diff --git a/.circleci/config.yml b/.circleci/config.yml index 5e77729df29..dfc539fb80e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -112,10 +112,10 @@ commands: node --version npm --version install_rust: - description: "Install pinned rustup (1.28.2) and Rust toolchain (1.97.1) with checksum verification. Adds ~/.cargo/bin to PATH. Run this before any `uv sync` or `uv build` of the workspace: the root package builds litellm-rust through maturin, and on an image without cargo maturin fetches an unpinned rustup and a floating toolchain by itself." + description: "Install pinned rustup (1.28.2) and Rust toolchain (1.98.0) with checksum verification. Adds ~/.cargo/bin to PATH. Run this before any `uv sync` or `uv build` of the workspace: the root package builds litellm-rust through maturin, and on an image without cargo maturin fetches an unpinned rustup and a floating toolchain by itself." steps: - run: - name: Install Rust (rustup 1.28.2, toolchain 1.97.1) + name: Install Rust (rustup 1.28.2, toolchain 1.98.0) command: | case "$(uname -m)" in x86_64) @@ -135,7 +135,7 @@ commands: "https://static.rust-lang.org/rustup/archive/1.28.2/${RUSTUP_TRIPLE}/rustup-init" echo "${RUSTUP_SHA256} /tmp/rustup-init" | sha256sum -c - chmod +x /tmp/rustup-init - /tmp/rustup-init -y --no-modify-path --profile minimal --default-toolchain 1.97.1 + /tmp/rustup-init -y --no-modify-path --profile minimal --default-toolchain 1.98.0 rm -f /tmp/rustup-init echo 'export PATH="$HOME/.cargo/bin:$PATH"' >> "$BASH_ENV" export PATH="$HOME/.cargo/bin:$PATH" @@ -300,7 +300,7 @@ jobs: if ($rustupActual -ne $rustupExpected) { throw "rustup installer hash mismatch: expected $rustupExpected got $rustupActual" } - & $rustupInit -y --profile minimal --default-toolchain stable + & $rustupInit -y --profile minimal --default-toolchain 1.98.0 if ($LASTEXITCODE -ne 0) { exit $LASTEXITCODE } @@ -430,7 +430,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm \ + --cov=./litellm --cov=./enterprise/litellm_enterprise \ --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=20 \ @@ -504,7 +504,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm \ + --cov=./litellm --cov=./enterprise/litellm_enterprise \ --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=20 \ @@ -631,7 +631,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -v -x \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 2" @@ -651,126 +651,6 @@ jobs: - auth_ui_unit_tests_coverage.xml - auth_ui_unit_tests_coverage - proxy_behavior_tests: - docker: - - *python312_image - - image: cimg/postgres:16.0@sha256:b125148bc76e8e8eee5eb3ad6020a3a14110a14e8192f1c645128afebe2e2f84 - environment: - POSTGRES_USER: postgres - POSTGRES_PASSWORD: postgres - POSTGRES_DB: litellm_test - working_directory: ~/project - environment: - DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/litellm_test" - steps: - - checkout - - skip_if_unrelated_changes - - setup_google_dns - - install_uv - - install_rust - - run: - name: Install Dependencies - command: | - uv sync --frozen --all-groups --all-extras --python 3.12 - - wait_for_service: - url: tcp://localhost:5432 - timeout: "60" - - run: - name: Seed DB schema via prisma db push - command: | - uv run --no-sync prisma db push --schema litellm/proxy/schema.prisma --accept-data-loss - - run: - name: Generate Prisma Client - command: uv run --no-sync python -m prisma generate - - run: - name: Run proxy management behavior tests - command: | - mkdir -p test-results - uv run --no-sync python -m pytest tests/proxy_behavior \ - -v --junitxml=test-results/junit.xml --durations=10 - no_output_timeout: 15m - - store_test_results: - path: test-results - - proxy_security_tests: - docker: - - *python312_image - - image: cimg/postgres:16.0@sha256:b125148bc76e8e8eee5eb3ad6020a3a14110a14e8192f1c645128afebe2e2f84 - environment: - POSTGRES_USER: postgres - POSTGRES_PASSWORD: postgres - POSTGRES_DB: litellm_test - working_directory: ~/project - environment: - DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/litellm_test" - steps: - - checkout - - skip_if_unrelated_changes - - setup_google_dns - - install_uv - - install_rust - - run: - name: Install Dependencies - command: | - uv sync --frozen --all-groups --all-extras --python 3.12 - - wait_for_service: - url: tcp://localhost:5432 - timeout: "60" - - run: - name: Seed DB schema via prisma db push - command: | - uv run --no-sync prisma db push --schema litellm/proxy/schema.prisma --accept-data-loss - - run: - name: Generate Prisma Client - command: uv run --no-sync python -m prisma generate - - run: - name: Run proxy security tests - command: | - mkdir -p test-results - uv run --no-sync python -m pytest tests/proxy_security_tests \ - -v --junitxml=test-results/junit.xml --durations=10 - no_output_timeout: 15m - - store_test_results: - path: test-results - - schema_migration_check: - docker: - - *python312_image - - image: cimg/postgres:16.0@sha256:b125148bc76e8e8eee5eb3ad6020a3a14110a14e8192f1c645128afebe2e2f84 - environment: - POSTGRES_USER: postgres - POSTGRES_PASSWORD: postgres - POSTGRES_DB: litellm_test - working_directory: ~/project - environment: - # An empty database; the test applies every committed migration itself. - DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/litellm_test" - steps: - - checkout - - skip_if_unrelated_changes - - setup_google_dns - - install_uv - - install_rust - - run: - name: Install Dependencies - command: | - uv sync --frozen --all-groups --all-extras --python 3.12 - - wait_for_service: - url: tcp://localhost:5432 - timeout: "60" - - run: - name: Generate Prisma Client - command: uv run --no-sync python -m prisma generate - - run: - name: Check schema.prisma is in sync with committed migrations - command: | - mkdir -p test-results - uv run --no-sync python -m pytest tests/proxy_migration_tests \ - -v --junitxml=test-results/junit.xml --durations=10 - no_output_timeout: 15m - - store_test_results: - path: test-results - litellm_router_testing: # Runs all tests with the "router" keyword docker: - *python312_image @@ -858,7 +738,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -v -x \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 4" @@ -985,7 +865,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=20 \ -n 4 \ @@ -1030,7 +910,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5" no_output_timeout: 15m @@ -1074,7 +954,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 2 \ @@ -1120,7 +1000,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ --retries 3 --retry-delay 5" @@ -1211,7 +1091,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 4" @@ -1255,7 +1135,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 4" @@ -1274,40 +1154,6 @@ jobs: paths: - search_coverage.xml - search_coverage - litellm_mapped_enterprise_tests: - docker: - - *python312_image - working_directory: ~/project - resource_class: large - - steps: - - checkout - - skip_if_unrelated_changes - - setup_google_dns - - install_uv - - install_rust - - run: - name: Install Dependencies - command: | - uv sync --frozen --all-groups --all-extras --python 3.12 - - setup_litellm_enterprise_pip - - run: - name: Run enterprise tests - command: | - uv run --no-sync python -m prisma generate - mkdir -p test-results - TEST_FILES=$(circleci tests glob "tests/enterprise/**/test_*.py") - echo "$TEST_FILES" | circleci tests run \ - --verbose \ - --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ - -v -x \ - --junitxml=test-results/junit-enterprise.xml \ - --durations=10 \ - -n 4" - no_output_timeout: 15m - # Store test results - - store_test_results: - path: test-results batches_testing: docker: - *python312_image @@ -1333,7 +1179,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 2" @@ -1377,7 +1223,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 2" @@ -1422,7 +1268,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 4" @@ -1501,7 +1347,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ -n 4 \ --junitxml=test-results/junit.xml \ --durations=5 \ @@ -1546,7 +1392,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5" no_output_timeout: 15m @@ -1599,7 +1445,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 -n 2 \ --reruns 2 --reruns-delay 1" @@ -2575,45 +2421,6 @@ jobs: - wait_for_service: url: http://localhost:4000 timeout: "300" - # Add Ruby installation and testing before the existing Node.js and Python tests - - run: - name: Install Ruby and Bundler - command: | - # Clone RVM at pinned tag and verify the commit SHA matches the - # published tag before running its install script. - RVM_VERSION="1.29.12" - RVM_EXPECTED_SHA="6bfc9213c9d6914fe756f524eb034a403d51db81" - git clone --depth 1 --branch "$RVM_VERSION" https://github.com/rvm/rvm.git /tmp/rvm - RVM_ACTUAL_SHA="$(git -C /tmp/rvm rev-parse HEAD)" - if [ "$RVM_ACTUAL_SHA" != "$RVM_EXPECTED_SHA" ]; then - echo "RVM tag $RVM_VERSION resolved to $RVM_ACTUAL_SHA; expected $RVM_EXPECTED_SHA" >&2 - exit 1 - fi - - # Import RVM signing keys (used by `rvm install` to verify Ruby tarballs) - gpg --keyserver hkp://keyserver.ubuntu.com --recv-keys 409B6B1796C275462A1703113804BB82D39DC0E3 7D2BAF1CF37B13E2069D6956105BD0E739499BDB - - # Install RVM from the verified checkout. The install script - # sources `scripts/functions/installer` using paths relative to - # its own working directory, so it must be run from /tmp/rvm. - (cd /tmp/rvm && ./install --path "$HOME/.rvm") - source "$HOME/.rvm/scripts/rvm" - - # Install Ruby 3.2.2 (RVM verifies the tarball PGP signature) - rvm install 3.2.2 - rvm use 3.2.2 --default - - # Install latest Bundler - gem install bundler - - - run: - name: Run Ruby tests - command: | - source $HOME/.rvm/scripts/rvm - cd tests/pass_through_tests/ruby_passthrough_tests - bundle install - bundle exec rspec - no_output_timeout: 30m # Install Node.js directly from nodejs.org with SHA256 verification, # instead of piping NodeSource's setup_24.x apt-repo installer into # sudo bash (which runs a mutable upstream script unattended). @@ -3105,12 +2912,6 @@ workflows: filters: *main_branches - auth_ui_unit_tests: filters: *main_branches - - proxy_behavior_tests: - filters: *main_branches - - proxy_security_tests: - filters: *main_branches - - schema_migration_check: - filters: *main_branches - build_docker_database_image: filters: *main_branches - e2e_ui_testing: @@ -3167,8 +2968,6 @@ workflows: filters: *main_branches - search_testing: filters: *main_branches - - litellm_mapped_enterprise_tests: - filters: *main_branches - batches_testing: filters: *main_branches - litellm_utils_testing: @@ -3191,7 +2990,6 @@ workflows: - guardrails_testing - ocr_testing - search_testing - - litellm_mapped_enterprise_tests - batches_testing - litellm_utils_testing - pass_through_unit_testing diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 7ae79aa666f..cfa0390e836 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,6 +1,9 @@ /ui/ @yuneng-berri @ryan-crabbe-berri /litellm/proxy/_experimental/out/ @yuneng-berri @ryan-crabbe-berri +/ui/Dockerfile +/ui/nginx.conf /ui/litellm-dashboard/src/lib/http/schema.d.ts +/ui/litellm-dashboard/tsconfig.tsbuildinfo /model_prices_and_context_window.json @mateo-berri /litellm/model_prices_and_context_window_backup.json @mateo-berri /litellm-proxy-extras/litellm_proxy_extras/migrations/ @yuneng-berri @ryan-crabbe-berri diff --git a/.github/actions/cache-cargo-build/action.yml b/.github/actions/cache-cargo-build/action.yml index 36c6c790b84..c3b8ce22c68 100644 --- a/.github/actions/cache-cargo-build/action.yml +++ b/.github/actions/cache-cargo-build/action.yml @@ -4,17 +4,16 @@ description: >- so only the first job on a given Cargo.lock compiles the bridge from scratch. litellm builds through maturin, which compiles litellm-rust/crates/python-bridge - in release mode before it can produce a wheel. `uv sync` therefore pays a full - build in every job that installs the workspace: measured at 2m40s per unit shard - on 2026-08-21, more than the whole unit tier spends running tests. Nothing caught - it, because the uv cache holds wheels uv downloads rather than wheels it builds, - and a path dependency whose source moves every commit could never hit that cache - anyway. Cargo rebuilds only what changed when its target directory survives, so a - warm job pays for the bridge crate alone. + in the dev profile for editable installs. `uv sync` therefore pays a full build + in every job that installs the workspace. Nothing caught it, because the uv cache + holds wheels uv downloads rather than wheels it builds, and a path dependency + whose source moves every commit could never hit that cache anyway. Cargo rebuilds + only what changed when its target directory survives, so a warm job pays for the + bridge crate alone. - The key namespace is separate from test-rust.yml's. Both cache the same directory, - but that workflow fills it with debug and clippy artifacts, which a release build - cannot reuse, and a shared key would let whichever ran first deny the other a save. + The key namespace is separate from test-rust.yml's check and release caches. They + cache the same directory for different workloads, and a shared key would let + whichever ran first deny the others a save. runs: using: composite @@ -26,6 +25,6 @@ runs: ~/.cargo/registry ~/.cargo/git litellm-rust/target - key: ${{ runner.os }}-cargo-release-${{ hashFiles('litellm-rust/Cargo.lock') }} + key: ${{ runner.os }}-maturin-dev-${{ hashFiles('litellm-rust/Cargo.lock') }} restore-keys: | - ${{ runner.os }}-cargo-release- + ${{ runner.os }}-maturin-dev- diff --git a/.github/ci-coverage-allowlist.yml b/.github/ci-coverage-allowlist.yml index 918589f84d1..f7a785b3b80 100644 --- a/.github/ci-coverage-allowlist.yml +++ b/.github/ci-coverage-allowlist.yml @@ -5,24 +5,21 @@ description: >- test_paths: - reason: >- - The caching suite in tests/local_testing, which runs nowhere. Every job that globs that - directory either deselects it (local_testing_part1 and part2 carry `-k "... and not caching - and not cache"`) or keeps only another keyword (langfuse, router, assistants), and no job - names these files the way redis_caching_unit_tests names test_dual_cache.py. Measured - 2026-08-20 by collecting the directory under each job's own selector: 118 tests across - these eight files are selected by none of them. Listed so the gap is a decision rather - than an accident, and so the --slices guard has a baseline to ratchet down from. Revisit - when tests/local_testing is ported off CircleCI, where the keyless part of this suite - belongs in a real job + What is left of the caching suite in tests/local_testing that runs nowhere. Every job that + globs that directory either deselects it (local_testing_part1 and part2 carry `-k "... and + not caching and not cache"`) or keeps only another keyword (langfuse, router, assistants), + and no job names these files the way redis_caching_unit_tests names test_dual_cache.py. + The gap was eight files and 118 tests when measured 2026-08-20; the five keyless ones now + run in the caching-local shard, leaving these three. Measured 2026-08-21 with no provider + credentials and no Redis: test_caching.py needs both (37 of 65 fail without them), + test_disk_cache_unit_tests.py needs OPENAI_API_KEY for 2 of its 4, and + test_gcs_cache_unit_tests.py needs GCS credentials for all 4. They want the keyless/live + split that porting tests/local_testing off CircleCI will force, not a job that is red by + construction paths: - - tests/local_testing/test_cache_preset_key.py - tests/local_testing/test_caching.py - - tests/local_testing/test_caching_handler.py - tests/local_testing/test_disk_cache_unit_tests.py - tests/local_testing/test_gcs_cache_unit_tests.py - - tests/local_testing/test_prompt_caching.py - - tests/local_testing/test_responses_stream_cache_keys.py - - tests/local_testing/test_unit_test_caching.py - reason: >- The end-to-end suite runs against a deployed proxy from its own in-cluster rig rather than from a pull request; it needs a live gateway and provider credentials no PR job holds @@ -92,17 +89,6 @@ test_paths: - tests/integration/sandbox/test_e2b_sandbox.py - tests/integration/test_oci_integration.py - tests/integration/test_oci_proxy_integration.py - - reason: >- - A unit test for the proxy-extras package that no job invokes, while the package's other - tests live under tests/proxy_migration_tests. Measured 2026-08-20: 24 of its 28 tests pass - and the 4 in TestMigrationSQLIdempotency fail, because 13 migrations from 2026-03 onward use - bare CREATE TABLE, ADD COLUMN, CREATE INDEX and ADD CONSTRAINT rather than the guarded forms - this file requires. It also matches those keywords inside SQL comments, so two further - migrations are reported that are in fact fine. Wiring it up means deciding what to do about - the 13 first, and they cannot simply be edited: Prisma checksums an applied migration, so a - changed one breaks migrate deploy for existing installs - paths: - - tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py dockerfiles: - reason: >- diff --git a/.github/mutmut-coverage.rc b/.github/mutmut-coverage.rc new file mode 100644 index 00000000000..c607df68853 --- /dev/null +++ b/.github/mutmut-coverage.rc @@ -0,0 +1,5 @@ +# mutmut's gather_coverage() looks covered lines up by absolute path, so the +# repo's `relative_files = true` makes every lookup miss and mutmut generates +# zero mutants. Point COVERAGE_RCFILE here for mutation runs only. +[run] +relative_files = false diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 4e428d8cebf..e85a397cbd2 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,7 +1,10 @@ + + ## TLDR - + Problem this solves: @@ -110,8 +113,20 @@ If you're seeing a delay in your PR being merged, ping the LiteLLM Team on [Slac ## Caveats (if any) - ## QA runbook @@ -134,6 +149,6 @@ Example checklists: - [ ] Sanity check: this test makes sense to add and is not hand-wavey (e.g., assert actual expected spend instead of just spend > 0) or potentially flaky --> -### Final Attestation +## Final Attestation - [ ] The tests check the right things, including the edge cases, and regressions in the respective real-world customer use-cases are not possible after this PR diff --git a/.github/scripts/assert_ci_coverage.py b/.github/scripts/assert_ci_coverage.py index c8572d9f6ef..411852acb98 100644 --- a/.github/scripts/assert_ci_coverage.py +++ b/.github/scripts/assert_ci_coverage.py @@ -312,8 +312,23 @@ def _matchable_names(relative_path: str) -> frozenset[str]: ) +def _workflow_named_tokens() -> frozenset[str]: + """Test tokens a GitHub Actions job names directly. + + A CircleCI `-k` that deselects a file no longer means the file runs nowhere once a + workflow names it, so the slice check has to credit those the same way the census does. + """ + return _invoked_test_tokens( + scalar + for path in _config_files() + if path != CIRCLECI_CONFIG + for scalar in _scalars(yaml.safe_load(path.read_text(encoding="utf-8")), path.name) + ) + + def _deselected_everywhere(allowlist: Allowlist) -> tuple[Finding, ...]: slices: Final = _slices() + named_by_workflow: Final = _workflow_named_tokens() globbed: Final = tuple( path for path in _test_files() @@ -326,6 +341,7 @@ def _deselected_everywhere(allowlist: Allowlist) -> tuple[Finding, ...]: ) for path in globbed if not allowlist.covers_test(path) + and not any(_token_covers(token, path) for token in named_by_workflow) and not any(slice_.claims(path, _matchable_names(path)) for slice_ in slices) ) diff --git a/.github/scripts/close_duplicate_issues.py b/.github/scripts/close_duplicate_issues.py deleted file mode 100755 index ec522af4f88..00000000000 --- a/.github/scripts/close_duplicate_issues.py +++ /dev/null @@ -1,230 +0,0 @@ -#!/usr/bin/env python3 -""" -Detect and close duplicate GitHub issues using title similarity. - -Modes: - --scan Compare all open issues against each other (batch) - --issue-number N Check a single issue against older open issues - -Requires the `gh` CLI to be authenticated. -""" - -import argparse -import difflib -import json -import re -import subprocess -import sys - - -def normalize_title(title: str) -> str: - """Strip common prefixes, lowercase, and collapse whitespace.""" - title = re.sub( - r"^\[?(bug|feature request|enhancement|question|docs)[:\]]?\s*", - "", - title, - flags=re.IGNORECASE, - ) - return " ".join(title.lower().split()) - - -def gh(*args: str) -> str: - """Run a gh CLI command and return stdout.""" - result = subprocess.run( - ["gh", *args], - capture_output=True, - text=True, - check=True, - ) - return result.stdout - - -def fetch_open_issues(repo: str | None) -> list[dict]: - """Fetch all open issues (excluding PRs) via gh api --paginate.""" - if repo: - endpoint = ( - f"repos/{repo}/issues?state=open&per_page=100&sort=created&direction=asc" - ) - else: - endpoint = "repos/{owner}/{repo}/issues?state=open&per_page=100&sort=created&direction=asc" - cmd = ["api", "--paginate", endpoint] - - raw = gh(*cmd) - # gh --paginate concatenates JSON arrays, so we may get multiple arrays - issues = [] - for line in raw.strip().splitlines(): - line = line.strip() - if not line: - continue - parsed = json.loads(line) - if isinstance(parsed, list): - issues.extend(parsed) - else: - issues.append(parsed) - - # Filter out pull requests (they also appear in the issues endpoint) - return [i for i in issues if "pull_request" not in i] - - -def close_as_duplicate( - issue_number: int, duplicate_of: int, repo: str | None, dry_run: bool -) -> None: - """Close an issue as duplicate of another, adding a comment and label.""" - repo_args = ["--repo", repo] if repo else [] - - if dry_run: - print( - f" [DRY RUN] Would close #{issue_number} as duplicate of #{duplicate_of}" - ) - return - - # Add comment - comment_body = ( - f"Closing as duplicate of #{duplicate_of}.\n\n" - "If you believe this is not a duplicate, please reopen and add context " - "explaining how this differs." - ) - gh("issue", "comment", str(issue_number), "--body", comment_body, *repo_args) - - # Add label - gh("issue", "edit", str(issue_number), "--add-label", "duplicate", *repo_args) - - # Close with not_planned reason - gh( - "api", - f"repos/{repo or '{owner}/{repo}'}/issues/{issue_number}", - "-X", - "PATCH", - "-f", - "state=closed", - "-f", - "state_reason=not_planned", - ) - - print(f" Closed #{issue_number} as duplicate of #{duplicate_of}") - - -def find_duplicate( - issue: dict, candidates: list[dict], threshold: float -) -> dict | None: - """Return the first candidate whose normalized title is above threshold.""" - norm = normalize_title(issue["title"]) - for candidate in candidates: - if candidate["number"] == issue["number"]: - continue - cand_norm = normalize_title(candidate["title"]) - ratio = difflib.SequenceMatcher(None, norm, cand_norm).ratio() - if ratio >= threshold: - return candidate - return None - - -def scan_all( - issues: list[dict], threshold: float, repo: str | None, dry_run: bool -) -> int: - """Compare every issue against all older issues. Returns count of duplicates found.""" - # Sort oldest first - issues.sort(key=lambda i: i["number"]) - closed_count = 0 - - for idx, issue in enumerate(issues): - older = issues[:idx] - if not older: - continue - dup = find_duplicate(issue, older, threshold) - if dup: - ratio = difflib.SequenceMatcher( - None, - normalize_title(issue["title"]), - normalize_title(dup["title"]), - ).ratio() - print( - f"#{issue['number']}: \"{issue['title']}\"\n" - f" -> duplicate of #{dup['number']}: \"{dup['title']}\" " - f"({ratio:.0%} similar)" - ) - close_as_duplicate(issue["number"], dup["number"], repo, dry_run) - closed_count += 1 - - return closed_count - - -def check_single( - issue_number: int, - issues: list[dict], - threshold: float, - repo: str | None, - dry_run: bool, -) -> bool: - """Check a single issue against all older open issues. Returns True if duplicate found.""" - target = None - for i in issues: - if i["number"] == issue_number: - target = i - break - - if target is None: - print(f"Issue #{issue_number} not found among open issues.") - return False - - older = [i for i in issues if i["number"] < issue_number] - dup = find_duplicate(target, older, threshold) - if dup: - ratio = difflib.SequenceMatcher( - None, - normalize_title(target["title"]), - normalize_title(dup["title"]), - ).ratio() - print( - f"#{target['number']}: \"{target['title']}\"\n" - f" -> duplicate of #{dup['number']}: \"{dup['title']}\" " - f"({ratio:.0%} similar)" - ) - close_as_duplicate(issue_number, dup["number"], repo, dry_run) - return True - - print(f"#{issue_number}: no duplicate found above threshold {threshold}") - return False - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Detect and close duplicate GitHub issues" - ) - mode = parser.add_mutually_exclusive_group(required=True) - mode.add_argument("--scan", action="store_true", help="Scan all open issues") - mode.add_argument("--issue-number", type=int, help="Check a single issue number") - parser.add_argument( - "--threshold", type=float, default=0.85, help="Similarity threshold (0-1)" - ) - parser.add_argument( - "--close", - action="store_true", - help="Actually close duplicates (default is dry-run)", - ) - parser.add_argument( - "--repo", type=str, help="Repository (owner/repo). Auto-detected if omitted." - ) - args = parser.parse_args() - - dry_run = not args.close - - if dry_run: - print("=== DRY RUN MODE (pass --close to actually close issues) ===\n") - - print("Fetching open issues...") - issues = fetch_open_issues(args.repo) - print(f"Found {len(issues)} open issues.\n") - - if args.scan: - count = scan_all(issues, args.threshold, args.repo, dry_run) - print(f"\nTotal duplicates {'found' if dry_run else 'closed'}: {count}") - else: - found = check_single( - args.issue_number, issues, args.threshold, args.repo, dry_run - ) - sys.exit(0 if found else 0) # Always exit 0; finding no dup is not an error - - -if __name__ == "__main__": - main() diff --git a/.github/scripts/e2e_egress_sentinel.py b/.github/scripts/e2e_egress_sentinel.py new file mode 100755 index 00000000000..b40c72d1fd7 --- /dev/null +++ b/.github/scripts/e2e_egress_sentinel.py @@ -0,0 +1,198 @@ +"""Prove an e2e replay run makes zero outbound provider calls, by counting them. + +`serve` pins each provider host (`--host`) to a local sink address in the hosts +file and binds a counting listener on that address, so any connection the proxy +or the record/replay edge opens to a real provider is redirected to the sink, +recorded as one line in `--hits-file`, and never leaves the box. The record and +replay edge only ever dials `127.0.0.1:` (a different host than the +pinned provider names), so in a clean replay the sink sees nothing; a single hit +means a provider call escaped the bundle. `assert-empty` turns that hit file into +the pass/fail check. + +Stdlib only, so CI runs it under the system interpreter as root (binding :443 and +editing the hosts file both need root); `--sink-address`, `--port`, and +`--hosts-file` are injectable so it runs unprivileged against a temp hosts file on +a high port under test. +""" + +# ruff: noqa: T201 # CLI script: its stdout/stderr progress and results are the interface +from __future__ import annotations + +import argparse +import json +import os +import signal +import socket +import sys +import threading +import time +from dataclasses import dataclass +from pathlib import Path +from types import FrameType +from typing import Final + +_BLOCK_BEGIN: Final = "# BEGIN e2e-egress-sentinel" +_BLOCK_END: Final = "# END e2e-egress-sentinel" + + +@dataclass(frozen=True, slots=True) +class ServeConfig: + hosts: tuple[str, ...] + sink_address: str + ports: tuple[int, ...] + hits_file: Path + hosts_file: Path + ready_file: Path | None + pid_file: Path | None + + +def _pin_block(sink_address: str, hosts: tuple[str, ...]) -> str: + lines = "\n".join(f"{sink_address}\t{host}" for host in hosts) + return f"\n{_BLOCK_BEGIN}\n{lines}\n{_BLOCK_END}\n" + + +def _install_pins(hosts_file: Path, sink_address: str, hosts: tuple[str, ...]) -> bytes: + original = hosts_file.read_bytes() if hosts_file.exists() else b"" + hosts_file.write_bytes(original + _pin_block(sink_address, hosts).encode()) + return original + + +def _restore_pins(hosts_file: Path, original: bytes) -> None: + hosts_file.write_bytes(original) + + +def _bind(sink_address: str, port: int) -> socket.socket: + listener = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + listener.bind((sink_address, port)) + listener.listen(128) + return listener + + +@dataclass(frozen=True, slots=True) +class _HitLog: + path: Path + _lock: threading.Lock + + def record(self, *, port: int, peer: tuple[str, int]) -> None: + entry = json.dumps({"ts": time.time(), "port": port, "peer": list(peer)}) + with self._lock: + with self.path.open("a", encoding="utf-8") as handle: + handle.write(entry + "\n") + + +def _serve_socket(listener: socket.socket, port: int, hits: _HitLog, stop: threading.Event) -> None: + while not stop.is_set(): + try: + conn, peer = listener.accept() + except OSError: + return + hits.record(port=port, peer=(peer[0], peer[1])) + try: + conn.close() + except OSError: + pass + + +def serve(config: ServeConfig) -> int: + config.hits_file.write_text("", encoding="utf-8") + original_hosts = _install_pins(config.hosts_file, config.sink_address, config.hosts) + try: + listeners = tuple(_bind(config.sink_address, port) for port in config.ports) + except OSError as exc: + _restore_pins(config.hosts_file, original_hosts) + print(f"egress sentinel could not bind a sink: {exc}", file=sys.stderr) + return 1 + + stop = threading.Event() + hits = _HitLog(path=config.hits_file, _lock=threading.Lock()) + threads = tuple( + threading.Thread(target=_serve_socket, args=(listener, port, hits, stop), daemon=True) + for listener, port in zip(listeners, config.ports) + ) + for thread in threads: + thread.start() + + def _handle(_signum: int, _frame: FrameType | None) -> None: + stop.set() + for listener in listeners: + try: + listener.close() + except OSError: + pass + + signal.signal(signal.SIGTERM, _handle) + signal.signal(signal.SIGINT, _handle) + + if config.pid_file is not None: + config.pid_file.write_text(str(os.getpid()), encoding="utf-8") + if config.ready_file is not None: + config.ready_file.write_text("ready", encoding="utf-8") + print( + f"egress sentinel up: pinned {', '.join(config.hosts)} to {config.sink_address} " + f"on port(s) {', '.join(str(p) for p in config.ports)}", + flush=True, + ) + + stop.wait() + _restore_pins(config.hosts_file, original_hosts) + if config.ready_file is not None and config.ready_file.exists(): + config.ready_file.unlink() + if config.pid_file is not None and config.pid_file.exists(): + config.pid_file.unlink() + return 0 + + +def assert_empty(hits_file: Path) -> int: + if not hits_file.exists(): + print(f"egress sentinel recorded no provider calls ({hits_file} absent): zero egress") + return 0 + hits = [line for line in hits_file.read_text(encoding="utf-8").splitlines() if line.strip()] + if not hits: + print("egress sentinel recorded no provider calls: zero egress") + return 0 + print(f"egress sentinel recorded {len(hits)} provider call(s); replay was not hermetic:", file=sys.stderr) + for line in hits: + print(f" {line}", file=sys.stderr) + return 1 + + +def _serve_from_args(args: argparse.Namespace) -> int: + config = ServeConfig( + hosts=tuple(args.host), + sink_address=args.sink_address, + ports=tuple(args.port), + hits_file=Path(args.hits_file), + hosts_file=Path(args.hosts_file), + ready_file=Path(args.ready_file) if args.ready_file else None, + pid_file=Path(args.pid_file) if args.pid_file else None, + ) + return serve(config) + + +def main(argv: tuple[str, ...]) -> int: + parser = argparse.ArgumentParser(description="count outbound provider calls during an e2e replay") + sub = parser.add_subparsers(dest="command", required=True) + + serve_parser = sub.add_parser("serve", help="pin provider hosts and count connection attempts") + serve_parser.add_argument("--host", action="append", required=True, help="provider host to pin and watch") + serve_parser.add_argument("--sink-address", default="127.0.0.1") + serve_parser.add_argument("--port", action="append", type=int, default=None) + serve_parser.add_argument("--hits-file", required=True) + serve_parser.add_argument("--hosts-file", default="/etc/hosts") + serve_parser.add_argument("--ready-file", default=None) + serve_parser.add_argument("--pid-file", default=None) + + assert_parser = sub.add_parser("assert-empty", help="exit non-zero if any provider call was recorded") + assert_parser.add_argument("--hits-file", required=True) + + args = parser.parse_args(argv) + if args.command == "serve": + if args.port is None: + args.port = [443] + return _serve_from_args(args) + return assert_empty(Path(args.hits_file)) + + +if __name__ == "__main__": + raise SystemExit(main(tuple(sys.argv[1:]))) diff --git a/.github/scripts/e2e_fetch_fixture_bundle.sh b/.github/scripts/e2e_fetch_fixture_bundle.sh new file mode 100755 index 00000000000..b76ced74b3b --- /dev/null +++ b/.github/scripts/e2e_fetch_fixture_bundle.sh @@ -0,0 +1,55 @@ +#!/usr/bin/env bash +set -euo pipefail + +REPO="${1:-${GITHUB_REPOSITORY:?REPO required}}" +ARTIFACT_NAME="${2:-e2e-fixtures-bundle}" +BASE_BRANCH="${3:?base branch required}" +DEST_DIR="${4:?destination bundle dir required}" + +: "${GH_TOKEN:?GH_TOKEN required to query and download artifacts}" + +WORKDIR="$(mktemp -d)" +trap 'rm -rf "${WORKDIR}"' EXIT + +echo "resolving newest non-expired '${ARTIFACT_NAME}' artifact on ${REPO}@${BASE_BRANCH}" + +SELECTED="$( + gh api "repos/${REPO}/actions/artifacts" -X GET -f per_page=100 --paginate \ + --jq ".artifacts[] | select(.name == \"${ARTIFACT_NAME}\" and .expired == false and .workflow_run.head_branch == \"${BASE_BRANCH}\") | {id, digest, created_at, run_id: .workflow_run.id, run_number: .workflow_run.run_number}" \ + | jq -s 'sort_by(.created_at) | reverse | .[0] // empty' +)" + +if [[ -z "${SELECTED}" ]]; then + echo "no usable '${ARTIFACT_NAME}' artifact on ${BASE_BRANCH}: the last record run produced none (a red Saturday), so there is nothing fresh to replay; failing loudly instead of replaying a stale bundle" >&2 + exit 1 +fi + +RUN_ID="$(echo "${SELECTED}" | jq -r '.run_id')" +RUN_NUMBER="$(echo "${SELECTED}" | jq -r '.run_number')" +ARTIFACT_ID="$(echo "${SELECTED}" | jq -r '.id')" +GH_DIGEST="$(echo "${SELECTED}" | jq -r '.digest // "unknown"')" +CREATED_AT="$(echo "${SELECTED}" | jq -r '.created_at')" + +echo "pinned bundle: run #${RUN_NUMBER} (run_id=${RUN_ID}, artifact_id=${ARTIFACT_ID}), recorded ${CREATED_AT}, github digest ${GH_DIGEST}" + +gh run download "${RUN_ID}" --repo "${REPO}" -n "${ARTIFACT_NAME}" -D "${WORKDIR}" + +TARBALL="$(find "${WORKDIR}" -name '*.tar.gz' -type f | head -n 1)" +if [[ -z "${TARBALL}" ]]; then + echo "downloaded artifact contained no tarball" >&2 + exit 1 +fi +SIDECAR="${TARBALL}.sha256" +if [[ ! -f "${SIDECAR}" ]]; then + echo "downloaded artifact has no ${SIDECAR}: cannot verify the bundle digest" >&2 + exit 1 +fi + +echo "verifying bundle against its recorded sha256 digest" +( cd "$(dirname "${TARBALL}")" && sha256sum -c "$(basename "${SIDECAR}")" ) + +mkdir -p "${DEST_DIR}" +tar xzf "${TARBALL}" -C "${DEST_DIR}" + +echo "extracted bundle into ${DEST_DIR}" +python3 -c "import json,sys; m=json.load(open(sys.argv[1])); print(' recorded_at', m['recorded_at'], 'harness', m['harness_version'], 'format_version', m['format_version'])" "${DEST_DIR}/manifest.json" diff --git a/.github/scripts/e2e_pack_fixture_bundle.sh b/.github/scripts/e2e_pack_fixture_bundle.sh new file mode 100755 index 00000000000..2105447cc65 --- /dev/null +++ b/.github/scripts/e2e_pack_fixture_bundle.sh @@ -0,0 +1,36 @@ +#!/usr/bin/env bash +set -euo pipefail + +if [[ $# -ne 2 ]]; then + echo "usage: $0 " >&2 + exit 2 +fi + +BUNDLE_DIR="$1" +OUT_TARBALL="$2" + +MANIFEST="${BUNDLE_DIR}/manifest.json" +if [[ ! -f "${MANIFEST}" ]]; then + echo "no ${MANIFEST}: refusing to publish a bundle with no manifest (record produced nothing)" >&2 + exit 1 +fi + +echo "packing fixture bundle from ${BUNDLE_DIR}" +python3 -c "import json,sys; m=json.load(open(sys.argv[1])); print(' format_version', m['format_version'], 'recorded_at', m['recorded_at'], 'harness', m['harness_version'])" "${MANIFEST}" + +TEST_DIRS=$(find "${BUNDLE_DIR}" -mindepth 1 -maxdepth 1 -type d | wc -l | tr -d ' ') +if [[ "${TEST_DIRS}" -eq 0 ]]; then + echo "bundle at ${BUNDLE_DIR} has a manifest but no recorded interactions; refusing to publish an empty bundle" >&2 + exit 1 +fi +echo " ${TEST_DIRS} recorded test director(ies)" + +mkdir -p "$(dirname "${OUT_TARBALL}")" +tar czf "${OUT_TARBALL}" -C "${BUNDLE_DIR}" . + +OUT_DIR="$(cd "$(dirname "${OUT_TARBALL}")" && pwd)" +OUT_BASE="$(basename "${OUT_TARBALL}")" +( cd "${OUT_DIR}" && sha256sum "${OUT_BASE}" > "${OUT_BASE}.sha256" ) + +echo "wrote ${OUT_TARBALL} ($(du -h "${OUT_TARBALL}" | cut -f1)) and ${OUT_BASE}.sha256" +cat "${OUT_DIR}/${OUT_BASE}.sha256" diff --git a/.github/scripts/smoke_test_native_wheel.py b/.github/scripts/smoke_test_native_wheel.py new file mode 100644 index 00000000000..577bb32fcf0 --- /dev/null +++ b/.github/scripts/smoke_test_native_wheel.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import subprocess +import sys +import tempfile +import zipfile +from pathlib import Path +from typing import Final + +CHILD_SCRIPT: Final = """ +from importlib.util import module_from_spec, spec_from_file_location +from pathlib import Path +import sys + +native_path = Path(sys.argv[1]) +spec = spec_from_file_location("litellm.rust_bridge._native", native_path) +if spec is None or spec.loader is None: + raise RuntimeError("cannot create native extension import specification") +module = module_from_spec(spec) +spec.loader.exec_module(module) + +before = module.gil_stats() +if not isinstance(before.get("releases"), int): + raise AssertionError(f"unexpected gil_stats result: {before!r}") + +try: + module._panic_for_test() +except BaseException as error: + if type(error).__name__ != "PanicException": + raise AssertionError(f"expected PanicException, got {type(error).__name__}") from error +else: + raise AssertionError("Rust panic returned without raising") + +after = module.gil_stats() +if not isinstance(after.get("releases"), int): + raise AssertionError(f"native module unusable after panic: {after!r}") +""" + + +def main() -> int: + if len(sys.argv) != 2: + sys.stderr.write(f"usage: {Path(sys.argv[0]).name} WHEEL\n") + return 2 + + wheel: Final = Path(sys.argv[1]) + with tempfile.TemporaryDirectory() as temporary_directory, zipfile.ZipFile(wheel) as archive: + native_members: Final = tuple( + member + for member in archive.infolist() + if member.filename.startswith("litellm/rust_bridge/_native.") and member.filename.endswith(".so") + ) + if len(native_members) != 1: + sys.stderr.write(f"expected one native extension, found {len(native_members)}\n") + return 1 + + native_path: Final = Path(temporary_directory) / Path(native_members[0].filename).name + native_path.write_bytes(archive.read(native_members[0])) + result: Final = subprocess.run((sys.executable, "-c", CHILD_SCRIPT, str(native_path)), check=False) + + if result.returncode != 0: + sys.stderr.write(f"native wheel smoke test exited with status {result.returncode}\n") + return 1 + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/scripts/verify_linux_native_wheel.py b/.github/scripts/verify_linux_native_wheel.py new file mode 100644 index 00000000000..899e2a211c0 --- /dev/null +++ b/.github/scripts/verify_linux_native_wheel.py @@ -0,0 +1,282 @@ +from __future__ import annotations + +import importlib.util +import os +import re +import subprocess +import sys +import zipfile +from collections.abc import Callable, Mapping, Sequence +from itertools import product +from pathlib import Path, PurePosixPath +from types import MappingProxyType, ModuleType +from typing import Final, Protocol + +EXPECTED_PYTHON_TAG: Final = "cp310" +EXPECTED_ABI_TAG: Final = "abi3" +EXPECTED_PLATFORM_TAG: Final = "linux_x86_64" + + +class CommandRunner(Protocol): + def __call__( + self, + command: tuple[str, ...], + *, + check: bool, + capture_output: bool, + text: bool, + ) -> subprocess.CompletedProcess[str]: ... + + +def _run_command( + command: tuple[str, ...], + *, + check: bool, + capture_output: bool, + text: bool, +) -> subprocess.CompletedProcess[str]: + return subprocess.run(command, check=check, capture_output=capture_output, text=text) + + +def _dist_info_directory(member: zipfile.ZipInfo) -> str | None: + parts: Final = PurePosixPath(member.filename).parts + if not parts or not parts[0].endswith(".dist-info"): + return None + return parts[0] + + +def _wheel_metadata_tags(archive: zipfile.ZipFile, members: tuple[zipfile.ZipInfo, ...]) -> tuple[str, ...]: + if len(members) != 1: + return () + lines: Final = archive.read(members[0]).splitlines() + return tuple(line.removeprefix(b"Tag:").strip().decode("ascii") for line in lines if line.startswith(b"Tag:")) + + +def _load_native_module(native_path: Path) -> ModuleType | None: + module_spec: Final = importlib.util.spec_from_file_location("litellm.rust_bridge._native", native_path) + if module_spec is None or module_spec.loader is None: + return None + try: + native_module: Final = importlib.util.module_from_spec(module_spec) + module_spec.loader.exec_module(native_module) + except Exception as error: # noqa: BLE001 # native module initialization can raise arbitrary exceptions + sys.stderr.write(f"native module load failed: {error}\n") + return None + return native_module + + +def main( + argv: Sequence[str] | None = None, + environment: Mapping[str, str] | None = None, + load_native_module: Callable[[Path], ModuleType | None] = _load_native_module, + run_command: CommandRunner = _run_command, +) -> int: + arguments: Final = tuple(sys.argv if argv is None else argv) + resolved_environment: Final = os.environ if environment is None else environment + if len(arguments) != 2: + sys.stderr.write(f"usage: {Path(arguments[0]).name} WHEEL\n") + return 2 + + wheel: Final = Path(arguments[1]) + wheel_tags: Final = wheel.stem.rsplit("-", maxsplit=3) + if len(wheel_tags) != 4: + sys.stderr.write(f"cannot parse wheel tags from {wheel.name}\n") + return 1 + + wheel_identity: Final = wheel_tags[0].split("-") + if len(wheel_identity) != 2 or wheel_identity[0] != "litellm" or not wheel_identity[1]: + sys.stderr.write(f"unexpected wheel identity: {wheel_tags[0]}\n") + return 1 + + expected_dist_info_directory: Final = f"{wheel_tags[0]}.dist-info" + expected_dist_info_directories: Final = frozenset((expected_dist_info_directory,)) + python_tag: Final = wheel_tags[1] + abi_tag: Final = wheel_tags[2] + platform_tag: Final = wheel_tags[3] + expanded_filename_tags: Final = frozenset( + "-".join(tag) for tag in product(python_tag.split("."), abi_tag.split("."), platform_tag.split(".")) + ) + + with zipfile.ZipFile(wheel) as archive: + wheel_members: Final = archive.infolist() + dist_info_directories: Final = frozenset( + directory for member in wheel_members if (directory := _dist_info_directory(member)) is not None + ) + required_dist_info_files: Final = ("METADATA", "RECORD", "WHEEL") + dist_info_file_counts: Final = MappingProxyType( + { + filename: sum( + member.filename == f"{expected_dist_info_directory}/{filename}" for member in wheel_members + ) + for filename in required_dist_info_files + } + ) + wheel_metadata_members: Final = tuple( + member for member in wheel_members if member.filename == f"{expected_dist_info_directory}/WHEEL" + ) + wheel_metadata_tags: Final = _wheel_metadata_tags(archive, wheel_metadata_members) + native_members: Final = tuple( + member + for member in wheel_members + if member.filename.startswith("litellm/rust_bridge/_native.") and member.filename.endswith(".so") + ) + if len(native_members) != 1: + sys.stderr.write(f"expected one native extension, found {len(native_members)}\n") + return 1 + + unexpected_members: Final = tuple( + member.filename + for member in wheel_members + if member.filename.endswith((".pdb", ".dwp", ".rlib", ".rmeta", "Cargo.toml", "Cargo.lock")) + or any(part.endswith(".dSYM") for part in PurePosixPath(member.filename).parts) + ) + native_member: Final = native_members[0] + uncompressed_wheel_size: Final = sum(member.file_size for member in wheel_members) + native_path: Final = wheel.parent / "native" / Path(native_member.filename).name + native_path.parent.mkdir(parents=True, exist_ok=True) + native_path.write_bytes(archive.read(native_member)) + + wheel_metadata_tags_match: Final = ( + len(wheel_metadata_tags) == len(expanded_filename_tags) + and frozenset(wheel_metadata_tags) == expanded_filename_tags + ) + commit_sha: Final = resolved_environment.get( + "RELEASE_WHEEL_COMMIT_SHA", resolved_environment.get("GITHUB_SHA", "unknown") + ) + rustc_version: Final = run_command( + ("rustc", "--version"), + check=True, + capture_output=True, + text=True, + ).stdout.strip() + pyproject: Final = (Path(__file__).parents[2] / "pyproject.toml").read_text() + maturin_match: Final = re.search(r'"maturin==([^";]+)', pyproject) + if maturin_match is None: + sys.stderr.write("build-system does not pin an exact Maturin version\n") + return 1 + + maturin_version: Final = maturin_match.group(1) + native_percentage: Final = native_member.file_size / uncompressed_wheel_size * 100 + size_report: Final = "\n".join( + ( + "## Native wheel build report", + "", + "| Build | Value |", + "| --- | --- |", + f"| Commit | `{commit_sha}` |", + f"| Platform | `{platform_tag}` |", + f"| Python ABI | `{python_tag}-{abi_tag}` |", + f"| Rust compiler | `{rustc_version}` |", + f"| Maturin | `{maturin_version}` |", + "| Cargo profile | `release` |", + "", + "| Artifact | Size |", + "| --- | ---: |", + f"| Compressed wheel | {wheel.stat().st_size / 1_000_000:.2f} MB |", + f"| Uncompressed wheel | {uncompressed_wheel_size / 1_000_000:.2f} MB |", + f"| Native extension | {native_member.file_size / 1_000_000:.2f} MB |", + f"| Native share | {native_percentage:.2f}% |", + "", + ) + ) + summary_path: Final = resolved_environment.get("GITHUB_STEP_SUMMARY") + if summary_path is None: + sys.stdout.write(size_report) + else: + Path(summary_path).write_text(size_report) + + sections: Final = run_command( + ("readelf", "--sections", "--wide", str(native_path)), + check=True, + capture_output=True, + text=True, + ).stdout + debug_sections: Final = tuple(section for section in (".debug_", ".zdebug_") if section in sections) + debug_sections_absent: Final = not debug_sections + static_symbol_table_absent: Final = ".symtab" not in sections + + dynamic_symbols: Final = run_command( + ("readelf", "--dyn-syms", "--wide", str(native_path)), + check=True, + capture_output=True, + text=True, + ).stdout + extension_entry_point_present: Final = "PyInit__native" in dynamic_symbols + native_module: Final = load_native_module(native_path) + native_module_loads: Final = native_module is not None + panic_test_hook_absent: Final = native_module is not None and not hasattr(native_module, "_panic_for_test") + native_size_limit: Final = 20_000_000 + native_size_within_limit: Final = native_member.file_size <= native_size_limit + validations: Final = ( + (f"Python tag is {EXPECTED_PYTHON_TAG}", python_tag == EXPECTED_PYTHON_TAG), + (f"ABI tag is {EXPECTED_ABI_TAG}", abi_tag == EXPECTED_ABI_TAG), + (f"Platform tag is {EXPECTED_PLATFORM_TAG}", platform_tag == EXPECTED_PLATFORM_TAG), + ("Wheel dist-info directory matches the filename", dist_info_directories == expected_dist_info_directories), + ( + "Required dist-info files are present exactly once", + all(count == 1 for count in dist_info_file_counts.values()), + ), + ("Wheel metadata tags match the filename", wheel_metadata_tags_match), + ("Debug sections are absent", debug_sections_absent), + ("Static symbol table is absent", static_symbol_table_absent), + ("Python extension entry point is present", extension_entry_point_present), + ("Native module loads", native_module_loads), + ("Production module omits the panic test hook", panic_test_hook_absent), + ("Native extension does not exceed 20 MB", native_size_within_limit), + ("Wheel contents are valid", not unexpected_members), + ) + + verified_report: Final = size_report + "\n".join( + ("", "| Validation | Expected | Result |", "| --- | --- | :---: |") + + tuple(f"| {label} | Yes | {'O' if passed else 'X'} |" for label, passed in validations) + + ("",) + ) + if summary_path is not None: + Path(summary_path).write_text(verified_report) + + invalid_dist_info_files: Final = any(count != 1 for count in dist_info_file_counts.values()) + validation_errors: Final = tuple( + message + for failed, message in ( + (bool(debug_sections), f"{native_member.filename} contains debug sections: {', '.join(debug_sections)}"), + (not static_symbol_table_absent, f"{native_member.filename} contains a static symbol table"), + (not extension_entry_point_present, "native extension does not export PyInit__native"), + ( + python_tag != EXPECTED_PYTHON_TAG, + f"unexpected Python tag: expected {EXPECTED_PYTHON_TAG}, found {python_tag}", + ), + (abi_tag != EXPECTED_ABI_TAG, f"unexpected ABI tag: expected {EXPECTED_ABI_TAG}, found {abi_tag}"), + ( + platform_tag != EXPECTED_PLATFORM_TAG, + f"unexpected platform tag: expected {EXPECTED_PLATFORM_TAG}, found {platform_tag}", + ), + ( + dist_info_directories != expected_dist_info_directories, + f"unexpected dist-info directories: expected {expected_dist_info_directory}, " + f"found {', '.join(sorted(dist_info_directories))}", + ), + (invalid_dist_info_files, f"required dist-info file counts are invalid: {dist_info_file_counts}"), + ( + not invalid_dist_info_files and not wheel_metadata_tags_match, + f"WHEEL tags do not match filename: expected {', '.join(sorted(expanded_filename_tags))}, " + f"found {', '.join(sorted(wheel_metadata_tags))}", + ), + ( + native_module is not None and not panic_test_hook_absent, + "production native module exposes _panic_for_test", + ), + ( + not native_size_within_limit, + f"native extension exceeds 20 MB: {native_member.file_size / 1_000_000:.2f} MB", + ), + (bool(unexpected_members), f"wheel contains unexpected build artifacts: {', '.join(unexpected_members)}"), + ) + if failed + ) + sys.stderr.write("".join(f"{message}\n" for message in validation_errors)) + + return 0 if all(passed for _, passed in validations) else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/workflows/_test-unit-base.yml b/.github/workflows/_test-unit-base.yml index b7d185bd0b9..c4045a08ffb 100644 --- a/.github/workflows/_test-unit-base.yml +++ b/.github/workflows/_test-unit-base.yml @@ -149,7 +149,7 @@ jobs: --reruns "${RERUNS}" \ --reruns-delay 1 \ --durations=20 \ - --cov=./litellm \ + --cov=./litellm --cov=./enterprise/litellm_enterprise \ --cov-report=xml:coverage.xml \ --cov-config=pyproject.toml else @@ -161,7 +161,7 @@ jobs: --reruns-delay 1 \ --dist="${DIST}" \ --durations=20 \ - --cov=./litellm \ + --cov=./litellm --cov=./enterprise/litellm_enterprise \ --cov-report=xml:coverage.xml \ --cov-config=pyproject.toml fi diff --git a/.github/workflows/auto-close-duplicates.yml b/.github/workflows/auto-close-duplicates.yml new file mode 100644 index 00000000000..d8256917805 --- /dev/null +++ b/.github/workflows/auto-close-duplicates.yml @@ -0,0 +1,69 @@ +name: Auto-close duplicate issues + +on: + schedule: + - cron: "0 9 * * *" + workflow_dispatch: + inputs: + dry_run: + description: Log which issues would close without closing anything + type: boolean + default: true + grace_period_days: + description: Days a duplicate notice must go unanswered before the close + type: number + default: 3 + pull_request: + paths: + - .github/workflows/auto-close-duplicates.yml + - scripts/auto-close-duplicates.ts + - scripts/auto-close-duplicates.test.ts + +permissions: {} + +jobs: + test: + if: github.event_name == 'pull_request' + runs-on: ubuntu-latest + timeout-minutes: 5 + permissions: + contents: read + steps: + - name: Checkout repository + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Setup Bun + uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6 # v2.2.0 + with: + bun-version: "1.4.0" + + - name: Test the sweep + run: bun test scripts/auto-close-duplicates.test.ts + + sweep: + if: github.event_name != 'pull_request' && github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + timeout-minutes: 10 + permissions: + contents: read + issues: write + steps: + - name: Checkout repository + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Setup Bun + uses: oven-sh/setup-bun@0c5077e51419868618aeaa5fe8019c62421857d6 # v2.2.0 + with: + # Exact version, never latest: the next step holds an issues: write token + bun-version: "1.4.0" + + - name: Close unanswered duplicates, reopen ones the reporter answered + run: bun run scripts/auto-close-duplicates.ts + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + DRY_RUN: ${{ inputs.dry_run == true }} + GRACE_PERIOD_DAYS: ${{ inputs.grace_period_days }} diff --git a/.github/workflows/check-ui-api-types.yml b/.github/workflows/check-ui-api-types.yml index 285676a0ddd..312a80103f8 100644 --- a/.github/workflows/check-ui-api-types.yml +++ b/.github/workflows/check-ui-api-types.yml @@ -83,6 +83,24 @@ jobs: if: steps.changes.outputs.relevant == 'true' run: uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma + - name: Regenerate the lazy OpenAPI snapshot + if: steps.changes.outputs.relevant == 'true' + run: uv run --no-sync python -m litellm.proxy._lazy_openapi_snapshot + + - name: Fail if the lazy OpenAPI snapshot is stale + if: steps.changes.outputs.relevant == 'true' + run: | + if ! git diff --exit-code -- litellm/proxy/_lazy_openapi_snapshot.json; then + echo "::error file=litellm/proxy/_lazy_openapi_snapshot.json::The lazy OpenAPI snapshot is out of sync with the lazily loaded routes." + echo "" + echo "A lazily loaded route or model changed without regenerating the snapshot that /openapi.json serves for unloaded features." + echo "To fix, run from the repo root:" + echo " uv run python -m litellm.proxy._lazy_openapi_snapshot" + echo "then run npm run gen:api from ui/litellm-dashboard and commit both files." + exit 1 + fi + echo "_lazy_openapi_snapshot.json is in sync with the lazily loaded routes." + - name: Set up Node.js if: steps.changes.outputs.relevant == 'true' uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 diff --git a/.github/workflows/check_duplicate_issues.yml b/.github/workflows/check_duplicate_issues.yml index 78198b2c7bb..41ec43a1d9b 100644 --- a/.github/workflows/check_duplicate_issues.yml +++ b/.github/workflows/check_duplicate_issues.yml @@ -1,12 +1,19 @@ name: Check Duplicate Issues +# Flagging only. "Auto-close duplicate issues" closes a flagged issue 3 days later, +# and only when its title is identical to an older open issue and nobody replied. +# The HTML marker below is the handshake between the two, so keep it in the template. + on: issues: types: [opened, edited] +permissions: {} + jobs: check-duplicate: runs-on: ubuntu-latest + timeout-minutes: 5 permissions: issues: write contents: read @@ -19,35 +26,12 @@ jobs: threshold: 0.6 reaction: eyes comment: | - **⚠️ Potential duplicate detected** + + **Potential duplicate detected** - This issue appears similar to existing issue(s): + This looks similar to: {{#issues}} - - [#{{number}}]({{html_url}}) - {{title}} ({{accuracy}}% similar) + - #{{number}} - {{title}} {{/issues}} - Please review the linked issue(s) to see if they address your concern. If this is not a duplicate, please provide additional context to help us understand the difference. - - - name: Checkout close script - if: github.event.action == 'opened' - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 - with: - sparse-checkout: .github/scripts - persist-credentials: false - - - name: Set up Python - if: github.event.action == 'opened' - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: "3.12" - - - name: Auto-close if high-confidence duplicate - if: github.event.action == 'opened' - env: - GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} - run: | - python3 .github/scripts/close_duplicate_issues.py \ - --issue-number ${{ github.event.issue.number }} \ - --repo ${{ github.repository }} \ - --threshold 0.85 \ - --close + If this is a duplicate, add a thumbs-up reaction to the existing issue and follow along there. When the title is identical to an older open issue, this issue closes automatically in 3 days unless someone responds. If it is not a duplicate, comment here or add a thumbs-down reaction to this comment and it stays open. diff --git a/.github/workflows/codspeed.yml b/.github/workflows/codspeed.yml index a69e50b5753..7e013b7bb0b 100644 --- a/.github/workflows/codspeed.yml +++ b/.github/workflows/codspeed.yml @@ -12,6 +12,7 @@ on: - "uv.lock" - ".github/workflows/codspeed.yml" - ".github/actions/setup-uv-with-retries/**" + - ".github/actions/cache-cargo-build/**" pull_request: branches: - main @@ -23,6 +24,7 @@ on: - "uv.lock" - ".github/workflows/codspeed.yml" - ".github/actions/setup-uv-with-retries/**" + - ".github/actions/cache-cargo-build/**" # Allow CodSpeed to trigger backtest performance analysis # in order to generate initial data workflow_dispatch: @@ -55,6 +57,26 @@ jobs: with: version: "0.10.9" + - name: Cache the Rust build + uses: ./.github/actions/cache-cargo-build + + # Build the wheel and resolve every dependency outside the CodSpeed + # runner: the same maturin build took 42 minutes inside `codspeed run` + # versus under 3 minutes as a plain step (LIT-6183) + - name: Build environment + run: > + env PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 + uv run --frozen --no-default-groups + --with pytest==8.3.5 + --with pytest-codspeed==4.3.0 + --with "mcp>=1.26.0,<2.0" + --with "a2a-sdk>=1.1.0,<2.0" + pytest + -p pytest_codspeed.plugin + tests/benchmarks/ + --codspeed + --collect-only -q + - name: Run benchmarks uses: CodSpeedHQ/action@1c8ae4843586d3ba879736b7f6b7b0c990757fab # v4.12.1 with: diff --git a/.github/workflows/e2e_record_replay.yml b/.github/workflows/e2e_record_replay.yml new file mode 100644 index 00000000000..ca52f0b4d81 --- /dev/null +++ b/.github/workflows/e2e_record_replay.yml @@ -0,0 +1,237 @@ +name: "E2E Record and Replay" + +on: + schedule: + - cron: "0 8 * * 6" + - cron: "0 8 * * 1-5" + workflow_dispatch: + inputs: + mode: + description: "record (hits real providers and publishes a fresh bundle) or replay (bundle only, zero provider egress)" + type: choice + options: + - record + - replay + default: record + +permissions: + contents: read + +jobs: + record: + name: "Record the e2e suite against real providers" + if: >- + (github.event_name != 'schedule' || github.repository == 'BerriAI/litellm') && + (github.event.schedule == '0 8 * * 6' || + (github.event_name == 'workflow_dispatch' && github.event.inputs.mode == 'record')) + runs-on: ubuntu-latest + timeout-minutes: 45 + services: + postgres: + image: postgres:16.6 + env: + POSTGRES_USER: llmproxy + POSTGRES_PASSWORD: dbpassword9090 + POSTGRES_DB: litellm + ports: + - 5432:5432 + options: >- + --health-cmd "pg_isready -U llmproxy" + --health-interval 5s + --health-timeout 5s + --health-retries 10 + env: + DATABASE_URL: postgresql://llmproxy:dbpassword9090@localhost:5432/litellm + LITELLM_MASTER_KEY: sk-e2e-record-replay + LITELLM_LOCAL_MODEL_COST_MAP: "True" + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Set up uv + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + + - name: Cache the Rust build + uses: ./.github/actions/cache-cargo-build + + - name: Install dependencies + run: | + .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra proxy + + - name: Cache Prisma binaries + uses: ./.github/actions/cache-prisma-binaries + + - name: Generate Prisma client + run: | + uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma + + - name: Start the proxy + run: | + nohup uv run --no-sync litellm --config tests/e2e/gateway/record_replay_ci_config.yml --port 4000 > proxy.log 2>&1 & + for _ in $(seq 1 90); do + if curl -fs http://localhost:4000/health/liveliness > /dev/null; then + exit 0 + fi + sleep 2 + done + echo "proxy never became live" + tail -n 100 proxy.log + exit 1 + + - name: Record the replayable e2e lane + env: + E2E_FIXTURE_MODE: record + run: | + uv run --no-sync pytest tests/e2e -m replayable --reruns 0 -v --tb=short -rA + + - name: Pack the fixture bundle + run: | + .github/scripts/e2e_pack_fixture_bundle.sh tests/e2e/.fixtures "${RUNNER_TEMP}/bundle/e2e-fixtures.tar.gz" + + - name: Publish the fixture bundle + uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 + with: + name: e2e-fixtures-bundle + path: | + ${{ runner.temp }}/bundle/e2e-fixtures.tar.gz + ${{ runner.temp }}/bundle/e2e-fixtures.tar.gz.sha256 + if-no-files-found: error + retention-days: 30 + + - name: Show proxy log on failure + if: failure() + run: tail -n 300 proxy.log + + replay: + name: "Replay the e2e suite from the pinned bundle with zero egress" + if: >- + (github.event_name != 'schedule' || github.repository == 'BerriAI/litellm') && + (github.event.schedule == '0 8 * * 1-5' || + (github.event_name == 'workflow_dispatch' && github.event.inputs.mode == 'replay')) + runs-on: ubuntu-latest + timeout-minutes: 45 + permissions: + contents: read + actions: read + services: + postgres: + image: postgres:16.6 + env: + POSTGRES_USER: llmproxy + POSTGRES_PASSWORD: dbpassword9090 + POSTGRES_DB: litellm + ports: + - 5432:5432 + options: >- + --health-cmd "pg_isready -U llmproxy" + --health-interval 5s + --health-timeout 5s + --health-retries 10 + env: + DATABASE_URL: postgresql://llmproxy:dbpassword9090@localhost:5432/litellm + LITELLM_MASTER_KEY: sk-e2e-record-replay + LITELLM_LOCAL_MODEL_COST_MAP: "True" + GH_TOKEN: ${{ github.token }} + OPENAI_API_KEY: sk-replay-must-never-reach-a-provider + ANTHROPIC_API_KEY: sk-ant-replay-must-never-reach-a-provider + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Set up uv + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + + - name: Cache the Rust build + uses: ./.github/actions/cache-cargo-build + + - name: Install dependencies + run: | + .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra proxy + + - name: Cache Prisma binaries + uses: ./.github/actions/cache-prisma-binaries + + - name: Generate Prisma client + run: | + uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma + + - name: Fetch the pinned fixture bundle by digest + env: + BASE_BRANCH: ${{ github.ref_name }} + run: | + .github/scripts/e2e_fetch_fixture_bundle.sh \ + "${GITHUB_REPOSITORY}" \ + e2e-fixtures-bundle \ + "${BASE_BRANCH}" \ + tests/e2e/.fixtures + + - name: Start the proxy + run: | + nohup uv run --no-sync litellm --config tests/e2e/gateway/record_replay_ci_config.yml --port 4000 > proxy.log 2>&1 & + for _ in $(seq 1 90); do + if curl -fs http://localhost:4000/health/liveliness > /dev/null; then + exit 0 + fi + sleep 2 + done + echo "proxy never became live" + tail -n 100 proxy.log + exit 1 + + - name: Start the egress sentinel + run: | + # shellcheck disable=SC2024 # the log redirect is deliberately the runner user's, so a later non-sudo cat can read it + sudo python3 .github/scripts/e2e_egress_sentinel.py serve \ + --host api.openai.com \ + --host api.anthropic.com \ + --hits-file "${RUNNER_TEMP}/egress-hits.jsonl" \ + --ready-file "${RUNNER_TEMP}/egress-ready" \ + --pid-file "${RUNNER_TEMP}/egress.pid" \ + > "${RUNNER_TEMP}/egress-sentinel.log" 2>&1 & + for _ in $(seq 1 30); do + if [[ -f "${RUNNER_TEMP}/egress-ready" ]]; then + cat "${RUNNER_TEMP}/egress-sentinel.log" + exit 0 + fi + sleep 1 + done + echo "egress sentinel never became ready" + cat "${RUNNER_TEMP}/egress-sentinel.log" + exit 1 + + - name: Replay the replayable e2e lane + env: + E2E_FIXTURE_MODE: replay + run: | + uv run --no-sync pytest tests/e2e -m replayable --reruns 0 -v --tb=short -rA + + - name: Stop the egress sentinel and assert zero provider egress + if: always() + run: | + if [[ -f "${RUNNER_TEMP}/egress.pid" ]]; then + sudo kill -TERM "$(cat "${RUNNER_TEMP}/egress.pid")" 2>/dev/null || true + sleep 2 + fi + python3 .github/scripts/e2e_egress_sentinel.py assert-empty --hits-file "${RUNNER_TEMP}/egress-hits.jsonl" + + - name: Show proxy log on failure + if: failure() + run: tail -n 300 proxy.log diff --git a/.github/workflows/image-scan.yml b/.github/workflows/image-scan.yml index 8faf3ef6229..206bb809e0c 100644 --- a/.github/workflows/image-scan.yml +++ b/.github/workflows/image-scan.yml @@ -17,10 +17,14 @@ on: - backend/Dockerfile - backend/main.py - docker/component_entrypoint.sh + - docker/entrypoint.sh + - litellm/proxy/prisma_migration.py - litellm-proxy-extras/** - tests/proxy_migration_tests/** - uv.lock - ui/litellm-dashboard/package-lock.json + - ui/Dockerfile + - ui/nginx.conf - .github/workflows/image-scan.yml schedule: - cron: "41 6 * * *" @@ -76,7 +80,7 @@ jobs: LITELLM_IMAGE: litellm-image-scan:${{ github.sha }} run: | python -m pip install "pytest==9.0.3" - python -m pytest tests/proxy_migration_tests/test_offline_image_migration.py -v + python -m pytest tests/proxy_migration_tests/test_offline_image_migration.py tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py -v # Scans the whole shipped artifact: OS/apk plus every language package # baked into the image, including ones no lockfile declares (e.g. prisma's @@ -120,7 +124,7 @@ jobs: LITELLM_IMAGE: litellm-runtime-scan:${{ github.sha }} run: | python -m pip install "pytest==9.0.3" - python -m pytest tests/proxy_migration_tests/test_offline_image_migration.py -v + python -m pytest tests/proxy_migration_tests/test_offline_image_migration.py tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py -v migrations-image: name: migrations-image @@ -181,7 +185,36 @@ jobs: LITELLM_COMPONENT_PORT: "4000" run: | python -m pip install "pytest==9.0.3" - python -m pytest tests/proxy_migration_tests/test_component_image_serves_offline.py -v + python -m pytest tests/proxy_migration_tests/test_component_image_serves_offline.py tests/proxy_migration_tests/test_image_bedrock_realtime_extra.py -v + + ui-image: + name: ui-image + runs-on: ubuntu-latest + if: >- + github.event_name != 'pull_request' || + github.event.pull_request.head.repo.full_name == github.repository + timeout-minutes: 30 + permissions: + contents: read + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Build UI image + run: docker build -f ui/Dockerfile -t litellm-ui-scan:${{ github.sha }} . + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Verify the UI serves offline as an arbitrary uid with a read-only root fs + env: + LITELLM_IMAGE: litellm-ui-scan:${{ github.sha }} + run: | + python -m pip install "pytest==9.0.3" + python -m pytest tests/proxy_migration_tests/test_ui_image_serves_offline.py -v backend-image: name: backend-image diff --git a/.github/workflows/mutation-test.yml b/.github/workflows/mutation-test.yml index 602c26a3e98..b7d28bcaae4 100644 --- a/.github/workflows/mutation-test.yml +++ b/.github/workflows/mutation-test.yml @@ -87,11 +87,20 @@ jobs: run: | uv pip uninstall pytest-retry || true + # Ends before the job's own deadline so a run that outlasts the budget is + # still followed by the report and upload steps. mutmut saves after every + # mutant result, to mutants/.meta, so an interrupted run + # still scores the mutants it finished and export-cicd-stats can read + # them; a cancelled job skips those steps and publishes nothing at all. - name: Run mutmut + timeout-minutes: 300 env: # Make the mutants/ sandbox win over site-packages on sys.path so the # trampolined files are imported instead of the installed copy. PYTHONPATH: ${{ github.workspace }}/mutants + # Without this mutmut finds no covered lines and generates 0 mutants. + # See the file itself for why. + COVERAGE_RCFILE: ${{ github.workspace }}/.github/mutmut-coverage.rc run: | set -o pipefail mkdir -p mutants @@ -130,6 +139,7 @@ jobs: mutmut-run.log mutants/mutmut-stats.json mutants/mutmut-cicd-stats.json + mutants/**/*.meta mutants/litellm/proxy/management_endpoints/**/*.py if-no-files-found: warn retention-days: 14 diff --git a/.github/workflows/report-rust-release-wheel.yml b/.github/workflows/report-rust-release-wheel.yml new file mode 100644 index 00000000000..1d93b56f77f --- /dev/null +++ b/.github/workflows/report-rust-release-wheel.yml @@ -0,0 +1,130 @@ +name: Report LiteLLM Rust release wheel + +on: # zizmor: ignore[dangerous-triggers] reporter executes no PR code and consumes no PR artifacts or outputs + workflow_run: + workflows: + - LiteLLM Rust + types: + - completed + +permissions: {} + +concurrency: + group: ${{ github.workflow }}-${{ github.event.workflow_run.pull_requests[0].number || github.event.workflow_run.id }} + cancel-in-progress: false + +jobs: + report-release-wheel: + name: report release wheel + if: >- + github.event.workflow_run.event == 'pull_request' && + github.event.workflow_run.path == '.github/workflows/test-rust.yml' && + github.event.workflow_run.head_repository.full_name == github.repository && + github.event.workflow_run.pull_requests[0].number != null + runs-on: ubuntu-latest + timeout-minutes: 5 + permissions: + issues: write # PR comments use the issues API + pull-requests: read # Current-head validation rejects stale workflow runs + + steps: + - name: Link release wheel report on PR + uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7.1.0 + env: + COMMENT_MARKER: "" + with: + script: | + const marker = process.env.COMMENT_MARKER; + const workflowRun = context.payload.workflow_run; + const allowedConclusions = new Set([ + "action_required", + "cancelled", + "failure", + "neutral", + "skipped", + "stale", + "startup_failure", + "success", + "timed_out", + ]); + if ( + !allowedConclusions.has(workflowRun.conclusion) || + workflowRun.event !== "pull_request" || + workflowRun.path !== ".github/workflows/test-rust.yml" || + workflowRun.head_repository?.full_name !== + `${context.repo.owner}/${context.repo.repo}` || + workflowRun.pull_requests?.length !== 1 + ) { + throw new Error("unexpected source workflow"); + } + const pullRequest = workflowRun.pull_requests[0]; + const pullRequestNumber = pullRequest.number; + const headSha = workflowRun.head_sha; + const runId = workflowRun.id; + if ( + !Number.isSafeInteger(pullRequestNumber) || + pullRequestNumber <= 0 || + !Number.isSafeInteger(runId) || + runId <= 0 || + !/^[0-9a-f]{40}$/.test(headSha) || + pullRequest.head?.sha !== headSha + ) { + throw new Error("invalid source workflow metadata"); + } + const runUrl = + `${context.serverUrl}/${context.repo.owner}/${context.repo.repo}` + + `/actions/runs/${runId}`; + const result = + workflowRun.conclusion === "success" + ? "successfully" + : `with \`${workflowRun.conclusion}\``; + const body = [ + marker, + "## LiteLLM Rust workflow", + "", + `Workflow completed ${result} for \`${headSha}\``, + "", + `[View workflow run](${runUrl})`, + ].join("\n"); + const comments = await github.paginate(github.rest.issues.listComments, { + owner: context.repo.owner, + repo: context.repo.repo, + issue_number: pullRequestNumber, + per_page: 100, + }); + const existing = comments.find( + (comment) => + comment.user?.login === "github-actions[bot]" && + comment.body?.startsWith(marker), + ); + const currentPullRequest = ( + await github.rest.pulls.get({ + owner: context.repo.owner, + repo: context.repo.repo, + pull_number: pullRequestNumber, + }) + ).data; + if ( + currentPullRequest.state !== "open" || + currentPullRequest.head.repo?.full_name !== + `${context.repo.owner}/${context.repo.repo}` || + currentPullRequest.head.sha !== headSha + ) { + core.info("source workflow no longer matches the current pull request head"); + return; + } + if (existing) { + await github.rest.issues.updateComment({ + owner: context.repo.owner, + repo: context.repo.repo, + comment_id: existing.id, + body, + }); + } else { + await github.rest.issues.createComment({ + owner: context.repo.owner, + repo: context.repo.repo, + issue_number: pullRequestNumber, + body, + }); + } diff --git a/.github/workflows/sync-together-ai-models.yml b/.github/workflows/sync-together-ai-models.yml new file mode 100644 index 00000000000..1daaadeabe2 --- /dev/null +++ b/.github/workflows/sync-together-ai-models.yml @@ -0,0 +1,68 @@ +name: Sync Together AI model registry + +on: + schedule: + - cron: "30 6 * * *" + workflow_dispatch: + +permissions: + contents: write + pull-requests: write + +jobs: + sync_together_ai_models: + if: github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + ref: litellm_internal_staging + persist-credentials: false + - name: Set up uv + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + - name: Look for an already-open sync PR + id: existing + run: | + open_pr="$(gh pr list --repo "$GITHUB_REPOSITORY" --state open --limit 1000 --json headRefName \ + --jq '[.[].headRefName | select(startswith("litellm_together_registry_sync_"))] | first // empty')" + echo "open_pr=$open_pr" >> "$GITHUB_OUTPUT" + if [ -n "$open_pr" ]; then + echo "An open sync PR already exists on branch $open_pr; skipping this run." + fi + env: + GH_TOKEN: ${{ secrets.GH_TOKEN || github.token }} + - name: Run the sync + if: steps.existing.outputs.open_pr == '' + run: | + uv run --frozen python scripts/sync_together_ai_models.py --write --pr-body-file "$RUNNER_TEMP/pr_body.md" + env: + TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }} + - name: Regenerate the JSON schema + if: steps.existing.outputs.open_pr == '' + run: | + uv run --frozen python ci_cd/generate_model_prices_schema.py + - name: Create a pull request when the registry changed + if: steps.existing.outputs.open_pr == '' + run: | + if git diff --quiet; then + echo "Registry already in sync; no PR needed." + exit 0 + fi + branch="litellm_together_registry_sync_$(date +'%Y-%m-%d')" + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + git checkout -b "$branch" + git add model_prices_and_context_window.json \ + litellm/model_prices_and_context_window_backup.json \ + model_prices_and_context_window.schema.json + git commit -m "feat(models): sync together_ai model registry $(date +'%Y-%m-%d')" + gh auth setup-git + git push origin "$branch" + gh pr create --title "feat(models): sync together_ai model registry" \ + --body-file "$RUNNER_TEMP/pr_body.md" \ + --head "$branch" \ + --base litellm_internal_staging + env: + GH_TOKEN: ${{ secrets.GH_TOKEN || github.token }} diff --git a/.github/workflows/test-code-quality.yml b/.github/workflows/test-code-quality.yml index 2a832d1956e..c112bf2bb22 100644 --- a/.github/workflows/test-code-quality.yml +++ b/.github/workflows/test-code-quality.yml @@ -131,6 +131,9 @@ jobs: - name: check_e2e_no_raw_requests run: uv run --no-sync python ./tests/code_coverage_tests/check_e2e_no_raw_requests.py + - name: check_migrations_no_data_rewrites + run: uv run --no-sync python ./tests/code_coverage_tests/check_migrations_no_data_rewrites.py + - name: memory_test run: uv run --no-sync python ./tests/code_coverage_tests/memory_test.py diff --git a/.github/workflows/test-linting.yml b/.github/workflows/test-linting.yml index ccb58f5cc9c..9f1283da19e 100644 --- a/.github/workflows/test-linting.yml +++ b/.github/workflows/test-linting.yml @@ -67,6 +67,17 @@ jobs: with: version: "0.10.9" + - name: Cache uv dependencies + if: steps.changes.outputs.decision != 'skip' + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cache/uv + .venv + key: ${{ runner.os }}-uv-lint-${{ hashFiles('uv.lock') }} + restore-keys: | + ${{ runner.os }}-uv-lint- + - name: Clean Python cache if: steps.changes.outputs.decision != 'skip' run: | diff --git a/.github/workflows/test-mcp.yml b/.github/workflows/test-mcp.yml index 6ea814dc2de..93ffcbe0586 100644 --- a/.github/workflows/test-mcp.yml +++ b/.github/workflows/test-mcp.yml @@ -60,4 +60,4 @@ jobs: - name: Run MCP tests if: steps.changes.outputs.decision != 'skip' run: | - uv run --no-sync pytest tests/mcp_tests -x -vv -n 4 --cov=./litellm --cov-report=xml --durations=5 + uv run --no-sync pytest tests/mcp_tests -x -vv -n 4 --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml --durations=5 diff --git a/.github/workflows/test-postgres.yml b/.github/workflows/test-postgres.yml new file mode 100644 index 00000000000..96c514dff7c --- /dev/null +++ b/.github/workflows/test-postgres.yml @@ -0,0 +1,145 @@ +name: "Postgres Tests" + +on: + pull_request: + branches: + - main + - litellm_internal_staging + - litellm_oss_staging + - "litellm_**" + push: + branches: + - main + - litellm_internal_staging + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + +jobs: + postgres: + name: ${{ matrix.shard }} + runs-on: ubuntu-latest + timeout-minutes: ${{ matrix.job-timeout-minutes }} + permissions: + contents: read + + services: + postgres: + image: postgres:16@sha256:e17e86066e5ef83e0952a9347f5c792b7ece00972e2aa787a6986f471b3dd3d5 + env: + POSTGRES_USER: postgres + POSTGRES_PASSWORD: postgres + POSTGRES_DB: litellm_test + ports: + - 5432:5432 + options: >- + --health-cmd pg_isready + --health-interval 10s + --health-timeout 5s + --health-retries 10 + + strategy: + fail-fast: false + matrix: + include: + - shard: proxy-behavior + test-path: "tests/proxy_behavior" + seed: db-push + workers: 0 + timeout-minutes: 25 + job-timeout-minutes: 50 + + - shard: proxy-security + test-path: "tests/proxy_security_tests" + seed: db-push + workers: 0 + timeout-minutes: 15 + job-timeout-minutes: 40 + + - shard: schema-migration + test-path: "tests/proxy_migration_tests" + seed: none + workers: 0 + timeout-minutes: 20 + job-timeout-minutes: 45 + + env: + DATABASE_URL: "postgresql://postgres:postgres@localhost:5432/litellm_test" + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + timeout-minutes: 3 + with: + persist-credentials: false + + - name: Detect relevant changes + id: changes + timeout-minutes: 2 + uses: ./.github/actions/detect-changes + + - name: Set up Python + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 3 + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Set up uv + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 3 + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + + - name: Cache uv dependencies + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 5 + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cache/uv + .venv + key: ${{ runner.os }}-uv-postgres-${{ hashFiles('uv.lock') }} + restore-keys: | + ${{ runner.os }}-uv-postgres- + + - name: Install dependencies + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 12 + run: | + .github/scripts/uv_sync_with_retries.sh --frozen --all-groups --all-extras + + - name: Cache Prisma binaries + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 3 + uses: ./.github/actions/cache-prisma-binaries + + - name: Generate Prisma client + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 5 + run: | + uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma + + - name: Seed database schema + if: steps.changes.outputs.decision != 'skip' && matrix.seed != 'none' + timeout-minutes: 10 + run: | + uv run --no-sync prisma db push --schema litellm/proxy/schema.prisma --accept-data-loss + + - name: Run tests + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: ${{ matrix.timeout-minutes }} + env: + TEST_PATH: ${{ matrix.test-path }} + WORKERS: ${{ matrix.workers }} + run: | + if [ "${WORKERS}" = "0" ]; then + uv run --no-sync pytest ${TEST_PATH:?} -vv --tb=short --durations=10 + else + uv run --no-sync pytest ${TEST_PATH:?} -vv --tb=short --durations=10 -n "${WORKERS}" + fi diff --git a/.github/workflows/test-redis-compat.yml b/.github/workflows/test-redis-compat.yml new file mode 100644 index 00000000000..f29755a74b1 --- /dev/null +++ b/.github/workflows/test-redis-compat.yml @@ -0,0 +1,77 @@ +name: "Unit Tests: Redis Client Version Compatibility" + +on: + pull_request: + branches: + - main + - litellm_internal_staging + - litellm_oss_staging + - "litellm_**" + paths: + - "litellm/_redis.py" + - "litellm/_redis_credential_provider.py" + - "tests/test_litellm/test_redis.py" + - "tests/test_litellm/caching/test_redis_connection_pool.py" + - ".github/workflows/test-redis-compat.yml" + - "pyproject.toml" + - "uv.lock" + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + redis-compat: + name: "redis-py ${{ matrix.redis-version }}" + runs-on: ubuntu-latest + timeout-minutes: 15 + + strategy: + fail-fast: false + matrix: + # 5.3.1 is the version pinned in uv.lock (redisvl caps it below 6); the + # newer legs prove the inspect.signature introspection in litellm/_redis.py + # keeps extracting kwargs on the redis-py releases people actually run now. + # Only the exact release 6.0.0 is skipped: rq (pulled by the proxy extra) + # specifies `redis != 6`, which excludes 6.0.0 alone, so 6.4.0 stands in + # for the 6.x line. + redis-version: ["5.3.1", "6.4.0", "7.4.1", "8.0.1"] + + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Set up uv + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + + - name: Install dependencies + run: | + .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router + + - name: Pin redis-py to the matrix version + env: + REDIS_VERSION: ${{ matrix.redis-version }} + run: | + uv pip install "redis==${REDIS_VERSION:?}" + uv run --no-sync python -c "import redis; assert redis.__version__ == '${REDIS_VERSION:?}', redis.__version__; print('redis-py', redis.__version__)" + + - name: Run redis unit tests + run: | + uv run --no-sync pytest \ + tests/test_litellm/test_redis.py \ + tests/test_litellm/caching/test_redis_connection_pool.py \ + --tb=short -vv \ + --reruns 2 \ + --reruns-delay 1 \ + --durations=20 diff --git a/.github/workflows/test-rust.yml b/.github/workflows/test-rust.yml index 21e1bcb90c6..aada0fcf239 100644 --- a/.github/workflows/test-rust.yml +++ b/.github/workflows/test-rust.yml @@ -4,6 +4,11 @@ on: push: paths: - "litellm-rust/**" + - ".cargo/**" + - "pyproject.toml" + - "rust-toolchain.toml" + - ".github/scripts/smoke_test_native_wheel.py" + - ".github/scripts/verify_linux_native_wheel.py" - ".github/workflows/test-rust.yml" pull_request: branches: @@ -13,6 +18,11 @@ on: - "litellm_**" paths: - "litellm-rust/**" + - ".cargo/**" + - "pyproject.toml" + - "rust-toolchain.toml" + - ".github/scripts/smoke_test_native_wheel.py" + - ".github/scripts/verify_linux_native_wheel.py" - ".github/workflows/test-rust.yml" permissions: @@ -40,9 +50,7 @@ jobs: persist-credentials: false - name: Set up Rust - run: | - rustup toolchain install stable --profile minimal --component clippy,rustfmt - rustup default stable + run: rustup toolchain install - name: Cache Cargo registry and target uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 @@ -51,7 +59,7 @@ jobs: ~/.cargo/registry ~/.cargo/git litellm-rust/target - key: ${{ runner.os }}-cargo-${{ hashFiles('litellm-rust/Cargo.lock') }} + key: ${{ runner.os }}-cargo-${{ hashFiles('rust-toolchain.toml', 'litellm-rust/Cargo.lock') }} restore-keys: | ${{ runner.os }}-cargo- @@ -69,3 +77,47 @@ jobs: - name: Run core tests with Bedrock auth run: cargo test -p litellm-core --features bedrock-auth --locked + + release-wheel: + name: release wheel + runs-on: ubuntu-latest + timeout-minutes: 20 + permissions: + contents: read + env: + CARGO_TERM_COLOR: always + + steps: + - name: Checkout repository + uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + persist-credentials: false + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + with: + python-version: "3.12" + + - name: Set up uv + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + + - name: Set up Rust + run: rustup toolchain install + + - name: Build release wheel + run: uv build --wheel --out-dir dist + + - name: Build panic contract wheel + run: >- + uv build --wheel --out-dir panic-dist + --config-setting "maturin.build-args=--features panic-test,extension-module" + + - name: Smoke-test native panic unwinding + run: python .github/scripts/smoke_test_native_wheel.py panic-dist/*.whl + + - name: Verify stripped native extension + env: + RELEASE_WHEEL_COMMIT_SHA: ${{ github.event.pull_request.head.sha || github.sha }} + run: python .github/scripts/verify_linux_native_wheel.py dist/*.whl diff --git a/.github/workflows/test-terraform-provider.yml b/.github/workflows/test-terraform-provider.yml index e46432e0e31..eb7b299fd1f 100644 --- a/.github/workflows/test-terraform-provider.yml +++ b/.github/workflows/test-terraform-provider.yml @@ -114,4 +114,4 @@ jobs: - name: Audit provider endpoints against the schema working-directory: terraform/provider - run: go run ./tools/endpointaudit -provider-dir ./litellm -spec "${RUNNER_TEMP}/openapi.json" + run: go run ./tools/endpointaudit -provider-dir ./litellm -spec "${RUNNER_TEMP}/openapi.json" -coverage-allowlist ./tools/endpointaudit/coverage_allowlist.txt diff --git a/.github/workflows/test-unit.yml b/.github/workflows/test-unit.yml index 71eb0958bec..c2dff805772 100644 --- a/.github/workflows/test-unit.yml +++ b/.github/workflows/test-unit.yml @@ -103,6 +103,7 @@ jobs: tests/test_litellm/completion_extras tests/test_litellm/compression tests/test_litellm/containers + tests/test_litellm/endpoints tests/test_litellm/experimental_mcp_client tests/test_litellm/models tests/test_litellm/repositories @@ -141,6 +142,7 @@ jobs: test-path: >- tests/test_litellm/proxy/analytics_endpoints tests/test_litellm/proxy/management_endpoints + tests/test_litellm/proxy/list_api tests/test_litellm/proxy/memory tests/test_litellm/proxy/guardrails tests/test_litellm/proxy/management_helpers @@ -164,11 +166,12 @@ jobs: tests/test_litellm/proxy/public_endpoints tests/test_litellm/proxy/prompts tests/test_litellm/proxy/rag_endpoints + tests/test_litellm/proxy/rerank_endpoints tests/test_litellm/proxy/realtime_endpoints tests/test_litellm/proxy/ui_crud_endpoints tests/test_litellm/proxy/config_resolvers tests/test_litellm/proxy/utils - workers: 2 + workers: 4 reruns: 2 timeout-minutes: 20 job-timeout-minutes: 60 @@ -195,11 +198,40 @@ jobs: tests/test_litellm/proxy/types_utils tests/test_litellm/proxy/logging_endpoints tests/test_litellm/proxy/test_*.py + workers: 4 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: caching-local + artifact-name: caching-local + test-path: >- + tests/local_testing/test_cache_preset_key.py + tests/local_testing/test_caching_handler.py + tests/local_testing/test_prompt_caching.py + tests/local_testing/test_responses_stream_cache_keys.py + tests/local_testing/test_unit_test_caching.py workers: 2 reruns: 2 timeout-minutes: 20 job-timeout-minutes: 60 + - shard: proxy-extras + artifact-name: proxy-extras + test-path: "tests/litellm-proxy-extras" + workers: 2 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: enterprise-package + artifact-name: enterprise-package + test-path: "tests/enterprise" + workers: 4 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + - shard: responses-caching-types artifact-name: responses-caching-types test-path: >- diff --git a/.gitignore b/.gitignore index 201e02f2189..deb0acae56e 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,8 @@ tests/e2e/.fixtures/ .venv-typecheck .venv_policy_test +.venv-mutmut +mutants/ .env .claude CLAUDE.local.md diff --git a/CLAUDE.md b/CLAUDE.md index b3383b4a895..d9e9e8f1586 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -23,6 +23,8 @@ When adding new features, add meaningful tests. Don't add tests that don't check Same thing for bug fixes. The tests should make it so that this specific bug can never happen again without failing tests (i.e., regression) +Never test structure of code only function of it + `tests/test_litellm/` mirrors `litellm/` in a parallel path (see `tests/test_litellm/readme.md`). Name tests `test_.py`, but always match the existing test file in the directory you touch — many provider dirs use longer descriptive names (e.g. `test_anthropic_chat_transformation.py`) to avoid ambiguity across sibling folders. For bug fixes, extend the existing mapped test file rather than creating a new one. Only create a new test file for a new feature (provider, endpoint, or transformation module) that has no mapped test yet, following that directory's naming convention (or `test_.py` if you're the first test there). One focused regression test beats many shallow ones End-to-end tests belong in `tests/e2e/` and must follow the harness conventions documented in that directory's `CLAUDE.md` @@ -37,13 +39,14 @@ If you're resolving a linear ticket, in the "## Linear ticket" section of the PR Never use `pytest` commands or the like as "Screenshots / Proof of Fix". We prefer curl'ing a live proxy instance running on localhost:4000 (I like to run it with `python litellm/proxy/proxy_cli.py --config litellm/proxy/dev_config.yaml --detailed_debug --reload --use_v2_migration_resolver 2>&1 | tee litellm.log`; the Admin UI dev server is `npm run dev` in `ui/litellm-dashboard`, served on port 3000) and showing both the command run and the output. Also, it should hit real LLM provider APIs, not mocks, and cost real $$$ because that is the most realistic test. The proof of fix should be exactly what the end user / customer would see / do. The run logs in PR #27703 is a prime example of how to do it (not a huge fan of using a python test script that future me and the team will have no visibility into; I prefer just curl commands or a short list of bash commands (e.g., using `for`)). If it's a UI thing, just tell me which URLs to go to (e.g., http://localhost:4000/ui/?page=logs), where to click, what fields to fill out, etc. along with the other commands to run in an ordered list, and I'll do it myself and post the screenshots after you make the PR -If you ever make public-facing PR descriptions, comments, issues, commit messages, etc., always follow these guidelines to sound less AI-y: +If you ever write any human-facing text (pull requests, issues, commit messages, discussion posts, github comments, release notes, docs, etc.), always follow these guidelines to sound less AI-y: - don't use emojis - don't use "—". Instead, reach for ",", ".", conjunction words, ":", ";", etc. in descending order of preference: vary among them, weighted toward the front of the list, and skip "," where it would cause a comma splice or the sentence is getting long. Overusing any one of them, ";" especially, also feels AI-y. A word cap does not penalize you for adding more sentences: when writing under tight word budgets, prefer a period split or a conjunction over ";", and keep to at most one ";" per message - don't use the pattern "It's not X, it's Y", "You're not X, you're Y", etc. -- don't use bulleted or numbered lists unless it would be nonsensical not to. Instead, prefer prose +- unless explicitly asked, don't use bulleted or numbered lists unless it would be nonsensical not to. Instead, prefer prose - don't add a trailing "." at the end of paragraphs (just like this file). That means every paragraph, not just the last one (of the markdown file, PR description, GitHub comment, etc.). Rule of thumb: if you're adding new line(s) before the next sentence, don't add a "." - don't use →. Instead, prefer not to use arrows, and if need be, use -> instead +- use plain, simple, everyday engineering language: the common phrase engineers actually say over rare compact phrasing, in grammatically complete sentences. When explicitly asked to use bullets or ordered lists and structure legitimately helps the reader, prefer nested bullets (any depth is fine) over dense lines in a flat structure Don't hesitate to use values in .env to get needed API keys and other secrets, as long as you never add them to conversation history, commit them, or include them in GitHub issues / PRs @@ -65,6 +68,8 @@ Commit and push your work when you're done without asking When referencing or running models (coding, QA'ing, writing docs, writing tests, etc.), use the latest model in that model family unless otherwise specified; treat your training knowledge, memories, configs, and tests as stale, and determine the family's latest with model_prices_and_context_window.json or the web +Always pull before starting any work. The checkout or worktree may be sitting on a stale branch + If you're an internal contributor, when creating a new PR, the typical flow is to branch off litellm_internal_staging and create a branch prefixed with litellm_. Do not create a branch prefixed with claude/ and generally do not have / in your branch names Do not add `Co-Authored-By: Claude` or any Claude attribution to commit messages. Never use a `claude/` prefix or put a `/` in a branch name. Do not add "Generated with Claude Code" (or any similar attribution) to PR descriptions or comments. Do not create a new PR/branch off the existing PR to fix/add something that is related and could've just been committed directly to the existing PR's branch @@ -79,6 +84,8 @@ Do not put names of customers or customer company names in code, PR descriptions CI supply-chain safety: Never pipe a remote script into a shell (`curl ... | bash`, `wget ... | sh`); download the artifact to a file, verify its SHA-256 checksum, then install. Pin every external tool to a specific version with a full URL (not `latest` or `stable`). Verify checksums for all downloaded binaries, using the provider's official `.sha256` / `.sha256sum` sidecar when available. These rules apply to every download in CI +Prisma migrations apply synchronously at proxy boot, before it serves traffic, so a migration must only change schema, never rewrite rows. No `UPDATE`, `DELETE` or `MERGE`, and no `INSERT ... SELECT`: on a spend-log-sized table any of those is minutes of downtime plus a doubled heap that plain autovacuum won't give back. `tests/code_coverage_tests/check_migrations_no_data_rewrites.py` enforces this. When a rewrite is genuinely bounded and has to ship inside the migration, mark the statement `-- data-migration-ok: ` + Follow these coding conventions for new/updated code (a three-line fix in a legacy file shouldn't trigger huge drive-by refactors): - Composition over inheritance diff --git a/Dockerfile b/Dockerfile index 66ce3af4a65..0a92aa9a68c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,10 +1,10 @@ # syntax=docker/dockerfile:1.7 # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43 @@ -40,8 +40,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN apk add --no-cache \ bash \ gcc \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ rust \ openssl \ openssl-dev \ @@ -51,6 +51,7 @@ RUN apk add --no-cache \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" # Copy dependency metadata first for layer caching @@ -65,7 +66,8 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 # Copy full source tree COPY . . @@ -86,7 +88,8 @@ RUN uv sync --frozen --no-default-groups --no-editable \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -100,8 +103,14 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root +# The base image only configures Chainguard's authenticated apk repo, which +# requires an enterprise subscription. Add the public Wolfi repo so `apk add` +# also works for anyone installing extra packages into a running container. +# https://github.com/BerriAI/litellm/issues/33518 +RUN echo "https://packages.wolfi.dev/os" >> /etc/apk/repositories + # node (without npm) is required by the prisma CLI at runtime -RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile +RUN apk add --no-cache bash openssl tzdata nodejs python-3.13 libsndfile WORKDIR /app ENV PATH="/app/.venv/bin:${PATH}" \ diff --git a/README.md b/README.md index 68aaa09ec98..92757fcbbc1 100644 --- a/README.md +++ b/README.md @@ -354,6 +354,8 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [Petals (`petals`)](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | | | | | | | | | [Pinstripes (`pinstripes`)](https://docs.litellm.ai/docs/providers/pinstripes) | ✅ | ✅ | ✅ | | | | | | | | | [Predibase (`predibase`)](https://docs.litellm.ai/docs/providers/predibase) | ✅ | ✅ | ✅ | | | | | | | | +| [Qwen AI Platform (`qwen_ai_platform`)](https://docs.litellm.ai/docs/providers/qwencloud) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | +| [QwenCloud (`qwencloud`)](https://docs.litellm.ai/docs/providers/qwencloud) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | | [Recraft (`recraft`)](https://docs.litellm.ai/docs/providers/recraft) | | | | | ✅ | | | | | | | [Replicate (`replicate`)](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | | | | | | | | | [Sagemaker Chat (`sagemaker_chat`)](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | | | | | | | | diff --git a/backend/Dockerfile b/backend/Dockerfile index 853c74b05ca..622fedcd70d 100644 --- a/backend/Dockerfile +++ b/backend/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -16,7 +16,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/ # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes. RUN for i in 1 2 3; do \ - apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \ + apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done @@ -46,7 +46,8 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra proxy-runtime \ --extra extra_proxy \ --extra semantic-router \ - --python python3 + --extra saml \ + --python python3.13 # Stage 2 — copy source and install the project + workspace members. COPY . . @@ -57,7 +58,8 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra proxy-runtime \ --extra extra_proxy \ --extra semantic-router \ - --python python3 + --extra saml \ + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -71,7 +73,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root RUN for i in 1 2 3; do \ - apk add --no-cache bash openssl tzdata python3 libsndfile libatomic && break; \ + apk add --no-cache bash openssl tzdata python-3.13 libsndfile libatomic && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json index 664e1669834..da788bf1ce3 100644 --- a/basedpyright-code-budget.json +++ b/basedpyright-code-budget.json @@ -1,36 +1,36 @@ { "reportAny": { - "limit": 19955 + "limit": 14076 }, "reportArgumentType": { - "limit": 2566 + "limit": 2216 }, "reportAssignmentType": { - "limit": 320 + "limit": 319 }, "reportAttributeAccessIssue": { - "limit": 488 + "limit": 480 }, "reportCallIssue": { - "limit": 114 + "limit": 112 }, "reportConstantRedefinition": { "limit": 40 }, "reportDeprecated": { - "limit": 213 + "limit": 211 }, "reportDuplicateImport": { "limit": 19 }, "reportExplicitAny": { - "limit": 6049 + "limit": 4128 }, "reportFunctionMemberAccess": { "limit": 7 }, "reportGeneralTypeIssues": { - "limit": 154 + "limit": 101 }, "reportIncompatibleMethodOverride": { "limit": 56 @@ -42,10 +42,10 @@ "limit": 12 }, "reportIndexIssue": { - "limit": 35 + "limit": 25 }, "reportInvalidTypeForm": { - "limit": 35 + "limit": 34 }, "reportInvalidTypeVarUse": { "limit": 2 @@ -54,10 +54,10 @@ "limit": 0 }, "reportMissingParameterType": { - "limit": 5663 + "limit": 5601 }, "reportMissingTypeArgument": { - "limit": 15555 + "limit": 15306 }, "reportMissingTypeStubs": { "limit": 40 @@ -72,7 +72,7 @@ "limit": 0 }, "reportOptionalMemberAccess": { - "limit": 1061 + "limit": 0 }, "reportOptionalOperand": { "limit": 0 @@ -84,46 +84,46 @@ "limit": 56 }, "reportPrivateUsage": { - "limit": 1822 + "limit": 1808 }, "reportRedeclaration": { "limit": 8 }, "reportReturnType": { - "limit": 213 + "limit": 181 }, "reportTypedDictNotRequiredAccess": { - "limit": 26 + "limit": 24 }, "reportUndefinedVariable": { "limit": 0 }, "reportUnknownArgumentType": { - "limit": 44655 + "limit": 44364 }, "reportUnknownLambdaType": { "limit": 109 }, "reportUnknownMemberType": { - "limit": 39011 + "limit": 38350 }, "reportUnknownParameterType": { - "limit": 19885 + "limit": 19626 }, "reportUnknownVariableType": { - "limit": 30569 + "limit": 29890 }, "reportUnnecessaryCast": { - "limit": 117 + "limit": 111 }, "reportUnnecessaryComparison": { - "limit": 699 + "limit": 692 }, "reportUnnecessaryContains": { "limit": 5 }, "reportUnnecessaryIsInstance": { - "limit": 836 + "limit": 826 }, "reportUntypedBaseClass": { "limit": 0 @@ -135,12 +135,12 @@ "limit": 21 }, "reportUnusedFunction": { - "limit": 139 + "limit": 138 }, "reportUnusedImport": { - "limit": 545 + "limit": 543 }, "reportUnusedVariable": { - "limit": 146 + "limit": 137 } } diff --git a/ci_cd/generate_model_prices_schema.py b/ci_cd/generate_model_prices_schema.py index b2bc3ebadb4..57cc742d5c4 100644 --- a/ci_cd/generate_model_prices_schema.py +++ b/ci_cd/generate_model_prices_schema.py @@ -73,6 +73,11 @@ ARRAY_KEYS: dict[str, JsonSchema] = { "description": "Output modalities the model can produce.", "items": {"type": "string", "enum": ["text", "image", "audio", "video", "code"]}, }, + "reasoning_effort_levels": { + "type": "array", + "description": "Exact reasoning_effort levels this deployment accepts; wins over supports_* flags.", + "items": {"type": "string", "enum": ["none", "minimal", "low", "medium", "high", "xhigh", "max"]}, + }, "supported_regions": { "type": "array", "description": "Cloud regions the model is available in ('global' or region ids).", @@ -157,6 +162,9 @@ COST_DESCRIPTIONS: dict[str, str] = { "input_cost_per_token": "USD per prompt token.", "output_cost_per_token": "USD per generated token.", "output_cost_per_reasoning_token": "USD per reasoning/thinking token, when billed separately.", + "google_maps_grounding_cost_per_query": ( + "USD per Grounding with Google Maps request; billed per query or per prompt per web_search_billing_unit." + ), "cache_creation_input_token_cost": "USD per token written to the provider's prompt cache.", "cache_read_input_token_cost": "USD per prompt token served from the provider's prompt cache.", "input_cost_per_token_batches": "USD per prompt token via the provider's batch API.", @@ -212,6 +220,15 @@ def string_key_schemas(modes: tuple) -> dict[str, JsonSchema]: "description": "Highest reasoning effort the Bedrock output_config accepts for this model.", "enum": ["low", "medium", "high", "max", "xhigh"], }, + "default_reasoning_effort": { + "type": "string", + "description": ( + "Reasoning effort the provider applies when the request omits reasoning_effort. " + "Gates whether a non-default temperature or the top_p/logprobs sampling params are " + "accepted, which hold only when the effort resolves to 'none'." + ), + "enum": ["none", "minimal", "low", "medium", "high", "xhigh"], + }, "comment": STRING, "audio_transcription_config": STRING, } diff --git a/codecov.yaml b/codecov.yaml index bc0b3604329..4d93c18f3ac 100644 --- a/codecov.yaml +++ b/codecov.yaml @@ -25,6 +25,8 @@ flag_management: carryforward: false - name: proxy-db-schema-migration carryforward: false + - name: circleci + carryforward: false component_management: individual_components: diff --git a/db_scripts/partition_spend_logs.sql b/db_scripts/partition_spend_logs.sql index 08fcbddb6f8..4e4a93539d7 100644 --- a/db_scripts/partition_spend_logs.sql +++ b/db_scripts/partition_spend_logs.sql @@ -10,6 +10,11 @@ -- partitioned, so existing installs are unaffected until you run this. -- -- IMPORTANT +-- * After partitioning, `prisma db push` (including the proxy's +-- --use_prisma_db_push startup mode) is NOT supported: it tries to rewrite +-- the primary key back to ("request_id"), which Postgres rejects on a +-- partitioned table. The proxy detects this and exits with guidance. +-- Use the default startup path (`prisma migrate deploy`) instead. -- * Test on a staging copy first and take a backup. -- * Postgres cannot convert a populated table to partitioned in place, so this -- renames the old table aside and creates a fresh partitioned table. diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index 4bf3ae2b417..e9ad2849bb2 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -1,10 +1,10 @@ # syntax=docker/dockerfile:1.7 # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43 @@ -39,8 +39,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN apk add --no-cache \ bash \ gcc \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ openssl \ openssl-dev \ nodejs \ @@ -49,6 +49,7 @@ RUN apk add --no-cache \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" # Copy dependency metadata first for layer caching @@ -63,7 +64,8 @@ RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-gr --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 # Copy full source tree COPY . . @@ -84,7 +86,8 @@ RUN uv sync --frozen --no-default-groups --no-editable \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -98,7 +101,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root # node (without npm) is required by the prisma CLI at runtime -RUN apk add --no-cache bash openssl tzdata nodejs python3 libsndfile +RUN apk add --no-cache bash openssl tzdata nodejs python-3.13 libsndfile WORKDIR /app ENV PATH="/app/.venv/bin:${PATH}" \ diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 7392cc09a0d..edf20e8bbff 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -1,8 +1,8 @@ # syntax=docker/dockerfile:1.7 # Base images -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG PROXY_EXTRAS_SOURCE=published ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a # Pinned by digest like the other base images; bump explicitly on Node upgrades. @@ -37,8 +37,8 @@ COPY --from=uvbin /uvx /usr/local/bin/uvx RUN for i in 1 2 3; do \ apk add --no-cache \ - python3 \ - python3-dev \ + python-3.13 \ + python-3.13-dev \ gcc \ rust \ bash \ @@ -52,6 +52,7 @@ RUN for i in 1 2 3; do \ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=0 \ PATH="/app/.venv/bin:${PATH}" \ LITELLM_NON_ROOT=true \ XDG_CACHE_HOME=/app/.cache @@ -69,7 +70,8 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 + --extra bedrock-realtime \ + --python python3.13 # Copy full source tree COPY . . @@ -96,7 +98,8 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3 \ + --extra bedrock-realtime \ + --python python3.13 \ --no-sources-package litellm-proxy-extras; \ else \ uv sync --frozen --no-default-groups --no-editable \ @@ -105,7 +108,8 @@ RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \ --extra extra_proxy \ --extra semantic-router \ --extra saml \ - --python python3; \ + --extra bedrock-realtime \ + --python python3.13; \ fi RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ @@ -124,7 +128,7 @@ RUN for i in 1 2 3; do \ apk upgrade --no-cache && break || sleep 5; \ done && \ for i in 1 2 3; do \ - apk add --no-cache python3 bash openssl tzdata libsndfile nodejs && break || sleep 5; \ + apk add --no-cache python-3.13 bash openssl tzdata libsndfile nodejs && break || sleep 5; \ done # Copy only what runtime needs. The application is installed inside the venv; diff --git a/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py b/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py index 18ac29b9781..b6f8bf2dc5b 100644 --- a/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py @@ -7,7 +7,7 @@ GET - /audit/{id} - Get audit log by id GET - /audit - Get all audit logs """ -from typing import Any, Dict, List, Optional +from typing import TYPE_CHECKING, Final #### AUDIT LOGGING #### from fastapi import APIRouter, Depends, HTTPException, Query @@ -18,11 +18,16 @@ from litellm_enterprise.types.proxy.audit_logging_endpoints import ( from litellm.proxy._types import CommonProxyErrors, UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth +from litellm.repositories.prisma_protocols import TableActions +from litellm.repositories.table_repositories import AuditLogRepository + +if TYPE_CHECKING: + from prisma import models as prisma_models router = APIRouter() -def _build_json_field_or_condition(json_key: str, value: str) -> Dict[str, Any]: +def _build_json_field_or_condition(json_key: str, value: str) -> dict[str, object]: """ Build an OR condition that matches a value inside a JSON column at the given key, checking both before_value and updated_values. @@ -53,33 +58,33 @@ async def get_audit_logs( page: int = Query(1, ge=1), page_size: int = Query(10, ge=1, le=100), # Filter parameters - changed_by: Optional[str] = Query( + changed_by: str | None = Query( None, description="Filter by user or system that performed the action" ), - changed_by_api_key: Optional[str] = Query( + changed_by_api_key: str | None = Query( None, description="Filter by API key hash that performed the action" ), - action: Optional[str] = Query( + action: str | None = Query( None, description="Filter by action type (create, update, delete)" ), - table_name: Optional[str] = Query( + table_name: str | None = Query( None, description="Filter by table name that was modified" ), - object_id: Optional[str] = Query( + object_id: str | None = Query( None, description="Filter by ID of the object that was modified" ), - start_date: Optional[str] = Query(None, description="Filter logs after this date"), - end_date: Optional[str] = Query(None, description="Filter logs before this date"), - object_team_id: Optional[str] = Query( + start_date: str | None = Query(None, description="Filter logs after this date"), + end_date: str | None = Query(None, description="Filter logs before this date"), + object_team_id: str | None = Query( None, description="Filter by team_id present in before_value or updated_values JSON (PostgreSQL only)", ), - object_key_hash: Optional[str] = Query( + object_key_hash: str | None = Query( None, description="Filter by token (key hash) present in before_value or updated_values JSON (PostgreSQL only)", ), # Sorting parameters - sort_by: Optional[str] = Query( + sort_by: str | None = Query( None, description="Column to sort by (e.g. 'updated_at', 'action', 'table_name')", ), @@ -101,46 +106,37 @@ async def get_audit_logs( detail={"message": CommonProxyErrors.db_not_connected_error.value}, ) - # Build filter conditions - where_conditions: Dict[str, Any] = {} - if changed_by: - where_conditions["changed_by"] = changed_by - if changed_by_api_key: - where_conditions["changed_by_api_key"] = changed_by_api_key - if action: - where_conditions["action"] = action - if table_name: - where_conditions["table_name"] = table_name - if object_id: - where_conditions["object_id"] = object_id - if start_date or end_date: - date_filter: Dict[str, Any] = {} - if start_date: - date_filter["gte"] = start_date - if end_date: - date_filter["lte"] = end_date - where_conditions["updated_at"] = date_filter + date_filter: Final[dict[str, str]] = { + **({"gte": start_date} if start_date else {}), + **({"lte": end_date} if end_date else {}), + } # JSON field filters (PostgreSQL only) — each filter is AND'd with the # others, but checks both before_value and updated_values internally (OR). - if object_team_id: - where_conditions["AND"] = where_conditions.get("AND", []) + [ - _build_json_field_or_condition("team_id", object_team_id) - ] - if object_key_hash: - where_conditions["AND"] = where_conditions.get("AND", []) + [ - _build_json_field_or_condition("token", object_key_hash) - ] + json_field_conditions: Final[list[dict[str, object]]] = [ + *([_build_json_field_or_condition("team_id", object_team_id)] if object_team_id else []), + *([_build_json_field_or_condition("token", object_key_hash)] if object_key_hash else []), + ] - # Build sort conditions - order_by: Dict[str, Any] = {} - if sort_by and isinstance(sort_by, str): - order_by[sort_by] = sort_order - else: - order_by["updated_at"] = sort_order # Default sort by updated_at + # Build filter conditions + where_conditions: Final[dict[str, object]] = { + **({"changed_by": changed_by} if changed_by else {}), + **({"changed_by_api_key": changed_by_api_key} if changed_by_api_key else {}), + **({"action": action} if action else {}), + **({"table_name": table_name} if table_name else {}), + **({"object_id": object_id} if object_id else {}), + **({"updated_at": date_filter} if start_date or end_date else {}), + **({"AND": json_field_conditions} if json_field_conditions else {}), + } + + order_by: Final[dict[str, str]] = ( + {sort_by: sort_order} if sort_by and isinstance(sort_by, str) else {"updated_at": sort_order} + ) + + audit_log_table: Final[TableActions["prisma_models.LiteLLM_AuditLog"]] = AuditLogRepository(prisma_client).table # Get paginated results - audit_logs = await prisma_client.db.litellm_auditlog.find_many( + audit_logs: Final = await audit_log_table.find_many( where=where_conditions, order=order_by, skip=(page - 1) * page_size, @@ -148,13 +144,14 @@ async def get_audit_logs( ) # Get total count for pagination - total_count = await prisma_client.db.litellm_auditlog.count(where=where_conditions) - total_pages = -(-total_count // page_size) # Ceiling division + total_count: Final = await audit_log_table.count(where=where_conditions) + total_pages: Final = -(-total_count // page_size) # Ceiling division # Return paginated response return PaginatedAuditLogResponse( audit_logs=[ - AuditLogResponse(**audit_log.model_dump()) for audit_log in audit_logs + AuditLogResponse.model_validate(audit_log.model_dump()) + for audit_log in audit_logs ] if audit_logs else [], @@ -198,8 +195,10 @@ async def get_audit_log_by_id( detail={"message": CommonProxyErrors.db_not_connected_error.value}, ) + audit_log_table: Final[TableActions["prisma_models.LiteLLM_AuditLog"]] = AuditLogRepository(prisma_client).table + # Get the audit log by ID - audit_log = await prisma_client.db.litellm_auditlog.find_unique(where={"id": id}) + audit_log: Final = await audit_log_table.find_unique(where={"id": id}) if audit_log is None: raise HTTPException( @@ -207,4 +206,4 @@ async def get_audit_log_by_id( ) # Convert to response model - return AuditLogResponse(**audit_log.model_dump()) + return AuditLogResponse.model_validate(audit_log.model_dump()) diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py index 4bb00408fc3..354a6ed2fd0 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -2,9 +2,10 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if the cost has been tracked. """ +from dataclasses import replace as dataclasses_replace from datetime import datetime, timedelta, timezone from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Dict, Final, List, Optional, Tuple +from typing import TYPE_CHECKING, Final, List, Literal, Optional, Tuple, cast from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -14,6 +15,8 @@ from litellm.constants import ( ) if TYPE_CHECKING: + from prisma import models as prisma_models + from litellm.integrations.prometheus import PrometheusLogger from litellm.proxy._types import LiteLLM_ManagedObjectTable from litellm.proxy.utils import PrismaClient, ProxyLogging @@ -84,7 +87,7 @@ class CheckBatchCost: return self.batch_processed_support_confirmed = True - async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> Dict[str, Any]: + async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> dict[str, str | None]: """ Look up user email and key alias by user_id for enriching the S3 callback metadata. Returns a dict with user_api_key_user_email and user_api_key_alias (both may be None). @@ -94,8 +97,10 @@ class CheckBatchCost: if not user_id: return {} try: - user_row = await self.prisma_client.db.litellm_usertable.find_unique( - where={"user_id": user_id} + user_row: prisma_models.LiteLLM_UserTable | None = ( + await self.prisma_client.db.litellm_usertable.find_unique( + where={"user_id": user_id} + ) ) if user_row is None: return {} @@ -112,8 +117,10 @@ class CheckBatchCost: if not api_key: return None try: - key_row = await self.prisma_client.db.litellm_verificationtoken.find_unique( - where={"token": api_key} + key_row: prisma_models.LiteLLM_VerificationToken | None = ( + await self.prisma_client.db.litellm_verificationtoken.find_unique( + where={"token": api_key} + ) ) return getattr(key_row, "key_alias", None) if key_row is not None else None except Exception as e: @@ -125,8 +132,10 @@ class CheckBatchCost: if not team_id: return None try: - team_row = await self.prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": team_id} + team_row: prisma_models.LiteLLM_TeamTable | None = ( + await self.prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": team_id} + ) ) return getattr(team_row, "team_alias", None) if team_row is not None else None except Exception as e: @@ -135,7 +144,7 @@ class CheckBatchCost: async def _build_creator_attribution_metadata( self, job: "LiteLLM_ManagedObjectTable", batch_id: str - ) -> Dict[str, Any]: + ) -> dict[str, object]: """ Rebuild the spend-tracking metadata for the key, team, and tags that created the batch so the batch-cost spend log is attributed the same way a non-batch request @@ -149,7 +158,7 @@ class CheckBatchCost: team_id = getattr(job, "team_id", None) request_tags = getattr(job, "request_tags", None) - metadata: Dict[str, Any] = { + metadata: dict[str, object] = { "user_api_key_user_id": job.created_by, "user_api_key": api_key, "user_api_key_team_id": team_id, @@ -351,7 +360,7 @@ class CheckBatchCost: return isinstance(error, (NotFoundError, openai.NotFoundError)) and output_file_id in str(error) async def _finalize_unbilled_terminal_job( - self, job: "LiteLLM_ManagedObjectTable", response: "LiteLLMBatch" + self, job: "prisma_models.LiteLLM_ManagedObjectTable", response: "LiteLLMBatch" ) -> None: """Persist a terminal batch that has nothing billable, converting any raw provider file ids to managed ids, and take it out of the poll page.""" @@ -624,6 +633,7 @@ class CheckBatchCost: later poll. """ from litellm.batches.batch_utils import ( + count_error_file_failed_requests, _get_file_content_as_dictionary, calculate_batch_cost_and_usage, ) @@ -759,16 +769,33 @@ class CheckBatchCost: model_id=model_id, deployment_model=litellm_model_name, ) - batch_cost, batch_usage, batch_models = ( - await calculate_batch_cost_and_usage( - file_content_dictionary=file_content_as_dict, - custom_llm_provider=llm_provider, # type: ignore - model_name=model_name, - model_info=deployment_model_info, + batch_file_provider: Final = cast( + Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"], llm_provider + ) + output_file_result: Final = await calculate_batch_cost_and_usage( + file_content_dictionary=file_content_as_dict, + custom_llm_provider=batch_file_provider, + model_name=model_name, + model_info=deployment_model_info, + ) + error_file_failed_requests: Final = await count_error_file_failed_requests( + response, + custom_llm_provider=batch_file_provider, + litellm_params={ + **credentials, + "_litellm_internal_model_credentials": MappingProxyType(dict(credentials)), + }, + ) + batch_result: Final = ( + output_file_result + if not error_file_failed_requests + else dataclasses_replace( + output_file_result, + failed_requests=output_file_result.failed_requests + error_file_failed_requests, ) ) logging_obj = LiteLLMLogging( - model=batch_models[0], + model=batch_result.models[0], messages=[{"role": "user", "content": ""}], stream=False, call_type="aretrieve_batch", @@ -800,9 +827,11 @@ class CheckBatchCost: try: await logging_obj.async_success_handler( result=response, - batch_cost=batch_cost, - batch_usage=batch_usage, - batch_models=batch_models, + batch_cost=batch_result.cost, + batch_usage=batch_result.usage, + batch_models=batch_result.models, + batch_successful_requests=batch_result.successful_requests, + batch_failed_requests=batch_result.failed_requests, ) except Exception: await self._release_job_claim(job) @@ -966,6 +995,16 @@ class CheckBatchCost: ) elif response.status in PROVIDER_TERMINAL_BATCH_STATUSES: + from litellm.proxy.openai_files_endpoints.common_utils import ( + _completed_batch_safe_to_retire, + ) + + if response.status in ("completed", "complete") and not _completed_batch_safe_to_retire(response): + verbose_proxy_logger.info( + f"CheckBatchCost: batch {batch_id} is completed but its output file id " + f"has not appeared yet; leaving job {job.id} for the next poll cycle" + ) + continue await self._finalize_unbilled_terminal_job(job, response) # Record polling run metrics (always, even if nothing was processed) diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py index 27837b0b5e4..06cf5fcf82f 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py @@ -1,6 +1,8 @@ """ Polls LiteLLM_ManagedObjectTable to check if the response is complete. -Cost tracking is handled automatically by the get-responses call. +Cost tracking is handled by the get-responses call, which prices normally only because the +poll stamps itself with BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN; user-facing reads of the +same route are non-inference and free. """ from datetime import datetime, timedelta, timezone @@ -9,12 +11,14 @@ from typing import TYPE_CHECKING, Dict, Optional, cast import litellm from litellm._logging import verbose_proxy_logger from litellm.constants import ( + INTERNAL_CALL_ORIGIN_METADATA_KEY, MANAGED_OBJECT_STALENESS_CUTOFF_DAYS, MAX_OBJECTS_PER_POLL_CYCLE, STALE_OBJECT_CLEANUP_BATCH_SIZE, ) from litellm.responses.utils import ResponsesAPIRequestUtils from litellm.types.llms.openai import ResponsesAPIResponse +from litellm.types.utils import BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN if TYPE_CHECKING: from litellm.proxy.utils import PrismaClient, ProxyLogging @@ -113,7 +117,8 @@ class CheckResponsesCost: Check if background responses are complete and track their cost. - Get all status="queued" or "in_progress" and file_purpose="response" jobs - Query the provider to check if response is complete - - Cost is automatically tracked by the get-responses call + - Cost is tracked by the get-responses call, billed because the poll is stamped + with BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN - Mark responses in a terminal state as complete in the database """ try: @@ -153,6 +158,7 @@ class CheckResponsesCost: # Prepare metadata with model information for cost tracking litellm_metadata = { "user_api_key_user_id": job.created_by or "default-user-id", + INTERNAL_CALL_ORIGIN_METADATA_KEY: BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN, } # Add model information if available diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index c986e835e4f..5cfcf6129f0 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -36,6 +36,7 @@ from litellm.llms.base_llm.managed_resources.isolation import ( build_list_page, build_owner_filter, can_access_resource, + resolve_resource_owner_id, ) from litellm.proxy._types import ( CallTypes, @@ -45,6 +46,8 @@ from litellm.proxy._types import ( UserAPIKeyAuth, ) from litellm.proxy.openai_files_endpoints.common_utils import ( + FILE_LIST_CONTINUATION_CHUNK_SIZE, + MAX_FILE_LIST_LIMIT, _is_base64_encoded_unified_file_id, apply_unified_file_ids, ensure_batch_response_managed_file_ids, @@ -54,6 +57,8 @@ from litellm.proxy.openai_files_endpoints.common_utils import ( map_raw_file_ids_to_unified, normalize_mime_type_for_provider, resolve_managed_output_file_model_name, + validate_file_list_limit, + validate_file_list_purpose, ) from litellm.proxy.pass_through_endpoints.llm_provider_handlers.batch_attribution import ( request_tags_from_metadata, @@ -63,9 +68,9 @@ from litellm.types.llms.openai import ( # pyright: ignore[reportAttributeAccess AsyncCursorPage, ChatCompletionFileObject, CreateFileRequest, + FileListPage, FileObject, OpenAIFileObject, - OpenAIFilesPurpose, ResponsesAPIResponse, ) from litellm.types.utils import ( @@ -144,7 +149,14 @@ class _ManagedFileRow(Protocol): class _ManagedFileTableActions(Protocol): async def find_first(self, where: Mapping[str, object]) -> Optional[_ManagedFileRow]: ... - async def find_many(self, where: Mapping[str, object]) -> Sequence[_ManagedFileRow]: ... + async def find_many( + self, + where: Mapping[str, object], + take: int = ..., + order: Union[Mapping[str, str], Sequence[Mapping[str, str]]] = ..., + cursor: Mapping[str, str] = ..., + skip: int = ..., + ) -> Sequence[_ManagedFileRow]: ... async def upsert(self, where: Mapping[str, str], data: Mapping[str, Mapping[str, object]]) -> _ManagedFileRow: ... @@ -170,6 +182,10 @@ class _ManagedObjectTableActions(Protocol): async def update_many(self, where: Mapping[str, object], data: Mapping[str, object]) -> int: ... +class _SchedulerWithJobLookup(Protocol): + def get_job(self, job_id: str) -> object: ... + + class _CursorPageArgs(TypedDict, total=False): cursor: Mapping[str, str] skip: int @@ -211,7 +227,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_object=file_object, model_mappings=model_mappings, flat_model_file_ids=list(model_mappings.values()), - created_by=user_api_key_dict.user_id, + created_by=resolve_resource_owner_id(user_api_key_dict), team_id=user_api_key_dict.team_id, updated_by=user_api_key_dict.user_id, ) @@ -227,7 +243,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "unified_file_id": file_id, "model_mappings": json.dumps(model_mappings), "flat_model_file_ids": list(model_mappings.values()), - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -331,7 +347,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "file_object": file_object.model_dump_json(), "model_object_id": model_object_id, "file_purpose": file_purpose, - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, "status": file_object.status, @@ -462,19 +478,56 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) page_size: Final = min(limit or 20, 100) - cursor_args: _CursorPageArgs = {"cursor": {"unified_object_id": after}, "skip": 1} if after else {} - - batches = await _managed_object_table(self.prisma_client).find_many( - where=where_clause, - take=page_size + 1, - order=[{"created_at": "desc"}, {"unified_object_id": "desc"}], - **cursor_args, + matches: Final = await self._collect_listed_batches( + where_clause=where_clause, + after=after, + wanted=page_size + 1, + user_api_key_dict=user_api_key_dict, ) + return build_list_page(list(matches[:page_size]), has_more=len(matches) > page_size) - has_more = len(batches) > page_size + async def _collect_listed_batches( + self, + where_clause: Mapping[str, object], + after: Optional[str], + wanted: int, + user_api_key_dict: UserAPIKeyAuth, + ) -> tuple[LiteLLMBatch, ...]: + """Read chunks newest-first until ``wanted`` batches survive parsing and + file-id resolution or the caller's rows run out, so a run of rows that will + not parse refills the page instead of emptying it. The first chunk is + ``wanted`` rows, so a healthy page still costs one query; a scan that has to + continue widens to ``FILE_LIST_CONTINUATION_CHUNK_SIZE`` like ``afile_list``, + and every chunk advances the keyset cursor, so the walk ends once the + caller's rows are exhausted.""" + matches: tuple[LiteLLMBatch, ...] = () # rebind-ok: accumulates survivors across chunks + cursor_id: Optional[str] = after # rebind-ok: keyset cursor advances to each chunk's last row + chunk_size: int = wanted # rebind-ok: widens once a scan has to continue past the first chunk + while len(matches) < wanted: + cursor_args: _CursorPageArgs = {"cursor": {"unified_object_id": cursor_id}, "skip": 1} if cursor_id else {} + chunk = await _managed_object_table(self.prisma_client).find_many( + where=where_clause, + take=chunk_size, + order=[{"created_at": "desc"}, {"unified_object_id": "desc"}], + **cursor_args, + ) + matches = matches + await self._resolve_listed_rows( + rows=chunk, wanted=wanted - len(matches), user_api_key_dict=user_api_key_dict + ) + if len(chunk) < chunk_size: + break + cursor_id = chunk[-1].unified_object_id + chunk_size = max(chunk_size, FILE_LIST_CONTINUATION_CHUNK_SIZE) + return matches + async def _resolve_listed_rows( + self, + rows: "Sequence[PrismaManagedObjectRow]", + wanted: int, + user_api_key_dict: UserAPIKeyAuth, + ) -> tuple[LiteLLMBatch, ...]: parsed_rows: Final = tuple( - (row, batch_obj) for row in batches[:page_size] if (batch_obj := _parse_managed_batch_row(row)) is not None + (row, batch_obj) for row in rows if (batch_obj := _parse_managed_batch_row(row)) is not None ) unified_id_by_raw_id: Final = await map_raw_file_ids_to_unified( raw_file_ids=frozenset( @@ -485,19 +538,19 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ), prisma_client=self.prisma_client, ) - resolved_batches: Final = [ - await self._resolve_listed_batch( + resolved: Final[list[LiteLLMBatch]] = [] # mutable-ok: resolution stops as soon as the page is full + for row, batch_obj in parsed_rows: + if len(resolved) == wanted: + break + resolved_batch = await self._resolve_listed_batch( row=row, batch_obj=batch_obj, unified_id_by_raw_id=unified_id_by_raw_id, user_api_key_dict=user_api_key_dict, ) - for row, batch_obj in parsed_rows - ] - return build_list_page( - [batch_obj for batch_obj in resolved_batches if batch_obj is not None], - has_more=has_more, - ) + if resolved_batch is not None: + resolved.append(resolved_batch) + return tuple(resolved) async def _resolve_listed_batch( self, @@ -804,7 +857,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_ids.append(file_id) return file_ids - def get_file_ids_from_responses_input(self, input: Union[str, List[Dict[str, Any]]]) -> List[str]: + def get_file_ids_from_responses_input(self, input: Union[str, List[Dict[str, object]]]) -> List[str]: """ Gets file ids from responses API input. @@ -829,7 +882,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Check for direct input_file type if item.get("type") == "input_file": file_id = item.get("file_id") - if file_id: + if isinstance(file_id, str) and file_id: file_ids.append(file_id) # Check for input_file in content array @@ -838,7 +891,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): for content_item in content: if isinstance(content_item, dict) and content_item.get("type") == "input_file": file_id = content_item.get("file_id") - if file_id: + if isinstance(file_id, str) and file_id: file_ids.append(file_id) return file_ids @@ -1178,7 +1231,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Handle both output_file_id and error_file_id for file_attr in ["output_file_id", "error_file_id"]: - file_id_value = getattr(response, file_attr, None) + file_id_value: str | None = getattr(response, file_attr, None) if file_id_value and model_id: decoded_output_file_id = _is_base64_encoded_unified_file_id(file_id_value) if decoded_output_file_id and "llm_output_file_id," in decoded_output_file_id: @@ -1365,12 +1418,76 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): async def afile_list( self, - purpose: Optional[OpenAIFilesPurpose], + purpose: Optional[str], litellm_parent_otel_span: Optional[Span], + user_api_key_dict: UserAPIKeyAuth, + limit: Optional[int] = None, + after: Optional[str] = None, **data: Dict, - ) -> List[OpenAIFileObject]: - """Handled in files_endpoints.py""" - return [] + ) -> FileListPage: + """List the managed files the caller owns, newest first. + + Pagination is keyset based on ``unified_file_id`` so a key that owns + every file on the proxy still reads one bounded page at a time. + ``purpose`` is applied after parsing, because the managed file table + keeps it inside the ``file_object`` blob instead of a column, and rows + whose blob will not parse drop out there too, so a chunk of rows can + yield fewer matches than the page holds. Successive chunks are read + until the page is full or the caller's rows run out, which keeps + ``data`` non-empty while matches remain and its last id usable as the + next cursor. A first chunk that fills the page costs one query; once a + scan has to continue past it, the chunk widens to + ``FILE_LIST_CONTINUATION_CHUNK_SIZE``, so the walk costs one query per + that many rows instead of one per page. That bound is per query, not + per request: the work is still linear in the rows the caller owns, and + a filter matching nothing reads every one of them, with no index + covering either the owner filter or the sort. + """ + validate_file_list_limit(limit) + validate_file_list_purpose(purpose) + + owner_filter: Final = build_owner_filter(user_api_key_dict) + if owner_filter is None: + return FileListPage(**build_list_page([])) + + if after: + cursor_row = await _managed_file_table(self.prisma_client).find_first( + where={**owner_filter, "unified_file_id": after} + ) + if cursor_row is None: + raise ProxyException( + message=f"Invalid 'after' cursor: no file found with id '{after}'.", + type="invalid_request_error", + param="after", + code=400, + openai_code="invalid_value", + ) + + page_size: Final = min(limit or MAX_FILE_LIST_LIMIT, MAX_FILE_LIST_LIMIT) + matches: Final[List[OpenAIFileObject]] = [] + cursor_id = after + chunk_size = page_size + 1 + + while len(matches) <= page_size: + cursor_args: _CursorPageArgs = {"cursor": {"unified_file_id": cursor_id}, "skip": 1} if cursor_id else {} + chunk = await _managed_file_table(self.prisma_client).find_many( + where=owner_filter, + take=chunk_size, + order=[{"created_at": "desc"}, {"unified_file_id": "desc"}], + **cursor_args, + ) + matches.extend( + parsed_file_object.model_copy(update={"id": row.unified_file_id}) + for row in chunk + if (parsed_file_object := _parse_managed_file_object(row.file_object, row.unified_file_id)) is not None + and (purpose is None or parsed_file_object.purpose == purpose) + ) + if len(chunk) < chunk_size: + break + cursor_id = chunk[-1].unified_file_id + chunk_size = max(chunk_size, FILE_LIST_CONTINUATION_CHUNK_SIZE) + + return FileListPage(**build_list_page(matches[:page_size], has_more=len(matches) > page_size)) def _is_batch_polling_enabled(self) -> bool: """ @@ -1383,7 +1500,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): import litellm.proxy.proxy_server as proxy_server_module # Check if the scheduler has the batch cost checking job registered - scheduler = getattr(proxy_server_module, "scheduler", None) + scheduler: Final[_SchedulerWithJobLookup | None] = getattr(proxy_server_module, "scheduler", None) if scheduler is None: return False @@ -1429,7 +1546,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) MAX_MATCHES_TO_RETURN = 10 - batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( + batches = await _managed_object_table(self.prisma_client).find_many( where={ "file_purpose": "batch", "batch_processed": False, @@ -1439,11 +1556,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): order={"created_at": "desc"}, ) - referencing_batches = [] + referencing_batches: Final[list[dict[str, object]]] = [] for batch in batches: try: # Parse the batch file_object to check for file references - batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object + decoded_file_object = _decode_json_blob(batch.file_object) + batch_data: Mapping[str, object] = ( + decoded_file_object if isinstance(decoded_file_object, Mapping) else {} + ) # Extract file IDs from batch # Batches typically reference the unified file ID in input_file_id diff --git a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py index 579f203554e..b2eda76f9ae 100644 --- a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py @@ -12,9 +12,10 @@ Endpoints for /project operations import json from collections.abc import Sequence -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Final from fastapi import APIRouter, Depends, HTTPException, Request +from pydantic import TypeAdapter from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -26,37 +27,50 @@ from litellm.proxy.management_helpers.utils import ( management_endpoint_wrapper, ) from litellm.proxy.utils import PrismaClient, handle_exception_on_proxy +from litellm.repositories.budget_repository import BudgetRepository +from litellm.repositories.object_permission_repository import ObjectPermissionRepository +from litellm.repositories.prisma_protocols import TableActions +from litellm.repositories.project_repository import ProjectRepository +from litellm.repositories.team_repository import TeamRepository +from litellm.repositories.user_repository import UserRepository +from litellm.repositories.verification_token_repository import VerificationTokenRepository if TYPE_CHECKING: from prisma import models as prisma_models - from prisma.actions import ( - LiteLLM_ProjectTableActions, - LiteLLM_TeamTableActions, - LiteLLM_VerificationTokenActions, - ) + + from litellm import Router router = APIRouter() - -def _team_table(prisma_client: PrismaClient) -> "LiteLLM_TeamTableActions[prisma_models.LiteLLM_TeamTable]": - team_table: LiteLLM_TeamTableActions[prisma_models.LiteLLM_TeamTable] = prisma_client.db.litellm_teamtable - return team_table +_OBJECT_PERMISSION_PAYLOAD: Final = TypeAdapter(dict[str, object]) -def _project_table(prisma_client: PrismaClient) -> "LiteLLM_ProjectTableActions[prisma_models.LiteLLM_ProjectTable]": - project_table: LiteLLM_ProjectTableActions[prisma_models.LiteLLM_ProjectTable] = ( - prisma_client.db.litellm_projecttable - ) - return project_table +def _team_table(prisma_client: PrismaClient) -> TableActions["prisma_models.LiteLLM_TeamTable"]: + return TeamRepository(prisma_client).table + + +def _project_table(prisma_client: PrismaClient) -> TableActions["prisma_models.LiteLLM_ProjectTable"]: + return ProjectRepository(prisma_client).table def _verification_token_table( prisma_client: PrismaClient, -) -> "LiteLLM_VerificationTokenActions[prisma_models.LiteLLM_VerificationToken]": - verification_token_table: LiteLLM_VerificationTokenActions[prisma_models.LiteLLM_VerificationToken] = ( - prisma_client.db.litellm_verificationtoken - ) - return verification_token_table +) -> TableActions["prisma_models.LiteLLM_VerificationToken"]: + return VerificationTokenRepository(prisma_client).table + + +def _budget_table(prisma_client: PrismaClient) -> TableActions["prisma_models.LiteLLM_BudgetTable"]: + return BudgetRepository(prisma_client).table + + +def _object_permission_table( + prisma_client: PrismaClient, +) -> TableActions["prisma_models.LiteLLM_ObjectPermissionTable"]: + return ObjectPermissionRepository(prisma_client).table + + +def _user_table(prisma_client: PrismaClient) -> TableActions["prisma_models.LiteLLM_UserTable"]: + return UserRepository(prisma_client).table def _jsonified(prisma_client: PrismaClient, payload: dict[str, object]) -> dict[str, object]: @@ -205,6 +219,114 @@ def _check_team_project_limits( ) +def _project_models_missing_positive_quota( + models: list[str] | None, + rpm_limits: Mapping[str, object] | None, + tpm_limits: Mapping[str, object] | None, +) -> list[str]: + """Return the models that lack a positive `rpm` AND `tpm` quota. + + A valid quota is a positive integer; null, zero, and negative are rejected + because downstream rate limiters treat a non-positive limit as immediately + exhausted (every request blocked). + """ + + def _is_positive(value: object) -> bool: + return isinstance(value, int) and not isinstance(value, bool) and value > 0 + + rpm = rpm_limits or {} + tpm = tpm_limits or {} + return [model for model in (models or []) if not _is_positive(rpm.get(model)) or not _is_positive(tpm.get(model))] + + +def _router_access_group_names(llm_router: "Router | None") -> frozenset[str]: + return frozenset(llm_router.get_model_access_groups()) if llm_router is not None else frozenset() + + +def _project_models_expanding_at_request_time( + models: Sequence[str] | None, access_group_names: frozenset[str] +) -> tuple[str, ...]: + """Entries project auth expands to many concrete models (`all-proxy-models`, `*` patterns, + access groups). The rate limiter looks quotas up by the exact requested model name, so a + quota keyed on one of these entries is never applied.""" + return tuple( + model + for model in (models or ()) + if model == SpecialModelNames.all_proxy_models.value or "*" in model or model in access_group_names + ) + + +def _raise_on_project_models_expanding_at_request_time( + models: Sequence[str] | None, access_group_names: frozenset[str] +) -> None: + expanding: Final = _project_models_expanding_at_request_time(models, access_group_names) + if not expanding: + return + raise HTTPException( + status_code=400, + detail={ + "error": f"models {list(expanding)} expand to multiple models at request time, so a per-model rpm/tpm quota cannot be enforced for them while 'enforce_project_model_quota' is enabled. List concrete model names instead." + }, + ) + + +def _raise_on_missing_project_model_quota( + data: NewProjectRequest | UpdateProjectRequest, access_group_names: frozenset[str] = frozenset() +) -> None: + """Require a positive `rpm`/`tpm` quota for every model on project CREATE. + + `model_rpm_limit`/`model_tpm_limit` are relocated into `metadata` by the request + model's `set_model_info` validator, so they are read from there. + + Only invoked when `general_settings.enforce_project_model_quota` is enabled + (default off), so it is opt-in and does not change behavior for existing users. + """ + _raise_on_project_models_expanding_at_request_time(data.models, access_group_names) + metadata = data.metadata or {} + missing = _project_models_missing_positive_quota( + data.models, metadata.get("model_rpm_limit"), metadata.get("model_tpm_limit") + ) + if not missing: + return + raise HTTPException( + status_code=400, + detail={ + "error": f"models {missing} added to project without a positive rpm/tpm quota. Set a positive model_rpm_limit and model_tpm_limit for each model." + }, + ) + + +def _raise_on_missing_project_model_quota_on_update( + data: UpdateProjectRequest, existing_project: object, access_group_names: frozenset[str] = frozenset() +) -> None: + """Require a positive `rpm`/`tpm` quota over the RESULTING state on project UPDATE. + + `/project/update` replaces `models` and `metadata` when they are provided, so the + check runs on what the project WILL look like: a partial update that doesn't touch + models/quota keeps the existing values, while one that adds a model or clears a + model's quota must leave every resulting model with a positive limit. + + Only invoked when `general_settings.enforce_project_model_quota` is enabled + (default off), so it is opt-in and does not change behavior for existing users. + """ + resulting_models = data.models if data.models is not None else (getattr(existing_project, "models", None) or []) + resulting_metadata = ( + data.metadata if data.metadata is not None else (getattr(existing_project, "metadata", None) or {}) + ) + _raise_on_project_models_expanding_at_request_time(resulting_models, access_group_names) + missing = _project_models_missing_positive_quota( + resulting_models, resulting_metadata.get("model_rpm_limit"), resulting_metadata.get("model_tpm_limit") + ) + if not missing: + return + raise HTTPException( + status_code=400, + detail={ + "error": f"models {missing} would be left on the project without a positive rpm/tpm quota. Set a positive model_rpm_limit and model_tpm_limit for each model." + }, + ) + + async def _create_budget_for_project( data: NewProjectRequest, user_id: str | None, @@ -219,7 +341,7 @@ async def _create_budget_for_project( new_budget = _jsonified(prisma_client, budget_row.model_dump(exclude_none=True)) - _budget: prisma_models.LiteLLM_BudgetTable = await prisma_client.db.litellm_budgettable.create( + _budget: Final = await _budget_table(prisma_client).create( data={ **new_budget, "created_by": user_id or litellm_proxy_admin_name, @@ -242,10 +364,8 @@ async def _set_project_object_permission( return None if data.object_permission is not None: - created_object_permission: prisma_models.LiteLLM_ObjectPermissionTable = ( - await prisma_client.db.litellm_objectpermissiontable.create( - data=data.object_permission.model_dump(exclude_none=True), - ) + created_object_permission: Final = await _object_permission_table(prisma_client).create( + data=data.object_permission.model_dump(exclude_none=True), ) del data.object_permission return created_object_permission.object_permission_id @@ -352,7 +472,9 @@ async def new_project( ``` """ from litellm.proxy.proxy_server import ( + general_settings, litellm_proxy_admin_name, + llm_router, premium_user, prisma_client, ) @@ -399,6 +521,10 @@ async def new_project( data=data, ) + # Opt-in (default off): require rpm/tpm for every model added to the project. + if general_settings.get("enforce_project_model_quota", False): + _raise_on_missing_project_model_quota(data, _router_access_group_names(llm_router)) + # Check if user has permission to create projects for this team # only team admins can create projects for their team has_permission = await _check_user_permission_for_project( @@ -470,10 +596,8 @@ async def new_project( new_project_row = _remove_budget_fields_from_project_data(new_project_row) verbose_proxy_logger.info(f"new_project_row: {json.dumps(new_project_row, indent=2)}") - response: prisma_models.LiteLLM_ProjectTable = await prisma_client.db.litellm_projecttable.create( - data={ - **new_project_row, # type: ignore - }, + response: Final = await _project_table(prisma_client).create( + data={**new_project_row}, include={"litellm_budget_table": True}, ) @@ -538,7 +662,9 @@ async def update_project( ``` """ from litellm.proxy.proxy_server import ( + general_settings, litellm_proxy_admin_name, + llm_router, premium_user, prisma_client, user_api_key_cache, @@ -642,6 +768,12 @@ async def update_project( data=data, ) + # Opt-in (default off): require rpm/tpm for every model the update would leave on the project. + if general_settings.get("enforce_project_model_quota", False): + _raise_on_missing_project_model_quota_on_update( + data, existing_project, _router_access_group_names(llm_router) + ) + # Prepare update data update_data = _jsonified(prisma_client, data.model_dump(exclude_none=True, exclude={"project_id"})) update_data["updated_by"] = user_api_key_dict.user_id or litellm_proxy_admin_name @@ -652,7 +784,7 @@ async def update_project( if budget_updates and existing_project.budget_id: # Update existing budget - await prisma_client.db.litellm_budgettable.update( + await _budget_table(prisma_client).update( where={"budget_id": existing_project.budget_id}, data={ **budget_updates, @@ -667,18 +799,17 @@ async def update_project( if "object_permission" in update_data: object_permission_data = update_data.pop("object_permission") if object_permission_data: + object_permission_payload: Final = _OBJECT_PERMISSION_PAYLOAD.validate_python(object_permission_data) if existing_project.object_permission_id: # Update existing permission - await prisma_client.db.litellm_objectpermissiontable.update( + await _object_permission_table(prisma_client).update( where={"object_permission_id": existing_project.object_permission_id}, - data=object_permission_data, + data=object_permission_payload, ) else: # Create new permission - created_permission: prisma_models.LiteLLM_ObjectPermissionTable = ( - await prisma_client.db.litellm_objectpermissiontable.create( - data=object_permission_data, - ) + created_permission: Final = await _object_permission_table(prisma_client).create( + data=object_permission_payload, ) update_data["object_permission_id"] = created_permission.object_permission_id @@ -694,7 +825,7 @@ async def update_project( update_data = _remove_budget_fields_from_project_data(update_data) # Update project - updated_project: prisma_models.LiteLLM_ProjectTable | None = await prisma_client.db.litellm_projecttable.update( + updated_project: Final = await _project_table(prisma_client).update( where={"project_id": data.project_id}, data=update_data, include={"litellm_budget_table": True, "object_permission": True}, @@ -934,7 +1065,7 @@ async def list_projects( # Look up the user's team memberships via the reverse-index on # LiteLLM_UserTable.teams (maintained by team_member_add alongside # members_with_roles). This avoids a full scan of all team rows. - user_record: prisma_models.LiteLLM_UserTable | None = await prisma_client.db.litellm_usertable.find_unique( + user_record: Final = await _user_table(prisma_client).find_unique( where={"user_id": user_api_key_dict.user_id}, ) user_team_ids: list[str] = user_record.teams if user_record is not None and user_record.teams else [] diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index ccfe7eda5e2..8360c0a077d 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.59" +version = "0.1.63" description = "Package for LiteLLM Enterprise features" readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.1.59" +version = "0.1.63" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/gateway/Dockerfile b/gateway/Dockerfile index 223df524d7c..308d70a6b26 100644 --- a/gateway/Dockerfile +++ b/gateway/Dockerfile @@ -1,5 +1,5 @@ -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -16,7 +16,7 @@ COPY --from=uvbin /uv /uvx /usr/local/bin/ # instead of nodeenv downloading one whose dynamic deps may not be in Wolfi # (e.g. Node 26.2.0 needs libatomic). Retry for transient apk.cgr.dev flakes. RUN for i in 1 2 3; do \ - apk add --no-cache bash gcc python3 python3-dev openssl openssl-dev libsndfile nodejs npm && break; \ + apk add --no-cache bash gcc python-3.13 python-3.13-dev openssl openssl-dev libsndfile nodejs npm && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done @@ -47,7 +47,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra extra_proxy \ --extra semantic-router \ --extra bedrock-realtime \ - --python python3 + --python python3.13 # Stage 2 — copy source and install the project + workspace members. COPY . . @@ -59,7 +59,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra extra_proxy \ --extra semantic-router \ --extra bedrock-realtime \ - --python python3 + --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ npm_config_cache=/root/.npm \ @@ -73,7 +73,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root RUN for i in 1 2 3; do \ - apk add --no-cache bash openssl tzdata python3 libsndfile libatomic && break; \ + apk add --no-cache bash openssl tzdata python-3.13 libsndfile libatomic && break; \ [ $i = 3 ] && { echo "apk add failed after 3 retries" >&2; exit 1; }; \ sleep 5; \ done diff --git a/gateway/routes/allowlist.py b/gateway/routes/allowlist.py index 05baf98bbb5..92b73867e67 100644 --- a/gateway/routes/allowlist.py +++ b/gateway/routes/allowlist.py @@ -86,6 +86,7 @@ GATEWAY_PATH_PREFIXES: tuple[str, ...] = ( "/comprehendmedical", "/cohere/", "/gemini/", + "/gigachat/", "/google/", "/vertex_ai/", "/vertex-ai/", diff --git a/helm/litellm-helm/Chart.yaml b/helm/litellm-helm/Chart.yaml index 3959d85edf3..a3cb388ffc6 100644 --- a/helm/litellm-helm/Chart.yaml +++ b/helm/litellm-helm/Chart.yaml @@ -18,7 +18,7 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 1.1.2 +version: 1.1.3 # This is the version number of the application being deployed. This version number should be # incremented each time you make changes to the application. Versions are not expected to diff --git a/helm/litellm-helm/README.md b/helm/litellm-helm/README.md index b242373de5d..bf4089404db 100644 --- a/helm/litellm-helm/README.md +++ b/helm/litellm-helm/README.md @@ -26,7 +26,7 @@ If `db.useStackgresOperator` is used (not yet implemented): | `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` | | `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A | | `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A | -| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A | +| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated on first install and reused on upgrades. | N/A | | `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | | `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | | `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` | @@ -212,6 +212,8 @@ service, the **Proxy Endpoint** should be set to `http://-litellm:4000` The **Proxy Key** is the value specified for `masterkey` or, if a `masterkey` was not provided to the helm command line, the `masterkey` is a randomly generated string in the `sk-...` format stored in the `-litellm-masterkey` Kubernetes Secret. +The key is generated once on the first install; later `helm upgrade` runs reuse the +value already in that Secret, so upgrading never rotates the master key. ```bash kubectl -n litellm get secret -litellm-masterkey -o jsonpath="{.data.masterkey}" diff --git a/helm/litellm-helm/templates/secret-masterkey.yaml b/helm/litellm-helm/templates/secret-masterkey.yaml index 7c8560cc2cc..60ab4e74c6b 100644 --- a/helm/litellm-helm/templates/secret-masterkey.yaml +++ b/helm/litellm-helm/templates/secret-masterkey.yaml @@ -1,9 +1,11 @@ {{- if not .Values.masterkeySecretName }} -{{ $masterkey := (.Values.masterkey | default (printf "sk-%s" (randAlphaNum 18))) }} +{{- $secretName := printf "%s-masterkey" (include "litellm.fullname" .) }} +{{- $existing := lookup "v1" "Secret" .Release.Namespace $secretName }} +{{- $masterkey := .Values.masterkey | default (dig "data" "masterkey" "" $existing | b64dec) | default (printf "sk-%s" (randAlphaNum 18)) }} apiVersion: v1 kind: Secret metadata: - name: {{ include "litellm.fullname" . }}-masterkey + name: {{ $secretName }} data: masterkey: {{ $masterkey | b64enc }} type: Opaque diff --git a/helm/litellm-helm/tests/hpa_tests.yaml b/helm/litellm-helm/tests/hpa_tests.yaml index ec18c3591d3..cd062dd5971 100644 --- a/helm/litellm-helm/tests/hpa_tests.yaml +++ b/helm/litellm-helm/tests/hpa_tests.yaml @@ -1,4 +1,4 @@ -suite: "hpa with behavior" +suite: "hpa" templates: - hpa.yaml tests: @@ -23,14 +23,44 @@ tests: - equal: { path: spec.behavior.scaleUp.stabilizationWindowSeconds, value: 60 } - equal: { path: spec.behavior.scaleDown.stabilizationWindowSeconds, value: 90 } ---- -suite: "hpa without behavior" -templates: - - hpa.yaml -tests: - it: "does not render behavior when not set" set: autoscaling.enabled: true asserts: - isKind: { of: HorizontalPodAutoscaler } - isNull: { path: spec.behavior } + + - it: "scales on cpu at the documented 60 percent by default" + set: + autoscaling.enabled: true + asserts: + - isKind: { of: HorizontalPodAutoscaler } + - equal: { path: "spec.metrics[0].resource.name", value: cpu } + - equal: { path: "spec.metrics[0].resource.target.type", value: Utilization } + - equal: { path: "spec.metrics[0].resource.target.averageUtilization", value: 60 } + + - it: "does not scale on memory by default" + set: + autoscaling.enabled: true + asserts: + - lengthEqual: { path: spec.metrics, count: 1 } + + - it: "honours an explicit cpu target override" + set: + autoscaling.enabled: true + autoscaling.targetCPUUtilizationPercentage: 75 + asserts: + - equal: { path: "spec.metrics[0].resource.target.averageUtilization", value: 75 } + + - it: "renders a memory metric only when a memory target is set" + set: + autoscaling.enabled: true + autoscaling.targetMemoryUtilizationPercentage: 80 + asserts: + - lengthEqual: { path: spec.metrics, count: 2 } + - equal: { path: "spec.metrics[1].resource.name", value: memory } + - equal: { path: "spec.metrics[1].resource.target.averageUtilization", value: 80 } + + - it: "renders no hpa when autoscaling is disabled" + asserts: + - hasDocuments: { count: 0 } diff --git a/helm/litellm-helm/tests/masterkey-secret_tests.yaml b/helm/litellm-helm/tests/masterkey-secret_tests.yaml index bbbade9d802..296f26755b8 100644 --- a/helm/litellm-helm/tests/masterkey-secret_tests.yaml +++ b/helm/litellm-helm/tests/masterkey-secret_tests.yaml @@ -15,6 +15,53 @@ tests: # Note: The masterkey is generated as "sk-<18-random-chars>" in plain text, # but stored as base64 encoded in Kubernetes secret (requirement). # "sk-" base64 encodes to "c2st", so we check for "^c2st" pattern. + - it: should reuse the master key already stored in the cluster instead of generating a new one on upgrade + template: secret-masterkey.yaml + set: + masterkeySecretName: "" + kubernetesProvider: + scheme: + "v1/Secret": + gvr: + version: "v1" + resource: "secrets" + namespaced: true + objects: + - kind: Secret + apiVersion: v1 + metadata: + name: RELEASE-NAME-litellm-masterkey + namespace: NAMESPACE + data: + masterkey: c2stZXhpc3Rpbmcta2V5 + asserts: + - equal: + path: data.masterkey + value: c2stZXhpc3Rpbmcta2V5 + - it: should let an explicit masterkey value override the one already stored in the cluster + template: secret-masterkey.yaml + set: + masterkeySecretName: "" + masterkey: sk-explicit + kubernetesProvider: + scheme: + "v1/Secret": + gvr: + version: "v1" + resource: "secrets" + namespaced: true + objects: + - kind: Secret + apiVersion: v1 + metadata: + name: RELEASE-NAME-litellm-masterkey + namespace: NAMESPACE + data: + masterkey: c2stZXhpc3Rpbmcta2V5 + asserts: + - equal: + path: data.masterkey + value: c2stZXhwbGljaXQ= - it: should not create a secret if masterkeySecretName is set template: secret-masterkey.yaml set: diff --git a/helm/litellm-helm/values.yaml b/helm/litellm-helm/values.yaml index f8df98de102..637be2322e3 100644 --- a/helm/litellm-helm/values.yaml +++ b/helm/litellm-helm/values.yaml @@ -200,7 +200,16 @@ autoscaling: enabled: false minReplicas: 1 maxReplicas: 100 - targetCPUUtilizationPercentage: 80 + # 60 is the documented recommendation. See "Recommended Machine Specifications" + # in https://docs.litellm.ai/docs/proxy/prod. A new replica clears the startupProbe + # above only after up to failureThreshold x periodSeconds = 300 seconds, so a target + # high enough to trip near saturation adds capacity minutes after it was needed. + targetCPUUtilizationPercentage: 60 + # Deliberately left unset rather than given a value. The prisma query engine's + # resident memory is a high-water mark that ratchets to the pod's worst-ever write + # and is never returned, so a memory target reads the largest write a pod ever did + # rather than what it is doing now, and replicas ratchet up without scaling back in. + # Memory is a floor to provision under 'resources', not a signal to scale on. # targetMemoryUtilizationPercentage: 80 # behavior: {} diff --git a/helm/litellm/templates/backend/deployment.yaml b/helm/litellm/templates/backend/deployment.yaml index 5c0431fc0bd..0db2f0b3d43 100644 --- a/helm/litellm/templates/backend/deployment.yaml +++ b/helm/litellm/templates/backend/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: backend spec: + {{- with .Values.backend.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.backend.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/templates/gateway/deployment.yaml b/helm/litellm/templates/gateway/deployment.yaml index d5363d0096e..5030ba2c9dc 100644 --- a/helm/litellm/templates/gateway/deployment.yaml +++ b/helm/litellm/templates/gateway/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: gateway spec: + {{- with .Values.gateway.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.gateway.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/templates/ingress.yaml b/helm/litellm/templates/ingress.yaml index ab609354d7b..f77ef537b02 100644 --- a/helm/litellm/templates/ingress.yaml +++ b/helm/litellm/templates/ingress.yaml @@ -5,6 +5,41 @@ {{- $gatewayPort := .Values.gateway.service.port -}} {{- $backendPort := .Values.backend.service.port -}} {{- $uiPort := .Values.ui.service.port -}} +{{/* + Backends addressable from ingress.extraPaths, keyed by the `service` field. +*/}} +{{- $extraPathBackends := dict + "gateway" (dict "name" $gatewayName "port" $gatewayPort) + "backend" (dict "name" $backendName "port" $backendPort) + "ui" (dict "name" $uiName "port" $uiPort) +-}} +{{/* + UI paths (Next.js static export). + + /ui/* is where the SPA serves its login + dashboard routes (e.g. /ui/login). + Without it, /ui/* falls into the catch-all → backend → 404. + + The App Router (output: "export", basePath: "") emits the RSC/flight payload + for every route as a ROOT-level .txt (/index.txt, /teams.txt, + /__next._tree.txt, ...). The client router fetches these on every soft + navigation / prefetch as .txt?_rsc= (the query string is + irrelevant to path matching). They are not under /ui, /_next, or + /litellm-asset-prefix, so without /*.txt they fall to the backend catch-all + → 404 → client-side navigation never settles and the login flow spins in an + infinite redirect loop (/ ⇄ /ui/login). ui/nginx.conf already serves *.txt + from the export; the rule only routes the request to it. Needs an ingress + controller whose ImplementationSpecific path is a wildcard pattern + (AWS ALB: `*` = 0+ chars); this chart targets the AWS Load Balancer + Controller. +*/}} +{{- $uiPaths := list + (dict "path" "/" "pathType" "Exact") + (dict "path" "/favicon.ico" "pathType" "Exact") + (dict "path" "/litellm-asset-prefix" "pathType" "Prefix") + (dict "path" "/_next" "pathType" "Prefix") + (dict "path" "/ui" "pathType" "Prefix") + (dict "path" "/*.txt" "pathType" "ImplementationSpecific") +-}} {{/* Gateway data-plane prefixes — must mirror gateway/routes/allowlist.py. Versioned paths are listed explicitly to avoid routing management routes @@ -39,6 +74,21 @@ routes at startup -> 404. So /test is rendered as a standalone Exact path and /test/* falls through to the backend catch-all. */}} +{{/* + Every "|" this template renders on its own. An + ingress.extraPaths entry that repeats one of these is rejected: duplicates + in a single rule are resolved by position or by controller-specific tie + breaking, so the operator entry could take over a built-in route (an entry + at "/" Prefix would swallow the whole backend management API) instead of + adding to it. +*/}} +{{- $builtinPathKeys := list "/test|Exact" "/|Prefix" -}} +{{- range $uiPaths }} +{{- $builtinPathKeys = append $builtinPathKeys (printf "%s|%s" .path .pathType) }} +{{- end }} +{{- range $gatewayPrefixes }} +{{- $builtinPathKeys = append $builtinPathKeys (printf "%s|Prefix" .) }} +{{- end }} apiVersion: networking.k8s.io/v1 kind: Ingress metadata: @@ -64,65 +114,15 @@ spec: http: paths: # --- UI (Next.js static export) --- - - path: / - pathType: Exact - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - - path: /favicon.ico - pathType: Exact - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - - path: /litellm-asset-prefix - pathType: Prefix - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - - path: /_next - pathType: Prefix - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - # /ui/* is where the Next.js SPA serves its login + dashboard - # routes (e.g. /ui/login). Without this, /ui/* falls into the - # catch-all → backend → 404. - - path: /ui - pathType: Prefix - backend: - service: - name: {{ $uiName }} - port: - number: {{ $uiPort }} - # Next.js App Router (output: "export", basePath: "") emits the - # RSC/flight payload for every route as a ROOT-level .txt - # (/index.txt, /teams.txt, /__next._tree.txt, ...). The client - # router fetches these on every soft navigation / prefetch as - # .txt?_rsc= (the query string is irrelevant to path - # matching). They are not under /ui, /_next, or - # /litellm-asset-prefix, so without this rule they fall to the - # backend catch-all → 404 → client-side navigation never settles - # and the login flow spins in an infinite redirect loop - # (/ ⇄ /ui/login). ui/nginx.conf already serves *.txt from the - # export; this rule only routes the request to it. Needs an - # ingress controller whose ImplementationSpecific path is a - # wildcard pattern (AWS ALB: `*` = 0+ chars); this chart targets - # the AWS Load Balancer Controller. - - path: /*.txt - pathType: ImplementationSpecific + {{- range $uiPaths }} + - path: {{ .path }} + pathType: {{ .pathType }} backend: service: name: {{ $uiName }} port: number: {{ $uiPort }} + {{- end }} # --- Gateway data plane --- # Exact /test only (see the $gatewayPrefixes comment above); # /test/* MCP management endpoints fall to the backend catch-all. @@ -142,6 +142,46 @@ spec: port: number: {{ $gatewayPort }} {{- end }} + {{- /* + --- Operator-supplied extra paths (ingress.extraPaths) --- + Rendered after every built-in path so an entry can never take + precedence over a default, and before the backend catch-all. + Position only decides the match on controllers that honour manifest + order: the AWS Load Balancer Controller this chart targets sorts + Exact paths first and Prefix paths longest-first, but keeps + ImplementationSpecific paths in manifest order, which is what the + /*.txt rule above already depends on. + */}} + {{- range $idx, $extra := .Values.ingress.extraPaths }} + {{- if not (kindIs "map" $extra) }} + {{- fail (printf "ingress.extraPaths[%d]: each entry must be a mapping with a 'path' key" $idx) }} + {{- end }} + {{- if not $extra.path }} + {{- fail (printf "ingress.extraPaths[%d]: 'path' is required" $idx) }} + {{- end }} + {{- $service := $extra.service | default "gateway" }} + {{- $target := get $extraPathBackends $service }} + {{- if not $target }} + {{- fail (printf "ingress.extraPaths[%d] (path %s): unknown service %q, expected one of backend, gateway, ui" $idx $extra.path $service) }} + {{- end }} + {{- $pathType := $extra.pathType | default "Prefix" }} + {{- if not (has $pathType (list "Prefix" "Exact" "ImplementationSpecific")) }} + {{- fail (printf "ingress.extraPaths[%d] (path %s): unknown pathType %q, expected one of Exact, ImplementationSpecific, Prefix" $idx $extra.path $pathType) }} + {{- end }} + {{- if eq $extra.path "/" }} + {{- fail (printf "ingress.extraPaths[%d]: path / is already routed in both directions, Exact to ui and Prefix to backend, so no pathType leaves a request for an entry here to capture" $idx) }} + {{- end }} + {{- if has (printf "%s|%s" $extra.path $pathType) $builtinPathKeys }} + {{- fail (printf "ingress.extraPaths[%d]: path %s with pathType %s is already routed by this chart, and a duplicate would take it over rather than add to it" $idx $extra.path $pathType) }} + {{- end }} + - path: {{ $extra.path | quote }} + pathType: {{ $pathType }} + backend: + service: + name: {{ $target.name }} + port: + number: {{ $target.port }} + {{- end }} # --- Catch-all → backend (management API: /key/*, /user/*, /team/*, ...) --- - path: / pathType: Prefix diff --git a/helm/litellm/templates/migrations-job.yaml b/helm/litellm/templates/migrations-job.yaml index 9cd8397f794..8d33081e72f 100644 --- a/helm/litellm/templates/migrations-job.yaml +++ b/helm/litellm/templates/migrations-job.yaml @@ -7,6 +7,8 @@ # # Running this pre-upgrade closes the window where new application pods would # otherwise serve traffic against the previous release's unmigrated schema. +# Argo CD users can swap the Helm hook for a PreSync hook through +# `migrationJob.hooks`, which re-runs the Job on every sync. apiVersion: batch/v1 kind: Job metadata: @@ -14,10 +16,18 @@ metadata: labels: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: migrations + {{- if or .Values.migrationJob.hooks.helm.enabled .Values.migrationJob.hooks.argocd.enabled }} annotations: + {{- if .Values.migrationJob.hooks.helm.enabled }} helm.sh/hook: pre-install,pre-upgrade helm.sh/hook-delete-policy: before-hook-creation - helm.sh/hook-weight: "0" + helm.sh/hook-weight: {{ .Values.migrationJob.hooks.helm.weight | default "0" | quote }} + {{- end }} + {{- if .Values.migrationJob.hooks.argocd.enabled }} + argocd.argoproj.io/hook: PreSync + argocd.argoproj.io/hook-delete-policy: BeforeHookCreation + {{- end }} + {{- end }} spec: backoffLimit: {{ .Values.migrationJob.backoffLimit }} ttlSecondsAfterFinished: {{ .Values.migrationJob.ttlSecondsAfterFinished }} diff --git a/helm/litellm/templates/ui/deployment.yaml b/helm/litellm/templates/ui/deployment.yaml index 91d6de39ea6..b992b347bad 100644 --- a/helm/litellm/templates/ui/deployment.yaml +++ b/helm/litellm/templates/ui/deployment.yaml @@ -7,6 +7,10 @@ metadata: {{- include "litellm.commonLabels" . | nindent 4 }} app.kubernetes.io/component: ui spec: + {{- with .Values.ui.strategy }} + strategy: + {{- toYaml . | nindent 4 }} + {{- end }} selector: matchLabels: {{- include "litellm.ui.selectorLabels" . | nindent 6 }} diff --git a/helm/litellm/tests/ingress_extra_paths_tests.yaml b/helm/litellm/tests/ingress_extra_paths_tests.yaml new file mode 100644 index 00000000000..fc7d5943278 --- /dev/null +++ b/helm/litellm/tests/ingress_extra_paths_tests.yaml @@ -0,0 +1,317 @@ +suite: test ingress.extraPaths +templates: + - ingress.yaml +values: + - ./values/required.yaml +tests: + - it: renders nothing extra between the built-in gateway prefixes and the backend catch-all when unset + set: + ingress.enabled: true + asserts: + - equal: + path: spec.rules[0].http.paths[-1] + value: + path: / + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-backend + port: + number: 4001 + - equal: + path: spec.rules[0].http.paths[-2] + value: + path: /metrics + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + + - it: routes an extra path to the gateway by default, immediately before the backend catch-all + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + asserts: + - equal: + path: spec.rules[0].http.paths[-2] + value: + path: /watsonx + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + - equal: + path: spec.rules[0].http.paths[-1] + value: + path: / + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-backend + port: + number: 4001 + + - it: keeps every built-in path when extra paths are supplied + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + asserts: + - contains: + path: spec.rules[0].http.paths + content: + path: / + pathType: Exact + backend: + service: + name: RELEASE-NAME-litellm-ui + port: + number: 3000 + - contains: + path: spec.rules[0].http.paths + content: + path: /ui + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-ui + port: + number: 3000 + - contains: + path: spec.rules[0].http.paths + content: + path: /test + pathType: Exact + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + - contains: + path: spec.rules[0].http.paths + content: + path: /v1/chat + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + - contains: + path: spec.rules[0].http.paths + content: + path: /vertex_ai + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + + - it: renders every entry in order and honours the service and pathType selectors + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + service: gateway + - path: /my-passthrough + pathType: Exact + service: backend + - path: /brand.txt + pathType: ImplementationSpecific + service: ui + asserts: + - equal: + path: spec.rules[0].http.paths[-4] + value: + path: /watsonx + pathType: Prefix + backend: + service: + name: RELEASE-NAME-litellm-gateway + port: + number: 4000 + - equal: + path: spec.rules[0].http.paths[-3] + value: + path: /my-passthrough + pathType: Exact + backend: + service: + name: RELEASE-NAME-litellm-backend + port: + number: 4001 + - equal: + path: spec.rules[0].http.paths[-2] + value: + path: /brand.txt + pathType: ImplementationSpecific + backend: + service: + name: RELEASE-NAME-litellm-ui + port: + number: 3000 + + - it: addresses the component services by their configured ports + set: + ingress.enabled: true + gateway.service.port: 8000 + backend.service.port: 8001 + ui.service.port: 8080 + ingress.extraPaths: + - path: /watsonx + - path: /my-passthrough + service: backend + - path: /brand.txt + service: ui + asserts: + - equal: + path: spec.rules[0].http.paths[-4].backend.service.port.number + value: 8000 + - equal: + path: spec.rules[0].http.paths[-3].backend.service.port.number + value: 8001 + - equal: + path: spec.rules[0].http.paths[-2].backend.service.port.number + value: 8080 + + - it: rejects an entry naming a service the chart does not deploy + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + service: proxy + asserts: + - failedTemplate: + errorMessage: 'ingress.extraPaths[0] (path /watsonx): unknown service "proxy", expected one of backend, gateway, ui' + + - it: rejects an entry whose pathType is not a kubernetes pathType + set: + ingress.enabled: true + ingress.extraPaths: + - path: /watsonx + pathType: prefix + asserts: + - failedTemplate: + errorMessage: 'ingress.extraPaths[0] (path /watsonx): unknown pathType "prefix", expected one of Exact, ImplementationSpecific, Prefix' + + - it: rejects an entry with no path + set: + ingress.enabled: true + ingress.extraPaths: + - service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: 'path' is required" + + + - it: rejects a root entry that would take over the backend catch-all + set: + ingress.enabled: true + ingress.extraPaths: + - path: / + service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path / is already routed in both directions, Exact to ui and Prefix to backend, so no pathType leaves a request for an entry here to capture" + + - it: rejects a root entry that would take over the UI root + set: + ingress.enabled: true + ingress.extraPaths: + - path: / + pathType: Exact + service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path / is already routed in both directions, Exact to ui and Prefix to backend, so no pathType leaves a request for an entry here to capture" + + # A root ImplementationSpecific entry duplicates no built-in pair, so the + # duplicate check alone would admit it. It is still dead: the built-in + # Exact / sorts ahead of it on the AWS Load Balancer Controller and claims + # the only request its pattern matches, so it renders and never routes. + - it: rejects a root entry that would render but never match + set: + ingress.enabled: true + ingress.extraPaths: + - path: / + pathType: ImplementationSpecific + service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path / is already routed in both directions, Exact to ui and Prefix to backend, so no pathType leaves a request for an entry here to capture" + + - it: rejects an entry that would take over a UI prefix + set: + ingress.enabled: true + ingress.extraPaths: + - path: /ui + service: gateway + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path /ui with pathType Prefix is already routed by this chart, and a duplicate would take it over rather than add to it" + + - it: rejects an entry that would take over the UI RSC payload rule + set: + ingress.enabled: true + ingress.extraPaths: + - path: /*.txt + pathType: ImplementationSpecific + service: backend + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path /*.txt with pathType ImplementationSpecific is already routed by this chart, and a duplicate would take it over rather than add to it" + + - it: rejects an entry that would take over a gateway data-plane prefix + set: + ingress.enabled: true + ingress.extraPaths: + - path: /v1/chat + service: backend + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path /v1/chat with pathType Prefix is already routed by this chart, and a duplicate would take it over rather than add to it" + + - it: rejects an entry that would take over the exact /test route + set: + ingress.enabled: true + ingress.extraPaths: + - path: /test + pathType: Exact + service: backend + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: path /test with pathType Exact is already routed by this chart, and a duplicate would take it over rather than add to it" + + - it: allows a built-in path under a different pathType, which is a distinct rule + set: + ingress.enabled: true + ingress.extraPaths: + - path: /ui + pathType: Exact + service: ui + asserts: + - equal: + path: spec.rules[0].http.paths[-2] + value: + path: /ui + pathType: Exact + backend: + service: + name: RELEASE-NAME-litellm-ui + port: + number: 3000 + + - it: rejects a bare string entry instead of failing on template internals + set: + ingress.enabled: true + ingress.extraPaths: + - /watsonx + asserts: + - failedTemplate: + errorMessage: "ingress.extraPaths[0]: each entry must be a mapping with a 'path' key" diff --git a/helm/litellm/tests/migration_job_hooks_tests.yaml b/helm/litellm/tests/migration_job_hooks_tests.yaml new file mode 100644 index 00000000000..650d2700429 --- /dev/null +++ b/helm/litellm/tests/migration_job_hooks_tests.yaml @@ -0,0 +1,63 @@ +suite: test migrations Job hook annotations +templates: + - migrations-job.yaml +values: + - ./values/required.yaml +tests: + - it: runs as a Helm pre-install / pre-upgrade hook by default + asserts: + - equal: + path: metadata.annotations["helm.sh/hook"] + value: pre-install,pre-upgrade + - equal: + path: metadata.annotations["helm.sh/hook-delete-policy"] + value: before-hook-creation + - equal: + path: metadata.annotations["helm.sh/hook-weight"] + value: "0" + - notExists: + path: metadata.annotations["argocd.argoproj.io/hook"] + + - it: adds the Argo CD PreSync hook when asked + set: + migrationJob.hooks.argocd.enabled: true + asserts: + - equal: + path: metadata.annotations["argocd.argoproj.io/hook"] + value: PreSync + - equal: + path: metadata.annotations["argocd.argoproj.io/hook-delete-policy"] + value: BeforeHookCreation + + - it: drops the Helm hook so Argo CD owns the Job + set: + migrationJob.hooks.argocd.enabled: true + migrationJob.hooks.helm.enabled: false + asserts: + - equal: + path: metadata.annotations["argocd.argoproj.io/hook"] + value: PreSync + - notExists: + path: metadata.annotations["helm.sh/hook"] + - notExists: + path: metadata.annotations["helm.sh/hook-delete-policy"] + - notExists: + path: metadata.annotations["helm.sh/hook-weight"] + + - it: renders an ordinary Job when both hooks are disabled + set: + migrationJob.hooks.helm.enabled: false + asserts: + - notExists: + path: metadata.annotations + - equal: + path: kind + value: Job + + - it: honours a custom Helm hook weight + set: + migrationJob.hooks.helm.weight: "-5" + asserts: + - equal: + path: metadata.annotations["helm.sh/hook-weight"] + value: "-5" diff --git a/helm/litellm/tests/rollout_strategy_tests.yaml b/helm/litellm/tests/rollout_strategy_tests.yaml new file mode 100644 index 00000000000..b12e2073c7c --- /dev/null +++ b/helm/litellm/tests/rollout_strategy_tests.yaml @@ -0,0 +1,66 @@ +suite: test rolling update strategy on the component deployments +templates: + - gateway/deployment.yaml + - gateway/configmap.yaml + - backend/deployment.yaml + - ui/deployment.yaml +values: + - ./values/required.yaml +tests: + - it: leaves the strategy to Kubernetes defaults when unset + asserts: + - notExists: + path: spec.strategy + + - it: renders the configured strategy on each deployment + set: + gateway.strategy: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 0 + maxSurge: 1 + backend.strategy: + type: RollingUpdate + rollingUpdate: + maxUnavailable: "25%" + maxSurge: 2 + ui.strategy: + type: Recreate + asserts: + - equal: + path: spec.strategy + value: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 0 + maxSurge: 1 + template: gateway/deployment.yaml + - equal: + path: spec.strategy + value: + type: RollingUpdate + rollingUpdate: + maxUnavailable: 25% + maxSurge: 2 + template: backend/deployment.yaml + - equal: + path: spec.strategy + value: + type: Recreate + template: ui/deployment.yaml + + - it: keeps a component on the cluster default when only another one sets a strategy + set: + gateway.strategy: + type: Recreate + asserts: + - equal: + path: spec.strategy.type + value: Recreate + template: gateway/deployment.yaml + - notExists: + path: spec.strategy + template: backend/deployment.yaml + - notExists: + path: spec.strategy + template: ui/deployment.yaml diff --git a/helm/litellm/values.yaml b/helm/litellm/values.yaml index 998d225a317..378c3b7a618 100644 --- a/helm/litellm/values.yaml +++ b/helm/litellm/values.yaml @@ -13,6 +13,27 @@ ingress: annotations: {} host: "" # optional; if set, becomes the rule's host tls: [] + # Extra HTTP paths appended to the ingress rule. Additive: every built-in + # UI / gateway / backend path is still rendered, these entries are placed + # after them and before the backend catch-all, and an entry that repeats a + # path the chart already routes is rejected at render time rather than + # silently taking it over. + # + # The chart's built-in gateway prefix list is a snapshot of the data-plane + # surface at release time. Use extraPaths for passthrough routes it does not + # cover: a provider prefix added upstream after this chart version, or a + # custom general_settings.pass_through_endpoints route. + # + # path required; the HTTP path to route + # service which component serves it: gateway (default), backend, or ui + # pathType Prefix (default), Exact, or ImplementationSpecific + # + # The target component only answers paths its own route allowlist keeps, so + # a path here still has to be one that component serves. + extraPaths: [] + # - path: /watsonx + # pathType: Prefix + # service: gateway # Per-component ServiceAccounts for gateway, backend, and ui. # @@ -54,6 +75,22 @@ serviceAccounts: # generate` — the migration engine doesn't need the generated client. migrationJob: enabled: true + # Which controller is responsible for running the Job. + # + # `helm.enabled` renders the Helm pre-install / pre-upgrade hook, so the Job + # runs whenever `helm upgrade` sees a change to apply. `argocd.enabled` + # renders an Argo CD PreSync hook instead, which runs the Job on every sync + # even when the rendered manifests are unchanged: the way to re-run + # migrations on demand from a GitOps pipeline. Turning the Helm hook off + # while the Argo CD hook is on leaves the Job out of Helm's own upgrade + # path, which is what Argo CD users want since Argo, not Helm, applies the + # manifests. + hooks: + helm: + enabled: true + weight: "0" + argocd: + enabled: false backoffLimit: 4 ttlSecondsAfterFinished: 120 # Wall-clock budget for the whole Job, shared across every `backoffLimit` @@ -236,6 +273,15 @@ gateway: initialDelaySeconds: 5 periodSeconds: 10 timeoutSeconds: 10 + # Rolling update tuning for the gateway Deployment. Empty by default, so + # Kubernetes applies its own RollingUpdate defaults (25% maxSurge / + # 25% maxUnavailable). Example, for a surge-only rollout behind a load + # balancer that must never lose capacity: + # type: RollingUpdate + # rollingUpdate: + # maxUnavailable: 0 + # maxSurge: 1 + strategy: {} # Optional startupProbe. Empty by default, so existing installs are unchanged # and liveness/readiness apply from container start. Set it to gate # liveness/readiness until a slow cold start finishes — a high failureThreshold @@ -348,6 +394,8 @@ backend: initialDelaySeconds: 5 periodSeconds: 10 timeoutSeconds: 10 + # Same shape as gateway.strategy. + strategy: {} # Optional startupProbe; same shape as gateway.startupProbe. Empty by default. startupProbe: {} hpa: @@ -412,6 +460,8 @@ ui: httpGet: { path: /, port: http } initialDelaySeconds: 2 periodSeconds: 10 + # Same shape as gateway.strategy. + strategy: {} # Optional startupProbe; same shape as gateway.startupProbe. Empty by default. startupProbe: {} hpa: @@ -428,9 +478,11 @@ ui: maxUnavailable: "" podAnnotations: {} # Same shape as the gateway blocks of the same name. The nginx runtime - # writes its pid, cache, and proxy temp files under the image's root - # filesystem, so `securityContext.readOnlyRootFilesystem: true` here needs - # emptyDir volumes mounted over those paths. + # writes its pid, cache, and proxy temp files under /tmp, so it boots as + # any (arbitrary, non-root) uid; `securityContext.readOnlyRootFilesystem: + # true` here needs an emptyDir volume mounted over /tmp. Images before + # the /tmp move instead need emptyDirs over /var/cache/nginx and /run to + # run as a non-root uid at all. podLabels: {} podSecurityContext: {} securityContext: {} diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260804162853_add_budget_window_spend_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260804162853_add_budget_window_spend_table/migration.sql new file mode 100644 index 00000000000..c3018006adb --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260804162853_add_budget_window_spend_table/migration.sql @@ -0,0 +1,12 @@ +CREATE TABLE IF NOT EXISTS "LiteLLM_BudgetWindowSpend" ( + "entity_type" TEXT NOT NULL, + "entity_id" TEXT NOT NULL, + "window_duration" TEXT NOT NULL, + "window_start" TIMESTAMP(3) NOT NULL, + "spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_at" TIMESTAMP(3) NOT NULL, + + CONSTRAINT "LiteLLM_BudgetWindowSpend_pkey" PRIMARY KEY ("entity_type","entity_id","window_duration") +); + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260824000000_add_gateway_injected_caching_savings_spend/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260824000000_add_gateway_injected_caching_savings_spend/migration.sql new file mode 100644 index 00000000000..dee5abfa269 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260824000000_add_gateway_injected_caching_savings_spend/migration.sql @@ -0,0 +1,18 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DailyUserSpend" ADD COLUMN IF NOT EXISTS "gateway_injected_caching_savings_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyOrganizationSpend" ADD COLUMN IF NOT EXISTS "gateway_injected_caching_savings_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyEndUserSpend" ADD COLUMN IF NOT EXISTS "gateway_injected_caching_savings_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyAgentSpend" ADD COLUMN IF NOT EXISTS "gateway_injected_caching_savings_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyTeamSpend" ADD COLUMN IF NOT EXISTS "gateway_injected_caching_savings_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyTagSpend" ADD COLUMN IF NOT EXISTS "gateway_injected_caching_savings_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260828000000_shadow_eval_cost_comparison/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260828000000_shadow_eval_cost_comparison/migration.sql new file mode 100644 index 00000000000..6a75024c5af --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260828000000_shadow_eval_cost_comparison/migration.sql @@ -0,0 +1,22 @@ +-- AlterTable +ALTER TABLE "LiteLLM_ShadowEvalAttempt" ADD COLUMN IF NOT EXISTS "real_cost" DOUBLE PRECISION; + +-- AlterTable +ALTER TABLE "LiteLLM_ShadowEvalAttempt" ADD COLUMN IF NOT EXISTS "real_classifier_cost" DOUBLE PRECISION NOT NULL DEFAULT 0; + +-- AlterTable +ALTER TABLE "LiteLLM_ShadowEvalAttempt" ADD COLUMN IF NOT EXISTS "shadow_classifier_cost" DOUBLE PRECISION NOT NULL DEFAULT 0; + +-- AlterTable +ALTER TABLE "LiteLLM_ShadowEvalAttempt" ADD COLUMN IF NOT EXISTS "real_cache_hit" BOOLEAN NOT NULL DEFAULT false; + +-- CreateTable +CREATE TABLE IF NOT EXISTS "LiteLLM_ShadowEvalFunnel" ( + "job_id" TEXT NOT NULL, + "not_sampled" INTEGER NOT NULL DEFAULT 0, + "unjudgeable" INTEGER NOT NULL DEFAULT 0, + "shed" INTEGER NOT NULL DEFAULT 0, + "withheld" INTEGER NOT NULL DEFAULT 0, + + CONSTRAINT "LiteLLM_ShadowEvalFunnel_pkey" PRIMARY KEY ("job_id") +); diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260829000000_add_model_access_group_budget_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260829000000_add_model_access_group_budget_table/migration.sql new file mode 100644 index 00000000000..62398da7f04 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260829000000_add_model_access_group_budget_table/migration.sql @@ -0,0 +1,20 @@ +-- CreateTable +CREATE TABLE IF NOT EXISTS "LiteLLM_ModelAccessGroupBudgetTable" ( + "access_group_name" TEXT NOT NULL, + "spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0, + "budget_id" TEXT, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "created_by" TEXT, + "updated_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_by" TEXT, + + CONSTRAINT "LiteLLM_ModelAccessGroupBudgetTable_pkey" PRIMARY KEY ("access_group_name") +); + +-- AddForeignKey +DO $$ +BEGIN + IF NOT EXISTS (SELECT 1 FROM pg_constraint WHERE conname = 'LiteLLM_ModelAccessGroupBudgetTable_budget_id_fkey') THEN + ALTER TABLE "LiteLLM_ModelAccessGroupBudgetTable" ADD CONSTRAINT "LiteLLM_ModelAccessGroupBudgetTable_budget_id_fkey" FOREIGN KEY ("budget_id") REFERENCES "LiteLLM_BudgetTable"("budget_id") ON DELETE SET NULL ON UPDATE CASCADE; + END IF; +END $$; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql new file mode 100644 index 00000000000..b7dbe931dd2 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260831000000_shadow_eval_typed_targets/migration.sql @@ -0,0 +1,21 @@ +DO $$ +BEGIN + IF EXISTS ( + SELECT 1 FROM information_schema.columns + WHERE table_name = 'LiteLLM_ShadowEvalJob' AND column_name = 'api_key_id' + ) THEN + ALTER TABLE "LiteLLM_ShadowEvalJob" RENAME COLUMN "api_key_id" TO "target_id"; + END IF; +END $$; + +ALTER TABLE "LiteLLM_ShadowEvalJob" ADD COLUMN IF NOT EXISTS "target_type" TEXT NOT NULL DEFAULT 'key'; + +DROP INDEX IF EXISTS "LiteLLM_ShadowEvalJob_one_active_per_key_direction"; + +CREATE UNIQUE INDEX IF NOT EXISTS "LiteLLM_ShadowEvalJob_one_active_per_target_direction" + ON "LiteLLM_ShadowEvalJob"("target_type", "target_id", "direction") WHERE "stopped_at" IS NULL; + +DROP INDEX IF EXISTS "LiteLLM_ShadowEvalJob_api_key_id_idx"; + +CREATE INDEX IF NOT EXISTS "LiteLLM_ShadowEvalJob_target_type_target_id_idx" + ON "LiteLLM_ShadowEvalJob"("target_type", "target_id"); diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql new file mode 100644 index 00000000000..90b21205310 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260901000000_shadow_eval_multi_router/migration.sql @@ -0,0 +1,3 @@ +ALTER TABLE "LiteLLM_ShadowEvalJob" ADD COLUMN IF NOT EXISTS "router_names" TEXT[] NOT NULL DEFAULT ARRAY[]::TEXT[]; + +ALTER TABLE "LiteLLM_ShadowEvalAttempt" ADD COLUMN IF NOT EXISTS "router_name" TEXT; diff --git a/litellm-proxy-extras/litellm_proxy_extras/prisma_toolchain.py b/litellm-proxy-extras/litellm_proxy_extras/prisma_toolchain.py index f3b55fd4d96..2283814ab35 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/prisma_toolchain.py +++ b/litellm-proxy-extras/litellm_proxy_extras/prisma_toolchain.py @@ -14,9 +14,22 @@ then fails on a Node binary that was never written. Deleting a cache directory that exists without a Node binary is what turns a killed bootstrap back into a recoverable one. -Both budgets are overridable so an operator can widen them without a release: -``LITELLM_PRISMA_BOOTSTRAP_TIMEOUT`` for the toolchain install and -``LITELLM_PRISMA_COMMAND_TIMEOUT`` for every individual Prisma command. +``prisma migrate deploy`` is the other command whose runtime is not a +constant: it grows with the number of pending migrations, so a fresh database +that has to replay every migration this package ships overruns a per-command +budget sized for the short bookkeeping commands, on a laptop as much as on a +slow CI runner. The Python ``prisma`` wrapper spawns Node and the schema engine +as separate children, so killing the wrapper on timeout leaves them running: +the retry then contends with that orphan for Prisma's advisory lock and cannot +finish any sooner. Migrate deploy therefore runs under its own budget. + +All three budgets are overridable so an operator can widen them without a +release: ``LITELLM_PRISMA_BOOTSTRAP_TIMEOUT`` for the toolchain install, +``LITELLM_PRISMA_MIGRATE_DEPLOY_TIMEOUT`` for ``prisma migrate deploy`` and +``LITELLM_PRISMA_COMMAND_TIMEOUT`` for every other Prisma command. The +per-command budget used to bound migrate deploy as well, so a deployment that +raised it above the deploy default keeps that larger budget for deploy unless +the deploy override says otherwise. """ import math @@ -36,10 +49,12 @@ except ImportError: PRISMA_COMMAND_TIMEOUT_ENV_VAR = "LITELLM_PRISMA_COMMAND_TIMEOUT" PRISMA_BOOTSTRAP_TIMEOUT_ENV_VAR = "LITELLM_PRISMA_BOOTSTRAP_TIMEOUT" +PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR = "LITELLM_PRISMA_MIGRATE_DEPLOY_TIMEOUT" NODEENV_CACHE_DIR_ENV_VAR = "PRISMA_NODEENV_CACHE_DIR" DEFAULT_PRISMA_COMMAND_TIMEOUT = 60.0 DEFAULT_PRISMA_BOOTSTRAP_TIMEOUT = 600.0 +DEFAULT_PRISMA_MIGRATE_DEPLOY_TIMEOUT = 600.0 BOOTSTRAP_ARG = "--version" @@ -88,6 +103,15 @@ def prisma_bootstrap_timeout() -> float: ) +def prisma_migrate_deploy_timeout() -> float: + """Seconds one ``prisma migrate deploy`` may run for, however many migrations are pending.""" + if os.getenv(PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR) is not None: + return _timeout_from_env( + PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR, DEFAULT_PRISMA_MIGRATE_DEPLOY_TIMEOUT + ) + return max(DEFAULT_PRISMA_MIGRATE_DEPLOY_TIMEOUT, prisma_command_timeout()) + + def nodeenv_cache_dir() -> Optional[Path]: """Where Prisma installs its private Node runtime, or None if unknowable.""" override = os.getenv(NODEENV_CACHE_DIR_ENV_VAR) diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index d9959677116..7604ceadf7a 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -29,6 +29,7 @@ model LiteLLM_BudgetTable { keys LiteLLM_VerificationToken[] // multiple keys can have the same budget end_users LiteLLM_EndUserTable[] // multiple end-users can have the same budget tags LiteLLM_TagTable[] // multiple tags can have the same budget + model_access_groups LiteLLM_ModelAccessGroupBudgetTable[] // multiple model access groups can have the same budget team_membership LiteLLM_TeamMembership[] // budgets of Users within a Team organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization } @@ -585,6 +586,20 @@ model LiteLLM_EndUserTable { blocked Boolean @default(false) } +// Budget and shared spend for a model access group. The groups themselves are not rows anywhere: +// they are free-text strings in LiteLLM_ProxyModelTable.model_info.access_groups, so a row here +// exists only once someone gives that group a budget. +model LiteLLM_ModelAccessGroupBudgetTable { + access_group_name String @id + spend Float @default(0.0) + budget_id String? + litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id]) + created_at DateTime @default(now()) @map("created_at") + created_by String? + updated_at DateTime @default(now()) @updatedAt + updated_by String? +} + // Track tags with budgets and spend model LiteLLM_TagTable { tag_name String @id @@ -649,6 +664,18 @@ model LiteLLM_SpendLogs { @@index([session_id]) } +model LiteLLM_BudgetWindowSpend { + entity_type String + entity_id String + window_duration String + window_start DateTime + spend Float @default(0.0) + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + + @@id([entity_type, entity_id, window_duration]) +} + // View spend, model, api_key per request model LiteLLM_ErrorLogs { request_id String @id @default(uuid()) @@ -754,6 +781,7 @@ model LiteLLM_DailyUserSpend { compression_saved_tokens BigInt @default(0) compression_savings_spend Float @default(0.0) prompt_caching_savings_spend Float @default(0.0) + gateway_injected_caching_savings_spend Float @default(0.0) autorouter_savings_spend Float @default(0.0) spend Float @default(0.0) api_requests BigInt @default(0) @@ -789,6 +817,7 @@ model LiteLLM_DailyOrganizationSpend { compression_saved_tokens BigInt @default(0) compression_savings_spend Float @default(0.0) prompt_caching_savings_spend Float @default(0.0) + gateway_injected_caching_savings_spend Float @default(0.0) autorouter_savings_spend Float @default(0.0) spend Float @default(0.0) api_requests BigInt @default(0) @@ -824,6 +853,7 @@ model LiteLLM_DailyEndUserSpend { compression_saved_tokens BigInt @default(0) compression_savings_spend Float @default(0.0) prompt_caching_savings_spend Float @default(0.0) + gateway_injected_caching_savings_spend Float @default(0.0) autorouter_savings_spend Float @default(0.0) spend Float @default(0.0) api_requests BigInt @default(0) @@ -858,6 +888,7 @@ model LiteLLM_DailyAgentSpend { compression_saved_tokens BigInt @default(0) compression_savings_spend Float @default(0.0) prompt_caching_savings_spend Float @default(0.0) + gateway_injected_caching_savings_spend Float @default(0.0) autorouter_savings_spend Float @default(0.0) spend Float @default(0.0) api_requests BigInt @default(0) @@ -892,6 +923,7 @@ model LiteLLM_DailyTeamSpend { compression_saved_tokens BigInt @default(0) compression_savings_spend Float @default(0.0) prompt_caching_savings_spend Float @default(0.0) + gateway_injected_caching_savings_spend Float @default(0.0) autorouter_savings_spend Float @default(0.0) spend Float @default(0.0) api_requests BigInt @default(0) @@ -929,6 +961,7 @@ model LiteLLM_DailyTagSpend { compression_saved_tokens BigInt @default(0) compression_savings_spend Float @default(0.0) prompt_caching_savings_spend Float @default(0.0) + gateway_injected_caching_savings_spend Float @default(0.0) autorouter_savings_spend Float @default(0.0) spend Float @default(0.0) api_requests BigInt @default(0) @@ -1496,14 +1529,16 @@ model LiteLLM_AutoRouterSession { model LiteLLM_ShadowEvalJob { id String @id @default(cuid()) group_id String // legs of one job share this; the API's job id - api_key_id String // hashed virtual key whose traffic this leg shadows - router_name String // the auto-router under evaluation, in either direction + target_type String @default("key") // key | team | user + target_id String // hashed virtual key, team_id, or user_id whose traffic this leg shadows + router_name String // first (often only) auto-router under evaluation; router_names is the full set + router_names String[] @default([]) // all routers this job runs as shadow arms; empty on legacy rows, whose set is (router_name) direction String @default("forward") // forward | reverse baseline_model String? // reverse only: the fixed model the router is judged against judge_model String shadow_percentage Float max_turns Int // sample-count ceiling: the whole budget on pre-max_budget jobs, the error-loop valve otherwise - max_budget Float? // per-key USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets + max_budget Float? // per-target USD cap on the eval's own shadow + judge spend; null on jobs from before spend budgets created_at DateTime @default(now()) created_by String? ends_at DateTime @@ -1511,7 +1546,7 @@ model LiteLLM_ShadowEvalJob { stopped_by String? // operator who stopped it early; null when it ended on its own @@index([group_id]) - @@index([api_key_id]) + @@index([target_type, target_id]) @@index([created_at]) } @@ -1521,18 +1556,34 @@ model LiteLLM_ShadowEvalAttempt { job_id String request_id String // the judged real request outcome String // real | shadow | tie | error + router_name String? // the arm this verdict scores; NULL on legacy rows, meaning the job's own router tier String? // router's tier for the prompt, when classified real_model String? shadow_model String? confidence Float? judge_cost Float @default(0) shadow_cost Float @default(0) + real_cost Float? // NULL = row predates cost measurement; comparisons read only measured rows + real_classifier_cost Float @default(0) + shadow_classifier_cost Float @default(0) + real_cache_hit Boolean @default(false) error String? created_at DateTime @default(now()) @@index([job_id]) } +// Per-leg sampling funnel counters the attempt rows cannot derive: requests an +// admitting job saw but did not judge. attempted = the leg's attempt rows; the +// leg's eligible traffic = not_sampled + unjudgeable + shed + withheld + attempted. +model LiteLLM_ShadowEvalFunnel { + job_id String @id + not_sampled Int @default(0) + unjudgeable Int @default(0) + shed Int @default(0) + withheld Int @default(0) +} + // --------------------------------------------------------------------------- // Workflow Run Tracking // diff --git a/litellm-proxy-extras/litellm_proxy_extras/utils.py b/litellm-proxy-extras/litellm_proxy_extras/utils.py index 5118865e43a..f6647268624 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/utils.py +++ b/litellm-proxy-extras/litellm_proxy_extras/utils.py @@ -15,8 +15,11 @@ from litellm_proxy_extras.replica_identity import ( apply_replica_identity_full, ) from litellm_proxy_extras.prisma_toolchain import ( + PRISMA_COMMAND_TIMEOUT_ENV_VAR, + PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR, ensure_prisma_toolchain, prisma_command_timeout, + prisma_migrate_deploy_timeout, ) @@ -40,6 +43,65 @@ def _get_prisma_env() -> dict: _MIGRATION_TS_RE = re.compile(r"^(\d{14})_") +_SPEND_LOGS_ALTER_RE = re.compile(r'^ALTER\s+TABLE\s+"LiteLLM_SpendLogs"\s', re.IGNORECASE) +_SPEND_LOGS_ARTIFACT_DROP_RE = re.compile( + r'^DROP\s+TABLE\s+"LiteLLM_SpendLogs_[^"]*"', re.IGNORECASE +) +_SPEND_LOGS_PK_CLAUSE_RE = re.compile( + r'^(?:DROP\s+CONSTRAINT\s+"[^"]*_pkey"' + r'|ADD\s+(?:CONSTRAINT\s+"[^"]*"\s+)?PRIMARY\s+KEY\s*\([^)]*\))$', + re.IGNORECASE, +) + +PARTITIONED_SPEND_LOGS_PUSH_ERROR = ( + "LiteLLM_SpendLogs is a partitioned table (see db_scripts/partition_spend_logs.sql), " + "so its primary key must include the partition key (\"startTime\"). `prisma db push` " + "reconciles the database against schema.prisma, which declares the unpartitioned " + "primary key (\"request_id\"), and Postgres rejects that rewrite with: unique " + "constraint on partitioned table must include all partitioning columns. Start the " + "proxy without --use_prisma_db_push so it uses `prisma migrate deploy`, which only " + "applies shipped migrations and leaves the partitioned primary key alone." +) + + +def _without_sql_comments(statement: str) -> str: + return "\n".join( + line + for line in statement.splitlines() + if line.strip() and not line.strip().startswith("--") + ).strip() + + +def _without_spend_logs_pk_clauses(statement: str) -> Optional[str]: + prefix_match = _SPEND_LOGS_ALTER_RE.match(statement) + if not prefix_match: + return statement + kept = tuple( + clause.strip() + for clause in statement[prefix_match.end():].split(",\n") + if not _SPEND_LOGS_PK_CLAUSE_RE.match(clause.strip()) + ) + if not kept: + return None + return statement[: prefix_match.end()] + ",\n".join(kept) + + +def filter_partitioned_spend_logs_diff(diff_sql: str) -> str: + """Drop statements from a `prisma migrate diff` script that fight the + SpendLogs partitioning runbook (db_scripts/partition_spend_logs.sql): the + primary-key rewrite on "LiteLLM_SpendLogs", which Postgres rejects on a + partitioned table, and drops of runbook artifacts such as + "LiteLLM_SpendLogs_legacy".""" + kept = tuple( + filtered + for statement in diff_sql.split(";") + for bare in (_without_sql_comments(statement),) + if bare and not _SPEND_LOGS_ARTIFACT_DROP_RE.match(bare) + for filtered in (_without_spend_logs_pk_clauses(bare),) + if filtered is not None + ) + return "".join(f"{statement};\n\n" for statement in kept) + def _migration_timestamp(name: str) -> int: """Extract the leading `YYYYMMDDHHMMSS` timestamp from a migration name. @@ -355,7 +417,24 @@ class ProxyExtrasDBManager: return logger.info(f"Migration diff created at {diff_sql_path}") + if ProxyExtrasDBManager.spend_logs_is_partitioned(): + filtered_sql = filter_partitioned_spend_logs_diff( + diff_sql_path.read_text() + ) + diff_sql_path.write_text(filtered_sql) + logger.info( + "LiteLLM_SpendLogs is partitioned; removed its primary-key " + "rewrite and partitioning artifacts from the drift script" + ) + if not filtered_sql.strip(): + logger.info("Drift script is empty after filtering; nothing to apply") + if not mark_all_applied: + return + ProxyExtrasDBManager._mark_migrations_applied(migrations_dir) + return + # 2. Run prisma db execute to apply the migration + applied_ok = False try: logger.info("Running prisma db execute to apply the migration diff...") result = subprocess.run( @@ -376,6 +455,7 @@ class ProxyExtrasDBManager: ) logger.info(f"prisma db execute stdout: {result.stdout}") logger.info("✅ Migration diff applied successfully") + applied_ok = True except subprocess.CalledProcessError as e: logger.warning(f"Failed to apply migration diff: {e.stderr}") except subprocess.TimeoutExpired: @@ -384,6 +464,16 @@ class ProxyExtrasDBManager: # 3. Mark all migrations as applied if not mark_all_applied: return + if not applied_ok: + logger.warning( + "Drift script failed to apply; NOT marking migrations as " + "applied so a later migration run can retry them" + ) + return + ProxyExtrasDBManager._mark_migrations_applied(migrations_dir) + + @staticmethod + def _mark_migrations_applied(migrations_dir: str) -> None: migration_names = ProxyExtrasDBManager._get_migration_names(migrations_dir) logger.info(f"Resolving {len(migration_names)} migrations") for migration_name in migration_names: @@ -410,6 +500,62 @@ class ProxyExtrasDBManager: f"Failed to resolve migration {migration_name}: {e.stderr}" ) + @staticmethod + def spend_logs_is_partitioned() -> bool: + """True when the connected database's LiteLLM_SpendLogs is a + partitioned table in Prisma's target schema (the `schema` URL param, + falling back to Prisma's default target, public), i.e. the operator + ran db_scripts/partition_spend_logs.sql. Returns False when psycopg is + unavailable or the database cannot be reached, preserving the + pre-existing behavior in those cases.""" + database_url = os.getenv("DATABASE_URL") + if not database_url: + return False + + try: + import psycopg + except ImportError: + logger.warning( + "psycopg is not installed; skipping the LiteLLM_SpendLogs " + "partition check. If this table is partitioned (see " + "db_scripts/partition_spend_logs.sql), schema reconciliation " + "will try to rewrite its primary key and fail. Install the " + "litellm[extra_proxy] extra, which now includes psycopg." + ) + return False + + cleaned_url = ProxyExtrasDBManager._strip_prisma_query_params(database_url) + try: + with psycopg.connect( + cleaned_url, connect_timeout=10, autocommit=True + ) as conn: + row = conn.execute( + "SELECT 1 " + "FROM pg_partitioned_table pt " + "JOIN pg_class c ON c.oid = pt.partrelid " + "JOIN pg_namespace n ON n.oid = c.relnamespace " + "WHERE c.relname = 'LiteLLM_SpendLogs' " + " AND n.nspname = %s", + ( + ProxyExtrasDBManager._prisma_schema_param(database_url) + or "public", + ), + ).fetchone() + except (psycopg.OperationalError, psycopg.DatabaseError): + return False + return row is not None + + @staticmethod + def _prisma_schema_param(url: str) -> Optional[str]: + """The `schema` query param Prisma uses to pick its target schema, + or None when the URL does not set one.""" + from urllib.parse import urlparse, parse_qsl + + return next( + (v for k, v in parse_qsl(urlparse(url).query) if k == "schema"), + None, + ) + @staticmethod def _strip_prisma_query_params(url: str) -> str: """Remove Prisma-specific query params (connection_limit, pool_timeout, @@ -528,7 +674,8 @@ class ProxyExtrasDBManager: migrations_dir = ProxyExtrasDBManager._get_prisma_dir() if not use_migrate: - # Preserve `prisma db push` path unchanged. + if ProxyExtrasDBManager.spend_logs_is_partitioned(): + raise RuntimeError(PARTITIONED_SPEND_LOGS_PUSH_ERROR) original_dir = os.getcwd() os.chdir(migrations_dir) try: @@ -554,12 +701,13 @@ class ProxyExtrasDBManager: original_dir = os.getcwd() os.chdir(migrations_dir) + deploy_timeout = prisma_migrate_deploy_timeout() try: for attempt in range(4): try: result = subprocess.run( [_get_prisma_command(), "migrate", "deploy"], - timeout=prisma_command_timeout(), + timeout=deploy_timeout, check=True, capture_output=True, text=True, @@ -569,8 +717,12 @@ class ProxyExtrasDBManager: return True except subprocess.TimeoutExpired: - logger.info( - f"prisma migrate deploy attempt {attempt + 1} timed out, retrying" + logger.warning( + "prisma migrate deploy attempt %s timed out after %ss, retrying. " + "Raise %s if this database needs longer to apply its pending migrations.", + attempt + 1, + deploy_timeout, + PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR, ) time.sleep(random.randrange(5, 15)) continue @@ -679,7 +831,8 @@ class ProxyExtrasDBManager: "Database migration failed after 4 attempts (retry loop " "exhausted by timeouts or repeated idempotent-recovery " "continues). Check database connectivity, load, and " - "_prisma_migrations ledger state." + "_prisma_migrations ledger state, and raise " + f"{PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR} if the attempts timed out." ) finally: os.chdir(original_dir) @@ -764,7 +917,7 @@ class ProxyExtrasDBManager: # Set migrations directory for Prisma result = subprocess.run( [_get_prisma_command(), "migrate", "deploy"], - timeout=prisma_command_timeout(), + timeout=prisma_migrate_deploy_timeout(), check=True, capture_output=True, text=True, @@ -972,6 +1125,8 @@ class ProxyExtrasDBManager: ) raise else: + if ProxyExtrasDBManager.spend_logs_is_partitioned(): + raise RuntimeError(PARTITIONED_SPEND_LOGS_PUSH_ERROR) # Use prisma db push with increased timeout subprocess.run( [_get_prisma_command(), "db", "push", "--accept-data-loss"], @@ -980,7 +1135,11 @@ class ProxyExtrasDBManager: ) return True except subprocess.TimeoutExpired: - logger.info(f"Attempt {attempt + 1} timed out") + logger.warning( + "Attempt %s timed out. Raise %s if this database needs longer to apply its schema.", + attempt + 1, + PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR if use_migrate else PRISMA_COMMAND_TIMEOUT_ENV_VAR, + ) time.sleep(random.randrange(5, 15)) except subprocess.CalledProcessError as e: attempts_left = 3 - attempt diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 98a3d8d535e..0944f99ad54 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.89" +version = "0.4.92" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.4.89" +version = "0.4.92" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", diff --git a/litellm-rust/AGENTS.md b/litellm-rust/AGENTS.md index 36a5ad5a8f4..b8b6291283d 100644 --- a/litellm-rust/AGENTS.md +++ b/litellm-rust/AGENTS.md @@ -1,6 +1,6 @@ # AGENTS.md -litellm-rust has exactly THREE crates. A crate is a LAYER, not a route. Routes (ocr, realtime, chat) and providers (mistral, openai) are MODULES inside the layers. +litellm-rust has four crates. A crate is a layer or shared foundation, not a route. Routes (ocr, realtime, chat) and providers (mistral, openai) are modules inside the layers. ## Crates @@ -8,9 +8,10 @@ litellm-rust has exactly THREE crates. A crate is a LAYER, not a route. Routes ( |-------|------| | litellm-core | The LiteLLM SDK in Rust. One public entrypoint per top-level call (`messages::messages()`), owning types, transforms, provider resolution, auth, and the provider HTTP call. Call it, get a typed response. | | litellm-ai-gateway | The axum server (behind the `server` feature) plus the WebSocket hosts. Translates HTTP/WS to core entrypoints; owns no provider logic and no handlers. | -| litellm-python-bridge | PyO3 cdylib exposing Rust to the litellm Python SDK — marshals Python objects and calls core entrypoints. | +| litellm-python-interop | Domain-neutral PyO3 foundation for GIL handling and typed Python/Serde conversion. | +| litellm-python-bridge | PyO3 cdylib exposing LiteLLM Rust APIs to the Python SDK. Owns API registration, domain wiring, and Python exception mapping. | -Dependency direction (acyclic): litellm-core ← litellm-ai-gateway ← litellm-python-bridge. +Dependency direction is acyclic: `litellm-python-bridge` depends on the domain layers and `litellm-python-interop`; the interop foundation depends on no LiteLLM domain crate. ## Where a route lives @@ -28,7 +29,7 @@ core/src/messages/ Handlers never live in `ai-gateway`. `ocr`, `audio_transcription`, and `realtime` are still hosted there from before this rule; they move to `core` as they are touched. -Adding a crate: default to a MODULE. New crate ONLY on a real trigger — separate artifact (binary/cdylib), proc-macro, shared foundation, or publishable standalone. A new provider or route is none of these. +Adding a crate: default to a module. A new crate requires a real trigger: separate artifact (binary/cdylib), proc-macro, shared foundation, or publishable standalone. A new provider or route is none of these. Adding a crate fails crates/core/tests/workspace_crate_allowlist.rs until you update its allowlist and this file — intentional. diff --git a/litellm-rust/CLAUDE.md b/litellm-rust/CLAUDE.md index fe6ceedbb86..3dcf1853efc 100644 --- a/litellm-rust/CLAUDE.md +++ b/litellm-rust/CLAUDE.md @@ -21,12 +21,13 @@ variants of it. The test for a good abstraction is that adding the next provider is a few declarative lines, not a new file of duplicated flow. Only diverge from the base when behavior is genuinely different, and say so explicitly in the PR. -## Crates (exactly three — see AGENTS.md) +## Crates (see AGENTS.md) `litellm-core` **is** the LiteLLM SDK in Rust: it makes the LLM call. `litellm-ai-gateway` is an HTTP/WebSocket server in front of it, and -`litellm-python-bridge` exposes it to the Python SDK. A crate is a **layer**, not -a route — add modules, not crates. +`litellm-python-bridge` exposes it to the Python SDK. `litellm-python-interop` +holds domain-neutral PyO3 primitives shared by Python-facing Rust code. A crate +is a layer or shared foundation, not a route; add modules, not crates. ## Core Boundary @@ -175,7 +176,7 @@ cd litellm-rust cargo fmt --check # the ai-gateway binary + server code is behind the `server` feature cargo clippy -p litellm-ai-gateway --all-targets --features server -- -D warnings -cargo clippy -p litellm-core -p litellm-python-bridge --all-targets -- -D warnings +cargo clippy -p litellm-core -p litellm-python-interop -p litellm-python-bridge --all-targets -- -D warnings cargo test --workspace ``` diff --git a/litellm-rust/Cargo.lock b/litellm-rust/Cargo.lock index ce28f737334..dd41cf0e84b 100644 --- a/litellm-rust/Cargo.lock +++ b/litellm-rust/Cargo.lock @@ -2,6 +2,36 @@ # It is not intended for manual editing. version = 4 +[[package]] +name = "aho-corasick" +version = "1.1.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c982642fa9e8606056828ee9a8505737230110bb1099153c79efe865c59d12ba" +dependencies = [ + "memchr", +] + +[[package]] +name = "alloca" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e5a7d05ea6aea7e9e64d25b9156ba2fee3fdd659e34e41063cd2fc7cd020d7f4" +dependencies = [ + "cc", +] + +[[package]] +name = "anes" +version = "0.1.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "4b46cbb362ab8752921c97e041f5e366ee6297bd428a31275b9fcf1e380f7299" + +[[package]] +name = "anstyle" +version = "1.0.14" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "940b3a0ca603d1eade50a4846a2afffd5ef57a9feac2c0e2ec2e14f9ead76000" + [[package]] name = "arc-swap" version = "1.9.2" @@ -506,6 +536,12 @@ dependencies = [ "either", ] +[[package]] +name = "cast" +version = "0.3.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "37b2a672a2cb129a2e41c10b1224bb368f9f37a2b16b612598138befd7b37eb5" + [[package]] name = "cc" version = "1.3.0" @@ -541,6 +577,58 @@ dependencies = [ "rand_core 0.10.1", ] +[[package]] +name = "ciborium" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "42e69ffd6f0917f5c029256a24d0161db17cea3997d185db0d35926308770f0e" +dependencies = [ + "ciborium-io", + "ciborium-ll", + "serde", +] + +[[package]] +name = "ciborium-io" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "05afea1e0a06c9be33d539b876f1ce3692f4afea2cb41f740e7743225ed1c757" + +[[package]] +name = "ciborium-ll" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "57663b653d948a338bfb3eeba9bb2fd5fcfaecb9e199e87e1eda4d9e8b240fd9" +dependencies = [ + "ciborium-io", + "half", +] + +[[package]] +name = "clap" +version = "4.6.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "473c7e07f409a8d772161724aa8db6a765a2532a70f9667eeb7b49d3d02fbdca" +dependencies = [ + "clap_builder", +] + +[[package]] +name = "clap_builder" +version = "4.6.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7b48fea5a88e9ae728a2dcbedbfc0e730f7d60da42e1cb049a83c9fb8b789889" +dependencies = [ + "anstyle", + "clap_lex", +] + +[[package]] +name = "clap_lex" +version = "1.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c8d4a3bb8b1e0c1050499d1815f5ab16d04f0959b233085fb31653fbfc9d98f9" + [[package]] name = "cmake" version = "0.1.58" @@ -596,6 +684,72 @@ dependencies = [ "libc", ] +[[package]] +name = "criterion" +version = "0.8.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "950046b2aa2492f9a536f5f4f9a3de7b9e2476e575e05bd6c333371add4d98f3" +dependencies = [ + "alloca", + "anes", + "cast", + "ciborium", + "clap", + "criterion-plot", + "itertools", + "num-traits", + "oorandom", + "page_size", + "plotters", + "rayon", + "regex", + "serde", + "serde_json", + "tinytemplate", + "walkdir", +] + +[[package]] +name = "criterion-plot" +version = "0.8.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d8d80a2f4f5b554395e47b5d8305bc3d27813bacb73493eb1001e8f76dae29ea" +dependencies = [ + "cast", + "itertools", +] + +[[package]] +name = "crossbeam-deque" +version = "0.8.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5181e0de7b61eb03a81e347d6dd8797bae9da5146707b51077e2d71a54ec0ceb" +dependencies = [ + "crossbeam-epoch", + "crossbeam-utils", +] + +[[package]] +name = "crossbeam-epoch" +version = "0.9.20" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2d6914041f254d6e9176c01941b21115dcfb7089e55135a35411081bd106ef3f" +dependencies = [ + "crossbeam-utils", +] + +[[package]] +name = "crossbeam-utils" +version = "0.8.22" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "61803da095bee82a81bb1a452ecc25d3b2f1416d1897eb86430c6159ef717c17" + +[[package]] +name = "crunchy" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "460fbee9c2c2f33933d720630a6a0bac33ba7053db5344fac858d4b8952d77d5" + [[package]] name = "crypto-common" version = "0.1.7" @@ -765,6 +919,12 @@ version = "0.3.33" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b231ed28831efb4a61a08580c4bc233ec56bc009f4cd8f52da2c3cb97df0c109" +[[package]] +name = "futures-timer" +version = "3.0.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "af43fadb8a98512d547e37b4e92e0ced13e205c061b87b4623eff01d918d6968" + [[package]] name = "futures-util" version = "0.3.33" @@ -818,6 +978,12 @@ dependencies = [ "wasm-bindgen", ] +[[package]] +name = "glob" +version = "0.3.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e4eba85ea1d0a966a983acd07deee566e67395d2d96b6fb39e62b5a833f1eb0b" + [[package]] name = "h2" version = "0.3.27" @@ -856,6 +1022,17 @@ dependencies = [ "tracing", ] +[[package]] +name = "half" +version = "2.7.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6ea2d84b969582b4b1864a92dc5d27cd2b77b622a8d79306834f1be5ba20d84b" +dependencies = [ + "cfg-if", + "crunchy", + "zerocopy", +] + [[package]] name = "hashbrown" version = "0.17.1" @@ -1179,6 +1356,15 @@ version = "2.12.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d98f6fed1fde3f8c21bc40a1abb88dd75e67924f9cffc3ef95607bad8017f8e2" +[[package]] +name = "itertools" +version = "0.13.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "413ee7dfc52ee1a4949ceeb7dbc8a33f2d6c088194d9f922fb8318faf1f01186" +dependencies = [ + "either", +] + [[package]] name = "itoa" version = "1.0.18" @@ -1255,14 +1441,27 @@ dependencies = [ name = "litellm-python-bridge" version = "0.1.0" dependencies = [ + "criterion", "litellm-ai-gateway", "litellm-core", + "litellm-python-interop", "pyo3", "pyo3-async-runtimes", "serde_json", "tokio", ] +[[package]] +name = "litellm-python-interop" +version = "0.1.0" +dependencies = [ + "pyo3", + "pythonize", + "rstest", + "serde", + "serde_json", +] + [[package]] name = "litemap" version = "0.8.2" @@ -1340,6 +1539,12 @@ version = "1.21.4" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9f7c3e4beb33f85d45ae3e3a1792185706c8e16d043238c593331cc7cd313b50" +[[package]] +name = "oorandom" +version = "11.1.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d6790f58c7ff633d8771f42965289203411a5e5c68388703c06e14f24770b41e" + [[package]] name = "openssl-probe" version = "0.2.1" @@ -1352,6 +1557,16 @@ version = "0.5.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1a80800c0488c3a21695ea981a54918fbb37abf04f4d0720c453632255e2ff0e" +[[package]] +name = "page_size" +version = "0.6.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "30d5b2194ed13191c1999ae0704b7839fb18384fa22e49b57eeaa97d79ce40da" +dependencies = [ + "libc", + "winapi", +] + [[package]] name = "percent-encoding" version = "2.3.2" @@ -1376,6 +1591,34 @@ version = "0.3.33" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "19f132c84eca552bf34cab8ec81f1c1dcc229b811638f9d283dceabe58c5569e" +[[package]] +name = "plotters" +version = "0.3.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5aeb6f403d7a4911efb1e33402027fc44f29b5bf6def3effcc22d7bb75f2b747" +dependencies = [ + "num-traits", + "plotters-backend", + "plotters-svg", + "wasm-bindgen", + "web-sys", +] + +[[package]] +name = "plotters-backend" +version = "0.3.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "df42e13c12958a16b3f7f4386b9ab1f3e7933914ecea48da7139435263a4172a" + +[[package]] +name = "plotters-svg" +version = "0.3.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "51bae2ac328883f7acdfea3d66a7c35751187f870bc81f94563733a154d7a670" +dependencies = [ + "plotters-backend", +] + [[package]] name = "portable-atomic" version = "1.14.0" @@ -1406,6 +1649,15 @@ dependencies = [ "zerocopy", ] +[[package]] +name = "proc-macro-crate" +version = "3.5.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e67ba7e9b2b56446f1d419b1d807906278ffa1a658a8a5d8a39dcb1f5a78614f" +dependencies = [ + "toml_edit", +] + [[package]] name = "proc-macro2" version = "1.0.107" @@ -1486,6 +1738,16 @@ dependencies = [ "syn 2.0.119", ] +[[package]] +name = "pythonize" +version = "0.29.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6ec376e1216e0c929a74964ce2020012a1a39f32d80e78aa688721219ea7fb89" +dependencies = [ + "pyo3", + "serde", +] + [[package]] name = "quinn" version = "0.11.11" @@ -1613,12 +1875,67 @@ dependencies = [ "rand_core 0.10.1", ] +[[package]] +name = "rayon" +version = "1.12.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "fb39b166781f92d482534ef4b4b1b2568f42613b53e5b6c160e24cfbfa30926d" +dependencies = [ + "either", + "rayon-core", +] + +[[package]] +name = "rayon-core" +version = "1.13.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "22e18b0f0062d30d4230b2e85ff77fdfe4326feb054b9783a3460d8435c8ab91" +dependencies = [ + "crossbeam-deque", + "crossbeam-utils", +] + +[[package]] +name = "regex" +version = "1.13.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f020237b6c8eed93db2e2cb53c00c60a8e1bc73da7d073199a1180401450218d" +dependencies = [ + "aho-corasick", + "memchr", + "regex-automata", + "regex-syntax", +] + +[[package]] +name = "regex-automata" +version = "0.4.18" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ad8553b9b26413251cbf30e620595c7a41b3887f03da04579c0e6b0d6a06b4b2" +dependencies = [ + "aho-corasick", + "memchr", + "regex-syntax", +] + [[package]] name = "regex-lite" version = "0.1.9" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "cab834c73d247e67f4fae452806d17d3c7501756d98c8808d7c9c7aa7d18f973" +[[package]] +name = "regex-syntax" +version = "0.8.11" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d6f6ff9a378485b298a5286656da665ba74413d36db0979633275d2e708145d4" + +[[package]] +name = "relative-path" +version = "1.9.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ba39f3699c378cd8970968dcbff9c43159ea4cfbd88d43c00b22f2ef10a435d2" + [[package]] name = "reqwest" version = "0.12.28" @@ -1676,6 +1993,35 @@ dependencies = [ "windows-sys 0.52.0", ] +[[package]] +name = "rstest" +version = "0.26.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f5a3193c063baaa2a95a33f03035c8a72b83d97a54916055ba22d35ed3839d49" +dependencies = [ + "futures-timer", + "futures-util", + "rstest_macros", +] + +[[package]] +name = "rstest_macros" +version = "0.26.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9c845311f0ff7951c5506121a9ad75aec44d083c31583b2ea5a30bcb0b0abba0" +dependencies = [ + "cfg-if", + "glob", + "proc-macro-crate", + "proc-macro2", + "quote", + "regex", + "relative-path", + "rustc_version", + "syn 2.0.119", + "unicode-ident", +] + [[package]] name = "rustc-hash" version = "2.1.3" @@ -1774,6 +2120,15 @@ version = "1.0.23" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9774ba4a74de5f7b1c1451ed6cd5285a32eddb5cccb8cc655a4e50009e06477f" +[[package]] +name = "same-file" +version = "1.0.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "93fc1dc3aaa9bfed95e02e6eadabb4baf7e3078b0bd1b4d7b6b0b68378900502" +dependencies = [ + "winapi-util", +] + [[package]] name = "schannel" version = "0.1.29" @@ -2099,6 +2454,16 @@ dependencies = [ "zerovec", ] +[[package]] +name = "tinytemplate" +version = "1.2.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "be4d6b5f19ff7664e8c98d03e2139cb510db9b0a60b55f8e8709b689d939b6bc" +dependencies = [ + "serde", + "serde_json", +] + [[package]] name = "tinyvec" version = "1.12.0" @@ -2189,6 +2554,36 @@ dependencies = [ "tokio", ] +[[package]] +name = "toml_datetime" +version = "1.1.1+spec-1.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3165f65f62e28e0115a00b2ebdd37eb6f3b641855f9d636d3cd4103767159ad7" +dependencies = [ + "serde_core", +] + +[[package]] +name = "toml_edit" +version = "0.25.13+spec-1.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6975367e4d2ef766d86af01ffad14b622fecc8d4357a998fbc4deb6e9bacaf9b" +dependencies = [ + "indexmap", + "toml_datetime", + "toml_parser", + "winnow", +] + +[[package]] +name = "toml_parser" +version = "1.1.3+spec-1.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1d38ac1cf9b95face32296c0a3ede1fdc270627c9d9c02a7274dd6d960dc4d56" +dependencies = [ + "winnow", +] + [[package]] name = "tower" version = "0.5.3" @@ -2363,6 +2758,16 @@ version = "0.8.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5c3082ca00d5a5ef149bb8b555a72ae84c9c59f7250f013ac822ac2e49b19c64" +[[package]] +name = "walkdir" +version = "2.5.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "29790946404f91d9c5d06f9874efddea1dc06c5efe94541a7d6863108e3a5e4b" +dependencies = [ + "same-file", + "winapi-util", +] + [[package]] name = "want" version = "0.3.1" @@ -2475,6 +2880,37 @@ dependencies = [ "rustls-pki-types", ] +[[package]] +name = "winapi" +version = "0.3.9" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5c839a674fcd7a98952e593242ea400abe93992746761e38641405d28b00f419" +dependencies = [ + "winapi-i686-pc-windows-gnu", + "winapi-x86_64-pc-windows-gnu", +] + +[[package]] +name = "winapi-i686-pc-windows-gnu" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6" + +[[package]] +name = "winapi-util" +version = "0.1.11" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c2a7b1c03c876122aa43f3020e6c3c3ee5c05081c9a00739faf7503aeba10d22" +dependencies = [ + "windows-sys 0.61.2", +] + +[[package]] +name = "winapi-x86_64-pc-windows-gnu" +version = "0.4.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "712e227841d057c1ee1cd2fb22fa7e5a5461ae8e48fa2ca79ec42cfc1931183f" + [[package]] name = "windows-link" version = "0.2.1" @@ -2563,6 +2999,15 @@ version = "0.52.6" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec" +[[package]] +name = "winnow" +version = "1.0.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "23b97319f7b8343df12cc98938e5c3eb436064524c8d2b4e30a1d3a36eecdf81" +dependencies = [ + "memchr", +] + [[package]] name = "writeable" version = "0.6.3" diff --git a/litellm-rust/Cargo.toml b/litellm-rust/Cargo.toml index 6d63be05d00..c447d915abe 100644 --- a/litellm-rust/Cargo.toml +++ b/litellm-rust/Cargo.toml @@ -2,6 +2,7 @@ members = [ "crates/core", "crates/ai-gateway", + "crates/python-interop", "crates/python-bridge", ] resolver = "2" @@ -15,11 +16,14 @@ repository = "https://github.com/BerriAI/litellm" [workspace.dependencies] litellm-core = { path = "crates/core" } litellm-ai-gateway = { path = "crates/ai-gateway", default-features = false } +litellm-python-interop = { path = "crates/python-interop" } axum = "0.7" pyo3 = "0.29.0" pyo3-async-runtimes = { version = "0.29.0", features = ["tokio-runtime"] } +pythonize = "0.29.0" rand = "0.8" reqwest = { version = "0.12", default-features = false, features = ["blocking", "json", "rustls-tls", "http2", "stream"] } +rstest = "0.26.1" serde = { version = "1.0", features = ["derive"] } serde_json = "1.0" sha2 = "0.10" @@ -29,3 +33,12 @@ tokio = { version = "1", features = ["rt-multi-thread", "macros", "time", "net"] tokio-tungstenite = { version = "0.24", default-features = false, features = ["connect", "rustls-tls-native-roots"] } futures-util = { version = "0.3", default-features = false, features = ["sink", "std"] } base64 = "0.22" + +[profile.release] +opt-level = 3 +lto = "thin" +codegen-units = 1 +panic = "unwind" +debug = false +incremental = false +strip = "symbols" diff --git a/litellm-rust/README.md b/litellm-rust/README.md index bcccf93300b..a0d79c6f0a5 100644 --- a/litellm-rust/README.md +++ b/litellm-rust/README.md @@ -26,9 +26,10 @@ coverage and production evidence. |-------|------| | litellm-core | The SDK. Per-route entrypoints (`messages::messages()`), types, provider transforms (modules under `providers/`), provider resolution, auth, the provider HTTP call, and the router. | | litellm-ai-gateway | The axum server (behind the `server` feature) and WebSocket hosts. Translates HTTP/WS to core entrypoints; no provider handlers. | -| litellm-python-bridge | PyO3 cdylib exposing Rust to the litellm Python SDK — marshals Python objects and calls core entrypoints. | +| litellm-python-interop | Domain-neutral PyO3 foundation for GIL handling and typed Python/Serde conversion. | +| litellm-python-bridge | PyO3 cdylib exposing LiteLLM Rust APIs to the Python SDK. Owns API registration, domain wiring, and Python exception mapping. | -Dependency direction (acyclic): litellm-core ← litellm-ai-gateway ← litellm-python-bridge. +Dependency direction is acyclic: `litellm-python-bridge` depends on the domain layers and `litellm-python-interop`; the interop foundation depends on no LiteLLM domain crate. ## Layout @@ -38,7 +39,8 @@ crates/ src/messages/ mod.rs (entrypoint), types, transformation, prepare, handler, client src/providers/anthropic/messages/transformation.rs ai-gateway/ Axum server + WebSocket hosts; calls core entrypoints. - python-bridge/ PyO3 bridge for Python LiteLLM. + python-interop/ Domain-neutral PyO3 conversion and GIL primitives. + python-bridge/ PyO3 API adapter for Python LiteLLM. ``` The folder shape follows the Python provider tree: diff --git a/litellm-rust/crates/CODING_STANDARDS/PROVIDER_CODING_STANDARDS.md b/litellm-rust/crates/CODING_STANDARDS/PROVIDER_CODING_STANDARDS.md index a1860d8a9c9..4a689cb9579 100644 --- a/litellm-rust/crates/CODING_STANDARDS/PROVIDER_CODING_STANDARDS.md +++ b/litellm-rust/crates/CODING_STANDARDS/PROVIDER_CODING_STANDARDS.md @@ -54,6 +54,6 @@ Rules for adding or changing an LLM provider/route in `litellm-rust`. `messages` cd litellm-rust cargo fmt --check cargo clippy -p litellm-ai-gateway --all-targets --features server -- -D warnings - cargo clippy -p litellm-core -p litellm-python-bridge --all-targets -- -D warnings + cargo clippy -p litellm-core -p litellm-python-interop -p litellm-python-bridge --all-targets -- -D warnings cargo test --workspace ``` diff --git a/litellm-rust/crates/ai-gateway/README.md b/litellm-rust/crates/ai-gateway/README.md index 7a6c620ee84..5cbb47220be 100644 --- a/litellm-rust/crates/ai-gateway/README.md +++ b/litellm-rust/crates/ai-gateway/README.md @@ -6,15 +6,16 @@ dials OpenAI upstream, and splices the two sockets frame-by-frame. ## Crates -`litellm-rust` is exactly three crates (a crate is a **layer**, not a route): +`litellm-rust` has four crates. A crate is a layer or shared foundation, not a route: | Crate | Role | |-------|------| | litellm-core | The LiteLLM SDK in Rust — per-route entrypoints (`messages::messages()`) that resolve the provider, transform, and make the call; plus types, provider transforms, and the router. | | litellm-ai-gateway | The Axum server (behind the `server` feature) and WebSocket hosts. Translates HTTP/WS to core entrypoints; no provider handlers. | -| litellm-python-bridge | PyO3 cdylib exposing Rust to the litellm Python SDK — marshals Python objects and calls core entrypoints. | +| litellm-python-interop | Domain-neutral PyO3 foundation for GIL handling and typed Python/Serde conversion. | +| litellm-python-bridge | PyO3 cdylib exposing LiteLLM Rust APIs to the Python SDK. | -Dependency direction (acyclic): litellm-core ← litellm-ai-gateway ← litellm-python-bridge. +Dependency direction is acyclic: `litellm-python-bridge` depends on the domain layers and `litellm-python-interop`; the interop foundation depends on no LiteLLM domain crate. - **Client endpoint:** `wss:///v1/realtime?model=` (WebSocket) - **Auth:** `Authorization: Bearer $LITELLM_MASTER_KEY` (fails closed if unset) diff --git a/litellm-rust/crates/ai-gateway/src/audio_transcription/common_utils.rs b/litellm-rust/crates/ai-gateway/src/audio_transcription/common_utils.rs index 270d5c2d97a..140bc8aeea8 100644 --- a/litellm-rust/crates/ai-gateway/src/audio_transcription/common_utils.rs +++ b/litellm-rust/crates/ai-gateway/src/audio_transcription/common_utils.rs @@ -1,10 +1,8 @@ -use std::collections::BTreeMap; - -use litellm_core::CoreResult; use litellm_core::audio_transcription::transformation::AudioTranscriptionProviderConfig; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::providers::bedrock::audio_transcription::BEDROCK_AUDIO_TRANSCRIPTION_CONFIG; use serde_json::{Map, Value}; +use std::collections::BTreeMap; pub(super) fn audio_transcription_provider_config( provider: &str, @@ -17,7 +15,7 @@ pub(super) fn audio_transcription_provider_config( pub(super) fn string_headers( headers: Option>, -) -> CoreResult> { +) -> Result, Error> { headers .unwrap_or_default() .into_iter() @@ -26,7 +24,7 @@ pub(super) fn string_headers( .as_str() .map(|value| (key.clone(), value.to_string())) .ok_or_else(|| { - CoreError::InvalidRequest(format!( + Error::InvalidRequest(format!( "audio transcription extra_headers.{key} must be a string" )) }) diff --git a/litellm-rust/crates/ai-gateway/src/audio_transcription/handler.rs b/litellm-rust/crates/ai-gateway/src/audio_transcription/handler.rs index 33c13550f58..1bdd4ae72a2 100644 --- a/litellm-rust/crates/ai-gateway/src/audio_transcription/handler.rs +++ b/litellm-rust/crates/ai-gateway/src/audio_transcription/handler.rs @@ -1,11 +1,9 @@ -use std::time::SystemTime; - -use litellm_core::CoreResult; use litellm_core::audio_transcription::transformation::AudioTranscriptionAuth; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::providers::bedrock::audio_transcription::aws_auth_config; use litellm_core::providers::bedrock::aws_base::{resolve_credentials, sign_bedrock_post}; use serde_json::Value; +use std::time::SystemTime; use super::common_utils::truncate_error_body; use super::types::ProviderAudioTranscriptionRequest; @@ -13,10 +11,9 @@ use crate::client::http_client; pub(crate) async fn execute_audio_transcription_provider_call( request: ProviderAudioTranscriptionRequest, -) -> CoreResult { - let body = serde_json::to_vec(&request.body).map_err(|error| { - CoreError::InvalidRequest(format!("invalid audio request body: {error}")) - })?; +) -> Result { + let body = serde_json::to_vec(&request.body) + .map_err(|error| Error::InvalidRequest(format!("invalid audio request body: {error}")))?; let mut request_builder = http_client().post(&request.url).body(body.clone()); for (key, value) in &request.upstream_headers { request_builder = request_builder.header(key, value); @@ -27,21 +24,20 @@ pub(crate) async fn execute_audio_transcription_provider_call( let response = request_builder .send() .await - .map_err(|error| CoreError::Network(error.to_string()))?; + .map_err(|error| Error::Network(error.to_string()))?; let status = response.status(); let text = response .text() .await - .map_err(|error| CoreError::Network(error.to_string()))?; + .map_err(|error| Error::Network(error.to_string()))?; if !status.is_success() { - return Err(CoreError::Http { + return Err(Error::Http { status: status.as_u16(), body: truncate_error_body(&text), }); } - let response_json: Value = serde_json::from_str(&text).map_err(|error| { - CoreError::InvalidResponse(format!("invalid audio response JSON: {error}")) - })?; + let response_json: Value = serde_json::from_str(&text) + .map_err(|error| Error::InvalidResponse(format!("invalid audio response JSON: {error}")))?; Ok(request .config .transform_transcription_response(&request.model, response_json)? @@ -51,14 +47,13 @@ pub(crate) async fn execute_audio_transcription_provider_call( pub(crate) async fn sign_request( request: &ProviderAudioTranscriptionRequest, optional_params: &serde_json::Map, -) -> CoreResult { +) -> Result { let env_lookup = environment_lookup; let auth = request .config .auth_strategy(&request.model, optional_params, &env_lookup)?; - let body = serde_json::to_vec(&request.body).map_err(|error| { - CoreError::InvalidRequest(format!("invalid audio request body: {error}")) - })?; + let body = serde_json::to_vec(&request.body) + .map_err(|error| Error::InvalidRequest(format!("invalid audio request body: {error}")))?; let mut headers = super::common_utils::string_headers(None)?; headers.insert("Content-Type".to_string(), "application/json".to_string()); headers.extend(request.upstream_headers.iter().cloned()); diff --git a/litellm-rust/crates/ai-gateway/src/audio_transcription/hooks.rs b/litellm-rust/crates/ai-gateway/src/audio_transcription/hooks.rs index 0c9faeda6e7..5e1240de759 100644 --- a/litellm-rust/crates/ai-gateway/src/audio_transcription/hooks.rs +++ b/litellm-rust/crates/ai-gateway/src/audio_transcription/hooks.rs @@ -1,11 +1,9 @@ -use std::future::Future; -use std::pin::Pin; - -use litellm_core::CoreResult; use litellm_core::audio_transcription::transformation::AudioTranscriptionAuth; use litellm_core::call_lifecycle::{CallLifecycleContext, CallLifecycleHooks, CallLifecycleTiming}; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use serde_json::{Map, Value, json}; +use std::future::Future; +use std::pin::Pin; use super::common_utils::{audio_transcription_provider_config, has_header, string_headers}; use super::handler::sign_request; @@ -26,7 +24,7 @@ pub(crate) struct AudioTranscriptionLifecycleHooks { request_metadata: RequestMetadata, } -type AudioFuture<'a, T> = Pin> + Send + 'a>>; +type AudioFuture<'a, T> = Pin> + Send + 'a>>; type AudioLogFuture<'a> = Pin + Send + 'a>>; impl AudioTranscriptionLifecycleHooks { @@ -45,7 +43,7 @@ impl AudioTranscriptionLifecycleHooks { async fn run_pre_call_guardrails( &self, request: PreparedAudioTranscriptionRequest, - ) -> CoreResult { + ) -> Result { if self.guardrail_runner.is_empty() { return Ok(request); } @@ -63,17 +61,17 @@ impl AudioTranscriptionLifecycleHooks { .await .map_err(guardrail_error_to_core_error)?; let Value::Object(mut data) = guardrail_request.data else { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "audio transcription pre_call guardrail must return an object".to_string(), )); }; let audio = data.remove("audio").ok_or_else(|| { - CoreError::InvalidRequest("audio transcription guardrail removed audio".to_string()) + Error::InvalidRequest("audio transcription guardrail removed audio".to_string()) })?; let optional_params = match data.remove("optional_params") { Some(Value::Object(value)) => value, Some(_) => { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "audio transcription optional_params must be an object".to_string(), )); } @@ -89,9 +87,9 @@ impl AudioTranscriptionLifecycleHooks { async fn prepare_provider_request( &self, request: PreparedAudioTranscriptionRequest, - ) -> CoreResult { + ) -> Result { let config = audio_transcription_provider_config(&request.custom_llm_provider) - .ok_or_else(|| CoreError::InvalidProvider(request.custom_llm_provider.clone()))?; + .ok_or_else(|| Error::InvalidProvider(request.custom_llm_provider.clone()))?; let env_lookup = super::handler::environment_lookup; let headers = string_headers(request.extra_headers)?; let url = config.complete_url( @@ -135,7 +133,7 @@ impl AudioTranscriptionLifecycleHooks { async fn run_during_call_guardrails( &self, request: ProviderAudioTranscriptionRequest, - ) -> CoreResult { + ) -> Result { if self.guardrail_runner.is_empty() { return Ok(request); } @@ -153,12 +151,12 @@ impl AudioTranscriptionLifecycleHooks { .await .map_err(guardrail_error_to_core_error)?; let Value::Object(mut data) = guardrail_request.data else { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "audio transcription during_call guardrail must return an object".to_string(), )); }; let body = data.remove("body").ok_or_else(|| { - CoreError::InvalidRequest("audio transcription guardrail removed body".to_string()) + Error::InvalidRequest("audio transcription guardrail removed body".to_string()) })?; Ok(ProviderAudioTranscriptionRequest { body, ..request }) } @@ -241,7 +239,7 @@ impl CallLifecycleHooks( &'a self, context: &'a CallLifecycleContext, - error: &'a CoreError, + error: &'a Error, timing: &'a CallLifecycleTiming, ) -> Self::FailureFuture<'a> { Box::pin(async move { @@ -281,22 +279,22 @@ fn guardrail_context(metadata: &RequestMetadata) -> GuardrailContext { } } -fn guardrail_error_to_core_error(error: GuardrailError) -> CoreError { - CoreError::InvalidRequest(format!("{}: {}", error.kind, error.message)) +fn guardrail_error_to_core_error(error: GuardrailError) -> Error { + Error::InvalidRequest(format!("{}: {}", error.kind, error.message)) } -fn core_error_kind(error: &CoreError) -> &'static str { +fn core_error_kind(error: &Error) -> &'static str { match error { - CoreError::Auth(_) => "AuthError", - CoreError::InvalidProvider(_) => "InvalidProvider", - CoreError::InvalidRequest(_) => "InvalidRequest", - CoreError::InvalidType { .. } => "InvalidType", - CoreError::MissingField(_) => "MissingField", - CoreError::Http { .. } => "HttpError", - CoreError::InvalidResponse(_) => "InvalidResponse", - CoreError::Network(_) => "NetworkError", - CoreError::Connect(_) => "ConnectError", - CoreError::Routing(_) => "RoutingError", - CoreError::Unsupported(_) => "UnsupportedRequest", + Error::Auth(_) => "AuthError", + Error::InvalidProvider(_) => "InvalidProvider", + Error::InvalidRequest(_) => "InvalidRequest", + Error::InvalidType { .. } => "InvalidType", + Error::MissingField(_) => "MissingField", + Error::Http { .. } => "HttpError", + Error::InvalidResponse(_) => "InvalidResponse", + Error::Network(_) => "NetworkError", + Error::Connect(_) => "ConnectError", + Error::Routing(_) => "RoutingError", + Error::Unsupported(_) => "UnsupportedRequest", } } diff --git a/litellm-rust/crates/ai-gateway/src/audio_transcription/mod.rs b/litellm-rust/crates/ai-gateway/src/audio_transcription/mod.rs index 5d33d912c40..3983846d7b6 100644 --- a/litellm-rust/crates/ai-gateway/src/audio_transcription/mod.rs +++ b/litellm-rust/crates/ai-gateway/src/audio_transcription/mod.rs @@ -1,4 +1,4 @@ -use litellm_core::CoreResult; +use litellm_core::Error; use litellm_core::call_lifecycle::CallLifecycle; use serde_json::Value; @@ -13,7 +13,7 @@ pub use types::AudioTranscriptionRequest; use handler::execute_audio_transcription_provider_call; use prepare::{PreparedAudioTranscriptionCall, prepare_audio_transcription_call}; -pub async fn audio_transcription(request: AudioTranscriptionRequest<'_>) -> CoreResult { +pub async fn audio_transcription(request: AudioTranscriptionRequest<'_>) -> Result { let PreparedAudioTranscriptionCall { request, hooks } = prepare_audio_transcription_call(request); CallLifecycle::default() diff --git a/litellm-rust/crates/ai-gateway/src/io/realtime.rs b/litellm-rust/crates/ai-gateway/src/io/realtime.rs index 845e7bf9527..662f7328982 100644 --- a/litellm-rust/crates/ai-gateway/src/io/realtime.rs +++ b/litellm-rust/crates/ai-gateway/src/io/realtime.rs @@ -15,8 +15,7 @@ use std::time::Duration; use futures_util::stream::{SplitSink, SplitStream}; use futures_util::{Sink, SinkExt, Stream, StreamExt}; -use litellm_core::CoreResult; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::realtime::transformation::RealtimeProviderConfig; use litellm_core::realtime::types::RealtimeEvent; use tokio::net::TcpStream; @@ -48,7 +47,7 @@ pub(crate) type UpstreamRx = SplitStream; /// Resolve the OpenAI API key from the explicit param or the environment. /// /// Blank/whitespace values are treated as absent (guard at resolution time). -pub(crate) fn resolve_api_key(api_key: Option<&str>) -> CoreResult { +pub(crate) fn resolve_api_key(api_key: Option<&str>) -> Result { api_key .map(str::trim) .filter(|key| !key.is_empty()) @@ -58,7 +57,7 @@ pub(crate) fn resolve_api_key(api_key: Option<&str>) -> CoreResult { .ok() .filter(|key| !key.trim().is_empty()) }) - .ok_or_else(|| CoreError::Auth(MISSING_KEY_MESSAGE.to_string())) + .ok_or_else(|| Error::Auth(MISSING_KEY_MESSAGE.to_string())) } /// Open the upstream WebSocket to OpenAI for `(model, api_key, api_base)`. @@ -70,24 +69,24 @@ pub(crate) async fn dial_upstream( model: &str, api_key: &str, api_base: Option<&str>, -) -> CoreResult { +) -> Result { let url = OPENAI_REALTIME_CONFIG.complete_url(api_base, model); let mut request = url .as_str() .into_client_request() - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; // GA realtime: only Authorization. The legacy OpenAI-Beta header triggers // beta_api_shape_disabled, so we do not send it. request.headers_mut().insert( AUTHORIZATION, HeaderValue::from_str(&format!("Bearer {api_key}")) - .map_err(|err| CoreError::Auth(err.to_string()))?, + .map_err(|err| Error::Auth(err.to_string()))?, ); let (upstream, _response) = connect_async(request) .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; Ok(upstream) } @@ -96,22 +95,22 @@ pub(crate) async fn dial_upstream( /// Used by the pool to pre-read OpenAI's unprompted `session.created`. Returns an /// error on a non-text frame, a closed socket, or undecodable JSON so the pool can /// discard a misbehaving socket rather than warm it. -pub(crate) async fn read_event(upstream_rx: &mut UpstreamRx) -> CoreResult { +pub(crate) async fn read_event(upstream_rx: &mut UpstreamRx) -> Result { loop { let message = upstream_rx .next() .await - .ok_or_else(|| CoreError::Network("upstream closed before first event".to_string()))? - .map_err(|err| CoreError::Network(err.to_string()))?; + .ok_or_else(|| Error::Network("upstream closed before first event".to_string()))? + .map_err(|err| Error::Network(err.to_string()))?; match message { Message::Text(text) => { return serde_json::from_str(&text) - .map_err(|err| CoreError::InvalidResponse(err.to_string())); + .map_err(|err| Error::InvalidResponse(err.to_string())); } // Ignore protocol frames (ping/pong) while waiting for the first event. Message::Ping(_) | Message::Pong(_) => continue, Message::Close(_) => { - return Err(CoreError::Network( + return Err(Error::Network( "upstream closed before first event".to_string(), )); } @@ -139,7 +138,7 @@ pub(crate) async fn splice( mut observe: impl FnMut(&RealtimeEvent) + Send, mut client_in: In, mut client_out: Out, -) -> CoreResult<()> +) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, @@ -154,7 +153,7 @@ where client_out .send(outbound) .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; } } @@ -175,26 +174,26 @@ where // inflate its own spend log. Logging observes upstream events only. for outbound in config.transform_realtime_request(&event, model)?.events { let payload = serde_json::to_string(&outbound) - .map_err(|err| CoreError::InvalidResponse(err.to_string()))?; + .map_err(|err| Error::InvalidResponse(err.to_string()))?; upstream_tx .send(Message::Text(payload)) .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; } } // upstream -> client upstream_message = upstream_rx.next() => { let Some(message) = upstream_message else { break }; // upstream closed - match message.map_err(|err| CoreError::Network(err.to_string()))? { + match message.map_err(|err| Error::Network(err.to_string()))? { Message::Text(text) => { let event: RealtimeEvent = serde_json::from_str(&text) - .map_err(|err| CoreError::InvalidResponse(err.to_string()))?; + .map_err(|err| Error::InvalidResponse(err.to_string()))?; observe(&event); for outbound in config.transform_realtime_response(&event, model)?.events { client_out .send(outbound) .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; } } Message::Close(_) => break, @@ -225,7 +224,7 @@ pub async fn realtime( observe: impl FnMut(&RealtimeEvent) + Send, client_in: In, client_out: Out, -) -> CoreResult<()> +) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, @@ -258,7 +257,7 @@ pub async fn realtime_warm( observe: impl FnMut(&RealtimeEvent) + Send, client_in: In, client_out: Out, -) -> CoreResult<()> +) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, diff --git a/litellm-rust/crates/ai-gateway/src/io/realtime_pool.rs b/litellm-rust/crates/ai-gateway/src/io/realtime_pool.rs index 4a1a3cd1166..49e9c459a88 100644 --- a/litellm-rust/crates/ai-gateway/src/io/realtime_pool.rs +++ b/litellm-rust/crates/ai-gateway/src/io/realtime_pool.rs @@ -28,7 +28,7 @@ use std::sync::{Arc, Mutex}; use std::time::{Duration, Instant}; use futures_util::StreamExt; -use litellm_core::CoreResult; +use litellm_core::Error; use litellm_core::realtime::types::RealtimeEvent; use crate::io::realtime::{ @@ -438,7 +438,7 @@ impl RealtimePool { /// /// `key.api_key` is already resolved (non-blank). The first frame OpenAI sends /// unprompted is `session.created`; we buffer exactly that and read nothing more. -async fn warm_one(key: &UpstreamKey) -> CoreResult { +async fn warm_one(key: &UpstreamKey) -> Result { let upstream: UpstreamWs = dial_upstream(&key.model, &key.api_key, key.api_base.as_deref()).await?; let (tx, mut rx) = upstream.split(); diff --git a/litellm-rust/crates/ai-gateway/src/io/responses_ws.rs b/litellm-rust/crates/ai-gateway/src/io/responses_ws.rs index 9b51019f4bc..0b01747b1a5 100644 --- a/litellm-rust/crates/ai-gateway/src/io/responses_ws.rs +++ b/litellm-rust/crates/ai-gateway/src/io/responses_ws.rs @@ -4,10 +4,10 @@ use std::time::Duration; use futures_util::stream::{SplitSink, SplitStream}; use futures_util::{Sink, SinkExt, Stream, StreamExt}; +use litellm_core::Error; use litellm_core::providers::openai::responses::transformation::OPENAI_RESPONSES_WS_CONFIG; use litellm_core::responses::types::ResponsesWsEvent; use litellm_core::responses::websocket::ResponsesWebSocketProviderConfig; -use litellm_core::{CoreError, CoreResult}; use tokio::net::TcpStream; use tokio::sync::Mutex; use tokio_tungstenite::tungstenite::Message; @@ -37,51 +37,49 @@ impl ResponsesWebSocketConnection { url: &str, headers: &HashMap, timeout: Option, - ) -> CoreResult { + ) -> Result { let mut request = url .into_client_request() - .map_err(|error| CoreError::Network(error.to_string()))?; + .map_err(|error| Error::Network(error.to_string()))?; for (name, value) in headers { let header_name = name .parse::() - .map_err(|error| CoreError::InvalidRequest(error.to_string()))?; + .map_err(|error| Error::InvalidRequest(error.to_string()))?; let header_value = HeaderValue::from_str(value) - .map_err(|error| CoreError::InvalidRequest(error.to_string()))?; + .map_err(|error| Error::InvalidRequest(error.to_string()))?; request.headers_mut().insert(header_name, header_value); } let connect = connect_async(request); let result = match timeout { Some(timeout) => tokio::time::timeout(timeout, connect).await.map_err(|_| { - CoreError::Network("Responses WebSocket connection timed out".to_string()) + Error::Network("Responses WebSocket connection timed out".to_string()) })?, None => connect.await, }; let (socket, _) = result.map_err(|error| match error { - tokio_tungstenite::tungstenite::Error::Http(response) => CoreError::Http { + tokio_tungstenite::tungstenite::Error::Http(response) => Error::Http { status: response.status().as_u16(), body: String::new(), }, - other => CoreError::Network(other.to_string()), + other => Error::Network(other.to_string()), })?; Ok(Self { socket: Arc::new(Mutex::new(Some(socket))), }) } - pub async fn send_text(&self, text: String) -> CoreResult<()> { + pub async fn send_text(&self, text: String) -> Result<(), Error> { let mut socket = self.socket.lock().await; let Some(socket) = socket.as_mut() else { - return Err(CoreError::Network( - "Responses WebSocket is closed".to_string(), - )); + return Err(Error::Network("Responses WebSocket is closed".to_string())); }; socket .send(Message::Text(text)) .await - .map_err(|error| CoreError::Network(error.to_string())) + .map_err(|error| Error::Network(error.to_string())) } - pub async fn recv_text(&self) -> CoreResult> { + pub async fn recv_text(&self) -> Result, Error> { let mut socket_guard = self.socket.lock().await; let Some(socket) = socket_guard.as_mut() else { return Ok(None); @@ -90,27 +88,27 @@ impl ResponsesWebSocketConnection { Some(Ok(Message::Text(text))) => Ok(Some(text)), Some(Ok(Message::Binary(bytes))) => String::from_utf8(bytes.to_vec()) .map(Some) - .map_err(|error| CoreError::InvalidResponse(error.to_string())), + .map_err(|error| Error::InvalidResponse(error.to_string())), Some(Ok(Message::Close(_))) | None => Ok(None), Some(Ok(_)) => Ok(None), - Some(Err(error)) => Err(CoreError::Network(error.to_string())), + Some(Err(error)) => Err(Error::Network(error.to_string())), } } - pub async fn close(&self) -> CoreResult<()> { + pub async fn close(&self) -> Result<(), Error> { let mut socket = self.socket.lock().await; if let Some(socket) = socket.as_mut() { socket .close(None) .await - .map_err(|error| CoreError::Network(error.to_string()))?; + .map_err(|error| Error::Network(error.to_string()))?; } *socket = None; Ok(()) } } -pub(crate) fn resolve_api_key(api_key: Option<&str>) -> CoreResult { +pub(crate) fn resolve_api_key(api_key: Option<&str>) -> Result { api_key .map(str::trim) .filter(|value| !value.is_empty()) @@ -120,38 +118,38 @@ pub(crate) fn resolve_api_key(api_key: Option<&str>) -> CoreResult { .ok() .filter(|value| !value.trim().is_empty()) }) - .ok_or_else(|| CoreError::Auth(MISSING_KEY_MESSAGE.to_string())) + .ok_or_else(|| Error::Auth(MISSING_KEY_MESSAGE.to_string())) } async fn dial_upstream( model: &str, api_key: &str, api_base: Option<&str>, -) -> CoreResult { +) -> Result { let url = OPENAI_RESPONSES_WS_CONFIG.complete_websocket_url(api_base, model); let mut request = url .as_str() .into_client_request() - .map_err(|error| CoreError::Network(error.to_string()))?; + .map_err(|error| Error::Network(error.to_string()))?; request.headers_mut().insert( AUTHORIZATION, HeaderValue::from_str(&format!("Bearer {api_key}")) - .map_err(|error| CoreError::Auth(error.to_string()))?, + .map_err(|error| Error::Auth(error.to_string()))?, ); let result = tokio::time::timeout( Duration::from_secs(DEFAULT_RESPONSES_WS_CONNECT_TIMEOUT_SECS), connect_async(request), ) .await - .map_err(|_| CoreError::Network("Responses WebSocket connection timed out".to_string()))?; + .map_err(|_| Error::Network("Responses WebSocket connection timed out".to_string()))?; result .map(|(socket, _)| socket) .map_err(|error| match error { - tokio_tungstenite::tungstenite::Error::Http(response) => CoreError::Http { + tokio_tungstenite::tungstenite::Error::Http(response) => Error::Http { status: response.status().as_u16(), body: String::new(), }, - other => CoreError::Network(other.to_string()), + other => Error::Network(other.to_string()), }) } @@ -166,7 +164,7 @@ impl ResponsesWebSocketStreaming { observe: impl FnMut(&ResponsesWsEvent) + Send, client_in: In, client_out: Out, - ) -> CoreResult<()> + ) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, @@ -193,7 +191,7 @@ pub(crate) async fn splice( mut observe: impl FnMut(&ResponsesWsEvent) + Send, mut client_in: In, mut client_out: Out, -) -> CoreResult<()> +) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, @@ -210,18 +208,18 @@ where .events { let payload = serde_json::to_string(&outbound) - .map_err(|error| CoreError::InvalidResponse(error.to_string()))?; + .map_err(|error| Error::InvalidResponse(error.to_string()))?; upstream_tx.send(Message::Text(payload)) .await - .map_err(|error| CoreError::Network(error.to_string()))?; + .map_err(|error| Error::Network(error.to_string()))?; } } message = upstream_rx.next() => { let Some(message) = message else { break }; - match message.map_err(|error| CoreError::Network(error.to_string()))? { + match message.map_err(|error| Error::Network(error.to_string()))? { Message::Text(text) => { let event = serde_json::from_str::(&text) - .map_err(|error| CoreError::InvalidResponse(error.to_string()))?; + .map_err(|error| Error::InvalidResponse(error.to_string()))?; observe(&event); for outbound in OPENAI_RESPONSES_WS_CONFIG .transform_ws_response(&event, model)? @@ -229,7 +227,7 @@ where { client_out.send(outbound) .await - .map_err(|error| CoreError::Network(error.to_string()))?; + .map_err(|error| Error::Network(error.to_string()))?; } } Message::Close(_) => break, @@ -252,7 +250,7 @@ pub async fn async_responses_websocket( mut observe: impl FnMut(&ResponsesWsEvent) + Send, client_in: In, client_out: Out, -) -> CoreResult<()> +) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, @@ -267,11 +265,11 @@ where .events { let payload = serde_json::to_string(&outbound) - .map_err(|error| CoreError::InvalidResponse(error.to_string()))?; + .map_err(|error| Error::InvalidResponse(error.to_string()))?; upstream_tx .send(Message::Text(payload)) .await - .map_err(|error| CoreError::Network(error.to_string()))?; + .map_err(|error| Error::Network(error.to_string()))?; } } ResponsesWebSocketStreaming::bidirectional_forward( @@ -296,7 +294,7 @@ pub async fn responses_ws( observe: impl FnMut(&ResponsesWsEvent) + Send, client_in: In, client_out: Out, -) -> CoreResult<()> +) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, @@ -514,7 +512,7 @@ mod tests { ) .await .expect_err("status error"); - assert!(matches!(error, CoreError::Http { status: 401, .. })); + assert!(matches!(error, Error::Http { status: 401, .. })); server.await.expect("server task"); } @@ -543,7 +541,7 @@ mod tests { ) .await .expect_err("status error"); - assert!(matches!(error, CoreError::Http { status: 500, .. })); + assert!(matches!(error, Error::Http { status: 500, .. })); server.await.expect("server task"); } } diff --git a/litellm-rust/crates/ai-gateway/src/ocr/common_utils.rs b/litellm-rust/crates/ai-gateway/src/ocr/common_utils.rs index 9bc2818b6e7..e0ce165dc93 100644 --- a/litellm-rust/crates/ai-gateway/src/ocr/common_utils.rs +++ b/litellm-rust/crates/ai-gateway/src/ocr/common_utils.rs @@ -3,8 +3,7 @@ use std::time::{Duration, Instant}; use base64::Engine; use base64::engine::general_purpose::STANDARD as BASE64_STANDARD; -use litellm_core::CoreResult; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::ocr::transformation::OcrProviderConfig; use reqwest::Url; use serde_json::{Map, Value}; @@ -56,7 +55,7 @@ fn is_azure_document_intelligence_model(model: &str) -> bool { pub(super) fn string_headers( extra_headers: Option>, -) -> CoreResult> { +) -> Result, Error> { extra_headers .unwrap_or_default() .into_iter() @@ -65,7 +64,7 @@ pub(super) fn string_headers( .as_str() .map(|value| (key.clone(), value.to_string())) .ok_or_else(|| { - CoreError::InvalidRequest(format!( + Error::InvalidRequest(format!( "OCR extra_headers.{key} must be a string, got {}", litellm_core::error::json_type_name(&value) )) @@ -80,7 +79,7 @@ pub(super) fn has_header(headers: &[(String, String)], name: &str) -> bool { .any(|(key, _)| key.eq_ignore_ascii_case(name)) } -fn document_url_field(document: &Value) -> CoreResult> { +fn document_url_field(document: &Value) -> Result, Error> { let Some(object) = document.as_object() else { return Ok(None); }; @@ -138,13 +137,13 @@ fn is_blocked_ip(ip: IpAddr) -> bool { } } -fn blocked_url_error(url: &Url) -> CoreError { - CoreError::InvalidRequest(format!( +fn blocked_url_error(url: &Url) -> Error { + Error::InvalidRequest(format!( "OCR document URL rejected by SSRF protection: {url}" )) } -async fn validate_safe_fetch_url(url: &Url) -> CoreResult<()> { +async fn validate_safe_fetch_url(url: &Url) -> Result<(), Error> { if !matches!(url.scheme(), "http" | "https") { return Err(blocked_url_error(url)); } @@ -162,7 +161,7 @@ async fn validate_safe_fetch_url(url: &Url) -> CoreResult<()> { .ok_or_else(|| blocked_url_error(url))?; let addresses = tokio::net::lookup_host((host, port)) .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; let mut saw_address = false; for address in addresses { saw_address = true; @@ -176,25 +175,25 @@ async fn validate_safe_fetch_url(url: &Url) -> CoreResult<()> { Ok(()) } -fn redirect_location(response: &reqwest::Response, url: &Url) -> CoreResult { +fn redirect_location(response: &reqwest::Response, url: &Url) -> Result { let location = response .headers() .get(reqwest::header::LOCATION) .and_then(|value| value.to_str().ok()) .ok_or_else(|| { - CoreError::InvalidResponse("OCR document redirect missing Location header".to_string()) + Error::InvalidResponse("OCR document redirect missing Location header".to_string()) })?; url.join(location) - .map_err(|err| CoreError::InvalidResponse(format!("invalid OCR document redirect: {err}"))) + .map_err(|err| Error::InvalidResponse(format!("invalid OCR document redirect: {err}"))) } -async fn safe_get_document_url(url: &str) -> CoreResult<(Url, reqwest::Response)> { +async fn safe_get_document_url(url: &str) -> Result<(Url, reqwest::Response), Error> { let client = reqwest::Client::builder() .redirect(reqwest::redirect::Policy::none()) .build() - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; let mut current_url = Url::parse(url) - .map_err(|err| CoreError::InvalidRequest(format!("invalid OCR document URL: {err}")))?; + .map_err(|err| Error::InvalidRequest(format!("invalid OCR document URL: {err}")))?; for _ in 0..MAX_SAFE_FETCH_REDIRECTS { validate_safe_fetch_url(¤t_url).await?; @@ -202,28 +201,28 @@ async fn safe_get_document_url(url: &str) -> CoreResult<(Url, reqwest::Response) .get(current_url.clone()) .send() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; if !response.status().is_redirection() { return Ok((current_url, response)); } current_url = redirect_location(&response, ¤t_url)?; } - Err(CoreError::InvalidRequest( + Err(Error::InvalidRequest( "Too many redirects while fetching OCR document URL".to_string(), )) } -fn enforce_download_size(content_length: u64, max_bytes: u64, url: &Url) -> CoreResult<()> { +fn enforce_download_size(content_length: u64, max_bytes: u64, url: &Url) -> Result<(), Error> { if max_bytes == 0 { - return Err(CoreError::InvalidRequest(format!( + return Err(Error::InvalidRequest(format!( "OCR document URL download is disabled (MAX_IMAGE_URL_DOWNLOAD_SIZE_MB=0). url={url}" ))); } if content_length > max_bytes { let size_mb = content_length as f64 / (1024.0 * 1024.0); let max_size_mb = max_bytes as f64 / (1024.0 * 1024.0); - return Err(CoreError::InvalidRequest(format!( + return Err(Error::InvalidRequest(format!( "OCR document size ({size_mb:.2}MB) exceeds maximum allowed size ({max_size_mb:.2}MB). url={url}" ))); } @@ -233,7 +232,7 @@ fn enforce_download_size(content_length: u64, max_bytes: u64, url: &Url) -> Core async fn read_response_with_limit( mut response: reqwest::Response, url: &Url, -) -> CoreResult> { +) -> Result, Error> { let max_bytes = max_document_download_bytes(); if let Some(content_length) = response.content_length() { enforce_download_size(content_length, max_bytes, url)?; @@ -246,7 +245,7 @@ async fn read_response_with_limit( while let Some(chunk) = response .chunk() .await - .map_err(|err| CoreError::Network(err.to_string()))? + .map_err(|err| Error::Network(err.to_string()))? { bytes_downloaded += chunk.len() as u64; enforce_download_size(bytes_downloaded, max_bytes, url)?; @@ -255,7 +254,7 @@ async fn read_response_with_limit( Ok(bytes) } -pub(super) async fn convert_document_url_to_data_uri(document: Value) -> CoreResult { +pub(super) async fn convert_document_url_to_data_uri(document: Value) -> Result { let Some((field, url)) = document_url_field(&document)? else { return Ok(document); }; @@ -267,7 +266,7 @@ pub(super) async fn convert_document_url_to_data_uri(document: Value) -> CoreRes let status = response.status(); if !status.is_success() { let body = response.text().await.unwrap_or_default(); - return Err(CoreError::Http { + return Err(Error::Http { status: status.as_u16(), body: truncate_error_body(&body), }); @@ -290,7 +289,7 @@ pub(super) async fn convert_document_url_to_data_uri(document: Value) -> CoreRes let mut transformed = document .as_object() .cloned() - .ok_or_else(|| CoreError::InvalidRequest("OCR document must be an object".to_string()))?; + .ok_or_else(|| Error::InvalidRequest("OCR document must be an object".to_string()))?; transformed.insert(field.to_string(), Value::String(data_uri)); Ok(Value::Object(transformed)) } @@ -316,11 +315,11 @@ fn retry_after_secs(response: &reqwest::Response) -> u64 { .unwrap_or(2) } -fn operation_status(response_json: &Value) -> CoreResult<&str> { +fn operation_status(response_json: &Value) -> Result<&str, Error> { let status = response_json .get("status") .and_then(Value::as_str) - .ok_or(CoreError::MissingField("status"))?; + .ok_or(Error::MissingField("status"))?; match status { "succeeded" => Ok("succeeded"), "running" | "notStarted" => Ok("running"), @@ -330,11 +329,11 @@ fn operation_status(response_json: &Value) -> CoreResult<&str> { .and_then(|error| error.get("message")) .and_then(Value::as_str) .unwrap_or("Unknown error"); - Err(CoreError::InvalidResponse(format!( + Err(Error::InvalidResponse(format!( "Azure Document Intelligence analysis failed: {message}" ))) } - other => Err(CoreError::InvalidResponse(format!( + other => Err(Error::InvalidResponse(format!( "Unknown operation status: {other}" ))), } @@ -345,9 +344,9 @@ pub(super) async fn poll_document_intelligence( original_url: &str, headers: &[(String, String)], timeout: Option, -) -> CoreResult { +) -> Result { if !same_origin(operation_url, original_url) { - return Err(CoreError::InvalidResponse( + return Err(Error::InvalidResponse( "Azure Document Intelligence: rejected cross-origin polling URL".to_string(), )); } @@ -358,7 +357,7 @@ pub(super) async fn poll_document_intelligence( )); loop { if start.elapsed() > timeout { - return Err(CoreError::Network(format!( + return Err(Error::Network(format!( "Azure Document Intelligence operation polling timed out after {} seconds", timeout.as_secs() ))); @@ -373,21 +372,21 @@ pub(super) async fn poll_document_intelligence( let response = request_builder .send() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; let retry_after = retry_after_secs(&response); let status = response.status(); let text = response .text() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; if !status.is_success() { - return Err(CoreError::Http { + return Err(Error::Http { status: status.as_u16(), body: truncate_error_body(&text), }); } let response_json: Value = serde_json::from_str(&text).map_err(|err| { - CoreError::InvalidResponse(format!("invalid Azure DI poll response JSON: {err}")) + Error::InvalidResponse(format!("invalid Azure DI poll response JSON: {err}")) })?; if operation_status(&response_json)? == "succeeded" { return Ok(response_json); @@ -426,7 +425,7 @@ mod tests { assert!(matches!( error, - CoreError::InvalidRequest(message) + Error::InvalidRequest(message) if message.contains("SSRF protection") )); } diff --git a/litellm-rust/crates/ai-gateway/src/ocr/handler.rs b/litellm-rust/crates/ai-gateway/src/ocr/handler.rs index 1de34eb400e..815bc84363a 100644 --- a/litellm-rust/crates/ai-gateway/src/ocr/handler.rs +++ b/litellm-rust/crates/ai-gateway/src/ocr/handler.rs @@ -1,5 +1,4 @@ -use litellm_core::CoreResult; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::ocr::transformation::OcrResponseHandling; use serde_json::Value; @@ -7,7 +6,7 @@ use super::common_utils::{poll_document_intelligence, truncate_error_body}; use super::types::ProviderOcrRequest; use crate::client::http_client; -pub(crate) async fn execute_ocr_provider_call(request: ProviderOcrRequest) -> CoreResult { +pub(crate) async fn execute_ocr_provider_call(request: ProviderOcrRequest) -> Result { let mut request_builder = http_client().post(&request.url).json(&request.body); for (key, value) in &request.upstream_headers { request_builder = request_builder.header(key, value); @@ -19,7 +18,7 @@ pub(crate) async fn execute_ocr_provider_call(request: ProviderOcrRequest) -> Co let response = request_builder .send() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; let status = response.status(); if request.config.response_handling() == OcrResponseHandling::AzureDocumentIntelligencePoll @@ -31,7 +30,7 @@ pub(crate) async fn execute_ocr_provider_call(request: ProviderOcrRequest) -> Co .and_then(|value| value.to_str().ok()) .map(str::to_string) .ok_or_else(|| { - CoreError::InvalidResponse( + Error::InvalidResponse( "Azure Document Intelligence returned 202 but no Operation-Location header found" .to_string(), ) @@ -52,17 +51,17 @@ pub(crate) async fn execute_ocr_provider_call(request: ProviderOcrRequest) -> Co let text = response .text() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; if !status.is_success() { - return Err(CoreError::Http { + return Err(Error::Http { status: status.as_u16(), body: truncate_error_body(&text), }); } let response_json: Value = serde_json::from_str(&text) - .map_err(|err| CoreError::InvalidResponse(format!("invalid OCR response JSON: {err}")))?; + .map_err(|err| Error::InvalidResponse(format!("invalid OCR response JSON: {err}")))?; Ok(request .config diff --git a/litellm-rust/crates/ai-gateway/src/ocr/hooks.rs b/litellm-rust/crates/ai-gateway/src/ocr/hooks.rs index 95df566dc53..401e26d3b29 100644 --- a/litellm-rust/crates/ai-gateway/src/ocr/hooks.rs +++ b/litellm-rust/crates/ai-gateway/src/ocr/hooks.rs @@ -1,11 +1,9 @@ -use std::future::Future; -use std::pin::Pin; - -use litellm_core::CoreResult; use litellm_core::call_lifecycle::{CallLifecycleContext, CallLifecycleHooks, CallLifecycleTiming}; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::ocr::transformation::OcrAuthStrategy; use serde_json::{Map, Value, json}; +use std::future::Future; +use std::pin::Pin; use super::common_utils::{ convert_document_url_to_data_uri, has_header, ocr_provider_config, string_headers, @@ -27,7 +25,7 @@ pub(crate) struct OcrLifecycleHooks { request_metadata: RequestMetadata, } -type OcrFuture<'a, T> = Pin> + Send + 'a>>; +type OcrFuture<'a, T> = Pin> + Send + 'a>>; type OcrLogFuture<'a> = Pin + Send + 'a>>; impl OcrLifecycleHooks { @@ -46,7 +44,7 @@ impl OcrLifecycleHooks { async fn run_pre_call_guardrails( &self, request: PreparedOcrRequest, - ) -> CoreResult { + ) -> Result { if self.guardrail_runner.is_empty() { return Ok(request); } @@ -74,9 +72,9 @@ impl OcrLifecycleHooks { async fn prepare_provider_request( &self, request: PreparedOcrRequest, - ) -> CoreResult { + ) -> Result { let config = ocr_provider_config(&request.custom_llm_provider, &request.model) - .ok_or_else(|| CoreError::InvalidProvider(request.custom_llm_provider.clone()))?; + .ok_or_else(|| Error::InvalidProvider(request.custom_llm_provider.clone()))?; let env_lookup = |key: &str| std::env::var(key).ok(); let headers = string_headers(request.extra_headers)?; let auth_strategy = config.auth_strategy(); @@ -120,7 +118,7 @@ impl OcrLifecycleHooks { custom_llm_provider: &str, url: &str, body: Value, - ) -> CoreResult { + ) -> Result { if self.guardrail_runner.is_empty() { return Ok(body); } @@ -217,7 +215,7 @@ impl CallLifecycleHooks for OcrLi fn async_log_failure_event<'a>( &'a self, context: &'a CallLifecycleContext, - error: &'a CoreError, + error: &'a Error, timing: &'a CallLifecycleTiming, ) -> Self::FailureFuture<'a> { Box::pin(async move { @@ -278,19 +276,19 @@ fn guardrail_context(metadata: &RequestMetadata) -> GuardrailContext { fn parse_ocr_pre_call_guardrail_request( request: GuardrailRequest, -) -> CoreResult<(Value, Map)> { +) -> Result<(Value, Map), Error> { let Value::Object(mut data) = request.data else { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "OCR pre_call guardrail must return an object".to_string(), )); }; let document = data.remove("document").ok_or_else(|| { - CoreError::InvalidRequest("OCR pre_call guardrail removed document".to_string()) + Error::InvalidRequest("OCR pre_call guardrail removed document".to_string()) })?; let optional_params = match data.remove("optional_params") { Some(Value::Object(params)) => params, Some(_) => { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "OCR pre_call guardrail optional_params must be an object".to_string(), )); } @@ -299,33 +297,32 @@ fn parse_ocr_pre_call_guardrail_request( Ok((document, optional_params)) } -fn parse_ocr_during_call_guardrail_request(request: GuardrailRequest) -> CoreResult { +fn parse_ocr_during_call_guardrail_request(request: GuardrailRequest) -> Result { let Value::Object(mut data) = request.data else { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "OCR during_call guardrail must return an object".to_string(), )); }; - data.remove("body").ok_or_else(|| { - CoreError::InvalidRequest("OCR during_call guardrail removed body".to_string()) - }) + data.remove("body") + .ok_or_else(|| Error::InvalidRequest("OCR during_call guardrail removed body".to_string())) } -fn guardrail_error_to_core_error(error: GuardrailError) -> CoreError { - CoreError::InvalidRequest(format!("{}: {}", error.kind, error.message)) +fn guardrail_error_to_core_error(error: GuardrailError) -> Error { + Error::InvalidRequest(format!("{}: {}", error.kind, error.message)) } -fn core_error_kind(error: &CoreError) -> &'static str { +fn core_error_kind(error: &Error) -> &'static str { match error { - CoreError::Auth(_) => "AuthError", - CoreError::InvalidProvider(_) => "InvalidProvider", - CoreError::InvalidRequest(_) => "InvalidRequest", - CoreError::InvalidType { .. } => "InvalidType", - CoreError::MissingField(_) => "MissingField", - CoreError::Http { .. } => "HttpError", - CoreError::InvalidResponse(_) => "InvalidResponse", - CoreError::Network(_) => "NetworkError", - CoreError::Connect(_) => "ConnectError", - CoreError::Routing(_) => "RoutingError", - CoreError::Unsupported(_) => "UnsupportedRequest", + Error::Auth(_) => "AuthError", + Error::InvalidProvider(_) => "InvalidProvider", + Error::InvalidRequest(_) => "InvalidRequest", + Error::InvalidType { .. } => "InvalidType", + Error::MissingField(_) => "MissingField", + Error::Http { .. } => "HttpError", + Error::InvalidResponse(_) => "InvalidResponse", + Error::Network(_) => "NetworkError", + Error::Connect(_) => "ConnectError", + Error::Routing(_) => "RoutingError", + Error::Unsupported(_) => "UnsupportedRequest", } } diff --git a/litellm-rust/crates/ai-gateway/src/ocr/mod.rs b/litellm-rust/crates/ai-gateway/src/ocr/mod.rs index c4c13e2300c..b59ab626fd3 100644 --- a/litellm-rust/crates/ai-gateway/src/ocr/mod.rs +++ b/litellm-rust/crates/ai-gateway/src/ocr/mod.rs @@ -1,4 +1,4 @@ -use litellm_core::CoreResult; +use litellm_core::Error; use litellm_core::call_lifecycle::CallLifecycle; use serde_json::Value; @@ -13,7 +13,7 @@ pub use types::OcrRequest; use handler::execute_ocr_provider_call; use prepare::{PreparedOcrCall, prepare_ocr_call}; -pub async fn ocr(request: OcrRequest<'_>) -> CoreResult { +pub async fn ocr(request: OcrRequest<'_>) -> Result { let PreparedOcrCall { request, hooks } = prepare_ocr_call(request); CallLifecycle::default() .run_request(request, &hooks, execute_ocr_provider_call) diff --git a/litellm-rust/crates/ai-gateway/src/ocr/tests.rs b/litellm-rust/crates/ai-gateway/src/ocr/tests.rs index bb2a6b06501..8c3f0425149 100644 --- a/litellm-rust/crates/ai-gateway/src/ocr/tests.rs +++ b/litellm-rust/crates/ai-gateway/src/ocr/tests.rs @@ -1,7 +1,7 @@ use std::sync::{Arc, Mutex}; use std::time::Duration; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::ocr::transformation::OcrResponseHandling; use serde_json::{Map, Value, json}; use tokio::io::{AsyncReadExt, AsyncWriteExt}; @@ -395,7 +395,7 @@ async fn ocr_lifecycle_runs_failure_hook_on_provider_error() { .await .expect_err("provider error propagates"); - assert!(matches!(err, CoreError::Http { status: 500, .. })); + assert!(matches!(err, Error::Http { status: 500, .. })); server.await.expect("server task completes"); assert_eq!( logger.events(), @@ -439,7 +439,7 @@ async fn ocr_lifecycle_pre_call_block_skips_provider_socket() { .await .expect_err("guardrail blocks request"); - assert!(matches!(err, CoreError::InvalidRequest(_))); + assert!(matches!(err, Error::InvalidRequest(_))); assert_eq!(guardrail.events(), vec!["async_pre_call_hook"]); assert_eq!( logger.events(), @@ -607,7 +607,7 @@ fn string_headers_rejects_non_string_values() { let err = string_headers(Some(headers)).expect_err("non-string header rejected"); assert_eq!( err, - CoreError::InvalidRequest( + Error::InvalidRequest( "OCR extra_headers.x-retry-count must be a string, got number".to_string() ) ); diff --git a/litellm-rust/crates/ai-gateway/src/python/config.rs b/litellm-rust/crates/ai-gateway/src/python/config.rs index c028d3d6b51..d5a4dd69c8d 100644 --- a/litellm-rust/crates/ai-gateway/src/python/config.rs +++ b/litellm-rust/crates/ai-gateway/src/python/config.rs @@ -6,33 +6,31 @@ //! (and recorded in [`crate::gil`]); the realtime hot path never touches Python. //! //! Compiled only under the `python-config` feature. - -use litellm_core::CoreResult; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::router::{Deployment, Router}; use pyo3::prelude::*; use crate::gil; /// Load the router's `model_list` from `config_path` via the Python reader. -pub fn load_router_from_config(config_path: &str) -> CoreResult { +pub fn load_router_from_config(config_path: &str) -> Result { gil::record_acquisition(); Python::attach(|py| { let model_list = py .import("litellm.proxy.read_model_list") .and_then(|module| module.getattr("read_model_list")) .and_then(|reader| reader.call1((config_path,))) - .map_err(|err| CoreError::Routing(format!("read_model_list failed: {err}")))?; + .map_err(|err| Error::Routing(format!("read_model_list failed: {err}")))?; let model_list_json: String = py .import("json") .and_then(|json| json.getattr("dumps")) .and_then(|dumps| dumps.call1((model_list,))) .and_then(|encoded| encoded.extract()) - .map_err(|err| CoreError::Routing(format!("serializing model_list failed: {err}")))?; + .map_err(|err| Error::Routing(format!("serializing model_list failed: {err}")))?; let deployments: Vec = serde_json::from_str(&model_list_json) - .map_err(|err| CoreError::Routing(format!("parsing model_list failed: {err}")))?; + .map_err(|err| Error::Routing(format!("parsing model_list failed: {err}")))?; Ok(Router::new(deployments)) }) diff --git a/litellm-rust/crates/ai-gateway/src/routes/messages/mod.rs b/litellm-rust/crates/ai-gateway/src/routes/messages/mod.rs index 7e38d10c6ff..e9f8c477f36 100644 --- a/litellm-rust/crates/ai-gateway/src/routes/messages/mod.rs +++ b/litellm-rust/crates/ai-gateway/src/routes/messages/mod.rs @@ -9,7 +9,7 @@ use axum::http::StatusCode; use axum::http::header::{CACHE_CONTROL, CONTENT_TYPE, HeaderMap, HeaderValue}; use axum::response::{IntoResponse, Response}; use axum::routing::post; -use litellm_core::CoreError; +use litellm_core::Error; use serde_json::{Map, Value}; use crate::auth::RequireMasterKey; @@ -46,7 +46,7 @@ fn stream_response(upstream: reqwest::Response) -> Result Result Result>, CoreError> { +fn forwarded_headers(headers: &HeaderMap) -> Result>, Error> { let forwarded = headers .iter() .filter(|(name, _)| { @@ -74,19 +74,19 @@ fn forwarded_headers(headers: &HeaderMap) -> Result>, }) .map(|(name, value)| { let value = value.to_str().map_err(|_| { - CoreError::InvalidRequest(format!("invalid value for header {}", name.as_str())) + Error::InvalidRequest(format!("invalid value for header {}", name.as_str())) })?; Ok((name.to_string(), Value::String(value.to_string()))) }) - .collect::, CoreError>>()?; + .collect::, Error>>()?; Ok((!forwarded.is_empty()).then_some(forwarded)) } #[derive(Debug)] -struct MessagesRouteError(CoreError); +struct MessagesRouteError(Error); -impl From for MessagesRouteError { - fn from(error: CoreError) -> Self { +impl From for MessagesRouteError { + fn from(error: Error) -> Self { Self(error) } } @@ -94,28 +94,28 @@ impl From for MessagesRouteError { impl IntoResponse for MessagesRouteError { fn into_response(self) -> Response { let (status, message) = match self.0 { - CoreError::InvalidRequest(message) => (StatusCode::BAD_REQUEST, message), - CoreError::InvalidProvider(_) | CoreError::Routing(_) => ( + Error::InvalidRequest(message) => (StatusCode::BAD_REQUEST, message), + Error::InvalidProvider(_) | Error::Routing(_) => ( StatusCode::NOT_FOUND, "no messages deployment is configured for this model".to_string(), ), - CoreError::Auth(_) => ( + Error::Auth(_) => ( StatusCode::BAD_GATEWAY, "messages provider authentication failed".to_string(), ), - CoreError::Http { .. } - | CoreError::Network(_) - | CoreError::Connect(_) - | CoreError::InvalidResponse(_) - | CoreError::InvalidType { .. } - | CoreError::MissingField(_) => ( + Error::Http { .. } + | Error::Network(_) + | Error::Connect(_) + | Error::InvalidResponse(_) + | Error::InvalidType { .. } + | Error::MissingField(_) => ( StatusCode::BAD_GATEWAY, "messages provider request failed".to_string(), ), // The gateway has no Python implementation to decline to, so a // request the core cannot serve is reported to the caller. The // reason is a fixed internal string, never provider content. - CoreError::Unsupported(reason) => ( + Error::Unsupported(reason) => ( StatusCode::BAD_REQUEST, format!("messages request is not supported: {reason}"), ), diff --git a/litellm-rust/crates/ai-gateway/src/routes/messages/service.rs b/litellm-rust/crates/ai-gateway/src/routes/messages/service.rs index 5f4c5fe8de4..4fd29db05d6 100644 --- a/litellm-rust/crates/ai-gateway/src/routes/messages/service.rs +++ b/litellm-rust/crates/ai-gateway/src/routes/messages/service.rs @@ -1,10 +1,10 @@ use std::sync::Arc; +use litellm_core::Error; use litellm_core::constants::ANTHROPIC_MESSAGES_PROVIDER; use litellm_core::messages::types::MessagesRequest; use litellm_core::messages::{messages, messages_stream}; use litellm_core::router::Router; -use litellm_core::{CoreError, CoreResult}; use serde_json::{Map, Value}; pub(crate) enum MessagesResponse { @@ -16,16 +16,16 @@ pub async fn run( router: &Arc, body: Value, extra_headers: Option>, -) -> CoreResult { +) -> Result { let model = body .get("model") .and_then(Value::as_str) .map(str::trim) .filter(|model| !model.is_empty()) - .ok_or_else(|| CoreError::InvalidRequest("messages body requires a model".to_string()))?; - let deployment = router.get_available_deployment(model).ok_or_else(|| { - CoreError::Routing(format!("no deployment available for model '{model}'")) - })?; + .ok_or_else(|| Error::InvalidRequest("messages body requires a model".to_string()))?; + let deployment = router + .get_available_deployment(model) + .ok_or_else(|| Error::Routing(format!("no deployment available for model '{model}'")))?; let provider_model = deployment.litellm_params.model.as_str(); let upstream_model = provider_model .split_once('/') @@ -37,7 +37,7 @@ pub async fn run( }; let mut body = body; body.as_object_mut() - .ok_or_else(|| CoreError::InvalidRequest("messages body must be an object".to_string()))? + .ok_or_else(|| Error::InvalidRequest("messages body must be an object".to_string()))? .insert( "model".to_string(), Value::String(upstream_model.to_string()), @@ -60,6 +60,6 @@ pub async fn run( serde_json::to_value(response) .map(MessagesResponse::Json) .map_err(|err| { - CoreError::InvalidResponse(format!("failed to serialize messages response: {err}")) + Error::InvalidResponse(format!("failed to serialize messages response: {err}")) }) } diff --git a/litellm-rust/crates/ai-gateway/src/routes/realtime/service.rs b/litellm-rust/crates/ai-gateway/src/routes/realtime/service.rs index 4ae8cfe7379..b8ee77c4269 100644 --- a/litellm-rust/crates/ai-gateway/src/routes/realtime/service.rs +++ b/litellm-rust/crates/ai-gateway/src/routes/realtime/service.rs @@ -11,8 +11,7 @@ use std::time::Duration; use crate::io::realtime_pool::{RealtimePool, upstream_key}; use futures_util::{Sink, Stream}; -use litellm_core::CoreResult; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::realtime::types::RealtimeEvent; use litellm_core::router::Router; @@ -29,15 +28,15 @@ pub async fn run( observe: impl FnMut(&RealtimeEvent) + Send, client_in: In, client_out: Out, -) -> CoreResult<()> +) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, >::Error: std::fmt::Display, { - let deployment = router.get_available_deployment(model).ok_or_else(|| { - CoreError::Routing(format!("no deployment available for model '{model}'")) - })?; + let deployment = router + .get_available_deployment(model) + .ok_or_else(|| Error::Routing(format!("no deployment available for model '{model}'")))?; let params = &deployment.litellm_params; // Strip a leading `openai/` so the OpenAI-only realtime fn gets the bare model. let provider_model = params diff --git a/litellm-rust/crates/ai-gateway/src/routes/responses/service.rs b/litellm-rust/crates/ai-gateway/src/routes/responses/service.rs index 165c95695d3..e8f840c0c8e 100644 --- a/litellm-rust/crates/ai-gateway/src/routes/responses/service.rs +++ b/litellm-rust/crates/ai-gateway/src/routes/responses/service.rs @@ -2,13 +2,13 @@ use std::sync::Arc; use std::time::Duration; use futures_util::{Sink, Stream}; +use litellm_core::Error; use litellm_core::call_lifecycle::{CallLifecycle, CallLifecycleContext}; use litellm_core::responses::instrumentation::{ ResponsesWsCallbackPayload, ResponsesWsInstrumentation, ResponsesWsLogOutcome, ResponsesWsMetadata, }; use litellm_core::responses::types::ResponsesWsEvent; -use litellm_core::{CoreError, CoreResult}; use crate::integrations::custom_logger::{ CallbackTiming, CallbackValue, CustomLogger, CustomLoggerRunner, LoggingError, ModelCallDetails, @@ -26,22 +26,22 @@ pub async fn run( metadata: RequestMetadata, client_in: In, client_out: Out, -) -> CoreResult<()> +) -> Result<(), Error> where In: Stream + Unpin + Send, Out: Sink + Unpin + Send, Out::Error: std::fmt::Display, { - let deployment = router.get_available_deployment(model).ok_or_else(|| { - CoreError::Routing(format!("no deployment available for model '{model}'")) - })?; + let deployment = router + .get_available_deployment(model) + .ok_or_else(|| Error::Routing(format!("no deployment available for model '{model}'")))?; let params = &deployment.litellm_params; let provider_model = params .model .strip_prefix("openai/") .unwrap_or(¶ms.model); if params.model.contains('/') && !params.model.starts_with("openai/") { - return Err(CoreError::InvalidProvider( + return Err(Error::InvalidProvider( "Responses WebSocket route supports OpenAI deployments only".to_string(), )); } diff --git a/litellm-rust/crates/core/src/audio_transcription/transformation.rs b/litellm-rust/crates/core/src/audio_transcription/transformation.rs index eab34c13843..16a28fbcac0 100644 --- a/litellm-rust/crates/core/src/audio_transcription/transformation.rs +++ b/litellm-rust/crates/core/src/audio_transcription/transformation.rs @@ -1,7 +1,6 @@ +use crate::Error; use serde_json::{Map, Value}; -use crate::CoreResult; - use super::types::{AudioTranscriptionRequestData, AudioTranscriptionResponseData}; #[derive(Clone, Debug, PartialEq, Eq)] @@ -32,13 +31,13 @@ pub trait AudioTranscriptionProviderConfig: Sync { model: &str, audio: Value, optional_params: Map, - ) -> CoreResult; + ) -> Result; fn transform_transcription_response( &self, model: &str, response_json: Value, - ) -> CoreResult; + ) -> Result; fn complete_url( &self, @@ -46,12 +45,12 @@ pub trait AudioTranscriptionProviderConfig: Sync { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult; + ) -> Result; fn auth_strategy( &self, model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult; + ) -> Result; } diff --git a/litellm-rust/crates/core/src/call_lifecycle/mod.rs b/litellm-rust/crates/core/src/call_lifecycle/mod.rs index d9b68a1b726..637c156e192 100644 --- a/litellm-rust/crates/core/src/call_lifecycle/mod.rs +++ b/litellm-rust/crates/core/src/call_lifecycle/mod.rs @@ -1,7 +1,7 @@ use std::future::Future; use std::time::{Instant, SystemTime, UNIX_EPOCH}; -use crate::{CoreError, CoreResult}; +use crate::Error; pub mod types; @@ -11,14 +11,14 @@ pub use types::{ }; pub trait CallLifecycleHooks: Send + Sync { - type PreCallFuture<'a>: Future> + Send + 'a + type PreCallFuture<'a>: Future> + Send + 'a where Self: 'a, InitialReq: 'a, ProviderReq: 'a, Resp: 'a; - type DuringCallFuture<'a>: Future> + Send + 'a + type DuringCallFuture<'a>: Future> + Send + 'a where Self: 'a, InitialReq: 'a, @@ -56,7 +56,7 @@ pub trait CallLifecycleHooks: Send + Sync { fn async_log_failure_event<'a>( &'a self, context: &'a CallLifecycleContext, - error: &'a CoreError, + error: &'a Error, timing: &'a CallLifecycleTiming, ) -> Self::FailureFuture<'a>; } @@ -86,12 +86,12 @@ impl<'a> CallLifecycle<'a> { request: InitialReq, hooks: &Hooks, provider_call: ProviderCall, - ) -> CoreResult + ) -> Result where InitialReq: CallLifecycleRequest, Hooks: CallLifecycleHooks, ProviderCall: FnOnce(ProviderReq) -> ProviderFuture, - ProviderFuture: Future>, + ProviderFuture: Future>, { let context = request.lifecycle_context(); self.run(context, request, hooks, provider_call).await @@ -103,11 +103,11 @@ impl<'a> CallLifecycle<'a> { request: InitialReq, hooks: &Hooks, provider_call: ProviderCall, - ) -> CoreResult + ) -> Result where Hooks: CallLifecycleHooks, ProviderCall: FnOnce(ProviderReq) -> ProviderFuture, - ProviderFuture: Future>, + ProviderFuture: Future>, { let call_start = epoch_seconds(); let mut phases = Vec::new(); @@ -166,7 +166,7 @@ impl<'a> CallLifecycle<'a> { &self, context: &CallLifecycleContext, hooks: &Hooks, - error: &CoreError, + error: &Error, call_start: f64, phases: &mut Vec, ) where @@ -251,8 +251,8 @@ mod tests { } impl CallLifecycleHooks for RecordingHooks { - type PreCallFuture<'a> = BoxFuture<'a, CoreResult>; - type DuringCallFuture<'a> = BoxFuture<'a, CoreResult>; + type PreCallFuture<'a> = BoxFuture<'a, Result>; + type DuringCallFuture<'a> = BoxFuture<'a, Result>; type SuccessFuture<'a> = BoxFuture<'a, ()>; type FailureFuture<'a> = BoxFuture<'a, ()>; @@ -294,7 +294,7 @@ mod tests { fn async_log_failure_event<'a>( &'a self, _context: &'a CallLifecycleContext, - _error: &'a CoreError, + _error: &'a Error, _timing: &'a CallLifecycleTiming, ) -> Self::FailureFuture<'a> { Box::pin(async move { @@ -304,8 +304,8 @@ mod tests { } impl CallLifecycleHooks for RecordingHooks { - type PreCallFuture<'a> = BoxFuture<'a, CoreResult>; - type DuringCallFuture<'a> = BoxFuture<'a, CoreResult>; + type PreCallFuture<'a> = BoxFuture<'a, Result>; + type DuringCallFuture<'a> = BoxFuture<'a, Result>; type SuccessFuture<'a> = BoxFuture<'a, ()>; type FailureFuture<'a> = BoxFuture<'a, ()>; @@ -345,7 +345,7 @@ mod tests { fn async_log_failure_event<'a>( &'a self, _context: &'a CallLifecycleContext, - _error: &'a CoreError, + _error: &'a Error, _timing: &'a CallLifecycleTiming, ) -> Self::FailureFuture<'a> { Box::pin(async move { @@ -383,13 +383,13 @@ mod tests { "request".to_string(), &hooks, |_request| async move { - Err::(CoreError::Network("provider down".to_string())) + Err::(Error::Network("provider down".to_string())) }, ) .await .expect_err("call fails"); - assert_eq!(error, CoreError::Network("provider down".to_string())); + assert_eq!(error, Error::Network("provider down".to_string())); assert_eq!(hooks.events(), vec!["pre_call", "during_call", "failure"]); } diff --git a/litellm-rust/crates/core/src/chat_completions/common_utils.rs b/litellm-rust/crates/core/src/chat_completions/common_utils.rs index 36eaf242a5a..ca51471eb7c 100644 --- a/litellm-rust/crates/core/src/chat_completions/common_utils.rs +++ b/litellm-rust/crates/core/src/chat_completions/common_utils.rs @@ -1,8 +1,7 @@ -use serde_json::{Map, Value}; - -use crate::error::CoreResult; +use crate::Error; use crate::http_utils::string_headers as shared_string_headers; use crate::providers::anthropic::chat_completions::transformation::ANTHROPIC_CHAT_COMPLETIONS_CONFIG; +use serde_json::{Map, Value}; use super::transformation::ChatCompletionsProviderConfig; @@ -23,6 +22,6 @@ pub(super) fn chat_completions_provider_config( pub(super) fn string_headers( extra_headers: Option>, -) -> CoreResult> { +) -> Result, Error> { shared_string_headers(HEADER_CONTEXT, extra_headers) } diff --git a/litellm-rust/crates/core/src/chat_completions/handler.rs b/litellm-rust/crates/core/src/chat_completions/handler.rs index afc4529fd26..7e2731442cc 100644 --- a/litellm-rust/crates/core/src/chat_completions/handler.rs +++ b/litellm-rust/crates/core/src/chat_completions/handler.rs @@ -1,6 +1,6 @@ use serde_json::Value; -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use crate::http_utils::truncate_error_body; use super::client::http_client; @@ -11,9 +11,9 @@ use super::types::{ pub(super) async fn execute_chat_completions_provider_call( request: ProviderChatCompletionsRequest, -) -> CoreResult { +) -> Result { let body = serde_json::to_vec(&request.body).map_err(|err| { - CoreError::InvalidRequest(format!( + Error::InvalidRequest(format!( "failed to serialize chat completions request: {err}" )) })?; @@ -32,9 +32,9 @@ pub(super) async fn execute_chat_completions_provider_call( // so the host can still serve it. Everything else here, a timeout // above all, may have reached the provider and been answered. if err.is_connect() || err.is_builder() { - CoreError::Connect(err.to_string()) + Error::Connect(err.to_string()) } else { - CoreError::Network(err.to_string()) + Error::Network(err.to_string()) } })?; @@ -42,17 +42,17 @@ pub(super) async fn execute_chat_completions_provider_call( let text = response .text() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; if !status.is_success() { - return Err(CoreError::Http { + return Err(Error::Http { status: status.as_u16(), body: truncate_error_body(&text), }); } let body: Value = serde_json::from_str(&text).map_err(|err| { - CoreError::InvalidResponse(format!("invalid chat completions response JSON: {err}")) + Error::InvalidResponse(format!("invalid chat completions response JSON: {err}")) })?; request .config @@ -69,10 +69,10 @@ pub(super) async fn execute_chat_completions_provider_call( /// second kind has already been billed, and a host that keeps a reference /// implementation must not retry those, so collapse them to one variant that /// can only mean the provider was already called. -pub(super) fn as_response_error(err: CoreError) -> CoreError { +pub(super) fn as_response_error(err: Error) -> Error { match err { - already @ (CoreError::InvalidResponse(_) | CoreError::Http { .. }) => already, - other => CoreError::InvalidResponse(other.to_string()), + already @ (Error::InvalidResponse(_) | Error::Http { .. }) => already, + other => Error::InvalidResponse(other.to_string()), } } @@ -80,7 +80,7 @@ pub(super) fn as_response_error(err: CoreError) -> CoreError { pub(super) async fn signed_headers( request: &ProviderChatCompletionsRequest, body: &[u8], -) -> CoreResult> { +) -> Result, Error> { use std::collections::BTreeMap; use std::time::SystemTime; @@ -101,7 +101,7 @@ pub(super) async fn signed_headers( .iter() .any(|(name, _)| is_sigv4_computed_header(name)) { - return Err(CoreError::Unsupported( + return Err(Error::Unsupported( "request forwards a header AWS SigV4 computes", )); } @@ -137,9 +137,9 @@ pub(super) async fn signed_headers( pub(super) async fn signed_headers( request: &ProviderChatCompletionsRequest, _body: &[u8], -) -> CoreResult> { +) -> Result, Error> { match &request.auth { - ChatCompletionsAuth::AwsSigV4 { .. } => Err(CoreError::Unsupported( + ChatCompletionsAuth::AwsSigV4 { .. } => Err(Error::Unsupported( "AWS SigV4 requires the bedrock-auth feature", )), _ => Ok(request.upstream_headers.clone()), diff --git a/litellm-rust/crates/core/src/chat_completions/mod.rs b/litellm-rust/crates/core/src/chat_completions/mod.rs index f30ac1a24bf..0d009d36d16 100644 --- a/litellm-rust/crates/core/src/chat_completions/mod.rs +++ b/litellm-rust/crates/core/src/chat_completions/mod.rs @@ -6,6 +6,7 @@ //! credentials, and it resolves the provider, translates the conversation, //! calls the provider, and returns a typed OpenAI-shaped response. +use crate::Error; mod client; mod common_utils; pub mod conversation; @@ -17,15 +18,13 @@ pub mod types; use serde_json::{Map, Value}; -use crate::error::CoreResult; - use handler::execute_chat_completions_provider_call; use prepare::{parse_messages, prepare_chat_completions_call, resolve_provider_config}; use types::{ChatCompletionsRequest, ChatCompletionsResponse}; pub async fn chat_completions( request: ChatCompletionsRequest<'_>, -) -> CoreResult { +) -> Result { execute_chat_completions_provider_call(prepare_chat_completions_call(request)?).await } diff --git a/litellm-rust/crates/core/src/chat_completions/prepare.rs b/litellm-rust/crates/core/src/chat_completions/prepare.rs index 1e1c8d1bafd..142b2f2aaed 100644 --- a/litellm-rust/crates/core/src/chat_completions/prepare.rs +++ b/litellm-rust/crates/core/src/chat_completions/prepare.rs @@ -1,6 +1,6 @@ use serde_json::Value; -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use crate::http_utils::has_header; use crate::routing_utils::provider::{CustomLlmProvider, get_custom_llm_provider}; @@ -11,7 +11,7 @@ use super::types::{ChatCompletionsRequest, ChatMessage, ProviderChatCompletionsR pub(super) fn resolve_provider_config<'a>( model: &'a str, custom_llm_provider: Option<&'a str>, -) -> CoreResult<(String, &'static dyn ChatCompletionsProviderConfig)> { +) -> Result<(String, &'static dyn ChatCompletionsProviderConfig), Error> { let provider_info = get_custom_llm_provider(model, custom_llm_provider) .or_else(|| { custom_llm_provider.map(|provider| CustomLlmProvider { @@ -20,35 +20,34 @@ pub(super) fn resolve_provider_config<'a>( }) }) .ok_or_else(|| { - CoreError::InvalidProvider( + Error::InvalidProvider( "unable to resolve custom_llm_provider for chat completions request".to_string(), ) })?; let config = chat_completions_provider_config(provider_info.custom_llm_provider) - .ok_or_else(|| CoreError::InvalidProvider(provider_info.custom_llm_provider.to_string()))?; + .ok_or_else(|| Error::InvalidProvider(provider_info.custom_llm_provider.to_string()))?; Ok((provider_info.model.to_string(), config)) } -pub(super) fn parse_messages(messages: Value) -> CoreResult> { - serde_json::from_value(messages).map_err(|err| { - CoreError::InvalidRequest(format!("invalid chat completions messages: {err}")) - }) +pub(super) fn parse_messages(messages: Value) -> Result, Error> { + serde_json::from_value(messages) + .map_err(|err| Error::InvalidRequest(format!("invalid chat completions messages: {err}"))) } pub(super) fn prepare_chat_completions_call( request: ChatCompletionsRequest<'_>, -) -> CoreResult { +) -> Result { let (model, config) = resolve_provider_config(request.model, request.custom_llm_provider)?; let env_lookup = |key: &str| std::env::var(key).ok(); let messages = parse_messages(request.messages)?; if messages.is_empty() { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "chat completions requires at least one message".to_string(), )); } if let Some(reason) = config.unsupported_reason(&messages, &request.optional_params) { - return Err(CoreError::Unsupported(reason.0)); + return Err(Error::Unsupported(reason.0)); } let mut headers = string_headers(request.extra_headers)?; diff --git a/litellm-rust/crates/core/src/chat_completions/tests.rs b/litellm-rust/crates/core/src/chat_completions/tests.rs index e2383723cb0..2858d180e27 100644 --- a/litellm-rust/crates/core/src/chat_completions/tests.rs +++ b/litellm-rust/crates/core/src/chat_completions/tests.rs @@ -1,6 +1,6 @@ use serde_json::{Map, Value, json}; -use crate::error::CoreError; +use crate::error::Error; use super::prepare::prepare_chat_completions_call; use super::transformation::ChatCompletionsAuth; @@ -29,7 +29,7 @@ fn request<'a>( /// `ProviderChatCompletionsRequest` deliberately has no `Debug` (its headers /// carry resolved credentials), so unwrap the failure case by hand. -fn decline(request: ChatCompletionsRequest<'_>) -> CoreError { +fn decline(request: ChatCompletionsRequest<'_>) -> Error { match prepare_chat_completions_call(request) { Err(error) => error, Ok(prepared) => panic!("expected a decline, prepared a call to {}", prepared.url), @@ -196,7 +196,7 @@ fn declines_an_unsupported_request_before_resolving_credentials() { call.api_key = None; // No api_key is set and no env is consulted: the gate must run first, so the // error is the decline rather than a missing-credential error. - assert_eq!(decline(call), CoreError::Unsupported("streaming")); + assert_eq!(decline(call), Error::Unsupported("streaming")); } #[test] @@ -208,7 +208,7 @@ fn rejects_an_unknown_provider() { json!([{"role": "user", "content": "hi"}]), json!({}), )), - CoreError::InvalidProvider("openai".to_string()) + Error::InvalidProvider("openai".to_string()) ); } @@ -221,7 +221,7 @@ fn rejects_a_model_with_no_resolvable_provider() { json!([{"role": "user", "content": "hi"}]), json!({}), )), - CoreError::InvalidProvider(_) + Error::InvalidProvider(_) )); } @@ -234,7 +234,7 @@ fn rejects_an_empty_or_malformed_message_list() { json!([]), json!({}), )), - CoreError::InvalidRequest("chat completions requires at least one message".to_string()) + Error::InvalidRequest("chat completions requires at least one message".to_string()) ); assert!(matches!( decline(request( @@ -243,7 +243,7 @@ fn rejects_an_empty_or_malformed_message_list() { json!("not a list"), json!({}), )), - CoreError::InvalidRequest(_) + Error::InvalidRequest(_) )); } @@ -258,7 +258,7 @@ fn rejects_non_string_extra_headers() { call.extra_headers = Some(Map::from_iter([("x-trace".to_string(), json!(7))])); assert_eq!( decline(call), - CoreError::InvalidRequest( + Error::InvalidRequest( "chat completions extra_headers.x-trace must be a string, got number".to_string() ) ); @@ -374,7 +374,7 @@ async fn a_forwarded_header_the_signer_computes_declines_to_python() { .await .expect_err("{forwarded} should decline instead of being signed"); assert!( - matches!(error, CoreError::Unsupported(_)), + matches!(error, Error::Unsupported(_)), "{forwarded} declined as {error:?}, which the host would not fall back on" ); } @@ -727,7 +727,7 @@ mod round_trip { .expect_err("response cannot be normalized"); handle.await.expect("server task"); assert!( - matches!(err, CoreError::InvalidResponse(_)), + matches!(err, Error::InvalidResponse(_)), "expected a post-send error, got {err:?}" ); } @@ -745,7 +745,7 @@ mod round_trip { .expect_err("response cannot be normalized"); handle.await.expect("server task"); assert!( - matches!(err, CoreError::InvalidResponse(_)), + matches!(err, Error::InvalidResponse(_)), "expected a post-send error, got {err:?}" ); } @@ -763,7 +763,7 @@ mod round_trip { .expect_err("upstream rejects"); handle.await.expect("server task"); assert!( - matches!(err, CoreError::Http { status: 429, .. }), + matches!(err, Error::Http { status: 429, .. }), "expected a 429, got {err:?}" ); } @@ -787,7 +787,7 @@ mod round_trip { .await .expect_err("nothing is listening"); assert!( - matches!(err, CoreError::Connect(_)), + matches!(err, Error::Connect(_)), "expected a pre-send connect failure, got {err:?}" ); } @@ -797,24 +797,24 @@ mod round_trip { use crate::chat_completions::handler::as_response_error; for original in [ - CoreError::MissingField("usage"), - CoreError::Unsupported("non-text response content block"), - CoreError::InvalidRequest("whatever".to_string()), - CoreError::Auth("whatever".to_string()), + Error::MissingField("usage"), + Error::Unsupported("non-text response content block"), + Error::InvalidRequest("whatever".to_string()), + Error::Auth("whatever".to_string()), ] { let label = format!("{original:?}"); assert!( - matches!(as_response_error(original), CoreError::InvalidResponse(_)), + matches!(as_response_error(original), Error::InvalidResponse(_)), "{label} must not stay retryable once the provider has answered" ); } // An upstream status is already unambiguous, so it survives intact. assert!(matches!( - as_response_error(CoreError::Http { + as_response_error(Error::Http { status: 500, body: "boom".to_string() }), - CoreError::Http { status: 500, .. } + Error::Http { status: 500, .. } )); } } diff --git a/litellm-rust/crates/core/src/chat_completions/transformation.rs b/litellm-rust/crates/core/src/chat_completions/transformation.rs index a30ce9dc77c..a0868209305 100644 --- a/litellm-rust/crates/core/src/chat_completions/transformation.rs +++ b/litellm-rust/crates/core/src/chat_completions/transformation.rs @@ -1,7 +1,6 @@ +use crate::Error; use serde_json::{Map, Value}; -use crate::error::CoreResult; - use super::types::{ ChatCompletionsResponse, ChatMessage, ChatMessageContent, ProviderChatRequestData, ProviderChatResponseData, @@ -39,7 +38,7 @@ pub trait ChatCompletionsProviderConfig: Sync { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult; + ) -> Result; fn auth( &self, @@ -47,7 +46,7 @@ pub trait ChatCompletionsProviderConfig: Sync { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult; + ) -> Result; fn default_headers(&self) -> &'static [(&'static str, &'static str)] { &[("content-type", "application/json")] @@ -91,13 +90,13 @@ pub trait ChatCompletionsProviderConfig: Sync { model: &str, messages: Vec, optional_params: Map, - ) -> CoreResult; + ) -> Result; fn transform_response( &self, model: &str, response: ProviderChatResponseData, - ) -> CoreResult; + ) -> Result; } pub fn unsupported_param( diff --git a/litellm-rust/crates/core/src/error.rs b/litellm-rust/crates/core/src/error.rs index 739532f8cb5..db3fa2ec704 100644 --- a/litellm-rust/crates/core/src/error.rs +++ b/litellm-rust/crates/core/src/error.rs @@ -1,9 +1,7 @@ -use thiserror::Error; +use thiserror::Error as ThisError; -pub type CoreResult = Result; - -#[derive(Debug, Error, PartialEq, Eq)] -pub enum CoreError { +#[derive(Debug, ThisError, PartialEq, Eq)] +pub enum Error { #[error("expected {expected}, got {actual}")] InvalidType { expected: &'static str, diff --git a/litellm-rust/crates/core/src/http_utils.rs b/litellm-rust/crates/core/src/http_utils.rs index c541f50275b..10661fadf96 100644 --- a/litellm-rust/crates/core/src/http_utils.rs +++ b/litellm-rust/crates/core/src/http_utils.rs @@ -3,7 +3,7 @@ use serde_json::{Map, Value}; use crate::constants::UPSTREAM_ERROR_BODY_MAX_CHARS; -use crate::error::{CoreError, CoreResult, json_type_name}; +use crate::error::{Error, json_type_name}; /// Bound an upstream error body before it crosses a host boundary, so provider /// bodies stay data-minimized. @@ -18,7 +18,7 @@ pub fn truncate_error_body(body: &str) -> String { pub fn string_headers( context: &'static str, extra_headers: Option>, -) -> CoreResult> { +) -> Result, Error> { extra_headers .unwrap_or_default() .into_iter() @@ -27,7 +27,7 @@ pub fn string_headers( .as_str() .map(|value| (key.clone(), value.to_string())) .ok_or_else(|| { - CoreError::InvalidRequest(format!( + Error::InvalidRequest(format!( "{context} extra_headers.{key} must be a string, got {}", json_type_name(&value) )) @@ -81,7 +81,7 @@ mod tests { let err = string_headers("chat completions", Some(headers)).expect_err("non-string value"); assert_eq!( err, - CoreError::InvalidRequest( + Error::InvalidRequest( "chat completions extra_headers.x-trace must be a string, got number".to_string() ) ); diff --git a/litellm-rust/crates/core/src/lib.rs b/litellm-rust/crates/core/src/lib.rs index dce4a425ea0..0e18d24e5d8 100644 --- a/litellm-rust/crates/core/src/lib.rs +++ b/litellm-rust/crates/core/src/lib.rs @@ -13,4 +13,4 @@ pub mod responses; pub mod router; pub mod routing_utils; -pub use error::{CoreError, CoreResult}; +pub use error::Error; diff --git a/litellm-rust/crates/core/src/messages/common_utils.rs b/litellm-rust/crates/core/src/messages/common_utils.rs index a14dffbc1fe..8dfdb2e361a 100644 --- a/litellm-rust/crates/core/src/messages/common_utils.rs +++ b/litellm-rust/crates/core/src/messages/common_utils.rs @@ -1,9 +1,8 @@ -use serde_json::{Map, Value}; - -use crate::error::CoreResult; +use crate::Error; use crate::http_utils::string_headers as shared_string_headers; use crate::providers::anthropic::messages::transformation::ANTHROPIC_MESSAGES_CONFIG; use crate::providers::azure_ai::messages::transformation::AZURE_ANTHROPIC_MESSAGES_CONFIG; +use serde_json::{Map, Value}; use super::transformation::AnthropicMessagesProviderConfig; @@ -23,6 +22,6 @@ pub(super) fn messages_provider_config( pub(super) fn string_headers( extra_headers: Option>, -) -> CoreResult> { +) -> Result, Error> { shared_string_headers(HEADER_CONTEXT, extra_headers) } diff --git a/litellm-rust/crates/core/src/messages/handler.rs b/litellm-rust/crates/core/src/messages/handler.rs index 1c895f66eba..13a65d86131 100644 --- a/litellm-rust/crates/core/src/messages/handler.rs +++ b/litellm-rust/crates/core/src/messages/handler.rs @@ -1,5 +1,5 @@ use crate::constants::ANTHROPIC_MESSAGES_PROVIDER; -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use super::client::http_client; use super::common_utils::truncate_error_body; @@ -7,7 +7,7 @@ use super::types::{AnthropicMessagesResponse, ProviderMessagesRequest}; pub(super) async fn execute_messages_provider_call( request: ProviderMessagesRequest, -) -> CoreResult { +) -> Result { let mut request_builder = http_client().post(&request.url).json(&request.body); for (key, value) in &request.upstream_headers { request_builder = request_builder.header(key, value); @@ -19,32 +19,31 @@ pub(super) async fn execute_messages_provider_call( let response = request_builder .send() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; let status = response.status(); let text = response .text() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; if !status.is_success() { - return Err(CoreError::Http { + return Err(Error::Http { status: status.as_u16(), body: truncate_error_body(&text), }); } - let response = serde_json::from_str(&text).map_err(|err| { - CoreError::InvalidResponse(format!("invalid messages response JSON: {err}")) - })?; + let response = serde_json::from_str(&text) + .map_err(|err| Error::InvalidResponse(format!("invalid messages response JSON: {err}")))?; request.config.transform_response(&request.model, response) } pub(super) async fn execute_messages_provider_stream( request: ProviderMessagesRequest, -) -> CoreResult { +) -> Result { if request.provider != ANTHROPIC_MESSAGES_PROVIDER { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "streaming messages is not supported for this provider".to_string(), )); } @@ -60,14 +59,14 @@ pub(super) async fn execute_messages_provider_stream( let response = request_builder .send() .await - .map_err(|err| CoreError::Network(err.to_string()))?; + .map_err(|err| Error::Network(err.to_string()))?; let status = response.status(); if !status.is_success() { let text = response .text() .await - .map_err(|err| CoreError::Network(err.to_string()))?; - return Err(CoreError::Http { + .map_err(|err| Error::Network(err.to_string()))?; + return Err(Error::Http { status: status.as_u16(), body: truncate_error_body(&text), }); diff --git a/litellm-rust/crates/core/src/messages/mod.rs b/litellm-rust/crates/core/src/messages/mod.rs index acb36d89daf..ee2877e61fc 100644 --- a/litellm-rust/crates/core/src/messages/mod.rs +++ b/litellm-rust/crates/core/src/messages/mod.rs @@ -7,6 +7,7 @@ //! is the streaming variant; it hands the raw upstream response back so a host //! can splice the event stream to its own caller. +use crate::Error; mod client; mod common_utils; mod handler; @@ -14,17 +15,15 @@ mod prepare; pub mod transformation; pub mod types; -use crate::error::CoreResult; - use handler::{execute_messages_provider_call, execute_messages_provider_stream}; use prepare::prepare_messages_call; use types::{AnthropicMessagesResponse, MessagesRequest}; -pub async fn messages(request: MessagesRequest<'_>) -> CoreResult { +pub async fn messages(request: MessagesRequest<'_>) -> Result { execute_messages_provider_call(prepare_messages_call(request)?).await } -pub async fn messages_stream(request: MessagesRequest<'_>) -> CoreResult { +pub async fn messages_stream(request: MessagesRequest<'_>) -> Result { execute_messages_provider_stream(prepare_messages_call(request)?).await } diff --git a/litellm-rust/crates/core/src/messages/prepare.rs b/litellm-rust/crates/core/src/messages/prepare.rs index 94b5b1eaed7..3b253ac3766 100644 --- a/litellm-rust/crates/core/src/messages/prepare.rs +++ b/litellm-rust/crates/core/src/messages/prepare.rs @@ -1,4 +1,4 @@ -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use crate::routing_utils::provider::{CustomLlmProvider, get_custom_llm_provider}; use super::common_utils::{has_bearer_auth, has_header, messages_provider_config, string_headers}; @@ -7,7 +7,7 @@ use super::types::{MessagesRequest, ProviderMessagesRequest}; pub(super) fn prepare_messages_call( request: MessagesRequest<'_>, -) -> CoreResult { +) -> Result { let provider_info = get_custom_llm_provider(request.model, request.custom_llm_provider) .or_else(|| { request @@ -18,7 +18,7 @@ pub(super) fn prepare_messages_call( }) }) .ok_or_else(|| { - CoreError::InvalidProvider( + Error::InvalidProvider( "unable to resolve custom_llm_provider for messages request".to_string(), ) })?; @@ -26,7 +26,7 @@ pub(super) fn prepare_messages_call( let provider = provider_info.custom_llm_provider; let config = messages_provider_config(provider) - .ok_or_else(|| CoreError::InvalidProvider(provider.to_string()))?; + .ok_or_else(|| Error::InvalidProvider(provider.to_string()))?; let env_lookup = |key: &str| std::env::var(key).ok(); let mut headers = string_headers(request.extra_headers)?; @@ -53,11 +53,11 @@ pub(super) fn prepare_messages_call( let url = config.complete_url(request.api_base, &model, &env_lookup)?; let typed_request = serde_json::from_value(request.body).map_err(|err| { - CoreError::InvalidRequest(format!("invalid Anthropic messages request: {err}")) + Error::InvalidRequest(format!("invalid Anthropic messages request: {err}")) })?; let transformed = config.transform_request(typed_request)?; let body = serde_json::to_value(transformed).map_err(|err| { - CoreError::InvalidRequest(format!( + Error::InvalidRequest(format!( "failed to serialize Anthropic messages request: {err}" )) })?; diff --git a/litellm-rust/crates/core/src/messages/tests.rs b/litellm-rust/crates/core/src/messages/tests.rs index 9fc1763683b..df9f7051011 100644 --- a/litellm-rust/crates/core/src/messages/tests.rs +++ b/litellm-rust/crates/core/src/messages/tests.rs @@ -4,7 +4,7 @@ use serde_json::{Map, Value, json}; use tokio::io::{AsyncReadExt, AsyncWriteExt}; use tokio::net::{TcpListener, TcpStream}; -use crate::error::CoreError; +use crate::error::Error; use super::common_utils::{ has_bearer_auth, has_header, messages_provider_config, string_headers, truncate_error_body, @@ -77,7 +77,7 @@ fn truncate_error_body_caps_long_payloads() { fn string_headers_rejects_non_string_values() { let headers = json!({"x-count": 3}).as_object().unwrap().clone(); let err = string_headers(Some(headers)).expect_err("non-string header rejected"); - assert!(matches!(err, CoreError::InvalidRequest(_))); + assert!(matches!(err, Error::InvalidRequest(_))); } #[test] @@ -341,7 +341,7 @@ async fn messages_requires_auth_when_no_key_and_no_header() { .await .expect_err("missing auth errors"); - assert!(matches!(err, CoreError::Auth(_))); + assert!(matches!(err, Error::Auth(_))); } #[tokio::test] @@ -420,7 +420,7 @@ async fn messages_maps_provider_error_status_to_http_error() { .await .expect_err("provider error propagates"); - assert!(matches!(err, CoreError::Http { status: 401, .. })); + assert!(matches!(err, Error::Http { status: 401, .. })); } #[tokio::test] @@ -437,5 +437,5 @@ async fn messages_rejects_unsupported_provider() { .await .expect_err("unsupported provider errors"); - assert!(matches!(err, CoreError::InvalidProvider(provider) if provider == "openai")); + assert!(matches!(err, Error::InvalidProvider(provider) if provider == "openai")); } diff --git a/litellm-rust/crates/core/src/messages/transformation.rs b/litellm-rust/crates/core/src/messages/transformation.rs index b478e20d24b..673a5728aca 100644 --- a/litellm-rust/crates/core/src/messages/transformation.rs +++ b/litellm-rust/crates/core/src/messages/transformation.rs @@ -1,6 +1,5 @@ -use crate::error::CoreResult; - use super::types::{AnthropicMessagesRequest, AnthropicMessagesResponse}; +use crate::Error; #[derive(Clone, Copy, Debug, PartialEq, Eq)] pub enum MessagesAuthStrategy { @@ -23,13 +22,13 @@ pub trait AnthropicMessagesProviderConfig: Sync { api_base: Option<&str>, model: &str, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult; + ) -> Result; fn resolve_api_key( &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult; + ) -> Result; fn auth_strategy(&self) -> MessagesAuthStrategy { MessagesAuthStrategy::Header("x-api-key") @@ -49,7 +48,7 @@ pub trait AnthropicMessagesProviderConfig: Sync { fn transform_request( &self, request: AnthropicMessagesRequest, - ) -> CoreResult { + ) -> Result { Ok(request) } @@ -57,7 +56,7 @@ pub trait AnthropicMessagesProviderConfig: Sync { &self, _model: &str, response: AnthropicMessagesResponse, - ) -> CoreResult { + ) -> Result { Ok(response) } } diff --git a/litellm-rust/crates/core/src/ocr/transformation.rs b/litellm-rust/crates/core/src/ocr/transformation.rs index cb3e735e533..3d3c16c8cb6 100644 --- a/litellm-rust/crates/core/src/ocr/transformation.rs +++ b/litellm-rust/crates/core/src/ocr/transformation.rs @@ -1,7 +1,6 @@ +use crate::Error; use serde_json::{Map, Value}; -use crate::CoreResult; - use super::types::{OcrRequestData, OcrResponseData}; #[derive(Clone, Copy, Debug, PartialEq, Eq)] @@ -43,13 +42,13 @@ pub trait OcrProviderConfig: Sync { model: &str, document: Value, optional_params: Map, - ) -> CoreResult; + ) -> Result; fn transform_ocr_response( &self, model: &str, response_json: Value, - ) -> CoreResult; + ) -> Result; fn complete_url( &self, @@ -57,13 +56,13 @@ pub trait OcrProviderConfig: Sync { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult; + ) -> Result; fn resolve_api_key( &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult; + ) -> Result; fn auth_strategy(&self) -> OcrAuthStrategy { OcrAuthStrategy::Bearer diff --git a/litellm-rust/crates/core/src/providers/anthropic/chat_completions/tests.rs b/litellm-rust/crates/core/src/providers/anthropic/chat_completions/tests.rs index 4534ac0182c..b22de6c47de 100644 --- a/litellm-rust/crates/core/src/providers/anthropic/chat_completions/tests.rs +++ b/litellm-rust/crates/core/src/providers/anthropic/chat_completions/tests.rs @@ -1,4 +1,5 @@ use super::*; +use crate::Error; use serde_json::json; fn messages(value: Value) -> Vec { @@ -19,7 +20,7 @@ fn transform(model: &str, msgs: Value, opts: Value) -> Value { .body } -fn transform_response(body: Value) -> CoreResult { +fn transform_response(body: Value) -> Result { ANTHROPIC_CHAT_COMPLETIONS_CONFIG .transform_response("claude-sonnet-4-5", ProviderChatResponseData { body }) } @@ -390,29 +391,26 @@ fn declines_a_response_carrying_a_non_text_block() { "usage": {"input_tokens": 1, "output_tokens": 1} })) .expect_err("non-text block"); - assert_eq!( - err, - CoreError::Unsupported("non-text response content block") - ); + assert_eq!(err, Error::Unsupported("non-text response content block")); } #[test] fn errors_on_a_response_missing_required_fields() { assert_eq!( transform_response(json!("nope")).expect_err("not an object"), - CoreError::InvalidResponse("messages response is not an object".to_string()) + Error::InvalidResponse("messages response is not an object".to_string()) ); assert_eq!( transform_response(json!({"model": "m", "usage": {}})).expect_err("no content"), - CoreError::MissingField("content") + Error::MissingField("content") ); assert_eq!( transform_response(json!({"model": "m", "content": []})).expect_err("no usage"), - CoreError::MissingField("usage") + Error::MissingField("usage") ); assert_eq!( transform_response(json!({"content": [], "usage": {}})).expect_err("no model"), - CoreError::MissingField("model") + Error::MissingField("model") ); } diff --git a/litellm-rust/crates/core/src/providers/anthropic/chat_completions/transformation.rs b/litellm-rust/crates/core/src/providers/anthropic/chat_completions/transformation.rs index 3658642b539..97cc48aa6f2 100644 --- a/litellm-rust/crates/core/src/providers/anthropic/chat_completions/transformation.rs +++ b/litellm-rust/crates/core/src/providers/anthropic/chat_completions/transformation.rs @@ -10,7 +10,7 @@ use crate::chat_completions::types::{ ProviderChatRequestData, ProviderChatResponseData, }; use crate::constants::ANTHROPIC_OAUTH_TOKEN_PREFIX; -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use crate::providers::anthropic::messages::transformation::{ complete_anthropic_url, resolve_anthropic_api_key, }; @@ -74,7 +74,7 @@ impl ChatCompletionsProviderConfig for AnthropicChatCompletionsConfig { _model: &str, _optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { Ok(complete_anthropic_url(api_base, env_lookup)) } @@ -84,7 +84,7 @@ impl ChatCompletionsProviderConfig for AnthropicChatCompletionsConfig { _model: &str, _optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { Ok(ChatCompletionsAuth::Header { name: "x-api-key", value: resolve_anthropic_api_key(api_key, env_lookup)?, @@ -137,7 +137,7 @@ impl ChatCompletionsProviderConfig for AnthropicChatCompletionsConfig { model: &str, messages: Vec, optional_params: Map, - ) -> CoreResult { + ) -> Result { Ok(ProviderChatRequestData { body: anthropic_body(model, &build_conversation(&messages), optional_params), }) @@ -147,15 +147,16 @@ impl ChatCompletionsProviderConfig for AnthropicChatCompletionsConfig { &self, _model: &str, response: ProviderChatResponseData, - ) -> CoreResult { - let body = response.body.as_object().ok_or_else(|| { - CoreError::InvalidResponse("messages response is not an object".into()) - })?; + ) -> Result { + let body = response + .body + .as_object() + .ok_or_else(|| Error::InvalidResponse("messages response is not an object".into()))?; let content = body .get("content") .and_then(Value::as_array) - .ok_or(CoreError::MissingField("content"))?; + .ok_or(Error::MissingField("content"))?; // The route declines tool and thinking requests, so a non-text block // means the response carries something this path never asked for. // Decline rather than silently dropping it; the host falls back. @@ -163,7 +164,7 @@ impl ChatCompletionsProviderConfig for AnthropicChatCompletionsConfig { .iter() .any(|block| block.get("type").and_then(Value::as_str) != Some("text")) { - return Err(CoreError::Unsupported("non-text response content block")); + return Err(Error::Unsupported("non-text response content block")); } let text: String = content .iter() @@ -173,7 +174,7 @@ impl ChatCompletionsProviderConfig for AnthropicChatCompletionsConfig { let usage = body .get("usage") .and_then(Value::as_object) - .ok_or(CoreError::MissingField("usage"))?; + .ok_or(Error::MissingField("usage"))?; let field = |name: &str| usage.get(name).and_then(Value::as_u64).unwrap_or(0); Ok(ChatCompletionsResponse { @@ -181,7 +182,7 @@ impl ChatCompletionsProviderConfig for AnthropicChatCompletionsConfig { model: body .get("model") .and_then(Value::as_str) - .ok_or(CoreError::MissingField("model"))? + .ok_or(Error::MissingField("model"))? .to_string(), choices: vec![ChatCompletionsChoice { index: 0, diff --git a/litellm-rust/crates/core/src/providers/anthropic/messages/transformation.rs b/litellm-rust/crates/core/src/providers/anthropic/messages/transformation.rs index 829f2260d3c..8fcc0f36c7d 100644 --- a/litellm-rust/crates/core/src/providers/anthropic/messages/transformation.rs +++ b/litellm-rust/crates/core/src/providers/anthropic/messages/transformation.rs @@ -1,4 +1,4 @@ -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use crate::messages::transformation::{AnthropicMessagesProviderConfig, MessagesAuthStrategy}; const ANTHROPIC_API_KEY_ENV: &str = "ANTHROPIC_API_KEY"; @@ -17,12 +17,12 @@ pub fn non_empty(value: Option<&str>) -> Option<&str> { pub fn resolve_anthropic_api_key( api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { non_empty(api_key) .map(str::to_string) .or_else(|| env_lookup(ANTHROPIC_API_KEY_ENV).filter(|value| !value.trim().is_empty())) .ok_or_else(|| { - CoreError::Auth( + Error::Auth( "Missing Anthropic API Key - Set `api_key` or the ANTHROPIC_API_KEY \ environment variable" .to_string(), @@ -52,7 +52,7 @@ impl AnthropicMessagesProviderConfig for AnthropicMessagesConfig { api_base: Option<&str>, _model: &str, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { Ok(complete_anthropic_url(api_base, env_lookup)) } @@ -60,7 +60,7 @@ impl AnthropicMessagesProviderConfig for AnthropicMessagesConfig { &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { resolve_anthropic_api_key(api_key, env_lookup) } @@ -121,7 +121,7 @@ mod tests { ); assert!(matches!( resolve_anthropic_api_key(None, &|_| None).expect_err("missing key"), - CoreError::Auth(_) + Error::Auth(_) )); } diff --git a/litellm-rust/crates/core/src/providers/azure_ai/messages/transformation.rs b/litellm-rust/crates/core/src/providers/azure_ai/messages/transformation.rs index 7b958c77ba3..70dad0300f1 100644 --- a/litellm-rust/crates/core/src/providers/azure_ai/messages/transformation.rs +++ b/litellm-rust/crates/core/src/providers/azure_ai/messages/transformation.rs @@ -1,4 +1,4 @@ -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use crate::messages::transformation::{AnthropicMessagesProviderConfig, MessagesAuthStrategy}; use crate::messages::types::{ AnthropicMessage, AnthropicMessagesRequest, AnthropicMessagesResponse, ContentBlock, @@ -28,12 +28,12 @@ pub const AZURE_ANTHROPIC_MESSAGES_CONFIG: AzureAnthropicMessagesConfig = pub fn resolve_azure_api_key( api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { non_empty(api_key) .map(str::to_string) .or_else(|| env_lookup(AZURE_API_KEY_ENV).filter(|value| !value.trim().is_empty())) .ok_or_else(|| { - CoreError::Auth( + Error::Auth( "Missing Azure API Key - Set `api_key` or the AZURE_API_KEY environment variable" .to_string(), ) @@ -43,12 +43,12 @@ pub fn resolve_azure_api_key( pub fn complete_azure_anthropic_url( api_base: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { let api_base = non_empty(api_base) .map(str::to_string) .or_else(|| env_lookup(AZURE_API_BASE_ENV).filter(|value| !value.trim().is_empty())) .ok_or_else(|| { - CoreError::Auth( + Error::Auth( "Missing Azure API Base - Set `api_base` or the AZURE_API_BASE environment variable. \ Expected format: https://.services.ai.azure.com/anthropic" .to_string(), @@ -147,7 +147,7 @@ impl AnthropicMessagesProviderConfig for AzureAnthropicMessagesConfig { api_base: Option<&str>, _model: &str, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { complete_azure_anthropic_url(api_base, env_lookup) } @@ -155,7 +155,7 @@ impl AnthropicMessagesProviderConfig for AzureAnthropicMessagesConfig { &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { resolve_azure_api_key(api_key, env_lookup) } @@ -174,7 +174,7 @@ impl AnthropicMessagesProviderConfig for AzureAnthropicMessagesConfig { fn transform_request( &self, request: AnthropicMessagesRequest, - ) -> CoreResult { + ) -> Result { let mut request = fold_system_role_messages(request); if let Some(system) = request.system.as_mut() { strip_scope_from_system(system); @@ -190,7 +190,7 @@ impl AnthropicMessagesProviderConfig for AzureAnthropicMessagesConfig { &self, model: &str, response: AnthropicMessagesResponse, - ) -> CoreResult { + ) -> Result { self.anthropic.transform_response(model, response) } } @@ -268,7 +268,7 @@ mod tests { "https://env.services.ai.azure.com/anthropic/v1/messages" ); let err = complete_azure_anthropic_url(Some(" "), &|_| None).expect_err("missing base"); - assert!(matches!(err, CoreError::Auth(_))); + assert!(matches!(err, Error::Auth(_))); } #[test] @@ -284,7 +284,7 @@ mod tests { ); assert!(matches!( resolve_azure_api_key(None, &|_| None).expect_err("missing key"), - CoreError::Auth(_) + Error::Auth(_) )); } diff --git a/litellm-rust/crates/core/src/providers/azure_ai/ocr/transformation.rs b/litellm-rust/crates/core/src/providers/azure_ai/ocr/transformation.rs index eabd15677cc..b26a7925e8a 100644 --- a/litellm-rust/crates/core/src/providers/azure_ai/ocr/transformation.rs +++ b/litellm-rust/crates/core/src/providers/azure_ai/ocr/transformation.rs @@ -1,6 +1,6 @@ use std::collections::BTreeSet; -use crate::error::{CoreError, CoreResult, json_type_name}; +use crate::error::{Error, json_type_name}; use crate::ocr::transformation::{OcrAuthStrategy, OcrProviderConfig, OcrResponseHandling}; use crate::ocr::types::{OcrRequestData, OcrResponseData}; use serde_json::{Map, Value, json}; @@ -32,17 +32,17 @@ fn resolve_value( env_name: &str, env_lookup: &dyn Fn(&str) -> Option, missing_message: &str, -) -> CoreResult { +) -> Result { non_empty(explicit) .map(str::to_string) .or_else(|| env_lookup(env_name).filter(|value| !value.trim().is_empty())) - .ok_or_else(|| CoreError::Auth(missing_message.to_string())) + .ok_or_else(|| Error::Auth(missing_message.to_string())) } pub fn resolve_azure_ai_api_key( api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { resolve_value( api_key, AZURE_AI_API_KEY_ENV, @@ -54,7 +54,7 @@ pub fn resolve_azure_ai_api_key( pub fn resolve_azure_ai_api_base( api_base: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { resolve_value( api_base, AZURE_AI_API_BASE_ENV, @@ -66,7 +66,7 @@ pub fn resolve_azure_ai_api_base( pub fn complete_azure_ai_url( api_base: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { let base = resolve_azure_ai_api_base(api_base, env_lookup)?; Ok(format!( "{}/providers/mistral/azure/ocr", @@ -77,7 +77,7 @@ pub fn complete_azure_ai_url( pub fn resolve_document_intelligence_api_key( api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { resolve_value( api_key, AZURE_DOCUMENT_INTELLIGENCE_API_KEY_ENV, @@ -89,7 +89,7 @@ pub fn resolve_document_intelligence_api_key( pub fn resolve_document_intelligence_endpoint( api_base: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { resolve_value( api_base, AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT_ENV, @@ -127,7 +127,7 @@ fn pages_token_is_valid(token: &str) -> bool { } } -fn normalize_pages_param(pages: &Value) -> CoreResult> { +fn normalize_pages_param(pages: &Value) -> Result, Error> { match pages { Value::String(value) => { let normalized = value @@ -138,7 +138,7 @@ fn normalize_pages_param(pages: &Value) -> CoreResult> { if normalized.split(',').all(pages_token_is_valid) { Ok(Some(normalized)) } else { - Err(CoreError::InvalidRequest(format!( + Err(Error::InvalidRequest(format!( "Invalid `pages` string for Azure Document Intelligence: {value:?}. Expected format like '1-3,5,7-9'." ))) } @@ -152,7 +152,7 @@ fn normalize_pages_param(pages: &Value) -> CoreResult> { for value in values { let page = value.as_i64().expect("checked is_i64"); if page < 0 { - return Err(CoreError::InvalidRequest( + return Err(Error::InvalidRequest( "`pages` integers must be >= 0 (Mistral 0-based indices)".to_string(), )); } @@ -176,16 +176,16 @@ fn normalize_pages_param(pages: &Value) -> CoreResult> { if normalized.split(',').all(pages_token_is_valid) { return Ok(Some(normalized)); } - return Err(CoreError::InvalidRequest(format!( + return Err(Error::InvalidRequest(format!( "Invalid `pages` list for Azure Document Intelligence: {values:?}. Expected tokens like '1' or '3-5'." ))); } - Err(CoreError::InvalidRequest( + Err(Error::InvalidRequest( "`pages` must be a list[int] (0-based, Mistral-style) or a string like '1-3,5,7-9'." .to_string(), )) } - _ => Err(CoreError::InvalidRequest( + _ => Err(Error::InvalidRequest( "`pages` must be a list[int] (0-based, Mistral-style) or a string like '1-3,5,7-9'." .to_string(), )), @@ -197,7 +197,7 @@ pub fn complete_document_intelligence_url( model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { let endpoint = resolve_document_intelligence_endpoint(api_base, env_lookup)?; let mut url = format!( "{}/documentintelligence/documentModels/{}:analyze?api-version={}", @@ -216,20 +216,20 @@ pub fn complete_document_intelligence_url( Ok(url) } -fn document_url_from_mistral_document(document: &Value) -> CoreResult<&str> { - let object = document.as_object().ok_or_else(|| CoreError::InvalidType { +fn document_url_from_mistral_document(document: &Value) -> Result<&str, Error> { + let object = document.as_object().ok_or_else(|| Error::InvalidType { expected: "object", actual: json_type_name(document), })?; let doc_type = object .get("type") .and_then(Value::as_str) - .ok_or(CoreError::MissingField("document.type"))?; + .ok_or(Error::MissingField("document.type"))?; let field_name = match doc_type { "document_url" => "document_url", "image_url" => "image_url", other => { - return Err(CoreError::InvalidRequest(format!( + return Err(Error::InvalidRequest(format!( "Invalid document type: {other}. Must be 'document_url' or 'image_url'" ))); } @@ -238,7 +238,7 @@ fn document_url_from_mistral_document(document: &Value) -> CoreResult<&str> { .get(field_name) .and_then(Value::as_str) .filter(|value| !value.is_empty()) - .ok_or(CoreError::MissingField(field_name)) + .ok_or(Error::MissingField(field_name)) } fn extract_base64_from_data_uri(data_uri: &str) -> &str { @@ -290,7 +290,7 @@ impl OcrProviderConfig for AzureAiOcrConfig { model: &str, document: Value, optional_params: Map, - ) -> CoreResult { + ) -> Result { MISTRAL_OCR_CONFIG.transform_ocr_request(model, document, optional_params) } @@ -298,7 +298,7 @@ impl OcrProviderConfig for AzureAiOcrConfig { &self, model: &str, response_json: Value, - ) -> CoreResult { + ) -> Result { MISTRAL_OCR_CONFIG.transform_ocr_response(model, response_json) } @@ -308,7 +308,7 @@ impl OcrProviderConfig for AzureAiOcrConfig { _model: &str, _optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { complete_azure_ai_url(api_base, env_lookup) } @@ -316,7 +316,7 @@ impl OcrProviderConfig for AzureAiOcrConfig { &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { resolve_azure_ai_api_key(api_key, env_lookup) } @@ -335,7 +335,7 @@ impl OcrProviderConfig for AzureDocumentIntelligenceOcrConfig { _model: &str, document: Value, _optional_params: Map, - ) -> CoreResult { + ) -> Result { let document_url = document_url_from_mistral_document(&document)?; let mut data = Map::new(); if document_url.starts_with("data:") { @@ -359,19 +359,19 @@ impl OcrProviderConfig for AzureDocumentIntelligenceOcrConfig { &self, model: &str, response_json: Value, - ) -> CoreResult { + ) -> Result { let response = response_json .as_object() - .ok_or_else(|| CoreError::InvalidType { + .ok_or_else(|| Error::InvalidType { expected: "object", actual: json_type_name(&response_json), })?; let status = response .get("status") .and_then(Value::as_str) - .ok_or(CoreError::MissingField("status"))?; + .ok_or(Error::MissingField("status"))?; if status != "succeeded" { - return Err(CoreError::InvalidResponse(format!( + return Err(Error::InvalidResponse(format!( "Azure Document Intelligence analysis failed with status: {status}" ))); } @@ -414,7 +414,7 @@ impl OcrProviderConfig for AzureDocumentIntelligenceOcrConfig { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { complete_document_intelligence_url(api_base, model, optional_params, env_lookup) } @@ -422,7 +422,7 @@ impl OcrProviderConfig for AzureDocumentIntelligenceOcrConfig { &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { resolve_document_intelligence_api_key(api_key, env_lookup) } diff --git a/litellm-rust/crates/core/src/providers/bedrock/audio_transcription.rs b/litellm-rust/crates/core/src/providers/bedrock/audio_transcription.rs index 5e885734182..bb4f6afe5f9 100644 --- a/litellm-rust/crates/core/src/providers/bedrock/audio_transcription.rs +++ b/litellm-rust/crates/core/src/providers/bedrock/audio_transcription.rs @@ -6,7 +6,7 @@ use crate::audio_transcription::transformation::{ use crate::audio_transcription::types::{ AudioTranscriptionRequestData, AudioTranscriptionResponseData, }; -use crate::error::{CoreError, CoreResult, json_type_name}; +use crate::error::{Error, json_type_name}; pub use super::aws_base::{aws_auth_config, bedrock_model_id_and_region, resolve_bedrock_region}; use super::constants::{BEDROCK_RUNTIME_ENDPOINT_TEMPLATE, BEDROCK_SERVICE}; @@ -18,8 +18,8 @@ pub static BEDROCK_AUDIO_TRANSCRIPTION_CONFIG: BedrockAudioTranscriptionConfig = pub struct BedrockAudioTranscriptionConfig; -fn audio_fields(audio: Value) -> CoreResult<(String, String)> { - let object = audio.as_object().ok_or_else(|| CoreError::InvalidType { +fn audio_fields(audio: Value) -> Result<(String, String), Error> { + let object = audio.as_object().ok_or_else(|| Error::InvalidType { expected: "object", actual: json_type_name(&audio), })?; @@ -27,13 +27,13 @@ fn audio_fields(audio: Value) -> CoreResult<(String, String)> { .get("data") .and_then(Value::as_str) .filter(|value| !value.is_empty()) - .ok_or(CoreError::MissingField("audio.data"))?; + .ok_or(Error::MissingField("audio.data"))?; let format = object .get("format") .and_then(Value::as_str) .filter(|value| matches!(*value, "wav" | "mp3" | "flac" | "ogg")) .ok_or_else(|| { - CoreError::InvalidRequest("audio.format must be wav, mp3, flac, or ogg".to_string()) + Error::InvalidRequest("audio.format must be wav, mp3, flac, or ogg".to_string()) })?; Ok((data.to_string(), format.to_string())) } @@ -55,7 +55,7 @@ impl AudioTranscriptionProviderConfig for BedrockAudioTranscriptionConfig { _model: &str, audio: Value, optional_params: Map, - ) -> CoreResult { + ) -> Result { let (data, format) = audio_fields(audio)?; let mut instruction = "Transcribe the audio. Respond with only the transcript.".to_string(); if let Some(language) = optional_string(&optional_params, "language") { @@ -87,14 +87,14 @@ impl AudioTranscriptionProviderConfig for BedrockAudioTranscriptionConfig { &self, _model: &str, response_json: Value, - ) -> CoreResult { + ) -> Result { let content = response_json .get("output") .and_then(|value| value.get("message")) .and_then(|value| value.get("content")) .and_then(Value::as_array) .ok_or_else(|| { - CoreError::InvalidResponse("Bedrock response has no output content".to_string()) + Error::InvalidResponse("Bedrock response has no output content".to_string()) })?; let mut text = String::new(); for block in content { @@ -111,7 +111,7 @@ impl AudioTranscriptionProviderConfig for BedrockAudioTranscriptionConfig { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { let (model_id, model_region) = bedrock_model_id_and_region(model); let region = resolve_bedrock_region(model_region.as_deref(), optional_params, env_lookup); let endpoint = optional_params @@ -133,7 +133,7 @@ impl AudioTranscriptionProviderConfig for BedrockAudioTranscriptionConfig { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { let (_, model_region) = bedrock_model_id_and_region(model); Ok(AudioTranscriptionAuth::AwsSigV4 { region: resolve_bedrock_region(model_region.as_deref(), optional_params, env_lookup), diff --git a/litellm-rust/crates/core/src/providers/bedrock/aws_base.rs b/litellm-rust/crates/core/src/providers/bedrock/aws_base.rs index b11639aa09b..e5e52bfce95 100644 --- a/litellm-rust/crates/core/src/providers/bedrock/aws_base.rs +++ b/litellm-rust/crates/core/src/providers/bedrock/aws_base.rs @@ -4,7 +4,7 @@ use std::time::Duration; use std::time::{SystemTime, UNIX_EPOCH}; use crate::caching::in_memory_cache::InMemoryCache; -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use aws_credential_types::Credentials; use aws_credential_types::provider::ProvideCredentials; use aws_sigv4::http_request::{ @@ -197,7 +197,7 @@ pub fn classify_auth( pub async fn resolve_credentials( config: AwsAuthConfig, env_lookup: &(dyn Fn(&str) -> Option + Sync), -) -> CoreResult { +) -> Result { let resolved = config.clone().with_environment(env_lookup); let flow = classify_auth(config, env_lookup); match flow { @@ -244,9 +244,10 @@ pub async fn resolve_credentials( let provider = aws_config::profile::ProfileFileCredentialsProvider::builder() .profile_name(name) .build(); - provider.provide_credentials().await.map_err(|error| { - CoreError::Auth(format!("AWS profile credentials failed: {error}")) - }) + provider + .provide_credentials() + .await + .map_err(|error| Error::Auth(format!("AWS profile credentials failed: {error}"))) } AwsAuthFlow::AssumeRole { role, session_name } => { if is_already_running_as_role(&role, &resolved).await? { @@ -260,7 +261,7 @@ pub async fn resolve_credentials( .build() .await; let credentials = provider.provide_credentials().await.map_err(|error| { - CoreError::Auth(format!("AWS default credentials failed: {error}")) + Error::Auth(format!("AWS default credentials failed: {error}")) })?; set_cached_credentials( key, @@ -301,7 +302,7 @@ pub async fn resolve_credentials( provider .provide_credentials() .await - .map_err(|error| CoreError::Auth(format!("AWS role credentials failed: {error}"))) + .map_err(|error| Error::Auth(format!("AWS role credentials failed: {error}"))) } AwsAuthFlow::WebIdentity { token, @@ -325,13 +326,13 @@ pub async fn resolve_credentials( .send() .await .map_err(|error| { - CoreError::Auth(format!("AWS web identity credentials failed: {error}")) + Error::Auth(format!("AWS web identity credentials failed: {error}")) })?; let credentials = response.credentials().ok_or_else(|| { - CoreError::Auth("AWS web identity response had no credentials".to_string()) + Error::Auth("AWS web identity response had no credentials".to_string()) })?; let expiration = SystemTime::try_from(*credentials.expiration()).map_err(|error| { - CoreError::Auth(format!("AWS web identity expiration was invalid: {error}")) + Error::Auth(format!("AWS web identity expiration was invalid: {error}")) })?; Ok(Credentials::new( credentials.access_key_id(), @@ -350,9 +351,10 @@ pub async fn resolve_credentials( aws_config::default_provider::credentials::DefaultCredentialsChain::builder() .build() .await; - let credentials = provider.provide_credentials().await.map_err(|error| { - CoreError::Auth(format!("AWS default credentials failed: {error}")) - })?; + let credentials = provider + .provide_credentials() + .await + .map_err(|error| Error::Auth(format!("AWS default credentials failed: {error}")))?; set_cached_credentials( key, credentials.clone(), @@ -363,7 +365,7 @@ pub async fn resolve_credentials( } } -async fn is_already_running_as_role(role: &str, config: &AwsAuthConfig) -> CoreResult { +async fn is_already_running_as_role(role: &str, config: &AwsAuthConfig) -> Result { if role_identity(role).is_none() { return Ok(false); } @@ -437,7 +439,7 @@ pub fn sign_bedrock_post( region: &str, credentials: &Credentials, signing_time: SystemTime, -) -> CoreResult> { +) -> Result, Error> { let identity: Identity = credentials.clone().into(); let params = v4::SigningParams::builder() .identity(&identity) @@ -447,14 +449,14 @@ pub fn sign_bedrock_post( .settings(SigningSettings::default()) .build() .map(SigningParams::from) - .map_err(|error| CoreError::Auth(format!("AWS signing parameters failed: {error}")))?; + .map_err(|error| Error::Auth(format!("AWS signing parameters failed: {error}")))?; let header_refs = headers .iter() .map(|(name, value)| (name.as_str(), value.as_str())); let request = SignableRequest::new("POST", url, header_refs, SignableBody::Bytes(body)) - .map_err(|error| CoreError::Auth(format!("AWS signable request failed: {error}")))?; + .map_err(|error| Error::Auth(format!("AWS signable request failed: {error}")))?; let (instructions, _) = sign(request, ¶ms) - .map_err(|error| CoreError::Auth(format!("AWS request signing failed: {error}")))? + .map_err(|error| Error::Auth(format!("AWS request signing failed: {error}")))? .into_parts(); Ok(instructions .headers() diff --git a/litellm-rust/crates/core/src/providers/bedrock/chat_completions/tests.rs b/litellm-rust/crates/core/src/providers/bedrock/chat_completions/tests.rs index 4b75dcb8e9d..c86f061b9ca 100644 --- a/litellm-rust/crates/core/src/providers/bedrock/chat_completions/tests.rs +++ b/litellm-rust/crates/core/src/providers/bedrock/chat_completions/tests.rs @@ -1,4 +1,5 @@ use super::*; +use crate::Error; use serde_json::json; fn messages(value: Value) -> Vec { @@ -23,7 +24,7 @@ fn transform(msgs: Value, opts: Value) -> Value { .body } -fn transform_response(body: Value) -> CoreResult { +fn transform_response(body: Value) -> Result { BEDROCK_CHAT_COMPLETIONS_CONFIG.transform_response( "anthropic.claude-sonnet-4-5-v1:0", ProviderChatResponseData { body }, @@ -478,25 +479,22 @@ fn declines_a_response_carrying_a_tool_use_block() { "usage": {"inputTokens": 1, "outputTokens": 1} })) .expect_err("tool use block"); - assert_eq!( - err, - CoreError::Unsupported("non-text response content block") - ); + assert_eq!(err, Error::Unsupported("non-text response content block")); } #[test] fn errors_on_a_response_missing_required_fields() { assert_eq!( transform_response(json!("nope")).expect_err("not an object"), - CoreError::InvalidResponse("converse response is not an object".to_string()) + Error::InvalidResponse("converse response is not an object".to_string()) ); assert_eq!( transform_response(json!({"usage": {}})).expect_err("no output"), - CoreError::MissingField("output.message.content") + Error::MissingField("output.message.content") ); assert_eq!( transform_response(json!({"output": {"message": {"content": []}}})).expect_err("no usage"), - CoreError::MissingField("usage") + Error::MissingField("usage") ); } diff --git a/litellm-rust/crates/core/src/providers/bedrock/chat_completions/transformation.rs b/litellm-rust/crates/core/src/providers/bedrock/chat_completions/transformation.rs index b107950748e..ef5f44b4a14 100644 --- a/litellm-rust/crates/core/src/providers/bedrock/chat_completions/transformation.rs +++ b/litellm-rust/crates/core/src/providers/bedrock/chat_completions/transformation.rs @@ -11,7 +11,7 @@ use crate::chat_completions::types::{ ChatCompletionsUsage, ChatMessage, ChatMessageContent, ProviderChatRequestData, ProviderChatResponseData, }; -use crate::error::{CoreError, CoreResult}; +use crate::error::Error; use super::super::aws_base::{bedrock_model_id_and_region, resolve_bedrock_region}; use super::super::constants::{AWS_BEARER_TOKEN_BEDROCK, BEDROCK_RUNTIME_ENDPOINT_TEMPLATE}; @@ -110,7 +110,7 @@ impl ChatCompletionsProviderConfig for BedrockChatCompletionsConfig { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { let (model_id, model_region) = bedrock_model_id_and_region(model); let region = resolve_bedrock_region(model_region.as_deref(), optional_params, env_lookup); let endpoint = optional_params @@ -137,7 +137,7 @@ impl ChatCompletionsProviderConfig for BedrockChatCompletionsConfig { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { // Python reads `api_key` as the Bedrock bearer token and consults the // env only when the caller passed none, so a caller-supplied empty key // falls through to SigV4 without reaching for the environment. An @@ -208,7 +208,7 @@ impl ChatCompletionsProviderConfig for BedrockChatCompletionsConfig { _model: &str, messages: Vec, optional_params: Map, - ) -> CoreResult { + ) -> Result { Ok(ProviderChatRequestData { body: converse_body(&build_conversation(&messages), &optional_params), }) @@ -218,17 +218,18 @@ impl ChatCompletionsProviderConfig for BedrockChatCompletionsConfig { &self, model: &str, response: ProviderChatResponseData, - ) -> CoreResult { - let body = response.body.as_object().ok_or_else(|| { - CoreError::InvalidResponse("converse response is not an object".into()) - })?; + ) -> Result { + let body = response + .body + .as_object() + .ok_or_else(|| Error::InvalidResponse("converse response is not an object".into()))?; let content = body .get("output") .and_then(|output| output.get("message")) .and_then(|message| message.get("content")) .and_then(Value::as_array) - .ok_or(CoreError::MissingField("output.message.content"))?; + .ok_or(Error::MissingField("output.message.content"))?; // The route declines tool requests, so anything other than a text block // is something this path never asked for. Decline; the host falls back. if content.iter().any(|block| { @@ -236,7 +237,7 @@ impl ChatCompletionsProviderConfig for BedrockChatCompletionsConfig { .as_object() .is_none_or(|block| block.len() != 1 || !block.contains_key("text")) }) { - return Err(CoreError::Unsupported("non-text response content block")); + return Err(Error::Unsupported("non-text response content block")); } let text: String = content .iter() @@ -246,7 +247,7 @@ impl ChatCompletionsProviderConfig for BedrockChatCompletionsConfig { let usage = body .get("usage") .and_then(Value::as_object) - .ok_or(CoreError::MissingField("usage"))?; + .ok_or(Error::MissingField("usage"))?; let field = |name: &str| usage.get(name).and_then(Value::as_u64).unwrap_or(0); let computed = usage_from_parts( field("inputTokens"), diff --git a/litellm-rust/crates/core/src/providers/mistral/ocr/transformation.rs b/litellm-rust/crates/core/src/providers/mistral/ocr/transformation.rs index dc720cc4244..6a8a38204a9 100644 --- a/litellm-rust/crates/core/src/providers/mistral/ocr/transformation.rs +++ b/litellm-rust/crates/core/src/providers/mistral/ocr/transformation.rs @@ -1,4 +1,4 @@ -use crate::error::{CoreError, CoreResult, json_type_name}; +use crate::error::{Error, json_type_name}; use crate::ocr::transformation::OcrProviderConfig; use crate::ocr::types::{OcrRequestData, OcrResponseData}; use serde_json::{Map, Value}; @@ -47,7 +47,7 @@ pub fn complete_url(api_base: Option<&str>) -> String { /// Resolve the Mistral API key from the explicit param or the environment. /// -/// Blank/whitespace values are treated as absent. Returns `CoreError::Auth` +/// Blank/whitespace values are treated as absent. Returns `Error::Auth` /// when no usable key is available. /// /// Note: the env fallback only reads the process environment. Secret-manager @@ -56,13 +56,13 @@ pub fn complete_url(api_base: Option<&str>) -> String { pub fn resolve_api_key( api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { api_key .map(str::trim) .filter(|key| !key.is_empty()) .map(str::to_string) .or_else(|| env_lookup(MISTRAL_API_KEY_ENV).filter(|key| !key.trim().is_empty())) - .ok_or_else(|| CoreError::Auth(MISSING_KEY_MESSAGE.to_string())) + .ok_or_else(|| Error::Auth(MISSING_KEY_MESSAGE.to_string())) } pub struct MistralOcrConfig; @@ -79,9 +79,9 @@ impl OcrProviderConfig for MistralOcrConfig { model: &str, document: Value, optional_params: Map, - ) -> CoreResult { + ) -> Result { if !document.is_object() { - return Err(CoreError::InvalidType { + return Err(Error::InvalidType { expected: "object", actual: json_type_name(&document), }); @@ -104,10 +104,10 @@ impl OcrProviderConfig for MistralOcrConfig { &self, model: &str, response_json: Value, - ) -> CoreResult { + ) -> Result { let response_object = response_json .as_object() - .ok_or_else(|| CoreError::InvalidType { + .ok_or_else(|| Error::InvalidType { expected: "object", actual: json_type_name(&response_json), })?; @@ -140,7 +140,7 @@ impl OcrProviderConfig for MistralOcrConfig { _model: &str, _optional_params: &Map, _env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { Ok(complete_url(api_base)) } @@ -148,7 +148,7 @@ impl OcrProviderConfig for MistralOcrConfig { &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { resolve_api_key(api_key, env_lookup) } } @@ -165,11 +165,11 @@ pub fn transform_ocr_request( model: &str, document: Value, optional_params: Map, -) -> CoreResult { +) -> Result { MISTRAL_OCR_CONFIG.transform_ocr_request(model, document, optional_params) } -pub fn transform_ocr_response(model: &str, response_json: Value) -> CoreResult { +pub fn transform_ocr_response(model: &str, response_json: Value) -> Result { MISTRAL_OCR_CONFIG.transform_ocr_response(model, response_json) } @@ -250,7 +250,7 @@ mod tests { assert_eq!( err, - CoreError::InvalidType { + Error::InvalidType { expected: "object", actual: "string", } @@ -307,6 +307,6 @@ mod tests { #[test] fn resolve_api_key_errors_when_absent() { let err = resolve_api_key(None, &|_| None).expect_err("missing key should error"); - assert_eq!(err, CoreError::Auth(MISSING_KEY_MESSAGE.to_string())); + assert_eq!(err, Error::Auth(MISSING_KEY_MESSAGE.to_string())); } } diff --git a/litellm-rust/crates/core/src/providers/openai/realtime/transformation.rs b/litellm-rust/crates/core/src/providers/openai/realtime/transformation.rs index b3f6b03b28a..f1985f81b7d 100644 --- a/litellm-rust/crates/core/src/providers/openai/realtime/transformation.rs +++ b/litellm-rust/crates/core/src/providers/openai/realtime/transformation.rs @@ -1,4 +1,4 @@ -use crate::CoreResult; +use crate::Error; use crate::realtime::transformation::RealtimeProviderConfig; use crate::realtime::types::{RealtimeEvent, RealtimeTransformResult}; @@ -72,7 +72,7 @@ impl RealtimeProviderConfig for OpenAiRealtimeConfig { &self, event: &RealtimeEvent, _model: &str, - ) -> CoreResult { + ) -> Result { Ok(RealtimeTransformResult::passthrough(event.clone())) } @@ -80,7 +80,7 @@ impl RealtimeProviderConfig for OpenAiRealtimeConfig { &self, event: &RealtimeEvent, _model: &str, - ) -> CoreResult { + ) -> Result { Ok(RealtimeTransformResult::passthrough(event.clone())) } } @@ -88,14 +88,14 @@ impl RealtimeProviderConfig for OpenAiRealtimeConfig { pub fn transform_realtime_request( event: &RealtimeEvent, model: &str, -) -> CoreResult { +) -> Result { OPENAI_REALTIME_CONFIG.transform_realtime_request(event, model) } pub fn transform_realtime_response( event: &RealtimeEvent, model: &str, -) -> CoreResult { +) -> Result { OPENAI_REALTIME_CONFIG.transform_realtime_response(event, model) } diff --git a/litellm-rust/crates/core/src/providers/openai/responses/transformation.rs b/litellm-rust/crates/core/src/providers/openai/responses/transformation.rs index e15197c468c..be86bb90311 100644 --- a/litellm-rust/crates/core/src/providers/openai/responses/transformation.rs +++ b/litellm-rust/crates/core/src/providers/openai/responses/transformation.rs @@ -1,4 +1,4 @@ -use crate::CoreResult; +use crate::Error; use crate::responses::types::{ResponsesWsEvent, ResponsesWsTransformResult}; use crate::responses::websocket::{ResponsesWebSocketProviderConfig, enforce_model}; @@ -15,7 +15,7 @@ impl ResponsesWebSocketProviderConfig for OpenAIResponsesWsConfig { &self, event: &ResponsesWsEvent, model: &str, - ) -> CoreResult { + ) -> Result { Ok(ResponsesWsTransformResult::passthrough(enforce_model( event, model, ))) @@ -25,7 +25,7 @@ impl ResponsesWebSocketProviderConfig for OpenAIResponsesWsConfig { &self, event: &ResponsesWsEvent, _model: &str, - ) -> CoreResult { + ) -> Result { Ok(ResponsesWsTransformResult::passthrough(event.clone())) } } diff --git a/litellm-rust/crates/core/src/providers/vertex_ai/ocr/transformation.rs b/litellm-rust/crates/core/src/providers/vertex_ai/ocr/transformation.rs index 6300149c237..ee095447028 100644 --- a/litellm-rust/crates/core/src/providers/vertex_ai/ocr/transformation.rs +++ b/litellm-rust/crates/core/src/providers/vertex_ai/ocr/transformation.rs @@ -1,4 +1,4 @@ -use crate::error::{CoreError, CoreResult, json_type_name}; +use crate::error::{Error, json_type_name}; use crate::ocr::transformation::OcrProviderConfig; use crate::ocr::types::{OcrRequestData, OcrResponseData}; use serde_json::{Map, Value, json}; @@ -43,7 +43,7 @@ pub fn is_deepseek_model(model: &str) -> bool { pub fn resolve_vertex_api_key( api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { api_key .map(str::trim) .filter(|key| !key.is_empty()) @@ -51,7 +51,7 @@ pub fn resolve_vertex_api_key( .or_else(|| env_lookup(VERTEX_AI_API_KEY_ENV).filter(|key| !key.trim().is_empty())) .or_else(|| env_lookup(VERTEXAI_API_KEY_ENV).filter(|key| !key.trim().is_empty())) .ok_or_else(|| { - CoreError::Auth( + Error::Auth( "Missing Vertex AI access token - pass api_key or provide Authorization via extra_headers" .to_string(), ) @@ -61,12 +61,12 @@ pub fn resolve_vertex_api_key( fn vertex_project( params: &Map, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { string_param(params, &["vertex_project", "vertex_ai_project"]) .map(str::to_string) .or_else(|| env_lookup(VERTEXAI_PROJECT_ENV).filter(|value| !value.trim().is_empty())) .ok_or_else(|| { - CoreError::InvalidRequest( + Error::InvalidRequest( "Missing vertex_project - Set VERTEXAI_PROJECT environment variable or pass vertex_project parameter" .to_string(), ) @@ -99,7 +99,7 @@ pub fn complete_vertex_mistral_url( model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { let project = vertex_project(optional_params, env_lookup)?; let location = vertex_location(optional_params, env_lookup); let base = vertex_mistral_api_base(api_base, &location); @@ -112,7 +112,7 @@ pub fn complete_vertex_deepseek_url( api_base: Option<&str>, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, -) -> CoreResult { +) -> Result { let project = vertex_project(optional_params, env_lookup)?; let location = vertex_location(optional_params, env_lookup); let base = api_base @@ -125,20 +125,20 @@ pub fn complete_vertex_deepseek_url( )) } -fn document_content_item(document: &Value) -> CoreResult { - let object = document.as_object().ok_or_else(|| CoreError::InvalidType { +fn document_content_item(document: &Value) -> Result { + let object = document.as_object().ok_or_else(|| Error::InvalidType { expected: "object", actual: json_type_name(document), })?; let doc_type = object .get("type") .and_then(Value::as_str) - .ok_or(CoreError::MissingField("document.type"))?; + .ok_or(Error::MissingField("document.type"))?; let url_field = match doc_type { "image_url" => "image_url", "document_url" => "document_url", other => { - return Err(CoreError::InvalidRequest(format!( + return Err(Error::InvalidRequest(format!( "Unsupported document type: {other}. Expected 'image_url' or 'document_url'" ))); } @@ -147,7 +147,7 @@ fn document_content_item(document: &Value) -> CoreResult { .get(url_field) .and_then(Value::as_str) .filter(|value| !value.is_empty()) - .ok_or(CoreError::MissingField(url_field))?; + .ok_or(Error::MissingField(url_field))?; Ok(json!({ "type": "image_url", @@ -163,7 +163,7 @@ fn deepseek_model_name(model: &str) -> String { } } -fn first_choice_content(response: &Value) -> CoreResult { +fn first_choice_content(response: &Value) -> Result { response .get("choices") .and_then(Value::as_array) @@ -176,9 +176,7 @@ fn first_choice_content(response: &Value) -> CoreResult { Value::Object(_) => true, _ => false, }) - .ok_or_else(|| { - CoreError::InvalidResponse("No content in DeepSeek OCR response".to_string()) - }) + .ok_or_else(|| Error::InvalidResponse("No content in DeepSeek OCR response".to_string())) } fn ocr_data_from_content(content: Value, usage: Option, model: &str) -> Value { @@ -219,7 +217,7 @@ impl OcrProviderConfig for VertexAiOcrConfig { model: &str, document: Value, optional_params: Map, - ) -> CoreResult { + ) -> Result { MISTRAL_OCR_CONFIG.transform_ocr_request(model, document, optional_params) } @@ -227,7 +225,7 @@ impl OcrProviderConfig for VertexAiOcrConfig { &self, model: &str, response_json: Value, - ) -> CoreResult { + ) -> Result { MISTRAL_OCR_CONFIG.transform_ocr_response(model, response_json) } @@ -237,7 +235,7 @@ impl OcrProviderConfig for VertexAiOcrConfig { model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { complete_vertex_mistral_url(api_base, model, optional_params, env_lookup) } @@ -245,7 +243,7 @@ impl OcrProviderConfig for VertexAiOcrConfig { &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { resolve_vertex_api_key(api_key, env_lookup) } @@ -264,7 +262,7 @@ impl OcrProviderConfig for VertexAiDeepSeekOcrConfig { model: &str, document: Value, optional_params: Map, - ) -> CoreResult { + ) -> Result { let mut data = Map::new(); data.insert( "model".to_string(), @@ -289,10 +287,10 @@ impl OcrProviderConfig for VertexAiDeepSeekOcrConfig { &self, model: &str, response_json: Value, - ) -> CoreResult { + ) -> Result { let response = response_json .as_object() - .ok_or_else(|| CoreError::InvalidType { + .ok_or_else(|| Error::InvalidType { expected: "object", actual: json_type_name(&response_json), })?; @@ -314,7 +312,7 @@ impl OcrProviderConfig for VertexAiDeepSeekOcrConfig { }); } - let object = ocr_data.as_object().ok_or_else(|| CoreError::InvalidType { + let object = ocr_data.as_object().ok_or_else(|| Error::InvalidType { expected: "object", actual: json_type_name(&ocr_data), })?; @@ -346,7 +344,7 @@ impl OcrProviderConfig for VertexAiDeepSeekOcrConfig { _model: &str, optional_params: &Map, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { complete_vertex_deepseek_url(api_base, optional_params, env_lookup) } @@ -354,7 +352,7 @@ impl OcrProviderConfig for VertexAiDeepSeekOcrConfig { &self, api_key: Option<&str>, env_lookup: &dyn Fn(&str) -> Option, - ) -> CoreResult { + ) -> Result { resolve_vertex_api_key(api_key, env_lookup) } } diff --git a/litellm-rust/crates/core/src/realtime/transformation.rs b/litellm-rust/crates/core/src/realtime/transformation.rs index 69b88687000..b08084514ef 100644 --- a/litellm-rust/crates/core/src/realtime/transformation.rs +++ b/litellm-rust/crates/core/src/realtime/transformation.rs @@ -1,4 +1,4 @@ -use crate::CoreResult; +use crate::Error; use crate::realtime::types::{RealtimeEvent, RealtimeTransformResult}; pub trait RealtimeProviderConfig { @@ -11,12 +11,12 @@ pub trait RealtimeProviderConfig { &self, event: &RealtimeEvent, model: &str, - ) -> CoreResult; + ) -> Result; /// Transform a backend → client event before it is forwarded downstream. fn transform_realtime_response( &self, event: &RealtimeEvent, model: &str, - ) -> CoreResult; + ) -> Result; } diff --git a/litellm-rust/crates/core/src/responses/instrumentation.rs b/litellm-rust/crates/core/src/responses/instrumentation.rs index ec04571da14..b1098f4d386 100644 --- a/litellm-rust/crates/core/src/responses/instrumentation.rs +++ b/litellm-rust/crates/core/src/responses/instrumentation.rs @@ -5,9 +5,9 @@ use std::time::{SystemTime, UNIX_EPOCH}; use serde_json::Value; +use crate::Error; use crate::call_lifecycle::{CallLifecycleContext, CallLifecycleHooks, CallLifecycleTiming}; use crate::responses::types::{ResponsesWsEvent, ResponsesWsEventType}; -use crate::{CoreError, CoreResult}; #[derive(Clone, Debug, Default, PartialEq, Eq)] pub struct ResponsesWsUsage { @@ -205,7 +205,7 @@ impl ResponsesWsInstrumentation { } } -type LifecycleFuture<'a, T> = Pin> + Send + 'a>>; +type LifecycleFuture<'a, T> = Pin> + Send + 'a>>; impl CallLifecycleHooks<(), (), ()> for ResponsesWsInstrumentation { type PreCallFuture<'a> = LifecycleFuture<'a, ()>; @@ -246,7 +246,7 @@ impl CallLifecycleHooks<(), (), ()> for ResponsesWsInstrumentation { fn async_log_failure_event<'a>( &'a self, _context: &'a CallLifecycleContext, - _error: &'a CoreError, + _error: &'a Error, _timing: &'a CallLifecycleTiming, ) -> Self::FailureFuture<'a> { Box::pin(async move { @@ -342,7 +342,7 @@ mod tests { ), (), &instrumentation, - |_| async { Ok::<(), CoreError>(()) }, + |_| async { Ok::<(), Error>(()) }, ) .await; diff --git a/litellm-rust/crates/core/src/responses/websocket.rs b/litellm-rust/crates/core/src/responses/websocket.rs index 92dc19627a0..5d037e9cf1b 100644 --- a/litellm-rust/crates/core/src/responses/websocket.rs +++ b/litellm-rust/crates/core/src/responses/websocket.rs @@ -1,4 +1,4 @@ -use crate::CoreResult; +use crate::Error; use crate::constants::{OPENAI_RESPONSES_DEFAULT_API_BASE, OPENAI_RESPONSES_PATH}; use crate::responses::types::{ResponsesWsEvent, ResponsesWsEventType, ResponsesWsTransformResult}; @@ -19,13 +19,13 @@ pub trait ResponsesWebSocketProviderConfig: Sync { &self, event: &ResponsesWsEvent, model: &str, - ) -> CoreResult; + ) -> Result; fn transform_ws_response( &self, event: &ResponsesWsEvent, model: &str, - ) -> CoreResult; + ) -> Result; } pub fn complete_websocket_url( diff --git a/litellm-rust/crates/core/tests/workspace_crate_allowlist.rs b/litellm-rust/crates/core/tests/workspace_crate_allowlist.rs index 656ba033b62..8a8a5ea263a 100644 --- a/litellm-rust/crates/core/tests/workspace_crate_allowlist.rs +++ b/litellm-rust/crates/core/tests/workspace_crate_allowlist.rs @@ -1,7 +1,8 @@ -//! Enforcement: the litellm-rust workspace has exactly three crates. +//! Enforcement: the litellm-rust workspace has exactly four crates. //! -//! `core` (pure translation), `ai-gateway` (routes + all network I/O), and -//! `python-bridge` (the PyO3 cdylib). Adding or removing a crate must be a +//! `core` (the Rust SDK), `ai-gateway` (the HTTP/WebSocket host), +//! `python-interop` (domain-neutral PyO3 primitives), and `python-bridge` (the +//! PyO3 cdylib). Adding or removing a crate must be a //! deliberate act: this test fails until the allowlist here is updated, forcing //! whoever changes the crate set to justify the new crate per the rule that a //! crate is a layer needing independent compilation / its own deps / a separate @@ -16,10 +17,15 @@ use std::path::{Path, PathBuf}; /// The one true crate set. Update BOTH this and `litellm-rust/AGENTS.md` when the /// workspace legitimately gains or loses a crate. -const EXPECTED_MEMBERS: &[&str] = &["crates/core", "crates/ai-gateway", "crates/python-bridge"]; +const EXPECTED_MEMBERS: &[&str] = &[ + "crates/core", + "crates/ai-gateway", + "crates/python-interop", + "crates/python-bridge", +]; /// The crate subdirectory names that must exist under `crates/`. -const EXPECTED_CRATE_DIRS: &[&str] = &["core", "ai-gateway", "python-bridge"]; +const EXPECTED_CRATE_DIRS: &[&str] = &["core", "ai-gateway", "python-interop", "python-bridge"]; const MISMATCH: &str = "litellm-rust crate set changed — update this allowlist AND litellm-rust/AGENTS.md, and justify the crate per the rule (crate = layer needing independent compilation / its own deps / a separate artifact)."; diff --git a/litellm-rust/crates/python-bridge/AGENTS.md b/litellm-rust/crates/python-bridge/AGENTS.md index ad3cddfa5fd..42282ca4da4 100644 --- a/litellm-rust/crates/python-bridge/AGENTS.md +++ b/litellm-rust/crates/python-bridge/AGENTS.md @@ -1,3 +1,3 @@ -litellm-python-bridge is the PyO3 cdylib that exposes Rust to the litellm Python SDK — a thin adapter (Python objects → Rust calls → Python results) over the litellm-core route entrypoints (e.g. `litellm_core::messages::messages`). +litellm-python-bridge is the PyO3 cdylib that exposes LiteLLM Rust APIs to the Python SDK. Keep API registration, domain dependency wiring, request assembly, and Python exception mapping here. Put domain-neutral Python/Serde conversion and GIL primitives in litellm-python-interop. Keep it thin: no business logic, no transforms, no I/O orchestration — just marshal in/out and call the core entrypoint. diff --git a/litellm-rust/crates/python-bridge/CLAUDE.md b/litellm-rust/crates/python-bridge/CLAUDE.md index 3ce8b8c639a..d25ae5a8130 100644 --- a/litellm-rust/crates/python-bridge/CLAUDE.md +++ b/litellm-rust/crates/python-bridge/CLAUDE.md @@ -5,8 +5,9 @@ Rules for `litellm-rust/crates/python-bridge`. ## Responsibility `python-bridge` is the PyO3 boundary between Python LiteLLM and Rust transforms. -Keep this crate thin. It adapts Python objects to Rust payloads and returns -Python-compatible dictionaries. +Keep this crate thin. It exposes LiteLLM Rust APIs, assembles domain requests, +maps domain errors to Python exceptions, and delegates generic conversion and +GIL handling to `litellm-python-interop`. ## Bridge Shape diff --git a/litellm-rust/crates/python-bridge/Cargo.toml b/litellm-rust/crates/python-bridge/Cargo.toml index 20a9ba789ce..498003de149 100644 --- a/litellm-rust/crates/python-bridge/Cargo.toml +++ b/litellm-rust/crates/python-bridge/Cargo.toml @@ -9,10 +9,24 @@ repository.workspace = true name = "_native" crate-type = ["cdylib"] +[features] +default = ["abi3"] +abi3 = ["pyo3/abi3-py310"] +extension-module = ["pyo3/extension-module"] +panic-test = [] + [dependencies] litellm-core = { workspace = true, features = ["bedrock-auth"] } litellm-ai-gateway = { workspace = true, default-features = false } -pyo3 = { workspace = true, features = ["extension-module"] } +litellm-python-interop.workspace = true +pyo3.workspace = true pyo3-async-runtimes.workspace = true serde_json.workspace = true tokio.workspace = true + +[dev-dependencies] +criterion = "0.8.2" + +[[bench]] +name = "serialization" +harness = false diff --git a/litellm-rust/crates/python-bridge/benches/serialization.rs b/litellm-rust/crates/python-bridge/benches/serialization.rs new file mode 100644 index 00000000000..0b9436d0cb7 --- /dev/null +++ b/litellm-rust/crates/python-bridge/benches/serialization.rs @@ -0,0 +1,102 @@ +use std::hint::black_box; +use std::time::Duration; + +use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main}; +use litellm_python_interop::{from_py, to_py}; +use pyo3::prelude::*; +use pyo3::types::PyDict; +use serde_json::{Value, json}; + +const PAYLOAD_SIZES: &[(&str, usize)] = &[ + ("1_KiB", 1024), + ("64_KiB", 64 * 1024), + ("1_MiB", 1024 * 1024), + ("4_MiB", 4 * 1024 * 1024), + ("16_MiB", 16 * 1024 * 1024), +]; + +fn former_json_roundtrip_from_py(py: Python<'_>, value: &Bound<'_, PyAny>) -> Value { + let json = py.import("json").expect("Python json module should import"); + let encoded: String = json + .call_method1("dumps", (value,)) + .expect("payload should serialize") + .extract() + .expect("json.dumps should return a string"); + serde_json::from_str(&encoded).expect("serialized JSON should parse") +} + +fn pythonize_from_py(value: &Bound<'_, PyAny>) -> Value { + from_py(value).expect("payload should depythonize") +} + +fn former_json_roundtrip_to_py(py: Python<'_>, value: &Value) -> Py { + let json = py.import("json").expect("Python json module should import"); + let encoded = serde_json::to_string(value).expect("response should serialize"); + json.call_method1("loads", (encoded,)) + .expect("serialized response should parse in Python") + .unbind() +} + +fn pythonize_to_py(py: Python<'_>, value: &Value) -> Py { + to_py(py, value).expect("response should pythonize") +} + +fn bridge_serialization(c: &mut Criterion) { + Python::initialize(); + Python::attach(|py| { + for &(label, payload_bytes) in PAYLOAD_SIZES { + let data_uri = format!("data:image/png;base64,{}", "A".repeat(payload_bytes)); + let document = PyDict::new(py); + document + .set_item("type", "image_url") + .expect("document type should be set"); + document + .set_item("image_url", &data_uri) + .expect("document URL should be set"); + let response = json!({ + "pages": [{ + "index": 0, + "markdown": "OCR text", + "images": [{"image_base64": data_uri}], + }], + "model": "mistral-ocr-latest", + "document_annotation": null, + "usage_info": {"pages_processed": 1}, + "object": "ocr", + }); + + c.bench_with_input( + BenchmarkId::new("python_to_rust_json", label), + &document, + |b, document| { + b.iter(|| former_json_roundtrip_from_py(py, black_box(document.as_any()))) + }, + ); + c.bench_with_input( + BenchmarkId::new("python_to_rust_pythonize", label), + &document, + |b, document| b.iter(|| pythonize_from_py(black_box(document.as_any()))), + ); + c.bench_with_input( + BenchmarkId::new("rust_to_python_json", label), + &response, + |b, response| b.iter(|| former_json_roundtrip_to_py(py, black_box(response))), + ); + c.bench_with_input( + BenchmarkId::new("rust_to_python_pythonize", label), + &response, + |b, response| b.iter(|| pythonize_to_py(py, black_box(response))), + ); + } + }); +} + +criterion_group! { + name = benches; + config = Criterion::default() + .sample_size(20) + .warm_up_time(Duration::from_secs(1)) + .measurement_time(Duration::from_secs(4)); + targets = bridge_serialization +} +criterion_main!(benches); diff --git a/litellm-rust/crates/python-bridge/src/gil.rs b/litellm-rust/crates/python-bridge/src/gil.rs deleted file mode 100644 index e887c8ec1e3..00000000000 --- a/litellm-rust/crates/python-bridge/src/gil.rs +++ /dev/null @@ -1,32 +0,0 @@ -//! GIL accounting. -//! -//! A single chokepoint for releasing the GIL around blocking work. Every -//! blocking call in the bridge goes through [`release_gil`] instead of calling -//! `Python::detach` directly, so the release count stays accurate and we -//! have one place to extend later (timing histograms, per-call labels, etc.). - -use std::sync::atomic::{AtomicU64, Ordering}; - -use pyo3::prelude::*; - -/// Number of times the bridge has released the GIL since process start. -static GIL_RELEASES: AtomicU64 = AtomicU64::new(0); - -/// Release the GIL around `f`, recording the release. -/// -/// `f` must not touch any Python state — that is what makes releasing the GIL -/// safe. Returning the value back to Python re-acquires the GIL at the call -/// site, after `f` has finished. -pub fn release_gil(py: Python<'_>, f: F) -> T -where - F: FnOnce() -> T + Send, - T: Send, -{ - GIL_RELEASES.fetch_add(1, Ordering::Relaxed); - py.detach(f) -} - -/// Total GIL releases performed by the bridge so far. -pub fn release_count() -> u64 { - GIL_RELEASES.load(Ordering::Relaxed) -} diff --git a/litellm-rust/crates/python-bridge/src/lib.rs b/litellm-rust/crates/python-bridge/src/lib.rs index c6f81cf6916..68aa9436b15 100644 --- a/litellm-rust/crates/python-bridge/src/lib.rs +++ b/litellm-rust/crates/python-bridge/src/lib.rs @@ -10,16 +10,15 @@ use litellm_core::chat_completions::types::{ChatCompletionsRequest, ChatCompleti use litellm_core::chat_completions::{ chat_completions as run_chat_completions, chat_completions_decline_reason, }; -use litellm_core::error::CoreError; +use litellm_core::error::Error; use litellm_core::messages::messages as run_messages; use litellm_core::messages::types::{AnthropicMessagesResponse, MessagesRequest}; +use litellm_python_interop::{from_py, release_count, release_gil, to_py}; use pyo3::exceptions::{PyRuntimeError, PyValueError}; use pyo3::prelude::*; use pyo3::types::{PyAny, PyDict}; use serde_json::{Map, Value}; -mod gil; - pyo3::create_exception!( _native, RustBridgeDeclined, @@ -41,44 +40,27 @@ type MarshaledOcrInputs = ( Option, ); -fn py_to_json(py: Python<'_>, value: &Bound<'_, PyAny>) -> PyResult { - let json = py.import("json")?; - let encoded: String = json.call_method1("dumps", (value,))?.extract()?; - serde_json::from_str(&encoded).map_err(|err| PyValueError::new_err(err.to_string())) -} - -fn json_to_py(py: Python<'_>, value: Value) -> PyResult> { - let json = py.import("json")?; - let encoded = - serde_json::to_string(&value).map_err(|err| PyValueError::new_err(err.to_string()))?; - Ok(json.call_method1("loads", (encoded,))?.unbind()) -} - fn messages_response_to_py( py: Python<'_>, response: AnthropicMessagesResponse, ) -> PyResult> { - let value = - serde_json::to_value(response).map_err(|err| PyValueError::new_err(err.to_string()))?; - json_to_py(py, value) + to_py(py, &response) } fn chat_completions_response_to_py( py: Python<'_>, response: ChatCompletionsResponse, ) -> PyResult> { - let value = - serde_json::to_value(response).map_err(|err| PyValueError::new_err(err.to_string()))?; - json_to_py(py, value) + to_py(py, &response) } -fn core_error_to_pyerr(err: CoreError) -> PyErr { +fn core_error_to_pyerr(err: Error) -> PyErr { match err { - CoreError::Auth(message) => PyValueError::new_err(message), - CoreError::InvalidProvider(_) - | CoreError::InvalidRequest(_) - | CoreError::InvalidType { .. } - | CoreError::MissingField(_) => PyValueError::new_err(err.to_string()), + Error::Auth(message) => PyValueError::new_err(message), + Error::InvalidProvider(_) + | Error::InvalidRequest(_) + | Error::InvalidType { .. } + | Error::MissingField(_) => PyValueError::new_err(err.to_string()), other => PyRuntimeError::new_err(other.to_string()), } } @@ -89,22 +71,22 @@ fn core_error_to_pyerr(err: CoreError) -> PyErr { /// Everything raised before the request goes out is safe for the host to retry /// on its own path; anything after it is not, because the provider has already /// done the work and billed for it. -fn chat_completions_error_to_pyerr(err: CoreError) -> PyErr { +fn chat_completions_error_to_pyerr(err: Error) -> PyErr { match err { - CoreError::Unsupported(_) - | CoreError::Auth(_) - | CoreError::InvalidProvider(_) - | CoreError::InvalidRequest(_) - | CoreError::InvalidType { .. } - | CoreError::MissingField(_) - | CoreError::Routing(_) + Error::Unsupported(_) + | Error::Auth(_) + | Error::InvalidProvider(_) + | Error::InvalidRequest(_) + | Error::InvalidType { .. } + | Error::MissingField(_) + | Error::Routing(_) // Nothing reached the provider, so serving it on Python cannot double // bill and is the only way the caller gets an answer at all. - | CoreError::Connect(_) => RustBridgeDeclined::new_err(err.to_string()), - CoreError::Http { status, body } => { + | Error::Connect(_) => RustBridgeDeclined::new_err(err.to_string()), + Error::Http { status, body } => { RustUpstreamError::new_err((status, format!("{status}: {body}"))) } - CoreError::Network(message) | CoreError::InvalidResponse(message) => { + Error::Network(message) | Error::InvalidResponse(message) => { RustUpstreamError::new_err((0u16, message)) } } @@ -116,7 +98,7 @@ fn optional_object_to_map( value: Option>, ) -> PyResult> { match value { - Some(value) => match py_to_json(py, value.bind(py))? { + Some(value) => match from_py(value.bind(py))? { Value::Object(map) => Ok(map), _ => Err(PyValueError::new_err(format!("{name} must be a dict"))), }, @@ -139,7 +121,7 @@ fn marshal_headers( headers: Option>, ) -> PyResult> { let value = match headers { - Some(headers) => py_to_json(py, headers.bind(py))?, + Some(headers) => from_py(headers.bind(py))?, None => Value::Object(Map::new()), }; let Value::Object(headers) = value else { @@ -211,7 +193,7 @@ fn marshal_inputs( optional_params: Option>, timeout_seconds: Option, ) -> PyResult { - let document = py_to_json(py, document.bind(py))?; + let document = from_py(document.bind(py))?; let extra_headers = match extra_headers { Some(headers) => Some(optional_object_to_map(py, "extra_headers", Some(headers))?), None => None, @@ -244,7 +226,7 @@ fn ocr( timeout_seconds, )?; - let result = gil::release_gil(py, || { + let result = release_gil(py, || { pyo3_async_runtimes::tokio::get_runtime().block_on(run_ocr(OcrRequest { model: &model, document, @@ -262,7 +244,7 @@ fn ocr( }); match result { - Ok(value) => json_to_py(py, value), + Ok(value) => to_py(py, &value), Err(err) => Err(core_error_to_pyerr(err)), } } @@ -307,7 +289,7 @@ fn aocr( .await .map_err(core_error_to_pyerr)?; - Python::attach(|py| json_to_py(py, value)) + Python::attach(|py| to_py(py, &value)) }) } @@ -325,14 +307,14 @@ fn transcription( optional_params: Option>, timeout_seconds: Option, ) -> PyResult> { - let audio = py_to_json(py, audio.bind(py))?; + let audio = from_py(audio.bind(py))?; let extra_headers = match extra_headers { Some(headers) => Some(optional_object_to_map(py, "extra_headers", Some(headers))?), None => None, }; let optional_params = optional_object_to_map(py, "optional_params", optional_params)?; let timeout = optional_timeout(timeout_seconds); - let result = gil::release_gil(py, || { + let result = release_gil(py, || { pyo3_async_runtimes::tokio::get_runtime().block_on(run_audio_transcription( AudioTranscriptionRequest { model: &model, @@ -351,7 +333,7 @@ fn transcription( )) }); match result { - Ok(value) => json_to_py(py, value), + Ok(value) => to_py(py, &value), Err(err) => Err(core_error_to_pyerr(err)), } } @@ -370,7 +352,7 @@ fn atranscription( optional_params: Option>, timeout_seconds: Option, ) -> PyResult> { - let audio = py_to_json(py, audio.bind(py))?; + let audio = from_py(audio.bind(py))?; let extra_headers = match extra_headers { Some(headers) => Some(optional_object_to_map(py, "extra_headers", Some(headers))?), None => None, @@ -394,7 +376,7 @@ fn atranscription( }) .await .map_err(core_error_to_pyerr)?; - Python::attach(|py| json_to_py(py, value)) + Python::attach(|py| to_py(py, &value)) }) } @@ -406,7 +388,7 @@ fn marshal_messages_inputs( extra_headers: Option>, timeout_seconds: Option, ) -> PyResult { - let body = py_to_json(py, body.bind(py))?; + let body: Value = from_py(body.bind(py))?; if !body.is_object() { return Err(PyValueError::new_err("body must be a dict")); } @@ -433,7 +415,7 @@ fn messages( let (body, extra_headers, timeout) = marshal_messages_inputs(py, body, extra_headers, timeout_seconds)?; - let result = gil::release_gil(py, || { + let result = release_gil(py, || { pyo3_async_runtimes::tokio::get_runtime().block_on(run_messages(MessagesRequest { model: &model, body, @@ -498,7 +480,7 @@ fn marshal_chat_completions_inputs( extra_headers: Option>, timeout_seconds: Option, ) -> PyResult { - let messages = py_to_json(py, messages.bind(py))?; + let messages: Value = from_py(messages.bind(py))?; if !messages.is_array() { return Err(PyValueError::new_err("messages must be a list")); } @@ -527,7 +509,7 @@ fn chat_completions_decline( optional_params: Option>, custom_llm_provider: Option, ) -> PyResult> { - let messages = py_to_json(py, messages.bind(py))?; + let messages = from_py(messages.bind(py))?; let optional_params = optional_object_to_map(py, "optional_params", optional_params)?; Ok(chat_completions_decline_reason( &model, @@ -560,7 +542,7 @@ fn chat_completions( timeout_seconds, )?; - let result = gil::release_gil(py, || { + let result = release_gil(py, || { pyo3_async_runtimes::tokio::get_runtime().block_on(run_chat_completions( ChatCompletionsRequest { model: &model, @@ -624,10 +606,16 @@ fn achat_completions( #[pyfunction] fn gil_stats(py: Python<'_>) -> PyResult> { let stats = PyDict::new(py); - stats.set_item("releases", gil::release_count())?; + stats.set_item("releases", release_count())?; Ok(stats.into_any().unbind()) } +#[cfg(feature = "panic-test")] +#[pyfunction] +fn _panic_for_test() { + panic!("intentional PyO3 panic smoke test"); +} + #[pymodule] fn _native(module: &Bound<'_, PyModule>) -> PyResult<()> { let py = module.py(); @@ -644,5 +632,7 @@ fn _native(module: &Bound<'_, PyModule>) -> PyResult<()> { module.add_function(wrap_pyfunction!(achat_completions, module)?)?; module.add_class::()?; module.add_function(wrap_pyfunction!(gil_stats, module)?)?; + #[cfg(feature = "panic-test")] + module.add_function(wrap_pyfunction!(_panic_for_test, module)?)?; Ok(()) } diff --git a/litellm-rust/crates/python-bridge/tests/marshal_boundary.rs b/litellm-rust/crates/python-bridge/tests/marshal_boundary.rs new file mode 100644 index 00000000000..d397d20b9fd --- /dev/null +++ b/litellm-rust/crates/python-bridge/tests/marshal_boundary.rs @@ -0,0 +1,49 @@ +use std::fs; +use std::path::{Path, PathBuf}; + +const DISALLOWED_OUTSIDE_INTEROP: &[&str] = &[ + "py.import(\"json\")", + "pythonize::", + "serde_json::to_string", + "serde_json::from_str", +]; + +fn source_root() -> PathBuf { + Path::new(env!("CARGO_MANIFEST_DIR")).join("src") +} + +fn rust_sources(directory: &Path) -> Vec { + fs::read_dir(directory) + .expect("bridge source directory should be readable") + .map(|entry| { + entry + .expect("bridge source entry should be readable") + .path() + }) + .flat_map(|path| { + if path.is_dir() { + rust_sources(&path) + } else if path.extension().is_some_and(|extension| extension == "rs") { + vec![path] + } else { + Vec::new() + } + }) + .collect() +} + +#[test] +fn serialization_uses_the_interop_boundary() { + let root = source_root(); + + for path in rust_sources(&root) { + let source = fs::read_to_string(&path).expect("bridge source should be readable"); + for disallowed in DISALLOWED_OUTSIDE_INTEROP { + assert!( + !source.contains(disallowed), + "{} bypasses litellm-python-interop with `{disallowed}`", + path.display() + ); + } + } +} diff --git a/litellm-rust/crates/python-interop/AGENTS.md b/litellm-rust/crates/python-interop/AGENTS.md new file mode 100644 index 00000000000..d1d61e5dfa0 --- /dev/null +++ b/litellm-rust/crates/python-interop/AGENTS.md @@ -0,0 +1 @@ +litellm-python-interop is the domain-neutral PyO3 foundation. Keep generic Python/Serde conversion and interpreter primitives here. Do not add LiteLLM domain crates, route types, API registration, or cdylib build features. diff --git a/litellm-rust/crates/python-interop/Cargo.toml b/litellm-rust/crates/python-interop/Cargo.toml new file mode 100644 index 00000000000..9da6af6e2e2 --- /dev/null +++ b/litellm-rust/crates/python-interop/Cargo.toml @@ -0,0 +1,15 @@ +[package] +name = "litellm-python-interop" +version = "0.1.0" +edition.workspace = true +license.workspace = true +repository.workspace = true + +[dependencies] +pyo3.workspace = true +pythonize.workspace = true +serde.workspace = true + +[dev-dependencies] +rstest.workspace = true +serde_json.workspace = true diff --git a/litellm-rust/crates/python-interop/src/gil.rs b/litellm-rust/crates/python-interop/src/gil.rs new file mode 100644 index 00000000000..04b966a6002 --- /dev/null +++ b/litellm-rust/crates/python-interop/src/gil.rs @@ -0,0 +1,21 @@ +use std::sync::atomic::{AtomicU64, Ordering}; + +use pyo3::prelude::*; + +static GIL_RELEASES: AtomicU64 = AtomicU64::new(0); + +/// Runs work detached from the interpreter and records the release. +/// +/// `f` must not access Python state while the interpreter is detached. +pub fn release_gil(py: Python<'_>, f: F) -> T +where + F: FnOnce() -> T + Send, + T: Send, +{ + GIL_RELEASES.fetch_add(1, Ordering::Relaxed); + py.detach(f) +} + +pub fn release_count() -> u64 { + GIL_RELEASES.load(Ordering::Relaxed) +} diff --git a/litellm-rust/crates/python-interop/src/lib.rs b/litellm-rust/crates/python-interop/src/lib.rs new file mode 100644 index 00000000000..df2bd260fdb --- /dev/null +++ b/litellm-rust/crates/python-interop/src/lib.rs @@ -0,0 +1,5 @@ +mod gil; +mod marshal; + +pub use gil::{release_count, release_gil}; +pub use marshal::{from_py, to_py}; diff --git a/litellm-rust/crates/python-interop/src/marshal.rs b/litellm-rust/crates/python-interop/src/marshal.rs new file mode 100644 index 00000000000..c3d0638427c --- /dev/null +++ b/litellm-rust/crates/python-interop/src/marshal.rs @@ -0,0 +1,20 @@ +use pyo3::exceptions::PyValueError; +use pyo3::prelude::*; +use serde::Serialize; +use serde::de::DeserializeOwned; + +pub fn from_py(value: &Bound<'_, PyAny>) -> PyResult +where + T: DeserializeOwned, +{ + pythonize::depythonize(value).map_err(|error| PyValueError::new_err(error.to_string())) +} + +pub fn to_py(py: Python<'_>, value: &T) -> PyResult> +where + T: Serialize + ?Sized, +{ + pythonize::pythonize(py, value) + .map(Bound::unbind) + .map_err(|error| PyValueError::new_err(error.to_string())) +} diff --git a/litellm-rust/crates/python-interop/tests/interop.rs b/litellm-rust/crates/python-interop/tests/interop.rs new file mode 100644 index 00000000000..9c456dcb938 --- /dev/null +++ b/litellm-rust/crates/python-interop/tests/interop.rs @@ -0,0 +1,44 @@ +use pyo3::Python; +use rstest::{fixture, rstest}; +use serde_json::{Value, json}; + +use litellm_python_interop::{from_py, release_count, release_gil, to_py}; + +struct InitializedPython; + +impl InitializedPython { + fn attach(&self, f: F) -> R + where + F: for<'py> FnOnce(Python<'py>) -> R, + { + Python::attach(f) + } +} + +#[fixture] +#[once] +fn initialized_python() -> InitializedPython { + Python::initialize(); + InitializedPython +} + +#[rstest] +fn serde_values_round_trip_through_python(#[from(initialized_python)] python: &InitializedPython) { + python.attach(|py| { + let expected = json!({"model": "test", "items": [1, true, null]}); + let python_value = to_py(py, &expected).expect("value should convert to Python"); + let actual: Value = + from_py(python_value.bind(py)).expect("Python value should convert to serde"); + + assert_eq!(actual, expected); + }); +} + +#[rstest] +fn release_gil_runs_work_and_records_it(#[from(initialized_python)] python: &InitializedPython) { + let before = release_count(); + let result = python.attach(|py| release_gil(py, || 42)); + + assert_eq!(result, 42); + assert_eq!(release_count(), before + 1); +} diff --git a/litellm/__init__.py b/litellm/__init__.py index e95b553c5d4..4eeececdb7e 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -7,6 +7,9 @@ warnings.filterwarnings("ignore", message=".*conflict with protected namespace.* # Suppress Pydantic 2.11+ deprecation warning about accessing model_fields on instances # This warning can accumulate during streaming and cause memory leaks warnings.filterwarnings("ignore", message=".*Accessing the.*attribute on the instance is deprecated.*") +# ReadOnly on TypedDict fields is repo-wide static discipline (LIT012); pydantic warns it +# cannot enforce it at runtime, which floods proxy boot once such a type is schema-walked +warnings.filterwarnings("ignore", message=".*`ReadOnly` qualifier.*") ### INIT VARIABLES ######################### import threading import os @@ -199,6 +202,7 @@ standard_logging_payload_excluded_fields: Optional[List[str]] = ( None # Fields to exclude from StandardLoggingPayload before callbacks receive it ) log_raw_request_response: bool = False +log_client_error_tracebacks: bool = False request_correlation_in_logs: bool = False redact_messages_in_exceptions: Optional[bool] = False redact_user_api_key_info: Optional[bool] = False @@ -273,7 +277,6 @@ databricks_key: Optional[str] = None openai_like_key: Optional[str] = None azure_key: Optional[str] = None anthropic_key: Optional[str] = None -autorouter_savings_baseline_model: Optional[str] = None replicate_key: Optional[str] = None bytez_key: Optional[str] = None gdc_key: Optional[str] = None @@ -444,6 +447,7 @@ max_ui_session_budget: Optional[float] = ( 1.0 # USD budget for each dashboard login session (playground, test connection) ) internal_user_budget_duration: Optional[str] = None +budget_rollover: bool = False # carry spend beyond max_budget into the next window instead of zeroing it tag_budget_config: Optional[Dict[str, "BudgetConfig"]] = None max_end_user_budget: Optional[float] = None max_end_user_budget_id: Optional[str] = None @@ -463,6 +467,11 @@ prometheus_metrics_config: Optional[List] = None prometheus_exclude_metrics: Optional[List[str]] = None prometheus_exclude_labels: Optional[List[str]] = None prometheus_emit_stream_label: bool = False +prometheus_deployment_and_latency_caller_identity: Literal[ + "api_key_alias", + "user_email", + "both", +] = "api_key_alias" # Opt-in: emit `rate_limit_category` and `rate_limit_type` labels on # `litellm_proxy_failed_requests_metric`. Off by default to preserve the # pre-unification label set so existing dashboards / recording rules keyed on @@ -480,6 +489,7 @@ public_mcp_servers: Optional[List[str]] = None public_mcp_hub_strict_whitelist: bool = True public_model_groups: Optional[List[str]] = None public_agent_groups: Optional[List[str]] = None +agent_search_embedding_model: Optional[str] = None # Supports both old format (Dict[str, str]) and new format (Dict[str, Dict[str, Any]]) # New format: { "displayName": { "url": "...", "index": 0 } } # Old format: { "displayName": "url" } (for backward compatibility) @@ -649,6 +659,8 @@ aiml_models: Set = set() deepgram_models: Set = set() elevenlabs_models: Set = set() dashscope_models: Set = set() +qwencloud_models: Set = set() +qwen_ai_platform_models: Set = set() moonshot_models: Set = set() publicai_models: Set = set() darkbloom_models: Set = set() @@ -899,6 +911,10 @@ def _populate_provider_model_sets(model_cost_map: Dict) -> None: heroku_models.add(key) elif value.get("litellm_provider") == "dashscope": dashscope_models.add(key) + elif value.get("litellm_provider") == "qwencloud": + qwencloud_models.add(key) + elif value.get("litellm_provider") == "qwen_ai_platform": + qwen_ai_platform_models.add(key) elif value.get("litellm_provider") == "modelscope": modelscope_models.add(key) elif value.get("litellm_provider") == "moonshot": @@ -1062,6 +1078,8 @@ model_list = list( | deepgram_models | elevenlabs_models | dashscope_models + | qwencloud_models + | qwen_ai_platform_models | moonshot_models | publicai_models | darkbloom_models @@ -1168,6 +1186,8 @@ def _build_models_by_provider() -> dict: "elevenlabs": elevenlabs_models, "heroku": heroku_models, "dashscope": dashscope_models, + "qwencloud": qwencloud_models, + "qwen_ai_platform": qwen_ai_platform_models, "modelscope": modelscope_models, "moonshot": moonshot_models, "publicai": publicai_models, @@ -1628,6 +1648,9 @@ if TYPE_CHECKING: AmazonMantleMessagesConfig as AmazonMantleMessagesConfig, ) from .llms.together_ai.chat import TogetherAIConfig as TogetherAIConfig + from .llms.together_ai.chat.transformation import ( + TogetherAIChatConfig as TogetherAIChatConfig, + ) from .llms.nlp_cloud.chat.handler import NLPCloudConfig as NLPCloudConfig from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig as VertexGeminiConfig, @@ -1801,6 +1824,9 @@ if TYPE_CHECKING: from .llms.gemini.interactions.transformation import ( GoogleAIStudioInteractionsConfig as GoogleAIStudioInteractionsConfig, ) + from .llms.vertex_ai.interactions.transformation import ( + VertexAIInteractionsConfig as VertexAIInteractionsConfig, + ) from .llms.openai.chat.o_series_transformation import ( OpenAIOSeriesConfig as OpenAIOSeriesConfig, OpenAIOSeriesConfig as OpenAIO1Config, @@ -1998,6 +2024,24 @@ if TYPE_CHECKING: from .llms.dashscope.rerank.transformation import ( DashScopeRerankConfig as DashScopeRerankConfig, ) + from .llms.dashscope.qwencloud import ( + QwenCloudChatConfig as QwenCloudChatConfig, + ) + from .llms.dashscope.qwencloud import ( + QwenCloudEmbeddingConfig as QwenCloudEmbeddingConfig, + ) + from .llms.dashscope.qwencloud import ( + QwenCloudRerankConfig as QwenCloudRerankConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformChatConfig as QwenAIPlatformChatConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformEmbeddingConfig as QwenAIPlatformEmbeddingConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformRerankConfig as QwenAIPlatformRerankConfig, + ) from .llms.modelscope.chat.transformation import ( ModelScopeChatConfig as ModelScopeChatConfig, ) diff --git a/litellm/_lazy_imports.py b/litellm/_lazy_imports.py index 933464d3f23..553aeb6680d 100644 --- a/litellm/_lazy_imports.py +++ b/litellm/_lazy_imports.py @@ -17,8 +17,11 @@ until they're actually needed. import importlib import sys -from collections.abc import Callable -from typing import Any, Final, cast +from collections.abc import Callable, Mapping +from types import ModuleType +from typing import TYPE_CHECKING, Any, Final, cast + +from typing_extensions import ReadOnly, TypedDict # Import all the data structures that define what can be lazy-loaded # These are just lists of names and maps of where to find them @@ -53,8 +56,12 @@ from ._lazy_imports_registry import ( UTILS_NAMES, ) +if TYPE_CHECKING: + import httpx + from tiktoken import Encoding -def get_litellm_globals() -> dict: + +def get_litellm_globals() -> dict[str, object]: """ Get the globals dictionary of the litellm module. @@ -64,7 +71,7 @@ def get_litellm_globals() -> dict: return sys.modules["litellm"].__dict__ -def _get_utils_globals() -> dict: +def _get_utils_globals() -> dict[str, object]: """ Get the globals dictionary of the utils module. @@ -74,14 +81,19 @@ def _get_utils_globals() -> dict: return sys.modules["litellm.utils"].__dict__ +def _get_module_level_client_timeout(litellm_globals: Mapping[str, Any]) -> "float | httpx.Timeout | None": + """Read the configured `litellm.request_timeout` used for the module level http clients.""" + return litellm_globals.get("request_timeout") + + # These are special lazy loaders for things that are used internally # They're separate from the main lazy import system because they have specific use cases # Lazy loader for default encoding - avoids importing heavy tiktoken library at startup -_default_encoding: Any | None = None +_default_encoding: "Encoding | None" = None -def _get_default_encoding() -> Any: +def _get_default_encoding() -> "Encoding": """ Lazily load and cache the default OpenAI encoding. @@ -100,10 +112,10 @@ def _get_default_encoding() -> Any: # Lazy loader for get_modified_max_tokens to avoid importing token_counter at module import time -_get_modified_max_tokens_func: Any | None = None +_get_modified_max_tokens_func: "Callable[..., int | None] | None" = None -def _get_modified_max_tokens() -> Any: +def _get_modified_max_tokens() -> "Callable[..., int | None]": """ Lazily load and cache the get_modified_max_tokens function. @@ -124,10 +136,10 @@ def _get_modified_max_tokens() -> Any: # Lazy loader for token_counter to avoid importing token_counter module at module import time -_token_counter_new_func: Any | None = None +_token_counter_new_func: "Callable[..., int] | None" = None -def _get_token_counter_new() -> Any: +def _get_token_counter_new() -> "Callable[..., int]": """ Lazily load and cache the token_counter function (aliased as token_counter_new). @@ -154,10 +166,10 @@ def _get_token_counter_new() -> Any: # This registry maps attribute names (like "ModelResponse") to handler functions # It's built once the first time someone accesses a lazy-loaded attribute # Example: {"ModelResponse": _lazy_import_utils, "Cache": _lazy_import_caching, ...} -_LAZY_IMPORT_REGISTRY: dict[str, Callable[[str], Any]] | None = None +_LAZY_IMPORT_REGISTRY: dict[str, Callable[[str], object]] | None = None -def _get_lazy_import_registry() -> dict[str, Callable[[str], Any]]: +def _get_lazy_import_registry() -> dict[str, Callable[[str], object]]: """ Build the registry that maps attribute names to their handler functions. @@ -206,7 +218,18 @@ def _get_lazy_import_registry() -> dict[str, Callable[[str], Any]]: return _LAZY_IMPORT_REGISTRY -def _generic_lazy_import(name: str, import_map: dict[str, tuple[str, str]], category: str) -> Any: +class _AttributeView(TypedDict): + """Holds one module attribute so the lazily fetched value is read back as ``object``.""" + + value: ReadOnly[object] + + +def _module_attribute(module: ModuleType, attr_name: str) -> object: + attribute: Final[_AttributeView] = {"value": getattr(module, attr_name)} + return attribute["value"] + + +def _generic_lazy_import(name: str, import_map: dict[str, tuple[str, str]], category: str) -> object: """ Generic function that handles lazy importing for most attributes. @@ -255,7 +278,7 @@ def _generic_lazy_import(name: str, import_map: dict[str, tuple[str, str]], cate # Step 6: Get the actual attribute from the module # Example: getattr(utils_module, "ModelResponse") returns the ModelResponse class - value: Final = getattr(module, attr_name) + value: Final = _module_attribute(module, attr_name) # Step 7: Cache it so we don't have to import again next time _globals[name] = value @@ -272,62 +295,62 @@ def _generic_lazy_import(name: str, import_map: dict[str, tuple[str, str]], cate # The registry (above) maps attribute names to these handler functions. -def _lazy_import_utils(name: str) -> Any: +def _lazy_import_utils(name: str) -> object: """Handler for utils module attributes (ModelResponse, token_counter, etc.)""" return _generic_lazy_import(name, _UTILS_IMPORT_MAP, "Utils") -def _lazy_import_cost_calculator(name: str) -> Any: +def _lazy_import_cost_calculator(name: str) -> object: """Handler for cost calculator functions (completion_cost, cost_per_token, etc.)""" return _generic_lazy_import(name, _COST_CALCULATOR_IMPORT_MAP, "Cost calculator") -def _lazy_import_token_counter(name: str) -> Any: +def _lazy_import_token_counter(name: str) -> object: """Handler for token counter utilities""" return _generic_lazy_import(name, _TOKEN_COUNTER_IMPORT_MAP, "Token counter") -def _lazy_import_bedrock_types(name: str) -> Any: +def _lazy_import_bedrock_types(name: str) -> object: """Handler for Bedrock type aliases""" return _generic_lazy_import(name, _BEDROCK_TYPES_IMPORT_MAP, "Bedrock types") -def _lazy_import_types_utils(name: str) -> Any: +def _lazy_import_types_utils(name: str) -> object: """Handler for types from litellm.types.utils (BudgetConfig, ImageObject, etc.)""" return _generic_lazy_import(name, _TYPES_UTILS_IMPORT_MAP, "Types utils") -def _lazy_import_caching(name: str) -> Any: +def _lazy_import_caching(name: str) -> object: """Handler for caching classes (Cache, DualCache, RedisCache, etc.)""" return _generic_lazy_import(name, _CACHING_IMPORT_MAP, "Caching") -def _lazy_import_dotprompt(name: str) -> Any: +def _lazy_import_dotprompt(name: str) -> object: """Handler for dotprompt integration globals""" return _generic_lazy_import(name, _DOTPROMPT_IMPORT_MAP, "Dotprompt") -def _lazy_import_types(name: str) -> Any: +def _lazy_import_types(name: str) -> object: """Handler for type classes (GuardrailItem, etc.)""" return _generic_lazy_import(name, _TYPES_IMPORT_MAP, "Types") -def _lazy_import_llm_configs(name: str) -> Any: +def _lazy_import_llm_configs(name: str) -> object: """Handler for LLM config classes (AnthropicConfig, OpenAILikeChatConfig, etc.)""" return _generic_lazy_import(name, _LLM_CONFIGS_IMPORT_MAP, "LLM config") -def _lazy_import_litellm_logging(name: str) -> Any: +def _lazy_import_litellm_logging(name: str) -> object: """Handler for litellm_logging module (Logging, modify_integration)""" return _generic_lazy_import(name, _LITELLM_LOGGING_IMPORT_MAP, "Litellm logging") -def _lazy_import_llm_provider_logic(name: str) -> Any: +def _lazy_import_llm_provider_logic(name: str) -> object: """Handler for LLM provider logic functions (get_llm_provider, etc.)""" return _generic_lazy_import(name, _LLM_PROVIDER_LOGIC_IMPORT_MAP, "LLM provider logic") -def _lazy_import_utils_module(name: str) -> Any: +def _lazy_import_utils_module(name: str) -> object: """ Handler for utils module lazy imports. @@ -355,7 +378,7 @@ def _lazy_import_utils_module(name: str) -> Any: module = importlib.import_module(module_path) # Get the actual attribute from the module - value: Final = getattr(module, attr_name) + value: Final = _module_attribute(module, attr_name) # Cache it so we don't have to import again next time _globals[name] = value @@ -370,7 +393,7 @@ def _lazy_import_utils_module(name: str) -> Any: # These handlers have custom logic that doesn't fit the generic pattern -def _lazy_import_llm_client_cache(name: str) -> Any: +def _lazy_import_llm_client_cache(name: str) -> object: """ Handler for LLM client cache - has special logic for singleton instance. @@ -386,8 +409,7 @@ def _lazy_import_llm_client_cache(name: str) -> Any: return _globals[name] # Import the class - module: Final = importlib.import_module("litellm.caching.llm_caching_handler") - LLMClientCache: Final = getattr(module, "LLMClientCache") + from litellm.caching.llm_caching_handler import LLMClientCache # If they want the class itself, return it if name == "LLMClientCache": @@ -403,7 +425,7 @@ def _lazy_import_llm_client_cache(name: str) -> Any: raise AttributeError(f"LLM client cache lazy import: unknown attribute {name!r}") -def _lazy_import_http_handlers(name: str) -> Any: +def _lazy_import_http_handlers(name: str) -> object: """ Handler for HTTP clients - has special logic for creating client instances. @@ -419,8 +441,8 @@ def _lazy_import_http_handlers(name: str) -> Any: from litellm.llms.custom_httpx.http_handler import get_async_httpx_client # Get timeout from module config (if set) - timeout = _globals.get("request_timeout") - params: Final = {"timeout": timeout, "client_alias": "module level aclient"} + async_timeout: Final = _get_module_level_client_timeout(_globals) + params: Final = {"timeout": async_timeout, "client_alias": "module level aclient"} # Create the client instance provider_id: Final = cast(Any, "litellm_module_level_client") @@ -437,8 +459,8 @@ def _lazy_import_http_handlers(name: str) -> Any: # Create a sync HTTP client from litellm.llms.custom_httpx.http_handler import HTTPHandler - timeout = _globals.get("request_timeout") - sync_client: Final = HTTPHandler(timeout=timeout) + sync_timeout: Final = _get_module_level_client_timeout(_globals) + sync_client: Final = HTTPHandler(timeout=sync_timeout) # Cache it _globals["module_level_client"] = sync_client diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 89c72acc06d..e9199e1ec80 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -177,6 +177,7 @@ LLM_CONFIG_NAMES: Final = ( "AmazonAnthropicClaudeMessagesConfig", "AmazonMantleMessagesConfig", "TogetherAIConfig", + "TogetherAIChatConfig", "NLPCloudConfig", "VertexGeminiConfig", "GoogleAIStudioGeminiConfig", @@ -242,6 +243,7 @@ LLM_CONFIG_NAMES: Final = ( "OpenRouterResponsesAPIConfig", "BedrockMantleResponsesAPIConfig", "GoogleAIStudioInteractionsConfig", + "VertexAIInteractionsConfig", "OpenAIOSeriesConfig", "AnthropicSkillsConfig", "BaseSkillsAPIConfig", @@ -308,6 +310,8 @@ LLM_CONFIG_NAMES: Final = ( "GigaChatConfig", "GigaChatEmbeddingConfig", "DashScopeChatConfig", + "QwenCloudChatConfig", + "QwenAIPlatformChatConfig", "ModelScopeChatConfig", "MoonshotChatConfig", "DockerModelRunnerChatConfig", @@ -740,6 +744,10 @@ _LLM_CONFIGS_IMPORT_MAP: Final = { "AmazonMantleMessagesConfig", ), "TogetherAIConfig": (".llms.together_ai.chat", "TogetherAIConfig"), + "TogetherAIChatConfig": ( + ".llms.together_ai.chat.transformation", + "TogetherAIChatConfig", + ), "NLPCloudConfig": (".llms.nlp_cloud.chat.handler", "NLPCloudConfig"), "VertexGeminiConfig": ( ".llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini", @@ -977,6 +985,10 @@ _LLM_CONFIGS_IMPORT_MAP: Final = { ".llms.gemini.interactions.transformation", "GoogleAIStudioInteractionsConfig", ), + "VertexAIInteractionsConfig": ( + ".llms.vertex_ai.interactions.transformation", + "VertexAIInteractionsConfig", + ), "OpenAIOSeriesConfig": ( ".llms.openai.chat.o_series_transformation", "OpenAIOSeriesConfig", @@ -1162,6 +1174,14 @@ _LLM_CONFIGS_IMPORT_MAP: Final = { ".llms.dashscope.chat.transformation", "DashScopeChatConfig", ), + "QwenCloudChatConfig": ( + ".llms.dashscope.qwencloud", + "QwenCloudChatConfig", + ), + "QwenAIPlatformChatConfig": ( + ".llms.dashscope.qwen_ai_platform", + "QwenAIPlatformChatConfig", + ), "GDCGeminiConfig": ( ".llms.gdc.chat.transformation", "GDCGeminiConfig", diff --git a/litellm/_logging.py b/litellm/_logging.py index 36fd51206c2..9435562f890 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -5,7 +5,7 @@ import os import sys from datetime import datetime from logging import Formatter -from typing import Any, Final +from typing import Any, Final, TextIO import litellm from litellm.constants import ( @@ -234,11 +234,69 @@ class CorrelationContextFilter(logging.Filter): _correlation_filter: Final = CorrelationContextFilter() -json_logs = bool(os.getenv("JSON_LOGS", False)) +_LOG_FORMAT_PREFIX: Final = "%(asctime)s - %(name)s:%(levelname)s" +_LOG_FORMAT_SUFFIX: Final = ": %(filename)s:%(lineno)s - %(message)s" +_PLAIN_LOG_FORMAT: Final = _LOG_FORMAT_PREFIX + _LOG_FORMAT_SUFFIX +_COLOR_LOG_FORMAT: Final = f"\033[92m{_LOG_FORMAT_PREFIX}\033[0m{_LOG_FORMAT_SUFFIX}" + + +def _stream_is_tty(stream: TextIO | None) -> bool: + """True when the stream is an open interactive terminal; never raises. + + A stream can be None (pythonw/embedded interpreters), lack isatty entirely + (GUI log-redirect shims), or be closed; import must survive all three. + """ + try: + return stream is not None and stream.isatty() + except (AttributeError, ValueError): + return False + + +def _plain_log_format(stdout: TextIO | None, stderr: TextIO | None) -> str: + """The plain-text log format, colorized only when both streams are an interactive terminal. + + Honors the NO_COLOR convention from no-color.org: color is disabled when + NO_COLOR is present with a non-empty value. + """ + if os.environ.get("NO_COLOR"): + return _PLAIN_LOG_FORMAT + return _COLOR_LOG_FORMAT if _stream_is_tty(stdout) and _stream_is_tty(stderr) else _PLAIN_LOG_FORMAT + + +class LevelRoutingStreamHandler(logging.StreamHandler): + """Writes records below WARNING and invalid-key warnings to stdout, others to stderr. + + Collectors that derive severity from the stream report every stderr line as an error. + Invalid-key warnings route to stdout so LITELLM_LOG=ERROR can suppress them. + """ + + def emit(self, record: logging.LogRecord) -> None: + is_stdout_record: Final = record.levelno < logging.WARNING or ( + record.levelno == logging.WARNING and record.name == verbose_proxy_stdout_logger.name + ) + preferred: Final = sys.stdout if is_stdout_record else sys.stderr + if preferred is None or getattr(preferred, "closed", False): + self.stream = sys.stderr # rebind-ok: fall back to the pre-fix stream rather than raising per record + else: + self.stream = preferred # rebind-ok: StreamHandler.emit writes self.stream under the handler lock + super().emit(record) + + +def _parse_json_logs_env(value: str | None) -> bool: + """Strict opt-in parse for the JSON_LOGS env var: only "true" (any case) enables JSON logs. + + Matches the reader in litellm-proxy-extras/_logging.py. The previous + bool(os.getenv(...)) treated any non-empty value, including "false" and "0", + as enabled. + """ + return (value or "").lower() == "true" + + +json_logs: Final = _parse_json_logs_env(os.getenv("JSON_LOGS")) # Create a handler for the logger (you may need to adapt this based on your needs) log_level: Final = os.getenv("LITELLM_LOG", "DEBUG") numeric_level: Final[str] = getattr(logging, log_level.upper()) -handler: Final = logging.StreamHandler() +handler: Final = LevelRoutingStreamHandler() handler.setLevel(numeric_level) handler.addFilter(_secret_filter) handler.addFilter(_correlation_filter) @@ -447,13 +505,16 @@ if json_logs: _setup_json_exception_handlers(JsonFormatter()) else: formatter: Final = CorrelationPlainFormatter( - "\033[92m%(asctime)s - %(name)s:%(levelname)s\033[0m: %(filename)s:%(lineno)s - %(message)s", + _plain_log_format(sys.stdout, sys.stderr), datefmt="%H:%M:%S", ) handler.setFormatter(formatter) verbose_proxy_logger = logging.getLogger("LiteLLM Proxy") +# Malformed virtual key rejections log through this child; LevelRoutingStreamHandler +# writes its WARNING records to stdout. It has no handler or level of its own. +verbose_proxy_stdout_logger: Final = verbose_proxy_logger.getChild("stdout") verbose_router_logger = logging.getLogger("LiteLLM Router") verbose_logger = logging.getLogger("LiteLLM") @@ -466,6 +527,7 @@ verbose_logger.addHandler(handler) # handlers (JSON mode, uvicorn log config, a host app's root handler). verbose_router_logger.addFilter(_stdout_truncation_filter) verbose_proxy_logger.addFilter(_stdout_truncation_filter) +verbose_proxy_stdout_logger.addFilter(_stdout_truncation_filter) verbose_logger.addFilter(_stdout_truncation_filter) @@ -628,7 +690,8 @@ def _turn_on_json(): - Adds a JSON formatter to all loggers """ - handler: Final = logging.StreamHandler() + handler: Final = LevelRoutingStreamHandler() + handler.setLevel(numeric_level) handler.setFormatter(JsonFormatter()) _initialize_loggers_with_handler(handler) # Set up exception handlers @@ -646,12 +709,14 @@ def _disable_debugging(): verbose_logger.disabled = True verbose_router_logger.disabled = True verbose_proxy_logger.disabled = True + verbose_proxy_stdout_logger.disabled = True def _enable_debugging(): verbose_logger.disabled = False verbose_router_logger.disabled = False verbose_proxy_logger.disabled = False + verbose_proxy_stdout_logger.disabled = False def print_verbose(print_statement): diff --git a/litellm/_redis.py b/litellm/_redis.py index 58f37cf569d..3e68d50cf16 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -12,8 +12,10 @@ import json # s/o [@Frank Colson](https://www.linkedin.com/in/frank-colson-422b9b183/) for this redis implementation import os -from collections.abc import Callable +from collections.abc import Callable, Mapping +from types import MappingProxyType from typing import Final +from urllib.parse import urlsplit, urlunsplit import redis import redis.asyncio as async_redis @@ -37,9 +39,25 @@ from ._logging import verbose_logger AZURE_REDIS_SCOPE: Final = "https://redis.azure.com/.default" -def _get_redis_kwargs(): - arg_spec: Final = inspect.getfullargspec(redis.Redis) +def _unwrapped_init_args(cls: type) -> frozenset[str]: + """Every parameter on a single class's own ``__init__``, decorator-unwrapped. + Unlike ``_init_arg_names`` below, this does not walk the MRO: ``redis.Redis`` + and ``redis.RedisCluster`` (sync and async) each declare every real + constructor parameter directly on their own ``__init__``, so MRO-walking is + unnecessary — and it actively breaks the several tests here that mock the + class with ``patch(..., autospec=True)``, since ``inspect.getmro`` needs a + real ``__mro__`` that an autospec'd stand-in for a class does not provide. + + Still unwraps first: redis-py >= 7.4 decorates these ``__init__``s with + ``@deprecated_args`` too, which the same class of bug as ``_init_arg_names`` + would otherwise silently empty this allowlist through (see its docstring). + """ + spec: Final = inspect.getfullargspec(inspect.unwrap(cls.__init__)) + return frozenset(spec.args + spec.kwonlyargs) + + +def _get_redis_kwargs(): # Only allow primitive arguments exclude_args: Final = { "self", @@ -50,6 +68,7 @@ def _get_redis_kwargs(): include_args: Final = { "url", "redis_connect_func", + "credential_provider", "gcp_service_account", "gcp_ssl_ca_certs", "azure_redis_ad_token", @@ -58,7 +77,7 @@ def _get_redis_kwargs(): "azure_client_secret", } - available_args: Final = {x for x in arg_spec.args if x not in exclude_args} | include_args + available_args: Final = {x for x in _unwrapped_init_args(redis.Redis) if x not in exclude_args} | include_args return available_args @@ -118,15 +137,23 @@ def _get_redis_url_kwargs(client: type | None = None) -> tuple[str, ...]: return tuple(x for x in _init_arg_names(connection_cls) if x not in exclude_args) + include_args -def _get_redis_cluster_kwargs(client=None): +def _get_redis_cluster_kwargs(client: type | None = None): + """Config kwargs the target cluster client's constructor actually accepts. + + Defaults to the sync ``redis.RedisCluster``, but the async cluster client + (``redis.asyncio.cluster.RedisCluster``) declares connection settings such as + ``decode_responses`` on its own constructor, where the sync class takes them + through ``**kwargs`` and so never names them in its signature. Introspecting + only the sync class regardless of which client is actually built silently + drops those for every async cluster caller. + """ if client is None: - client = redis.Redis.from_url - arg_spec: Final = inspect.getfullargspec(redis.RedisCluster) + client = redis.RedisCluster # Only allow primitive arguments exclude_args: Final = {"self", "connection_pool", "retry", "host", "port", "startup_nodes"} - available_args = {x for x in arg_spec.args if x not in exclude_args} + available_args = {x for x in _unwrapped_init_args(client) if x not in exclude_args} available_args |= { "password", "username", @@ -155,7 +182,81 @@ def _get_redis_cluster_kwargs(client=None): def _get_redis_env_kwarg_mapping(): PREFIX: Final = "REDIS_" - return {f"{PREFIX}{x.upper()}": x for x in _get_redis_kwargs()} + exclude_from_environment: Final = frozenset({"credential_provider"}) + return {f"{PREFIX}{x.upper()}": x for x in _get_redis_kwargs() if x not in exclude_from_environment} + + +def _str_to_bool(value: str) -> bool: + return value.lower() in ("true", "1", "yes") + + +def _coerce_redis_kwargs_types( + redis_kwargs: Mapping[str, object], + client: type | tuple[type, ...] = redis.Redis, +) -> dict[str, object]: # mutable-ok: a caller mutates the returned kwargs before constructing its client + """Coerces string values to the numeric/boolean type ``client``'s constructor + declares for that parameter. ``client`` may be a tuple of client classes; a + parameter's type is taken from the first signature that declares it, which + lets cluster callers coerce cluster-only kwargs such as + ``cluster_error_retry_attempts`` alongside the shared connection kwargs. + + Environment variables are always strings, and Helm ``--set`` stringifies values + too, so a config value like ``health_check_interval`` or ``socket_timeout`` + can arrive as ``"30"``/``"5.5"`` rather than a real number. redis-py's own + connection-health-check arithmetic (``loop.time() + self.health_check_interval``) + then raises ``TypeError`` on every Redis operation instead of connecting. + + ``max_connections``, ``socket_timeout``, and ``socket_connect_timeout`` use an + explicit target type rather than the parameter's own signature default: redis-py + 8.x changed the timeout defaults from ``None`` to int ``5``, so inferring the + type from the default would make a fractional ``"5.5"`` fail ``int()`` and get + silently dropped on 8.x while working on older versions. ``socket_keepalive`` + is explicit too: its signature default is ``None``, which carries no type to + infer from, and leaving it a string makes ``"false"`` truthy. + """ + signatures: Final = tuple(inspect.signature(c) for c in (client if isinstance(client, tuple) else (client,))) + explicit_param_types: Final = MappingProxyType( + { + "max_connections": int, + "socket_timeout": float, + "socket_connect_timeout": float, + "socket_keepalive": bool, + } + ) + result: Final = dict(redis_kwargs) # mutable-ok: per-key try/except coercion below needs to drop individual keys + for key, value in redis_kwargs.items(): + if not isinstance(value, str): + continue + param = next((sig.parameters[key] for sig in signatures if key in sig.parameters), None) + if param is None: + continue + explicit_type = explicit_param_types.get(key) + if explicit_type is bool: + result[key] = _str_to_bool(value) + continue + if explicit_type is not None: + try: + result[key] = explicit_type(value) + except (ValueError, TypeError): + del result[key] + continue + default: object = param.default # pyright: ignore[reportAny] # inspect.Parameter.default is stubbed as Any + if default is inspect.Parameter.empty: + continue + # bool must be checked before int, since bool subclasses int + if isinstance(default, bool): + result[key] = _str_to_bool(value) + elif isinstance(default, int): + try: + result[key] = int(value) + except (ValueError, TypeError): + del result[key] + elif isinstance(default, float): + try: + result[key] = float(value) + except (ValueError, TypeError): + del result[key] + return result def _redis_kwargs_from_environment(): @@ -353,6 +454,12 @@ def get_redis_url_from_environment(): return f"{redis_protocol}://{auth_part}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" +def _url_without_userinfo(url: str) -> str: + parts: Final = urlsplit(url) + netloc: Final = parts.netloc.rsplit("@", 1)[-1] + return urlunsplit((parts.scheme, netloc, parts.path, parts.query, parts.fragment)) + + def _get_redis_client_logic(**env_overrides): """ Common functionality across sync + async redis client implementations @@ -410,54 +517,58 @@ def _get_redis_client_logic(**env_overrides): if _service_name is not None: redis_kwargs["service_name"] = _service_name - # Handle GCP IAM authentication - _gcp_service_account: Final = redis_kwargs.get("gcp_service_account") or get_secret_str("REDIS_GCP_SERVICE_ACCOUNT") - _gcp_ssl_ca_certs: Final = redis_kwargs.get("gcp_ssl_ca_certs") or get_secret_str("REDIS_GCP_SSL_CA_CERTS") - - if _gcp_service_account is not None: - verbose_logger.debug("Setting up GCP IAM authentication for Redis with service account.") - redis_kwargs["redis_connect_func"] = create_gcp_iam_redis_connect_func( - service_account=_gcp_service_account, ssl_ca_certs=_gcp_ssl_ca_certs + if redis_kwargs.get("credential_provider") is None: + # Handle GCP IAM authentication + _gcp_service_account: Final = redis_kwargs.get("gcp_service_account") or get_secret_str( + "REDIS_GCP_SERVICE_ACCOUNT" ) - # Store GCP service account in redis_connect_func for async cluster access - redis_kwargs["redis_connect_func"]._gcp_service_account = _gcp_service_account + _gcp_ssl_ca_certs: Final = redis_kwargs.get("gcp_ssl_ca_certs") or get_secret_str("REDIS_GCP_SSL_CA_CERTS") - # Remove GCP-specific kwargs that shouldn't be passed to Redis client - redis_kwargs.pop("gcp_service_account", None) - redis_kwargs.pop("gcp_ssl_ca_certs", None) + if _gcp_service_account is not None: + verbose_logger.debug("Setting up GCP IAM authentication for Redis with service account.") + redis_kwargs["redis_connect_func"] = create_gcp_iam_redis_connect_func( + service_account=_gcp_service_account, ssl_ca_certs=_gcp_ssl_ca_certs + ) + # Store GCP service account in redis_connect_func for async cluster access + redis_kwargs["redis_connect_func"]._gcp_service_account = _gcp_service_account - # Only enable SSL if explicitly requested AND SSL CA certs are provided - if _gcp_ssl_ca_certs and redis_kwargs.get("ssl", False): - redis_kwargs["ssl_ca_certs"] = _gcp_ssl_ca_certs + # Only enable SSL if explicitly requested AND SSL CA certs are provided + if _gcp_ssl_ca_certs and redis_kwargs.get("ssl", False): + redis_kwargs["ssl_ca_certs"] = _gcp_ssl_ca_certs - # Handle Azure AD authentication (after GCP IAM block) - _azure_redis_ad_token: Final = redis_kwargs.get("azure_redis_ad_token") or get_secret("REDIS_AZURE_AD_TOKEN") + # Handle Azure AD authentication (after GCP IAM block) + _azure_redis_ad_token: Final = redis_kwargs.get("azure_redis_ad_token") or get_secret("REDIS_AZURE_AD_TOKEN") - _azure_ad_enabled: Final = _azure_redis_ad_token is not None and str(_azure_redis_ad_token).lower() == "true" + _azure_ad_enabled: Final = _azure_redis_ad_token is not None and str(_azure_redis_ad_token).lower() == "true" - if _azure_ad_enabled and _gcp_service_account is not None: - verbose_logger.warning( - "Both GCP IAM (gcp_service_account) and Azure AD (azure_redis_ad_token) are configured for Redis. " - "Using GCP IAM. Remove one to avoid misconfiguration." - ) + if _azure_ad_enabled and _gcp_service_account is not None: + verbose_logger.warning( + "Both GCP IAM (gcp_service_account) and Azure AD (azure_redis_ad_token) are configured for Redis. " + "Using GCP IAM. Remove one to avoid misconfiguration." + ) - if _azure_ad_enabled and _gcp_service_account is None: - _azure_client_id: Final = redis_kwargs.get("azure_client_id") or get_secret_str("AZURE_CLIENT_ID") - _azure_tenant_id: Final = redis_kwargs.get("azure_tenant_id") or get_secret_str("AZURE_TENANT_ID") - _azure_client_secret: Final = redis_kwargs.get("azure_client_secret") or get_secret_str("AZURE_CLIENT_SECRET") + if _azure_ad_enabled and _gcp_service_account is None: + _azure_client_id: Final = redis_kwargs.get("azure_client_id") or get_secret_str("AZURE_CLIENT_ID") + _azure_tenant_id: Final = redis_kwargs.get("azure_tenant_id") or get_secret_str("AZURE_TENANT_ID") + _azure_client_secret: Final = redis_kwargs.get("azure_client_secret") or get_secret_str( + "AZURE_CLIENT_SECRET" + ) - verbose_logger.debug("Setting up Azure AD authentication for Redis.") - redis_kwargs["redis_connect_func"] = create_azure_ad_redis_connect_func( - azure_client_id=_azure_client_id, - azure_tenant_id=_azure_tenant_id, - azure_client_secret=_azure_client_secret, - ) - # Marker for async paths to detect Azure AD auth. The live credential - # object is attached separately as `_azure_credential` by - # `create_azure_ad_redis_connect_func`; the raw client_id/tenant_id/secret - # are intentionally NOT exposed on the function to avoid leaking - # credentials via inspection or logging. - redis_kwargs["redis_connect_func"]._azure_redis_ad_token = True + verbose_logger.debug("Setting up Azure AD authentication for Redis.") + redis_kwargs["redis_connect_func"] = create_azure_ad_redis_connect_func( + azure_client_id=_azure_client_id, + azure_tenant_id=_azure_tenant_id, + azure_client_secret=_azure_client_secret, + ) + # Marker for async paths to detect Azure AD auth. The live credential + # object is attached separately as `_azure_credential` by + # `create_azure_ad_redis_connect_func`; the raw client_id/tenant_id/secret + # are intentionally NOT exposed on the function to avoid leaking + # credentials via inspection or logging. + redis_kwargs["redis_connect_func"]._azure_redis_ad_token = True + + redis_kwargs.pop("gcp_service_account", None) + redis_kwargs.pop("gcp_ssl_ca_certs", None) # Always remove Azure-specific kwargs that shouldn't be passed to Redis client redis_kwargs.pop("azure_redis_ad_token", None) @@ -465,6 +576,13 @@ def _get_redis_client_logic(**env_overrides): redis_kwargs.pop("azure_tenant_id", None) redis_kwargs.pop("azure_client_secret", None) + if redis_kwargs.get("credential_provider") is not None: + redis_kwargs.pop("redis_connect_func", None) + redis_kwargs.pop("username", None) + redis_kwargs.pop("password", None) + if redis_kwargs.get("url") is not None: + redis_kwargs["url"] = _url_without_userinfo(redis_kwargs["url"]) + if "url" in redis_kwargs and redis_kwargs["url"] is not None: # Only strip host/port/db/password when not routing to a cluster. # When startup_nodes is also present the cluster path takes priority and @@ -485,7 +603,12 @@ def _get_redis_client_logic(**env_overrides): raise ValueError("Either 'host' or 'url' must be specified for redis.") # litellm.print_verbose(f"redis_kwargs: {redis_kwargs}") - return redis_kwargs + coercion_client: Final = ( + (redis.Redis, redis.RedisCluster, async_redis.RedisCluster) + if redis_kwargs.get("startup_nodes") + else redis.Redis + ) + return _coerce_redis_kwargs_types(redis_kwargs, client=coercion_client) def init_redis_cluster(redis_kwargs) -> redis.RedisCluster: @@ -532,8 +655,7 @@ def _init_redis_sentinel(redis_kwargs) -> redis.Redis: service_name: Final = redis_kwargs.get("service_name") connection_kwargs: Final = _get_redis_sentinel_connection_kwargs(redis_kwargs) connection_kwargs.setdefault("socket_timeout", REDIS_SOCKET_TIMEOUT) - sentinel_kwargs: Final = dict(connection_kwargs) - sentinel_kwargs["password"] = sentinel_password + sentinel_kwargs: Final = _sentinel_auth_kwargs(connection_kwargs, sentinel_password) if not sentinel_nodes or not service_name: raise ValueError("Both 'sentinel_nodes' and 'service_name' are required for Redis Sentinel.") @@ -605,7 +727,12 @@ def _async_credential_provider(redis_connect_func: object | None) -> CredentialP def _async_auth_kwargs(redis_kwargs: dict) -> dict: """Swaps a connect func an async path cannot run for the equivalent credential provider, which supersedes any static username or password redis-py would otherwise reject it with.""" - credential_provider: Final = _async_credential_provider(redis_kwargs.get("redis_connect_func")) + explicit_provider: Final = redis_kwargs.get("credential_provider") + credential_provider: Final = ( + explicit_provider + if explicit_provider is not None + else _async_credential_provider(redis_kwargs.get("redis_connect_func")) + ) if credential_provider is None: return redis_kwargs @@ -633,7 +760,9 @@ def get_redis_client(**env_overrides): if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs: return _init_redis_sentinel(redis_kwargs) - return redis.Redis(**redis_kwargs) + return redis.Redis( # pyright: ignore[reportCallIssue] # object-valued kwargs match no overload statically + **redis_kwargs, # pyright: ignore[reportArgumentType] # allow-listed and coerced against this signature + ) def get_redis_async_client( @@ -645,7 +774,7 @@ def get_redis_async_client( if "startup_nodes" in redis_kwargs: from redis.cluster import ClusterNode - args = _get_redis_cluster_kwargs() + args = _get_redis_cluster_kwargs(async_redis.RedisCluster) cluster_kwargs: Final = {} for arg in redis_kwargs: if arg in args: @@ -738,8 +867,20 @@ def get_redis_connection_pool( return async_redis.BlockingConnectionPool(timeout=REDIS_CONNECTION_POOL_TIMEOUT, **redis_kwargs) +def _redis_kwargs_for_logging(redis_kwargs: Mapping[str, object]) -> Mapping[str, object]: + return { + key: "" + if key == "credential_provider" and value is not None + else "" + if key == "redis_connect_func" and value is not None + else value + for key, value in redis_kwargs.items() + } + + def _pretty_print_redis_config(redis_kwargs: dict) -> None: """Pretty print the Redis configuration using rich with sensitive data masking""" + redis_kwargs_for_logging: Final = _redis_kwargs_for_logging(redis_kwargs) try: import logging @@ -757,7 +898,7 @@ def _pretty_print_redis_config(redis_kwargs: dict) -> None: masker = SensitiveDataMasker() # Mask sensitive data in redis_kwargs - masked_redis_kwargs = masker.mask_dict(redis_kwargs) + masked_redis_kwargs = masker.mask_dict(redis_kwargs_for_logging) # Create main panel title title: Final = Text("Redis Configuration", style="bold blue") @@ -820,7 +961,7 @@ def _pretty_print_redis_config(redis_kwargs: dict) -> None: except ImportError: # Fallback to simple logging if rich is not available masker = SensitiveDataMasker() - masked_redis_kwargs = masker.mask_dict(redis_kwargs) + masked_redis_kwargs = masker.mask_dict(redis_kwargs_for_logging) verbose_logger.info("Redis configuration: %s", masked_redis_kwargs) except Exception as e: verbose_logger.error("Error pretty printing Redis configuration: %s", e) diff --git a/litellm/a2a_protocol/card_resolver.py b/litellm/a2a_protocol/card_resolver.py index d14d892256b..25f2e1a9a0d 100644 --- a/litellm/a2a_protocol/card_resolver.py +++ b/litellm/a2a_protocol/card_resolver.py @@ -4,6 +4,7 @@ Custom A2A Card Resolver for LiteLLM. Extends the A2A SDK's card resolver to support multiple well-known paths. """ +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final from litellm._logging import verbose_logger @@ -48,6 +49,43 @@ def is_localhost_or_internal_url(url: str | None) -> bool: return any(pattern in url_lower for pattern in LOCALHOST_URL_PATTERNS) +_CANONICAL_PROTOCOL_BINDINGS: Final = MappingProxyType( + { + "jsonrpc": "JSONRPC", + "http+json": "HTTP+JSON", + "grpc": "GRPC", + } +) + +_LEGACY_PROTOCOL_VERSION: Final = "0.3" + + +def normalize_agent_card_interfaces(agent_card: "AgentCard") -> "AgentCard": + """ + Canonicalize the supported interfaces of spec-adjacent agent cards. + + Some A2A servers (e.g. LangGraph Platform) serve agent cards with lowercase + bindings like "jsonrpc", but a2a-sdk's ClientFactory matches bindings + case-sensitively against its uppercase TransportProtocol constants and fails + with "no compatible transports found." for spec-adjacent casings. + + The same servers also speak the A2A 0.3 JSON dialect ("kind"-discriminated + payloads) while declaring protocolVersion "1.0", which a2a-sdk's strict v1 + proto parsing rejects. A mis-cased binding fingerprints such a server, so its + declared version is downgraded to 0.3 to route the SDK's ClientFactory onto + its v0.3 compat transport, which speaks that dialect. + """ + normalized: Final = type(agent_card)() + normalized.CopyFrom(agent_card) + for interface in normalized.supported_interfaces: + canonical: str | None = _CANONICAL_PROTOCOL_BINDINGS.get(interface.protocol_binding.lower()) + if canonical is None or canonical == interface.protocol_binding: + continue + interface.protocol_binding = canonical + interface.protocol_version = _LEGACY_PROTOCOL_VERSION + return normalized + + def get_agent_card_url(agent_card: "AgentCard") -> str | None: """Return the agent endpoint URL from the resolved SDK card.""" url: Final = getattr(agent_card, "url", None) diff --git a/litellm/a2a_protocol/litellm_completion_bridge/transformation.py b/litellm/a2a_protocol/litellm_completion_bridge/transformation.py index 15cf77708f9..838c0fd8373 100644 --- a/litellm/a2a_protocol/litellm_completion_bridge/transformation.py +++ b/litellm/a2a_protocol/litellm_completion_bridge/transformation.py @@ -17,11 +17,27 @@ A2A Streaming Events: - Artifact update (kind: "artifact-update") - Content/artifact delivery """ +from collections.abc import Mapping, MutableMapping, Sequence from datetime import datetime, timezone -from typing import Any, Final +from typing import TYPE_CHECKING, Final from uuid import uuid4 +from pydantic import JsonValue, TypeAdapter, ValidationError + from litellm._logging import verbose_logger +from litellm.types.utils import ModelResponse + +if TYPE_CHECKING: + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper + +_STR_KEY_MAPPING_ADAPTER: Final = TypeAdapter(Mapping[str, object]) + + +def _as_object_mapping(value: object) -> Mapping[str, object]: + try: + return _STR_KEY_MAPPING_ADAPTER.validate_python(value) + except ValidationError: + return {} class A2AStreamingContext: @@ -30,7 +46,7 @@ class A2AStreamingContext: Tracks task_id, context_id, and message accumulation. """ - def __init__(self, request_id: str, input_message: dict[str, Any]): + def __init__(self, request_id: str, input_message: Mapping[str, JsonValue]): self.request_id = request_id self.task_id = str(uuid4()) self.context_id = str(uuid4()) @@ -46,44 +62,46 @@ class A2ACompletionBridgeTransformation: """ @staticmethod - def _extract_text_from_a2a_parts(parts: list[dict[str, Any]]) -> str: + def _text_from_a2a_part(part: JsonValue) -> str | None: + if not isinstance(part, dict): + return None + text: Final = part.get("text") + if text is None: + return None + if part.get("kind") not in (None, "", "text"): + return None + return str(text) + + @staticmethod + def _extract_text_from_a2a_parts(parts: Sequence[JsonValue]) -> str: """Extract text from A2A parts (with or without explicit ``kind``).""" - content_parts: Final[list[str]] = [] - for part in parts: - if not isinstance(part, dict): - continue - kind = part.get("kind") - text = part.get("text") - if text is None: - continue - if kind in (None, "", "text"): - content_parts.append(str(text)) - return "\n".join(content_parts) + extracted: Final = (A2ACompletionBridgeTransformation._text_from_a2a_part(part) for part in parts) + return "\n".join(text for text in extracted if text is not None) @staticmethod def get_forward_metadata( - a2a_message: dict[str, Any], - params: dict[str, Any] | None = None, - ) -> dict[str, Any] | None: + a2a_message: Mapping[str, JsonValue], + params: Mapping[str, JsonValue] | None = None, + ) -> Mapping[str, JsonValue] | None: """ Merge A2A metadata from MessageSendParams and the message for downstream providers. Forwarded once on the LangGraph run payload (``metadata``), not duplicated on each input message — see ``apply_forward_metadata_to_completion_params``. """ - merged: Final[dict[str, Any]] = {} - if params and isinstance(params.get("metadata"), dict): - merged.update(params["metadata"]) + params_metadata: Final = params.get("metadata") if params else None message_metadata: Final = a2a_message.get("metadata") - if isinstance(message_metadata, dict): - merged.update(message_metadata) + merged: Final[dict[str, JsonValue]] = { + **(params_metadata if isinstance(params_metadata, dict) else {}), + **(message_metadata if isinstance(message_metadata, dict) else {}), + } return merged or None @staticmethod def apply_forward_metadata_to_completion_params( - completion_params: dict[str, Any], - a2a_message: dict[str, Any], - params: dict[str, Any] | None = None, + completion_params: MutableMapping[str, object], + a2a_message: Mapping[str, JsonValue], + params: Mapping[str, JsonValue] | None = None, ) -> None: """ Attach A2A metadata to completion kwargs for provider bridges (e.g. LangGraph). @@ -97,24 +115,20 @@ class A2ACompletionBridgeTransformation: if not forward_metadata: return - extra_body = completion_params.get("extra_body") - if not isinstance(extra_body, dict): - extra_body = {} + extra_body: Final = _as_object_mapping(completion_params.get("extra_body")) # Layer client-supplied A2A metadata under any agent-owner-configured # ``extra_body.metadata`` so the configured keys remain authoritative # and an A2A caller cannot overwrite server-set run metadata. - existing_metadata: Final = extra_body.get("metadata") - existing_dict: Final[dict[str, Any]] = existing_metadata if isinstance(existing_metadata, dict) else {} - merged_metadata: Final[dict[str, Any]] = {**forward_metadata, **existing_dict} - extra_body = {**extra_body, "metadata": merged_metadata} - completion_params["extra_body"] = extra_body + existing_dict: Final = _as_object_mapping(extra_body.get("metadata")) + merged_metadata: Final[dict[str, object]] = {**forward_metadata, **existing_dict} + completion_params["extra_body"] = {**extra_body, "metadata": merged_metadata} verbose_logger.debug("A2A -> completion forward metadata keys=%s", list(forward_metadata.keys())) @staticmethod def a2a_message_to_openai_messages( - a2a_message: dict[str, Any], - ) -> list[dict[str, Any]]: + a2a_message: Mapping[str, JsonValue], + ) -> list[dict[str, object]]: """ Transform an A2A message to OpenAI message format. @@ -125,25 +139,19 @@ class A2ACompletionBridgeTransformation: List of OpenAI-format messages """ role: Final = a2a_message.get("role", "user") - parts = a2a_message.get("parts", []) + raw_parts: Final = a2a_message.get("parts", []) # Map A2A roles to OpenAI roles - openai_role = role - if role == "user": - openai_role = "user" - elif role == "assistant": - openai_role = "assistant" - elif role == "system": - openai_role = "system" - - if not isinstance(parts, list): - parts = [] + openai_role: Final = ( + "user" if role == "user" else "assistant" if role == "assistant" else "system" if role == "system" else role + ) + parts: Final = raw_parts if isinstance(raw_parts, list) else [] content: Final = A2ACompletionBridgeTransformation._extract_text_from_a2a_parts(parts) # Do not attach A2A message.metadata here — the completion bridge forwards it # once at run level via extra_body.metadata (LangGraph POST /runs/wait shape). - openai_message: Final[dict[str, Any]] = {"role": openai_role, "content": content} + openai_message: Final[dict[str, object]] = {"role": openai_role, "content": content} verbose_logger.debug( "A2A -> OpenAI transform: role=%s -> %s, content_length=%s", role, openai_role, len(content) @@ -151,11 +159,20 @@ class A2ACompletionBridgeTransformation: return [openai_message] + @staticmethod + def _extract_response_content(response: "ModelResponse | CustomStreamWrapper") -> str: + if not isinstance(response, ModelResponse) or not response.choices: + return "" + choice: Final = response.choices[0] + if not choice.message: + return "" + return choice.message.content or "" + @staticmethod def openai_response_to_a2a_response( - response: Any, + response: "ModelResponse | CustomStreamWrapper", request_id: str | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Transform a LiteLLM ModelResponse to A2A SendMessageResponse format. @@ -166,12 +183,7 @@ class A2ACompletionBridgeTransformation: Returns: A2A SendMessageResponse dict """ - # Extract content from response - content = "" - if hasattr(response, "choices") and response.choices: - choice: Final = response.choices[0] - if hasattr(choice, "message") and choice.message: - content = choice.message.content or "" + content: Final = A2ACompletionBridgeTransformation._extract_response_content(response) # Build A2A message a2a_message: Final = { @@ -182,7 +194,7 @@ class A2ACompletionBridgeTransformation: } # Build A2A response - a2a_response: Final = { + a2a_response: Final[dict[str, object]] = { "jsonrpc": "2.0", "id": request_id, "result": a2a_message, @@ -200,7 +212,7 @@ class A2ACompletionBridgeTransformation: @staticmethod def create_task_event( ctx: A2AStreamingContext, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Create the initial task event with status 'submitted'. @@ -235,7 +247,7 @@ class A2ACompletionBridgeTransformation: state: str, final: bool = False, message_text: str | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Create a status update event. @@ -245,7 +257,7 @@ class A2ACompletionBridgeTransformation: final: Whether this is the final event message_text: Optional message text for 'working' status """ - status: Final[dict[str, Any]] = { + status: Final[dict[str, object]] = { "state": state, "timestamp": A2ACompletionBridgeTransformation._get_timestamp(), } @@ -277,7 +289,7 @@ class A2ACompletionBridgeTransformation: def create_artifact_update_event( ctx: A2AStreamingContext, text: str, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Create an artifact update event with content. diff --git a/litellm/a2a_protocol/main.py b/litellm/a2a_protocol/main.py index 1c6ebf0b95c..0e8b8136c19 100644 --- a/litellm/a2a_protocol/main.py +++ b/litellm/a2a_protocol/main.py @@ -73,6 +73,7 @@ except ImportError: from litellm.a2a_protocol.card_resolver import ( LiteLLMA2ACardResolver, get_agent_card_url, + normalize_agent_card_interfaces, ) from litellm.a2a_protocol.exception_mapping_utils import ( handle_a2a_localhost_retry, @@ -85,7 +86,7 @@ A2ACardResolver: Final = LiteLLMA2ACardResolver def _set_usage_on_logging_obj( - kwargs: dict[str, Any], + kwargs: Mapping[str, object], prompt_tokens: int, completion_tokens: int, ) -> None: @@ -98,7 +99,7 @@ def _set_usage_on_logging_obj( completion_tokens: Number of output tokens """ litellm_logging_obj: Final = kwargs.get("litellm_logging_obj") - if litellm_logging_obj is not None: + if isinstance(litellm_logging_obj, Logging): usage: Final = litellm.Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, @@ -108,7 +109,7 @@ def _set_usage_on_logging_obj( def _set_agent_id_on_logging_obj( - kwargs: dict[str, Any], + kwargs: Mapping[str, object], agent_id: str | None, ) -> None: """ @@ -122,7 +123,7 @@ def _set_agent_id_on_logging_obj( return litellm_logging_obj: Final = kwargs.get("litellm_logging_obj") - if litellm_logging_obj is not None: + if isinstance(litellm_logging_obj, Logging): # Set agent_id directly on model_call_details (same pattern as custom_llm_provider) litellm_logging_obj.model_call_details["agent_id"] = agent_id @@ -131,7 +132,7 @@ _A2A_COST_PARAM_KEYS: Final = ("cost_per_query", "input_cost_per_token", "output def _set_litellm_params_on_logging_obj( - kwargs: dict[str, Any], + kwargs: Mapping[str, object], litellm_params: Mapping[str, object], ) -> None: """ @@ -143,18 +144,22 @@ def _set_litellm_params_on_logging_obj( context, so merge the pricing keys in rather than replacing the dict. """ logging_obj: Final = kwargs.get("litellm_logging_obj") - if logging_obj is None: + if not isinstance(logging_obj, Logging): return - cost_params = {key: litellm_params[key] for key in _A2A_COST_PARAM_KEYS if litellm_params.get(key) is not None} + cost_params: Final = { + key: litellm_params[key] for key in _A2A_COST_PARAM_KEYS if litellm_params.get(key) is not None + } if not cost_params: return - existing: Final = logging_obj.model_call_details.get("litellm_params") or {} - logging_obj.model_call_details["litellm_params"] = {**existing, **cost_params} + logging_obj.model_call_details["litellm_params"] = { + **(logging_obj.model_call_details.get("litellm_params") or {}), + **cost_params, + } -def _get_a2a_model_info(a2a_client: "A2AClientType", kwargs: dict[str, Any]) -> str: +def _get_a2a_model_info(a2a_client: "A2AClientType", kwargs: Mapping[str, object]) -> str: """ Extract agent info and set model/custom_llm_provider for cost tracking. @@ -174,7 +179,7 @@ def _get_a2a_model_info(a2a_client: "A2AClientType", kwargs: dict[str, Any]) -> # Set on litellm_logging_obj if available (for standard logging payload) litellm_logging_obj: Final = kwargs.get("litellm_logging_obj") - if litellm_logging_obj is not None: + if isinstance(litellm_logging_obj, Logging): litellm_logging_obj.model = model litellm_logging_obj.custom_llm_provider = custom_llm_provider litellm_logging_obj.model_call_details["model"] = model @@ -497,7 +502,7 @@ async def asend_message( response: Final = LiteLLMSendMessageResponse.from_a2a_response(a2a_response, request_id=str(request.id)) # Calculate token usage from request and response - response_dict: Final[dict[str, object]] = a2a_response.model_dump(mode="json", exclude_none=True) + response_dict: Final[dict[str, object]] = a2a_response.root.model_dump(mode="json", exclude_none=True) ( prompt_tokens, completion_tokens, @@ -782,13 +787,17 @@ async def create_a2a_client( if extra_headers: verbose_proxy_logger.debug("A2A client created with extra_headers=%s", list(extra_headers.keys())) + resolver: Final = A2ACardResolver(httpx_client=httpx_client, base_url=base_url) + agent_card: Final = normalize_agent_card_interfaces( + await resolver.get_agent_card(http_kwargs={"headers": extra_headers} if extra_headers else None) + ) + a2a_client: Final = await create_client( # pyright: ignore[reportOptionalCall] - base_url, + agent_card, client_config=ClientConfig( # pyright: ignore[reportOptionalCall] httpx_client=httpx_client, streaming=streaming, ), - resolver_http_kwargs={"headers": extra_headers} if extra_headers else None, ) # Stash LiteLLM-owned handles on the client so the localhost-retry path can reuse # the configured httpx client and this agent's headers without excavating @@ -799,9 +808,7 @@ async def create_a2a_client( if extra_headers else None ) - agent_card: Final = getattr(a2a_client, "_card", None) - if agent_card is not None: - a2a_client._litellm_agent_card = agent_card + a2a_client._litellm_agent_card = agent_card verbose_logger.info("A2A client created for %s", base_url) diff --git a/litellm/a2a_protocol/providers/bedrock_agentcore/transformation.py b/litellm/a2a_protocol/providers/bedrock_agentcore/transformation.py index 32252711997..1e8cc4ff90e 100644 --- a/litellm/a2a_protocol/providers/bedrock_agentcore/transformation.py +++ b/litellm/a2a_protocol/providers/bedrock_agentcore/transformation.py @@ -10,8 +10,19 @@ from collections.abc import AsyncIterator, Mapping from typing import Any, Final from litellm._logging import verbose_logger +from litellm.a2a_protocol.litellm_completion_bridge.handler import ( + A2A_USER_API_KEY_HASH_PARAM, +) +from litellm.a2a_protocol.utils import ( + get_session_id_from_a2a_params, + scope_session_to_principal, +) +from litellm.exceptions import BadRequestError from litellm.llms.bedrock.chat.agentcore.transformation import AmazonAgentCoreConfig +RUNTIME_SESSION_ID_MIN_LENGTH: Final = 33 +RUNTIME_SESSION_ID_MAX_LENGTH: Final = 256 + # Reserved outbound header names that must never be sourced from per-request # ``agent_extra_headers`` for AgentCore requests. ``agent_extra_headers`` carries # values rewritten from the client-controlled ``x-a2a-{agent}-*`` convention, so @@ -19,8 +30,9 @@ from litellm.llms.bedrock.chat.agentcore.transformation import AmazonAgentCoreCo # request identity / SigV4 metadata by overwriting headers the proxy sets from # trusted server-side config. # -# The runtime headers (session / user id) are derived server-side from -# ``runtimeSessionId`` / ``runtimeUserId`` in the agent's ``litellm_params``; +# The runtime headers (session / user id) are derived server-side from the A2A +# ``message.contextId`` and ``runtimeSessionId`` / ``runtimeUserId`` in the +# agent's ``litellm_params``; # ``authorization`` is set by the AgentCore signer (JWT or SigV4); ``host`` and # the ``x-amz-*`` family are owned by SigV4 itself. _RESERVED_EXACT_HEADERS: Final = frozenset( @@ -66,6 +78,31 @@ def _filter_reserved_headers( return filtered or None +def _request_scoped_runtime_session_id( + params: Mapping[str, Any], + litellm_params: Mapping[str, Any], +) -> str | None: + context_id: Final = get_session_id_from_a2a_params(params) + if not isinstance(context_id, str) or not context_id: + return None + return scope_session_to_principal(context_id, litellm_params.get(A2A_USER_API_KEY_HASH_PARAM)) + + +def _validate_runtime_session_id(session_id: str, model: str) -> str: + if RUNTIME_SESSION_ID_MIN_LENGTH <= len(session_id) <= RUNTIME_SESSION_ID_MAX_LENGTH: + return session_id + raise BadRequestError( + message=( + f"Invalid AgentCore runtime session id {session_id!r}: AWS requires " + f"{RUNTIME_SESSION_ID_MIN_LENGTH}-{RUNTIME_SESSION_ID_MAX_LENGTH} characters. It is built from the A2A " + "message.contextId (prefixed with a 16-hex-char hash of the calling key and '-') when set, " + "otherwise from the agent's configured runtimeSessionId." + ), + model=model, + llm_provider="bedrock", + ) + + class BedrockAgentCoreA2ATransformation: """ Request/response transformation for Bedrock AgentCore A2A agents. @@ -100,7 +137,9 @@ class BedrockAgentCoreA2ATransformation: here to prevent a caller-controlled ``x-a2a-{agent}-*`` header from spoofing the AgentCore runtime user id or other SigV4 metadata. Use ``api_key`` / ``runtimeUserId`` / ``runtimeSessionId`` in litellm_params - (not ``agent_extra_headers``) to override those values. + (not ``agent_extra_headers``) to override those values. The runtime + session id is taken from ``params["message"]["contextId"]`` (scoped to + the calling key) when present, then ``runtimeSessionId``, else generated. Returns: Tuple of (url, signed_headers, signed_body_bytes) @@ -139,7 +178,11 @@ class BedrockAgentCoreA2ATransformation: # Set required AgentCore session headers (normally set by transform_request, # which we skip because it also builds {"prompt": "..."}) headers: Final[dict] = {} - session_id: Final = agentcore_config._get_runtime_session_id(optional_params) + session_id: Final = _validate_runtime_session_id( + _request_scoped_runtime_session_id(params, litellm_params) + or agentcore_config._get_runtime_session_id(optional_params), + model=model, + ) headers["X-Amzn-Bedrock-AgentCore-Runtime-Session-Id"] = session_id runtime_user_id: Final = agentcore_config._get_runtime_user_id(optional_params) if runtime_user_id: diff --git a/litellm/a2a_protocol/utils.py b/litellm/a2a_protocol/utils.py index f2e61f66105..7c459daf720 100644 --- a/litellm/a2a_protocol/utils.py +++ b/litellm/a2a_protocol/utils.py @@ -2,6 +2,8 @@ Utility functions for A2A protocol. """ +import hashlib +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import litellm @@ -140,6 +142,29 @@ class A2ARequestUtils: return prompt_tokens, completion_tokens, total_tokens +def get_session_id_from_a2a_params(params: Mapping[str, Any]) -> str | None: + message: Final = params.get("message", {}) + if isinstance(message, dict): + return message.get("contextId") + return getattr(message, "contextId", None) + + +def scope_session_to_principal(session_id: str, principal: str | None) -> str: + """ + Bind a client-supplied A2A contextId to the authenticated principal. + + Without this, two distinct keys authorized for the same agent could set the + same contextId and read/append to each other's backend memory. The + principal is hashed (it is already a hashed token) so the raw value is never + sent to the agent backend, while the original contextId is kept as a suffix + for operator-side correlation. + """ + if not principal: + return session_id + principal_prefix: Final = hashlib.sha256(principal.encode("utf-8")).hexdigest()[:16] + return f"{principal_prefix}-{session_id}" + + # Backwards compatibility aliases def extract_text_from_a2a_message(message: Any) -> str: return A2ARequestUtils.extract_text_from_message(message) diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 6eb13d2cba7..3831f57a10d 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -1,6 +1,8 @@ import json from collections.abc import Iterable, Iterator, Mapping from dataclasses import dataclass +from dataclasses import replace as dataclasses_replace +from enum import Enum from typing import Any, Final, Literal import litellm @@ -12,12 +14,23 @@ from litellm.types.utils import CallTypes, ModelInfo, Usage from litellm.utils import token_counter +@dataclass(frozen=True, slots=True) +class BatchCostUsageResult: + """Aggregate cost, usage, and per-line pass/fail counts for a completed batch.""" + + cost: float + usage: Usage + models: list[str] + successful_requests: int + failed_requests: int + + async def calculate_batch_cost_and_usage( file_content_dictionary: list[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"], model_name: str | None = None, model_info: ModelInfo | None = None, -) -> tuple[float, Usage, list[str]]: +) -> BatchCostUsageResult: """ Calculate the cost and usage of a batch. @@ -32,8 +45,7 @@ async def calculate_batch_cost_and_usage( and model_name and getattr(litellm, "disable_vertex_batch_output_transformation", False) ): - batch_cost, batch_usage = calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) - return batch_cost, batch_usage, [model_name] + return calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) return _aggregate_batch_cost_usage_models( entries=file_content_dictionary, @@ -49,7 +61,7 @@ async def _handle_completed_batch( model_name: str | None = None, litellm_params: dict | None = None, model_info: ModelInfo | None = None, -) -> tuple[float, Usage, list[str]]: +) -> BatchCostUsageResult: """Fetch a completed batch's output file and aggregate its cost, usage, and models in a single pass over the JSONL lines, so the parsed file content is never materialized in memory. @@ -72,27 +84,49 @@ async def _handle_completed_batch( # The generic retrieval helper keeps raising for callers that explicitly ask # for a missing output file. if batch.output_file_id is None: - return 0.0, Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0), [] + return BatchCostUsageResult( + cost=0.0, + usage=Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0), + models=[], # mutable-ok: no output file means no model was ever priced; BatchCostUsageResult.models requires list[str] + successful_requests=0, + failed_requests=await count_error_file_failed_requests( + batch, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params + ), + ) file_content = await _fetch_batch_output_file_content(batch, custom_llm_provider, litellm_params=litellm_params) - - if ( - custom_llm_provider == "vertex_ai" - and model_name - and getattr(litellm, "disable_vertex_batch_output_transformation", False) - ): - batch_cost, batch_usage = calculate_vertex_ai_batch_cost_and_usage( - _get_file_content_as_dictionary(file_content), model_name - ) - return batch_cost, batch_usage, [model_name] - - return _aggregate_batch_cost_usage_models( - entries=_iter_batch_output_entries(file_content), - custom_llm_provider=custom_llm_provider, - model_name=model_name, - model_info=model_info, + error_file_failed_requests: Final = await count_error_file_failed_requests( + batch, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params ) + output_file_result: Final = ( + calculate_vertex_ai_batch_cost_and_usage(_get_file_content_as_dictionary(file_content), model_name) + if ( + custom_llm_provider == "vertex_ai" + and model_name + and getattr(litellm, "disable_vertex_batch_output_transformation", False) + ) + else _aggregate_batch_cost_usage_models( + entries=_iter_batch_output_entries(file_content), + custom_llm_provider=custom_llm_provider, + model_name=model_name, + model_info=model_info, + ) + ) + + if not error_file_failed_requests: + return output_file_result + return dataclasses_replace( + output_file_result, failed_requests=output_file_result.failed_requests + error_file_failed_requests + ) + + +class _LineOutcome(Enum): + """A batch output line that yielded no billable stats.""" + + PROVIDER_FAILED = "provider_failed" + UNCOSTABLE = "uncostable" + @dataclass(frozen=True, slots=True) class _BatchOutputLineStats: @@ -102,19 +136,27 @@ class _BatchOutputLineStats: total_tokens: int cache_read_tokens: int cache_creation_tokens: int + reasoning_tokens: int model: str | None -def _iter_successful_output_line_stats( +def _classify_output_line_stats( entries: Iterable[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"], model_name: str | None, model_info: ModelInfo | None, -) -> Iterator[_BatchOutputLineStats]: +) -> Iterator[_BatchOutputLineStats | _LineOutcome]: + """Classify every output line in a single pass, so counting failures never needs + a second read of a potentially huge output file. A line the provider reported as + failed yields ``PROVIDER_FAILED``; a successful line litellm could not price + yields ``UNCOSTABLE`` and still counts as a successful request billed at $0, so + the counts stay reconcilable with the provider's own ``request_counts``.""" for entry in entries: + if not _batch_response_was_successful(entry, custom_llm_provider): + yield _LineOutcome.PROVIDER_FAILED + continue stats = _safe_output_line_stats(entry, custom_llm_provider, model_name, model_info) - if stats is not None: - yield stats + yield stats if stats is not None else _LineOutcome.UNCOSTABLE def _safe_output_line_stats( @@ -123,13 +165,11 @@ def _safe_output_line_stats( model_name: str | None, model_info: ModelInfo | None, ) -> _BatchOutputLineStats | None: - """Return the stats for one batch output line, or None for a line that is - unsuccessful or cannot be costed, so a single bad line never aborts the - whole batch's cost accounting.""" + """Return the stats for one provider-successful batch output line, or None when + it cannot be costed, so a single bad line never aborts the whole batch's cost + accounting.""" custom_id: Final = entry.get("custom_id") if isinstance(entry, dict) else None try: - if not _batch_response_was_successful(entry, custom_llm_provider): - return None return _compute_output_line_stats(entry, custom_llm_provider, model_name, model_info) except Exception as e: # noqa: BLE001 # any single line's costing failure must not abort the whole batch verbose_logger.warning( @@ -152,6 +192,7 @@ def _compute_output_line_stats( prompt_details: Final = parse_prompt_tokens_details(usage) raw_model: Final = response_body.get("model") response_model: Final = raw_model if isinstance(raw_model, str) and raw_model else None + completion_details: Final = usage.completion_tokens_details return _BatchOutputLineStats( cost=_output_line_cost( response_body=response_body, @@ -166,6 +207,7 @@ def _compute_output_line_stats( total_tokens=usage.total_tokens, cache_read_tokens=prompt_details["cache_hit_tokens"], cache_creation_tokens=prompt_details["cache_creation_tokens"], + reasoning_tokens=(completion_details.reasoning_tokens if completion_details else None) or 0, model=response_model, ) @@ -203,10 +245,14 @@ def _aggregate_batch_cost_usage_models( custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock"], model_name: str | None = None, model_info: ModelInfo | None = None, -) -> tuple[float, Usage, list[str]]: - """Aggregate cost, usage, and models from batch output entries in a single - pass, holding one small stats record per line instead of the parsed file.""" - line_stats: Final = tuple(_iter_successful_output_line_stats(entries, custom_llm_provider, model_name, model_info)) +) -> BatchCostUsageResult: + """Aggregate cost, usage, models, and pass/fail counts from batch output + entries in a single pass, holding one small stats record per line instead + of the parsed file.""" + all_results: Final = tuple(_classify_output_line_stats(entries, custom_llm_provider, model_name, model_info)) + line_stats: Final = tuple(result for result in all_results if isinstance(result, _BatchOutputLineStats)) + failed_requests: Final = sum(1 for result in all_results if result is _LineOutcome.PROVIDER_FAILED) + successful_requests: Final = len(all_results) - failed_requests cache_token_params: Final = { key: tokens @@ -220,18 +266,32 @@ def _aggregate_batch_cost_usage_models( total_tokens=sum(stats.total_tokens for stats in line_stats), prompt_tokens=sum(stats.prompt_tokens for stats in line_stats), completion_tokens=sum(stats.completion_tokens for stats in line_stats), + reasoning_tokens=sum(stats.reasoning_tokens for stats in line_stats), **cache_token_params, ) batch_models: Final = [model_name] if model_name else [stats.model for stats in line_stats if stats.model] total_cost: Final = sum((stats.cost for stats in line_stats), 0.0) - verbose_logger.debug("batch output aggregate: cost=%s usage=%s models=%s", total_cost, batch_usage, batch_models) - return total_cost, batch_usage, batch_models + verbose_logger.debug( + "batch output aggregate: cost=%s usage=%s models=%s successful=%d failed=%d", + total_cost, + batch_usage, + batch_models, + successful_requests, + failed_requests, + ) + return BatchCostUsageResult( + cost=total_cost, + usage=batch_usage, + models=batch_models, + successful_requests=successful_requests, + failed_requests=failed_requests, + ) def calculate_vertex_ai_batch_cost_and_usage( vertex_ai_batch_responses: list[dict], model_name: str | None = None, -) -> tuple[float, Usage]: +) -> BatchCostUsageResult: """ Calculate both cost and usage from raw Vertex AI batch responses. @@ -242,6 +302,10 @@ def calculate_vertex_ai_batch_cost_and_usage( {"request": ..., "response": {"candidates": [...], "usageMetadata": {...}}} usageMetadata contains promptTokenCount, candidatesTokenCount, totalTokenCount. + + A row with no ``response`` is counted as failed - the same signal already + used to skip it from cost/usage aggregation, since Vertex batch prediction + output doesn't establish a distinct error shape in this (non-default) path. """ from litellm.cost_calculator import batch_cost_calculator @@ -249,12 +313,16 @@ def calculate_vertex_ai_batch_cost_and_usage( total_tokens = 0 prompt_tokens = 0 completion_tokens = 0 + successful_requests = 0 # rebind-ok: loop accumulator, matches total_cost/total_tokens above + failed_requests = 0 # rebind-ok: loop accumulator, matches total_cost/total_tokens above actual_model_name: Final = model_name or "gemini-2.0-flash-001" for response in vertex_ai_batch_responses: response_body = response.get("response") if response_body is None: + failed_requests += 1 continue + successful_requests += 1 usage_metadata = response_body.get("usageMetadata", {}) _prompt = usage_metadata.get("promptTokenCount", 0) or 0 @@ -282,17 +350,25 @@ def calculate_vertex_ai_batch_cost_and_usage( total_tokens += _total verbose_logger.info( - "vertex_ai batch cost: cost=%s, prompt=%d, completion=%d, total=%d", + "vertex_ai batch cost: cost=%s, prompt=%d, completion=%d, total=%d, successful=%d, failed=%d", total_cost, prompt_tokens, completion_tokens, total_tokens, + successful_requests, + failed_requests, ) - return total_cost, Usage( - total_tokens=total_tokens, - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, + return BatchCostUsageResult( + cost=total_cost, + usage=Usage( + total_tokens=total_tokens, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ), + models=[actual_model_name], + successful_requests=successful_requests, + failed_requests=failed_requests, ) @@ -322,6 +398,36 @@ def _provider_output_file_id(output_file_id: str) -> str: return extracted +async def _fetch_batch_managed_file_content( + file_id: str, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai", + litellm_params: dict | None = None, +) -> bytes: + """ + Fetch a batch's output or error file and return its raw JSONL bytes. + + Args: + file_id: The provider or unified (litellm-managed) file id to fetch + custom_llm_provider: The LLM provider + litellm_params: Optional litellm parameters containing credentials (api_key, api_base, etc.) + Required for Azure and other providers that need authentication + """ + from litellm.files.main import afile_content + + # Build kwargs for afile_content with credentials from litellm_params + file_content_kwargs: Final = { + "file_id": _provider_output_file_id(file_id), + "custom_llm_provider": custom_llm_provider, + } + + # Extract and add credentials for file access + credentials: Final = _extract_file_access_credentials(litellm_params) + file_content_kwargs.update(credentials) + + _file_content: Final = await afile_content(**file_content_kwargs) + return _file_content.content + + async def _fetch_batch_output_file_content( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai", @@ -336,25 +442,36 @@ async def _fetch_batch_output_file_content( litellm_params: Optional litellm parameters containing credentials (api_key, api_base, etc.) Required for Azure and other providers that need authentication """ - from litellm.files.main import afile_content - if batch.output_file_id is None: raise ValueError("Output file id is None cannot retrieve file content") - file_id: Final = _provider_output_file_id(batch.output_file_id) + return await _fetch_batch_managed_file_content( + batch.output_file_id, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params + ) - # Build kwargs for afile_content with credentials from litellm_params - file_content_kwargs: Final = { - "file_id": file_id, - "custom_llm_provider": custom_llm_provider, - } - # Extract and add credentials for file access - credentials: Final = _extract_file_access_credentials(litellm_params) - file_content_kwargs.update(credentials) +async def count_error_file_failed_requests( + batch: Batch, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"], + litellm_params: dict | None, +) -> int: + """Count failed requests reported only in the batch's separate error file. - _file_content: Final = await afile_content(**file_content_kwargs) - return _file_content.content + OpenAI-shaped batch providers write successful lines to ``output_file_id`` + and per-request failures (e.g. a rejected param) to a distinct + ``error_file_id`` - they never appear in the output file at all, so + counting failures from the output file alone silently undercounts them. + """ + if batch.error_file_id is None: + return 0 + try: + error_file_content = await _fetch_batch_managed_file_content( + batch.error_file_id, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params + ) + except Exception as e: # noqa: BLE001 # a failed/missing error file must not abort cost tracking for the batch + verbose_logger.debug("Failed to fetch batch error file %s: %s", batch.error_file_id, e) + return 0 + return sum(1 for _ in _iter_batch_input_lines(error_file_content)) def _extract_file_access_credentials(litellm_params: dict | None) -> dict: @@ -551,7 +668,7 @@ def _get_batch_job_usage_from_response_body( return usage -def _get_anthropic_result_from_batch_results_line(batch_results_line: Mapping[str, Any]) -> dict: +def _get_anthropic_result_from_batch_results_line(batch_results_line: Mapping[str, Any]) -> Mapping[str, Any]: """ Get the ``result`` object from a line of an Anthropic message batch results JSONL file. @@ -563,7 +680,7 @@ def _get_anthropic_result_from_batch_results_line(batch_results_line: Mapping[st def _get_response_from_batch_job_output_file( batch_job_output_file: Mapping[str, Any], custom_llm_provider: str = "openai" -) -> Any: +) -> Mapping[str, Any]: """ Get the response from the batch job output file """ diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 2aa7b527c57..c8360a81c7a 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -390,7 +390,7 @@ def _handle_retrieve_batch_providers_without_provider_config( custom_llm_provider: Literal[ "openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "litellm_proxy", "anthropic" ] = "openai", - logging_obj: Any | None = None, + logging_obj: LiteLLMLoggingObj | None = None, ): api_base: str | None = None if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index cefe6aae9ed..754815fce47 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -12,6 +12,7 @@ import hashlib import json import time import traceback +from collections.abc import Mapping from enum import Enum from typing import Any, Final @@ -506,7 +507,7 @@ class Cache: def _get_cache_logic( self, - cached_result: Any | None, + cached_result: object | None, max_age: float | None, ): """ @@ -538,8 +539,8 @@ class Cache: return cached_result @staticmethod - def _get_safe_cache_lookup_kwargs(kwargs: dict[str, Any]) -> dict[str, Any]: - cache_lookup_kwargs: Final[dict[str, Any]] = {} + def _get_safe_cache_lookup_kwargs(kwargs: Mapping[str, object]) -> dict[str, object]: + cache_lookup_kwargs: Final[dict[str, object]] = {} for prompt_kwarg in ("messages", "input"): if prompt_kwarg in kwargs: cache_lookup_kwargs[prompt_kwarg] = kwargs[prompt_kwarg] @@ -552,7 +553,7 @@ class Cache: @staticmethod def _update_metadata_from_cache_lookup_kwargs( - original_kwargs: dict[str, Any], cache_lookup_kwargs: dict[str, Any] + original_kwargs: Mapping[str, object], cache_lookup_kwargs: Mapping[str, object] ) -> None: original_metadata: Final = original_kwargs.get("metadata") cache_lookup_metadata: Final = cache_lookup_kwargs.get("metadata") diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py index 7526dfd4e4c..8fe60876b4e 100644 --- a/litellm/caching/caching_handler.py +++ b/litellm/caching/caching_handler.py @@ -18,7 +18,7 @@ import asyncio import datetime import inspect import time -from collections.abc import AsyncGenerator, AsyncIterator, Callable, Generator, Mapping +from collections.abc import AsyncGenerator, AsyncIterator, Awaitable, Callable, Generator, Mapping from typing import TYPE_CHECKING, Any, Final, Optional, TypeVar from pydantic import BaseModel @@ -27,6 +27,7 @@ import litellm from litellm._logging import print_verbose, verbose_logger from litellm.caching import InMemoryCache from litellm.caching.caching import S3Cache +from litellm.constants import CACHE_WRITE_SHUTDOWN_FLUSH_TIMEOUT_SECONDS from litellm.litellm_core_utils.llm_response_utils.response_metadata import ( update_response_metadata, ) @@ -124,6 +125,29 @@ def _prompt_tokens_details_as_mapping(details: "PromptTokensDetailsWrapper") -> return details.model_dump(exclude_none=True) if hasattr(details, "model_dump") else {} +_PENDING_CACHE_WRITES: Final[set["asyncio.Task[None]"]] = set() # mutable-ok: strong refs to pending write tasks + + +async def _complete_cache_write_despite_cancellation(write_factory: Callable[[], Awaitable[None]]) -> None: + try: + await write_factory() + except asyncio.CancelledError: + try: + await asyncio.wait_for(write_factory(), timeout=CACHE_WRITE_SHUTDOWN_FLUSH_TIMEOUT_SECONDS) + except Exception as flush_error: # noqa: BLE001 # shutdown flush failures are logged, never raised + verbose_logger.warning( + "LiteLLM Cache: pending cache write failed during event loop shutdown: %s", flush_error + ) + raise + + +def create_cache_write_task(write_factory: Callable[[], Awaitable[None]]) -> "asyncio.Task[None]": + task: Final = asyncio.create_task(_complete_cache_write_despite_cancellation(write_factory)) + _PENDING_CACHE_WRITES.add(task) + task.add_done_callback(_PENDING_CACHE_WRITES.discard) + return task + + def _request_cache_key(request_kwargs: Mapping[str, Any]) -> str | None: """Read the caller-supplied ``cache_key`` off the request kwargs.""" return request_kwargs.get("cache_key", None) @@ -983,6 +1007,7 @@ class LLMCachingHandler: if litellm.cache is None: return + cache: Final = litellm.cache new_kwargs: Final = kwargs.copy() new_kwargs.update( @@ -1004,24 +1029,24 @@ class LLMCachingHandler: ): if ( isinstance(result, EmbeddingResponse) - and litellm.cache is not None - and not isinstance(litellm.cache.cache, S3Cache) # s3 doesn't support bulk writing. Exclude. + and not isinstance(cache.cache, S3Cache) # s3 doesn't support bulk writing. Exclude. ): - asyncio.create_task( - litellm.cache.async_add_cache_pipeline( + create_cache_write_task( + lambda: cache.async_add_cache_pipeline( result, dynamic_cache_object=self.dual_cache, **new_kwargs ) ) else: - asyncio.create_task( - litellm.cache.async_add_cache( - result.model_dump_json(), + result_json: Final = result.model_dump_json() + create_cache_write_task( + lambda: cache.async_add_cache( + result_json, dynamic_cache_object=self.dual_cache, **new_kwargs, ) ) else: - asyncio.create_task(litellm.cache.async_add_cache(result, **new_kwargs)) + create_cache_write_task(lambda: cache.async_add_cache(result, **new_kwargs)) def sync_set_cache( self, diff --git a/litellm/caching/qdrant_semantic_cache.py b/litellm/caching/qdrant_semantic_cache.py index 4898700c403..c5876e993d3 100644 --- a/litellm/caching/qdrant_semantic_cache.py +++ b/litellm/caching/qdrant_semantic_cache.py @@ -12,7 +12,7 @@ import ast import asyncio import json import os -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, cast import litellm from litellm._logging import print_verbose @@ -39,6 +39,12 @@ if TYPE_CHECKING: from litellm.router import Router +class _QdrantCollectionDetailsResponse(Protocol): + """The qdrant `/collections/{name}` response, whose body is kept as an opaque JSON object.""" + + def json(self) -> dict[str, object]: ... + + class QdrantSemanticCache(BaseCache): CACHE_KEY_FIELD_NAME = "litellm_cache_key" embedding_max_input_tokens: int | None = None @@ -115,15 +121,15 @@ class QdrantSemanticCache(BaseCache): raise ValueError(f"Error from qdrant checking if /collections exist {collection_exists.text}") if collection_exists.json()["result"]["exists"]: - collection_details = self.sync_client.get( + collection_details: _QdrantCollectionDetailsResponse = self.sync_client.get( url=f"{self.qdrant_api_base}/collections/{self.collection_name}", headers=self.headers, ) - self.collection_info = collection_details.json() + self.collection_info: dict[str, object] = collection_details.json() print_verbose(f"Collection already exists.\nCollection details:{self.collection_info}") self._ensure_cache_key_payload_index() else: - quantization_params: dict[str, Any] + quantization_params: dict[str, dict[str, object]] if quantization_config is None or quantization_config == "binary": quantization_params = { "binary": { @@ -214,7 +220,7 @@ class QdrantSemanticCache(BaseCache): resolve_embedding_max_input_tokens(self.embedding_max_input_tokens, self.embedding_model, router), ) - def _get_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: + def _get_embedding(self, prompt: str, metadata: dict[str, object] | None = None) -> EmbeddingResponse: """Embed via the proxy Router when it serves the model, else direct.""" try: from litellm.proxy.proxy_server import llm_model_list, llm_router @@ -241,7 +247,7 @@ class QdrantSemanticCache(BaseCache): num_retries=0, ) - async def _get_async_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: + async def _get_async_embedding(self, prompt: str, metadata: dict[str, object] | None = None) -> EmbeddingResponse: try: from litellm.proxy.proxy_server import llm_model_list, llm_router except ImportError: diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index 934ba500ef9..2b04a075114 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -18,7 +18,7 @@ import time from collections.abc import Awaitable, Callable, Sequence from contextvars import ContextVar from datetime import timedelta -from typing import TYPE_CHECKING, Any, Final, TypeVar, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, TypeVar, cast import litellm from litellm._logging import print_verbose, verbose_logger @@ -58,6 +58,26 @@ else: Span = Any +class _AsyncRedisCommands(Protocol): + """Async redis commands this cache issues. + + redis-py's type stubs omit these methods on RedisCluster, so the union returned by + init_async_client() is untyped at every call site without this protocol. + """ + + def ping(self) -> Awaitable[bool]: ... + + def delete(self, *names: str) -> Awaitable[int]: ... + + def ttl(self, name: str) -> Awaitable[int]: ... + + def rpush(self, name: str, *values: str | bytes | float) -> Awaitable[int]: ... + + def lpop(self, name: str, count: int | None = None) -> Awaitable[object]: ... + + def pipeline(self, transaction: bool = True) -> "Pipeline[bytes]": ... + + def _get_call_stack_info(num_frames: int = 2) -> str: """ Get the function names from the previous 1-2 functions in the call stack. @@ -175,6 +195,10 @@ _RedisCallResult = TypeVar("_RedisCallResult") _swallowed_redis_failures: Final[ContextVar[int]] = ContextVar("litellm_swallowed_redis_failures", default=0) +def _opaque_kwarg_key(value: object) -> str: + return f"{type(value).__name__}-{id(value)}" + + @functools.lru_cache(maxsize=1) def _redis_health_error_types() -> tuple[type, ...]: """Exception types that mean the Redis backend itself is unhealthy. @@ -399,10 +423,9 @@ class RedisCache(BaseCache): Generate a cache key for the async Redis client based on connection parameters. This ensures different Redis configurations use different cached clients. """ - # Create a stable representation of redis_kwargs for hashing # Sort keys to ensure consistent hash regardless of parameter order sorted_kwargs: Final = sorted(self.redis_kwargs.items()) - kwargs_str: Final = json.dumps(sorted_kwargs, sort_keys=True) + kwargs_str: Final = json.dumps(sorted_kwargs, sort_keys=True, default=_opaque_kwarg_key) kwargs_hash: Final = hashlib.sha256(kwargs_str.encode()).hexdigest()[:16] return f"async-redis-client-{kwargs_hash}" @@ -426,13 +449,16 @@ class RedisCache(BaseCache): self.redis_async_client = redis_async_client return redis_async_client + def _async_commands(self) -> _AsyncRedisCommands: + return self.init_async_client() + def check_and_fix_namespace(self, key: str) -> str: """ Make sure each key starts with the given namespace """ if key is None: return key - if self.namespace is not None and not key.startswith(self.namespace): + if self.namespace and not key.startswith(self.namespace + ":"): key = self.namespace + ":" + key return key @@ -1052,19 +1078,17 @@ class RedisCache(BaseCache): await self.async_set_cache_pipeline(self.redis_batch_writing_buffer) self.redis_batch_writing_buffer = [] - def _get_cache_logic(self, cached_response: Any): + def _get_cache_logic(self, cached_response: bytes | str | None): """ Common 'get_cache_logic' across sync + async redis client implementations """ if cached_response is None: - return cached_response - # cached_response is in `b{} convert it to ModelResponse - cached_response = cached_response.decode("utf-8") # Convert bytes to string + return None + decoded: Final = cached_response.decode("utf-8") if isinstance(cached_response, bytes) else cached_response try: - cached_response = json.loads(cached_response) # Convert string to dictionary + return json.loads(decoded) except Exception: - cached_response = ast.literal_eval(cached_response) - return cached_response + return ast.literal_eval(decoded) def get_cache(self, key, parent_otel_span: Span | None = None, **kwargs): try: @@ -1311,8 +1335,7 @@ class RedisCache(BaseCache): raise e async def ping(self) -> bool: - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `ping` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() print_verbose("Pinging Async Redis Cache") try: @@ -1346,8 +1369,7 @@ class RedisCache(BaseCache): @_redis_circuit_breaker_guard async def delete_cache_keys(self, keys): - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `delete` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() keys = [self.check_and_fix_namespace(key=key) for key in keys] # keys is a list, unpack it so it gets passed as individual elements to delete await _redis_client.delete(*keys) @@ -1384,10 +1406,10 @@ class RedisCache(BaseCache): dict: {"status": "success" | "failed", "message": str, "error": Optional[str]} """ try: - import redis.asyncio as redis_async + from .._redis import get_redis_async_client # Create a fresh Redis client with current settings - redis_client: Final = redis_async.Redis(**self.redis_kwargs) + redis_client: Final = get_redis_async_client(**self.redis_kwargs) # Test the connection ping_result: Final = await redis_client.ping() @@ -1412,8 +1434,7 @@ class RedisCache(BaseCache): @_redis_circuit_breaker_guard async def async_delete_cache(self, key: str): - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `delete` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) # keys is str return await _redis_client.delete(key) @@ -1520,8 +1541,7 @@ class RedisCache(BaseCache): Redis ref: https://redis.io/docs/latest/commands/ttl/ """ try: - # typed as Any, redis python lib has incomplete type stubs for RedisCluster and does not include `ttl` - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) ttl: Final = await _redis_client.ttl(key) if ttl <= -1: # -1 means the key does not exist, -2 key does not exist @@ -1551,7 +1571,7 @@ class RedisCache(BaseCache): Returns: int: The length of the list after the push operation """ - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) start_time: Final = time.time() try: @@ -1618,7 +1638,7 @@ class RedisCache(BaseCache): if len(rpush_list) == 0: return [] - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() try: @@ -1675,7 +1695,7 @@ class RedisCache(BaseCache): parent_otel_span: Span | None = None, **kwargs, ) -> Any | list[Any]: - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() key = self.check_and_fix_namespace(key=key) start_time: Final = time.time() print_verbose(f"LPOP from Redis list: key: {key}, count: {count}") @@ -1807,7 +1827,7 @@ class RedisCache(BaseCache): if len(lpop_list) == 0: return [] - _redis_client: Final[Any] = self.init_async_client() + _redis_client: Final = self._async_commands() start_time: Final = time.time() try: diff --git a/litellm/caching/redis_cluster_cache.py b/litellm/caching/redis_cluster_cache.py index b6dd8047fd4..12d285ca5a8 100644 --- a/litellm/caching/redis_cluster_cache.py +++ b/litellm/caching/redis_cluster_cache.py @@ -64,22 +64,9 @@ class RedisClusterCache(RedisCache): dict: {"status": "success" | "failed", "message": str, "error": Optional[str]} """ try: - import redis.asyncio as redis_async - from redis.cluster import ClusterNode + from .._redis import get_redis_async_client - # Create ClusterNode objects from startup_nodes - cluster_kwargs: Final = self.redis_kwargs.copy() - startup_nodes: Final = cluster_kwargs.pop("startup_nodes", []) - - new_startup_nodes: Final[list[ClusterNode]] = [] - for item in startup_nodes: - new_startup_nodes.append(ClusterNode(**item)) - - # Create a fresh Redis Cluster client with current settings - redis_client: Final = redis_async.RedisCluster( - startup_nodes=new_startup_nodes, - **cluster_kwargs, - ) + redis_client: Final = get_redis_async_client(**self.redis_kwargs) # Test the connection ping_result: Final = await redis_client.ping() diff --git a/litellm/caching/redis_cluster_node_isolation.py b/litellm/caching/redis_cluster_node_isolation.py index 8b0c120e80c..ae8c78709d9 100644 --- a/litellm/caching/redis_cluster_node_isolation.py +++ b/litellm/caching/redis_cluster_node_isolation.py @@ -18,6 +18,14 @@ already does when one of its pooled connections errors), leaving every other nod connections untouched. Every other branch (MOVED, ASK, CLUSTERDOWN, slot-not-covered, retry-exhaustion) is unchanged from upstream, since those already carry real evidence the topology changed. + +redis-py 8.x fixed this upstream with gentler machinery than this override's +``node.disconnect()`` (which also kills connections other coroutines are mid-operation +on, so one timeout cascades into a reconnect storm and, with TLS, a fresh handshake per +killed connection): it marks in-use connections for reconnect only after their current +operation completes, disconnects only the idle pooled ones, and defers reinitialization +to the outer retry loop. When the installed ``ClusterNode`` has that per-connection +recovery API, the factory returns the base ``RedisCluster`` unmodified. """ import asyncio @@ -72,8 +80,16 @@ class _ClusterAttrs(Protocol): _VERIFIED_REDIS_VERSIONS: Final = frozenset({"5.3.1"}) -def get_litellm_async_redis_cluster_class() -> type["_AsyncRedisClusterType"]: - """Builds the ``RedisCluster`` subclass with the per-node isolation fix. +def get_litellm_async_redis_cluster_class( + cluster_node_class: type | None = None, +) -> type["_AsyncRedisClusterType"]: + """Returns the base ``RedisCluster`` when the installed redis-py already recovers a + node-level connection error per-connection (8.x+), else builds the ``RedisCluster`` + subclass with the per-node isolation fix for older versions whose upstream branch + tears down the whole cluster client. + + ``cluster_node_class`` exists for dependency injection in tests; production callers + leave it unset and the installed ``ClusterNode`` is used. Imported lazily because this module is reachable from a base ``import litellm`` while redis is not a base dependency. Cheap to call repeatedly: the underlying redis @@ -81,7 +97,10 @@ def get_litellm_async_redis_cluster_class() -> type["_AsyncRedisClusterType"]: """ import redis from redis.asyncio.cluster import ( - RedisCluster as _BaseAsyncRedisCluster, # pyright: ignore[reportUnknownVariableType] # redis-py ships no resolvable stub for this class under the repo's current (stale) types-redis pin + ClusterNode as _AsyncClusterNode, # pyright: ignore[reportUnknownVariableType] # redis-py ships no resolvable stub for this class under the repo's current (stale) types-redis pin + ) + from redis.asyncio.cluster import ( + RedisCluster as _BaseAsyncRedisCluster, # pyright: ignore[reportUnknownVariableType] # same stale-stub gap as the import above ) from redis.cluster import get_node_name from redis.commands import READ_COMMANDS @@ -98,6 +117,15 @@ def get_litellm_async_redis_cluster_class() -> type["_AsyncRedisClusterType"]: from redis.exceptions import ConnectionError as _RedisConnectionError from redis.exceptions import TimeoutError as _RedisTimeoutError + node_class: Final = cluster_node_class if cluster_node_class is not None else _AsyncClusterNode + if hasattr(node_class, "update_active_connections_for_reconnect"): + verbose_logger.debug( + "redis-py %s recovers a node-level connection error per-connection upstream; " + "using the base RedisCluster without litellm's node-isolation override.", + redis.__version__, + ) + return _BaseAsyncRedisCluster + if redis.__version__ not in _VERIFIED_REDIS_VERSIONS: verbose_logger.warning( "redis-py %s is not in the set this cluster-teardown-storm fix was verified " diff --git a/litellm/caching/valkey_semantic_cache.py b/litellm/caching/valkey_semantic_cache.py index c66f6873383..58b76d98d6d 100644 --- a/litellm/caching/valkey_semantic_cache.py +++ b/litellm/caching/valkey_semantic_cache.py @@ -17,6 +17,7 @@ RedisSemanticCache since those are backend agnostic. import asyncio import hashlib import os +from collections.abc import Mapping, Sequence from dataclasses import dataclass from typing import Any, Final @@ -64,7 +65,7 @@ class ValkeySemanticCache(RedisSemanticCache): async_client: AsyncRedis | None = None, embedding_max_input_tokens: int | None = None, embedding_timeout: float | None = None, - **kwargs: Any, + **kwargs: object, ): if similarity_threshold is None: raise ValueError("similarity_threshold must be provided, passed None") @@ -87,11 +88,13 @@ class ValkeySemanticCache(RedisSemanticCache): self.key_prefix = f"{self.index_name}:" self._index_dim: int | None = None - resolved_url = None - if sync_client is None or async_client is None: - resolved_url = redis_url or self._build_valkey_url(host, port, password, ssl) - self.sync_client = sync_client if sync_client is not None else Redis.from_url(resolved_url) - self.async_client = async_client if async_client is not None else AsyncRedis.from_url(resolved_url) + if sync_client is not None and async_client is not None: + self.sync_client = sync_client + self.async_client = async_client + else: + resolved_url: Final = redis_url or self._build_valkey_url(host, port, password, ssl) + self.sync_client = sync_client if sync_client is not None else Redis.from_url(resolved_url) + self.async_client = async_client if async_client is not None else AsyncRedis.from_url(resolved_url) print_verbose(f"Valkey semantic-cache initializing index - {self.index_name}") @@ -118,7 +121,7 @@ class ValkeySemanticCache(RedisSemanticCache): return hashlib.sha256(str(key).encode("utf-8")).hexdigest() @staticmethod - def _embedding_to_bytes(embedding: list[float]) -> bytes: + def _embedding_to_bytes(embedding: Sequence[float]) -> bytes: return pack_vector(embedding) def _index_schema(self, dim: int) -> tuple[TagField, VectorField]: @@ -192,7 +195,9 @@ class ValkeySemanticCache(RedisSemanticCache): def _doc_key(self, key: str) -> str: return f"{self.key_prefix}{self._scope_tag(key)}:{uuid.uuid4()}" - def _doc_mapping(self, key: str, prompt: str, value_str: str, embedding: list[float]) -> dict: + def _doc_mapping( + self, key: str, prompt: str, value_str: str, embedding: Sequence[float] + ) -> Mapping[str | bytes, str | bytes]: return { self.CACHE_KEY_FIELD_NAME: self._scope_tag(key), self.PROMPT_FIELD_NAME: prompt, @@ -208,30 +213,49 @@ class ValkeySemanticCache(RedisSemanticCache): ) return Query(query_string).return_fields(self.RESPONSE_FIELD_NAME, self.DISTANCE_FIELD_NAME).dialect(2) + async def _async_search(self, key: str, embedding: Sequence[float]) -> object: + """Run the KNN query on the async client, stopping the untyped search surface here.""" + return await self.async_client.ft(self.index_name).search( + self._knn_query(key), + query_params={"vec": self._embedding_to_bytes(embedding)}, # pyright: ignore[reportArgumentType] # redis stubs omit bytes; KNN vectors are raw bytes at runtime + ) + @classmethod - def _first_hit(cls, search_result: Any) -> _ValkeyCacheHit | None: - docs: Final = getattr(search_result, "docs", []) + def _first_hit(cls, search_result: object) -> _ValkeyCacheHit | None: + docs: Final[Sequence[object]] = getattr(search_result, "docs", []) if not docs: return None doc: Final = docs[0] + response_field: Final[object] = getattr(doc, cls.RESPONSE_FIELD_NAME) + distance_field: Final[str | bytes | float] = getattr(doc, cls.DISTANCE_FIELD_NAME) return _ValkeyCacheHit( - response=str(getattr(doc, cls.RESPONSE_FIELD_NAME)), - distance=float(getattr(doc, cls.DISTANCE_FIELD_NAME)), + response=str(response_field), + distance=float(distance_field), ) - def _resolve_hit(self, hit: _ValkeyCacheHit | None, key: str, **kwargs: Any) -> Any: + @staticmethod + def _record_similarity(kwargs: dict[str, Any], similarity: float) -> None: + """Stamp the semantic-similarity score onto the request metadata carried in ``kwargs``.""" + kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity + + @staticmethod + def _embedding_metadata(kwargs: dict[str, Any]) -> dict[str, Any] | None: + """The request metadata forwarded to the embedding call.""" + return kwargs.get("metadata") + + def _resolve_hit(self, hit: _ValkeyCacheHit | None, key: str, **kwargs: object) -> object: if hit is None: - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) return None similarity: Final = 1 - hit.distance - kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity + self._record_similarity(kwargs, similarity) if similarity < self.similarity_threshold: return None return self._get_cache_logic(cached_response=hit.response) - def set_cache(self, key: str, value: Any, **kwargs: Any) -> None: + def set_cache(self, key: str, value: object, **kwargs: object) -> None: print_verbose(f"Valkey semantic-cache set_cache, kwargs: {kwargs}") try: prompt: Final = self._get_prompt_from_kwargs(**kwargs) @@ -250,12 +274,12 @@ class ValkeySemanticCache(RedisSemanticCache): except Exception as e: print_verbose(f"Error in Valkey semantic-cache set_cache: {e}") - def get_cache(self, key: str, **kwargs: Any) -> Any: + def get_cache(self, key: str, **kwargs: object) -> object: print_verbose(f"Valkey semantic-cache get_cache, kwargs: {kwargs}") try: prompt: Final = self._get_prompt_from_kwargs(**kwargs) if prompt is None: - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) return None embedding: Final = self._get_embedding(prompt) @@ -263,14 +287,14 @@ class ValkeySemanticCache(RedisSemanticCache): search_result: Final = self.sync_client.ft(self.index_name).search( self._knn_query(key), - query_params={"vec": self._embedding_to_bytes(embedding)}, + query_params={"vec": self._embedding_to_bytes(embedding)}, # pyright: ignore[reportArgumentType] # redis stubs omit bytes; KNN vectors are raw bytes at runtime ) return self._resolve_hit(self._first_hit(search_result), key, **kwargs) except Exception as e: print_verbose(f"Error in Valkey semantic-cache get_cache: {e}") - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) - async def async_set_cache(self, key: str, value: Any, **kwargs: Any) -> None: + async def async_set_cache(self, key: str, value: object, **kwargs: object) -> None: print_verbose(f"Async Valkey semantic-cache set_cache, kwargs: {kwargs}") try: prompt: Final = self._get_prompt_from_kwargs(**kwargs) @@ -278,7 +302,7 @@ class ValkeySemanticCache(RedisSemanticCache): print_verbose("No prompt provided for semantic caching") return - embedding: Final = await self._get_async_embedding(prompt, metadata=kwargs.get("metadata")) + embedding: Final = await self._get_async_embedding(prompt, metadata=self._embedding_metadata(kwargs)) await self._ensure_index_async(len(embedding)) doc_key: Final = self._doc_key(key) @@ -289,31 +313,28 @@ class ValkeySemanticCache(RedisSemanticCache): except Exception as e: print_verbose(f"Error in async Valkey semantic-cache set_cache: {e}") - async def async_get_cache(self, key: str, **kwargs: Any) -> Any: + async def async_get_cache(self, key: str, **kwargs: object) -> object: print_verbose(f"Async Valkey semantic-cache get_cache, kwargs: {kwargs}") try: prompt: Final = self._get_prompt_from_kwargs(**kwargs) if prompt is None: - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) return None - embedding: Final = await self._get_async_embedding(prompt, metadata=kwargs.get("metadata")) + embedding: Final = await self._get_async_embedding(prompt, metadata=self._embedding_metadata(kwargs)) await self._ensure_index_async(len(embedding)) - search_result: Final = await self.async_client.ft(self.index_name).search( - self._knn_query(key), - query_params={"vec": self._embedding_to_bytes(embedding)}, - ) + search_result: Final[object] = await self._async_search(key, embedding) return self._resolve_hit(self._first_hit(search_result), key, **kwargs) except Exception as e: print_verbose(f"Error in async Valkey semantic-cache get_cache: {e}") - kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + self._record_similarity(kwargs, 0.0) - async def async_set_cache_pipeline(self, cache_list: list[tuple[str, Any]], **kwargs: Any) -> None: + async def async_set_cache_pipeline(self, cache_list: list[tuple[str, object]], **kwargs: object) -> None: try: await asyncio.gather(*[self.async_set_cache(key, value, **kwargs) for key, value in cache_list]) except Exception as e: print_verbose(f"Error in Valkey semantic-cache async_set_cache_pipeline: {e}") - async def _index_info(self) -> dict: + async def _index_info(self) -> Mapping[str, object]: return await self.async_client.ft(self.index_name).info() diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index 727c39c16ec..f494d6610a1 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -45,14 +45,14 @@ class ResponsesToCompletionBridgeHandler: return bool(stream) @staticmethod - def _is_preformatted_cached_chat_stream(result: Any) -> bool: + def _is_preformatted_cached_chat_stream(result: object) -> bool: from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper return isinstance(result, CustomStreamWrapper) and result.custom_llm_provider == "cached_response" @staticmethod def _coerce_response_object( - response_obj: Any, + response_obj: object, hidden_params: dict | None, ) -> "ResponsesAPIResponse": if isinstance(response_obj, ResponsesAPIResponse): @@ -78,8 +78,8 @@ class ResponsesToCompletionBridgeHandler: for _ in stream_iter: pass - completed: Final = getattr(stream_iter, "completed_response", None) - response_obj: Final = getattr(completed, "response", None) if completed else None + completed: Final[object] = getattr(stream_iter, "completed_response", None) + response_obj: Final[object] = getattr(completed, "response", None) if completed else None if response_obj is None: raise ValueError("Stream ended without a completed response") @@ -93,8 +93,8 @@ class ResponsesToCompletionBridgeHandler: async for _ in stream_iter: pass - completed: Final = getattr(stream_iter, "completed_response", None) - response_obj: Final = getattr(completed, "response", None) if completed else None + completed: Final[object] = getattr(stream_iter, "completed_response", None) + response_obj: Final[object] = getattr(completed, "response", None) if completed else None if response_obj is None: raise ValueError("Stream ended without a completed response") @@ -157,7 +157,7 @@ class ResponsesToCompletionBridgeHandler: def completion( self, *args, **kwargs ) -> Union[ - Coroutine[Any, Any, Union["ModelResponse", "CustomStreamWrapper"]], + Coroutine[None, None, Union["ModelResponse", "CustomStreamWrapper"]], "ModelResponse", "CustomStreamWrapper", ]: diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 6103b1bf484..7368de1e968 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -5,7 +5,7 @@ Handler for transforming /chat/completions api requests to litellm.responses req import json import os from collections.abc import AsyncIterator, Callable, Iterable, Iterator, Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict, Union, cast +from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict, Union, cast, get_args from openai.types.responses.custom_tool_param import CustomToolParam from openai.types.responses.response_input_param import ( @@ -21,6 +21,9 @@ from pydantic import BaseModel import litellm from litellm import ModelResponse from litellm._logging import verbose_logger +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + responses_reasoning_item_from_thinking_blocks, +) from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator from litellm.llms.base_llm.bridges.completion_transformation import ( CompletionTransformationBridge, @@ -32,6 +35,7 @@ from litellm.responses.sse_output_recovery import ( ) from litellm.responses.utils import normalize_responses_api_stream_options from litellm.types.llms.openai import ( + REASONING_EFFORT, ChatCompletionAnnotation, ChatCompletionReasoningItem, ChatCompletionToolCallChunk, @@ -55,9 +59,11 @@ if TYPE_CHECKING: from litellm.types.llms.openai import ( ALL_RESPONSES_API_TOOL_PARAMS, AllMessageValues, + ChatCompletionFileObject, ChatCompletionImageObject, ChatCompletionRedactedThinkingBlock, ChatCompletionThinkingBlock, + ChatCompletionToolReferenceObject, OpenAIMessageContentListBlock, ) from litellm.types.utils import Choices @@ -85,6 +91,22 @@ def _get_reasoning_items( return [] +def _reasoning_input_items(msg: "AllMessageValues") -> list[dict[str, object]]: # mutable-ok: API message payload + """Reasoning input items for an assistant message. + + Stored reasoning items win because they carry an id the Responses API minted; thinking + blocks are the fallback for turns that arrived over another API surface. + """ + items: Final = _get_reasoning_items(msg) + stored: Final = [_reasoning_item_to_response_input(item) for item in items] # mutable-ok: API message payload + if stored: + return stored + raw_blocks: Final = msg.get("thinking_blocks") or () + blocks: Final = cast("Iterable[ChatCompletionThinkingBlock]", raw_blocks) # cast-ok: untyped client json + from_thinking: Final = responses_reasoning_item_from_thinking_blocks(blocks) + return [] if from_thinking is None else [dict(from_thinking)] # mutable-ok: API message payload + + def _build_reasoning_item( item_id: str, encrypted_content: str | None, @@ -155,6 +177,16 @@ def _map_incomplete_reason_to_finish_reason(incomplete_reason: str | None) -> Li return "length" +def _input_file_from_file_value(file_value: object) -> dict[str, object]: + if not isinstance(file_value, dict): + return {"type": "input_file"} + file_dict: Final = cast("dict[str, object]", file_value) # cast-ok: runtime dict checked + return { + "type": "input_file", + **{key: file_dict[key] for key in ("file_id", "file_data", "filename") if key in file_dict}, + } + + def _incomplete_reason_from_response_payload(response_payload: object) -> str | None: if not isinstance(response_payload, Mapping): return None @@ -180,7 +212,8 @@ def _tool_call_dict_from_output_item(item: Mapping[str, Any], index: int) -> _Ch LiteLLMCompletionResponsesConfig, ) - is_custom: Final = item.get("type") == "custom_tool_call" + item_type: Final[object] = item.get("type") + is_custom: Final = item_type == "custom_tool_call" arguments: Final = (item.get("input") if is_custom else item.get("arguments")) or "" name: Final = item.get("name") or ("custom_tool" if is_custom else "") function_chunk: Final = ChatCompletionToolCallFunctionChunk(name=name, arguments=arguments) @@ -190,7 +223,7 @@ def _tool_call_dict_from_output_item(item: Mapping[str, Any], index: int) -> _Ch function=function_chunk, index=index, ) - raw_provider_fields: Final = item.get("provider_specific_fields") + raw_provider_fields: Final[object] = item.get("provider_specific_fields") if isinstance(raw_provider_fields, dict): provider_specific_fields = raw_provider_fields elif raw_provider_fields and hasattr(raw_provider_fields, "__dict__"): @@ -372,8 +405,15 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): ) ) elif role == "assistant" and tool_calls and isinstance(tool_calls, list): - for r_item in _get_reasoning_items(msg): - input_items.append(_reasoning_item_to_response_input(r_item)) + input_items.extend(_reasoning_input_items(msg)) + if content: + input_items.append( + { # mutable-ok: API message payload + "type": "message", + "role": "assistant", + "content": self._convert_content_to_responses_format(content, "assistant"), + } + ) for tool_call in tool_calls: function = tool_call.get("function") custom = tool_call.get("custom") @@ -400,15 +440,16 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): raise ValueError(f"tool call not supported: {tool_call}") elif content is not None: if role == "assistant": - for r_item in _get_reasoning_items(msg): - input_items.append(_reasoning_item_to_response_input(r_item)) + input_items.extend(_reasoning_input_items(msg)) input_items.append( - { + { # mutable-ok: API message payload "type": "message", "role": role, "content": self._convert_content_to_responses_format(content, cast(str, role)), } ) + elif role == "assistant": + input_items.extend(_reasoning_input_items(msg)) return input_items, instructions @@ -467,7 +508,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): def _merge_responses_api_request_into_request_data( self, - request_data: dict[str, Any], + request_data: dict[str, object], responses_api_request: "ResponsesAPIOptionalRequestParams", instructions: str | None, ) -> None: @@ -929,7 +970,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): content: str | list[object] | Iterable[ - Union["OpenAIMessageContentListBlock", "ChatCompletionThinkingBlock", "ChatCompletionRedactedThinkingBlock"] + Union[ + "OpenAIMessageContentListBlock", + "ChatCompletionThinkingBlock", + "ChatCompletionRedactedThinkingBlock", + "ChatCompletionToolReferenceObject", + ] ] | None, role: str, @@ -978,17 +1024,15 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): result.append(converted) verbose_logger.debug("Chat provider: image -> %s", converted) elif item_type == "file": - # Map Chat Completion file to Responses API input_file - # {"type": "file", "file": {"file_data": "...", "filename": "..."}} - # -> {"type": "input_file", "file_data": "...", "filename": "..."} - file_data = item.get("file", {}) - converted = {"type": "input_file"} - if isinstance(file_data, dict): - for key in ["file_id", "file_data", "filename"]: - if key in file_data: - converted[key] = file_data[key] + converted = _input_file_from_file_value( + cast("ChatCompletionFileObject", item).get("file"), # cast-ok: type tag checked + ) result.append(converted) verbose_logger.debug("Chat provider: file -> %s", converted) + elif item_type == "tool_reference": + verbose_logger.debug( + "Chat provider: tool_reference has no responses API equivalent; skipped" + ) elif item_type in [ "input_text", "input_image", @@ -1086,22 +1130,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): litellm.reasoning_auto_summary or os.getenv("LITELLM_REASONING_AUTO_SUMMARY", "false").lower() == "true" ) - # If string is passed, map with optional summary based on flag/env var - if reasoning_effort == "none": - return Reasoning(effort="none", summary="detailed") if auto_summary_enabled else Reasoning(effort="none") - elif reasoning_effort == "high": - return Reasoning(effort="high", summary="detailed") if auto_summary_enabled else Reasoning(effort="high") - elif reasoning_effort == "xhigh": - return Reasoning(effort="xhigh", summary="detailed") if auto_summary_enabled else Reasoning(effort="xhigh") - elif reasoning_effort == "medium": + if reasoning_effort in get_args(REASONING_EFFORT): return ( - Reasoning(effort="medium", summary="detailed") if auto_summary_enabled else Reasoning(effort="medium") - ) - elif reasoning_effort == "low": - return Reasoning(effort="low", summary="detailed") if auto_summary_enabled else Reasoning(effort="low") - elif reasoning_effort == "minimal": - return ( - Reasoning(effort="minimal", summary="detailed") if auto_summary_enabled else Reasoning(effort="minimal") + Reasoning(effort=reasoning_effort, summary="detailed") + if auto_summary_enabled + else Reasoning(effort=reasoning_effort) ) return None diff --git a/litellm/constants.py b/litellm/constants.py index c33e5a53b76..1c1939bd350 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -9,10 +9,48 @@ DEFAULT_HEALTH_CHECK_PROMPT: Final = str(os.getenv("DEFAULT_HEALTH_CHECK_PROMPT" AZURE_DEFAULT_RESPONSES_API_VERSION: Final = str(os.getenv("AZURE_DEFAULT_RESPONSES_API_VERSION", "preview")) ROUTER_MAX_FALLBACKS: Final = int(os.getenv("ROUTER_MAX_FALLBACKS", 5)) ROUTER_FALLBACK_ERROR_DETAIL_MAX_CHARS: Final = 2000 +RUNTIME_UPDATABLE_ROUTER_SETTINGS: Final[frozenset[str]] = frozenset( + { + "routing_strategy_args", + "routing_strategy", + "routing_groups", + "allowed_fails", + "cooldown_time", + "num_retries", + "timeout", + "max_retries", + "retry_after", + "fallbacks", + "context_window_fallbacks", + "retry_policy", + "model_group_retry_policy", + "model_group_alias", + "enable_weighted_failover", + "enable_tag_filtering", + "tag_routing_prefix", + "optional_pre_call_checks", + } +) +ROUTER_SETTINGS_MANAGED_OUTSIDE_CONFIG: Final[frozenset[str]] = frozenset( + { + "model_list", + "search_tools", + "assistants_config", + "router_general_settings", + "ignore_invalid_deployments", + "fallback_access_check", + } +) DEFAULT_BATCH_SIZE: Final = int(os.getenv("DEFAULT_BATCH_SIZE", 512)) DEFAULT_FLUSH_INTERVAL_SECONDS: Final = int(os.getenv("DEFAULT_FLUSH_INTERVAL_SECONDS", 5)) DEFAULT_S3_FLUSH_INTERVAL_SECONDS: Final = int(os.getenv("DEFAULT_S3_FLUSH_INTERVAL_SECONDS", 10)) DEFAULT_S3_BATCH_SIZE: Final = int(os.getenv("DEFAULT_S3_BATCH_SIZE", 512)) +# https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-keys.html +MAX_S3_OBJECT_KEY_BYTES: Final = 1024 +S3_BOUNDED_OBJECT_KEY_HEAD_BYTES: Final = 64 +S3_PREFIX_DIGEST_CHARS: Final = 16 +# s3 allows 2048 bytes of combined metadata headers, which Content-Disposition counts against +MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES: Final = 1024 DEFAULT_SQS_FLUSH_INTERVAL_SECONDS: Final = int(os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10)) DEFAULT_NUM_WORKERS_LITELLM_PROXY: Final = int(os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1)) DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE = int(os.getenv("DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE", 1)) @@ -35,6 +73,7 @@ DEFAULT_COOLDOWN_TIME_SECONDS: Final = int(os.getenv("DEFAULT_COOLDOWN_TIME_SECO DEFAULT_REPLICATE_POLLING_RETRIES: Final = int(os.getenv("DEFAULT_REPLICATE_POLLING_RETRIES", 5)) DEFAULT_REPLICATE_POLLING_DELAY_SECONDS: Final = int(os.getenv("DEFAULT_REPLICATE_POLLING_DELAY_SECONDS", 1)) DEFAULT_IMAGE_TOKEN_COUNT: Final = int(os.getenv("DEFAULT_IMAGE_TOKEN_COUNT", 250)) +HF_CONFIG_FETCH_TIMEOUT_SECONDS: Final = 10.0 # Maximum wall-clock seconds a streaming response is allowed to run. # Streams exceeding this duration are terminated with a Timeout error. @@ -48,6 +87,9 @@ LITELLM_MAX_STREAMING_DURATION_SECONDS: Final = ( # Data URIs exceeding this are replaced with a size placeholder. # Set to 0 to disable truncation. MAX_BASE64_LENGTH_FOR_LOGGING: Final = int(os.getenv("MAX_BASE64_LENGTH_FOR_LOGGING", 64)) +REDACTED_BY_LITELLM: Final = "redacted-by-litellm" +# in-memory stand-in handed to provider converters for redacted arguments; never stored +REDACTED_TOOL_CALL_ARGUMENTS_PLACEHOLDER: Final = "{}" MAX_STRING_LENGTH_STDOUT_LOG: Final = get_env_int("MAX_STRING_LENGTH_STDOUT_LOG", 4096) @@ -126,6 +168,7 @@ MCP_CLIENT_TIMEOUT: Final = float(os.getenv("LITELLM_MCP_CLIENT_TIMEOUT", "60.0" MCP_TOOL_LISTING_TIMEOUT: Final = float(os.getenv("LITELLM_MCP_TOOL_LISTING_TIMEOUT", "30.0")) MCP_METADATA_TIMEOUT: Final = float(os.getenv("LITELLM_MCP_METADATA_TIMEOUT", "10.0")) MCP_HEALTH_CHECK_TIMEOUT: Final = float(os.getenv("LITELLM_MCP_HEALTH_CHECK_TIMEOUT", "10.0")) +MCP_TOOL_LISTING_MAX_PAGES: Final = 1000 # Allowlist of commands permitted for MCP stdio transport. # Prevents arbitrary command execution via /mcp-rest/test/* endpoints or server creation. @@ -146,6 +189,7 @@ LITELLM_UI_ALLOW_HEADERS: Final = [ "x-litellm-adaptive-router-model", "x-litellm-applied-guardrails", "x-litellm-guardrail-scan-id", + "x-litellm-cache-key", ] # Gemini model-specific minimal thinking budget constants @@ -284,6 +328,7 @@ REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_daily_org_spend_update REDIS_DAILY_END_USER_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_daily_end_user_spend_update_buffer" REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_daily_agent_spend_update_buffer" REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_daily_tag_spend_update_buffer" +REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY: Final = "litellm_window_spend_update_buffer" MAX_REDIS_BUFFER_DEQUEUE_COUNT: Final = int(os.getenv("MAX_REDIS_BUFFER_DEQUEUE_COUNT", 100)) # Bounds asyncio.Queue() instances (log queues, spend update queues, etc.) to prevent unbounded memory growth LITELLM_ASYNCIO_QUEUE_MAXSIZE: Final = int(os.getenv("LITELLM_ASYNCIO_QUEUE_MAXSIZE", 1000)) @@ -292,6 +337,9 @@ GUARDRAIL_SCANNED_MESSAGES_CACHE_TTL_SECONDS: Final = int( os.getenv("GUARDRAIL_SCANNED_MESSAGES_CACHE_TTL_SECONDS", 24 * 60 * 60) ) BEDROCK_APPLY_GUARDRAIL_CHUNK_BUDGET_CHARS: Final = 25_000 +DEFAULT_PRESIDIO_ANALYZE_CHUNK_SIZE_BYTES: Final = 500_000 +PRESIDIO_ANALYZE_CHUNK_OVERLAP_CHARS: Final = 4096 +PRESIDIO_ANALYZE_CHUNK_CONCURRENCY: Final = 8 # Aggregation threshold: default to 80% of the asyncio queue maxsize so the check can always trigger. # Must be < LITELLM_ASYNCIO_QUEUE_MAXSIZE; if set higher the aggregation logic will never fire. MAX_SIZE_IN_MEMORY_QUEUE: Final = int(os.getenv("MAX_SIZE_IN_MEMORY_QUEUE", int(LITELLM_ASYNCIO_QUEUE_MAXSIZE * 0.8))) @@ -377,6 +425,7 @@ AZURE_OPERATION_POLLING_TIMEOUT: Final = int(os.getenv("AZURE_OPERATION_POLLING_ AZURE_DOCUMENT_INTELLIGENCE_API_VERSION: Final = str(os.getenv("AZURE_DOCUMENT_INTELLIGENCE_API_VERSION", "2024-11-30")) AZURE_DOCUMENT_INTELLIGENCE_DEFAULT_DPI: Final = int(os.getenv("AZURE_DOCUMENT_INTELLIGENCE_DEFAULT_DPI", 96)) REDIS_SOCKET_TIMEOUT: Final = float(os.getenv("REDIS_SOCKET_TIMEOUT", 0.1)) +CACHE_WRITE_SHUTDOWN_FLUSH_TIMEOUT_SECONDS: Final[float] = 5.0 REDIS_CONNECTION_POOL_TIMEOUT: Final = int(os.getenv("REDIS_CONNECTION_POOL_TIMEOUT", 5)) REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD: Final = int(os.getenv("REDIS_CIRCUIT_BREAKER_FAILURE_THRESHOLD", 5)) REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT: Final = int(os.getenv("REDIS_CIRCUIT_BREAKER_RECOVERY_TIMEOUT", 60)) @@ -460,6 +509,8 @@ CONNECTION_ERROR_PATTERNS: Final[list[str]] = [ ] STREAM_SSE_DONE_STRING: Final[str] = "[DONE]" STREAM_SSE_DATA_PREFIX: Final[str] = "data: " +STREAM_SSE_KEEPALIVE_PING_CHUNK: Final[str] = 'event: ping\ndata: {"type": "ping"}\n\n' +STREAM_SSE_KEEPALIVE_PING_BYTES: Final[bytes] = STREAM_SSE_KEEPALIVE_PING_CHUNK.encode("utf-8") ### SPEND TRACKING ### DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND: Final = float( os.getenv("DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND", 0.001400) @@ -472,6 +523,22 @@ FIREWORKS_AI_80_B: Final = int(os.getenv("FIREWORKS_AI_80_B", 80)) #### Logging callback constants #### REDACTED_BY_LITELM_STRING: Final = "REDACTED_BY_LITELM" MAX_LANGFUSE_INITIALIZED_CLIENTS: Final = int(os.getenv("MAX_LANGFUSE_INITIALIZED_CLIENTS", 50)) +# Backpressure + lifetime bounds for the /v1/messages streaming relay (see +# BaseAnthropicMessagesStreamingIterator.async_sse_wrapper). The relay queue is +# bounded so a slow client throttles the upstream pump instead of letting it +# buffer the whole response in memory; the detached-drain cap bounds how many +# post-disconnect drains may run concurrently so client behavior can't create +# unbounded worker state. +ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE: Final = int( + os.getenv("ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE", "1024") +) +# Setting this to 0 disables detached draining entirely: every post-disconnect +# pump bills whatever partial output it has already collected and aborts the +# upstream stream immediately, instead of continuing to drain for the real +# terminal usage. +ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS: Final = int( + os.getenv("ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS", "100") +) LOGGING_WORKER_CONCURRENCY: Final = int(os.getenv("LOGGING_WORKER_CONCURRENCY", 100)) # Must be above 0 LOGGING_WORKER_MAX_QUEUE_SIZE: Final = int(os.getenv("LOGGING_WORKER_MAX_QUEUE_SIZE", 50_000)) LOGGING_WORKER_MAX_TIME_PER_COROUTINE: Final = float(os.getenv("LOGGING_WORKER_MAX_TIME_PER_COROUTINE", 20.0)) @@ -602,6 +669,8 @@ LITELLM_CHAT_PROVIDERS: Final = [ "nscale", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "publicai", @@ -619,6 +688,15 @@ LITELLM_CHAT_PROVIDERS: Final = [ "amazon_nova", ] +# Resolving these providers runs an OAuth device flow (their provider info IS the login), so any +# metadata or capability lookup against them can block for minutes waiting on a human. +PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO: Final = frozenset( + { + "github_copilot", + "chatgpt", + } +) + LITELLM_EMBEDDING_PROVIDERS_SUPPORTING_INPUT_ARRAY_OF_TOKENS: Final = [ "openai", "azure", @@ -749,6 +827,7 @@ openai_compatible_endpoints: Final[list] = [ "api.groq.com/openai/v1", "https://integrate.api.nvidia.com/v1", "api.deepseek.com/v1", + "api.together.ai/v1", "api.together.xyz/v1", "app.empower.dev/api/v1", "https://api.friendli.ai/serverless/v1", @@ -761,6 +840,7 @@ openai_compatible_endpoints: Final[list] = [ "inference.api.nscale.com/v1", "api.studio.nebius.ai/v1", "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + "https://dashscope.aliyuncs.com/compatible-mode/v1", "https://api-inference.modelscope.cn/v1", "https://api.moonshot.ai/v1", "https://api.publicai.co/v1", @@ -784,6 +864,7 @@ openai_compatible_endpoints: Final[list] = [ "https://api.meta.ai/v1", "https://api.cognition.ai/v1", "https://api.scx.ai/v1", + "https://gigachat.devices.sberbank.ru/api/v1", ] @@ -833,6 +914,8 @@ openai_compatible_providers: Final[list] = [ "nscale", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "v0", @@ -863,6 +946,8 @@ openai_text_completion_compatible_providers: Final[list] = [ # providers that s "featherless_ai", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "publicai", @@ -1070,7 +1155,7 @@ nebius_models: Final[set] = set( ] ) -dashscope_models: Final[set] = set( +dashscope_models: Final[frozenset] = frozenset( [ "qwen-turbo", "qwen-plus", @@ -1085,6 +1170,10 @@ dashscope_models: Final[set] = set( ] ) +qwencloud_models: Final[frozenset] = frozenset(dashscope_models) + +qwen_ai_platform_models: Final[frozenset] = frozenset(dashscope_models) + nebius_embedding_models: Final[set] = set( [ "BAAI/bge-en-icl", @@ -1201,6 +1290,7 @@ BEDROCK_CONVERSE_MODELS: Final = [ "openai.gpt-oss-120b-1:0", "anthropic.claude-haiku-4-5-20251001-v1:0", "anthropic.claude-sonnet-4-5-20250929-v1:0", + "anthropic.claude-fable-5-1", "anthropic.claude-fable-5", "anthropic.claude-sonnet-5", "anthropic.claude-opus-5", @@ -1356,8 +1446,6 @@ X_LITELLM_DISABLE_CALLBACKS: Final = "x-litellm-disable-callbacks" LITELLM_METADATA_FIELD: Final = "litellm_metadata" OLD_LITELLM_METADATA_FIELD: Final = "metadata" RETURN_RAW_MODEL_NAME_METADATA_KEY: Final = "_complexity_router_return_raw_model_name" -AUTO_ROUTED_REQUEST_METADATA_KEY: Final = "_auto_routed_request" -ROUTER_MODEL_NAME_RESPONSE_FIELD: Final = "router_model_name" SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: Final = "_session_deployment_affinity_ttl" CONSUMED_REQUEST_TAGS_METADATA_KEY: Final = "_consumed_request_tags" INTERNAL_CALL_ORIGIN_METADATA_KEY: Final = "internal_call_origin" @@ -1390,6 +1478,12 @@ DEFAULT_SOFT_BUDGET: Final = float( ) # by default all litellm proxy keys have a soft budget of 50.0 # makes it clear this is a rate limit error for a litellm virtual key RATE_LIMIT_ERROR_MESSAGE_FOR_VIRTUAL_KEY: Final = "LiteLLM Virtual Key user_api_key_hash" +# Prefix of the 401 raised when a submitted virtual key is not shaped like one. +INVALID_VIRTUAL_KEY_ERROR_MESSAGE: Final = "LiteLLM Virtual Key expected" +# Attribute stamped on that 401 at its raise site so log routing recognises it by +# provenance. Message text is caller-influenceable on other 401s, so it must not +# be used to classify. +INVALID_VIRTUAL_KEY_ERROR_MARKER: Final = "_litellm_invalid_virtual_key_error" # Python garbage collection threshold configuration # Format: "gen0,gen1,gen2" e.g., "1000,50,50" @@ -1467,6 +1561,12 @@ LITELLM_PROXY_MASTER_KEY_ALIAS: Final = "litellm_proxy_master_key" # ``ProxyLogging._handle_logging_proxy_only_error``. LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL: Final = "litellm_no_upstream_llm_call" +# Key/team metadata fields naming the OTel Resource ``service.name``, highest +# precedence first. Shared between the OTel v2 tenant router (which reads them +# out of ``user_api_key_auth_metadata``) and proxy request setup (which re-applies +# the key's values after the team metadata merge so a key outranks its team). +OTEL_SERVICE_NAME_METADATA_KEYS: Final = ("otel_service_name_override", "otel_service_name") + # Key Rotation Constants LITELLM_KEY_ROTATION_ENABLED: Final = os.getenv("LITELLM_KEY_ROTATION_ENABLED", "false") LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS: Final = int( @@ -1522,6 +1622,7 @@ KEY_ROTATION_JOB_NAME: Final = "litellm_key_rotation_job" EXPIRED_UI_SESSION_KEY_CLEANUP_JOB_NAME: Final = "litellm_expired_ui_session_key_cleanup_job" WEEKLY_SPEND_REPORT_JOB_ID: Final = "weekly_spend_report_job" MONTHLY_SPEND_REPORT_JOB_ID: Final = "monthly_spend_report_job" +USER_SPEND_ALERTS_JOB_ID: Final = "user_spend_alerts_job" PROMETHEUS_FALLBACK_STATS_JOB_ID: Final = "prometheus_fallback_stats_job" SLACK_DAILY_REPORT_LOCK_ID: Final = "slack_daily_report" SLACK_MODEL_DEPRECATION_LOCK_ID: Final = "slack_model_deprecation_warning" @@ -1542,6 +1643,8 @@ SPEND_LOG_WRITE_BATCH_MAX_ROWS: Final = max(1, int(os.getenv("SPEND_LOG_WRITE_BA SPEND_LOG_QUEUE_SIZE_THRESHOLD: Final = int(os.getenv("SPEND_LOG_QUEUE_SIZE_THRESHOLD", 100)) SPEND_LOG_QUEUE_MAX_BYTES: Final = max(1, int(os.getenv("SPEND_LOG_QUEUE_MAX_BYTES", "64000000"))) SPEND_LOG_QUEUE_POLL_INTERVAL: Final = float(os.getenv("SPEND_LOG_QUEUE_POLL_INTERVAL", 2.0)) +RESPONSES_SESSION_LOOKUP_MAX_ATTEMPTS: Final = max(1, int(os.getenv("RESPONSES_SESSION_LOOKUP_MAX_ATTEMPTS", "3"))) +RESPONSES_SESSION_LOOKUP_RETRY_INTERVAL: Final = float(os.getenv("RESPONSES_SESSION_LOOKUP_RETRY_INTERVAL", "0.2")) SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE: Final = int(os.getenv("SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE", 10000)) DEFAULT_CRON_JOB_LOCK_TTL_SECONDS: Final = int(os.getenv("DEFAULT_CRON_JOB_LOCK_TTL_SECONDS", 60)) # 1 minute PROXY_BUDGET_RESCHEDULER_MIN_TIME: Final = int(os.getenv("PROXY_BUDGET_RESCHEDULER_MIN_TIME", 597)) @@ -1561,6 +1664,19 @@ STALE_OBJECT_CLEANUP_BATCH_SIZE: Final = max(1, int(os.getenv("STALE_OBJECT_CLEA # installations with large numbers of stale managed objects). _batch_polling_env: Final = os.getenv("PROXY_BATCH_POLLING_ENABLED", "true").lower() PROXY_BATCH_POLLING_ENABLED: Final = _batch_polling_env == "true" +BACKGROUND_INTERACTION_COST_POLL_INITIAL_INTERVAL_SECONDS: Final = float( + os.getenv("BACKGROUND_INTERACTION_COST_POLL_INITIAL_INTERVAL_SECONDS", "5") +) +BACKGROUND_INTERACTION_COST_POLL_MAX_INTERVAL_SECONDS: Final = float( + os.getenv("BACKGROUND_INTERACTION_COST_POLL_MAX_INTERVAL_SECONDS", "60") +) +BACKGROUND_INTERACTION_COST_POLL_TIMEOUT_SECONDS: Final = float( + os.getenv("BACKGROUND_INTERACTION_COST_POLL_TIMEOUT_SECONDS", "3600") +) +_background_interaction_cost_polling_env: Final = os.getenv( + "BACKGROUND_INTERACTION_COST_POLLING_ENABLED", "true" +).lower() +BACKGROUND_INTERACTION_COST_POLLING_ENABLED: Final = _background_interaction_cost_polling_env == "true" PROXY_BUDGET_RESCHEDULER_MAX_TIME: Final = int(os.getenv("PROXY_BUDGET_RESCHEDULER_MAX_TIME", 605)) PROXY_BATCH_WRITE_AT: Final = int(os.getenv("PROXY_BATCH_WRITE_AT", 10)) # in seconds, increased from 10 PROXY_CONFIG_RELOAD_INTERVAL_SECONDS: Final = get_env_int("PROXY_CONFIG_RELOAD_INTERVAL_SECONDS", 30) @@ -1625,6 +1741,7 @@ LITELLM_SETTINGS_SAFE_DB_OVERRIDES: Final = [ "enable_anthropic_prompt_caching", "anthropic_prompt_caching_ttl", "max_ui_session_budget", + "budget_rollover", ] SPECIAL_LITELLM_AUTH_TOKEN: Final = ["ui-token"] DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL = int(os.getenv("DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL", 60)) @@ -1641,6 +1758,7 @@ DEFAULT_MCP_NAMESPACE_CSV_MAX_TOKENS: Final = 16 # Ceilings on the cached auth registries; larger tables fall back to per-row lookups # instead of holding an unbounded id set in every worker. TAG_REGISTRY_MAX_SIZE: Final = 5000 +MODEL_ACCESS_GROUP_REGISTRY_MAX_SIZE: Final = 5000 END_USER_RESTRICTED_REGISTRY_MAX_SIZE: Final = 5000 # How long a failed registry load is remembered as "unusable", so a degraded Postgres # is not re-scanned on every request on top of the per-id lookups it falls back to. @@ -1685,6 +1803,7 @@ SENTRY_DENYLIST: Final = [ "jwt_token", "private_key", "SLACK_WEBHOOK_URL", + "ALERTING_WEBHOOK_URL", "webhook_url", "LANGFUSE_SECRET_KEY", # Email Configuration @@ -1791,6 +1910,43 @@ BROWSER_SECURITY_HEADERS: Final[frozenset[str]] = frozenset( UNSAFE_PROXY_RESPONSE_HEADERS: Final[frozenset[str]] = HTTP_FRAMING_HEADERS | BROWSER_SECURITY_HEADERS +# A retrieved response replays the usage of the call that created it, so pricing these +# read/management routes like inference bills the same tokens twice. +NON_INFERENCE_CALL_TYPES: Final[frozenset[str]] = frozenset( + { + "get_responses", + "aget_responses", + "delete_responses", + "adelete_responses", + "cancel_responses", + "acancel_responses", + "list_input_items", + "alist_input_items", + "vector_store_create", + "avector_store_create", + "vector_store_retrieve", + "avector_store_retrieve", + "vector_store_list", + "avector_store_list", + "vector_store_update", + "avector_store_update", + "vector_store_delete", + "avector_store_delete", + "vector_store_file_create", + "avector_store_file_create", + "vector_store_file_list", + "avector_store_file_list", + "vector_store_file_retrieve", + "avector_store_file_retrieve", + "vector_store_file_content", + "avector_store_file_content", + "vector_store_file_update", + "avector_store_file_update", + "vector_store_file_delete", + "avector_store_file_delete", + } +) + # PTU reservation rollup writes rows to LiteLLM_DailyTeamSpend with this # sentinel api_key so PTU flat cost stays distinguishable from real per-request # spend under the table's composite unique constraint. diff --git a/litellm/containers/main.py b/litellm/containers/main.py index 97ca11872c1..90d59af009f 100644 --- a/litellm/containers/main.py +++ b/litellm/containers/main.py @@ -1,7 +1,7 @@ import asyncio import contextvars import json -from collections.abc import Coroutine, Mapping +from collections.abc import Callable, Coroutine, Mapping from functools import partial from typing import Final, Literal, overload @@ -47,6 +47,13 @@ __all__ = [ ##### Container Create ####################### +async def _encode_created_container_id( + pending: Coroutine[object, object, ContainerObject], + encode: Callable[[ContainerObject], ContainerObject], +) -> ContainerObject: + return encode(await pending) + + @client async def acreate_container( name: str, @@ -256,16 +263,16 @@ def create_container( _is_async=_is_async, ) - # Encode container_id with provider/model metadata for routing + encode: Final = partial( + ContainerRequestUtils.encode_container_id_in_response, + custom_llm_provider=custom_llm_provider, + litellm_metadata=kwargs.get("litellm_metadata"), + extra_body=extra_body, + ) if isinstance(container_obj, ContainerObject): - container_obj = ContainerRequestUtils.encode_container_id_in_response( - response_obj=container_obj, - custom_llm_provider=custom_llm_provider, - litellm_metadata=kwargs.get("litellm_metadata"), - extra_body=extra_body, - ) + return encode(container_obj) - return container_obj + return _encode_created_container_id(pending=container_obj, encode=encode) except Exception as e: raise litellm.exception_type( diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 8f7cd09d364..b83e9b395a8 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -2,6 +2,7 @@ ## File for 'response_cost' calculation in Logging import logging import time +from collections.abc import Mapping, Sequence from functools import lru_cache from typing import TYPE_CHECKING, Any, Final, Literal, cast @@ -19,6 +20,7 @@ from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( StandardBuiltInToolCostTracking, ) from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import ( + InteractionsUsageObjectTransformation, TranscriptionUsageObjectTransformation, ) from litellm.litellm_core_utils.llm_cost_calc.utils import ( @@ -74,7 +76,10 @@ from litellm.llms.perplexity.cost_calculator import ( from litellm.llms.tencent.cost_calculator import ( cost_per_token as tencent_cost_per_token, ) -from litellm.llms.together_ai.cost_calculator import get_model_params_and_category +from litellm.llms.together_ai.cost_calculator import ( + get_model_params_and_category, + has_together_registry_pricing, +) from litellm.llms.vertex_ai.cost_calculator import ( cost_per_character as google_cost_per_character, ) @@ -150,6 +155,7 @@ _VIDEO_CALL_TYPES: Final = frozenset( } ) + _SPEECH_CALL_TYPES: Final = frozenset( { CallTypes.speech.value, @@ -554,9 +560,10 @@ def cost_per_token( ) elif call_type == "atranscription" or call_type == "transcription": if _transcription_usage_has_token_details(usage_block): - return openai_cost_per_token( + return generic_cost_per_token( model=model_without_prefix, usage=usage_block, + custom_llm_provider=custom_llm_provider, service_tier=service_tier, data_residency=data_residency, ) @@ -589,6 +596,7 @@ def cost_per_token( prompt_characters=prompt_characters, completion_characters=completion_characters, usage=usage_block, + service_tier=service_tier, vertex_location=vertex_location, ) elif cost_router == "cost_per_token": @@ -633,12 +641,12 @@ def cost_per_token( return xai_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "lemonade": return lemonade_cost_per_token(model=model, usage=usage_block) - elif custom_llm_provider == "dashscope": + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): from litellm.llms.dashscope.cost_calculator import ( cost_per_token as dashscope_cost_per_token, ) - return dashscope_cost_per_token(model=model, usage=usage_block) + return dashscope_cost_per_token(model=model, usage=usage_block, custom_llm_provider=custom_llm_provider) elif custom_llm_provider == "azure_ai": return azure_ai_cost_per_token( model=model, @@ -731,6 +739,13 @@ def _get_provider_for_cost_calc( return custom_llm_provider +def _get_hidden_str_for_cost_calc(hidden_params: object, key: str) -> str | None: + if not isinstance(hidden_params, Mapping): + return None + value: Final[object] = hidden_params.get(key) + return value if isinstance(value, str) and value else None + + def _select_model_name_for_cost_calc( model: str | None, completion_response: object | None, @@ -747,7 +762,6 @@ def _select_model_name_for_cost_calc( """ return_model: str | None = None - region_name: str | None = None custom_llm_provider = _get_provider_for_cost_calc(model=model, custom_llm_provider=custom_llm_provider) completion_response_model: str | None = None @@ -757,6 +771,14 @@ def _select_model_name_for_cost_calc( elif isinstance(completion_response, dict): completion_response_model = completion_response.get("model", None) hidden_params: Final[dict | None] = getattr(completion_response, "_hidden_params", None) + provider_response_model: Final = _get_hidden_str_for_cost_calc(hidden_params, "provider_response_model") + explicit_pricing: Final = custom_pricing is True or base_model is not None + priced_from_response: Final = provider_response_model is not None or completion_response_model is not None + region_name: Final = ( + _get_hidden_str_for_cost_calc(hidden_params, "region_name") + if not explicit_pricing and priced_from_response + else None + ) if custom_pricing is True: if router_model_id is not None and router_model_id in litellm.model_cost: @@ -772,14 +794,12 @@ def _select_model_name_for_cost_calc( else: return_model = model - elif base_model is not None: - return_model = base_model + elif base_model is not None or provider_response_model is not None: + return_model = base_model if base_model is not None else provider_response_model elif completion_response_model is None and hidden_params is not None: if hidden_params.get("model", None) is not None and len(hidden_params["model"]) > 0: return_model = hidden_params.get("model", model) - elif hidden_params is not None and hidden_params.get("region_name", None) is not None: - region_name = hidden_params.get("region_name", None) if return_model is None and completion_response_model is not None: return_model = completion_response_model @@ -792,14 +812,27 @@ def _select_model_name_for_cost_calc( and custom_llm_provider is not None and not _model_contains_known_llm_provider(return_model) ): # add provider prefix if not already present, to match model_cost - if region_name is not None: - return_model = f"{custom_llm_provider}/{region_name}/{return_model}" - else: - return_model = f"{custom_llm_provider}/{return_model}" + provider_prefix: Final = custom_llm_provider if region_name is None else f"{custom_llm_provider}/{region_name}" + return_model = _strip_unregistered_leading_segments(f"{provider_prefix}/{return_model}", region_name) return return_model +def _strip_unregistered_leading_segments(model: str, region_name: str | None) -> str: + """Resolve a provider-prefixed slash alias like "vertex_ai/vertex/claude-opus-5" to the + registered cost key ("vertex_ai/claude-opus-5"), keeping the model unchanged when it already + resolves downstream (custom-priced router ids) or no stripped candidate is registered (#38069).""" + segments: Final = model.split("/") + if "/".join(segments[1:]) in litellm.model_cost: + return model + head_len: Final = 2 if region_name is not None and len(segments) > 2 and segments[1] == region_name else 1 + head: Final = "/".join(segments[:head_len]) + tail: Final = segments[head_len:] + strippable: Final = next((index for index, segment in enumerate(tail) if segment in LlmProvidersSet), len(tail)) + candidates: Final = (f"{head}/{'/'.join(tail[start:])}" for start in range(min(strippable, len(tail) - 1) + 1)) + return next((candidate for candidate in candidates if candidate in litellm.model_cost), model) + + @lru_cache(maxsize=DEFAULT_MAX_LRU_CACHE_SIZE) def _model_contains_known_llm_provider(model: str) -> bool: """ @@ -830,9 +863,11 @@ def _get_response_model(completion_response: object) -> str | None: _GEMINI_TRAFFIC_TYPE_TO_SERVICE_TIER: Final[dict] = { # ON_DEMAND_PRIORITY maps to "priority" — selects input_cost_per_token_priority, etc. "ON_DEMAND_PRIORITY": "priority", - # FLEX / BATCH maps to "flex" — selects input_cost_per_token_flex, etc. + # FLEX / BATCH / ON_DEMAND_FLEX maps to "flex" — selects input_cost_per_token_flex, etc. + # Vertex AI reports flex/shared-capacity traffic as ON_DEMAND_FLEX, not FLEX. "FLEX": "flex", "BATCH": "flex", + "ON_DEMAND_FLEX": "flex", # ON_DEMAND is standard pricing — no service_tier suffix applied "ON_DEMAND": None, } @@ -847,9 +882,9 @@ def _map_traffic_type_to_service_tier(traffic_type: str | None) -> str | None: trafficType values seen in practice ------------------------------------ - ON_DEMAND -> standard pricing (service_tier = None) - ON_DEMAND_PRIORITY -> priority pricing (service_tier = "priority") - FLEX / BATCH -> batch/flex pricing (service_tier = "flex") + ON_DEMAND -> standard pricing (service_tier = None) + ON_DEMAND_PRIORITY -> priority pricing (service_tier = "priority") + FLEX / BATCH / ON_DEMAND_FLEX -> batch/flex pricing (service_tier = "flex") """ if traffic_type is None: return None @@ -912,6 +947,8 @@ def _get_usage_object( usage_obj, ) ) + elif isinstance(usage_obj, dict) and InteractionsUsageObjectTransformation.is_interactions_usage_object(usage_obj): + return InteractionsUsageObjectTransformation.transform_interactions_usage_object(usage_obj) elif isinstance(usage_obj, dict): return Usage(**usage_obj) elif isinstance(usage_obj, BaseModel): @@ -1288,6 +1325,10 @@ def completion_cost( ) if tr_usage is not None: _usage = tr_usage.model_dump() + elif InteractionsUsageObjectTransformation.is_interactions_usage_object(_usage): + _usage = InteractionsUsageObjectTransformation.transform_interactions_usage_object( + _usage + ).model_dump() else: _usage = _usage @@ -1372,23 +1413,36 @@ def completion_cost( if custom_pricing and litellm_logging_obj is not None: _litellm_params = getattr(litellm_logging_obj, "litellm_params", None) if _litellm_params is not None: - _metadata = _litellm_params.get("metadata", {}) or {} - _video_model_info = _metadata.get("model_info", None) + _video_model_info = next( + ( + model_info + for _metadata_key in ("metadata", "litellm_metadata") + if (model_info := (_litellm_params.get(_metadata_key) or {}).get("model_info")) + is not None + ), + None, + ) usage_obj = getattr(completion_response, "usage", None) duration_seconds: float | None = None video_resolution: str | None = None + provider_reported_cost: float | None = None if completion_response is not None and usage_obj: # Handle both dict and Pydantic Usage object if isinstance(usage_obj, dict): duration_seconds = usage_obj.get("duration_seconds", None) _vr = usage_obj.get("video_resolution", None) + provider_reported_cost = usage_obj.get("provider_reported_cost_usd", None) else: duration_seconds = getattr(usage_obj, "duration_seconds", None) _vr = getattr(usage_obj, "video_resolution", None) + provider_reported_cost = getattr(usage_obj, "provider_reported_cost_usd", None) if _vr is not None: video_resolution = str(_vr).strip().lower() + if _video_model_info is None and provider_reported_cost is not None: + return float(provider_reported_cost) + if duration_seconds is not None: # Calculate cost based on video duration using video-specific cost calculation from litellm.llms.openai.cost_calculation import ( @@ -1530,10 +1584,9 @@ def completion_cost( return MCPCostCalculator.calculate_mcp_tool_call_cost(litellm_logging_obj=litellm_logging_obj) # Calculate cost based on prompt_tokens, completion_tokens - if "togethercomputer" in model or "together_ai" in model or custom_llm_provider == "together_ai": - # together ai prices based on size of llm - # get_model_params_and_category takes a model name and returns the category of LLM size it is in model_prices_and_context_window.json - + if ( + "togethercomputer" in model or "together_ai" in model or custom_llm_provider == "together_ai" + ) and not has_together_registry_pricing(model, litellm.model_cost): model = get_model_params_and_category(model, call_type=CallTypes(call_type)) # replicate llms are calculate based on time for request running @@ -1857,12 +1910,15 @@ def ocr_cost( if credits is not None and cost_per_credit is not None: return cost_per_credit * credits, 0.0 - ocr_cost_per_page: float | None = None - if model_info is not None: - ocr_cost_per_page = model_info.get("ocr_cost_per_page") + ocr_cost_per_page: Final = model_info.get("ocr_cost_per_page") if model_info is not None else None + annotation_cost_per_page: Final = model_info.get("annotation_cost_per_page") if model_info is not None else None + annotation_rate: Final = annotation_cost_per_page if annotation_cost_per_page is not None else ocr_cost_per_page pages_processed: Final = response.usage_info.pages_processed - if pages_processed is None: + annotation_pages: Final = response.usage_info.pages_processed_annotation or 0 + has_billable_annotation_pages: Final = annotation_rate is not None and annotation_pages > 0 + + if pages_processed is None and not has_billable_annotation_pages: if cost_per_credit is not None or ocr_cost_per_page is None: # Surface missing usage data instead of silently under-reporting # cost. The previous behavior raised ValueError; we now return 0.0 @@ -1878,7 +1934,7 @@ def ocr_cost( return 0.0, 0.0 raise ValueError("OCR response pages_processed is None") - if ocr_cost_per_page is None: + if ocr_cost_per_page is None and not has_billable_annotation_pages: # No per-page pricing configured. Either the model is on credit-based # pricing (and credits weren't returned, so the credit branch above did # not match) or the model has no OCR pricing entry at all. Surface a @@ -1894,8 +1950,9 @@ def ocr_cost( ) return 0.0, 0.0 - total_ocr_processing_cost: Final[float] = ocr_cost_per_page * pages_processed - return total_ocr_processing_cost, 0.0 + ocr_pages_cost: Final = (ocr_cost_per_page or 0.0) * (pages_processed or 0) + annotation_pages_cost: Final = (annotation_rate or 0.0) * annotation_pages + return ocr_pages_cost + annotation_pages_cost, 0.0 def vector_store_search_cost( @@ -2215,6 +2272,10 @@ def batch_cost_calculator( return total_prompt_cost, total_completion_cost +def _attribute_value(obj: object, name: str) -> object: + return getattr(obj, name) + + def _summable_prompt_token_fields(prompt_tokens_details: BaseModel) -> list[str]: field_names: Final = list(type(prompt_tokens_details).model_fields) if getattr(prompt_tokens_details, "cache_write_tokens", None) is None: @@ -2240,7 +2301,7 @@ class BaseTokenUsageProcessor: for usage in usage_objects: # Handle direct attributes by checking what exists in the model for attr in dir(usage): - if not attr.startswith("_") and not callable(getattr(usage, attr)): + if not attr.startswith("_") and not callable(_attribute_value(usage, attr)): current_val = getattr(combined, attr, 0) new_val = getattr(usage, attr, 0) if ( @@ -2260,7 +2321,7 @@ class BaseTokenUsageProcessor: if ( hasattr(usage.prompt_tokens_details, attr) and not attr.startswith("_") - and not callable(getattr(usage.prompt_tokens_details, attr)) + and not callable(_attribute_value(usage.prompt_tokens_details, attr)) ): current_val = getattr(combined.prompt_tokens_details, attr, 0) or 0 new_val = getattr(usage.prompt_tokens_details, attr, 0) or 0 @@ -2279,7 +2340,9 @@ class BaseTokenUsageProcessor: # Check what keys exist in the model's completion_tokens_details # Access model_fields on the class, not the instance, to avoid Pydantic 2.11+ deprecation warnings for attr in type(usage.completion_tokens_details).model_fields: - if not attr.startswith("_") and not callable(getattr(usage.completion_tokens_details, attr)): + if not attr.startswith("_") and not callable( + _attribute_value(usage.completion_tokens_details, attr) + ): current_val = getattr(combined.completion_tokens_details, attr, 0) or 0 new_val = getattr(usage.completion_tokens_details, attr, 0) or 0 if isinstance(new_val, (int, float)): @@ -2336,6 +2399,64 @@ class RealtimeAPITokenUsageProcessor(BaseTokenUsageProcessor): _TRANSCRIPTION_COMPLETED_EVENT_TYPE: Final = "conversation.item.input_audio_transcription.completed" +def _candidate_realtime_token_costs( + model_name: str, + combined_usage_object: Usage, + custom_llm_provider: str, + data_residency: str | None, +) -> tuple[float, float] | None: + try: + return generic_cost_per_token( + model=model_name, + usage=combined_usage_object, + custom_llm_provider=custom_llm_provider, + data_residency=data_residency, + ) + except Exception: + return None + + +def _cost_map_entry_declares_pricing(model_name: str, custom_llm_provider: str) -> bool: + entries: Final = ( + litellm.model_cost.get(model_name), + litellm.model_cost.get(f"{custom_llm_provider}/{model_name}"), + ) + return any( + entry is not None and any("cost_per" in field and value is not None for field, value in entry.items()) + for entry in entries + ) + + +def _first_priced_realtime_token_costs( + potential_model_names: Sequence[str | None], + combined_usage_object: Usage, + custom_llm_provider: str, + data_residency: str | None, +) -> tuple[float, float]: + candidate_costs: Final = ( + (model_name, costs) + for model_name in potential_model_names + if model_name is not None + and ( + costs := _candidate_realtime_token_costs( + model_name=model_name, + combined_usage_object=combined_usage_object, + custom_llm_provider=custom_llm_provider, + data_residency=data_residency, + ) + ) + is not None + ) + return next( + ( + costs + for model_name, costs in candidate_costs + if sum(costs) > 0 or _cost_map_entry_declares_pricing(model_name, custom_llm_provider) + ), + (0.0, 0.0), + ) + + def handle_realtime_stream_cost_calculation( results: OpenAIRealtimeStreamList, combined_usage_object: Usage, @@ -2360,24 +2481,12 @@ def handle_realtime_stream_cost_calculation( potential_model_names.append(received_model) potential_model_names.append(litellm_model_name) - input_cost_per_token = 0.0 - output_cost_per_token = 0.0 - - for model_name in potential_model_names: - try: - if model_name is None: - continue - _input_cost_per_token, _output_cost_per_token = generic_cost_per_token( - model=model_name, - usage=combined_usage_object, - custom_llm_provider=custom_llm_provider, - data_residency=data_residency, - ) - except Exception: - continue - input_cost_per_token += _input_cost_per_token - output_cost_per_token += _output_cost_per_token - break # exit if we find a valid model + input_cost_per_token, output_cost_per_token = _first_priced_realtime_token_costs( + potential_model_names=potential_model_names, + combined_usage_object=combined_usage_object, + custom_llm_provider=custom_llm_provider, + data_residency=data_residency, + ) transcription_cost: Final = ( handle_realtime_transcription_cost_calculation( results=results, diff --git a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py index 9e949db625a..6c33621ec89 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py @@ -115,9 +115,11 @@ class SpeechToCompletionBridgeHandler: **request_data, ) + requested_response_format: Final = optional_params.get("response_format") if isinstance(result, ModelResponse): return self.transformation_handler.transform_response( model_response=result, + response_format=requested_response_format if isinstance(requested_response_format, str) else None, ) else: raise Exception(f"Unmapped response type. Got type: {type(result)}") diff --git a/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py b/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py index a9429b673e4..2ed140c0208 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py @@ -1,14 +1,83 @@ +from collections.abc import Mapping +from types import MappingProxyType from typing import TYPE_CHECKING, Final, cast +from typing_extensions import NotRequired, ReadOnly, TypedDict + from litellm.constants import OPENAI_CHAT_COMPLETION_PARAMS if TYPE_CHECKING: from litellm import Logging as LiteLLMLoggingObj - from litellm.types.llms.openai import HttpxBinaryResponseContent + from litellm.types.llms.openai import ChatCompletionUserMessage, HttpxBinaryResponseContent from litellm.types.utils import ModelResponse +def _completion_response_cost(model_response: "ModelResponse") -> float | None: + hidden_params: Final = getattr(model_response, "_hidden_params", None) + if not isinstance(hidden_params, dict): + return None + response_cost: Final = hidden_params.get("response_cost") + return response_cost if isinstance(response_cost, float) else None + + +GEMINI_TTS_CHAT_AUDIO_FORMAT: Final = "pcm16" +GEMINI_TTS_RAW_RESPONSE_FORMAT: Final = "pcm" +GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS: Final = frozenset({"wav", GEMINI_TTS_RAW_RESPONSE_FORMAT}) + + +class ChatAudioParam(TypedDict): + voice: ReadOnly[str] + format: ReadOnly[NotRequired[str]] + + class SpeechToCompletionBridgeTransformationHandler: + def _validate_response_format( + self, model: str, custom_llm_provider: str, optional_params: Mapping[str, object] + ) -> None: + if not self._is_gemini_tts_model(model): + return + response_format: Final = optional_params.get("response_format") + if not isinstance(response_format, str) or response_format in GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS: + return + from litellm.exceptions import BadRequestError + + supported: Final = ", ".join(sorted(GEMINI_TTS_SUPPORTED_RESPONSE_FORMATS)) + raise BadRequestError( + message=( + f"Gemini TTS only produces raw PCM16 audio, so response_format='{response_format}'" + f" is not supported. Supported response formats: {supported}." + ), + model=model, + llm_provider=custom_llm_provider, + ) + + def _chat_completion_params(self, optional_params: Mapping[str, object]) -> Mapping[str, object]: + return MappingProxyType( + { + param: value + for param, value in optional_params.items() + if param in OPENAI_CHAT_COMPLETION_PARAMS and param != "response_format" + } + ) + + def _chat_audio_format(self, model: str, optional_params: Mapping[str, object]) -> str | None: + if self._is_gemini_tts_model(model): + return GEMINI_TTS_CHAT_AUDIO_FORMAT + response_format: Final = optional_params.get("response_format") + return response_format if isinstance(response_format, str) else None + + def _chat_audio_param( + self, model: str, voice: str | Mapping[str, object] | None, optional_params: Mapping[str, object] + ) -> ChatAudioParam | None: + if not isinstance(voice, str): + return None + audio_format: Final = self._chat_audio_format(model, optional_params) + if audio_format is None: + voice_only: Final[ChatAudioParam] = {"voice": voice} + return voice_only + audio: Final[ChatAudioParam] = {"voice": voice, "format": audio_format} + return audio + def transform_request( self, model: str, @@ -20,36 +89,20 @@ class SpeechToCompletionBridgeTransformationHandler: litellm_logging_obj: "LiteLLMLoggingObj", custom_llm_provider: str, ) -> dict: - passed_optional_params: Final = {} - for op in optional_params: - if op in OPENAI_CHAT_COMPLETION_PARAMS: - passed_optional_params[op] = optional_params[op] - - if voice is not None: - if isinstance(voice, str): - passed_optional_params["audio"] = {"voice": voice} - if "response_format" in optional_params: - passed_optional_params["audio"]["format"] = optional_params["response_format"] - - return_kwargs = { + self._validate_response_format(model, custom_llm_provider, optional_params) + user_message: Final[ChatCompletionUserMessage] = {"role": "user", "content": input} + return_kwargs: Final = { "model": model, - "messages": [ - { - "role": "user", - "content": input, - } - ], + "messages": [user_message], "modalities": ["audio"], - **passed_optional_params, + **self._chat_completion_params(optional_params), + "audio": self._chat_audio_param(model, voice, optional_params), **litellm_params, "headers": headers, "litellm_logging_obj": litellm_logging_obj, "custom_llm_provider": custom_llm_provider, } - - # filter out None values - return_kwargs = {k: v for k, v in return_kwargs.items() if v is not None} - return return_kwargs + return {k: v for k, v in return_kwargs.items() if v is not None} def _convert_pcm16_to_wav(self, pcm_data: bytes, sample_rate: int = 24000, channels: int = 1) -> bytes: """ @@ -95,7 +148,14 @@ class SpeechToCompletionBridgeTransformationHandler: """Check if the model is a Gemini TTS model that returns PCM16 data.""" return "gemini" in model.lower() and ("tts" in model.lower() or "preview-tts" in model.lower()) - def transform_response(self, model_response: "ModelResponse") -> "HttpxBinaryResponseContent": + def _gemini_tts_response_body(self, decoded_audio: bytes, response_format: str | None) -> tuple[bytes, str]: + if response_format == GEMINI_TTS_RAW_RESPONSE_FORMAT: + return decoded_audio, "audio/pcm" + return self._convert_pcm16_to_wav(decoded_audio), "audio/wav" + + def transform_response( + self, model_response: "ModelResponse", response_format: str | None + ) -> "HttpxBinaryResponseContent": import base64 import httpx @@ -106,21 +166,17 @@ class SpeechToCompletionBridgeTransformationHandler: audio_part: Final = cast(Choices, model_response.choices[0]).message.audio if audio_part is None: raise ValueError("No audio part found in the response") - audio_content: Final = audio_part.data + decoded_audio: Final = base64.b64decode(audio_part.data) - # Decode base64 to get binary content - binary_data = base64.b64decode(audio_content) - - # Check if this is a Gemini TTS model that returns raw PCM16 data model: Final = getattr(model_response, "model", "") - headers: Final = {} - if self._is_gemini_tts_model(model): - # Convert PCM16 to WAV format for proper audio file playback - binary_data = self._convert_pcm16_to_wav(binary_data) - headers["Content-Type"] = "audio/wav" - else: - headers["Content-Type"] = "audio/mpeg" - - # Create an httpx.Response object - response: Final = httpx.Response(status_code=200, content=binary_data, headers=headers) - return HttpxBinaryResponseContent(response) + content, content_type = ( + self._gemini_tts_response_body(decoded_audio, response_format) + if self._is_gemini_tts_model(model) + else (decoded_audio, "audio/mpeg") + ) + response: Final = httpx.Response( + status_code=200, content=content, headers=MappingProxyType({"Content-Type": content_type}) + ) + binary_response: Final = HttpxBinaryResponseContent(response) + binary_response.set_response_cost(_completion_response_cost(model_response)) + return binary_response diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index 11b15a63484..ea81e323da4 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -7,6 +7,8 @@ import base64 import os from collections.abc import Awaitable, Callable, Generator from datetime import timedelta +from functools import partial +from importlib import metadata from typing import Any, Final, TypeVar import httpx @@ -21,6 +23,18 @@ try: streamable_http_client = getattr(streamable_http_module, "streamable_http_client", None) except ImportError: pass + +MCP_STREAMABLE_HTTP_REQUIREMENT: Final = "mcp>=1.28.1" + + +def missing_streamable_http_client_error() -> ImportError: + return ImportError( + f"MCP streamable HTTP transport requires {MCP_STREAMABLE_HTTP_REQUIREMENT}, but the installed " + f"mcp {metadata.version('mcp')} does not provide streamable_http_client. " + "Fix with: pip install 'litellm[mcp]' (or upgrade mcp directly: pip install -U mcp)" + ) + + from mcp.types import CallToolRequestParams as MCPCallToolRequestParams from mcp.types import CallToolResult as MCPCallToolResult from mcp.types import ( @@ -34,7 +48,8 @@ from mcp.types import Tool as MCPTool from pydantic import AnyUrl from litellm._logging import verbose_logger -from litellm.constants import MCP_CLIENT_TIMEOUT, MCP_NPM_CACHE_DIR +from litellm.constants import MCP_CLIENT_TIMEOUT, MCP_NPM_CACHE_DIR, MCP_TOOL_LISTING_TIMEOUT +from litellm.experimental_mcp_client.tools import list_tools_with_pagination from litellm.llms.custom_httpx.http_handler import get_ssl_configuration from litellm.types.llms.custom_http import VerifyTypes from litellm.types.mcp import ( @@ -43,6 +58,9 @@ from litellm.types.mcp import ( MCPStdioConfig, MCPTransport, MCPTransportType, + credential_redirect_hook, + has_header, + without_header, ) @@ -260,6 +278,7 @@ class MCPClient: transport_type: MCPTransportType = MCPTransport.http, auth_type: MCPAuthType = None, auth_value: str | dict[str, str] | None = None, + auth_header_name: str | None = None, timeout: float | None = None, stdio_config: MCPStdioConfig | None = None, extra_headers: dict[str, str] | None = None, @@ -275,6 +294,11 @@ class MCPClient: self.auth_type: MCPAuthType = auth_type self.timeout: float = timeout if timeout is not None else MCP_CLIENT_TIMEOUT self._mcp_auth_value: str | dict[str, str] | None = None + # The one place this client decides which header its credential occupies: the operator's + # configured slot on the v1 path, or the slot the v2 resolver's auth object already owns. + # Every consumer reads this rather than re-deriving it, since each re-derivation so far + # picked up a different bug. + self._credential_slot: str | None = auth_header_name or getattr(resolved_auth, "header_name", None) self.stdio_config: MCPStdioConfig | None = stdio_config self.extra_headers: dict[str, str] | None = extra_headers self.ssl_verify: VerifyTypes | None = ssl_verify @@ -323,7 +347,7 @@ class MCPClient: ) # HTTP transport (default) if streamable_http_client is None: - raise ImportError("streamable_http_client is not available. Please install mcp with HTTP support.") + raise missing_streamable_http_client_error() headers = self._get_auth_headers() httpx_client_factory = self._create_httpx_client_factory() verbose_logger.debug("litellm headers for streamable_http_client: %s", headers) @@ -488,26 +512,33 @@ class MCPClient: else: self._mcp_auth_value = mcp_auth_value + def _header_slot(self, default: str) -> str: + return self._credential_slot or default + def _get_auth_headers(self) -> dict: """Generate authentication headers based on auth type.""" headers: Final = {} if self._mcp_auth_value: if isinstance(self._mcp_auth_value, str): if self.auth_type == MCPAuth.bearer_token: - headers["Authorization"] = f"Bearer {strip_auth_scheme(self._mcp_auth_value, 'Bearer')}" + static_bearer: Final = strip_auth_scheme(self._mcp_auth_value, "Bearer") + headers[self._header_slot("Authorization")] = f"Bearer {static_bearer}" elif self.auth_type == MCPAuth.basic: - headers["Authorization"] = f"Basic {self._mcp_auth_value}" + headers[self._header_slot("Authorization")] = f"Basic {self._mcp_auth_value}" elif self.auth_type == MCPAuth.api_key: - headers["X-API-Key"] = self._mcp_auth_value + headers[self._header_slot("X-API-Key")] = self._mcp_auth_value elif self.auth_type == MCPAuth.authorization: # This auth type means the caller owns the whole header value. - headers["Authorization"] = self._mcp_auth_value + headers[self._header_slot("Authorization")] = self._mcp_auth_value elif self.auth_type == MCPAuth.oauth2: - headers["Authorization"] = f"Bearer {strip_auth_scheme(self._mcp_auth_value, 'Bearer')}" + oauth2_bearer: Final = strip_auth_scheme(self._mcp_auth_value, "Bearer") + headers[self._header_slot("Authorization")] = f"Bearer {oauth2_bearer}" elif self.auth_type == MCPAuth.token: - headers["Authorization"] = f"token {strip_auth_scheme(self._mcp_auth_value, 'token')}" + scheme_token: Final = strip_auth_scheme(self._mcp_auth_value, "token") + headers[self._header_slot("Authorization")] = f"token {scheme_token}" elif self.auth_type == MCPAuth.oauth2_token_exchange: - headers["Authorization"] = f"Bearer {strip_auth_scheme(self._mcp_auth_value, 'Bearer')}" + exchanged_bearer: Final = strip_auth_scheme(self._mcp_auth_value, "Bearer") + headers[self._header_slot("Authorization")] = f"Bearer {exchanged_bearer}" elif isinstance(self._mcp_auth_value, dict): headers.update(self._mcp_auth_value) # Note: aws_sigv4 auth is not handled here — SigV4 requires per-request @@ -515,7 +546,14 @@ class MCPClient: # of static headers. See MCPSigV4Auth and _create_httpx_client_factory(). # update the headers with the extra headers if self.extra_headers: - headers.update(self.extra_headers) + # Mirrors _resolve_v2_auth: when the operator named a slot for the credential the + # gateway resolved, no injected header may shadow it, case-insensitively, since HTTP + # header names are. Without a configured slot the old precedence stands unchanged. + slot: Final = self._credential_slot + injected: Final = ( + without_header(self.extra_headers, slot) if slot and has_header(headers, slot) else self.extra_headers + ) + headers.update(injected or {}) return _strip_header_whitespace(headers) def _create_httpx_client_factory(self) -> Callable[..., httpx.AsyncClient]: @@ -543,12 +581,14 @@ class MCPClient: # SigV4 aws_auth. Both are None for the common case — no behavior change. fallback_auth: Final = self._resolved_auth if self._resolved_auth is not None else self._aws_auth effective_auth: Final = auth if auth is not None else fallback_auth + guard: Final = credential_redirect_hook(self.server_url, self._credential_slot) return httpx.AsyncClient( headers=headers, timeout=timeout, auth=effective_auth, verify=ssl_config, follow_redirects=True, + event_hooks={"request": [guard]} if guard else {}, ) return factory @@ -565,17 +605,19 @@ class MCPClient: """ verbose_logger.debug("MCP client listing tools from %s", self.server_url or "stdio") - async def _list_tools_operation(session: ClientSession): - return await session.list_tools() - try: - result: Final = await self.run_with_session(_list_tools_operation, quiet_on_error=raise_on_error) - tool_count: Final = len(result.tools) - tool_names: Final = [tool.name for tool in result.tools] + # A per-server timeout above the global default extends the whole-walk deadline + listing_deadline: Final = max(self.timeout, MCP_TOOL_LISTING_TIMEOUT) + tools: Final = await self.run_with_session( + partial(list_tools_with_pagination, listing_deadline=listing_deadline), + quiet_on_error=raise_on_error, + ) + tool_count: Final = len(tools) + tool_names: Final = tuple(tool.name for tool in tools) verbose_logger.info( "MCP client listed %s tools from %s: %s", tool_count, self.server_url or "stdio", tool_names ) - return result.tools + return tools except asyncio.CancelledError: verbose_logger.warning("MCP client list_tools was cancelled") raise diff --git a/litellm/experimental_mcp_client/tools.py b/litellm/experimental_mcp_client/tools.py index 30d50e2a74b..51d2139ef3b 100644 --- a/litellm/experimental_mcp_client/tools.py +++ b/litellm/experimental_mcp_client/tools.py @@ -1,14 +1,22 @@ import json from typing import Final, Literal +import anyio from mcp import ClientSession from mcp.types import CallToolRequestParams as MCPCallToolRequestParams from mcp.types import CallToolResult as MCPCallToolResult +from mcp.types import PaginatedRequestParams from mcp.types import Tool as MCPTool from openai.types.chat import ChatCompletionToolParam from openai.types.responses.function_tool_param import FunctionToolParam from openai.types.shared_params.function_definition import FunctionDefinition +from litellm._logging import verbose_logger +from litellm.constants import ( + MCP_CLIENT_TIMEOUT, + MCP_TOOL_LISTING_MAX_PAGES, + MCP_TOOL_LISTING_TIMEOUT, +) from litellm.types.llms.anthropic import AnthropicMessagesTool from litellm.types.utils import ChatCompletionMessageToolCall @@ -90,6 +98,64 @@ def transform_mcp_tool_to_anthropic_tool(mcp_tool: MCPTool) -> AnthropicMessages ) +async def list_tools_with_pagination( + session: ClientSession, listing_deadline: float | None = None +) -> list[MCPTool]: # mutable-ok: list return contract + """Collect tools from every tools/list page by following nextCursor. + + Stops and returns the tools collected so far when the upstream repeats a + cursor, the page cap is reached, or the whole-walk deadline expires, so a + buggy or slow upstream yields a partial catalog instead of an error. + listing_deadline overrides the default whole-walk deadline; callers with a + per-server timeout above the global default pass it through here. + """ + tools: Final[list[MCPTool]] = [] # mutable-ok: accumulates each page's tools + seen_cursors: Final[set[str]] = set() # mutable-ok: guards against cursor loops + cursor: str | None = None # rebind-ok: advances to each page's nextCursor + # The per-request session read timeout restarts on every page, so a multi-page + # walk needs its own overall deadline. max() keeps the pre-pagination guarantee + # that a single page slower than the listing timeout but within the client + # timeout still succeeds. + effective_deadline: Final = ( + listing_deadline if listing_deadline is not None else max(MCP_CLIENT_TIMEOUT, MCP_TOOL_LISTING_TIMEOUT) + ) + + with anyio.move_on_after(effective_deadline): + for _ in range(MCP_TOOL_LISTING_MAX_PAGES): + result = ( + await session.list_tools() + if cursor is None + else await session.list_tools(params=PaginatedRequestParams(cursor=cursor)) + ) + tools.extend(result.tools) + + next_cursor = getattr(result, "nextCursor", None) + if not isinstance(next_cursor, str) or not next_cursor: + return tools + if next_cursor in seen_cursors: + verbose_logger.warning( + "MCP server repeated a tools/list cursor while listing tools; returning %s tools collected so far", + len(tools), + ) + return tools + seen_cursors.add(next_cursor) + cursor = next_cursor + + verbose_logger.warning( + "MCP server tools/list pagination exceeded the maximum of %s pages; returning %s tools collected so far", + MCP_TOOL_LISTING_MAX_PAGES, + len(tools), + ) + return tools + + verbose_logger.warning( + "MCP server tools/list pagination exceeded the %s second listing deadline; returning %s tools collected so far", + effective_deadline, + len(tools), + ) + return tools + + async def load_mcp_tools( session: ClientSession, format: Literal["mcp", "openai"] = "mcp" ) -> list[MCPTool] | list[ChatCompletionToolParam]: @@ -103,10 +169,12 @@ async def load_mcp_tools( If format is set to "openai", the tools are converted to OpenAI API compatible tools. """ - tools: Final = await session.list_tools() + tools: Final = await list_tools_with_pagination(session) if format == "openai": - return [transform_mcp_tool_to_openai_tool(mcp_tool=tool) for tool in tools.tools] - return tools.tools + return [ # mutable-ok: public API returns a list + transform_mcp_tool_to_openai_tool(mcp_tool=tool) for tool in tools + ] + return tools ######################################################## diff --git a/litellm/google_genai/adapters/transformation.py b/litellm/google_genai/adapters/transformation.py index 7c86ceafd7f..6a698bb6018 100644 --- a/litellm/google_genai/adapters/transformation.py +++ b/litellm/google_genai/adapters/transformation.py @@ -1,8 +1,9 @@ import json -from collections.abc import AsyncIterator, Iterator, Sequence -from typing import Any, Final, TypedDict, cast +from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence +from types import MappingProxyType +from typing import Any, Final, TypeAlias, cast -from typing_extensions import ReadOnly +from typing_extensions import ReadOnly, TypedDict from litellm import verbose_logger from litellm.litellm_core_utils.json_validation_rule import normalize_tool_schema @@ -11,7 +12,6 @@ from litellm.types.llms.openai import ( ChatCompletionAssistantMessage, ChatCompletionAssistantToolCall, ChatCompletionImageObject, - ChatCompletionRequest, ChatCompletionSystemMessage, ChatCompletionTextObject, ChatCompletionToolCallFunctionChunk, @@ -23,35 +23,63 @@ from litellm.types.llms.openai import ( from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import ( AdapterCompletionStreamWrapper, + ChatCompletionDeltaCustomToolCall, + ChatCompletionDeltaToolCall, + ChatCompletionMessageCustomToolCall, + ChatCompletionMessageToolCall, Choices, + Delta, + Function, + Message, ModelResponse, ModelResponseStream, StreamingChoices, - Usage, ) - -class _GenAITextPart(TypedDict, total=False): - text: ReadOnly[str] +_JsonDict: TypeAlias = dict[str, object] +_JsonDictList: TypeAlias = list[_JsonDict] -class _GenAISystemInstruction(TypedDict, total=False): - parts: ReadOnly[list[_GenAITextPart]] +class _ToolCallAccumulator(TypedDict): + name: ReadOnly[str] + arguments: ReadOnly[str] + + +class _GenAIFunctionCall(TypedDict): + name: ReadOnly[str] + args: ReadOnly[Mapping[str, object]] class _GenAIPart(TypedDict, total=False): text: ReadOnly[str] - functionCall: ReadOnly[dict[str, object]] + functionCall: ReadOnly[_GenAIFunctionCall] + + +class _GenAIFunctionResponse(TypedDict, total=False): + name: ReadOnly[str] + response: ReadOnly[object] + + +class _GenAIRequestFunctionCall(TypedDict, total=False): + name: ReadOnly[str] + args: ReadOnly[Mapping[str, object]] + + +class _GenAIContentPart(TypedDict, total=False): + text: ReadOnly[str] + inline_data: ReadOnly[Mapping[str, str]] + functionResponse: ReadOnly[_GenAIFunctionResponse] + functionCall: ReadOnly[_GenAIRequestFunctionCall] class _GenAIFunctionDeclaration(TypedDict, total=False): name: ReadOnly[str] description: ReadOnly[str] - parametersJsonSchema: ReadOnly[dict[str, object]] + parametersJsonSchema: ReadOnly[object] class _GenAITool(TypedDict, total=False): - functionDeclarations: ReadOnly[list[_GenAIFunctionDeclaration]] + functionDeclarations: ReadOnly[Sequence[_GenAIFunctionDeclaration]] class _GenAIFunctionCallingConfig(TypedDict, total=False): @@ -62,9 +90,11 @@ class _GenAIToolConfig(TypedDict, total=False): functionCallingConfig: ReadOnly[_GenAIFunctionCallingConfig] -def _decode_tool_call_arguments(raw_arguments: str) -> object: - """Decode a tool call's JSON-encoded arguments into the value Google GenAI expects.""" - return json.loads(raw_arguments) +class _GenAISystemInstruction(TypedDict, total=False): + parts: ReadOnly[Sequence[Mapping[str, str]]] + + +_EMPTY_STR_MAPPING: Final[Mapping[str, str]] = MappingProxyType({}) class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): @@ -74,12 +104,11 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): """ sent_first_chunk: bool = False - # State tracking for accumulating partial tool calls - accumulated_tool_calls: dict[int, dict[str, str]] + _parse_accumulated_args: Callable[[str], Mapping[str, object]] = staticmethod(json.loads) def __init__(self, completion_stream: object): self.sent_first_chunk = False - self.accumulated_tool_calls = {} + self.accumulated_tool_calls = dict[int, _ToolCallAccumulator]() self._returned_response = False super().__init__(completion_stream) @@ -124,7 +153,7 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): # After the stream is exhausted, check for any remaining accumulated tool calls if self.accumulated_tool_calls: try: - parts: Final[list[_GenAIPart]] = [] + parts: Final = list[_GenAIPart]() for ( tool_call_index, tool_call_data, @@ -132,7 +161,9 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): try: # For tool calls with no arguments, accumulated_args will be "", which is not valid JSON. # We default to an empty JSON object in this case. - parsed_args = _decode_tool_call_arguments(tool_call_data["arguments"] or "{}") + parsed_args: Mapping[str, object] = self._parse_accumulated_args( + tool_call_data["arguments"] or "{}" + ) function_call_part: _GenAIPart = { "functionCall": { "name": tool_call_data["name"] or "undefined_tool_name", @@ -149,7 +180,7 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): tool_call_data["arguments"], ) if parts: - final_chunk: Final[dict[str, object]] = { + final_chunk: Final = { "candidates": [ { "content": {"parts": parts, "role": "model"}, @@ -211,14 +242,16 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper): class GoogleGenAIAdapter: """Adapter for transforming Google GenAI generate_content requests to/from litellm.completion format""" + _parse_tool_call_args: Callable[[str], Mapping[str, object]] = staticmethod(json.loads) + def __init__(self) -> None: pass def translate_generate_content_to_completion( self, model: str, - contents: list[dict[str, Any]] | dict[str, Any], - config: dict[str, Any] | None = None, + contents: _JsonDictList | _JsonDict, + config: Mapping[str, object] | None = None, litellm_params: GenericLiteLLMParams | None = None, **kwargs, ) -> dict[str, Any]: @@ -250,7 +283,7 @@ class GoogleGenAIAdapter: messages: Final = self._transform_contents_to_messages(contents_list, system_instruction=system_instruction) # Create base request as dict (which is compatible with ChatCompletionRequest) - completion_request: Final[ChatCompletionRequest] = { + completion_request: Final[_JsonDict] = { "model": model, "messages": messages, } @@ -312,9 +345,9 @@ class GoogleGenAIAdapter: def _add_generic_litellm_params_to_request( self, - completion_request_dict: dict[str, object], + completion_request_dict: _JsonDict, litellm_params: GenericLiteLLMParams | None = None, - ) -> dict[str, object]: + ) -> _JsonDict: """Add generic litellm params to request. e.g add api_base, api_key, api_version, etc. Args: @@ -326,7 +359,7 @@ class GoogleGenAIAdapter: """ allowed_fields: Final = GenericLiteLLMParams.model_fields.keys() if litellm_params: - litellm_dict: Final = litellm_params.model_dump(exclude_none=True) + litellm_dict: Final[_JsonDict] = litellm_params.model_dump(exclude_none=True) for key, value in litellm_dict.items(): if key in allowed_fields: completion_request_dict[key] = value @@ -346,12 +379,12 @@ class GoogleGenAIAdapter: tools: Sequence[_GenAITool], ) -> list[ChatCompletionToolParam]: """Transform Google GenAI tools to OpenAI tools format""" - openai_tools: Final[list[dict[str, object]]] = [] + openai_tools: Final = list[_JsonDict]() for tool in tools: if "functionDeclarations" in tool: for func_decl in tool["functionDeclarations"]: - function_chunk: dict[str, object] = { + function_chunk: _JsonDict = { "name": func_decl.get("name", ""), } @@ -360,7 +393,7 @@ class GoogleGenAIAdapter: if "parametersJsonSchema" in func_decl: function_chunk["parameters"] = func_decl["parametersJsonSchema"] - openai_tool: dict[str, object] = {"type": "function", "function": function_chunk} + openai_tool: _JsonDict = {"type": "function", "function": function_chunk} openai_tools.append(openai_tool) # normalize the tool schemas @@ -391,13 +424,13 @@ class GoogleGenAIAdapter: # Handle system instruction if system_instruction: - system_parts: Final = system_instruction.get("parts", []) + system_parts: Final[Sequence[Mapping[str, str]]] = system_instruction.get("parts", []) if system_parts and "text" in system_parts[0]: messages.append(ChatCompletionSystemMessage(role="system", content=system_parts[0]["text"])) for content in contents: role = content.get("role", "user") - parts = content.get("parts", []) + parts: Sequence[_GenAIContentPart | str | None] = content.get("parts", []) if role == "user": # Handle user messages with potential function responses @@ -500,7 +533,7 @@ class GoogleGenAIAdapter: def translate_completion_to_generate_content( self, response: ModelResponse, - ) -> dict[str, object]: + ) -> _JsonDict: """ Transform litellm completion response to Google GenAI generate_content format @@ -523,13 +556,13 @@ class GoogleGenAIAdapter: parts = self._transform_openai_message_to_google_genai_parts(choice.message) else: # Fallback for generic choice objects - message_content = getattr(choice, "message", {}).get("content", "") or getattr(choice, "delta", {}).get( - "content", "" - ) + message_content: str = getattr(choice, "message", _EMPTY_STR_MAPPING).get("content", "") or getattr( + choice, "delta", _EMPTY_STR_MAPPING + ).get("content", "") parts = [{"text": message_content}] if message_content else [] # Create Google GenAI format response - generate_content_response: Final[dict[str, object]] = { + generate_content_response: Final[_JsonDict] = { "candidates": [ { "content": {"parts": parts, "role": "model"}, @@ -563,7 +596,7 @@ class GoogleGenAIAdapter: self, response: ModelResponse | ModelResponseStream, wrapper: GoogleGenAIStreamWrapper, - ) -> dict[str, object] | None: + ) -> Mapping[str, object] | None: """ Transform streaming litellm completion chunk to Google GenAI generate_content format @@ -590,7 +623,7 @@ class GoogleGenAIAdapter: finish_reason: str | None = getattr(choice, "finish_reason", None) else: # Fallback for generic choice objects - message_content: Final = getattr(choice, "delta", {}).get("content", "") + message_content: Final[str] = getattr(choice, "delta", _EMPTY_STR_MAPPING).get("content", "") parts = [{"text": message_content}] if message_content else [] finish_reason = getattr(choice, "finish_reason", None) @@ -599,7 +632,7 @@ class GoogleGenAIAdapter: return None # Create Google GenAI streaming format response - streaming_chunk: Final[dict[str, object]] = { + streaming_chunk: Final[_JsonDict] = { "candidates": [ { "content": {"parts": parts, "role": "model"}, @@ -635,10 +668,10 @@ class GoogleGenAIAdapter: def _transform_openai_message_to_google_genai_parts( self, - message: Any, - ) -> list[_GenAIPart]: + message: Message, + ) -> Sequence[_GenAIPart]: """Transform OpenAI message to Google GenAI parts format""" - parts: Final[list[_GenAIPart]] = [] + parts: Final = list[_GenAIPart]() # Add text content if present if hasattr(message, "content") and message.content: @@ -646,20 +679,22 @@ class GoogleGenAIAdapter: # Add tool calls if present if hasattr(message, "tool_calls") and message.tool_calls: - for tool_call in message.tool_calls: - if hasattr(tool_call, "function") and tool_call.function: + tool_calls: Final[Sequence[ChatCompletionMessageToolCall | ChatCompletionMessageCustomToolCall]] = ( + message.tool_calls + ) + for tool_call in tool_calls: + function: Function | None = getattr(tool_call, "function", None) + if function: try: - args = ( - _decode_tool_call_arguments(tool_call.function.arguments) - if tool_call.function.arguments - else {} + args: Mapping[str, object] = ( + self._parse_tool_call_args(function.arguments) if function.arguments else {} ) except json.JSONDecodeError: args = {} function_call_part: _GenAIPart = { "functionCall": { - "name": tool_call.function.name or "undefined_tool_name", + "name": function.name or "undefined_tool_name", "args": args, } } @@ -668,24 +703,26 @@ class GoogleGenAIAdapter: return parts if parts else [{"text": ""}] def _transform_openai_delta_to_google_genai_parts_with_accumulation( - self, delta: Any, wrapper: GoogleGenAIStreamWrapper - ) -> list[_GenAIPart]: + self, delta: Delta, wrapper: GoogleGenAIStreamWrapper + ) -> Sequence[_GenAIPart]: """Transforms OpenAI delta to Google GenAI parts, accumulating streaming tool calls.""" # 1. Initialize wrapper state if it doesn't exist if not hasattr(wrapper, "accumulated_tool_calls"): wrapper.accumulated_tool_calls = {} - parts: Final[list[_GenAIPart]] = [] + parts: Final = list[_GenAIPart]() if hasattr(delta, "content") and delta.content: parts.append({"text": delta.content}) # 2. Ensure tool_calls is iterable - tool_calls: Final = delta.tool_calls or [] + tool_calls: Final[Sequence[ChatCompletionDeltaToolCall | ChatCompletionDeltaCustomToolCall]] = ( + delta.tool_calls or [] + ) for tool_call in tool_calls: - if not hasattr(tool_call, "function"): + if not hasattr(tool_call, "function") or isinstance(tool_call, ChatCompletionDeltaCustomToolCall): continue # 3. Use `index` as the primary key for accumulation @@ -701,19 +738,20 @@ class GoogleGenAIAdapter: } # Accumulate name and arguments - function_name = getattr(tool_call.function, "name", None) - args_chunk = getattr(tool_call.function, "arguments", None) + delta_function: Function | None = getattr(tool_call, "function", None) + function_name: str | None = getattr(delta_function, "name", None) + args_chunk: str | None = getattr(delta_function, "arguments", None) # Optimization: Skip chunks that have no new data if not function_name and not args_chunk: verbose_logger.debug("Skipping empty tool call chunk for index: %s", tool_call_index) continue - if function_name: - wrapper.accumulated_tool_calls[tool_call_index]["name"] = function_name - - if args_chunk: - wrapper.accumulated_tool_calls[tool_call_index]["arguments"] += args_chunk + previous_data: _ToolCallAccumulator = wrapper.accumulated_tool_calls[tool_call_index] + wrapper.accumulated_tool_calls[tool_call_index] = _ToolCallAccumulator( + name=function_name or previous_data["name"], + arguments=previous_data["arguments"] + (args_chunk or ""), + ) # Attempt to parse and emit a complete tool call accumulated_data = wrapper.accumulated_tool_calls[tool_call_index] @@ -723,7 +761,7 @@ class GoogleGenAIAdapter: # 5. Attempt to parse arguments even if name hasn't arrived. try: # Attempt to parse the accumulated arguments string - parsed_args = _decode_tool_call_arguments(accumulated_args) + parsed_args: Mapping[str, object] = self._parse_tool_call_args(accumulated_args) # If parsing succeeds, but we don't have a name yet, wait. # The part will be created by a later chunk that brings the name. @@ -757,7 +795,7 @@ class GoogleGenAIAdapter: return mapping.get(finish_reason, "STOP") - def _map_usage(self, usage: Usage | None) -> dict[str, int]: + def _map_usage(self, usage: object) -> Mapping[str, int]: """Map OpenAI usage to Google GenAI usage format""" return { "promptTokenCount": getattr(usage, "prompt_tokens", 0) or 0, diff --git a/litellm/google_genai/main.py b/litellm/google_genai/main.py index b5815bd3f7c..c1822e4720d 100644 --- a/litellm/google_genai/main.py +++ b/litellm/google_genai/main.py @@ -52,10 +52,10 @@ class GenerateContentSetupResult(BaseModel): model_config: ClassVar[ConfigDict] = ConfigDict(arbitrary_types_allowed=True) model: str - request_body: dict[str, Any] + request_body: dict[str, object] custom_llm_provider: str generate_content_provider_config: BaseGoogleGenAIGenerateContentConfig | None - generate_content_config_dict: dict[str, Any] + generate_content_config_dict: dict[str, object] native_request_fields: dict[str, object] litellm_params: GenericLiteLLMParams litellm_logging_obj: LiteLLMLoggingObj @@ -68,7 +68,7 @@ class GenerateContentHelper: @staticmethod def mock_generate_content_response( mock_response: str = "This is a mock response from Google GenAI generate_content.", - ) -> dict[str, Any]: + ) -> dict[str, object]: """Mock response for generate_content for testing purposes""" return { "text": mock_response, @@ -239,9 +239,9 @@ async def agenerate_content( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -307,9 +307,9 @@ def generate_content( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -397,9 +397,9 @@ async def agenerate_content_stream( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -492,9 +492,9 @@ def generate_content_stream( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, diff --git a/litellm/google_genai/streaming_iterator.py b/litellm/google_genai/streaming_iterator.py index e03f7ee745f..a49e43e7bdc 100644 --- a/litellm/google_genai/streaming_iterator.py +++ b/litellm/google_genai/streaming_iterator.py @@ -2,6 +2,7 @@ import asyncio from datetime import datetime from typing import TYPE_CHECKING, Any, Final +import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.proxy.pass_through_endpoints.success_handler import ( PassThroughEndpointLogging, @@ -65,6 +66,7 @@ class BaseGoogleGenAIGenerateContentStreamingIterator: litellm_logging_obj: LiteLLMLoggingObj, request_body: dict, model: str, + custom_llm_provider: str, hidden_params: dict[str, Any] | None = None, ): self.litellm_logging_obj = litellm_logging_obj @@ -72,6 +74,10 @@ class BaseGoogleGenAIGenerateContentStreamingIterator: self.start_time = datetime.now() self.collected_chunks: list[bytes] = [] self.model = model + self.custom_llm_provider = custom_llm_provider + self.endpoint_type: Final = ( + EndpointType.GEMINI if custom_llm_provider == litellm.LlmProviders.GEMINI.value else EndpointType.VERTEX_AI + ) self._hidden_params: dict[str, Any] = hidden_params or {} async def _handle_async_streaming_logging( @@ -89,7 +95,7 @@ class BaseGoogleGenAIGenerateContentStreamingIterator: passthrough_success_handler_obj=GLOBAL_PASS_THROUGH_SUCCESS_HANDLER_OBJ, url_route="/v1/generateContent", request_body=self.request_body or {}, - endpoint_type=EndpointType.VERTEX_AI, + endpoint_type=self.endpoint_type, start_time=self.start_time, raw_bytes=self.collected_chunks, end_time=end_time, @@ -118,13 +124,13 @@ class GoogleGenAIGenerateContentStreamingIterator(BaseGoogleGenAIGenerateContent litellm_logging_obj=logging_obj, request_body=request_body or {}, model=model, + custom_llm_provider=custom_llm_provider, hidden_params=hidden_params, ) self.response = response self.model = model self.generate_content_provider_config = generate_content_provider_config self.litellm_metadata = litellm_metadata - self.custom_llm_provider = custom_llm_provider # Gemini streamGenerateContent uses SSE line framing; iter_lines keeps # large inlineData payloads (e.g. image/jpeg) intact within one event. self.stream_iterator = response.iter_lines() @@ -169,13 +175,13 @@ class AsyncGoogleGenAIGenerateContentStreamingIterator(BaseGoogleGenAIGenerateCo litellm_logging_obj=logging_obj, request_body=request_body or {}, model=model, + custom_llm_provider=custom_llm_provider, hidden_params=hidden_params, ) self.response = response self.model = model self.generate_content_provider_config = generate_content_provider_config self.litellm_metadata = litellm_metadata - self.custom_llm_provider = custom_llm_provider # Gemini streamGenerateContent uses SSE line framing; aiter_lines keeps # large inlineData payloads (e.g. image/jpeg) intact within one event. self.stream_iterator = response.aiter_lines() diff --git a/litellm/images/main.py b/litellm/images/main.py index ae4818b1967..6a94e7c8df2 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -3,7 +3,7 @@ import contextvars import importlib from collections.abc import Coroutine from functools import partial -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast, overload +from typing import TYPE_CHECKING, Final, Literal, Optional, cast, overload if TYPE_CHECKING: from litellm.images.utils import ImageEditRequestUtils @@ -19,6 +19,7 @@ from litellm.constants import request_timeout as DEFAULT_REQUEST_TIMEOUT from litellm.exceptions import LiteLLMUnknownProvider from litellm.litellm_core_utils.litellm_logging import Logging from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.llm_request_utils import flatten_form_field_values from litellm.litellm_core_utils.mock_functions import mock_image_generation from litellm.llms.base_llm import BaseImageEditConfig, BaseImageGenerationConfig from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler @@ -150,7 +151,7 @@ def image_generation( *, aimg_generation: Literal[True], **kwargs, -) -> Coroutine[Any, Any, ImageResponse]: +) -> Coroutine[object, object, ImageResponse]: ... @@ -196,7 +197,7 @@ def image_generation( api_version: str | None = None, custom_llm_provider=None, **kwargs, -) -> ImageResponse | Coroutine[Any, Any, ImageResponse]: +) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Maps the https://api.openai.com/v1/images/generations endpoint. @@ -385,6 +386,8 @@ def image_generation( litellm.LlmProviders.VERTEX_AI, litellm.LlmProviders.OPENROUTER, litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, ): if image_generation_config is None: raise ValueError(f"image generation config is not supported for {custom_llm_provider}") @@ -422,24 +425,32 @@ def image_generation( aimg_generation=aimg_generation, ) elif custom_llm_provider == "azure_ai": - from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + from litellm.llms.azure_ai.common_utils import ( + AzureFoundryModelInfo, + get_azure_ai_auth_headers, + ) api_base = AzureFoundryModelInfo.get_api_base(api_base) api_key = AzureFoundryModelInfo.get_api_key(api_key) if extra_headers is not None: optional_params["extra_headers"] = extra_headers - default_headers = { + caller_header_names = frozenset(name.lower() for name in headers) + caller_set_auth = "api-key" in caller_header_names or "authorization" in caller_header_names + auth_headers = ( + headers + if caller_set_auth + else get_azure_ai_auth_headers( + api_key=api_key, + litellm_params=litellm_params_dict, + api_key_header="api-key", + ) + ) + request_headers: Final = { "Content-Type": "application/json", + **auth_headers, + **headers, } - # Only add api-key header if api_key is not None - # Azure AD authentication will use Authorization header instead - if api_key is not None: - default_headers["api-key"] = api_key - - for k, v in default_headers.items(): - if k not in headers: - headers[k] = v model_response = azure_chat_completions.image_generation( model=model, @@ -455,7 +466,7 @@ def image_generation( api_version=api_version, aimg_generation=aimg_generation, client=client, - headers=headers, + headers=request_headers, litellm_params=litellm_params_dict, ) elif ( @@ -714,14 +725,14 @@ def image_edit( user: str | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, **kwargs, -) -> ImageResponse | Coroutine[Any, Any, ImageResponse]: +) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Maps the image edit functionality, similar to OpenAI's images/edits endpoint. """ @@ -760,7 +771,7 @@ def image_edit( images: Final = image if isinstance(image, list) else ([image] if image is not None else []) headers_from_kwargs: Final = kwargs.get("headers") - merged_extra_headers: Final[dict[str, Any]] = {} + merged_extra_headers: Final[dict[str, object]] = {} if isinstance(headers_from_kwargs, dict): merged_extra_headers.update(headers_from_kwargs) if isinstance(extra_headers, dict): @@ -846,6 +857,18 @@ def image_edit( additional_drop_params=kwargs.get("additional_drop_params"), ) + if ( + custom_llm_provider == "openai" + or custom_llm_provider == "azure" + or custom_llm_provider in litellm.openai_compatible_providers + ): + image_edit_request_params.update( + flatten_form_field_values( + non_default_params, + extra_body if isinstance(extra_body, dict) else None, + ) + ) + # Pre Call logging litellm_logging_obj.update_from_kwargs( kwargs=kwargs, @@ -953,9 +976,9 @@ async def aimage_edit( user: str | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -995,6 +1018,9 @@ async def aimage_edit( response_format=response_format, size=size, user=user, + extra_headers=extra_headers, + extra_query=extra_query, + extra_body=extra_body, timeout=timeout, custom_llm_provider=custom_llm_provider, **kwargs, @@ -1020,7 +1046,7 @@ async def aimage_edit( ) -def __getattr__(name: str) -> Any: +def __getattr__(name: str) -> type["ImageEditRequestUtils"]: """Lazy import handler for images.main module""" if name == "ImageEditRequestUtils": # Lazy load ImageEditRequestUtils to avoid heavy import from images.utils at module load time diff --git a/litellm/integrations/SlackAlerting/batching_handler.py b/litellm/integrations/SlackAlerting/batching_handler.py index a7febdadacd..1c35a15d5a1 100644 --- a/litellm/integrations/SlackAlerting/batching_handler.py +++ b/litellm/integrations/SlackAlerting/batching_handler.py @@ -10,6 +10,8 @@ from typing import TYPE_CHECKING, Any, Final from litellm._logging import verbose_proxy_logger +from .ms_teams import MS_TEAMS_ALERTING_DESTINATION, build_ms_teams_payload + if TYPE_CHECKING: from .slack_alerting import SlackAlerting as _SlackAlerting @@ -62,14 +64,17 @@ async def send_to_webhook(slackAlertingInstance: SlackAlertingType, item, count) if count > 1: payload["text"] = f"[Num Alerts: {count}]\n\n{payload['text']}" + request_body: Final = ( + build_ms_teams_payload(payload["text"]) if item.get("format") == MS_TEAMS_ALERTING_DESTINATION else payload + ) response: Final = await slackAlertingInstance.async_http_handler.post( url=item["url"], headers=item["headers"], - data=json.dumps(payload), + data=json.dumps(request_body), ) if response.status_code != 200: - verbose_proxy_logger.debug("Error sending slack alert to url=%s. Error=%s", item["url"], response.text) + verbose_proxy_logger.debug("Error sending alert to url=%s. Error=%s", item["url"], response.text) except Exception as e: - verbose_proxy_logger.debug("Error sending slack alert: %s", e) + verbose_proxy_logger.debug("Error sending alert: %s", e) finally: _print_alerting_payload_warning(payload, slackAlertingInstance=slackAlertingInstance) diff --git a/litellm/integrations/SlackAlerting/ms_teams.py b/litellm/integrations/SlackAlerting/ms_teams.py new file mode 100644 index 00000000000..a8988c045b2 --- /dev/null +++ b/litellm/integrations/SlackAlerting/ms_teams.py @@ -0,0 +1,75 @@ +"""Microsoft Teams alert delivery helpers. + +Teams incoming webhooks (Workflows and legacy connectors) accept an Adaptive +Card wrapped in a message attachment, so alert text is delivered as a single +wrapped TextBlock. +""" + +import os +from collections.abc import Mapping +from types import MappingProxyType +from typing import Final + +from typing_extensions import ReadOnly, TypedDict + +from litellm.types.integrations.slack_alerting import AlertType + +MS_TEAMS_WEBHOOK_URL_ENV: Final = "MS_TEAMS_WEBHOOK_URL" + +MS_TEAMS_ALERTING_DESTINATION: Final = "ms_teams" + +MS_TEAMS_ALERT_HEADERS: Final[Mapping[str, str]] = MappingProxyType({"Content-type": "application/json"}) + + +class MSTeamsTextBlock(TypedDict): + type: ReadOnly[str] + text: ReadOnly[str] + wrap: ReadOnly[bool] + + +class MSTeamsAdaptiveCard(TypedDict): + type: ReadOnly[str] + version: ReadOnly[str] + body: ReadOnly[tuple[MSTeamsTextBlock, ...]] + + +class MSTeamsAttachment(TypedDict): + contentType: ReadOnly[str] + content: ReadOnly[MSTeamsAdaptiveCard] + + +class MSTeamsMessage(TypedDict): + type: ReadOnly[str] + attachments: ReadOnly[tuple[MSTeamsAttachment, ...]] + + +class MSTeamsAlertText(TypedDict): + text: ReadOnly[str] + + +class MSTeamsQueueItem(TypedDict): + url: ReadOnly[str] + headers: ReadOnly[Mapping[str, str]] + payload: ReadOnly[MSTeamsAlertText] + alert_type: ReadOnly[AlertType] + format: ReadOnly[str] + + +def get_ms_teams_webhook_url() -> str | None: + return os.getenv(MS_TEAMS_WEBHOOK_URL_ENV) + + +def build_ms_teams_payload(text: str) -> MSTeamsMessage: + return MSTeamsMessage( + type="message", + attachments=( + MSTeamsAttachment( + contentType="application/vnd.microsoft.card.adaptive", + content=MSTeamsAdaptiveCard( + type="AdaptiveCard", + version="1.4", + body=(MSTeamsTextBlock(type="TextBlock", text=text, wrap=True),), + ), + ), + ), + ) diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 65f4774a693..748ef938cea 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -57,10 +57,18 @@ from litellm.types.proxy.model_deprecation import ( from ..email_templates.templates import * from .batching_handler import send_to_webhook, squash_payloads +from .ms_teams import ( + MS_TEAMS_ALERT_HEADERS, + MS_TEAMS_ALERTING_DESTINATION, + MSTeamsAlertText, + MSTeamsQueueItem, + get_ms_teams_webhook_url, +) from .utils import process_slack_alerting_variables if TYPE_CHECKING: from litellm.proxy.db.db_transaction_queue.pod_lock_manager import PodLockManager + from litellm.proxy.utils import PrismaClient from litellm.router import Router as _Router Router = _Router @@ -538,7 +546,6 @@ class SlackAlerting(CustomBatchLogger): # Get the appropriate budget alert type handler budget_alert_class: Final = get_budget_alert_type(type) _id: Final = budget_alert_class.get_id(user_info) - user_info_json: Final = user_info.model_dump(exclude_none=True) user_info_str: Final = self._get_user_info_str(user_info) event_message = budget_alert_class.get_event_message() @@ -568,7 +575,22 @@ class SlackAlerting(CustomBatchLogger): webhook_event = WebhookEvent( event=event, event_message=event_message, - **user_info_json, + spend=user_info.spend, + max_budget=user_info.max_budget, + soft_budget=user_info.soft_budget, + token=user_info.token, + customer_id=user_info.customer_id, + user_id=user_info.user_id, + team_id=user_info.team_id, + team_alias=user_info.team_alias, + organization_id=user_info.organization_id, + user_email=user_info.user_email, + key_alias=user_info.key_alias, + projected_exceeded_date=user_info.projected_exceeded_date, + projected_spend=user_info.projected_spend, + event_group=user_info.event_group, + alert_emails=user_info.alert_emails, + max_budget_alert_emails=user_info.max_budget_alert_emails, ) await self.send_alert( message=event_message + "\n\n" + user_info_str, @@ -650,7 +672,7 @@ class SlackAlerting(CustomBatchLogger): """ Create a standard message for a budget alert """ - _all_fields_as_dict: Final = user_info.model_dump(exclude_none=True) + _all_fields_as_dict: Final[dict[str, object]] = user_info.model_dump(exclude_none=True) _all_fields_as_dict.pop("token") msg = "" for k, v in _all_fields_as_dict.items(): @@ -999,7 +1021,7 @@ class SlackAlerting(CustomBatchLogger): except Exception: pass - async def model_added_alert(self, model_name: str, litellm_model_name: str, passed_model_info: Any): + async def model_added_alert(self, model_name: str, litellm_model_name: str, passed_model_info: object): base_model_from_user: Final = getattr(passed_model_info, "base_model", None) model_info = {} base_model = "" @@ -1431,13 +1453,43 @@ Model Info: # only send budget alerts over Email await self.send_email_alert_using_smtp(webhook_event=user_info, alert_type=alert_type) - if "slack" not in self.alerting: + send_to_slack: Final = "slack" in self.alerting + send_to_ms_teams: Final = MS_TEAMS_ALERTING_DESTINATION in self.alerting + if not send_to_slack and not send_to_ms_teams: return if alert_type not in self.alert_types: return from datetime import datetime + current_time: Final = datetime.now().strftime("%H:%M:%S") + _proxy_base_url: Final = os.getenv("PROXY_BASE_URL", None) + alert_type_name: Final = getattr(alert_type, "name", alert_type) + alert_type_formatted: Final = f"Alert type: `{alert_type_name}`" + if alert_type == "daily_reports" or alert_type == "new_model_added": + formatted_message = alert_type_formatted + message + else: + formatted_message = ( + f"{alert_type_formatted}\nLevel: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" + ) + + if kwargs: + for key, value in kwargs.items(): + formatted_message += f"\n\n{key}: `{value}`\n\n" + if alerting_metadata: + for key, value in alerting_metadata.items(): + formatted_message += f"\n\n*Alerting Metadata*: \n{key}: `{value}`\n\n" + if _proxy_base_url is not None: + formatted_message += f"\n\nProxy URL: `{_proxy_base_url}`" + + if send_to_ms_teams: + self._enqueue_ms_teams_alert(formatted_message=formatted_message, alert_type=alert_type) + + if not send_to_slack: + if len(self.log_queue) >= self.batch_size: + await self.flush_queue() + return + # Check if digest mode is enabled for this alert type alert_type_name_str: Final = getattr(alert_type, "value", str(alert_type)) _atc: Final = self.alert_type_config.get(alert_type_name_str) @@ -1448,9 +1500,9 @@ Model Info: elif self.default_webhook_url is not None: _digest_webhook = self.default_webhook_url else: - _digest_webhook = os.getenv("SLACK_WEBHOOK_URL", None) + _digest_webhook = os.getenv("SLACK_WEBHOOK_URL") or os.getenv("ALERTING_WEBHOOK_URL") if _digest_webhook is None: - raise ValueError("Missing SLACK_WEBHOOK_URL from environment") + raise ValueError("Missing SLACK_WEBHOOK_URL / ALERTING_WEBHOOK_URL from environment") digest_key: Final = f"{alert_type_name_str}:{request_model or ''}:{api_base or ''}" @@ -1473,38 +1525,16 @@ Model Info: ) return # Suppress immediate alert; will be emitted by _flush_digest_buckets - # Get the current timestamp - current_time: Final = datetime.now().strftime("%H:%M:%S") - _proxy_base_url: Final = os.getenv("PROXY_BASE_URL", None) - # Use .name if it's an enum, otherwise use as is - alert_type_name: Final = getattr(alert_type, "name", alert_type) - alert_type_formatted: Final = f"Alert type: `{alert_type_name}`" - if alert_type == "daily_reports" or alert_type == "new_model_added": - formatted_message = alert_type_formatted + message - else: - formatted_message = ( - f"{alert_type_formatted}\nLevel: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" - ) - - if kwargs: - for key, value in kwargs.items(): - formatted_message += f"\n\n{key}: `{value}`\n\n" - if alerting_metadata: - for key, value in alerting_metadata.items(): - formatted_message += f"\n\n*Alerting Metadata*: \n{key}: `{value}`\n\n" - if _proxy_base_url is not None: - formatted_message += f"\n\nProxy URL: `{_proxy_base_url}`" - # check if we find the slack webhook url in self.alert_to_webhook_url if self.alert_to_webhook_url is not None and alert_type in self.alert_to_webhook_url: slack_webhook_url: str | list[str] | None = self.alert_to_webhook_url[alert_type] elif self.default_webhook_url is not None: slack_webhook_url = self.default_webhook_url else: - slack_webhook_url = os.getenv("SLACK_WEBHOOK_URL", None) + slack_webhook_url = os.getenv("SLACK_WEBHOOK_URL") or os.getenv("ALERTING_WEBHOOK_URL") if slack_webhook_url is None: - raise ValueError("Missing SLACK_WEBHOOK_URL from environment") + raise ValueError("Missing SLACK_WEBHOOK_URL / ALERTING_WEBHOOK_URL from environment") payload: Final = {"text": formatted_message} headers: Final = {"Content-type": "application/json"} @@ -1531,6 +1561,24 @@ Model Info: if len(self.log_queue) >= self.batch_size: await self.flush_queue() + def _enqueue_ms_teams_alert(self, formatted_message: str, alert_type: AlertType) -> None: + ms_teams_webhook_url: Final = get_ms_teams_webhook_url() + if ms_teams_webhook_url is None: + verbose_proxy_logger.error( + "MS Teams alerting is enabled but MS_TEAMS_WEBHOOK_URL is not set. Dropping alert type=%s", + alert_type, + ) + return + payload: Final[MSTeamsAlertText] = {"text": formatted_message} + item: Final[MSTeamsQueueItem] = { + "url": ms_teams_webhook_url, + "headers": MS_TEAMS_ALERT_HEADERS, + "payload": payload, + "alert_type": alert_type, + "format": MS_TEAMS_ALERTING_DESTINATION, + } + self.log_queue.append(item) + async def async_send_batch(self): if not self.log_queue: return @@ -1897,6 +1945,69 @@ Model Info: except Exception as e: verbose_proxy_logger.exception("Error sending weekly spend report %s", e) + async def send_user_spend_alerts(self, prisma_client: "PrismaClient | None" = None) -> None: + """Check per-user daily/monthly spend thresholds and spend anomalies, alerting once per user per period.""" + if self.alerting is None or "slack" not in self.alerting: + return + + thresholds_enabled: Final = AlertType.user_spend_thresholds in self.alert_types + anomalies_enabled: Final = AlertType.user_spend_anomalies in self.alert_types + if not thresholds_enabled and not anomalies_enabled: + return + + if prisma_client is None: + from litellm.proxy.proxy_server import prisma_client as global_prisma_client + + prisma_client = global_prisma_client # rebind-ok: fall back to the proxy's global client + if prisma_client is None: + return + + from litellm.integrations.SlackAlerting.user_spend_alerts import ( + evaluate_user_spend, + fetch_user_spend_rows, + ) + + try: + today: Final = datetime.datetime.now(datetime.timezone.utc).date() + rows: Final = await fetch_user_spend_rows( + prisma_client=prisma_client, + today=today, + baseline_days=self.alerting_args.spend_anomaly_baseline_days, + ) + all_events: Final = tuple( + event + for row in rows + for event in evaluate_user_spend( + row=row, + args=self.alerting_args, + today=today, + thresholds_enabled=thresholds_enabled, + anomalies_enabled=anomalies_enabled, + ) + ) + cached_flags: Final = await asyncio.gather( + *(self.internal_usage_cache.async_get_cache(key=event.cache_key) for event in all_events) + ) + new_events: Final = tuple(event for event, cached in zip(all_events, cached_flags) if not cached) + for alert_type in (AlertType.user_spend_thresholds, AlertType.user_spend_anomalies): + typed_events = tuple(event for event in new_events if event.alert_type == alert_type) + if not typed_events: + continue + await self.send_alert( + message="\n\n".join(event.message for event in typed_events), + level="High", + alert_type=alert_type, + alerting_metadata={}, # mutable-ok: send_alert takes a dict payload + ) + for event in typed_events: + await self.internal_usage_cache.async_set_cache( + key=event.cache_key, + value="SENT", + ttl=event.cache_ttl, + ) + except Exception as e: # noqa: BLE001 # background job must not crash the scheduler + verbose_proxy_logger.exception("Error sending user spend alerts: %s", e) + async def send_fallback_stats_from_prometheus(self): """ Helper to send fallback statistics from prometheus server -> to slack @@ -1940,7 +2051,7 @@ Model Info: try: message = f"`{event_name}`\n" - key_event_dict: Final = key_event.model_dump() + key_event_dict: Final[dict[str, object]] = key_event.model_dump() # Add Created by information first message += "*Action Done by:*\n" diff --git a/litellm/integrations/SlackAlerting/user_spend_alerts.py b/litellm/integrations/SlackAlerting/user_spend_alerts.py new file mode 100644 index 00000000000..38794735c1b --- /dev/null +++ b/litellm/integrations/SlackAlerting/user_spend_alerts.py @@ -0,0 +1,139 @@ +"""Per-user daily/monthly spend threshold alerts and spend anomaly detection.""" + +import datetime +from dataclasses import dataclass +from typing import TYPE_CHECKING, Final, Literal + +from pydantic import TypeAdapter + +from litellm.constants import HOURS_IN_A_DAY +from litellm.types.integrations.slack_alerting import AlertType, SlackAlertingArgs + +if TYPE_CHECKING: + from litellm.proxy.utils import PrismaClient + +DAY_SECONDS: Final = HOURS_IN_A_DAY * 60 * 60 +MONTHLY_ALERT_TTL_SECONDS: Final = 32 * DAY_SECONDS + +USER_SPEND_QUERY: Final = """ +SELECT + user_id, + COALESCE(SUM(spend) FILTER (WHERE date = $1), 0)::float AS daily_spend, + COALESCE(SUM(spend) FILTER (WHERE date >= $2), 0)::float AS monthly_spend, + COALESCE(SUM(spend) FILTER (WHERE date >= $3 AND date < $1), 0)::float AS baseline_spend +FROM "LiteLLM_DailyUserSpend" +WHERE date >= LEAST($2, $3) AND user_id IS NOT NULL +GROUP BY user_id +HAVING COALESCE(SUM(spend) FILTER (WHERE date >= $2), 0) > 0 +""" + + +@dataclass(frozen=True, slots=True) +class UserSpendRow: + user_id: str + daily_spend: float + monthly_spend: float + baseline_spend: float + + +@dataclass(frozen=True, slots=True) +class UserSpendAlertEvent: + kind: Literal["daily_threshold", "monthly_threshold", "anomaly"] + alert_type: AlertType + message: str + cache_key: str + cache_ttl: int + + +USER_SPEND_ROWS_ADAPTER: Final = TypeAdapter(tuple[UserSpendRow, ...]) + + +async def fetch_user_spend_rows( + prisma_client: "PrismaClient", + today: datetime.date, + baseline_days: int, +) -> tuple[UserSpendRow, ...]: + today_str: Final = today.strftime("%Y-%m-%d") + month_start_str: Final = today.replace(day=1).strftime("%Y-%m-%d") + baseline_start_str: Final = (today - datetime.timedelta(days=max(baseline_days, 1))).strftime("%Y-%m-%d") + raw: Final = await prisma_client.db.query_raw(USER_SPEND_QUERY, today_str, month_start_str, baseline_start_str) + return USER_SPEND_ROWS_ADAPTER.validate_python(raw) + + +def _daily_threshold_event(row: UserSpendRow, args: SlackAlertingArgs, today_str: str) -> UserSpendAlertEvent | None: + threshold: Final = args.daily_spend_per_user_threshold + if threshold is None or row.daily_spend < threshold: + return None + return UserSpendAlertEvent( + kind="daily_threshold", + alert_type=AlertType.user_spend_thresholds, + message=( + f"User Daily Spend Threshold Crossed:\n" + f"User: `{row.user_id}`\n" + f"Spend Today: `${row.daily_spend:.2f}`\n" + f"Daily Threshold: `${threshold:.2f}`" + ), + cache_key=f"user_spend_alert_daily_{row.user_id}_{today_str}", + cache_ttl=DAY_SECONDS, + ) + + +def _monthly_threshold_event(row: UserSpendRow, args: SlackAlertingArgs, month_str: str) -> UserSpendAlertEvent | None: + threshold: Final = args.monthly_spend_per_user_threshold + if threshold is None or row.monthly_spend < threshold: + return None + return UserSpendAlertEvent( + kind="monthly_threshold", + alert_type=AlertType.user_spend_thresholds, + message=( + f"User Monthly Spend Threshold Crossed:\n" + f"User: `{row.user_id}`\n" + f"Spend This Month: `${row.monthly_spend:.2f}`\n" + f"Monthly Threshold: `${threshold:.2f}`" + ), + cache_key=f"user_spend_alert_monthly_{row.user_id}_{month_str}", + cache_ttl=MONTHLY_ALERT_TTL_SECONDS, + ) + + +def _anomaly_event(row: UserSpendRow, args: SlackAlertingArgs, today_str: str) -> UserSpendAlertEvent | None: + if row.daily_spend < args.spend_anomaly_min_spend: + return None + baseline_daily_avg: Final = row.baseline_spend / args.spend_anomaly_baseline_days + if row.baseline_spend > 0 and row.daily_spend <= args.spend_anomaly_multiplier * baseline_daily_avg: + return None + return UserSpendAlertEvent( + kind="anomaly", + alert_type=AlertType.user_spend_anomalies, + message=( + f"User Spend Anomaly Detected:\n" + f"User: `{row.user_id}`\n" + f"Spend Today: `${row.daily_spend:.2f}`\n" + f"Daily Average (last {args.spend_anomaly_baseline_days} days): `${baseline_daily_avg:.2f}`\n" + f"Trigger: spend above `{args.spend_anomaly_multiplier}x` the daily average " + f"(minimum `${args.spend_anomaly_min_spend:.2f}`)" + ), + cache_key=f"user_spend_alert_anomaly_{row.user_id}_{today_str}", + cache_ttl=DAY_SECONDS, + ) + + +def evaluate_user_spend( + row: UserSpendRow, + args: SlackAlertingArgs, + today: datetime.date, + thresholds_enabled: bool, + anomalies_enabled: bool, +) -> tuple[UserSpendAlertEvent, ...]: + today_str: Final = today.strftime("%Y-%m-%d") + month_str: Final = today.strftime("%Y-%m") + threshold_events: Final = ( + ( + _daily_threshold_event(row=row, args=args, today_str=today_str), + _monthly_threshold_event(row=row, args=args, month_str=month_str), + ) + if thresholds_enabled + else () + ) + anomaly_events: Final = (_anomaly_event(row=row, args=args, today_str=today_str),) if anomalies_enabled else () + return tuple(event for event in (*threshold_events, *anomaly_events) if event is not None) diff --git a/litellm/integrations/anthropic_cache_control_hook.py b/litellm/integrations/anthropic_cache_control_hook.py index f4f3b00dda0..545b0f40018 100644 --- a/litellm/integrations/anthropic_cache_control_hook.py +++ b/litellm/integrations/anthropic_cache_control_hook.py @@ -24,6 +24,8 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( with_prompt_cache_breakpoint, ) from litellm.types.integrations.anthropic_cache_control_hook import ( + GATEWAY_INJECTED_CACHE_METADATA_KEY, + GATEWAY_INJECTED_FOR_EVERY_DEPLOYMENT, CacheControlInjectionPoint, CacheControlMessageInjectionPoint, ) @@ -104,6 +106,13 @@ def _accepts_prompt_cache_breakpoint(block: object) -> bool: return isinstance(block, dict) and block.get("type") in OPENAI_PROMPT_CACHE_BREAKPOINT_BLOCK_TYPES +# Set by a caller whose message list is not the one that goes upstream -- today the +# Responses API layer, whose `instructions` only becomes a system message further down. +# Tells this hook to hand role-targeted points to the pass holding the final messages +# rather than spending them on a list that is still missing some of their targets. +CARRY_UNMATCHED_MESSAGE_POINTS: Final = "_litellm_carry_unmatched_cache_control_points" + + class AnthropicCacheControlHook(CustomPromptManagement): def get_chat_completion_prompt( self, @@ -128,6 +137,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): - non_default_params: dict - params with any global cache controls """ # Extract cache control injection points + carry_unmatched: Final = bool(non_default_params.pop(CARRY_UNMATCHED_MESSAGE_POINTS, False)) injection_points: Final[list[CacheControlInjectionPoint]] = non_default_params.pop( "cache_control_injection_points", [] ) @@ -161,26 +171,44 @@ class AnthropicCacheControlHook(CustomPromptManagement): non_default_params.get("prompt_cache_options"), ) ) + # A provisional message list defers every role-targeted point to the pass holding + # the final one: a role with no message here may have one there, and settling all + # of them in one pass is what lets config order decide the shared breakpoint + # budget. An ordinal names a different message once a later layer builds its own + # list, so it is placed here or not at all. + carried_message_points: Final[Sequence[CacheControlMessageInjectionPoint]] = ( + tuple(point for point in message_points if point.get("index") is None) if carry_unmatched else () + ) + applied_message_points: Final[Sequence[CacheControlMessageInjectionPoint]] = ( + tuple(point for point in message_points if point.get("index") is not None) + if carry_unmatched + else tuple(message_points) + ) reserved_blocks: Final = ( 1 if not openai_dialect and any(p.get("location") == "tool_config" for p in remaining_points) else 0 ) - breakpoints_before: Final = AnthropicCacheControlHook._count_request_cache_breakpoints(processed_messages) + breakpoints_before: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(processed_messages) processed_messages = self._apply_message_injections( - points=message_points, + points=applied_message_points, messages=processed_messages, max_blocks=MAX_CACHE_CONTROL_BLOCKS - reserved_blocks, openai_dialect=openai_dialect, ) if ( openai_dialect - and AnthropicCacheControlHook._count_request_cache_breakpoints(processed_messages) > breakpoints_before + and AnthropicCacheControlHook.count_request_cache_breakpoints(processed_messages) > breakpoints_before ): non_default_params.setdefault("prompt_cache_options", PromptCacheOptions(mode="explicit")) - # Pass through non-message injection points for provider-specific handling - if remaining_points: + # Points this pass did not place: non-message ones for the provider transform, and + # the deferred role-targeted ones. Deferring is what reaches the Responses API's + # `instructions`, which is only a system message once the bridge builds one. The + # judged stamp is what makes it safe: the next pass must not re-judge points + # against messages this pass already marked (see `_should_stand_down`). + carried_points: Final[Sequence[CacheControlInjectionPoint]] = (*remaining_points, *carried_message_points) + if carried_points: non_default_params["cache_control_injection_points"] = AnthropicCacheControlHook._stamped_as_judged( - remaining_points + carried_points ) return model, processed_messages, non_default_params @@ -210,7 +238,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): return provider @staticmethod - def _count_request_cache_breakpoints(messages: Iterable[object], system: object = None) -> int: + def count_request_cache_breakpoints(messages: Iterable[object], system: object = None) -> int: system_blocks: Final = ( sum(1 for block in system if _carries_cache_breakpoint(block)) if isinstance(system, list) else 0 ) @@ -218,7 +246,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): @staticmethod def _apply_message_injections( - points: list[CacheControlMessageInjectionPoint], + points: Sequence[CacheControlMessageInjectionPoint], messages: list[AllMessageValues], max_blocks: int, openai_dialect: bool = False, @@ -232,7 +260,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): ``max_blocks`` is reached. Injection points are honored in config order, so earlier points win when slots are scarce. """ - used_blocks = AnthropicCacheControlHook._count_request_cache_breakpoints(messages) + used_blocks = AnthropicCacheControlHook.count_request_cache_breakpoints(messages) limit_reached = False for point in points: @@ -350,7 +378,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): # 2. list of objects - only apply to last item per Anthropic spec elif isinstance(message_content, list): if len(message_content) > 0 and isinstance(message_content[-1], dict): - message_content[-1]["cache_control"] = control + message_content[-1]["cache_control"] = control # pyright: ignore[reportGeneralTypeIssues] # loose runtime dict return message @staticmethod @@ -428,8 +456,8 @@ class AnthropicCacheControlHook(CustomPromptManagement): ) max_blocks: Final = MAX_CACHE_CONTROL_BLOCKS - reserved_blocks - message_blocks: Final = AnthropicCacheControlHook._count_request_cache_breakpoints(processed_messages) - system_blocks = AnthropicCacheControlHook._count_request_cache_breakpoints((), processed_system) + message_blocks: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(processed_messages) + system_blocks = AnthropicCacheControlHook.count_request_cache_breakpoints((), processed_system) if system_points and processed_system is not None and message_blocks + system_blocks < max_blocks: system_already_has_cc: Final = isinstance(processed_system, list) and any( @@ -563,7 +591,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): carry the mark either at the top level (Anthropic shape) or nested under ``function`` (OpenAI shape); the Anthropic chat transform accepts both. """ - if AnthropicCacheControlHook._count_request_cache_breakpoints(messages, system) > 0: + if AnthropicCacheControlHook.count_request_cache_breakpoints(messages, system) > 0: return True if tools is not None: return any( @@ -723,6 +751,64 @@ class AnthropicCacheControlHook(CustomPromptManagement): if points: non_default_params["cache_control_injection_points"] = points + @staticmethod + def record_gateway_injection( + request_kwargs: Mapping[str, object], + added: int, + ) -> None: + """Name the deployment whose payload the gateway, not the client, put breakpoints on. + + Spend accounting only asks whether litellm acted, so what it needs is which + deployment, not a count. Recording that is what makes the mark attempt-scoped: the + metadata bucket is one dict shared by every retry, failover and fallback of a + request, and ``litellm_call_id`` is shared with it, so anything request-scoped + written by one attempt is read by all of them and each boundary would have to + remember to strip it. The deployment is the part that actually changes when the + request moves, so a leg that injected nothing is never credited for one that did. + + It also makes a zero delta (hook re-entry) and a negative one (a prompt manager + replacing the messages) harmless, since neither rewrites an earlier mark. + + A pass that runs before a deployment is chosen, which is what the proxy does for + prompt templates, injects into the payload every leg goes on to send, so it marks + the request for all of them rather than for one. + + Only what this pass actually placed counts. A ``tool_config`` point is placed by + the Bedrock converse transform, and only when the request carries tools, so the + presence of one here says nothing about whether a breakpoint reaches the wire; + claiming it marked three request shapes out of four that inject nothing. Missing + that Bedrock credit is the fail-closed direction, and the alternative is a + provider transform that carries spend-attribution state. + + Reads whichever bucket the request actually carries rather than asking the shared + name resolver, which answers on key presence: ``litellm_params`` declares + ``litellm_metadata`` as None on every request, so the resolver names a bucket that + is not there and the mark is dropped. + + Never CREATES the bucket. The proxy seeds it on every request and is the marker's + only reader, so a request without one is a bare SDK call nothing would consume it + from. Creating it would also add a key to a dict call sites splat as ``**kwargs``, + and on the Responses API ``metadata`` is both this bucket's default name and an + explicit parameter, so the splat collides with the caller's own value. + """ + if added <= 0: + return + bucket: Final = next( + ( + candidate + for candidate in (request_kwargs.get("litellm_metadata"), request_kwargs.get("metadata")) + if isinstance(candidate, dict) + ), + None, + ) + if bucket is not None: + model_info: Final = request_kwargs.get("model_info") + bucket[GATEWAY_INJECTED_CACHE_METADATA_KEY] = ( + model_info.get("id", GATEWAY_INJECTED_FOR_EVERY_DEPLOYMENT) + if isinstance(model_info, dict) + else GATEWAY_INJECTED_FOR_EVERY_DEPLOYMENT + ) + @staticmethod def maybe_inject_cache_control( messages: list[dict], @@ -772,17 +858,18 @@ class AnthropicCacheControlHook(CustomPromptManagement): openai_dialect: Final = AnthropicCacheControlHook._targets_openai_prompt_cache_breakpoint( model, custom_llm_provider, api_base, kwargs.get("prompt_cache_options") ) - breakpoints_before: Final = AnthropicCacheControlHook._count_request_cache_breakpoints(messages, system) + breakpoints_before: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(messages, system) messages, system, remaining = AnthropicCacheControlHook.apply_to_anthropic_messages_request( messages=messages, system=system, injection_points=injection_points, openai_dialect=openai_dialect, ) - if ( - openai_dialect - and AnthropicCacheControlHook._count_request_cache_breakpoints(messages, system) > breakpoints_before - ): + breakpoints_added: Final = ( + AnthropicCacheControlHook.count_request_cache_breakpoints(messages, system) - breakpoints_before + ) + AnthropicCacheControlHook.record_gateway_injection(kwargs, breakpoints_added) + if openai_dialect and breakpoints_added > 0: kwargs.setdefault("prompt_cache_options", PromptCacheOptions(mode="explicit")) if remaining: kwargs["cache_control_injection_points"] = AnthropicCacheControlHook._stamped_as_judged(remaining) diff --git a/litellm/integrations/arize/arize_phoenix_prompt_manager.py b/litellm/integrations/arize/arize_phoenix_prompt_manager.py index 71f4902bbe5..0c9e868c146 100644 --- a/litellm/integrations/arize/arize_phoenix_prompt_manager.py +++ b/litellm/integrations/arize/arize_phoenix_prompt_manager.py @@ -3,10 +3,12 @@ Arize Phoenix prompt manager that integrates with LiteLLM's prompt management sy Fetches prompt versions from Arize Phoenix and provides workspace-based access control. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, cast from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import ReadOnly, TypedDict from litellm.integrations.custom_prompt_management import CustomPromptManagement from litellm.integrations.prompt_management_base import ( @@ -20,6 +22,31 @@ from litellm.types.utils import StandardCallbackDynamicParams from .arize_phoenix_client import ArizePhoenixClient +class ArizePhoenixContentPart(TypedDict, total=False): + type: ReadOnly[str] + text: ReadOnly[str] + + +class ArizePhoenixTemplateMessage(TypedDict, total=False): + role: ReadOnly[str] + content: ReadOnly[Sequence[ArizePhoenixContentPart]] + + +class ArizePhoenixTemplateBody(TypedDict, total=False): + messages: ReadOnly[Sequence[ArizePhoenixTemplateMessage]] + + +class ArizePhoenixPromptMetadata(TypedDict): + model_name: ReadOnly[str | None] + model_provider: ReadOnly[str | None] + description: ReadOnly[str] + template_type: ReadOnly[str | None] + template_format: ReadOnly[str] + invocation_parameters: ReadOnly[Mapping[str, Mapping[str, object]]] + temperature: ReadOnly[float | None] + max_tokens: ReadOnly[int | None] + + class ArizePhoenixPromptTemplate: """ Represents a prompt template loaded from Arize Phoenix. @@ -28,10 +55,10 @@ class ArizePhoenixPromptTemplate: def __init__( self, template_id: str, - messages: list[dict[str, Any]], - metadata: dict[str, Any], + messages: Sequence[ArizePhoenixTemplateMessage], + metadata: ArizePhoenixPromptMetadata, model: str | None = None, - ): + ) -> None: self.template_id = template_id self.messages = messages self.metadata = metadata @@ -43,7 +70,7 @@ class ArizePhoenixPromptTemplate: self.description = metadata.get("description", "") self.template_format = metadata.get("template_format", "MUSTACHE") - def __repr__(self): + def __repr__(self) -> str: return f"ArizePhoenixPromptTemplate(id='{self.template_id}', model='{self.model}')" @@ -109,7 +136,7 @@ class ArizePhoenixTemplateManager: def _parse_prompt_data(self, data: dict[str, Any], prompt_version_id: str) -> ArizePhoenixPromptTemplate: """Parse Arize Phoenix prompt data and extract messages and metadata.""" - template_data: Final = data.get("template", {}) + template_data: Final[ArizePhoenixTemplateBody] = data.get("template", {}) messages: Final = template_data.get("messages", []) # Extract invocation parameters @@ -129,7 +156,7 @@ class ArizePhoenixTemplateManager: break # Build metadata dictionary - metadata: Final = { + metadata: Final[ArizePhoenixPromptMetadata] = { "model_name": data.get("model_name"), "model_provider": data.get("model_provider"), "description": data.get("description", ""), @@ -146,7 +173,9 @@ class ArizePhoenixTemplateManager: metadata=metadata, ) - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> list[AllMessageValues]: + def render_template( + self, template_id: str, variables: Mapping[str, object] | None = None + ) -> list[AllMessageValues]: """Render a template with the given variables and return formatted messages.""" if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") @@ -174,7 +203,9 @@ class ArizePhoenixTemplateManager: # Combine rendered content final_content = " ".join(rendered_content_parts) - rendered_messages.append({"role": role, "content": final_content}) + rendered_messages.append( + cast("AllMessageValues", {"role": role, "content": final_content}) # cast-ok: Phoenix roles are OpenAI + ) return rendered_messages @@ -243,8 +274,8 @@ class ArizePhoenixPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, - ) -> tuple[list[AllMessageValues], dict[str, Any]]: + prompt_variables: Mapping[str, object] | None = None, + ) -> tuple[list[AllMessageValues], dict[str, object]]: """ Get a prompt template and render it with variables. @@ -263,7 +294,7 @@ class ArizePhoenixPromptManager(CustomPromptManagement): rendered_messages: Final = self.prompt_manager.render_template(prompt_id, prompt_variables or {}) # Extract metadata - metadata: Final = { + metadata: Final[dict[str, object]] = { "model": template.model, "temperature": template.temperature, "max_tokens": template.max_tokens, @@ -271,7 +302,7 @@ class ArizePhoenixPromptManager(CustomPromptManagement): # Add additional invocation parameters invocation_params: Final = template.invocation_parameters - provider_params = {} + provider_params: Mapping[str, object] = {} if "openai" in invocation_params: provider_params = invocation_params["openai"] @@ -289,12 +320,12 @@ class ArizePhoenixPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: dict[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: dict[str, object] | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: """ Pre-call hook that processes the prompt template before making the LLM call. """ @@ -335,9 +366,9 @@ class ArizePhoenixPromptManager(CustomPromptManagement): except Exception as e: # Log error but don't fail the call - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in Arize Phoenix prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in Arize Phoenix prompt pre_call_hook: %s", e) return messages, litellm_params def get_available_prompts(self) -> list[str]: @@ -393,7 +424,8 @@ class ArizePhoenixPromptManager(CustomPromptManagement): rendered_messages, prompt_metadata = self.get_prompt_template(prompt_id, prompt_variables) # Extract model from metadata (if specified) - template_model: Final = prompt_metadata.get("model") + raw_template_model: Final = prompt_metadata.get("model") + template_model: Final = raw_template_model if isinstance(raw_template_model, str) else None # Extract optional parameters from metadata optional_params: Final = {} diff --git a/litellm/integrations/bitbucket/bitbucket_client.py b/litellm/integrations/bitbucket/bitbucket_client.py index e06e5ab358f..e7256da0237 100644 --- a/litellm/integrations/bitbucket/bitbucket_client.py +++ b/litellm/integrations/bitbucket/bitbucket_client.py @@ -4,11 +4,38 @@ BitBucket API client for fetching .prompt files from BitBucket repositories. import base64 import urllib.parse -from typing import Any, Final +from collections.abc import Mapping +from typing import Final, TypedDict + +from typing_extensions import NotRequired, ReadOnly from litellm.llms.custom_httpx.http_handler import HTTPHandler +class BitBucketSrcEntry(TypedDict): + path: ReadOnly[NotRequired[str]] + type: ReadOnly[NotRequired[str]] + + +class BitBucketSrcListing(TypedDict): + values: ReadOnly[NotRequired[list[BitBucketSrcEntry]]] + + +class BitBucketBranch(TypedDict): + name: ReadOnly[NotRequired[str]] + type: ReadOnly[NotRequired[str]] + + +class BitBucketBranchListing(TypedDict): + values: ReadOnly[NotRequired[list[BitBucketBranch]]] + + +class BitBucketFileMetadata(TypedDict): + content_type: ReadOnly[str | None] + content_length: ReadOnly[str | None] + last_modified: ReadOnly[str | None] + + def _sanitize_file_path(file_path: str) -> str: """Reject path traversal and URL-encode each path segment.""" if "#" in file_path or "?" in file_path: @@ -31,7 +58,7 @@ class BitBucketClient: - Branch-specific file fetching """ - def __init__(self, config: dict[str, Any]): + def __init__(self, config: Mapping[str, object]): """ Initialize the BitBucket client. @@ -135,16 +162,12 @@ class BitBucketClient: response: Final = self.http_handler.get(url, headers=self.headers) response.raise_for_status() - data: Final = response.json() - files: Final = [] - - for item in data.get("values", []): - if item.get("type") == "commit_file": - file_path = item.get("path", "") - if file_path.endswith(file_extension): - files.append(file_path) - - return files + data: Final[BitBucketSrcListing] = response.json() + return [ + file_path + for item in data.get("values", []) + if item.get("type") == "commit_file" and (file_path := item.get("path", "")).endswith(file_extension) + ] except Exception as e: # Check if it's an HTTP error @@ -162,7 +185,7 @@ class BitBucketClient: else: raise Exception(f"Error listing files in '{directory_path}': {e}") - def get_repository_info(self) -> dict[str, Any]: + def get_repository_info(self) -> Mapping[str, object]: """ Get information about the repository. @@ -191,7 +214,7 @@ class BitBucketClient: except Exception: return False - def get_branches(self) -> list[dict[str, Any]]: + def get_branches(self) -> list[BitBucketBranch]: """ Get list of branches in the repository. @@ -204,12 +227,12 @@ class BitBucketClient: response: Final = self.http_handler.get(url, headers=self.headers) response.raise_for_status() - data: Final = response.json() + data: Final[BitBucketBranchListing] = response.json() return data.get("values", []) except Exception as e: raise Exception(f"Failed to get branches: {e}") - def get_file_metadata(self, file_path: str) -> dict[str, Any] | None: + def get_file_metadata(self, file_path: str) -> BitBucketFileMetadata | None: """ Get metadata about a file (size, last modified, etc.). diff --git a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py index 6a03e3ee93c..ff34bd91e31 100644 --- a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py +++ b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py @@ -3,6 +3,7 @@ BitBucket prompt manager that integrates with LiteLLM's prompt management system Fetches .prompt files from BitBucket repositories and provides team-based access control. """ +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final from jinja2 import DictLoader, select_autoescape @@ -65,7 +66,7 @@ class BitBucketTemplateManager: def __init__( self, - bitbucket_config: dict[str, Any], + bitbucket_config: Mapping[str, object], prompt_id: str | None = None, ): self.bitbucket_config = bitbucket_config @@ -123,7 +124,7 @@ class BitBucketTemplateManager: template_content = content # Parse YAML frontmatter - metadata: dict[str, Any] = {} + metadata: dict[str, object] = {} if frontmatter_str: try: import yaml @@ -141,9 +142,9 @@ class BitBucketTemplateManager: metadata=metadata, ) - def _parse_yaml_basic(self, yaml_str: str) -> dict[str, Any]: + def _parse_yaml_basic(self, yaml_str: str) -> dict[str, object]: """Basic YAML parser for simple cases when PyYAML is not available.""" - result: Final[dict[str, Any]] = {} + result: Final[dict[str, object]] = {} for line in yaml_str.split("\n"): line = line.strip() if ":" in line and not line.startswith("#"): @@ -162,7 +163,7 @@ class BitBucketTemplateManager: result[key] = value.strip("\"'") return result - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> str: + def render_template(self, template_id: str, variables: Mapping[str, object] | None = None) -> str: """Render a template with the given variables.""" if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") @@ -209,7 +210,7 @@ class BitBucketPromptManager(CustomPromptManagement): def __init__( self, - bitbucket_config: dict[str, Any], + bitbucket_config: Mapping[str, object], prompt_id: str | None = None, ): self.bitbucket_config = bitbucket_config @@ -234,7 +235,7 @@ class BitBucketPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, ) -> tuple[str, dict[str, Any]]: """ Get a prompt template and render it with variables. @@ -267,12 +268,12 @@ class BitBucketPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: """ Pre-call hook that processes the prompt template before making the LLM call. """ @@ -316,9 +317,9 @@ class BitBucketPromptManager(CustomPromptManagement): except Exception as e: # Log error but don't fail the call - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in BitBucket prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in BitBucket prompt pre_call_hook: %s", e) return messages, litellm_params def _parse_prompt_to_messages(self, prompt_content: str) -> list[AllMessageValues]: @@ -384,14 +385,14 @@ class BitBucketPromptManager(CustomPromptManagement): def post_call_hook( self, user_id: str | None, - response: Any, + response: object, input_messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: Mapping[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> Any: + ) -> object: """ Post-call hook for any post-processing after the LLM call. """ diff --git a/litellm/integrations/callback_configs.json b/litellm/integrations/callback_configs.json index 590c848767a..7a2295a35ae 100644 --- a/litellm/integrations/callback_configs.json +++ b/litellm/integrations/callback_configs.json @@ -220,6 +220,12 @@ "ui_name": "Host URL", "description": "Langfuse host URL (default: https://cloud.langfuse.com)", "required": false + }, + "langfuse_environment": { + "type": "text", + "ui_name": "Tracing Environment", + "description": "Langfuse tracing environment (lowercase; falls back to LANGFUSE_TRACING_ENVIRONMENT)", + "required": false } }, "description": "Langfuse v2 Logging Integration" @@ -247,6 +253,12 @@ "ui_name": "Host URL", "description": "Langfuse host URL (default: https://cloud.langfuse.com)", "required": false + }, + "langfuse_environment": { + "type": "text", + "ui_name": "Tracing Environment", + "description": "Langfuse tracing environment (lowercase; falls back to LANGFUSE_TRACING_ENVIRONMENT)", + "required": false } }, "description": "Langfuse v3 OTEL Logging Integration" @@ -294,12 +306,18 @@ "id": "newrelic", "displayName": "New Relic", "logo": "newrelic.png", - "supports_key_team_logging": false, + "supports_key_team_logging": true, "dynamic_params": { - "NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED": { + "newrelic_api_key": { + "type": "password", + "ui_name": "New Relic Ingest License Key", + "description": "Per-team ingest (license) key. Team traces export to this key's New Relic account over OTLP.", + "required": false + }, + "newrelic_region": { "type": "text", - "ui_name": "Record AI Content (default: true)", - "description": "Whether to record AI message content. Set to false to disable.", + "ui_name": "New Relic Region (us or eu)", + "description": "Data center region for this team's account. Defaults to us.", "required": false } }, diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index f0d4d67fc22..ffc8fe1c1f5 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -19,14 +19,29 @@ """Transform LiteLLM data to CloudZero AnyCost CBF format.""" from datetime import datetime -from typing import Any, Final +from typing import Final, SupportsFloat, SupportsIndex, SupportsInt import polars as pl +from typing_extensions import Buffer from ...types.integrations.cloudzero import CBFRecord from .cz_resource_names import CZEntityType, CZRNGenerator +def _as_int(value: object) -> int: + """The integer form of a spend table cell, computed the way :func:`int` computes it.""" + if isinstance(value, (str, Buffer, SupportsInt, SupportsIndex)): + return int(value) + raise TypeError(f"int() argument must be a string or a number, not {type(value).__name__!r}") + + +def _as_float(value: object) -> float: + """The floating point form of a spend table cell, computed the way :func:`float` computes it.""" + if isinstance(value, (str, Buffer, SupportsFloat, SupportsIndex)): + return float(value) + raise TypeError(f"float() argument must be a string or a number, not {type(value).__name__!r}") + + class CBFTransformer: """Transform LiteLLM usage data to CloudZero Billing Format (CBF).""" @@ -82,15 +97,15 @@ class CBFTransformer: return pl.DataFrame(cbf_data) - def _create_cbf_record(self, row: dict[str, Any]) -> CBFRecord: + def _create_cbf_record(self, row: dict[str, object]) -> CBFRecord: """Create a single CBF record from LiteLLM daily spend row.""" # Parse date (daily spend tables use date strings like '2025-04-19') usage_date: Final = self._parse_date(row.get("date")) # Calculate total tokens - prompt_tokens: Final = int(row.get("prompt_tokens", 0)) - completion_tokens: Final = int(row.get("completion_tokens", 0)) + prompt_tokens: Final = _as_int(row.get("prompt_tokens", 0)) + completion_tokens: Final = _as_int(row.get("completion_tokens", 0)) total_tokens: Final = prompt_tokens + completion_tokens # Create CloudZero Resource Name (CZRN) as resource_id @@ -154,7 +169,7 @@ class CBFTransformer: "time/usage_start": ( usage_date.isoformat() if usage_date else None ), # Required: ISO-formatted UTC datetime - "cost/cost": float(row.get("spend", 0.0)), # Required: billed cost + "cost/cost": _as_float(row.get("spend", 0.0)), # Required: billed cost "resource/id": resource_id, # CZRN (CloudZero Resource Name) # Usage metrics for token consumption "usage/amount": total_tokens, # Numeric value of tokens consumed @@ -187,7 +202,7 @@ class CBFTransformer: return CBFRecord(cbf_record) - def _parse_date(self, date_str) -> datetime | None: + def _parse_date(self, date_str: object) -> datetime | None: """Parse date string from daily spend tables (e.g., '2025-04-19').""" if date_str is None: return None diff --git a/litellm/integrations/compression_interception/handler.py b/litellm/integrations/compression_interception/handler.py index 7ea60053e6f..1be7a01ba3a 100644 --- a/litellm/integrations/compression_interception/handler.py +++ b/litellm/integrations/compression_interception/handler.py @@ -7,7 +7,10 @@ litellm_content_retrieve tool calls server-side via the typed agentic loop plan. import time import uuid -from typing import Any, Final, cast +from collections.abc import Mapping, Sequence +from typing import Any, ClassVar, Final, Protocol, cast + +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm.compression import compress @@ -26,6 +29,19 @@ LITELLM_CONTENT_RETRIEVE_TOOL_NAME: Final = "litellm_content_retrieve" _CACHE_TTL_SECONDS: Final = 15 * 60 +class _AgenticLoopParams(TypedDict, total=False): + """The ``agentic_loop_params`` entry the agentic loop driver records on the logging object.""" + + model: ReadOnly[str] + + +class _AgenticLoopLoggingObj(Protocol): + """Logging object view exposing the untyped call details this handler reads.""" + + @property + def model_call_details(self) -> Mapping[str, _AgenticLoopParams]: ... + + def _compression_savings_from_counts( original_tokens: object, compressed_tokens: object ) -> CompressionSavingsMetadata | None: @@ -72,13 +88,15 @@ class CompressionInterceptionLogger(CustomLogger): 4. Build typed rerun plan with tool_result blocks from the compressed cache. """ + server_fulfilled_tool_names: ClassVar[frozenset[str]] = frozenset({LITELLM_CONTENT_RETRIEVE_TOOL_NAME}) + def __init__( self, enabled: bool = True, compression_trigger: int = 200_000, compression_target: int | None = None, embedding_model: str | None = None, - embedding_model_params: dict[str, Any] | None = None, + embedding_model_params: dict[str, object] | None = None, ): super().__init__() self.enabled = enabled @@ -101,7 +119,7 @@ class CompressionInterceptionLogger(CustomLogger): @staticmethod def initialize_from_proxy_config( litellm_settings: dict[str, Any], - callback_specific_params: dict[str, Any], + callback_specific_params: Mapping[str, object], ) -> "CompressionInterceptionLogger": compression_params: CompressionInterceptionConfig = {} if "compression_interception_params" in litellm_settings: @@ -115,7 +133,9 @@ class CompressionInterceptionLogger(CustomLogger): ) return CompressionInterceptionLogger.from_config_yaml(compression_params) - async def async_pre_call_deployment_hook(self, kwargs: dict[str, Any], call_type: CallTypes | None) -> dict | None: + async def async_pre_call_deployment_hook( + self, kwargs: dict[str, Any], call_type: CallTypes | None + ) -> dict[str, object] | None: if not self.enabled: return None if call_type is not None and call_type != CallTypes.anthropic_messages: @@ -145,7 +165,7 @@ class CompressionInterceptionLogger(CustomLogger): cache: Final = cast(dict[str, str], compressed.get("cache", {})) skip_reason: Final = cast(str | None, compressed.get("compression_skipped_reason")) - compressed_tools: Final = cast(list[dict[str, Any]], compressed.get("tools", [])) + compressed_tools: Final = cast(list[dict[str, object]], compressed.get("tools", [])) # Only mutate kwargs when compression actually produced a result. # If compression was a no-op (below trigger, invalid tool sequence, etc.), @@ -156,7 +176,7 @@ class CompressionInterceptionLogger(CustomLogger): kwargs["messages"] = compressed["messages"] if compressed_tools: kwargs["tools"] = self._merge_tools( - existing_tools=cast(list[dict[str, Any]] | None, kwargs.get("tools")), + existing_tools=cast(list[dict[str, object]] | None, kwargs.get("tools")), compressed_tools=compressed_tools, ) call_id = cast(str | None, kwargs.get("litellm_call_id")) @@ -189,14 +209,14 @@ class CompressionInterceptionLogger(CustomLogger): async def async_should_run_agentic_loop( self, - response: Any, + response: object, model: str, - messages: list[dict], - tools: list[dict] | None, + messages: Sequence[Mapping[str, object]], + tools: Sequence[Mapping[str, object]] | None, stream: bool, custom_llm_provider: str, - kwargs: dict, - ) -> tuple[bool, dict]: + kwargs: Mapping[str, object], + ) -> tuple[bool, dict[str, object]]: if not self.enabled: return False, {} if not self._has_retrieval_tool(tools): @@ -214,19 +234,19 @@ class CompressionInterceptionLogger(CustomLogger): async def async_build_agentic_loop_plan( self, - tools: dict, + tools: Mapping[str, object], model: str, - messages: list[dict], - response: Any, - anthropic_messages_provider_config: Any, - anthropic_messages_optional_request_params: dict, - logging_obj: Any, + messages: list[dict[str, object]], + response: object, + anthropic_messages_provider_config: object, + anthropic_messages_optional_request_params: Mapping[str, object], + logging_obj: _AgenticLoopLoggingObj | None, stream: bool, - kwargs: dict, + kwargs: Mapping[str, object], ) -> AgenticLoopPlan: self._prune_expired_cache() - tool_calls: Final = cast(list[dict[str, Any]], tools.get("tool_calls", [])) - thinking_blocks: Final = cast(list[dict[str, Any]], tools.get("thinking_blocks", [])) + tool_calls: Final = cast(list[dict[str, object]], tools.get("tool_calls", [])) + thinking_blocks: Final = cast(list[dict[str, object]], tools.get("thinking_blocks", [])) call_id: Final = self._resolve_call_id(logging_obj=logging_obj, kwargs=kwargs) cache: Final = self._get_cache(call_id=call_id) @@ -269,7 +289,7 @@ class CompressionInterceptionLogger(CustomLogger): full_model_name = model if logging_obj is not None: agentic_params: Final = logging_obj.model_call_details.get("agentic_loop_params", {}) - full_model_name = cast(str, agentic_params.get("model", model)) + full_model_name = agentic_params.get("model", model) request_patch: Final = AgenticLoopRequestPatch( model=full_model_name, @@ -304,15 +324,15 @@ class CompressionInterceptionLogger(CustomLogger): return {} return cache_entry[0] - def _resolve_call_id(self, logging_obj: Any, kwargs: dict[str, Any]) -> str | None: + def _resolve_call_id(self, logging_obj: _AgenticLoopLoggingObj | None, kwargs: Mapping[str, object]) -> str | None: if logging_obj is not None: logging_call_id: Final = getattr(logging_obj, "litellm_call_id", None) if isinstance(logging_call_id, str) and logging_call_id: return logging_call_id kwargs_call_id: Final = kwargs.get("litellm_call_id") - return cast(str | None, kwargs_call_id if isinstance(kwargs_call_id, str) else None) + return kwargs_call_id if isinstance(kwargs_call_id, str) else None - def _resolve_retrieval_content(self, tool_call: dict[str, Any], cache: dict[str, str]) -> str: + def _resolve_retrieval_content(self, tool_call: Mapping[str, object], cache: Mapping[str, str]) -> str: raw_input: Final = tool_call.get("input", {}) key = "" if isinstance(raw_input, dict): @@ -323,7 +343,9 @@ class CompressionInterceptionLogger(CustomLogger): return cache[key] return f"[compressed content key '{key}' not found]" - def _extract_retrieval_tool_calls(self, response: Any) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + def _extract_retrieval_tool_calls( + self, response: object + ) -> tuple[list[dict[str, object]], list[dict[str, object]]]: if isinstance(response, dict): content = response.get("content", []) else: @@ -332,8 +354,8 @@ class CompressionInterceptionLogger(CustomLogger): if not isinstance(content, list): return [], [] - tool_calls: Final[list[dict[str, Any]]] = [] - thinking_blocks: Final[list[dict[str, Any]]] = [] + tool_calls: Final[list[dict[str, object]]] = [] + thinking_blocks: Final[list[dict[str, object]]] = [] for block in content: if isinstance(block, dict): @@ -380,13 +402,13 @@ class CompressionInterceptionLogger(CustomLogger): return tool_calls, thinking_blocks - def _prepare_followup_kwargs(self, kwargs: dict[str, Any]) -> dict[str, Any]: + def _prepare_followup_kwargs(self, kwargs: Mapping[str, object]) -> dict[str, object]: internal_keys: Final = {"litellm_logging_obj"} return { k: v for k, v in kwargs.items() if not k.startswith("_compression_interception") and k not in internal_keys } - def _has_retrieval_tool(self, tools: Any) -> bool: + def _has_retrieval_tool(self, tools: object) -> bool: if not isinstance(tools, list): return False for tool in tools: @@ -402,9 +424,9 @@ class CompressionInterceptionLogger(CustomLogger): def _merge_tools( self, - existing_tools: list[dict[str, Any]] | None, - compressed_tools: list[dict[str, Any]], - ) -> list[dict[str, Any]]: + existing_tools: Sequence[Mapping[str, object]] | None, + compressed_tools: Sequence[Mapping[str, object]], + ) -> list[Mapping[str, object]]: merged: Final = list(existing_tools or []) if self._has_retrieval_tool(merged): return merged diff --git a/litellm/integrations/custom_batch_logger.py b/litellm/integrations/custom_batch_logger.py index c9e24913900..bfc78b93715 100644 --- a/litellm/integrations/custom_batch_logger.py +++ b/litellm/integrations/custom_batch_logger.py @@ -45,7 +45,7 @@ class CustomBatchLogger(CustomLogger): super().__init__(**kwargs) - async def periodic_flush(self): + async def periodic_flush(self) -> None: while True: await asyncio.sleep(self.flush_interval) verbose_logger.debug("CustomLogger periodic flush after %s seconds", self.flush_interval) diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index f2e390625f5..372c9bf6b91 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -1,7 +1,9 @@ import contextvars +import copy import hashlib import os import secrets +from collections.abc import Mapping from datetime import datetime from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, Optional, get_args @@ -38,6 +40,7 @@ except ImportError: if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation dc: Final = DualCache() @@ -60,6 +63,12 @@ _PRE_CALL_EXECUTED_TOKEN: Final = secrets.token_hex(16) _GUARDRAIL_BLOCK_STATUS_CODES: Final = frozenset({400, 403, 422}) +DEFAULT_ADVISORY_MESSAGE: Final = ( + "The user's latest message was flagged for {reason} by a content safety " + "guardrail. This may be a false positive. Use your judgment: respond " + "helpfully if the request is legitimate, or decline if it is not." +) + _guardrail_self_recorded: Final[contextvars.ContextVar[bool]] = contextvars.ContextVar( "litellm_guardrail_self_recorded", default=False ) @@ -158,6 +167,7 @@ class CustomGuardrail(CustomLogger): sensitive_data_route_to_model: str | None = None, sticky_session_routing: bool = True, run_in_parallel: bool = False, + scan_raw_request: bool = False, only_scan_new_messages: bool = False, **kwargs, ): @@ -180,6 +190,13 @@ class CustomGuardrail(CustomLogger): run_in_parallel: When True, this pre_call or post_call guardrail runs concurrently with other opted-in guardrails of the same hook. Only safe for block-only guardrails that do not mutate the request or response. + scan_raw_request: When True, this pre_call guardrail always evaluates the request as it + was before any guardrail in this hook ran, regardless of where it's declared in the + guardrails list -- so an earlier guardrail that masks/rewrites content (e.g. PII + redaction) can never hide a violation from this one. Only safe for block-only + guardrails: any data this guardrail returns is discarded, matching run_in_parallel's + contract, since applying its mutations on top of a stale snapshot would silently + undo whatever later guardrails already did to the live request. """ self.guardrail_name = guardrail_name self.supported_event_hooks = supported_event_hooks @@ -195,6 +212,7 @@ class CustomGuardrail(CustomLogger): self.sensitive_data_route_to_model: str | None = sensitive_data_route_to_model self.sticky_session_routing: bool = sticky_session_routing self.run_in_parallel: bool = run_in_parallel + self.scan_raw_request: bool = scan_raw_request self.only_scan_new_messages: bool = only_scan_new_messages if supported_event_hooks: @@ -212,13 +230,13 @@ class CustomGuardrail(CustomLogger): ) super().__init__(**kwargs) - def render_violation_message(self, default: str, context: dict[str, Any] | None = None) -> str: + def render_violation_message(self, default: str, context: Mapping[str, object] | None = None) -> str: """Return a custom violation message if template is configured.""" if not self.violation_message_template: return default - format_context: Final[dict[str, Any]] = {"default_message": default} + format_context: Final[dict[str, object]] = {"default_message": default} if context: format_context.update(context) try: @@ -281,6 +299,82 @@ class CustomGuardrail(CustomLogger): original_response=original_response, ) + def inject_advisory_message( + self, + data: dict[str, Any], # mutable-ok: caller's dict is mutated in place, matching mark_pre_call_hook_ran + message: str, + ) -> bool: + """ + Append an advisory system message to the request in place, so the LLM + itself can weigh a possible false-positive guardrail flag rather than + the request being hard-blocked or silently allowed. + + Unlike raise_passthrough_exception, this does NOT short-circuit the LLM + call; the request proceeds normally with the extra message appended. + Guardrails should call this from on_flagged handling analogous to how + passthrough-supporting guardrails call raise_passthrough_exception. + + Args: + data: The request data dictionary, mutated in place to append the + advisory message to its "messages" list and/or "input"/ + "instructions" text. + message: The formatted advisory message to append as a system message. + + Returns: + True if the advisory was actually written somewhere the model will + see it. False if ``data["input"]`` is a structured Responses-API + list (not a plain string) -- the Responses API reads only + ``input``, so appending to ``messages`` would be inert regardless + of whether a ``messages`` list also happens to be present, and + there is no field this helper can safely append into. The caller + must treat this like any other case where the mitigation can't + land and degrade to blocking instead of silently letting the + flagged request through unmodified. + """ + advisory_message: Final = {"role": "system", "content": message} # mutable-ok: plain dict for live request + existing_messages: Final = data.get("messages") + existing_input: Final = data.get("input") + existing_instructions: Final = data.get("instructions") + if isinstance(existing_instructions, str): + # Responses API "instructions" is the privileged, developer-set + # system-level field the model treats as authoritative -- unlike + # "input", which the caller controls and could use to tell the + # model to disregard a trailing warning. Prefer it over "input" + # whenever present. + if isinstance(existing_messages, list): + messages_with_instructions_note: Final = [ # mutable-ok: fresh list + *existing_messages, + advisory_message, + ] + data["messages"] = messages_with_instructions_note # rebind-ok: mutates caller's dict by design + data["instructions"] = f"{existing_instructions}\n\n{message}" # rebind-ok: mutates caller's dict by design + return True + if isinstance(existing_input, str): + # A plain-string "input" doesn't rule out "messages" also being a + # real, read field (e.g. a chat-completions call carrying a stray + # "input"), so write to both when both are present. + if isinstance(existing_messages, list): + messages_with_input_note: Final = [*existing_messages, advisory_message] # mutable-ok: fresh list + data["messages"] = messages_with_input_note # rebind-ok: mutates caller's dict by design + # The Responses API reads "input", not "messages" -- appending only to + # "messages" would leave the advisory unreachable for that endpoint. + data["input"] = f"{existing_input}\n\n{message}" # rebind-ok: mutates caller's dict by design + return True + if existing_input is not None: + # existing_input is a structured (non-string) Responses-API item + # list. That endpoint reads only "input", so appending to + # "messages" -- even if "messages" also happens to be present -- + # would never reach the model. Leave data untouched and report + # non-delivery so the caller degrades to blocking. + return False + if isinstance(existing_messages, list): + messages_without_input_note: Final = [*existing_messages, advisory_message] # mutable-ok: fresh list + data["messages"] = messages_without_input_note # rebind-ok: mutates caller's dict by design + return True + sole_message: Final = [advisory_message] # mutable-ok: plain list for the live JSON request + data["messages"] = sole_message # rebind-ok: mutates caller's dict by design + return True + def raise_sensitive_data_route_exception( self, route_to_model: str, @@ -570,7 +664,7 @@ class CustomGuardrail(CustomLogger): value: Final = self._get_admin_metadata(data).get("opted_out_global_guardrails") return value if isinstance(value, list) else [] - def _is_valid_response_type(self, result: Any) -> bool: + def _is_valid_response_type(self, result: object) -> bool: """ Check if result is a valid LLMResponseTypes instance. @@ -631,7 +725,7 @@ class CustomGuardrail(CustomLogger): return None return f"{_PRE_CALL_EXECUTED_TOKEN}:{name}" - def mark_pre_call_hook_ran(self, data: dict[str, Any]) -> None: + def mark_pre_call_hook_ran(self, data: dict[str, object]) -> None: """ Record that this guardrail's ``async_pre_call_hook`` already ran for this request, so the deployment-level hook does not run it a second time. @@ -656,7 +750,7 @@ class CustomGuardrail(CustomLogger): return data["metadata"] = {PRE_CALL_EXECUTED_GUARDRAILS_KEY: [marker]} - def _pre_call_hook_already_ran(self, data: dict[str, Any]) -> bool: + def _pre_call_hook_already_ran(self, data: dict[str, object]) -> bool: marker: Final = self._pre_call_marker() if marker is None: return False @@ -760,6 +854,69 @@ class CustomGuardrail(CustomLogger): return result + async def async_logging_hook( + self, + kwargs: dict, # mutable-ok: CustomLogger.async_logging_hook contract + result: object, + call_type: str, + ) -> tuple[dict, object]: # mutable-ok: CustomLogger.async_logging_hook contract + """logging_only: run apply_guardrail on copies of the logged request/response and record the verdict.""" + from litellm.llms import get_guardrail_translation_mapping + + if not self.uses_apply_guardrail_interface() or self.use_native_lifecycle_hooks: + return kwargs, result + try: + translation: Final = get_guardrail_translation_mapping(CallTypes(call_type))() + except ValueError: + verbose_logger.debug( + "Guardrail %s: no guardrail translation for call_type=%s, skipping logging_only scan", + self.guardrail_name, + call_type, + ) + return kwargs, result + litellm_params: Final = kwargs.get("litellm_params") or {} + scratch_metadata: Final = { + key: value + for key, value in (litellm_params.get("metadata") or {}).items() + if key != "standard_logging_guardrail_information" + } + try: + await self._scan_logged_call(kwargs, result, translation, scratch_metadata) + except Exception as e: + verbose_logger.warning("Guardrail %s: logging_only scan raised: %s", self.guardrail_name, e) + recorded: Final = scratch_metadata.get("standard_logging_guardrail_information") + standard_logging_object: Final = kwargs.get("standard_logging_object") + if not recorded or not isinstance(standard_logging_object, dict): + return kwargs, result + entries: Final = recorded if isinstance(recorded, list) else [recorded] + existing: Final = standard_logging_object.get("guardrail_information") or [] + return { + **kwargs, + "standard_logging_object": {**standard_logging_object, "guardrail_information": [*existing, *entries]}, + }, result + + async def _scan_logged_call( + self, + kwargs: dict, # mutable-ok: CustomLogger.async_logging_hook contract + result: object, + translation: "BaseTranslation", + scratch_metadata: dict, # mutable-ok: apply_guardrail records its verdict into request metadata + ) -> None: + optional_params: Final = kwargs.get("optional_params") or {} + scratch_input: Final = copy.deepcopy(kwargs.get("messages") or kwargs.get("input")) + scratch_request: Final = { + "model": kwargs.get("model"), + "messages": scratch_input, + "input": scratch_input, + "tools": copy.deepcopy(optional_params.get("tools")), + "litellm_call_id": kwargs.get("litellm_call_id"), + "metadata": scratch_metadata, + } + await translation.process_input_messages(data=scratch_request, guardrail_to_apply=self) + await translation.process_output_response( + response=copy.deepcopy(result), guardrail_to_apply=self, request_data=scratch_request + ) + def supports_scan_only_tool_results(self) -> bool: """Whether this guardrail can scan tool-result content. @@ -1079,7 +1236,7 @@ class CustomGuardrail(CustomLogger): This gets logged on downsteam Langfuse, DataDog, etc. """ # Convert None to empty dict to satisfy type requirements - guardrail_response: dict[str, Any] | str = {} if response is None else response + guardrail_response: dict[str, object] | str = {} if response is None else response # For apply_guardrail functions in custom_code_guardrail scenario, # simplify the logged response to "allow", "deny", or "mask" diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index 195eb85c07d..8f03e08f02d 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -2,8 +2,8 @@ # On success, logs events to Promptlayer import re import traceback -from collections.abc import AsyncGenerator -from typing import TYPE_CHECKING, Any, Final, Optional +from collections.abc import AsyncGenerator, Mapping, Sequence +from typing import TYPE_CHECKING, Any, ClassVar, Final, Optional from pydantic import BaseModel @@ -31,6 +31,9 @@ if TYPE_CHECKING: from litellm.caching.caching import DualCache from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.llms.base_llm.anthropic_messages.transformation import ( + BaseAnthropicMessagesConfig, + ) from litellm.proxy._types import UserAPIKeyAuth from litellm.types.mcp import ( MCPPostCallResponseObject, @@ -39,7 +42,7 @@ if TYPE_CHECKING: ) from litellm.types.router import PreRoutingHookResponse - Span = _Span | Any + Span = _Span else: Span = Any LiteLLMLoggingObj = Any @@ -60,6 +63,7 @@ _BASE64_INLINE_PATTERN: Final = re.compile( class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback#callback-class # Class variables or attributes + server_fulfilled_tool_names: ClassVar[frozenset[str]] = frozenset() enforces_request_content: bool = False """ @@ -122,11 +126,11 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac return [] callbacks: Final = AllCallbacks() - callback_info: Final = getattr(callbacks, lookup_name, None) + callback_info: Final[object] = getattr(callbacks, lookup_name, None) if callback_info is None: return [] - params: Final = getattr(callback_info, "litellm_callback_params", None) + params: Final[Sequence[str] | None] = getattr(callback_info, "litellm_callback_params", None) if not params: return [] @@ -267,7 +271,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac ) -> list[dict]: return healthy_deployments - async def async_pre_call_deployment_hook(self, kwargs: dict[str, Any], call_type: CallTypes | None) -> dict | None: + async def async_pre_call_deployment_hook( + self, kwargs: dict[str, object], call_type: CallTypes | None + ) -> dict | None: """ Allow modifying the request just before it's sent to the deployment. @@ -292,12 +298,60 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac Allow modifying / reviewing the response just after it's received from the deployment. """ + async def async_post_call_failure_deployment_hook( + self, + request_data: Mapping[str, object], + exception: Exception, + call_type: CallTypes | None, + fallback_depth: int | None = None, + ) -> None: + """ + Called once per failed deployment attempt - attempt 1, every retry, and + every fallback chain step - because the router re-invokes the wrapped + function on each attempt, re-entering this hook's call site fresh + every time. + + This is a DEPLOYMENT-LEVEL signal, distinct from the REQUEST-LEVEL + ``async_log_failure_event``, which fires once per logical client + request behind a dedup gate. ``request_data`` is mostly this + attempt's own kwargs, with one exception: it omits + ``attempted_targets``, the router's own bookkeeping of which fallback + targets this request has already tried, since that one object *is* + shared by reference across every hop of the live fallback walk. + + Pairs with ``async_pre_call_deployment_hook`` and + ``async_post_call_success_deployment_hook`` to complete the + pre-call/success/failure lifecycle for a single deployment attempt. + + ``fallback_depth`` is best-effort: ``None`` on the first attempt and on + any call made without a ``Router`` (a bare SDK call has no fallback + chain to be at a depth in), ``1`` on the first fallback hop, ``2`` on + the second, and so on. It reflects ``Router``'s own internal fallback + bookkeeping (``kwargs["fallback_depth"]``), not a value this hook + computes or guarantees the shape of across versions. It tracks + fallback hops only, not retries within the same model group - a + retry-only failure (no fallback yet) also reports ``None``. If an + override predates this field it's simply never passed, rather than + raising - safe to leave off an override written before it existed. + + ``exception`` is a same-class snapshot, not the exact object about to + be re-raised to the real caller: read it freely, but setting an + attribute on it (e.g. ``status_code``) has no effect on what the + caller actually receives. + + Default: no-op. Opt in by overriding. Keep overrides fast - this + runs on the request's exception path, so a slow implementation + delays error propagation to the caller. The reported failure + duration is captured before this hook runs, so a slow override + doesn't inflate that metric, but the caller still waits for it. + """ + async def async_post_call_streaming_deployment_hook( self, request_data: dict, - response_chunk: Any, + response_chunk: object, call_type: CallTypes | None, - ) -> Any | None: + ) -> object | None: """ Allow modifying streaming chunks just before they're returned to the user. @@ -329,7 +383,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac """ def translate_completion_output_params_streaming( - self, completion_stream: Any + self, completion_stream: object ) -> AdapterCompletionStreamWrapper | None: """ Translates the streaming chunk, from the OpenAI format to the custom format. @@ -369,9 +423,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac self, data: dict, user_api_key_dict: UserAPIKeyAuth, - response: Any, + response: object, request_headers: dict[str, str] | None = None, - litellm_call_info: dict[str, Any] | None = None, + litellm_call_info: dict[str, object] | None = None, ) -> dict[str, str] | None: """ Called after an LLM API call (success or failure) to allow injecting custom HTTP response headers. @@ -422,11 +476,11 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac ) -> Any: pass - async def async_logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + async def async_logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: """For masking logged request/response. Return a modified version of the request/result.""" return kwargs, result - def logging_hook(self, kwargs: dict, result: Any, call_type: str) -> tuple[dict, Any]: + def logging_hook(self, kwargs: dict, result: object, call_type: str) -> tuple[dict, object]: """For masking logged request/response. Return a modified version of the request/result.""" return kwargs, result @@ -532,7 +586,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_should_run_agentic_loop( self, - response: Any, + response: object, model: str, messages: list[dict], tools: list[dict] | None, @@ -593,8 +647,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, - anthropic_messages_provider_config: Any, + response: object, + anthropic_messages_provider_config: "BaseAnthropicMessagesConfig | None", anthropic_messages_optional_request_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -662,8 +716,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, - anthropic_messages_provider_config: Any, + response: object, + anthropic_messages_provider_config: "BaseAnthropicMessagesConfig | None", anthropic_messages_optional_request_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -679,7 +733,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_post_agentic_loop_response_hook( self, - response: Any, + response: object, plan: AgenticLoopPlan, kwargs: dict, ) -> Any: @@ -718,7 +772,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_should_run_chat_completion_agentic_loop( self, - response: Any, + response: object, model: str, messages: list[dict], tools: list[dict] | None, @@ -736,12 +790,12 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, + response: object, optional_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, kwargs: dict, - ) -> Any: + ) -> object: """ Hook to execute chat completion agentic loop based on context from should_run hook. """ @@ -751,7 +805,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac tools: dict, model: str, messages: list[dict], - response: Any, + response: object, optional_params: dict, logging_obj: "LiteLLMLoggingObj", stream: bool, @@ -802,7 +856,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac - Converting to string and then truncating the logged content catches this 2. We want to avoid modifying the original `messages`, `response`, and `error_str` in the logging payload since these are in kwargs and could be returned to the user """ - field_value: Final = standard_logging_object.get(field_name) + field_value: Final[object] = standard_logging_object.get(field_name) if field_value: str_value: Final = str(field_value) if len(str_value) > max_length: @@ -956,8 +1010,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac • Keep untyped or text content. • Recursively redact inline base64 blobs in *any* string field, at any depth. """ - raw_messages: Final[Any] = payload.get("messages", []) - messages: Final[list[Any]] = raw_messages if isinstance(raw_messages, list) else [] + raw_messages: Final[object] = payload.get("messages", []) + messages: Final[list[object]] = raw_messages if isinstance(raw_messages, list) else [] verbose_logger.debug("[CustomLogger] Stripping base64 from %s messages", len(messages)) if messages: @@ -988,8 +1042,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac • Keep untyped or text content. • Recursively redact inline base64 blobs in *any* string field, at any depth. """ - raw_messages: Final[Any] = payload.get("messages", []) - messages: Final[list[Any]] = raw_messages if isinstance(raw_messages, list) else [] + raw_messages: Final[object] = payload.get("messages", []) + messages: Final[list[object]] = raw_messages if isinstance(raw_messages, list) else [] verbose_logger.debug("[CustomLogger] Stripping base64 from %s messages", len(messages)) if messages: @@ -1007,10 +1061,10 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac def _redact_base64( self, - value: Any, + value: object, depth: int = 0, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER, - ) -> Any: + ) -> object: """Recursively redact inline base64 from any nested structure with a max recursion depth limit.""" if depth > max_depth: verbose_logger.warning("[CustomLogger] Max recursion depth %s reached while redacting base64", max_depth) @@ -1030,7 +1084,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac return value - def _should_keep_content(self, content: Any) -> bool: + def _should_keep_content(self, content: object) -> bool: """Return True if this content item should be retained.""" if not isinstance(content, dict): return True @@ -1041,16 +1095,16 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac def _process_messages( self, - messages: list[Any], + messages: list[object], max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER, - ) -> list[dict[str, Any]]: - filtered_messages: Final[list[dict[str, Any]]] = [] + ) -> list[dict[str, object]]: + filtered_messages: Final[list[dict[str, object]]] = [] for msg in messages: if not isinstance(msg, dict): continue - contents: Any = msg.get("content") + contents: object = msg.get("content") if isinstance(contents, list): - cleaned: list[Any] = [] + cleaned: list[object] = [] for c in contents: if self._should_keep_content(content=c): cleaned.append(self._redact_base64(value=c, max_depth=max_depth)) diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 04f1c6dff15..866076a3c49 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -20,10 +20,11 @@ import time import traceback from collections.abc import Sequence from datetime import datetime as datetimeObj -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx from httpx import Response +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -62,6 +63,18 @@ from litellm.types.utils import StandardLoggingPayload from ..additional_logging_utils import AdditionalLoggingUtils +if TYPE_CHECKING: + from fastapi import HTTPException + + from litellm.proxy._types import UserAPIKeyAuth + + +class _DatadogLoggingKwargs(TypedDict, total=False): + """The subset of logging ``kwargs`` that the Datadog payload builder reads.""" + + standard_logging_object: ReadOnly[StandardLoggingPayload | None] + + # max number of logs DD API can accept @@ -87,6 +100,11 @@ def _resolve_dd_batch_size() -> int: return max(1, min(value, DD_MAX_BATCH_SIZE)) +def _span_attribute(span: object, name: str) -> object: + """Read an optional attribute off whatever span object the active tracer hands back.""" + return getattr(span, name, None) + + class DataDogLogger( CustomBatchLogger, AdditionalLoggingUtils, @@ -271,9 +289,9 @@ class DataDogLogger( self, request_data: dict, original_exception: Exception, - user_api_key_dict: Any, + user_api_key_dict: "UserAPIKeyAuth", traceback_str: str | None = None, - ) -> Any | None: + ) -> "HTTPException | None": """ Log proxy-level failures (e.g. 401 auth, DB connection errors) to Datadog. @@ -297,7 +315,7 @@ class DataDogLogger( status_code = int(_code) # Use project-standard sanitized user context when running in proxy - user_context: dict[str, Any] = {} + user_context: dict[str, object] = {} try: from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, @@ -553,8 +571,8 @@ class DataDogLogger( def create_datadog_logging_payload( self, - kwargs: dict | Any, - response_obj: Any, + kwargs: _DatadogLoggingKwargs, + response_obj: object, start_time: datetime.datetime, end_time: datetime.datetime, ) -> DatadogPayload: @@ -562,8 +580,8 @@ class DataDogLogger( Helper function to create a datadog payload for logging Args: - kwargs (Union[dict, Any]): request kwargs - response_obj (Any): llm api response + kwargs: request kwargs, read for its standard logging object + response_obj: llm api response start_time (datetime.datetime): start time of request end_time (datetime.datetime): end time of request @@ -625,7 +643,7 @@ class DataDogLogger( self, payload: ServiceLoggerPayload, error: str | None = "", - parent_otel_span: Any | None = None, + parent_otel_span: object = None, start_time: datetimeObj | float | None = None, end_time: float | datetimeObj | None = None, event_metadata: dict | None = None, @@ -659,7 +677,7 @@ class DataDogLogger( self, payload: ServiceLoggerPayload, error: str | None = "", - parent_otel_span: Any | None = None, + parent_otel_span: object = None, start_time: datetimeObj | float | None = None, end_time: float | datetimeObj | None = None, event_metadata: dict | None = None, @@ -696,7 +714,7 @@ class DataDogLogger( def _create_v0_logging_payload( self, - kwargs: dict | Any, + kwargs: dict, response_obj: Any, start_time: datetime.datetime, end_time: datetime.datetime, @@ -810,11 +828,11 @@ class DataDogLogger( if current_span is None: return None - trace_id: Final = getattr(current_span, "trace_id", None) + trace_id: Final = _span_attribute(current_span, "trace_id") if trace_id is None: return None - span_id: Final = getattr(current_span, "span_id", None) + span_id: Final = _span_attribute(current_span, "span_id") trace_context: Final[dict[str, str]] = {"trace_id": str(trace_id)} if span_id is not None: trace_context["span_id"] = str(span_id) diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 704f0323e95..5e116b7301a 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -9,7 +9,9 @@ API Reference: https://docs.datadoghq.com/llm_observability/setup/api/?tab=examp import asyncio import json import os +from collections.abc import Mapping, Sequence from datetime import datetime +from types import MappingProxyType from typing import Any, Final, Literal import httpx @@ -29,12 +31,16 @@ from litellm.integrations.datadog.datadog_mock_client import ( ) from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.prompt_templates.common_utils import ( + convert_content_list_to_str, handle_any_messages_to_chat_completion_str_messages_conversion, ) +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps +from litellm.litellm_core_utils.safe_json_loads import safe_json_loads from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) +from litellm.proxy.spend_tracking.savings import extract_cache_creation_tokens, extract_cache_read_tokens from litellm.types.integrations.datadog_llm_obs import * from litellm.types.utils import ( CallTypes, @@ -43,6 +49,189 @@ from litellm.types.utils import ( StandardLoggingPayloadErrorInformation, ) +_EMPTY_MAPPING: Final[Mapping[str, Any]] = MappingProxyType({}) +_EMPTY_MESSAGE: Final[Message] = {"role": "", "content": ""} +_MAX_PARSED_TOOL_ARGUMENT_CHARS: Final = 256 * 1024 + + +def _mapping_field(source: Mapping[str, Any], key: str) -> Mapping[str, Any]: + """The value at `key` when it is a mapping, else an empty one.""" + value: Final = source.get(key) + return value if isinstance(value, dict) else _EMPTY_MAPPING + + +def _content_blocks(message: Mapping[str, Any]) -> tuple[Mapping[str, Any], ...]: + content: Final = message.get("content") + if not isinstance(content, list): + return () + return tuple(block for block in content if isinstance(block, dict)) + + +def _to_dd_arguments(raw_arguments: object) -> dict[str, Any] | str: + """ + Arguments as the object LLM Obs types them as, or the raw string when they are not one. + + Strings past the size bound ship unparsed: decoding multiplies memory on hostile compact + JSON, and the raw string is what the intake receives either way. + """ + if not isinstance(raw_arguments, str): + return raw_arguments if isinstance(raw_arguments, dict) else str(raw_arguments) + if len(raw_arguments) > _MAX_PARSED_TOOL_ARGUMENT_CHARS: + return raw_arguments + parsed: Final = safe_json_loads(raw_arguments) + return parsed if isinstance(parsed, dict) else raw_arguments + + +def _to_dd_tool_calls(message: Mapping[str, Any]) -> tuple[ToolCall, ...]: + """ + The tool calls a message carries, in LLM Obs' ToolCall schema, from either dialect. + + OpenAI puts them in `tool_calls` with the callee nested under `function` and `arguments` + serialized; Anthropic puts them in `content` as `tool_use` blocks with `input` already an + object. LLM Obs reads `name` / `arguments` / `tool_id` either way. + """ + raw_tool_calls: Final = message.get("tool_calls") + openai_calls: Final = tuple( + ToolCall( + name=function.get("name", ""), + arguments=_to_dd_arguments(function.get("arguments", "")), + tool_id=tool_call.get("id", ""), + type=tool_call.get("type", "function"), + ) + for tool_call in (raw_tool_calls if isinstance(raw_tool_calls, list) else ()) + if isinstance(tool_call, dict) + for function in [_mapping_field(tool_call, "function")] + ) + anthropic_calls: Final = tuple( + ToolCall( + name=block.get("name", ""), + arguments=_to_dd_arguments(block.get("input") or {}), + tool_id=block.get("id", ""), + type="tool_use", + ) + for block in _content_blocks(message) + if block.get("type") == "tool_use" + ) + return openai_calls + anthropic_calls + + +def _to_dd_tool_results(message: Mapping[str, Any], tool_call_names: Mapping[str, str]) -> tuple[ToolResult, ...]: + """ + The tool results a message carries, linked back to the call each answers. + + OpenAI models a result as a whole `role: "tool"` message keyed by `tool_call_id`; + Anthropic nests `tool_result` blocks inside a user message, keyed by `tool_use_id`. + """ + + def to_result(tool_id: str, result: object) -> ToolResult: + return ToolResult( + name=tool_call_names.get(tool_id, ""), + result=result if isinstance(result, str) else safe_dumps(result), + tool_id=tool_id, + type="function", + ) + + if message.get("role") == "tool": + return (to_result(str(message.get("tool_call_id", "")), message.get("content") or ""),) + return tuple( + to_result(str(block.get("tool_use_id", "")), block.get("content") or "") + for block in _content_blocks(message) + if block.get("type") == "tool_result" + ) + + +def _tool_call_names_by_id(messages: Sequence[object]) -> Mapping[str, str]: + """Ids to tool names for result linking; reads names structurally and parses nothing.""" + openai_pairs: Final = tuple( + (tool_call.get("id"), function.get("name", "")) + for message in messages + if isinstance(message, dict) and isinstance(message.get("tool_calls"), list) + for tool_call in message["tool_calls"] + if isinstance(tool_call, dict) + for function in [_mapping_field(tool_call, "function")] + ) + anthropic_pairs: Final = tuple( + (block.get("id"), block.get("name", "")) + for message in messages + if isinstance(message, dict) + for block in _content_blocks(message) + if block.get("type") == "tool_use" + ) + return MappingProxyType({str(tool_id): str(name) for tool_id, name in openai_pairs + anthropic_pairs if tool_id}) + + +def _to_dd_message(message: object, tool_call_names: Mapping[str, str]) -> Message: + """ + Map one chat message onto LLM Obs' Message schema, adding fields and never destroying content. + + Content collapses to its text only when it has text; a content list with none (tool blocks, + images) rides along unchanged so nothing the caller logged is lost. Tool calls and results + move into the fields the LLM Obs Tools panel reads, from both the OpenAI and Anthropic shapes. + """ + if not isinstance(message, dict): + converted: Final = handle_any_messages_to_chat_completion_str_messages_conversion(message) + return converted[0] if converted else _EMPTY_MESSAGE + + text: Final = convert_content_list_to_str(message) # pyright: ignore[reportArgumentType] # caller-supplied dict + original_content: Final = message.get("content") + content: Final = ( + text if text or not isinstance(original_content, list) or not original_content else original_content + ) + reasoning: Final = message.get("reasoning_content") + tool_calls: Final = _to_dd_tool_calls(message) + tool_results: Final = _to_dd_tool_results(message, tool_call_names) + dd_message: Final[Message] = { + "role": message.get("role", ""), + "content": content, + **({"reasoning_content": reasoning} if reasoning is not None else {}), + **({"tool_calls": tool_calls} if tool_calls else {}), + **({"tool_results": tool_results} if tool_results else {}), + } + return dd_message + + +def _to_dd_messages(messages: object) -> tuple[Message, ...]: + """Map a whole conversation, resolving each tool result against the calls that precede it.""" + if messages is None: + return () + if not isinstance(messages, list): + return tuple(handle_any_messages_to_chat_completion_str_messages_conversion(messages)) + tool_call_names: Final = _tool_call_names_by_id(messages) + return tuple(_to_dd_message(message, tool_call_names) for message in messages) + + +def _to_dd_tool_definition(entry: Mapping[str, Any]) -> ToolDefinition | None: + function: Final = entry.get("function") + declared: Final[Mapping[str, Any]] = function if isinstance(function, dict) else entry + name: Final = declared.get("name") + if not name: + return None + schema: Final = declared.get("parameters") or declared.get("input_schema") + description: Final = declared.get("description", "") + if not isinstance(schema, dict): + return ToolDefinition(name=name, description=description) + return ToolDefinition(name=name, description=description, schema=schema) + + +def _to_dd_tool_definitions(model_parameters: object) -> tuple[ToolDefinition, ...]: + """ + Map the request's declared tools onto LLM Obs' ToolDefinition schema. + + Handles the wrapped chat-completions shape and the bare shape the Anthropic and + Responses surfaces use, since both reach this logger through `model_parameters`. + """ + if not isinstance(model_parameters, dict): + return () + raw_tools: Final = model_parameters.get("tools") or model_parameters.get("functions") + if not isinstance(raw_tools, list): + return () + return tuple( + definition + for entry in raw_tools + if isinstance(entry, dict) + if (definition := _to_dd_tool_definition(entry)) is not None + ) + class DataDogLLMObsLogger(CustomBatchLogger): def __init__(self, **kwargs): @@ -221,12 +410,9 @@ class DataDogLLMObsLogger(CustomBatchLogger): if standard_logging_payload is None: raise Exception("DataDogLLMObs: standard_logging_object is not set") - messages = standard_logging_payload["messages"] - messages = self._ensure_string_content(messages=messages) - metadata: Final = kwargs.get("litellm_params", {}).get("metadata", {}) - input_meta: Final = InputMeta(messages=handle_any_messages_to_chat_completion_str_messages_conversion(messages)) + input_meta: Final = InputMeta(messages=_to_dd_messages(standard_logging_payload["messages"])) output_meta: Final = OutputMeta( messages=self._get_response_messages( standard_logging_payload=standard_logging_payload, @@ -240,22 +426,20 @@ class DataDogLLMObsLogger(CustomBatchLogger): if isinstance(metadata, dict): metadata_parent_id = metadata.get("parent_id") - meta: Final = Meta( - kind=self._get_datadog_span_kind(standard_logging_payload.get("call_type"), metadata_parent_id), - input=input_meta, - output=output_meta, - metadata=self._get_dd_llm_obs_payload_metadata(standard_logging_payload), - error=error_info, - ) + tool_definitions: Final = _to_dd_tool_definitions(standard_logging_payload.get("model_parameters")) + span_kind: Final = self._get_datadog_span_kind(standard_logging_payload.get("call_type"), metadata_parent_id) + payload_metadata: Final = self._get_dd_llm_obs_payload_metadata(standard_logging_payload) - # Calculate metrics (you may need to adjust these based on available data) - metrics: Final = LLMMetrics( - input_tokens=float(standard_logging_payload.get("prompt_tokens", 0)), - output_tokens=float(standard_logging_payload.get("completion_tokens", 0)), - total_tokens=float(standard_logging_payload.get("total_tokens", 0)), - total_cost=float(standard_logging_payload.get("response_cost", 0)), - time_to_first_token=self._get_time_to_first_token_seconds(standard_logging_payload), - ) + meta: Final[Meta] = { + "kind": span_kind, + "input": input_meta, + "output": output_meta, + "metadata": payload_metadata, + "error": error_info, + **({"tool_definitions": tool_definitions} if tool_definitions else {}), + } + + metrics: Final = self._assemble_metrics(standard_logging_payload) payload: Final[LLMObsPayload] = LLMObsPayload( parent_id=metadata_parent_id if metadata_parent_id else "undefined", @@ -313,6 +497,45 @@ class DataDogLLMObsLogger(CustomBatchLogger): ) return error_info + def _assemble_metrics(self, standard_logging_payload: StandardLoggingPayload) -> LLMMetrics: + """ + Build the span metrics, including the prompt-cache counts LLM Obs charts cache savings from. + + Cache counts resolve through the same owners the savings dashboard uses, so every provider + spelling is covered, and `non_cached_input_tokens` subtracts BOTH cache categories because + litellm's normalized prompt count includes both (the invariant the cost calculator's custom + pricing helper documents). A zero residual on a fully cached request is real data and is + emitted; a zero read or write count is absence and is not. + """ + prompt_tokens: Final = float(standard_logging_payload.get("prompt_tokens", 0)) + completion_tokens: Final = float(standard_logging_payload.get("completion_tokens", 0)) + total_tokens: Final = float(standard_logging_payload.get("total_tokens", 0)) + total_cost: Final = float(standard_logging_payload.get("response_cost", 0)) + time_to_first_token: Final = self._get_time_to_first_token_seconds(standard_logging_payload) + + raw_usage: Final = (standard_logging_payload.get("metadata") or {}).get("usage_object") + usage_object: Final = raw_usage if isinstance(raw_usage, dict) else None + cache_read: Final = float(extract_cache_read_tokens(usage_object)) + cache_write: Final = float(extract_cache_creation_tokens(usage_object)) + + metrics: Final[LLMMetrics] = { + "input_tokens": prompt_tokens, + "output_tokens": completion_tokens, + "total_tokens": total_tokens, + "total_cost": total_cost, + "time_to_first_token": time_to_first_token, + **( + { + **({"cache_read_input_tokens": cache_read} if cache_read else {}), + **({"cache_write_input_tokens": cache_write} if cache_write else {}), + "non_cached_input_tokens": max(prompt_tokens - cache_read - cache_write, 0.0), + } + if cache_read or cache_write + else {} + ), + } + return metrics + def _get_time_to_first_token_seconds(self, standard_logging_payload: StandardLoggingPayload) -> float: """ Get the time to first token in seconds @@ -334,7 +557,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): def _get_response_messages( self, standard_logging_payload: StandardLoggingPayload, call_type: str | None - ) -> list[Any]: + ) -> tuple[Message, ...]: """ Get the messages from the response object @@ -343,7 +566,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): response_obj = standard_logging_payload.get("response") if response_obj is None: - return [] + return () # edge case: handle response_obj is a string representation of a dict if isinstance(response_obj, str): @@ -356,7 +579,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): # fallback to json parsing response_obj = json.loads(str(response_obj)) except json.JSONDecodeError: - return [] + return () if call_type in [ CallTypes.completion.value, @@ -374,12 +597,12 @@ class DataDogLLMObsLogger(CustomBatchLogger): if isinstance(response_obj, dict) and "choices" in response_obj: choices: Final = response_obj["choices"] if choices and len(choices) > 0 and "message" in choices[0]: - return [choices[0]["message"]] - return [] + return _to_dd_messages([choices[0]["message"]]) + return () except (KeyError, IndexError, TypeError): # In case of any error accessing the response structure, return empty list - return [] - return [] + return () + return () def _get_datadog_span_kind( self, call_type: str | None, parent_id: str | None = None @@ -484,22 +707,11 @@ class DataDogLLMObsLogger(CustomBatchLogger): # Default fallback for unknown or passthrough operations return "llm" - def _ensure_string_content(self, messages: str | list[Any] | dict[Any, Any] | None) -> list[Any]: - if messages is None: - return [] - if isinstance(messages, str): - return [messages] - elif isinstance(messages, list): - return [message for message in messages] - elif isinstance(messages, dict): - return [str(messages.get("content", ""))] - return [] - - def _get_dd_llm_obs_payload_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, Any]: + def _get_dd_llm_obs_payload_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, object]: """ Fields to track in DD LLM Observability metadata from litellm standard logging payload """ - _metadata: Final[dict[str, Any]] = { + _metadata: Final[dict[str, object]] = { "model_name": standard_logging_payload.get("model", "unknown"), "model_provider": standard_logging_payload.get("custom_llm_provider", "unknown"), "id": standard_logging_payload.get("id", "unknown"), @@ -523,10 +735,6 @@ class DataDogLLMObsLogger(CustomBatchLogger): spend_metrics: Final = self._get_spend_metrics(standard_logging_payload) _metadata.update({"spend_metrics": dict(spend_metrics)}) - ## extract tool calls and add to metadata - tool_call_metadata: Final = self._extract_tool_call_metadata(standard_logging_payload) - _metadata.update(tool_call_metadata) - _standard_logging_metadata: Final[dict] = dict(standard_logging_payload.get("metadata", {})) or {} _metadata.update(_standard_logging_metadata) return _metadata @@ -646,107 +854,3 @@ class DataDogLLMObsLogger(CustomBatchLogger): verbose_logger.debug("Original value: %s", user_api_key_budget_reset_at) return spend_metrics - - def _process_input_messages_preserving_tool_calls(self, messages: list[Any]) -> list[dict[str, Any]]: - """ - Process input messages while preserving tool_calls and tool message types. - - This bypasses the lossy string conversion when tool calls are present, - allowing complex nested tool_calls objects to be preserved for Datadog. - """ - processed: Final = [] - for msg in messages: - if isinstance(msg, dict): - # Preserve messages with tool_calls or tool role as-is - if "tool_calls" in msg or msg.get("role") == "tool": - processed.append(msg) - else: - # For regular messages, still apply string conversion - converted = handle_any_messages_to_chat_completion_str_messages_conversion([msg]) - processed.extend(converted) - else: - # For non-dict messages, apply string conversion - converted = handle_any_messages_to_chat_completion_str_messages_conversion([msg]) - processed.extend(converted) - return processed - - @staticmethod - def _tool_calls_kv_pair(tool_calls: list[dict[str, Any]]) -> dict[str, Any]: - """ - Extract tool call information into key-value pairs for Datadog metadata. - - Similar to OpenTelemetry's implementation but adapted for Datadog's format. - """ - kv_pairs: Final[dict[str, Any]] = {} - for idx, tool_call in enumerate(tool_calls): - try: - # Extract tool call ID - tool_id = tool_call.get("id") - if tool_id: - kv_pairs[f"tool_calls.{idx}.id"] = tool_id - - # Extract tool call type - tool_type = tool_call.get("type") - if tool_type: - kv_pairs[f"tool_calls.{idx}.type"] = tool_type - - # Extract function information - function = tool_call.get("function") - if function: - function_name = function.get("name") - if function_name: - kv_pairs[f"tool_calls.{idx}.function.name"] = function_name - - function_arguments = function.get("arguments") - if function_arguments: - # Store arguments as JSON string for Datadog - if isinstance(function_arguments, str): - kv_pairs[f"tool_calls.{idx}.function.arguments"] = function_arguments - else: - import json - - kv_pairs[f"tool_calls.{idx}.function.arguments"] = json.dumps(function_arguments) - except (KeyError, TypeError, ValueError) as e: - verbose_logger.debug("DataDogLLMObs: Error processing tool call %s: %s", idx, e) - continue - - return kv_pairs - - def _extract_tool_call_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, Any]: - """ - Extract tool call information from both input messages and response for Datadog metadata. - """ - tool_call_metadata: Final[dict[str, Any]] = {} - - try: - # Extract tool calls from input messages - messages: Final = standard_logging_payload.get("messages", []) - if messages and isinstance(messages, list): - for message in messages: - if isinstance(message, dict) and "tool_calls" in message: - tool_calls = message.get("tool_calls") - if tool_calls: - input_tool_calls_kv = self._tool_calls_kv_pair(tool_calls) - # Prefix with "input_" to distinguish from response tool calls - for key, value in input_tool_calls_kv.items(): - tool_call_metadata[f"input_{key}"] = value - - # Extract tool calls from response - response_obj: Final = standard_logging_payload.get("response") - if response_obj and isinstance(response_obj, dict): - choices: Final = response_obj.get("choices", []) - for choice in choices: - if isinstance(choice, dict): - message = choice.get("message") - if message and isinstance(message, dict): - tool_calls = message.get("tool_calls") - if tool_calls: - response_tool_calls_kv = self._tool_calls_kv_pair(tool_calls) - # Prefix with "output_" to distinguish from input tool calls - for key, value in response_tool_calls_kv.items(): - tool_call_metadata[f"output_{key}"] = value - - except Exception as e: - verbose_logger.debug("DataDogLLMObs: Error extracting tool call metadata: %s", e) - - return tool_call_metadata diff --git a/litellm/integrations/dotprompt/__init__.py b/litellm/integrations/dotprompt/__init__.py index 07d83bc34d5..1188bce27da 100644 --- a/litellm/integrations/dotprompt/__init__.py +++ b/litellm/integrations/dotprompt/__init__.py @@ -62,12 +62,16 @@ def prompt_initializer(litellm_params: "PromptLiteLLMParams", prompt_spec: "Prom if dotprompt_content and not prompt_data and not prompt_file: prompt_data = _get_prompt_data_from_dotprompt_content(dotprompt_content) + from .prompt_manager import strip_version_suffix + + registration_prompt_id: Final = prompt_id or strip_version_suffix(prompt_spec.prompt_id) or prompt_spec.prompt_id + try: dot_prompt_manager: Final = DotpromptManager( prompt_directory=prompt_directory, prompt_data=prompt_data, prompt_file=prompt_file, - prompt_id=prompt_id, + prompt_id=registration_prompt_id, ) return dot_prompt_manager diff --git a/litellm/integrations/dotprompt/dotprompt_manager.py b/litellm/integrations/dotprompt/dotprompt_manager.py index e5e868f0523..f1ef011cdb7 100644 --- a/litellm/integrations/dotprompt/dotprompt_manager.py +++ b/litellm/integrations/dotprompt/dotprompt_manager.py @@ -96,7 +96,7 @@ class DotpromptManager(CustomPromptManagement): if prompt_id is None: return False try: - return prompt_id in self.prompt_manager.list_prompts() + return self.prompt_manager.get_prompt(prompt_id) is not None except Exception: # If there's any error accessing prompts, don't run prompt management return False @@ -209,6 +209,8 @@ class DotpromptManager(CustomPromptManagement): prompt_spec=prompt_spec, prompt_label=prompt_label, prompt_version=prompt_version, + ignore_prompt_manager_model=ignore_prompt_manager_model, + ignore_prompt_manager_optional_params=ignore_prompt_manager_optional_params, ) async def async_get_chat_completion_prompt( diff --git a/litellm/integrations/dotprompt/prompt_manager.py b/litellm/integrations/dotprompt/prompt_manager.py index 46750ed9799..9c82ff7c5ba 100644 --- a/litellm/integrations/dotprompt/prompt_manager.py +++ b/litellm/integrations/dotprompt/prompt_manager.py @@ -3,12 +3,28 @@ Based on Google's GenAI Kit dotprompt implementation: https://google.github.io/d """ import re +from collections.abc import Mapping from pathlib import Path from typing import Any, Final import yaml from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import NotRequired, ReadOnly, TypedDict + + +class _PromptFileJson(TypedDict): + """JSON form of a .prompt file: rendered template text plus its frontmatter.""" + + content: ReadOnly[NotRequired[str]] + metadata: ReadOnly[NotRequired[dict[str, object]]] + + +def strip_version_suffix(prompt_id: str) -> str | None: + base, separator, version = prompt_id.rpartition(".v") + if separator and base and version.isdigit(): + return base + return None class PromptTemplate: @@ -124,11 +140,13 @@ class PromptManager: "content": "template content", "metadata": {"model": "gpt-4", "temperature": 0.7, ...} } + prompt_id - """ - if prompt_id: - prompt_data = {prompt_id: prompt_data} - for prompt_id, prompt_info in prompt_data.items(): + A dict carrying a "content" key is a single flat template registered under + prompt_id; anything else is treated as already keyed by template ID. + """ + keyed_prompts: Final = {prompt_id: prompt_data} if prompt_id and "content" in prompt_data else prompt_data + + for template_id, prompt_info in keyed_prompts.items(): try: content = prompt_info.get("content", "") metadata = prompt_info.get("metadata", {}) @@ -136,11 +154,10 @@ class PromptManager: template = PromptTemplate( content=content, metadata=metadata, - template_id=prompt_id, + template_id=template_id, ) - self.prompts[prompt_id] = template + self.prompts[template_id] = template except Exception: - # Optional: print(f"Error loading prompt from JSON: {prompt_id}") pass def _load_prompt_file(self, file_path: str | Path, prompt_id: str) -> PromptTemplate: @@ -159,7 +176,7 @@ class PromptManager: template_id=prompt_id, ) - def _parse_frontmatter(self, content: str) -> tuple[dict[str, Any], str]: + def _parse_frontmatter(self, content: str) -> tuple[dict[str, object], str]: """Parse YAML frontmatter from prompt content.""" # Match YAML frontmatter between --- delimiters frontmatter_pattern: Final = r"^---\s*\n(.*?)\n---\s*\n(.*)$" @@ -170,7 +187,7 @@ class PromptManager: template_content = match.group(2) try: - frontmatter = yaml.safe_load(frontmatter_yaml) or {} + frontmatter: dict[str, object] = yaml.safe_load(frontmatter_yaml) or {} except yaml.YAMLError as e: raise ValueError(f"Invalid YAML frontmatter: {e}") else: @@ -183,7 +200,7 @@ class PromptManager: def render( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, version: int | None = None, ) -> str: """ @@ -223,7 +240,7 @@ class PromptManager: except Exception as e: raise ValueError(f"Error rendering template '{prompt_id}': {e}") - def _validate_input(self, variables: dict[str, Any], schema: dict[str, Any]) -> None: + def _validate_input(self, variables: Mapping[str, object], schema: Mapping[str, str]) -> None: """Basic validation of input variables against schema.""" for field_name, field_type in schema.items(): if field_name in variables: @@ -272,14 +289,18 @@ class PromptManager: if versioned_id in self.prompts: return self.prompts[versioned_id] - # Fall back to base prompt_id - return self.prompts.get(prompt_id) + direct_match: Final = self.prompts.get(prompt_id) + if direct_match is not None: + return direct_match + + base_prompt_id: Final = strip_version_suffix(prompt_id) + return self.prompts.get(base_prompt_id) if base_prompt_id else None def list_prompts(self) -> list[str]: """Get a list of all available prompt IDs.""" return list(self.prompts.keys()) - def get_prompt_metadata(self, prompt_id: str) -> dict[str, Any] | None: + def get_prompt_metadata(self, prompt_id: str) -> dict[str, object] | None: """Get metadata for a specific prompt.""" template: Final = self.prompts.get(prompt_id) return template.metadata if template else None @@ -290,12 +311,12 @@ class PromptManager: if self.prompt_directory: self._load_prompts() - def add_prompt(self, prompt_id: str, content: str, metadata: dict[str, Any] | None = None) -> None: + def add_prompt(self, prompt_id: str, content: str, metadata: dict[str, object] | None = None) -> None: """Add a prompt template programmatically.""" template: Final = PromptTemplate(content=content, metadata=metadata or {}, template_id=prompt_id) self.prompts[prompt_id] = template - def prompt_file_to_json(self, file_path: str | Path) -> dict[str, Any]: + def prompt_file_to_json(self, file_path: str | Path) -> _PromptFileJson: """Convert a .prompt file to JSON format. Args: @@ -312,7 +333,7 @@ class PromptManager: return {"content": template_content.strip(), "metadata": frontmatter} - def json_to_prompt_file(self, prompt_data: dict[str, Any]) -> str: + def json_to_prompt_file(self, prompt_data: _PromptFileJson) -> str: """Convert JSON prompt data to .prompt file format. Args: diff --git a/litellm/integrations/focus/destinations/mavvrik_destination.py b/litellm/integrations/focus/destinations/mavvrik_destination.py index dad5526eb18..4e7765b9e5d 100644 --- a/litellm/integrations/focus/destinations/mavvrik_destination.py +++ b/litellm/integrations/focus/destinations/mavvrik_destination.py @@ -9,9 +9,12 @@ Flow: from __future__ import annotations import gzip -from typing import Any, Final +from collections.abc import Mapping +from typing import Final, Protocol from urllib.parse import urlparse +from typing_extensions import NotRequired, ReadOnly, TypedDict + from litellm._logging import verbose_logger from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, @@ -28,6 +31,34 @@ _MAVVRIK_ALLOWED_SUFFIXES: Final = (".mavvrik.dev", ".mavvrik.ai", ".mavvrik.app _GCS_CHUNK_SIZE: Final = 8 * 1024 * 1024 # 8 MB +class MavvrikRegisterBody(TypedDict): + metricsMarker: ReadOnly[NotRequired[int | str]] + + +class MavvrikUploadUrlBody(TypedDict): + url: ReadOnly[NotRequired[str]] + + +class _RegisterResponse(Protocol): + def json(self) -> MavvrikRegisterBody: ... + + +class _UploadUrlResponse(Protocol): + def json(self) -> MavvrikUploadUrlBody: ... + + +def _register_body(response: _RegisterResponse) -> MavvrikRegisterBody: + return response.json() + + +def _upload_url_body(response: _UploadUrlResponse) -> MavvrikUploadUrlBody: + return response.json() + + +def _header_value(headers: Mapping[str, str], name: str) -> str | None: + return headers.get(name) + + def _validate_api_endpoint(api_endpoint: str) -> None: if not api_endpoint.startswith("https://"): raise ValueError("MAVVRIK_API_ENDPOINT must be an HTTPS URL") @@ -56,12 +87,12 @@ class FocusMavvrikDestination(FocusDestination): self, *, prefix: str, - config: dict[str, Any] | None = None, + config: Mapping[str, str] | None = None, ) -> None: - config = config or {} - api_key: Final = config.get("api_key") - api_endpoint: Final = config.get("api_endpoint") - connection_id: Final = config.get("connection_id") + resolved_config: Final[Mapping[str, str]] = config or {} + api_key: Final = resolved_config.get("api_key") + api_endpoint: Final = resolved_config.get("api_endpoint") + connection_id: Final = resolved_config.get("connection_id") if not api_key: raise ValueError( @@ -100,7 +131,7 @@ class FocusMavvrikDestination(FocusDestination): def _auth_headers(self) -> dict[str, str]: return {"Content-Type": "application/json", "x-api-key": self.api_key} - async def _ensure_registered(self) -> int | None: + async def _ensure_registered(self) -> int | str | None: """POST agent endpoint to register/initialize the connector (once per instance). Returns metricsMarker from the Mavvrik response — the last date index @@ -127,7 +158,7 @@ class FocusMavvrikDestination(FocusDestination): if resp.status_code >= 400: raise RuntimeError(f"Mavvrik FOCUS destination: register failed ({resp.status_code}): {resp.text[:200]}") self._registered = True - metrics_marker: Final = resp.json().get("metricsMarker", 0) + metrics_marker: Final = _register_body(resp).get("metricsMarker", 0) verbose_logger.debug( "Mavvrik FOCUS destination: connector registered (metricsMarker=%s)", metrics_marker, @@ -148,7 +179,7 @@ class FocusMavvrikDestination(FocusDestination): raise RuntimeError( f"Mavvrik FOCUS destination: failed to get signed URL ({resp.status_code}): {resp.text[:200]}" ) - signed_url: Final = resp.json().get("url") + signed_url: Final = _upload_url_body(resp).get("url") if not signed_url: raise RuntimeError(f"Mavvrik FOCUS destination: response missing 'url' field: {resp.json()}") _validate_gcs_url(signed_url, "signed URL") @@ -190,7 +221,7 @@ class FocusMavvrikDestination(FocusDestination): f"Mavvrik FOCUS destination: GCS session init failed ({init_resp.status_code}): {init_resp.text[:400]}" ) - session_uri: Final = init_resp.headers.get("Location") + session_uri: Final = _header_value(init_resp.headers, "Location") if not session_uri: raise RuntimeError("Mavvrik FOCUS destination: GCS session init missing Location header") _validate_gcs_url(session_uri, "session URI") @@ -264,7 +295,7 @@ class FocusMavvrikDestination(FocusDestination): ) verbose_logger.debug("Mavvrik FOCUS destination: metricsMarker advanced to %s", date_epoch) - async def get_metrics_marker(self) -> int | None: + async def get_metrics_marker(self) -> int | str | None: """Register with Mavvrik and return the current metricsMarker. Always calls the Mavvrik register API — unlike deliver() which skips @@ -287,7 +318,7 @@ class FocusMavvrikDestination(FocusDestination): if resp.status_code >= 400: raise RuntimeError(f"Mavvrik FOCUS destination: register failed ({resp.status_code}): {resp.text[:200]}") self._registered = True - metrics_marker: Final = resp.json().get("metricsMarker", 0) + metrics_marker: Final = _register_body(resp).get("metricsMarker", 0) verbose_logger.debug("Mavvrik FOCUS destination: got metricsMarker=%s", metrics_marker) return metrics_marker diff --git a/litellm/integrations/galileo.py b/litellm/integrations/galileo.py index 23727801a6f..b27618993a3 100644 --- a/litellm/integrations/galileo.py +++ b/litellm/integrations/galileo.py @@ -6,10 +6,11 @@ import re import uuid from collections.abc import Mapping, Sequence from datetime import datetime, timezone, tzinfo -from typing import Any, Final, TypedDict, cast +from typing import Any, Final, Protocol, cast import httpx from pydantic import BaseModel, Field +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -35,6 +36,34 @@ GALILEO_CLOUD_API_BASE_URL: Final = "https://api.galileo.ai" GALILEO_MAX_IN_MEMORY_RECORDS: Final = 1000 +class _GalileoLoginBody(TypedDict): + """Decoded body of the Galileo login response.""" + + access_token: ReadOnly[str] + + +class _GalileoLoginResponse(Protocol): + """The login call's HTTP response, read for the access token it carries.""" + + def json(self) -> _GalileoLoginBody: ... + + +class _JsonResponse(Protocol): + """An HTTP response read only for whatever JSON body it decodes to.""" + + def json(self) -> object: ... + + +def _login_access_token(response: _GalileoLoginResponse) -> str: + """Read the bearer token out of a Galileo login response body.""" + return response.json()["access_token"] + + +def _decoded_body(response: _JsonResponse) -> object: + """Decode a response body without asserting anything about its shape.""" + return response.json() + + class GalileoStandardLoggingFields(TypedDict, total=False): call_type: str model: str @@ -156,7 +185,7 @@ class GalileoObserve(CustomLogger): }, ) galileo_login_response.raise_for_status() - access_token: Final = galileo_login_response.json()["access_token"] + access_token: Final = _login_access_token(galileo_login_response) self.headers = { "accept": "application/json", "Content-Type": "application/json", @@ -421,7 +450,7 @@ class GalileoObserve(CustomLogger): try: verbose_logger.debug( "Galileo Logger HTTP error response json: %s", - response.json(), + _decoded_body(response), ) except Exception: pass diff --git a/litellm/integrations/generic_prompt_management/generic_prompt_manager.py b/litellm/integrations/generic_prompt_management/generic_prompt_manager.py index fbbf50fb340..bed3bdb58d1 100644 --- a/litellm/integrations/generic_prompt_management/generic_prompt_manager.py +++ b/litellm/integrations/generic_prompt_management/generic_prompt_manager.py @@ -416,17 +416,8 @@ class GenericPromptManager(CustomPromptManagement): tools=tools, prompt_label=prompt_label, prompt_version=prompt_version, - ignore_prompt_manager_model=( - ignore_prompt_manager_model or prompt_spec.litellm_params.ignore_prompt_manager_model - if prompt_spec - else False - ), - ignore_prompt_manager_optional_params=( - ignore_prompt_manager_optional_params - or prompt_spec.litellm_params.ignore_prompt_manager_optional_params - if prompt_spec - else False - ), + ignore_prompt_manager_model=ignore_prompt_manager_model, + ignore_prompt_manager_optional_params=ignore_prompt_manager_optional_params, ) def get_chat_completion_prompt( @@ -457,17 +448,8 @@ class GenericPromptManager(CustomPromptManagement): prompt_spec=prompt_spec, prompt_label=prompt_label, prompt_version=prompt_version, - ignore_prompt_manager_model=( - ignore_prompt_manager_model or prompt_spec.litellm_params.ignore_prompt_manager_model - if prompt_spec - else False - ), - ignore_prompt_manager_optional_params=( - ignore_prompt_manager_optional_params - or prompt_spec.litellm_params.ignore_prompt_manager_optional_params - if prompt_spec - else False - ), + ignore_prompt_manager_model=ignore_prompt_manager_model, + ignore_prompt_manager_optional_params=ignore_prompt_manager_optional_params, ) def clear_cache(self) -> None: diff --git a/litellm/integrations/gitlab/gitlab_client.py b/litellm/integrations/gitlab/gitlab_client.py index 0690ccc8c15..813a2ef2821 100644 --- a/litellm/integrations/gitlab/gitlab_client.py +++ b/litellm/integrations/gitlab/gitlab_client.py @@ -4,12 +4,80 @@ Now supports selecting a tag via `config["tag"]`; falls back to branch ("main"). """ import base64 -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Protocol, TypedDict from urllib.parse import quote +from typing_extensions import ReadOnly + from litellm.llms.custom_httpx.http_handler import HTTPHandler +class GitLabFilePayload(TypedDict, total=False): + """A repository-files API entry.""" + + content: ReadOnly[str] + encoding: ReadOnly[str] + + +class GitLabTreeEntry(TypedDict, total=False): + """A repository-tree API entry.""" + + path: ReadOnly[str] + type: ReadOnly[str] + + +class GitLabBranch(TypedDict, total=False): + """A repository-branches API entry.""" + + name: ReadOnly[str] + type: ReadOnly[str] + + +class GitLabFileMetadata(TypedDict): + """The response headers a raw file request exposes as metadata.""" + + content_type: ReadOnly[str | None] + content_length: ReadOnly[str | None] + last_modified: ReadOnly[str | None] + + +class _FileJsonResponse(Protocol): + def json(self) -> GitLabFilePayload: ... + + +class _TreeJsonResponse(Protocol): + def json(self) -> Sequence[GitLabTreeEntry] | None: ... + + +class _ProjectJsonResponse(Protocol): + def json(self) -> Mapping[str, object]: ... + + +class _BranchesJsonResponse(Protocol): + def json(self) -> Sequence[GitLabBranch] | None: ... + + +def _file_payload(resp: _FileJsonResponse) -> GitLabFilePayload: + """The JSON body of a repository-files response.""" + return resp.json() + + +def _tree_entries(resp: _TreeJsonResponse) -> Sequence[GitLabTreeEntry]: + """The entries of a repository-tree response.""" + return resp.json() or [] + + +def _project_info(resp: _ProjectJsonResponse) -> Mapping[str, object]: + """The JSON body of a project response.""" + return resp.json() + + +def _branch_entries(resp: _BranchesJsonResponse) -> Sequence[GitLabBranch] | None: + """The JSON body of a repository-branches response.""" + return resp.json() + + class GitLabClient: """ Client for interacting with the GitLab API to fetch files. @@ -42,12 +110,12 @@ class GitLabClient: self.project: str | int = project self.access_token: str = str(access_token) - self.auth_method = config.get("auth_method", "token") # 'token' or 'oauth' + self.auth_method: str = config.get("auth_method", "token") # 'token' or 'oauth' self.branch = config.get("branch", None) if not self.branch: self.branch = "main" self.tag = config.get("tag") - self.base_url = config.get("base_url", "https://gitlab.com/api/v4") + self.base_url: str = config.get("base_url", "https://gitlab.com/api/v4") if not all([self.project, self.access_token]): raise ValueError("project and access_token are required") @@ -159,7 +227,7 @@ class GitLabClient: if resp.status_code == 404: return None resp.raise_for_status() - data: Final = resp.json() + data: Final = _file_payload(resp) content: Final = data.get("content") encoding: Final = data.get("encoding", "") if content and encoding == "base64": @@ -208,7 +276,7 @@ class GitLabClient: return [] resp.raise_for_status() - data: Final = resp.json() or [] + data: Final = _tree_entries(resp) files: Final[list[str]] = [] for item in data: if item.get("type") == "blob": @@ -229,13 +297,13 @@ class GitLabClient: raise Exception("Authentication failed. Check your GitLab token and auth_method.") raise Exception(f"Failed to list files in '{directory_path}': {e}") - def get_repository_info(self) -> dict[str, Any]: + def get_repository_info(self) -> Mapping[str, object]: """Get information about the project/repository.""" url: Final = f"{self.base_url}/projects/{self._project_enc}" try: resp: Final = self.http_handler.get(url, headers=self.headers) resp.raise_for_status() - return resp.json() + return _project_info(resp) except Exception as e: raise Exception(f"Failed to get repository info: {e}") @@ -247,18 +315,18 @@ class GitLabClient: except Exception: return False - def get_branches(self) -> list[dict[str, Any]]: + def get_branches(self) -> list[GitLabBranch]: """Get list of branches in the repository.""" url: Final = f"{self.base_url}/projects/{self._project_enc}/repository/branches" try: resp: Final = self.http_handler.get(url, headers=self.headers) resp.raise_for_status() - data: Final = resp.json() + data: Final = _branch_entries(resp) return data if isinstance(data, list) else [] except Exception as e: raise Exception(f"Failed to get branches: {e}") - def get_file_metadata(self, file_path: str, *, ref: str | None = None) -> dict[str, Any] | None: + def get_file_metadata(self, file_path: str, *, ref: str | None = None) -> GitLabFileMetadata | None: """ Get minimal metadata about a file via RAW endpoint headers at a given ref. diff --git a/litellm/integrations/gitlab/gitlab_prompt_manager.py b/litellm/integrations/gitlab/gitlab_prompt_manager.py index c41d9dd240f..d4602176650 100644 --- a/litellm/integrations/gitlab/gitlab_prompt_manager.py +++ b/litellm/integrations/gitlab/gitlab_prompt_manager.py @@ -2,10 +2,12 @@ GitLab prompt manager with configurable prompts folder. """ -from typing import TYPE_CHECKING, Any, Final +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, TypeVar from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import ReadOnly, TypedDict from litellm.integrations.custom_prompt_management import CustomPromptManagement @@ -24,6 +26,19 @@ from litellm.types.utils import StandardCallbackDynamicParams GITLAB_PREFIX: Final = "gitlab::" +_ResponseT = TypeVar("_ResponseT") + + +class GitLabCachedPrompt(TypedDict): + id: ReadOnly[str] + path: ReadOnly[str] + content: ReadOnly[str] + metadata: ReadOnly[Mapping[str, object]] + model: ReadOnly[str | None] + temperature: ReadOnly[float | None] + max_tokens: ReadOnly[int | None] + optional_params: ReadOnly[Mapping[str, object]] + def encode_prompt_id(raw_id: str) -> str: """Convert GitLab path IDs like 'invoice/extract' → 'gitlab::invoice::extract'""" @@ -206,7 +221,7 @@ class GitLabTemplateManager: result[key] = value.strip("\"'") return result - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> str: + def render_template(self, template_id: str, variables: Mapping[str, object] | None = None) -> str: if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") template: Final = self.prompts[template_id] @@ -313,7 +328,7 @@ class GitLabPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, *, ref: str | None = None, ) -> tuple[str, dict[str, Any]]: @@ -338,13 +353,13 @@ class GitLabPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, prompt_version: str | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: if not prompt_id: return messages, litellm_params try: @@ -377,9 +392,9 @@ class GitLabPromptManager(CustomPromptManagement): return final_messages, litellm_params except Exception as e: - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in GitLab prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in GitLab prompt pre_call_hook: %s", e) return messages, litellm_params def _parse_prompt_to_messages(self, prompt_content: str) -> list[AllMessageValues]: @@ -435,14 +450,14 @@ class GitLabPromptManager(CustomPromptManagement): def post_call_hook( self, user_id: str | None, - response: Any, + response: _ResponseT, input_messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: Mapping[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> Any: + ) -> _ResponseT: return response def get_available_prompts(self) -> list[str]: @@ -498,7 +513,7 @@ class GitLabPromptManager(CustomPromptManagement): messages: Final = self._parse_prompt_to_messages(rendered_prompt) template_model: Final = prompt_metadata.get("model") - optional_params: Final[dict[str, Any]] = {} + optional_params: Final[dict[str, object]] = {} for param in [ "temperature", "max_tokens", @@ -658,14 +673,14 @@ class GitLabPromptCache: self.template_manager: GitLabTemplateManager = self.prompt_manager.prompt_manager # In-memory stores - self._by_file: dict[str, dict[str, Any]] = {} - self._by_id: dict[str, dict[str, Any]] = {} + self._by_file: dict[str, GitLabCachedPrompt] = {} + self._by_id: dict[str, GitLabCachedPrompt] = {} # ------------------------- # Public API # ------------------------- - def load_all(self, *, recursive: bool = True) -> dict[str, dict[str, Any]]: + def load_all(self, *, recursive: bool = True) -> dict[str, GitLabCachedPrompt]: """ Scan GitLab for all .prompt files under prompts_path, load and parse each, and return the mapping of repo file path -> JSON-like dict. @@ -695,7 +710,7 @@ class GitLabPromptCache: return self._by_id - def reload(self, *, recursive: bool = True) -> dict[str, dict[str, Any]]: + def reload(self, *, recursive: bool = True) -> dict[str, GitLabCachedPrompt]: """Clear the cache and re-load from GitLab.""" self._by_file.clear() self._by_id.clear() @@ -709,11 +724,11 @@ class GitLabPromptCache: """Return the template IDs (relative to prompts_path, without extension) currently cached.""" return list(self._by_id.keys()) - def get_by_file(self, file_path: str) -> dict[str, Any] | None: + def get_by_file(self, file_path: str) -> GitLabCachedPrompt | None: """Get a cached prompt JSON by repo file path.""" return self._by_file.get(file_path) - def get_by_id(self, prompt_id: str) -> dict[str, Any] | None: + def get_by_id(self, prompt_id: str) -> GitLabCachedPrompt | None: """Get a cached prompt JSON by prompt ID (relative to prompts_path).""" if prompt_id in self._by_id: return self._by_id[prompt_id] @@ -728,7 +743,7 @@ class GitLabPromptCache: # Internals # ------------------------- - def _template_to_json(self, prompt_id: str, tmpl: GitLabPromptTemplate) -> dict[str, Any]: + def _template_to_json(self, prompt_id: str, tmpl: GitLabPromptTemplate) -> GitLabCachedPrompt: """ Normalize a GitLabPromptTemplate into a JSON-like dict that is easy to serialize. """ diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index da924a81e0c..9576eabaa34 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -1,9 +1,11 @@ #### What this does #### # On success, logs events to Langfuse +import inspect import os import traceback from collections.abc import Callable, Iterable, Mapping from datetime import datetime +from functools import lru_cache from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, cast @@ -21,6 +23,9 @@ from litellm.litellm_core_utils.core_helpers import ( reconstruct_model_name, safe_deep_copy, ) +from litellm.litellm_core_utils.initialize_dynamic_callback_params import ( + validate_langfuse_environment_value, +) from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info from litellm.llms.custom_httpx.http_handler import _get_httpx_client from litellm.secret_managers.main import str_to_bool @@ -84,7 +89,7 @@ def _extract_cache_read_input_tokens(usage_obj) -> int: # Check prompt_tokens_details.cached_tokens (used by Gemini and other providers) if hasattr(usage_obj, "prompt_tokens_details"): - prompt_tokens_details: Final = getattr(usage_obj, "prompt_tokens_details", None) + prompt_tokens_details: Final[object] = getattr(usage_obj, "prompt_tokens_details", None) if prompt_tokens_details is not None and hasattr(prompt_tokens_details, "cached_tokens"): cached_tokens: Final = getattr(prompt_tokens_details, "cached_tokens", None) if cached_tokens is not None and isinstance(cached_tokens, (int, float)) and cached_tokens > 0: @@ -133,6 +138,16 @@ def resolve_langfuse_credentials( return public_key, secret_key, resolved_host +@lru_cache(maxsize=8) +def _warn_invalid_deployment_environment(raw_value: str, error: str) -> None: + verbose_logger.warning( + "Ignoring invalid LANGFUSE_TRACING_ENVIRONMENT=%r for the langfuse callback: %s. " + "Traces will be sent to Langfuse's default environment.", + raw_value, + error, + ) + + class LangFuseLogger: # Class variables or attributes def __init__( @@ -140,6 +155,7 @@ class LangFuseLogger: langfuse_public_key=None, langfuse_secret=None, langfuse_host=None, + langfuse_environment: str | None = None, flush_interval=1, allow_env_credentials: bool = True, ): @@ -159,6 +175,12 @@ class LangFuseLogger: if not (self.langfuse_host.startswith("http://") or self.langfuse_host.startswith("https://")): # add http:// if unset, assume communicating over private network - e.g. render self.langfuse_host = "http://" + self.langfuse_host + _env_override: Final = str(langfuse_environment).strip() if langfuse_environment is not None else None + if _env_override: + validate_langfuse_environment_value(_env_override) + self.langfuse_environment: str | None = _env_override + else: + self.langfuse_environment = self.resolve_deployment_environment() self.langfuse_release = os.getenv("LANGFUSE_RELEASE") self.langfuse_debug = os.getenv("LANGFUSE_DEBUG") self.langfuse_flush_interval = LangFuseLogger._get_langfuse_flush_interval(flush_interval) @@ -182,6 +204,8 @@ class LangFuseLogger: } self.langfuse_sdk_version: str = langfuse.version.__version__ + if "environment" in inspect.signature(Langfuse.__init__).parameters: + parameters["environment"] = self.langfuse_environment if Version(self.langfuse_sdk_version) >= Version("2.6.0"): parameters["sdk_integration"] = "litellm" self.Langfuse: Langfuse = self.safe_init_langfuse_client(parameters) @@ -599,9 +623,16 @@ class LangFuseLogger: ) # Apply custom masking function if provided - if masking_function is not None and callable(masking_function): - input = self._apply_masking_function(input, masking_function) - output = self._apply_masking_function(output, masking_function) + masked_input: Final[object] = ( + self._apply_masking_function(input, masking_function) + if masking_function is not None and callable(masking_function) + else input + ) + masked_output: Final[object] = ( + self._apply_masking_function(output, masking_function) + if masking_function is not None and callable(masking_function) + else output + ) clean_metadata = redact_user_api_key_info(metadata=clean_metadata) @@ -627,15 +658,15 @@ class LangFuseLogger: # Special keys that are found in the function arguments and not the metadata if "input" in update_trace_keys: - trace_params["input"] = input if not mask_input else "redacted-by-litellm" + trace_params["input"] = masked_input if not mask_input else "redacted-by-litellm" if "output" in update_trace_keys: - trace_params["output"] = output if not mask_output else "redacted-by-litellm" + trace_params["output"] = masked_output if not mask_output else "redacted-by-litellm" else: # don't overwrite an existing trace trace_params = { "id": trace_id, "name": trace_name, "session_id": session_id, - "input": input if not mask_input else "redacted-by-litellm", + "input": masked_input if not mask_input else "redacted-by-litellm", "version": clean_metadata.pop( "trace_version", clean_metadata.get("version", None) ), # If provided just version, it will applied to the trace as well, if applied a trace version it will take precedence @@ -645,9 +676,9 @@ class LangFuseLogger: trace_params[key.replace("trace_", "")] = clean_metadata.pop(key, None) if level == "ERROR": - trace_params["status_message"] = output + trace_params["status_message"] = masked_output else: - trace_params["output"] = output if not mask_output else "redacted-by-litellm" + trace_params["output"] = masked_output if not mask_output else "redacted-by-litellm" if debug is True or (isinstance(debug, str) and debug.lower() == "true"): debug_metadata: Final = { @@ -684,7 +715,7 @@ class LangFuseLogger: ("aws_region_name", aws_region_name, bool(aws_region_name)), ("cache_hit", kwargs.get("cache_hit") or False, self._supports_tags() and "cache_hit" in kwargs), ) - enrichments: Final[Mapping[str, Any]] = { + enrichments: Final[Mapping[str, object]] = { key: value for key, value, include in candidate_enrichments if include } @@ -778,8 +809,8 @@ class LangFuseLogger: "end_time": end_time, "model": model_name, "model_parameters": optional_params, - "input": input if not mask_input else "redacted-by-litellm", - "output": output if not mask_output else "redacted-by-litellm", + "input": masked_input if not mask_input else "redacted-by-litellm", + "output": masked_output if not mask_output else "redacted-by-litellm", "usage": usage, "usage_details": usage_details, "metadata": { @@ -801,8 +832,8 @@ class LangFuseLogger: prompt_management_metadata=prompt_management_metadata, langfuse_client=self.Langfuse, ) - if output is not None and isinstance(output, str) and level == "ERROR": - generation_params["status_message"] = output + if masked_output is not None and isinstance(masked_output, str) and level == "ERROR": + generation_params["status_message"] = masked_output if self._supports_completion_start_time(): generation_params["completion_start_time"] = kwargs.get("completion_start_time", None) @@ -911,7 +942,7 @@ class LangFuseLogger: return Version(self.langfuse_sdk_version) >= Version("2.7.3") @staticmethod - def _apply_masking_function(data: Any, masking_function: Callable[[Any], Any]) -> Any: + def _apply_masking_function(data: object, masking_function: Callable[[object], object]) -> object: """ Apply a masking function to data, handling different data types. @@ -942,6 +973,20 @@ class LangFuseLogger: verbose_logger.warning("Failed to apply masking function: %s. Returning original data.", e) return data + @staticmethod + def resolve_deployment_environment() -> str | None: + """Resolve LANGFUSE_TRACING_ENVIRONMENT: stripped value, "default" plus a warning when invalid, None when unset.""" + raw: Final = os.getenv("LANGFUSE_TRACING_ENVIRONMENT") + if not raw: + return None + value: Final = raw.strip() + try: + validate_langfuse_environment_value(value) + except ValueError as e: + _warn_invalid_deployment_environment(raw, str(e)) + return "default" + return value + @staticmethod def _get_langfuse_flush_interval(flush_interval: int) -> int: """ @@ -1011,7 +1056,7 @@ def _add_prompt_to_generation_params( generation_params: dict, clean_metadata: dict, prompt_management_metadata: StandardLoggingPromptManagementMetadata | None, - langfuse_client: Any, + langfuse_client: object, ) -> dict: from langfuse import Langfuse from langfuse.model import ( diff --git a/litellm/integrations/langfuse/langfuse_handler.py b/litellm/integrations/langfuse/langfuse_handler.py index f4dd80f91f5..c74866c7a9e 100644 --- a/litellm/integrations/langfuse/langfuse_handler.py +++ b/litellm/integrations/langfuse/langfuse_handler.py @@ -6,6 +6,7 @@ Used to get the LangFuseLogger for a given request Handles Key/Team Based Langfuse Logging """ +import os from typing import TYPE_CHECKING, Any, Final from litellm.litellm_core_utils.litellm_logging import StandardCallbackDynamicParams @@ -108,6 +109,7 @@ class LangFuseHandler: langfuse_public_key=credentials.get("langfuse_public_key"), langfuse_secret=credentials.get("langfuse_secret") or credentials.get("langfuse_secret_key"), langfuse_host=credentials.get("langfuse_host"), + langfuse_environment=credentials.get("langfuse_environment"), allow_env_credentials=credentials.get("langfuse_host") is None, ) in_memory_dynamic_logger_cache.set_cache( @@ -135,8 +137,33 @@ class LangFuseHandler: or standard_callback_dynamic_params.get("langfuse_secret_key"), langfuse_public_key=standard_callback_dynamic_params.get("langfuse_public_key"), langfuse_host=standard_callback_dynamic_params.get("langfuse_host"), + langfuse_environment=LangFuseHandler._meaningful_dynamic_environment(standard_callback_dynamic_params), ) + @staticmethod + def _meaningful_dynamic_environment( + standard_callback_dynamic_params: StandardCallbackDynamicParams, + ) -> str | None: + """Return the per-request environment only when it changes behavior. + + Empty/whitespace values and values equal to the deployment-wide + LANGFUSE_TRACING_ENVIRONMENT fallback are treated as absent so an + environment-only override that matches the default does not mint a + duplicate SDK client (each client costs threads and counts against + MAX_LANGFUSE_INITIALIZED_CLIENTS). + """ + raw = standard_callback_dynamic_params.get("langfuse_environment") + if raw is None: + return None + value = str(raw).strip() + if ( + not value + or value == os.getenv("LANGFUSE_TRACING_ENVIRONMENT") + or value == LangFuseLogger.resolve_deployment_environment() + ): + return None + return value + @staticmethod def _dynamic_langfuse_credentials_are_passed( standard_callback_dynamic_params: StandardCallbackDynamicParams, @@ -153,6 +180,7 @@ class LangFuseHandler: or standard_callback_dynamic_params.get("langfuse_public_key") is not None or standard_callback_dynamic_params.get("langfuse_secret") is not None or standard_callback_dynamic_params.get("langfuse_secret_key") is not None + or LangFuseHandler._meaningful_dynamic_environment(standard_callback_dynamic_params) is not None ): return True return False diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index a93c45ef840..a96fac32c2a 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -10,6 +10,7 @@ from litellm.integrations.langfuse.langfuse_otel_attributes import ( LangfuseLLMObsOTELAttributes, ) from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig +from litellm.litellm_core_utils.safe_json_loads import safe_json_loads from litellm.types.integrations.langfuse_otel import ( LangfuseSpanAttributes, ) @@ -197,7 +198,11 @@ class LangfuseOtelLogger(OpenTelemetry): ) elif item_type == "function_call": arguments_str = getattr(item, "arguments", "{}") - arguments_obj = json.loads(arguments_str) if isinstance(arguments_str, str) else arguments_str + arguments_obj = ( + safe_json_loads(arguments_str, default={}) + if isinstance(arguments_str, str) + else arguments_str + ) langfuse_tool_call = { "id": getattr(item, "id", ""), "name": getattr(item, "name", ""), @@ -226,7 +231,10 @@ class LangfuseOtelLogger(OpenTelemetry): from litellm.integrations.arize._utils import safe_set_attribute from litellm.litellm_core_utils.safe_json_dumps import safe_dumps - langfuse_environment: Final = os.environ.get("LANGFUSE_TRACING_ENVIRONMENT") + dynamic_params: Final = kwargs.get("standard_callback_dynamic_params") + langfuse_environment: Final = ( + dynamic_params.get("langfuse_environment") if dynamic_params else None + ) or os.environ.get("LANGFUSE_TRACING_ENVIRONMENT") if langfuse_environment: safe_set_attribute( span, diff --git a/litellm/integrations/langfuse/langfuse_prompt_management.py b/litellm/integrations/langfuse/langfuse_prompt_management.py index d8d03b73d14..90db0626e23 100644 --- a/litellm/integrations/langfuse/langfuse_prompt_management.py +++ b/litellm/integrations/langfuse/langfuse_prompt_management.py @@ -2,6 +2,7 @@ Call Hook for LiteLLM Proxy which allows Langfuse prompt management. """ +import inspect import os from functools import lru_cache from typing import TYPE_CHECKING, Any, Final, Literal, TypeAlias, cast @@ -109,6 +110,9 @@ def langfuse_client_init( cert=os.getenv("SSL_CERTIFICATE", litellm.ssl_certificate), ) + if "environment" in inspect.signature(Langfuse.__init__).parameters: + parameters["environment"] = LangFuseLogger.resolve_deployment_environment() + client: Final = Langfuse(**parameters) return client diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 89f1a30c143..9607eccef52 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -168,17 +168,20 @@ class LangsmithLogger(CustomBatchLogger): return outputs def _ensure_required_ids(self, data: dict, run_id: str | None): + resolved_id: Final = run_id or str(uuid.uuid4()) if "id" not in data or data["id"] is None: - run_id = str(uuid.uuid4()) - data["id"] = run_id + data["id"] = resolved_id - if "trace_id" not in data or data["trace_id"] is None: - if run_id is not None and isinstance(run_id, str): - data["trace_id"] = run_id + # LangSmith rejects the whole ingest batch unless a root run's trace_id + # equals the run id embedded in the first segment of dotted_order + posts_as_root: Final = ("parent_run_id" not in data or data["parent_run_id"] is None) and ( + "dotted_order" not in data or data["dotted_order"] is None + ) + if posts_as_root or "trace_id" not in data or data["trace_id"] is None: + data["trace_id"] = resolved_id if "dotted_order" not in data or data["dotted_order"] is None: - if run_id is not None and isinstance(run_id, str): - data["dotted_order"] = self.make_dot_order(run_id=run_id) + data["dotted_order"] = self.make_dot_order(run_id=resolved_id) def _prepare_log_data( self, @@ -193,6 +196,11 @@ class LangsmithLogger(CustomBatchLogger): metadata = _litellm_params.get("metadata", {}) or {} fields: Final = self._extract_metadata_fields(metadata, credentials) + # the proxy header fan-out mirrors one value into both keys, and LangSmith + # rejects the whole ingest batch when run-body session_id is not an + # existing tracer-session uuid + if fields["session_id"] == fields["trace_id"]: + fields["session_id"] = None verbose_logger.debug( "Langsmith Logging - project_name: %s, run_name %s", fields["project_name"], fields["run_name"] ) diff --git a/litellm/integrations/newrelic/newrelic_metrics.py b/litellm/integrations/newrelic/newrelic_metrics.py new file mode 100644 index 00000000000..25dbfc2bdb2 --- /dev/null +++ b/litellm/integrations/newrelic/newrelic_metrics.py @@ -0,0 +1,395 @@ +""" +New Relic Metric API Integration - sends per-team cost/usage metrics to /metric/v1 + +NR Reference API: https://docs.newrelic.com/docs/data-apis/ingest-apis/metric-api/introduction-metric-api/ + +`async_log_success_event` / `async_log_failure_event` queue one record per request; +at flush the queue is aggregated by (team, model group, model, provider, status) +into count/summary metrics. `interval.ms` is the real window between flushes, +computed at flush time. + +Team-scoped by construction: the ingest key is injected explicitly and there is +deliberately no environment-variable fallback, so a team's metrics are never sent +with the proxy operator's credentials (mirrors ``allow_env_credentials=False`` on +the Datadog team logger). + +Error policy on flush: 4xx drops the batch (a retry would fail identically; 403 +is a permanent credential failure), 5xx/network re-queues capped at +``max_queue_size`` records with the oldest dropped. + +For batching specific details see CustomBatchLogger class +""" + +import asyncio +import gzip +import time +import traceback +from collections.abc import Mapping +from math import ceil +from types import MappingProxyType +from typing import Final + +from httpx import HTTPStatusError, Response + +from litellm._logging import verbose_logger +from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps +from litellm.llms.custom_httpx.http_handler import ( + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.integrations.newrelic import ( + NEWRELIC_DEFAULT_REGION, + NEWRELIC_METRIC_ATTRIBUTE_MAX_LEN, + NEWRELIC_METRIC_COMPLETION_TOKENS, + NEWRELIC_METRIC_COST_USD, + NEWRELIC_METRIC_ENDPOINT_BY_REGION, + NEWRELIC_METRIC_PROMPT_TOKENS, + NEWRELIC_METRIC_REQUEST_DURATION_MS, + NEWRELIC_METRIC_REQUESTS, + NEWRELIC_METRIC_TOTAL_TOKENS, + NEWRELIC_METRICS_MAX_BATCH_SIZE, + NEWRELIC_METRICS_MAX_DRAIN_PASSES, + NEWRELIC_METRICS_MAX_RETRY_QUEUE_SIZE, + NewRelicCountMetric, + NewRelicMetric, + NewRelicMetricCommon, + NewRelicMetricEnvelope, + NewRelicMetricRecord, + NewRelicSummaryMetric, + NewRelicSummaryValue, +) +from litellm.types.utils import StandardLoggingPayload + +# 408 (request timeout) and 429 (rate limit) are transient client errors the +# Metric API expects a retry on, unlike 400/403 which a retry would only repeat. +_RETRYABLE_CLIENT_STATUSES: Final = frozenset({408, 429}) + + +def resolve_newrelic_metric_endpoint(newrelic_region: str | None) -> str: + if not newrelic_region: + return NEWRELIC_METRIC_ENDPOINT_BY_REGION[NEWRELIC_DEFAULT_REGION] + endpoint: Final = NEWRELIC_METRIC_ENDPOINT_BY_REGION.get(newrelic_region.lower()) + if endpoint is None: + verbose_logger.warning( + "New Relic: unknown newrelic_region %r; supported regions: %s. Using the default (US) endpoint.", + newrelic_region, + ", ".join(sorted(NEWRELIC_METRIC_ENDPOINT_BY_REGION)), + ) + return NEWRELIC_METRIC_ENDPOINT_BY_REGION[NEWRELIC_DEFAULT_REGION] + return endpoint + + +def _metric_record_from_payload(standard_logging_object: StandardLoggingPayload) -> NewRelicMetricRecord: + metadata: Final = standard_logging_object.get("metadata") + team_id: Final = ((metadata.get("user_api_key_team_id") or metadata.get("team_id")) if metadata else None) or "" + team_alias: Final = ( + (metadata.get("user_api_key_team_alias") or metadata.get("team_alias")) if metadata else None + ) or "" + return NewRelicMetricRecord( + team_id=team_id, + team_alias=team_alias, + model_group=standard_logging_object.get("model_group") or "", + model=standard_logging_object.get("model") or "", + custom_llm_provider=standard_logging_object.get("custom_llm_provider") or "", + status=str(standard_logging_object.get("status") or "success"), + response_cost=float(standard_logging_object.get("response_cost") or 0.0), + prompt_tokens=int(standard_logging_object.get("prompt_tokens") or 0), + completion_tokens=int(standard_logging_object.get("completion_tokens") or 0), + total_tokens=int(standard_logging_object.get("total_tokens") or 0), + duration_ms=float(standard_logging_object.get("response_time") or 0.0) * 1000.0, + ) + + +def _bucket_metrics(bucket_records: tuple[NewRelicMetricRecord, ...]) -> tuple[NewRelicMetric, ...]: + first: Final = bucket_records[0] + attributes: Final[Mapping[str, str]] = { # mutable-ok: JSON leaf; safe_dumps stringifies MappingProxyType + key: value[:NEWRELIC_METRIC_ATTRIBUTE_MAX_LEN] + for key, value in ( + ("team_id", first.team_id), + ("team_alias", first.team_alias), + ("model_group", first.model_group), + ("model", first.model), + ("custom_llm_provider", first.custom_llm_provider), + ("status", first.status), + ) + if value + } + durations: Final = tuple(record.duration_ms for record in bucket_records) + counts: Final[tuple[tuple[str, float], ...]] = ( + (NEWRELIC_METRIC_REQUESTS, float(len(bucket_records))), + (NEWRELIC_METRIC_COST_USD, sum(record.response_cost for record in bucket_records)), + (NEWRELIC_METRIC_PROMPT_TOKENS, float(sum(record.prompt_tokens for record in bucket_records))), + (NEWRELIC_METRIC_COMPLETION_TOKENS, float(sum(record.completion_tokens for record in bucket_records))), + (NEWRELIC_METRIC_TOTAL_TOKENS, float(sum(record.total_tokens for record in bucket_records))), + ) + count_metrics: Final[tuple[NewRelicMetric, ...]] = tuple( + NewRelicCountMetric(name=name, type="count", value=value, attributes=attributes) for name, value in counts + ) + summary_metric: Final = NewRelicSummaryMetric( + name=NEWRELIC_METRIC_REQUEST_DURATION_MS, + type="summary", + value=NewRelicSummaryValue( + count=len(durations), + sum=sum(durations), + min=min(durations), + max=max(durations), + ), + attributes=attributes, + ) + return (*count_metrics, summary_metric) + + +def build_metric_payload( + records: tuple[NewRelicMetricRecord, ...], + *, + window_start: float, + now: float, +) -> tuple[NewRelicMetricEnvelope, ...]: + """Aggregates records into one Metric API envelope for the flush window.""" + interval_ms: Final = max(1, int((now - window_start) * 1000)) + bucket_keys: Final = tuple(dict.fromkeys(record.bucket_key for record in records)) + metrics: Final = tuple( + metric + for key in bucket_keys + for metric in _bucket_metrics(tuple(record for record in records if record.bucket_key == key)) + ) + common: Final[NewRelicMetricCommon] = { + "timestamp": int(window_start * 1000), + "interval.ms": interval_ms, + } + return (NewRelicMetricEnvelope(common=common, metrics=metrics),) + + +class NewRelicMetricsLogger(CustomBatchLogger): + def __init__( + self, + newrelic_api_key: str, + newrelic_region: str | None = None, + ) -> None: + if not newrelic_api_key: + raise ValueError( + "newrelic_api_key is required for NewRelicMetricsLogger; " + "team-scoped metrics never fall back to environment credentials" + ) + self.newrelic_api_key: Final = newrelic_api_key + self.metric_api_url: Final = resolve_newrelic_metric_endpoint(newrelic_region) + self.async_client = get_async_httpx_client(llm_provider=httpxSpecialProvider.LoggingCallback) + self._stopped: bool = False + self._drain_lock = asyncio.Lock() + asyncio.create_task(self.periodic_flush()) + self.flush_lock = asyncio.Lock() + super().__init__( + flush_lock=self.flush_lock, + batch_size=NEWRELIC_METRICS_MAX_BATCH_SIZE, + max_queue_size=NEWRELIC_METRICS_MAX_RETRY_QUEUE_SIZE, + ) + + def stop(self) -> None: + """Ends the periodic flush loop; called on DynamicLoggingCache eviction. + + Schedules one final drain of anything still queued, so eviction never + silently discards records. Guarded so it can never raise into the + cache's eviction path. + """ + self._stopped = True + try: + asyncio.get_running_loop().create_task(self._final_drain()) + except Exception: # noqa: BLE001 # no running loop / shutdown; the periodic loop's final drain still runs + verbose_logger.debug("New Relic Metrics: could not schedule final drain on stop()", exc_info=True) + + async def _drain_with_retry(self) -> None: + """Deliver everything queued on a stopped logger, or drop it with a log. + + A stopped logger has no periodic loop left, so every post-stop path + funnels through here. ``_drain_lock`` serializes drains: a callback that + appends and starts its own drain queues behind the running one instead + of racing it. Each pass attempts the whole current queue in + ``batch_size`` chunks, unlike the periodic path it does not stop at the + first failing chunk, so a persistently failing head never starves the + tail. Only after ``_MAX_DRAIN_PASSES`` against a permanently failing + destination is the remainder dropped, and then only the records that were + queued when this drain began, so every dropped record got the full retry + budget: a record a callback appended mid-drain is not in that snapshot, + so it is left for its own serialized drain rather than dropped after + fewer attempts, and is never stranded. + """ + async with self._drain_lock: + attempted: Final = tuple(self.log_queue) + for _pass in range(NEWRELIC_METRICS_MAX_DRAIN_PASSES): + await self._drain_flush_once() + if not self.log_queue: + return + if _pass < NEWRELIC_METRICS_MAX_DRAIN_PASSES - 1: + await asyncio.sleep(2**_pass) + async with self.flush_lock: + tried_ids: Final = frozenset(id(record) for record in attempted) + survivors: Final = tuple(record for record in self.log_queue if id(record) not in tried_ids) + dropped: Final = len(self.log_queue) - len(survivors) + if dropped: + verbose_logger.warning( + "New Relic Metrics: dropping %s records after %s drain passes", + dropped, + NEWRELIC_METRICS_MAX_DRAIN_PASSES, + ) + self.log_queue[:] = list(survivors) # mutable-ok: leave late arrivals for the next serialized drain + + async def _drain_flush_once(self) -> None: + """Attempt every queued record once, in ``batch_size`` chunks, without + stopping at the first failing chunk so a persistently failing head does + not starve the tail (the periodic ``flush_queue`` deliberately stops + instead). Takes the queue under ``flush_lock`` and re-queues only the + chunks a 5xx/network error left undelivered, so records a concurrent + request appends during the sends survive for the next pass.""" + async with self.flush_lock: + pending: Final = tuple(self.log_queue) + window_start: Final = self.last_flush_time + self.last_flush_time = time.time() + del self.log_queue[:] + if not pending: + return + chunks: Final = tuple( + pending[start : start + self.batch_size] for start in range(0, len(pending), self.batch_size) + ) + delivered: Final = tuple([await self._classify_and_send(chunk, window_start) for chunk in chunks]) + failed: Final = tuple(record for chunk, ok in zip(chunks, delivered) for record in (() if ok else chunk)) + if failed: + self._requeue(failed) + + async def _final_drain(self) -> None: + await self._drain_with_retry() + + async def periodic_flush(self) -> None: + while not self._stopped: + await asyncio.sleep(self.flush_interval) + if self._stopped: + break + await self.flush_queue() + await self._final_drain() + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time) -> None: + try: + await self._log_async_event(standard_logging_object=kwargs.get("standard_logging_object", None)) + except Exception as e: # noqa: BLE001 # logging must never break the request path + verbose_logger.exception("New Relic Metrics Layer Error - %s\n%s", e, traceback.format_exc()) + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time) -> None: + try: + await self._log_async_event(standard_logging_object=kwargs.get("standard_logging_object", None)) + except Exception as e: # noqa: BLE001 # logging must never break the request path + verbose_logger.exception("New Relic Metrics Layer Error - %s\n%s", e, traceback.format_exc()) + + async def _log_async_event(self, standard_logging_object: StandardLoggingPayload | None) -> None: + if standard_logging_object is None: + raise ValueError("standard_logging_object not found in kwargs") + self.log_queue.append(_metric_record_from_payload(standard_logging_object)) + if self._stopped: + # A stopped logger has no periodic loop left; an in-flight callback + # that appends after the eviction drain delivers its own record. + await self._drain_with_retry() + return + if len(self.log_queue) >= self.batch_size: + await self.flush_queue() + + async def flush_queue(self) -> None: + async with self.flush_lock: + window_start: Final = self.last_flush_time + self.last_flush_time = time.time() + queued: Final = len(self.log_queue) + if not queued: + return + verbose_logger.debug("New Relic Metrics: Flushing %s queued records", queued) + # Bounded by what is queued now: records appended mid-flush belong to + # the next window, and looping until empty would never end under load. + for _chunk in range(ceil(queued / self.batch_size)): + if not await self.async_send_batch(window_start=window_start): + return + + async def async_send_batch(self, window_start: float | None = None) -> bool: + """Sends the oldest ``batch_size`` records only, so a queue grown past that + by re-queues cannot breach the Metric API data point cap in one request. + Returns False once a chunk fails and is re-queued, so the caller stops.""" + if not self.log_queue: + return False + + batch_to_send: Final[tuple[NewRelicMetricRecord, ...]] = tuple(self.log_queue[: self.batch_size]) + del self.log_queue[: len(batch_to_send)] + + delivered: Final = await self._classify_and_send( + batch_to_send, window_start if window_start is not None else self.last_flush_time + ) + if not delivered: + self._requeue(batch_to_send) + return delivered + + async def _classify_and_send(self, batch: tuple[NewRelicMetricRecord, ...], window_start: float) -> bool: + """Send one chunk and classify the outcome, never touching the queue. + Returns True when the batch is done with (delivered on any 2xx, or a 4xx + a retry would only repeat, 403 being a permanent bad-key rejection), and + False when a 5xx or network error means the caller should re-queue it. + + ``AsyncHTTPHandler.post`` raises ``HTTPStatusError`` on any non-2xx, so a + 4xx never returns a response here; the status is read off the raised + error to keep the client-error path (drop) distinct from 5xx (retry).""" + payload: Final = build_metric_payload(records=batch, window_start=window_start, now=time.time()) + try: + status = ( + await self.async_send_compressed_data(payload) + ).status_code # rebind-ok: reassigned from the raised HTTPStatusError below + except HTTPStatusError as e: + status = e.response.status_code + except Exception as e: # noqa: BLE001 # transport/network failure re-queues the batch + verbose_logger.warning( + "New Relic Metrics: network error sending %s records, will retry - %s", + len(batch), + e, + ) + return False + + if 200 <= status < 300: + return True + + if 400 <= status < 500 and status not in _RETRYABLE_CLIENT_STATUSES: + verbose_logger.warning( + "New Relic Metrics: %s from Metric API%s, dropping %s records.", + status, + " (permanent credential failure: invalid or revoked team ingest key)" if status == 403 else "", + len(batch), + ) + return True + + verbose_logger.warning( + "New Relic Metrics: %s from Metric API, will retry %s records", + status, + len(batch), + ) + return False + + def _requeue(self, batch: tuple[NewRelicMetricRecord, ...]) -> None: + """Prepends ``batch`` in place (never by assignment: records appended by + concurrent requests during the flush await must survive), keeping + chronological order so the cap drops the oldest records first.""" + self.log_queue[:0] = batch + overflow: Final = len(self.log_queue) - self.max_queue_size + if overflow > 0: + del self.log_queue[:overflow] + verbose_logger.warning( + "New Relic Metrics: retry queue exceeded max_queue_size=%s; dropped %s oldest records.", + self.max_queue_size, + overflow, + ) + + async def async_send_compressed_data(self, payload: tuple[NewRelicMetricEnvelope, ...]) -> Response: + compressed_data: Final = gzip.compress(safe_dumps(payload).encode("utf-8")) + headers: Final[Mapping[str, str]] = MappingProxyType( + { + "Content-Type": "application/json", + "Content-Encoding": "gzip", + "Api-Key": self.newrelic_api_key, + } + ) + return await self.async_client.post( + url=self.metric_api_url, + data=compressed_data, + headers=headers, + ) diff --git a/litellm/integrations/newrelic/newrelic_team_handler.py b/litellm/integrations/newrelic/newrelic_team_handler.py new file mode 100644 index 00000000000..ae52a6d4efb --- /dev/null +++ b/litellm/integrations/newrelic/newrelic_team_handler.py @@ -0,0 +1,90 @@ +""" +New Relic Team Handler + +Used to get the NewRelicMetricsLogger for a given request. +Handles Key/Team Based New Relic metrics, following the same pattern as DataDogHandler. +""" + +from typing import TYPE_CHECKING, Final + +from typing_extensions import ReadOnly, TypedDict + +from litellm._logging import verbose_logger +from litellm.litellm_core_utils.litellm_logging import StandardCallbackDynamicParams + +from .newrelic_metrics import NewRelicMetricsLogger + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import DynamicLoggingCache + + +class NewRelicLoggingConfig(TypedDict): + newrelic_api_key: ReadOnly[str | None] + newrelic_region: ReadOnly[str | None] + + +class NewRelicHandler: + @staticmethod + def get_newrelic_logger_for_request( + standard_callback_dynamic_params: StandardCallbackDynamicParams, + in_memory_dynamic_logger_cache: "DynamicLoggingCache", + ) -> NewRelicMetricsLogger: + """ + Get a team-scoped NewRelicMetricsLogger for a given request. + + Resolves and caches per-team NewRelicMetricsLogger instances using + DynamicLoggingCache, keyed by the team's New Relic credentials. Each unique + set of credentials gets its own logger instance with its own batch/flush loop. + + Note: This handler is only called when a team-scoped newrelic_api_key is + present. The trace logger for the ``newrelic`` callback (OTel v2 / legacy + agent) is managed separately by _init_custom_logger_compatible_class via + _in_memory_loggers. + """ + _credentials: Final = NewRelicHandler.get_dynamic_newrelic_logging_config( + standard_callback_dynamic_params=standard_callback_dynamic_params, + ) + + temp_newrelic_logger = in_memory_dynamic_logger_cache.get_cache( + credentials=_credentials, service_name="newrelic" + ) + + if temp_newrelic_logger is None: + temp_newrelic_logger = NewRelicHandler._create_newrelic_logger_from_credentials( + credentials=_credentials, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + + return temp_newrelic_logger + + @staticmethod + def _create_newrelic_logger_from_credentials( + credentials: NewRelicLoggingConfig, + in_memory_dynamic_logger_cache: "DynamicLoggingCache", + ) -> NewRelicMetricsLogger: + newrelic_logger: Final = NewRelicMetricsLogger( + newrelic_api_key=credentials.get("newrelic_api_key") or "", + newrelic_region=credentials.get("newrelic_region"), + ) + in_memory_dynamic_logger_cache.set_cache( + credentials=credentials, + service_name="newrelic", + logging_obj=newrelic_logger, + ) + verbose_logger.debug("New Relic: Created and cached new NewRelicMetricsLogger for team-scoped credentials") + return newrelic_logger + + @staticmethod + def get_dynamic_newrelic_logging_config( + standard_callback_dynamic_params: StandardCallbackDynamicParams, + ) -> NewRelicLoggingConfig: + return NewRelicLoggingConfig( + newrelic_api_key=standard_callback_dynamic_params.get("newrelic_api_key"), + newrelic_region=standard_callback_dynamic_params.get("newrelic_region"), + ) + + @staticmethod + def _dynamic_newrelic_credentials_are_passed( + standard_callback_dynamic_params: StandardCallbackDynamicParams, + ) -> bool: + return standard_callback_dynamic_params.get("newrelic_api_key") is not None diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 78081837ae3..e8f3b305139 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -22,6 +22,7 @@ from litellm.integrations.opentelemetry_utils.gen_ai_semconv import ( ) from litellm.integrations.otel.model.db_endpoint import db_span_attributes from litellm.integrations.otel.model.semconv import Metric +from litellm.litellm_core_utils.internal_call_metadata import is_unbilled_non_inference_call_from_params from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.litellm_core_utils.secret_redaction import redact_string from litellm.litellm_core_utils.service_tier_utils import ( @@ -1643,7 +1644,12 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): if self._operation_duration_histogram: self._operation_duration_histogram.record(duration_s, attributes=common_attrs) - if response_obj and (usage := response_obj.get("usage")) and self._token_usage_histogram: + if ( + self._token_usage_histogram + and response_obj + and not is_unbilled_non_inference_call_from_params(kwargs.get("call_type"), params, response_obj) + and (usage := response_obj.get("usage")) + ): in_attrs: Final = {**common_attrs, TOKEN_TYPE_ATTRIBUTE: "input"} out_attrs: Final = {**common_attrs, TOKEN_TYPE_ATTRIBUTE: "output"} self._token_usage_histogram.record(usage.get("prompt_tokens", 0), attributes=in_attrs) @@ -1719,6 +1725,11 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): if not self._time_per_output_token_histogram: return + if is_unbilled_non_inference_call_from_params( + kwargs.get("call_type"), kwargs.get("litellm_params"), response_obj + ): + return + # Get completion tokens from response_obj completion_tokens = None if response_obj and (usage := response_obj.get("usage")): @@ -2049,6 +2060,26 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): # serialise to JSON once so set_attribute never coerces. guardrail_span.set_attribute("guardrail_violation_categories", safe_dumps(violation_categories)) + # Billable usage counters and USD cost stamped by the provider hook + # (e.g. Azure Prompt Shield text records, Bedrock policy units). + guardrail_usage = guardrail_information.get("guardrail_usage") + if guardrail_usage is not None: + guardrail_span.set_attribute("guardrail_usage", safe_dumps(guardrail_usage)) + guardrail_cost = guardrail_information.get("guardrail_cost") + if guardrail_cost is not None: + self.safe_set_attribute( + span=guardrail_span, + key="guardrail_cost", + value=guardrail_cost, + ) + guardrail_cost_in_spend = guardrail_information.get("guardrail_cost_in_spend") + if isinstance(guardrail_cost_in_spend, bool): + self.safe_set_attribute( + span=guardrail_span, + key="guardrail_cost_in_spend", + value=guardrail_cost_in_spend, + ) + self._set_team_attributes_from_kwargs(guardrail_span, kwargs) guardrail_span.end(end_time=self._to_ns(end_time_datetime)) @@ -2468,7 +2499,14 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): self._set_service_tier_attributes(span=span, standard_logging_payload=standard_logging_payload) - usage: Final = response_obj and response_obj.get("usage") + usage: Final = ( + response_obj.get("usage") + if response_obj + and not is_unbilled_non_inference_call_from_params( + kwargs.get("call_type"), litellm_params, response_obj + ) + else None + ) if usage: self.safe_set_attribute( span=span, diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py index fae93f03d1e..ce47d7fe27a 100644 --- a/litellm/integrations/opik/opik.py +++ b/litellm/integrations/opik/opik.py @@ -4,9 +4,12 @@ Opik Logger that logs LLM events to an Opik server import asyncio import traceback +from collections.abc import Mapping from datetime import datetime from typing import Any, Final +from typing_extensions import ReadOnly, TypedDict, Unpack + from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.llms.custom_httpx.http_handler import ( @@ -23,7 +26,7 @@ except Exception: opik_client = None -def _should_skip_event(kwargs: dict[str, Any]) -> bool: +def _should_skip_event(kwargs: Mapping[str, object]) -> bool: """Check if event should be skipped due to missing standard_logging_object.""" if kwargs.get("standard_logging_object") is None: verbose_logger.debug("OpikLogger skipping event; no standard_logging_object found") @@ -31,12 +34,24 @@ def _should_skip_event(kwargs: dict[str, Any]) -> bool: return False +class _OpikLoggerKwargs(TypedDict, total=False): + """Constructor options accepted by ``OpikLogger``.""" + + project_name: ReadOnly[str | None] + url: ReadOnly[str | None] + api_key: ReadOnly[str | None] + workspace: ReadOnly[str | None] + batch_size: ReadOnly[int | None] + flush_interval: ReadOnly[int | None] + max_queue_size: ReadOnly[int | None] + + class OpikLogger(CustomBatchLogger): """ Opik Logger for logging events to an Opik Server """ - def __init__(self, **kwargs: Any) -> None: + def __init__(self, **kwargs: Unpack[_OpikLoggerKwargs]) -> None: self.async_httpx_client = get_async_httpx_client(llm_provider=httpxSpecialProvider.LoggingCallback) self.sync_httpx_client = _get_httpx_client() @@ -95,7 +110,7 @@ class OpikLogger(CustomBatchLogger): async def async_log_success_event( self, - kwargs: dict[str, Any], + kwargs: dict[str, object], response_obj: Any, start_time: datetime, end_time: datetime, @@ -163,7 +178,7 @@ class OpikLogger(CustomBatchLogger): except Exception as e: verbose_logger.exception("OpikLogger failed to log success event - %s\n%s", e, traceback.format_exc()) - def _sync_send(self, url: str, headers: dict[str, str], batch: dict[str, Any]) -> None: + def _sync_send(self, url: str, headers: dict[str, str], batch: dict[str, object]) -> None: try: response: Final = self.sync_httpx_client.post( url=url, @@ -178,7 +193,7 @@ class OpikLogger(CustomBatchLogger): def log_success_event( self, - kwargs: dict[str, Any], + kwargs: dict[str, object], response_obj: Any, start_time: datetime, end_time: datetime, @@ -247,7 +262,7 @@ class OpikLogger(CustomBatchLogger): except Exception as e: verbose_logger.exception("OpikLogger failed to log success event - %s\n%s", e, traceback.format_exc()) - async def _submit_batch(self, url: str, headers: dict[str, str], batch: dict[str, Any]) -> None: + async def _submit_batch(self, url: str, headers: dict[str, str], batch: dict[str, object]) -> None: try: response: Final = await self.async_httpx_client.post( url=url, diff --git a/litellm/integrations/opik/opik_payload_builder/extractors.py b/litellm/integrations/opik/opik_payload_builder/extractors.py index 92a7eca7f3e..4dd3d40fae3 100644 --- a/litellm/integrations/opik/opik_payload_builder/extractors.py +++ b/litellm/integrations/opik/opik_payload_builder/extractors.py @@ -1,6 +1,7 @@ """Data extraction functions for Opik payload building.""" import json +from collections.abc import Mapping from typing import Any, Final from litellm import _logging @@ -35,8 +36,8 @@ def normalize_provider_name(provider: str | None) -> str | None: def extract_opik_metadata( - litellm_metadata: dict[str, Any], - standard_logging_metadata: dict[str, Any], + litellm_metadata: Mapping[str, Any], + standard_logging_metadata: Mapping[str, Any], ) -> dict[str, Any]: """ Merge Opik metadata from three sources in increasing priority order: @@ -97,7 +98,7 @@ def extract_span_identifiers( def extract_tags( - opik_metadata: dict[str, Any], + opik_metadata: Mapping[str, Any], custom_llm_provider: str | None, ) -> list[str]: """ @@ -122,7 +123,7 @@ def apply_proxy_header_overrides( project_name: str, tags: list[str], thread_id: str | None, - proxy_headers: dict[str, Any], + proxy_headers: Mapping[str, str], ) -> tuple[str, list[str], str | None]: """ Apply overrides from proxy request headers (opik_* prefix). @@ -148,7 +149,7 @@ def apply_proxy_header_overrides( thread_id = value elif param_key == "tags": try: - parsed_tags = json.loads(value) + parsed_tags: object = json.loads(value) if isinstance(parsed_tags, list): tags.extend(parsed_tags) except (json.JSONDecodeError, TypeError): @@ -158,11 +159,11 @@ def apply_proxy_header_overrides( def extract_and_build_metadata( - opik_metadata: dict[str, Any], - standard_logging_metadata: dict[str, Any], - standard_logging_object: dict[str, Any], - litellm_kwargs: dict[str, Any], -) -> dict[str, Any]: + opik_metadata: Mapping[str, object], + standard_logging_metadata: Mapping[str, object], + standard_logging_object: Mapping[str, object], + litellm_kwargs: Mapping[str, object], +) -> dict[str, object]: """ Build the complete metadata dictionary from all available sources. diff --git a/litellm/integrations/opik/opik_payload_builder/payload_builders.py b/litellm/integrations/opik/opik_payload_builder/payload_builders.py index e40d72ea542..855b84ba4c8 100644 --- a/litellm/integrations/opik/opik_payload_builder/payload_builders.py +++ b/litellm/integrations/opik/opik_payload_builder/payload_builders.py @@ -17,12 +17,12 @@ def build_trace_payload( end_time: datetime, input_data: Any, output_data: Any, - metadata: dict[str, Any], + metadata: dict[str, object], tags: list[str], thread_id: str | None, ) -> types.TracePayload: """Build a complete trace payload.""" - trace_name: Final = response_obj.get("object", "unknown type") + trace_name: Final[str] = response_obj.get("object", "unknown type") return types.TracePayload( project_name=project_name, @@ -47,7 +47,7 @@ def build_span_payload( end_time: datetime, input_data: Any, output_data: Any, - metadata: dict[str, Any], + metadata: dict[str, object], tags: list[str], usage: dict[str, int], provider: str | None = None, @@ -56,9 +56,9 @@ def build_span_payload( """Build a complete span payload.""" span_id: Final = utils.create_uuid7() - model: Final = response_obj.get("model", "unknown-model") - obj_type: Final = response_obj.get("object", "unknown-object") - created: Final = response_obj.get("created", 0) + model: Final[str] = response_obj.get("model", "unknown-model") + obj_type: Final[str] = response_obj.get("object", "unknown-object") + created: Final[int] = response_obj.get("created", 0) span_name: Final = f"{model}_{obj_type}_{created}" _logging.verbose_logger.debug("OpikLogger creating span with id %s for trace %s", span_id, trace_id) diff --git a/litellm/integrations/otel/__init__.py b/litellm/integrations/otel/__init__.py index 9c1205bb277..d7627d4d63d 100644 --- a/litellm/integrations/otel/__init__.py +++ b/litellm/integrations/otel/__init__.py @@ -49,6 +49,7 @@ from litellm.integrations.otel.model.semconv import ( Error, GenAI, GenAIOperation, + GenAIOutputType, GenAIProvider, JsonRpc, LiteLLM, @@ -60,6 +61,7 @@ from litellm.integrations.otel.model.semconv import ( RpcSystem, Server, resolve_operation, + resolve_output_type, resolve_provider, ) from litellm.integrations.otel.model.spans import ( @@ -84,6 +86,7 @@ __all__ = [ "Error", "GenAI", "GenAIOperation", + "GenAIOutputType", "GenAIProvider", "GuardrailSpanData", "JsonRpc", @@ -116,6 +119,7 @@ __all__ = [ "is_otel_v2_enabled", "promoted_baggage", "resolve_operation", + "resolve_output_type", "resolve_provider", "span_role_for_service", "validate_registry", diff --git a/litellm/integrations/otel/emitter.py b/litellm/integrations/otel/emitter.py index 0850d867c7b..101dbc6538d 100644 --- a/litellm/integrations/otel/emitter.py +++ b/litellm/integrations/otel/emitter.py @@ -146,7 +146,7 @@ class SpanEmitter: For callers that own and manage their own span lifecycle. ``tracer`` overrides the bound tracer for this span only, used for per-request multi-tenant credential routing. ``links`` records related-but-not-parent - spans (e.g. the transport span of an MCP message, per MCP semconv). + spans (e.g. the trace context an MCP client propagated in ``params._meta``). """ return (tracer or self._tracer).start_span( name, @@ -156,6 +156,12 @@ class SpanEmitter: links=list(links) if links else None, ) + def mark_emitted(self, dedup_key: str | None, role: SpanRole) -> None: + """Register a span emitted outside :meth:`emit` (the boundary-opened + LLM-call span closed via :meth:`finish_span`) so a later :meth:`emit` + for the same ``(dedup_key, role)`` deduplicates against it.""" + self._seen(dedup_key, role) + def _seen(self, dedup_key: str | None, role: SpanRole) -> bool: """Return True once a ``(dedup_key, role)`` pair has been emitted. @@ -190,8 +196,8 @@ class SpanEmitter: Return the span, or ``None`` if it was deduplicated away. ``tracer`` overrides the bound tracer for this span, used for per-request routing. - ``links`` records related-but-not-parent spans (the transport span of an - MCP message). + ``links`` records related-but-not-parent spans (e.g. the trace context an + MCP client propagated in ``params._meta``). """ # LLM-call and MCP tool-call spans carry a dedup key (their request's # call id), so a sync+async double-firing coalesces. ``isinstance`` narrows diff --git a/litellm/integrations/otel/logger.py b/litellm/integrations/otel/logger.py index 53b9829023c..d2a32ef73b6 100644 --- a/litellm/integrations/otel/logger.py +++ b/litellm/integrations/otel/logger.py @@ -390,10 +390,10 @@ class OpenTelemetryV2(CustomLogger): MCP tool calls reach the success/failure callbacks like any other request (with ``call_type`` ``call_mcp_tool``), but they are not LLM calls and have - no ``pre_call`` carrier — so they get their own CLIENT span here. Per the MCP - semconv it parents to the trace context the client propagated in - ``params._meta`` (or starts a new root) and links the transport span, rather - than nesting under the HTTP/session span. Returns whether it handled the + no ``pre_call`` carrier — so they get their own CLIENT span here. It nests + under the transport span of the request carrying this message, and trace + context the client propagated in ``params._meta`` is recorded as a span + link (see ``resolve_mcp_span_context``). Returns whether it handled the event, so the caller skips the LLM-call path. The whole span is emitted at once (there is no boundary to open it at), deduped on the call id. """ @@ -436,9 +436,9 @@ class OpenTelemetryV2(CustomLogger): Like a tool call, listing reaches the success/failure callbacks (here with ``call_type`` ``list_mcp_tools``) with no ``pre_call`` carrier, so it gets its - own CLIENT span. Per the MCP semconv it parents to the ``params._meta`` trace - context (or starts a new root) and links the transport span, rather than - nesting under the HTTP/session span. Returns whether it handled the event so + own CLIENT span, nested under the transport span of the request carrying + this message with any ``params._meta`` trace context recorded as a span + link (see ``resolve_mcp_span_context``). Returns whether it handled the event so the caller skips the LLM-call path. """ raw_payload: Final = kwargs.get("standard_logging_object") @@ -484,10 +484,15 @@ class OpenTelemetryV2(CustomLogger): # ``pop`` is the dedup: this method runs from both the success and failure # paths, and whichever fires first removes the carrier and closes the span. carrier: Final = self._open_llm_calls.pop(call_id, None) if call_id else None - if carrier is None: + # A missing carrier does not always mean nothing happened: a team/key-scoped + # logger is a success/failure callback only, so ``pre_call`` never reaches it + # and no carrier exists. The payload plus the request-level provider-handoff + # stamp (``upstream_started``) is the affirmative signal of a real call; a + # gate rejection carries ``is_no_upstream_call`` and gets no span. + if carrier is None and (call.is_no_upstream_call or not call.upstream_started or call.payload is None): return None try: - return self._finish_carrier(carrier, call, end_time) + return self._finish_carrier(carrier, call, start_time, end_time) finally: # After the span has ended, so a release-triggered provider shutdown # force-flushes it out rather than racing its enqueue. @@ -497,8 +502,11 @@ class OpenTelemetryV2(CustomLogger): """Remember an in-flight LLM call, evicting the oldest if over budget. A call that opens but never closes (a stream that only fires stream - events) would linger otherwise; the evicted span is simply dropped - (never exported). + events) would linger otherwise. Eviction only drops the boundary carrier, + not the call: if that call later closes as a real completed call, it still + emits through the deferred branch in ``_close_llm_call`` (the same path a + team/key-scoped logger uses, since it never opens a carrier), deduplicated + by call id. Only a call that is evicted and never closes goes unexported. """ self._open_llm_calls[call_id] = carrier if len(self._open_llm_calls) > _OPEN_CALLS_MAX: @@ -512,15 +520,20 @@ class OpenTelemetryV2(CustomLogger): def _finish_carrier( self, - carrier: _LLMCallSpan, + carrier: "_LLMCallSpan | None", call: LLMCallEvent, + start_time: datetime | float | None, end_time: datetime | float | None, ) -> Span | None: payload: Final = call.payload + call_id: Final = call.call_id if payload is None: - if carrier.span is not None: + if carrier is not None and carrier.span is not None: # Opened at the boundary but the payload never materialized — end - # it (named provisionally) so it isn't leaked as an open span. + # it (named provisionally) so it isn't leaked as an open span, and + # register the dedup marker so a later payload-carrying close for + # the same call id cannot re-emit through the deferred branch. + self._emitter.mark_emitted(call_id, SpanRole.LLM_CALL) carrier.span.end(end_time=to_ns(end_time)) return None data: Final = LLMCallSpanData.from_standard_logging_payload( @@ -529,10 +542,13 @@ class OpenTelemetryV2(CustomLogger): time_to_first_chunk_seconds=call.time_to_first_chunk_seconds, ) end_time_ns: Final = to_ns(end_time) - if carrier.span is not None: + if carrier is not None and carrier.span is not None: # Born at the boundary: stamp attributes from the typed payload, set # status, and end it. Its parent (the server span) was captured at - # creation from real ambient context. + # creation from real ambient context. Register the dedup marker so a + # second close for the same call id (success then failure on one + # logging object) cannot re-emit through the deferred branch. + self._emitter.mark_emitted(call_id, SpanRole.LLM_CALL) self._emitter.finish_span(SpanRole.LLM_CALL, carrier.span, data, end_time_ns=end_time_ns) return carrier.span # Deferred: ``pre_call`` saw no recordable parent, so create the span now. @@ -549,7 +565,7 @@ class OpenTelemetryV2(CustomLogger): SpanRole.LLM_CALL, data, parent_context=(set_span_in_context(INVALID_SPAN, parent_ctx) if route.detached else parent_ctx), - start_time_ns=carrier.start_time_ns, + start_time_ns=(carrier.start_time_ns if carrier is not None else to_ns(start_time)), end_time_ns=end_time_ns, tracer=route.tracer, links=_request_trace_links(parent_ctx) if route.detached else None, diff --git a/litellm/integrations/otel/mappers/genai.py b/litellm/integrations/otel/mappers/genai.py index 79487e69ac4..3ac92b04c27 100644 --- a/litellm/integrations/otel/mappers/genai.py +++ b/litellm/integrations/otel/mappers/genai.py @@ -42,6 +42,7 @@ class GenAIMapper: _LLM_CALL_ATTRS: dict[str, Callable[[LLMCallSpanData], AttrValue | None]] = { GenAI.OPERATION_NAME: lambda d: d.operation.value, GenAI.PROVIDER_NAME: lambda d: d.provider or None, + GenAI.OUTPUT_TYPE: lambda d: d.output_type.value if d.output_type else None, GenAI.REQUEST_MODEL: lambda d: d.request_model or None, GenAI.REQUEST_TEMPERATURE: lambda d: d.request_params.temperature, GenAI.REQUEST_TOP_P: lambda d: d.request_params.top_p, @@ -61,10 +62,13 @@ class GenAIMapper: GenAI.RESPONSE_TIME_TO_FIRST_CHUNK: lambda d: d.time_to_first_chunk_seconds, GenAI.USAGE_INPUT_TOKENS: lambda d: d.usage.input_tokens, GenAI.USAGE_OUTPUT_TOKENS: lambda d: d.usage.output_tokens, + GenAI.USAGE_CACHE_CREATION_INPUT_TOKENS: lambda d: d.usage.cache_creation_input_tokens, + GenAI.USAGE_CACHE_READ_INPUT_TOKENS: lambda d: d.usage.cache_read_input_tokens, Error.TYPE: lambda d: d.error.error_type if d.error else None, Server.ADDRESS: lambda d: d.server.address if d.server else None, Server.PORT: lambda d: d.server.port if d.server else None, LiteLLM.CALL_ID: lambda d: d.identity.call_id or None, + LiteLLM.CALL_TYPE: lambda d: d.call_type, # The provider/underlying model is only known once routing has picked a # deployment, so it can't ride identity Baggage (seeded at auth, before # routing) onto the boundary-born LLM span — stamp it directly here. @@ -134,6 +138,9 @@ class GenAIMapper: LiteLLM.GUARDRAIL_ID: lambda d: d.guardrail_id, LiteLLM.GUARDRAIL_POLICY_TEMPLATE: lambda d: d.policy_template, LiteLLM.GUARDRAIL_DETECTION_METHOD: lambda d: d.detection_method, + LiteLLM.GUARDRAIL_USAGE: lambda d: d.usage_json, + LiteLLM.GUARDRAIL_COST: lambda d: d.cost, + LiteLLM.GUARDRAIL_COST_IN_SPEND: lambda d: d.cost_in_spend, } _SERVICE_ATTRS: dict[str, Callable[[ServiceSpanData], AttrValue | None]] = { diff --git a/litellm/integrations/otel/model/config.py b/litellm/integrations/otel/model/config.py index 53178b48991..9e3064c2bff 100644 --- a/litellm/integrations/otel/model/config.py +++ b/litellm/integrations/otel/model/config.py @@ -39,6 +39,7 @@ class ExporterOwner(str, Enum): WEAVE_OTEL = "weave_otel" LEVO = "levo" AGENTOPS = "agentops" + NEWRELIC = "newrelic" class _OTelV2Flag(BaseSettings): @@ -97,6 +98,15 @@ class ExporterSpec(BaseModel): "auto (Simple for console/in_memory, Batch otherwise)." ), ) + requires_headers: bool = Field( + default=False, + description=( + "Skip this exporter when no headers are resolved. For destinations " + "that reject unauthenticated exports (e.g. New Relic), a spec kept " + "only as the per-request credential-stamping target would otherwise " + "export keyless traffic and produce a 4xx for every span batch." + ), + ) class OpenTelemetryV2Config(BaseSettings): diff --git a/litellm/integrations/otel/model/metadata.py b/litellm/integrations/otel/model/metadata.py index de6366e7dbd..062b2ca20b4 100644 --- a/litellm/integrations/otel/model/metadata.py +++ b/litellm/integrations/otel/model/metadata.py @@ -203,6 +203,11 @@ class LLMCallEvent: # True for synthetic proxy-gate logs (auth / rate-limit rejections): they fire # the ``pre_call`` hook but never made an upstream call, so they get no span. is_no_upstream_call: bool + # True once the request handed off to a provider (``pre_call`` stamped + # ``api_call_start_time``). The affirmative signal that an LLM call was + # actually attempted — router pre-call rejections, SDK failures before the + # provider handoff, and standalone guardrail runs all lack it. + upstream_started: bool # A best-effort ``"{operation} {model}"`` name known at ``pre_call`` time. The # span is renamed from the typed payload at close (``finish_span``); this only # needs to be reasonable for a span that never gets closed (a leak). @@ -221,6 +226,7 @@ class LLMCallEvent: dynamic_params=kwargs.get("standard_callback_dynamic_params"), auth_metadata=auth_metadata(payload, kwargs), is_no_upstream_call=bool(kwargs.get(LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL)), + upstream_started=kwargs.get("api_call_start_time") is not None, provisional_span_name=f"{operation.value} {model}".strip(), time_to_first_chunk_seconds=time_to_first_chunk_seconds(kwargs), ) diff --git a/litellm/integrations/otel/model/payloads.py b/litellm/integrations/otel/model/payloads.py index aba9cc80240..e8ed269f6cb 100644 --- a/litellm/integrations/otel/model/payloads.py +++ b/litellm/integrations/otel/model/payloads.py @@ -6,6 +6,7 @@ import json from collections.abc import Mapping from dataclasses import dataclass, field from enum import Enum +from types import MappingProxyType from typing import TYPE_CHECKING, ClassVar, Final, cast from urllib.parse import urlsplit @@ -15,8 +16,10 @@ from litellm.integrations.otel.model.metadata import ( ) from litellm.integrations.otel.model.semconv import ( GenAIOperation, + GenAIOutputType, MCPMethod, resolve_operation, + resolve_output_type, resolve_provider, ) from litellm.integrations.otel.model.utils import ( @@ -60,6 +63,31 @@ if TYPE_CHECKING: # --- typed sub-structures ---------------------------------------------------- # +def _cache_token_value(*values: object) -> int | None: + explicit_zero = False + invalid_before_zero = False + for raw_value in values: + if raw_value is None: + continue + if isinstance(raw_value, bool): + parsed = None + else: + try: + parsed = as_int(raw_value) + except (OverflowError, ValueError): + parsed = None + if parsed is None: + if not explicit_zero: + invalid_before_zero = True + elif parsed > 0: + return parsed + elif parsed == 0: + explicit_zero = True + elif not explicit_zero: + invalid_before_zero = True + return 0 if explicit_zero and not invalid_before_zero else None + + @dataclass(frozen=True) class LLMRequestParams: temperature: float | None = None @@ -93,6 +121,35 @@ class LLMUsage: input_tokens: int | None = None output_tokens: int | None = None total_tokens: int | None = None + cache_creation_input_tokens: int | None = None + cache_read_input_tokens: int | None = None + + @classmethod + def from_standard_logging_payload(cls, payload: StandardLoggingPayload) -> LLMUsage: + # Cache token counts only exist on the raw provider usage object under metadata + metadata: Final[Mapping[str, object]] = payload.get("metadata") or {} + raw_usage: Final = metadata.get("usage_object") + usage_object: Final[Mapping[str, object]] = raw_usage if isinstance(raw_usage, Mapping) else {} + raw_details: Final = usage_object.get("prompt_tokens_details") + prompt_details: Final[Mapping[str, object]] = ( + raw_details if isinstance(raw_details, Mapping) else MappingProxyType({}) + ) + return cls( + input_tokens=as_int(payload.get("prompt_tokens")), + output_tokens=as_int(payload.get("completion_tokens")), + total_tokens=as_int(payload.get("total_tokens")), + cache_creation_input_tokens=_cache_token_value( + usage_object.get("cache_creation_input_tokens"), + prompt_details.get("cache_write_tokens"), + prompt_details.get("cache_creation_tokens"), + prompt_details.get("cache_creation_input_tokens"), + ), + cache_read_input_tokens=_cache_token_value( + usage_object.get("cache_read_input_tokens"), + prompt_details.get("cached_tokens"), + usage_object.get("prompt_cache_hit_tokens"), + ), + ) @dataclass(frozen=True) @@ -188,6 +245,15 @@ class GuardrailSpanData: guardrail_id: str | None = None policy_template: str | None = None detection_method: str | None = None + # Provider-reported billable usage counters (JSON-serialized) and the USD cost + # priced from them by the provider hook (``guardrail_usage`` / + # ``guardrail_cost`` on ``StandardLoggingGuardrailInformation``). + usage_json: str | None = None + cost: float | None = None + # Whether ``cost`` participates in the request's billed spend (absent means + # billed, the default; False means report-only). Mirrors + # ``guardrail_cost_in_spend`` so trace consumers can avoid double-counting. + cost_in_spend: bool | None = None # Set when the guardrail intervened/blocked or failed, so the emitter marks # the span ERROR — a blocking guardrail is an error outcome for that span. error: SpanError | None = None @@ -207,6 +273,8 @@ class GuardrailSpanData: get: Final = cast(Mapping[str, object], entry).get status: Final = as_str(get("guardrail_status")) response: Final = get("guardrail_response") + usage: Final = get("guardrail_usage") + in_spend: Final = get("guardrail_cost_in_spend") error: Final = ( SpanError(error_type=status, message=as_str(get("guardrail_action"))) if status in cls._ERROR_STATUSES @@ -229,6 +297,9 @@ class GuardrailSpanData: guardrail_id=as_str(get("guardrail_id")), policy_template=as_str(get("policy_template")), detection_method=as_str(get("detection_method")), + usage_json=_json_or_none(usage) if usage is not None else None, + cost=as_float(get("guardrail_cost")), + cost_in_spend=in_spend if isinstance(in_spend, bool) else None, error=error, ) @@ -310,6 +381,11 @@ class LLMCallSpanData: choices_out: tuple[Mapping[str, object], ...] = () system_fingerprint: str | None = None time_to_first_chunk_seconds: float | None = None + # The requested output modality, set only on the routes that pin one (image + # generation, speech, transcription, OCR), and the litellm route itself, which + # keeps routes the convention folds into one operation distinguishable. + output_type: GenAIOutputType | None = None + call_type: str | None = None @classmethod def from_standard_logging_payload( @@ -334,18 +410,15 @@ class LLMCallSpanData: # otherwise the content-bearing mappers receive empty sequences and emit # no prompt/response text. finish_reasons: Final = _finish_reasons(choices_out) + call_type: Final = as_str(payload.get("call_type")) return cls( - operation=resolve_operation(as_str(payload.get("call_type"))), + operation=resolve_operation(call_type), provider=resolve_provider(as_str(payload.get("custom_llm_provider"))), request_model=context.request_model, response_model=context.response_model, response_id=as_str(response.get("id")), request_params=LLMRequestParams.from_model_parameters(params), - usage=LLMUsage( - input_tokens=as_int(payload.get("prompt_tokens")), - output_tokens=as_int(payload.get("completion_tokens")), - total_tokens=as_int(payload.get("total_tokens")), - ), + usage=LLMUsage.from_standard_logging_payload(payload), finish_reasons=finish_reasons, error=_parse_error(payload), response_cost=as_float(payload.get("response_cost")), @@ -358,6 +431,8 @@ class LLMCallSpanData: choices_out=choices_out if capture_content else (), system_fingerprint=as_str(response.get("system_fingerprint")), time_to_first_chunk_seconds=time_to_first_chunk_seconds, + output_type=resolve_output_type(call_type), + call_type=call_type or None, ) diff --git a/litellm/integrations/otel/model/semconv.py b/litellm/integrations/otel/model/semconv.py index ada2822ba66..f7a6280f95b 100644 --- a/litellm/integrations/otel/model/semconv.py +++ b/litellm/integrations/otel/model/semconv.py @@ -3,7 +3,9 @@ Keys follow the OpenTelemetry GenAI semantic conventions (experimental). Anythin without a semconv equivalent lives under the ``litellm.*`` vendor namespace. """ +from collections.abc import Mapping from enum import Enum +from types import MappingProxyType from typing import Final from litellm._logging import verbose_logger @@ -30,6 +32,22 @@ class GenAIOperation(str, Enum): EXECUTE_TOOL = "execute_tool" # MCP tool-call spans LITELLM_VECTOR_STORE_MANAGEMENT = "litellm.vector_store_management" LITELLM_VECTOR_STORE_FILE_MANAGEMENT = "litellm.vector_store_file_management" + LITELLM_RESPONSES_MANAGEMENT = "litellm.responses_management" + LITELLM_MODERATION = "litellm.moderation" + + +class GenAIOutputType(str, Enum): + """Values for ``gen_ai.output.type``, the modality the client asked for. + + It is what separates the inference routes that share ``generate_content``: + image generation requests ``image``, speech requests ``speech``, and + transcription and OCR both request ``text``. + """ + + TEXT = "text" + JSON = "json" + IMAGE = "image" + SPEECH = "speech" class GenAIProvider(str, Enum): @@ -92,6 +110,8 @@ class GenAI: # usage USAGE_INPUT_TOKENS: Final = "gen_ai.usage.input_tokens" USAGE_OUTPUT_TOKENS: Final = "gen_ai.usage.output_tokens" + USAGE_CACHE_CREATION_INPUT_TOKENS: Final = "gen_ai.usage.cache_creation.input_tokens" + USAGE_CACHE_READ_INPUT_TOKENS: Final = "gen_ai.usage.cache_read.input_tokens" # content (opt-in, gated by capture mode) INPUT_MESSAGES: Final = "gen_ai.input.messages" OUTPUT_MESSAGES: Final = "gen_ai.output.messages" @@ -258,6 +278,11 @@ class LiteLLM: """Vendor-extension keys (no semconv equivalent). Always ``litellm.*``.""" CALL_ID: Final = "litellm.call_id" + # The litellm route that produced the call. Needed because the convention maps + # several routes onto one operation: transcription and OCR are both + # ``generate_content`` with a ``text`` output type, so this is the only thing + # that tells them apart. + CALL_TYPE: Final = "litellm.call_type" COST_PREFIX: Final = "litellm.cost." METADATA_PREFIX: Final = "litellm.metadata." TEAM_ID: Final = "litellm.team.id" @@ -285,6 +310,15 @@ class LiteLLM: GUARDRAIL_ID: Final = "litellm.guardrail.id" GUARDRAIL_POLICY_TEMPLATE: Final = "litellm.guardrail.policy_template" GUARDRAIL_DETECTION_METHOD: Final = "litellm.guardrail.detection_method" + # Provider-reported billable usage counters, JSON-serialized into one value. + GUARDRAIL_USAGE: Final = "litellm.guardrail.usage" + # Numeric USD cost of the guardrail invocation; lives under the litellm.cost.* + # namespace (COST_PREFIX) beside the LLM call's litellm.cost.total. + GUARDRAIL_COST: Final = "litellm.cost.guardrail" + # Whether litellm.cost.guardrail is already inside litellm.cost.total (True, + # the billed default) or reported alongside it (False) — without this a trace + # consumer cannot tell whether adding the two double-counts. + GUARDRAIL_COST_IN_SPEND: Final = "litellm.guardrail.cost_in_spend" SERVICE_NAME: Final = "litellm.service.name" SERVICE_CALL_TYPE: Final = "litellm.service.call_type" PREPROCESSING_MS: Final = "litellm.preprocessing.duration_ms" @@ -352,6 +386,24 @@ _OPERATION_BY_CALL_TYPE: Final[dict[str, GenAIOperation]] = { "aembedding": GenAIOperation.EMBEDDINGS, "responses": GenAIOperation.CHAT, "aresponses": GenAIOperation.CHAT, + "get_responses": GenAIOperation.LITELLM_RESPONSES_MANAGEMENT, + "aget_responses": GenAIOperation.LITELLM_RESPONSES_MANAGEMENT, + "delete_responses": GenAIOperation.LITELLM_RESPONSES_MANAGEMENT, + "adelete_responses": GenAIOperation.LITELLM_RESPONSES_MANAGEMENT, + "cancel_responses": GenAIOperation.LITELLM_RESPONSES_MANAGEMENT, + "acancel_responses": GenAIOperation.LITELLM_RESPONSES_MANAGEMENT, + "list_input_items": GenAIOperation.LITELLM_RESPONSES_MANAGEMENT, + "alist_input_items": GenAIOperation.LITELLM_RESPONSES_MANAGEMENT, + "image_generation": GenAIOperation.GENERATE_CONTENT, + "aimage_generation": GenAIOperation.GENERATE_CONTENT, + "moderation": GenAIOperation.LITELLM_MODERATION, + "amoderation": GenAIOperation.LITELLM_MODERATION, + "ocr": GenAIOperation.GENERATE_CONTENT, + "aocr": GenAIOperation.GENERATE_CONTENT, + "speech": GenAIOperation.GENERATE_CONTENT, + "aspeech": GenAIOperation.GENERATE_CONTENT, + "transcription": GenAIOperation.GENERATE_CONTENT, + "atranscription": GenAIOperation.GENERATE_CONTENT, "call_mcp_tool": GenAIOperation.EXECUTE_TOOL, "vector_store_search": GenAIOperation.RETRIEVAL, "avector_store_search": GenAIOperation.RETRIEVAL, @@ -385,6 +437,23 @@ _OPERATION_BY_CALL_TYPE: Final[dict[str, GenAIOperation]] = { } +# litellm ``call_type`` -> ``gen_ai.output.type``. Only the call types whose route +# fixes the requested modality are listed; the attribute is conditionally required +# on a request that asks for an output format, so anything else is left unstamped. +_OUTPUT_TYPE_BY_CALL_TYPE: Final[Mapping[str, GenAIOutputType]] = MappingProxyType( + { + "image_generation": GenAIOutputType.IMAGE, + "aimage_generation": GenAIOutputType.IMAGE, + "speech": GenAIOutputType.SPEECH, + "aspeech": GenAIOutputType.SPEECH, + "transcription": GenAIOutputType.TEXT, + "atranscription": GenAIOutputType.TEXT, + "ocr": GenAIOutputType.TEXT, + "aocr": GenAIOutputType.TEXT, + } +) + + def resolve_provider(custom_llm_provider: str | None) -> str: """Map a litellm provider string to a ``gen_ai.provider.name`` value. @@ -416,3 +485,11 @@ def resolve_operation(call_type: str | None) -> GenAIOperation: GenAIOperation.CHAT.value, ) return GenAIOperation.CHAT + + +def resolve_output_type(call_type: str | None) -> GenAIOutputType | None: + """Map a litellm ``call_type`` to a ``gen_ai.output.type`` value, or ``None`` + for a route that doesn't pin the output modality.""" + if not call_type: + return None + return _OUTPUT_TYPE_BY_CALL_TYPE.get(call_type.lower()) diff --git a/litellm/integrations/otel/model/spans.py b/litellm/integrations/otel/model/spans.py index 08318f78b7c..35fc50a2a83 100644 --- a/litellm/integrations/otel/model/spans.py +++ b/litellm/integrations/otel/model/spans.py @@ -10,6 +10,8 @@ Canonical hierarchy:: │ └── DB_CALL (CLIENT) # its key/user/team lookups nest here ├── GUARDRAIL (INTERNAL) # request-lifecycle hook, sibling of LLM_CALL ├── LLM_CALL (CLIENT) + ├── MCP_TOOL_CALL (CLIENT) # nests under the POST carrying the message + ├── MCP_LIST_TOOLS (CLIENT) # (client-propagated context is a span link) └── DB_CALL (CLIENT) # e.g. the spend-log write Guardrails parent to PROXY_REQUEST, not LLM_CALL: pre/during/post-call guardrail @@ -18,14 +20,14 @@ before the LLM call even starts), so a guardrail is a sibling of the LLM call, not a child of it. The emitter parents every span to the ambient OTel context (the active server span), which matches this. -MCP spans (``MCP_TOOL_CALL``, ``MCP_LIST_TOOLS``) have two shapes, chosen at emit -time by :func:`resolve_mcp_span_context`. When the client propagates trace context -in ``params._meta`` MCP and the HTTP transport are independent contexts per the -OTel GenAI MCP semconv, so the span parents to that propagated context and records -the ``PROXY_REQUEST`` transport span as a span *link*, never a parent — the shape -this registry's ``parent=None, links=PROXY_REQUEST`` entry encodes. When nothing is -propagated (the common case) the span nests under the transport span of the request -carrying that message, so the tool call stays in one trace. +MCP spans (``MCP_TOOL_CALL``, ``MCP_LIST_TOOLS``) are parented at emit time by +:func:`resolve_mcp_span_context`: they nest under the ``PROXY_REQUEST`` transport +span of the request carrying that message, so the tool call stays in one trace. +Trace context the client propagated in ``params._meta`` (SEP-414) is recorded as +a span *link*, never the parent — a remote parent would root the span in a trace +whose root never reaches the gateway's tracing backend. Links always target that +remote client context, never a registry role, so ``SpanSpec`` declares no link +field; the concrete transport parent is resolved per message at emit time. Not every service call becomes a span — :func:`span_role_for_service` decides: @@ -85,25 +87,19 @@ class SpanSpec: role: SpanRole kind: LiteLLMSpanKind parent: SpanRole | None - links: SpanRole | None = None SPAN_REGISTRY: Final[dict[SpanRole, SpanSpec]] = { SpanRole.PROXY_REQUEST: SpanSpec(SpanRole.PROXY_REQUEST, LiteLLMSpanKind.SERVER, parent=None), SpanRole.LLM_CALL: SpanSpec(SpanRole.LLM_CALL, LiteLLMSpanKind.CLIENT, parent=SpanRole.PROXY_REQUEST), # The proxy is an MCP client to the upstream server, so MCP spans are CLIENT - # spans. With trace context propagated in ``params._meta``, MCP and the HTTP - # transport are independent contexts (OTel GenAI MCP semconv): the span parents - # to the propagated context and records the PROXY_REQUEST transport span as a - # span *link*, never a parent — the shape ``parent=None, links=PROXY_REQUEST`` - # encodes. With nothing propagated, ``resolve_mcp_span_context`` nests the span - # under that message's transport span instead, keeping the call in one trace. - SpanRole.MCP_TOOL_CALL: SpanSpec( - SpanRole.MCP_TOOL_CALL, LiteLLMSpanKind.CLIENT, parent=None, links=SpanRole.PROXY_REQUEST - ), - SpanRole.MCP_LIST_TOOLS: SpanSpec( - SpanRole.MCP_LIST_TOOLS, LiteLLMSpanKind.CLIENT, parent=None, links=SpanRole.PROXY_REQUEST - ), + # spans. ``resolve_mcp_span_context`` nests them under the PROXY_REQUEST + # transport span of the request carrying that message (resolved per message at + # emit time), keeping the call in one trace. Trace context the client + # propagated in ``params._meta`` becomes a span *link* to that remote context, + # which is not a registry role, so ``SpanSpec`` has no link field. + SpanRole.MCP_TOOL_CALL: SpanSpec(SpanRole.MCP_TOOL_CALL, LiteLLMSpanKind.CLIENT, parent=SpanRole.PROXY_REQUEST), + SpanRole.MCP_LIST_TOOLS: SpanSpec(SpanRole.MCP_LIST_TOOLS, LiteLLMSpanKind.CLIENT, parent=SpanRole.PROXY_REQUEST), SpanRole.GUARDRAIL: SpanSpec(SpanRole.GUARDRAIL, LiteLLMSpanKind.INTERNAL, parent=SpanRole.PROXY_REQUEST), SpanRole.DB_CALL: SpanSpec(SpanRole.DB_CALL, LiteLLMSpanKind.CLIENT, parent=SpanRole.PROXY_REQUEST), SpanRole.SERVICE: SpanSpec(SpanRole.SERVICE, LiteLLMSpanKind.INTERNAL, parent=SpanRole.PROXY_REQUEST), @@ -209,8 +205,8 @@ def service_span_name(data: "ServiceSpanData") -> str: def root_roles() -> list[SpanRole]: - """Roles with no in-process parent. They start a new trace unless they adopt a - remote parent (e.g. an MCP span joining the client's propagated context).""" + """Roles with no in-process parent, i.e. they start a new trace (only the + instrumentor-owned ``PROXY_REQUEST`` server span today).""" return [role for role, spec in SPAN_REGISTRY.items() if spec.parent is None] @@ -227,8 +223,6 @@ def validate_registry( raise ValueError(f"SPAN_REGISTRY[{role}] has mismatched role {spec.role}") if spec.parent is not None and spec.parent not in reg: raise ValueError(f"span role {role} declares unknown parent {spec.parent}") - if spec.links is not None and spec.links not in reg: - raise ValueError(f"span role {role} declares unknown link target {spec.links}") missing: Final = [role for role in SpanRole if role not in reg] if missing: raise ValueError(f"SPAN_REGISTRY is missing roles: {missing}") diff --git a/litellm/integrations/otel/plumbing/context.py b/litellm/integrations/otel/plumbing/context.py index 19b36c0b967..159a84b121f 100644 --- a/litellm/integrations/otel/plumbing/context.py +++ b/litellm/integrations/otel/plumbing/context.py @@ -57,8 +57,8 @@ def request_root_span() -> "Span | None": # The W3C trace-context carrier (``traceparent``/``tracestate``/``baggage``) the # MCP client propagated in the current request's ``params._meta``. The MCP gateway -# sets it per message so the MCP span can parent to the client's span rather than -# to the transport. A ``ContextVar`` because, like the root-span anchor, it must +# sets it per message so the MCP span can record the client's span as a span +# link. A ``ContextVar`` because, like the root-span anchor, it must # ride the request task and be readable by the inline success-logging callback. _mcp_message_trace_carrier: Final["ContextVar[Mapping[str, str] | None]"] = ContextVar( "litellm_otel_mcp_message_trace_carrier", default=None @@ -148,10 +148,10 @@ def _mcp_transport_span_context() -> "SpanContext | None": Prefers the transport the gateway published for this specific message; falls back to the ambient request anchor for paths that emit an MCP span on the - request task itself (the REST MCP endpoints, the SDK). Parenting and linking - only need the immutable context, and unlike ``mcp_message_transport_span`` they - stay correct against a transport that has already finished, so this does not - require the span to still be recording. + request task itself (the REST MCP endpoints). Parenting needs only the + immutable context, and unlike ``mcp_message_transport_span`` it stays correct + against a transport that has already finished, so this does not require the + span to still be recording. """ published: Final = _mcp_message_transport_span.get() if published is not None: @@ -222,25 +222,31 @@ def resolve_mcp_span_context( ) -> "tuple[Context, tuple[Link, ...]]": """Parent context + links for an MCP message span. + The span always nests under the transport span of the request carrying this + message, so a tool call and the ``POST`` that carried it stay in one trace. + The transport comes from :func:`_mcp_transport_span_context`, which is the + *current message's* POST rather than whatever request happened to open the + session, so a long-lived session does not glue every message under its first + request. + When the client propagates W3C trace context in the request's ``params._meta`` - (SEP-414), MCP and the underlying transport are independent lifecycles — one - streamable-HTTP session multiplexes many messages, and the client's own span is - the truthful parent. So, per the OTel GenAI MCP semconv: + (SEP-414), that remote context is recorded as a span *link*, never the parent. + The OTel GenAI MCP semconv prefers the inverse (remote parent, transport link), + but the gateway's tracing backend only ever receives the gateway's half of such + a trace: parenting into the client's trace id roots the span in a trace whose + root span never reaches the backend, so the span is unreachable from the trace + view and the transport transaction shows a dangling link (observed with + clients that propagate synthetic trace ids). Anchoring to the gateway's own + request and linking the client's context keeps every trace renderable while + preserving the client-side correlation. - * parent to the trace context the client propagated (a *remote* parent), and - * record the transport span as a *link*, never the parent. - - Almost no client implements SEP-414 yet, so in practice nothing is propagated. - Rooting the span there splits a single tool call into two disconnected traces - joined only by a link, which is how it surfaces in APM: the ``POST`` transaction - and the ``tools/call`` span share no trace. With no remote parent to honor, - parent to the transport span of the request carrying this message instead, so - the call stays in one trace; no link is added since the transport is now the - real parent. The transport comes from :func:`_mcp_transport_span_context`, which - is the *current message's* POST rather than whatever request happened to open - the session, so a long-lived session does not glue every message under its - first request. With neither a remote parent nor a transport the returned context - carries no span and the span legitimately starts its own root trace. + With no transport at all the span starts its own root trace, still carrying + the link — the client context is only ever a link, so this event keeps one + shape everywhere. Both returned contexts are built on an explicitly empty + base, so ambient (stale session) state can never leak in, and the span + inherits the transport's sampling decision exactly like every other + request-level span — a client's sampled flag neither forces nor suppresses + recording. Only trace context (``traceparent``/``tracestate``) is extracted, never the client's W3C Baggage: ``params._meta`` is caller-controlled, and the otel @@ -251,13 +257,12 @@ def resolve_mcp_span_context( never fall through to the ambient (stale session) span. """ source: Final = carrier if carrier is not None else _mcp_message_trace_carrier.get() - parent: Final = _PROPAGATOR.extract(dict(source or {}), context=Context()) + propagated: Final = get_current_span(_PROPAGATOR.extract(dict(source or {}), context=Context())) + links: Final = (Link(propagated.get_span_context()),) if is_recordable_span(propagated) else () transport: Final = _mcp_transport_span_context() - if is_recordable_span(get_current_span(parent)): - return parent, (Link(transport),) if transport is not None else () - if transport is not None: - return context_from_span(NonRecordingSpan(transport)), () - return parent, () + if transport is None: + return Context(), links + return context_from_span(NonRecordingSpan(transport), context=Context()), links def is_recordable_span(obj: object) -> bool: diff --git a/litellm/integrations/otel/plumbing/metrics.py b/litellm/integrations/otel/plumbing/metrics.py index 548a6440126..e1623f4697f 100644 --- a/litellm/integrations/otel/plumbing/metrics.py +++ b/litellm/integrations/otel/plumbing/metrics.py @@ -11,9 +11,10 @@ identical metrics. The attribute cardinality filter is reused from v1 by import from collections.abc import Mapping from dataclasses import dataclass from datetime import datetime -from typing import Any, Final, TypeAlias +from typing import Any, Final, Literal, Protocol, TypeAlias from opentelemetry.metrics import Histogram, Meter +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -32,6 +33,7 @@ from litellm.integrations.otel.model.semconv import ( resolve_provider, ) from litellm.integrations.otel.model.utils import to_seconds +from litellm.litellm_core_utils.internal_call_metadata import is_unbilled_non_inference_call_from_params from litellm.litellm_core_utils.safe_json_dumps import safe_dumps @@ -150,6 +152,29 @@ METRIC_ATTRIBUTE_CEILING: Final[frozenset[str]] = frozenset( BOUNDED_HIDDEN_PARAM_KEYS: Final[tuple[str, ...]] = ("model_id",) +class _TokenUsage(TypedDict, total=False): + """The token counts a response's ``usage`` carries, as the recorder reads them.""" + + prompt_tokens: ReadOnly[int] + completion_tokens: ReadOnly[int] + + +class _ResponseView(Protocol): + """The one read the recorder makes on a litellm response object.""" + + def get(self, key: Literal["usage"], /) -> _TokenUsage | None: ... + + +class _MetricKwargs(TypedDict, total=False): + """The logging kwargs the recorder reads directly.""" + + call_type: ReadOnly[str | None] + litellm_params: ReadOnly[Mapping[str, object] | None] + response_cost: ReadOnly[float | None] + completion_start_time: ReadOnly[datetime | float | str | None] + api_call_start_time: ReadOnly[datetime | float | str | None] + + def resolve_error_type(kwargs: Mapping[str, Any]) -> str: """The ``error.type`` value for a failed request. @@ -191,28 +216,33 @@ class GenAIMetricRecorder: def record( self, - kwargs: Mapping[str, Any], - response_obj: Any, + kwargs: _MetricKwargs, + response_obj: _ResponseView | None, start_time: datetime, end_time: datetime, ) -> None: common_attrs: Final = self._filter_attributes(self._bounded_attributes(kwargs)) duration_s: Final = (end_time - start_time).total_seconds() + usage_is_replayed: Final = is_unbilled_non_inference_call_from_params( + kwargs.get("call_type"), kwargs.get("litellm_params"), response_obj + ) self._metrics.operation_duration.record(duration_s, attributes=common_attrs) - self._record_token_usage(response_obj, common_attrs) + if not usage_is_replayed: + self._record_token_usage(response_obj, common_attrs) cost: Final = kwargs.get("response_cost") if cost: self._metrics.token_cost.record(cost, attributes=common_attrs) self._record_time_to_first_token(kwargs, common_attrs) - self._record_time_per_output_token(kwargs, response_obj, end_time, duration_s, common_attrs) + if not usage_is_replayed: + self._record_time_per_output_token(kwargs, response_obj, end_time, duration_s, common_attrs) self._record_response_duration(kwargs, end_time, common_attrs) def record_failure( self, - kwargs: Mapping[str, Any], + kwargs: _MetricKwargs, start_time: datetime, end_time: datetime, ) -> None: @@ -336,7 +366,7 @@ class GenAIMetricRecorder: # Per-metric recording # ------------------------------------------------------------------ # - def _record_token_usage(self, response_obj: Any, common_attrs: dict) -> None: + def _record_token_usage(self, response_obj: _ResponseView | None, common_attrs: dict) -> None: if not response_obj: return usage: Final = response_obj.get("usage") @@ -347,7 +377,7 @@ class GenAIMetricRecorder: self._metrics.token_usage.record(usage.get("prompt_tokens", 0), attributes=in_attrs) self._metrics.token_usage.record(usage.get("completion_tokens", 0), attributes=out_attrs) - def _record_time_to_first_token(self, kwargs: Mapping[str, Any], common_attrs: dict) -> None: + def _record_time_to_first_token(self, kwargs: _MetricKwargs, common_attrs: dict) -> None: time_to_first_chunk: Final = time_to_first_chunk_seconds(kwargs) if time_to_first_chunk is None: return @@ -355,15 +385,14 @@ class GenAIMetricRecorder: def _record_time_per_output_token( self, - kwargs: Mapping[str, Any], - response_obj: Any, + kwargs: _MetricKwargs, + response_obj: _ResponseView | None, end_time: datetime, duration_s: float, common_attrs: dict, ) -> None: - completion_tokens = None - if response_obj and (usage := response_obj.get("usage")): - completion_tokens = usage.get("completion_tokens") + usage: Final = response_obj.get("usage") if response_obj else None + completion_tokens: Final = usage.get("completion_tokens") if usage else None if completion_tokens is None or completion_tokens <= 0: return diff --git a/litellm/integrations/otel/plumbing/providers.py b/litellm/integrations/otel/plumbing/providers.py index 80a8d01c061..fb74ff85e5b 100644 --- a/litellm/integrations/otel/plumbing/providers.py +++ b/litellm/integrations/otel/plumbing/providers.py @@ -1,7 +1,7 @@ """Provider / exporter factory + the Baggage span processor.""" from collections.abc import Callable, Iterable -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Literal from opentelemetry import _logs, baggage, metrics from opentelemetry._events import EventLogger @@ -135,14 +135,36 @@ def parse_headers(raw: str | None) -> dict[str, str]: return dict(parse_env_headers(raw, liberal=True)) +_IN_MEMORY_KINDS: Final = ("in_memory", "inmemory", "memory") +_OTLP_HTTP_KINDS: Final = ("otlp_http", "http", "http/protobuf", "http/json") +_OTLP_GRPC_KINDS: Final = ("otlp_grpc", "grpc") + + +def exporter_transport(kind: str) -> Literal["http", "grpc", "headerless"]: + """How an exporter of this ``kind`` carries credentials, per ``_exporter_from_spec``. + + ``http``/``grpc`` exporters (and any registered factory, which builds an + OTLP exporter) stamp ``spec.headers``; ``console``, ``in_memory``, and any + unrecognized kind (which falls back to a header-ignoring console exporter) + are ``headerless``. Routability decisions must read this rather than a + denylist, so a typo'd or unavailable kind is not mistaken for OTLP. + """ + resolved: Final = kind.lower() + if resolved in _OTLP_HTTP_KINDS or resolved in _EXPORTER_FACTORIES: + return "http" + if resolved in _OTLP_GRPC_KINDS: + return "grpc" + return "headerless" + + def _exporter_from_spec(spec: ExporterSpec) -> SpanExporter: kind: Final = (spec.kind or "console").lower() factory: Final = _EXPORTER_FACTORIES.get(kind) if factory is not None: return factory(spec) - if kind in ("in_memory", "inmemory", "memory"): + if kind in _IN_MEMORY_KINDS: return InMemorySpanExporter() - if kind in ("otlp_http", "http", "http/protobuf", "http/json"): + if kind in _OTLP_HTTP_KINDS: from opentelemetry.exporter.otlp.proto.http.trace_exporter import ( OTLPSpanExporter as HTTPExporter, ) @@ -151,7 +173,7 @@ def _exporter_from_spec(spec: ExporterSpec) -> SpanExporter: endpoint=_otlp_traces_endpoint(spec.endpoint), headers=parse_headers(spec.headers), ) - if kind in ("otlp_grpc", "grpc"): + if kind in _OTLP_GRPC_KINDS: from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import ( OTLPSpanExporter as GRPCExporter, ) @@ -436,6 +458,8 @@ def build_tracer_provider( # ``config._normalize`` guarantees at least one spec (it folds the top-level # ``exporter``/``endpoint``/``headers`` fields in when ``exporters`` is empty). for spec in config.exporters: + if spec.requires_headers and not spec.headers: + continue exp = _exporter_from_spec(spec) provider.add_span_processor( _processor_for( diff --git a/litellm/integrations/otel/plumbing/routing.py b/litellm/integrations/otel/plumbing/routing.py index f231df9e914..227e18f3663 100644 --- a/litellm/integrations/otel/plumbing/routing.py +++ b/litellm/integrations/otel/plumbing/routing.py @@ -2,12 +2,13 @@ When a request carries team/key vendor credentials in ``standard_callback_dynamic_params``, or the key/team config resolved at auth -names a destination project, its spans must export through a -``TracerProvider`` whose OTLP headers carry those credentials / that project. -``TenantTracerCache`` builds and caches one provider per distinct -(credentials, project) pair, and otherwise hands back the logger's default -tracer. This lets a single logger fan requests out to many tenants without -needing a logger per tenant. +names a destination project or a service name, its spans must export through a +``TracerProvider`` whose OTLP headers carry those credentials / that project, +or whose Resource carries that ``service.name``. ``TenantTracerCache`` builds +and caches one provider per distinct (credentials, project, service name) +tuple, and otherwise hands back the logger's default tracer. This lets a +single logger fan requests out to many tenants without needing a logger per +tenant. """ import threading @@ -15,22 +16,26 @@ from collections import OrderedDict from collections.abc import Mapping from dataclasses import dataclass from types import MappingProxyType -from typing import Any, Final, TypeAlias +from typing import Final, TypeAlias from urllib.parse import quote from opentelemetry.sdk.trace import TracerProvider from opentelemetry.trace import Tracer from litellm._logging import verbose_logger +from litellm.constants import OTEL_SERVICE_NAME_METADATA_KEYS from litellm.integrations.otel.model.config import ExporterSpec, OpenTelemetryV2Config from litellm.integrations.otel.plumbing.providers import ( build_tracer_provider, + exporter_transport, get_tracer, ) from litellm.integrations.otel.presets import ( + dynamic_otlp_endpoint, dynamic_otlp_headers, project_routing_headers, ) +from litellm.types.utils import StandardCallbackDynamicParams # Exporter kinds that ignore headers — never rewritten with dynamic credentials. _NON_OTLP_KINDS: Final = ("console", "in_memory", "inmemory", "memory") @@ -63,8 +68,30 @@ _MAX_RETIRED_PROVIDERS: Final = 64 _HeaderItems: TypeAlias = tuple[tuple[str, str], ...] +_RouteKey: TypeAlias = tuple[_HeaderItems, _HeaderItems, str | None, str | None] + _NO_HEADERS: Final[Mapping[str, str]] = MappingProxyType({}) +#: Key/team config fields naming the Resource ``service.name``, highest +#: precedence first. Read only from ``user_api_key_auth_metadata`` (the config +#: the proxy resolved at auth), never from client-supplied request metadata: +#: the service name picks the dataset/service traces land in (Honeycomb routes +#: datasets by it), so a caller must not be able to choose one. +_SERVICE_NAME_KEYS: Final = OTEL_SERVICE_NAME_METADATA_KEYS + + +def tenant_service_name(auth_metadata: Mapping[str, str] | None) -> str | None: + """The per-request ``service.name`` override for this key/team, if any. + + ``None`` keeps the env-configured default (``OTEL_SERVICE_NAME``). + """ + if not auth_metadata: + return None + return next( + (stripped for key in _SERVICE_NAME_KEYS if (stripped := (auth_metadata.get(key) or "").strip())), + None, + ) + def _shutdown_provider(provider: TracerProvider) -> None: """Flush + stop an evicted provider's processors (reclaims their threads). @@ -95,13 +122,27 @@ def _encoded_header_string(headers: Mapping[str, str]) -> str: class TenantRoute: """The tracer to create a span on, plus whether it must root its own trace. - ``detached`` is True when project routing engaged. Phoenix assigns a whole + ``detached`` is True when the routed span exports to a DIFFERENT backend + than the request's root span, which always exports through the default + tracer. A detached span roots a fresh trace with a link back to the request + trace for correlation, so the destination account is not left holding a + child whose parent it never received. It is driven by whether routing + headers were actually applied to an owned exporter, not merely requested: + a credential or project route whose callback owns no exporter those headers + can reach exports through the default backend unchanged, so it stays + parented like an unrouted span. + + Credential routing (a team/key's own vendor account) is one detaching case: + the root, auth, and db spans stay on the operator's default backend while + the LLM-call span exports to the tenant's account, so parenting it into the + request trace makes the tenant account show a fragmented span with a missing + parent. Project routing (Phoenix) is the other: Phoenix assigns a whole trace to one project by whichever of its spans arrives first, so a project-routed span parented into the request trace gets dragged into the project of the default-exported request spans and the header does nothing. - The span must therefore start a fresh trace (with a link back to the - request trace for correlation) — which is also how the v1 Phoenix logger - behaved, exporting each request under its own Phoenix-local parent span. + Both mirror the v1 loggers, which exported each request under its own + backend-local root. Service-name routing does NOT detach: it relabels + ``service.name`` on the SAME operator backend, where the parent is present. """ tracer: Tracer @@ -114,7 +155,7 @@ class TenantRoute: class TenantTracerCache: - """Credential/project-scoped ``TracerProvider`` cache keyed by the routing headers.""" + """Tenant-scoped ``TracerProvider`` cache keyed by routing headers and service name.""" def __init__( self, @@ -129,15 +170,26 @@ class TenantTracerCache: # thread-pool workers concurrently with the event loop, so cache # updates, span counts, and retirement must be atomic. self._lock: Final = threading.Lock() - self._providers: OrderedDict[tuple[_HeaderItems, _HeaderItems], TracerProvider] = OrderedDict() + self._providers: OrderedDict[_RouteKey, TracerProvider] = ( + OrderedDict() # mutable-ok: bounded LRU; eviction needs in-place ordered mutation + ) self._open_span_counts: dict[TracerProvider, int] = {} # mutable-ok: live refcount state # Oldest-first so an overflow of draining providers sheds the stalest. self._retired: OrderedDict[TracerProvider, None] = OrderedDict() # mutable-ok: draining evicted providers - self._project_routable = any( - spec.owner == callback_name and spec.kind.lower() not in (*_NON_OTLP_KINDS, *_GRPC_KINDS) - for spec in config.exporters + # An owned exporter is routable only when its kind actually resolves to a + # header-carrying OTLP exporter. A denylist would accept a typo'd or + # unavailable kind, which ``_exporter_from_spec`` falls back to a + # header-ignoring console exporter: detaching such a span would strand it + # on the operator's console, never reaching the tenant backend. Project + # headers are HTTP-only; credentials ride gRPC metadata too (Arize's + # default exporter is gRPC), so they accept either OTLP transport. + owned_transports: Final = tuple( + exporter_transport(spec.kind) for spec in config.exporters if spec.owner == callback_name ) + self._project_routable = "http" in owned_transports + self._credential_routable = "http" in owned_transports or "grpc" in owned_transports self._warned_project_unroutable = False + self._warned_credential_unroutable = False def release(self, provider: TracerProvider | None) -> None: """Drop one open-span count; shut a retired provider down once drained. @@ -163,52 +215,65 @@ class TenantTracerCache: def route_for( self, default: Tracer, - dynamic_params: Any, + dynamic_params: StandardCallbackDynamicParams | None, auth_metadata: Mapping[str, str] | None = None, ) -> TenantRoute: """Return the tracer (and trace-detachment flag) for this request. - Use ``default`` unless the request's dynamic credentials or its key/team - project require a scoped tracer, in which case build (or reuse) one. The - cache is a bounded LRU: the least-recently-used provider is flushed and - shut down on overflow so its exporter threads don't accumulate. + Use ``default`` unless the request's dynamic credentials, its key/team + project, or its key/team service name require a scoped tracer, in + which case build (or reuse) one. The cache is a bounded LRU: the + least-recently-used provider is flushed and shut down on overflow so + its exporter threads don't accumulate. A routed provider is returned already held — its open-span count is incremented in the same critical section as the cache update — so a concurrent overflow eviction can't shut it down between selection and the caller's span start. The caller must ``release`` it exactly once. """ - credential_headers: Final = dynamic_otlp_headers(self._callback_name, dynamic_params) or _NO_HEADERS + credential_headers: Final = self._credential_headers(dynamic_params) project_headers: Final = self._project_headers(auth_metadata) - if not credential_headers and not project_headers: + service_name: Final = tenant_service_name(auth_metadata) + if not credential_headers and not project_headers and service_name is None: return TenantRoute(tracer=default, detached=False) + # A fixed per-integration region endpoint (New Relic us/eu), never a + # caller-supplied host; ``None`` keeps the preset's own endpoint. + endpoint: Final = dynamic_otlp_endpoint(self._callback_name, dynamic_params) cache_key: Final = ( tuple(sorted(credential_headers.items())), tuple(sorted(project_headers.items())), + endpoint, + service_name, ) with self._lock: - provider: Final = self._cached_provider_locked(cache_key, credential_headers, project_headers) + provider: Final = self._cached_provider_locked( + cache_key, credential_headers, project_headers, endpoint, service_name + ) self._open_span_counts[provider] = self._open_span_counts.get(provider, 0) + 1 evicted: Final = self._evicted_on_overflow_locked() if evicted is not None: _shutdown_provider(evicted) return TenantRoute( tracer=get_tracer(provider, self._tracer_name), - detached=bool(project_headers), + detached=bool(project_headers) or bool(credential_headers), provider=provider, ) def _cached_provider_locked( self, - cache_key: tuple[_HeaderItems, _HeaderItems], + cache_key: _RouteKey, credential_headers: Mapping[str, str], project_headers: Mapping[str, str], + endpoint: str | None, + service_name: str | None, ) -> TracerProvider: cached: Final = self._providers.get(cache_key) if cached is not None: self._providers.move_to_end(cache_key) return cached - built: Final = build_tracer_provider(self._routed_config(credential_headers, project_headers)) + built: Final = build_tracer_provider( + self._routed_config(credential_headers, project_headers, endpoint, service_name) + ) self._providers[cache_key] = built return built @@ -234,6 +299,26 @@ class TenantTracerCache: self._open_span_counts.pop(overflowed, None) return overflowed + def _credential_headers(self, dynamic_params: StandardCallbackDynamicParams | None) -> Mapping[str, str]: + """The per-request dynamic OTLP credentials, if this cache can apply them. + + A callback owning only a console/in_memory exporter has nowhere to stamp + them, so the span would export to the operator's default backend + unchanged; routing there and detaching would orphan it on the very + backend that holds its parent. Warn once and keep the default tracer. + """ + requested: Final = dynamic_otlp_headers(self._callback_name, dynamic_params) or _NO_HEADERS + if not requested or self._credential_routable: + return requested + if not self._warned_credential_unroutable: + self._warned_credential_unroutable = True + verbose_logger.warning( + "OTel V2: %s request carries dynamic credentials, but the callback owns no " + "OTLP exporter to stamp them onto; spans export to the default backend.", + self._callback_name, + ) + return _NO_HEADERS + def _project_headers(self, auth_metadata: Mapping[str, str] | None) -> Mapping[str, str]: """The per-request project-routing headers, if this cache can apply them. @@ -257,6 +342,8 @@ class TenantTracerCache: self, credential_headers: Mapping[str, str], project_headers: Mapping[str, str], + endpoint: str | None = None, + service_name: str | None = None, ) -> OpenTelemetryV2Config: """Clone the config, rewriting headers on the callback's own exporter. @@ -272,15 +359,20 @@ class TenantTracerCache: ``Authorization``), which must survive routing to a project. """ exporters: Final = [ - self._routed_exporter(spec, credential_headers, project_headers) for spec in self._config.exporters + self._routed_exporter(spec, credential_headers, project_headers, endpoint) + for spec in self._config.exporters ] - return self._config.model_copy(update={"exporters": exporters}) + update: Final = ( + {"exporters": exporters} if service_name is None else {"exporters": exporters, "service_name": service_name} + ) + return self._config.model_copy(update=update) def _routed_exporter( self, spec: ExporterSpec, credential_headers: Mapping[str, str], project_headers: Mapping[str, str], + endpoint: str | None = None, ) -> ExporterSpec: kind: Final = spec.kind.lower() if spec.owner != self._callback_name or kind in _NON_OTLP_KINDS: @@ -291,4 +383,10 @@ class TenantTracerCache: if project_headers and kind not in _GRPC_KINDS else base ) - return spec if routed == spec.headers else spec.model_copy(update={"headers": routed}) + update: Final = { # mutable-ok: model_copy(update=...) requires a plain dict + field: value + for field, value in (("headers", routed), ("endpoint", endpoint)) + if (field == "headers" and routed != spec.headers) + or (field == "endpoint" and endpoint is not None and endpoint != spec.endpoint) + } + return spec if not update else spec.model_copy(update=update) diff --git a/litellm/integrations/otel/presets/__init__.py b/litellm/integrations/otel/presets/__init__.py index 35b0584c697..a0cd5b3fd98 100644 --- a/litellm/integrations/otel/presets/__init__.py +++ b/litellm/integrations/otel/presets/__init__.py @@ -21,6 +21,11 @@ from litellm.integrations.otel.presets.langfuse import ( ) from litellm.integrations.otel.presets.langtrace import langtrace_preset from litellm.integrations.otel.presets.levo import levo_preset +from litellm.integrations.otel.presets.newrelic import ( + newrelic_dynamic_endpoint, + newrelic_dynamic_headers, + newrelic_preset, +) from litellm.integrations.otel.presets.phoenix import ( phoenix_preset, phoenix_project_headers, @@ -30,25 +35,45 @@ from litellm.types.utils import StandardCallbackDynamicParams #: Callback name → preset. The ``Preset`` annotation makes mypy verify every #: registered value matches the preset interface. -PRESET_BY_CALLBACK: Final[dict[str, Preset]] = { - "agentops": agentops_preset, - "arize": arize_preset, - "arize_phoenix": phoenix_preset, - "langfuse_otel": langfuse_preset, - "langtrace": langtrace_preset, - "levo": levo_preset, - "weave_otel": weave_preset, -} +PRESET_BY_CALLBACK: Final[Mapping[str, Preset]] = MappingProxyType( + { + "agentops": agentops_preset, + "arize": arize_preset, + "arize_phoenix": phoenix_preset, + "langfuse_otel": langfuse_preset, + "langtrace": langtrace_preset, + "levo": levo_preset, + "newrelic": newrelic_preset, + "weave_otel": weave_preset, + } +) #: Callback name → per-request OTLP header builder (team/key multi-tenant #: routing). Only integrations that support dynamic credentials appear here — #: Arize-Phoenix/Langtrace/Levo/AgentOps don't, so they use the logger's #: default tracer. -DYNAMIC_HEADERS_BY_CALLBACK: Final[dict[str, Callable[[StandardCallbackDynamicParams], dict[str, str]]]] = { - "arize": arize_dynamic_headers, - "langfuse_otel": langfuse_dynamic_headers, - "weave_otel": weave_dynamic_headers, -} +DYNAMIC_HEADERS_BY_CALLBACK: Final[Mapping[str, Callable[[StandardCallbackDynamicParams], dict[str, str]]]] = ( + MappingProxyType( + { + "arize": arize_dynamic_headers, + "langfuse_otel": langfuse_dynamic_headers, + "newrelic": newrelic_dynamic_headers, + "weave_otel": weave_dynamic_headers, + } + ) +) + +#: Callback name → per-request OTLP endpoint resolver. Only integrations whose +#: destination host varies per tenant (from a fixed region table, never a +#: caller-supplied URL) appear here; for everyone else the preset's endpoint is +#: authoritative. +DYNAMIC_ENDPOINT_BY_CALLBACK: Final[Mapping[str, Callable[[StandardCallbackDynamicParams], str | None]]] = ( + MappingProxyType( + { + "newrelic": newrelic_dynamic_endpoint, + } + ) +) #: Callback name → per-request *routing* header builder, sourced from the key/team @@ -98,17 +123,34 @@ def project_routing_headers( return builder(auth_metadata) +def dynamic_otlp_endpoint( + callback_name: str | None, + dynamic_params: StandardCallbackDynamicParams | None, +) -> str | None: + """Per-request OTLP endpoint for ``callback_name``, or ``None`` if N/A. + + ``None`` means "keep the preset's own endpoint". + """ + resolver: Final = DYNAMIC_ENDPOINT_BY_CALLBACK.get(callback_name or "") + if resolver is None or not dynamic_params: + return None + return resolver(dynamic_params) + + __all__ = [ + "DYNAMIC_ENDPOINT_BY_CALLBACK", "DYNAMIC_HEADERS_BY_CALLBACK", "PRESET_BY_CALLBACK", "PROJECT_HEADERS_BY_CALLBACK", "Preset", "agentops_preset", "arize_preset", + "dynamic_otlp_endpoint", "dynamic_otlp_headers", "langfuse_preset", "langtrace_preset", "levo_preset", + "newrelic_preset", "phoenix_preset", "project_routing_headers", "weave_preset", diff --git a/litellm/integrations/otel/presets/newrelic.py b/litellm/integrations/otel/presets/newrelic.py new file mode 100644 index 00000000000..4660a707355 --- /dev/null +++ b/litellm/integrations/otel/presets/newrelic.py @@ -0,0 +1,104 @@ +"""New Relic preset — OTLP/HTTP exporter to New Relic + GenAI vocabulary.""" + +from collections.abc import Mapping +from types import MappingProxyType +from typing import Final + +from pydantic import Field +from pydantic_settings import BaseSettings, SettingsConfigDict + +from litellm._logging import verbose_logger +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) +from litellm.integrations.otel.presets.utils import ensure_mappers +from litellm.types.utils import StandardCallbackDynamicParams + +#: Region -> OTLP base endpoint. A fixed table by design: team config picks a +#: region enum rather than a free-form endpoint, so callback vars can never +#: redirect telemetry to an arbitrary host. +NEWRELIC_OTLP_ENDPOINT_BY_REGION: Final[Mapping[str, str]] = MappingProxyType( + { + "us": "https://otlp.nr-data.net", + "eu": "https://otlp.eu01.nr-data.net", + } +) + +_DEFAULT_REGION: Final = "us" + + +class _NewRelicSettings(BaseSettings): + model_config = SettingsConfigDict(case_sensitive=False, extra="ignore") + + # The same env vars the agent-based integration documents; the key is the + # operator-level fallback for traffic without team credentials, the region + # picks that fallback's data center, and the record-content flag keeps its + # documented meaning when the OTel path replaces the agent. + license_key: str | None = Field(default=None, validation_alias="NEW_RELIC_LICENSE_KEY") + region: str | None = Field(default=None, validation_alias="NEW_RELIC_REGION") + record_content: bool | None = Field(default=None, validation_alias="NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED") + + +def newrelic_preset( + *, + config_overrides: OpenTelemetryV2Config | None = None, +) -> OpenTelemetryV2Config: + settings: Final = _NewRelicSettings() + base: Final = config_overrides or OpenTelemetryV2Config() + endpoint: Final = NEWRELIC_OTLP_ENDPOINT_BY_REGION.get( + (settings.region or _DEFAULT_REGION).lower(), NEWRELIC_OTLP_ENDPOINT_BY_REGION[_DEFAULT_REGION] + ) + return base.model_copy( + update={ + "exporters": [ + *base.exporters, + ExporterSpec( + kind="otlp_http", + endpoint=endpoint, + headers=(f"api-key={settings.license_key}" if settings.license_key else None), + owner=ExporterOwner.NEWRELIC, + requires_headers=True, + ), + ], + # New Relic ingests the OTLP GenAI semantic conventions natively. + "mapper_names": ensure_mappers(base.mapper_names, "genai"), + **( + {"capture_message_content": ("span_only" if settings.record_content else "no_content")} + if settings.record_content is not None + else {} + ), + } + ) + + +def newrelic_dynamic_headers(params: StandardCallbackDynamicParams) -> dict[str, str]: + """Per-request New Relic OTLP headers from team/key dynamic params.""" + api_key: Final = params.get("newrelic_api_key") + return {header: value for header, value in (("api-key", api_key),) if value} + + +def newrelic_dynamic_endpoint(params: StandardCallbackDynamicParams) -> str: + """Per-request OTLP endpoint for the team's ``newrelic_region``. + + Always the team's own region endpoint, defaulting to US when the team left + the region unset. It never falls through to the preset's endpoint, which + follows the operator's ``NEW_RELIC_REGION`` env; a team that saved only its + ingest key must not inherit the operator's region and have its US-account + spans rejected by an EU-configured default (or vice versa). An unknown + region likewise resolves to the documented US default rather than a guess. + """ + region: Final = params.get("newrelic_region") + default_endpoint: Final = NEWRELIC_OTLP_ENDPOINT_BY_REGION[_DEFAULT_REGION] + if not region: + return default_endpoint + endpoint: Final = NEWRELIC_OTLP_ENDPOINT_BY_REGION.get(region.lower()) + if endpoint is None: + verbose_logger.warning( + "New Relic: unknown newrelic_region %r; supported regions: %s. Using the default (US) endpoint.", + region, + ", ".join(sorted(NEWRELIC_OTLP_ENDPOINT_BY_REGION)), + ) + return default_endpoint + return endpoint diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py index db9610a5a3c..4f7dff952e6 100644 --- a/litellm/integrations/posthog.py +++ b/litellm/integrations/posthog.py @@ -12,7 +12,10 @@ For batching specific details see CustomBatchLogger class import asyncio import atexit import os -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Final + +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm._uuid import uuid @@ -34,6 +37,21 @@ from litellm.types.integrations.posthog import ( from litellm.types.utils import StandardCallbackDynamicParams, StandardLoggingPayload +class PostHogBatchPayload(TypedDict): + api_key: ReadOnly[str] + batch: ReadOnly[Sequence[PostHogEventPayload]] + + +class PostHogLiteLLMParams(TypedDict, total=False): + metadata: ReadOnly[Mapping[str, object]] + + +class PostHogLogKwargs(TypedDict, total=False): + standard_logging_object: ReadOnly[StandardLoggingPayload] + standard_callback_dynamic_params: ReadOnly[StandardCallbackDynamicParams] + litellm_params: ReadOnly[PostHogLiteLLMParams] + + class PostHogLogger(CustomBatchLogger): def __init__(self, **kwargs): """ @@ -137,7 +155,7 @@ class PostHogLogger(CustomBatchLogger): if len(self.log_queue) >= self.batch_size: await self.flush_queue() - def create_posthog_event_payload(self, kwargs: dict[str, Any]) -> PostHogEventPayload: + def create_posthog_event_payload(self, kwargs: PostHogLogKwargs) -> PostHogEventPayload: """ Helper function to create a PostHog event payload for logging @@ -171,11 +189,11 @@ class PostHogLogger(CustomBatchLogger): def _create_posthog_properties( self, standard_logging_object: StandardLoggingPayload, - kwargs: dict[str, Any], + kwargs: PostHogLogKwargs, event_name: str, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Create PostHog properties following LLM Analytics spec""" - properties: Final = {} + properties: Final[dict[str, object]] = {} # Core model information properties["$ai_model"] = self._safe_get(standard_logging_object, "model", "") @@ -211,16 +229,19 @@ class PostHogLogger(CustomBatchLogger): properties["$ai_error"] = error_str # Add trace properties - self._add_trace_properties(properties, kwargs) + self._add_trace_properties(properties, standard_logging_object, kwargs) # Add custom metadata fields self._add_custom_metadata_properties(properties, kwargs) return properties - def _add_trace_properties(self, properties: dict[str, Any], kwargs: dict[str, Any]): - standard_logging_object: Final = self._safe_get(kwargs, "standard_logging_object", {}) - + def _add_trace_properties( + self, + properties: dict[str, object], + standard_logging_object: StandardLoggingPayload, + kwargs: PostHogLogKwargs, + ) -> None: trace_id: Final = self._safe_get(standard_logging_object, "trace_id", self._safe_uuid()) properties["$ai_trace_id"] = trace_id @@ -232,7 +253,7 @@ class PostHogLogger(CustomBatchLogger): if parent_id: properties["$ai_parent_id"] = parent_id - def _add_custom_metadata_properties(self, properties: dict[str, Any], kwargs: dict[str, Any]): + def _add_custom_metadata_properties(self, properties: dict[str, object], kwargs: PostHogLogKwargs) -> None: """Add custom metadata fields to PostHog properties""" metadata: Final = self._extract_metadata(kwargs) if not isinstance(metadata, dict): @@ -277,7 +298,7 @@ class PostHogLogger(CustomBatchLogger): if key not in litellm_internal_fields: properties[key] = value - def _get_distinct_id(self, standard_logging_object: StandardLoggingPayload, kwargs: dict[str, Any]) -> str: + def _get_distinct_id(self, standard_logging_object: StandardLoggingPayload, kwargs: PostHogLogKwargs) -> str: metadata: Final = self._extract_metadata(kwargs) user_id: Final = self._safe_get(metadata, "user_id") if user_id: @@ -291,7 +312,7 @@ class PostHogLogger(CustomBatchLogger): return self._safe_uuid() - def _get_credentials_for_request(self, kwargs: dict[str, Any]) -> tuple[str | None, str | None]: + def _get_credentials_for_request(self, kwargs: PostHogLogKwargs) -> tuple[str | None, str | None]: """ Get PostHog credentials for this request. @@ -334,7 +355,7 @@ class PostHogLogger(CustomBatchLogger): verbose_logger.debug("[POSTHOG MOCK] Mock mode enabled - API calls will be intercepted") # Group events by credentials for batch sending - batches_by_credentials: Final[dict[tuple[str, str], list]] = {} + batches_by_credentials: Final[dict[tuple[str, str], list[PostHogEventPayload]]] = {} for item in self.log_queue: key = (item["api_key"], item["api_url"]) if key not in batches_by_credentials: @@ -380,18 +401,19 @@ class PostHogLogger(CustomBatchLogger): verbose_logger.error("PostHog: Failed to initialize async components: %s", e) raise - def _extract_metadata(self, kwargs: dict[str, Any]) -> dict[str, Any]: - litellm_params: Final = kwargs.get("litellm_params", {}) or {} - return litellm_params.get("metadata", {}) or {} + def _extract_metadata(self, kwargs: PostHogLogKwargs) -> Mapping[str, object]: + litellm_params: Final[PostHogLiteLLMParams] = kwargs.get("litellm_params", {}) or {} + metadata: Final[Mapping[str, object]] = litellm_params.get("metadata", {}) or {} + return metadata def _safe_uuid(self) -> str: return str(uuid.uuid4()) - def _create_posthog_payload(self, events: list, api_key: str) -> dict[str, Any]: + def _create_posthog_payload(self, events: Sequence[PostHogEventPayload], api_key: str) -> PostHogBatchPayload: return {"api_key": api_key, "batch": events} - def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any: - if obj is None or not hasattr(obj, "get"): + def _safe_get(self, obj: Mapping[str, object] | None, key: str, default: object = None) -> object: + if not isinstance(obj, Mapping): return default return obj.get(key, default) @@ -412,7 +434,7 @@ class PostHogLogger(CustomBatchLogger): try: # Group events by credentials (same logic as async_send_batch) - batches_by_credentials: Final[dict[tuple[str, str], list]] = {} + batches_by_credentials: Final[dict[tuple[str, str], list[PostHogEventPayload]]] = {} for item in self.log_queue: key = (item["api_key"], item["api_url"]) if key not in batches_by_credentials: diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 76066f4a305..975a9bd8639 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -8,6 +8,7 @@ import math import os import sys from collections.abc import Awaitable, Callable, Mapping, Sequence +from dataclasses import replace from datetime import datetime, timedelta from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, TypeVar, cast @@ -49,6 +50,7 @@ from litellm.types.integrations.prometheus import * from litellm.types.integrations.prometheus import ( _sanitize_prometheus_label_name, _sanitize_prometheus_label_value, + validate_prometheus_deployment_and_latency_caller_identity, ) from litellm.types.utils import ( StandardLoggingGuardrailInformation, @@ -57,7 +59,10 @@ from litellm.types.utils import ( if TYPE_CHECKING: from apscheduler.schedulers.asyncio import AsyncIOScheduler + from prometheus_client import Gauge from prometheus_client.metrics import MetricWrapperBase + + from litellm.router import Router else: AsyncIOScheduler = Any @@ -66,6 +71,8 @@ _TableRowT: Final = TypeVar("_TableRowT", bound=BaseModel) _DEFAULT_BUDGET_METRICS_PER_REQUEST_TIMEOUT: Final = 5.0 +UNRECOGNIZED_REQUESTED_MODEL_LABEL: Final = "other" + _NON_ENUM_METRIC_LABELS: Final[frozenset[str]] = frozenset( ( "guardrail_name", @@ -96,7 +103,10 @@ class _PaginatedPrismaTable(Protocol[_TableRowT]): def _paginated_table(repository: BaseRepository[_TableRowT]) -> _PaginatedPrismaTable[_TableRowT]: """View a repository's prisma table through the pagination surface budget metrics need.""" - return repository.table + return cast( + _PaginatedPrismaTable[_TableRowT], + repository.table, # cast-ok: prisma rows carry the budget columns the domain model declares + ) class _OrgBudgetRow(Protocol): @@ -150,6 +160,44 @@ def _get_budget_metrics_per_request_timeout() -> float: return parsed +def _get_proxy_llm_router() -> Router | None: + try: + from litellm.proxy.proxy_server import llm_router + except Exception: + return None + return llm_router + + +def _bounded_requested_model_label(requested_model: str | None, router_originated: bool = False) -> str | None: + """ + Bound ``requested_model`` label cardinality: names the router recognizes + (model names, deployment ids, aliases, routing groups, team public model + names) or matches via a global or team wildcard/pattern route keep their + own label value; any other client-supplied string collapses into the + single ``other`` bucket. With no proxy router to vouch for the string, + client-supplied values collapse to ``other`` while ``router_originated`` + values (emitted by an SDK ``Router``'s own deployment failure and + fallback events, where the proxy router never exists) pass through. + """ + if not requested_model: + return requested_model + llm_router: Final = _get_proxy_llm_router() + if llm_router is None: + return requested_model if router_originated else UNRECOGNIZED_REQUESTED_MODEL_LABEL + if llm_router.is_recognized_model(requested_model): + return requested_model + if requested_model in llm_router.team_public_model_names: + return requested_model + if llm_router.pattern_router.route(requested_model) is not None: + return requested_model + if any( + team_pattern_router.route(requested_model) is not None + for team_pattern_router in llm_router.team_pattern_routers.values() + ): + return requested_model + return UNRECOGNIZED_REQUESTED_MODEL_LABEL + + class PrometheusLogger(CustomLogger): # Class variables or attributes @@ -172,6 +220,11 @@ class PrometheusLogger(CustomLogger): try: from prometheus_client import Counter, Gauge, Histogram + # Validate the caller-identity mode before any collector registers so an + # invalid value cannot leave partially-registered metrics behind in the + # process-global registry. + validate_prometheus_deployment_and_latency_caller_identity() + # Always initialize label_filters, even for non-premium users self.label_filters = self._parse_prometheus_config() @@ -215,7 +268,9 @@ class PrometheusLogger(CustomLogger): # request latency metrics self.litellm_request_total_latency_metric = self._histogram_factory( "litellm_request_total_latency_metric", - "Total latency (seconds) for a request to LiteLLM", + "End-to-end latency (seconds) for a request to LiteLLM Proxy Server, from the moment " + "the request reached the proxy through the end of processing -- includes " + "authentication, pre-call hooks, the LLM API call, and post-call processing", labelnames=self.get_labels_for_metric("litellm_request_total_latency_metric"), buckets=self.latency_buckets, ) @@ -423,6 +478,30 @@ class PrometheusLogger(CustomLogger): labelnames=self.get_labels_for_metric("litellm_remaining_api_key_tokens_for_model"), ) + self.litellm_api_key_rate_limit_allowed_metric = self._gauge_factory( + "litellm_api_key_rate_limit_allowed_metric", + "Configured rate limit for the API Key in the current window (rpm_limit / tpm_limit), by rate_limit_type", + labelnames=self.get_labels_for_metric("litellm_api_key_rate_limit_allowed_metric"), + ) + + self.litellm_api_key_rate_limit_used_metric = self._gauge_factory( + "litellm_api_key_rate_limit_used_metric", + "Requests or tokens the API Key has consumed in the current rate limit window, by rate_limit_type", + labelnames=self.get_labels_for_metric("litellm_api_key_rate_limit_used_metric"), + ) + + self.litellm_team_rate_limit_allowed_metric = self._gauge_factory( + "litellm_team_rate_limit_allowed_metric", + "Configured rate limit for the Team in the current window (team rpm_limit / tpm_limit), by rate_limit_type", + labelnames=self.get_labels_for_metric("litellm_team_rate_limit_allowed_metric"), + ) + + self.litellm_team_rate_limit_used_metric = self._gauge_factory( + "litellm_team_rate_limit_used_metric", + "Requests or tokens the Team has consumed in the current rate limit window, by rate_limit_type", + labelnames=self.get_labels_for_metric("litellm_team_rate_limit_used_metric"), + ) + ######################################## # LLM API Deployment Metrics / analytics ######################################## @@ -458,7 +537,8 @@ class PrometheusLogger(CustomLogger): # Request queue time metric self.litellm_request_queue_time_metric = self._histogram_factory( "litellm_request_queue_time_seconds", - "Time spent in request queue before processing starts (seconds)", + "Time (seconds) from request arrival at the proxy to the start of pre-call " + "processing -- includes authentication and any ASGI-level queueing", labelnames=self.get_labels_for_metric("litellm_request_queue_time_seconds"), buckets=self.latency_buckets, ) @@ -1421,6 +1501,11 @@ class PrometheusLogger(CustomLogger): model_id=enum_values.model_id, ) + self._set_key_and_team_rate_limit_metrics( + standard_logging_payload=standard_logging_payload, # pyright: ignore[reportArgumentType] # isinstance(dict) above narrows the TypedDict to dict[Unknown, Unknown] + enum_values=enum_values, + ) + # set latency metrics self._set_latency_metrics( kwargs=kwargs, @@ -1948,17 +2033,102 @@ class PrometheusLogger(CustomLogger): """ if standard_logging_payload is None: return None + return PrometheusLogger._get_int_from_v3_rate_limit_headers( + standard_logging_payload=standard_logging_payload, + header_name=f"x-ratelimit-model_per_key-remaining-{rate_limit_type}", + ) + + @staticmethod + def _get_int_from_v3_rate_limit_headers( + standard_logging_payload: StandardLoggingPayload, + header_name: str, + ) -> int | None: hidden_params: Final = standard_logging_payload.get("hidden_params") if hidden_params is None: return None - additional_headers: Final = hidden_params.get("additional_headers") + additional_headers: Final[Mapping[str, object] | None] = hidden_params.get("additional_headers") if additional_headers is None: return None - value: Final = dict(additional_headers).get(f"x-ratelimit-model_per_key-remaining-{rate_limit_type}") + value: Final = additional_headers.get(header_name) if isinstance(value, bool) or not isinstance(value, int): return None return value + def _set_key_and_team_rate_limit_metrics( + self, + standard_logging_payload: StandardLoggingPayload, + enum_values: UserAPIKeyLabelValues, + ) -> None: + """ + Export the key-level and team-level RPM / TPM limit and current window + usage from the ``x-ratelimit-{api_key,team}-{limit,remaining}-*`` + headers the v3 rate limiter mirrors into the logging payload. The + limiter already read these counters (from Redis when configured) on + the request path, so no extra store lookup happens here. Descriptors + without a configured limit emit no header, so their series is removed + rather than left at the value from before the limit was dropped. + """ + descriptor_gauges: Final[ + tuple[tuple[Literal["api_key", "team"], DEFINED_PROMETHEUS_METRICS, Gauge, Gauge], ...] + ] = ( + ( + "api_key", + "litellm_api_key_rate_limit_allowed_metric", + self.litellm_api_key_rate_limit_allowed_metric, + self.litellm_api_key_rate_limit_used_metric, + ), + ( + "team", + "litellm_team_rate_limit_allowed_metric", + self.litellm_team_rate_limit_allowed_metric, + self.litellm_team_rate_limit_used_metric, + ), + ) + for descriptor_key, metric_name, allowed_gauge, used_gauge in descriptor_gauges: + for rate_limit_type in ("requests", "tokens"): + self._set_rate_limit_allowed_and_used_gauges( + standard_logging_payload=standard_logging_payload, + enum_values=enum_values, + descriptor_key=descriptor_key, + metric_name=metric_name, + allowed_gauge=allowed_gauge, + used_gauge=used_gauge, + rate_limit_type=rate_limit_type, + ) + + def _set_rate_limit_allowed_and_used_gauges( + self, + standard_logging_payload: StandardLoggingPayload, + enum_values: UserAPIKeyLabelValues, + descriptor_key: Literal["api_key", "team"], + metric_name: DEFINED_PROMETHEUS_METRICS, + allowed_gauge: Gauge, + used_gauge: Gauge, + rate_limit_type: Literal["requests", "tokens"], + ) -> None: + limit: Final = self._get_int_from_v3_rate_limit_headers( + standard_logging_payload=standard_logging_payload, + header_name=f"x-ratelimit-{descriptor_key}-limit-{rate_limit_type}", + ) + remaining: Final = self._get_int_from_v3_rate_limit_headers( + standard_logging_payload=standard_logging_payload, + header_name=f"x-ratelimit-{descriptor_key}-remaining-{rate_limit_type}", + ) + labelled_values: Final = replace(enum_values, rate_limit_type=rate_limit_type) + labelnames: Final = self.get_labels_for_metric(metric_name) + labels: Final = prometheus_label_factory( + supported_enum_labels=labelnames, + enum_values=labelled_values, + label_context=PrometheusLabelFactoryContext(labelled_values), + ) + if limit is None or remaining is None: + label_values: Final = tuple(labels.get(label) for label in labelnames) + self._bounded_prometheus_series_tracker.remove_series(allowed_gauge, label_values) + self._bounded_prometheus_series_tracker.remove_series(used_gauge, label_values) + return + allowed_gauge.labels(**labels).set(limit) + used_gauge.labels(**labels).set(limit - remaining) + def _set_virtual_key_rate_limit_metrics( self, user_api_key: str | None, @@ -2078,27 +2248,37 @@ class PrometheusLogger(CustomLogger): _labels, ) - # total request latency + # request queue time (time from arrival to processing start) -- read first so + # it can be folded into the total-latency metric below. start_time/end_time + # only span from after auth completes, so without this the "total" latency + # metric silently excludes auth and pre-call hook time. + _litellm_params: Final = kwargs.get("litellm_params", {}) or {} + queue_time_seconds: Final = (_litellm_params.get("metadata") or {}).get("queue_time_seconds") + + # total request latency: true end-to-end, from request arrival (queue_time_seconds, + # when available) through the end of processing. total_time_seconds: Final = self._safe_duration_seconds( start_time=start_time, end_time=end_time, ) if total_time_seconds is not None: + _observed_total_time_seconds: Final = ( + total_time_seconds + queue_time_seconds + if queue_time_seconds is not None and queue_time_seconds >= 0 + else total_time_seconds + ) _labels = prometheus_label_factory( supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_request_total_latency_metric"), enum_values=enum_values, label_context=label_context, ) - self.litellm_request_total_latency_metric.labels(**_labels).observe(total_time_seconds) + self.litellm_request_total_latency_metric.labels(**_labels).observe(_observed_total_time_seconds) self._track_end_user_metric_series( self.litellm_request_total_latency_metric, "litellm_request_total_latency_metric", _labels, ) - # request queue time (time from arrival to processing start) - _litellm_params: Final = kwargs.get("litellm_params", {}) or {} - queue_time_seconds: Final = (_litellm_params.get("metadata") or {}).get("queue_time_seconds") if queue_time_seconds is not None and queue_time_seconds >= 0: _labels = prometheus_label_factory( supported_enum_labels=self.get_labels_for_metric(metric_name="litellm_request_queue_time_seconds"), @@ -2385,7 +2565,7 @@ class PrometheusLogger(CustomLogger): team_alias=user_api_key_dict.team_alias, org_id=user_api_key_dict.org_id, org_alias=user_api_key_dict.organization_alias, - requested_model=request_data.get("model", ""), + requested_model=_bounded_requested_model_label(request_data.get("model", "")), status_code=str(status_code), exception_status=str(status_code), exception_class=self._get_exception_class_name(original_exception), @@ -2449,6 +2629,7 @@ class PrometheusLogger(CustomLogger): else: _metadata = { "user_api_key_alias": getattr(_metadata_raw, "user_api_key_alias", None), + "user_api_key_user_email": getattr(_metadata_raw, "user_api_key_user_email", None), "user_api_key_team_id": getattr(_metadata_raw, "user_api_key_team_id", None), "user_api_key_team_alias": getattr(_metadata_raw, "user_api_key_team_alias", None), "user_api_key_hash": getattr(_metadata_raw, "user_api_key_hash", None), @@ -2471,6 +2652,17 @@ class PrometheusLogger(CustomLogger): return getattr(user_api_key_auth, "key_alias", None) return None + def _get_user_email() -> str | None: + from_metadata: Final = _metadata.get("user_api_key_user_email") + if from_metadata is not None: + return from_metadata + from_params: Final = _litellm_params_metadata.get("user_api_key_user_email") + if from_params is not None: + return from_params + if user_api_key_auth is not None: + return self._safe_get(user_api_key_auth, "user_email") + return None + def _get_team_id() -> str | None: val = _metadata.get("user_api_key_team_id") if val is not None: @@ -2506,6 +2698,7 @@ class PrometheusLogger(CustomLogger): return { "api_key_alias": _get_api_key_alias(), + "user_email": _get_user_email(), "team": _get_team_id(), "team_alias": _get_team_alias(), "hashed_api_key": _get_hashed_api_key(), @@ -2563,6 +2756,7 @@ class PrometheusLogger(CustomLogger): _metadata: Final = standard_logging_payload.get("metadata", {}) or {} hashed_api_key: Final = fallback_values.get("hashed_api_key") or _metadata.get("user_api_key_hash") api_key_alias: Final = fallback_values.get("api_key_alias") or _metadata.get("user_api_key_alias") + user_email: Final = fallback_values.get("user_email") team: Final = fallback_values.get("team") or _metadata.get("user_api_key_team_id") team_alias: Final = fallback_values.get("team_alias") or _metadata.get("user_api_key_team_alias") client_ip: Final = fallback_values.get("client_ip") or _metadata.get("requester_ip_address") @@ -2591,7 +2785,9 @@ class PrometheusLogger(CustomLogger): label_model_id = "" label_api_base = "" label_api_provider = "" - label_requested_model = litellm_model_name or model_group or "" + label_requested_model = ( + _bounded_requested_model_label(litellm_model_name or model_group, router_originated=True) or "" + ) enum_values: Final = UserAPIKeyLabelValues( litellm_model_name=label_litellm_model_name, @@ -2603,6 +2799,7 @@ class PrometheusLogger(CustomLogger): requested_model=label_requested_model, hashed_api_key=hashed_api_key, api_key_alias=api_key_alias, + user_email=user_email, team=team, team_alias=team_alias, tags=standard_logging_payload.get("request_tags", []), @@ -3149,7 +3346,7 @@ class PrometheusLogger(CustomLogger): _tags: Final = cast(list[str], kwargs.get("tags") or []) enum_values: Final = UserAPIKeyLabelValues( - requested_model=original_model_group, + requested_model=_bounded_requested_model_label(original_model_group, router_originated=True), fallback_model=_new_model, hashed_api_key=standard_metadata["user_api_key_hash"], api_key_alias=standard_metadata["user_api_key_alias"], @@ -3190,7 +3387,7 @@ class PrometheusLogger(CustomLogger): ) enum_values: Final = UserAPIKeyLabelValues( - requested_model=original_model_group, + requested_model=_bounded_requested_model_label(original_model_group, router_originated=True), fallback_model=_new_model, hashed_api_key=standard_metadata["user_api_key_hash"], api_key_alias=standard_metadata["user_api_key_alias"], @@ -3539,7 +3736,9 @@ class PrometheusLogger(CustomLogger): except Exception as e: verbose_logger.exception("Error initializing user/team count metrics: %s", e) - async def _set_key_list_budget_metrics(self, keys: list[str | UserAPIKeyAuth | LiteLLM_DeletedVerificationToken]): + async def _set_key_list_budget_metrics( + self, keys: list[str | UserAPIKeyAuth | LiteLLM_DeletedVerificationToken] + ) -> None: """Helper function to set budget metrics for a list of keys""" for key in keys: if isinstance(key, UserAPIKeyAuth): diff --git a/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py b/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py index c54790b8ae7..c1ccf09d5d6 100644 --- a/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py +++ b/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py @@ -60,6 +60,10 @@ class BoundedPrometheusSeriesTracker: break del series[tracked_label_values] + def remove_series(self, metric: object, label_values: tuple[str | None, ...]) -> bool: + """Drop one child series, True when it is gone (removed or never existed).""" + return self._remove_metric_child(metric, label_values) + def _should_run_ttl_cleanup( self, metric_name: str, diff --git a/litellm/integrations/prometheus_helpers/prometheus_api.py b/litellm/integrations/prometheus_helpers/prometheus_api.py index 9f77f87a670..e111474bd4d 100644 --- a/litellm/integrations/prometheus_helpers/prometheus_api.py +++ b/litellm/integrations/prometheus_helpers/prometheus_api.py @@ -7,6 +7,9 @@ import time from datetime import datetime, timedelta from typing import Final +from pydantic import BaseModel, TypeAdapter +from typing_extensions import ReadOnly, TypedDict + from litellm import get_secret from litellm._logging import verbose_logger from litellm.llms.custom_httpx.http_handler import ( @@ -18,10 +21,32 @@ PROMETHEUS_URL: Final[str | None] = get_secret("PROMETHEUS_URL") PROMETHEUS_SELECTED_INSTANCE: Final[str | None] = get_secret("PROMETHEUS_SELECTED_INSTANCE") async_http_handler: Final = get_async_httpx_client(llm_provider=httpxSpecialProvider.LoggingCallback) +_RAW_JSON_PAYLOAD: Final = TypeAdapter(object) + + +class PrometheusRangeSample(BaseModel): + """One ``matrix`` series of the Prometheus HTTP query API.""" + + metric: dict[str, object] + values: list[tuple[float, str]] + + +class PrometheusQueryData(BaseModel): + result: list[PrometheusRangeSample] + + +class PrometheusQueryResponse(BaseModel): + data: PrometheusQueryData + + +class PrometheusDailySpend(TypedDict): + date: ReadOnly[str] + spend: ReadOnly[float] + async def get_metric_from_prometheus( metric_name: str, -): +) -> list[PrometheusRangeSample]: # Get the start of the current day in Unix timestamp if PROMETHEUS_URL is None: raise ValueError("PROMETHEUS_URL not set please set 'PROMETHEUS_URL=<>' in .env") @@ -31,13 +56,13 @@ async def get_metric_from_prometheus( response: Final = await async_http_handler.get( f"{PROMETHEUS_URL}/api/v1/query", params={"query": query, "time": now} ) # End of the day - _json_response: Final = response.json() + _json_response: Final = _RAW_JSON_PAYLOAD.validate_python(response.json()) verbose_logger.debug("json response from prometheus /query api %s", _json_response) - results: Final = response.json()["data"]["result"] + results: Final = PrometheusQueryResponse.model_validate(_json_response).data.result return results -async def get_fallback_metric_from_prometheus(): +async def get_fallback_metric_from_prometheus() -> str: """ Gets fallback metrics from prometheus for the last 24 hours """ @@ -55,17 +80,17 @@ async def get_fallback_metric_from_prometheus(): verbose_logger.debug("response json %s", response_json) for result in response_json: verbose_logger.debug("result= %s", result) - metric = result["metric"] - metric_values = result["values"] + metric_labels = result.metric + metric_values = result.values most_recent_value = metric_values[0] if PROMETHEUS_SELECTED_INSTANCE is not None: - if metric.get("instance") != PROMETHEUS_SELECTED_INSTANCE: + if metric_labels.get("instance") != PROMETHEUS_SELECTED_INSTANCE: continue value = int(float(most_recent_value[1])) # Convert value to integer - primary_model = metric.get("primary_model", "Unknown") - fallback_model = metric.get("fallback_model", "Unknown") + primary_model = metric_labels.get("primary_model", "Unknown") + fallback_model = metric_labels.get("fallback_model", "Unknown") response_message += f"`{value} successful fallback requests` with primary model=`{primary_model}` -> fallback model=`{fallback_model}`" response_message += "\n" verbose_logger.debug("response message %s", response_message) @@ -96,7 +121,7 @@ def _quote_promql_string_literal(value: str) -> str: return json.dumps(value, ensure_ascii=False) -async def get_daily_spend_from_prometheus(api_key: str | None): +async def get_daily_spend_from_prometheus(api_key: str | None) -> list[PrometheusDailySpend]: """ Expected Response Format: [ @@ -133,17 +158,16 @@ async def get_daily_spend_from_prometheus(api_key: str | None): } response: Final = await async_http_handler.get(url, params=params) - _json_response: Final = response.json() + _json_response: Final = _RAW_JSON_PAYLOAD.validate_python(response.json()) verbose_logger.debug("json response from prometheus /query api %s", _json_response) - results: Final = response.json()["data"]["result"] - formatted_results: Final = [] - - for result in results: - metric_data = result["values"] - for timestamp, value in metric_data: - # Convert timestamp to ISO 8601 string with UTC offset - date = datetime.fromtimestamp(float(timestamp)).isoformat() + "+00:00" - spend = float(value) - formatted_results.append({"date": date, "spend": spend}) + results: Final = PrometheusQueryResponse.model_validate(_json_response).data.result + formatted_results: Final[list[PrometheusDailySpend]] = [ + { + "date": datetime.fromtimestamp(float(timestamp)).isoformat() + "+00:00", + "spend": float(value), + } + for result in results + for timestamp, value in result.values + ] return formatted_results diff --git a/litellm/integrations/prompt_management_base.py b/litellm/integrations/prompt_management_base.py index 81c01599e77..3c6b5284041 100644 --- a/litellm/integrations/prompt_management_base.py +++ b/litellm/integrations/prompt_management_base.py @@ -19,6 +19,19 @@ class PromptManagementClient(TypedDict): completed_messages: list[AllMessageValues] | None +def resolve_prompt_manager_ignore_flags( + prompt_spec: PromptSpec | None, + ignore_prompt_manager_model: bool | None, + ignore_prompt_manager_optional_params: bool | None, +) -> tuple[bool, bool]: + spec_params: Final = prompt_spec.litellm_params if prompt_spec is not None else None + return ( + bool(ignore_prompt_manager_model) or bool(spec_params is not None and spec_params.ignore_prompt_manager_model), + bool(ignore_prompt_manager_optional_params) + or bool(spec_params is not None and spec_params.ignore_prompt_manager_optional_params), + ) + + class PromptManagementBase(ABC): @property @abstractmethod @@ -182,13 +195,18 @@ class PromptManagementBase(ABC): prompt_version=prompt_version, ) + resolved_ignore_model, resolved_ignore_optional_params = resolve_prompt_manager_ignore_flags( + prompt_spec=prompt_spec, + ignore_prompt_manager_model=ignore_prompt_manager_model, + ignore_prompt_manager_optional_params=ignore_prompt_manager_optional_params, + ) return self.post_compile_prompt_processing( prompt_template=prompt_template, messages=messages, non_default_params=non_default_params, model=model, - ignore_prompt_manager_model=ignore_prompt_manager_model, - ignore_prompt_manager_optional_params=ignore_prompt_manager_optional_params, + ignore_prompt_manager_model=resolved_ignore_model, + ignore_prompt_manager_optional_params=resolved_ignore_optional_params, ) async def async_get_chat_completion_prompt( @@ -224,11 +242,16 @@ class PromptManagementBase(ABC): prompt_version=prompt_version, ) + resolved_ignore_model, resolved_ignore_optional_params = resolve_prompt_manager_ignore_flags( + prompt_spec=prompt_spec, + ignore_prompt_manager_model=ignore_prompt_manager_model, + ignore_prompt_manager_optional_params=ignore_prompt_manager_optional_params, + ) return self.post_compile_prompt_processing( prompt_template=prompt_template, messages=messages, non_default_params=non_default_params, model=model, - ignore_prompt_manager_model=ignore_prompt_manager_model, - ignore_prompt_manager_optional_params=ignore_prompt_manager_optional_params, + ignore_prompt_manager_model=resolved_ignore_model, + ignore_prompt_manager_optional_params=resolved_ignore_optional_params, ) diff --git a/litellm/integrations/rubrik.py b/litellm/integrations/rubrik.py index a474a11601d..c9e511905a6 100644 --- a/litellm/integrations/rubrik.py +++ b/litellm/integrations/rubrik.py @@ -8,11 +8,12 @@ import uuid from collections import Counter from collections.abc import Awaitable, Mapping, Sequence from dataclasses import dataclass +from datetime import datetime from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict +from typing import TYPE_CHECKING, Final, Literal, Optional, Protocol, TypedDict, overload import httpx -from typing_extensions import Never, ReadOnly +from typing_extensions import Never, ReadOnly, Required from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger @@ -30,6 +31,7 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolCallChunk from litellm.types.utils import ( ChatCompletionMessageToolCall, Function, @@ -52,17 +54,102 @@ _DROP_WARNING_INTERVAL_SECONDS: Final = 60.0 _EMPTY_MAPPING: Final[Mapping[str, Never]] = MappingProxyType({}) -class _ServiceToolCall(TypedDict): - id: ReadOnly[str] +class _ModerationToolCall(TypedDict, total=False): + id: ReadOnly[Required[str]] -class _ServiceMessage(TypedDict, total=False): +class _ModerationMessage(TypedDict, total=False): + content: ReadOnly[str | None] + tool_calls: ReadOnly[Sequence[_ModerationToolCall] | None] + + +class _ModerationChoice(TypedDict, total=False): + message: ReadOnly[_ModerationMessage | None] + + +class _ModerationResponse(TypedDict, total=False): + choices: ReadOnly[Sequence[_ModerationChoice]] + + +class _LogEventKwargs(TypedDict, total=False): + standard_logging_object: ReadOnly[Required[StandardLoggingPayload]] + litellm_call_id: ReadOnly[str] + + +class _HasCallId(Protocol): + def get(self, key: Literal["litellm_call_id"], /) -> str | None: ... + + +class _HasModelAttr(Protocol): + model: str | None + + +class _ResponseSource(Protocol): + def get(self, key: Literal["response"], /) -> "_HasModelAttr | None": ... + + +class _ModelSource(Protocol): + def get(self, key: Literal["model"], default: str, /) -> str: ... + + +class _FallbackSource(Protocol): + @overload + def get(self, key: Literal["start_time"], /) -> datetime | None: ... + @overload + def get(self, key: str, /) -> object | None: ... + + +class _RequestContextSource(Protocol): + @overload + def get(self, key: Literal["optional_params"], /) -> Mapping[str, object] | None: ... + @overload + def get(self, key: str, /) -> object | None: ... + def __contains__(self, key: object, /) -> bool: ... + def __getitem__(self, key: str, /) -> object: ... + + +class _ToolCallLike(Protocol): + id: str | None + type: str | None + function: Function + + +class _ModerationSourceToolCall(TypedDict, total=False): + function: ReadOnly[Mapping[str, object] | None] + + +class _ModerationSourceMessage(TypedDict, total=False): + role: ReadOnly[str] + function_call: ReadOnly[Mapping[str, object] | None] + tool_calls: ReadOnly[Sequence[_ModerationSourceToolCall | None] | None] + + +class _FlattenedModerationMessage(TypedDict): + role: ReadOnly[str | None] content: ReadOnly[str] - tool_calls: ReadOnly[Sequence[_ServiceToolCall]] -class _ServiceChoice(TypedDict, total=False): - message: ReadOnly[_ServiceMessage] +class _CorrelatablePayload(TypedDict): + id: str # writable-ok: _apply_correlation_id overwrites the provider id on a deep-copied payload + + +class _SystemPromptCarrier(TypedDict, total=False): + messages: object # writable-ok: _prepend_system_prompt rebinds messages on the copied payload by design + + +class _BlockFailurePayload(TypedDict, total=False): + id: object # writable-ok: correlation id is pinned after copying the base payload + model: ReadOnly[object] + model_group: ReadOnly[object] + model_id: ReadOnly[str] + model_parameters: ReadOnly[object] + startTime: ReadOnly[float | None] + endTime: ReadOnly[float | None] + completionStartTime: ReadOnly[float | None] + messages: object # writable-ok: passed to _prepend_system_prompt, which rebinds messages + metadata: ReadOnly[StandardLoggingUserAPIKeyMetadata] + response: str # writable-ok: block failure text replaces the copied response + status: ReadOnly[str] class _MalformedToolBlockingResponseError(Exception): @@ -385,7 +472,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): @staticmethod def _stash_block_context( logging_obj: Optional["LiteLLMLoggingObj"], - request_data: dict, + request_data: dict[str, object], ) -> None: """Stash signals so the deferred success-event skips this request and ``async_post_call_failure_hook`` can build the failure payload. @@ -414,12 +501,16 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): request_data["_rubrik_logging_obj"] = logging_obj @staticmethod - def _normalize_tool_calls(tool_calls: Sequence[object]) -> tuple[ChatCompletionMessageToolCall, ...]: + def _normalize_tool_calls( + tool_calls: Sequence[ChatCompletionToolCallChunk | ChatCompletionMessageToolCall | _ToolCallLike], + ) -> tuple[ChatCompletionMessageToolCall, ...]: """Convert tool_calls from inputs to ChatCompletionMessageToolCall objects.""" return tuple(RubrikLogger._normalize_tool_call(tc) for tc in tool_calls) @staticmethod - def _normalize_tool_call(tc: Any) -> ChatCompletionMessageToolCall: + def _normalize_tool_call( + tc: ChatCompletionToolCallChunk | ChatCompletionMessageToolCall | _ToolCallLike, + ) -> ChatCompletionMessageToolCall: if isinstance(tc, ChatCompletionMessageToolCall): return tc if isinstance(tc, dict): @@ -460,12 +551,15 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): ``content`` is sent so the webhook can moderate the response text; ``None`` when the assistant produced no text (tool-call-only response). """ - message: Final[dict[str, object]] = { + message: Final[Mapping[str, object]] = { "role": "assistant", "content": content or None, + **( + {"tool_calls": tuple(tc.model_dump(exclude_none=True) for tc in tool_calls)} + if tool_calls + else _EMPTY_MAPPING + ), } - if tool_calls: - message["tool_calls"] = tuple(tc.model_dump(exclude_none=True) for tc in tool_calls) return { "id": request_id or f"chatcmpl-{uuid.uuid4()}", "object": "chat.completion", @@ -481,7 +575,9 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): } @staticmethod - def _flatten_messages_for_moderation(messages: Sequence[object] | None) -> tuple[Mapping[str, Any], ...]: + def _flatten_messages_for_moderation( + messages: Sequence[AllMessageValues | None] | None, + ) -> tuple[_FlattenedModerationMessage, ...]: """Collapse each message's content to a plain string for the webhook. litellm normalizes Anthropic ``/v1/messages`` requests to OpenAI shape, @@ -502,7 +598,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): ) @staticmethod - def _moderation_text_parts(message: Mapping[str, Any]) -> tuple[str, ...]: + def _moderation_text_parts(message: _ModerationSourceMessage) -> tuple[str, ...]: """Every attacker-controlled text segment of a message: its content plus the arguments of any tool call or deprecated function call.""" fc: Final = message.get("function_call") @@ -530,16 +626,8 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): ``/v1/messages`` requests too. Optional fields are sent only when present so the payload stays clean. """ - payload: Final[dict[str, object]] = { - "model": inputs.get("model") or request_data.get("model") or "", - "messages": RubrikLogger._flatten_messages_for_moderation(inputs.get("structured_messages")), - } tools: Final = inputs.get("tools") - if tools is not None: - payload["tools"] = tools user: Final = request_data.get("user") - if user: - payload["user"] = user # Fall back to litellm_call_id, the stable cross-provider join key the # response/tool path uses (see _correlation_id). LiteLLM does not # populate request_data["correlation_key"]; it carries litellm_call_id. @@ -547,14 +635,18 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): # when correlation_key is empty, so without this the block fires but no # log is ever written. An explicit correlation_key still wins. correlation_key: Final = request_data.get("correlation_key") or request_data.get("litellm_call_id") - if correlation_key: - payload["correlation_key"] = correlation_key - return payload + return { + "model": inputs.get("model") or request_data.get("model") or "", + "messages": RubrikLogger._flatten_messages_for_moderation(inputs.get("structured_messages")), + **({"tools": tools} if tools is not None else _EMPTY_MAPPING), + **({"user": user} if user else _EMPTY_MAPPING), + **({"correlation_key": correlation_key} if correlation_key else _EMPTY_MAPPING), + } @staticmethod def _extract_request_data( - call_details: Mapping[str, Any], - request_data: Mapping[str, object] | None, + call_details: _RequestContextSource, + request_data: _RequestContextSource | None, ) -> Mapping[str, object]: """Extract original request data from model_call_details for the response moderation service envelope. @@ -590,7 +682,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): } @staticmethod - def _sanitize_proxy_server_request(proxy_server_request: object) -> object: + def _sanitize_proxy_server_request(proxy_server_request: Mapping[str, object] | str | None) -> object: """Allowlist only routing fields (``url``, ``method``) when forwarding ``proxy_server_request`` to an external webhook, dropping inbound ``headers`` (Authorization, Cookie, x-api-key, ...) and the raw @@ -600,18 +692,19 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): return {key: proxy_server_request[key] for key in ("url", "method") if key in proxy_server_request} @staticmethod - def _resolve_model(request_data: Mapping[str, object], call_details: Mapping[str, str]) -> str: + def _resolve_model(request_data: _ResponseSource, call_details: _ModelSource) -> str: """Get the model name for the ModifyResponseException.""" response: Final = request_data.get("response") if response and hasattr(response, "model"): - response_model: Final[str | None] = getattr(response, "model", None) - return response_model or "unknown" + return response.model or "unknown" return call_details.get("model", "unknown") # -- Logging hooks --------------------------------------------------------- @staticmethod - def _correlation_id(call_details: Mapping[str, str], request_data: Mapping[str, str] | None = None) -> str | None: + def _correlation_id( + call_details: _HasCallId | _LogEventKwargs, request_data: _HasCallId | None = None + ) -> str | None: """The id that joins a blocked request's two S3 logs by filename: the moderation (``_blocking``) log and the failure (response) log. @@ -625,7 +718,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): return call_details.get("litellm_call_id") or (request_data or _EMPTY_MAPPING).get("litellm_call_id") @classmethod - def _apply_correlation_id(cls, payload: dict[str, object], source: Mapping[str, str]) -> None: + def _apply_correlation_id(cls, payload: _CorrelatablePayload, source: _HasCallId | _LogEventKwargs) -> None: """Pin ``payload["id"]`` to ``litellm_call_id`` in place so this log shares its S3 filename id with the moderation (``_blocking``) and failure logs for the same request -- for every provider. @@ -645,7 +738,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): payload["id"] = correlated @staticmethod - def _prepend_system_prompt(payload: dict[str, object], source: Mapping[str, object]) -> None: + def _prepend_system_prompt(payload: _SystemPromptCarrier, source: Mapping[str, object]) -> None: """Prepend ``source["system"]`` onto ``payload["messages"]``. Builds a NEW messages list rather than mutating ``payload["messages"]`` @@ -673,9 +766,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): exc_info=True, ) - async def _prepare_log_payload( - self, kwargs: Mapping[str, object], event_type: str - ) -> StandardLoggingPayload | None: + async def _prepare_log_payload(self, kwargs: _LogEventKwargs, event_type: str) -> StandardLoggingPayload | None: """Shared logic for success logging (sampled).""" if random.random() > self.sampling_rate: verbose_logger.debug("Skipping Rubrik %s logging (sampling_rate=%s)", event_type, self.sampling_rate) @@ -684,12 +775,12 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): # Deep-copy so mutations don't affect other callbacks sharing this object standard_logging_payload: Final[StandardLoggingPayload] = safe_deep_copy(kwargs["standard_logging_object"]) - self._apply_correlation_id(standard_logging_payload, kwargs) # pyright: ignore[reportArgumentType] # StandardLoggingPayload is dict[str,Any] at runtime + self._apply_correlation_id(standard_logging_payload, kwargs) self._prepend_system_prompt(standard_logging_payload, kwargs) # pyright: ignore[reportArgumentType] # StandardLoggingPayload is dict[str,Any] at runtime return standard_logging_payload - async def _append_and_maybe_flush(self, payload) -> None: + async def _append_and_maybe_flush(self, payload: Mapping[str, object]) -> None: self._ensure_periodic_flush_task() self.log_queue.append(payload) self._enforce_max_queue_size() @@ -714,7 +805,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): self._dropped_since_warning = 0 self._last_drop_warning_time = now - async def _enqueue_log_event(self, kwargs: Mapping[str, object], event_type: str): + async def _enqueue_log_event(self, kwargs: _LogEventKwargs, event_type: str): try: payload: Final = await self._prepare_log_payload(kwargs, event_type) if payload is None: @@ -835,7 +926,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): logging_obj: "LiteLLMLoggingObj", exception: "ModifyResponseException", user_api_key_dict: "UserAPIKeyAuth", - ) -> StandardLoggingPayload: + ) -> _BlockFailurePayload: """Build a failure-style payload using the exception text as response. Blocked-tool events are security-relevant and **bypass sampling**: @@ -877,9 +968,9 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): call_details: Final = logging_obj.model_call_details exception_text: Final = f"{type(exception).__name__}: {exception.message}" - base: Final = call_details.get("standard_logging_object") + base: Final[StandardLoggingPayload | None] = call_details.get("standard_logging_object") if base is not None: - payload: dict[str, object] = safe_deep_copy(base) + payload: _BlockFailurePayload = self._copy_block_payload_base(base) else: verbose_logger.debug( "Rubrik: standard_logging_object not yet on model_call_details " @@ -901,6 +992,10 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): return payload + @staticmethod + def _copy_block_payload_base(base: StandardLoggingPayload) -> _BlockFailurePayload: + return safe_deep_copy(base) + @staticmethod def _caller_metadata(user_api_key_dict: "UserAPIKeyAuth") -> StandardLoggingUserAPIKeyMetadata: """Identify the caller whose request was blocked. @@ -923,9 +1018,9 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): @classmethod def _build_fallback_payload( cls, - call_details: Mapping[str, Any], + call_details: _FallbackSource, user_api_key_dict: "UserAPIKeyAuth", - ) -> dict[str, object]: + ) -> _BlockFailurePayload: # Convert datetime to a Unix float so json.dumps can serialize it. # httpx's json= parameter uses stdlib json.dumps with no custom encoder. _raw_start: Final = call_details.get("start_time") @@ -959,7 +1054,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): response: Final = await self.async_httpx_client.post( url=self.logging_endpoint, json=data, - headers=self._headers, + headers=dict(self._headers), ) response.raise_for_status() except httpx.HTTPStatusError as e: @@ -1013,7 +1108,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): # -- Webhook services ------------------------------------------------------ - async def _post_json(self, endpoint: str, payload: Mapping[str, object], service_name: str) -> Mapping[str, Any]: + async def _post_json(self, endpoint: str, payload: Mapping[str, object], service_name: str) -> _ModerationResponse: """POST ``payload`` to a Rubrik webhook and return its dict response. Raises: @@ -1023,11 +1118,11 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): verbose_logger.debug("Sending request to %s: %s", service_name, endpoint) http_response: Final = await self.moderation_client.post( endpoint, - json=payload, - headers=self._headers, + json=dict(payload), + headers=dict(self._headers), ) http_response.raise_for_status() - result: Final[object] = http_response.json() + result: Final[_ModerationResponse | None] = http_response.json() if not isinstance(result, dict): raise TypeError( f"{service_name} returned non-dict JSON " @@ -1040,7 +1135,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): self, response_data: Mapping[str, object], request_data: Mapping[str, object], - ) -> Mapping[str, Any]: + ) -> _ModerationResponse: """Post the ``{request, response}`` envelope to the after_completion webhook and return its (possibly rewritten) response. @@ -1056,7 +1151,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): "Response moderation service", ) - async def _post_to_prompt_moderation_endpoint(self, payload: Mapping[str, object]) -> Mapping[str, Any]: + async def _post_to_prompt_moderation_endpoint(self, payload: Mapping[str, object]) -> _ModerationResponse: """Post a bare OpenAI request to the before_prompt webhook. Returns ``{}`` (passthrough) or a synthetic chat.completion (block). @@ -1064,14 +1159,14 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): return await self._post_json(self.prompt_moderation_endpoint, payload, "Prompt moderation service") @staticmethod - def _extract_prompt_refusal(service_response: Mapping[str, Any]) -> str | None: + def _extract_prompt_refusal(service_response: _ModerationResponse) -> str | None: """Return the refusal text when the prompt was blocked, else None. The before_prompt webhook returns ``{}`` (passthrough) or a synthetic chat.completion whose ``choices[0].message.content`` is the refusal explanation. """ - choices: Final[Sequence[_ServiceChoice] | None] = service_response.get("choices") + choices: Final = service_response.get("choices") if not choices: return None message: Final = choices[0].get("message") or _EMPTY_MAPPING @@ -1080,7 +1175,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): @staticmethod def _extract_response_block( - service_response: Mapping[str, Any], + service_response: _ModerationResponse, all_tool_calls: Sequence[ChatCompletionMessageToolCall], sent_content: str, ) -> BlockedResponseResult | None: @@ -1103,7 +1198,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger): Expects service_response in OpenAI chat completion format: {"choices": [{"message": {"tool_calls": [...], "content": "..."}}]} """ - choices: Final[Sequence[_ServiceChoice]] = service_response.get("choices") or () + choices: Final = service_response.get("choices") or () if not choices: raise _MalformedToolBlockingResponseError("Response moderation service returned empty response") diff --git a/litellm/integrations/s3.py b/litellm/integrations/s3.py index ddeb410c54a..8ce461eea5b 100644 --- a/litellm/integrations/s3.py +++ b/litellm/integrations/s3.py @@ -1,11 +1,18 @@ #### What this does #### # On success + failure, log events to Supabase +import hashlib from datetime import datetime from typing import Final, cast import litellm from litellm._logging import print_verbose, verbose_logger +from litellm.constants import ( + MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES, + MAX_S3_OBJECT_KEY_BYTES, + S3_BOUNDED_OBJECT_KEY_HEAD_BYTES, + S3_PREFIX_DIGEST_CHARS, +) from litellm.types.utils import StandardLoggingPayload @@ -133,9 +140,7 @@ class S3Logger: s3_file_name, ) - s3_object_download_filename: Final = ( - "time-" + start_time.strftime("%Y-%m-%dT%H-%M-%S-%f") + "_" + payload["id"] + ".json" - ) + s3_object_download_filename: Final = get_s3_object_download_filename(start_time, payload["id"]) from litellm.litellm_core_utils.safe_json_dumps import safe_dumps @@ -198,6 +203,47 @@ def resolve_sse_params( return algorithm, valid_key_id +S3_MIN_BOUNDED_FILE_NAME_BYTES: Final = 64 + + +def _truncate_to_utf8_bytes(value: str, max_bytes: int) -> str: + """Trim `value` so its UTF-8 encoding fits `max_bytes`, never splitting a character.""" + if max_bytes <= 0: + return "" + encoded: Final = value.encode("utf-8") + if len(encoded) <= max_bytes: + return value + return encoded[:max_bytes].decode("utf-8", errors="ignore") + + +def get_s3_object_download_filename(start_time: datetime, response_id: str) -> str: + """Content-Disposition filename for the uploaded object, bounded to the metadata header cap.""" + sanitized_response_id: Final = response_id.replace("/", "_").replace('"', "_") + file_name: Final = f"time-{start_time.strftime('%Y-%m-%dT%H-%M-%S-%f')}_{response_id}" + sanitized_file_name: Final = f"time-{start_time.strftime('%Y-%m-%dT%H-%M-%S-%f')}_{sanitized_response_id}" + budget: Final = MAX_S3_OBJECT_DOWNLOAD_FILENAME_BYTES - len(b".json") + if len(sanitized_file_name.encode("utf-8")) <= budget: + return sanitized_file_name + ".json" + return _bounded_s3_file_name(file_name, sanitized_file_name, budget) + ".json" + + +def _bounded_s3_file_name(s3_file_name: str, sanitized_s3_file_name: str, max_bytes: int) -> str: + """As much of the file name as `max_bytes` allows, then the sha256 of the whole name.""" + digest: Final = hashlib.sha256(s3_file_name.encode("utf-8")).hexdigest() + head_budget: Final = min(S3_BOUNDED_OBJECT_KEY_HEAD_BYTES, max_bytes - len(digest) - 1) + head: Final = _truncate_to_utf8_bytes(sanitized_s3_file_name, head_budget) + return f"{head}_{digest}" if head else digest + + +def _bounded_s3_prefix(configured_prefix: str, max_bytes: int) -> str: + """As much of the configured prefix as fits, then a digest segment naming the full prefix.""" + digest_segment: Final = hashlib.sha256(configured_prefix.encode("utf-8")).hexdigest()[:S3_PREFIX_DIGEST_CHARS] + "/" + if max_bytes < len(digest_segment): + return "" + head: Final = _truncate_to_utf8_bytes(configured_prefix, max_bytes - len(digest_segment) - 1).rstrip("/") + return f"{head}/{digest_segment}" if head else digest_segment + + def get_s3_object_key( s3_path: str, prefix: str, @@ -205,12 +251,23 @@ def get_s3_object_key( s3_file_name: str, ) -> str: sanitized_s3_file_name: Final = s3_file_name.replace("/", "_") - s3_object_key = ( - (s3_path.rstrip("/") + "/" if s3_path else "") - + prefix - + start_time.strftime("%Y-%m-%d") - + "/" - + sanitized_s3_file_name - ) # we need the s3 key to include the time, so we log cache hits too - s3_object_key += ".json" - return s3_object_key + configured_prefix: Final = (s3_path.rstrip("/") + "/" if s3_path else "") + prefix + date_segment: Final = start_time.strftime("%Y-%m-%d") + "/" + # we need the s3 key to include the time, so we log cache hits too + s3_object_key: Final = configured_prefix + date_segment + sanitized_s3_file_name + ".json" + if len(s3_object_key.encode("utf-8")) <= MAX_S3_OBJECT_KEY_BYTES: + return s3_object_key + + # shorten the response id first and only trim the configured prefix if that is what does not + # fit, so prefix scoped IAM policies and lifecycle rules keep matching + budget: Final = MAX_S3_OBJECT_KEY_BYTES - len(date_segment.encode("utf-8")) - len(b".json") + prefix_bytes: Final = len(configured_prefix.encode("utf-8")) + if prefix_bytes + S3_MIN_BOUNDED_FILE_NAME_BYTES <= budget: + bounded_file_name: Final = _bounded_s3_file_name(s3_file_name, sanitized_s3_file_name, budget - prefix_bytes) + return configured_prefix + date_segment + bounded_file_name + ".json" + + shortest_file_name: Final = _bounded_s3_file_name( + s3_file_name, sanitized_s3_file_name, S3_MIN_BOUNDED_FILE_NAME_BYTES + ) + bounded_prefix: Final = _bounded_s3_prefix(configured_prefix, budget - len(shortest_file_name.encode("utf-8"))) + return bounded_prefix + date_segment + shortest_file_name + ".json" diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py index d52bcda525f..712ce41d09e 100644 --- a/litellm/integrations/s3_v2.py +++ b/litellm/integrations/s3_v2.py @@ -11,11 +11,17 @@ import time from collections.abc import Mapping from datetime import datetime from typing import Final, cast +from urllib.parse import quote import litellm from litellm._logging import print_verbose, verbose_logger from litellm.constants import DEFAULT_S3_BATCH_SIZE, DEFAULT_S3_FLUSH_INTERVAL_SECONDS -from litellm.integrations.s3 import get_s3_object_key, resolve_sse_params +from litellm.integrations.s3 import ( + get_s3_object_download_filename, + get_s3_object_key, + resolve_sse_params, +) +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM @@ -206,6 +212,26 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): params.get("s3_sse_kms_key_id") or s3_sse_kms_key_id, ) + def _build_object_url(self, s3_object_key: str) -> str: + """ + Build the exact URL that is both signed and sent, with the key percent-encoded once. + + S3SigV4Auth signs the path verbatim while S3 canonicalizes the received path with reserved + characters encoded, so an unencoded `=`, `+`, `&`, `#`, `?`, `%` or space in the key makes + the two signatures disagree (403 SignatureDoesNotMatch). + """ + encoded_key: Final = quote(s3_object_key, safe="/") + if self.s3_endpoint_url and self.s3_bucket_name: + if self.s3_use_virtual_hosted_style: + endpoint_host: Final = self.s3_endpoint_url.replace("https://", "").replace("http://", "") + protocol: Final = "https://" if self.s3_endpoint_url.startswith("https://") else "http://" + return f"{protocol}{self.s3_bucket_name}.{endpoint_host}/{encoded_key}" + return f"{self.s3_endpoint_url}/{self.s3_bucket_name}/{encoded_key}" + return ( + f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}." + f"{get_aws_dns_suffix(self.s3_region_name)}/{encoded_key}" + ) + def _sse_headers(self) -> Mapping[str, str]: candidates: Final = { "x-amz-server-side-encryption": self.s3_server_side_encryption, @@ -237,11 +263,11 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): now: Final = datetime.now(timezone.utc) audit_log_id: Final = audit_log.get("id", "unknown") - s3_path = cast(str | None, self.s3_path) or "" - s3_path = s3_path.rstrip("/") + "/" if s3_path else "" - - s3_object_key: Final = ( - f"{s3_path}audit_logs/{now.strftime('%Y-%m-%d')}/{now.strftime('%H-%M-%S')}_{audit_log_id}.json" + s3_object_key: Final = get_s3_object_key( + cast(str | None, self.s3_path) or "", + "audit_logs/", + now, + f"{now.strftime('%H-%M-%S')}_{audit_log_id}", ) element: Final = s3BatchLoggingElement( @@ -292,7 +318,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): import base64 import hashlib - import requests from botocore.auth import S3SigV4Auth from botocore.awsrequest import AWSRequest except ImportError: @@ -316,18 +341,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): verbose_logger.debug("s3_v2 logger - uploading data to s3 - %s", batch_logging_element.s3_object_key) verbose_logger.debug("s3_v2 logger - s3_verify setting: %s", self.s3_verify) - # Prepare the URL - url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}" - - if self.s3_endpoint_url and self.s3_bucket_name: - if self.s3_use_virtual_hosted_style: - # Virtual-hosted-style: bucket.endpoint/key - endpoint_host: Final = self.s3_endpoint_url.replace("https://", "").replace("http://", "") - protocol: Final = "https://" if self.s3_endpoint_url.startswith("https://") else "http://" - url = f"{protocol}{self.s3_bucket_name}.{endpoint_host}/{batch_logging_element.s3_object_key}" - else: - # Path-style: endpoint/bucket/key - url = self.s3_endpoint_url + "/" + self.s3_bucket_name + "/" + batch_logging_element.s3_object_key + url: Final = self._build_object_url(batch_logging_element.s3_object_key) # Convert JSON to string json_string: Final = safe_dumps(batch_logging_element.payload) @@ -348,29 +362,19 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): "Cache-Control": "private, immutable, max-age=31536000, s-maxage=0", **self._sse_headers(), } - req: Final = requests.Request("PUT", url, data=json_string, headers=headers) - prepped: Final = req.prepare() # Sign the request - aws_request: Final = AWSRequest( - method=prepped.method, - url=prepped.url, - data=prepped.body, - headers=prepped.headers, - ) + aws_request: Final = AWSRequest(method="PUT", url=url, data=json_string, headers=headers) aws_region_name: Final = self.get_aws_region_name_for_non_llm_api_calls(aws_region_name=self.s3_region_name) S3SigV4Auth(credentials, "s3", aws_region_name).add_auth(aws_request) # Prepare the signed headers signed_headers: Final = dict(aws_request.headers.items()) - # Use prepared URL so path segments match SigV4 canonical request (e.g. %20 for spaces). - request_url: Final = prepped.url or url - # Make the request with retry for transient S3 errors (500/503) max_retries: Final = 3 for attempt in range(max_retries): - response = await self.async_httpx_client.put(request_url, data=json_string, headers=signed_headers) + response = await self.async_httpx_client.put(url, data=json_string, headers=signed_headers) if response.status_code in (500, 503) and attempt < max_retries - 1: wait_time = 2**attempt # 1s, 2s verbose_logger.warning( @@ -463,9 +467,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): ) verbose_logger.debug("s3_object_key=%s", s3_object_key) - s3_object_download_filename: Final = ( - f"time-{start_time.strftime('%Y-%m-%dT%H-%M-%S-%f')}_{standard_logging_payload['id']}.json" - ) + s3_object_download_filename: Final = get_s3_object_download_filename(start_time, standard_logging_payload["id"]) return s3BatchLoggingElement( payload=dict(standard_logging_payload), @@ -478,7 +480,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): import base64 import hashlib - import requests from botocore.auth import S3SigV4Auth from botocore.awsrequest import AWSRequest from botocore.credentials import Credentials @@ -493,18 +494,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): aws_region_name=self.s3_region_name, ) - # Prepare the URL - url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}" - - if self.s3_endpoint_url and self.s3_bucket_name: - if self.s3_use_virtual_hosted_style: - # Virtual-hosted-style: bucket.endpoint/key - endpoint_host: Final = self.s3_endpoint_url.replace("https://", "").replace("http://", "") - protocol: Final = "https://" if self.s3_endpoint_url.startswith("https://") else "http://" - url = f"{protocol}{self.s3_bucket_name}.{endpoint_host}/{batch_logging_element.s3_object_key}" - else: - # Path-style: endpoint/bucket/key - url = self.s3_endpoint_url + "/" + self.s3_bucket_name + "/" + batch_logging_element.s3_object_key + url: Final = self._build_object_url(batch_logging_element.s3_object_key) # Convert JSON to string json_string: Final = safe_dumps(batch_logging_element.payload) @@ -525,32 +515,22 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): "Cache-Control": "private, immutable, max-age=31536000, s-maxage=0", **self._sse_headers(), } - req: Final = requests.Request("PUT", url, data=json_string, headers=headers) - prepped: Final = req.prepare() # Sign the request - aws_request: Final = AWSRequest( - method=prepped.method, - url=prepped.url, - data=prepped.body, - headers=prepped.headers, - ) + aws_request: Final = AWSRequest(method="PUT", url=url, data=json_string, headers=headers) aws_region_name: Final = self.get_aws_region_name_for_non_llm_api_calls(aws_region_name=self.s3_region_name) S3SigV4Auth(credentials, "s3", aws_region_name).add_auth(aws_request) # Prepare the signed headers signed_headers: Final = dict(aws_request.headers.items()) - # Use prepared URL so path segments match SigV4 canonical request (e.g. %20 for spaces). - request_url: Final = prepped.url or url - httpx_client: Final = _get_httpx_client( params=({"ssl_verify": self.s3_verify} if self.s3_verify is not None else None) ) # Make the request with retry for transient S3 errors (500/503) max_retries: Final = 3 for attempt in range(max_retries): - response = httpx_client.put(request_url, data=json_string, headers=signed_headers) + response = httpx_client.put(url, data=json_string, headers=signed_headers) if response.status_code in (500, 503) and attempt < max_retries - 1: wait_time = 2**attempt # 1s, 2s verbose_logger.warning( @@ -582,7 +562,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): try: import hashlib - import requests from botocore.auth import S3SigV4Auth from botocore.awsrequest import AWSRequest except ImportError: @@ -607,18 +586,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): verbose_logger.debug("s3_v2 logger - downloading data from s3 - %s", s3_object_key) - # Prepare the URL - url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{s3_object_key}" - - if self.s3_endpoint_url and self.s3_bucket_name: - if self.s3_use_virtual_hosted_style: - # Virtual-hosted-style: bucket.endpoint/key - endpoint_host: Final = self.s3_endpoint_url.replace("https://", "").replace("http://", "") - protocol: Final = "https://" if self.s3_endpoint_url.startswith("https://") else "http://" - url = f"{protocol}{self.s3_bucket_name}.{endpoint_host}/{s3_object_key}" - else: - # Path-style: endpoint/bucket/key - url = self.s3_endpoint_url + "/" + self.s3_bucket_name + "/" + s3_object_key + url: Final = self._build_object_url(s3_object_key) # Prepare the request for GET operation # For GET requests, we need x-amz-content-sha256 with hash of empty string @@ -626,22 +594,15 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): headers: Final = { "x-amz-content-sha256": empty_string_hash, } - req: Final = requests.Request("GET", url, headers=headers) - prepped: Final = req.prepare() # Sign the request - aws_request: Final = AWSRequest( - method=prepped.method, - url=prepped.url, - headers=prepped.headers, - ) + aws_request: Final = AWSRequest(method="GET", url=url, headers=headers) S3SigV4Auth(credentials, "s3", self.s3_region_name).add_auth(aws_request) # Prepare the signed headers signed_headers: Final = dict(aws_request.headers.items()) - request_url: Final = prepped.url or url - response: Final = await self.async_httpx_client.get(request_url, headers=signed_headers) + response: Final = await self.async_httpx_client.get(url, headers=signed_headers) if response.status_code != 200: verbose_logger.exception("S3 object not found, saw response=", response.text) diff --git a/litellm/integrations/shadow_eval_logger.py b/litellm/integrations/shadow_eval_logger.py index 5f4e7c71395..27da785331a 100644 --- a/litellm/integrations/shadow_eval_logger.py +++ b/litellm/integrations/shadow_eval_logger.py @@ -1,8 +1,11 @@ """Shadow Eval Logger: samples a shadowed key's successful LLM requests (chat completions, Anthropic Messages, and Responses API surfaces, each normalized to chat shape), duplicates -each against the job's other arm in a detached task (the auto-router for a forward job, the -fixed baseline model for a reverse one), blind-judges real vs shadow, and appends one -``LiteLLM_ShadowEvalAttempt`` row (verdict or error) as the feature's only hot-path write. +each through every shadow arm in one detached task (each candidate auto-router for a +forward job, the fixed baseline model for a reverse one), blind-judges real vs each arm, +and appends one ``LiteLLM_ShadowEvalAttempt`` row per arm (verdict or error) as the +feature's only hot-path write. A multi-router job's arms therefore score the identical +sampled requests against the identical real responses, which is what makes their win +rates comparable head-to-head. Counts, status, and spend derive from those rows at read time, so nothing can disagree across pods or stop races; the hook reads active jobs through a short-TTL cache.""" @@ -38,6 +41,7 @@ from litellm.types.management_endpoints.auto_router_endpoints import ShadowEvalD from litellm.types.utils import SHADOW_EVAL_JUDGE_CALL_ORIGIN, SHADOW_EVAL_ROUTER_CALL_ORIGIN if TYPE_CHECKING: + from litellm.proxy.db.shadow_eval_funnel import ShadowEvalFunnelStage from litellm.proxy.utils import PrismaClient from litellm.router import Router from litellm.types.utils import StandardLoggingPayload @@ -386,6 +390,13 @@ def _judge_user_prompt(conversation: str, response_a: str, response_b: str) -> s ) +def _leg_eval_spend(sums: Mapping[str, object]) -> float: + return sum( + float(raw) if isinstance(raw := sums.get(column), (int, float)) else 0.0 + for column in ("judge_cost", "shadow_cost", "shadow_classifier_cost") + ) + + def _job_spend_counter_key(job_id: str) -> str: return f"spend:shadow_eval:{job_id}" @@ -412,6 +423,15 @@ async def _add_job_spend_to_counter(counter_key: str, cost: float) -> None: verbose_logger.warning("shadow_eval: spend counter increment failed for %s: %s", counter_key, e) +def _record_funnel_event(job_id: str, stage: "ShadowEvalFunnelStage") -> None: + try: + from litellm.proxy.db.shadow_eval_funnel import record_shadow_eval_funnel_event + + record_shadow_eval_funnel_event(job_id, stage) + except Exception as e: # noqa: BLE001 # coverage stats are advisory; sampling must proceed + verbose_logger.debug("shadow_eval: funnel increment failed for %s: %s", job_id, e) + + async def _key_or_team_is_over_budget(metadata: Mapping[str, object]) -> bool: """Whether the shadowed key or its team is over budget, decided by the same owners the request path uses, so counter keys and thresholds can never drift from auth's. @@ -452,6 +472,14 @@ async def _key_or_team_is_over_budget(metadata: Mapping[str, object]) -> bool: return False +def _forwarded_team_id(metadata: Mapping[str, object]) -> str | None: + """The shadowed key's team, the identity the judge call already carries in its metadata + and the router already selects deployments with. Read here too so the arm choice, which + happens before the router sees the call, is made under the same team.""" + team_id: Final = metadata.get("user_api_key_team_id") + return team_id if isinstance(team_id, str) and team_id else None + + def _routing_decision(metadata: Mapping[str, object]) -> Mapping[str, object]: """The routing decision a pre-routing strategy wrote to a call's metadata, empty when a plain model served it. Read off the sampled request for the control arm, and off the @@ -466,12 +494,23 @@ def _routed_tier(metadata: Mapping[str, object]) -> str | None: return str(raw) if raw is not None else None -def _request_was_routed_by(request_metadata: Mapping[str, object], router_name: str) -> bool: - """Whether the router under evaluation served this request, which is what decides - the direction it belongs to. A forward job skips its own router's traffic, since - duplicating it would compare the router to itself: guaranteed ties, judge spend for - zero information. A reverse job samples exactly that traffic and nothing else.""" - return _routing_decision(request_metadata).get("router_model_name") == router_name +def _decision_classifier_cost(metadata: Mapping[str, object]) -> float: + """What the arm's own routing decision says its classifier call billed: the money a + completion cost alone omits, and 0 for a plain model that never classifies.""" + raw: Final = _routing_decision(metadata).get("classifier_cost") + return float(raw) if isinstance(raw, (int, float)) else 0.0 + + +def _direction_admits(request_metadata: Mapping[str, object], job: "ActiveShadowEvalJob") -> bool: + """Whether this request belongs to the job's direction. A forward job skips traffic + any of its candidate routers served: duplicating a router's own request compares it + to itself (guaranteed ties), and judging a sibling against another candidate's live + response would score candidates against each other instead of against the incumbent. + A reverse job samples exactly its one router's traffic and nothing else.""" + routed_by: Final = _routing_decision(request_metadata).get("router_model_name") + if job.direction == "reverse": + return routed_by == job.router_name + return routed_by not in job.arm_router_names @dataclass(frozen=True, slots=True) @@ -481,6 +520,7 @@ class _CallFailure: error: str cost: float = 0.0 + classifier_cost: float = 0.0 @dataclass(frozen=True, slots=True) @@ -491,6 +531,7 @@ class _ShadowResponse: model: str tier: str | None cost: float + classifier_cost: float @dataclass(frozen=True, slots=True) @@ -512,6 +553,7 @@ class ActiveShadowEvalJob(BaseModel): id: str router_name: str + router_names: tuple[str, ...] = () direction: ShadowEvalDirection = "forward" baseline_model: str | None = None shadow_percentage: float @@ -533,12 +575,25 @@ class ActiveShadowEvalJob(BaseModel): raise ValueError("baseline_model is set for exactly the reverse jobs") return self + @model_validator(mode="after") + def _reverse_evaluates_one_router(self) -> "ActiveShadowEvalJob": + """A reverse row naming several routers is unsamplable (there is no one traffic + slice they share) and fails closed.""" + if self.direction == "reverse" and len(self.arm_router_names) > 1: + raise ValueError("a reverse job evaluates exactly one router") + return self + @property - def shadow_target(self) -> str: - """The model the duplicated arm calls: the router itself for a forward job, the - fixed baseline for a reverse one. Total because the validator above pins + def arm_router_names(self) -> tuple[str, ...]: + """The job's full router set; rows from before router_names existed hold it in + router_name alone. The one place that reading lives on the sampling side.""" + return self.router_names or (self.router_name,) + + def arm_target(self, arm_router: str) -> str: + """The model one duplicated arm calls: the candidate router itself for a forward + job, the fixed baseline for a reverse one. Total because the validator above pins baseline_model to reverse jobs and only those.""" - return self.baseline_model or self.router_name + return self.baseline_model or arm_router def _as_active_job(record: object, attempts: int, spend: float) -> ActiveShadowEvalJob | None: @@ -558,7 +613,12 @@ _JOBS_CACHE_KEY: Final = "shadow_eval:active_jobs" class ShadowEvalLogger(CustomLogger): - """Fires blind pairwise shadow evaluations for keys with an active shadow-eval job.""" + """Fires blind pairwise shadow evaluations for targets with an active shadow-eval job. + + A job targets a virtual key, a team, or a user; a request qualifies for a job when + any of its resolved identities (key hash, team id, user id) matches the job's + target, so team and user jobs cover JWT-authenticated traffic, which carries no + key hash at all.""" def __init__( self, @@ -567,6 +627,7 @@ class ShadowEvalLogger(CustomLogger): jobs_cache: InMemoryCache | None = None, job_spend_reader: Callable[[str, float, float], Awaitable[float]] | None = None, job_spend_writer: Callable[[str, float], Awaitable[None]] | None = None, + funnel_recorder: Callable[[str, "ShadowEvalFunnelStage"], None] | None = None, ) -> None: """Providers are callables so the proxy's lazily-initialized globals are resolved at call time, not at logger construction. The spend reader and writer wrap the @@ -576,15 +637,16 @@ class ShadowEvalLogger(CustomLogger): self._jobs_cache = jobs_cache or _jobs_cache self._read_job_spend = job_spend_reader or _job_spend_from_counter self._write_job_spend = job_spend_writer or _add_job_spend_to_counter + self._record_funnel = funnel_recorder or _record_funnel_event self._inflight_shadow_tasks: int = 0 # Starts per job since the last cache fill, never decremented within a # generation; the refill absorbs written rows and resets. self._job_starts: dict[str, int] = {} # mutable-ok: per-generation counter - async def _active_jobs(self) -> Mapping[str, tuple[ActiveShadowEvalJob, ...]]: - """Active jobs by api_key_id, cache-first. A key holds at most one job per - direction, so the value is a collection. A DB fault returns empty without - caching, so sampling pauses for that request and the next one retries.""" + async def _active_jobs(self) -> Mapping[tuple[str, str], tuple[ActiveShadowEvalJob, ...]]: + """Active jobs by (target_type, target_id), cache-first. A target holds at most + one job per direction, so the value is a collection. A DB fault returns empty + without caching, so sampling pauses for that request and the next one retries.""" cached: Final = await self._jobs_cache.async_get_cache(_JOBS_CACHE_KEY) if cached is not None: return cached # pyright: ignore[reportReturnType] # cache stores exactly this mapping shape @@ -602,7 +664,8 @@ class ShadowEvalLogger(CustomLogger): await prisma.db.litellm_shadowevalattempt.group_by( by=["job_id"], count=True, - sum={"judge_cost": True, "shadow_cost": True}, # mutable-ok: Prisma aggregate spec + # mutable-ok: Prisma aggregate spec + sum={"judge_cost": True, "shadow_cost": True, "shadow_classifier_cost": True}, where={"job_id": {"in": [str(record.id) for record in records]}}, # mutable-ok: Prisma filter ) if records @@ -611,15 +674,14 @@ class ShadowEvalLogger(CustomLogger): attempt_stats: Final = { # mutable-ok: frozen snapshot of the grouped read str(row["job_id"]): ( int(row["_count"]["_all"]), - float((row["_sum"] or {}).get("judge_cost") or 0.0) - + float((row["_sum"] or {}).get("shadow_cost") or 0.0), + _leg_eval_spend(row["_sum"] or _EMPTY_METADATA), ) for row in grouped or [] } - by_key: Final = tuple( + by_target: Final = tuple( sorted( ( - (str(record.api_key_id), job) + ((str(record.target_type), str(record.target_id)), job) for record in records or [] if (job := _as_active_job(record, *attempt_stats.get(str(record.id), (0, 0.0)))) is not None ), @@ -627,7 +689,7 @@ class ShadowEvalLogger(CustomLogger): ) ) jobs: Final = MappingProxyType( - {key: tuple(job for _, job in group) for key, group in groupby(by_key, key=itemgetter(0))} + {target: tuple(job for _, job in group) for target, group in groupby(by_target, key=itemgetter(0))} ) await self._jobs_cache.async_set_cache(_JOBS_CACHE_KEY, jobs) self._job_starts = {} # rebind-ok: new generation, counts absorbed into the fill @@ -638,6 +700,32 @@ class ShadowEvalLogger(CustomLogger): #### hook #### + def _sampled_jobs( + self, + active_jobs: Sequence[ActiveShadowEvalJob], + request_metadata: Mapping[str, object], + request_id: str, + ) -> tuple[ActiveShadowEvalJob, ...]: + """The jobs that sample this request. A key can hold one job per direction, and a + request routed by one job's router while bypassing the other's qualifies for both; + each is separately budgeted, so both fire. An admitting job that loses the sampling + dice is counted, so results can weigh judged rows against the traffic they stand for.""" + eligible: list[ActiveShadowEvalJob] = [] # mutable-ok: bucketed per-job admission + now: Final = datetime.now(timezone.utc) + for job in active_jobs: + if ( + now >= job.ends_at + or job.attempts + self._job_starts.get(job.id, 0) >= job.max_turns + or (job.max_budget is not None and job.spend >= job.max_budget) + or not _direction_admits(request_metadata, job) + ): + continue + if not _sample_hits(request_id, job.id, job.shadow_percentage): + self._record_funnel(job.id, "not_sampled") + continue + eligible.append(job) + return tuple(eligible) + async def async_log_success_event( self, kwargs: Mapping[str, object], @@ -658,8 +746,18 @@ class ShadowEvalLogger(CustomLogger): if should_redact_message_logging(dict(kwargs)): # mutable-ok: predicate takes a plain dict return metadata: Final = payload.get("metadata") or _EMPTY_METADATA - api_key_hash: Final = metadata.get("user_api_key_hash") - if not api_key_hash: + # Each identity the request resolved to is a candidate target; JWT-auth + # requests carry no key hash but do carry a team and user. + targets: Final = tuple( + (target_type, str(value)) + for target_type, value in ( + ("key", metadata.get("user_api_key_hash")), + ("team", metadata.get("user_api_key_team_id")), + ("user", metadata.get("user_api_key_user_id")), + ) + if value + ) + if not targets: return request_id: Final = payload.get("id") or "" if not request_id: @@ -669,18 +767,11 @@ class ShadowEvalLogger(CustomLogger): return # only surfaces this table can normalize are comparable; unknown types fail closed if ops.wire_params and _request_mutating_guardrail_ran(request_metadata): return # the wire-body snapshot predates the rewrite; replaying it would resurrect stripped content - # A key can hold one job per direction, and a request routed by one job's - # router while bypassing the other's qualifies for both. Each is separately - # budgeted, so both fire; the request is normalized once, and only when at - # least one job sampled it. - eligible: Final = tuple( - job - for job in (await self._active_jobs()).get(str(api_key_hash), ()) - if datetime.now(timezone.utc) < job.ends_at - and job.attempts + self._job_starts.get(job.id, 0) < job.max_turns - and (job.max_budget is None or job.spend < job.max_budget) - and _sample_hits(request_id, job.id, job.shadow_percentage) - and _request_was_routed_by(request_metadata, job.router_name) == (job.direction == "reverse") + active_jobs: Final = await self._active_jobs() + eligible: Final = self._sampled_jobs( + tuple(job for target in targets for job in active_jobs.get(target, ())), + request_metadata, + request_id, ) if not eligible: return @@ -691,13 +782,22 @@ class ShadowEvalLogger(CustomLogger): response_obj, ) if sample is None: + for job in eligible: + self._record_funnel(job.id, "unjudgeable") return messages, shadow_params, real_text = sample control_tier: Final = _routed_tier(request_metadata) + real_cost: Final = float(payload.get("response_cost") or 0.0) + real_cache_hit: Final = payload.get("cache_hit") is True + real_classifier_cost: Final = _decision_classifier_cost(request_metadata) for job in eligible: if self._inflight_shadow_tasks >= _MAX_CONCURRENT_SHADOW_TASKS: - return - self._job_starts[job.id] = self._job_starts.get(job.id, 0) + 1 + self._record_funnel(job.id, "shed") + continue + # One start writes one attempt row per arm, and max_turns is a row + # ceiling, so admission must pre-count every arm or a multi-router + # job overshoots the valve N-fold within a cache generation. + self._job_starts[job.id] = self._job_starts.get(job.id, 0) + len(job.arm_router_names) self._inflight_shadow_tasks += 1 asyncio.create_task( self._run_shadow_eval( @@ -706,6 +806,9 @@ class ShadowEvalLogger(CustomLogger): messages=messages, real_text=real_text, real_model=payload.get("model") or "", + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, control_tier=control_tier, shadow_params=shadow_params, parent_metadata=MappingProxyType(dict(request_metadata)), # mutable-ok: frozen snapshot @@ -726,37 +829,110 @@ class ShadowEvalLogger(CustomLogger): messages: Sequence[Mapping[str, object]], real_text: str, real_model: str, + real_cost: float, + real_classifier_cost: float, + real_cache_hit: bool, control_tier: str | None, shadow_params: Mapping[str, object], parent_metadata: Mapping[str, object], ) -> None: - """Budget gate -> shadow call -> blind judge -> one attempt row. The prisma gate - sits above the dispatch so no provider spend happens without a place to record - the outcome, and the budget read lives here rather than in the success hook.""" + """Budget gates once per sampled request, then every router arm in turn: shadow + call -> blind judge -> one attempt row stamped with the arm. The gates that + decline to spend on an admitted sample (no DB to record into, an over-budget key, + an unverifiable or exhausted eval budget) count the REQUEST withheld before any + arm runs, so funnel counters stay per-request and a leg's eligible traffic still + reconciles as not_sampled + unjudgeable + shed + withheld + sampled requests, + where each sampled request writes one attempt row per arm. A budget crossed + mid-loop lets the remaining arms overshoot by one round, the same class of + overshoot as the samples already in flight when the cap is crossed. The prisma + gate sits above the dispatch so no provider spend happens without a place to + record the outcome, and the budget read lives here rather than in the success + hook.""" prisma: Final = self._prisma_provider() + if prisma is None: + self._record_funnel(job.id, "withheld") + return + if await _key_or_team_is_over_budget(parent_metadata): + self._record_funnel(job.id, "withheld") + return + if job.max_budget is not None: + try: + spend: Final = await self._read_job_spend(_job_spend_counter_key(job.id), job.spend, job.max_budget) + except Exception as e: # noqa: BLE001 # unverifiable budget: skip the sample rather than spend on it + verbose_logger.warning("shadow_eval: budget unverifiable for %s, sample skipped: %s", job.id, e) + self._record_funnel(job.id, "withheld") + return + if spend >= job.max_budget: + self._record_funnel(job.id, "withheld") + return + for arm_router in job.arm_router_names: + await self._run_shadow_arm( + prisma=prisma, + job=job, + arm_router=arm_router, + request_id=request_id, + messages=messages, + real_text=real_text, + real_model=real_model, + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, + control_tier=control_tier, + shadow_params=shadow_params, + parent_metadata=parent_metadata, + ) + + async def _run_shadow_arm( + self, + prisma: "PrismaClient", + job: ActiveShadowEvalJob, + arm_router: str, + request_id: str, + messages: Sequence[Mapping[str, object]], + real_text: str, + real_model: str, + real_cost: float, + real_classifier_cost: float, + real_cache_hit: bool, + control_tier: str | None, + shadow_params: Mapping[str, object], + parent_metadata: Mapping[str, object], + ) -> None: + """One arm's pipeline: shadow call -> blind judge -> one attempt row, every exit + recording this arm's outcome, so one arm's fault never silences a sibling arm.""" try: - if prisma is None: - return - if await _key_or_team_is_over_budget(parent_metadata): - return - if job.max_budget is not None: - try: - spend: Final = await self._read_job_spend(_job_spend_counter_key(job.id), job.spend, job.max_budget) - except Exception as e: # noqa: BLE001 # unverifiable budget: skip the sample rather than spend on it - verbose_logger.warning("shadow_eval: budget unverifiable for %s, sample skipped: %s", job.id, e) - return - if spend >= job.max_budget: - return - shadow: Final = await self._call_router_shadow(job.shadow_target, messages, shadow_params, parent_metadata) + shadow: Final = await self._call_router_shadow( + job.arm_target(arm_router), messages, shadow_params, parent_metadata + ) except Exception as e: # noqa: BLE001 # detached task: nothing billed yet, record and never raise verbose_logger.debug("shadow_eval: pipeline failed for %s: %s", request_id, e) await self._record_attempt( - prisma, job, request_id, control_tier, outcome="error", error=f"pipeline error: {e}" + prisma, + job, + request_id, + control_tier, + router_name=arm_router, + outcome="error", + error=f"pipeline error: {e}", + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, ) return if isinstance(shadow, _CallFailure): await self._record_attempt( - prisma, job, request_id, control_tier, outcome="error", error=shadow.error, shadow_cost=shadow.cost + prisma, + job, + request_id, + control_tier, + router_name=arm_router, + outcome="error", + error=shadow.error, + shadow_cost=shadow.cost, + shadow_classifier_cost=shadow.classifier_cost, + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, ) return # From here the shadow call has billed, so every exit records its cost. @@ -774,11 +950,16 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=verdict.error, shadow=shadow, judge_cost=verdict.cost, shadow_cost=shadow.cost, + shadow_classifier_cost=shadow.classifier_cost, + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, ) return await self._record_attempt( @@ -786,12 +967,17 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome=verdict.preference, shadow=shadow, real_model=real_model, confidence=verdict.confidence, judge_cost=verdict.cost, shadow_cost=shadow.cost, + shadow_classifier_cost=shadow.classifier_cost, + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, ) except Exception as e: # noqa: BLE001 # detached task: the shadow call billed, record its cost, never raise verbose_logger.debug("shadow_eval: pipeline failed for %s: %s", request_id, e) @@ -800,10 +986,15 @@ class ShadowEvalLogger(CustomLogger): job, request_id, control_tier, + router_name=arm_router, outcome="error", error=f"pipeline error: {e}", shadow=shadow, shadow_cost=shadow.cost, + shadow_classifier_cost=shadow.classifier_cost, + real_cost=real_cost, + real_classifier_cost=real_classifier_cost, + real_cache_hit=real_cache_hit, ) async def _record_attempt( @@ -813,16 +1004,22 @@ class ShadowEvalLogger(CustomLogger): request_id: str, control_tier: str | None, *, + router_name: str, outcome: str, + real_cost: float, + real_classifier_cost: float, + real_cache_hit: bool, shadow: _ShadowResponse | None = None, real_model: str = "", confidence: float | None = None, judge_cost: float = 0.0, shadow_cost: float = 0.0, + shadow_classifier_cost: float = 0.0, error: str | None = None, ) -> None: - if judge_cost + shadow_cost > 0: - await self._write_job_spend(_job_spend_counter_key(job.id), judge_cost + shadow_cost) + eval_spend: Final = judge_cost + shadow_cost + shadow_classifier_cost + if eval_spend > 0: + await self._write_job_spend(_job_spend_counter_key(job.id), eval_spend) if prisma is None: return try: @@ -830,6 +1027,7 @@ class ShadowEvalLogger(CustomLogger): data={ # mutable-ok: Prisma payload "job_id": job.id, "request_id": request_id, + "router_name": router_name, "outcome": outcome, "tier": control_tier if job.direction == "reverse" else (shadow.tier if shadow else None), "real_model": real_model or None, @@ -837,6 +1035,10 @@ class ShadowEvalLogger(CustomLogger): "confidence": confidence, "judge_cost": judge_cost, "shadow_cost": shadow_cost, + "shadow_classifier_cost": shadow_classifier_cost, + "real_cost": real_cost, + "real_classifier_cost": real_classifier_cost, + "real_cache_hit": real_cache_hit, "error": error[:_MAX_ERROR_CHARS] if error else None, } ) @@ -873,15 +1075,23 @@ class ShadowEvalLogger(CustomLogger): ) except Exception as e: # noqa: BLE001 # provider errors become error rows, not crashes verbose_logger.debug("shadow_eval: router call failed: %s", e) - return _CallFailure(f"shadow router call failed: {_failure_detail(e)}") + return _CallFailure( + f"shadow router call failed: {_failure_detail(e)}", + classifier_cost=_decision_classifier_cost(shadow_metadata), + ) text: Final = _chat_final_text(response) if not text: - return _CallFailure("shadow router returned an empty response", cost=_call_cost(response)) + return _CallFailure( + "shadow router returned an empty response", + cost=_call_cost(response), + classifier_cost=_decision_classifier_cost(shadow_metadata), + ) return _ShadowResponse( text=text, model=str(getattr(response, "model", None) or _routing_decision(shadow_metadata).get("routed_model") or ""), tier=_routed_tier(shadow_metadata), cost=_call_cost(response), + classifier_cost=_decision_classifier_cost(shadow_metadata), ) async def _call_judge( @@ -915,6 +1125,7 @@ class ShadowEvalLogger(CustomLogger): self._router_provider(), judge_model, judge_messages, # pyright: ignore[reportArgumentType] # plain SDK message dicts + team_id=_forwarded_team_id(parent_metadata), temperature=0, max_tokens=JUDGE_MAX_OUTPUT_TOKENS, response_format=PAIRWISE_JUDGE_RESPONSE_FORMAT, @@ -936,7 +1147,7 @@ class ShadowEvalLogger(CustomLogger): ) -_EMPTY_JOBS: Final[Mapping[str, tuple[ActiveShadowEvalJob, ...]]] = MappingProxyType({}) +_EMPTY_JOBS: Final[Mapping[tuple[str, str], tuple[ActiveShadowEvalJob, ...]]] = MappingProxyType({}) def _default_prisma_provider() -> "PrismaClient | None": diff --git a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py index aa29162ba1f..07d4f959489 100644 --- a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py +++ b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py @@ -13,7 +13,7 @@ from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage from litellm.types.prompts.init_prompts import PromptSpec -from litellm.types.utils import StandardCallbackDynamicParams +from litellm.types.utils import CallTypes, StandardCallbackDynamicParams from litellm.types.vector_stores import ( LiteLLM_ManagedVectorStore, VectorStoreResultContent, @@ -226,7 +226,7 @@ class VectorStorePreCallHook(CustomLogger): self, request_data: dict, response: Any, - call_type: Any | None, + call_type: CallTypes | None, ) -> Any | None: """ Add search results to the response after successful LLM call. @@ -283,7 +283,7 @@ class VectorStorePreCallHook(CustomLogger): self, request_data: dict, response_chunk: Any, - call_type: Any | None, + call_type: CallTypes | None, ) -> Any | None: """ Add search results to the final streaming chunk. diff --git a/litellm/integrations/websearch_interception/ARCHITECTURE.md b/litellm/integrations/websearch_interception/ARCHITECTURE.md index ce7f01c5a2a..4ea7a7ae527 100644 --- a/litellm/integrations/websearch_interception/ARCHITECTURE.md +++ b/litellm/integrations/websearch_interception/ARCHITECTURE.md @@ -207,6 +207,59 @@ response = await litellm.messages.acreate( --- +## Loop Ceiling + +One intercepted request can chain several follow-up model calls, since the model often searches again after +reading the first set of results. `max_agentic_loops` caps how many of those follow-ups run, and it defaults +to 3. LiteLLM also breaks the loop early when the model asks for the exact same tool call twice in a row. + +Set the ceiling on the feature, which the interceptor applies to `/v1/messages` requests: + +```yaml +litellm_settings: + websearch_interception_params: + enabled_providers: ["bedrock"] + max_agentic_loops: 5 +``` + +Or per deployment, which wins over the feature-level setting: + +```yaml +model_list: + - model_name: claude-sonnet-4-5 + litellm_params: + model: bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0 + max_agentic_loops: 5 +``` + +Clients cannot set it. `max_agentic_loops` is on the proxy's untrusted-field list, so a request body that +carries it is ignored and one request can never drive an unbounded number of upstream model calls. + +Both places are validated at config load, and a value that is not an integer of at least 1 stops the proxy +from starting rather than surfacing later. The per-deployment one is checked while the model list is read, +not on `LiteLLM_Params`, because the proxy builds its router with `ignore_invalid_deployments=True` and a +validator down there would drop the deployment silently instead of refusing to start. + +When the ceiling is reached on a non-streaming `/v1/messages` request, the turn ends there and the client gets +the last response back with the internal `litellm_web_search` tool call removed and `stop_reason: end_turn`. +The client never declared that tool, so leaving the block in would hand it a tool call it has no way to answer. +The answer can be less complete than it would have been with more loops, which is the tradeoff the ceiling +buys. Where the refused call was the only block left, the turn comes back with no text in it at all. + +Non-streaming is not a limitation on the client here, because a client that asked for a stream gets the same +treatment. Interception converts an intercepted `stream=True` request to non-streaming before the loop runs and +rebuilds the SSE stream from the finalized turn afterwards, so the ceiling is always reached on a response the +client has not seen yet. `AgenticStreamingIterator` is the one caller that reaches the loop with its events +already on the wire, and it keeps raising, because a finalized turn would arrive there as a second message +rather than as a replacement. + +Two other surfaces do not get that treatment yet. `/v1/responses` returns its own shape that the finalizer does +not rewrite, so it still hands back the internal call. And `/v1/chat/completions` runs its own copy of these +rails in `litellm_core_utils/chat_completion_agentic_loop.py`, which still raises rather than ending the turn. +Both are tracked separately + +--- + ## Streaming Support WebSearch interception works transparently with both streaming and non-streaming requests. diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index e59ef0449d0..dc61ee38a8c 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -10,7 +10,7 @@ import asyncio import math import uuid from collections.abc import AsyncIterator, Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, TypedDict, cast +from typing import TYPE_CHECKING, Any, Final, Literal, Never, TypedDict, TypeVar, cast from typing_extensions import ReadOnly @@ -31,6 +31,9 @@ from litellm.integrations.websearch_interception.tools import ( from litellm.integrations.websearch_interception.transformation import ( WebSearchTransformation, ) +from litellm.litellm_core_utils.agentic_loop_settings import ( + validated_max_agentic_loops, +) from litellm.llms.base_llm.search.transformation import SearchResponse from litellm.types.integrations.custom_logger import ( CHAT_COMPLETION_AGENTIC_SURFACE, @@ -43,7 +46,13 @@ from litellm.types.integrations.websearch_interception import ( AnthropicServerToolUseBlock, WebSearchInterceptionConfig, ) -from litellm.types.llms.openai import AllMessageValues +from litellm.types.llms.anthropic import AnthropicThinkingParam +from litellm.types.llms.openai import ( + AllMessageValues, + ChatCompletionAudioParam, + ChatCompletionPredictionContentParam, + OpenAIWebSearchOptions, +) from litellm.types.utils import ( AgenticLoopParams, CallTypes, @@ -53,6 +62,8 @@ from litellm.types.utils import ( from litellm.utils import ProviderConfigManager if TYPE_CHECKING: + from aiohttp import ClientSession + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, @@ -74,6 +85,10 @@ WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY: Final = "_websearch_interception_emit_native_b # ``web_search_tool_result`` blocks to inject into the final response. WEBSEARCH_NATIVE_BLOCKS_METADATA_KEY: Final = "websearch_native_blocks" +_RESPONSE_CONTENT_FIELD: Final = "content" + +_ResponseT: Final = TypeVar("_ResponseT") + class _PlanMetadataView(TypedDict): websearch_native_blocks: Sequence[Mapping[str, object]] | None @@ -87,23 +102,98 @@ class _WebSearchSettingsView(TypedDict): websearch_interception_params: WebSearchInterceptionConfig +class _SearchToolLitellmParams(TypedDict, total=False): + search_provider: ReadOnly[str | None] + + class _SearchToolConfig(TypedDict, total=False): search_tool_name: str - litellm_params: Mapping[str, object] | None + litellm_params: ReadOnly[_SearchToolLitellmParams | None] -class _DeploymentKwargsView(TypedDict): - """Typed reads of the untyped request kwargs seen by the deployment hook.""" - +class _LitellmParamsProviderView(TypedDict, total=False): custom_llm_provider: ReadOnly[str] - litellm_params: ReadOnly[Mapping[str, object]] + + +class _DeploymentCallKwargsView(TypedDict): + custom_llm_provider: ReadOnly[str] + litellm_params: ReadOnly[_LitellmParamsProviderView] model: ReadOnly[str] -class _UserAuthView(TypedDict): - """Typed read of the optional team attached to the caller's auth object.""" +class _AcreateNamedParams(TypedDict, total=False): + metadata: ReadOnly[Never] + stop_sequences: ReadOnly[Never] + stream: ReadOnly[bool | None] + system: ReadOnly[str | None] + temperature: ReadOnly[float | None] + thinking: ReadOnly[Never] + tool_choice: ReadOnly[Never] + tools: ReadOnly[Never] + top_k: ReadOnly[int | None] + top_p: ReadOnly[float | None] + container: ReadOnly[Never] - team_id: ReadOnly[str | None] + +class _AsearchNamedParams(TypedDict, total=False): + max_results: ReadOnly[int | None] + search_domain_filter: ReadOnly[Never] + max_tokens_per_page: ReadOnly[int | None] + country: ReadOnly[str | None] + api_key: ReadOnly[str | None] + api_base: ReadOnly[str | None] + timeout: ReadOnly[float | None] + extra_headers: ReadOnly[Never] + + +class _AcompletionNamedParams(TypedDict, total=False): + functions: ReadOnly[Never] + function_call: ReadOnly[str | None] + timeout: ReadOnly[float | None] + temperature: ReadOnly[float | None] + top_p: ReadOnly[float | None] + n: ReadOnly[int | None] + stream: ReadOnly[bool | None] + stream_options: ReadOnly[Never] + stop: ReadOnly[Never] + max_tokens: ReadOnly[int | None] + max_completion_tokens: ReadOnly[int | None] + modalities: ReadOnly[Never] + prediction: ReadOnly[ChatCompletionPredictionContentParam | None] + audio: ReadOnly[ChatCompletionAudioParam | None] + presence_penalty: ReadOnly[float | None] + frequency_penalty: ReadOnly[float | None] + logit_bias: ReadOnly[Never] + user: ReadOnly[str | None] + response_format: ReadOnly[Never] + seed: ReadOnly[int | None] + tools: ReadOnly[Never] + tool_choice: ReadOnly[Never] + parallel_tool_calls: ReadOnly[bool | None] + logprobs: ReadOnly[bool | None] + top_logprobs: ReadOnly[int | None] + deployment_id: ReadOnly[str | None] + reasoning_effort: ReadOnly[Literal["none", "minimal", "low", "medium", "high", "xhigh", "default"] | None] + verbosity: ReadOnly[Literal["low", "medium", "high"] | None] + safety_identifier: ReadOnly[str | None] + service_tier: ReadOnly[str | None] + store: ReadOnly[bool | None] + prompt_cache_key: ReadOnly[str | None] + base_url: ReadOnly[str | None] + api_version: ReadOnly[str | None] + api_key: ReadOnly[str | None] + model_list: ReadOnly[Never] + extra_headers: ReadOnly[Never] + thinking: ReadOnly[AnthropicThinkingParam | None] + web_search_options: ReadOnly[OpenAIWebSearchOptions | None] + include_server_side_tool_invocations: ReadOnly[bool | None] + shared_session: ReadOnly["ClientSession | None"] + enable_json_schema_validation: ReadOnly[bool | None] + + +_NO_ACREATE_NAMED: Final[_AcreateNamedParams] = {} +_NO_ASEARCH_NAMED: Final[_AsearchNamedParams] = {} +_NO_ACOMPLETION_NAMED: Final[_AcompletionNamedParams] = {} class WebSearchInterceptionLogger(CustomLogger): @@ -122,6 +212,7 @@ class WebSearchInterceptionLogger(CustomLogger): self, enabled_providers: list[LlmProviders | str] | None = None, search_tool_name: str | None = None, + max_agentic_loops: int | None = None, ): """ Args: @@ -131,6 +222,9 @@ class WebSearchInterceptionLogger(CustomLogger): Default: None (all providers enabled) search_tool_name: Name of search tool configured in router's search_tools. If None, will attempt to use first available search tool. + max_agentic_loops: How many follow-up model calls one intercepted request + may chain before the loop is refused and the turn ends. + If None, LiteLLM's default of 3 applies. """ super().__init__() # Convert enum values to strings for comparison @@ -139,8 +233,16 @@ class WebSearchInterceptionLogger(CustomLogger): else: self.enabled_providers = [p.value if isinstance(p, LlmProviders) else p for p in enabled_providers] self.search_tool_name = search_tool_name + self.max_agentic_loops = self._validated_max_agentic_loops(max_agentic_loops) self._request_has_websearch = False # Track if current request has web search + @staticmethod + def _validated_max_agentic_loops(max_agentic_loops: object) -> int | None: + """ + Reject loop ceilings the agentic loop cannot honor, at config load time. + """ + return validated_max_agentic_loops(max_agentic_loops, field="websearch_interception_params.max_agentic_loops") + async def try_short_circuit_search( self, model: str, @@ -293,17 +395,17 @@ class WebSearchInterceptionLogger(CustomLogger): """ # Check if this is for an enabled provider # Try top-level kwargs first, then nested litellm_params, then derive from model name - kwargs_view: Final[_DeploymentKwargsView] = { + call_kwargs_view: Final[_DeploymentCallKwargsView] = { "custom_llm_provider": kwargs.get("custom_llm_provider", ""), "litellm_params": kwargs.get("litellm_params", {}), "model": kwargs.get("model", ""), } - custom_llm_provider = kwargs_view["custom_llm_provider"] or kwargs_view["litellm_params"].get( + custom_llm_provider = call_kwargs_view["custom_llm_provider"] or call_kwargs_view["litellm_params"].get( "custom_llm_provider", "" ) if not custom_llm_provider: try: - _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=kwargs_view["model"]) + _, custom_llm_provider, _, _ = litellm.get_llm_provider(model=call_kwargs_view["model"]) except Exception: custom_llm_provider = "" if custom_llm_provider not in self.enabled_providers: @@ -398,6 +500,7 @@ class WebSearchInterceptionLogger(CustomLogger): websearch_interception_params: enabled_providers: ["bedrock"] search_tool_name: "my-perplexity-search" + max_agentic_loops: 5 Usage: config = litellm_settings.get("websearch_interception_params", {}) @@ -406,6 +509,7 @@ class WebSearchInterceptionLogger(CustomLogger): # Extract parameters from config enabled_providers_str: Final = config.get("enabled_providers", None) search_tool_name: Final = config.get("search_tool_name", None) + max_agentic_loops: Final = config.get("max_agentic_loops", None) # Convert string provider names to LlmProviders enum values enabled_providers: list[LlmProviders | str] | None = None @@ -423,6 +527,7 @@ class WebSearchInterceptionLogger(CustomLogger): return cls( enabled_providers=enabled_providers, search_tool_name=search_tool_name, + max_agentic_loops=max_agentic_loops, ) @staticmethod @@ -493,6 +598,10 @@ class WebSearchInterceptionLogger(CustomLogger): verbose_logger.debug("WebSearchInterception: Pre-request hook triggered for provider=%s", custom_llm_provider) + deployment_max_agentic_loops: Final = kwargs.get("max_agentic_loops") + if self.max_agentic_loops is not None and deployment_max_agentic_loops is None: + kwargs["max_agentic_loops"] = self.max_agentic_loops # rebind-ok: this hook returns the kwargs it edits + # If the client sent an Anthropic-native web_search_* tool, mark the # request so the agentic loop emits native web_search_tool_result # blocks in the final response (for citations panels, etc.). The flag @@ -926,17 +1035,17 @@ class WebSearchInterceptionLogger(CustomLogger): ) @staticmethod - def _inject_native_blocks(response: Any, native_blocks: Sequence[Mapping[str, object]]) -> Any: + def _inject_native_blocks(response: _ResponseT, native_blocks: Sequence[Mapping[str, object]]) -> _ResponseT: """Prepend native blocks to response content, dict or object form.""" if not native_blocks: return response if isinstance(response, dict): - existing = response.get("content") or [] - response["content"] = list(native_blocks) + list(existing) + existing = response.get(_RESPONSE_CONTENT_FIELD) or [] + response[_RESPONSE_CONTENT_FIELD] = list(native_blocks) + list(existing) return response - existing = getattr(response, "content", None) or [] + existing = getattr(response, _RESPONSE_CONTENT_FIELD, None) or [] try: - response.content = list(native_blocks) + list(existing) + setattr(response, _RESPONSE_CONTENT_FIELD, list(native_blocks) + list(existing)) except (AttributeError, TypeError): # Object refused write — fall through and leave the response # untouched rather than crash the request. @@ -1192,10 +1301,10 @@ class WebSearchInterceptionLogger(CustomLogger): messages: list[dict], tool_calls: list[dict], thinking_blocks: list[dict], - anthropic_messages_optional_request_params: dict, + anthropic_messages_optional_request_params: Mapping[str, object], logging_obj: "LiteLLMLoggingObj | None", stream: bool, - kwargs: dict, + kwargs: Mapping[str, object], ) -> "AnthropicMessagesResponse | AsyncIterator[object]": """Legacy path: execute search + build patch + run follow-up call.""" request_patch, structured_results = await self._build_anthropic_request_patch( @@ -1203,9 +1312,9 @@ class WebSearchInterceptionLogger(CustomLogger): messages=messages, tool_calls=tool_calls, thinking_blocks=thinking_blocks, - anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + anthropic_messages_optional_request_params=dict[str, object](anthropic_messages_optional_request_params), logging_obj=logging_obj, - kwargs=kwargs, + kwargs=dict[str, object](kwargs), ) if request_patch.messages is None: raise ValueError("WebSearchInterception: missing follow-up messages") @@ -1220,12 +1329,14 @@ class WebSearchInterceptionLogger(CustomLogger): if max_tokens is None: max_tokens = cast(int, kwargs.get("max_tokens", 1024)) + patch_kwargs: Final = dict[str, object](request_patch.kwargs) response: AnthropicMessagesResponse | AsyncIterator[object] = await anthropic_messages.acreate( max_tokens=max_tokens, messages=request_patch.messages, model=request_patch.model or model, + **_NO_ACREATE_NAMED, **optional_params, - **request_patch.kwargs, + **patch_kwargs, ) # Legacy path: the new path goes through the typed plan + core @@ -1367,12 +1478,13 @@ class WebSearchInterceptionLogger(CustomLogger): search_tool: Final = self._select_search_tool_from_router(llm_router=llm_router) search_provider: str | None = None - search_litellm_params: dict[str, Any] = {} + search_litellm_params: Mapping[str, object] = {} search_tool_name: Final = self._selected_search_tool_name(search_tool=search_tool) if search_tool is not None: await self._authorize_search_tool(search_tool=search_tool, kwargs=kwargs) - search_litellm_params = dict(search_tool.get("litellm_params", {}) or {}) - search_provider = search_litellm_params.get("search_provider") + tool_params: Final[_SearchToolLitellmParams] = search_tool.get("litellm_params", {}) or {} + search_litellm_params = dict[str, object](tool_params) + search_provider = tool_params.get("search_provider") # Fallback to perplexity if no router or no search tools configured if not search_provider: @@ -1400,12 +1512,15 @@ class WebSearchInterceptionLogger(CustomLogger): if key != "search_provider" and value is not None } result: Final = ( - await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs) + await litellm.asearch( + query=query, search_provider=search_provider, **_NO_ASEARCH_NAMED, **search_kwargs + ) if search_metadata is None else await litellm.asearch( query=query, search_provider=search_provider, litellm_metadata=search_metadata, + **_NO_ASEARCH_NAMED, **search_kwargs, ) ) @@ -1445,8 +1560,7 @@ class WebSearchInterceptionLogger(CustomLogger): valid_token=user_api_key_auth, ) - auth_view: Final[_UserAuthView] = {"team_id": getattr(user_api_key_auth, "team_id", None)} - team_id: Final = auth_view["team_id"] + team_id: Final[str | None] = getattr(user_api_key_auth, "team_id", None) if team_id: from litellm.proxy.proxy_server import ( prisma_client, @@ -1519,16 +1633,18 @@ class WebSearchInterceptionLogger(CustomLogger): def _select_search_tool_from_router(self, llm_router: object) -> "_SearchToolConfig | None": if llm_router is None or not hasattr(llm_router, "search_tools"): return None - search_tools: Final = list(getattr(llm_router, "search_tools") or []) + search_tools: Final = tuple(getattr(llm_router, "search_tools", None) or ()) return self._select_search_tool_from_list(search_tools=search_tools, source="router") def _select_search_tool_from_list( self, - search_tools: list[_SearchToolConfig], + search_tools: Sequence[_SearchToolConfig], source: str, ) -> "_SearchToolConfig | None": if self.search_tool_name: - matching_tools = [tool for tool in search_tools if tool.get("search_tool_name") == self.search_tool_name] + matching_tools: Final = tuple( + tool for tool in search_tools if tool.get("search_tool_name") == self.search_tool_name + ) if matching_tools: search_provider = (matching_tools[0].get("litellm_params", {}) or {}).get("search_provider") verbose_logger.debug( @@ -1561,10 +1677,10 @@ class WebSearchInterceptionLogger(CustomLogger): model: str, messages: list[dict], tool_calls: list[dict], - optional_params: dict, + optional_params: Mapping[str, object], logging_obj: "LiteLLMLoggingObj | None", stream: bool, - kwargs: dict, + kwargs: Mapping[str, object], response_format: str = "openai", ) -> "ModelResponse | CustomStreamWrapper": """Legacy path: execute search + build patch + run follow-up call.""" @@ -1572,8 +1688,8 @@ class WebSearchInterceptionLogger(CustomLogger): model=model, messages=messages, tool_calls=tool_calls, - optional_params=optional_params, - kwargs=kwargs, + optional_params=dict[str, object](optional_params), + kwargs=dict[str, object](kwargs), response_format=response_format, ) if request_patch.messages is None: @@ -1581,11 +1697,13 @@ class WebSearchInterceptionLogger(CustomLogger): params: Final = dict(optional_params) params.update(request_patch.optional_params) params.pop("tool_choice", None) + patch_kwargs: Final = dict[str, object](request_patch.kwargs) return await litellm.acompletion( model=request_patch.model or model, messages=request_patch.messages, + **_NO_ACOMPLETION_NAMED, **params, - **request_patch.kwargs, + **patch_kwargs, ) async def _build_chat_completion_request_patch( diff --git a/litellm/interactions/background_cost_polling.py b/litellm/interactions/background_cost_polling.py new file mode 100644 index 00000000000..51325354e7d --- /dev/null +++ b/litellm/interactions/background_cost_polling.py @@ -0,0 +1,313 @@ +""" +Cost tracking for background interactions. + +A create request with ``background=true`` returns ``in_progress`` with no +usage block, and GET polls are deliberately never billed (billing them would +double-charge every poll; the GET response also does not echo ``background``, +so a poll cannot be told apart from a re-fetch of an already-billed +interaction). The create call is therefore the only place that can own +billing: it schedules a poll task that fetches the interaction until it +reaches a terminal status and logs the final usage as a single success event +attributed to the original request. + +``requires_action`` is terminal for the interaction it names. The API has no +operation that resumes one: a caller answers a tool request by creating a new +interaction whose ``previous_interaction_id`` points at it, and that new +interaction bills itself. The paused interaction keeps the tokens it already +spent producing the tool request, so it is billed and settled where it stops +rather than polled until the timeout, which would both lose that usage and +hold its budget reservation open for the whole timeout window. + +Deleting an interaction makes every subsequent poll fail, which would let a +caller retrieve the completed output themselves and then delete it before the +poll task settles, leaving the work unbilled and the budget reservation +refunded at the poll timeout. ``adelete`` therefore settles any pending poll +for the interaction before dispatching the delete: it fetches the current +state with the create's credentials, bills it if it is terminal with usage, +and releases the reservation otherwise. A settlement gate on the create's +logging object makes the poll task and the delete path mutually exclusive, so +the interaction is billed exactly once no matter who settles first. +""" + +import asyncio +from collections.abc import Awaitable, Callable, Iterator, Mapping +from dataclasses import dataclass +from typing import TYPE_CHECKING, Final, TypeAlias + +from litellm._logging import verbose_logger +from litellm.constants import ( + BACKGROUND_INTERACTION_COST_POLL_INITIAL_INTERVAL_SECONDS, + BACKGROUND_INTERACTION_COST_POLL_MAX_INTERVAL_SECONDS, + BACKGROUND_INTERACTION_COST_POLL_TIMEOUT_SECONDS, + BACKGROUND_INTERACTION_COST_POLLING_ENABLED, +) +from litellm.litellm_core_utils.core_helpers import get_litellm_metadata_from_kwargs +from litellm.types.interactions import InteractionsAPIResponse + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + +_TERMINAL_STATUSES: Final = frozenset( + {"completed", "failed", "cancelled", "incomplete", "budget_exceeded", "requires_action"} +) + +_POLLABLE_STATUSES: Final = frozenset({"in_progress", "queued"}) + +_STATUSES_THAT_PRODUCED_OUTPUT: Final = frozenset({"completed", "requires_action"}) + + +@dataclass(frozen=True, slots=True) +class BackgroundInteractionPollContext: + interaction_id: str + custom_llm_provider: str + logging_obj: "LiteLLMLoggingObj" + api_key: str | None = None + api_base: str | None = None + initial_interval_seconds: float = BACKGROUND_INTERACTION_COST_POLL_INITIAL_INTERVAL_SECONDS + max_interval_seconds: float = BACKGROUND_INTERACTION_COST_POLL_MAX_INTERVAL_SECONDS + timeout_seconds: float = BACKGROUND_INTERACTION_COST_POLL_TIMEOUT_SECONDS + + +FetchInteraction: TypeAlias = Callable[[BackgroundInteractionPollContext], Awaitable[InteractionsAPIResponse]] + + +async def _fetch_interaction(context: BackgroundInteractionPollContext) -> InteractionsAPIResponse: + from litellm.interactions import aget + + return await aget( + interaction_id=context.interaction_id, + custom_llm_provider=context.custom_llm_provider, + api_key=context.api_key, + api_base=context.api_base, + **{ + "no-log": True + }, # mutable-ok: "no-log" is not a valid identifier, so it can only be passed through a mapping + ) + + +def _poll_intervals(initial: float, maximum: float, timeout: float) -> Iterator[float]: + elapsed = 0.0 + interval = initial + while interval > 0 and elapsed + interval <= timeout: + yield interval + elapsed += interval + interval = min(interval * 2, maximum) + + +_SETTLED_KEY = "background_interaction_settled" + + +def _is_settled(logging_obj: "LiteLLMLoggingObj") -> bool: + return logging_obj.model_call_details.get(_SETTLED_KEY) is True + + +def _claim_settlement(logging_obj: "LiteLLMLoggingObj") -> bool: + """ + Exactly-once gate between the poll task and the delete-time settlement: + both run on the same event loop and neither awaits between reading and + setting the flag, so whichever claims first owns billing or release. + """ + if _is_settled(logging_obj): + return False + logging_obj.model_call_details[_SETTLED_KEY] = True # rebind-ok: both settlers must see the same settlement flag + return True + + +async def poll_and_log_background_interaction_cost( + context: BackgroundInteractionPollContext, + fetch_interaction: FetchInteraction = _fetch_interaction, +) -> None: + last_seen_status: str | None = None + for interval in _poll_intervals( + initial=context.initial_interval_seconds, + maximum=context.max_interval_seconds, + timeout=context.timeout_seconds, + ): + await asyncio.sleep(interval) + if _is_settled(context.logging_obj): + return + try: + response = await fetch_interaction(context) + except Exception as e: # noqa: BLE001 # any fetch error must not kill the billing poll loop + verbose_logger.debug( + "Background interaction cost poll for %s failed, will retry: %s", + context.interaction_id, + e, + ) + continue + last_seen_status = response.status + if response.status not in _TERMINAL_STATUSES: + continue + if not _claim_settlement(context.logging_obj): + return + if response.usage is not None: + await _bill_settled_interaction(logging_obj=context.logging_obj, response=response) + else: + await _release_open_budget_reservation(logging_obj=context.logging_obj) + return + if not _claim_settlement(context.logging_obj): + return + if last_seen_status is not None and last_seen_status not in _POLLABLE_STATUSES: + verbose_logger.error( + "Gave up cost polling for background interaction %s after %ss: its last status %r is in neither " + "the pollable nor the terminal set, so this proxy never learned how to settle it and its usage " + "will not be tracked", + context.interaction_id, + context.timeout_seconds, + last_seen_status, + ) + else: + verbose_logger.warning( + "Gave up cost polling for background interaction %s after %ss; its usage will not be tracked", + context.interaction_id, + context.timeout_seconds, + ) + await _release_open_budget_reservation(logging_obj=context.logging_obj) + + +async def _release_open_budget_reservation(logging_obj: "LiteLLMLoggingObj") -> None: + """ + The proxy keeps the pre-call budget reservation open for an in-progress + background interaction so concurrent creates cannot stack past the budget. + The completion success event reconciles it to the actual cost; when the + interaction terminates without billable usage (or polling gives up, or it + is deleted before settling), no such event fires, so whoever claims the + settlement must release the reservation here or the spend counters stay + pinned at the estimated cost. + """ + metadata = get_litellm_metadata_from_kwargs(kwargs=logging_obj.model_call_details) + budget_reservation = metadata.get("user_api_key_budget_reservation") + if not isinstance(budget_reservation, dict): + return + + from litellm.proxy.spend_tracking.budget_reservation import release_budget_reservation + + try: + await release_budget_reservation(budget_reservation=budget_reservation) + except Exception: # noqa: BLE001 # a failed release must not crash the poll task; counters expire via TTL + verbose_logger.exception("Failed to release budget reservation for an unbilled background interaction") + + +async def _bill_settled_interaction(logging_obj: "LiteLLMLoggingObj", response: InteractionsAPIResponse) -> None: + """ + Claiming the settlement makes the claimer solely responsible for the + reservation, and no one retries a claim that is already set. A billing + failure here must therefore release the reservation on its way out, or it + stays pinned at the estimated cost until the whole poll times out. + """ + try: + await logging_obj.async_log_background_interaction_completion(result=response) + except Exception: + await _release_open_budget_reservation(logging_obj=logging_obj) + raise + + +def is_pollable_background_interaction(response: InteractionsAPIResponse) -> bool: + """ + The single gate deciding whether a create's response gets a poll task. + The proxy's success callback defers releasing the budget reservation for + exactly these responses, on the promise that a poll task will settle them, + so a response one site accepts and the other refuses strands its + reservation on the spend counters with nothing left to reconcile it. + + ``queued`` belongs here alongside ``in_progress``. It is the API's + not-started-yet state, so it reaches a terminal status the same way and + needs polling for the same reason: nothing else in the proxy ever bills a + create that came back without usage, so a status missing from both this + set and ``_TERMINAL_STATUSES`` is billed nowhere and alerts nobody. + """ + return response.status in _POLLABLE_STATUSES and bool(response.id) + + +def missing_usage_is_expected(response: InteractionsAPIResponse) -> bool: + """ + Whether a response arriving with no usage block is a normal outcome rather + than lost billing data. An interaction that is still running, or that + stopped at ``failed``, ``cancelled``, ``incomplete`` or ``budget_exceeded``, + has nothing to charge for and should not raise a cost-tracking alarm. + + ``completed`` and ``requires_action`` both mean the model produced output, + so a usage block is always expected with them. If one arrives without it + the charge for real work has been lost, which is precisely what the + proxy's cost-tracking alert exists to surface. + """ + return response.status not in _STATUSES_THAT_PRODUCED_OUTPUT + + +@dataclass(frozen=True, slots=True) +class _ActiveBackgroundPoll: + task: "asyncio.Task[None]" + context: BackgroundInteractionPollContext + + +_ACTIVE_POLLS: dict[str, _ActiveBackgroundPoll] = {} # mutable-ok: asyncio needs strong refs to running poll tasks + + +def _discard_poll(interaction_id: str, task: "asyncio.Task[None]") -> None: + entry = _ACTIVE_POLLS.get(interaction_id) + if entry is not None and entry.task is task: + del _ACTIVE_POLLS[interaction_id] + + +def maybe_schedule_background_interaction_cost_polling( + response: object, + create_kwargs: Mapping[str, object], + custom_llm_provider: str, +) -> "asyncio.Task[None] | None": + from litellm.litellm_core_utils.litellm_logging import Logging + + if not BACKGROUND_INTERACTION_COST_POLLING_ENABLED: + return None + if not isinstance(response, InteractionsAPIResponse): + return None + if not is_pollable_background_interaction(response): + return None + logging_obj = create_kwargs.get("litellm_logging_obj") + if not isinstance(logging_obj, Logging): + return None + try: + asyncio.get_running_loop() + except RuntimeError: + return None + api_key = create_kwargs.get("api_key") + api_base = create_kwargs.get("api_base") + context = BackgroundInteractionPollContext( + interaction_id=response.id, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + api_key=api_key if isinstance(api_key, str) else None, + api_base=api_base if isinstance(api_base, str) else None, + ) + task = asyncio.create_task(poll_and_log_background_interaction_cost(context)) + _ACTIVE_POLLS[context.interaction_id] = _ActiveBackgroundPoll(task=task, context=context) + task.add_done_callback( + lambda finished, interaction_id=context.interaction_id: _discard_poll(interaction_id, finished) + ) + return task + + +async def maybe_settle_background_interaction_before_delete( + interaction_id: str, + fetch_interaction: FetchInteraction = _fetch_interaction, +) -> None: + entry = _ACTIVE_POLLS.get(interaction_id) + if entry is None: + return + context = entry.context + try: + response = await fetch_interaction(context) + except Exception as e: # noqa: BLE001 # unfetchable pre-delete state settles by releasing the reservation + verbose_logger.debug( + "Could not fetch background interaction %s before delete, releasing its reservation: %s", + interaction_id, + e, + ) + if _claim_settlement(context.logging_obj): + await _release_open_budget_reservation(logging_obj=context.logging_obj) + return + if not _claim_settlement(context.logging_obj): + return + if response.status in _TERMINAL_STATUSES and response.usage is not None: + await _bill_settled_interaction(logging_obj=context.logging_obj, response=response) + return + await _release_open_budget_reservation(logging_obj=context.logging_obj) diff --git a/litellm/interactions/main.py b/litellm/interactions/main.py index 3e8c381fdf7..a2c3d510fae 100644 --- a/litellm/interactions/main.py +++ b/litellm/interactions/main.py @@ -40,6 +40,10 @@ from typing import Any, Final import httpx import litellm +from litellm.interactions.background_cost_polling import ( + maybe_schedule_background_interaction_cost_polling, + maybe_settle_background_interaction_before_delete, +) from litellm.interactions.http_handler import interactions_http_handler from litellm.interactions.utils import ( InteractionsAPIRequestUtils, @@ -171,6 +175,12 @@ async def acreate( else: response = init_response + maybe_schedule_background_interaction_cost_polling( + response=response, + create_kwargs=kwargs, + custom_llm_provider=custom_llm_provider, + ) + return response except Exception as e: raise litellm.exception_type( @@ -462,6 +472,8 @@ async def adelete( loop: Final = asyncio.get_event_loop() kwargs["adelete_interaction"] = True + await maybe_settle_background_interaction_before_delete(interaction_id=interaction_id) + func: Final = partial( delete, interaction_id=interaction_id, diff --git a/litellm/interactions/utils.py b/litellm/interactions/utils.py index 8a1e8836894..3895a85061d 100644 --- a/litellm/interactions/utils.py +++ b/litellm/interactions/utils.py @@ -47,6 +47,13 @@ def get_provider_interactions_api_config( return GoogleAIStudioInteractionsConfig() + if provider in (LlmProviders.VERTEX_AI.value, LlmProviders.VERTEX_AI_BETA.value): + from litellm.llms.vertex_ai.interactions.transformation import ( + VertexAIInteractionsConfig, + ) + + return VertexAIInteractionsConfig() + return None diff --git a/litellm/litellm_core_utils/agentic_loop_settings.py b/litellm/litellm_core_utils/agentic_loop_settings.py new file mode 100644 index 00000000000..3dd8d437aef --- /dev/null +++ b/litellm/litellm_core_utils/agentic_loop_settings.py @@ -0,0 +1,59 @@ +""" +Shared validation for the agentic loop ceiling. + +``max_agentic_loops`` can be set in two places, and the two disagreed about +what a bad value means. The feature-level +``litellm_settings.websearch_interception_params.max_agentic_loops`` was +checked at config load, while a per-deployment +``model_list[].litellm_params.max_agentic_loops`` was passed straight through +to ``int(... or 3)``. That let a per-deployment ``0`` read as the default 3, +turning the tightest ceiling into the loosest one, and let a per-deployment +``"three"`` boot the proxy and then fail every request to that model. + +Both settings now go through :func:`validated_max_agentic_loops`, which names +the field it rejected so the error says which line of the config to fix. + +Anything that spells a whole number is still accepted, because the old +``int(... or 3)`` accepted those and a ceiling is routinely parameterized as +``max_agentic_loops: os.environ/MAX_AGENTIC_LOOPS``, which resolves to a +string. Rejecting ``"5"`` would stop such a proxy from booting on upgrade. +""" + +from typing import Final + +DEFAULT_MAX_AGENTIC_LOOPS: Final = 3 + + +def _as_whole_number(value: object) -> int | None: + """ + Return ``value`` as an int when it spells a whole number, else ``None``. + + ``bool`` is excluded explicitly because it is an ``int`` subclass, so + ``max_agentic_loops: true`` would otherwise be read as a ceiling of 1. + """ + if isinstance(value, bool): + return None + if isinstance(value, int): + return value + if isinstance(value, float): + return int(value) if value.is_integer() else None + if isinstance(value, str): + try: + return int(value.strip()) + except ValueError: + return None + return None + + +def validated_max_agentic_loops(max_agentic_loops: object, field: str) -> int | None: + """ + Return ``max_agentic_loops`` as an int, or raise naming ``field``. + """ + if max_agentic_loops is None: + return None + ceiling: Final = _as_whole_number(max_agentic_loops) + if ceiling is None: + raise TypeError(f"{field} must be an integer, got {max_agentic_loops!r}") + if ceiling < 1: + raise ValueError(f"{field} must be at least 1, got {ceiling}") + return ceiling diff --git a/litellm/litellm_core_utils/audio_utils/subtitle_utils.py b/litellm/litellm_core_utils/audio_utils/subtitle_utils.py new file mode 100644 index 00000000000..91427ba09ad --- /dev/null +++ b/litellm/litellm_core_utils/audio_utils/subtitle_utils.py @@ -0,0 +1,279 @@ +"""Provider-agnostic SRT/WebVTT subtitle synthesis from timestamped transcription tokens.""" + +import unicodedata +from collections.abc import Sequence +from dataclasses import dataclass +from itertools import accumulate, groupby +from typing import Final + +from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError + +CUE_MAX_CHARS: Final = 84 +CUE_MAX_DURATION_MS: Final = 7000 +CUE_GAP_MS: Final = 700 + +SRT_RESPONSE_FORMAT: Final = "srt" +VTT_RESPONSE_FORMAT: Final = "vtt" +SUBTITLE_RESPONSE_FORMATS: Final = frozenset((SRT_RESPONSE_FORMAT, VTT_RESPONSE_FORMAT)) + +_SENTENCE_END_CHARS: Final = (".", "!", "?", "。", "！", "？", "؟", "۔", "।", "॥", "։", "።") + +_CJK_RANGES: Final = ( + (0x3400, 0x4DBF), + (0x4E00, 0x9FFF), + (0xF900, 0xFAFF), + (0x3040, 0x309F), + (0x30A0, 0x30FF), + (0x31F0, 0x31FF), +) + +_CJK_NO_BREAK_BEFORE: Final = "、。，．！？：；・ー…」』）〉》】〕" + +_CJK_NO_BREAK_AFTER: Final = "「『（〈《【〔" + + +@dataclass(frozen=True, slots=True) +class SubtitleToken: + text: str + start_ms: int | None = None + end_ms: int | None = None + speaker: str | int | None = None + + +@dataclass(frozen=True, slots=True) +class SubtitleCue: + start_ms: int + end_ms: int + text: str + + +@dataclass(frozen=True, slots=True) +class _Word: + text: str + start_ms: int | None + end_ms: int | None + speaker: str | int | None + + +def _is_cjk(ch: str) -> bool: + cp: Final = ord(ch) + return any(lo <= cp <= hi for lo, hi in _CJK_RANGES) + + +def _is_cjk_word_boundary(prev_ch: str, next_ch: str) -> bool: + if not (_is_cjk(prev_ch) or _is_cjk(next_ch)): + return False + return next_ch not in _CJK_NO_BREAK_BEFORE and prev_ch not in _CJK_NO_BREAK_AFTER + + +def _text_width(text: str) -> int: + return sum(2 if unicodedata.east_asian_width(ch) in ("W", "F") else 1 for ch in text) + + +def _starts_new_word(prev: SubtitleToken, token: SubtitleToken) -> bool: + prev_last: Final = prev.text[-1:] + first: Final = token.text[0] + return ( + first.isspace() + or prev_last.isspace() + or token.speaker != prev.speaker + or _is_cjk_word_boundary(prev_last, first) + ) + + +def _build_word(group: Sequence[SubtitleToken]) -> _Word: + return _Word( + text="".join(t.text for t in group), + start_ms=next((t.start_ms for t in group if t.start_ms is not None), None), + end_ms=next((t.end_ms for t in reversed(group) if t.end_ms is not None), None), + speaker=group[0].speaker, + ) + + +def _merge_tokens_into_words(tokens: Sequence[SubtitleToken]) -> tuple[_Word, ...]: + """ + Merge subword tokens (e.g. ``"Hel"``, ``"lo"``) into whole words. + + A token starts a new word when its text begins with whitespace, when the + previous token's text ends with whitespace, when the speaker changes, or + at a CJK character boundary (CJK scripts carry no spaces, so without this + an entire utterance would fuse into a single unbreakable "word"; CJK + punctuation stays attached to the preceding character per kinsoku rules). + Each word carries the first/last available timestamps of its tokens. + """ + kept: Final = tuple(t for t in tokens if t.text != "") + starts: Final = tuple(i for i, t in enumerate(kept) if i == 0 or _starts_new_word(kept[i - 1], t)) + return tuple(_build_word(kept[begin:end]) for begin, end in zip(starts, (*starts[1:], len(kept)))) + + +def _cue_start(ws: Sequence[_Word]) -> int | None: + return next((w.start_ms for w in ws if w.start_ms is not None), None) + + +def _cue_end(ws: Sequence[_Word]) -> int | None: + return next((w.end_ms for w in reversed(ws) if w.end_ms is not None), _cue_start(ws)) + + +def _cue_text(ws: Sequence[_Word]) -> str: + return "".join(w.text for w in ws).strip() + + +def _should_break(cue: Sequence[_Word], word: _Word) -> bool: + speaker_changed: Final = word.speaker is not None and any( + w.speaker is not None and w.speaker != word.speaker for w in cue + ) + cue_start: Final = _cue_start(cue) + cue_end: Final = _cue_end(cue) + gap_exceeded: Final = word.start_ms is not None and cue_end is not None and (word.start_ms - cue_end) >= CUE_GAP_MS + chars_exceeded: Final = _text_width(_cue_text(cue)) + _text_width(word.text) > CUE_MAX_CHARS + word_end: Final = word.end_ms if word.end_ms is not None else word.start_ms + duration_exceeded: Final = ( + word_end is not None and cue_start is not None and (word_end - cue_start) > CUE_MAX_DURATION_MS + ) + return speaker_changed or gap_exceeded or chars_exceeded or duration_exceeded + + +def _cue_start_indices(words: Sequence[_Word]) -> tuple[int, ...]: + def next_start(start: int, index: int) -> int: + if words[index - 1].text.rstrip().endswith(_SENTENCE_END_CHARS): + return index + if _should_break(words[start:index], words[index]): + return index + return start + + if not words: + return () + return tuple(start for start, _ in groupby(accumulate(range(1, len(words)), next_start, initial=0))) + + +def _build_cue(ws: Sequence[_Word]) -> SubtitleCue | None: + text: Final = _cue_text(ws) + start: Final = _cue_start(ws) + if not text or start is None: + return None + end: Final = _cue_end(ws) + return SubtitleCue(start_ms=start, end_ms=end if end is not None else start, text=text) + + +def group_subtitle_tokens_into_cues(tokens: Sequence[SubtitleToken]) -> tuple[SubtitleCue, ...]: + """ + Group transcription tokens into subtitle cues aligned to the actual speech. + + Cues only ever break at word boundaries (tokens may be subwords, so they + are first merged into words). A new cue starts when: + - the speaker changes (if diarization is on), + - a silence gap of at least CUE_GAP_MS separates two words, so + subtitles never bridge pauses in speech, + - adding the next word would exceed CUE_MAX_CHARS of display width + (~two subtitle lines; East-Asian wide characters count double), or + - adding the next word would make the cue span more than + CUE_MAX_DURATION_MS. + A cue also ends after sentence-final punctuation, which keeps cue breaks + at natural seams. Cue timestamps come straight from token timestamps; + words without timestamps stay attached to the surrounding cue, and a cue + whose words carry no timestamps at all is dropped. + """ + words: Final = _merge_tokens_into_words(tokens) + starts: Final = _cue_start_indices(words) + return tuple( + cue + for begin, end in zip(starts, (*starts[1:], len(words))) + if (cue := _build_cue(words[begin:end])) is not None + ) + + +def _format_timestamp(total_ms: int, millis_separator: str) -> str: + clamped: Final = max(total_ms, 0) + hours, hour_remainder = divmod(clamped, 3_600_000) + minutes, minute_remainder = divmod(hour_remainder, 60_000) + seconds, millis = divmod(minute_remainder, 1_000) + return f"{hours:02d}:{minutes:02d}:{seconds:02d}{millis_separator}{millis:03d}" + + +def _render_srt(cues: Sequence[SubtitleCue]) -> str: + lines: Final = tuple( + line + for index, cue in enumerate(cues, start=1) + for line in ( + str(index), + f"{_format_timestamp(cue.start_ms, ',')} --> {_format_timestamp(cue.end_ms, ',')}", + cue.text, + "", + ) + ) + return "\n".join(lines) + + +def _render_vtt(cues: Sequence[SubtitleCue]) -> str: + cue_lines: Final = tuple( + line + for cue in cues + for line in ( + f"{_format_timestamp(cue.start_ms, '.')} --> {_format_timestamp(cue.end_ms, '.')}", + cue.text, + "", + ) + ) + return "\n".join(("WEBVTT", "", *cue_lines)) + + +def render_subtitle_tokens_as_srt(tokens: Sequence[SubtitleToken]) -> str: + """Render tokens as an SRT document; empty string when no token has timestamp data.""" + cues: Final = group_subtitle_tokens_into_cues(tokens) + if not cues: + return "" + return _render_srt(cues) + + +def render_subtitle_tokens_as_vtt(tokens: Sequence[SubtitleToken]) -> str: + """Render tokens as a WebVTT document; the WEBVTT header is emitted even without cues.""" + return _render_vtt(group_subtitle_tokens_into_cues(tokens)) + + +class TranscriptionWordTiming(BaseModel): + model_config = ConfigDict(frozen=True, extra="ignore") + + word: str = "" + start: float | None = None + end: float | None = None + speaker: str | None = None + + +_WORD_TIMINGS_ADAPTER: Final = TypeAdapter(tuple[TranscriptionWordTiming, ...]) + + +def _seconds_to_ms(seconds: float | None) -> int | None: + if seconds is None: + return None + return round(seconds * 1000) + + +def _word_to_subtitle_token(word: TranscriptionWordTiming) -> SubtitleToken: + return SubtitleToken( + text=f"{word.word} ", + start_ms=_seconds_to_ms(word.start), + end_ms=_seconds_to_ms(word.end), + speaker=word.speaker, + ) + + +def _parse_word_timings(words: object) -> tuple[TranscriptionWordTiming, ...]: + try: + return _WORD_TIMINGS_ADAPTER.validate_python(words) + except ValidationError: + return () + + +def synthesize_subtitle_document(words: object, response_format: str) -> str | None: + """ + Build an SRT/VTT document from OpenAI verbose_json-style word dicts + (word/start/end in float seconds, optional speaker). Returns None when the + format is not a subtitle format or the words carry no usable timestamps. + """ + if response_format not in SUBTITLE_RESPONSE_FORMATS: + return None + tokens: Final = tuple(_word_to_subtitle_token(word) for word in _parse_word_timings(words)) + cues: Final = group_subtitle_tokens_into_cues(tokens) + if not cues: + return None + return _render_srt(cues) if response_format == SRT_RESPONSE_FORMAT else _render_vtt(cues) diff --git a/litellm/litellm_core_utils/audio_utils/utils.py b/litellm/litellm_core_utils/audio_utils/utils.py index 3b3775a8fe6..dab3e48f91a 100644 --- a/litellm/litellm_core_utils/audio_utils/utils.py +++ b/litellm/litellm_core_utils/audio_utils/utils.py @@ -7,7 +7,13 @@ import os from dataclasses import dataclass from typing import Final -from litellm.types.files import get_file_mime_type_from_extension +from litellm.types.files import ( + AUDIO_FILE_TYPES, + FILE_EXTENSIONS, + FILE_MIME_TYPES, + FileType, + get_file_mime_type_from_extension, +) from litellm.types.utils import FileTypes @@ -323,3 +329,75 @@ def calculate_request_duration(file: FileTypes) -> float | None: except Exception: # Silently fail if duration extraction fails return None + + +DEFAULT_SPEECH_MEDIA_TYPE: Final = "audio/mpeg" + + +def _speech_media_type_for_response_format(response_format: str) -> str | None: + file_type: Final = next( + (candidate for candidate, extensions in FILE_EXTENSIONS.items() if response_format.lower() in extensions), + None, + ) + if file_type is None or file_type not in AUDIO_FILE_TYPES: + return None + return FILE_MIME_TYPES[file_type] + + +def resolve_speech_media_type(upstream_content_type: str | None, response_format: str | None) -> str: + upstream_media_type: Final = (upstream_content_type or "").split(";", 1)[0].strip().lower() + if upstream_media_type.startswith("audio/"): + return upstream_media_type + requested_media_type: Final = ( + None if response_format is None else _speech_media_type_for_response_format(response_format) + ) + return requested_media_type or DEFAULT_SPEECH_MEDIA_TYPE + + +_OGG_OPUS_HEAD_WINDOW: Final = 64 +_ADTS_SYNC_AND_LAYER_MASK: Final = 0xF6 +_ADTS_SYNC_AND_LAYER: Final = 0xF0 +_ADTS_SAMPLE_RATE_INDEX_LIMIT: Final = 13 +_MPEG_SYNC_MASK: Final = 0xE0 +_MPEG_LAYER_MASK: Final = 0x06 +_MPEG_RESERVED_VERSION: Final = 0x01 +_MPEG_INVALID_BITRATE_INDEX: Final = 0x0F +_MPEG_RESERVED_SAMPLE_RATE_INDEX: Final = 0x03 + + +def _adts_aac_frame_media_type(header: bytes) -> str | None: + sample_rate_index: Final = (header[2] >> 2) & 0x0F + return FILE_MIME_TYPES[FileType.AAC] if sample_rate_index < _ADTS_SAMPLE_RATE_INDEX_LIMIT else None + + +def _mpeg_audio_frame_media_type(header: bytes) -> str | None: + version: Final = (header[1] >> 3) & 0x03 + layer: Final = header[1] & _MPEG_LAYER_MASK + bitrate_index: Final = header[2] >> 4 + sample_rate_index: Final = (header[2] >> 2) & 0x03 + if ( + (header[1] & _MPEG_SYNC_MASK) != _MPEG_SYNC_MASK + or version == _MPEG_RESERVED_VERSION + or layer == 0 + or bitrate_index == _MPEG_INVALID_BITRATE_INDEX + or sample_rate_index == _MPEG_RESERVED_SAMPLE_RATE_INDEX + ): + return None + return FILE_MIME_TYPES[FileType.MP3] + + +def speech_media_type_from_audio_bytes(audio: bytes) -> str | None: + if audio[:4] == b"RIFF" and audio[8:12] == b"WAVE": + return FILE_MIME_TYPES[FileType.WAV] + if audio[:4] == b"fLaC": + return FILE_MIME_TYPES[FileType.FLAC] + if audio[:4] == b"OggS": + is_opus: Final = b"OpusHead" in audio[:_OGG_OPUS_HEAD_WINDOW] + return FILE_MIME_TYPES[FileType.OPUS if is_opus else FileType.OGG] + if audio[:3] == b"ID3": + return FILE_MIME_TYPES[FileType.MP3] + if len(audio) < 3 or audio[0] != 0xFF: + return None + if (audio[1] & _ADTS_SYNC_AND_LAYER_MASK) == _ADTS_SYNC_AND_LAYER: + return _adts_aac_frame_media_type(audio) + return _mpeg_audio_frame_media_type(audio) diff --git a/litellm/litellm_core_utils/aws_partition.py b/litellm/litellm_core_utils/aws_partition.py new file mode 100644 index 00000000000..f8ca3aa4473 --- /dev/null +++ b/litellm/litellm_core_utils/aws_partition.py @@ -0,0 +1,55 @@ +import re +from types import MappingProxyType +from typing import Final, NamedTuple + + +class AwsPartition(NamedTuple): + partition: str + dns_suffix: str + + +_COMMERCIAL_PARTITION: Final = AwsPartition(partition="aws", dns_suffix="amazonaws.com") + +_PARTITIONS_BY_REGION_PREFIX: Final = MappingProxyType( + { + "cn-": AwsPartition(partition="aws-cn", dns_suffix="amazonaws.com.cn"), + "us-gov-": AwsPartition(partition="aws-us-gov", dns_suffix="amazonaws.com"), + "us-isob-": AwsPartition(partition="aws-iso-b", dns_suffix="sc2s.sgov.gov"), + "us-isof-": AwsPartition(partition="aws-iso-f", dns_suffix="csp.hci.ic.gov"), + "us-iso-": AwsPartition(partition="aws-iso", dns_suffix="c2s.ic.gov"), + "eu-isoe-": AwsPartition(partition="aws-iso-e", dns_suffix="cloud.adc-e.uk"), + } +) + +_BEDROCK_ARN_PATTERN: Final = re.compile(r"arn:aws(?:-[a-z0-9-]+)?:bedrock") +_BEDROCK_ARN_PREFIX_PATTERN: Final = re.compile(r"\Aarn:aws(?:-[a-z0-9-]+)?:bedrock:") +_AWS_ARN_PATTERN: Final = re.compile(r"arn:aws(?:-[a-z0-9-]+)?:") + + +def get_aws_partition(aws_region_name: str | None) -> AwsPartition: + if not aws_region_name: + return _COMMERCIAL_PARTITION + return next( + (partition for prefix, partition in _PARTITIONS_BY_REGION_PREFIX.items() if aws_region_name.startswith(prefix)), + _COMMERCIAL_PARTITION, + ) + + +def get_aws_dns_suffix(aws_region_name: str | None) -> str: + return get_aws_partition(aws_region_name).dns_suffix + + +def get_aws_arn_prefix(aws_region_name: str | None) -> str: + return f"arn:{get_aws_partition(aws_region_name).partition}:" + + +def contains_bedrock_arn(value: str) -> bool: + return _BEDROCK_ARN_PATTERN.search(value) is not None + + +def is_bedrock_arn(value: str) -> bool: + return _BEDROCK_ARN_PREFIX_PATTERN.match(value) is not None + + +def contains_aws_arn(value: str) -> bool: + return _AWS_ARN_PATTERN.search(value) is not None diff --git a/litellm/litellm_core_utils/chat_completion_agentic_loop.py b/litellm/litellm_core_utils/chat_completion_agentic_loop.py index b91c1785a54..c8e9e2583ba 100644 --- a/litellm/litellm_core_utils/chat_completion_agentic_loop.py +++ b/litellm/litellm_core_utils/chat_completion_agentic_loop.py @@ -1,12 +1,20 @@ # this is a patch to allow for agentic loops covering llm_http_handler.py and openai sdk based calling flows for the .completion() api import json +from collections.abc import Mapping from typing import Final, cast from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.agentic_loop_settings import ( + DEFAULT_MAX_AGENTIC_LOOPS, + validated_max_agentic_loops, +) +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObject +from litellm.llms.base_llm.base_model_iterator import MockResponseIterator from litellm.types.integrations.custom_logger import ( CHAT_COMPLETION_AGENTIC_SURFACE, + HEADROOM_CONVERTED_STREAM_KEY, NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES, AgenticLoopPlan, AgenticLoopRequestPatch, @@ -46,13 +54,22 @@ def _post_hook_overridden(callback: CustomLogger) -> bool: return getattr(func, "__func__", func) is not getattr(base, "__func__", base) +def _converted_stream_requested(kwargs: Mapping[str, object]) -> bool: + return bool( + kwargs.get("_code_interpreter_interception_converted_stream") or kwargs.get(HEADROOM_CONVERTED_STREAM_KEY) + ) + + def _coerce_int(value: object, default: int) -> int: return int(value) if isinstance(value, (int, str)) else default def _agentic_loop_settings(kwargs: dict[str, object]) -> tuple[int, int, list[str]]: depth: Final = _coerce_int(kwargs.get("_agentic_loop_depth"), 0) - max_loops: Final = max(_coerce_int(kwargs.get("max_agentic_loops"), 3), 1) + configured: Final = validated_max_agentic_loops( + kwargs.get("max_agentic_loops"), field="litellm_params.max_agentic_loops" + ) + max_loops: Final = DEFAULT_MAX_AGENTIC_LOOPS if configured is None else configured raw_fingerprints: Final = kwargs.get("_agentic_loop_fingerprints") fingerprints: Final = [str(fp) for fp in raw_fingerprints] if isinstance(raw_fingerprints, list) else [] return depth, max_loops, fingerprints @@ -80,16 +97,24 @@ def _check_agentic_loop_safety( return fingerprint -def _wrap_response_as_fake_stream(response: object) -> object: - if getattr(response, "object", None) == "chat.completion.chunk": +def _wrap_response_as_fake_stream( + response: object, + *, + model: str, + custom_llm_provider: str, + logging_obj: object, +) -> object: + if isinstance(response, CustomStreamWrapper): return response - if not hasattr(response, "choices"): + if not isinstance(response, ModelResponse) or not isinstance(logging_obj, LiteLLMLoggingObject): return response - from litellm.llms.base_llm.base_model_iterator import ( - convert_model_response_to_streaming, - ) - return convert_model_response_to_streaming(cast(ModelResponse, response)) + return CustomStreamWrapper( + completion_stream=MockResponseIterator(model_response=response), + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + ) def _add_agentic_loop_metadata(kwargs_for_followup: dict[str, object]) -> None: @@ -170,8 +195,13 @@ async def _execute_chat_completion_agentic_plan( model, str(e), ) - if kwargs.get("_code_interpreter_interception_converted_stream") and not depth: - return _wrap_response_as_fake_stream(response_followup) + if _converted_stream_requested(kwargs) and not depth: + return _wrap_response_as_fake_stream( + response_followup, + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + ) return response_followup finally: try: @@ -295,9 +325,14 @@ async def maybe_run_chat_completion_agentic_loop( str(e), ) - if kwargs.get("_code_interpreter_interception_converted_stream") and not depth and hasattr(response, "choices"): + if _converted_stream_requested(kwargs) and not depth: return cast( "ModelResponse | CustomStreamWrapper", - _wrap_response_as_fake_stream(response), + _wrap_response_as_fake_stream( + response, + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging_obj, + ), ) return None diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index de1092bc02f..1738e30d865 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -58,6 +58,67 @@ def safe_divide( return numerator / denominator +def _is_litellm_limit_rejection(exception: BaseException) -> bool: + from litellm.exceptions import RateLimitErrorCategory + + litellm_limit_categories: Final = frozenset( + (RateLimitErrorCategory.LITELLM_RATE_LIMIT.value, RateLimitErrorCategory.LITELLM_BATCH_RATE_LIMIT.value) + ) + return getattr(exception, "category", None) in litellm_limit_categories + + +def _is_proxy_rejection(exception: BaseException) -> bool: + if _is_litellm_limit_rejection(exception): + return True + try: + from starlette.exceptions import HTTPException + except ImportError: + return False + return isinstance(exception, HTTPException) + + +def _is_provider_originated(exception: BaseException) -> bool: + if _is_proxy_rejection(exception): + return False + if getattr(exception, "llm_provider", None): + return True + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + return isinstance(exception, BaseLLMException) + + +def is_expected_client_error(exception: BaseException | None) -> bool: + """ + True when the proxy itself rejected the request with an HTTP 4xx before any + provider call (bad key, budget, unknown model, guardrail). A 4xx returned by + a provider is an upstream or deployment problem, so it is never an expected + client error and keeps its traceback: a mapped litellm exception carries + ``llm_provider``, and the raw ``BaseLLMException`` that provider handlers + raise before mapping (the /v1/messages route surfaces it as-is) is one too. + The proxy's own limiters raise ``HTTPException`` subclasses that also carry + an ``llm_provider``, so any ``HTTPException`` stays a proxy rejection, and + so does any exception whose unified rate-limit ``category`` names litellm's + own limiter (``BudgetExceededError`` is a plain ``Exception`` that the auth + handler decorates with the requested model's provider). + + ProxyException stores the status on .code (as a str), HTTPException and + litellm exceptions on .status_code. + """ + if exception is None: + return False + if _is_provider_originated(exception): + return False + code: Final[object] = getattr(exception, "code", None) + status_code: Final[object] = code if code is not None else getattr(exception, "status_code", None) + if status_code is None or isinstance(status_code, bool): + return False + try: + status: Final = int(str(status_code)) + except ValueError: + return False + return 400 <= status < 500 + + def coerce_token_limit(value: object) -> int | None: """ Coerce a max_input_tokens / max_output_tokens value to an int, treating a @@ -393,6 +454,62 @@ def safe_deep_copy(data): return new_data +def independent_snapshot( + data: dict, # mutable-ok: caller-defined request-payload shape +) -> dict: # mutable-ok: caller-defined request-payload shape + """ + A copy of ``data`` whose top-level keys are deep-copied independently + where possible -- always attempted, regardless of + ``litellm.safe_memory_mode``. Unlike ``safe_deep_copy``, which can return + the *original* object outright under that mode (defeating any isolation + guarantee for every key, not just the ones that need it), this never + skips copying wholesale. + + Real proxy requests carry ``data["litellm_logging_obj"]`` (a ``Logging`` + instance nesting a live OTel span with a real lock) by the time + ``pre_call_hook`` runs, which can never be deep-copied. Any individual + key that fails to deep-copy falls back to sharing its original + reference, same crash tolerance as ``safe_deep_copy``'s own per-key + fallback; callers needing true isolation (e.g. a guardrail's + ``scan_raw_request`` snapshot) only depend on the keys that are plain, + cleanly-copyable structures (``messages``/``input``, + ``metadata``/``litellm_metadata``). + """ + sanitized: Final = { + key: ( + { # mutable-ok: same request-payload shape as data + inner_key: ("placeholder" if inner_key == "litellm_parent_otel_span" else inner_value) + for inner_key, inner_value in value.items() + } + if key in ("metadata", "litellm_metadata") and isinstance(value, dict) + else value + ) + for key, value in data.items() + } + + def _copied_value(key: str, sanitized_value: object) -> object: + try: + copied_value: Final = copy.deepcopy(sanitized_value) + except Exception: # noqa: BLE001 # any unpicklable value falls back to the original reference for this key only + return data.get(key) + original_value: Final = data.get(key) + if ( + key in ("metadata", "litellm_metadata") + and isinstance(copied_value, dict) + and isinstance(original_value, dict) + and "litellm_parent_otel_span" in original_value + ): + return { # mutable-ok: same request-payload shape as data + **copied_value, + "litellm_parent_otel_span": original_value["litellm_parent_otel_span"], + } + return copied_value + + return { # mutable-ok: same request-payload shape as data + key: _copied_value(key, value) for key, value in sanitized.items() + } + + def filter_exceptions_from_params(data: Any, max_depth: int = 20) -> Any: """ Recursively filter out Exception objects and callable objects from dicts/lists. diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 4a25eb218c0..8f8c955d971 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -550,6 +550,13 @@ def _map_anthropic_exception( llm_provider="anthropic", model=model, ) + elif original_exception.status_code == 403: + raise PermissionDeniedError( + message=f"AnthropicException - {error_str}", + llm_provider="anthropic", + model=model, + response=original_exception.response, + ) elif original_exception.status_code == 400 or original_exception.status_code == 413: raise BadRequestError( message=f"AnthropicException - {error_str}", @@ -755,12 +762,19 @@ def _map_openai_like_exception( llm_provider=custom_llm_provider, model=model, ) - elif original_exception.status_code == 401 or original_exception.status_code == 403: + elif original_exception.status_code == 401: raise AuthenticationError( message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) + elif original_exception.status_code == 403: + raise PermissionDeniedError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=_response_or_stub(original_exception, status_code=403), + ) elif original_exception.status_code == 400: raise BadRequestError( message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", @@ -2187,6 +2201,122 @@ def _map_openrouter_exception( ) +def _response_or_stub(original_exception: _ProviderHTTPException, status_code: int) -> httpx.Response: + response: Final = original_exception.response if hasattr(original_exception, "response") else None + if response is not None: + return response + return httpx.Response( + status_code=status_code, request=httpx.Request(method="POST", url="https://docs.litellm.ai/docs") + ) + + +def _map_exception_by_status( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_provider: str, + extra_information: str, +) -> None: + status_code: Final = original_exception.status_code if hasattr(original_exception, "status_code") else None + if not isinstance(status_code, int) or status_code < 400: + return + if getattr(original_exception, "status_code_is_synthesized", False): + return + message: Final = f"{exception_provider} - {error_str}" + response: Final = original_exception.response if hasattr(original_exception, "response") else None + match status_code: + case 401: + raise AuthenticationError( + message=message, + llm_provider=custom_llm_provider, + model=model, + response=response, + litellm_debug_info=extra_information, + ) + case 403: + raise PermissionDeniedError( + message=message, + llm_provider=custom_llm_provider, + model=model, + response=_response_or_stub(original_exception, status_code=status_code), + litellm_debug_info=extra_information, + ) + case 404: + raise NotFoundError( + message=message, + model=model, + llm_provider=custom_llm_provider, + response=response, + litellm_debug_info=extra_information, + ) + case 408: + raise Timeout( + message=message, + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + case 429: + raise RateLimitError( + message=message, + model=model, + llm_provider=custom_llm_provider, + response=response, + litellm_debug_info=extra_information, + ) + case 500: + raise InternalServerError( + message=message, + llm_provider=custom_llm_provider, + model=model, + response=response, + litellm_debug_info=extra_information, + ) + case 502: + raise BadGatewayError( + message=message, + llm_provider=custom_llm_provider, + model=model, + response=response, + litellm_debug_info=extra_information, + ) + case 503: + raise ServiceUnavailableError( + message=message, + llm_provider=custom_llm_provider, + model=model, + response=response, + litellm_debug_info=extra_information, + ) + case 504: + raise Timeout( + message=message, + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=status_code, + ) + case _ if status_code < 500: + raise BadRequestError( + message=message, + model=model, + llm_provider=custom_llm_provider, + response=response, + litellm_debug_info=extra_information, + ) + case _: + raise APIError( + status_code=status_code, + message=message, + llm_provider=custom_llm_provider, + model=model, + request=original_exception.request if hasattr(original_exception, "request") else None, + litellm_debug_info=extra_information, + ) + + def exception_type( model, original_exception, @@ -2213,6 +2343,7 @@ def exception_type( litellm_response_headers: Final = _get_response_headers(original_exception=original_exception) try: error_str = redact_string(str(original_exception)) if _ENABLE_SECRET_REDACTION else str(original_exception) + extra_information = "" if model or custom_llm_provider: if hasattr(original_exception, "message"): error_str = ( @@ -2229,7 +2360,6 @@ def exception_type( # Common Extra information needed for all providers # We pass num retries, api_base, vertex_deployment etc to the exception here ################################################################################ - extra_information = "" try: _api_base: Final = litellm.get_api_base(model=model, optional_params=extra_kwargs) messages: Final = litellm.get_first_chars_messages(kwargs=completion_kwargs) @@ -2301,6 +2431,7 @@ def exception_type( or custom_llm_provider == "custom_openai" or custom_llm_provider in litellm.openai_compatible_providers or custom_llm_provider == "mistral" + or custom_llm_provider == "runwayml" ): _map_openai_exception( model=model, @@ -2500,6 +2631,14 @@ def exception_type( For unmapped exceptions - raise the exception with traceback - https://github.com/BerriAI/litellm/issues/4201 """ exception_mapping_worked = True + _map_exception_by_status( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_provider=exception_provider, + extra_information=extra_information, + ) if hasattr(original_exception, "request"): raise APIConnectionError( message=f"{exception_provider} - {error_str}", diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index b12c715c9f5..389e6f7f501 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -50,6 +50,9 @@ OPTIONAL_KWARGS_KEYS: Final = ( "vertex_ai_project", "vertex_ai_location", "vertex_ai_credentials", + "gigachat_scope", + "gigachat_auth_url", + "gigachat_access_token", "tpm", "rpm", "itpm", diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index e674fc37673..ce51fb19970 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -2,7 +2,7 @@ from typing import Final, cast from urllib.parse import urlparse import litellm -from litellm.constants import REPLICATE_MODEL_NAME_WITH_ID_LENGTH +from litellm.constants import PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO, REPLICATE_MODEL_NAME_WITH_ID_LENGTH from litellm.litellm_core_utils.fallback_generalizations import ( match_routing_generalization, ) @@ -127,6 +127,18 @@ def handle_anthropic_text_model_custom_llm_provider( return model, custom_llm_provider +def declared_authenticating_provider(model: str | None, custom_llm_provider: str | None = None) -> str | None: + """The authenticating provider this pair already names, or None. + + get_llm_provider runs the OAuth device flow for github_copilot and chatgpt, because their + provider info includes the key it unlocks. For a metadata question that flow is pure hazard, + and for a declared pair the resolver's answer is the declaration itself, so metadata callers + adopt the declaration instead of resolving. + """ + declared: Final = custom_llm_provider or (model.split("/", 1)[0] if model and "/" in model else None) + return declared if declared in PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO else None + + def get_llm_provider( model: str, custom_llm_provider: str | None = None, @@ -272,6 +284,14 @@ def get_llm_provider( elif endpoint == "api.deepseek.com/v1": custom_llm_provider = "deepseek" dynamic_api_key = get_secret_str("DEEPSEEK_API_KEY") + elif endpoint == "api.together.ai/v1" or endpoint == "api.together.xyz/v1": + custom_llm_provider = "together_ai" + dynamic_api_key = api_key or ( + get_secret_str("TOGETHER_API_KEY") + or get_secret_str("TOGETHER_AI_API_KEY") + or get_secret_str("TOGETHERAI_API_KEY") + or get_secret_str("TOGETHER_AI_TOKEN") + ) elif endpoint == "ollama.com": custom_llm_provider = "ollama" dynamic_api_key = get_secret_str("OLLAMA_API_KEY") @@ -349,6 +369,9 @@ def get_llm_provider( elif endpoint == "https://api.meta.ai/v1": custom_llm_provider = "meta" dynamic_api_key = get_secret_str("META_API_KEY") + elif endpoint == "https://gigachat.devices.sberbank.ru/api/v1": + custom_llm_provider = "gigachat" + dynamic_api_key = get_secret_str("GIGACHAT_API_KEY") elif (json_provider := JSONProviderRegistry.get_by_base_url(endpoint)) is not None: custom_llm_provider = json_provider.slug dynamic_api_key = api_key if api_key is not None else get_secret_str(json_provider.api_key_env) @@ -513,6 +536,14 @@ def get_llm_provider( ) +def _dashscope_family_chat_config(custom_llm_provider: str) -> "litellm.DashScopeChatConfig": + if custom_llm_provider == "qwencloud": + return litellm.QwenCloudChatConfig() + if custom_llm_provider == "qwen_ai_platform": + return litellm.QwenAIPlatformChatConfig() + return litellm.DashScopeChatConfig() + + def _get_openai_compatible_provider_info( model: str, api_base: str | None, @@ -707,7 +738,7 @@ def _get_openai_compatible_provider_info( dynamic_api_key, ) = litellm.ZAIChatConfig()._get_openai_compatible_provider_info(api_base, api_key) elif custom_llm_provider == "together_ai": - api_base = api_base or get_secret_str("TOGETHER_AI_API_BASE") or "https://api.together.xyz/v1" + api_base = api_base or get_secret_str("TOGETHER_AI_API_BASE") or "https://api.together.ai/v1" dynamic_api_key = api_key or ( get_secret_str("TOGETHER_API_KEY") or get_secret_str("TOGETHER_AI_API_KEY") @@ -762,11 +793,11 @@ def _get_openai_compatible_provider_info( api_base, dynamic_api_key, ) = litellm.HerokuChatConfig()._get_openai_compatible_provider_info(api_base, api_key) - elif custom_llm_provider == "dashscope": + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): ( api_base, dynamic_api_key, - ) = litellm.DashScopeChatConfig()._get_openai_compatible_provider_info(api_base, api_key) + ) = _dashscope_family_chat_config(custom_llm_provider)._get_openai_compatible_provider_info(api_base, api_key) elif custom_llm_provider == "modelscope": ( api_base, @@ -847,6 +878,9 @@ def _get_openai_compatible_provider_info( # Manus is OpenAI compatible for responses API api_base = api_base or get_secret_str("MANUS_API_BASE") or "https://api.manus.im" dynamic_api_key = api_key or get_secret_str("MANUS_API_KEY") + elif custom_llm_provider == "gigachat": + api_base = api_base or get_secret_str("GIGACHAT_API_BASE") or "https://gigachat.devices.sberbank.ru/api/v1" + dynamic_api_key = api_key or get_secret_str("GIGACHAT_API_KEY") if api_base is not None and not isinstance(api_base, str): raise Exception(f"api base needs to be a string. api_base={api_base}") diff --git a/litellm/litellm_core_utils/get_model_cost_map.py b/litellm/litellm_core_utils/get_model_cost_map.py index 2043a9e2f89..9cba5db8ab7 100644 --- a/litellm/litellm_core_utils/get_model_cost_map.py +++ b/litellm/litellm_core_utils/get_model_cost_map.py @@ -12,6 +12,7 @@ import asyncio import json import os import random +import time from collections.abc import Awaitable, Callable from dataclasses import dataclass from datetime import datetime, timezone @@ -154,18 +155,6 @@ class GetModelCostMap: return True - @staticmethod - def fetch_remote_model_cost_map(url: str, timeout: int = 5) -> dict: - """ - Fetch the model cost map from a remote URL. - - Returns the parsed JSON dict. Raises on network/parse errors - (caller is expected to handle). - """ - response: Final = httpx.get(url, timeout=timeout) - response.raise_for_status() - return response.json() - RETRYABLE_FETCH_STATUS_CODES: Final = frozenset({429, 500, 502, 503, 504}) MODEL_COST_MAP_FETCH_MAX_ATTEMPTS: Final = 3 @@ -212,6 +201,13 @@ class _AsyncGetClient(Protocol): def get(self, url: str, *, timeout: float | None = None) -> Awaitable[httpx.Response]: ... +class _SyncGetClient(Protocol): + def get(self, url: str, *, timeout: float | None = None) -> httpx.Response: ... + + +_FetchAttemptOutcome = ModelCostMapReloaded | ModelCostMapReloadUnavailable | _FetchAttemptRetryable + + def _default_reload_client() -> _AsyncGetClient: from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.custom_http import httpxSpecialProvider @@ -219,13 +215,30 @@ def _default_reload_client() -> _AsyncGetClient: return get_async_httpx_client(llm_provider=httpxSpecialProvider.ModelCostMap) -async def _attempt_fetch( - client: _AsyncGetClient, url: str, timeout: int -) -> ModelCostMapReloaded | ModelCostMapReloadUnavailable | _FetchAttemptRetryable: +def _classify_fetch_error(error: httpx.HTTPError | httpx.InvalidURL, url: str) -> _FetchAttemptOutcome: + reason: Final = f"{type(error).__name__} fetching {url}: {error}" + if isinstance(error, (httpx.InvalidURL, httpx.UnsupportedProtocol)): + return ModelCostMapReloadUnavailable(reason=reason) + return _FetchAttemptRetryable(reason=reason, retry_after_seconds=None) + + +async def _attempt_fetch(client: _AsyncGetClient, url: str, timeout: int) -> _FetchAttemptOutcome: try: response: Final = await client.get(url, timeout=timeout) - except httpx.HTTPError as e: - return _FetchAttemptRetryable(reason=f"{type(e).__name__} fetching {url}: {e}", retry_after_seconds=None) + except (httpx.HTTPError, httpx.InvalidURL) as e: + return _classify_fetch_error(e, url) + return _classify_fetch_response(response, url) + + +def _attempt_fetch_sync(client: _SyncGetClient, url: str, timeout: int) -> _FetchAttemptOutcome: + try: + response: Final = client.get(url, timeout=timeout) + except (httpx.HTTPError, httpx.InvalidURL) as e: + return _classify_fetch_error(e, url) + return _classify_fetch_response(response, url) + + +def _classify_fetch_response(response: httpx.Response, url: str) -> _FetchAttemptOutcome: if response.status_code in RETRYABLE_FETCH_STATUS_CODES: return _FetchAttemptRetryable( reason=f"HTTP {response.status_code} from {url}", @@ -242,6 +255,22 @@ async def _attempt_fetch( return ModelCostMapReloaded(model_cost_map=parsed) +def _next_retry_wait( + outcome: _FetchAttemptRetryable, attempt: int, max_attempts: int, rng: random.Random +) -> float | ModelCostMapReloadUnavailable: + if attempt == max_attempts: + return ModelCostMapReloadUnavailable(reason=f"{outcome.reason} (after {max_attempts} attempts)") + wait_seconds: Final = _retry_wait_seconds(outcome=outcome, attempt=attempt, rng=rng) + verbose_logger.warning( + "LiteLLM: model cost map fetch attempt %d/%d failed (%s); retrying in %.1fs", + attempt, + max_attempts, + outcome.reason, + wait_seconds, + ) + return wait_seconds + + async def _fetch_remote_model_cost_map_with_retry( url: str, timeout: int, @@ -254,20 +283,32 @@ async def _fetch_remote_model_cost_map_with_retry( outcome = await _attempt_fetch(client=client, url=url, timeout=timeout) if not isinstance(outcome, _FetchAttemptRetryable): return outcome - if attempt == max_attempts: - return ModelCostMapReloadUnavailable(reason=f"{outcome.reason} (after {max_attempts} attempts)") - wait_seconds = _retry_wait_seconds(outcome=outcome, attempt=attempt, rng=rng) - verbose_logger.warning( - "LiteLLM: model cost map fetch attempt %d/%d failed (%s); retrying in %.1fs", - attempt, - max_attempts, - outcome.reason, - wait_seconds, - ) + wait_seconds = _next_retry_wait(outcome=outcome, attempt=attempt, max_attempts=max_attempts, rng=rng) + if isinstance(wait_seconds, ModelCostMapReloadUnavailable): + return wait_seconds await sleep(wait_seconds) return ModelCostMapReloadUnavailable(reason="model cost map fetch failed") +def _fetch_remote_model_cost_map_with_retry_sync( + url: str, + timeout: int, + max_attempts: int, + sleep: Callable[[float], None], + rng: random.Random, + client: _SyncGetClient, +) -> ModelCostMapReloadResult: + for attempt in range(1, max_attempts + 1): + outcome = _attempt_fetch_sync(client=client, url=url, timeout=timeout) + if not isinstance(outcome, _FetchAttemptRetryable): + return outcome + wait_seconds = _next_retry_wait(outcome=outcome, attempt=attempt, max_attempts=max_attempts, rng=rng) + if isinstance(wait_seconds, ModelCostMapReloadUnavailable): + return wait_seconds + sleep(wait_seconds) + return ModelCostMapReloadUnavailable(reason="model cost map fetch failed") + + async def refetch_model_cost_map( url: str, timeout: int = 5, @@ -423,13 +464,21 @@ def _finalize_model_cost_map(model_cost: dict) -> dict: return _expand_model_aliases(model_cost) -def get_model_cost_map(url: str) -> dict: +def get_model_cost_map( + url: str, + timeout: int = 5, + max_attempts: int = MODEL_COST_MAP_FETCH_MAX_ATTEMPTS, + sleep: Callable[[float], None] = time.sleep, + rng: random.Random | None = None, + client: "_SyncGetClient | None" = None, +) -> dict: """ Public entry point — returns the model cost map dict. 1. If ``LITELLM_LOCAL_MODEL_COST_MAP`` is set, uses the local backup only. - 2. Otherwise fetches from ``url``, validates integrity, and falls back - to the local backup on any failure. + 2. Otherwise fetches from ``url``, retrying transient HTTP errors + (429/5xx/transport) with Retry-After-aware backoff, validates + integrity, and falls back to the local backup on any failure. Only the backup model count is cached (a single int) for validation. The full backup dict is only parsed when it must be *returned* as a @@ -448,17 +497,24 @@ def get_model_cost_map(url: str) -> dict: _cost_map_source_info.url = url _cost_map_source_info.is_env_forced = False - try: - content: Final = GetModelCostMap.fetch_remote_model_cost_map(url) - except Exception as e: + result: Final = _fetch_remote_model_cost_map_with_retry_sync( + url=url, + timeout=timeout, + max_attempts=max_attempts, + sleep=sleep, + rng=rng if rng is not None else random.Random(), + client=client if client is not None else httpx, + ) + if isinstance(result, ModelCostMapReloadUnavailable): verbose_logger.warning( "LiteLLM: Failed to fetch remote model cost map from %s: %s. Falling back to local backup.", url, - str(e), + result.reason, ) _cost_map_source_info.source = "local" - _cost_map_source_info.fallback_reason = f"Remote fetch failed: {e}" + _cost_map_source_info.fallback_reason = f"Remote fetch failed: {result.reason}" return _finalize_model_cost_map(GetModelCostMap.load_local_model_cost_map()) + content: Final = result.model_cost_map # Validate using cached count (cheap int comparison, no file I/O) if not GetModelCostMap.validate_model_cost_map( diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index 72f36661f4c..915a03025d9 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -2,6 +2,7 @@ from typing import Final, Literal import litellm from litellm.exceptions import BadRequestError +from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider from litellm.types.utils import LlmProviders, LlmProvidersSet @@ -30,6 +31,10 @@ def get_supported_openai_params( - List if custom_llm_provider is mapped - None if unmapped """ + if not custom_llm_provider: + custom_llm_provider = declared_authenticating_provider( + model + ) # rebind-ok: resolving would run the provider's OAuth flow if not custom_llm_provider: try: custom_llm_provider = litellm.get_llm_provider(model=model)[1] @@ -172,7 +177,7 @@ def get_supported_openai_params( if request_type == "embeddings": return litellm.JinaAIEmbeddingConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "together_ai": - return litellm.TogetherAIConfig().get_supported_openai_params(model=model) + return litellm.TogetherAIChatConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "databricks": if request_type == "chat_completion": return litellm.DatabricksConfig().get_supported_openai_params(model=model) diff --git a/litellm/litellm_core_utils/health_check_helpers.py b/litellm/litellm_core_utils/health_check_helpers.py index 3a79eb78b17..c745bbea5c4 100644 --- a/litellm/litellm_core_utils/health_check_helpers.py +++ b/litellm/litellm_core_utils/health_check_helpers.py @@ -2,17 +2,32 @@ Helper functions for health check calls. """ -from collections.abc import Callable +import base64 +from collections.abc import Awaitable, Callable from typing import TYPE_CHECKING, Final, Literal from litellm.types.utils import LIST_BATCHES_SUPPORTED_PROVIDERS if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.types.utils import ImageResponse # Minimal PDF for health checks - base64 encoded 1-page PDF with just "test" TEST_PDF_URL = "data:application/pdf;base64,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" +# Minimal image for health checks - base64 encoded 512x512 blue circle on a white background PNG +TEST_IMAGE_BASE64 = "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" + + +IMAGE_EDIT_HEALTH_CHECK_PROMPT: Final = ( + "Add a small yellow star in the top right corner of this simple drawing of a blue circle on a white background" +) + + +def get_image_file_for_health_check() -> bytes: + """Return the image used for health checks.""" + return base64.b64decode(TEST_IMAGE_BASE64) + class HealthCheckHelpers: @staticmethod @@ -112,6 +127,17 @@ class HealthCheckHelpers: else: return await litellm.acompletion(**model_params) + @staticmethod + async def _image_edit_health_check(edit_request: Callable[[], Awaitable["ImageResponse"]]) -> "ImageResponse": + import litellm + + try: + return await edit_request() + except litellm.BadRequestError as e: + if isinstance(e, litellm.ContentPolicyViolationError) or "moderation_blocked" in str(e): + return litellm.ImageResponse() + raise + @staticmethod def get_mode_handlers( model: str, @@ -127,6 +153,7 @@ class HealthCheckHelpers: "audio_speech", "audio_transcription", "image_generation", + "image_edit", "video_generation", "rerank", "realtime", @@ -185,6 +212,13 @@ class HealthCheckHelpers: **_filter_model_params(model_params=model_params), prompt=prompt, ), + "image_edit": lambda: HealthCheckHelpers._image_edit_health_check( + edit_request=lambda: litellm.aimage_edit( + **_filter_model_params(model_params=model_params), + image=get_image_file_for_health_check(), + prompt=IMAGE_EDIT_HEALTH_CHECK_PROMPT, + ), + ), "video_generation": lambda: litellm.avideo_generation( **_filter_model_params(model_params=model_params), prompt=prompt or "test video generation", diff --git a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py index 284989ab20f..65c5b0d9799 100644 --- a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py +++ b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py @@ -1,3 +1,4 @@ +import re from collections.abc import Iterator, Mapping from typing import Any, Final @@ -45,12 +46,29 @@ def validate_no_callback_env_reference(param: str, value: object, *, source: str _raise_env_reference_error(param, source=source) +# Langfuse rejects events whose environment does not match this pattern +# (lowercase alphanumerics, hyphens, underscores; no "langfuse" prefix). +# Validating here fails fast at config/init time instead of silently +# dropping every trace server-side. +LANGFUSE_ENVIRONMENT_PATTERN: Final = r"^(?!langfuse)[a-z0-9-_]+$" + + +def validate_langfuse_environment_value(value: str) -> None: + if not re.match(LANGFUSE_ENVIRONMENT_PATTERN, value): + raise ValueError( + f"Invalid langfuse_environment {value!r}: must be lowercase " + "alphanumerics/hyphens/underscores and must not start with " + f"'langfuse' (pattern {LANGFUSE_ENVIRONMENT_PATTERN})" + ) + + # Hardcoded list of supported callback params to avoid runtime inspection issues with TypedDict -_supported_callback_params: Final = [ +_supported_callback_params: Final[tuple[str, ...]] = ( "langfuse_public_key", "langfuse_secret", "langfuse_secret_key", "langfuse_host", + "langfuse_environment", "langfuse_prompt_version", "langsmith_api_key", "langsmith_project", @@ -72,8 +90,10 @@ _supported_callback_params: Final = [ "dd_site", "dd_agent_host", "dd_agent_port", + "newrelic_api_key", + "newrelic_region", "turn_off_message_logging", -] +) _request_blocked_callback_params: Final = frozenset( { @@ -83,6 +103,20 @@ _request_blocked_callback_params: Final = frozenset( "dd_site", "dd_agent_host", "dd_agent_port", + "newrelic_api_key", + "newrelic_region", + } +) + +# Request-blocked params that must still reach ``standard_callback_dynamic_params`` +# when the proxy itself stamped them from admin-configured team/key callback +# settings (the trusted-vars channel). The OTel per-tenant tracer routing reads +# ``standard_callback_dynamic_params``, so without this overlay a blocked param +# could never drive routing at all. +_trusted_overlay_callback_params: Final = frozenset( + { + "newrelic_api_key", + "newrelic_region", } ) @@ -121,7 +155,9 @@ def initialize_standard_callback_dynamic_params( if param in kwargs: _param_value = kwargs.get(param) validate_no_callback_env_reference(param, _param_value, source="request body") - standard_callback_dynamic_params[param] = _param_value + standard_callback_dynamic_params[param] = ( # pyright: ignore[reportGeneralTypeIssues] # several supported params predate their StandardCallbackDynamicParams fields + _param_value + ) for slot_label, metadata in iter_client_callback_metadata_dicts(kwargs): for param in _supported_callback_params: @@ -130,6 +166,12 @@ def initialize_standard_callback_dynamic_params( if param not in standard_callback_dynamic_params and param in metadata: _param_value = metadata.get(param) validate_no_callback_env_reference(param, _param_value, source=slot_label) - standard_callback_dynamic_params[param] = _param_value + standard_callback_dynamic_params[param] = ( # pyright: ignore[reportGeneralTypeIssues] # several supported params predate their StandardCallbackDynamicParams fields + _param_value + ) + + for param, trusted_value in get_trusted_callback_params(kwargs): + if param in _trusted_overlay_callback_params: + standard_callback_dynamic_params[param] = trusted_value return standard_callback_dynamic_params diff --git a/litellm/litellm_core_utils/internal_call_metadata.py b/litellm/litellm_core_utils/internal_call_metadata.py index 6815727de69..4d043701f40 100644 --- a/litellm/litellm_core_utils/internal_call_metadata.py +++ b/litellm/litellm_core_utils/internal_call_metadata.py @@ -20,11 +20,18 @@ from __future__ import annotations from collections.abc import Mapping from typing import Final -from litellm.constants import INTERNAL_CALL_ORIGIN_METADATA_KEY -from litellm.types.utils import InternalCallOrigin +from litellm.constants import INTERNAL_CALL_ORIGIN_METADATA_KEY, NON_INFERENCE_CALL_TYPES +from litellm.types.utils import BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN, InternalCallOrigin BUDGET_RESERVATION_METADATA_KEYS: Final = frozenset({"user_api_key_budget_reservation"}) +MODEL_ACCESS_GROUP_METADATA_KEY: Final = "user_api_key_matched_model_access_groups" +"""Where auth records the model access groups that authorized the request, for the spend writer. + +The ``user_api_key`` prefix is load-bearing, not cosmetic: when a request carries both +``metadata`` and ``litellm_metadata``, ``get_litellm_metadata_from_kwargs`` returns the latter and +copies a key across only when ``user_api_key`` appears in its name.""" + _USER_API_KEY_AUTH_KEY: Final = "user_api_key_auth" FORWARDABLE_IDENTITY_METADATA_KEYS: Final = frozenset( @@ -45,6 +52,60 @@ budget-checked like the request that spawned it. Everything else on the parent's be a lie on a sub-call that runs after it returned.""" +def is_background_response(response: object) -> bool: + """Whether a retrieved object is a response created with ``background=true``. + + Such a create returns ``status="queued"`` and no usage at all, so nothing has billed the + job by the time anyone reads it back. Accepts the response as a mapping or a model, + because the callers hold it in both shapes. + """ + if isinstance(response, Mapping): + return response.get("background") is True + return getattr(response, "background", None) is True + + +def is_unbilled_non_inference_call( + call_type: str | None, + metadata: Mapping[str, object] | None, + response: object, +) -> bool: + """A read/management route priced at zero, because the usage it reports belongs to the + call that created the object it just read. + + Retrieving a background response is the exception, and the enterprise cost poller's read + is the same exception seen from the other side: that job's create billed nothing, so its + retrieval is the only place the spend is ever visible. Pricing those at zero would lose + the spend rather than deduplicate it. + """ + if call_type not in NON_INFERENCE_CALL_TYPES: + return False + if is_background_response(response): + return False + if metadata is None: + return True + return metadata.get(INTERNAL_CALL_ORIGIN_METADATA_KEY) != BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN + + +def is_unbilled_non_inference_call_from_params( + call_type: str | None, + litellm_params: Mapping[str, object] | None, + response: object, +) -> bool: + """:func:`is_unbilled_non_inference_call` for callers holding raw ``litellm_params``. + + The call-type membership test runs first so that inference traffic, which is every + request in a normal workload, never pays for the metadata merge behind it. + """ + if call_type not in NON_INFERENCE_CALL_TYPES: + return False + from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup + + metadata: Final = ( + StandardLoggingPayloadSetup.merge_litellm_metadata(litellm_params) if litellm_params is not None else None + ) + return is_unbilled_non_inference_call(call_type, metadata, response) + + def sanitize_user_api_key_auth(auth: object) -> object: """Copy of the auth object with its budget reservation removed; the cost callback falls back to reading the reservation from inside the auth object.""" diff --git a/litellm/litellm_core_utils/json_fragment_accumulator.py b/litellm/litellm_core_utils/json_fragment_accumulator.py new file mode 100644 index 00000000000..81d18dd0119 --- /dev/null +++ b/litellm/litellm_core_utils/json_fragment_accumulator.py @@ -0,0 +1,97 @@ +import json +from typing import Final, cast # noqa: TID251 # raw_decode returns tuple[Any, int]; no cast-free unpack + + +class JSONFragmentAccumulator: + """ + Buffers a JSON value that arrives piecemeal over a stream (SSE data split + across TCP packets, one shard per network read, etc) without the O(n^2) + cost of repeated `buffer += fragment` string concatenation, and without + the O(n^2) cost of re-copying the unconsumed remainder on every peeled + value when one payload holds many concatenated JSON values. + + Fragments are appended to a list in O(1). The buffer is only rebuilt into + a single string, and only decoded, when a caller asks for a value via + `pop_next_value`, and `could_close_json` lets callers skip that rebuild + entirely for fragments that plainly cannot close a JSON value yet. Once + rebuilt, consumed values are dropped by advancing a cursor rather than + slicing a new string, so draining N concatenated values already sitting + in the buffer costs O(n) total, not O(n^2). + """ + + def __init__(self) -> None: + self._chunks: list[str] = [] # mutable-ok: O(1) append; string concat would copy the buffer each time + self._buffer: str = ( + "" # mutable-ok: lazily materialized join of _chunks, rebuilt only when _chunks is non-empty + ) + self._offset: int = 0 # mutable-ok: cursor past already-consumed values; avoids re-slicing on every pop + self._could_close: bool = False # mutable-ok: cached heuristic; rescanning past fragments was itself O(n^2) + + def __bool__(self) -> bool: + return bool(self._chunks) or self._offset < len(self._buffer) + + def append(self, fragment: str) -> None: + self._chunks.append(fragment) # mutable-ok: see __init__ + stripped: Final = fragment.rstrip() + if stripped: + self._could_close = stripped[-1] in ("}", "]") # mutable-ok: see __init__ + + def could_close_json(self) -> bool: + """ + Whether the buffer's logical last non-whitespace byte is "}" or "]", + i.e. whether a JSON value could plausibly be complete. Tracked + incrementally in `append` rather than rescanned here, so a run of + blank keepalive fragments (e.g. from a malformed upstream stream) + can't make this, or the join+parse it gates, cost O(n^2). + """ + return self._could_close + + def _materialize(self) -> None: + if not self._chunks: + return + unconsumed: Final = self._buffer[self._offset :] + self._buffer = unconsumed + "".join(self._chunks) # mutable-ok: merge pending fragments, once per append batch + self._offset = 0 # mutable-ok: see __init__ + self._chunks = [] # mutable-ok: see __init__ + + def pop_next_value(self) -> tuple[bool, object]: + """ + Attempt to decode one complete JSON value from the front of the + buffer. On success, advances a cursor past that value (keeping any + unconsumed tail, e.g. a second concatenated value, in place rather + than copying it) and returns (True, value). If the buffer is empty + or holds no complete value yet, it is left untouched and this + returns (False, None). + """ + self._materialize() + length: Final = len(self._buffer) + start = self._offset + while start < length and self._buffer[start].isspace(): + start += 1 + if start >= length: + self._offset = start # mutable-ok: see __init__ + return False, None + decoder: Final = json.JSONDecoder() + try: + raw_value: Final = decoder.raw_decode(self._buffer, start) + except json.JSONDecodeError: + return False, None + decoded, end_index = cast("tuple[object, int]", raw_value) # cast-ok: raw_decode returns tuple[Any, int] + self._offset = end_index # mutable-ok: see __init__ + if self._offset >= len(self._buffer): + self._buffer = "" # mutable-ok: see __init__ + self._offset = 0 # mutable-ok: see __init__ + self._could_close = False # mutable-ok: buffer is empty, nothing can close + return True, decoded + + def snapshot(self) -> str: + self._materialize() + return self._buffer[self._offset :] + + def set(self, value: str) -> None: + """Replace the buffer's contents with a single fragment.""" + self._chunks = [] # mutable-ok: see __init__ + self._buffer = value # mutable-ok: see __init__ + self._offset = 0 # mutable-ok: see __init__ + stripped: Final = value.rstrip() + self._could_close = bool(stripped) and stripped[-1] in ("}", "]") # mutable-ok: see __init__ diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index c14dd6c3d8b..9a6fb11f978 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -62,8 +62,12 @@ from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.deepeval.deepeval import DeepEvalLogger from litellm.integrations.mlflow import MlflowLogger from litellm.integrations.sqs import SQSLogger -from litellm.litellm_core_utils.core_helpers import reconstruct_model_name +from litellm.litellm_core_utils.core_helpers import is_expected_client_error, reconstruct_model_name from litellm.litellm_core_utils.get_litellm_params import get_litellm_params +from litellm.litellm_core_utils.internal_call_metadata import ( + MODEL_ACCESS_GROUP_METADATA_KEY, + is_unbilled_non_inference_call, +) from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import ( cost_breakdown_with_guardrail, guardrail_information_cost, @@ -71,6 +75,9 @@ from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import ( from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( StandardBuiltInToolCostTracking, ) +from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import ( + InteractionsUsageObjectTransformation, +) from litellm.litellm_core_utils.logging_utils import truncate_base64_in_messages from litellm.litellm_core_utils.model_param_helper import ModelParamHelper from litellm.litellm_core_utils.redact_messages import ( @@ -83,6 +90,10 @@ from litellm.llms.base_llm.search.transformation import SearchResponse from litellm.responses.utils import ResponseAPILoggingUtils from litellm.types.agents import LiteLLMSendMessageResponse from litellm.types.containers.main import ContainerObject +from litellm.types.interactions import ( + InteractionsAPIResponse, + InteractionsAPIStreamingResponse, +) from litellm.types.llms.openai import ( AllMessageValues, Batch, @@ -100,6 +111,7 @@ from litellm.types.mcp import MCPPostCallResponseObject from litellm.types.prompts.init_prompts import PromptSpec from litellm.types.rerank import RerankResponse from litellm.types.utils import ( + DEPLOYMENT_SCOPED_PRICING_FIELDS, CachingDetails, CallTypes, CostBreakdown, @@ -244,6 +256,7 @@ _STANDARD_LOGGING_METADATA_KEYS: Final[frozenset[str]] = frozenset(StandardLoggi # Cache custom pricing keys as frozenset for O(1) lookups instead of looping through 49 keys _CUSTOM_PRICING_KEYS: Final[frozenset[str]] = frozenset(CustomPricingLiteLLMParams.model_fields.keys()) +_MODEL_INFO_CUSTOM_PRICING_KEYS: Final[frozenset[str]] = _CUSTOM_PRICING_KEYS | DEPLOYMENT_SCOPED_PRICING_FIELDS sentry_sdk_instance = None capture_exception = None @@ -536,6 +549,9 @@ class Logging(LiteLLMLoggingBaseClass): # Init Caching related details self.caching_details: CachingDetails | None = None + # Timing for results that cannot carry ``_hidden_params`` (plain-dict /v1/messages + # responses and the bridge stream wrappers); see ``update_response_metadata``. + self.response_timing_metrics: Mapping[str, float] = {} # mutable-ok: kept deep-copyable # Passthrough endpoint guardrails config for field targeting self.passthrough_guardrails_config: dict[str, Any] | None = None @@ -555,6 +571,10 @@ class Logging(LiteLLMLoggingBaseClass): self._defer_async_logging: bool = False self._enqueue_deferred_logging: Callable[[], None] | None = None + def set_response_timing_metrics(self, timing_metrics: Mapping[str, float]) -> None: + """Keep ``_response_ms`` / ``litellm_overhead_time_ms`` for a result that has no ``_hidden_params``.""" + self.response_timing_metrics = dict(timing_metrics) # mutable-ok: kept deep-copyable + def process_dynamic_callbacks(self): """ Initializes CustomLogger compatible callbacks in self.dynamic_* callbacks @@ -605,37 +625,60 @@ class Logging(LiteLLMLoggingBaseClass): processed_list: Final[list[str | Callable | CustomLogger]] = [] for callback in callback_list: if isinstance(callback, str) and callback in litellm._known_custom_logger_compatible_callbacks: - # For callbacks that support team-scoped credentials (e.g. datadog), - # pass only the relevant dynamic params as custom_logger_init_args. - _custom_logger_init_args: dict | None = None - if callback == "datadog": - # dd_* params are blocked from standard_callback_dynamic_params - # (request-level security); only the proxy-stamped team/key - # callback vars are admin-configured and trusted. - _custom_logger_init_args = {k: v for k, v in self._trusted_callback_vars if k.startswith("dd_")} - - callback_class = _init_custom_logger_compatible_class( - callback, - internal_usage_cache=None, - llm_router=None, - custom_logger_init_args=_custom_logger_init_args, - ) - if callback_class is not None: - processed_list.append(callback_class) + for callback_instance in self._resolve_dynamic_callback_string(callback): + processed_list.append(callback_instance) # If processing dynamic_success_callbacks, add to dynamic_async_success_callbacks if dynamic_callbacks_type == "success": if self.dynamic_async_success_callbacks is None: self.dynamic_async_success_callbacks = [] - self.dynamic_async_success_callbacks.append(callback_class) + self.dynamic_async_success_callbacks.append(callback_instance) elif dynamic_callbacks_type == "failure": if self.dynamic_async_failure_callbacks is None: self.dynamic_async_failure_callbacks = [] - self.dynamic_async_failure_callbacks.append(callback_class) + self.dynamic_async_failure_callbacks.append(callback_instance) else: processed_list.append(callback) return processed_list + def _resolve_dynamic_callback_string(self, callback: str) -> "tuple[CustomLogger, ...]": + """ + Resolve a known callback name to the logger instance(s) it dispatches to. + + For callbacks that support team-scoped credentials (datadog, newrelic), + only the proxy-stamped team/key callback vars are passed as + custom_logger_init_args: dd_*/newrelic_* params are blocked from + standard_callback_dynamic_params (request-level security), so the + trusted-vars channel is the only way credentials reach a per-team logger. + """ + _trusted_var_prefix: Final = "dd_" if callback == "datadog" else "newrelic_" if callback == "newrelic" else None + _custom_logger_init_args: Final[dict | None] = ( + {k: v for k, v in self._trusted_callback_vars if k.startswith(_trusted_var_prefix)} + if _trusted_var_prefix is not None + else None + ) + + callback_class: Final = _init_custom_logger_compatible_class( + callback, + internal_usage_cache=None, + llm_router=None, + custom_logger_init_args=_custom_logger_init_args, + ) + if callback_class is None: + return () + + # With team creds, "newrelic" resolves to the per-team METRICS logger; + # resolve the name again without creds so the trace logger (OTel v2 / + # legacy agent) keeps receiving this request. + _newrelic_trace_class: Final = ( + _init_custom_logger_compatible_class(callback, internal_usage_cache=None, llm_router=None) + if callback == "newrelic" and _custom_logger_init_args and _custom_logger_init_args.get("newrelic_api_key") + else None + ) + if _newrelic_trace_class is not None and _newrelic_trace_class is not callback_class: + return (callback_class, _newrelic_trace_class) + return (callback_class,) + def initialize_standard_callback_dynamic_params(self, kwargs: dict | None = None) -> StandardCallbackDynamicParams: """ Initialize the standard callback dynamic params from the kwargs @@ -857,7 +900,10 @@ class Logging(LiteLLMLoggingBaseClass): prompt_management_logger: CustomLogger | None = None, prompt_label: str | None = None, prompt_version: int | None = None, + request_kwargs: dict[str, object] | None = None, # mutable-ok: marker stamped into live request kwargs ) -> tuple[str, list[AllMessageValues], dict]: + from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook + custom_logger: Final = prompt_management_logger or self.get_custom_logger_for_prompt_management( model=model, non_default_params=non_default_params, @@ -867,6 +913,7 @@ class Logging(LiteLLMLoggingBaseClass): ) if custom_logger: + breakpoints_before: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(messages) ( model, messages, @@ -882,6 +929,11 @@ class Logging(LiteLLMLoggingBaseClass): prompt_label=prompt_label, prompt_version=prompt_version, ) + if request_kwargs is not None: + AnthropicCacheControlHook.record_gateway_injection( + request_kwargs, + AnthropicCacheControlHook.count_request_cache_breakpoints(messages) - breakpoints_before, + ) self.messages = messages return model, messages, non_default_params @@ -897,7 +949,10 @@ class Logging(LiteLLMLoggingBaseClass): tools: list[dict] | None = None, prompt_label: str | None = None, prompt_version: int | None = None, + request_kwargs: dict[str, object] | None = None, # mutable-ok: marker stamped into live request kwargs ) -> tuple[str, list[AllMessageValues], dict]: + from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook + custom_logger: Final = prompt_management_logger or self.get_custom_logger_for_prompt_management( model=model, tools=tools, @@ -908,6 +963,7 @@ class Logging(LiteLLMLoggingBaseClass): ) if custom_logger: + breakpoints_before: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(messages) ( model, messages, @@ -925,6 +981,11 @@ class Logging(LiteLLMLoggingBaseClass): prompt_label=prompt_label, prompt_version=prompt_version, ) + if request_kwargs is not None: + AnthropicCacheControlHook.record_gateway_injection( + request_kwargs, + AnthropicCacheControlHook.count_request_cache_breakpoints(messages) - breakpoints_before, + ) self.messages = messages return model, messages, non_default_params @@ -1579,11 +1640,16 @@ class Logging(LiteLLMLoggingBaseClass): if cache_hit is True: return 0.0 + if is_unbilled_non_inference_call( + self.call_type, StandardLoggingPayloadSetup.merge_litellm_metadata(self.litellm_params), result + ): + return 0.0 + transformed_result: Final = self._generate_content_result_as_model_response(result) if transformed_result is not None: result = transformed_result - if isinstance(result, BaseModel) and hasattr(result, "_hidden_params"): + if isinstance(result, (BaseModel, HttpxBinaryResponseContent)) and hasattr(result, "_hidden_params"): hidden_params: Final = getattr(result, "_hidden_params", {}) if ( "response_cost" in hidden_params and hidden_params["response_cost"] is not None @@ -2077,6 +2143,9 @@ class Logging(LiteLLMLoggingBaseClass): logging_result: Final = self.normalize_logging_result(result=result) + if isinstance(result, Response) and isinstance(logging_result, (ModelResponse, EmbeddingResponse)): + result = logging_result + if standard_logging_object is None and result is not None and self.stream is not True: if self._is_recognized_call_type_for_logging(logging_result=logging_result) or isinstance( logging_result, (dict, list) @@ -2145,6 +2214,11 @@ class Logging(LiteLLMLoggingBaseClass): or isinstance(logging_result, OpenAIModerationResponse) or isinstance(logging_result, OCRResponse) # OCR or isinstance(logging_result, SearchResponse) # Search API + or ( + isinstance(logging_result, InteractionsAPIResponse) + and logging_result.usage is not None + and self._is_interactions_create_call_type() + ) or isinstance(logging_result, dict) and logging_result.get("object") == "vector_store.search_results.page" or isinstance(logging_result, dict) @@ -2157,6 +2231,87 @@ class Logging(LiteLLMLoggingBaseClass): return True return False + def _is_interactions_create_call_type(self) -> bool: + """ + Only interaction creation is billable. GET polls, deletes, and cancels + also return an ``InteractionsAPIResponse`` (with usage once completed), + so recognizing those would write spend on every poll of a background + interaction. The proxy sets ``call_type`` from its route_type + (``create_interaction``/``acreate_interaction``); the SDK sets it from + the decorated function name (``create``/``acreate``). + + Recognition additionally requires a usage block (checked at the call + site): a ``background=true`` create returns ``in_progress`` without + usage, and billing it would write a $0 spend log under the interaction + id that collides with the row the background poll task writes once the + interaction completes (see + ``litellm.interactions.background_cost_polling``). + """ + return self.call_type in ( + CallTypes.create_interaction.value, + CallTypes.acreate_interaction.value, + "create", + "acreate", + ) + + async def async_log_background_interaction_completion( + self, + result: InteractionsAPIResponse, + ) -> None: + """ + Log the terminal result of a background interaction as a fresh success + event. The create request already ran success logging for its + ``in_progress`` response (no usage, so no cost was tracked); clearing + the dedup flags lets the completed result flow through cost calculation + and spend tracking exactly once, spanning create to completion. + + The poll fetched this body through its own client call, which priced it + against a throwaway logging object holding none of this request's + deployment context: no ``model_info``, no router ``model_id``, no + deployment ``litellm_params``. Keeping that price would bill a + custom-priced deployment at the wrong rate, and it would also satisfy + the "already calculated" shortcut and skip repricing here, leaving the + cost breakdown at the zeros the usage-less create stamped and writing + those zeros to the spend log. Dropping it makes this event price the + settled body itself, against the deployment that served the create. + + The same throwaway call stamped the deployment identity that travels + with the price, so ``model_id`` and ``litellm_model_name`` go with it. + Left in place they overwrite the create's real deployment with the + poll's empty one in the payload every logging integration reads. + """ + settled_hidden_params: Final = getattr(result, "_hidden_params", None) + if isinstance(settled_hidden_params, dict): + for poll_scoped_key in ("response_cost", "model_id", "litellm_model_name"): + settled_hidden_params.pop(poll_scoped_key, None) + self._reset_success_emission_dedupe() + await self.async_success_handler(result=result) + + def _reset_success_emission_dedupe(self) -> None: + """ + Success callbacks dedupe per request, because the sync and async + handlers both fire on some paths and would otherwise report one call + twice. A settled background interaction is a genuinely second success + event on the same request, so every such marker has to be cleared or + the completion, the only event that carries usage and cost, is + discarded as a duplicate of the in-progress create. + """ + self.model_call_details.pop("has_logged_async_success", None) + litellm_params = self.model_call_details.get("litellm_params") + if not isinstance(litellm_params, dict): + return + metadata = litellm_params.get("metadata") + if not isinstance(metadata, dict): + return + otel_internal = metadata.get("_otel_internal") + if not isinstance(otel_internal, dict): + return + spans_logged = otel_internal.get("spans_logged") + if not isinstance(spans_logged, dict): + return + for scope in [key for key in spans_logged if isinstance(key, tuple) and key[-1:] == ("success",)]: + del spans_logged[scope] + def _flush_passthrough_collected_chunks_helper( self, raw_bytes: list[bytes], @@ -2282,7 +2437,9 @@ class Logging(LiteLLMLoggingBaseClass): is_sync_request: Final = self._is_sync_litellm_request(litellm_params) try: ## BUILD COMPLETE STREAMED RESPONSE - complete_streaming_response: ModelResponse | TextCompletionResponse | ResponsesAPIResponse | None = None + complete_streaming_response: ( + ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse | None + ) = None if "complete_streaming_response" in self.model_call_details: return # break out of this. complete_streaming_response = self._get_assembled_streaming_response( @@ -2730,6 +2887,8 @@ class Logging(LiteLLMLoggingBaseClass): batch_cost: Final = kwargs.get("batch_cost", None) batch_usage = kwargs.get("batch_usage", None) batch_models = kwargs.get("batch_models", None) + batch_successful_requests: Final = kwargs.get("batch_successful_requests", None) + batch_failed_requests: Final = kwargs.get("batch_failed_requests", None) has_explicit_batch_data: Final = all(x is not None for x in (batch_cost, batch_usage, batch_models)) should_compute_batch_data: Final = ( @@ -2738,14 +2897,12 @@ class Logging(LiteLLMLoggingBaseClass): if has_explicit_batch_data: result._hidden_params["response_cost"] = batch_cost result._hidden_params["batch_models"] = batch_models + result._hidden_params["batch_successful_requests"] = batch_successful_requests # pyright: ignore[reportPrivateUsage] # rebind-ok: same result._hidden_params pattern as response_cost/batch_models above + result._hidden_params["batch_failed_requests"] = batch_failed_requests # pyright: ignore[reportPrivateUsage] # rebind-ok: same pattern as above result.usage = batch_usage elif should_compute_batch_data: - ( - response_cost, - batch_usage, - batch_models, - ) = await _handle_completed_batch( + batch_result: Final = await _handle_completed_batch( batch=result, custom_llm_provider=self.custom_llm_provider, model_name=self.get_deployment_model_for_cost(), @@ -2753,9 +2910,11 @@ class Logging(LiteLLMLoggingBaseClass): model_info=self.get_router_deployment_model_info(), ) - result._hidden_params["response_cost"] = response_cost - result._hidden_params["batch_models"] = batch_models - result.usage = batch_usage + result._hidden_params["response_cost"] = batch_result.cost + result._hidden_params["batch_models"] = batch_result.models + result._hidden_params["batch_successful_requests"] = batch_result.successful_requests # pyright: ignore[reportPrivateUsage] # rebind-ok: same pattern as above + result._hidden_params["batch_failed_requests"] = batch_result.failed_requests # pyright: ignore[reportPrivateUsage] # rebind-ok: same pattern as above + result.usage = batch_result.usage start_time, end_time, result = self._success_handler_helper_fn( start_time=start_time, @@ -2768,14 +2927,14 @@ class Logging(LiteLLMLoggingBaseClass): ## BUILD COMPLETE STREAMED RESPONSE if "async_complete_streaming_response" in self.model_call_details: return # break out of this. - complete_streaming_response: Final[ModelResponse | TextCompletionResponse | ResponsesAPIResponse | None] = ( - self._get_assembled_streaming_response( - result=result, - start_time=start_time, - end_time=end_time, - is_async=True, - streaming_chunks=self.streaming_chunks, - ) + complete_streaming_response: Final[ + ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse | None + ] = self._get_assembled_streaming_response( + result=result, + start_time=start_time, + end_time=end_time, + is_async=True, + streaming_chunks=self.streaming_chunks, ) if complete_streaming_response is not None: @@ -2800,13 +2959,25 @@ class Logging(LiteLLMLoggingBaseClass): "Model=%s not found in completion cost map. Setting 'response_cost' to None", self.model ) self.model_call_details["response_cost"] = None + except Exception: # noqa: BLE001 # cost calculation must never block later callbacks (slot release) + verbose_logger.exception( + "Error calculating streaming response cost for model=%s. Setting 'response_cost' to None", + self.model, + ) + self.model_call_details["response_cost"] = None self._merge_hidden_params_from_response_into_metadata(complete_streaming_response) ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = self._build_standard_logging_payload( - complete_streaming_response, start_time, end_time - ) + try: + self.model_call_details["standard_logging_object"] = self._build_standard_logging_payload( + complete_streaming_response, start_time, end_time + ) + except Exception: # noqa: BLE001 # payload build must never block later callbacks (slot release) + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception building the standard logging payload " + "for a streaming response; callbacks still run without it" + ) # print standard logging payload if (standard_logging_payload := self.model_call_details.get("standard_logging_object")) is not None: @@ -2846,32 +3017,39 @@ class Logging(LiteLLMLoggingBaseClass): ## LOGGING HOOK ## for callback in callbacks: - if isinstance(callback, CustomGuardrail): - from litellm.types.guardrails import GuardrailEventHooks + try: + if isinstance(callback, CustomGuardrail): + from litellm.types.guardrails import GuardrailEventHooks - if ( - callback.should_run_guardrail( - data=self.model_call_details, - event_type=GuardrailEventHooks.logging_only, + if ( + callback.should_run_guardrail( + data=self.model_call_details, + event_type=GuardrailEventHooks.logging_only, + ) + is not True + ): + continue + + self.model_call_details, result = await callback.async_logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, ) - is not True - ): - continue - - self.model_call_details, result = await callback.async_logging_hook( - kwargs=self.model_call_details, - result=result, - call_type=self.call_type, - ) - elif isinstance(callback, CustomLogger): - result = redact_message_input_output_from_custom_logger( - result=result, litellm_logging_obj=self, custom_logger=callback - ) - self.model_call_details, result = await callback.async_logging_hook( - kwargs=self.model_call_details, - result=result, - call_type=self.call_type, + elif isinstance(callback, CustomLogger): + result = redact_message_input_output_from_custom_logger( + result=result, litellm_logging_obj=self, custom_logger=callback + ) + self.model_call_details, result = await callback.async_logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, + ) + except Exception: # noqa: BLE001 # one failing hook must not skip later callbacks (slot release) + verbose_logger.error( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred in async_logging_hook %s", + traceback.format_exc(), ) + self._handle_callback_failure(callback=callback) self.has_run_logging(event_type="async_success") @@ -3029,6 +3207,13 @@ class Logging(LiteLLMLoggingBaseClass): if not hasattr(self, "model_call_details"): self.model_call_details = {} + if ( + self.model_call_details.get("log_event_type") == "failed_api_call" + and self.model_call_details.get("exception") is exception + and self.model_call_details.get("standard_logging_object") is not None + ): + return start_time, self.model_call_details["end_time"] + self.model_call_details["log_event_type"] = "failed_api_call" self.model_call_details["exception"] = exception self.model_call_details["traceback_exception"] = ( @@ -3558,7 +3743,7 @@ class Logging(LiteLLMLoggingBaseClass): end_time: datetime.datetime, is_async: bool, streaming_chunks: list[object], - ) -> ModelResponse | TextCompletionResponse | ResponsesAPIResponse | None: + ) -> ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse | None: if self.stream is not True: return None if isinstance(result, ModelResponse) or isinstance(result, TextCompletionResponse): @@ -3583,9 +3768,40 @@ class Logging(LiteLLMLoggingBaseClass): ), ) return result.response + elif isinstance(result, InteractionsAPIStreamingResponse): + return self._assemble_completed_interaction_response(result) else: return None + @staticmethod + def _assemble_completed_interaction_response( + result: InteractionsAPIStreamingResponse, + ) -> InteractionsAPIResponse | None: + """ + The Interactions API streaming iterator hands the terminal event to the + success handlers: the new schema (Api-Revision: 2026-05-20) emits + ``interaction.completed`` carrying the full interaction object, the + legacy schema (2026-05-07) emits a chunk with ``status="completed"`` + and usage on the chunk itself. Build the equivalent non-streaming + response so cost calculation and spend tracking see one shape. + """ + if result.event_type == "interaction.completed" and result.interaction is not None: + return InteractionsAPIResponse(**result.interaction) + if result.status == "completed": + return InteractionsAPIResponse( + **result.model_dump( + exclude={ # mutable-ok: pydantic types exclude as set[str], which a frozenset does not satisfy + "event_type", + "delta", + "index", + "step", + "interaction_id", + "interaction", + } + ) + ) + return None + def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse: """ Handles logging for Anthropic messages responses. @@ -4503,6 +4719,22 @@ def _init_custom_logger_compatible_class( _in_memory_loggers.append(gitlab_logger) return gitlab_logger elif logging_integration == "newrelic": + if custom_logger_init_args.get("newrelic_api_key"): + # Team-scoped credentials: per-team METRICS logger, isolated per + # credential set via DynamicLoggingCache. The trace logger for + # this name stays on the global path below. + from litellm.integrations.newrelic.newrelic_team_handler import ( + NewRelicHandler, + ) + + return NewRelicHandler.get_newrelic_logger_for_request( + standard_callback_dynamic_params=custom_logger_init_args, + in_memory_dynamic_logger_cache=in_memory_dynamic_logger_cache, + ) + + _v2 = _maybe_construct_otel_v2("newrelic", _in_memory_loggers) + if _v2 is not None: + return _v2 for callback in _in_memory_loggers: if isinstance(callback, NewRelicLogger): return callback @@ -4789,7 +5021,11 @@ def get_custom_logger_compatible_class( if isinstance(callback, SMTPEmailLogger): return callback elif logging_integration == "newrelic": + from litellm.integrations.otel.logger import OpenTelemetryV2 + for callback in _in_memory_loggers: + if isinstance(callback, OpenTelemetryV2) and callback.callback_name == "newrelic": + return callback if isinstance(callback, NewRelicLogger): return callback return None @@ -4818,7 +5054,9 @@ def use_custom_pricing_for_model(litellm_params: dict | None) -> bool: """ Check if the model uses custom pricing - Returns True if any of `SPECIAL_MODEL_INFO_PARAMS` are present in `litellm_params` or `model_info` + Returns True if any custom pricing field is present in `litellm_params`, or if + any custom pricing or deployment-scoped pricing field (such as + ``off_peak_pricing``) is present in the metadata ``model_info`` """ if litellm_params is None: return False @@ -4836,7 +5074,7 @@ def use_custom_pricing_for_model(litellm_params: dict | None) -> bool: model_info: dict = metadata.get("model_info", {}) or {} if model_info: - matching_keys = _CUSTOM_PRICING_KEYS & model_info.keys() + matching_keys = _MODEL_INFO_CUSTOM_PRICING_KEYS & model_info.keys() for key in matching_keys: if model_info.get(key) is not None: return True @@ -4849,6 +5087,42 @@ def is_valid_sha256_hash(value: str) -> bool: return bool(re.fullmatch(r"[a-fA-F0-9]{64}", value)) +def coerce_model_access_groups(value: object) -> tuple[str, ...]: + """Model access group names out of untrusted request metadata, deduped and order preserving.""" + if not isinstance(value, (list, tuple)): + return () + return tuple(dict.fromkeys(group for group in value if isinstance(group, str) and group)) + + +def _model_access_groups_on_auth_object(user_api_key_auth: object) -> object: + if isinstance(user_api_key_auth, Mapping): + return user_api_key_auth.get("matched_model_access_groups") + return getattr(user_api_key_auth, "matched_model_access_groups", None) + + +def _model_access_groups_from_metadata(metadata: Mapping[str, object]) -> tuple[str, ...]: + stamped: Final = coerce_model_access_groups(metadata.get(MODEL_ACCESS_GROUP_METADATA_KEY)) + if stamped: + return stamped + return coerce_model_access_groups(_model_access_groups_on_auth_object(metadata.get("user_api_key_auth"))) + + +def request_model_access_groups_from_litellm_params(litellm_params: Mapping[str, object]) -> tuple[str, ...]: + """Access groups the auth layer stamped onto this request, from whichever metadata field carries them. + + Detached internal sub-calls only inherit the identity keys, so the auth object is the + fallback there, exactly as _get_budget_reservation_from_metadata does for reservations. + """ + for metadata_variable_name in ("metadata", "litellm_metadata"): + metadata = litellm_params.get(metadata_variable_name) + if not isinstance(metadata, Mapping): + continue + model_access_groups = _model_access_groups_from_metadata(metadata) + if model_access_groups: + return model_access_groups + return () + + class StandardLoggingPayloadSetup: @staticmethod def cleanup_timestamps( @@ -4917,7 +5191,7 @@ class StandardLoggingPayloadSetup: return messages @staticmethod - def merge_litellm_metadata(litellm_params: dict) -> dict: + def merge_litellm_metadata(litellm_params: Mapping[str, object]) -> dict: """ Merge both litellm_metadata and metadata from litellm_params. @@ -5085,6 +5359,8 @@ class StandardLoggingPayloadSetup: elif isinstance(usage, dict): if ResponseAPILoggingUtils._is_response_api_usage(usage): return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(usage) + if InteractionsUsageObjectTransformation.is_interactions_usage_object(usage): + return InteractionsUsageObjectTransformation.transform_interactions_usage_object(usage) return Usage(**usage) raise ValueError(f"usage is required, got={usage} of type {type(usage)}") @@ -5111,6 +5387,8 @@ class StandardLoggingPayloadSetup: if isinstance(_raw, dict): if ResponseAPILoggingUtils._is_response_api_usage(_raw): return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(_raw).model_dump() + if InteractionsUsageObjectTransformation.is_interactions_usage_object(_raw): + return InteractionsUsageObjectTransformation.transform_interactions_usage_object(_raw).model_dump() return _raw if isinstance(_raw, Usage): return _raw.model_dump() @@ -5218,6 +5496,8 @@ class StandardLoggingPayloadSetup: additional_headers=None, litellm_overhead_time_ms=None, batch_models=None, + batch_successful_requests=None, + batch_failed_requests=None, litellm_model_name=None, usage_object=None, ) @@ -5318,9 +5598,10 @@ class StandardLoggingPayloadSetup: error_class: Final[str] = str(original_exception.__class__.__name__) if original_exception else "" _llm_provider_in_exception: Final = getattr(original_exception, "llm_provider", "") - # Get traceback information (first 100 lines) traceback_info = traceback_str or "" - if original_exception: + if original_exception and ( + litellm.log_client_error_tracebacks or not is_expected_client_error(original_exception) + ): tb: Final[TracebackType | None] = getattr(original_exception, "__traceback__", None) if tb: tb_lines: Final = traceback.format_tb(tb) @@ -5607,6 +5888,8 @@ def _extract_response_obj_and_hidden_params( response_cost=None, litellm_overhead_time_ms=None, batch_models=None, + batch_successful_requests=None, + batch_failed_requests=None, litellm_model_name=None, usage_object=None, ) @@ -5674,7 +5957,7 @@ def get_standard_logging_object_payload( cache_hit: Final = kwargs.get("cache_hit", False) # Extract usage as a plain dict, avoiding Pydantic round-trip raw_usage_dict: Final = StandardLoggingPayloadSetup.get_usage_as_dict( - response_obj=response_obj, + response_obj=None if is_unbilled_non_inference_call(call_type, metadata, response_obj) else response_obj, combined_usage_object=cast(Usage | None, kwargs.get("combined_usage_object")), ) usage_dict: Final = ( @@ -5691,6 +5974,7 @@ def get_standard_logging_object_payload( request_tags: Final = StandardLoggingPayloadSetup._get_request_tags( litellm_params=litellm_params, proxy_server_request=proxy_server_request ) + request_model_access_groups: Final = request_model_access_groups_from_litellm_params(litellm_params) # cleanup timestamps ( @@ -5754,6 +6038,13 @@ def get_standard_logging_object_payload( clean_hidden_params: Final = StandardLoggingPayloadSetup.get_hidden_params(hidden_params) if clean_hidden_params["response_cost"] is None and raw_response_cost is not None: clean_hidden_params["response_cost"] = llm_response_cost + if clean_hidden_params["litellm_overhead_time_ms"] is None and status == "success": + # /v1/messages dict results and the bridge stream wrappers keep it on the logging object; + # failure payloads stay None like every response type that carries its own _hidden_params + timing_metrics: Final = ( + getattr(logging_obj, "response_timing_metrics", None) or {} # mutable-ok: empty fallback + ) + clean_hidden_params["litellm_overhead_time_ms"] = timing_metrics.get("litellm_overhead_time_ms") model_cost_information: Final = StandardLoggingPayloadSetup.get_model_cost_information( base_model=base_model, @@ -5793,11 +6084,15 @@ def get_standard_logging_object_payload( response_model_name = final_response_obj.get("model") # For Azure Model Router, preserve the actual model in the top-level standard - # logging payload only when the user has opted in. + # logging payload. + from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + requested_model: Final = kwargs.get("model") - if ( - isinstance(requested_model, str) - and ("model_router" in requested_model.lower() or "model-router" in requested_model.lower()) + stamped_selected_model: Final = AzureFoundryModelInfo.get_model_router_selected_model(hidden_params) + if stamped_selected_model is not None: + model_name = stamped_selected_model + elif ( + AzureFoundryModelInfo.is_model_router_call(model=requested_model, hidden_params=hidden_params) and isinstance(response_model_name, str) and response_model_name ): @@ -5849,7 +6144,8 @@ def get_standard_logging_object_payload( prompt_tokens=usage_dict.get("prompt_tokens", 0), completion_tokens=usage_dict.get("completion_tokens", 0), request_tags=request_tags, - end_user=end_user_id or "", + request_model_access_groups=request_model_access_groups, + end_user=end_user_id, api_base=StandardLoggingPayloadSetup.strip_trailing_slash(litellm_params.get("api_base", "")) or "", model_group=_model_group, model_id=_model_id, @@ -5882,7 +6178,10 @@ def get_standard_logging_object_payload( def emit_standard_logging_payload(payload: StandardLoggingPayload): if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): - print(json.dumps(payload, indent=4), flush=True) # noqa: T201 + try: + print(json.dumps(payload, indent=4, default=str), flush=True) # noqa: T201 + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception("Error serializing standard logging payload for debug output: %s", e) def get_standard_logging_metadata( @@ -6019,6 +6318,8 @@ def create_dummy_standard_logging_payload() -> StandardLoggingPayload: additional_headers=None, litellm_overhead_time_ms=None, batch_models=None, + batch_successful_requests=None, + batch_failed_requests=None, litellm_model_name=None, usage_object=None, ) @@ -6060,6 +6361,7 @@ def create_dummy_standard_logging_payload() -> StandardLoggingPayload: cache_key=None, saved_cache_cost=saved_cache_cost, request_tags=[], + request_model_access_groups=(), end_user=None, requester_ip_address="127.0.0.1", messages=messages, diff --git a/litellm/litellm_core_utils/llm_cost_calc/guardrail_cost.py b/litellm/litellm_core_utils/llm_cost_calc/guardrail_cost.py index 4645a8c3074..ad1880d4cc2 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/guardrail_cost.py +++ b/litellm/litellm_core_utils/llm_cost_calc/guardrail_cost.py @@ -21,11 +21,13 @@ class GuardrailCostEntry(BaseModel): model_config = ConfigDict(extra="ignore", frozen=True) guardrail_cost: float | None = None + # ``bool | None`` because the TypedDict sanctions None; None means "not set" + # and keeps the default billed behavior, so a None-carrying entry must not + # fail union validation and silently zero a sibling entry's real cost. + guardrail_cost_in_spend: bool | None = True -GuardrailInformationShape = tuple[GuardrailCostEntry, ...] | GuardrailCostEntry | None - -_GUARDRAIL_INFORMATION_ADAPTER: Final[TypeAdapter[GuardrailInformationShape]] = TypeAdapter(GuardrailInformationShape) +_GUARDRAIL_COST_ENTRY_ADAPTER: Final[TypeAdapter[GuardrailCostEntry]] = TypeAdapter(GuardrailCostEntry) def _bedrock_guardrail_pricing(aws_region_name: str | None) -> GuardrailPricing | None: @@ -47,23 +49,55 @@ def bedrock_guardrail_cost(usage_units: Mapping[str, int], aws_region_name: str return sum(units * pricing.guardrail_cost_per_unit.get(counter, 0.0) for counter, units in usage_units.items()) +AZURE_PROMPT_SHIELD_TEXT_RECORD_UNIT: Final = "text_records" + + +def azure_prompt_shield_guardrail_cost( + usage_units: Mapping[str, int], + cost_tier: str | None, + price_per_1000_text_records: float | None, +) -> float | None: + """USD cost of an Azure Prompt Shield invocation from its text-record count. + + Returns 0.0 on the free tier, ``text_records * price / 1000`` when a price is + configured, and None when pricing is not configured (usage-only tracking). + """ + if cost_tier == "free": + return 0.0 + if price_per_1000_text_records is None: + return None + return usage_units.get(AZURE_PROMPT_SHIELD_TEXT_RECORD_UNIT, 0) * price_per_1000_text_records / 1000.0 + + def _billable_entry_cost(entry: GuardrailCostEntry) -> float: + if entry.guardrail_cost_in_spend is False: + return 0.0 cost: Final = entry.guardrail_cost if cost is None or not math.isfinite(cost) or cost <= 0.0: return 0.0 return cost -def guardrail_information_cost(guardrail_information: object) -> float: +def _validated_entry_cost(raw: object) -> float: + """Billable cost of one raw ``guardrail_information`` entry. + + Validated per entry so one malformed entry (e.g. a custom hook stamping a + non-boolean ``guardrail_cost_in_spend``) prices to 0.0 by itself instead of + failing a whole-payload validation and silently zeroing a sibling entry's + real billable cost.""" try: - parsed: Final = _GUARDRAIL_INFORMATION_ADAPTER.validate_python(guardrail_information) - except ValidationError: + return _billable_entry_cost(_GUARDRAIL_COST_ENTRY_ADAPTER.validate_python(raw)) + except ValidationError as e: + verbose_logger.warning("Ignoring malformed guardrail_information entry for guardrail cost: %s", e) return 0.0 - if parsed is None: + + +def guardrail_information_cost(guardrail_information: object) -> float: + if guardrail_information is None: return 0.0 - if isinstance(parsed, GuardrailCostEntry): - return _billable_entry_cost(parsed) - return sum(_billable_entry_cost(entry) for entry in parsed) + if isinstance(guardrail_information, (list, tuple)): + return sum(_validated_entry_cost(entry) for entry in guardrail_information) + return _validated_entry_cost(guardrail_information) def cost_breakdown_with_guardrail(cost_breakdown: CostBreakdown | None, guardrail_cost: float) -> CostBreakdown | None: diff --git a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py index 887f167c262..5504756ceb8 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py +++ b/litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py @@ -3,17 +3,20 @@ Helper utilities for tracking the cost of built-in tools. """ from collections.abc import Mapping -from typing import Any, Final, Literal +from typing import Final, Literal import litellm from litellm.constants import OPENAI_FILE_SEARCH_COST_PER_1K_CALLS -from litellm.litellm_core_utils.llm_cost_calc.utils import _get_web_search_requests +from litellm.litellm_core_utils.llm_cost_calc.utils import ( + get_web_search_requests_from_usage, +) from litellm.types.llms.openai import ( FileSearchTool, ResponsesAPIResponse, WebSearchOptions, ) from litellm.types.utils import ( + ChatCompletionAnnotation, Message, ModelInfo, ModelResponse, @@ -47,7 +50,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def get_cost_for_built_in_tools( model: str, - response_object: Any, + response_object: object, usage: Usage | None = None, custom_llm_provider: str | None = None, standard_built_in_tools_params: StandardBuiltInToolsParams | None = None, @@ -64,11 +67,17 @@ class StandardBuiltInToolCostTracking: """ standard_built_in_tools_params = standard_built_in_tools_params or {} + google_maps_grounding_cost: Final = StandardBuiltInToolCostTracking._handle_google_maps_grounding_cost( + model=model, + custom_llm_provider=custom_llm_provider, + usage=usage, + ) + # Handle web search if StandardBuiltInToolCostTracking.response_object_includes_web_search_call( response_object=response_object, usage=usage ): - return StandardBuiltInToolCostTracking._handle_web_search_cost( + return google_maps_grounding_cost + StandardBuiltInToolCostTracking._handle_web_search_cost( model=model, custom_llm_provider=custom_llm_provider, usage=usage, @@ -78,19 +87,56 @@ class StandardBuiltInToolCostTracking: # Handle file search if StandardBuiltInToolCostTracking.response_object_includes_file_search_call(response_object=response_object): - return StandardBuiltInToolCostTracking._handle_file_search_cost( + return google_maps_grounding_cost + StandardBuiltInToolCostTracking._handle_file_search_cost( model=model, custom_llm_provider=custom_llm_provider, standard_built_in_tools_params=standard_built_in_tools_params, ) # Handle Azure assistant features - return StandardBuiltInToolCostTracking._handle_azure_assistant_costs( + return google_maps_grounding_cost + StandardBuiltInToolCostTracking._handle_azure_assistant_costs( model=model, custom_llm_provider=custom_llm_provider, standard_built_in_tools_params=standard_built_in_tools_params, ) + @staticmethod + def _resolve_model_info(model: str, custom_llm_provider: str | None) -> tuple[ModelInfo | None, str | None]: + direct: Final = StandardBuiltInToolCostTracking._safe_get_model_info( + model=model, custom_llm_provider=custom_llm_provider + ) + if direct is not None: + return direct, custom_llm_provider or direct["litellm_provider"] + if "/" not in model: + return None, custom_llm_provider + by_prefix: Final = StandardBuiltInToolCostTracking._safe_get_model_info(model=model) + if by_prefix is None: + return None, custom_llm_provider + return by_prefix, by_prefix["litellm_provider"] + + @staticmethod + def _handle_google_maps_grounding_cost( + model: str, + custom_llm_provider: str | None, + usage: Usage | None, + ) -> float: + from litellm.llms import get_cost_for_google_maps_grounding_request + from litellm.llms.gemini.cost_calculator import google_maps_grounding_requests + + if usage is None or google_maps_grounding_requests(usage) is None: + return 0.0 + model_info, resolved_provider = StandardBuiltInToolCostTracking._resolve_model_info( + model=model, custom_llm_provider=custom_llm_provider + ) + if model_info is None or resolved_provider is None: + return 0.0 + return ( + get_cost_for_google_maps_grounding_request( + custom_llm_provider=resolved_provider, usage=usage, model_info=model_info + ) + or 0.0 + ) + @staticmethod def _handle_web_search_cost( model: str, @@ -102,29 +148,21 @@ class StandardBuiltInToolCostTracking: """Handle web search cost calculation.""" from litellm.llms import get_cost_for_web_search_request - model_info = StandardBuiltInToolCostTracking._safe_get_model_info( + # A provider-prefixed model (e.g. gemini/gemini-3.1-flash-lite) may not map under the + # request's custom_llm_provider. _resolve_model_info re-resolves from the prefix and adopts + # that provider so the cost is routed and priced with the model_info that was actually + # resolved, instead of feeding a re-resolved model into the original provider's calculator. + model_info, resolved_provider = StandardBuiltInToolCostTracking._resolve_model_info( model=model, custom_llm_provider=custom_llm_provider ) - # A provider-prefixed model (e.g. gemini/gemini-3.1-flash-lite) may not map under the - # request's custom_llm_provider. Re-resolve from the prefix and adopt that provider so the - # cost is routed and priced with the model_info that was actually resolved, instead of - # feeding a re-resolved model into the original provider's calculator. - if model_info is None and "/" in model: - model_info = StandardBuiltInToolCostTracking._safe_get_model_info(model=model) - if model_info is not None: - custom_llm_provider = model_info["litellm_provider"] - - if custom_llm_provider is None and model_info is not None: - custom_llm_provider = model_info["litellm_provider"] - resolved_usage: Final = StandardBuiltInToolCostTracking._usage_with_anthropic_web_search( usage=usage, response_object=response_object ) - if model_info is not None and resolved_usage is not None and custom_llm_provider is not None: + if model_info is not None and resolved_usage is not None and resolved_provider is not None: result: Final = get_cost_for_web_search_request( - custom_llm_provider=custom_llm_provider, + custom_llm_provider=resolved_provider, usage=resolved_usage, model_info=model_info, ) @@ -164,8 +202,7 @@ class StandardBuiltInToolCostTracking: model_info: Final = StandardBuiltInToolCostTracking._safe_get_model_info( model=model, custom_llm_provider=custom_llm_provider ) - file_search_raw: Final[Any] = standard_built_in_tools_params.get("file_search", {}) - file_search_usage: Final[FileSearchTool | None] = FileSearchTool(**file_search_raw) if file_search_raw else None + file_search_usage: Final[FileSearchTool | None] = standard_built_in_tools_params.get("file_search") or None # Convert model_info to dict and extract usage parameters model_info_dict: Final = dict(model_info) if model_info is not None else None @@ -208,7 +245,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def _extract_file_search_params( - file_search_usage: Any, + file_search_usage: object, ) -> tuple[float | None, float | None]: """Extract and convert file search parameters safely.""" storage_gb = None @@ -298,7 +335,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def _extract_token_counts( - computer_use_usage: Any, + computer_use_usage: object, ) -> tuple[int | None, int | None]: """Extract and convert token counts safely.""" input_tokens = None @@ -314,9 +351,9 @@ class StandardBuiltInToolCostTracking: return input_tokens, output_tokens @staticmethod - def _safe_convert_to_int(value: Any) -> int | None: + def _safe_convert_to_int(value: object) -> int | None: """Safely convert a value to int.""" - if value is not None: + if isinstance(value, (int, float, str)): try: return int(value) except (TypeError, ValueError): @@ -333,7 +370,7 @@ class StandardBuiltInToolCostTracking: get_anthropic_web_search_requests_from_response, ) - if usage is not None and (_get_web_search_requests(getattr(usage, "server_tool_use", None)) is not None): + if usage is not None and (get_web_search_requests_from_usage(usage) is not None): return usage web_search_requests: Final = get_anthropic_web_search_requests_from_response(response_object) if web_search_requests is None: @@ -344,7 +381,7 @@ class StandardBuiltInToolCostTracking: return usage.model_copy(update={"server_tool_use": server_tool_use}) @staticmethod - def response_object_includes_web_search_call(response_object: Any, usage: Usage | None = None) -> bool: + def response_object_includes_web_search_call(response_object: object, usage: Usage | None = None) -> bool: """ Check if the response object includes a web search call. @@ -381,7 +418,7 @@ class StandardBuiltInToolCostTracking: # Anthropic Claude (direct API and Vertex AI) uses server_tool_use.web_search_requests. # Without this check, Claude ModelResponse always falls through to return False # and _handle_web_search_cost() is never called. - if hasattr(usage, "server_tool_use") and _get_web_search_requests(usage.server_tool_use) is not None: + if get_web_search_requests_from_usage(usage) is not None: return True # xAI reports usage.server_side_tool_usage_details.web_search_calls; a searched # answer with no url_citation annotations has no other chat-path signal @@ -394,16 +431,12 @@ class StandardBuiltInToolCostTracking: response_object=response_object, output_type="web_search_call" ) elif usage is not None: - if ( - hasattr(usage, "server_tool_use") - and _get_web_search_requests(usage.server_tool_use) is not None - or ( - hasattr(usage, "prompt_tokens_details") - and usage.prompt_tokens_details is not None - and isinstance(usage.prompt_tokens_details, PromptTokensDetailsWrapper) - and hasattr(usage.prompt_tokens_details, "web_search_requests") - and usage.prompt_tokens_details.web_search_requests is not None - ) + if get_web_search_requests_from_usage(usage) is not None or ( + hasattr(usage, "prompt_tokens_details") + and usage.prompt_tokens_details is not None + and isinstance(usage.prompt_tokens_details, PromptTokensDetailsWrapper) + and hasattr(usage.prompt_tokens_details, "web_search_requests") + and usage.prompt_tokens_details.web_search_requests is not None ): return True if _usage_reports_server_side_web_search_calls(usage): @@ -413,7 +446,7 @@ class StandardBuiltInToolCostTracking: @staticmethod def response_object_includes_file_search_call( - response_object: Any, + response_object: object, ) -> bool: """ Check if the response object includes a file search call. @@ -444,11 +477,11 @@ class StandardBuiltInToolCostTracking: message: Message | None = getattr(choice, "message", None) if message is None: continue - if annotations := getattr(message, "annotations", None): - if len(annotations) > 0: - for annotation in annotations: - if annotation.get("type", None) == annotation_type: - return True + annotations: list[ChatCompletionAnnotation] | None = getattr(message, "annotations", None) + if annotations: + for annotation in annotations: + if annotation.get("type", None) == annotation_type: + return True return False @staticmethod @@ -489,10 +522,8 @@ class StandardBuiltInToolCostTracking: if model_info is None: return 0.0 - search_context_raw: Final[Any] = model_info.get("search_context_cost_per_query", {}) - search_context_pricing: Final[SearchContextCostPerQuery] = ( - SearchContextCostPerQuery(**search_context_raw) if search_context_raw else SearchContextCostPerQuery() - ) + search_context_raw: Final = model_info.get("search_context_cost_per_query") + search_context_pricing: Final[SearchContextCostPerQuery] = search_context_raw or SearchContextCostPerQuery() if web_search_options.get("search_context_size", None) == "low": return search_context_pricing.get("search_context_size_low", 0.0) elif web_search_options.get("search_context_size", None) == "medium": @@ -512,10 +543,8 @@ class StandardBuiltInToolCostTracking: """ if model_info is None: return 0.0 - search_context_raw: Final[Any] = model_info.get("search_context_cost_per_query", {}) or {} - search_context_pricing: Final[SearchContextCostPerQuery] = ( - SearchContextCostPerQuery(**search_context_raw) if search_context_raw else SearchContextCostPerQuery() - ) + search_context_raw: Final = model_info.get("search_context_cost_per_query") + search_context_pricing: Final[SearchContextCostPerQuery] = search_context_raw or SearchContextCostPerQuery() return search_context_pricing.get("search_context_size_medium", 0.0) @staticmethod @@ -681,7 +710,7 @@ class StandardBuiltInToolCostTracking: response_object: ModelResponse, ) -> bool: for _choice in response_object.choices: - message = getattr(_choice, "message", None) + message: Message | None = getattr(_choice, "message", None) if ( message is not None and hasattr(message, "annotations") diff --git a/litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py b/litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py index 210ac72cd8a..f11f6d46fb2 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py +++ b/litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py @@ -1,6 +1,9 @@ -from typing import Any +from collections.abc import Mapping, Sequence +from types import MappingProxyType +from typing import Any, Final from litellm.types.utils import ( + CompletionTokensDetailsWrapper, PromptTokensDetailsWrapper, TranscriptionUsageDurationObject, TranscriptionUsageTokensObject, @@ -34,3 +37,130 @@ class TranscriptionUsageObjectTransformation: ), ) return None + + +_INTERACTIONS_MODALITY_FIELDS: Final[Mapping[str, str]] = MappingProxyType( + { + "text": "text_tokens", + "audio": "audio_tokens", + "image": "image_tokens", + "video": "video_tokens", + "document": "text_tokens", + } +) + + +def _modality_field(entry: Mapping[str, Any]) -> str | None: + return _INTERACTIONS_MODALITY_FIELDS.get(str(entry.get("modality", "")).lower()) + + +def _token_count(value: object) -> int: + return value if isinstance(value, int) else 0 + + +def _modality_token_sums(entries: Sequence[Mapping[str, Any]]) -> Mapping[str, int]: + fields: Final = frozenset(field for entry in entries if (field := _modality_field(entry)) is not None) + return MappingProxyType( + { + field: sum(_token_count(entry.get("tokens")) for entry in entries if _modality_field(entry) == field) + for field in fields + } + ) + + +def _google_search_query_count(usage_object: Mapping[str, Any]) -> int: + entries: Final = usage_object.get("grounding_tool_count") + if not isinstance(entries, Sequence): + return 0 + return sum( + _token_count(entry.get("count")) + for entry in entries + if isinstance(entry, Mapping) and entry.get("type") == "google_search" + ) + + +def _subtract_cached_from_input( + input_sums: Mapping[str, int], + cached_sums: Mapping[str, int], + total_cached_tokens: int, +) -> Mapping[str, int]: + if cached_sums: + return MappingProxyType( + {field: max(0, tokens - cached_sums.get(field, 0)) for field, tokens in input_sums.items()} + ) + if total_cached_tokens and "text_tokens" in input_sums: + return MappingProxyType( + { + **input_sums, + "text_tokens": max(0, input_sums["text_tokens"] - total_cached_tokens), + } + ) + return input_sums + + +class InteractionsUsageObjectTransformation: + """ + Maps the Google Interactions API usage block (total_input_tokens, + output_tokens_by_modality, ...) into LiteLLM's chat-format ``Usage`` so the + generic cost calculator and spend tracking can bill it. + """ + + @staticmethod + def is_interactions_usage_object(usage_object: object) -> bool: + if not isinstance(usage_object, dict): + return False + if "prompt_tokens" in usage_object or "input_tokens" in usage_object: + return False + return "total_input_tokens" in usage_object or "total_output_tokens" in usage_object + + @staticmethod + def transform_interactions_usage_object(usage_object: Mapping[str, Any]) -> Usage: + input_entries: Final = tuple(usage_object.get("input_tokens_by_modality") or ()) + tuple( + usage_object.get("tool_use_tokens_by_modality") or () + ) + cached_sums: Final = _modality_token_sums(tuple(usage_object.get("cached_tokens_by_modality") or ())) + output_sums: Final = _modality_token_sums(tuple(usage_object.get("output_tokens_by_modality") or ())) + + total_cached_tokens: Final = _token_count(usage_object.get("total_cached_tokens")) + input_sums: Final = _subtract_cached_from_input( + input_sums=_modality_token_sums(input_entries), + cached_sums=cached_sums, + total_cached_tokens=total_cached_tokens, + ) + + reasoning_tokens: Final = _token_count(usage_object.get("total_reasoning_tokens")) or _token_count( + usage_object.get("total_thought_tokens") + ) + prompt_tokens: Final = _token_count(usage_object.get("total_input_tokens")) + _token_count( + usage_object.get("total_tool_use_tokens") + ) + completion_tokens: Final = _token_count(usage_object.get("total_output_tokens")) + reasoning_tokens + total_tokens: Final = _token_count(usage_object.get("total_tokens")) or (prompt_tokens + completion_tokens) + + web_search_requests: Final = _google_search_query_count(usage_object) + prompt_tokens_details: Final = ( + PromptTokensDetailsWrapper( + cached_tokens=total_cached_tokens or None, + web_search_requests=web_search_requests or None, + **input_sums, + ) + if input_sums or total_cached_tokens or web_search_requests + else None + ) + completion_tokens_details: Final = ( + CompletionTokensDetailsWrapper( + reasoning_tokens=reasoning_tokens or None, + **output_sums, + ) + if output_sums or reasoning_tokens + else None + ) + + return Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + prompt_tokens_details=prompt_tokens_details, + completion_tokens_details=completion_tokens_details, + cache_read_input_tokens=total_cached_tokens or None, + ) diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 0a52e1d283e..b34c416cd40 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1,10 +1,13 @@ # What is this? ## Helper utilities for cost_per_token() -from collections.abc import Mapping +import re +from collections.abc import Mapping, Sequence from dataclasses import dataclass +from datetime import datetime, timezone, tzinfo from types import MappingProxyType from typing import Any, Final, Literal, TypedDict, cast +from zoneinfo import ZoneInfo, ZoneInfoNotFoundError import litellm from litellm._logging import verbose_logger @@ -72,7 +75,20 @@ def _get_token_detail_value(details: object, key: str) -> int | None: return value if isinstance(value, int) else None -def _get_web_search_requests(server_tool_use: Any) -> int | None: +_IMAGE_SIZE_PATTERN: Final = re.compile(r"\d+(?:x|-x-)\d+") + + +def _requested_image_param(optional_params: Mapping[str, object] | None, key: str) -> str | None: + value: Final = None if optional_params is None else optional_params.get(key) + return value if isinstance(value, str) else None + + +def _requested_image_size(optional_params: Mapping[str, object] | None) -> str | None: + value: Final = _requested_image_param(optional_params, "size") + return value if value is not None and _IMAGE_SIZE_PATTERN.fullmatch(value) else None + + +def get_web_search_requests(server_tool_use: Any) -> int | None: """ Tolerantly read ``web_search_requests`` from a ``server_tool_use`` value that may be ``None``, a ``dict``, a ``ServerToolUse`` pydantic instance, @@ -92,6 +108,16 @@ def _get_web_search_requests(server_tool_use: Any) -> int | None: return getattr(server_tool_use, "web_search_requests", None) +def get_web_search_requests_from_usage(usage: Usage) -> int | None: + """Read ``web_search_requests`` from a ``Usage``'s ``server_tool_use``. + + ``Usage`` deletes unset optional fields from ``__dict__`` (see + ``SafeAttributeModel``), so direct attribute access can raise + ``AttributeError``; ``getattr`` with a default is required here. + """ + return get_web_search_requests(getattr(usage, "server_tool_use", None)) + + def _is_above_128k(tokens: float) -> bool: if tokens > 128000: return True @@ -266,10 +292,187 @@ def _get_tiered_base_costs(model_info: ModelInfo, usage: Usage) -> tuple[float, ) +def _is_within_off_peak_window(off_peak_hours_utc: str | Sequence[str], current_time: datetime | None = None) -> bool: + """Return True if current_time (UTC, defaulting to now) falls inside any off-peak window. + + off_peak_hours_utc is a "HH:MM-HH:MM" string in UTC, or a list of such strings for providers + with multiple daily windows (e.g. ["16:30-00:30", "04:00-06:00"]). A window may wrap past + midnight, and a window whose start equals its end covers the whole day. The start is + inclusive and the end is exclusive; malformed windows are ignored. + + An aware current_time is converted to UTC. A naive one is taken to already be UTC rather + than being localised, so callers must pass datetime.now(timezone.utc), never datetime.now(), + or every window shifts by the host's offset. + """ + reference: Final = current_time if current_time is not None else datetime.now(timezone.utc) + now: Final = (reference.astimezone(timezone.utc) if reference.tzinfo is not None else reference).time() + windows: Final = (off_peak_hours_utc,) if isinstance(off_peak_hours_utc, str) else off_peak_hours_utc + for window in windows: + try: + start_str, end_str = window.split("-") + start = datetime.strptime(start_str.strip(), "%H:%M").replace(tzinfo=timezone.utc).time() + end = datetime.strptime(end_str.strip(), "%H:%M").replace(tzinfo=timezone.utc).time() + except (ValueError, AttributeError): + continue + if start < end: + if start <= now < end: + return True + elif now >= start or now < end: + return True + return False + + +_WEEKDAY_NUMBERS: Final = MappingProxyType( + { + "mon": 1, + "monday": 1, + "tue": 2, + "tues": 2, + "tuesday": 2, + "wed": 3, + "wednesday": 3, + "thu": 4, + "thur": 4, + "thurs": 4, + "thursday": 4, + "fri": 5, + "friday": 5, + "sat": 6, + "saturday": 6, + "sun": 7, + "sunday": 7, + } +) + + +def _normalize_weekday(value: object) -> int | None: + if isinstance(value, bool): + return None + if isinstance(value, int): + return value if 1 <= value <= 7 else None + if isinstance(value, str): + return _WEEKDAY_NUMBERS.get(value.strip().lower()) + return None + + +def _weekday_calendar(weekday_timezone: object) -> tzinfo: + if isinstance(weekday_timezone, str) and weekday_timezone.strip(): + try: + return ZoneInfo(weekday_timezone.strip()) + except (ValueError, ZoneInfoNotFoundError): + return timezone.utc + return timezone.utc + + +def _matches_weekdays(reference_utc: datetime, weekdays: object, weekday_timezone: object) -> bool: + """Return True when reference_utc falls on one of the rule's weekdays, read on the calendar + named by weekday_timezone (default UTC). An absent weekdays means every day. The calendar + matters even when UTC and vendor-local weekdays agree at every currently priced hour: a + window past 16:00 UTC is where an Asia/Shanghai weekday diverges from the UTC one. + """ + if weekdays is None: + return True + if isinstance(weekdays, str) or not isinstance(weekdays, Sequence): + return False + allowed: Final = frozenset(day for day in map(_normalize_weekday, weekdays) if day is not None) + return reference_utc.astimezone(_weekday_calendar(weekday_timezone)).isoweekday() in allowed + + +def _as_window_strings(value: object) -> tuple[str, ...]: + if isinstance(value, str): + return (value,) + if isinstance(value, Sequence): + return tuple(entry for entry in value if isinstance(entry, str)) + return () + + +def _is_off_peak(off_peak: Mapping[str, object], current_time: datetime | None = None) -> bool: + """Return True when current_time (UTC, defaulting to now) is off-peak under the block's + rules: the flat hours_utc windows, which apply every day, or any entry in windows, whose + hours apply only on its weekdays. + """ + reference: Final = current_time if current_time is not None else datetime.now(timezone.utc) + reference_utc: Final = ( + reference.astimezone(timezone.utc) if reference.tzinfo is not None else reference.replace(tzinfo=timezone.utc) + ) + flat_windows: Final = _as_window_strings(off_peak.get("hours_utc")) + if flat_windows and _is_within_off_peak_window(flat_windows, reference_utc): + return True + windows: Final = off_peak.get("windows") + if isinstance(windows, str) or not isinstance(windows, Sequence): + return False + weekday_timezone: Final = off_peak.get("weekday_timezone") + for rule in windows: + if not isinstance(rule, Mapping): + continue + rule_windows = _as_window_strings(rule.get("hours_utc")) + if not rule_windows: + continue + if not _matches_weekdays(reference_utc, rule.get("weekdays"), weekday_timezone): + continue + if _is_within_off_peak_window(rule_windows, reference_utc): + return True + return False + + +def _coerce_off_peak_rate(value: object, default: float) -> float: + if isinstance(value, bool): + return default + if isinstance(value, (int, float)): + return float(value) + if isinstance(value, str): + try: + return float(value) + except ValueError: + return default + return default + + +def _apply_off_peak_pricing( + model_info: ModelInfo, + current_time: datetime | None, + prompt_base_cost: float, + completion_base_cost: float, + cache_read_cost: float, +) -> tuple[float, float, float]: + """Swap in off-peak per-token rates when the current UTC time is inside one of the model's + off_peak_pricing rules, the every-day hours_utc windows or a day-of-week-qualified entry in + windows. An off-peak rate replaces the rate that would otherwise apply rather than + discounting it, so a model that also has tiered or above-threshold pricing bills the flat + off-peak rate for the whole request while the window is open. Any rate left unset in + off_peak_pricing falls back to the standard rate. + """ + off_peak: Final = model_info.get("off_peak_pricing") + if not isinstance(off_peak, Mapping) or not _is_off_peak(off_peak, current_time): + return prompt_base_cost, completion_base_cost, cache_read_cost + return ( + _coerce_off_peak_rate(off_peak.get("input_cost_per_token"), prompt_base_cost), + _coerce_off_peak_rate(off_peak.get("output_cost_per_token"), completion_base_cost), + _coerce_off_peak_rate(off_peak.get("cache_read_input_token_cost"), cache_read_cost), + ) + + +def _apply_off_peak_to_base_costs( + model_info: ModelInfo, + current_time: datetime | None, + base_costs: tuple[float, float, float, float, float], +) -> tuple[float, float, float, float, float]: + """Apply off-peak rates to an already-resolved set of base costs, whichever pricing path + produced them. Cache-creation rates are passed through untouched, since off_peak_pricing + has no field for them. + """ + prompt, completion, cache_creation, cache_creation_above_1hr, cache_read = base_costs + off_peak_prompt, off_peak_completion, off_peak_cache_read = _apply_off_peak_pricing( + model_info, current_time, prompt, completion, cache_read + ) + return (off_peak_prompt, off_peak_completion, cache_creation, cache_creation_above_1hr, off_peak_cache_read) + + def _get_token_base_cost( model_info: ModelInfo, usage: Usage, service_tier: str | None = None, + current_time: datetime | None = None, *, threshold_is_inclusive: bool = False, ) -> tuple[float, float, float, float, float]: @@ -287,7 +490,7 @@ def _get_token_base_cost( """ tiered_base_costs: Final = _get_tiered_base_costs(model_info=model_info, usage=usage) if tiered_base_costs is not None: - return tiered_base_costs + return _apply_off_peak_to_base_costs(model_info, current_time, tiered_base_costs) # Get service tier aware cost keys input_cost_key: Final = _get_service_tier_cost_key("input_cost_per_token", service_tier) @@ -321,12 +524,16 @@ def _get_token_base_cost( k for k in model_info if k.startswith("input_cost_per_token_above_") and not k.endswith(_SERVICE_TIER_SUFFIXES) ] if not threshold_keys: - return ( - prompt_base_cost, - completion_base_cost, - cache_creation_cost, - cache_creation_cost_above_1hr, - cache_read_cost, + return _apply_off_peak_to_base_costs( + model_info, + current_time, + ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ), ) # Only sort the threshold keys (typically 1-2 keys instead of 66+) @@ -427,12 +634,16 @@ def _get_token_base_cost( except Exception: continue - return ( - prompt_base_cost, - completion_base_cost, - cache_creation_cost, - cache_creation_cost_above_1hr, - cache_read_cost, + return _apply_off_peak_to_base_costs( + model_info, + current_time, + ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ), ) @@ -889,11 +1100,22 @@ def generic_cost_per_token( total_details: Final = text_tokens + cache_hit + audio_tokens + cache_creation + image_tokens + video_tokens has_double_counting: Final = (cache_hit > 0 or cache_creation > 0) and total_details > usage.prompt_tokens - if (text_tokens == 0 and prompt_tokens_details["image_count"] == 0) or has_double_counting: - text_tokens = usage.prompt_tokens - cache_hit - audio_tokens - cache_creation - image_tokens - video_tokens + if has_double_counting: + # cached and per-modality counts are both subsets of prompt_tokens and may overlap, so a + # modality can only bill what the cache did not already cover or the overlap is billed twice + uncached_budget: Final = max(usage.prompt_tokens - cache_hit - cache_creation, 0) + billable_audio: Final = min(audio_tokens, uncached_budget) + billable_image: Final = min(image_tokens, uncached_budget - billable_audio) + billable_video: Final = min(video_tokens, uncached_budget - billable_audio - billable_image) + prompt_tokens_details["audio_tokens"] = billable_audio + prompt_tokens_details["image_tokens"] = billable_image + prompt_tokens_details["video_tokens"] = billable_video + prompt_tokens_details["text_tokens"] = uncached_budget - billable_audio - billable_image - billable_video + elif text_tokens == 0 and prompt_tokens_details["image_count"] == 0: # Clamp to zero: inconsistent streaming usage - text_tokens = max(text_tokens, 0) - prompt_tokens_details["text_tokens"] = text_tokens + prompt_tokens_details["text_tokens"] = max( + usage.prompt_tokens - cache_hit - audio_tokens - cache_creation - image_tokens - video_tokens, 0 + ) ( prompt_base_cost, @@ -1063,15 +1285,17 @@ def get_token_type_cost_breakdown( reasoning_tokens = _coerce_token_count(getattr(usage, "reasoning_tokens", 0)) # Reasoning is billed at the selected tier's reasoning rate for tiered models, - # else at the explicit per-reasoning-token rate when the model defines one, - # otherwise at the standard output-token rate - this mirrors how the total - # completion cost is computed, so the breakdown can never diverge from it. + # else at the service-tier-aware per-reasoning-token rate - this mirrors how the + # total completion cost is computed, so the breakdown can never diverge from it. tiered_reasoning_rate: Final = _get_tiered_reasoning_rate(model_info=model_info, usage=usage) - flat_reasoning_rate: Final = _get_cost_per_unit(model_info, "output_cost_per_reasoning_token", None) reasoning_rate: Final = ( tiered_reasoning_rate if tiered_reasoning_rate is not None - else (flat_reasoning_rate if flat_reasoning_rate is not None else completion_base_cost) + else _resolve_reasoning_token_cost( + model_info=model_info, + service_tier=service_tier, + completion_base_cost=completion_base_cost, + ) ) reasoning_cost = float(reasoning_tokens) * reasoning_rate @@ -1288,12 +1512,13 @@ class CostCalculatorUtils: cost_calculator as vertex_ai_image_cost_calculator, ) - if size is None: - size = completion_response.size or "1024-x-1024" - if quality is None: - quality = completion_response.quality or "standard" - if n is None: - n = len(completion_response.data) if completion_response.data else 0 + resolved_size: Final = ( + size or completion_response.size or _requested_image_size(optional_params) or "1024-x-1024" + ) + resolved_quality: Final = ( + quality or completion_response.quality or _requested_image_param(optional_params, "quality") or "standard" + ) + resolved_n: Final = n if n is not None else (len(completion_response.data) if completion_response.data else 0) if custom_llm_provider == litellm.LlmProviders.VERTEX_AI.value: if isinstance(completion_response, ImageResponse): @@ -1305,7 +1530,7 @@ class CostCalculatorUtils: if isinstance(completion_response, ImageResponse): return bedrock_image_cost_calculator( model=model, - size=size, + size=resolved_size, image_response=completion_response, optional_params=optional_params, ) @@ -1401,19 +1626,19 @@ class CostCalculatorUtils: # Fall through to default for DALL-E models return default_image_cost_calculator( model=model, - quality=quality, + quality=resolved_quality, custom_llm_provider=custom_llm_provider, - n=n, - size=size, + n=resolved_n, + size=resolved_size, optional_params=optional_params, ) else: return default_image_cost_calculator( model=model, - quality=quality, + quality=resolved_quality, custom_llm_provider=custom_llm_provider, - n=n, - size=size, + n=resolved_n, + size=resolved_size, optional_params=optional_params, ) return 0.0 diff --git a/litellm/litellm_core_utils/llm_judge.py b/litellm/litellm_core_utils/llm_judge.py index 4ad8d719402..b632d3a9af9 100644 --- a/litellm/litellm_core_utils/llm_judge.py +++ b/litellm/litellm_core_utils/llm_judge.py @@ -4,7 +4,9 @@ from __future__ import annotations import json import re -from typing import TYPE_CHECKING, Final +from dataclasses import dataclass +from functools import lru_cache +from typing import TYPE_CHECKING, Final, Literal import litellm @@ -56,17 +58,62 @@ def extract_text_from_content(content: object) -> str: return "" -def router_resolves_model(router: Router | None, model: str) -> bool: - """Whether the model name resolves through the proxy's router (configured deployment - or model-group alias), the same check the judge dispatch itself makes, so start-time - validation cannot accept a name the call path then fails on.""" - return router is not None and bool(model in router.model_group_alias or router.get_model_list(model_name=model)) +@lru_cache(maxsize=512) +def _provider_qualified(model: str) -> str | None: + """`model` in the one spelling litellm itself resolves it to, or None if it maps to no + provider. + + A deployment may be configured as `openai/gpt-4o` and a judge given as `gpt-4o`; both + reach the same model, so an identity that keeps them apart reports two models where + there is one. None is a different answer from "unchanged": a name that is already + provider-qualified normalises to itself, and reading that as a failure would call every + correctly-spelled public model unresolvable. + """ + try: + stripped, provider, _, _ = litellm.get_llm_provider(model=model) + except Exception: # noqa: BLE001 # an unmapped name has no provider, which is the answer + return None + return f"{provider}/{stripped}" if provider and stripped else None + + +@dataclass(frozen=True, slots=True) +class JudgeTarget: + """Where a call to one model name goes for one caller, and what answers it. + + The single answer to that question: the resolvability gate, the judge-vs-candidate + gate and the dispatch all read it, so none of them can decide it differently. Splitting + it is what let start-time validation accept a team's own model while dispatch sent the + literal name to the SDK. + """ + + via: Literal["router", "sdk", "nothing"] + models: frozenset[str] + + +def judge_target(router: Router | None, model: str, team_id: str | None = None) -> JudgeTarget: + """Resolve `model` the way a call from `team_id` would be. + + Three outcomes and no others: the router serves it (a deployment, a team-public name, + an alias, a routing group or a wildcard, exactly the channels `get_model_list` + composes); the SDK serves it because litellm recognises the provider; or nothing does, + which is the only case a caller may refuse on. + + `team_id` is part of the question, not a refinement of it. A team-public name resolves + only for its own team and a team's own deployment resolves for nobody else, so asking + without it answers for a caller who does not exist. + """ + served: Final = router.resolved_litellm_models(model, team_id=team_id) if router is not None else () + if served: + return JudgeTarget("router", frozenset(_provider_qualified(m) or m for m in served)) + qualified: Final = _provider_qualified(model) + return JudgeTarget("sdk", frozenset({qualified})) if qualified is not None else JudgeTarget("nothing", frozenset()) async def judge_acompletion( router: Router | None, judge_model: str, messages: list[AllMessageValues], # mutable-ok: the SDK acompletion signature takes a list + team_id: str | None = None, **params: object, ) -> ModelResponse: """Dispatch a judge call through the proxy's router when the judge model is a @@ -74,9 +121,13 @@ async def judge_acompletion( provider-qualified public names. The router path never retries or falls back: a failed judge call is the caller's counted failure, not a spend multiplier. Sampling preferences are advisory: models that removed sampling params (e.g. - claude-sonnet-5) drop them instead of rejecting the judge call.""" - if router_resolves_model(router, judge_model): - return await router.acompletion( # pyright: ignore[reportOptionalMemberAccess] # router_resolves_model implies router is not None + claude-sonnet-5) drop them instead of rejecting the judge call. + + The arm is chosen by `judge_target` under the caller's own team, the same call + start-time validation makes, so a judge a team can reach cannot be validated as a + deployment and then dispatched as a public name the SDK has never heard of.""" + if judge_target(router, judge_model, team_id).via == "router": + return await router.acompletion( # pyright: ignore[reportOptionalMemberAccess] # a router target implies router is not None model=judge_model, messages=messages, num_retries=0, diff --git a/litellm/litellm_core_utils/llm_request_utils.py b/litellm/litellm_core_utils/llm_request_utils.py index b4e27b129fe..04824a5bf39 100644 --- a/litellm/litellm_core_utils/llm_request_utils.py +++ b/litellm/litellm_core_utils/llm_request_utils.py @@ -1,6 +1,113 @@ +from collections.abc import Mapping from typing import Final import litellm +from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH + + +def _form_field_value(value: object) -> str: + if value is True: + return "true" + if value is False: + return "false" + return str(value) + + +def _flatten_form_field(key: str, value: object) -> tuple[tuple[str, str], ...]: + pending_fields: Final[ # mutable-ok: depth-capped stack walks nested JSON into multipart names + list[tuple[str, object, int]] + ] = [ # mutable-ok: depth-capped stack walks nested JSON into multipart names + (key, value, 0) + ] + flat_fields: Final[list[tuple[str, str]]] = [] # mutable-ok: local accumulator + while pending_fields: + current_key, current_value, depth = pending_fields.pop() + if depth > DEFAULT_MAX_RECURSE_DEPTH: + raise ValueError("form field nesting exceeds max depth") + if isinstance(current_value, Mapping): + pending_fields.extend( + (f"{current_key}[{subkey}]", subvalue, depth + 1) + for subkey, subvalue in reversed(tuple(current_value.items())) + ) + continue + if isinstance(current_value, (list, tuple)): + pending_fields.extend((f"{current_key}[]", entry, depth + 1) for entry in reversed(tuple(current_value))) + continue + if current_value is None: + continue + serialized = _form_field_value(current_value) + if serialized: + flat_fields.append((current_key, serialized)) + return tuple(flat_fields) + + +def _is_form_scalar(value: object) -> bool: + return value is not None and not isinstance(value, (Mapping, list, tuple)) + + +def _flatten_form_data_field(key: str, value: object) -> tuple[tuple[str, str | tuple[str, ...]], ...]: + pending_fields: Final[ # mutable-ok: depth-capped stack walks nested JSON into multipart names + list[tuple[str, object, int]] + ] = [ # mutable-ok: depth-capped stack walks nested JSON into multipart names + (key, value, 0) + ] + flat_fields: Final[list[tuple[str, str | tuple[str, ...]]]] = [] # mutable-ok: local accumulator + while pending_fields: + current_key, current_value, depth = pending_fields.pop() + if depth > DEFAULT_MAX_RECURSE_DEPTH: + raise ValueError("form field nesting exceeds max depth") + if isinstance(current_value, Mapping): + pending_fields.extend( + (f"{current_key}[{subkey}]", subvalue, depth + 1) + for subkey, subvalue in reversed(tuple(current_value.items())) + ) + continue + if isinstance(current_value, (list, tuple)): + if all(_is_form_scalar(entry) for entry in current_value): + serialized_fields = tuple(field for entry in current_value if (field := _form_field_value(entry))) + if serialized_fields: + flat_fields.append((current_key, serialized_fields)) + continue + pending_fields.extend((f"{current_key}[]", entry, depth + 1) for entry in reversed(tuple(current_value))) + continue + if current_value is None: + continue + serialized = _form_field_value(current_value) + if serialized: + flat_fields.append((current_key, serialized)) + return tuple(flat_fields) + + +def flatten_form_field_values(*sources: Mapping[str, object] | None) -> tuple[tuple[str, str | tuple[str, ...]], ...]: + """ + Flatten JSON-shaped bodies into ``(name, value)`` form fields for a ``dict``-backed + multipart body, applying ``sources`` in order so a later source wins on a key collision + under ``dict.update``. Nested objects become ``key[subkey]`` fields the way the OpenAI SDK + serializes them, so provider params reach a multipart request without handing the httpx + encoder a nested value it rejects with ``Invalid type for value``. A scalar list becomes a + single field carrying a tuple value, which httpx emits as one repeated part per element, so + every element survives instead of collapsing to the last under ``dict.update``. + """ + return tuple( + pair + for source in sources + if source is not None + for top_key, top_value in source.items() + for pair in _flatten_form_data_field(top_key, top_value) + ) + + +def serialize_multipart_form_fields(data: Mapping[str, object]) -> tuple[tuple[str, tuple[None, str]], ...]: + """ + Encode a JSON-shaped body as OpenAI-SDK-style multipart file-tuples so a file-less + request is still sent as multipart/form-data, working around httpx downgrading a + file-less ``data=`` payload to application/x-www-form-urlencoded. + """ + return tuple( + (key, (None, serialized)) + for top_key, top_value in data.items() + for key, serialized in _flatten_form_field(top_key, top_value) + ) def _ensure_extra_body_is_safe(extra_body: dict | None) -> dict | None: diff --git a/litellm/litellm_core_utils/llm_response_utils/response_metadata.py b/litellm/litellm_core_utils/llm_response_utils/response_metadata.py index 44fed944d2a..b53a2d36753 100644 --- a/litellm/litellm_core_utils/llm_response_utils/response_metadata.py +++ b/litellm/litellm_core_utils/llm_response_utils/response_metadata.py @@ -1,6 +1,9 @@ import datetime +from collections.abc import Mapping from typing import Any, Final +import httpx + from litellm.constants import LITELLM_DETAILED_TIMING from litellm.litellm_core_utils.core_helpers import process_response_headers from litellm.litellm_core_utils.llm_response_utils.get_api_base import get_api_base @@ -13,6 +16,39 @@ from litellm.types.utils import ( ) +def response_timing_metrics( + start_time: datetime.datetime, + end_time: datetime.datetime, + logging_obj: LiteLLMLoggingObject, + include_overhead: bool = True, +) -> Mapping[str, float]: + """``_response_ms`` for the whole call, plus ``litellm_overhead_time_ms`` when it can be derived. + + On a cache hit the overhead is the total minus the cache read; otherwise it is the total minus + the provider call (``llm_api_duration_ms``). It is omitted when neither duration was recorded, + and when ``include_overhead`` is False because the two durations cover different windows. + """ + total_response_time_ms: Final = (end_time - start_time).total_seconds() * 1000 + if not include_overhead: + return {"_response_ms": total_response_time_ms} # mutable-ok: read-only timing result + caching_details: Final = logging_obj.caching_details + cache_duration_ms: Final = ( + caching_details.get("cache_duration_ms") + if caching_details is not None and caching_details.get("cache_hit") is True + else None + ) + llm_api_duration_ms: Final = logging_obj.model_call_details.get("llm_api_duration_ms") + if cache_duration_ms is not None: + overhead_ms: float | None = total_response_time_ms - cache_duration_ms + elif llm_api_duration_ms is not None: + overhead_ms = round(total_response_time_ms - llm_api_duration_ms, 4) + else: + overhead_ms = None + if overhead_ms is None: + return {"_response_ms": total_response_time_ms} + return {"_response_ms": total_response_time_ms, "litellm_overhead_time_ms": overhead_ms} + + class ResponseMetadata: """ Handles setting and managing `_hidden_params`, `response_time_ms`, and `litellm_overhead_time_ms` for LiteLLM responses @@ -25,11 +61,7 @@ class ResponseMetadata: @property def supports_response_time(self) -> bool: """Check if response type supports timing metrics""" - return ( - isinstance(self.result, ModelResponse) - or isinstance(self.result, EmbeddingResponse) - or isinstance(self.result, TranscriptionResponse) - ) + return isinstance(self.result, (ModelResponse, EmbeddingResponse, TranscriptionResponse)) def set_hidden_params(self, logging_obj: LiteLLMLoggingObject, model: str | None, kwargs: dict) -> None: """Set hidden parameters on the response""" @@ -45,14 +77,14 @@ class ResponseMetadata: result=self.result, litellm_model_name=model, router_model_id=model_id ), "additional_headers": process_response_headers( - self._get_value_from_hidden_params("additional_headers") or {}, + self._get_additional_headers_from_hidden_params() or {}, preserve_litellm_internal_headers=True, ), "litellm_model_name": model, } self._update_hidden_params(new_params) - def _update_hidden_params(self, new_params: dict) -> None: + def _update_hidden_params(self, new_params: Mapping[str, object]) -> None: """ Update hidden params - handles when self._hidden_params is a dict or HiddenParams object """ @@ -64,51 +96,38 @@ class ResponseMetadata: for key, value in new_params.items(): setattr(self._hidden_params, key, value) - def _get_value_from_hidden_params(self, key: str) -> Any | None: - """Get value from hidden params - handles when self._hidden_params is a dict or HiddenParams object""" + def _get_additional_headers_from_hidden_params(self) -> httpx.Headers | dict[str, str] | None: + """Get `additional_headers` from hidden params - handles when self._hidden_params is a dict or HiddenParams object""" if isinstance(self._hidden_params, dict): - return self._hidden_params.get(key, None) + return self._hidden_params.get("additional_headers", None) elif isinstance(self._hidden_params, HiddenParams): - return getattr(self._hidden_params, key, None) + return getattr(self._hidden_params, "additional_headers", None) def set_timing_metrics( self, start_time: datetime.datetime, end_time: datetime.datetime, logging_obj: LiteLLMLoggingObject, + include_overhead: bool = True, ) -> None: """Set response timing metrics""" - total_response_time_ms: Final = (end_time - start_time).total_seconds() * 1000 + timing_metrics: Final = response_timing_metrics(start_time, end_time, logging_obj, include_overhead) + total_response_time_ms: Final = timing_metrics["_response_ms"] # Set total response time if supported if self.supports_response_time: self.result._response_ms = total_response_time_ms ######################################################### - # 1. Add _response_ms total duration + # 1. Add _response_ms total duration and the LiteLLM overhead within it + # (total minus the cache read on a cache hit, else total minus the provider call) ######################################################### - self._update_hidden_params( - { - "_response_ms": total_response_time_ms, - } - ) + self._update_hidden_params(timing_metrics) ######################################################### - # 2. Add LiteLLM overhead duration + # 2. Add callback processing duration ######################################################### - llm_api_duration_ms: Final = logging_obj.model_call_details.get("llm_api_duration_ms") - if llm_api_duration_ms is not None: - overhead_ms = round(total_response_time_ms - llm_api_duration_ms, 4) - self._update_hidden_params( - { - "litellm_overhead_time_ms": overhead_ms, - } - ) - - ######################################################### - # 3. Add callback processing duration - ######################################################### - callback_duration_ms: Final = getattr(logging_obj, "callback_duration_ms", None) + callback_duration_ms: Final[float | None] = getattr(logging_obj, "callback_duration_ms", None) if callback_duration_ms is not None: self._update_hidden_params( { @@ -117,36 +136,21 @@ class ResponseMetadata: ) ######################################################### - # 4. Add duration for reading from cache - # In this case overhead from litellm is the difference between the cache read duration and the total response time - ######################################################### - if ( - logging_obj.caching_details is not None - and logging_obj.caching_details.get("cache_hit") is True - and (cache_duration_ms := logging_obj.caching_details.get("cache_duration_ms")) is not None - ): - overhead_ms = total_response_time_ms - cache_duration_ms - self._update_hidden_params( - { - "litellm_overhead_time_ms": overhead_ms, - } - ) - - ######################################################### - # 5. Detailed per-phase timing (opt-in via env var) + # 3. Detailed per-phase timing (opt-in via env var) ######################################################### + llm_api_duration_ms: Final = logging_obj.model_call_details.get("llm_api_duration_ms") if LITELLM_DETAILED_TIMING and llm_api_duration_ms is not None: - detailed: Final[dict] = { + detailed: Final[dict[str, float]] = { "timing_llm_api_ms": round(llm_api_duration_ms, 4), } # message copy time from Logging.__init__() - msg_copy_ms: Final = getattr(logging_obj, "message_copy_duration_ms", None) + msg_copy_ms: Final[float | None] = getattr(logging_obj, "message_copy_duration_ms", None) if msg_copy_ms is not None: detailed["timing_message_copy_ms"] = round(msg_copy_ms, 4) # pre-processing = time from request start to LLM API call start - api_call_start: Final = logging_obj.model_call_details.get("api_call_start_time") + api_call_start: Final[datetime.datetime | None] = logging_obj.model_call_details.get("api_call_start_time") if api_call_start is not None and start_time is not None: pre_ms: Final = (api_call_start - start_time).total_seconds() * 1000 detailed["timing_pre_processing_ms"] = round(pre_ms, 4) @@ -170,6 +174,7 @@ def update_response_metadata( kwargs: dict, start_time: datetime.datetime, end_time: datetime.datetime, + include_overhead: bool = True, ) -> None: """ Updates response metadata including hidden params and timing metrics @@ -177,11 +182,22 @@ def update_response_metadata( - response._hidden_params - response._hidden_params["litellm_overhead_time_ms"] - response.response_time_ms + A result that cannot hold ``_hidden_params`` gets its timing on ``logging_obj`` instead. + Callers whose ``end_time`` covers more than the recorded provider call (a stream read to + completion) pass ``include_overhead=False``, since the overhead cannot be derived there. """ if result is None: return + if not hasattr(result, "_hidden_params"): + # /v1/messages returns a plain dict and the Anthropic / Responses bridge stream wrappers + # cannot hold ``_hidden_params``: keep only the timing on the logging object (no cost + # recompute) so the proxy headers and the standard logging payload can still read it. + logging_obj.set_response_timing_metrics( + response_timing_metrics(start_time, end_time, logging_obj, include_overhead) + ) + return metadata: Final = ResponseMetadata(result) metadata.set_hidden_params(logging_obj, model, kwargs) - metadata.set_timing_metrics(start_time, end_time, logging_obj) + metadata.set_timing_metrics(start_time, end_time, logging_obj, include_overhead) metadata.apply() diff --git a/litellm/litellm_core_utils/logging_utils.py b/litellm/litellm_core_utils/logging_utils.py index a17415f3ab8..91c8ba36b26 100644 --- a/litellm/litellm_core_utils/logging_utils.py +++ b/litellm/litellm_core_utils/logging_utils.py @@ -3,6 +3,7 @@ import functools import inspect import re import time +from collections.abc import Mapping from datetime import datetime from typing import TYPE_CHECKING, Any, Final @@ -268,6 +269,16 @@ def _set_duration_in_model_call_details( verbose_logger.warning("Error setting `llm_api_duration_ms`: %s", e) +def speech_request_body(model: str, voice: str, optional_params: Mapping[str, object]) -> Mapping[str, object]: + """Speech request body for telemetry, without the caller headers the provider SDKs + take as request kwargs rather than body fields.""" + return { # mutable-ok: loggers isinstance-check the request body as a dict + "model": model, + "voice": voice, + **{key: value for key, value in optional_params.items() if key != "extra_headers"}, + } + + def track_llm_api_timing(): """ Decorator to track LLM API call timing for both sync and async functions. diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py index 41d2af27eeb..1d74595781a 100644 --- a/litellm/litellm_core_utils/logging_worker.py +++ b/litellm/litellm_core_utils/logging_worker.py @@ -4,8 +4,9 @@ import asyncio import atexit import contextvars +import inspect import logging -from collections.abc import Coroutine +from collections.abc import Coroutine, Iterator from typing import Final from typing_extensions import TypedDict @@ -53,6 +54,7 @@ class LoggingWorker: self._queue: asyncio.Queue[LoggingTask] | None = None self._worker_task: asyncio.Task | None = None self._running_tasks: set[asyncio.Task] = set() + self._dequeued_tasks: dict[int, LoggingTask] = {} # mutable-ok: refs so flush can rescue never-started tasks self._sem: asyncio.Semaphore | None = None self._bound_loop: asyncio.AbstractEventLoop | None = None self._last_aggressive_clear_time: float = 0.0 @@ -61,6 +63,51 @@ class LoggingWorker: # Register cleanup handler to flush remaining events on exit atexit.register(self._flush_on_exit) + def _track_dequeued(self, task: LoggingTask) -> None: + self._dequeued_tasks[id(task)] = task + + def _untrack_dequeued(self, task: LoggingTask) -> None: + self._dequeued_tasks.pop(id(task), None) + + def _unstarted_dequeued_tasks(self) -> tuple[LoggingTask, ...]: + return tuple( + task + for task in self._dequeued_tasks.values() + if inspect.getcoroutinestate(task["coroutine"]) == inspect.CORO_CREATED + ) + + def _requeue_unstarted_dequeued(self, new_queue: "asyncio.Queue[LoggingTask]") -> int: + revived: Final = self._unstarted_dequeued_tasks() + self._dequeued_tasks.clear() + for index, revived_task in enumerate(revived): + try: + new_queue.put_nowait(revived_task) + except asyncio.QueueFull: + for leftover in revived[index:]: + self._track_dequeued(leftover) + return index + return len(revived) + + def _run_coroutine_silently(self, loop: asyncio.AbstractEventLoop, coroutine: Coroutine) -> bool: + try: + loop.run_until_complete(asyncio.wait_for(coroutine, timeout=self.timeout)) + except (Exception, asyncio.CancelledError): # noqa: BLE001 # atexit flush must never break the user's program + return False + return True + + @staticmethod + def _drain_pending(queue: "asyncio.Queue[LoggingTask]") -> tuple[LoggingTask, ...]: + """Pop every task still queued, without awaiting them, so they can be moved to another queue.""" + + def _pop_until_empty() -> Iterator[LoggingTask]: + while True: + try: + yield queue.get_nowait() + except asyncio.QueueEmpty: + return + + return tuple(_pop_until_empty()) + def _ensure_queue(self) -> None: """Initialize the queue if it doesn't exist or if event loop has changed.""" try: @@ -69,14 +116,29 @@ class LoggingWorker: # No running loop, can't initialize return - # Check if we need to reinitialize due to event loop change + # The queue, semaphore and worker task are all bound to the loop that created them. On a + # loop change we hand the still-pending tasks to a fresh queue instead of dropping them, + # so queued spend-logging coroutines are not silently discarded (and never left un-awaited). if self._queue is not None and self._bound_loop is not current_loop: - verbose_logger.debug("LoggingWorker: Event loop changed, reinitializing queue and worker") - # Clear old state - these are bound to the old loop - self._queue = None + carried_over: Final = self._drain_pending(self._queue) + new_queue: Final[asyncio.Queue[LoggingTask]] = asyncio.Queue(maxsize=self.max_queue_size) + for carried_task in carried_over: + new_queue.put_nowait(carried_task) + revived_count: Final = self._requeue_unstarted_dequeued(new_queue) + if carried_over or revived_count: + verbose_logger.warning( + "LoggingWorker: event loop changed; carried %d pending and revived %d dequeued logging task(s) onto the new loop", + len(carried_over), + revived_count, + ) + else: + verbose_logger.debug("LoggingWorker: Event loop changed, reinitializing queue and worker") self._sem = None self._worker_task = None self._running_tasks.clear() + self._queue = new_queue + self._bound_loop = current_loop + return if self._queue is None: self._queue = asyncio.Queue(maxsize=self.max_queue_size) @@ -103,6 +165,7 @@ class LoggingWorker: except Exception as e: verbose_logger.exception("LoggingWorker error: %s", e) finally: + self._untrack_dequeued(task) self._queue.task_done() finally: # Always release semaphore, even if queue is None @@ -120,6 +183,7 @@ class LoggingWorker: await self._sem.acquire() try: task = await self._queue.get() + self._track_dequeued(task) # Track each spawned coroutine so we can cancel on shutdown. processing_task = asyncio.create_task(self._process_log_task(task, self._sem)) self._running_tasks.add(processing_task) @@ -272,9 +336,10 @@ class LoggingWorker: extracted_tasks: Final = [] for _ in range(items_to_extract): try: - extracted_tasks.append(self._queue.get_nowait()) + extracted_tasks.append(extracted := self._queue.get_nowait()) except asyncio.QueueEmpty: break + self._track_dequeued(extracted) return extracted_tasks @@ -292,6 +357,7 @@ class LoggingWorker: # Add new task to extracted tasks to process directly if new_task is not None: + self._track_dequeued(new_task) extracted_tasks.append(new_task) # Process extracted tasks directly @@ -317,6 +383,7 @@ class LoggingWorker: # Suppress errors during processing to ensure we keep going pass finally: + self._untrack_dequeued(task) self._queue.task_done() async def _process_extracted_tasks(self, tasks: list[LoggingTask]) -> None: @@ -460,11 +527,12 @@ class LoggingWorker: self._safe_log("debug", "[LoggingWorker] atexit: No queue initialized") return - if self._queue.empty(): + unstarted_dequeued: Final = self._unstarted_dequeued_tasks() + if self._queue.empty() and not unstarted_dequeued: self._safe_log("debug", "[LoggingWorker] atexit: Queue is empty") return - queue_size: Final = self._queue.qsize() + queue_size: Final = self._queue.qsize() + len(unstarted_dequeued) self._safe_log("info", f"[LoggingWorker] atexit: Flushing {queue_size} remaining events...") # Create a new event loop since the original is closed @@ -483,6 +551,16 @@ class LoggingWorker: previous_raise_exceptions: Final = logging.raiseExceptions logging.raiseExceptions = False try: + for pending in unstarted_dequeued: + if ( + processed >= MAX_ITERATIONS_TO_CLEAR_QUEUE + or loop.time() - start_time >= MAX_TIME_TO_CLEAR_QUEUE + ): + break + if self._run_coroutine_silently(loop, pending["coroutine"]): + processed += 1 + self._untrack_dequeued(pending) + while not self._queue.empty() and processed < MAX_ITERATIONS_TO_CLEAR_QUEUE: if loop.time() - start_time >= MAX_TIME_TO_CLEAR_QUEUE: self._safe_log( @@ -500,11 +578,8 @@ class LoggingWorker: # Note: We run the coroutine directly, not via create_task, # since we're in a new event loop context try: - loop.run_until_complete(task["coroutine"]) - processed += 1 - except Exception: - # Silent failure to not break user's program - pass + if self._run_coroutine_silently(loop, task["coroutine"]): + processed += 1 finally: # Clear reference to prevent memory leaks task = None diff --git a/litellm/litellm_core_utils/model_response_utils.py b/litellm/litellm_core_utils/model_response_utils.py index ea4be1c856f..dc4f375daa7 100644 --- a/litellm/litellm_core_utils/model_response_utils.py +++ b/litellm/litellm_core_utils/model_response_utils.py @@ -2,9 +2,21 @@ Utility functions for ModelResponse and ModelResponseStream objects. """ -from typing import Any, Final +from collections.abc import Mapping +from typing import Final -from litellm.types.utils import Delta, ModelResponseBase, ModelResponseStream +from typing_extensions import ReadOnly, TypedDict + +from litellm.types.utils import Delta, ModelResponseBase, ModelResponseStream, StreamingChoices + + +class _AttributeView(TypedDict): + value: ReadOnly[object] + + +def _attribute_of(source: object, name: str) -> object: + attribute: Final[_AttributeView] = {"value": getattr(source, name)} + return attribute["value"] def is_model_response_stream_empty(model_response: ModelResponseStream) -> bool: @@ -40,10 +52,10 @@ def is_model_response_stream_empty(model_response: ModelResponseStream) -> bool: return False # Check model_extra for dynamically added fields (this is where Pydantic stores them) - if hasattr(model_response, "model_extra") and model_response.model_extra: - for extra_field_name, extra_field_value in model_response.model_extra.items(): - if _has_meaningful_content(extra_field_value): - return False + stream_extra_fields: Final[Mapping[str, object]] = model_response.model_extra or {} + for extra_field_value in stream_extra_fields.values(): + if _has_meaningful_content(extra_field_value): + return False # Check for any non-base fields that are set # Access model_fields on the class, not the instance, to avoid Pydantic 2.11+ deprecation warnings @@ -57,7 +69,7 @@ def is_model_response_stream_empty(model_response: ModelResponseStream) -> bool: continue # Check if any other field has meaningful content - model_response_value = getattr(model_response, model_response_field, None) + model_response_value: object = getattr(model_response, model_response_field, None) if _has_meaningful_content(model_response_value): return False @@ -71,7 +83,7 @@ def is_model_response_stream_empty(model_response: ModelResponseStream) -> bool: return True -def _has_meaningful_content(value: Any) -> bool: +def _has_meaningful_content(value: object) -> bool: """ Check if a value contains meaningful content. @@ -102,7 +114,7 @@ def _has_meaningful_content(value: Any) -> bool: return True -def _is_choice_non_empty(choice: Any) -> bool: +def _is_choice_non_empty(choice: StreamingChoices) -> bool: """ Deep check if a choice contains any meaningful content. @@ -113,41 +125,40 @@ def _is_choice_non_empty(choice: Any) -> bool: bool: True if the choice has meaningful content, False otherwise """ # Check finish_reason - if hasattr(choice, "finish_reason") and choice.finish_reason is not None: + if getattr(choice, "finish_reason", None) is not None: return True # Check logprobs - if hasattr(choice, "logprobs") and choice.logprobs is not None: + if getattr(choice, "logprobs", None) is not None: return True # Check enhancements (if present) - if hasattr(choice, "enhancements") and choice.enhancements is not None: + if getattr(choice, "enhancements", None) is not None: return True # Deep check delta object - if hasattr(choice, "delta") and choice.delta is not None: - if _is_delta_non_empty(choice.delta): - return True + choice_delta: Final[Delta | None] = getattr(choice, "delta", None) + if choice_delta is not None and _is_delta_non_empty(choice_delta): + return True # Check model_extra for dynamically added fields on the choice - if hasattr(choice, "model_extra") and choice.model_extra: - for extra_field_name, extra_field_value in choice.model_extra.items(): - # Skip certain structural fields that are just default/None placeholders - if extra_field_name == "index" and extra_field_value == 0: - continue - if extra_field_name in {"finish_reason", "logprobs"} and extra_field_value is None: - continue - if extra_field_name == "delta": - continue - if _has_meaningful_content(extra_field_value): - return True + choice_extra_fields: Final[Mapping[str, object]] = choice.model_extra or {} + for extra_field_name, extra_field_value in choice_extra_fields.items(): + if extra_field_name == "index" and extra_field_value == 0: + continue + if extra_field_name in {"finish_reason", "logprobs"} and extra_field_value is None: + continue + if extra_field_name == "delta": + continue + if _has_meaningful_content(extra_field_value): + return True # Check for any other non-standard fields on the choice for attr_name in dir(choice): # Skip private attributes, methods, and known empty fields if ( attr_name.startswith("_") - or callable(getattr(choice, attr_name)) + or callable(_attribute_of(choice, attr_name)) or attr_name.startswith("model_") or attr_name in { @@ -160,8 +171,8 @@ def _is_choice_non_empty(choice: Any) -> bool: ): continue - attr_value = getattr(choice, attr_name, None) - if _has_meaningful_content(attr_value): + choice_attr_value: object = getattr(choice, attr_name, None) + if _has_meaningful_content(choice_attr_value): return True return False @@ -178,20 +189,19 @@ def _is_delta_non_empty(delta: Delta) -> bool: bool: True if the delta has meaningful content, False otherwise """ # Check model_extra for dynamically added fields (this is where Pydantic stores them) - if hasattr(delta, "model_extra") and delta.model_extra: - for extra_field_name, extra_field_value in delta.model_extra.items(): - # Even structural fields are meaningful if they have actual content - if _has_meaningful_content(extra_field_value): - return True + delta_extra_fields: Final[Mapping[str, object]] = delta.model_extra or {} + for extra_field_value in delta_extra_fields.values(): + if _has_meaningful_content(extra_field_value): + return True # Check all regular attributes of the delta object for attr_name in dir(delta): # Skip private attributes, methods, and Pydantic-specific fields - if attr_name.startswith("_") or callable(getattr(delta, attr_name)) or attr_name.startswith("model_"): + if attr_name.startswith("_") or callable(_attribute_of(delta, attr_name)) or attr_name.startswith("model_"): continue - attr_value = getattr(delta, attr_name, None) - if _has_meaningful_content(attr_value): + delta_attr_value: object = getattr(delta, attr_name, None) + if _has_meaningful_content(delta_attr_value): return True return False diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 2db5776047b..ff46440ff5c 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -10,6 +10,7 @@ from collections.abc import Iterable, Mapping, Sequence from itertools import groupby from os import PathLike from pathlib import Path +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, TypeVar, cast from openai.types.chat.chat_completion_custom_tool_param import ( @@ -28,8 +29,12 @@ from litellm.types.llms.openai import ( ChatCompletionAssistantMessage, ChatCompletionFileObject, ChatCompletionImageObject, + ChatCompletionReasoningItem, + ChatCompletionReasoningSummaryTextBlock, + ChatCompletionRedactedThinkingBlock, ChatCompletionResponseMessage, ChatCompletionTextObject, + ChatCompletionThinkingBlock, ChatCompletionToolParam, ChatCompletionUserMessage, ) @@ -200,6 +205,41 @@ def is_non_content_values_set(message: AllMessageValues) -> bool: return any(message.get(key, None) is not None for key in message if key not in ignore_keys) +_IMAGE_CONTENT_PART_TYPES: Final = frozenset({"image_url", "input_image", "image"}) +_IMAGE_SCAN_MAX_DEPTH: Final = 4 + + +def _content_parts_contain_image(parts: Sequence[object]) -> bool: + """Depth-bounded frontier walk over nested content lists, iterative because the repo bans + recursion; an Anthropic tool_result nests its image parts exactly one level down.""" + frontier = parts # rebind-ok: depth-bounded frontier walk + for _ in range(_IMAGE_SCAN_MAX_DEPTH): + if any(isinstance(part, Mapping) and part.get("type") in _IMAGE_CONTENT_PART_TYPES for part in frontier): + return True + frontier = tuple( # rebind-ok: depth-bounded frontier walk + nested + for part in frontier + if isinstance(part, Mapping) + for content in (part.get("content"),) + if isinstance(content, list) + for nested in content + ) + if not frontier: + return False + return False + + +def request_contains_image_content(messages: Sequence[Mapping[str, object]]) -> bool: + """Whether any message carries an image content part, across the dialects that reach + pre-routing hooks untranslated: chat-completions ``image_url``, Responses ``input_image``, + and Anthropic Messages ``image``, including images nested inside ``tool_result`` blocks.""" + return any( + isinstance(content, list) and _content_parts_contain_image(content) + for message in messages + for content in (message.get("content"),) + ) + + def _audio_or_image_in_message_content(message: AllMessageValues) -> bool: """ Checks if message content contains an image or audio @@ -466,6 +506,8 @@ def update_messages_with_model_file_ids( from litellm.proxy.openai_files_endpoints.common_utils import ( _is_base64_encoded_unified_file_id, convert_b64_uid_to_unified_uid, + get_original_file_id, + is_model_embedded_id, ) for message in messages: @@ -504,6 +546,8 @@ def update_messages_with_model_file_ids( unified_file_id = convert_b64_uid_to_unified_uid(file_id) if "llm_output_file_id," in unified_file_id: provider_file_id = unified_file_id.split("llm_output_file_id,")[1].split(";")[0] + if not provider_file_id and is_model_embedded_id(file_id): + provider_file_id = get_original_file_id(file_id) file_object_file_field["file_id"] = provider_file_id or file_id if format: file_object_file_field["format"] = format @@ -511,10 +555,10 @@ def update_messages_with_model_file_ids( def update_responses_input_with_model_file_ids( - input: Any, + input: object, model_id: str | None = None, model_file_id_mapping: dict[str, dict[str, str]] | None = None, -) -> str | list[dict[str, Any]]: +) -> object: """ Updates responses API input with provider-specific file IDs. File IDs are always inside the content array, not as direct input_file items. @@ -531,6 +575,8 @@ def update_responses_input_with_model_file_ids( from litellm.proxy.openai_files_endpoints.common_utils import ( _is_base64_encoded_unified_file_id, convert_b64_uid_to_unified_uid, + get_original_file_id, + is_model_embedded_id, ) if isinstance(input, str): @@ -574,6 +620,10 @@ def update_responses_input_with_model_file_ids( updated_content_item = content_item.copy() updated_content_item["file_id"] = provider_file_id updated_content.append(updated_content_item) + elif is_model_embedded_id(file_id): + updated_content_item = content_item.copy() + updated_content_item["file_id"] = get_original_file_id(file_id) + updated_content.append(updated_content_item) else: # Not a managed file, keep as-is updated_content.append(content_item) @@ -589,8 +639,8 @@ def update_responses_input_with_model_file_ids( def _decode_vector_store_ids_in_tools( - tools: list[dict[str, Any]] | None, -) -> list[dict[str, Any]] | None: + tools: list[dict[str, object]] | None, +) -> list[dict[str, object]] | None: """ Decodes unified (LiteLLM-managed) vector_store_ids in file_search tools to provider-native IDs. Non-unified IDs are passed through unchanged. @@ -642,10 +692,10 @@ def _decode_vector_store_ids_in_tools( def update_responses_tools_with_model_file_ids( - tools: list[dict[str, Any]] | None, + tools: list[dict[str, object]] | None, model_id: str | None = None, model_file_id_mapping: dict[str, dict[str, str]] | None = None, -) -> list[dict[str, Any]] | None: +) -> list[dict[str, object]] | None: """ Updates responses API tools with provider-specific file IDs. @@ -838,7 +888,7 @@ def extract_file_data(file_data: FileTypes) -> ExtractedFileData: # --------------------------------------------------------------------------- -def _estimate_json_bytes(obj: Any) -> int: +def _estimate_json_bytes(obj: object) -> int: """Estimate the JSON-serialised byte size of ``obj`` without materialising JSON. Walks iteratively (no recursion stack risk). @@ -1075,6 +1125,175 @@ def sanitize_input_schema_for_anthropic(input_schema: dict) -> "AnthropicInputSc return AnthropicInputSchema(**filtered) +_TOP_LEVEL_SCHEMA_COMBINATORS: Final = ("allOf", "anyOf", "oneOf") +_OPENAI_REJECTED_TOP_LEVEL_SCHEMA_KEYS: Final = ("enum", "const", "not") +_LOCAL_SCHEMA_REF_PREFIXES: Final = (("#/$defs/", "$defs"), ("#/definitions/", "definitions")) +_MAX_SCHEMA_FLATTEN_DEPTH: Final = 32 +_EMPTY_SCHEMA: Final[Mapping[str, object]] = MappingProxyType({}) + + +def _schema_properties(schema: Mapping[str, object]) -> Mapping[str, object]: + properties: Final = schema.get("properties") + return properties if isinstance(properties, dict) else _EMPTY_SCHEMA + + +def _schema_branches(schema: Mapping[str, object], combinator: str) -> tuple[object, ...]: + branches: Final = schema.get(combinator) + return tuple(branches) if isinstance(branches, list) else () + + +def _schema_required_names(schema: Mapping[str, object]) -> frozenset[str]: + required: Final = schema.get("required") + if not isinstance(required, list): + return frozenset() + return frozenset(name for name in required if isinstance(name, str)) + + +def _combinator_required_names(combinator: str, branches: tuple[Mapping[str, object], ...]) -> frozenset[str]: + branch_names: Final = tuple(_schema_required_names(branch) for branch in branches) + if not branch_names: + return frozenset() + if combinator == "allOf": + return branch_names[0].union(*branch_names[1:]) + return branch_names[0].intersection(*branch_names[1:]) + + +def _resolve_local_schema_ref(root: Mapping[str, object], ref: str) -> Mapping[str, object] | None: + matched: Final = next( + ((prefix, container) for prefix, container in _LOCAL_SCHEMA_REF_PREFIXES if ref.startswith(prefix)), + None, + ) + if matched is None: + return None + prefix, container = matched + definitions: Final = root.get(container) + if not isinstance(definitions, dict): + return None + target: Final = definitions.get(ref[len(prefix) :]) + return target if isinstance(target, dict) else None + + +def _mergeable_branch( + root: Mapping[str, object], + branch: object, + seen_refs: frozenset[str], + depth: int, + expanded_refs: dict[str, Mapping[str, object] | None], # mutable-ok: per-call memo bounding repeated $ref work +) -> Mapping[str, object] | None: + if not isinstance(branch, dict) or depth > _MAX_SCHEMA_FLATTEN_DEPTH: + return None + ref: Final = branch.get("$ref") + if not isinstance(ref, str): + flattened: Final = _flatten_schema_against_root(branch, root, seen_refs, depth, expanded_refs) + if any(combinator in flattened for combinator in _TOP_LEVEL_SCHEMA_COMBINATORS): + return None + return flattened + if ref in expanded_refs: + return expanded_refs[ref] + if ref in seen_refs: + return None + target: Final = _resolve_local_schema_ref(root, ref) + expanded: Final = ( + None + if target is None + else _mergeable_branch(root, target, seen_refs | frozenset((ref,)), depth + 1, expanded_refs) + ) + expanded_refs[ref] = expanded + return expanded + + +def _is_object_schema(schema: Mapping[str, object]) -> bool: + return schema.get("type") == "object" or ("type" not in schema and "properties" in schema) + + +def _flatten_schema_against_root( + schema: Mapping[str, object], + root: Mapping[str, object], + seen_refs: frozenset[str], + depth: int, + expanded_refs: dict[str, Mapping[str, object] | None], # mutable-ok: per-call memo bounding repeated $ref work +) -> Mapping[str, object]: + raw_branch_groups: Final = tuple( + ( + combinator, + tuple( + _mergeable_branch(root, branch, seen_refs, depth + 1, expanded_refs) + for branch in _schema_branches(schema, combinator) + ), + ) + for combinator in _TOP_LEVEL_SCHEMA_COMBINATORS + if isinstance(schema.get(combinator), list) + ) + dropped: Final = ( + *(combinator for combinator, _ in raw_branch_groups), + *(key for key in _OPENAI_REJECTED_TOP_LEVEL_SCHEMA_KEYS if key in schema), + ) + if not dropped: + return schema + + if any(branch is None for _, group in raw_branch_groups for branch in group): + return schema + branch_groups: Final = tuple( + (combinator, tuple(branch for branch in group if branch is not None)) for combinator, group in raw_branch_groups + ) + branches: Final = tuple(branch for _, group in branch_groups for branch in group) + is_object_schema: Final = _is_object_schema(schema) or ( + "type" not in schema and branches != () and all(_is_object_schema(branch) for branch in branches) + ) + if not is_object_schema: + return schema + + merged_properties: Final = { # mutable-ok: tool parameters are JSON dicts + name: value for source in (*reversed(branches), schema) for name, value in _schema_properties(source).items() + } + required_names: Final = _schema_required_names(schema).union( + *(_combinator_required_names(combinator, group) for combinator, group in branch_groups) + ) + kept: Final = MappingProxyType({key: value for key, value in schema.items() if key not in dropped}) + required_update: Final = MappingProxyType({"required": sorted(required_names)}) if required_names else _EMPTY_SCHEMA + return { # mutable-ok: tool parameters are JSON dicts + **kept, + "type": "object", + "properties": merged_properties, + **required_update, + } + + +def flatten_top_level_schema_combinators(schema: Mapping[str, object]) -> Mapping[str, object]: + """Merge top-level ``allOf``/``anyOf``/``oneOf`` branches into an object tool schema. + + OpenAI's function-calling validator rejects tool ``parameters`` carrying + 'oneOf'/'anyOf'/'allOf'/'enum'/'const'/'not' at the top level (nested uses + are accepted), while lenient backends such as the ChatGPT backend Codex + talks to natively accept them, so an MCP tool declaring a top-level union + 400s through LiteLLM. Branch properties merge without clobbering (the + top-level schema wins, then earlier branches); ``required`` becomes the + top-level list plus the intersection of the branch lists for anyOf/oneOf + or their union for allOf. Branches that are local ``$ref``s + (``#/$defs/...`` or ``#/definitions/...``) are resolved first, each ref + at most once per call, and branches that are themselves combinators are + flattened recursively up to a fixed depth; a branch that cannot be fully + merged (a boolean schema, an external or cyclic ``$ref``, a non-object + union, or nesting past the depth cap) leaves the whole schema untouched so + OpenAI's own validation still applies. Non-object schemas pass through + unchanged and the input is never mutated. + """ + return _flatten_schema_against_root(schema, schema, frozenset(), 0, {}) # mutable-ok: fresh per-call $ref memo + + +def tool_with_flattened_parameters(tool: Mapping[str, object]) -> Mapping[str, object]: + function: Final = tool.get("function") + if not isinstance(function, dict): + return tool + parameters: Final = function.get("parameters") + if not isinstance(parameters, dict): + return tool + flattened: Final = flatten_top_level_schema_combinators(parameters) + if flattened is parameters: + return tool + return {**tool, "function": {**function, "parameters": flattened}} # mutable-ok: request tools are JSON dicts + + def _get_image_mime_type_from_url(url: str) -> str | None: """ Get mime type for common image URLs @@ -1549,6 +1768,44 @@ def _extract_reasoning_content(message: dict) -> tuple[str | None, str | None]: return None, message_content +def _readable_thinking_text( + block: ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock, +) -> str: + """The text a chat model can read back, empty for redacted blocks and malformed ones.""" + if block.get("type") != "thinking": + return "" + thinking: Final = cast(ChatCompletionThinkingBlock, block).get("thinking") # cast-ok: narrowed by the type tag + return str(thinking or "") + + +def reasoning_content_from_thinking_blocks( + thinking_blocks: Iterable[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock], +) -> str: + """Flatten Anthropic thinking blocks into the `reasoning_content` string chat models expect. + + Redacted blocks carry no readable text, so they contribute nothing. + """ + return "\n".join(text for block in thinking_blocks if (text := _readable_thinking_text(block))) + + +def responses_reasoning_item_from_thinking_blocks( + thinking_blocks: Iterable[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock], +) -> ChatCompletionReasoningItem | None: + """Build a Responses API `reasoning` input item from Anthropic thinking blocks. + + The item carries no `id`: the Responses API rejects an empty one and 404s on any id it + did not mint itself, while an item without an id is always accepted. + """ + summary: Final[list[ChatCompletionReasoningSummaryTextBlock]] = [ # mutable-ok: API message payload + ChatCompletionReasoningSummaryTextBlock(type="summary_text", text=text) + for block in thinking_blocks + if (text := _readable_thinking_text(block)) + ] + if not summary: + return None + return ChatCompletionReasoningItem(type="reasoning", summary=summary) + + def _parse_content_for_reasoning( message_text: str | None, ) -> tuple[str | None, str | None]: @@ -1695,7 +1952,47 @@ def hoist_images_from_tool_messages( ] -def _attempt_json_repair(s: str) -> Any | None: +def _is_tool_reference_part(part: object) -> bool: + return isinstance(part, dict) and part.get("type") == "tool_reference" + + +def _tool_message_carries_tool_reference(message: AllMessageValues) -> bool: + if message.get("role") != "tool": + return False + content = message.get("content") + return isinstance(content, list) and any(_is_tool_reference_part(part) for part in content) + + +def _drop_tool_reference_parts(message: AllMessageValues) -> AllMessageValues: + if not _tool_message_carries_tool_reference(message): + return message + content = cast(list, message.get("content")) # cast-ok: shape checked by _tool_message_carries_tool_reference + remaining_parts = [ # mutable-ok: tool message content must stay a json list + part for part in content if not _is_tool_reference_part(part) + ] + new_content = remaining_parts if remaining_parts else "" + rewritten = {**message, "content": new_content} # mutable-ok: chat messages are plain json dicts + return cast(AllMessageValues, rewritten) # cast-ok: dict spread keeps keys like cache_control + + +def drop_tool_reference_parts_from_tool_messages( + messages: list[AllMessageValues], # mutable-ok: message pipelines type messages as mutable lists +) -> list[AllMessageValues]: # mutable-ok: message pipelines type messages as mutable lists + """ + Remove tool_reference content parts from role:"tool" messages. + + The OpenAI chat spec only accepts text in tool messages, so a tool_reference + part carried through the Anthropic adapter makes strict providers reject the + request. The reference names an already-declared tool rather than carrying + content, so it is dropped; a reference-only result keeps its tool message with + empty text so the preceding tool_call stays answered. + """ + if not any(_tool_message_carries_tool_reference(message) for message in messages): + return messages + return [_drop_tool_reference_parts(message) for message in messages] # mutable-ok: pipelines mutate message lists + + +def _attempt_json_repair(s: str) -> object | None: """ Attempt to repair truncated JSON produced by LLM tool calls. @@ -1811,7 +2108,7 @@ def parse_tool_call_arguments( raise ValueError(error_message) from original_error -def split_concatenated_json_objects(raw: str) -> list[dict[str, Any]]: +def split_concatenated_json_objects(raw: str) -> list[dict[str, object]]: """ Split a string that contains one or more concatenated JSON objects into a list of parsed dicts. @@ -1847,7 +2144,7 @@ def split_concatenated_json_objects(raw: str) -> list[dict[str, Any]]: return [] decoder: Final = json.JSONDecoder() - results: Final[list[dict[str, Any]]] = [] + results: Final[list[dict[str, object]]] = [] idx = 0 length: Final = len(raw) diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index b676077ab0e..ba59e3fa997 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -16,6 +16,7 @@ import litellm.types import litellm.types.llms from litellm import verbose_logger from litellm._uuid import uuid +from litellm.constants import REDACTED_BY_LITELLM from litellm.litellm_core_utils.url_utils import async_safe_get, safe_get from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client from litellm.types.files import get_file_extension_from_mime_type @@ -642,49 +643,6 @@ def claude_2_1_pt( return prompt -### TOGETHER AI - - -def get_model_info(token, model): - try: - headers: Final = {"Authorization": f"Bearer {token}"} - client: Final = HTTPHandler(concurrent_limit=1) - response: Final = client.get("https://api.together.xyz/models/info", headers=headers) - if response.status_code == 200: - model_info: Final = response.json() - for m in model_info: - if m["name"].lower().strip() == model.strip(): - return m["config"].get("prompt_format", None), m["config"].get("chat_template", None) - return None, None - else: - return None, None - except Exception: # safely fail a prompt template request - return None, None - - -## OLD TOGETHER AI FLOW -# def format_prompt_togetherai(messages, prompt_format, chat_template): -# if prompt_format is None: -# return default_pt(messages) - -# human_prompt, assistant_prompt = prompt_format.split("{prompt}") - -# if chat_template is not None: -# prompt = hf_chat_template( -# model=None, messages=messages, chat_template=chat_template -# ) -# elif prompt_format is not None: -# prompt = custom_prompt( -# role_dict={}, -# messages=messages, -# initial_prompt_value=human_prompt, -# final_prompt_value=assistant_prompt, -# ) -# else: -# prompt = default_pt(messages) -# return prompt - - ### IBM Granite @@ -1454,7 +1412,7 @@ def convert_to_gemini_tool_call_result( ) except Exception as e: verbose_logger.warning("Failed to process image in tool response: %s", e) - elif content_type in ("file", "input_file"): + elif content_type in ("file", "input_file"): # pyright: ignore[reportUnnecessaryContains] # loose runtime dict # Extract file for inline_data (for tool results with PDF, audio, video, etc.) file_data = content.get("file_data", "") if not file_data: @@ -1606,14 +1564,23 @@ def convert_to_anthropic_tool_result( } """ anthropic_content: ( - str | list[AnthropicMessagesToolResultContent | AnthropicMessagesImageParam | AnthropicMessagesDocumentParam] + str + | list[ + AnthropicMessagesToolResultContent + | AnthropicMessagesImageParam + | AnthropicMessagesDocumentParam + | ToolReference + ] ) = "" if isinstance(message["content"], str): anthropic_content = message["content"] elif isinstance(message["content"], list): content_list: Final = message["content"] anthropic_content_list: list[ - AnthropicMessagesToolResultContent | AnthropicMessagesImageParam | AnthropicMessagesDocumentParam + AnthropicMessagesToolResultContent + | AnthropicMessagesImageParam + | AnthropicMessagesDocumentParam + | ToolReference ] = [] for content in content_list: if content["type"] == "text": @@ -1656,6 +1623,8 @@ def convert_to_anthropic_tool_result( original_content_element=content, ) anthropic_content_list.append(cast(AnthropicMessagesImageParam, _anthropic_image_param)) + elif content["type"] == "tool_reference": + anthropic_content_list.append(ToolReference(type="tool_reference", tool_name=content["tool_name"])) elif content["type"] == "file": file_content = cast(ChatCompletionFileObject, content) _file_block = anthropic_process_openai_file_message(file_content) @@ -1725,6 +1694,18 @@ def convert_function_to_anthropic_tool_invoke( raise e +def _find_server_tool_result( + tool_id: str, + web_search_results: Sequence[object] | None, + tool_results: Sequence[object] | None, +) -> dict[str, object] | None: + candidates: Final = (*(web_search_results or ()), *(tool_results or ())) + return next( + (result for result in candidates if isinstance(result, dict) and result.get("tool_use_id") == tool_id), + None, + ) + + def convert_to_anthropic_tool_invoke( tool_calls: list[ChatCompletionAssistantToolCall], web_search_results: list[Any] | None = None, @@ -1789,32 +1770,22 @@ def convert_to_anthropic_tool_invoke( context="Anthropic tool invoke", ) - # Check if this is a server-side tool (web_search, tool_search, etc.) - # Server tool IDs start with "srvtoolu_" - if tool_id.startswith("srvtoolu_"): - # Create server_tool_use block instead of tool_use - _anthropic_server_tool_use: dict[str, object] = { - "type": "server_tool_use", - "id": tool_id, - "name": tool_name, - "input": tool_input, - } - anthropic_tool_invoke.append(_anthropic_server_tool_use) - - # Add corresponding tool result if available. - # Check both web_search_results (web_search_tool_result / web_fetch_tool_result) - # and tool_results (bash_code_execution_tool_result, etc.) - _all_tool_results: list[Any] = [] - if web_search_results: - _all_tool_results.extend(web_search_results) - if tool_results: - _all_tool_results.extend(tool_results) - for result in _all_tool_results: - if result.get("tool_use_id") == tool_id: - anthropic_tool_invoke.append(result) - break + server_tool_result = ( + _find_server_tool_result(tool_id, web_search_results, tool_results) + if tool_id.startswith("srvtoolu_") + else None + ) + if server_tool_result is not None: + anthropic_tool_invoke.append( + { + "type": "server_tool_use", + "id": tool_id, + "name": tool_name, + "input": tool_input, + } + ) + anthropic_tool_invoke.append(server_tool_result) else: - # Regular tool_use sanitized_tool_id = _sanitize_anthropic_tool_use_id(tool_id) _anthropic_tool_use_param = AnthropicMessagesToolUseParam( type="tool_use", @@ -4986,10 +4957,13 @@ def make_valid_bedrock_tool_name(input_tool_name: str) -> str: def add_cache_point_tool_block(tool: dict, model: str | None = None) -> BedrockToolBlock | None: - from litellm.llms.bedrock.common_utils import is_claude_4_5_on_bedrock + from litellm.llms.bedrock.common_utils import ( + bedrock_model_accepts_cache_points, + is_claude_4_5_on_bedrock, + ) cache_control: Final = tool.get("cache_control", None) - if cache_control is not None: + if cache_control is not None and bedrock_model_accepts_cache_points(model): cache_point: Final = cache_control.get("type", "ephemeral") if cache_point == "ephemeral": cache_point_block: Final[CachePointBlock] = {"type": "default"} @@ -5383,12 +5357,13 @@ def _parse_tool_call_arguments(raw: Any, tool_name: str | None, context: str) -> return raw if not isinstance(raw, str): return {} + normalized_raw: Final = "{}" if raw == REDACTED_BY_LITELLM else raw from litellm.litellm_core_utils.prompt_templates.common_utils import ( parse_tool_call_arguments, ) try: - parsed: Final = parse_tool_call_arguments(raw, tool_name=tool_name, context=context) + parsed: Final = parse_tool_call_arguments(normalized_raw, tool_name=tool_name, context=context) except ValueError as e: verbose_logger.warning("Failed to parse tool call arguments: %s", e) return {} diff --git a/litellm/litellm_core_utils/ptu_pricing.py b/litellm/litellm_core_utils/ptu_pricing.py index 021210d9175..f545ba4aa3b 100644 --- a/litellm/litellm_core_utils/ptu_pricing.py +++ b/litellm/litellm_core_utils/ptu_pricing.py @@ -28,6 +28,7 @@ PTU_ZEROED_PRICING_FIELDS: Final = tuple(f for f in MirroredPricingParams.model_ "cache_creation_input_token_cost_above_1hr", "cache_creation_input_token_cost_above_200k_tokens", "cache_read_input_token_cost_above_200k_tokens", + "google_maps_grounding_cost_per_query", ) # tiered_pricing is emptied rather than zeroed: its tiers outrank the zeros written beside # them, so a zero here would leave the cost map's tiers billing the traffic the reserved diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 10056d64a20..8479e108d17 100644 --- a/litellm/litellm_core_utils/realtime_streaming.py +++ b/litellm/litellm_core_utils/realtime_streaming.py @@ -330,6 +330,24 @@ class RealTimeStreaming: except (AttributeError, TypeError): pass + def _flush_unbilled_transcription_usage(self) -> None: + if self.provider_config is None: + return + usage: Final = self.provider_config.unbilled_usage_on_session_close(self.model) + if usage is None: + return + flush_event: Final = ( + cast( # cast-ok: usage-only partial event, the same shape _capture_transcription_usage logs + OpenAIRealtimeEvents, + { + "type": "conversation.item.input_audio_transcription.completed", + "usage": usage, + }, + ) + ) + self.store_message(flush_event) + self._capture_transcription_usage(flush_event) + def _collect_tool_calls_from_response_done(self, event_obj: dict | OpenAIRealtimeEvents) -> None: """Extract function_call items from response.done events for spend logging.""" try: @@ -955,6 +973,7 @@ class RealTimeStreaming: transcript = event.get("transcript", "") self._collect_user_input_from_backend_event(cast(dict, event)) self.store_message(event_str) + self._capture_transcription_usage(event) await self._send_event_to_client(event, event_str) blocked = await self.run_realtime_guardrails( cast(str, transcript), @@ -1068,6 +1087,7 @@ class RealTimeStreaming: except Exception as e: verbose_logger.exception("Error in backend to client send messages: %s", e) finally: + self._flush_unbilled_transcription_usage() await self.log_messages() @staticmethod @@ -1480,6 +1500,6 @@ class RealTimeStreaming: pass -def client_sent_openai_beta_realtime_header(websocket: Any) -> bool: +def client_sent_openai_beta_realtime_header(websocket: _ScopedWebSocket) -> bool: """True when the client WebSocket includes ``OpenAI-Beta: realtime=v1``.""" return RealTimeStreaming._detect_beta_header(websocket) diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py index 0d590e1ceba..9402d465712 100644 --- a/litellm/litellm_core_utils/redact_messages.py +++ b/litellm/litellm_core_utils/redact_messages.py @@ -10,9 +10,11 @@ import asyncio import copy import inspect +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import litellm +from litellm.constants import REDACTED_BY_LITELLM from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.core_helpers import ( get_metadata_variable_name_from_kwargs, @@ -84,29 +86,31 @@ def _redact_tool_calls(tool_calls) -> None: for tool_call in tool_calls: function = getattr(tool_call, "function", None) if function is not None and hasattr(function, "arguments"): - function.arguments = "redacted-by-litellm" + function.arguments = REDACTED_BY_LITELLM def _redact_function_call(function_call) -> None: """Redact legacy assistant function_call arguments.""" if function_call is not None and hasattr(function_call, "arguments"): - function_call.arguments = "redacted-by-litellm" + function_call.arguments = REDACTED_BY_LITELLM def _redact_choice_content(choice): """Helper to redact content in a choice (message or delta).""" if isinstance(choice, litellm.Choices): - choice.message.content = "redacted-by-litellm" - if hasattr(choice.message, "reasoning_content"): - choice.message.reasoning_content = "redacted-by-litellm" + if choice.message.content is not None: + choice.message.content = REDACTED_BY_LITELLM + if getattr(choice.message, "reasoning_content", None) is not None: + choice.message.reasoning_content = REDACTED_BY_LITELLM if hasattr(choice.message, "thinking_blocks"): choice.message.thinking_blocks = None _redact_tool_calls(getattr(choice.message, "tool_calls", None)) _redact_function_call(getattr(choice.message, "function_call", None)) elif isinstance(choice, litellm.utils.StreamingChoices): - choice.delta.content = "redacted-by-litellm" - if hasattr(choice.delta, "reasoning_content"): - choice.delta.reasoning_content = "redacted-by-litellm" + if choice.delta.content is not None: + choice.delta.content = REDACTED_BY_LITELLM + if getattr(choice.delta, "reasoning_content", None) is not None: + choice.delta.reasoning_content = REDACTED_BY_LITELLM if hasattr(choice.delta, "thinking_blocks"): choice.delta.thinking_blocks = None _redact_tool_calls(getattr(choice.delta, "tool_calls", None)) @@ -116,23 +120,23 @@ def _redact_choice_content(choice): def _redact_responses_api_output(output_items): """Helper to redact ResponsesAPIResponse output items.""" for output_item in output_items: - if hasattr(output_item, "text"): - output_item.text = "redacted-by-litellm" + if getattr(output_item, "text", None) is not None: + output_item.text = REDACTED_BY_LITELLM if hasattr(output_item, "content") and isinstance(output_item.content, list): for content_part in output_item.content: - if hasattr(content_part, "text"): - content_part.text = "redacted-by-litellm" + if getattr(content_part, "text", None) is not None: + content_part.text = REDACTED_BY_LITELLM # Redact reasoning items in output array if hasattr(output_item, "type") and output_item.type == "reasoning": if hasattr(output_item, "summary") and isinstance(output_item.summary, list): for summary_item in output_item.summary: - if hasattr(summary_item, "text"): - summary_item.text = "redacted-by-litellm" + if getattr(summary_item, "text", None) is not None: + summary_item.text = REDACTED_BY_LITELLM if hasattr(output_item, "type") and output_item.type == "function_call" and hasattr(output_item, "arguments"): - output_item.arguments = "redacted-by-litellm" + output_item.arguments = REDACTED_BY_LITELLM def _redact_responses_api_output_dict(output_items, redacted_str: str): @@ -141,17 +145,17 @@ def _redact_responses_api_output_dict(output_items, redacted_str: str): if not isinstance(output_item, dict): continue - if "text" in output_item: + if output_item.get("text") is not None: output_item["text"] = redacted_str if isinstance(output_item.get("content"), list): for content_item in output_item["content"]: - if isinstance(content_item, dict) and "text" in content_item: + if isinstance(content_item, dict) and content_item.get("text") is not None: content_item["text"] = redacted_str if output_item.get("type") == "reasoning" and isinstance(output_item.get("summary"), list): for summary_item in output_item["summary"]: - if isinstance(summary_item, dict) and "text" in summary_item: + if isinstance(summary_item, dict) and summary_item.get("text") is not None: summary_item["text"] = redacted_str if output_item.get("type") == "function_call" and "arguments" in output_item: @@ -164,7 +168,7 @@ def _redact_standard_logging_object(model_call_details: dict): if standard_logging_object is None: return - redacted_str: Final = "redacted-by-litellm" + redacted_str: Final = REDACTED_BY_LITELLM if standard_logging_object.get("messages") is not None: standard_logging_object["messages"] = [{"role": "user", "content": redacted_str}] @@ -188,40 +192,42 @@ def _redact_standard_logging_object(model_call_details: dict): standard_logging_object["response"] = {"text": redacted_str} -def _redact_tool_calls_dict(message: dict, redacted_str: str) -> None: +def _redact_tool_calls_dict(message: Mapping[str, object]) -> None: """Redact tool call / function_call arguments in a dict-form message or delta.""" tool_calls: Final = message.get("tool_calls") if isinstance(tool_calls, list): for tool_call in tool_calls: if isinstance(tool_call, dict) and isinstance(tool_call.get("function"), dict): - tool_call["function"]["arguments"] = redacted_str + tool_call["function"]["arguments"] = REDACTED_BY_LITELLM function_call: Final = message.get("function_call") if isinstance(function_call, dict) and "arguments" in function_call: - function_call["arguments"] = redacted_str + function_call["arguments"] = REDACTED_BY_LITELLM def _redact_model_response_dict_choices(choices, redacted_str: str): for choice in choices: if isinstance(choice, dict): if "message" in choice and isinstance(choice["message"], dict): - choice["message"]["content"] = redacted_str - if "reasoning_content" in choice["message"]: + if choice["message"].get("content") is not None: + choice["message"]["content"] = redacted_str + if choice["message"].get("reasoning_content") is not None: choice["message"]["reasoning_content"] = redacted_str if "thinking_blocks" in choice["message"]: choice["message"]["thinking_blocks"] = None if "audio" in choice["message"]: choice["message"]["audio"] = None - _redact_tool_calls_dict(choice["message"], redacted_str) + _redact_tool_calls_dict(choice["message"]) elif "delta" in choice and isinstance(choice["delta"], dict): - choice["delta"]["content"] = redacted_str - if "reasoning_content" in choice["delta"]: + if choice["delta"].get("content") is not None: + choice["delta"]["content"] = redacted_str + if choice["delta"].get("reasoning_content") is not None: choice["delta"]["reasoning_content"] = redacted_str if "thinking_blocks" in choice["delta"]: choice["delta"]["thinking_blocks"] = None if "audio" in choice["delta"]: choice["delta"]["audio"] = None - _redact_tool_calls_dict(choice["delta"], redacted_str) + _redact_tool_calls_dict(choice["delta"]) else: _redact_choice_content(choice) @@ -235,7 +241,7 @@ def perform_redaction(model_call_details: dict, result, redact_streaming_respons copy via redact_streaming_responses_for_custom_logger instead. """ # Redact model_call_details - model_call_details["messages"] = [{"role": "user", "content": "redacted-by-litellm"}] + model_call_details["messages"] = [{"role": "user", "content": REDACTED_BY_LITELLM}] model_call_details["prompt"] = "" model_call_details["input"] = "" _redact_standard_logging_object(model_call_details) @@ -256,13 +262,13 @@ def perform_redaction(model_call_details: dict, result, redact_streaming_respons or hasattr(result, "__anext__") # async generator ): # async iterator # For async objects, return a simple redacted response without deepcopy - return {"text": "redacted-by-litellm"} + return {"text": REDACTED_BY_LITELLM} if not ( isinstance(result, (litellm.ModelResponse, litellm.ResponsesAPIResponse, litellm.EmbeddingResponse)) or (isinstance(result, dict) and ("choices" in result or "output" in result)) ): - return {"text": "redacted-by-litellm"} + return {"text": REDACTED_BY_LITELLM} _result: Final = copy.deepcopy(result) if isinstance(_result, litellm.ModelResponse): @@ -273,11 +279,11 @@ def perform_redaction(model_call_details: dict, result, redact_streaming_respons elif isinstance(_result, dict) and "choices" in _result: # Handle dict representation of ModelResponse (e.g., from model_dump()) if _result.get("choices") is not None: - _redact_model_response_dict_choices(_result["choices"], "redacted-by-litellm") + _redact_model_response_dict_choices(_result["choices"], REDACTED_BY_LITELLM) redact_vertex_ai_metadata_from_logged_object(_result) elif isinstance(_result, dict) and "output" in _result: if isinstance(_result.get("output"), list): - _redact_responses_api_output_dict(_result["output"], "redacted-by-litellm") + _redact_responses_api_output_dict(_result["output"], REDACTED_BY_LITELLM) elif isinstance(_result, litellm.ResponsesAPIResponse): if hasattr(_result, "output"): _redact_responses_api_output(_result.output) @@ -288,7 +294,7 @@ def perform_redaction(model_call_details: dict, result, redact_streaming_respons if hasattr(_result, "data") and _result.data is not None: _result.data = [] else: - return {"text": "redacted-by-litellm"} + return {"text": REDACTED_BY_LITELLM} return _result diff --git a/litellm/litellm_core_utils/specialty_caches/dynamic_logging_cache.py b/litellm/litellm_core_utils/specialty_caches/dynamic_logging_cache.py index f63c60dd430..da3ac366bfd 100644 --- a/litellm/litellm_core_utils/specialty_caches/dynamic_logging_cache.py +++ b/litellm/litellm_core_utils/specialty_caches/dynamic_logging_cache.py @@ -13,6 +13,7 @@ import json from typing import Any, Final import litellm +from litellm._logging import verbose_logger from litellm.constants import _DEFAULT_TTL_FOR_HTTPX_CLIENTS from ...caching import InMemoryCache @@ -46,6 +47,15 @@ class LangfuseInMemoryCache(InMemoryCache): _created_langfuse_logger.Langfuse.flush() _created_langfuse_logger.Langfuse.shutdown() + # Loggers with a periodic flush task (e.g. NewRelicMetricsLogger) expose + # stop() so eviction actually ends the task instead of leaking it. + _evicted_stop: Final = getattr(self.cache_dict[key], "stop", None) + if callable(_evicted_stop): + try: + _evicted_stop() + except Exception: # noqa: BLE001 # a failing stop() must not block eviction + verbose_logger.debug("DynamicLoggingCache: stop() raised during eviction", exc_info=True) + ######################################################### # Call parent class to remove key from cache ######################################################### diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index ee0518c4aec..0e01577b20e 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -36,6 +36,8 @@ from litellm.types.utils import ( from litellm.utils import print_verbose, token_counter if TYPE_CHECKING: + from openai.types.completion_usage import CompletionUsage + from litellm.litellm_core_utils.litellm_logging import Logging from litellm.types.litellm_core_utils.streaming_chunk_builder_utils import ( UsagePerChunk, @@ -73,6 +75,18 @@ class _ContentChunk(TypedDict): choices: Sequence[_ContentChoice] +class _FunctionCallDelta(TypedDict): + function_call: ReadOnly[FunctionCall] + + +class _FunctionCallChoice(TypedDict): + delta: ReadOnly[_FunctionCallDelta] + + +class _FunctionCallChunk(TypedDict): + choices: ReadOnly[Sequence[_FunctionCallChoice]] + + class _AudioDelta(TypedDict, total=False): audio: ChatCompletionAudioDelta | None @@ -173,6 +187,27 @@ def attach_cache_creation_token_details( return prompt_tokens_details.model_copy(update={"cache_creation_token_details": cache_creation_token_details}) +def apply_grounding_request_counts( + prompt_tokens_details: PromptTokensDetailsWrapper | None, + web_search_requests: int | None, + google_maps_grounding_requests: int | None, +) -> PromptTokensDetailsWrapper | None: + updates: Final = MappingProxyType( + { + field: value + for field, value in ( + ("web_search_requests", web_search_requests), + ("google_maps_grounding_requests", google_maps_grounding_requests), + ) + if value is not None + } + ) + if not updates: + return prompt_tokens_details + counted: Final = prompt_tokens_details if prompt_tokens_details is not None else PromptTokensDetailsWrapper() + return counted.model_copy(update=updates) + + class ChunkProcessor: def __init__(self, chunks: list, messages: list | None = None): self.chunks = self._sort_chunks(chunks) @@ -218,6 +253,22 @@ class ChunkProcessor: model_response._hidden_params = chunk.get("_hidden_params", {}) return model_response + @staticmethod + def _get_provider_response_model( + chunks: Sequence["_BaseChunk"], + first_chunk_model: str, + ) -> str | None: + models: Final = tuple( + model + for chunk in chunks + if isinstance((hidden_params := chunk.get("_hidden_params")), Mapping) + if isinstance((model := hidden_params.get("provider_response_model")), str) and model + ) + return next( + (model for model in models if model != first_chunk_model), + models[0] if models else None, + ) + @staticmethod def apply_provider_assembled_streaming_metadata( response: ModelResponse, @@ -339,6 +390,15 @@ class ChunkProcessor: ) response = self.update_model_response_with_hidden_params(model_response=response, chunk=chunk) + provider_response_model: Final = self._get_provider_response_model( + chunks, + first_chunk_model, + ) + if provider_response_model is not None: + response._hidden_params = dict( # pyright: ignore[reportPrivateUsage] # ModelResponse exposes no public hidden-params setter + response._hidden_params, # pyright: ignore[reportPrivateUsage] # ModelResponse exposes no public hidden-params getter + provider_response_model=provider_response_model, + ) return response @staticmethod @@ -542,7 +602,7 @@ class ChunkProcessor: return tool_calls_list - def get_combined_function_call_content(self, function_call_chunks: list[dict[str, Any]]) -> FunctionCall: + def get_combined_function_call_content(self, function_call_chunks: Sequence["_FunctionCallChunk"]) -> FunctionCall: argument_list: Final = [] delta = function_call_chunks[0]["choices"][0]["delta"] function_call = delta.get("function_call", "") @@ -736,7 +796,7 @@ class ChunkProcessor: @staticmethod def _extract_usage_chunk(chunk: "_UsageBearingChunk | ModelResponse | ModelResponseStream") -> Usage | None: - usage_chunk: Usage | None = None + usage_chunk: Usage | CompletionUsage | None = None if hasattr(chunk, "usage") and chunk.usage is not None: usage_chunk = chunk.usage elif "usage" in chunk: @@ -748,7 +808,9 @@ class ChunkProcessor: if isinstance(usage_chunk, dict): return Usage(**usage_chunk) - return usage_chunk + if usage_chunk is None or isinstance(usage_chunk, Usage): + return usage_chunk + return Usage(**usage_chunk.model_dump()) def _calculate_usage_per_chunk( self, @@ -778,6 +840,7 @@ class ChunkProcessor: server_tool_use: ServerToolUse | None = None web_search_requests: int | None = None + google_maps_grounding_requests: int | None = None completion_tokens_details: CompletionTokensDetails | None = None prompt_tokens_details: PromptTokensDetailsWrapper | None = None # Anthropic emits the cache-creation TTL breakdown (5m/1h split) only on @@ -827,6 +890,13 @@ class ChunkProcessor: ) if chunk_web_search_requests is not None: web_search_requests = chunk_web_search_requests + chunk_google_maps_grounding_requests: int | None = getattr( + usage_chunk_dict["prompt_tokens_details"], + "google_maps_grounding_requests", + None, + ) + if chunk_google_maps_grounding_requests is not None: + google_maps_grounding_requests = chunk_google_maps_grounding_requests prompt_tokens_details = usage_chunk_dict["prompt_tokens_details"] or prompt_tokens_details @@ -852,6 +922,7 @@ class ChunkProcessor: cache_read_input_tokens=cache_read_input_tokens, server_tool_use=server_tool_use, web_search_requests=web_search_requests, + google_maps_grounding_requests=google_maps_grounding_requests, completion_tokens_details=completion_tokens_details, prompt_tokens_details=prompt_tokens_details, cost=cost, @@ -939,6 +1010,7 @@ class ChunkProcessor: server_tool_use: Final[ServerToolUse | None] = calculated_usage_per_chunk["server_tool_use"] web_search_requests: Final[int | None] = calculated_usage_per_chunk["web_search_requests"] + google_maps_grounding_requests: Final[int | None] = calculated_usage_per_chunk["google_maps_grounding_requests"] completion_tokens_details: Final[CompletionTokensDetails | None] = calculated_usage_per_chunk[ "completion_tokens_details" ] @@ -998,13 +1070,11 @@ class ChunkProcessor: if server_tool_use is not None: returned_usage.server_tool_use = server_tool_use - if web_search_requests is not None: - if returned_usage.prompt_tokens_details is None: - returned_usage.prompt_tokens_details = PromptTokensDetailsWrapper( - web_search_requests=web_search_requests - ) - else: - returned_usage.prompt_tokens_details.web_search_requests = web_search_requests + returned_usage.prompt_tokens_details = apply_grounding_request_counts( + returned_usage.prompt_tokens_details, + web_search_requests, + google_maps_grounding_requests, + ) if cost is not None: setattr(returned_usage, "cost", cost) diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index f6340426c1b..480b1921c18 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -8,11 +8,12 @@ import time import traceback from collections.abc import AsyncIterator, Callable, Iterable, Iterator, Mapping, Sequence from dataclasses import dataclass +from types import MappingProxyType from typing import Any, Final, NoReturn, Protocol, TypeVar, cast import anyio import httpx -from pydantic import BaseModel +from pydantic import BaseModel, ValidationError from typing_extensions import NotRequired, TypedDict import litellm @@ -92,7 +93,7 @@ def print_verbose(print_statement: object): @dataclass(frozen=True, slots=True) class _ProviderChunkParsed: - response_obj: dict[str, Any] + response_obj: dict[str, object] @dataclass(frozen=True, slots=True) @@ -182,6 +183,48 @@ class _VertexChunkLike(Protocol): candidates: Sequence[_VertexCandidateLike] +class _ParsedChunkHiddenParams(BaseModel): + provider_specific_fields: Mapping[str, object] | None = None + + +def _provider_response_model(chunk: object) -> str | None: + model: Final[object] = chunk.get("model") if isinstance(chunk, Mapping) else getattr(chunk, "model", None) + return model if isinstance(model, str) and model else None + + +def _parsed_provider_hidden_params(hidden: object) -> _ParsedChunkHiddenParams | None: + if not isinstance(hidden, dict): + return None + try: + return _ParsedChunkHiddenParams.model_validate(hidden) + except ValidationError: + return None + + +def _provider_hidden_params( + chunk: object, + provider_response_model: str | None, +) -> Mapping[str, object] | None: + hidden: Final[object] = getattr(chunk, "_hidden_params", None) + parsed: Final = _parsed_provider_hidden_params(hidden) + provider_specific_fields: Final[object | None] = ( + dict(parsed.provider_specific_fields) # mutable-ok: stream assembly merges provider metadata into this dict + if parsed is not None and parsed.provider_specific_fields + else None + ) + params: Final[Mapping[str, object]] = MappingProxyType( + { + key: value + for key, value in ( + ("provider_response_model", provider_response_model), + ("provider_specific_fields", provider_specific_fields), + ) + if value is not None + } + ) + return params or None + + class CustomStreamWrapper: def __init__( self, @@ -211,6 +254,7 @@ class CustomStreamWrapper: self.thinking_content = "" self.system_fingerprint: str | None = None + self._provider_response_model: str | None = None self.received_finish_reason: str | None = None self.intermittent_finish_reason: str | None = None # finish reasons that show up mid-stream self.special_tokens = [ @@ -801,7 +845,9 @@ class CustomStreamWrapper: except Exception as e: raise e - def model_response_creator(self, chunk: dict | None = None, hidden_params: dict | None = None): + def model_response_creator( + self, chunk: dict | None = None, hidden_params: Mapping[str, object] | None = None + ) -> ModelResponseStream: _model: Final = self._cached_model_name _logging_obj_llm_provider: Final = self._cached_logging_llm_provider @@ -816,6 +862,8 @@ class CustomStreamWrapper: model_response: Final = ModelResponseStream(**args) if self.response_id is not None: model_response.id = self.response_id + elif model_response.id: + self.response_id = model_response.id if self.system_fingerprint is not None: model_response.system_fingerprint = self.system_fingerprint @@ -1242,7 +1290,7 @@ class CustomStreamWrapper: for key, value in anthropic_response_obj["provider_specific_fields"].items(): setattr(model_response, key, value) - response_obj = cast(dict[str, Any], anthropic_response_obj) + response_obj = cast(dict[str, object], anthropic_response_obj) elif self.model == "replicate" or self.custom_llm_provider == "replicate": response_obj = self.handle_replicate_chunk(chunk) completion_obj["content"] = response_obj["text"] @@ -1398,7 +1446,7 @@ class CustomStreamWrapper: if not isinstance(chunk, str): raise ValueError(f"chunk is not a string: {chunk}") response_obj = cast( - dict[str, Any], + dict[str, object], litellm.CodestralTextCompletionConfig()._chunk_parser(chunk), ) completion_obj["content"] = response_obj["text"] @@ -1504,7 +1552,12 @@ class CustomStreamWrapper: def chunk_creator(self, chunk: Any): if hasattr(chunk, "id"): self.response_id = chunk.id - model_response = self.model_response_creator() + provider_response_model: Final = _provider_response_model(chunk) + if provider_response_model is not None: + self._provider_response_model = provider_response_model + model_response = self.model_response_creator( + hidden_params=_provider_hidden_params(chunk, self._provider_response_model) + ) response_obj: dict[str, Any] = {} try: # return this for all models @@ -2318,6 +2371,7 @@ class CustomStreamWrapper: partial_response: Final = litellm.stream_chunk_builder( chunks=self.chunks, messages=self.messages if isinstance(self.messages, list) else None, + logging_obj=self.logging_obj, ) if partial_response is None: return @@ -2499,7 +2553,7 @@ def calculate_total_usage(chunks: list[ModelResponse]) -> Usage: prompt_tokens: int = 0 completion_tokens: int = 0 - latest_usage_chunk = None + latest_usage_chunk: Usage | Mapping[str, int] | None = None prompt_tokens_details: PromptTokensDetailsWrapper | None = None completion_tokens_details: CompletionTokensDetailsWrapper | None = None cache_creation_token_details: CacheCreationTokenDetails | None = None diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index 858b078d626..3732ffd734c 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -3,9 +3,10 @@ import base64 import io import struct -from collections.abc import Callable, Mapping -from typing import Any, Final, Literal, cast +from collections.abc import Callable, Iterable, Mapping, Sequence +from typing import Final, Literal, cast +import httpx import tiktoken import litellm @@ -25,14 +26,21 @@ from litellm.litellm_core_utils.default_encoding import encoding as default_enco from litellm.litellm_core_utils.url_utils import safe_get from litellm.llms.custom_httpx.http_handler import _get_httpx_client from litellm.types.llms.anthropic import ( + AnthropicContentParamSource, + AnthropicContentParamSourceFileId, + AnthropicContentParamSourceUrl, + AnthropicMessagesDocumentParam, + AnthropicMessagesImageParam, + AnthropicMessagesTextParam, AnthropicMessagesToolResultParam, AnthropicMessagesToolUseParam, ) from litellm.types.llms.openai import ( AllMessageValues, + ChatCompletionDocumentObject, ChatCompletionNamedToolChoiceParam, ChatCompletionToolParam, - OpenAIMessageContent, + OpenAIMessageContentListBlock, ) from litellm.types.utils import Message, SelectTokenizerResponse @@ -164,6 +172,10 @@ def calculate_tiles_needed( return total_tiles +def _unpack_ints(fmt: str, buffer: bytes) -> tuple[int, ...]: + return struct.unpack(fmt, buffer) + + def get_image_type(image_data: bytes) -> str | None: """take an image (really only the first ~100 bytes max are needed) and return 'png' 'gif' 'jpeg' 'webp' 'heic' or None. method added to @@ -203,9 +215,9 @@ def get_image_dimensions( if data.startswith(("http://", "https://")): try: client: Final = _get_httpx_client() - response: Final = safe_get(client, data) + response: Final[httpx.Response] = safe_get(client, data) max_bytes: Final = int(MAX_IMAGE_URL_DOWNLOAD_SIZE_MB * 1024 * 1024) - content_length: Final = response.headers.get("Content-Length") + content_length: Final[str | None] = response.headers.get("Content-Length") if content_length is not None and int(content_length) > max_bytes: pass # skip download; img_data stays None else: @@ -222,10 +234,10 @@ def get_image_dimensions( img_type: Final = get_image_type(img_data) if img_type == "png": - w, h = struct.unpack(">LL", img_data[16:24]) + w, h = _unpack_ints(">LL", img_data[16:24]) return w, h elif img_type == "gif": - w, h = struct.unpack("H", fhandle.read(2))[0] - 2 + size = _unpack_ints(">H", fhandle.read(2))[0] - 2 fhandle.seek(1, 1) - h, w = struct.unpack(">HH", fhandle.read(4)) + h, w = _unpack_ints(">HH", fhandle.read(4)) return w, h elif img_type == "webp": # For WebP, the dimensions are stored at different offsets depending on the format # Check for VP8X (extended format) if img_data[12:16] == b"VP8X": - w = struct.unpack("> 14) & 0x3FFF) + 1 return w, h @@ -346,7 +358,7 @@ def token_counter( model="", custom_tokenizer: dict | SelectTokenizerResponse | None = None, text: str | list[str] | None = None, - messages: list[AllMessageValues | Message] | None = None, + messages: Sequence[AllMessageValues | Message] | None = None, count_response_tokens: bool | None = False, tools: list[ChatCompletionToolParam] | None = None, tool_choice: ChatCompletionNamedToolChoiceParam | None = None, @@ -413,8 +425,8 @@ def token_counter( def _count_function_call_tokens( key: str, - value: Any, - message: Mapping[str, Any], + value: object, + message: Mapping[str, object], count_function: TokenCounterFunction, ) -> int: """ @@ -580,7 +592,7 @@ def _fix_model_name(model: str) -> str: def _count_image_tokens( - image_url: Any, + image_url: object, use_default_image_token_count: bool, ) -> int: """ @@ -620,7 +632,7 @@ def _count_image_tokens( raise ValueError(f"Invalid image_url type: {type(image_url).__name__}. Expected str or dict with 'url' field.") -def _validate_anthropic_content(content: Mapping[str, Any]) -> type: +def _validate_anthropic_content(content: Mapping[str, object]) -> type: """ Validate and determine which Anthropic TypedDict applies. @@ -635,7 +647,7 @@ def _validate_anthropic_content(content: Mapping[str, Any]) -> type: "tool_result": AnthropicMessagesToolResultParam, } - expected_cls: Final = mapping.get(content_type) + expected_cls: Final = mapping.get(content_type) if isinstance(content_type, str) else None if expected_cls is None: raise ValueError(f"Unknown Anthropic content type: '{content_type}'") @@ -646,8 +658,68 @@ def _validate_anthropic_content(content: Mapping[str, Any]) -> type: return expected_cls +def _anthropic_image_source_data( + source: AnthropicContentParamSource | AnthropicContentParamSourceUrl | AnthropicContentParamSourceFileId, +) -> str: + if source["type"] == "base64": + data: Final = source.get("data") + if not data: + return "" + media_type: Final = source.get("media_type") or "image/png" + return f"data:{media_type};base64,{data}" + if source["type"] == "url": + return source.get("url") or "" + return "" + + +def _count_document_tokens( + document: ChatCompletionDocumentObject | AnthropicMessagesDocumentParam, + count_function: TokenCounterFunction, + use_default_image_token_count: bool, + default_token_count: int | None, +) -> int: + source: Final = document["source"] + metadata_tokens: Final = sum( + count_function(text) for text in (document.get("title"), document.get("context")) if text + ) + if source["type"] == "text": + return metadata_tokens + count_function(source["data"]) + if source["type"] == "content": + content: Final = source["content"] + if isinstance(content, str): + return metadata_tokens + count_function(content) + return metadata_tokens + _count_content_list( + count_function, content, use_default_image_token_count, default_token_count + ) + return metadata_tokens + calculate_img_tokens( + data=_anthropic_image_source_data(source), + mode="auto", + use_default_image_token_count=use_default_image_token_count, + ) + + +def _count_file_tokens( + file_value: object, + count_function: TokenCounterFunction, + use_default_image_token_count: bool, +) -> int: + """An OpenAI `file` block is the chat-completions spelling of a document, so it prices like one.""" + if not isinstance(file_value, Mapping): + return 0 + filename: Final = file_value.get("filename") + file_data: Final = file_value.get("file_data") + name_tokens: Final = count_function(filename) if isinstance(filename, str) and filename else 0 + if not isinstance(file_data, str) or not file_data: + return name_tokens + return name_tokens + calculate_img_tokens( + data=file_data, + mode="auto", + use_default_image_token_count=use_default_image_token_count, + ) + + def _count_anthropic_content( - content: Mapping[str, Any], + content: Mapping[str, object], count_function: TokenCounterFunction, use_default_image_token_count: bool, default_token_count: int | None, @@ -662,7 +734,7 @@ def _count_anthropic_content( avoiding hardcoded field names. """ typeddict_cls: Final = _validate_anthropic_content(content) - type_hints: Final = getattr(typeddict_cls, "__annotations__", {}) + type_hints: Final[Mapping[str, object]] = getattr(typeddict_cls, "__annotations__", {}) tokens = 0 # Fields to skip (metadata/identifiers that don't contribute to prompt tokens) @@ -697,13 +769,17 @@ def _count_anthropic_content( def _count_content_list( count_function: TokenCounterFunction, - content_list: OpenAIMessageContent, + content_list: str + | Iterable[ + OpenAIMessageContentListBlock + | AnthropicMessagesTextParam + | AnthropicMessagesImageParam + | AnthropicMessagesDocumentParam + ], use_default_image_token_count: bool, default_token_count: int | None, ) -> int: - """ - Recursively count tokens from a list of content blocks. - """ + """Recursively count tokens from a list of content blocks.""" try: num_tokens = 0 for c in content_list: @@ -714,6 +790,25 @@ def _count_content_list( elif c["type"] == "image_url": image_url = c.get("image_url") num_tokens += _count_image_tokens(image_url, use_default_image_token_count) + elif c["type"] == "image": + num_tokens += calculate_img_tokens( + data=_anthropic_image_source_data(c["source"]), + mode="auto", + use_default_image_token_count=use_default_image_token_count, + ) + elif c["type"] == "document": + num_tokens += _count_document_tokens( + c, + count_function, + use_default_image_token_count, + default_token_count, + ) + elif c["type"] == "file": + num_tokens += _count_file_tokens( + c.get("file"), + count_function, + use_default_image_token_count, + ) elif c["type"] in ("tool_use", "tool_result"): num_tokens += _count_anthropic_content( c, @@ -742,7 +837,8 @@ def _count_content_list( content_type = c.get("type", type(c).__name__) if isinstance(c, dict) else type(c).__name__ raise ValueError( f"Invalid content item type: {content_type}. " - f"Expected str or dict with 'type' field (text, image_url, tool_use, tool_result, thinking, tool_reference)." + f"Expected str or dict with 'type' field " + f"(text, image_url, image, document, file, tool_use, tool_result, thinking, tool_reference)." ) return num_tokens except Exception as e: diff --git a/litellm/litellm_core_utils/url_utils.py b/litellm/litellm_core_utils/url_utils.py index 0a59eaa75d3..1e43117933d 100644 --- a/litellm/litellm_core_utils/url_utils.py +++ b/litellm/litellm_core_utils/url_utils.py @@ -21,13 +21,61 @@ Admins can opt out via two ``litellm`` globals (wired from proxy config): import socket from ipaddress import ip_address, ip_network -from typing import Any, Final +from typing import Any, Final, Protocol from urllib.parse import quote, urlparse, urlunparse import httpx +from typing_extensions import ReadOnly, TypedDict import litellm +_SockAddr = tuple[str, int] | tuple[str, int, int, int] | tuple[int, bytes] + + +class _LocationHeaderView(TypedDict): + location: ReadOnly[object] + + +class _ResponseView(TypedDict): + response: ReadOnly[httpx.Response] + + +class _UrlFetcher(Protocol): + """The slice of ``httpx.Client`` / ``HTTPHandler`` that ``safe_get`` drives.""" + + def get( + self, + url: str, + *, + headers: dict[str, str] | None = None, + follow_redirects: bool = False, + ) -> httpx.Response: ... + + +class _AsyncUrlFetcher(Protocol): + """The slice of ``httpx.AsyncClient`` / ``AsyncHTTPHandler`` that ``async_safe_get`` drives.""" + + async def get( + self, + url: str, + *, + headers: dict[str, str] | None = None, + follow_redirects: bool = False, + ) -> httpx.Response: ... + + +class _FetcherView(TypedDict): + fetcher: ReadOnly[_UrlFetcher] + + +class _AsyncFetcherView(TypedDict): + fetcher: ReadOnly[_AsyncUrlFetcher] + + +class _CallerHeadersView(TypedDict): + headers: ReadOnly[dict[str, str]] + + # Globally-routable IPs that are cloud-internal. Everything else # non-public is caught by ``not ip.is_global`` (RFC 6890, as implemented by # Python's ``ipaddress`` module). This list only holds IPs that are @@ -44,7 +92,7 @@ class SSRFError(ValueError): """Raised when a URL targets a blocked network.""" -def encode_url_path_segment(value: Any, *, field_name: str = "path parameter") -> str: +def encode_url_path_segment(value: object, *, field_name: str = "path parameter") -> str: """Percent-encode one user-controlled URL path segment. ``urllib.parse.quote(..., safe="")`` intentionally leaves RFC 3986 @@ -64,7 +112,7 @@ def encode_url_path_segment(value: Any, *, field_name: str = "path parameter") - return quote(value_str, safe="") -def encode_url_path_segments(value: Any, *, field_name: str = "path") -> str: +def encode_url_path_segments(value: object, *, field_name: str = "path") -> str: """Percent-encode a user-controlled URL path made of multiple segments. Empty segments are rejected, so leading, trailing, or consecutive slashes @@ -77,11 +125,7 @@ def encode_url_path_segments(value: Any, *, field_name: str = "path") -> str: if value_str == "": raise ValueError(f"{field_name} is required") - encoded_segments: Final = [] - for segment in value_str.split("/"): - encoded_segments.append(encode_url_path_segment(segment, field_name=field_name)) - - return "/".join(encoded_segments) + return "/".join(encode_url_path_segment(segment, field_name=field_name) for segment in value_str.split("/")) def _is_blocked_ip(addr: str) -> bool: @@ -202,7 +246,7 @@ def _format_host_header(hostname: str, port: int, default_port: int) -> str: return f"{bracketed}:{port}" -def _sockaddr_host(sockaddr: Any) -> str: +def _sockaddr_host(sockaddr: _SockAddr) -> str: """Return the host element of a ``getaddrinfo`` sockaddr as ``str``. ``getaddrinfo`` with ``IPPROTO_TCP`` returns AF_INET / AF_INET6 sockaddrs @@ -285,8 +329,8 @@ def validate_url(url: str) -> tuple[str, str]: raise SSRFError(f"No addresses found for '{hostname}'") if not is_allowlisted: - for family, type_, proto, canonname, sockaddr in addrinfo: - resolved_ip = _sockaddr_host(sockaddr) + for addrinfo_entry in addrinfo: + resolved_ip = _sockaddr_host(addrinfo_entry[4]) if _is_blocked_ip(resolved_ip): raise SSRFError( f"URL targets a blocked address ({resolved_ip}). " @@ -363,16 +407,17 @@ def assert_same_origin(candidate_url: str, expected_url: str) -> None: _MAX_REDIRECTS: Final = 10 -def _extract_redirect_url(response: Any, request_url: str) -> str: +def _extract_redirect_url(response: httpx.Response, request_url: str) -> str: """Extract and resolve the redirect target from a response's Location header.""" - location: Final = response.headers.get("location") + header_view: Final[_LocationHeaderView] = {"location": response.headers.get("location")} + location: Final = header_view["location"] if not isinstance(location, str) or not location: raise SSRFError("Redirect response has no Location header") # Resolve relative URLs against the request URL return str(httpx.URL(request_url).join(location)) -def safe_get(client: Any, url: str, **kwargs: Any) -> Any: +def safe_get(client: Any, url: str, **kwargs: Any) -> httpx.Response: """ Fetch a user-supplied URL with SSRF protection on every redirect hop. @@ -393,14 +438,17 @@ def safe_get(client: Any, url: str, **kwargs: Any) -> Any: """ if not getattr(litellm, "user_url_validation", True): kwargs.setdefault("follow_redirects", True) - return client.get(url, **kwargs) + unvalidated: Final[_ResponseView] = {"response": client.get(url, **kwargs)} + return unvalidated["response"] + fetcher_view: Final[_FetcherView] = {"fetcher": client} + fetcher: Final = fetcher_view["fetcher"] kwargs.pop("follow_redirects", None) - caller_headers: Final = kwargs.pop("headers", {}) + headers_view: Final[_CallerHeadersView] = {"headers": kwargs.pop("headers", {})} for _ in range(_MAX_REDIRECTS): validated_url, original_host = validate_url(url) - response = client.get( + response = fetcher.get( validated_url, - headers={**caller_headers, "Host": original_host}, + headers={**headers_view["headers"], "Host": original_host}, follow_redirects=False, **kwargs, ) @@ -412,18 +460,21 @@ def safe_get(client: Any, url: str, **kwargs: Any) -> Any: raise SSRFError("Too many redirects") -async def async_safe_get(client: Any, url: str, **kwargs: Any) -> Any: +async def async_safe_get(client: Any, url: str, **kwargs: Any) -> httpx.Response: """Async version of safe_get.""" if not getattr(litellm, "user_url_validation", True): kwargs.setdefault("follow_redirects", True) - return await client.get(url, **kwargs) + unvalidated: Final[_ResponseView] = {"response": await client.get(url, **kwargs)} + return unvalidated["response"] + fetcher_view: Final[_AsyncFetcherView] = {"fetcher": client} + fetcher: Final = fetcher_view["fetcher"] kwargs.pop("follow_redirects", None) - caller_headers: Final = kwargs.pop("headers", {}) + headers_view: Final[_CallerHeadersView] = {"headers": kwargs.pop("headers", {})} for _ in range(_MAX_REDIRECTS): validated_url, original_host = validate_url(url) - response = await client.get( + response = await fetcher.get( validated_url, - headers={**caller_headers, "Host": original_host}, + headers={**headers_view["headers"], "Host": original_host}, follow_redirects=False, **kwargs, ) diff --git a/litellm/llms/__init__.py b/litellm/llms/__init__.py index c178ad12a0f..88a44f38c57 100644 --- a/litellm/llms/__init__.py +++ b/litellm/llms/__init__.py @@ -14,6 +14,21 @@ if TYPE_CHECKING: from litellm.types.utils import ModelInfo, Usage +def get_cost_for_google_maps_grounding_request( + custom_llm_provider: str, usage: "Usage", model_info: "ModelInfo" +) -> float | None: + """ + Get the cost of Grounding with Google Maps for a given model. Only Gemini models on the + Gemini API and Vertex AI can populate the Maps grounding counter, so every other provider + returns None. + """ + if custom_llm_provider != "gemini" and not custom_llm_provider.startswith("vertex_ai"): + return None + from .gemini.cost_calculator import cost_per_google_maps_grounding_request + + return cost_per_google_maps_grounding_request(usage=usage, model_info=model_info) + + def get_cost_for_web_search_request(custom_llm_provider: str, usage: "Usage", model_info: "ModelInfo") -> float | None: """ Get the cost for a web search request for a given model. diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py index 1c5ba951942..f1c7451796d 100644 --- a/litellm/llms/a2a/chat/guardrail_translation/handler.py +++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py @@ -11,8 +11,11 @@ A2A Protocol Format: """ import json +from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Final, Optional +from typing_extensions import ReadOnly, TypedDict + from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.types.utils import GenericGuardrailAPIInputs @@ -23,6 +26,13 @@ if TYPE_CHECKING: from litellm.proxy._types import UserAPIKeyAuth +class _A2ATextPart(TypedDict, total=False): + """The subset of an A2A message part this handler reads text from.""" + + kind: ReadOnly[str] + text: ReadOnly[str] + + class A2AGuardrailHandler(BaseTranslation): """ Handler for processing A2A Protocol messages with guardrails. @@ -41,7 +51,7 @@ class A2AGuardrailHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, - ) -> Any: + ) -> dict: """ Process A2A input messages by applying guardrails to text content. @@ -214,12 +224,12 @@ class A2AGuardrailHandler(BaseTranslation): async def process_output_streaming_response( self, - responses_so_far: list[Any], + responses_so_far: list[object], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, request_data: dict | None = None, - ) -> list[Any]: + ) -> list[object]: """ Process A2A streaming output by applying guardrails to accumulated text. @@ -305,11 +315,12 @@ class A2AGuardrailHandler(BaseTranslation): def _parse_streaming_responses( self, - responses_so_far: list[Any], - ) -> tuple[list[dict[str, Any] | None], list[tuple[int, dict[str, Any]]]]: + responses_so_far: list[object], + ) -> tuple[list[dict[str, object] | None], list[tuple[int, dict[str, object]]]]: """Parse JSON-RPC items, returning aligned parsed list and valid entries.""" - parsed: Final[list[dict[str, Any] | None]] = [None] * len(responses_so_far) + parsed: Final[list[dict[str, object] | None]] = [None] * len(responses_so_far) for i, item in enumerate(responses_so_far): + obj: dict[str, object] if isinstance(item, dict): obj = item elif isinstance(item, str): @@ -326,7 +337,7 @@ class A2AGuardrailHandler(BaseTranslation): def _collect_text_from_parsed_chunks( self, - valid_parsed: list[tuple[int, dict[str, Any]]], + valid_parsed: list[tuple[int, dict[str, object]]], ) -> tuple[str, list[int]]: """Collect text from parsed chunks, returning combined text and indices.""" from litellm.llms.a2a.common_utils import extract_text_from_a2a_response @@ -411,7 +422,7 @@ class A2AGuardrailHandler(BaseTranslation): def _extract_texts_from_parts( self, - parts: list[dict[str, Any]], + parts: Sequence[_A2ATextPart], path: tuple[str, ...], texts_to_check: list[str], task_mappings: list[tuple[tuple[str, ...], int]], diff --git a/litellm/llms/a2a/chat/transformation.py b/litellm/llms/a2a/chat/transformation.py index 8ebf8958416..f6cb14c0836 100644 --- a/litellm/llms/a2a/chat/transformation.py +++ b/litellm/llms/a2a/chat/transformation.py @@ -4,7 +4,7 @@ A2A Protocol Transformation for LiteLLM import uuid from collections.abc import Iterator -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx @@ -20,6 +20,11 @@ from ..common_utils import ( ) from .streaming_iterator import A2AModelResponseIterator +if TYPE_CHECKING: + import tiktoken + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class A2AConfig(BaseConfig): """ @@ -246,12 +251,12 @@ class A2AConfig(BaseConfig): model: str, raw_response: httpx.Response, model_response: ModelResponse, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", request_data: dict, messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/aiml/image_generation/transformation.py b/litellm/llms/aiml/image_generation/transformation.py index ba641c0a752..4f4cd074165 100644 --- a/litellm/llms/aiml/image_generation/transformation.py +++ b/litellm/llms/aiml/image_generation/transformation.py @@ -14,6 +14,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -169,7 +171,7 @@ class AimlImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/aiohttp_openai/chat/transformation.py b/litellm/llms/aiohttp_openai/chat/transformation.py index 21adab2d5b1..530896bf9b0 100644 --- a/litellm/llms/aiohttp_openai/chat/transformation.py +++ b/litellm/llms/aiohttp_openai/chat/transformation.py @@ -16,6 +16,8 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import Choices, ModelResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -66,7 +68,7 @@ class AiohttpOpenAIChatConfig(OpenAILikeChatConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/amazon_nova/chat/transformation.py b/litellm/llms/amazon_nova/chat/transformation.py index c26182643df..7551fb28c21 100644 --- a/litellm/llms/amazon_nova/chat/transformation.py +++ b/litellm/llms/amazon_nova/chat/transformation.py @@ -2,7 +2,7 @@ Translate from OpenAI's `/v1/chat/completions` to Amazon Nova's `/v1/chat/completions` """ -from typing import Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -16,6 +16,9 @@ from litellm.types.utils import ModelResponse from ...openai_like.chat.transformation import OpenAILikeChatConfig +if TYPE_CHECKING: + import tiktoken + class AmazonNovaChatConfig(OpenAILikeChatConfig): max_completion_tokens: int | None = None @@ -83,7 +86,7 @@ class AmazonNovaChatConfig(OpenAILikeChatConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/anthropic/batches/transformation.py b/litellm/llms/anthropic/batches/transformation.py index 3f8fd2c27f4..4f4d39f09b0 100644 --- a/litellm/llms/anthropic/batches/transformation.py +++ b/litellm/llms/anthropic/batches/transformation.py @@ -1,9 +1,11 @@ import json import time +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final, Literal, cast import httpx from httpx import Headers, Response +from typing_extensions import ReadOnly, TypedDict from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig @@ -12,6 +14,8 @@ from litellm.types.llms.openai import AllMessageValues, CreateBatchRequest from litellm.types.utils import LiteLLMBatch, LlmProviders, ModelResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -19,6 +23,29 @@ else: LoggingClass = Any +class AnthropicBatchRequestCounts(TypedDict, total=False): + """The ``request_counts`` object of an Anthropic Message Batch.""" + + processing: ReadOnly[int] + succeeded: ReadOnly[int] + errored: ReadOnly[int] + canceled: ReadOnly[int] + expired: ReadOnly[int] + + +class AnthropicMessageBatch(TypedDict, total=False): + """The fields of an Anthropic Message Batch that map onto an OpenAI Batch.""" + + id: ReadOnly[str] + processing_status: ReadOnly[str] + created_at: ReadOnly[str | None] + ended_at: ReadOnly[str | None] + expires_at: ReadOnly[str | None] + cancel_initiated_at: ReadOnly[str | None] + archived_at: ReadOnly[str | None] + request_counts: ReadOnly[AnthropicBatchRequestCounts] + + class AnthropicBatchesConfig(BaseBatchesConfig): def __init__(self): from ..chat.transformation import AnthropicConfig @@ -83,7 +110,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): create_batch_data: CreateBatchRequest, optional_params: dict, litellm_params: dict, - ) -> bytes | str | dict[str, Any]: + ) -> bytes | str | dict[str, object]: """ Transform the batch creation request to Anthropic format. @@ -133,7 +160,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): batch_id: str, optional_params: dict, litellm_params: dict, - ) -> bytes | str | dict[str, Any]: + ) -> bytes | str | dict[str, object]: """ Transform batch retrieval request for Anthropic. @@ -152,7 +179,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): ) -> LiteLLMBatch: """Transform Anthropic MessageBatch retrieval response to LiteLLM format.""" try: - response_data: Final = raw_response.json() + response_data: Final[AnthropicMessageBatch] = raw_response.json() except Exception as e: raise ValueError(f"Failed to parse Anthropic batch response: {e}") @@ -161,18 +188,20 @@ class AnthropicBatchesConfig(BaseBatchesConfig): processing_status: Final = response_data.get("processing_status", "in_progress") # Map Anthropic processing_status to OpenAI status - status_mapping: dict[ - str, - Literal[ - "validating", - "failed", - "in_progress", - "finalizing", - "completed", - "expired", - "cancelling", - "cancelled", - ], + status_mapping: Final[ + Mapping[ + str, + Literal[ + "validating", + "failed", + "in_progress", + "finalizing", + "completed", + "expired", + "cancelling", + "cancelled", + ], + ] ] = { "in_progress": "in_progress", "canceling": "cancelling", @@ -261,7 +290,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -279,7 +308,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig): if not line: continue try: - response_json = json.loads(line) + response_json: Mapping[str, Mapping[str, dict[str, object]]] = json.loads(line) # Update model_response with the parsed JSON completion_response = response_json["result"]["message"] transformed_response = self.anthropic_chat_config.transform_parsed_response( diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 721a6653597..c7d12e5cf3a 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -16,18 +16,20 @@ import json from collections.abc import Mapping, Sequence from copy import deepcopy from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, cast, overload, runtime_checkable -from typing_extensions import assert_never +from typing_extensions import ReadOnly, TypedDict, assert_never from litellm._logging import verbose_proxy_logger from litellm.llms.anthropic.chat.transformation import AnthropicConfig from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( LiteLLMAnthropicMessagesAdapter, + is_provider_native_tool_dict, ) from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.llms.base_llm.guardrail_translation.utils import ( anthropic_tool_name, + anthropic_tool_names, effective_scan_only_tool_results_for_guardrail, effective_skip_system_message_for_guardrail, effective_skip_tool_message_for_guardrail, @@ -56,6 +58,8 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + from fastapi import HTTPException + from litellm.integrations.custom_guardrail import ( CustomGuardrail, ModifyResponseException, @@ -96,6 +100,38 @@ InputWriteBackTarget = ( ) +def _as_str_mapping(value: Mapping[str, object]) -> Mapping[str, object]: + return value + + +def _content_block_at(blocks: Sequence[object], index: int) -> object: + return blocks[index] + + +@runtime_checkable +class _ModelDumpBlock(Protocol): + def model_dump(self) -> Mapping[str, object]: ... + + +@runtime_checkable +class _TextAttrBlock(Protocol): + text: str + + +class _WritableMessage(Protocol): + @overload + def get(self, key: str, /) -> object | None: ... + + @overload + def get(self, key: str, default: object, /) -> object: ... + + def __setitem__(self, key: str, value: object, /) -> None: ... + + +def _as_writable(value: _WritableMessage) -> _WritableMessage: + return value + + @dataclass(frozen=True, slots=True) class ScannedText: text: str @@ -111,6 +147,16 @@ class ExtractedInput: EMPTY_EXTRACTED_INPUT: Final = ExtractedInput(scanned=(), images=()) +class _AnthropicSSEDelta(TypedDict, total=False): + type: ReadOnly[str] + text: ReadOnly[str] + stop_reason: ReadOnly[str | None] + + +class _AnthropicSSEEvent(TypedDict, total=False): + delta: ReadOnly[_AnthropicSSEDelta] + + class AnthropicMessagesHandler(BaseTranslation): """Process Anthropic messages with guardrails. @@ -124,7 +170,7 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _build_streaming_usage_response( - responses_so_far: list[object], + responses_so_far: Sequence[object], request_data: dict | None, ) -> ModelResponse | None: chunks: Final = tuple(response for response in responses_so_far if isinstance(response, (str, bytes))) @@ -142,7 +188,7 @@ class AnthropicMessagesHandler(BaseTranslation): self, exc: "ModifyResponseException", stream_started: bool = False, - responses_so_far: list[object] | None = None, + responses_so_far: Sequence[object] | None = None, ) -> list[bytes]: """ Build an Anthropic SSE sequence delivering the guardrail block message @@ -160,9 +206,22 @@ class AnthropicMessagesHandler(BaseTranslation): would make Anthropic clients reject the stream. """ if stream_started: - return self._block_continuation_chunks(exc, responses_so_far or []) + return list(self._block_continuation_chunks(exc, responses_so_far or [])) return self._standalone_block_chunks(exc) + def build_stream_error_items( + self, + exc: "HTTPException", + responses_so_far: Sequence[Any] | None = None, + ) -> Sequence[Any] | None: + from litellm.proxy.common_request_processing import ( + serialize_http_exception_detail, + ) + from litellm.proxy.guardrails.anthropic_sse import anthropic_sse_error_frames + + message, _ = serialize_http_exception_detail(exc.detail) + return tuple(anthropic_sse_error_frames(message)) + def _standalone_block_chunks(self, exc: "ModifyResponseException") -> list[bytes]: import uuid @@ -185,7 +244,9 @@ class AnthropicMessagesHandler(BaseTranslation): ) return list(FakeAnthropicMessagesStreamIterator(response=block_response)) - def _block_continuation_chunks(self, exc: "ModifyResponseException", responses_so_far: list[object]) -> list[bytes]: + def _block_continuation_chunks( + self, exc: "ModifyResponseException", responses_so_far: Sequence[object] + ) -> Sequence[bytes]: """Continue an already-started message: close the open content block, append the block message as a new text block, then end the message -- without a second message_start.""" @@ -197,7 +258,7 @@ class AnthropicMessagesHandler(BaseTranslation): def _sse(event_type: str, payload: dict) -> bytes: return f"event: {event_type}\ndata: {json.dumps(payload)}\n\n".encode() - output_tokens: Final = blocked_response_usage(getattr(exc, "original_response", None))["output_tokens"] + output_tokens: Final = blocked_response_usage(getattr(exc, "original_response", None)).get("output_tokens", 0) open_index, max_index = self._content_block_state(responses_so_far) new_index: Final = (max_index + 1) if max_index is not None else 0 chunks: list[bytes] = [] @@ -235,7 +296,7 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _content_block_state( - responses_so_far: list[object], + responses_so_far: Sequence[object], ) -> tuple[int | None, int | None]: """From the SSE chunks already sent to the client, return (open content-block index or None, highest content-block index seen or None). @@ -261,7 +322,20 @@ class AnthropicMessagesHandler(BaseTranslation): return open_index, max_index @staticmethod - def _iter_sse_events(item: object) -> list[dict[str, object]]: + def _parse_sse_data_line(raw_line: str) -> tuple[Mapping[str, object], ...]: + line: Final = raw_line.strip() + if not line.startswith("data:"): + return () + try: + parsed: Final[object] = json.loads(line[len("data:") :].strip()) + except json.JSONDecodeError: + return () + if not isinstance(parsed, dict): + return () + return (_as_str_mapping(parsed),) + + @staticmethod + def _iter_sse_events(item: object) -> Sequence[Mapping[str, object]]: """Yield the event-data dicts in one stream chunk. Handles both formats this stream can carry (see @@ -269,24 +343,15 @@ class AnthropicMessagesHandler(BaseTranslation): several events separated by a blank line -- and an already-parsed event ``dict``.""" if isinstance(item, dict): - return [item] + return (_as_str_mapping(item),) if not isinstance(item, (bytes, bytearray)): - return [] - events: Final[list[dict[str, object]]] = [] - for block in item.decode("utf-8", errors="replace").split("\n\n"): - for line in block.split("\n"): - line = line.strip() - if not line.startswith("data:"): - continue - try: - parsed: str | int | float | bool | None | Sequence[object] | Mapping[str, object] = json.loads( - line[len("data:") :].strip() - ) - except json.JSONDecodeError: - continue - if isinstance(parsed, dict): - events.append(parsed) - return events + return () + return tuple( + event + for block in item.decode("utf-8", errors="replace").split("\n\n") + for line in block.split("\n") + for event in AnthropicMessagesHandler._parse_sse_data_line(line) + ) def _translate_to_openai(self, data: dict) -> ChatCompletionRequest: """Translate Anthropic request to OpenAI chat completion format.""" @@ -319,7 +384,7 @@ class AnthropicMessagesHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - ) -> Any: + ) -> Mapping[str, object]: """ Process input messages by applying guardrails to text content. """ @@ -360,7 +425,13 @@ class AnthropicMessagesHandler(BaseTranslation): structured_messages: Final = [full_structured_messages[index] for index in scoped_message_indices] tools_to_check: Final[list[ChatCompletionToolParam]] = ( - [] if scan_only_tool_results else chat_completion_compatible_request.get("tools", []) + [] + if scan_only_tool_results + else [ + tool + for tool in chat_completion_compatible_request.get("tools", []) + if not is_provider_native_tool_dict(tool) + ] ) # Step 1: Extract all text content and images @@ -419,7 +490,10 @@ class AnthropicMessagesHandler(BaseTranslation): tool_name=anthropic_tool_name, ) if scan_only_tool_results - else anthropic_tools + else [ + *(tool for tool in data.get("tools") or [] if is_provider_native_tool_dict(tool)), + *anthropic_tools, + ] ) guardrailed_structured_messages: Final = guardrailed_inputs.get("structured_messages") @@ -470,7 +544,7 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _openai_system_message_to_anthropic( - message: dict[str, object], + message: Mapping[str, object], ) -> dict[str, object] | None: # mutable-ok: API message payload """Convert an OpenAI system message to the client's Anthropic-shaped entry.""" content: Final = message.get("content") @@ -550,7 +624,7 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _defer_systems_inside_tool_exchanges( - structured_messages: list, # mutable-ok: API message payload + structured_messages: Sequence[Mapping[str, object]], ) -> list: """Hold a system row until the tool exchange around it completes so the call/result pair converts together.""" from litellm.litellm_core_utils.prompt_templates.factory import group_tool_exchanges @@ -677,12 +751,9 @@ class AnthropicMessagesHandler(BaseTranslation): ) def extract_request_tool_names(self, data: dict) -> list[str]: - """Extract tool names from Anthropic messages request (tools[].name).""" - names: Final[list[str]] = [] - for tool in data.get("tools") or []: - if isinstance(tool, dict) and tool.get("name"): - names.append(str(tool["name"])) - return names + """Extract every tool name in an Anthropic messages request: tools[].name, plus + tools[].function.name for OpenAI-format tools the bridge forwards verbatim.""" + return [name for tool in data.get("tools") or [] for name in anthropic_tool_names(tool)] @classmethod def _extract_input_text_and_images( @@ -747,7 +818,7 @@ class AnthropicMessagesHandler(BaseTranslation): if scan_only_tool_results: return EMPTY_EXTRACTED_INPUT - text_str: Final = content_item.get("text", None) + text_str: Final[str | None] = content_item.get("text") return ExtractedInput( scanned=( () if text_str is None else (ScannedText(text_str, ContentBlockTextTarget(msg_idx, content_idx)),) @@ -788,16 +859,32 @@ class AnthropicMessagesHandler(BaseTranslation): @staticmethod def _image_sources(block: Mapping[str, object]) -> tuple[str, ...]: + """Normalize an Anthropic image block into strings a guardrail can read. + + base64 becomes a data URI so the format travels with the payload, which is what + the OpenAI path already puts in this field. A file source yields nothing: those + bytes live behind the Files API and this extractor has no client to fetch them. + """ source: Final = block.get("source") if not isinstance(source, Mapping): return () - # Could be base64 or url + + source_type: Final = source.get("type") + if source_type == "url": + url: Final = source.get("url") + return (url,) if isinstance(url, str) and url else () + data: Final = source.get("data") - return (data,) if data else () + if not isinstance(data, str) or not data: + return () + media_type: Final = source.get("media_type") + if isinstance(media_type, str) and media_type: + return (f"data:{media_type};base64,{data}",) + return (data,) async def _apply_guardrail_responses_to_input( self, - messages: list[dict[str, object]], + messages: Sequence[_WritableMessage], responses: list[str], scanned: tuple[ScannedText, ...], ) -> None: @@ -923,7 +1010,7 @@ class AnthropicMessagesHandler(BaseTranslation): litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, - ) -> list[Any]: + ) -> Sequence[object]: """ Process output streaming response by applying guardrails to text content. @@ -1019,7 +1106,7 @@ class AnthropicMessagesHandler(BaseTranslation): return request_data @staticmethod - def _get_response_content(response: object) -> list[Any]: + def _get_response_content(response: object) -> Sequence[object]: """Extract content list from a dict or object response.""" if isinstance(response, dict): return response.get("content", []) or [] @@ -1029,7 +1116,7 @@ class AnthropicMessagesHandler(BaseTranslation): def _extract_from_content_blocks( self, - response_content: list[Any], + response_content: Sequence[object], texts_to_check: list[str], images_to_check: list[str], task_mappings: list[tuple[int, int | None]], @@ -1037,21 +1124,10 @@ class AnthropicMessagesHandler(BaseTranslation): ) -> None: """Extract text, images, and tool calls from content blocks.""" for content_idx, content_block in enumerate(response_content): - block_dict: dict[str, object] = {} - if isinstance(content_block, dict): - block_type = content_block.get("type") - block_dict = cast(dict[str, object], content_block) - elif hasattr(content_block, "type"): - block_type = getattr(content_block, "type", None) - if hasattr(content_block, "model_dump"): - block_dict = content_block.model_dump() - else: - block_dict = { - "type": block_type, - "text": getattr(content_block, "text", None), - } - else: + fields = self._output_block_fields(content_block) + if fields is None: continue + block_type, block_dict = fields if block_type in ["text", "tool_use"]: self._extract_output_text_and_images( @@ -1063,6 +1139,21 @@ class AnthropicMessagesHandler(BaseTranslation): tool_calls_to_check=tool_calls_to_check, ) + @staticmethod + def _output_block_fields(content_block: object) -> "tuple[object, Mapping[str, object]] | None": + if isinstance(content_block, dict): + block_dict: Final = _as_str_mapping(content_block) + return block_dict.get("type"), block_dict + if not hasattr(content_block, "type"): + return None + block_type: Final = getattr(content_block, "type", None) + if isinstance(content_block, _ModelDumpBlock): + return block_type, content_block.model_dump() + return block_type, { + "type": block_type, + "text": getattr(content_block, "text", None), + } + @staticmethod def _build_guardrail_inputs( texts_to_check: list[str], @@ -1085,7 +1176,7 @@ class AnthropicMessagesHandler(BaseTranslation): inputs["model"] = response_model return inputs - def get_streaming_string_so_far(self, responses_so_far: list[Any]) -> str: + def get_streaming_string_so_far(self, responses_so_far: Sequence[object]) -> str: """ Parse streaming responses and extract accumulated text content. @@ -1156,8 +1247,8 @@ class AnthropicMessagesHandler(BaseTranslation): # Only process content_block_delta events if event_type == "content_block_delta" and data_line: try: - data = json.loads(data_line) - delta = data.get("delta", {}) + data: _AnthropicSSEEvent = json.loads(data_line) + delta: _AnthropicSSEDelta = data.get("delta", {}) if delta.get("type") == "text_delta": text += delta.get("text", "") except json.JSONDecodeError: @@ -1168,7 +1259,7 @@ class AnthropicMessagesHandler(BaseTranslation): return text - def _check_streaming_has_ended(self, responses_so_far: list[Any]) -> bool: + def _check_streaming_has_ended(self, responses_so_far: Sequence[object]) -> bool: """ Check if streaming response has ended by looking for non-null stop_reason. @@ -1219,9 +1310,9 @@ class AnthropicMessagesHandler(BaseTranslation): # Check for message_delta event with stop_reason if event_type == "message_delta" and data_line: try: - data = json.loads(data_line) - delta = data.get("delta", {}) - stop_reason = delta.get("stop_reason") + data: _AnthropicSSEEvent = json.loads(data_line) + delta: _AnthropicSSEDelta = data.get("delta", {}) + stop_reason: str | None = delta.get("stop_reason") if stop_reason is not None: return True except json.JSONDecodeError: @@ -1263,7 +1354,7 @@ class AnthropicMessagesHandler(BaseTranslation): def _extract_output_text_and_images( self, - content_block: dict[str, object], + content_block: Mapping[str, object], content_idx: int, texts_to_check: list[str], images_to_check: list[str], @@ -1286,7 +1377,7 @@ class AnthropicMessagesHandler(BaseTranslation): task_mappings.append((content_idx, None)) # Extract tool calls - elif content_type == "tool_use": + elif content_type == "tool_use" and isinstance(content_block, dict): tool_call: Final = AnthropicConfig.convert_tool_use_to_openai_format( anthropic_tool_content=content_block, index=content_idx, @@ -1311,7 +1402,7 @@ class AnthropicMessagesHandler(BaseTranslation): content_idx = cast(int, mapping[0]) # Handle both dict and object responses - response_content: list[Any] = [] + response_content: Sequence[object] = [] if isinstance(response, dict): response_content = response.get("content", []) or [] elif hasattr(response, "content"): @@ -1327,14 +1418,15 @@ class AnthropicMessagesHandler(BaseTranslation): if content_idx >= len(response_content): continue - content_block = response_content[content_idx] + content_block = _content_block_at(response_content, content_idx) # Verify it's a text block and update the text field # Handle both dict and Pydantic object content blocks if isinstance(content_block, dict): - if content_block.get("type") == "text": - cast(dict[str, object], content_block)["text"] = guardrail_response + block = _as_writable(content_block) + if block.get("type") == "text": + block["text"] = guardrail_response elif hasattr(content_block, "type") and getattr(content_block, "type", None) == "text": # Update Pydantic object's text attribute - if hasattr(content_block, "text"): + if isinstance(content_block, _TextAttrBlock): content_block.text = guardrail_response diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index d9bb0d7abff..c82be07a5c5 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -18,6 +18,7 @@ from litellm.anthropic_beta_headers_manager import ( ) from litellm.constants import RESPONSE_FORMAT_TOOL_NAME from litellm.litellm_core_utils.core_helpers import map_finish_reason +from litellm.litellm_core_utils.json_fragment_accumulator import JSONFragmentAccumulator from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, HTTPHandler, @@ -65,6 +66,10 @@ if TYPE_CHECKING: from litellm.llms.base_llm.chat.transformation import BaseConfig +def _loads_stream_chunk(payload: str) -> dict[str, object]: + return json.loads(payload) + + async def make_call( client: AsyncHTTPHandler | None, api_base: str, @@ -77,7 +82,7 @@ async def make_call( json_mode: bool, speed: str | None = None, tool_name_reverse_map: dict[str, str] | None = None, -) -> tuple[Any, httpx.Headers]: +) -> tuple["ModelResponseIterator", httpx.Headers]: if client is None: client = litellm.module_level_aclient @@ -92,7 +97,7 @@ async def make_call( ) except httpx.HTTPStatusError as e: error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) raise AnthropicError( @@ -137,7 +142,7 @@ def make_sync_call( json_mode: bool, speed: str | None = None, tool_name_reverse_map: dict[str, str] | None = None, -) -> tuple[Any, httpx.Headers]: +) -> tuple["ModelResponseIterator", httpx.Headers]: if client is None: client = litellm.module_level_client # re-use a module level client @@ -152,7 +157,7 @@ def make_sync_call( ) except httpx.HTTPStatusError as e: error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) raise AnthropicError( @@ -291,7 +296,7 @@ class AnthropicChatCompletion(BaseLLM): status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) error_text = getattr(e, "text", str(e)) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) if error_response and hasattr(error_response, "text"): @@ -592,7 +597,7 @@ class AnthropicChatCompletion(BaseLLM): status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) error_text = getattr(e, "text", str(e)) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) if error_response and hasattr(error_response, "text"): @@ -654,7 +659,7 @@ class ModelResponseIterator: # For handling partial JSON chunks from fragmentation # See: https://github.com/BerriAI/litellm/issues/17473 - self.accumulated_json: str = "" + self._json_buffer = JSONFragmentAccumulator() self.chunk_type: Literal["valid_json", "accumulated_json"] = "valid_json" # Track current content block type to avoid emitting tool calls for non-tool blocks @@ -663,10 +668,10 @@ class ModelResponseIterator: # Accumulate web_search_tool_result blocks for multi-turn reconstruction # See: https://github.com/BerriAI/litellm/issues/17737 - self.web_search_results: list[dict[str, Any]] = [] + self.web_search_results: list[dict[str, object]] = [] # Accumulate compaction blocks for multi-turn reconstruction - self.compaction_blocks: list[dict[str, Any]] = [] + self.compaction_blocks: list[dict[str, object]] = [] # Accumulate streamed thinking text so final usage can split reasoning # tokens from regular output tokens. @@ -678,6 +683,14 @@ class ModelResponseIterator: self._current_server_tool_id: str | None = None self._container_id: str | None = None + @property + def accumulated_json(self) -> str: + return self._json_buffer.snapshot() + + @accumulated_json.setter + def accumulated_json(self, value: str) -> None: + self._json_buffer.set(value) + def check_empty_tool_call_args(self) -> bool: """ Check if the tool call block so far has been an empty string @@ -703,11 +716,14 @@ class ModelResponseIterator: def _handle_usage(self, anthropic_usage_chunk: dict | UsageDelta) -> Usage: reasoning_content: Final = "".join(self.reasoning_content_chunks) if self.reasoning_content_chunks else None - return AnthropicConfig().calculate_usage( + usage: Final = AnthropicConfig().calculate_usage( usage_object=cast(dict, anthropic_usage_chunk), reasoning_content=reasoning_content, speed=self.speed, ) + if usage.speed is not None: + self.speed = usage.speed + return usage def _content_block_delta_helper( self, chunk: dict @@ -715,7 +731,7 @@ class ModelResponseIterator: str, ChatCompletionToolCallChunk | None, list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock], - dict[str, Any], + dict[str, object], str | None, ]: """ @@ -723,7 +739,7 @@ class ModelResponseIterator: """ text = "" tool_use: ChatCompletionToolCallChunk | None = None - provider_specific_fields: Final = {} + provider_specific_fields: Final[dict[str, object]] = {} reasoning_content: str | None = None content_block: Final = ContentBlockDelta(**chunk) thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] = [] @@ -797,8 +813,8 @@ class ModelResponseIterator: def _handle_redacted_thinking_content( self, content_block_start: ContentBlockStart, - provider_specific_fields: dict[str, Any], - ) -> tuple[list[ChatCompletionRedactedThinkingBlock], dict[str, Any]]: + provider_specific_fields: dict[str, object], + ) -> tuple[list[ChatCompletionRedactedThinkingBlock], dict[str, object]]: """ Handle the redacted thinking content """ @@ -866,7 +882,7 @@ class ModelResponseIterator: tool_use: ChatCompletionToolCallChunk | None = None finish_reason = "" usage: Usage | None = None - provider_specific_fields: dict[str, Any] = {} + provider_specific_fields: dict[str, object] = {} reasoning_content: str | None = None thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None = None @@ -1149,31 +1165,39 @@ class ModelResponseIterator: container: Final = message_delta["delta"].get("container") return finish_reason, usage, container - def _handle_accumulated_json_chunk(self, data_str: str) -> ModelResponseStream | None: + def _handle_accumulated_json_chunk(self, data_str: str, is_final: bool = False) -> ModelResponseStream | None: """ Handle partial JSON chunks by accumulating them until valid JSON is received. This fixes network fragmentation issues where SSE data chunks may be split across TCP packets. See: https://github.com/BerriAI/litellm/issues/17473 + Mid-stream, defer parsing until the buffer's last byte can close a value: + attempting a parse after every fragment of one large object is O(n^2) and + holds the GIL, freezing the event loop. At end of stream (is_final) no more + data is coming, so drain whatever complete values remain regardless of the + trailing byte. + Args: data_str: The JSON string to parse (without "data:" prefix) + is_final: True when called from the end-of-stream drain, where the + trailing-byte heuristic no longer applies Returns: ModelResponseStream if JSON is complete, None if still accumulating """ - # Accumulate JSON data - self.accumulated_json += data_str + self._json_buffer.append(data_str) - # Try to parse the accumulated JSON - try: - data_json: Final = json.loads(self.accumulated_json) - self.accumulated_json = "" # Reset after successful parsing - return self.chunk_parser(chunk=data_json) - except json.JSONDecodeError: - # If it's not valid JSON yet, continue to the next chunk + if not is_final and not self._json_buffer.could_close_json(): return None + while True: + found, decoded = self._json_buffer.pop_next_value() + if not found: + return None + if isinstance(decoded, dict): + return self.chunk_parser(chunk=decoded) + def _parse_sse_data(self, str_line: str) -> ModelResponseStream | None: """ Parse SSE data line, handling both complete and partial JSON chunks. @@ -1192,7 +1216,7 @@ class ModelResponseIterator: # Try to parse as valid JSON first try: - data_json: Final = json.loads(data_str) + data_json: Final = _loads_stream_chunk(data_str) return self.chunk_parser(chunk=data_json) except json.JSONDecodeError: # Switch to accumulation mode and start accumulating @@ -1209,13 +1233,10 @@ class ModelResponseIterator: chunk = self.response_iterator.__next__() except StopIteration: # If we have accumulated JSON when stream ends, try to parse it - if self.accumulated_json: - try: - data_json = json.loads(self.accumulated_json) - self.accumulated_json = "" - return self.chunk_parser(chunk=data_json) - except json.JSONDecodeError: - pass + if self._json_buffer: + result = self._handle_accumulated_json_chunk(data_str="", is_final=True) + if result is not None: + return result raise StopIteration except ValueError as e: raise RuntimeError(f"Error receiving chunk from stream: {e}") @@ -1258,13 +1279,10 @@ class ModelResponseIterator: chunk = await self.async_response_iterator.__anext__() except StopAsyncIteration: # If we have accumulated JSON when stream ends, try to parse it - if self.accumulated_json: - try: - data_json = json.loads(self.accumulated_json) - self.accumulated_json = "" - return self.chunk_parser(chunk=data_json) - except json.JSONDecodeError: - pass + if self._json_buffer: + result = self._handle_accumulated_json_chunk(data_str="", is_final=True) + if result is not None: + return result raise StopAsyncIteration except ValueError as e: raise RuntimeError(f"Error receiving chunk from stream: {e}") @@ -1316,7 +1334,7 @@ class ModelResponseIterator: str_line = str_line[index:] if str_line.startswith("data:"): - data_json: Final = json.loads(str_line[5:]) + data_json: Final = _loads_stream_chunk(str_line[5:]) return self.chunk_parser(chunk=data_json) else: return ModelResponseStream(id=self.response_id) diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index ef278c8f723..aa805ccea71 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -1,11 +1,13 @@ import json import re import time -from collections.abc import Mapping, Sequence +from collections.abc import Callable, Mapping, Sequence +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, NoReturn, cast import httpx from pydantic import ValidationError +from typing_extensions import ReadOnly, TypedDict import litellm from litellm.constants import ( @@ -91,6 +93,8 @@ from ..common_utils import ( ) if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -121,6 +125,50 @@ else: # response side. _ANTHROPIC_TOOL_NAME_INVALID_CHARS: Final = re.compile(r"[^a-zA-Z0-9_-]") _ANTHROPIC_TOOL_NAME_MAX_LEN: Final = 128 + + +class _AnthropicUsageIteration(TypedDict, total=False): + """One entry of the ``usage.iterations`` array on an Anthropic response.""" + + input_tokens: ReadOnly[int | None] + output_tokens: ReadOnly[int | None] + cache_creation_input_tokens: ReadOnly[int | None] + cache_read_input_tokens: ReadOnly[int | None] + + +class _AnthropicToolResultBlock(TypedDict, total=False): + """A ``*_tool_result`` content block on an Anthropic response.""" + + type: ReadOnly[str] + tool_use_id: ReadOnly[str] + content: ReadOnly[object] + + +_ENUM_TYPE_CHECKS: Final[Mapping[str, Callable[[object], bool]]] = MappingProxyType( + { + "null": lambda v: v is None, + "boolean": lambda v: isinstance(v, bool), + "integer": lambda v: isinstance(v, int) and not isinstance(v, bool), + "number": lambda v: isinstance(v, (int, float)) and not isinstance(v, bool), + "string": lambda v: isinstance(v, str), + "array": lambda v: isinstance(v, list), + "object": lambda v: isinstance(v, dict), + } +) + + +def _enum_conflicts_with_declared_type(schema: Mapping[str, Any]) -> bool: + """Whether ``schema``'s ``enum`` cannot match its declared ``type``.""" + enum_values: Final = schema.get("enum") + declared_type: Final = schema.get("type") + if not isinstance(enum_values, list) or declared_type is None: + return False + if isinstance(declared_type, list): + return True + check: Final = _ENUM_TYPE_CHECKS.get(declared_type) + return check is not None and not all(check(value) for value in enum_values) + + # Single, internal-only key on ``litellm_params`` used to thread the per- # request reverse map (sanitized -> original) from request build to response # parsing. ``litellm_params`` is never serialized to a provider; ``optional_ @@ -411,7 +459,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): optional_params.pop("speed", None) @staticmethod - def _raise_invalid_reasoning_effort(model: str, value: Any, llm_provider: str) -> NoReturn: + def _raise_invalid_reasoning_effort(model: str, value: object, llm_provider: str) -> NoReturn: """Raise a ``BadRequestError`` for an unrecognised ``reasoning_effort``. Args: @@ -565,9 +613,13 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): else: result["description"] = constraint_note + drops_conflicting_type: Final = _enum_conflicts_with_declared_type(schema) + for key, value in schema.items(): if key in unsupported_fields: continue + if key == "type" and drops_conflicting_type: + continue if key == "description" and "description" in result: # Already handled above continue @@ -1184,8 +1236,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if reasoning_effort is None or reasoning_effort == "none": return None if AnthropicConfig._is_adaptive_thinking_model(model, custom_llm_provider): + # without display, Anthropic defaults adaptive thinking to + # display="omitted" and returns a blank thinking block return AnthropicThinkingParam( type="adaptive", + display="summarized", ) elif reasoning_effort == "low": return AnthropicThinkingParam( @@ -1232,7 +1287,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) @staticmethod - def _cap_thinking_budget_to_max_tokens( + def cap_thinking_budget_to_max_tokens( thinking: AnthropicThinkingParam, max_tokens: int | None ) -> AnthropicThinkingParam | None: """Cap a legacy ``thinking.budget_tokens`` below ``max_tokens`` (Anthropic @@ -1430,7 +1485,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) if _tool_choice is not None: - optional_params["tool_choice"] = _tool_choice + optional_params["tool_choice"] = AnthropicConfig._apply_forced_tool_choice( + model=model, tool_choice=_tool_choice, drop_params=drop_params + ) elif param == "stream" and value is True: optional_params["stream"] = value elif param == "stop" and (isinstance(value, str) or isinstance(value, list)): @@ -1459,7 +1516,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _tool = self.map_response_format_to_anthropic_tool(value, optional_params, is_thinking_enabled) if _tool is None: continue - if not is_thinking_enabled: + if not is_thinking_enabled and not AnthropicModelInfo.forced_tool_use_unsupported(model): _tool_choice = { "name": RESPONSE_FORMAT_TOOL_NAME, "type": "tool", @@ -1494,7 +1551,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): llm_provider=self._resolved_provider, ) capped_thinking = ( - AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) + AnthropicConfig.cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) if legacy_thinking is not None else None ) @@ -1956,19 +2013,35 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return data def _apply_output_config(self, data: dict, model: str, optional_params: dict) -> None: - """Validate and apply output_config to the request data.""" + """Validate and apply output_config to the request data. + + The ``drop_params`` gate here is an effort gate: ``format`` is a + structured-output field, not an effort field, so it survives the drop + and is vetted where it is consumed (the map's + ``supports_native_structured_output`` flag on emission paths). + """ if "output_config" not in optional_params: return output_config: Final = optional_params.get("output_config") if not output_config or not isinstance(output_config, dict): return - if litellm.drop_params is True and not self._model_supports_effort_param(model, self._resolved_provider): + if ( + litellm.drop_params is True + and any(key != "format" for key in output_config) + and not self._model_supports_effort_param(model, self._resolved_provider) + ): litellm.verbose_logger.warning( DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, model, ) - optional_params.pop("output_config", None) - data.pop("output_config", None) + preserved_format: Final = output_config.get("format") + if preserved_format is None: + optional_params.pop("output_config", None) + data.pop("output_config", None) + return + format_only: Final = {"format": preserved_format} # mutable-ok: json body + optional_params["output_config"] = format_only # rebind-ok: out-param store + data["output_config"] = format_only # rebind-ok: out-param store return effort: Final = output_config.get("effort") valid_efforts: Final = ["high", "medium", "low", "xhigh", "max"] @@ -2023,22 +2096,22 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): self, completion_response: dict ) -> tuple[ str, - list[Any] | None, + list[object] | None, list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None, str | None, list[ChatCompletionToolCallChunk], - list[Any] | None, - list[Any] | None, - list[Any] | None, + list[object] | None, + list[_AnthropicToolResultBlock] | None, + list[object] | None, ]: text_content = "" - citations: list[Any] | None = None + citations: list[object] | None = None thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None = None reasoning_content: str | None = None tool_calls: Final[list[ChatCompletionToolCallChunk]] = [] - web_search_results: list[Any] | None = None - tool_results: list[Any] | None = None - compaction_blocks: list[Any] | None = None + web_search_results: list[object] | None = None + tool_results: list[_AnthropicToolResultBlock] | None = None + compaction_blocks: list[object] | None = None for idx, content in enumerate(completion_response["content"]): if content["type"] == "text": text_content += content["text"] @@ -2113,7 +2186,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) @staticmethod - def _thinking_tokens_from_usage(usage_object: Mapping[str, object]) -> int | None: + def thinking_tokens_from_usage(usage_object: Mapping[str, object]) -> int | None: details: Final = usage_object.get("output_tokens_details") if not isinstance(details, Mapping): return None @@ -2145,7 +2218,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): reported_thinking_tokens: Final = ( iteration_thinking_tokens if iteration_thinking_tokens is not None - else self._thinking_tokens_from_usage(usage_object) + else self.thinking_tokens_from_usage(usage_object) ) if reported_thinking_tokens is not None: capped_reported: Final = min(max(0, reported_thinking_tokens), completion_tokens) @@ -2168,7 +2241,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _sum_iteration_thinking_tokens(self, iterations: Sequence[object]) -> int | None: per_iteration: Final = tuple( - self._thinking_tokens_from_usage(iteration) if isinstance(iteration, Mapping) else None + self.thinking_tokens_from_usage(iteration) if isinstance(iteration, Mapping) else None for iteration in iterations ) reported: Final = tuple(tokens for tokens in per_iteration if tokens is not None) @@ -2245,8 +2318,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): str | None, _usage.get("service_tier"), ) + raw_speed: Final = _usage.get("speed") + resolved_speed: Final = raw_speed if isinstance(raw_speed, str) else speed - iterations: Final[list[Any] | None] = _usage.get("iterations") + iterations: Final[Sequence[_AnthropicUsageIteration] | None] = _usage.get("iterations") if iterations: prompt_tokens = sum(it.get("input_tokens", 0) or 0 for it in iterations) completion_tokens = sum(it.get("output_tokens", 0) or 0 for it in iterations) @@ -2319,7 +2394,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): else None ), inference_geo=inference_geo, - speed=speed, + speed=resolved_speed, service_tier=service_tier, ) return usage @@ -2339,7 +2414,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _build_code_interpreter_results( self, - tool_results: list[Any], + tool_results: Sequence[_AnthropicToolResultBlock], code_by_id: dict[str, str], container_id: str | None, ) -> list[OutputCodeInterpreterCall]: @@ -2365,11 +2440,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _build_provider_specific_fields( self, completion_response: dict, - citations: list[Any] | None, + citations: Sequence[object] | None, thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None, - web_search_results: list[Any] | None, - tool_results: list[Any] | None, - compaction_blocks: list[Any] | None, + web_search_results: Sequence[object] | None, + tool_results: Sequence[_AnthropicToolResultBlock] | None, + compaction_blocks: Sequence[object] | None, tool_calls: list[ChatCompletionToolCallChunk], ) -> dict[str, Any]: provider_specific_fields: Final[dict[str, Any]] = { @@ -2539,7 +2614,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 3297aa95715..d23690976ad 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -4,7 +4,7 @@ This file contains common utils for anthropic calls. import copy import re -from collections.abc import Mapping, Sequence +from collections.abc import Mapping, MutableMapping, Sequence from datetime import datetime, timezone from types import MappingProxyType from typing import Any, Final, Literal @@ -28,16 +28,36 @@ from litellm.types.llms.anthropic import ( ANTHROPIC_OAUTH_TOKEN_PREFIX, AllAnthropicToolsValues, AnthropicMcpServerTool, + AnthropicMessagesToolChoice, ) from litellm.types.llms.openai import AllMessageValues from litellm.types.proxy.model_listing import ModelInfoResponse +DROP_FORCED_TOOL_CHOICE_WARNING: Final = ( + "Downgrading forced tool_choice to 'auto' for model=%s (drop_params=True): this model rejects tool_choice type " + "'any'/'tool' with a 400 because thinking is always on and a forced call would skip it." +) DROP_DISABLED_THINKING_WARNING: Final = ( "Dropping `thinking={'type': 'disabled'}` for model=%s: thinking is always on for this model and cannot be " "disabled (the alternative is a provider 400). The model will still think adaptively, its response can contain " "thinking blocks, and those thinking tokens are billed as output tokens." ) +# Anthropic error `type` (both the JSON error body and SSE `event: error` +# payloads use this field) mapped to the HTTP status code it corresponds to. +ANTHROPIC_ERROR_STATUS_CODE_MAP: Final = MappingProxyType( + { + "invalid_request_error": 400, + "authentication_error": 401, + "permission_error": 403, + "not_found_error": 404, + "rate_limit_error": 429, + "api_error": 500, + "overloaded_error": 503, + "timeout_error": 504, + } +) + _BEDROCK_VERSION_SUFFIX_RE: Final = re.compile(r"-v\d+(?::\d+)?$") _INFERENCE_PROFILE_MINOR_RE: Final = re.compile(r":\d+$") _DATED_RELEASE_SUFFIX_RE: Final = re.compile(r"-\d{8}$") @@ -78,8 +98,8 @@ def optionally_handle_anthropic_oauth(headers: dict, api_key: str | None) -> tup """ Handle Anthropic OAuth token detection and header setup. - If an OAuth token is detected in the Authorization header, extracts it - and sets the required OAuth headers. + If an OAuth token is detected in the Authorization header (any casing), + extracts it and sets the required OAuth headers. Args: headers: Request headers dict @@ -89,16 +109,21 @@ def optionally_handle_anthropic_oauth(headers: dict, api_key: str | None) -> tup Tuple of (updated headers, api_key) """ # Check Authorization header (passthrough / forwarded requests) - auth_header: Final = headers.get("authorization", "") - if auth_header and auth_header.startswith(f"Bearer {ANTHROPIC_OAUTH_TOKEN_PREFIX}"): - api_key = auth_header.replace("Bearer ", "") - headers.pop("x-api-key", None) + auth_header: Final = next((value for name, value in headers.items() if name.lower() == "authorization"), "") + if auth_header.startswith(f"Bearer {ANTHROPIC_OAUTH_TOKEN_PREFIX}"): + api_key = auth_header.removeprefix("Bearer ") + for name in tuple( + header_name for header_name in headers if header_name.lower() in ("x-api-key", "authorization") + ): + headers.pop(name) + headers["authorization"] = auth_header headers["anthropic-beta"] = _merge_beta_headers(headers.get("anthropic-beta"), ANTHROPIC_OAUTH_BETA_HEADER) headers["anthropic-dangerous-direct-browser-access"] = "true" return headers, api_key # Check api_key directly (standard chat/completion flow) if api_key and api_key.startswith(ANTHROPIC_OAUTH_TOKEN_PREFIX): - headers.pop("x-api-key", None) + for name in tuple(header_name for header_name in headers if header_name.lower() == "x-api-key"): + headers.pop(name) headers["authorization"] = f"Bearer {api_key}" headers["anthropic-beta"] = _merge_beta_headers(headers.get("anthropic-beta"), ANTHROPIC_OAUTH_BETA_HEADER) headers["anthropic-dangerous-direct-browser-access"] = "true" @@ -300,6 +325,45 @@ class AnthropicModelInfo(BaseLLMModelInfo): status_code=400, ) + @staticmethod + def forced_tool_use_unsupported(model: str) -> bool: + return AnthropicModelInfo._get_model_capability(model, "supports_forced_tool_use") is False + + @staticmethod + def forced_tool_use_downgraded(model: str, drop_params: bool) -> bool: + """True when the model map flags the model with + ``supports_forced_tool_use: false`` (Fable 5.1 / Mythos 5.1 400 on + ``any``/``tool``) and ``drop_params`` asks for the ``auto`` downgrade; + raises a clean client-side 400 for such models without ``drop_params``.""" + if not AnthropicModelInfo.forced_tool_use_unsupported(model): + return False + if not (litellm.drop_params or drop_params): + raise litellm.utils.UnsupportedParamsError( + message=( + f"{model} does not support forced tool use (tool_choice='required' or a named tool). " + "Use tool_choice='auto' and tell the model in the prompt when to call the tool, or set " + "`litellm.drop_params = True` to downgrade to 'auto' automatically." + ), + status_code=400, + ) + litellm.verbose_logger.warning(DROP_FORCED_TOOL_CHOICE_WARNING, model) + return True + + @staticmethod + def _apply_forced_tool_choice( + model: str, + tool_choice: AnthropicMessagesToolChoice, + drop_params: bool, + ) -> AnthropicMessagesToolChoice: + if tool_choice["type"] not in ("any", "tool"): + return tool_choice + if not AnthropicModelInfo.forced_tool_use_downgraded(model, drop_params): + return tool_choice + disable_parallel: Final = tool_choice.get("disable_parallel_tool_use") + if disable_parallel is None: + return AnthropicMessagesToolChoice(type="auto") + return AnthropicMessagesToolChoice(type="auto", disable_parallel_tool_use=disable_parallel) + @staticmethod def _strip_version_suffix(model: str) -> str: at: Final = model.rfind("@") @@ -440,10 +504,20 @@ class AnthropicModelInfo(BaseLLMModelInfo): """ return AnthropicModelInfo._supports_model_capability(model, "thinking_always_on", custom_llm_provider) + @staticmethod + def _supports_legacy_thinking(model: str, custom_llm_provider: str) -> bool: + """Whether ``model`` is an adaptive-thinking model that still accepts legacy + ``thinking.type=enabled`` with ``budget_tokens`` (the Claude 4.6 family). + The model cost map is authoritative: an explicit ``supports_legacy_thinking`` + entry resolved under ``custom_llm_provider``, or a ``fallback_generalizations`` + rule for unmapped 4.6 ids. Absent flag means the model rejects the legacy shape. + """ + return AnthropicModelInfo._supports_model_capability(model, "supports_legacy_thinking", custom_llm_provider) + @staticmethod def maybe_drop_disabled_thinking( model: str, - optional_params: dict, # mutable-ok: in-place out-param, same contract as AnthropicConfig._maybe_drop_speed_param + optional_params: MutableMapping[str, object], # mutable-ok: in-place out-param, as in _maybe_drop_speed_param custom_llm_provider: str, ) -> None: """Omit ``thinking={'type': 'disabled'}`` for always-on-thinking models @@ -835,13 +909,9 @@ class AnthropicModelInfo(BaseLLMModelInfo): f"Failed to fetch models from Anthropic. Status code: {response.status_code}, Response: {response.text}" ) - models: Final = response.json()["data"] + models: Final[Sequence[Mapping[str, str]]] = response.json()["data"] - litellm_model_names: Final = [] - for model in models: - stripped_model_name = model["id"] - litellm_model_name = "anthropic/" + stripped_model_name - litellm_model_names.append(litellm_model_name) + litellm_model_names: Final = ["anthropic/" + model["id"] for model in models] return litellm_model_names def get_token_counter(self) -> BaseTokenCounter | None: @@ -944,19 +1014,25 @@ def strip_advisor_blocks_from_messages(messages: list[Any], replace_with_text: b return messages -def is_anthropic_invalid_thinking_signature_error(error_text: str) -> bool: +def is_anthropic_invalid_thinking_block_error(error_text: str) -> bool: """ - Detect Anthropic 400 errors caused by missing or invalid thinking signatures. + Detect Anthropic 400 errors caused by invalid thinking blocks in replayed + history: a missing or invalid signature, or a block with empty thinking text. Known error formats: {"message":"messages.2.content.0.thinking.signature.str: Input should be a valid string"} messages.N.content.M.thinking.signature.str: Input should be a valid string messages.N.content.M: Invalid `signature` in `thinking` block + messages.N.content.M.thinking: each thinking block must contain thinking """ if not error_text: return False lower: Final = error_text.lower() - return "thinking" in lower and "signature" in lower and ("invalid" in lower or "valid string" in lower) + if "thinking" not in lower: + return False + if "signature" in lower and ("invalid" in lower or "valid string" in lower): + return True + return "must contain thinking" in lower def strip_thinking_blocks_from_anthropic_messages(messages: list[Any]) -> list[Any]: @@ -998,22 +1074,29 @@ def strip_thinking_blocks_from_anthropic_messages_request_dict( data.pop("thinking", None) -def strip_empty_text_blocks_from_anthropic_messages( +def strip_empty_content_blocks_from_anthropic_messages( messages: list[Any], ) -> list[Any]: """ Return a new message list with empty or whitespace-only ``{"type": "text"}`` - content blocks removed. + and ``{"type": "thinking"}`` content blocks removed. Anthropic's API rejects requests containing such blocks with - ``"messages: text content blocks must be non-empty"``, but assistant - messages from Anthropic routinely arrive with ``{"type": "text", "text": ""}`` - alongside ``tool_use`` blocks (see anthropics/anthropic-sdk-python#461). + ``"messages: text content blocks must be non-empty"`` and + ``"messages.N.content.M.thinking: each thinking block must contain + thinking"`` respectively. Assistant messages routinely arrive with + ``{"type": "text", "text": ""}`` alongside ``tool_use`` blocks (see + anthropics/anthropic-sdk-python#461), and a turn served by a + non-Anthropic reasoning model through the /v1/messages bridge can carry + ``{"type": "thinking", "thinking": ""}`` when the model produced no + reasoning text (e.g. it went straight to parallel tool calls). Multi-turn tool-use clients (e.g. Claude Code) loop these prior responses back as conversation history, which then causes the next request to 400 on the unified ``/v1/messages`` path. ``/v1/chat/completions`` already handles this in ``anthropic_messages_pt``; this helper provides the equivalent guarantee for the native Anthropic Messages path. + ``redacted_thinking`` blocks are never touched: they carry opaque + ``data`` instead of thinking text. Messages whose content is a list and becomes empty after stripping are omitted, matching :func:`strip_thinking_blocks_from_anthropic_messages`. @@ -1026,7 +1109,7 @@ def strip_empty_text_blocks_from_anthropic_messages( out.append(m) continue content = m["content"] - filtered = [b for b in content if not _is_empty_text_block(b)] + filtered = [b for b in content if not _is_empty_text_block(b) and not is_empty_thinking_block(b)] if len(filtered) == len(content): out.append(m) elif filtered: @@ -1034,13 +1117,47 @@ def strip_empty_text_blocks_from_anthropic_messages( return out -def _is_empty_text_block(block: Any) -> bool: +def _is_empty_text_block(block: object) -> bool: if not isinstance(block, dict) or block.get("type") != "text": return False text: Final = block.get("text") return not isinstance(text, str) or not text.strip() +def is_empty_thinking_block(block: object) -> bool: + """ + True for a ``{"type": "thinking"}`` content block whose thinking text is + missing, not a string, or empty/whitespace-only after ``.strip()``. + Anthropic rejects such blocks with ``"each thinking block must contain + thinking"`` (whitespace-only included, verified live), regardless of any + signature they carry. ``redacted_thinking`` blocks are a different type + and always return False. + """ + if not isinstance(block, dict) or block.get("type") != "thinking": + return False + thinking: Final = block.get("thinking") + return not isinstance(thinking, str) or not thinking.strip() + + +def is_empty_unsigned_thinking_block(block: object) -> bool: + """ + True for an empty ``{"type": "thinking"}`` block carrying no signature. + + The emit-side predicate: response paths drop a thinking block only when it + holds nothing the client could need. A signature-only block is a real + provider response (Bedrock Converse under adaptive thinking emits a + reasoning block with empty text and only a signature) and the client needs + the signature to replay reasoning across tool-use turns, so it must be + emitted. Request paths keep using :func:`is_empty_thinking_block`: + Anthropic rejects empty thinking blocks in request history regardless of + signature, and the inbound strip self-heals a replayed signature-only + block. + """ + if not isinstance(block, dict) or not is_empty_thinking_block(block): + return False + return not block.get("signature") + + def normalize_anthropic_tool_use_id(raw_id: str) -> str: """ Normalize a tool_use / tool_result id for Anthropic's ``^[a-zA-Z0-9_-]+$`` @@ -1054,7 +1171,7 @@ def normalize_anthropic_tool_use_id(raw_id: str) -> str: return sanitized or "tool_use_id" -def _sanitize_tool_use_id_content_block(block: Any) -> Any: +def _sanitize_tool_use_id_content_block(block: object) -> object: if not isinstance(block, dict): return block block_type: Final = block.get("type") @@ -1261,31 +1378,38 @@ def process_anthropic_headers(headers: httpx.Headers | dict) -> dict: return additional_headers -def _anthropic_model_entry(model: ModelInfoResponse, created_at: str) -> Mapping[str, object]: +def _anthropic_model_entry( + model: ModelInfoResponse, created_at: str, display_names: Mapping[str, str] +) -> Mapping[str, object]: return { # mutable-ok: JSON response body, serialized by the route and never mutated "type": "model", "id": model["id"], - "display_name": model["id"], + "display_name": display_names.get(model["id"], model["id"]), "created_at": created_at, "max_input_tokens": model.get("max_input_tokens"), "max_tokens": model.get("max_output_tokens"), } -def create_anthropic_model_list_response(models: Sequence[ModelInfoResponse]) -> Mapping[str, object]: +def create_anthropic_model_list_response( + models: Sequence[ModelInfoResponse], + display_names: Mapping[str, str] = MappingProxyType({}), +) -> Mapping[str, object]: """Build the Anthropic-native /v1/models envelope. Clients that send an anthropic-version header parse the Anthropic Models API shape (type/display_name/created_at plus has_more/first_id/last_id) and filter the list themselves, so every model is returned here. The token limits carry over from the OpenAI-shaped listing, named as the Messages API names them, and - are always present because the vendor shape declares them nullable, not optional + are always present because the vendor shape declares them nullable, not optional. + display_names maps a listed model id to a configured human-readable name; ids + without an entry fall back to the id itself, matching the vendor behavior """ created_at: Final = ( datetime.fromtimestamp(DEFAULT_MODEL_CREATED_AT_TIME, tz=timezone.utc).isoformat().replace("+00:00", "Z") ) data: Final = [ # mutable-ok: JSON response body, serialized by the route and never mutated - _anthropic_model_entry(model, created_at) for model in models + _anthropic_model_entry(model, created_at, display_names) for model in models ] return { # mutable-ok: JSON response body, serialized by the route and never mutated "data": data, diff --git a/litellm/llms/anthropic/completion/transformation.py b/litellm/llms/anthropic/completion/transformation.py index d4e2b3db166..b15b0159bd9 100644 --- a/litellm/llms/anthropic/completion/transformation.py +++ b/litellm/llms/anthropic/completion/transformation.py @@ -7,7 +7,7 @@ Litellm provider slug: `anthropic_text/` import json import time from collections.abc import AsyncIterator, Iterator -from typing import Final +from typing import TYPE_CHECKING, Final import httpx @@ -32,6 +32,9 @@ from litellm.types.utils import ( Usage, ) +if TYPE_CHECKING: + import tiktoken + class AnthropicTextError(BaseLLMException): def __init__(self, status_code, message): @@ -182,7 +185,7 @@ class AnthropicTextConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: str, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -202,9 +205,10 @@ class AnthropicTextConfig(BaseConfig): model_response.choices[0].finish_reason = completion_response["stop_reason"] ## CALCULATING USAGE - prompt_tokens: Final = len(encoding.encode(prompt)) ##[TODO] use the anthropic tokenizer here + tokenizer: Final = encoding if encoding is not None else litellm.encoding + prompt_tokens: Final = len(tokenizer.encode(prompt)) ##[TODO] use the anthropic tokenizer here completion_tokens: Final = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) + tokenizer.encode(model_response["choices"][0]["message"].get("content", "")) ) ##[TODO] use the anthropic tokenizer here model_response.created = int(time.time()) diff --git a/litellm/llms/anthropic/cost_calculation.py b/litellm/llms/anthropic/cost_calculation.py index 7bb3e0294f0..95615b8e748 100644 --- a/litellm/llms/anthropic/cost_calculation.py +++ b/litellm/llms/anthropic/cost_calculation.py @@ -8,12 +8,9 @@ from typing import TYPE_CHECKING, Final, Optional from pydantic import BaseModel, ValidationError from litellm.litellm_core_utils.llm_cost_calc.utils import ( - _get_token_base_cost, - _get_web_search_requests, - calculate_cache_writing_cost, generic_cost_per_token, get_provider_specific_geo_multiplier, - parse_prompt_tokens_details, + get_web_search_requests_from_usage, ) if TYPE_CHECKING: @@ -21,43 +18,6 @@ if TYPE_CHECKING: import litellm -def _compute_cache_only_cost(model_info: "ModelInfo", usage: "Usage", service_tier: str | None = None) -> float: - """ - Return only the cache-related portion of the prompt cost (cache read + cache write). - - These costs must NOT be scaled by the ``fast`` speed multiplier because the old - explicit ``fast/`` model entries carried unchanged cache rates while - multiplying only the regular input/output token costs. Regional pricing, by - contrast, uplifts every token type, so the geo multiplier does scale them. - """ - if usage.prompt_tokens_details is None: - return 0.0 - - prompt_tokens_details: Final = parse_prompt_tokens_details(usage) - ( - _, - _, - cache_creation_cost, - cache_creation_cost_above_1hr, - cache_read_cost, - ) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier) - - cache_cost = float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost - - if ( - prompt_tokens_details["cache_creation_tokens"] - or prompt_tokens_details["cache_creation_token_details"] is not None - ): - cache_cost += calculate_cache_writing_cost( - cache_creation_tokens=prompt_tokens_details["cache_creation_tokens"], - cache_creation_token_details=prompt_tokens_details["cache_creation_token_details"], - cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, - cache_creation_cost=cache_creation_cost, - ) - - return cache_cost - - def cost_per_token(model: str, usage: "Usage", service_tier: str | None = None) -> tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -89,8 +49,7 @@ def cost_per_token(model: str, usage: "Usage", service_tier: str | None = None) ) if speed_multiplier != 1.0: - cache_cost: Final = _compute_cache_only_cost(model_info=model_info, usage=usage, service_tier=service_tier) - prompt_cost = (prompt_cost - cache_cost) * speed_multiplier + cache_cost + prompt_cost *= speed_multiplier completion_cost *= speed_multiplier if geo_multiplier != 1.0: @@ -145,7 +104,7 @@ def get_cost_for_anthropic_web_search( if usage is None: return 0.0 - web_search_requests: Final = _get_web_search_requests(getattr(usage, "server_tool_use", None)) + web_search_requests: Final = get_web_search_requests_from_usage(usage) if web_search_requests is None: return 0.0 diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index 30b5df1e4ee..cc5879df56d 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -1029,6 +1029,8 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): @staticmethod def _is_blank_delta(chunk: "ModelResponseStream") -> bool: + from litellm.llms.anthropic.common_utils import is_empty_unsigned_thinking_block + choice: Final = chunk.choices[0] if choice.finish_reason is not None: return False @@ -1039,7 +1041,14 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): return False if getattr(delta, "reasoning_content", None): return False - if getattr(delta, "thinking_blocks", None): + # thinking_blocks whose entries are all empty AND unsigned must not + # open a block: the emitted {"type": "thinking", "thinking": ""} gets + # replayed as history and Anthropic rejects it (LIT-6357). A signed + # entry opens the block so the client receives the replay signature. + thinking_blocks: Final = getattr(delta, "thinking_blocks", None) + if thinking_blocks and any( + isinstance(b, dict) and not is_empty_unsigned_thinking_block(b) for b in thinking_blocks + ): return False return True diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 34c2d837127..199a8ab77e7 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -1,8 +1,8 @@ import copy import hashlib import json -from collections.abc import AsyncIterator, Iterator, Mapping -from typing import TYPE_CHECKING, Any, Final, Literal, TypeVar, cast +from collections.abc import AsyncIterator, Iterator, Mapping, Sequence +from typing import TYPE_CHECKING, Any, Final, Literal, TypeAlias, TypeVar, cast import litellm from litellm.llms.anthropic.experimental_pass_through.utils import ( @@ -18,6 +18,40 @@ TOOL_NAME_PREFIX_LENGTH: Final = OPENAI_MAX_TOOL_NAME_LENGTH - TOOL_NAME_HASH_LE PROVIDERS_PROXYING_AN_UNKNOWN_BACKEND: Final = frozenset({"litellm_proxy"}) +def _optional_attr(source: object, name: str) -> object: + return getattr(source, name, None) + + +def _as_string_mapping(value: object) -> Mapping[str, object] | None: + if isinstance(value, Mapping): + return value + return None + + +def _thought_signature(provider_specific_fields: object) -> str | None: + fields: Final = _as_string_mapping(provider_specific_fields) + if fields is None: + return None + signature: Final = fields.get("thought_signature") + return signature if isinstance(signature, str) else None + + +_ANTHROPIC_TOOL_SCHEMA_KEYS: Final = frozenset( + {"name", "type", "input_schema", "description", "cache_control", "strict"} +) + + +def _is_openai_function_tool(tool: Mapping[str, object]) -> bool: + return tool.get("type") == "function" and "function" in tool + + +def is_provider_native_tool_dict(tool: Mapping[str, object]) -> bool: + if len(tool) != 1: + return False + key, value = next(iter(tool.items())) + return key not in _ANTHROPIC_TOOL_SCHEMA_KEYS and isinstance(value, dict) + + def truncate_tool_name(name: str) -> str: """ Truncate tool names that exceed OpenAI's 64-character limit. @@ -40,7 +74,7 @@ def truncate_tool_name(name: str) -> str: def create_tool_name_mapping( - tools: list[dict[str, Any]], + tools: Sequence[Mapping[str, object]], ) -> dict[str, str]: """ Create a mapping of truncated tool names to original names. @@ -54,6 +88,8 @@ def create_tool_name_mapping( mapping: Final[dict[str, str]] = {} for tool in tools: original_name = tool.get("name", "") + if not isinstance(original_name, str): + continue truncated_name = truncate_tool_name(original_name) if truncated_name != original_name: mapping[truncated_name] = original_name @@ -64,6 +100,7 @@ from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingCho from litellm.litellm_core_utils.prompt_templates.common_utils import ( parse_tool_call_arguments, + reasoning_content_from_thinking_blocks, with_prompt_cache_breakpoint, ) from litellm.litellm_core_utils.prompt_templates.factory import ( @@ -72,7 +109,10 @@ from litellm.litellm_core_utils.prompt_templates.factory import ( from litellm.litellm_core_utils.reasoning_effort_utils import ( reasoning_effort_from_thinking_budget, ) -from litellm.llms.anthropic.common_utils import normalize_anthropic_tool_use_id +from litellm.llms.anthropic.common_utils import ( + is_empty_unsigned_thinking_block, + normalize_anthropic_tool_use_id, +) from litellm.llms.anthropic.experimental_pass_through.context_management import ( PolyfillResult, ) @@ -98,6 +138,7 @@ from litellm.types.llms.anthropic import ( ContextManagementResponse, MessageBlockDelta, MessageDelta, + ServerToolUsage, StreamingContentBlockDeltaType, UsageDelta, UsageIteration, @@ -124,7 +165,9 @@ from litellm.types.llms.openai import ( ChatCompletionToolMessage, ChatCompletionToolParam, ChatCompletionToolParamFunctionChunk, + ChatCompletionToolReferenceObject, ChatCompletionUserMessage, + ToolMessageContentPart, ) from litellm.types.utils import Choices, ModelResponse, StreamingChoices, Usage @@ -133,6 +176,8 @@ from .streaming_iterator import AnthropicStreamWrapper if TYPE_CHECKING: from litellm.types.llms.anthropic import ContentBlockContentBlockDict +ToolResultContent: TypeAlias = str | list[ToolMessageContentPart] + class AnthropicAdapter: def __init__(self) -> None: @@ -261,44 +306,44 @@ class LiteLLMAnthropicMessagesAdapter: ### FOR [BETA] `/v1/messages` endpoint support - def _extract_signature_from_tool_call(self, tool_call: Any) -> str | None: + def _extract_signature_from_tool_call(self, tool_call: object) -> str | None: """ Extract signature from a tool call's provider_specific_fields. Only checks provider_specific_fields, not thinking blocks. """ - signature = None + fields: Final = _optional_attr(tool_call, "provider_specific_fields") + if fields: + return _thought_signature(fields) - if hasattr(tool_call, "provider_specific_fields") and tool_call.provider_specific_fields: - if "thought_signature" in tool_call.provider_specific_fields: - signature = tool_call.provider_specific_fields["thought_signature"] - elif hasattr(tool_call.function, "provider_specific_fields") and tool_call.function.provider_specific_fields: - if "thought_signature" in tool_call.function.provider_specific_fields: - signature = tool_call.function.provider_specific_fields["thought_signature"] + function_fields: Final = _optional_attr(_optional_attr(tool_call, "function"), "provider_specific_fields") + if function_fields: + return _thought_signature(function_fields) - return signature + return None - def _extract_signature_from_tool_use_content(self, content: dict[str, Any]) -> str | None: + def _extract_signature_from_tool_use_content(self, content: Mapping[str, object]) -> str | None: """ Extract signature from a tool_use content block's provider_specific_fields. """ - provider_specific_fields: Final = content.get("provider_specific_fields", {}) + provider_specific_fields: Final = _as_string_mapping(content.get("provider_specific_fields", {})) if provider_specific_fields: - return provider_specific_fields.get("signature") + signature: Final = provider_specific_fields.get("signature") + return signature if isinstance(signature, str) else None return None def _add_cache_control_if_applicable( self, - source: Any, - target: Any, + source: object, + target: object, model: str | None, ) -> None: """ Extract cache_control from source and add to target if it should be preserved. - This method accepts Any type to support both regular dicts and TypedDict objects. - TypedDict objects (like ChatCompletionTextObject, ChatCompletionImageObject, etc.) - are dicts at runtime but have specific types at type-check time. Using Any allows - this method to work with both while maintaining runtime correctness. + This method accepts an unconstrained type to support both regular dicts and + TypedDict objects. TypedDict objects (like ChatCompletionTextObject, + ChatCompletionImageObject, etc.) are dicts at runtime but have specific types at + type-check time, so the widest parameter type works with both. Args: source: Dict or TypedDict containing potential cache_control field @@ -410,90 +455,13 @@ class LiteLLMAnthropicMessagesAdapter: self._add_cache_control_if_applicable(content, doc_obj, model) new_user_content_list.append(doc_obj) elif content.get("type") == "tool_result": - if "content" not in content: - tool_result = ChatCompletionToolMessage( - role="tool", - tool_call_id=content.get("tool_use_id", ""), - content="", - ) - self._add_cache_control_if_applicable(content, tool_result, model) - tool_message_list.append(tool_result) - elif isinstance(content.get("content"), str): - tool_result = ChatCompletionToolMessage( - role="tool", - tool_call_id=content.get("tool_use_id", ""), - content=str(content.get("content", "")), - ) - self._add_cache_control_if_applicable(content, tool_result, model) - tool_message_list.append(tool_result) - elif isinstance(content.get("content"), list): - # Combine all content items into a single tool message - # to avoid creating multiple tool_result blocks with the same ID - # (each tool_use must have exactly one tool_result) - content_items = list(content.get("content", [])) - - # Single-item text keeps the backward-compatible string format; a single - # image becomes a structured image_url part - if len(content_items) == 1: - c = content_items[0] - if isinstance(c, str): - tool_result = ChatCompletionToolMessage( - role="tool", - tool_call_id=content.get("tool_use_id", ""), - content=c, - ) - self._add_cache_control_if_applicable(content, tool_result, model) - tool_message_list.append(tool_result) - elif isinstance(c, dict): - if c.get("type") == "text": - tool_result = ChatCompletionToolMessage( - role="tool", - tool_call_id=content.get("tool_use_id", ""), - content=c.get("text", ""), - ) - self._add_cache_control_if_applicable(content, tool_result, model) - tool_message_list.append(tool_result) - elif c.get("type") == "image": - image_part = self._tool_result_image_part(c.get("source")) - tool_result = ChatCompletionToolMessage( - role="tool", - tool_call_id=content.get("tool_use_id", ""), - content=[image_part] # mutable-ok: content must be a json list - if image_part - else "", - ) - self._add_cache_control_if_applicable(content, tool_result, model) - tool_message_list.append(tool_result) - else: - # For multiple content items, combine into a single tool message - # with list content to preserve all items while having one tool_use_id - combined_content_parts: list[ - ChatCompletionTextObject | ChatCompletionImageObject - ] = [] - for c in content_items: - if isinstance(c, str): - combined_content_parts.append(ChatCompletionTextObject(type="text", text=c)) - elif isinstance(c, dict): - if c.get("type") == "text": - combined_content_parts.append( - ChatCompletionTextObject( - type="text", - text=c.get("text", ""), - ) - ) - elif c.get("type") == "image": - image_part = self._tool_result_image_part(c.get("source")) - if image_part: - combined_content_parts.append(image_part) - # Create a single tool message with combined content - if combined_content_parts: - tool_result = ChatCompletionToolMessage( - role="tool", - tool_call_id=content.get("tool_use_id", ""), - content=combined_content_parts, - ) - self._add_cache_control_if_applicable(content, tool_result, model) - tool_message_list.append(tool_result) + tool_result = ChatCompletionToolMessage( + role="tool", + tool_call_id=content.get("tool_use_id", ""), + content=self._tool_result_content(content.get("content")), + ) + self._add_cache_control_if_applicable(content, tool_result, model) + tool_message_list.append(tool_result) if len(tool_message_list) > 0: new_messages.extend(tool_message_list) @@ -592,6 +560,9 @@ class LiteLLMAnthropicMessagesAdapter: assistant_message["tool_calls"] = tool_calls if len(thinking_blocks) > 0: assistant_message["thinking_blocks"] = thinking_blocks + reasoning_content = reasoning_content_from_thinking_blocks(thinking_blocks) + if reasoning_content: + assistant_message["reasoning_content"] = reasoning_content new_messages.append(assistant_message) return new_messages @@ -766,6 +737,10 @@ class LiteLLMAnthropicMessagesAdapter: new_tools.append(tool) continue + if _is_openai_function_tool(tool) or is_provider_native_tool_dict(tool): + new_tools.append(cast(ChatCompletionToolParam, tool)) # cast-ok: passed through verbatim to provider + continue + raw_name = tool.get("name") if raw_name is None or (isinstance(raw_name, str) and not str(raw_name).strip()): original_name = f"litellm_unnamed_tool_{idx}" @@ -796,7 +771,7 @@ class LiteLLMAnthropicMessagesAdapter: return new_tools, tool_name_mapping - def translate_anthropic_output_format_to_openai(self, output_format: Any) -> dict[str, object] | None: + def translate_anthropic_output_format_to_openai(self, output_format: object) -> dict[str, object] | None: """ Translate Anthropic's output_format to OpenAI's response_format. @@ -938,6 +913,31 @@ class LiteLLMAnthropicMessagesAdapter: ) return "prompt_cache_key" in (supported_params or ()) + @staticmethod + def _target_declares_reasoning_effort(model: str, custom_llm_provider: str | None) -> bool: + """Whether the target declares ``reasoning_effort`` among its supported params. + + A Claude-family target is recognized by name, which says nothing about the carrier the + provider serving it accepts: Snowflake serves Claude over the Anthropic dialect and + declares ``thinking`` alone, so storing the tier there raises before the request reaches + the wire. + + Without a resolved provider the tier stays behind, which is what this bridge sent before + it carried one at all. Reading the declaration from the model's own prefix instead would + resolve the provider through a lookup that runs an OAuth device flow for two of them, and + this runs inside a logging callback as well as on the request path. + + Unlike ``_supports_prompt_cache_key`` this does not exclude a provider that proxies an + unknown backend, because that provider declares this param and forwards it to a proxy + that resolves the real target itself, where a derived cache key has no such guarantee. + """ + if not model or not custom_llm_provider: + return False + supported_params: Final = litellm.get_supported_openai_params( + model=model, custom_llm_provider=custom_llm_provider + ) + return "reasoning_effort" in (supported_params or ()) + def _translate_metadata_to_openai( self, anthropic_message_request: AnthropicMessagesRequest, @@ -1026,8 +1026,32 @@ class LiteLLMAnthropicMessagesAdapter: self, anthropic_message_request: AnthropicMessagesRequest, new_kwargs: ChatCompletionRequest, + *, + custom_llm_provider: str | None = None, ) -> None: - """Translate Anthropic thinking to either thinking or reasoning_effort.""" + """Translate Anthropic thinking to either thinking or reasoning_effort. + + A Claude-family target keeps ``thinking`` verbatim, since every bridged provider serving one + speaks that param. Carrying its adaptive effort tier alongside takes two different params, + because the two are not interchangeable at the provider mapping below. + + Bedrock takes ``output_config`` directly, which attaches the tier and leaves ``thinking`` + alone. Another bridged Claude target takes ``reasoning_effort`` if it declares that param, + and used to be sent no tier at all, so an adaptive request arrived byte-identical whichever + effort the caller asked for. That tier stays a plain string there, since the summary it + would otherwise be wrapped with already travels inside the forwarded ``thinking`` block, + and the wrapped dict is rejected outright by some of these providers. + + A target declaring neither carrier keeps its bare ``thinking`` block. Being Claude-family + is a fact about the model, not about the params the provider in front of it accepts, so + the tier is offered only where the target says it is taken. + + ``reasoning_effort`` is not a substitute for ``output_config`` on the Bedrock side: an + application inference profile ARN resolves to neither, so the tier is dropped, and providers + that rebuild ``output_config`` from it overwrite a caller-set ``thinking.display`` doing so. + An adaptive request with no tier stays untouched either way, so the provider's own default + still applies. + """ if "thinking" not in anthropic_message_request: return @@ -1036,35 +1060,40 @@ class LiteLLMAnthropicMessagesAdapter: return model: Final = new_kwargs.get("model", "") - if self.is_anthropic_claude_model(model) or self.is_bedrock_arn_model(model): + is_bedrock_target: Final = model.startswith(("bedrock/", "converse/", "invoke/")) or self.is_bedrock_arn_model( + model + ) + is_claude_target: Final = self.is_anthropic_claude_model(model) or self.is_bedrock_arn_model(model) + output_config: Final = anthropic_message_request.get("output_config") + + if is_claude_target: new_kwargs["thinking"] = thinking - # Adaptive thinking without its effort tier makes Bedrock Converse - # return zero reasoning blocks, so forward output_config (minus - # `format`, already translated to response_format) for Bedrock - # targets only: other bridged providers reject the raw param, and - # get_llm_provider strips the `bedrock/` prefix before this runs. - if model.startswith(("bedrock/", "converse/", "invoke/")) or self.is_bedrock_arn_model(model): - claude_output_config: Final = anthropic_message_request.get("output_config") - if isinstance(claude_output_config, dict): - effort_config: Final = {k: v for k, v in claude_output_config.items() if k != "format"} + if is_bedrock_target: + if isinstance(output_config, dict): + effort_config: Final = {k: v for k, v in output_config.items() if k != "format"} if effort_config: new_kwargs["output_config"] = effort_config # rebind-ok: out-param store like thinking above + return + if not self._target_declares_reasoning_effort(model, custom_llm_provider): + return + + thinking_type: Final = thinking.get("type") if isinstance(thinking, dict) else None + declared_effort: Final = ( + output_config.get("effort") if thinking_type == "adaptive" and isinstance(output_config, dict) else None + ) + if is_claude_target and not declared_effort: return - reasoning_effort = self.translate_anthropic_thinking_to_reasoning_effort(cast(AnthropicThinkingParam, thinking)) + reasoning_effort: Final = declared_effort or self.translate_anthropic_thinking_to_reasoning_effort( + cast(AnthropicThinkingParam, thinking) + ) if not reasoning_effort: return - thinking_type: Final = thinking.get("type") if isinstance(thinking, dict) else None - - # For adaptive thinking, override with output_config.effort if available - if thinking_type == "adaptive": - output_config: Final = anthropic_message_request.get("output_config") - if isinstance(output_config, dict) and output_config.get("effort"): - reasoning_effort = output_config["effort"] - - new_kwargs["reasoning_effort"] = self._apply_reasoning_summary_wrapping( - reasoning_effort, cast(dict[str, object], thinking) + new_kwargs["reasoning_effort"] = ( + reasoning_effort + if is_claude_target + else self._apply_reasoning_summary_wrapping(reasoning_effort, cast(dict[str, object], thinking)) ) def _translate_output_format_to_openai( @@ -1160,6 +1189,7 @@ class LiteLLMAnthropicMessagesAdapter: self._translate_thinking_to_openai( anthropic_message_request=anthropic_message_request, new_kwargs=new_kwargs, + custom_llm_provider=custom_llm_provider, ) ## CONVERT STOP_SEQUENCES self._translate_stop_sequences_to_openai( @@ -1205,6 +1235,39 @@ class LiteLLMAnthropicMessagesAdapter: return None + def _tool_result_content(self, raw_content: object) -> ToolResultContent: + if isinstance(raw_content, str): + return raw_content + if not isinstance(raw_content, list): + return "" + items: Final = cast(Sequence[object], raw_content) # cast-ok: untrusted client payload + parts: Final = tuple(part for part in (self._tool_result_part(item) for item in items) if part is not None) + match parts: + case (): + return "" + case ({"type": "text", "text": str(text)},): + return text + case _: + return list(parts) # mutable-ok: content must be a json list + + def _tool_result_part(self, item: object) -> ToolMessageContentPart | None: + if isinstance(item, str): + return ChatCompletionTextObject(type="text", text=item) + if not isinstance(item, dict): + return None + block: Final = cast(Mapping[str, object], item) # cast-ok: untrusted client payload + match block.get("type"): + case "text": + return ChatCompletionTextObject(type="text", text=str(block.get("text") or "")) + case "image" | "document": + return self._tool_result_image_part(block.get("source")) + case "tool_reference": + return ChatCompletionToolReferenceObject( + type="tool_reference", tool_name=str(block.get("tool_name") or "") + ) + case _: + return None + def _tool_result_image_part(self, image_source: object) -> ChatCompletionImageObject | None: if not isinstance(image_source, dict): return None @@ -1224,6 +1287,8 @@ class LiteLLMAnthropicMessagesAdapter: if hasattr(choice.message, "thinking_blocks") and choice.message.thinking_blocks: for thinking_block in choice.message.thinking_blocks: if thinking_block.get("type") == "thinking": + if is_empty_unsigned_thinking_block(thinking_block): + continue thinking_value = thinking_block.get("thinking", "") signature_value = thinking_block.get("signature", "") new_content.append( @@ -1321,7 +1386,7 @@ class LiteLLMAnthropicMessagesAdapter: @classmethod def _first_positive_prompt_tokens_detail_value(cls, usage: Usage, field_names: tuple[str, ...]) -> int: - prompt_tokens_details: Final = getattr(usage, "prompt_tokens_details", None) + prompt_tokens_details: Final = _optional_attr(usage, "prompt_tokens_details") if prompt_tokens_details is None: return 0 @@ -1329,7 +1394,7 @@ class LiteLLMAnthropicMessagesAdapter: if isinstance(prompt_tokens_details, dict): value = cls._positive_int(prompt_tokens_details.get(field_name)) else: - value = cls._positive_int(getattr(prompt_tokens_details, field_name, None)) + value = cls._positive_int(_optional_attr(prompt_tokens_details, field_name)) if value > 0: return value return 0 @@ -1350,10 +1415,22 @@ class LiteLLMAnthropicMessagesAdapter: return explicit_value return cls._first_positive_prompt_tokens_detail_value(usage, ("cache_creation_tokens", "cache_write_tokens")) + @classmethod + def _get_web_search_request_count(cls, usage: Usage) -> int: + from litellm.litellm_core_utils.llm_cost_calc.utils import ( + get_web_search_requests_from_usage, + ) + + from_server_tool_use: Final = cls._positive_int(get_web_search_requests_from_usage(usage)) + if from_server_tool_use > 0: + return from_server_tool_use + return cls._first_positive_prompt_tokens_detail_value(usage, ("web_search_requests",)) + @classmethod def _translate_openai_usage_to_anthropic_usage_delta(cls, usage: Usage) -> UsageDelta: cache_read_input_tokens: Final = cls._get_cache_read_input_tokens(usage) cache_creation_input_tokens: Final = cls._get_cache_creation_input_tokens(usage) + web_search_requests: Final = cls._get_web_search_request_count(usage) input_tokens: Final = max( (usage.prompt_tokens or 0) - cache_read_input_tokens - cache_creation_input_tokens, 0, @@ -1367,6 +1444,11 @@ class LiteLLMAnthropicMessagesAdapter: usage_delta["cache_creation_input_tokens"] = cache_creation_input_tokens if cache_read_input_tokens > 0: usage_delta["cache_read_input_tokens"] = cache_read_input_tokens + if web_search_requests > 0: + return UsageDelta( + **usage_delta, + server_tool_use=ServerToolUsage(web_search_requests=web_search_requests), + ) return usage_delta @classmethod diff --git a/litellm/llms/anthropic/experimental_pass_through/context_management/dispatcher.py b/litellm/llms/anthropic/experimental_pass_through/context_management/dispatcher.py index 41795fa0f32..902808647c0 100644 --- a/litellm/llms/anthropic/experimental_pass_through/context_management/dispatcher.py +++ b/litellm/llms/anthropic/experimental_pass_through/context_management/dispatcher.py @@ -2,7 +2,7 @@ import inspect from collections.abc import Awaitable, Callable -from typing import Any, Final, cast +from typing import TYPE_CHECKING, Final, TypeAlias from litellm._logging import verbose_logger from litellm.types.llms.anthropic import AppliedEdit @@ -11,7 +11,13 @@ from .constants import CLEAR_TOOL_USES_EDIT_TYPE, COMPACT_EDIT_TYPE from .editors import apply_clear_tool_uses_20250919, apply_compact_20260112 from .result import PolyfillResult -EditorFn = Callable[..., Any] +if TYPE_CHECKING: + from litellm.proxy._types import UserAPIKeyAuth + from litellm.router import Router + +EditorResult: TypeAlias = "PolyfillResult | tuple[list[dict[str, object]], AppliedEdit | None]" + +EditorFn: TypeAlias = "Callable[..., EditorResult | Awaitable[EditorResult]]" _EDITOR_REGISTRY: Final[dict[str, EditorFn]] = { CLEAR_TOOL_USES_EDIT_TYPE: apply_clear_tool_uses_20250919, @@ -19,23 +25,31 @@ _EDITOR_REGISTRY: Final[dict[str, EditorFn]] = { } -def _normalize_spec( - spec: dict[str, Any] | list[dict[str, Any]] | None, -) -> list[dict[str, Any]] | None: - """Accept Anthropic-native dict form or OpenAI list form; return edits list.""" - if isinstance(spec, list): - # Local import to avoid an import cycle at module load. - from litellm.llms.anthropic.chat.transformation import AnthropicConfig - - spec = AnthropicConfig.map_openai_context_management_to_anthropic(spec) - - edits: Final = spec.get("edits") if isinstance(spec, dict) else None +def _edits_from(normalized: dict[str, object] | None) -> list[dict[str, object]] | None: + edits: Final = normalized.get("edits") if isinstance(normalized, dict) else None if not edits or not isinstance(edits, list): return None return [edit for edit in edits if isinstance(edit, dict)] -def _wrap_editor_return(raw: Any, *, fallback_system: Any) -> PolyfillResult: +def _normalize_spec( + spec: dict[str, object] | list[dict[str, object]] | None, +) -> list[dict[str, object]] | None: + """Accept Anthropic-native dict form or OpenAI list form; return edits list.""" + if isinstance(spec, list): + # Local import to avoid an import cycle at module load. + from litellm.llms.anthropic.chat.transformation import AnthropicConfig + + return _edits_from(AnthropicConfig.map_openai_context_management_to_anthropic(spec)) + + return _edits_from(spec) + + +def _wrap_editor_return( + raw: EditorResult, + *, + fallback_system: str | list[dict[str, object]] | None, +) -> PolyfillResult: """Coerce an editor's native return shape into a ``PolyfillResult``. v0 sync editors (e.g. ``clear_tool_uses_20250919``) return a 2-tuple @@ -46,7 +60,7 @@ def _wrap_editor_return(raw: Any, *, fallback_system: Any) -> PolyfillResult: return raw # Legacy 2-tuple return — sync editors don't mutate ``system``, so # carry the caller's value forward. - messages, applied = cast(tuple[list[dict[str, Any]], Any], raw) + messages, applied = raw return PolyfillResult( messages=messages, system=fallback_system, @@ -57,13 +71,13 @@ def _wrap_editor_return(raw: Any, *, fallback_system: Any) -> PolyfillResult: async def apply_context_management( *, model: str, - messages: list[dict[str, Any]], - tools: list[dict[str, Any]] | None, - system: Any, - context_management_spec: dict[str, Any] | list[dict[str, Any]] | None, - litellm_metadata: dict[str, Any] | None = None, - llm_router: Any = None, - user_api_key_auth: Any = None, + messages: list[dict[str, object]], + tools: list[dict[str, object]] | None, + system: str | list[dict[str, object]] | None, + context_management_spec: dict[str, object] | list[dict[str, object]] | None, + litellm_metadata: dict[str, object] | None = None, + llm_router: "Router | None" = None, + user_api_key_auth: "UserAPIKeyAuth | None" = None, ) -> PolyfillResult: """Run edits in order; return a single ``PolyfillResult``. @@ -92,22 +106,30 @@ async def apply_context_management( ) continue - kwargs: dict[str, Any] = { - "model": model, - "messages": current_messages, - "tools": tools, - "system": current_system, - "edit_spec": edit_spec, - } # Only async editors accept these — passing them to sync v0 editors # would break their signature. - if inspect.iscoroutinefunction(editor): - kwargs["litellm_metadata"] = litellm_metadata - kwargs["llm_router"] = llm_router - kwargs["user_api_key_auth"] = user_api_key_auth - raw_result = await cast(Callable[..., Awaitable[Any]], editor)(**kwargs) - else: - raw_result = editor(**kwargs) + editor_is_async = inspect.iscoroutinefunction(editor) + editor_return = ( + editor( + model=model, + messages=current_messages, + tools=tools, + system=current_system, + edit_spec=edit_spec, + litellm_metadata=litellm_metadata, + llm_router=llm_router, + user_api_key_auth=user_api_key_auth, + ) + if editor_is_async + else editor( + model=model, + messages=current_messages, + tools=tools, + system=current_system, + edit_spec=edit_spec, + ) + ) + raw_result = editor_return if isinstance(editor_return, (PolyfillResult, tuple)) else await editor_return result = _wrap_editor_return(raw_result, fallback_system=current_system) diff --git a/litellm/llms/anthropic/experimental_pass_through/context_management/editors/clear_tool_uses.py b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/clear_tool_uses.py index 393c0507d2b..00ecb315bf1 100644 --- a/litellm/llms/anthropic/experimental_pass_through/context_management/editors/clear_tool_uses.py +++ b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/clear_tool_uses.py @@ -2,6 +2,8 @@ from typing import Any, Final, cast +from typing_extensions import ReadOnly, TypedDict + import litellm from litellm._logging import verbose_logger from litellm.types.llms.anthropic import AppliedEdit @@ -14,7 +16,18 @@ from ..constants import ( from ..placeholders import build_cleared_tool_result_content -def _count_tool_uses(messages: list[dict[str, Any]]) -> int: +class ClearToolUsesEditSpec(TypedDict, total=False): + """The ``clear_tool_uses_20250919`` entry of a ``context_management`` spec.""" + + type: ReadOnly[str] + trigger: ReadOnly[dict[str, object]] + keep: ReadOnly[dict[str, object]] + clear_at_least: ReadOnly[object] + exclude_tools: ReadOnly[object] + clear_tool_inputs: ReadOnly[object] + + +def _count_tool_uses(messages: list[dict[str, object]]) -> int: """Return the number of tool_use content blocks across all messages. Only counts blocks with a string ``id`` to stay consistent with @@ -32,7 +45,7 @@ def _count_tool_uses(messages: list[dict[str, Any]]) -> int: return count -def _collect_tool_use_ids_in_order(messages: list[dict[str, Any]]) -> list[str]: +def _collect_tool_use_ids_in_order(messages: list[dict[str, object]]) -> list[str]: """Return tool_use ids in the chronological order they appear in messages.""" ids: Final[list[str]] = [] for msg in messages: @@ -47,10 +60,10 @@ def _collect_tool_use_ids_in_order(messages: list[dict[str, Any]]) -> list[str]: def _trigger_met( - trigger: dict[str, Any], + trigger: dict[str, object], model: str, - messages: list[dict[str, Any]], - tools: list[dict[str, Any]] | None, + messages: list[dict[str, object]], + tools: list[dict[str, object]] | None, ) -> tuple[bool, int | None]: """Return (trigger_met, input_tokens if counted for reuse).""" trigger_type: Final = trigger.get("type", "input_tokens") @@ -73,7 +86,7 @@ def _trigger_met( return current_tokens > threshold, current_tokens -def _resolve_keep_count(keep: dict[str, Any]) -> int: +def _resolve_keep_count(keep: dict[str, object]) -> int: keep_type: Final = keep.get("type", "tool_uses") if keep_type != "tool_uses": return DEFAULT_KEEP_TOOL_USES @@ -84,7 +97,7 @@ def _resolve_keep_count(keep: dict[str, Any]) -> int: def _last_completed_tool_use_id( - messages: list[dict[str, Any]], + messages: list[dict[str, object]], ) -> str | None: """Latest completed tool_result id; never cleared.""" last_id: str | None = None @@ -99,17 +112,19 @@ def _last_completed_tool_use_id( return last_id -def _clear_tool_results(messages: list[dict[str, Any]], ids_to_clear: set) -> tuple[list[dict[str, Any]], int]: +def _clear_tool_results( + messages: list[dict[str, object]], ids_to_clear: set[str] +) -> tuple[list[dict[str, object]], int]: """Clear matching tool_result content; return (messages, cleared_count).""" cleared = 0 - new_messages: Final[list[dict[str, Any]]] = [] + new_messages: Final[list[dict[str, object]]] = [] for msg in messages: content = msg.get("content") if not isinstance(content, list): new_messages.append(msg) continue - new_blocks: list[Any] = [] + new_blocks: list[object] = [] mutated = False for block in content: if ( @@ -138,11 +153,11 @@ def _clear_tool_results(messages: list[dict[str, Any]], ids_to_clear: set) -> tu def apply_clear_tool_uses_20250919( *, model: str, - messages: list[dict[str, Any]], - tools: list[dict[str, Any]] | None, - system: Any, - edit_spec: dict[str, Any], -) -> tuple[list[dict[str, Any]], AppliedEdit | None]: + messages: list[dict[str, object]], + tools: list[dict[str, object]] | None, + system: str | list[dict[str, object]] | None, + edit_spec: ClearToolUsesEditSpec, +) -> tuple[list[dict[str, object]], AppliedEdit | None]: """Apply clear_tool_uses; return (messages, AppliedEdit or None).""" ignored_knobs = [knob for knob in ("clear_at_least", "exclude_tools", "clear_tool_inputs") if knob in edit_spec] for ignored_knob in ignored_knobs: @@ -153,11 +168,11 @@ def apply_clear_tool_uses_20250919( CLEAR_TOOL_USES_EDIT_TYPE, ) - trigger: Final = edit_spec.get("trigger") or { + trigger: Final[dict[str, object]] = edit_spec.get("trigger") or { "type": "input_tokens", "value": DEFAULT_INPUT_TOKENS_TRIGGER, } - keep: Final = edit_spec.get("keep") or { + keep: Final[dict[str, object]] = edit_spec.get("keep") or { "type": "tool_uses", "value": DEFAULT_KEEP_TOOL_USES, } diff --git a/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py index 2a87afb5990..050ab67c86c 100644 --- a/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py +++ b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py @@ -13,10 +13,10 @@ Mirrors Anthropic's native ``compact_20260112`` for non-Anthropic providers: """ import re -from collections.abc import Mapping, Sequence -from typing import TYPE_CHECKING, Any, Final, Literal, NotRequired, Optional, TypedDict, Union, cast +from collections.abc import Awaitable, Mapping, Sequence +from typing import TYPE_CHECKING, Final, Literal, Optional, Protocol, TypeVar, Union, cast -from typing_extensions import ReadOnly +from typing_extensions import NotRequired, ReadOnly, TypedDict, Unpack import litellm from litellm._logging import verbose_logger @@ -29,6 +29,7 @@ from litellm.types.llms.anthropic import ( if TYPE_CHECKING: from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.hooks.parallel_request_limiter_v3 import RateLimitDescriptor, RateLimitResponse from litellm.router import Router from litellm.types.llms.anthropic import ( AllAnthropicPassThroughMessageValues, @@ -84,6 +85,77 @@ _PROPAGATED_METADATA_KEYS: Final = ( _SUMMARY_TAG_RE: Final = re.compile(r"(.*?)", re.IGNORECASE | re.DOTALL) +_MsgT: Final = TypeVar("_MsgT", bound=Mapping[str, object]) + + +def _as_object(value: object) -> object: + return value + + +def _is_tool_result_block(block: object) -> bool: + return isinstance(block, dict) and block.get("type") in ("tool_result",) + + +class _SummaryCallKwargs(TypedDict): + model: ReadOnly[str] + max_tokens: ReadOnly[int] + timeout: ReadOnly[float] + litellm_metadata: ReadOnly[Mapping[str, object]] + user: ReadOnly[NotRequired[str]] + allowed_model_region: ReadOnly[NotRequired[str]] + + +class _SummaryOptionalKwargs(TypedDict, total=False): + user: ReadOnly[str] + allowed_model_region: ReadOnly[str] + + +class _SummaryAcompletion(Protocol): + def __call__( + self, + *, + messages: Sequence[Mapping[str, object]], + **kwargs: Unpack[_SummaryCallKwargs], # kwargs-ok: forwarded verbatim to acompletion, which owns them + ) -> "Awaitable[ModelResponse | CustomStreamWrapper]": ... + + +class _CreateRateLimitDescriptors(Protocol): + def __call__( + self, + *, + user_api_key_dict: "UserAPIKeyAuth", + data: Mapping[str, str], + rpm_limit_type: object, + tpm_limit_type: object, + model_has_failures: bool, + ) -> "Sequence[RateLimitDescriptor]": ... + + +class _AddModelRateLimitDescriptor(Protocol): + def __call__( + self, + *, + user_api_key_dict: "UserAPIKeyAuth", + requested_model: str, + descriptors: "Sequence[RateLimitDescriptor]", + ) -> None: ... + + +class _CreateOrgRateLimitDescriptors(Protocol): + def __call__( + self, user_api_key_dict: "UserAPIKeyAuth", requested_model: str | None = None + ) -> "Sequence[RateLimitDescriptor]": ... + + +class _ShouldRateLimit(Protocol): + def __call__( + self, + *, + descriptors: "Sequence[RateLimitDescriptor]", + parent_otel_span: object, + read_only: bool, + ) -> "Awaitable[RateLimitResponse]": ... + def _read_summary_model_setting() -> str | None: """Look up the configured summarization model from proxy general_settings.""" @@ -159,11 +231,11 @@ async def _check_summary_model_access( return True key_models: Final = list(getattr(user_api_key_auth, "models", None) or []) - team_id: Final = getattr(user_api_key_auth, "team_id", None) - team_model_aliases: Final = getattr(user_api_key_auth, "team_model_aliases", None) + team_id: Final[str | None] = getattr(user_api_key_auth, "team_id", None) + team_model_aliases: Final[dict[str, str] | None] = getattr(user_api_key_auth, "team_model_aliases", None) team_models: Final = list(getattr(user_api_key_auth, "team_models", None) or []) - user_id: Final = getattr(user_api_key_auth, "user_id", None) - project_id: Final = getattr(user_api_key_auth, "project_id", None) + user_id: Final[str | None] = getattr(user_api_key_auth, "user_id", None) + project_id: Final[str | None] = getattr(user_api_key_auth, "project_id", None) checks: Final[tuple[tuple[Literal["key", "team"], list[str]], ...]] = ( ("key", key_models), @@ -352,8 +424,8 @@ async def _check_summary_model_budget( ) return False - user_model_max_budget: Final = getattr(user_api_key_auth, "user_model_max_budget", None) - user_id: Final = getattr(user_api_key_auth, "user_id", None) + user_model_max_budget: Final = user_api_key_auth.user_model_max_budget + user_id: Final = user_api_key_auth.user_id if isinstance(user_model_max_budget, dict) and user_model_max_budget and user_id is not None: try: await model_max_budget_limiter.is_user_within_model_budget( @@ -371,8 +443,10 @@ async def _check_summary_model_budget( ) return False - end_user_model_max_budget: Final = getattr(user_api_key_auth, "end_user_model_max_budget", None) - end_user_id: Final = getattr(user_api_key_auth, "end_user_id", None) + end_user_model_max_budget: Final[dict[str, object] | None] = getattr( + user_api_key_auth, "end_user_model_max_budget", None + ) + end_user_id: Final[str | None] = getattr(user_api_key_auth, "end_user_id", None) if isinstance(end_user_model_max_budget, dict) and end_user_model_max_budget and end_user_id is not None: try: await model_max_budget_limiter.is_end_user_within_model_budget( @@ -424,40 +498,57 @@ async def _check_summary_model_rate_limit( except Exception: return True - limiter: Final = getattr(proxy_logging_obj, "max_parallel_request_limiter", None) + limiter: Final[object] = getattr(proxy_logging_obj, "max_parallel_request_limiter", None) + should_rate_limit_check: Final[_ShouldRateLimit | None] = getattr(limiter, "should_rate_limit", None) + create_descriptors: Final[_CreateRateLimitDescriptors | None] = getattr( + limiter, "_create_rate_limit_descriptors", None + ) + add_team_descriptor: Final[_AddModelRateLimitDescriptor | None] = getattr( + limiter, "_add_team_model_rate_limit_descriptor_from_metadata", None + ) + add_project_descriptor: Final[_AddModelRateLimitDescriptor | None] = getattr( + limiter, "_add_project_model_rate_limit_descriptor_from_metadata", None + ) + create_org_descriptors: Final[_CreateOrgRateLimitDescriptors | None] = getattr( + limiter, "create_organization_rate_limit_descriptor", None + ) if ( limiter is None - or not hasattr(limiter, "should_rate_limit") - or not hasattr(limiter, "_create_rate_limit_descriptors") + or should_rate_limit_check is None + or create_descriptors is None + or add_team_descriptor is None + or add_project_descriptor is None + or create_org_descriptors is None ): return True try: - metadata: Final = getattr(user_api_key_auth, "metadata", None) or {} + metadata: Final[Mapping[str, object]] = getattr(user_api_key_auth, "metadata", None) or {} data: Final = {"model": summary_model} - descriptors: Final = limiter._create_rate_limit_descriptors( + base_descriptors: Final = create_descriptors( user_api_key_dict=user_api_key_auth, data=data, rpm_limit_type=metadata.get("rpm_limit_type"), tpm_limit_type=metadata.get("tpm_limit_type"), model_has_failures=False, ) - limiter._add_team_model_rate_limit_descriptor_from_metadata( + add_team_descriptor( user_api_key_dict=user_api_key_auth, requested_model=summary_model, - descriptors=descriptors, + descriptors=base_descriptors, ) - limiter._add_project_model_rate_limit_descriptor_from_metadata( + add_project_descriptor( user_api_key_dict=user_api_key_auth, requested_model=summary_model, - descriptors=descriptors, + descriptors=base_descriptors, ) - descriptors.extend(limiter.create_organization_rate_limit_descriptor(user_api_key_auth, summary_model)) + descriptors: Final = (*base_descriptors, *create_org_descriptors(user_api_key_auth, summary_model)) if not descriptors: return True - response: Final = await limiter.should_rate_limit( + parent_otel_span: Final[object] = getattr(user_api_key_auth, "parent_otel_span", None) + response: Final[RateLimitResponse] = await should_rate_limit_check( descriptors=descriptors, - parent_otel_span=getattr(user_api_key_auth, "parent_otel_span", None), + parent_otel_span=parent_otel_span, read_only=True, ) except Exception as e: @@ -471,7 +562,7 @@ async def _check_summary_model_rate_limit( def _find_latest_compaction_index( - messages: list[dict[str, object]], + messages: Sequence[Mapping[str, object]], ) -> tuple[int | None, int | None]: """Return (message_index, block_index) of the most recent compaction block. @@ -490,8 +581,8 @@ def _find_latest_compaction_index( def _slice_around_compaction_block( - messages: list[dict[str, Any]], -) -> tuple[list[dict[str, object]], dict[str, object] | None]: + messages: Sequence[_MsgT], +) -> tuple[Sequence[_MsgT | dict[str, object]], dict[str, object] | None]: """Apply Anthropic's "drop everything before the compaction block" rule. Returns ``(sliced_messages_with_compaction_block, compaction_block_dict)`` @@ -506,19 +597,21 @@ def _slice_around_compaction_block( original_msg: Final = messages[msg_idx] original_content: Final = original_msg["content"] - compaction_block: Final = cast(dict[str, object], original_content[blk_idx]) + if not isinstance(original_content, list): + return messages, None + original_blocks: Final = cast("Sequence[dict[str, object]]", original_content) + compaction_block: Final = original_blocks[blk_idx] # Per Anthropic's contract everything before the compaction block is # dropped, including earlier blocks within the same assistant message. - sliced_content: Final = list(original_content[blk_idx:]) + sliced_content: Final = list(original_blocks[blk_idx:]) - sliced_messages: Final[list[dict[str, object]]] = [{**original_msg, "content": sliced_content}] - sliced_messages.extend(messages[msg_idx + 1 :]) + sliced_messages: Final = [{**original_msg, "content": sliced_content}, *messages[msg_idx + 1 :]] return sliced_messages, compaction_block def _strip_compaction_blocks( - messages: list[dict[str, object]], + messages: Sequence[dict[str, object]], ) -> list[dict[str, object]]: """Drop any ``compaction`` content blocks from messages. @@ -625,7 +718,7 @@ def _propagate_metadata( def _count_effective_tokens( model: str, - effective_messages: list[dict[str, object]], + effective_messages: Sequence[dict[str, object]], compaction_block: CompactionBlock | None, tools: list[dict[str, object]] | None, system: str | list[dict[str, object]] | None = None, @@ -704,17 +797,18 @@ def _system_to_text( return "" if isinstance(system, str): return system - parts: Final[list[str]] = [] - for block in system: - if isinstance(block, dict) and block.get("type") == "text": - text = block.get("text") - if isinstance(text, str) and text: - parts.append(text) - return "\n".join(parts) + return "\n".join( + text + for block in system + if isinstance(block, dict) + and block.get("type") == "text" + and isinstance(text := block.get("text"), str) + and text + ) def _select_last_user_question( - messages: list[dict[str, object]], + messages: Sequence[dict[str, object]], ) -> list[dict[str, object]]: """Pick the most recent ``user`` turn that is a real question. @@ -729,16 +823,18 @@ def _select_last_user_question( turns, or contained no user turns at all). The downstream call always needs a non-empty user message. """ + blocks: Sequence[object] for msg in reversed(messages): if msg.get("role") != "user": continue content = msg.get("content") if isinstance(content, list): - filtered = [blk for blk in content if not (isinstance(blk, dict) and blk.get("type") == "tool_result")] + blocks = [*map(_as_object, content)] + filtered = [blk for blk in blocks if not _is_tool_result_block(blk)] if not filtered: # Purely tool_result — skip and look for an earlier turn. continue - if len(filtered) < len(content): + if len(filtered) < len(blocks): return [{**msg, "content": filtered}] return [msg] return [ @@ -760,7 +856,7 @@ def _extract_summary_text(raw: str | None) -> str | None: def _system_to_openai_message( - system: str | list[dict[str, Any]] | None, + system: str | list[dict[str, object]] | None, ) -> dict[str, object] | None: """Translate Anthropic-shaped ``system`` to an OpenAI system message. @@ -772,17 +868,19 @@ def _system_to_openai_message( if isinstance(system, str): return {"role": "system", "content": system} if system else None if isinstance(system, list): - parts = [block.get("text", "") for block in system if isinstance(block, dict) and block.get("type") == "text"] - joined: Final = "\n\n".join(part for part in parts if part) + parts: Final[list[object]] = [ + block.get("text", "") for block in system if isinstance(block, dict) and block.get("type") == "text" + ] + joined: Final = "\n\n".join(part for part in parts if isinstance(part, str) and part) return {"role": "system", "content": joined} if joined else None return None def _build_summary_messages( - effective_messages: list[dict[str, object]], + effective_messages: Sequence[dict[str, object]], prompt: str, system: str | list[dict[str, object]] | None = None, -) -> list[dict[str, object]]: +) -> Sequence[Mapping[str, object]]: """Build the OpenAI-shape message list for the summary call. The caller's ``system`` prompt is prepended (the default summarization @@ -810,7 +908,7 @@ def _build_summary_messages( ) openai_messages = stripped - summary_messages: Final[list[dict[str, object]]] = [] + summary_messages: Final[list[Mapping[str, object]]] = [] system_message: Final = _system_to_openai_message(system) if system_message is not None: summary_messages.append(system_message) @@ -845,35 +943,17 @@ def _append_text_to_content(content: object, extra_text: str) -> object: if isinstance(content, str): return f"{content}\n\n{extra_text}" if isinstance(content, list): - appended: Final[list[object]] = [*content, {"type": "text", "text": extra_text}] + appended: Final[Sequence[object]] = [*map(_as_object, content), {"type": "text", "text": extra_text}] return appended return [content, {"type": "text", "text": extra_text}] -class _SummaryCallUserKwarg(TypedDict, total=False): - user: ReadOnly[object] - - -class _SummaryCallRegionKwarg(TypedDict, total=False): - allowed_model_region: ReadOnly[str] - - -class _SummaryCallKwargs(TypedDict): - model: ReadOnly[str] - messages: ReadOnly[list[dict[str, object]]] - max_tokens: ReadOnly[int] - timeout: ReadOnly[float] - litellm_metadata: ReadOnly[Mapping[str, object]] - user: NotRequired[ReadOnly[object]] - allowed_model_region: NotRequired[ReadOnly[str]] - - async def _call_summary_model( *, summary_model: str, - summary_messages: list[dict[str, object]], + summary_messages: Sequence[Mapping[str, object]], metadata: Mapping[str, object], - llm_router: Any, + llm_router: Optional["Router"], allowed_model_region: str | None = None, max_tokens: int = COMPACT_SUMMARY_MAX_TOKENS, ) -> Union["ModelResponse", "CustomStreamWrapper"]: @@ -909,28 +989,37 @@ async def _call_summary_model( # than from ``litellm_metadata``, so without it the summary tokens would not # debit the caller's end-user counters. end_user_id: Final = metadata.get("user_api_key_end_user_id") + user_kwargs: Final = ( + _SummaryOptionalKwargs(user=end_user_id) + if isinstance(end_user_id, str) and end_user_id + else _SummaryOptionalKwargs() + ) + region_kwargs: Final = ( + _SummaryOptionalKwargs(allowed_model_region=allowed_model_region) + if allowed_model_region is not None + else _SummaryOptionalKwargs() + ) call_kwargs: Final[_SummaryCallKwargs] = { "model": summary_model, - "messages": summary_messages, "max_tokens": max_tokens, "timeout": COMPACT_SUMMARY_TIMEOUT_SECONDS, "litellm_metadata": metadata, - **(_SummaryCallUserKwarg(user=end_user_id) if end_user_id else _SummaryCallUserKwarg()), - **( - _SummaryCallRegionKwarg(allowed_model_region=allowed_model_region) - if allowed_model_region is not None - else _SummaryCallRegionKwarg() - ), + **user_kwargs, + **region_kwargs, } - if llm_router is not None and hasattr(llm_router, "acompletion"): - return await llm_router.acompletion(**call_kwargs) - return await litellm.acompletion(**call_kwargs) + router_acompletion: Final[_SummaryAcompletion | None] = getattr(llm_router, "acompletion", None) + if llm_router is not None and router_acompletion is not None: + return await router_acompletion(messages=summary_messages, **call_kwargs) + return await litellm.acompletion(messages=[*summary_messages], **call_kwargs) -def _extract_response_text(response: Any) -> str | None: +def _extract_response_text(response: object) -> str | None: try: - choice: Final = response.choices[0] - message: Final = choice.message + choices: Final[Sequence[object] | None] = getattr(response, "choices", None) + if choices is None: + return None + choice: Final = choices[0] + message: Final = getattr(choice, "message", None) content: Final = getattr(message, "content", None) if isinstance(content, str): return content @@ -946,13 +1035,12 @@ def _extract_response_text(response: Any) -> str | None: def _extract_usage(response: object) -> tuple[int, int]: - usage: Final = getattr(response, "usage", None) + usage: Final[object] = getattr(response, "usage", None) if usage is None: return 0, 0 - return ( - int(getattr(usage, "prompt_tokens", 0) or 0), - int(getattr(usage, "completion_tokens", 0) or 0), - ) + prompt_tokens: Final[int | None] = getattr(usage, "prompt_tokens", 0) + completion_tokens: Final[int | None] = getattr(usage, "completion_tokens", 0) + return int(prompt_tokens or 0), int(completion_tokens or 0) def apply_client_compaction_block_history( diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/agentic_streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/messages/agentic_streaming_iterator.py index 5c4fa4700c0..171f5156594 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/agentic_streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/agentic_streaming_iterator.py @@ -6,13 +6,44 @@ yields every chunk to the caller (preserving real streaming), collects all bytes, and on stream exhaustion rebuilds the full Anthropic response to run through agentic completion hooks. If an agentic hook fires, the follow-up response is chained as Phase 2 of the same iterator. + +In hold-back mode (``hold_back=True``) chunks are buffered instead of yielded +live, keepalive pings run whenever no other byte is ready, and then either the +follow-up replaces the message or the buffer replays, except that a tool_use for +a server-fulfilled tool fails the turn rather than reaching a client that cannot +execute it. """ +import asyncio +import contextlib import json from collections.abc import AsyncIterator -from typing import Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, cast from litellm._logging import verbose_logger +from litellm.constants import STREAM_SSE_KEEPALIVE_PING_BYTES + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + +HOLD_BACK_PING_INTERVAL_SECONDS: Final = 15.0 +SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES: Final = ( + b"event: error\n" + b'data: {"type": "error", "error": {"type": "api_error", "message": ' + b'"Server-side tool retrieval failed, so this turn could not be completed. Please retry."}}\n\n' +) + + +def is_server_fulfilled_tool_leak_error(chunk: object) -> bool: + return chunk == SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES + + +async def _anext_or_none(iterator: AsyncIterator) -> bytes | None: + try: + return await iterator.__anext__() + except StopAsyncIteration: + return None + # --------------------------------------------------------------------------- # SSE parsing helpers (module-level to keep the class lean) @@ -153,9 +184,12 @@ class AgenticAnthropicStreamingIterator: messages: list[dict], anthropic_messages_provider_config: Any, anthropic_messages_optional_request_params: dict, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", custom_llm_provider: str, kwargs: dict, + hold_back: bool = False, + server_fulfilled_tool_names: frozenset[str] = frozenset(), + ping_interval_seconds: float = HOLD_BACK_PING_INTERVAL_SECONDS, ): self._inner = completion_stream.__aiter__() self._http_handler = http_handler @@ -166,16 +200,32 @@ class AgenticAnthropicStreamingIterator: self._logging_obj = logging_obj self._custom_llm_provider = custom_llm_provider self._kwargs = kwargs + self._hold_back = hold_back + self._server_fulfilled_tool_names = server_fulfilled_tool_names + self._ping_interval_seconds = ping_interval_seconds self._collected_bytes: list[bytes] = [] self._stream_exhausted = False self._hook_processing_done = False self._follow_up_iterator: AsyncIterator | None = None + self._drain_task: asyncio.Task | None = None + self._hook_task: asyncio.Task | None = None + self._follow_up_chunk_task: asyncio.Task | None = None + self._replay_index = 0 + self._error_emitted = False + + @property + def has_buffered_provider_output(self) -> bool: + """Whether provider output was received but withheld from the client behind keepalive pings.""" + return self._hold_back and bool(self._collected_bytes) def __aiter__(self): return self async def __anext__(self) -> bytes: + if self._hold_back: + return await self._anext_held_back() + # Phase 1: yield from upstream, collect bytes if not self._stream_exhausted: try: @@ -194,11 +244,102 @@ class AgenticAnthropicStreamingIterator: raise StopAsyncIteration + async def _drain_upstream(self) -> None: + try: + while True: + self._collected_bytes.append(await self._inner.__anext__()) + except StopAsyncIteration: + return + + async def _completed_within_ping_interval(self, task: asyncio.Task) -> bool: + try: + await asyncio.wait_for(asyncio.shield(task), timeout=self._ping_interval_seconds) + except asyncio.TimeoutError: + return False + return True + + async def _anext_held_back(self) -> bytes: + if self._drain_task is None: + self._drain_task = asyncio.create_task(self._drain_upstream()) + return STREAM_SSE_KEEPALIVE_PING_BYTES + + if not self._stream_exhausted: + if not await self._completed_within_ping_interval(self._drain_task): + return STREAM_SSE_KEEPALIVE_PING_BYTES + self._stream_exhausted = True + + if self._hook_task is None: + self._hook_task = asyncio.create_task(self._process_agentic_hooks()) + if not await self._completed_within_ping_interval(self._hook_task): + return STREAM_SSE_KEEPALIVE_PING_BYTES + + if self._follow_up_iterator is not None: + return await self._next_follow_up_chunk(self._follow_up_iterator) + + if self._buffer_holds_server_fulfilled_tool_use(): + if self._error_emitted: + raise StopAsyncIteration + self._error_emitted = True + verbose_logger.error( + "AgenticStreamingIterator: hooks did not replace a message containing a server-fulfilled " + "tool_use [model=%s]; emitting an SSE error instead of leaking the tool call to the client", + self._model, + ) + return SERVER_FULFILLED_TOOL_LEAK_ERROR_SSE_BYTES + + if self._replay_index < len(self._collected_bytes): + chunk: Final = self._collected_bytes[self._replay_index] + self._replay_index += 1 + return chunk + + raise StopAsyncIteration + + async def _next_follow_up_chunk(self, follow_up_iterator: AsyncIterator) -> bytes: + if self._follow_up_chunk_task is None: + self._follow_up_chunk_task = asyncio.create_task(_anext_or_none(follow_up_iterator)) + if not await self._completed_within_ping_interval(self._follow_up_chunk_task): + return STREAM_SSE_KEEPALIVE_PING_BYTES + chunk: Final = self._follow_up_chunk_task.result() + self._follow_up_chunk_task = None + if chunk is None: + raise StopAsyncIteration + return chunk + + def _buffer_holds_server_fulfilled_tool_use(self) -> bool: + if not self._server_fulfilled_tool_names: + return False + started_blocks: Final = ( + data.get("content_block") + for event_type, data in _parse_sse_events(b"".join(self._collected_bytes)) + if event_type == "content_block_start" + ) + return any( + isinstance(block, dict) + and block.get("type") == "tool_use" + and block.get("name") in self._server_fulfilled_tool_names + for block in started_blocks + ) + + @staticmethod + async def _settle_task(task: asyncio.Task | None) -> None: + if task is None: + return + if task.done(): + if not task.cancelled(): + task.exception() + return + task.cancel() + with contextlib.suppress(asyncio.CancelledError): + await task + async def aclose(self) -> None: from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( aclose_if_supported, ) + await self._settle_task(self._drain_task) + await self._settle_task(self._hook_task) + await self._settle_task(self._follow_up_chunk_task) await aclose_if_supported(self._inner) await aclose_if_supported(self._follow_up_iterator) @@ -217,11 +358,6 @@ class AgenticAnthropicStreamingIterator: verbose_logger.debug("AgenticStreamingIterator: Could not rebuild response from SSE bytes") return - [ - (f"{b.get('type')}({b.get('name', '')})" if b.get("type") == "tool_use" else b.get("type")) - for b in rebuilt.get("content", []) - ] - result: Final = await self._http_handler._call_agentic_completion_hooks( response=rebuilt, model=self._model, diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/fake_stream_iterator.py b/litellm/llms/anthropic/experimental_pass_through/messages/fake_stream_iterator.py index 215d4a5b42b..14f1b7697cf 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/fake_stream_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/fake_stream_iterator.py @@ -113,6 +113,14 @@ class FakeAnthropicMessagesStreamIterator: } chunks.append(f"event: content_block_delta\ndata: {json.dumps(content_block_delta)}\n\n".encode()) + else: + passthrough_start: Final = { + "type": "content_block_start", + "index": index, + "content_block": block_dict, + } + chunks.append(f"event: content_block_start\ndata: {json.dumps(passthrough_start)}\n\n".encode()) + content_block_stop: Final = {"type": "content_block_stop", "index": index} chunks.append(f"event: content_block_stop\ndata: {json.dumps(content_block_stop)}\n\n".encode()) return chunks diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py index f4d24bb933c..69985bcdaa3 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py @@ -12,15 +12,17 @@ from functools import partial from typing import Any, Final, cast import litellm +from litellm.litellm_core_utils.exception_mapping_utils import exception_type from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.anthropic.common_utils import ( flatten_unencrypted_web_search_results_in_anthropic_messages, sanitize_tool_use_ids_in_anthropic_messages, - strip_empty_text_blocks_from_anthropic_messages, + strip_empty_content_blocks_from_anthropic_messages, ) from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, ) +from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.types.llms.anthropic_messages.anthropic_request import AnthropicMetadata @@ -240,17 +242,20 @@ async def anthropic_messages( """ Async: Make llm api request in Anthropic /messages API spec. - Runs the empty-text-block sanitizer before any backend dispatch. + Runs the empty-content-block sanitizer before any backend dispatch. """ # Anthropic's API rejects requests containing empty / whitespace-only - # text content blocks with "messages: text content blocks must be - # non-empty". Multi-turn tool-use clients (e.g. Claude Code) routinely - # loop assistant responses that contain {"type": "text", "text": ""} - # alongside tool_use blocks back as conversation history, which then - # causes the next /v1/messages call to 400. /v1/chat/completions - # already handles this in anthropic_messages_pt; sanitize the native - # Anthropic Messages path here for the same guarantee. See #22930. - messages = strip_empty_text_blocks_from_anthropic_messages(messages) + # text content blocks ("messages: text content blocks must be + # non-empty") and empty thinking blocks ("each thinking block must + # contain thinking"). Multi-turn tool-use clients (e.g. Claude Code) + # routinely loop assistant responses that contain such blocks — an empty + # text block alongside tool_use, or an empty thinking block from a turn + # a non-Anthropic reasoning model served through the bridge — back as + # conversation history, which then causes the next /v1/messages call to + # 400. /v1/chat/completions already handles this in + # anthropic_messages_pt; sanitize the native Anthropic Messages path + # here for the same guarantee. See #22930. + messages = strip_empty_content_blocks_from_anthropic_messages(messages) # Replay of cross-provider tool history (e.g. kimi -> Anthropic) may carry # ids like ``functions.Bash:0`` that violate Anthropic's id pattern. messages = sanitize_tool_use_ids_in_anthropic_messages(messages) @@ -372,7 +377,7 @@ async def anthropic_messages( api_base=api_base, client=client, custom_llm_provider=custom_llm_provider, - # messages were already empty-text-block sanitized at the top of this + # messages were already empty-content-block sanitized at the top of this # function and are NOT reassigned before this dispatch, so the handler # can skip its (otherwise redundant) second full-messages scan. Passed # explicitly (not via **kwargs) so it only affects this direct @@ -382,13 +387,18 @@ async def anthropic_messages( ) ctx: Final = contextvars.copy_context() func_with_context: Final = partial(ctx.run, func) - init_response: Final = await loop.run_in_executor(None, func_with_context) - - if asyncio.iscoroutine(init_response): - response = await init_response - else: - response = init_response - return response + try: + init_response: Final = await loop.run_in_executor(None, func_with_context) + if asyncio.iscoroutine(init_response): + return await init_response + return init_response + except BaseLLMException as e: + raise exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + extra_kwargs=kwargs, + ) def validate_anthropic_api_metadata(metadata: dict | None = None) -> dict | None: @@ -444,7 +454,7 @@ def anthropic_messages_handler( # ``_litellm_messages_presanitized`` to skip this redundant second # full-messages scan. Pop it so it never leaks into provider params. if not kwargs.pop("_litellm_messages_presanitized", False): - messages = strip_empty_text_blocks_from_anthropic_messages(messages) + messages = strip_empty_content_blocks_from_anthropic_messages(messages) messages = sanitize_tool_use_ids_in_anthropic_messages(messages) messages = flatten_unencrypted_web_search_results_in_anthropic_messages(messages) @@ -561,7 +571,34 @@ def anthropic_messages_handler( anthropic_messages_provider_config = OpenAILikeAnthropicMessagesConfig() if anthropic_messages_provider_config is None: # Route to Responses API for OpenAI / Azure, chat/completions for everything else. - _shared_kwargs: Final = dict( + if _should_route_to_responses_api(custom_llm_provider, original_model, model): + return LiteLLMMessagesToResponsesAPIHandler.anthropic_messages_handler( + max_tokens=max_tokens, + messages=messages, + model=original_model, + metadata=metadata, + stop_sequences=stop_sequences, + stream=stream, + system=system, + temperature=temperature, + thinking=thinking, + tool_choice=tool_choice, + tools=tools, + top_k=top_k, + top_p=top_p, + _is_async=is_async, + api_key=api_key, + api_base=api_base, + client=client, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) + + # The in-gateway context_management polyfill runs inside + # ``async_anthropic_messages_handler`` so it can ``await`` the + # summarization model for ``compact_20260112``. ``context_management`` + # is passed through as a regular kwarg. + return LiteLLMMessagesToCompletionTransformationHandler.anthropic_messages_handler( max_tokens=max_tokens, messages=messages, model=original_model, @@ -582,16 +619,6 @@ def anthropic_messages_handler( custom_llm_provider=custom_llm_provider, **kwargs, ) - if _should_route_to_responses_api(custom_llm_provider, original_model, model): - return LiteLLMMessagesToResponsesAPIHandler.anthropic_messages_handler(**_shared_kwargs) - - # The in-gateway context_management polyfill runs inside - # ``async_anthropic_messages_handler`` so it can ``await`` the - # summarization model for ``compact_20260112``. ``context_management`` - # is passed through as a regular kwarg. - return LiteLLMMessagesToCompletionTransformationHandler.anthropic_messages_handler( - **_shared_kwargs, - ) if custom_llm_provider is None: raise ValueError( diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/response_cache.py b/litellm/llms/anthropic/experimental_pass_through/messages/response_cache.py index 9ac5187681b..86dfe8ff451 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/response_cache.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/response_cache.py @@ -46,6 +46,10 @@ class AnthropicMessagesStreamCacheWriter: stream._hidden_params if isinstance(stream, AnthropicMessagesStreamingResponse) else _EMPTY_MAPPING ) + @property + def has_buffered_provider_output(self) -> bool: + return getattr(self.stream, "has_buffered_provider_output", False) is True + def __aiter__(self) -> "AnthropicMessagesStreamCacheWriter": return self diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py index 922769dbbfd..66e36dab2ba 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/streaming_iterator.py @@ -1,6 +1,6 @@ import asyncio import json -from collections.abc import AsyncIterator +from collections.abc import AsyncIterator, Mapping from datetime import datetime from typing import Any, Final, Protocol, runtime_checkable @@ -8,17 +8,26 @@ import httpx from pydantic import TypeAdapter from typing_extensions import TypedDict +from litellm.constants import ( + ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS, + ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE, +) from litellm.litellm_core_utils.core_helpers import process_response_headers from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER +from litellm.llms.anthropic.common_utils import ANTHROPIC_ERROR_STATUS_CODE_MAP from litellm.proxy.pass_through_endpoints.success_handler import ( PassThroughEndpointLogging, ) +from litellm.types.llms.anthropic_messages.anthropic_response import AnthropicMessagesResponse from litellm.types.passthrough_endpoints.pass_through_endpoints import EndpointType from litellm.types.utils import GenericStreamingChunk, ModelResponseStream GLOBAL_PASS_THROUGH_SUCCESS_HANDLER_OBJ: Final = PassThroughEndpointLogging() +_UPSTREAM_PUMP_TASKS: Final[set[asyncio.Task[None]]] = set() # mutable-ok: stdlib strong-ref set for pump tasks +_DETACHED_STREAM_DRAINS: Final[set[asyncio.Task[None]]] = set() # mutable-ok: bounded strong-ref set, detached drains + INCOMPLETE_STREAM_ERROR_MESSAGE: Final = ( "Provider stream ended before emitting a message_stop event; " "the response is incomplete and any partial content (e.g. tool_use input JSON) may be truncated." @@ -33,26 +42,286 @@ def _is_message_stop_chunk(chunk: object) -> bool: return False -def _is_provider_error_chunk(chunk: object) -> bool: +def is_anthropic_ping_chunk(chunk: object) -> bool: + """ + Whether a chunk is a pure ``ping`` keepalive frame. It carries no content + and can recur indefinitely on a slow-starting or idle connection, so a + mid-stream fallback wrapper drops it outright while still deciding + whether to commit to the primary stream, rather than buffering it. + + A physical transport chunk that coalesces a ping with any other SSE + event (``message_start``, ``content_block_delta``, ``event: error``, ...) + is NOT a pure ping - dropping it whole would discard those events - so + only a chunk whose every ``event:`` line is ``event: ping`` qualifies. + """ if isinstance(chunk, dict): - return chunk.get("type") == "error" + return chunk.get("type") == "ping" if isinstance(chunk, (bytes, bytearray)): - return any(line == b"event: error" for line in chunk.splitlines()) + event_lines: Final = tuple(line for line in chunk.splitlines() if line.startswith(b"event:")) + return bool(event_lines) and all(line == b"event: ping" for line in event_lines) return False +def is_anthropic_content_delta_chunk(chunk: object) -> bool: + """ + Whether a chunk carries actual assistant-generated output (a + ``content_block_delta`` frame), as opposed to a lifecycle/bookkeeping + frame (``message_start``, ``content_block_start``/``stop``, + ``message_delta``, ``message_stop``, ``ping``) that carries nothing + worth preserving before an invisible mid-stream fallback retry. + """ + if isinstance(chunk, dict): + return chunk.get("type") == "content_block_delta" + if isinstance(chunk, (bytes, bytearray)): + return any(line == b"event: content_block_delta" for line in chunk.splitlines()) + return False + + +def _decoded_sse_data_line(line: bytes) -> object | None: + if not line.startswith(b"data:"): + return None + try: + return json.loads(line[len(b"data:") :].strip()) + except (ValueError, TypeError): + return None + + +def _anthropic_event_payload(chunk: object, event_type: str) -> Mapping[str, object] | None: + if isinstance(chunk, dict): + return chunk if chunk.get("type") == event_type else None + if isinstance(chunk, (bytes, bytearray)): + decoded_lines: Final = (_decoded_sse_data_line(line) for line in chunk.splitlines()) + return next( + ( + candidate + for candidate in decoded_lines + if isinstance(candidate, dict) and candidate.get("type") == event_type + ), + None, + ) + return None + + +def _anthropic_error_event_payload(chunk: object) -> Mapping[str, object] | None: + return _anthropic_event_payload(chunk, "error") + + +def parse_anthropic_refusal_stop_details(chunk: object) -> Mapping[str, object] | None: + """ + Return the ``stop_details`` object of an Anthropic SSE ``message_delta`` + chunk whose delta carries ``stop_reason: "refusal"`` (a safeguard refusal: + https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback), + or None for any other chunk, a plain refusal without ``stop_details`` included. + """ + payload: Final = _anthropic_event_payload(chunk, "message_delta") + delta: Final = payload.get("delta") if payload is not None else None + if not isinstance(delta, dict) or delta.get("stop_reason") != "refusal": + return None + stop_details: Final = delta.get("stop_details") + return stop_details if isinstance(stop_details, dict) else None + + +def _anthropic_error_body(chunk: object) -> Mapping[str, object] | None: + """Return the ``error`` object of an Anthropic SSE ``event: error`` chunk, or None.""" + payload: Final = _anthropic_error_event_payload(chunk) + error_body: Final = payload.get("error") if payload is not None else None + return error_body if isinstance(error_body, dict) else None + + +def _is_provider_error_chunk(chunk: object) -> bool: + return _anthropic_error_body(chunk) is not None + + +def parse_anthropic_error_event(chunk: object) -> tuple[str, str, int] | None: + """ + Extract ``(error_type, message, http_status_code)`` from an Anthropic SSE + ``event: error`` chunk (raw bytes or an already-decoded dict), or None if + ``chunk`` is not an error event. + + The status code is looked up via ANTHROPIC_ERROR_STATUS_CODE_MAP, + defaulting to 500 for an error ``type`` Anthropic hasn't documented yet. + """ + error_body: Final = _anthropic_error_body(chunk) + if error_body is None: + return None + error_type: Final = error_body.get("type") + if not isinstance(error_type, str): + return None + message: Final = error_body.get("message") + return ( + error_type, + message if isinstance(message, str) else error_type, + ANTHROPIC_ERROR_STATUS_CODE_MAP.get(error_type, 500), + ) + + def _is_terminal_stream_chunk(chunk: object) -> bool: return _is_message_stop_chunk(chunk) or _is_provider_error_chunk(chunk) +def _try_claim_detached_drain_slot() -> bool: + """Claim a detached-drain slot for the current task, bounding concurrency. + + Returns True if a slot was claimed (the caller may keep draining upstream + for billing) or False if the cap is already reached (the caller should stop + and bill what it has). Only touched from the event loop, so the check + + insert need no lock. + """ + if len(_DETACHED_STREAM_DRAINS) >= ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS: + return False + current_task: Final = asyncio.current_task() + if current_task is not None: + _DETACHED_STREAM_DRAINS.add(current_task) + current_task.add_done_callback(_DETACHED_STREAM_DRAINS.discard) + return True + + +def _exception_left_unconsumed(queue: "asyncio.Queue[bytes | None | BaseException]", exc: BaseException) -> bool: + """After client detach the relay never reads the queue again, so drain it here. + + The forwarded exception still sitting in the queue means the relay tore + down before re-raising it, so the proxy's failure handling never ran and + the caller must salvage spend itself. + """ + remaining: Final = tuple(queue.get_nowait() for _ in range(queue.qsize())) + return any(item is exc for item in remaining) + + +def _sse_event(event_type: str, payload: Mapping[str, object]) -> bytes: + return f"event: {event_type}\ndata: {json.dumps(payload)}\n\n".encode() + + def _incomplete_stream_error_sse_event() -> bytes: - payload: Final = json.dumps( - { - "type": "error", - "error": {"type": "api_error", "message": INCOMPLETE_STREAM_ERROR_MESSAGE}, - } + return _sse_event( # mutable-ok: one-shot JSON payload, never mutated after construction + "error", + {"type": "error", "error": {"type": "api_error", "message": INCOMPLETE_STREAM_ERROR_MESSAGE}}, + ) + + +def _anthropic_content_block_start_and_deltas( + block: Mapping[str, object], +) -> tuple[Mapping[str, object], tuple[Mapping[str, object], ...]]: + """ + ``(content_block_start.content_block, content_block_delta.delta events)`` + for one Anthropic response content block. A thinking block emits both a + thinking_delta and a trailing signature_delta - a real Anthropic stream + does the same, and dropping the signature makes any replay of that + assistant message (a follow-up turn, a tool-use continuation) fail + Anthropic's thinking-signature verification. redacted_thinking has no + delta at all - it is sent complete in content_block_start. + """ + match block.get("type"): + case "tool_use": + return ( + { # mutable-ok: one-shot payload + "id": block.get("id"), + "name": block.get("name"), + "input": {}, # mutable-ok: one-shot payload + "type": "tool_use", + }, + ( + { # mutable-ok: one-shot payload + "partial_json": json.dumps(block.get("input") or {}), # mutable-ok: one-shot payload + "type": "input_json_delta", + }, + ), + ) + case "thinking": + signature: Final = block.get("signature") + signature_deltas: Final = ( + ({"signature": signature, "type": "signature_delta"},) # mutable-ok: one-shot payload + if isinstance(signature, str) and signature + else () + ) + return ( + {"thinking": "", "signature": "", "type": "thinking"}, # mutable-ok: one-shot payload + ( + {"thinking": block.get("thinking") or "", "type": "thinking_delta"}, # mutable-ok: one-shot payload + *signature_deltas, + ), + ) + case "redacted_thinking": + return ({"type": "redacted_thinking", "data": block.get("data")}, ()) # mutable-ok: one-shot JSON payload + case _: + return ( + {"type": "text", "text": ""}, # mutable-ok: one-shot JSON payload + ({"type": "text_delta", "text": block.get("text") or ""},), # mutable-ok: one-shot JSON payload + ) + + +def anthropic_messages_response_as_sse_events(response: AnthropicMessagesResponse) -> tuple[bytes, ...]: + """ + Render a complete (non-streaming) AnthropicMessagesResponse as the SSE + event sequence a real streaming request would have produced. + + A mid-stream fallback can resolve to a non-streaming response even + though the client asked to stream (e.g. an agentic tool-use loop that + intercepts and returns a complete message) - yielding that dict directly + into a `/v1/messages` SSE byte stream would produce a malformed + response, so it's synthesized into the message_start/content_block_*/ + message_delta/message_stop lifecycle a real stream would have sent. + """ + content_blocks: Final = response.get("content") or () + content_events: Final = ( + event for index, block in enumerate(content_blocks) for event in _anthropic_content_block_events(index, block) + ) + # A real message_start always carries a null stop_reason/stop_sequence and + # a zero output_tokens - those are only known once generation finishes, so + # copying the completed response's final values here would let a client + # treat the message as already finished, or double-count output tokens. + message_start_usage: Final = { # mutable-ok: one-shot JSON payload + **(response.get("usage") or {}), + "output_tokens": 0, + } + message_start_payload: Final = { # mutable-ok: one-shot JSON payload, never mutated after construction + "type": "message_start", + "message": { # mutable-ok: one-shot JSON payload + **response, + "content": [], # mutable-ok: one-shot JSON payload + "stop_reason": None, + "stop_sequence": None, + "usage": message_start_usage, + }, + } + message_delta_payload: Final = { # mutable-ok: one-shot JSON payload, never mutated after construction + "type": "message_delta", + "delta": { # mutable-ok: one-shot JSON payload + "stop_reason": response.get("stop_reason"), + "stop_sequence": response.get("stop_sequence"), + }, + "usage": response.get("usage") or {}, # mutable-ok: one-shot JSON payload + } + return ( + _sse_event("message_start", message_start_payload), + *content_events, + _sse_event("message_delta", message_delta_payload), + _sse_event("message_stop", {"type": "message_stop"}), # mutable-ok: one-shot JSON payload + ) + + +def _anthropic_content_block_events(index: int, block: Mapping[str, object]) -> tuple[bytes, ...]: + start_block, deltas = _anthropic_content_block_start_and_deltas(block) + start_payload: Final = { # mutable-ok: one-shot payload + "type": "content_block_start", + "index": index, + "content_block": start_block, + } + stop_payload: Final = { # mutable-ok: one-shot payload + "type": "content_block_stop", + "index": index, + } + delta_events: Final = tuple( + _sse_event( + "content_block_delta", + {"type": "content_block_delta", "index": index, "delta": delta}, # mutable-ok: one-shot payload + ) + for delta in deltas + ) + return ( + _sse_event("content_block_start", start_payload), + *delta_events, + _sse_event("content_block_stop", stop_payload), ) - return f"event: error\ndata: {payload}\n\n".encode() class AnthropicMessagesStreamHiddenParams(TypedDict): @@ -97,6 +366,10 @@ class AnthropicMessagesStreamingResponse: self.completion_stream = completion_stream self._hidden_params = hidden_params + @property + def has_buffered_provider_output(self) -> bool: + return getattr(self.completion_stream, "has_buffered_provider_output", False) is True + def __aiter__(self) -> "AnthropicMessagesStreamingResponse": return self @@ -123,7 +396,7 @@ class BaseAnthropicMessagesStreamingIterator: self.start_time = datetime.now() self.completion_start_time: datetime | None = None - async def _handle_streaming_logging(self, collected_chunks: list[bytes]): + async def _handle_streaming_logging(self, collected_chunks: list[bytes], *, stream_teardown: bool = False): """Handle the logging after all chunks have been collected.""" from litellm.proxy.pass_through_endpoints.streaming_handler import ( PassThroughStreamingHandler, @@ -135,21 +408,26 @@ class BaseAnthropicMessagesStreamingIterator: if self.completion_start_time is not None: self.litellm_logging_obj.completion_start_time = self.completion_start_time self.litellm_logging_obj.model_call_details["completion_start_time"] = self.completion_start_time + logging_coroutine: Final = PassThroughStreamingHandler._route_streaming_logging_to_handler( + litellm_logging_obj=self.litellm_logging_obj, + passthrough_success_handler_obj=GLOBAL_PASS_THROUGH_SUCCESS_HANDLER_OBJ, + url_route="/v1/messages", + request_body=self.request_body or {}, + endpoint_type=EndpointType.ANTHROPIC, + start_time=self.start_time, + raw_bytes=collected_chunks, + end_time=end_time, + ) + deferred_dispatch_armed: Final = ( + getattr(self.litellm_logging_obj, "_on_deferred_stream_complete", None) is not None + ) + if deferred_dispatch_armed and not stream_teardown: + self.litellm_logging_obj._deferred_stream_complete_args = (logging_coroutine,) + return # Enqueue on the rooted logging worker rather than asyncio.create_task: # this also runs during generator teardown after a client disconnect, # where an unrooted task could be garbage-collected before it bills. - GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue( - async_coroutine=PassThroughStreamingHandler._route_streaming_logging_to_handler( - litellm_logging_obj=self.litellm_logging_obj, - passthrough_success_handler_obj=GLOBAL_PASS_THROUGH_SUCCESS_HANDLER_OBJ, - url_route="/v1/messages", - request_body=self.request_body or {}, - endpoint_type=EndpointType.ANTHROPIC, - start_time=self.start_time, - raw_bytes=collected_chunks, - end_time=end_time, - ) - ) + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(async_coroutine=logging_coroutine) def get_async_streaming_response_iterator( self, @@ -190,17 +468,167 @@ class BaseAnthropicMessagesStreamingIterator: async def async_sse_wrapper( self, - completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | dict], + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], ) -> AsyncIterator[bytes]: """ Generic async SSE wrapper that converts streaming chunks to SSE format and handles logging. + The upstream read runs in a detached background task (``_pump_upstream``) + so that a client disconnect tears down only this client-facing generator, + never the upstream drain + billing. The provider (e.g. Bedrock) keeps + generating and billing the full response regardless of the client, so + draining it to completion is what lets spend tracking see the real + terminal ``message_delta`` / ``message_stop`` usage instead of a + truncated placeholder count. + + Chunks reach the client through a bounded queue. While the client is + connected the pump blocks on a full queue (racing the disconnect + signal), so a slow reader throttles the upstream read exactly as the old + direct ``yield`` did instead of letting the whole response buffer in + memory. Once the client goes away the pump stops enqueueing and only + keeps a single ``collected_chunks`` copy for billing, and the number of + such post-disconnect drains running at once is capped so client behavior + can't create unbounded worker state; over the cap the pump bills what it + has rather than draining further. Detached-drain lifetime is otherwise + bounded by the upstream stream/read timeout. + + An upstream failure (Bedrock read / decode / chunk-conversion error) + that happens while the client is still connected is forwarded through + the queue and re-raised here, so the original provider exception (and + its status) reaches the proxy's failure handling unchanged rather than + being masked by a generic incomplete-stream event. + This method provides the common logic for both Anthropic and Bedrock implementations. """ - collected_chunks: Final = [] - saw_terminal_event = False + queue: Final[asyncio.Queue[bytes | None | BaseException]] = asyncio.Queue( + maxsize=ANTHROPIC_MESSAGES_STREAM_RELAY_QUEUE_MAXSIZE + ) + client_detached: Final = asyncio.Event() + pump_task: Final = asyncio.create_task(self._pump_upstream_to_queue(completion_stream, queue, client_detached)) + _UPSTREAM_PUMP_TASKS.add(pump_task) + pump_task.add_done_callback(_UPSTREAM_PUMP_TASKS.discard) + + reached_end = False # rebind-ok: flipped once the relay consumes the end-of-stream sentinel + try: + while True: + item = await queue.get() + if item is None: + reached_end = True + break + if isinstance(item, BaseException): + raise item + yield item + finally: + client_detached.set() + if not reached_end: + self._dispatch_pending_deferred_logging() + + def _dispatch_pending_deferred_logging(self) -> None: + """Fire deferred billing that a torn-down response would otherwise drop. + + When the pump finishes draining while the client is still connected it + stores the logging coroutine for ProxyLogging._fire_deferred_stream_logging, + which the proxy only fires on a normally completed response: a client + disconnect (GeneratorExit / CancelledError) re-raises past it. Without + this dispatch that window loses the spend row entirely. + """ + deferred_cb: Final = getattr(self.litellm_logging_obj, "_on_deferred_stream_complete", None) + deferred_args: Final = getattr(self.litellm_logging_obj, "_deferred_stream_complete_args", None) + if deferred_cb is None or deferred_args is None: + return + self.litellm_logging_obj._on_deferred_stream_complete = None + self.litellm_logging_obj._deferred_stream_complete_args = None + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(async_coroutine=deferred_cb(*deferred_args)) + + async def _bill_collected_chunks( + self, + collected_chunks: list[bytes], # mutable-ok: SSE buffer forwarded to list-typed _handle_streaming_logging + *, + stream_teardown: bool, + ) -> None: + from litellm._logging import verbose_proxy_logger + + try: + await self._handle_streaming_logging(collected_chunks, stream_teardown=stream_teardown) + except Exception as exc: # noqa: BLE001 # billing is best-effort; never crash the pump + verbose_proxy_logger.warning( + "async_sse_wrapper billing failed after %d chunks: %s(%s)", + len(collected_chunks), + type(exc).__name__, + exc, + ) + + @staticmethod + async def _abort_upstream( + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], + ) -> None: + """Close the upstream provider stream so it stops generating and billing.""" + from litellm._logging import verbose_proxy_logger + + try: + await aclose_if_supported(completion_stream) + except Exception as exc: # noqa: BLE001 # abort is best-effort; log and continue + verbose_proxy_logger.warning( + "async_sse_wrapper failed to abort upstream stream: %s(%s)", + type(exc).__name__, + exc, + ) + + @staticmethod + async def _enqueue_for_client( + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + item: bytes | None | BaseException, + ) -> bool: + """Deliver one item to the client, applying backpressure. + + Returns True if the item was queued, False if the client disconnected + before there was room (the item is then dropped, since a gone client + can't receive it). Never blocks once the client has detached. + """ + if client_detached.is_set(): + return False + try: + queue.put_nowait(item) + except asyncio.QueueFull: + pass + else: + return True + put_task: Final = asyncio.ensure_future(queue.put(item)) + detached_task: Final = asyncio.ensure_future(client_detached.wait()) + try: + await asyncio.wait(frozenset((put_task, detached_task)), return_when=asyncio.FIRST_COMPLETED) + finally: + if not detached_task.done(): + detached_task.cancel() + if put_task.done() and not put_task.cancelled(): + return True + put_task.cancel() + return False + + async def _pump_upstream_to_queue( + self, + completion_stream: AsyncIterator[bytes | GenericStreamingChunk | ModelResponseStream | Mapping[str, object]], + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + ) -> None: + """Drain the whole upstream into ``queue`` (backpressured) and bill once. + + Runs detached so a client disconnect can't interrupt the upstream read; + see ``async_sse_wrapper`` for the full rationale. On a completed drain + the success billing (or deferred park) happens before the end-of-stream + sentinel is enqueued: the relay can only tear down after consuming the + sentinel, so its teardown can never outrun the park and get mistaken + for a client disconnect, and a sentinel the client never consumes falls + back to dispatching the parked billing here. + """ + from litellm._logging import verbose_proxy_logger + + collected_chunks: Final[list[bytes]] = [] # mutable-ok: SSE billing buffer appended to across the drain + saw_terminal_event = False # rebind-ok: accumulates across the upstream loop + draining_detached = False # rebind-ok: set once this pump claims a detached-drain slot try: async for chunk in completion_stream: if self.completion_start_time is None: @@ -208,17 +636,62 @@ class BaseAnthropicMessagesStreamingIterator: saw_terminal_event = saw_terminal_event or _is_terminal_stream_chunk(chunk) encoded_chunk = self._convert_chunk_to_sse_format(chunk) collected_chunks.append(encoded_chunk) - yield encoded_chunk - except (GeneratorExit, asyncio.CancelledError): - # A client disconnect tears the generator down at the yield, so the - # post-loop logging below never runs and the tokens already streamed - # (and billed by the provider) would never reach spend tracking. See LIT-5839. - if collected_chunks: - await self._handle_streaming_logging(collected_chunks) - raise + if not client_detached.is_set(): + await self._enqueue_for_client(queue, client_detached, encoded_chunk) + continue + if not draining_detached: + if not _try_claim_detached_drain_slot(): + verbose_proxy_logger.warning( + "async_sse_wrapper: detached-drain cap (%d) reached; billing %d partial " + "chunks and aborting the upstream stream to stop provider billing", + ANTHROPIC_MESSAGES_MAX_DETACHED_STREAM_DRAINS, + len(collected_chunks), + ) + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + await self._abort_upstream(completion_stream) + return + draining_detached = True + except Exception as exc: # noqa: BLE001 # upstream errors are handled/forwarded by _handle_pump_upstream_error + await self._handle_pump_upstream_error(queue, client_detached, collected_chunks, exc) + return - if not saw_terminal_event: - yield _incomplete_stream_error_sse_event() + if client_detached.is_set(): + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + return + if not saw_terminal_event and not await self._enqueue_for_client( + queue, client_detached, _incomplete_stream_error_sse_event() + ): + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) + return + await self._bill_collected_chunks(collected_chunks, stream_teardown=False) + if not await self._enqueue_for_client(queue, client_detached, None): + self._dispatch_pending_deferred_logging() - # Handle logging after all chunks are processed - await self._handle_streaming_logging(collected_chunks) + async def _handle_pump_upstream_error( + self, + queue: "asyncio.Queue[bytes | None | BaseException]", + client_detached: "asyncio.Event", + collected_chunks: list[bytes], # mutable-ok: SSE buffer forwarded to list-typed _bill_collected_chunks + exc: BaseException, + ) -> None: + """Forward a provider error to a still-connected client, else salvage partial spend. + + Handing the original exception to the client-facing generator lets it + re-raise so the proxy's failure handling keeps the provider status and + owns logging (no success-bill). If the client already went away, or + disconnects before ever consuming the queued exception, no failure hook + runs, so bill the partial instead of dropping the request. + """ + from litellm._logging import verbose_proxy_logger + + if not client_detached.is_set() and await self._enqueue_for_client(queue, client_detached, exc): + await client_detached.wait() + if not _exception_left_unconsumed(queue, exc): + return + verbose_proxy_logger.warning( + "async_sse_wrapper upstream pump failed after client disconnect (%d chunks): %s(%s)", + len(collected_chunks), + type(exc).__name__, + exc, + ) + await self._bill_collected_chunks(collected_chunks, stream_teardown=True) diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index adabfa2d62d..3d62b8b4784 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -8,6 +8,7 @@ from litellm.constants import ( DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_XHIGH_THINKING_BUDGET, ) +from litellm.exceptions import AuthenticationError from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.litellm_logging import verbose_logger from litellm.llms.base_llm.anthropic_messages.transformation import ( @@ -39,6 +40,11 @@ DROP_UNSUPPORTED_ADAPTIVE_EFFORT_WARNING: Final = ( "minimum thinking budget." ) +DROP_UNFITTING_REASONING_EFFORT_WARNING: Final = ( + "Dropping `thinking` mapped from reasoning_effort=%s for model=%s: max_tokens=%s " + "is too small to fit the minimum thinking budget." +) + class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): @property @@ -307,10 +313,20 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): # Check for Anthropic OAuth token in Authorization header headers, api_key = optionally_handle_anthropic_oauth(headers=headers, api_key=api_key) - if "x-api-key" not in headers and "authorization" not in headers: + header_names: Final = frozenset(name.lower() for name in headers) + if "x-api-key" not in header_names and "authorization" not in header_names: auth_header: Final = AnthropicModelInfo.get_auth_header(api_key) - if auth_header is not None: - headers.update(auth_header) + if auth_header is None: + raise AuthenticationError( + message=( + "Missing Anthropic API Key - A call is being made to anthropic but no key is set " + "either in the environment variables or via params. Please set `ANTHROPIC_API_KEY` " + "or `ANTHROPIC_AUTH_TOKEN` in your environment vars" + ), + llm_provider=self._resolved_provider, + model=model, + ) + headers.update(auth_header) if "anthropic-version" not in headers: headers["anthropic-version"] = DEFAULT_ANTHROPIC_API_VERSION if "content-type" not in headers: @@ -324,11 +340,15 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): return headers, api_base @staticmethod - def _translate_reasoning_effort_to_anthropic(model: str, optional_params: dict, custom_llm_provider: str) -> None: + def _translate_reasoning_effort_to_anthropic( + model: str, optional_params: dict, max_tokens: int | None, custom_llm_provider: str + ) -> None: """Map OpenAI-style ``reasoning_effort`` to native Anthropic params. Caller-supplied ``thinking`` / ``output_config`` win over the alias. - ``effort='none'`` clears both. Invalid efforts raise a 400. + ``effort='none'`` clears both. Invalid efforts raise a 400. A mapped + thinking budget is capped below ``max_tokens`` and dropped when even + the minimum budget cannot fit. """ from litellm.exceptions import BadRequestError as _BadRequestError from litellm.llms.anthropic.chat.transformation import ( @@ -354,7 +374,12 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): optional_params.pop("output_config", None) return - optional_params.setdefault("thinking", mapped_thinking) + fitted_thinking: Final = AnthropicConfig.cap_thinking_budget_to_max_tokens(mapped_thinking, max_tokens) + if fitted_thinking is None: + verbose_logger.warning(DROP_UNFITTING_REASONING_EFFORT_WARNING, reasoning_effort, model, max_tokens) + return + + optional_params.setdefault("thinking", fitted_thinking) if AnthropicModelInfo._is_adaptive_thinking_model(model, custom_llm_provider): mapped_effort: Final = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(reasoning_effort) if mapped_effort is None: @@ -379,13 +404,19 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): def _translate_legacy_thinking_for_adaptive_model( model: str, optional_params: dict, custom_llm_provider: str ) -> None: - """Translate legacy ``thinking.type=enabled`` to adaptive for 4.6/4.7. - Caller-provided ``output_config.effort`` is never overridden. + """Translate legacy ``thinking.type=enabled`` to adaptive for the + adaptive-thinking models that reject it (4.7+ and the 5 families). + Models flagged ``supports_legacy_thinking`` (the 4.6 family) accept the + legacy shape natively, so it is forwarded verbatim and the caller's + ``budget_tokens`` cap keeps applying. Caller-provided + ``output_config.effort`` is never overridden. """ from litellm.llms.anthropic.chat.transformation import AnthropicConfig if not AnthropicModelInfo._is_adaptive_thinking_model(model, custom_llm_provider): return + if AnthropicModelInfo._supports_legacy_thinking(model, custom_llm_provider): + return thinking: Final = optional_params.get("thinking") if not isinstance(thinking, dict) or thinking.get("type") != "enabled": return @@ -493,7 +524,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): except _BadRequestError as e: raise AnthropicError(message=str(e.message), status_code=400) capped_thinking: Final = ( - AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) + AnthropicConfig.cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) if legacy_thinking is not None else None ) @@ -565,6 +596,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): self._translate_reasoning_effort_to_anthropic( model=model, optional_params=anthropic_messages_optional_request_params, + max_tokens=max_tokens, custom_llm_provider=self._resolved_provider, ) diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py index 02d82887dde..9deff950724 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py @@ -1,11 +1,40 @@ +from collections.abc import Mapping from functools import lru_cache -from typing import Any, Final, cast, get_type_hints +from typing import TYPE_CHECKING, Any, Final, cast, get_type_hints from litellm.types.llms.anthropic import AnthropicMessagesRequestOptionalParams from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, ) +if TYPE_CHECKING: + from litellm.exceptions import ContentPolicyViolationError + + +def get_safeguard_refusal_stop_details(response: object) -> Mapping[str, Any] | None: + """ + Return the ``stop_details`` of an Anthropic Messages response refused by a + safeguard (``stop_reason: "refusal"`` carrying ``stop_details``: + https://platform.claude.com/docs/en/build-with-claude/refusals-and-fallback), + or None for any other response, a plain refusal without ``stop_details`` included. + """ + if not isinstance(response, dict) or response.get("stop_reason") != "refusal": + return None + stop_details: Final = response.get("stop_details") + return stop_details if isinstance(stop_details, dict) else None + + +def safeguard_refusal_error(model: str, stop_details: Mapping[str, object]) -> "ContentPolicyViolationError": + """The exception a safeguard-refused Anthropic response converts into so the + content-policy fallback chain can re-dispatch it.""" + from litellm.exceptions import ContentPolicyViolationError + + return ContentPolicyViolationError( + message=f"Anthropic safeguard refusal (category: {stop_details.get('category')}).", + model=model, + llm_provider="anthropic", + ) + @lru_cache(maxsize=1) def _anthropic_messages_optional_param_keys() -> frozenset[str]: @@ -100,14 +129,12 @@ def mock_response( model=model, ) return AnthropicMessagesResponse( - **{ - "content": [{"text": mock_response, "type": "text"}], - "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF", - "model": "claude-sonnet-4-20250514", - "role": "assistant", - "stop_reason": "end_turn", - "stop_sequence": None, - "type": "message", - "usage": {"input_tokens": 2095, "output_tokens": 503}, - } + content=[{"text": mock_response, "type": "text"}], + id="msg_013Zva2CMHLNnXjNJJKqJ2EF", + model="claude-sonnet-4-20250514", + role="assistant", + stop_reason="end_turn", + stop_sequence=None, + type="message", + usage={"input_tokens": 2095, "output_tokens": 503}, ) diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py index c1ea39fd72c..ec0560016da 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py @@ -5,10 +5,11 @@ Used when the target model is an OpenAI or Azure model. """ from collections.abc import AsyncIterator, Coroutine, Mapping -from typing import Any, Final +from typing import Any, Final, TypeAlias import litellm from litellm.types.llms.anthropic import ( + AllAnthropicMessageValues, AllAnthropicToolsValues, AnthropicMessagesRequest, AnthropicOutputConfig, @@ -23,6 +24,8 @@ from ..utils import local_model_name from .streaming_iterator import AnthropicResponsesStreamWrapper from .transformation import LiteLLMAnthropicToResponsesAPIAdapter +AnthropicRequestMessages: TypeAlias = list[AllAnthropicMessageValues] | list[dict[str, object]] + _ADAPTER: Final = LiteLLMAnthropicToResponsesAPIAdapter() @@ -34,22 +37,22 @@ def _forwarded_kwargs(extra_kwargs: Mapping[str, object] | None) -> Mapping[str, def _build_responses_kwargs( *, max_tokens: int, - messages: list[dict], + messages: AnthropicRequestMessages, model: str, - context_management: dict | None = None, - metadata: dict | None = None, + context_management: dict[str, object] | None = None, + metadata: dict[str, object] | None = None, output_config: AnthropicOutputConfig | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, system: str | None = None, temperature: float | None = None, - thinking: dict | None = None, - tool_choice: dict | None = None, - tools: list[AllAnthropicToolsValues | dict] | None = None, + thinking: dict[str, object] | None = None, + tool_choice: dict[str, object] | None = None, + tools: list[AllAnthropicToolsValues | dict[str, object]] | None = None, top_k: int | None = None, top_p: float | None = None, output_format: AnthropicOutputSchema | None = None, - extra_kwargs: dict[str, Any] | None = None, + extra_kwargs: Mapping[str, object] | None = None, ) -> dict[str, Any]: """ Build the kwargs dict to pass directly to litellm.responses() / litellm.aresponses(). @@ -83,30 +86,32 @@ def _build_responses_kwargs( anthropic_request: Final = AnthropicMessagesRequest(**request_data) responses_kwargs: Final = _ADAPTER.translate_request(anthropic_request) + forwarded_kwargs: Final = _forwarded_kwargs(extra_kwargs) # Normalize reasoning effort based on model capabilities # (e.g. "max" → "xhigh"/"high", "minimal" → "low" if unsupported) reasoning: Final = responses_kwargs.get("reasoning") - if isinstance(reasoning, dict) and "effort" in reasoning: - from litellm.llms.anthropic.experimental_pass_through.utils import ( - normalize_reasoning_effort_value, - ) + if isinstance(reasoning, dict): + effort: Final[object] = reasoning.get("effort") + if isinstance(effort, str): + from litellm.llms.anthropic.experimental_pass_through.utils import ( + normalize_reasoning_effort_value, + ) - effort: Final = reasoning["effort"] - normalized: Final = normalize_reasoning_effort_value( - effort, - model=model, - custom_llm_provider=(extra_kwargs or {}).get("custom_llm_provider"), - ) - if normalized != effort: - responses_kwargs["reasoning"] = {**reasoning, "effort": normalized} + provider_hint: Final = forwarded_kwargs.get("custom_llm_provider") + normalized: Final = normalize_reasoning_effort_value( + effort, + model=model, + custom_llm_provider=provider_hint if isinstance(provider_hint, str) else None, + ) + if normalized != effort: + responses_kwargs["reasoning"] = {**reasoning, "effort": normalized} if stream: responses_kwargs["stream"] = True # Forward litellm-specific kwargs (api_key, api_base, logging obj, etc.) excluded: Final = {"anthropic_messages"} - forwarded_kwargs: Final = _forwarded_kwargs(extra_kwargs) for key, value in forwarded_kwargs.items(): if key == "litellm_logging_obj" and value is not None: from litellm.litellm_core_utils.litellm_logging import ( @@ -140,22 +145,22 @@ class LiteLLMMessagesToResponsesAPIHandler: @staticmethod async def async_anthropic_messages_handler( max_tokens: int, - messages: list[dict], + messages: AnthropicRequestMessages, model: str, - context_management: dict | None = None, - metadata: dict | None = None, + context_management: dict[str, object] | None = None, + metadata: dict[str, object] | None = None, output_config: AnthropicOutputConfig | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, system: str | None = None, temperature: float | None = None, - thinking: dict | None = None, - tool_choice: dict | None = None, - tools: list[AllAnthropicToolsValues | dict] | None = None, + thinking: dict[str, object] | None = None, + tool_choice: dict[str, object] | None = None, + tools: list[AllAnthropicToolsValues | dict[str, object]] | None = None, top_k: int | None = None, top_p: float | None = None, output_format: AnthropicOutputSchema | None = None, - **kwargs, + **kwargs: object, ) -> AnthropicMessagesResponse | AsyncIterator[bytes]: responses_kwargs: Final = _build_responses_kwargs( max_tokens=max_tokens, @@ -193,23 +198,23 @@ class LiteLLMMessagesToResponsesAPIHandler: @staticmethod def anthropic_messages_handler( max_tokens: int, - messages: list[dict], + messages: AnthropicRequestMessages, model: str, - context_management: dict | None = None, - metadata: dict | None = None, + context_management: dict[str, object] | None = None, + metadata: dict[str, object] | None = None, output_config: AnthropicOutputConfig | None = None, stop_sequences: list[str] | None = None, stream: bool | None = False, system: str | None = None, temperature: float | None = None, - thinking: dict | None = None, - tool_choice: dict | None = None, - tools: list[AllAnthropicToolsValues | dict] | None = None, + thinking: dict[str, object] | None = None, + tool_choice: dict[str, object] | None = None, + tools: list[AllAnthropicToolsValues | dict[str, object]] | None = None, top_k: int | None = None, top_p: float | None = None, output_format: AnthropicOutputSchema | None = None, _is_async: bool = False, - **kwargs, + **kwargs: object, ) -> ( AnthropicMessagesResponse | AsyncIterator[bytes] diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py index e2ad9c9c6d3..292d2622c7f 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/streaming_iterator.py @@ -152,7 +152,10 @@ class AnthropicResponsesStreamWrapper: if block_idx < 0: if not delta: return - block_idx = self._open_block(item_id, {"type": "thinking", "thinking": ""}) + block_idx = self._open_block( + item_id, + {"type": "thinking", "thinking": "", "signature": ""}, # mutable-ok: API message payload + ) self._chunk_queue.append( { "type": "content_block_delta", diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py index 25d729d8606..0eb0e38a46e 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -6,12 +6,14 @@ path used for OpenAI and Azure models. """ import json -from collections.abc import Iterable +from collections.abc import Iterable, Mapping +from itertools import groupby from typing import Any, Final, cast from litellm.litellm_core_utils.prompt_templates.common_utils import ( TOOL_RESULT_IMAGE_BOUNDARY, TOOL_RESULT_IMAGE_PLACEHOLDER, + responses_reasoning_item_from_thinking_blocks, with_prompt_cache_breakpoint, ) from litellm.litellm_core_utils.reasoning_effort_utils import ( @@ -36,7 +38,11 @@ from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, AnthropicUsage, ) -from litellm.types.llms.openai import ResponseAPIUsage, ResponsesAPIResponse +from litellm.types.llms.openai import ( + ChatCompletionThinkingBlock, + ResponseAPIUsage, + ResponsesAPIResponse, +) class LiteLLMAnthropicToResponsesAPIAdapter: @@ -81,6 +87,51 @@ class LiteLLMAnthropicToResponsesAPIAdapter: return source.get("url") return None + @staticmethod + def _translate_anthropic_document_block_to_file_part( + block: Mapping[str, object], + ) -> dict[str, str] | None: # mutable-ok: API message payload + """Convert an Anthropic document block to a Responses input_file part.""" + raw_source: Final = block.get("source") + if not isinstance(raw_source, Mapping): + return None + source: Final = cast(Mapping[str, object], raw_source) # cast-ok: untrusted client payload + source_type: Final = source.get("type") + if source_type == "base64": + data: Final = source.get("data") + if not isinstance(data, str) or not data: + return None + raw_media_type: Final = source.get("media_type") + media_type: Final = ( + raw_media_type if isinstance(raw_media_type, str) and raw_media_type else "application/pdf" + ) + raw_title: Final = block.get("title") + filename: Final = raw_title if isinstance(raw_title, str) and raw_title else "document.pdf" + return { # mutable-ok: API message payload + "type": "input_file", + "filename": filename, + "file_data": f"data:{media_type};base64,{data}", + } + if source_type == "url": + url: Final = source.get("url") + if not isinstance(url, str) or not url: + return None + return {"type": "input_file", "file_url": url} # mutable-ok: API message payload + return None + + @staticmethod + def _tool_result_output_value( + output_text: str, + file_parts: tuple[dict[str, str], ...], # mutable-ok: json content parts + ) -> str | list[dict[str, str]]: # mutable-ok: API message payload + """Plain string output, or a part list when document file parts are present.""" + if not file_parts: + return output_text + text_parts: Final = ( + [{"type": "input_text", "text": output_text}] if output_text else [] # mutable-ok: API message payload + ) + return [*text_parts, *file_parts] # mutable-ok: API message payload + @staticmethod def _translate_midturn_system_content_to_responses( content: str | Iterable[AnthropicSystemMessageContent], @@ -100,10 +151,62 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if isinstance(block, dict) and block.get("type") == "text" and (text := block.get("text")) # pyright: ignore[reportUnnecessaryIsInstance] # untrusted client payload ] + @staticmethod + def _summary_part_text(part: object) -> str: + if isinstance(part, Mapping): + mapping: Final = cast(Mapping[str, Any], part) # cast-ok: summary parts are untyped provider json + return str(mapping.get("text") or "") + return str(getattr(part, "text", None) or "") + + @classmethod + def _thinking_blocks_from_reasoning_item( + cls, + summary: Iterable[object], + ) -> tuple[dict[str, Any], ...]: # mutable-ok: API message payload + """Anthropic thinking blocks for one Responses reasoning item. + + The signature stays empty: only Anthropic can sign a thinking block, and a stand-in + value would be replayed as a real one and rejected by every backend that verifies it. + """ + return tuple( + AnthropicResponseContentBlockThinking( + type="thinking", + thinking=text, + signature=None, + ).model_dump() + for part in summary + if (text := cls._summary_part_text(part)) + ) + + @staticmethod + def _assistant_block_group_key(indexed_block: tuple[int, Mapping[str, object]]) -> str: + """Group a run of consecutive thinking blocks together; keep every other block alone.""" + index, block = indexed_block + return "thinking" if block.get("type") == "thinking" else f"block:{index}" + + @classmethod + def _assistant_group_to_input_item( + cls, group: tuple[Mapping[str, object], ...] + ) -> dict[str, Any] | None: # mutable-ok: API message payload + first: Final = group[0] + btype: Final = first.get("type") + if btype == "thinking": + blocks: Final = cast(tuple[ChatCompletionThinkingBlock, ...], group) # cast-ok: untrusted client payload + reasoning_item: Final = responses_reasoning_item_from_thinking_blocks(blocks) + return None if reasoning_item is None else dict(reasoning_item) # mutable-ok: API message payload + if btype == "tool_use": + return { # mutable-ok: API message payload + "type": "function_call", + "call_id": first.get("id", ""), + "name": first.get("name", ""), + "arguments": json.dumps(first.get("input", {})), # mutable-ok: API message payload + } + return None + def translate_messages_to_responses_input( self, messages: list[AllAnthropicPassThroughMessageValues], - ) -> list[dict[str, Any]]: + ) -> list[dict[str, object]]: """ Convert Anthropic messages list to Responses API `input` items. @@ -111,11 +214,13 @@ class LiteLLMAnthropicToResponsesAPIAdapter: system text -> message(role=system, input_text) user text -> message(role=user, input_text) user image -> message(role=user, input_image) + user document -> message(role=user, input_file) user tool_result -> function_call_output assistant text -> message(role=assistant, output_text) + assistant thinking -> reasoning assistant tool_use -> function_call """ - input_items: Final[list[dict[str, Any]]] = [] + input_items: Final[list[dict[str, object]]] = [] for m in messages: if m["role"] == "system": @@ -143,7 +248,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: } ) elif isinstance(content, list): - user_parts: list[dict[str, Any]] = [] + user_parts: list[Mapping[str, object]] = [] tool_image_parts: list[dict[str, Any]] = [] # mutable-ok: json content parts for block in content: if not isinstance(block, dict): @@ -164,9 +269,25 @@ class LiteLLMAnthropicToResponsesAPIAdapter: {"type": "input_image", "image_url": url}, block.get("prompt_cache_breakpoint") ) ) + elif btype == "document": + file_part = self._translate_anthropic_document_block_to_file_part(block) + if file_part: + user_parts.append( + with_prompt_cache_breakpoint(file_part, block.get("prompt_cache_breakpoint")) + ) elif btype == "tool_result": tool_use_id = block.get("tool_use_id", "") inner = block.get("content") + document_candidates = ( + tuple( + self._translate_anthropic_document_block_to_file_part(c) + for c in inner + if isinstance(c, dict) and c.get("type") == "document" + ) + if isinstance(inner, list) + else () + ) + tool_file_parts = tuple(part for part in document_candidates if part is not None) if inner is None: output_text = "" elif isinstance(inner, str): @@ -199,7 +320,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: { "type": "function_call_output", "call_id": tool_use_id, - "output": output_text, + "output": self._tool_result_output_value(output_text, tool_file_parts), } ) if tool_image_parts: @@ -233,27 +354,17 @@ class LiteLLMAnthropicToResponsesAPIAdapter: } ) elif isinstance(content, list): - asst_parts: list[dict[str, Any]] = [] - for block in content: - if not isinstance(block, dict): - continue - btype = block.get("type") - if btype == "text": - asst_parts.append({"type": "output_text", "text": block.get("text", "")}) - elif btype == "tool_use": - # tool_use becomes a top-level function_call item - input_items.append( - { - "type": "function_call", - "call_id": block.get("id", ""), - "name": block.get("name", ""), - "arguments": json.dumps(block.get("input", {})), - } - ) - elif btype == "thinking": - thinking_text = block.get("thinking", "") - if thinking_text: - asst_parts.append({"type": "output_text", "text": thinking_text}) + blocks = tuple(block for block in content if isinstance(block, dict)) + input_items.extend( + item + for _, group in groupby(enumerate(blocks), key=self._assistant_block_group_key) + if (item := self._assistant_group_to_input_item(tuple(block for _, block in group))) is not None + ) + asst_parts: list[dict[str, Any]] = [ # mutable-ok: API message payload + {"type": "output_text", "text": block.get("text", "")} # mutable-ok: API message payload + for block in blocks + if block.get("type") == "text" + ] if asst_parts: input_items.append( { @@ -268,9 +379,9 @@ class LiteLLMAnthropicToResponsesAPIAdapter: def translate_tools_to_responses_api( self, tools: list[AllAnthropicToolsValues], - ) -> list[dict[str, Any]]: + ) -> list[dict[str, object]]: """Convert Anthropic tool definitions to Responses API function tools.""" - result: Final[list[dict[str, Any]]] = [] + result: Final[list[dict[str, object]]] = [] for tool in tools: tool_dict = cast(dict[str, Any], tool) tool_type = tool_dict.get("type", "") @@ -281,7 +392,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: continue # Responses turns strict mode on when `strict` is omitted, silently rewriting # `required` to every property. Anthropic tools are non-strict unless asked. - func_tool: dict[str, Any] = { + func_tool: dict[str, object] = { "type": "function", "name": tool_name, "strict": bool(tool_dict.get("strict")), @@ -296,7 +407,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_tool_choice_to_responses_api( tool_choice: AnthropicMessagesToolChoice, - ) -> str | dict[str, Any]: + ) -> str | dict[str, object]: """Convert Anthropic tool_choice to Responses API tool_choice.""" tc_type: Final = tool_choice.get("type") if tc_type == "any": @@ -309,8 +420,8 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_context_management_to_responses_api( - context_management: dict[str, Any], - ) -> list[dict[str, Any]] | None: + context_management: dict[str, object], + ) -> list[dict[str, object]] | None: """ Convert Anthropic context_management dict to OpenAI Responses API array format. @@ -324,13 +435,13 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if not isinstance(edits, list): return None - result: Final[list[dict[str, Any]]] = [] + result: Final[list[dict[str, object]]] = [] for edit in edits: if not isinstance(edit, dict): continue edit_type = edit.get("type", "") if edit_type == "compact_20260112": - entry: dict[str, Any] = {"type": "compaction"} + entry: dict[str, object] = {"type": "compaction"} trigger = edit.get("trigger") if isinstance(trigger, dict) and trigger.get("value") is not None: entry["compact_threshold"] = int(trigger["value"]) @@ -340,9 +451,9 @@ class LiteLLMAnthropicToResponsesAPIAdapter: @staticmethod def translate_thinking_to_reasoning( - thinking: dict[str, Any], - output_config: dict[str, Any] | None = None, - ) -> dict[str, Any] | None: + thinking: dict[str, object], + output_config: dict[str, object] | None = None, + ) -> dict[str, object] | None: """ Convert Anthropic thinking param to Responses API reasoning param. @@ -362,12 +473,14 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if isinstance(output_config, dict) and output_config.get("effort"): effort = output_config["effort"] elif thinking_type == "enabled": - effort = reasoning_effort_from_thinking_budget(thinking.get("budget_tokens", 0)) + raw_budget: Final = thinking.get("budget_tokens", 0) + budget_tokens: Final = int(raw_budget) if isinstance(raw_budget, (int, float)) else 0 + effort = reasoning_effort_from_thinking_budget(budget_tokens) else: return None auto_summary: Final = is_reasoning_auto_summary_enabled() - result: Final[dict[str, Any]] = {"effort": effort} + result: Final[dict[str, object]] = {"effort": effort} summary: Final = thinking.get("summary") if summary: result["summary"] = summary @@ -459,7 +572,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: # output_format / output_config.format -> text format # output_format: {"type": "json_schema", "schema": {...}} # output_config: {"format": {"type": "json_schema", "schema": {...}}} - output_format: Any = anthropic_request.get("output_format") + output_format: object = anthropic_request.get("output_format") output_config = anthropic_request.get("output_config") if not isinstance(output_format, dict) and isinstance(output_config, dict): output_format = output_config.get("format") @@ -471,7 +584,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: "type": "json_schema", "name": "structured_output", "schema": schema, - "strict": True, + "strict": output_format.get("strict", False), } } @@ -509,21 +622,12 @@ class LiteLLMAnthropicToResponsesAPIAdapter: ResponseReasoningItem, ) - content: Final[list[dict[str, Any]]] = [] + content: Final[list[dict[str, object]]] = [] stop_reason: AnthropicFinishReason = "end_turn" for item in response.output: if isinstance(item, ResponseReasoningItem): - for summary in item.summary: - text = getattr(summary, "text", "") - if text: - content.append( - AnthropicResponseContentBlockThinking( - type="thinking", - thinking=text, - signature=None, - ).model_dump() - ) + content.extend(self._thinking_blocks_from_reasoning_item(item.summary)) elif isinstance(item, ResponseOutputMessage): for part in item.content: @@ -555,6 +659,12 @@ class LiteLLMAnthropicToResponsesAPIAdapter: content.append( AnthropicResponseContentBlockText(type="text", text=part.get("text", "")).model_dump() ) + elif item_type == "reasoning": + content.extend( + self._thinking_blocks_from_reasoning_item( + cast(Iterable[object], item.get("summary") or ()), # cast-ok: untyped provider json + ) + ) elif item_type == "function_call": try: input_data = json.loads(item.get("arguments", "{}")) diff --git a/litellm/llms/anthropic/experimental_pass_through/utils.py b/litellm/llms/anthropic/experimental_pass_through/utils.py index 29661572b73..716a4f54778 100644 --- a/litellm/llms/anthropic/experimental_pass_through/utils.py +++ b/litellm/llms/anthropic/experimental_pass_through/utils.py @@ -1,4 +1,6 @@ import os +from collections.abc import Mapping +from types import MappingProxyType from typing import Final import litellm @@ -6,6 +8,15 @@ from litellm.types.utils import ModelInfo OPENAI_MAX_PROMPT_CACHE_KEY_LENGTH: Final = 64 +_EFFORT_DEGRADATION_CHAIN: Final[Mapping[str, tuple[str, ...]]] = MappingProxyType( + { + "max": ("max", "xhigh", "high"), + "xhigh": ("xhigh", "high"), + "minimal": ("minimal", "low"), + } +) +_THINKING_OFF: Final = "none" + def prompt_cache_key_from_user_id(user_id: object) -> str | None: if user_id is None: @@ -28,38 +39,33 @@ def normalize_reasoning_effort_value( model: str, custom_llm_provider: str | None = None, ) -> str: - """ - Normalize a reasoning effort value based on model capabilities. + """Lower a tier the deployment does not accept to the nearest one it does, leaving others alone. - Degradation chains: - - "max" → max / xhigh / high - - "xhigh" → xhigh / high - - "minimal" → minimal / low - - other values pass through unchanged + The accepted set is resolved by the same owner that answers ``/model_group/info``, so a level + the proxy advertises is a level this path forwards. + + A deployment that refuses every step of a chain falls back to an accepted level read off that + same set rather than to an assumed one, since an entry naming its levels outright can exclude + the tiers the per-level flags treat as unconditional. ``none`` is never that fallback and is + never degraded to, being an off switch rather than a tier; an always-on-thinking model is + handled where the thinking block is built. A deployment accepting no tier at all keeps the + chain's floor, which is what every deployment degraded to before there was anything to ask. """ - if effort not in ("max", "xhigh", "minimal"): + chain: Final = _EFFORT_DEGRADATION_CHAIN.get(effort) + if chain is None: return effort + from litellm.router_utils.reasoning_effort_capability import resolve_supported_reasoning_efforts from litellm.utils import get_model_info - model_info: ModelInfo | None = None try: - model_info = get_model_info(model=model, custom_llm_provider=custom_llm_provider) + model_info: Final[ModelInfo] = get_model_info(model=model, custom_llm_provider=custom_llm_provider) except Exception: - model_info = None + return chain[-1] - if effort == "max": - if model_info and model_info.get("supports_max_reasoning_effort"): - return "max" - if model_info and model_info.get("supports_xhigh_reasoning_effort"): - return "xhigh" - return "high" - elif effort == "xhigh": - if model_info and model_info.get("supports_xhigh_reasoning_effort"): - return "xhigh" - return "high" - elif effort == "minimal": - if model_info and model_info.get("supports_minimal_reasoning_effort"): - return "minimal" - return "low" - return "medium" + supported: Final = resolve_supported_reasoning_efforts(model_info, deployment_is_mapped=True) + if not supported: + return chain[-1] + + accepted_tiers: Final = tuple(level for level in supported if level != _THINKING_OFF) + return next((level for level in (*chain, *accepted_tiers) if level in supported), chain[-1]) diff --git a/litellm/llms/anthropic/files/handler.py b/litellm/llms/anthropic/files/handler.py index 0c62418708f..dfd62ca575b 100644 --- a/litellm/llms/anthropic/files/handler.py +++ b/litellm/llms/anthropic/files/handler.py @@ -2,7 +2,7 @@ import asyncio import json import time from collections.abc import Coroutine -from typing import Any, Final +from typing import Final import httpx @@ -22,19 +22,7 @@ from litellm.types.llms.openai import ( from litellm.types.utils import CallTypes, LlmProviders, ModelResponse from ..chat.transformation import AnthropicConfig -from ..common_utils import AnthropicModelInfo - -# Map Anthropic error types to HTTP status codes -ANTHROPIC_ERROR_STATUS_CODE_MAP: Final = { - "invalid_request_error": 400, - "authentication_error": 401, - "permission_error": 403, - "not_found_error": 404, - "rate_limit_error": 429, - "api_error": 500, - "overloaded_error": 503, - "timeout_error": 504, -} +from ..common_utils import ANTHROPIC_ERROR_STATUS_CODE_MAP, AnthropicModelInfo class AnthropicFilesHandler: @@ -128,7 +116,7 @@ class AnthropicFilesHandler: api_key: str | None = None, timeout: float | httpx.Timeout = 600.0, max_retries: int | None = None, - ) -> HttpxBinaryResponseContent | Coroutine[Any, Any, HttpxBinaryResponseContent]: + ) -> HttpxBinaryResponseContent | Coroutine[object, object, HttpxBinaryResponseContent]: """ Retrieve file content from Anthropic. diff --git a/litellm/llms/anthropic/skills/transformation.py b/litellm/llms/anthropic/skills/transformation.py index 566322bbdd6..448e2dc2584 100644 --- a/litellm/llms/anthropic/skills/transformation.py +++ b/litellm/llms/anthropic/skills/transformation.py @@ -2,9 +2,10 @@ Anthropic Skills API configuration and transformations """ -from typing import Any, Final +from typing import Final import httpx +from pydantic import TypeAdapter from litellm._logging import verbose_logger from litellm.litellm_core_utils.url_utils import encode_url_path_segment @@ -22,6 +23,8 @@ from litellm.types.llms.anthropic_skills import ( from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders +_RAW_JSON_PAYLOAD: Final = TypeAdapter(object) + class AnthropicSkillsConfig(BaseSkillsAPIConfig): """Anthropic-specific Skills API configuration""" @@ -104,10 +107,10 @@ class AnthropicSkillsConfig(BaseSkillsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> Skill: """Transform Anthropic response to Skill object""" - response_json: Final = raw_response.json() + response_json: Final = _RAW_JSON_PAYLOAD.validate_python(raw_response.json()) verbose_logger.debug("Transforming create skill response: %s", response_json) - return Skill(**response_json) + return Skill.model_validate(response_json) def transform_list_skills_request( self, @@ -122,13 +125,12 @@ class AnthropicSkillsConfig(BaseSkillsAPIConfig): url: Final = self.get_complete_url(api_base=api_base, endpoint="skills") # Build query parameters - query_params: Final[dict[str, Any]] = {} - if "limit" in list_params and list_params["limit"]: - query_params["limit"] = list_params["limit"] - if "page" in list_params and list_params["page"]: - query_params["page"] = list_params["page"] - if "source" in list_params and list_params["source"]: - query_params["source"] = list_params["source"] + limit: Final = list_params.get("limit") + page: Final = list_params.get("page") + source: Final = list_params.get("source") + query_params: Final[dict[str, int | str]] = { + key: value for key, value in (("limit", limit), ("page", page), ("source", source)) if value + } verbose_logger.debug( "List skills request made to Anthropic Skills endpoint with params: %s", @@ -143,10 +145,10 @@ class AnthropicSkillsConfig(BaseSkillsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> ListSkillsResponse: """Transform Anthropic response to ListSkillsResponse""" - response_json: Final = raw_response.json() + response_json: Final = _RAW_JSON_PAYLOAD.validate_python(raw_response.json()) verbose_logger.debug("Transforming list skills response: %s", response_json) - return ListSkillsResponse(**response_json) + return ListSkillsResponse.model_validate(response_json) def transform_get_skill_request( self, @@ -168,10 +170,10 @@ class AnthropicSkillsConfig(BaseSkillsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> Skill: """Transform Anthropic response to Skill object""" - response_json: Final = raw_response.json() + response_json: Final = _RAW_JSON_PAYLOAD.validate_python(raw_response.json()) verbose_logger.debug("Transforming get skill response: %s", response_json) - return Skill(**response_json) + return Skill.model_validate(response_json) def transform_delete_skill_request( self, @@ -193,7 +195,7 @@ class AnthropicSkillsConfig(BaseSkillsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> DeleteSkillResponse: """Transform Anthropic response to DeleteSkillResponse""" - response_json: Final = raw_response.json() + response_json: Final = _RAW_JSON_PAYLOAD.validate_python(raw_response.json()) verbose_logger.debug("Transforming delete skill response: %s", response_json) - return DeleteSkillResponse(**response_json) + return DeleteSkillResponse.model_validate(response_json) diff --git a/litellm/llms/aws_polly/text_to_speech/transformation.py b/litellm/llms/aws_polly/text_to_speech/transformation.py index 68630335ca7..8f96f80d15e 100644 --- a/litellm/llms/aws_polly/text_to_speech/transformation.py +++ b/litellm/llms/aws_polly/text_to_speech/transformation.py @@ -11,6 +11,7 @@ from typing import TYPE_CHECKING, Any, Final, Union import httpx +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.llms.base_llm.text_to_speech.transformation import ( BaseTextToSpeechConfig, TextToSpeechRequestData, @@ -238,7 +239,7 @@ class AWSPollyTextToSpeechConfig(BaseTextToSpeechConfig, BaseAWSLLM): return api_base.rstrip("/") + "/v1/speech" aws_region_name: Final = litellm_params.get("aws_region_name", self.DEFAULT_REGION) - return f"https://polly.{aws_region_name}.amazonaws.com/v1/speech" + return f"https://polly.{aws_region_name}.{get_aws_dns_suffix(aws_region_name)}/v1/speech" def is_ssml_input(self, input: str) -> bool: """ diff --git a/litellm/llms/azure/audio_transcriptions.py b/litellm/llms/azure/audio_transcriptions.py index 3ab0bd18b45..4a5ed2ccb0c 100644 --- a/litellm/llms/azure/audio_transcriptions.py +++ b/litellm/llms/azure/audio_transcriptions.py @@ -1,5 +1,5 @@ from collections.abc import Coroutine -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final from openai import AsyncAzureOpenAI, AzureOpenAI from pydantic import BaseModel @@ -16,6 +16,9 @@ from litellm.utils import ( from .azure import AzureChatCompletion from .common_utils import AzureOpenAIError +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class AzureAudioTranscription(AzureChatCompletion): def audio_transcriptions( @@ -23,7 +26,7 @@ class AzureAudioTranscription(AzureChatCompletion): model: str, audio_file: FileTypes, optional_params: dict, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", model_response: TranscriptionResponse, timeout: float, max_retries: int, @@ -112,7 +115,7 @@ class AzureAudioTranscription(AzureChatCompletion): data: dict, model_response: TranscriptionResponse, timeout: float, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", api_version: str | None = None, api_key: str | None = None, api_base: str | None = None, diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index c8f94b575ad..46a9dd1a531 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -2,7 +2,7 @@ import asyncio import json import time from collections.abc import Callable, Coroutine -from typing import Any, Final +from typing import Final import httpx from openai import ( @@ -17,7 +17,7 @@ from openai import ( import litellm from litellm.constants import AZURE_OPERATION_POLLING_TIMEOUT, DEFAULT_MAX_RETRIES from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj -from litellm.litellm_core_utils.logging_utils import track_llm_api_timing +from litellm.litellm_core_utils.logging_utils import speech_request_body, track_llm_api_timing from litellm.litellm_core_utils.url_utils import SSRFError, assert_same_origin from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, @@ -374,7 +374,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) error_body: Final = getattr(e, "body", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) @@ -392,7 +392,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): model: str, api_base: str, data: dict, - timeout: Any, + timeout: float | httpx.Timeout, dynamic_params: bool, model_response: ModelResponse, logging_obj: LiteLLMLoggingObj, @@ -502,7 +502,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): dynamic_params: bool, data: dict[str, object], model: str, - timeout: Any, + timeout: float | httpx.Timeout, max_retries: int, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, @@ -578,7 +578,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): dynamic_params: bool, data: dict, model: str, - timeout: Any, + timeout: float | httpx.Timeout, max_retries: int, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, @@ -634,7 +634,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) message: Final = getattr(e, "message", str(e)) error_body: Final = getattr(e, "body", None) if error_headers is None and error_response: @@ -754,7 +754,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): aembedding=None, headers: dict | None = None, litellm_params: dict | None = None, - ) -> EmbeddingResponse | Coroutine[Any, Any, EmbeddingResponse]: + ) -> EmbeddingResponse | Coroutine[object, object, EmbeddingResponse]: if headers: optional_params["extra_headers"] = headers if self._client_session is None: @@ -846,6 +846,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_key: str, data: dict, headers: dict, + deployment_name: str | None = None, ) -> httpx.Response: """ Implemented for azure dall-e-2 image gen calls @@ -957,7 +958,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): content=json.dumps(result).encode("utf-8"), request=httpx.Request(method="POST", url="https://api.openai.com/v1"), ) - request_json: Final = azure_deployment_image_generation_json_body(api_base, data) + request_json: Final = azure_deployment_image_generation_json_body(api_base, data, deployment_name) return await async_handler.post( url=api_base, json=request_json, @@ -973,6 +974,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_key: str, data: dict, headers: dict, + deployment_name: str | None = None, ) -> httpx.Response: """ Implemented for azure dall-e-2 image gen calls @@ -1073,7 +1075,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): content=json.dumps(result).encode("utf-8"), request=httpx.Request(method="POST", url="https://api.openai.com/v1"), ) - request_json: Final = azure_deployment_image_generation_json_body(api_base, data) + request_json: Final = azure_deployment_image_generation_json_body(api_base, data, deployment_name) return sync_handler.post( url=api_base, json=request_json, @@ -1091,9 +1093,10 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): AzureFoundryMAIImageGenerationConfig, ) - api_base: str = azure_client_params.get("azure_endpoint", "") # "https://example-endpoint.openai.azure.com" - if api_base.endswith("/"): - api_base = api_base.rstrip("/") + # deployment-scoped endpoints are moved to "base_url" by select_azure_base_url_or_endpoint + api_base: str = (azure_client_params.get("azure_endpoint") or azure_client_params.get("base_url") or "").rstrip( + "/" + ) api_version: Final[str] = azure_client_params.get("api_version", "") if model is None: model = "" @@ -1113,6 +1116,14 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_version=api_version, ) + v1_url: Final = BaseAzureLLM.get_azure_v1_image_url( + api_base=api_base, + api_version=api_version, + route="/openai/images/generations", + ) + if v1_url is not None: + return v1_url + if "/openai/deployments/" in api_base: base_url_with_deployment = api_base else: @@ -1167,6 +1178,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_key=api_key, data=data, headers=headers, + deployment_name=model, ) provider_config: Final = get_azure_image_generation_config(data.get("model", "dall-e-2")) @@ -1256,7 +1268,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): headers["Authorization"] = f"Bearer {azure_ad_token}" # init AzureOpenAI Client - azure_client_params: Final[dict[str, Any]] = self.initialize_azure_sdk_client( + azure_client_params: Final[dict[str, object]] = self.initialize_azure_sdk_client( litellm_params=litellm_params or {}, api_key=api_key, model_name=model or "", @@ -1302,6 +1314,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): api_key=api_key or "", data=data, headers=headers, + deployment_name=model, ) provider_config: Final = get_azure_image_generation_config(data.get("model", "dall-e-2")) if isinstance(provider_config, AzureFoundryMAIImageGenerationConfig): @@ -1352,6 +1365,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): organization: str | None, max_retries: int, timeout: float | httpx.Timeout, + logging_obj: LiteLLMLoggingObj, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, aspeech: bool | None = None, @@ -1373,6 +1387,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): azure_ad_token_provider=azure_ad_token_provider, max_retries=max_retries, timeout=timeout, + logging_obj=logging_obj, client=client, litellm_params=litellm_params, ) @@ -1387,6 +1402,15 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): litellm_params=litellm_params, ) + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ # mutable-ok: loggers isinstance-check this payload as a dict + "complete_input_dict": speech_request_body(model, voice, optional_params), + "api_base": str(azure_client.base_url), + }, + ) + response: Final = azure_client.audio.speech.create( model=model, voice=voice, @@ -1408,6 +1432,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): azure_ad_token_provider: Callable | None, max_retries: int, timeout: float | httpx.Timeout, + logging_obj: LiteLLMLoggingObj, client=None, litellm_params: dict | None = None, ) -> HttpxBinaryResponseContent: @@ -1421,6 +1446,15 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): litellm_params=litellm_params, ) + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ # mutable-ok: loggers isinstance-check this payload as a dict + "complete_input_dict": speech_request_body(model, voice, optional_params), + "api_base": str(azure_client.base_url), + }, + ) + azure_response: Final = await azure_client.audio.speech.create( model=model, voice=voice, diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py index d7584083327..6fdd277a04f 100644 --- a/litellm/llms/azure/chat/gpt_5_transformation.py +++ b/litellm/llms/azure/chat/gpt_5_transformation.py @@ -19,19 +19,19 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config): GPT5_SERIES_ROUTE = "gpt5_series/" @classmethod - def _supports_reasoning_effort_level(cls, model: str, level: str) -> bool: - """Override to handle gpt5_series/ prefix used for Azure routing. + def _model_map_lookup_name(cls, model: str) -> str: + """Normalise an Azure routing name to its cost-map key. - The parent class calls ``_supports_factory(model, custom_llm_provider=None)`` - which fails to resolve ``gpt5_series/gpt-5.1`` to the correct Azure model - entry. Strip the prefix and prepend ``azure/`` so the lookup finds - ``azure/gpt-5.1`` in model_prices_and_context_window.json. + Neither ``gpt5_series/gpt-5.1`` nor a bare ``gpt-5.1`` is a key in + model_prices_and_context_window.json; ``azure/gpt-5.1`` is. Overriding the shared + resolver rather than one lookup means the supports, explicitly-disabled and + default-effort answers all read the same entry. """ if model.startswith(cls.GPT5_SERIES_ROUTE): - model = "azure/" + model[len(cls.GPT5_SERIES_ROUTE) :] - elif not model.startswith("azure/"): - model = "azure/" + model - return super()._supports_reasoning_effort_level(model, level) + return "azure/" + model[len(cls.GPT5_SERIES_ROUTE) :] + if model.startswith("azure/"): + return model + return "azure/" + model @classmethod def is_model_gpt_5_model(cls, model: str) -> bool: diff --git a/litellm/llms/azure/chat/gpt_transformation.py b/litellm/llms/azure/chat/gpt_transformation.py index 0d50609555a..0ac0662205a 100644 --- a/litellm/llms/azure/chat/gpt_transformation.py +++ b/litellm/llms/azure/chat/gpt_transformation.py @@ -1,10 +1,14 @@ +from collections.abc import Mapping +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final from httpx._models import Headers, Response import litellm from litellm.litellm_core_utils.prompt_templates.common_utils import ( + drop_tool_reference_parts_from_tool_messages, hoist_images_from_tool_messages, + tool_with_flattened_parameters, ) from litellm.litellm_core_utils.prompt_templates.factory import ( convert_to_azure_openai_messages, @@ -22,6 +26,8 @@ from ...base_llm.chat.transformation import BaseConfig from ..common_utils import AzureOpenAIError if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -29,6 +35,19 @@ else: LoggingClass = Any +_NO_TOOLS_UPDATE: Final[Mapping[str, object]] = MappingProxyType({}) + + +def flattened_tools_update(optional_params: Mapping[str, object]) -> Mapping[str, object]: + tools: Final = optional_params.get("tools") + if not isinstance(tools, list): + return _NO_TOOLS_UPDATE + flattened: Final = [ # mutable-ok: request tools are a JSON list + tool_with_flattened_parameters(tool) if isinstance(tool, dict) else tool for tool in tools + ] + return MappingProxyType({"tools": flattened}) + + class AzureOpenAIConfig(BaseConfig): """ Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions @@ -252,11 +271,13 @@ class AzureOpenAIConfig(BaseConfig): litellm_params: dict, headers: dict, ) -> dict: - azure_messages: Final = convert_to_azure_openai_messages(hoist_images_from_tool_messages(messages)) + stripped_messages: Final = drop_tool_reference_parts_from_tool_messages(messages) + azure_messages: Final = convert_to_azure_openai_messages(hoist_images_from_tool_messages(stripped_messages)) return { "model": model, "messages": azure_messages, **optional_params, + **flattened_tools_update(optional_params), } def transform_response( @@ -269,7 +290,7 @@ class AzureOpenAIConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/azure/chat/o_series_transformation.py b/litellm/llms/azure/chat/o_series_transformation.py index 6cbd91bab5d..246bf69cb5f 100644 --- a/litellm/llms/azure/chat/o_series_transformation.py +++ b/litellm/llms/azure/chat/o_series_transformation.py @@ -20,6 +20,7 @@ from litellm.types.llms.openai import AllMessageValues from litellm.utils import get_model_info, supports_reasoning from ...openai.chat.o_series_transformation import OpenAIOSeriesConfig +from .gpt_transformation import flattened_tools_update class AzureOpenAIO1Config(OpenAIOSeriesConfig): @@ -108,4 +109,8 @@ class AzureOpenAIO1Config(OpenAIOSeriesConfig): headers: dict, ) -> dict: model = model.replace("o_series/", "") # handle o_series/my-random-deployment-name - return super().transform_request(model, messages, optional_params, litellm_params, headers) + flattened_params: Final = { # mutable-ok: transform_request's contract takes a plain JSON params dict + **optional_params, + **flattened_tools_update(optional_params), + } + return super().transform_request(model, messages, flattened_params, litellm_params, headers) diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py index b77ba2f9460..6cb7d09cec4 100644 --- a/litellm/llms/azure/common_utils.py +++ b/litellm/llms/azure/common_utils.py @@ -3,6 +3,8 @@ import hashlib import json import os from collections.abc import Callable, Mapping +from functools import lru_cache +from types import MappingProxyType from typing import Any, Final, Literal, NamedTuple, cast import httpx @@ -75,6 +77,24 @@ def process_azure_headers(headers: httpx.Headers | dict) -> dict: return {**llm_response_headers, **openai_headers} +@lru_cache(maxsize=128) +def _cached_entra_id_token_provider( + tenant_id: str, + client_id: str, + client_secret: str, + scope: str, +) -> Callable[[], str]: + """Build (once per credential set) a bearer token provider backed by a `ClientSecretCredential`. + + The credential caches the access token internally and only talks to Entra ID when it is close + to expiry, so reusing the provider keeps one AAD round trip per token lifetime instead of one + per request. + """ + from azure.identity import ClientSecretCredential, get_bearer_token_provider + + return get_bearer_token_provider(ClientSecretCredential(tenant_id, client_id, client_secret), scope) + + def get_azure_ad_token_from_entra_id( tenant_id: str, client_id: str, @@ -93,8 +113,6 @@ def get_azure_ad_token_from_entra_id( Returns: callable that returns a bearer token. """ - from azure.identity import ClientSecretCredential, get_bearer_token_provider - verbose_logger.debug("Getting Azure AD Token from Entra ID") if tenant_id.startswith("os.environ/"): @@ -120,9 +138,13 @@ def get_azure_ad_token_from_entra_id( ) if _tenant_id is None or _client_id is None or _client_secret is None: raise ValueError("tenant_id, client_id, and client_secret must be provided") - credential: Final = ClientSecretCredential(_tenant_id, _client_id, _client_secret) - token_provider: Final = get_bearer_token_provider(credential, scope) + token_provider: Final = _cached_entra_id_token_provider( + tenant_id=_tenant_id, + client_id=_client_id, + client_secret=_client_secret, + scope=scope, + ) verbose_logger.debug("token_provider %s", token_provider) @@ -768,6 +790,32 @@ class BaseAzureLLM(BaseOpenAILLM): return str(final_url) + @staticmethod + def get_azure_v1_image_url(api_base: str, api_version: str | None, route: str) -> str | None: + """ + Azure's v1 surface serves images at ``/openai/v1/images/{generations,edits}`` and routes by + ``model`` in the request body, so any deployment path and stale ``api-version`` in + ``api_base`` have to be dropped. + + Returns None when ``api_version`` is a dated one, which still uses the deployment route. + """ + if not BaseAzureLLM._is_azure_v1_api_version(api_version): + return None + + base_url: Final = httpx.URL(api_base) + openai_path_start: Final = base_url.path.find("/openai") + resource_base: Final = str( + base_url.copy_with( + path=base_url.path if openai_path_start == -1 else base_url.path[:openai_path_start], + params=httpx.QueryParams(tuple((k, v) for k, v in base_url.params.multi_items() if k != "api-version")), + ) + ) + return BaseAzureLLM._get_base_azure_url( + api_base=resource_base, + litellm_params=MappingProxyType({"api_version": api_version}), + route=route, + ) + @staticmethod def _is_azure_v1_api_version(api_version: str | None) -> bool: if api_version is None: diff --git a/litellm/llms/azure/completion/handler.py b/litellm/llms/azure/completion/handler.py index 728968e12e7..80934e994f6 100644 --- a/litellm/llms/azure/completion/handler.py +++ b/litellm/llms/azure/completion/handler.py @@ -193,7 +193,7 @@ class AzureTextCompletion(BaseAzureLLM): data: dict, timeout: Any, model_response: ModelResponse, - logging_obj: Any, + logging_obj: LiteLLMLoggingObj, max_retries: int, azure_ad_token: str | None = None, client=None, # this is the AsyncAzureOpenAI diff --git a/litellm/llms/azure/files/handler.py b/litellm/llms/azure/files/handler.py index 4f93896699f..67bf47c2359 100644 --- a/litellm/llms/azure/files/handler.py +++ b/litellm/llms/azure/files/handler.py @@ -48,7 +48,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): verbose_logger.debug("create_file_data=%s", create_file_data) response = await openai_client.files.create(**self._prepare_create_file_data(create_file_data)) verbose_logger.debug("create_file_response=%s", response) - return OpenAIFileObject(**response.model_dump()) + return OpenAIFileObject.model_validate(response.model_dump()) def create_file( self, @@ -60,8 +60,8 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): timeout: float | httpx.Timeout, max_retries: int | None, client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = None, - litellm_params: dict | None = None, - ) -> OpenAIFileObject | Coroutine[Any, Any, OpenAIFileObject]: + litellm_params: dict[str, object] | None = None, + ) -> OpenAIFileObject | Coroutine[object, object, OpenAIFileObject]: openai_client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = self.get_azure_openai_client( litellm_params=litellm_params or {}, api_key=api_key, @@ -84,7 +84,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): response: Final = cast(AzureOpenAI | OpenAI, openai_client).files.create( **self._prepare_create_file_data(create_file_data) ) - return OpenAIFileObject(**response.model_dump()) + return OpenAIFileObject.model_validate(response.model_dump()) async def afile_content( self, @@ -104,8 +104,8 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): max_retries: int | None, api_version: str | None = None, client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = None, - litellm_params: dict | None = None, - ) -> HttpxBinaryResponseContent | Coroutine[Any, Any, HttpxBinaryResponseContent]: + litellm_params: dict[str, object] | None = None, + ) -> HttpxBinaryResponseContent | Coroutine[object, object, HttpxBinaryResponseContent]: openai_client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = self.get_azure_openai_client( litellm_params=litellm_params or {}, api_key=api_key, @@ -150,7 +150,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): max_retries: int | None, api_version: str | None = None, client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = None, - litellm_params: dict | None = None, + litellm_params: dict[str, object] | None = None, ): openai_client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = self.get_azure_openai_client( litellm_params=litellm_params or {}, @@ -200,7 +200,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): organization: str | None = None, api_version: str | None = None, client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = None, - litellm_params: dict | None = None, + litellm_params: dict[str, object] | None = None, ): openai_client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = self.get_azure_openai_client( litellm_params=litellm_params or {}, @@ -252,7 +252,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): purpose: str | None = None, api_version: str | None = None, client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = None, - litellm_params: dict | None = None, + litellm_params: dict[str, object] | None = None, ): openai_client: AzureOpenAI | AsyncAzureOpenAI | OpenAI | AsyncOpenAI | None = self.get_azure_openai_client( litellm_params=litellm_params or {}, diff --git a/litellm/llms/azure/image_edit/transformation.py b/litellm/llms/azure/image_edit/transformation.py index 15592968bad..e4716289a34 100644 --- a/litellm/llms/azure/image_edit/transformation.py +++ b/litellm/llms/azure/image_edit/transformation.py @@ -93,8 +93,6 @@ class AzureImageEditConfig(OpenAIImageEditConfig): raise ValueError( f"api_base is required for Azure AI Studio. Please set the api_base parameter. Passed `api_base={api_base}`" ) - original_url: Final = httpx.URL(api_base) - # Resolve api_version: litellm_params > litellm.api_version > AZURE_API_VERSION env > default. # Mirrors the fallback chain used by the Azure chat path in common_utils.py, # so callers that set a global / env api_version don't get an unversioned URL. @@ -105,6 +103,16 @@ class AzureImageEditConfig(OpenAIImageEditConfig): or litellm.AZURE_DEFAULT_API_VERSION ) + v1_url: Final = BaseAzureLLM.get_azure_v1_image_url( + api_base=api_base, + api_version=api_version, + route="/openai/images/edits", + ) + if v1_url is not None: + return v1_url + + original_url: Final = httpx.URL(api_base) + # Create a new dictionary with existing params query_params: Final = dict(original_url.params) diff --git a/litellm/llms/azure/image_generation/http_utils.py b/litellm/llms/azure/image_generation/http_utils.py index 03c425eeffc..1aa5757ca95 100644 --- a/litellm/llms/azure/image_generation/http_utils.py +++ b/litellm/llms/azure/image_generation/http_utils.py @@ -1,7 +1,9 @@ """HTTP helpers for Azure OpenAI image generation (REST, not SDK).""" +from typing import Final -def azure_deployment_image_generation_json_body(api_base: str, data: dict) -> dict: + +def azure_deployment_image_generation_json_body(api_base: str, data: dict, deployment_name: str | None = None) -> dict: """ Build the JSON body for Azure OpenAI image generation POSTs. @@ -9,9 +11,20 @@ def azure_deployment_image_generation_json_body(api_base: str, data: dict) -> di deployment in the URL only; sending ``model`` in the body (especially the deployment name) breaks some models (e.g. gpt-image-2). See LiteLLM #26316. + For the v1 surface (``.../openai/v1/images/...``), Azure routes by the deployment + name in the body ``model`` field, so the deployment name must replace any base + model name there or Azure answers 404 DeploymentNotFound. + Provider-style URLs (e.g. ``/providers/...`` for FLUX on Azure AI) keep all keys so non–OpenAI-deployment payloads still work. """ - if "images/generations" in api_base and "/openai/deployments/" in api_base: - return {k: v for k, v in data.items() if k != "model"} - return data + drop_model: Final = "images/generations" in api_base and "/openai/deployments/" in api_base + v1_route: Final = "/openai/v1/images/" in api_base and bool(deployment_name) + if not drop_model and not v1_route: + return data + entries: Final = ( + tuple((k, v) for k, v in data.items() if k != "model") + if drop_model + else (*data.items(), ("model", deployment_name)) + ) + return {k: v for k, v in entries} diff --git a/litellm/llms/azure/realtime/handler.py b/litellm/llms/azure/realtime/handler.py index e3e1ef8ecd5..88492ef996e 100644 --- a/litellm/llms/azure/realtime/handler.py +++ b/litellm/llms/azure/realtime/handler.py @@ -4,6 +4,8 @@ This file contains the calling Azure OpenAI's `/openai/realtime` endpoint. This requires websockets, and is currently only supported on LiteLLM Proxy. """ +from collections.abc import Mapping +from types import MappingProxyType from typing import Any, Final, cast from litellm._logging import _redact_string, verbose_proxy_logger @@ -30,6 +32,21 @@ async def forward_messages(client_ws: Any, backend_ws: Any): class AzureOpenAIRealtime(AzureChatCompletion): + @staticmethod + def get_auth_headers(api_key: str | None, azure_ad_token: str | None) -> Mapping[str, str]: + """ + Build the websocket handshake auth headers, preferring a static api-key and falling back to + an Azure AD (Entra ID) bearer token. Never sends both. + """ + if api_key: + return MappingProxyType({"api-key": api_key}) + if azure_ad_token: + return MappingProxyType({"Authorization": f"Bearer {azure_ad_token}"}) + raise ValueError( + "Missing Azure credentials for the realtime endpoint. Set an api_key, or configure Azure AD auth " + "(azure_ad_token, tenant_id/client_id/client_secret, or a managed identity)" + ) + def _construct_url( self, api_base: str, @@ -117,13 +134,13 @@ class AzureOpenAIRealtime(AzureChatCompletion): query_params=query_params, ) + auth_headers: Final = self.get_auth_headers(api_key=api_key, azure_ad_token=azure_ad_token) + try: ssl_context: Final = get_shared_realtime_ssl_context() async with websockets.connect( url, - additional_headers={ - "api-key": api_key, - }, + additional_headers=auth_headers, max_size=REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES, ssl=ssl_context, ) as backend_ws: diff --git a/litellm/llms/azure/search/__init__.py b/litellm/llms/azure/search/__init__.py new file mode 100644 index 00000000000..2414ba2b1e8 --- /dev/null +++ b/litellm/llms/azure/search/__init__.py @@ -0,0 +1,3 @@ +from litellm.llms.azure.search.transformation import BingGroundingSearchConfig + +__all__ = ("BingGroundingSearchConfig",) diff --git a/litellm/llms/azure/search/transformation.py b/litellm/llms/azure/search/transformation.py new file mode 100644 index 00000000000..0754c9b1fda --- /dev/null +++ b/litellm/llms/azure/search/transformation.py @@ -0,0 +1,442 @@ +""" +Calls the Microsoft Foundry Responses API with the `bing_grounding` or `web_search` +tool to search the web (Grounding with Bing Search). + +Microsoft docs: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/bing-grounding + +Setup: + 1. Set BING_GROUNDING_PROJECT_ENDPOINT to the Foundry project endpoint, e.g. + https://.services.ai.azure.com/api/projects/ + 2. Set BING_GROUNDING_MODEL to a model deployment in that project (e.g. gpt-4.1); + it runs the grounded search and its tokens are billed on that deployment + 3. Optional: set BING_GROUNDING_CONNECTION_ID to a Grounding with Bing Search + project connection id to use the `bing_grounding` tool; without it the + project's built-in `web_search` tool is used + 4. Auth: pass api_key (an Azure API key, sent in the api-key header), or set + BING_GROUNDING_TOKEN to an Entra bearer token for scope + https://ai.azure.com/.default, or configure azure-identity (AZURE_CLIENT_ID / + AZURE_CLIENT_SECRET / AZURE_TENANT_ID, managed identity, or any + DefaultAzureCredential source) and the token is minted automatically + +Usage: + response = litellm.search( + query="latest AI developments", + search_provider="bing_grounding", + max_results=5, + ) +""" + +from __future__ import annotations + +from collections.abc import Callable, Mapping +from types import MappingProxyType +from typing import TYPE_CHECKING, Final, Literal + +import httpx +from pydantic import BaseModel, ConfigDict, ValidationError + +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.search.transformation import ( + BaseSearchConfig, + SearchResponse, + SearchResult, +) +from litellm.secret_managers.main import get_secret_str + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + +_DOCS_URL: Final = "https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/bing-grounding" + +PROJECT_ENDPOINT_ENV: Final = "BING_GROUNDING_PROJECT_ENDPOINT" +MODEL_ENV: Final = "BING_GROUNDING_MODEL" +CONNECTION_ID_ENV: Final = "BING_GROUNDING_CONNECTION_ID" +TOKEN_ENV: Final = "BING_GROUNDING_TOKEN" + +ENTRA_SCOPE: Final = "https://ai.azure.com/.default" + +_RESPONSES_PATH: Final = "/openai/v1/responses" +_SNIPPET_FALLBACK_LENGTH: Final = 300 +_UPSTREAM_ERROR_STATUS: Final = 502 +_RESPONSE_COST_HEADER: Final = "llm_provider-x-litellm-response-cost" + + +class _Annotation(BaseModel): + model_config = ConfigDict(extra="ignore", frozen=True) + + type: str = "" + url: str | None = None + title: str | None = None + start_index: int | None = None + end_index: int | None = None + + +class _ContentPart(BaseModel): + model_config = ConfigDict(extra="ignore", frozen=True) + + type: str = "" + text: str = "" + annotations: tuple[_Annotation, ...] = () + + +class _OutputItem(BaseModel): + model_config = ConfigDict(extra="ignore", frozen=True) + + type: str = "" + content: tuple[_ContentPart, ...] = () + + +class _ErrorBody(BaseModel): + model_config = ConfigDict(extra="ignore", frozen=True) + + message: str | None = None + + +class _IncompleteDetails(BaseModel): + model_config = ConfigDict(extra="ignore", frozen=True) + + reason: str | None = None + + +class _ResponsesEnvelope(BaseModel): + """A Foundry Responses API body. `output` is required: a body without it is not a + Responses API response and must not be reported as a successful empty search. + + A 200 body can still carry `status` `failed` or `incomplete`; those are surfaced as + errors rather than reported as a successful empty search.""" + + model_config = ConfigDict(extra="ignore", frozen=True) + + output: tuple[_OutputItem, ...] + status: str | None = None + error: _ErrorBody | None = None + incomplete_details: _IncompleteDetails | None = None + + +class _ErrorEnvelope(BaseModel): + model_config = ConfigDict(extra="ignore", frozen=True) + + error: _ErrorBody | None = None + + +def _unwrap_error_detail(error_message: str) -> str: + """ + Surface the human-readable message inside Foundry's error envelope. + + Tool failures nest a second JSON document as a string inside `error.message` + (observed live for `bing_grounding` connection errors), so the unwrap runs twice. + Falls back to the raw body for anything else. + """ + try: + envelope: Final = _ErrorEnvelope.model_validate_json(error_message) + except ValidationError: + return error_message + message: Final = envelope.error.message if envelope.error else None + if message is None: + return error_message + try: + nested: Final = _ErrorBody.model_validate_json(message) + except ValidationError: + return message + return nested.message or message + + +def _snippet(text: str, annotation: _Annotation) -> str: + """ + The text a citation supports, not the citation marker itself. + + A url_citation's start/end indices span the inline marker ("([host](url))"), + which follows the claim it backs, so the snippet is the marker's own line up + to where the marker starts. + """ + start: Final = annotation.start_index + marker_start: Final = start if start is not None and 0 <= start <= len(text) else len(text) + claim: Final = text[:marker_start].rsplit("\n", 1)[-1].strip() + if claim: + return claim[-_SNIPPET_FALLBACK_LENGTH:] + return text[:_SNIPPET_FALLBACK_LENGTH] + + +def _citation_results(envelope: _ResponsesEnvelope) -> tuple[SearchResult, ...]: + """One result per cited URL: first occurrence wins, order preserved as answered.""" + cited: Final = tuple( + SearchResult( + title=annotation.title or "", + url=annotation.url or "", + snippet=_snippet(part.text, annotation), + date=None, + last_updated=None, + ) + for item in envelope.output + if item.type == "message" + for part in item.content + if part.type == "output_text" + for annotation in part.annotations + if annotation.type == "url_citation" and annotation.url + ) + first_by_url: Final = MappingProxyType({result.url: result for result in reversed(cited)}) + return tuple(first_by_url[url] for url in dict.fromkeys(result.url for result in cited)) + + +def _valid_max_results(max_results: object) -> int | None: + """A positive-int `max_results`, else None. Rejects bools, an `int` subclass, and + non-positive values so neither the request-side `count` nor the response-side cap + forwards a value the other would silently ignore. + """ + if isinstance(max_results, bool) or not isinstance(max_results, int): + return None + return max_results if max_results > 0 else None + + +def _requested_max_results(response_kwargs: Mapping[str, object]) -> int | None: + """The unified `max_results` cap the caller asked for, if any. + + The built-in web_search tool has no server-side result-count knob, so the cap is + enforced here after the fact; connection mode also honors it as a hard ceiling on + top of the tool's `count` hint. + """ + optional_params: Final = response_kwargs.get("optional_params") + if not isinstance(optional_params, Mapping): + return None + return _valid_max_results(optional_params.get("max_results")) + + +def _capped(results: tuple[SearchResult, ...], max_results: int | None) -> tuple[SearchResult, ...]: + return results[:max_results] if max_results is not None else results + + +class _SearchConfiguration(BaseModel): + model_config = ConfigDict(frozen=True) + + project_connection_id: str + count: int | None = None + + +class _BingGroundingParams(BaseModel): + model_config = ConfigDict(frozen=True) + + search_configurations: tuple[_SearchConfiguration, ...] + + +class _BingGroundingTool(BaseModel): + model_config = ConfigDict(frozen=True) + + type: Literal["bing_grounding"] = "bing_grounding" + bing_grounding: _BingGroundingParams + + +class _UserLocation(BaseModel): + model_config = ConfigDict(frozen=True) + + type: Literal["approximate"] = "approximate" + country: str + + +class _WebSearchTool(BaseModel): + model_config = ConfigDict(frozen=True) + + type: Literal["web_search"] = "web_search" + user_location: _UserLocation | None = None + + +class _ResponsesRequest(BaseModel): + model_config = ConfigDict(frozen=True) + + model: str + input: str + tools: tuple[_BingGroundingTool | _WebSearchTool, ...] + + +def _search_tool(optional_params: Mapping[str, object]) -> _BingGroundingTool | _WebSearchTool: + connection_id: Final = get_secret_str(CONNECTION_ID_ENV) + max_results: Final = optional_params.get("max_results") + country: Final = optional_params.get("country") + if connection_id: + configuration: Final = _SearchConfiguration( + project_connection_id=connection_id, + count=_valid_max_results(max_results), + ) + return _BingGroundingTool(bing_grounding=_BingGroundingParams(search_configurations=(configuration,))) + location: Final = _UserLocation(country=country.upper()) if isinstance(country, str) else None + return _WebSearchTool(user_location=location) + + +def _default_entra_token_minter() -> str: + from litellm.secret_managers.get_azure_ad_token_provider import get_azure_ad_token_provider + + return get_azure_ad_token_provider(azure_scope=ENTRA_SCOPE)() + + +class BingGroundingSearchConfig(BaseSearchConfig): + def __init__(self, entra_token_minter: Callable[[], str] | None = None) -> None: + super().__init__() + self._entra_token_minter = entra_token_minter + + @staticmethod + def ui_friendly_name() -> str: + return "Grounding with Bing Search" + + def validate_environment( + self, + headers: dict[str, str], # mutable-ok: BaseSearchConfig.validate_environment signature + api_key: str | None = None, + api_base: str | None = None, + **kwargs: object, # kwargs-ok: BaseSearchConfig.validate_environment signature + ) -> dict[str, str]: # mutable-ok: the http handler passes this straight to httpx as headers + """ + Validate environment and return headers. + + Returns a new dict rather than mutating ``headers``: the http handler calls this + a second time after ``litellm/search/main.py`` already did, so it has to be idempotent. + """ + return { # mutable-ok: httpx requires a plain dict of headers + **headers, + **self._auth_header(api_key, api_base), + "Content-Type": "application/json", + } + + def _auth_header(self, api_key: str | None, api_base: str | None) -> Mapping[str, str]: + """ + A caller-supplied ``api_key`` is an Azure API key and rides the ``api-key`` header; + an Entra bearer token (``BING_GROUNDING_TOKEN`` or one minted via azure-identity) + rides ``Authorization: Bearer``. Foundry rejects the wrong scheme for each. + """ + if api_key: + return MappingProxyType({"api-key": api_key}) + token: Final = self.resolve_server_api_key( + caller_api_key=None, + caller_api_base=api_base, + key_env_vars=(TOKEN_ENV,), + base_env_var=PROJECT_ENDPOINT_ENV, + default_api_base=None, + ) or self._mint_entra_token(api_base) + return MappingProxyType({"Authorization": f"Bearer {token}"}) + + def _mint_entra_token(self, caller_api_base: str | None) -> str: + self._assert_trusted_api_base_for_server_credential( + caller_api_base, None, PROJECT_ENDPOINT_ENV, "Azure AD token" + ) + minter: Final = self._entra_token_minter or _default_entra_token_minter + try: + return minter() + except Exception as e: + raise ValueError( + f"Grounding with Bing Search: no credential available. Pass api_key, set {TOKEN_ENV} " + f"to an Entra bearer token, or configure azure-identity (AZURE_CLIENT_ID / " + f"AZURE_CLIENT_SECRET / AZURE_TENANT_ID or any DefaultAzureCredential source) " + f"for scope {ENTRA_SCOPE}. Underlying error: {e}" + ) from e + + def get_complete_url( + self, + api_base: str | None, + optional_params: dict[str, object], # mutable-ok: BaseSearchConfig.get_complete_url signature + data: dict[str, object] | list[dict[str, object]] | None = None, # mutable-ok: base signature + **kwargs: object, # kwargs-ok: BaseSearchConfig.get_complete_url signature + ) -> str: + resolved_base: Final = api_base or get_secret_str(PROJECT_ENDPOINT_ENV) + if not resolved_base: + raise ValueError( + f"{PROJECT_ENDPOINT_ENV} is not set. Set it to your Microsoft Foundry project " + f"endpoint, e.g. https://.services.ai.azure.com/api/projects/." + ) + trimmed: Final = resolved_base.rstrip("/") + if trimmed.endswith(_RESPONSES_PATH): + return trimmed + return f"{trimmed}{_RESPONSES_PATH}" + + def transform_search_request( + self, + query: str | list[str], # mutable-ok: BaseSearchConfig.transform_search_request signature + optional_params: dict[str, object], # mutable-ok: base signature + **kwargs: object, # kwargs-ok: BaseSearchConfig.transform_search_request signature + ) -> dict[str, object]: # mutable-ok: the http handler passes this straight to httpx as the JSON body + """ + Transform Search request to the Foundry Responses API format. + + The unified params map as far as the API allows: + - max_results -> the bing_grounding search configuration's `count`; the built-in + web_search tool has no result-count knob, so that mode instead caps the returned + results after the fact (see transform_search_response) + - country -> web_search's approximate `user_location` (bing_grounding's `market` + wants a full locale like en-US, which a bare country code cannot fill) + - search_domain_filter, max_tokens_per_page -> no API equivalent, dropped + """ + model: Final = get_secret_str(MODEL_ENV) + if not model: + raise ValueError( + f"{MODEL_ENV} is not set. Set it to a model deployment in the Foundry project " + f"that runs the grounded search, e.g. gpt-4.1." + ) + request: Final = _ResponsesRequest( + model=model, + input=" ".join(query) if isinstance(query, list) else query, + tools=(_search_tool(optional_params),), + ) + return request.model_dump(mode="json", exclude_none=True) + + def transform_search_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + **kwargs: object, # kwargs-ok: BaseSearchConfig.transform_search_response signature + ) -> SearchResponse: + try: + parsed: Final = _ResponsesEnvelope.model_validate_json(raw_response.content) + except ValidationError as e: + raise self.get_error_class( + error_message=f"response does not match the Foundry Responses API schema: {e}", + status_code=raw_response.status_code, + headers=dict(raw_response.headers), # mutable-ok: BaseSearchConfig.get_error_class signature + ) + if parsed.status == "failed": + detail: Final = ( + parsed.error.message if parsed.error and parsed.error.message else "the grounded search failed" + ) + raise self._upstream_error(detail, raw_response) + results: Final = _capped(_citation_results(parsed), _requested_max_results(kwargs)) + if not results and parsed.status == "incomplete": + reason: Final = ( + parsed.incomplete_details.reason + if parsed.incomplete_details and parsed.incomplete_details.reason + else "unknown reason" + ) + raise self._upstream_error(f"the grounded search was incomplete: {reason}", raw_response) + return self._priced(results) + + def _upstream_error(self, detail: str, raw_response: httpx.Response) -> Exception: + return self.get_error_class( + error_message=detail, + status_code=_UPSTREAM_ERROR_STATUS, + headers=dict(raw_response.headers), # mutable-ok: BaseSearchConfig.get_error_class signature + ) + + def _priced(self, results: tuple[SearchResult, ...]) -> SearchResponse: + """web_search mode runs no paid Grounding with Bing transaction, so it must not + inherit the connection-mode ``bing_grounding/search`` price; zero its per-query + cost while leaving connection mode to the cost map.""" + response: Final = SearchResponse( + results=list(results), # mutable-ok: SearchResponse.results is list[SearchResult] + object="search", + ) + if get_secret_str(CONNECTION_ID_ENV): + return response + response._hidden_params[ + "additional_headers" + ] = { # mutable-ok: response_cost_calculator writes into _hidden_params + _RESPONSE_COST_HEADER: 0.0 + } + return response + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: dict[str, str], # mutable-ok: BaseSearchConfig.get_error_class signature + ) -> Exception: + detail: Final = _unwrap_error_detail(error_message).rstrip(". ") + return BaseLLMException( + status_code=status_code, + message=f"Grounding with Bing Search: {detail}. See {_DOCS_URL} for details.", + headers=headers, + ) diff --git a/litellm/llms/azure_ai/agents/handler.py b/litellm/llms/azure_ai/agents/handler.py index a13b1300e55..f7382190fca 100644 --- a/litellm/llms/azure_ai/agents/handler.py +++ b/litellm/llms/azure_ai/agents/handler.py @@ -51,15 +51,13 @@ else: AsyncHTTPHandler = Any -class _AzureRawAnnotation(TypedDict, total=False): - type: ReadOnly[str] +class _AzureRawAnnotation(ChatCompletionAnnotation, total=False): text: ReadOnly[str] start_index: ReadOnly[int] end_index: ReadOnly[int] - url_citation: ReadOnly[ChatCompletionAnnotationURLCitation] -_TransformedAnnotation: TypeAlias = ChatCompletionAnnotation | _AzureRawAnnotation +_TransformedAnnotation: TypeAlias = ChatCompletionAnnotation class _AzureText(TypedDict, total=False): @@ -223,18 +221,11 @@ class AzureAIAgentsHandler: """Build the ModelResponse from agent output.""" from litellm.types.utils import Choices, Message, Usage - message_kwargs: Final[dict[str, Any]] = { - "content": content, - "role": "assistant", - } - if annotations: - message_kwargs["annotations"] = annotations - model_response.choices = [ Choices( finish_reason="stop", index=0, - message=Message(**message_kwargs), + message=Message(content=content, role="assistant", annotations=annotations or None), ) ] model_response.model = model @@ -655,9 +646,6 @@ class AzureAIAgentsHandler: if data_str == "[DONE]": # Send final chunk with finish_reason - final_delta_kwargs: dict[str, Any] = {"content": None} - if collected_annotations: - final_delta_kwargs["annotations"] = collected_annotations final_chunk = ModelResponseStream( id=response_id, created=created, @@ -667,7 +655,7 @@ class AzureAIAgentsHandler: StreamingChoices( finish_reason="stop", index=0, - delta=Delta(**final_delta_kwargs), + delta=Delta(content=None, annotations=collected_annotations or None), ) ], ) diff --git a/litellm/llms/azure_ai/agents/transformation.py b/litellm/llms/azure_ai/agents/transformation.py index b81e6b0d62d..60ce81a23c7 100644 --- a/litellm/llms/azure_ai/agents/transformation.py +++ b/litellm/llms/azure_ai/agents/transformation.py @@ -34,6 +34,8 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler @@ -295,7 +297,7 @@ class AzureAIAgentsConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/azure_ai/azure_model_router/transformation.py b/litellm/llms/azure_ai/azure_model_router/transformation.py index 1a924088390..9e35e396e15 100644 --- a/litellm/llms/azure_ai/azure_model_router/transformation.py +++ b/litellm/llms/azure_ai/azure_model_router/transformation.py @@ -5,7 +5,7 @@ The Model Router is a special Azure AI deployment that automatically routes requ to the best available model. It has specific cost tracking requirements. """ -from typing import Any, Final +from typing import TYPE_CHECKING, Final from httpx import Response @@ -14,6 +14,9 @@ from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse +if TYPE_CHECKING: + import tiktoken + class AzureModelRouterConfig(AzureAIStudioConfig): """ @@ -56,7 +59,7 @@ class AzureModelRouterConfig(AzureAIStudioConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -65,15 +68,24 @@ class AzureModelRouterConfig(AzureAIStudioConfig): Extracts the actual model used from the Azure response (e.g., gpt-5-nano-2025-08-07) and returns it with the azure_ai/ prefix for proper display and cost tracking. + + Also stamps that model onto ``_hidden_params`` so downstream consumers (spend logs, + response restamping) can read it instead of guessing the route from the model string. """ - from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + from litellm.llms.azure_ai.common_utils import ( + AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY, + AzureFoundryModelInfo, + ) + from litellm.router_utils.add_retry_fallback_headers import ( + get_hidden_params_dict, + ) # Get base model for the parent call (strips routing prefixes for API compatibility) base_model: Final[str] = AzureFoundryModelInfo.get_base_model(model) # Call parent transform_response first - this will extract the actual model # from the raw response (e.g., "gpt-5-nano-2025-08-07") - model_response = super().transform_response( + transformed_response: Final = super().transform_response( model=base_model, raw_response=raw_response, model_response=model_response, @@ -86,7 +98,15 @@ class AzureModelRouterConfig(AzureAIStudioConfig): api_key=api_key, json_mode=json_mode, ) - return model_response + selected_model: Final = transformed_response.model + if selected_model: + # Rebuilt rather than mutated in place: ModelResponseBase declares _hidden_params as a + # class-level dict, so an in-place write can bleed into unrelated responses. + transformed_response._hidden_params = { # pyright: ignore[reportPrivateUsage] # ModelResponse exposes no public hidden-params setter # mutable-ok: ModelResponse requires _hidden_params to be a plain dict + **get_hidden_params_dict(transformed_response), + AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY: selected_model, + } + return transformed_response def calculate_additional_costs(self, model: str, prompt_tokens: int, completion_tokens: int) -> dict | None: """ diff --git a/litellm/llms/azure_ai/chat/transformation.py b/litellm/llms/azure_ai/chat/transformation.py index bc8ea31ea8c..7fe9d3dec52 100644 --- a/litellm/llms/azure_ai/chat/transformation.py +++ b/litellm/llms/azure_ai/chat/transformation.py @@ -1,7 +1,7 @@ import copy import enum import re -from typing import Any, Final, cast +from typing import TYPE_CHECKING, Final, cast from urllib.parse import urlparse import httpx @@ -25,12 +25,20 @@ from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import ModelResponse, ProviderField from litellm.utils import _add_path_to_api_base, supports_tool_choice +if TYPE_CHECKING: + import tiktoken + class AzureFoundryErrorStrings(str, enum.Enum): SET_EXTRA_PARAMETERS_TO_PASS_THROUGH = "Set extra-parameters to 'pass-through'" -NON_OPENAI_SPEC_MESSAGE_FIELDS: Final = ("thinking_blocks", "provider_specific_fields", "cache_control") +NON_OPENAI_SPEC_MESSAGE_FIELDS: Final = ( + "thinking_blocks", + "reasoning_content", + "provider_specific_fields", + "cache_control", +) class AzureAIStudioConfig(OpenAIConfig): @@ -173,7 +181,8 @@ class AzureAIStudioConfig(OpenAIConfig): """ - Azure AI Studio doesn't support content as a list. This handles: 1. Strips message fields that are not part of the OpenAI chat-completions - schema (thinking_blocks, provider_specific_fields, cache_control). + schema (thinking_blocks, reasoning_content, provider_specific_fields, + cache_control). Azure AI Foundry backends set additionalProperties=false and reject these with "Extra inputs are not permitted", which breaks multi-turn Anthropic-format clients that echo thinking blocks back as history. @@ -252,7 +261,7 @@ class AzureAIStudioConfig(OpenAIConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/azure_ai/common_utils.py b/litellm/llms/azure_ai/common_utils.py index d25a8fd6561..26a90157455 100644 --- a/litellm/llms/azure_ai/common_utils.py +++ b/litellm/llms/azure_ai/common_utils.py @@ -1,9 +1,57 @@ +from collections.abc import Mapping from typing import Final, Literal import litellm from litellm.llms.base_llm.base_utils import BaseLLMModelInfo, BaseTokenCounter from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues +from litellm.types.router import GenericLiteLLMParams + +AzureAIApiKeyHeader = Literal["Authorization", "api-key", "Api-Key", "Ocp-Apim-Subscription-Key"] + + +def get_azure_ai_entra_token(litellm_params: Mapping[str, object] | None = None) -> str | None: + """ + Resolve an Entra ID / OAuth access token for an Azure AI Foundry deployment. + + Accepts the same credential set as the `azure` provider: service principal + (`tenant_id` / `client_id` / `client_secret`), a pre-fetched `azure_ad_token`, an OIDC + federated token, username/password, or `DefaultAzureCredential` / managed identity. + """ + from litellm.llms.azure.common_utils import get_azure_ad_token + + params = GenericLiteLLMParams.model_validate(litellm_params) if litellm_params else GenericLiteLLMParams() + + return get_azure_ad_token(params) + + +def get_azure_ai_auth_headers( + api_key: str | None, + litellm_params: Mapping[str, object] | None = None, + api_key_header: AzureAIApiKeyHeader = "Authorization", + api_key_env_var: str = "AZURE_AI_API_KEY", +) -> Mapping[str, str]: + """ + Build the auth headers for an Azure AI Foundry route. + + Prefers the API key when one is configured, and otherwise falls back to Entra ID / OAuth, + sending the access token as a bearer token. + """ + if api_key: + return {api_key_header: f"Bearer {api_key}" if api_key_header == "Authorization" else api_key} + + azure_ad_token = get_azure_ai_entra_token(litellm_params=litellm_params) + if azure_ad_token: + return {"Authorization": f"Bearer {azure_ad_token}"} + + raise ValueError( + f"Missing Azure AI credentials - set an API key (`api_key` or {api_key_env_var}), or Entra ID / OAuth " + "credentials (`tenant_id` + `client_id` + `client_secret`, `azure_ad_token`, an OIDC token, or a managed " + "identity with `litellm.enable_azure_ad_token_refresh = True`)" + ) + + +AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY: Final = "azure_model_router_selected_model" class AzureFoundryModelInfo(BaseLLMModelInfo): @@ -37,13 +85,48 @@ class AzureFoundryModelInfo(BaseLLMModelInfo): return "model_router" return "default" + @staticmethod + def get_model_router_selected_model(hidden_params: Mapping[str, object] | None) -> str | None: + """The model Azure Model Router actually served, stamped by ``AzureModelRouterConfig``. + + Reading this beats re-deriving the route from a model string: the stamp is set on the + code path that was actually taken, so it holds no matter what the caller named the model. + """ + if not hidden_params: + return None + selected: Final = hidden_params.get(AZURE_MODEL_ROUTER_SELECTED_MODEL_KEY) + if isinstance(selected, str) and selected: + return selected + return None + + @staticmethod + def is_model_router_call( + model: str | None = None, + hidden_params: Mapping[str, object] | None = None, + ) -> bool: + """Whether a request went down the Azure Model Router route. + + Prefers the response stamp, then the deployment's litellm model path, and only then the + caller-supplied name. The last two go through ``get_azure_ai_route`` so the model-router + name heuristic lives in exactly one place. + """ + if AzureFoundryModelInfo.get_model_router_selected_model(hidden_params) is not None: + return True + deployment_model: Final = ( + hidden_params.get("litellm_model_name") or hidden_params.get("model") if hidden_params is not None else None + ) + return any( + isinstance(candidate, str) and AzureFoundryModelInfo.get_azure_ai_route(candidate) == "model_router" + for candidate in (deployment_model, model) + ) + @staticmethod def get_api_base(api_base: str | None = None) -> str | None: return api_base or litellm.api_base or get_secret_str("AZURE_AI_API_BASE") @staticmethod def get_api_key(api_key: str | None = None) -> str | None: - return api_key or litellm.api_key or litellm.openai_key or get_secret_str("AZURE_AI_API_KEY") + return api_key or litellm.api_key or get_secret_str("AZURE_AI_API_KEY") @property def api_version(self, api_version: str | None = None) -> str | None: diff --git a/litellm/llms/azure_ai/image_edit/flux2_transformation.py b/litellm/llms/azure_ai/image_edit/flux2_transformation.py index 3aac08ddcaf..a09a80985b7 100644 --- a/litellm/llms/azure_ai/image_edit/flux2_transformation.py +++ b/litellm/llms/azure_ai/image_edit/flux2_transformation.py @@ -5,7 +5,10 @@ from typing import Any, Final from httpx._types import RequestFiles import litellm -from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo +from litellm.llms.azure_ai.common_utils import ( + AzureFoundryModelInfo, + get_azure_ai_auth_headers, +) from litellm.llms.azure_ai.image_generation.flux_transformation import ( AzureFoundryFluxImageGenerationConfig, ) @@ -71,16 +74,13 @@ class AzureFoundryFlux2ImageEditConfig(OpenAIImageEditConfig): """ Validate Azure AI Foundry environment and set up authentication """ - api_key = AzureFoundryModelInfo.get_api_key(api_key) - - if not api_key: - raise ValueError( - f"Azure AI API key is required for model {model}. Set AZURE_AI_API_KEY environment variable or pass api_key parameter." - ) - headers.update( { - "Api-Key": api_key, + **get_azure_ai_auth_headers( + api_key=AzureFoundryModelInfo.get_api_key(api_key), + litellm_params=litellm_params, + api_key_header="Api-Key", + ), "Content-Type": "application/json", } ) diff --git a/litellm/llms/azure_ai/image_edit/mai_transformation.py b/litellm/llms/azure_ai/image_edit/mai_transformation.py index 73bd9957b8f..e639c20292b 100644 --- a/litellm/llms/azure_ai/image_edit/mai_transformation.py +++ b/litellm/llms/azure_ai/image_edit/mai_transformation.py @@ -3,7 +3,10 @@ from typing import TYPE_CHECKING, Any, Final, cast import httpx from httpx._types import RequestFiles -from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo +from litellm.llms.azure_ai.common_utils import ( + AzureFoundryModelInfo, + get_azure_ai_auth_headers, +) from litellm.llms.azure_ai.image_generation.mai_transformation import ( AzureFoundryMAIImageGenerationConfig, ) @@ -91,15 +94,13 @@ class AzureFoundryMAIImageEditConfig(OpenAIImageEditConfig): litellm_params: dict | None = None, api_base: str | None = None, ) -> dict: - api_key = AzureFoundryModelInfo.get_api_key(api_key) - - if not api_key: - raise ValueError( - f"Azure AI API key is required for model {model}. " - "Set AZURE_AI_API_KEY environment variable or pass api_key parameter." + headers.update( + get_azure_ai_auth_headers( + api_key=AzureFoundryModelInfo.get_api_key(api_key), + litellm_params=litellm_params, + api_key_header="api-key", ) - - headers.update({"api-key": api_key}) + ) return headers def get_complete_url( diff --git a/litellm/llms/azure_ai/image_edit/transformation.py b/litellm/llms/azure_ai/image_edit/transformation.py index efe3d8b2b88..1c626458df4 100644 --- a/litellm/llms/azure_ai/image_edit/transformation.py +++ b/litellm/llms/azure_ai/image_edit/transformation.py @@ -3,7 +3,10 @@ from typing import Final import httpx import litellm -from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo +from litellm.llms.azure_ai.common_utils import ( + AzureFoundryModelInfo, + get_azure_ai_auth_headers, +) from litellm.llms.openai.image_edit.transformation import OpenAIImageEditConfig from litellm.secret_managers.main import get_secret_str from litellm.utils import _add_path_to_api_base @@ -30,19 +33,14 @@ class AzureFoundryFluxImageEditConfig(OpenAIImageEditConfig): ) -> dict: """ Validate Azure AI Foundry environment and set up authentication - Uses Api-Key header format + Uses the Api-Key header format, or an Entra ID / OAuth bearer token when no key is set """ - api_key = AzureFoundryModelInfo.get_api_key(api_key) - - if not api_key: - raise ValueError( - f"Azure AI API key is required for model {model}. Set AZURE_AI_API_KEY environment variable or pass api_key parameter." - ) - headers.update( - { - "Api-Key": api_key, # Azure AI Foundry uses Api-Key header format - } + get_azure_ai_auth_headers( + api_key=AzureFoundryModelInfo.get_api_key(api_key), + litellm_params=litellm_params, + api_key_header="Api-Key", + ) ) return headers diff --git a/litellm/llms/azure_ai/image_generation/mai_transformation.py b/litellm/llms/azure_ai/image_generation/mai_transformation.py index 02e62f27d02..64f81956ad7 100644 --- a/litellm/llms/azure_ai/image_generation/mai_transformation.py +++ b/litellm/llms/azure_ai/image_generation/mai_transformation.py @@ -11,6 +11,7 @@ from litellm.types.utils import ImageResponse from litellm.utils import convert_to_model_response_object if TYPE_CHECKING: + import tiktoken from litellm.litellm_core_utils.logging import Logging as LiteLLMLoggingObj @@ -199,7 +200,7 @@ class AzureFoundryMAIImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py b/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py index e7b94b3812b..f5126f81006 100644 --- a/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py +++ b/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py @@ -12,7 +12,7 @@ import asyncio import re import time from collections.abc import Mapping -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final from urllib.parse import quote import httpx @@ -26,6 +26,7 @@ from litellm.constants import ( ) from litellm.exceptions import UnsupportedParamsError from litellm.litellm_core_utils.url_utils import SSRFError, assert_same_origin, encode_url_path_segment +from litellm.llms.azure_ai.common_utils import get_azure_ai_auth_headers from litellm.llms.base_llm.ocr.transformation import ( OCR_REQUEST_FORMAT_PARAM, BaseOCRConfig, @@ -40,6 +41,9 @@ from litellm.llms.base_llm.ocr.transformation import ( ) from litellm.secret_managers.main import get_secret_str +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + AZURE_DOCUMENT_INTELLIGENCE_API_KEY_ENV_VAR: Final = "AZURE_DOCUMENT_INTELLIGENCE_API_KEY" @@ -236,17 +240,13 @@ class AzureDocumentIntelligenceOCRConfig(BaseOCRConfig): """ Validate environment and return headers for Azure Document Intelligence. - Authentication uses Ocp-Apim-Subscription-Key header. + Authentication uses the Ocp-Apim-Subscription-Key header, or an Entra ID / OAuth bearer + token when no subscription key is set. """ # Get API key from environment if not provided if api_key is None: api_key = get_secret_str(AZURE_DOCUMENT_INTELLIGENCE_API_KEY_ENV_VAR) - if api_key is None: - raise ValueError( - "Missing Azure Document Intelligence API Key - Set AZURE_DOCUMENT_INTELLIGENCE_API_KEY environment variable or pass api_key parameter" - ) - # Validate API base/endpoint is provided if api_base is None: api_base = get_secret_str("AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT") @@ -257,7 +257,12 @@ class AzureDocumentIntelligenceOCRConfig(BaseOCRConfig): ) headers = { - "Ocp-Apim-Subscription-Key": api_key, + **get_azure_ai_auth_headers( + api_key=api_key, + litellm_params=litellm_params, + api_key_header="Ocp-Apim-Subscription-Key", + api_key_env_var=AZURE_DOCUMENT_INTELLIGENCE_API_KEY_ENV_VAR, + ), "Content-Type": "application/json", **headers, } @@ -674,7 +679,7 @@ class AzureDocumentIntelligenceOCRConfig(BaseOCRConfig): self, model: str, raw_response: httpx.Response, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", **kwargs, ) -> OCRResponse: """ @@ -749,7 +754,7 @@ class AzureDocumentIntelligenceOCRConfig(BaseOCRConfig): self, model: str, raw_response: httpx.Response, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", **kwargs, ) -> OCRResponse: """ diff --git a/litellm/llms/azure_ai/ocr/transformation.py b/litellm/llms/azure_ai/ocr/transformation.py index 24f96868eb3..dff6af71c99 100644 --- a/litellm/llms/azure_ai/ocr/transformation.py +++ b/litellm/llms/azure_ai/ocr/transformation.py @@ -9,6 +9,7 @@ from litellm.litellm_core_utils.prompt_templates.image_handling import ( async_convert_url_to_base64, convert_url_to_base64, ) +from litellm.llms.azure_ai.common_utils import get_azure_ai_auth_headers from litellm.llms.base_llm.ocr.transformation import DocumentType, OCRRequestData from litellm.llms.mistral.ocr.transformation import MistralOCRConfig from litellm.secret_managers.main import get_secret_str @@ -47,17 +48,12 @@ class AzureAIOCRConfig(MistralOCRConfig): """ Validate environment and return headers for Azure AI OCR. - Azure AI uses Bearer token authentication with AZURE_AI_API_KEY. + Authenticates with AZURE_AI_API_KEY, or with an Entra ID / OAuth token when no key is set. """ # Get API key from environment if not provided if api_key is None: api_key = get_secret_str(AZURE_AI_OCR_API_KEY_ENV_VAR) - if api_key is None: - raise ValueError( - "Missing Azure AI API Key - A call is being made to Azure AI but no key is set either in the environment variables or via params" - ) - # Validate API base is provided if api_base is None: api_base = get_secret_str("AZURE_AI_API_BASE") @@ -68,7 +64,7 @@ class AzureAIOCRConfig(MistralOCRConfig): ) headers = { - "Authorization": f"Bearer {api_key}", + **get_azure_ai_auth_headers(api_key=api_key, litellm_params=litellm_params), "Content-Type": "application/json", **headers, } diff --git a/litellm/llms/azure_ai/rerank/transformation.py b/litellm/llms/azure_ai/rerank/transformation.py index 1212d2c1689..64372c53f09 100644 --- a/litellm/llms/azure_ai/rerank/transformation.py +++ b/litellm/llms/azure_ai/rerank/transformation.py @@ -2,12 +2,14 @@ Translate between Cohere's `/rerank` format and Azure AI's `/rerank` format. """ +from collections.abc import Mapping from typing import Final import httpx import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.azure_ai.common_utils import get_azure_ai_auth_headers from litellm.llms.cohere.rerank.transformation import CohereRerankConfig from litellm.secret_managers.main import get_secret_str from litellm.types.utils import RerankResponse @@ -64,15 +66,13 @@ class AzureAIRerankConfig(CohereRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: if api_key is None: api_key = get_secret_str("AZURE_AI_API_KEY") or litellm.azure_key - if api_key is None: - raise ValueError("Azure AI API key is required. Please set 'AZURE_AI_API_KEY' or 'litellm.azure_key'") - default_headers: Final = { - "Authorization": f"Bearer {api_key}", + **get_azure_ai_auth_headers(api_key=api_key, litellm_params=litellm_params), "accept": "application/json", "content-type": "application/json", } diff --git a/litellm/llms/azure_ai/vector_stores/transformation.py b/litellm/llms/azure_ai/vector_stores/transformation.py index 5e61d0a1dd9..64e72b819b1 100644 --- a/litellm/llms/azure_ai/vector_stores/transformation.py +++ b/litellm/llms/azure_ai/vector_stores/transformation.py @@ -19,6 +19,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -115,6 +116,7 @@ class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict[str, Any]]: """ Transform search request for Azure AI Search API diff --git a/litellm/llms/base.py b/litellm/llms/base.py index 7dec5509c46..8f6f45f4d35 100644 --- a/litellm/llms/base.py +++ b/litellm/llms/base.py @@ -6,6 +6,7 @@ import httpx import litellm if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm.types.utils import ModelResponse, TextCompletionResponse @@ -19,7 +20,7 @@ class BaseLLM: response: httpx.Response, model_response: "ModelResponse", stream: bool, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", optional_params: dict, api_key: str, data: dict | str, @@ -38,7 +39,7 @@ class BaseLLM: response: httpx.Response, model_response: "TextCompletionResponse", stream: bool, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", optional_params: dict, api_key: str, data: dict | str, diff --git a/litellm/llms/base_llm/anthropic_messages/transformation.py b/litellm/llms/base_llm/anthropic_messages/transformation.py index 6455bb010f4..8e7c22930fa 100644 --- a/litellm/llms/base_llm/anthropic_messages/transformation.py +++ b/litellm/llms/base_llm/anthropic_messages/transformation.py @@ -159,20 +159,20 @@ class BaseAnthropicMessagesConfig(ABC): and issue one more attempt (bounded by max_retry_on_anthropic_messages_http_error). """ from litellm.llms.anthropic.common_utils import ( - is_anthropic_invalid_thinking_signature_error, + is_anthropic_invalid_thinking_block_error, ) - return e.response.status_code == 400 and is_anthropic_invalid_thinking_signature_error(e.response.text) + return e.response.status_code == 400 and is_anthropic_invalid_thinking_block_error(e.response.text) def transform_anthropic_messages_request_on_http_error(self, e: httpx.HTTPStatusError, request_data: dict) -> dict: """ Mutates request_data in place when retrying after a recoverable HTTP error. """ from litellm.llms.anthropic.common_utils import ( - is_anthropic_invalid_thinking_signature_error, + is_anthropic_invalid_thinking_block_error, strip_thinking_blocks_from_anthropic_messages_request_dict, ) - if e.response.status_code == 400 and is_anthropic_invalid_thinking_signature_error(e.response.text): + if e.response.status_code == 400 and is_anthropic_invalid_thinking_block_error(e.response.text): strip_thinking_blocks_from_anthropic_messages_request_dict(request_data) return request_data diff --git a/litellm/llms/base_llm/audio_transcription/transformation.py b/litellm/llms/base_llm/audio_transcription/transformation.py index 6d087102816..b323c4812b5 100644 --- a/litellm/llms/base_llm/audio_transcription/transformation.py +++ b/litellm/llms/base_llm/audio_transcription/transformation.py @@ -12,6 +12,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import FileTypes, ModelResponse, TranscriptionResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -40,6 +42,16 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC): def get_supported_openai_params(self, model: str) -> list[OpenAIAudioTranscriptionOptionalParams]: pass + @property + def supports_subtitle_synthesis(self) -> bool: + """ + Opt-in for providers without a native srt/vtt response body: when True + and the user asked for response_format srt/vtt, the http handler + synthesizes the subtitle document from the word timestamps the + provider's TranscriptionResponse carries in `words`. + """ + return False + def get_complete_url( self, api_base: str | None, @@ -100,7 +112,7 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/base_llm/bridges/completion_transformation.py b/litellm/llms/base_llm/bridges/completion_transformation.py index 2d5879dc8e3..87b55152d09 100644 --- a/litellm/llms/base_llm/bridges/completion_transformation.py +++ b/litellm/llms/base_llm/bridges/completion_transformation.py @@ -4,9 +4,10 @@ Bridge for transforming API requests to another API requests from abc import ABC, abstractmethod from collections.abc import AsyncIterator, Iterator -from typing import TYPE_CHECKING, Any, Union +from typing import TYPE_CHECKING, Union if TYPE_CHECKING: + import tiktoken from pydantic import BaseModel from litellm import LiteLLMLoggingObj, ModelResponse @@ -38,7 +39,7 @@ class CompletionTransformationBridge(ABC): messages: list["AllMessageValues"], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> "ModelResponse": diff --git a/litellm/llms/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py index d147063df73..bbe1cc85df1 100644 --- a/litellm/llms/base_llm/chat/transformation.py +++ b/litellm/llms/base_llm/chat/transformation.py @@ -21,6 +21,8 @@ from litellm.types.llms.openai import ( ) if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm.types.utils import ModelResponse @@ -46,8 +48,10 @@ class BaseLLMException(Exception): request: httpx.Request | None = None, response: httpx.Response | None = None, body: dict | None = None, + status_code_is_synthesized: bool = False, ): self.status_code = status_code + self.status_code_is_synthesized = status_code_is_synthesized self.message: str = message self.headers = headers if request: @@ -340,7 +344,7 @@ class BaseConfig(ABC): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> "ModelResponse": diff --git a/litellm/llms/base_llm/completion/transformation.py b/litellm/llms/base_llm/completion/transformation.py index c38199b0966..fb472dfa63b 100644 --- a/litellm/llms/base_llm/completion/transformation.py +++ b/litellm/llms/base_llm/completion/transformation.py @@ -8,6 +8,8 @@ from litellm.types.llms.openai import AllMessageValues, OpenAITextCompletionUser from litellm.types.utils import ModelResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -66,7 +68,7 @@ class BaseTextCompletionConfig(BaseConfig, ABC): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/base_llm/embedding/transformation.py b/litellm/llms/base_llm/embedding/transformation.py index 0330c0118bd..da87dcc7f98 100644 --- a/litellm/llms/base_llm/embedding/transformation.py +++ b/litellm/llms/base_llm/embedding/transformation.py @@ -8,6 +8,8 @@ from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues from litellm.types.utils import EmbeddingResponse, ModelResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -78,7 +80,7 @@ class BaseEmbeddingConfig(BaseConfig, ABC): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/base_llm/files/transformation.py b/litellm/llms/base_llm/files/transformation.py index 174be93448b..7a7088c2fb5 100644 --- a/litellm/llms/base_llm/files/transformation.py +++ b/litellm/llms/base_llm/files/transformation.py @@ -11,15 +11,17 @@ from litellm.types.llms.openai import ( AllMessageValues, CreateFileRequest, FileContentRequest, + FileListPage, OpenAICreateFileRequestOptionalParams, OpenAIFileObject, - OpenAIFilesPurpose, ) from litellm.types.utils import LlmProviders, ModelResponse from ..chat.transformation import BaseConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.router import Router as _Router from litellm.types.llms.openai import HttpxBinaryResponseContent @@ -207,7 +209,7 @@ class BaseFilesConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -240,10 +242,13 @@ class BaseFileEndpoints(ABC): @abstractmethod async def afile_list( self, - purpose: OpenAIFilesPurpose | None, + purpose: str | None, litellm_parent_otel_span: Span | None, + user_api_key_dict: UserAPIKeyAuth, + limit: int | None = None, + after: str | None = None, **data: dict, - ) -> list[OpenAIFileObject]: + ) -> FileListPage: pass @abstractmethod diff --git a/litellm/llms/base_llm/guardrail_translation/base_translation.py b/litellm/llms/base_llm/guardrail_translation/base_translation.py index ba96ab3dc99..220fcedb0f8 100644 --- a/litellm/llms/base_llm/guardrail_translation/base_translation.py +++ b/litellm/llms/base_llm/guardrail_translation/base_translation.py @@ -1,8 +1,11 @@ from abc import ABC, abstractmethod +from collections.abc import Sequence from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Final, Optional if TYPE_CHECKING: + from fastapi import HTTPException + from litellm.integrations.custom_guardrail import ( CustomGuardrail, ModifyResponseException, @@ -73,6 +76,31 @@ class BaseTranslation(ABC): return transformed + @staticmethod + def merge_user_api_key_metadata_into_request( + request_data: dict[str, Any], # mutable-ok: proxy hooks share and mutate the request payload dict in place + user_api_key_dict: Optional["UserAPIKeyAuth"], + ) -> None: + """ + Add the prefixed ``user_api_key_*`` metadata to the request's resolved + metadata bucket without overwriting existing keys. + + Writes must go through ``get_or_create_metadata_bucket``: creating a + ``litellm_metadata`` key on a route whose bucket is ``metadata`` (chat + completions) flips the bucket for every later metadata write, and spend + logging never sees those writes (e.g. guardrail_information). + """ + from litellm.litellm_core_utils.core_helpers import ( + get_or_create_metadata_bucket, + ) + + user_metadata: Final = BaseTranslation.transform_user_api_key_dict_to_metadata(user_api_key_dict) + if not user_metadata: + return + _, metadata_bucket = get_or_create_metadata_bucket(request_data) + for key, value in user_metadata.items(): + metadata_bucket.setdefault(key, value) + @abstractmethod async def process_input_messages( self, @@ -127,8 +155,8 @@ class BaseTranslation(ABC): self, exc: "ModifyResponseException", stream_started: bool = False, - responses_so_far: list[Any] | None = None, - ) -> list[bytes] | None: + responses_so_far: Sequence[Any] | None = None, + ) -> Sequence[bytes] | None: """ Build the streaming chunks that deliver a guardrail block message and cleanly terminate the stream in this provider's wire format. @@ -147,6 +175,26 @@ class BaseTranslation(ABC): """ return None + def build_stream_error_items( + self, + exc: "HTTPException", + responses_so_far: Sequence[Any] | None = None, + ) -> Sequence[Any] | None: + """ + Build the stream items that surface a guardrail HTTPException (a block + with the default exception-on-block config, or a failed scan) after the + response has already started streaming, in this endpoint's wire format. + + Called only once chunks have been sent: the HTTP status is gone, so the + failure must travel as an in-stream error frame. ``responses_so_far`` + holds the chunks the client has already received, for formats whose + error frame continues the stream (e.g. sequence numbers). + + Returns None when the format has no in-stream error frame; the caller + then re-raises ``exc``. Override in endpoint subclasses. + """ + return None + def get_structured_messages(self, data: dict) -> list["AllMessageValues"] | None: """ Convert request data to OpenAI-spec structured messages. diff --git a/litellm/llms/base_llm/guardrail_translation/utils.py b/litellm/llms/base_llm/guardrail_translation/utils.py index 1546adbb0bd..9b6f9c47105 100644 --- a/litellm/llms/base_llm/guardrail_translation/utils.py +++ b/litellm/llms/base_llm/guardrail_translation/utils.py @@ -124,6 +124,61 @@ def blocked_responses_api_usage(original_response: object) -> ResponseAPIUsage: ) +def stream_item_field(item: object, field: str) -> object | None: + if isinstance(item, dict): + return item.get(field) + return getattr(item, field, None) + + +def blocked_chat_stream_usage(original_response: object) -> tuple[int, int]: + """ + ``(prompt_tokens, completion_tokens)`` for a synthetic guardrail-blocked + chat completions stream. + + A mid-stream block carries the chunks received so far as a list; real usage + rides on the final chunk when the upstream sent one + (``stream_options.include_usage``). Non-list originals defer to + ``blocked_response_usage``. + """ + if not isinstance(original_response, list): + usage: Final = blocked_response_usage(original_response) + return usage.get("input_tokens", 0), usage.get("output_tokens", 0) + usage_obj: Final = next( + ( + chunk_usage + for item in reversed(original_response) + if (chunk_usage := stream_item_field(item, "usage")) is not None + ), + None, + ) + return ( + _usage_tokens(usage_obj, "prompt_tokens", "input_tokens"), + _usage_tokens(usage_obj, "completion_tokens", "output_tokens"), + ) + + +def blocked_responses_stream_usage(original_response: object) -> ResponseAPIUsage: + """ + ``ResponseAPIUsage`` for a synthetic guardrail-blocked /v1/responses stream. + + A mid-stream block carries the events received so far as a list; real usage + rides on the ``response.completed`` event's response when the upstream sent + one. Non-list originals defer to ``blocked_responses_api_usage``. + """ + if not isinstance(original_response, list): + return blocked_responses_api_usage(original_response) + completed: Final = next( + ( + response + for item in reversed(original_response) + if stream_item_field(item, "type") == "response.completed" + and (response := stream_item_field(item, "response")) is not None + ), + None, + ) + return blocked_responses_api_usage(completed) + + def effective_skip_system_message_for_guardrail(guardrail_to_apply: Any) -> bool: per: Final = getattr(guardrail_to_apply, "skip_system_message_in_guardrail", None) if per is not None: @@ -158,6 +213,22 @@ def openai_messages_without_tool( return tuple(m for m in messages if _message_role(m) != "tool") +def filter_messages_by_skip_flags( + guardrail_to_apply: object, messages: Sequence[AllMessageValues] +) -> tuple[tuple[AllMessageValues, ...], bool]: + system_filtered = ( + openai_messages_without_system(messages) + if effective_skip_system_message_for_guardrail(guardrail_to_apply) + else tuple(messages) + ) + fully_filtered = ( + openai_messages_without_tool(system_filtered) + if effective_skip_tool_message_for_guardrail(guardrail_to_apply) + else system_filtered + ) + return fully_filtered, len(fully_filtered) != len(messages) + + def effective_scan_only_tool_results_for_guardrail(guardrail_to_apply: object) -> bool: return getattr(guardrail_to_apply, "scan_only_tool_results", None) is True @@ -209,9 +280,20 @@ def openai_tool_name(tool: object) -> str | None: return flat_name if isinstance(flat_name, str) else None +def anthropic_tool_names(tool: object) -> tuple[str, ...]: + """Every name a /v1/messages tool dict can act under: the flat Anthropic ``name`` plus + ``function.name`` for OpenAI-format tools the bridge forwards verbatim. Allowlist checks + must see both, or a decoy flat name could smuggle a disallowed ``function.name`` through.""" + if not isinstance(tool, dict): + return () + function: Final = tool.get("function") if tool.get("type") == "function" else None + function_name: Final = function.get("name") if isinstance(function, dict) else None + return tuple(name for name in (tool.get("name"), function_name) if isinstance(name, str) and name) + + def anthropic_tool_name(tool: object) -> str | None: - name: Final = tool.get("name") if isinstance(tool, dict) else None - return name if isinstance(name, str) else None + names: Final = anthropic_tool_names(tool) + return names[0] if names else None def merge_returned_tools_into_request_tools( diff --git a/litellm/llms/base_llm/image_generation/transformation.py b/litellm/llms/base_llm/image_generation/transformation.py index 4ce4add0432..4616441133e 100644 --- a/litellm/llms/base_llm/image_generation/transformation.py +++ b/litellm/llms/base_llm/image_generation/transformation.py @@ -11,6 +11,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -91,7 +93,7 @@ class BaseImageGenerationConfig(ABC): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/base_llm/image_variations/transformation.py b/litellm/llms/base_llm/image_variations/transformation.py index beae828c301..d3e02139e0e 100644 --- a/litellm/llms/base_llm/image_variations/transformation.py +++ b/litellm/llms/base_llm/image_variations/transformation.py @@ -17,6 +17,8 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -80,7 +82,7 @@ class BaseImageVariationConfig(BaseConfig, ABC): image: FileTypes, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, ) -> ImageResponse: pass @@ -96,7 +98,7 @@ class BaseImageVariationConfig(BaseConfig, ABC): image: FileTypes, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, ) -> ImageResponse: pass @@ -123,7 +125,7 @@ class BaseImageVariationConfig(BaseConfig, ABC): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/base_llm/managed_resources/base_managed_resource.py b/litellm/llms/base_llm/managed_resources/base_managed_resource.py index 2a59eddf88a..4fbc0ce51b0 100644 --- a/litellm/llms/base_llm/managed_resources/base_managed_resource.py +++ b/litellm/llms/base_llm/managed_resources/base_managed_resource.py @@ -5,13 +5,15 @@ import base64 import json from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Final, Generic, TypeVar, cast +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, Generic, Protocol, TypeVar, cast, runtime_checkable from litellm import verbose_logger from litellm.llms.base_llm.managed_resources.isolation import ( build_list_page, build_owner_filter, can_access_resource, + resolve_resource_owner_id, ) from litellm.proxy._types import UserAPIKeyAuth from litellm.types.utils import SpecialEnums @@ -37,6 +39,30 @@ else: ResourceObjectType = TypeVar("ResourceObjectType") +@runtime_checkable +class _HasIdentifier(Protocol): + id: str + + +class _ManagedResourceRecord(Protocol[ResourceObjectType]): + unified_resource_id: str + resource_object: ResourceObjectType + + def model_dump(self) -> dict[str, object]: ... + + +class _ManagedResourceTable(Protocol[ResourceObjectType]): + async def create(self, *, data: Mapping[str, object]) -> object: ... + + async def find_first(self, *, where: Mapping[str, object]) -> _ManagedResourceRecord[ResourceObjectType] | None: ... + + async def find_many( + self, *, where: Mapping[str, object], take: int, order: Mapping[str, str] + ) -> list[_ManagedResourceRecord[ResourceObjectType]]: ... + + async def delete(self, *, where: Mapping[str, object]) -> object: ... + + class BaseManagedResource(ABC, Generic[ResourceObjectType]): """ Base class for managing resources with target_model_names support. @@ -63,6 +89,9 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): self.internal_usage_cache = internal_usage_cache self.prisma_client = prisma_client + def _resource_table(self) -> _ManagedResourceTable[ResourceObjectType]: + return getattr(self.prisma_client.db, self.table_name) + # ============================================================================ # ABSTRACT METHODS # ============================================================================ @@ -136,7 +165,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): litellm_parent_otel_span: Span | None, model_mappings: dict[str, str], user_api_key_dict: UserAPIKeyAuth, - additional_db_fields: dict[str, Any] | None = None, + additional_db_fields: Mapping[str, object] | None = None, ) -> None: """ Store unified resource ID with model mappings in cache and database. @@ -152,12 +181,12 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): verbose_logger.info("Storing LiteLLM Managed %s with id=%s in cache", self.resource_type, unified_resource_id) # Prepare cache data - cache_data: Final = { + cache_data: Final[dict[str, object]] = { "unified_resource_id": unified_resource_id, "resource_object": resource_object, "model_mappings": model_mappings, "flat_model_resource_ids": list(model_mappings.values()), - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -175,11 +204,11 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): ) # Prepare database data - db_data: Final = { + db_data: Final[dict[str, object]] = { "unified_resource_id": unified_resource_id, "model_mappings": json.dumps(model_mappings), "flat_model_resource_ids": list(model_mappings.values()), - "created_by": user_api_key_dict.user_id, + "created_by": resolve_resource_owner_id(user_api_key_dict), "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -204,7 +233,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): db_data.update(additional_db_fields) # Store in database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() result: Final = await table.create(data=db_data) verbose_logger.debug( @@ -239,7 +268,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): return result # Check database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() db_object: Final = await table.find_first(where={"unified_resource_id": unified_resource_id}) if db_object: @@ -263,7 +292,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): The deleted resource object or None if not found """ # Get old value from database - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() initial_value: Final = await table.find_first(where={"unified_resource_id": unified_resource_id}) if initial_value is None: @@ -514,7 +543,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): user_api_key_dict: UserAPIKeyAuth, limit: int | None = None, after: str | None = None, - additional_filters: dict[str, Any] | None = None, + additional_filters: Mapping[str, object] | None = None, ) -> dict[str, Any]: """ List resources created by a user. @@ -532,7 +561,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): if owner_filter is None: return build_list_page([]) - where_clause: Final[dict[str, Any]] = {**owner_filter} + where_clause: Final[dict[str, object]] = {**owner_filter} if after: where_clause["id"] = {"gt": after} @@ -543,14 +572,14 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): # Fetch resources fetch_limit: Final = limit or 20 - table: Final = getattr(self.prisma_client.db, self.table_name) + table: Final = self._resource_table() resources: Final = await table.find_many( where=where_clause, take=fetch_limit, order={"created_at": "desc"}, ) - resource_objects: Final[list[Any]] = [] + resource_objects: Final[list[object]] = [] for resource in resources: try: # Stop once we have enough @@ -558,12 +587,13 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): break # Parse resource object - resource_data = resource.resource_object - if isinstance(resource_data, str): - resource_data = json.loads(resource_data) + stored_resource = resource.resource_object + resource_data: object = ( + json.loads(stored_resource) if isinstance(stored_resource, str) else stored_resource + ) # Set unified ID - if hasattr(resource_data, "id"): + if isinstance(resource_data, _HasIdentifier): resource_data.id = resource.unified_resource_id elif isinstance(resource_data, dict): resource_data["id"] = resource.unified_resource_id diff --git a/litellm/llms/base_llm/managed_resources/isolation.py b/litellm/llms/base_llm/managed_resources/isolation.py index e1b204214d7..6a71e8e9223 100644 --- a/litellm/llms/base_llm/managed_resources/isolation.py +++ b/litellm/llms/base_llm/managed_resources/isolation.py @@ -3,10 +3,11 @@ Tenant-isolation helpers for managed file/batch/vector-store resources. Returns a Prisma filter and an ownership check that scope managed resources to the caller's identity: proxy admins see everything, user-keyed callers -see records they created, and service-account keys (no user_id) fall back -to the resource's owning team. Callers with no admin role and no -identifying ids are denied so an empty user_id can never select an -unscoped query. +see records they created, service-account keys (no user_id) fall back to +the resource's owning team, and keys with neither a user_id nor a team_id +fall back to their own hashed token so they can still reach the resources +they created. Callers with no admin role and no identifying ids at all +are denied so an empty user_id can never select an unscoped query. """ from typing import Any, Final @@ -19,6 +20,32 @@ from litellm.proxy._types import ( ) +def resolve_resource_owner_id( + user_api_key_dict: UserAPIKeyAuth, +) -> str | None: + """Return the identity to stamp on (and match against) a managed + resource's ``created_by``. + + A key with neither a user_id nor a team_id would otherwise stamp + ``created_by=None`` and be locked out of its own resources, so it owns + them under its hashed token instead, using the ``key:`` scope prefix + already used by ``proxy/common_utils/resource_ownership.py``. ``None`` + means the caller has no usable identity of its own and must fall back + to team scoping, or be denied. + """ + if user_api_key_dict.user_id is not None: + return user_api_key_dict.user_id + + if user_api_key_dict.team_id is not None: + return None + + token: Final = user_api_key_dict.token or user_api_key_dict.api_key + if token: + return f"key:{token}" + + return None + + def build_list_page(items: list[Any], has_more: bool = False) -> dict[str, Any]: """Build the OpenAI-style paginated list response shape used by managed file/batch/vector-store listings. ``first_id`` and ``last_id`` are @@ -39,7 +66,8 @@ def build_owner_filter( to records the caller is allowed to see. - ``{}`` means no scoping (proxy admins). - - ``{"created_by": }`` for user-keyed callers. + - ``{"created_by": }`` for user-keyed callers, and for keys + with no user_id and no team_id (owner id is their hashed token). - ``{"team_id": }`` for service-account callers that have a team but no user_id. - ``{"OR": [...]}`` when the caller has both — listing must include @@ -62,12 +90,13 @@ def build_owner_filter( ] } - if user_id is not None: - return {"created_by": user_id} - if team_id is not None: return {"team_id": team_id} + owner_id: Final = resolve_resource_owner_id(user_api_key_dict) + if owner_id is not None: + return {"created_by": owner_id} + return None @@ -86,8 +115,8 @@ def can_access_resource( if _user_has_admin_view(user_api_key_dict): return True - user_id: Final = user_api_key_dict.user_id - if user_id is not None and created_by is not None and created_by == user_id: + owner_id: Final = resolve_resource_owner_id(user_api_key_dict) + if owner_id is not None and created_by is not None and created_by == owner_id: return True team_id: Final = user_api_key_dict.team_id diff --git a/litellm/llms/base_llm/ocr/transformation.py b/litellm/llms/base_llm/ocr/transformation.py index d1c77186ea8..3b302837032 100644 --- a/litellm/llms/base_llm/ocr/transformation.py +++ b/litellm/llms/base_llm/ocr/transformation.py @@ -75,6 +75,7 @@ class OCRUsageInfo(LiteLLMPydanticObjectBase): """Usage information from OCR response.""" pages_processed: int | None = None + pages_processed_annotation: int | None = None credits: float | None = None doc_size_bytes: int | None = None diff --git a/litellm/llms/base_llm/realtime/transformation.py b/litellm/llms/base_llm/realtime/transformation.py index 26c189504df..cfcde7c6e9e 100644 --- a/litellm/llms/base_llm/realtime/transformation.py +++ b/litellm/llms/base_llm/realtime/transformation.py @@ -5,6 +5,7 @@ import httpx from litellm.types.llms.openai import OpenAIRealtimeStreamSessionEvents from litellm.types.realtime import ( + RealtimeInputAudioTranscriptionUsage, RealtimeResponseTransformInput, RealtimeResponseTypedDict, ) @@ -70,6 +71,9 @@ class BaseRealtimeConfig(ABC): def session_configuration_request(self, model: str) -> str | None: # message sent to setup the realtime session return None + def unbilled_usage_on_session_close(self, model: str) -> RealtimeInputAudioTranscriptionUsage | None: + return None + def transform_session_created_event( self, model: str, diff --git a/litellm/llms/base_llm/rerank/transformation.py b/litellm/llms/base_llm/rerank/transformation.py index 3a946fb4af4..5d2f92b5e82 100644 --- a/litellm/llms/base_llm/rerank/transformation.py +++ b/litellm/llms/base_llm/rerank/transformation.py @@ -1,4 +1,5 @@ from abc import ABC, abstractmethod +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import httpx @@ -24,6 +25,7 @@ class BaseRerankConfig(ABC): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: pass diff --git a/litellm/llms/base_llm/vector_store/transformation.py b/litellm/llms/base_llm/vector_store/transformation.py index 02a51a8bace..63e99c0915a 100644 --- a/litellm/llms/base_llm/vector_store/transformation.py +++ b/litellm/llms/base_llm/vector_store/transformation.py @@ -17,6 +17,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router from ..chat.transformation import BaseLLMException as _BaseLLMException @@ -57,6 +58,7 @@ class BaseVectorStoreConfig: litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: pass @@ -69,6 +71,7 @@ class BaseVectorStoreConfig: litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """ Optional async version of transform_search_vector_store_request. @@ -84,6 +87,7 @@ class BaseVectorStoreConfig: litellm_logging_obj=litellm_logging_obj, litellm_params=litellm_params, extra_body=extra_body, + router=router, ) @abstractmethod @@ -197,6 +201,7 @@ class BaseDirectVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: Mapping[str, object], extra_body: Mapping[str, object] | None = None, + router: "Router | None" = None, ) -> NoReturn: raise NotImplementedError("Direct vector store providers execute the search themselves; no HTTP request shape") diff --git a/litellm/llms/base_llm/videos/transformation.py b/litellm/llms/base_llm/videos/transformation.py index 1aea3cafe33..f725b295d0f 100644 --- a/litellm/llms/base_llm/videos/transformation.py +++ b/litellm/llms/base_llm/videos/transformation.py @@ -1,9 +1,10 @@ import types from abc import ABC, abstractmethod +from collections.abc import Mapping from typing import TYPE_CHECKING, Any import httpx -from httpx._types import RequestFiles +from httpx._types import FileContent, RequestFiles from litellm.types.responses.main import * from litellm.types.router import GenericLiteLLMParams @@ -91,6 +92,14 @@ class BaseVideoConfig(ABC): raise ValueError("api_base is required") return api_base + def use_multipart_form_data(self) -> bool: + """ + Whether video create requests without files must still be sent as + multipart/form-data (the encoding the OpenAI SDK always uses for + /videos), instead of falling back to JSON. + """ + return False + @abstractmethod def transform_video_create_request( self, @@ -332,14 +341,18 @@ class BaseVideoConfig(ABC): api_base: str, litellm_params: GenericLiteLLMParams, headers: dict, + video_file: FileContent | None = None, extra_body: dict[str, Any] | None = None, prefetched_source_data: dict[str, Any] | None = None, - ) -> tuple[str, dict]: + ) -> tuple[str, Mapping[str, object], RequestFiles | None]: """ - Transform the video edit request into a URL and JSON data. + Transform the video edit request into a URL plus either JSON data or + multipart form fields and files. Returns: - Tuple[str, Dict]: (url, data) for the POST request + tuple[str, Mapping[str, object], RequestFiles | None]: (url, data, + files). When files is None the handler sends data as JSON; otherwise + data holds the form fields and files holds the uploaded source video. """ raise NotImplementedError("video edit is not supported for this provider") diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index db6f2c0d491..1e634ced29b 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -23,6 +23,7 @@ from litellm.constants import ( BEDROCK_MAX_POLICY_SIZE, STS_CREDENTIAL_EXPIRY_SAFETY_MARGIN_SECONDS, ) +from litellm.litellm_core_utils.aws_partition import contains_bedrock_arn, get_aws_dns_suffix from litellm.litellm_core_utils.dd_tracing import tracer from litellm.secret_managers.main import get_secret, get_secret_str @@ -348,7 +349,7 @@ class BaseAWSLLM: def _get_aws_region_from_model_arn(self, model: str | None) -> str | None: try: # First check if the string contains the expected prefix - if not isinstance(model, str) or "arn:aws:bedrock" not in model: + if not isinstance(model, str) or not contains_bedrock_arn(model): return None # Split the ARN and check if we have enough parts @@ -625,24 +626,29 @@ class BaseAWSLLM: return match.group(1) if match else None @staticmethod - def _resolve_sts_region(aws_sts_endpoint: str | None = None) -> str | None: - """STS signing region: parsed from aws_sts_endpoint else AWS_REGION / AWS_DEFAULT_REGION.""" + def _resolve_sts_region( + aws_sts_endpoint: str | None = None, + aws_region_name: str | None = None, + ) -> str | None: + """STS signing region: parsed from aws_sts_endpoint, else AWS_REGION / AWS_DEFAULT_REGION, else the configured aws_region_name.""" return ( BaseAWSLLM._parse_sts_region_from_endpoint(aws_sts_endpoint) or os.getenv("AWS_REGION") or os.getenv("AWS_DEFAULT_REGION") + or aws_region_name ) def _build_sts_client_kwargs( self, aws_sts_endpoint: str | None = None, ssl_verify: bool | str | None = None, + aws_region_name: str | None = None, ) -> dict: """STS client kwargs with aligned endpoint_url and region_name (SigV4).""" kwargs: Final[dict] = {"verify": self._get_ssl_verify(ssl_verify)} if aws_sts_endpoint is not None: kwargs["endpoint_url"] = aws_sts_endpoint - sts_region: Final = self._resolve_sts_region(aws_sts_endpoint) + sts_region: Final = self._resolve_sts_region(aws_sts_endpoint, aws_region_name) if sts_region is not None: kwargs["region_name"] = sts_region return kwargs @@ -837,6 +843,7 @@ class BaseAWSLLM: sts_client_kwargs: Final = self._build_sts_client_kwargs( aws_sts_endpoint=aws_sts_endpoint, ssl_verify=ssl_verify, + aws_region_name=aws_region_name, ) with tracer.trace("boto3.client(sts)"): @@ -948,6 +955,7 @@ class BaseAWSLLM: aws_external_id: str | None = None, aws_sts_endpoint: str | None = None, ssl_verify: bool | str | None = None, + aws_region_name: str | None = None, ) -> dict: """Handle cross-account role assumption for IRSA.""" import boto3 @@ -961,6 +969,7 @@ class BaseAWSLLM: irsa_sts_kwargs: Final = self._build_sts_client_kwargs( aws_sts_endpoint=aws_sts_endpoint, ssl_verify=ssl_verify, + aws_region_name=aws_region_name, ) # Create an STS client without credentials @@ -1017,6 +1026,7 @@ class BaseAWSLLM: aws_external_id: str | None = None, aws_sts_endpoint: str | None = None, ssl_verify: bool | str | None = None, + aws_region_name: str | None = None, ) -> dict: """Handle same-account role assumption for IRSA.""" import boto3 @@ -1024,6 +1034,7 @@ class BaseAWSLLM: irsa_sts_kwargs: Final = self._build_sts_client_kwargs( aws_sts_endpoint=aws_sts_endpoint, ssl_verify=ssl_verify, + aws_region_name=aws_region_name, ) verbose_logger.debug("Same account role assumption, using automatic IRSA") @@ -1153,6 +1164,7 @@ class BaseAWSLLM: aws_external_id, aws_sts_endpoint=aws_sts_endpoint, ssl_verify=ssl_verify, + aws_region_name=aws_region_name, ) else: sts_response = self._handle_irsa_same_account( @@ -1161,6 +1173,7 @@ class BaseAWSLLM: aws_external_id, aws_sts_endpoint=aws_sts_endpoint, ssl_verify=ssl_verify, + aws_region_name=aws_region_name, ) return self._extract_credentials_and_ttl(sts_response) @@ -1182,6 +1195,7 @@ class BaseAWSLLM: sts_client_kwargs: Final = self._build_sts_client_kwargs( aws_sts_endpoint=aws_sts_endpoint, ssl_verify=ssl_verify, + aws_region_name=aws_region_name, ) if aws_access_key_id is None and aws_secret_access_key is None: with tracer.trace("boto3.client(sts)"): @@ -1363,14 +1377,15 @@ class BaseAWSLLM: """ Select the default endpoint url based on the endpoint type - Default endpoint url is https://bedrock-runtime.{aws_region_name}.amazonaws.com + Default endpoint url is https://bedrock-runtime.{aws_region_name}.{partition dns suffix} """ + dns_suffix: Final = get_aws_dns_suffix(aws_region_name) if endpoint_type == "agent": - return f"https://bedrock-agent-runtime.{aws_region_name}.amazonaws.com" + return f"https://bedrock-agent-runtime.{aws_region_name}.{dns_suffix}" elif endpoint_type == "agentcore": - return f"https://bedrock-agentcore.{aws_region_name}.amazonaws.com" + return f"https://bedrock-agentcore.{aws_region_name}.{dns_suffix}" else: - return f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" + return f"https://bedrock-runtime.{aws_region_name}.{dns_suffix}" def _get_boto_credentials_from_optional_params( self, optional_params: dict, model: str | None = None @@ -1427,16 +1442,19 @@ class BaseAWSLLM: @tracer.wrap() def get_request_headers( self, - credentials: Credentials, + credentials: Credentials | None, aws_region_name: str, extra_headers: dict | None, endpoint_url: str, data: str | bytes, headers: dict, api_key: str | None = None, + supports_bearer_token: bool = True, ) -> AWSPreparedRequest: - if api_key is not None: - aws_bearer_token: str | None = api_key + if not supports_bearer_token: + aws_bearer_token: str | None = None + elif api_key is not None: + aws_bearer_token = api_key else: aws_bearer_token = get_secret_str("AWS_BEARER_TOKEN_BEDROCK") @@ -1451,9 +1469,13 @@ class BaseAWSLLM: try: from botocore.auth import SigV4Auth from botocore.awsrequest import AWSRequest + from botocore.exceptions import NoCredentialsError except ImportError: raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + if credentials is None: + raise NoCredentialsError() + # Filter headers for AWS signature calculation # AWS SigV4 only includes specific headers in signature calculation aws_signature_headers: Final = self._filter_headers_for_aws_signature(headers) diff --git a/litellm/llms/bedrock/batches/handler.py b/litellm/llms/bedrock/batches/handler.py index 6efdd17f98d..4b500897642 100644 --- a/litellm/llms/bedrock/batches/handler.py +++ b/litellm/llms/bedrock/batches/handler.py @@ -1,9 +1,11 @@ +from collections.abc import Mapping from datetime import datetime from typing import TYPE_CHECKING, Any, Final, cast from openai.types.batch import BatchRequestCounts from openai.types.batch import Metadata as OpenAIBatchMetadata +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.types.utils import LiteLLMBatch if TYPE_CHECKING: @@ -68,6 +70,19 @@ def _predict_output_file_uri(output_prefix: str, input_uri: str, job_id: str | N return f"{output_prefix}{job_id}/{input_basename}.out" +def _record_counts_from_response(response: Mapping[str, object]) -> BatchRequestCounts | None: + total_records: Final = response.get("totalRecordCount") + success_records: Final = response.get("successRecordCount") + if not isinstance(total_records, int) or not isinstance(success_records, int): + return None + error_records: Final = response.get("errorRecordCount") + return BatchRequestCounts( + total=total_records, + completed=success_records, + failed=error_records if isinstance(error_records, int) else 0, + ) + + def _to_epoch(value: Any) -> int | None: if value is None: return None @@ -271,11 +286,11 @@ class BedrockBatchesHandler: ``aws_external_id``). Unknown keys are ignored. Returns: - ``LiteLLMBatch`` shaped like an OpenAI Batch resource. Note that - ``request_counts`` is always ``(0, 0, 0)`` because - ``GetModelInvocationJob`` does not surface per-record counts; - callers that need accurate counts should parse - ``manifest.json.out`` from the output S3 prefix. + ``LiteLLMBatch`` shaped like an OpenAI Batch resource. + ``request_counts`` maps ``GetModelInvocationJob``'s + ``totalRecordCount`` / ``successRecordCount`` / ``errorRecordCount`` + when the provider reports them, and is ``None`` when it does not + (older botocore, or a status that omits counts). """ try: import boto3 @@ -323,7 +338,9 @@ class BedrockBatchesHandler: api_key="", additional_args={ "complete_input_dict": {"jobIdentifier": batch_id}, - "api_base": (f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{url_path_id}"), + "api_base": ( + f"https://bedrock.{region}.{get_aws_dns_suffix(region)}/model-invocation-job/{url_path_id}" + ), }, ) @@ -386,7 +403,7 @@ class BedrockBatchesHandler: failed_at=completed_at if openai_status == "failed" else None, cancelled_at=completed_at if openai_status == "cancelled" else None, expired_at=completed_at if openai_status == "expired" else None, - request_counts=BatchRequestCounts(total=0, completed=0, failed=0), + request_counts=_record_counts_from_response(response), metadata=openai_batch_metadata, completion_window="24h", endpoint="/v1/chat/completions", diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index 04f395f2bf1..7729cdfdb0d 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -1,11 +1,12 @@ import os import re import time -from typing import Any, Final, Literal, cast +from typing import TYPE_CHECKING, Any, Final, Literal, cast from httpx import Headers, Response from pydantic import TypeAdapter, ValidationError +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix, is_bedrock_arn from litellm.litellm_core_utils.cloud_storage_security import ( BEDROCK_MANAGED_S3_BATCH_PREFIX, ) @@ -34,6 +35,9 @@ from ..common_utils import ( resolve_s3_encryption_key_id, ) +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + # Bedrock batch input files are uploaded as # s3://bucket/litellm-bedrock-files-{model, ":" -> "-"}-{uuid4}.jsonl (see # BedrockFilesTransformation._get_s3_object_name). A uuid4 is always 36 hex/dash @@ -138,8 +142,10 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): aws_region_name: Final = self._get_aws_region_name(request_params, model) # Bedrock model invocation job endpoint - # Format: https://bedrock.{region}.amazonaws.com/model-invocation-job - bedrock_endpoint: Final = f"https://bedrock.{aws_region_name}.amazonaws.com/model-invocation-job" + # Format: https://bedrock.{region}.{partition dns suffix}/model-invocation-job + bedrock_endpoint: Final = ( + f"https://bedrock.{aws_region_name}.{get_aws_dns_suffix(aws_region_name)}/model-invocation-job" + ) return bedrock_endpoint @@ -238,8 +244,9 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): # For Bedrock, we need to return a pre-signed request with AWS auth headers # Use common utility for AWS signing request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params) + aws_region_name: Final = self._get_aws_region_name(request_params, model) endpoint_url: Final = ( - f"https://bedrock.{self._get_aws_region_name(request_params, model)}.amazonaws.com/model-invocation-job" + f"https://bedrock.{aws_region_name}.{get_aws_dns_suffix(aws_region_name)}/model-invocation-job" ) signed_headers, signed_data = self.common_utils.sign_aws_request( service_name="bedrock", @@ -261,7 +268,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): self, model: str | None, raw_response: Response, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", litellm_params: dict, ) -> LiteLLMBatch: """ @@ -371,7 +378,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): """ # For Bedrock, batch_id should be the full job ARN # The GetModelInvocationJob API expects the full ARN as the identifier - if not batch_id.startswith("arn:aws:bedrock:"): + if not is_bedrock_arn(batch_id): raise ValueError(f"Invalid batch_id format. Expected ARN, got: {batch_id}") # Extract the job identifier from the ARN - use the full ARN path part @@ -390,7 +397,9 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): import urllib.parse as _ul encoded_arn: Final = _ul.quote(batch_id, safe="") - endpoint_url: Final = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}" + endpoint_url: Final = ( + f"https://bedrock.{region}.{get_aws_dns_suffix(region)}/model-invocation-job/{encoded_arn}" + ) # Use common utility for AWS signing request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params) @@ -527,7 +536,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): self, model: str | None, raw_response: Response, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", litellm_params: dict, ) -> LiteLLMBatch: """ diff --git a/litellm/llms/bedrock/chat/agentcore/transformation.py b/litellm/llms/bedrock/chat/agentcore/transformation.py index 4a2db621421..690040dd93b 100644 --- a/litellm/llms/bedrock/chat/agentcore/transformation.py +++ b/litellm/llms/bedrock/chat/agentcore/transformation.py @@ -13,6 +13,7 @@ import httpx from litellm._logging import verbose_logger from litellm._uuid import uuid +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.prompt_templates.common_utils import ( convert_content_list_to_str, ) @@ -38,6 +39,8 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler @@ -97,7 +100,7 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): if aws_bedrock_runtime_endpoint: base_url = aws_bedrock_runtime_endpoint else: - base_url = f"https://bedrock-agentcore.{region}.amazonaws.com" + base_url = f"https://bedrock-agentcore.{region}.{get_aws_dns_suffix(region)}" # Based on boto3 client.invoke_agent_runtime, the path is: # /runtimes/{URL-ENCODED-ARN}/invocations?qualifier= @@ -974,7 +977,7 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index ca5f1298360..7d5f99ca893 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -1,4 +1,6 @@ import json +from collections.abc import Mapping +from types import MappingProxyType from typing import Any, Final import httpx @@ -24,6 +26,22 @@ from ..common_utils import BedrockError, _get_all_bedrock_regions from .invoke_handler import AWSEventStreamDecoder, MockResponseIterator, make_call +def _sigv4_principal(credentials: Credentials | None) -> Mapping[str, str]: + if credentials is None: + return MappingProxyType({}) + return MappingProxyType( + { + key: value + for key, value in ( + ("aws_access_key_id", credentials.access_key), + ("aws_secret_access_key", credentials.secret_key), + ("aws_session_token", credentials.token), + ) + if value is not None + } + ) + + def make_sync_call( client: HTTPHandler | None, api_base: str, @@ -95,7 +113,7 @@ class BedrockConverseLLM(BaseAWSLLM): stream, optional_params: dict, litellm_params: dict, - credentials: Credentials, + credentials: Credentials | None, logger_fn=None, headers={}, client: AsyncHTTPHandler | None = None, @@ -167,7 +185,7 @@ class BedrockConverseLLM(BaseAWSLLM): stream, optional_params: dict, litellm_params: dict, - credentials: Credentials, + credentials: Credentials | None, logger_fn=None, headers: dict = {}, client: AsyncHTTPHandler | None = None, @@ -331,7 +349,7 @@ class BedrockConverseLLM(BaseAWSLLM): litellm_params["aws_region_name"] = aws_region_name # [DO NOT DELETE] important for async calls - credentials: Final[Credentials] = self.get_credentials( + credentials: Final[Credentials | None] = self.get_credentials( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, aws_session_token=aws_session_token, @@ -368,19 +386,13 @@ class BedrockConverseLLM(BaseAWSLLM): # The Rust core owns the whole call for the subset it accepts. Ask # before transforming so whichever path runs emits pre_call once, and # hand down the credentials, region and endpoint this handler already - # resolved so both paths sign as the same principal. + # resolved so both paths sign as the same principal. Bearer-token auth + # resolves no SigV4 principal at all, and each path reads that token + # itself. rust_optional_params: Final = { # mutable-ok: json.dumps in the bridge rejects a mappingproxy **optional_params, - **{ # mutable-ok: merged into its mutable parent above - key: value - for key, value in ( - ("aws_access_key_id", credentials.access_key), - ("aws_secret_access_key", credentials.secret_key), - ("aws_session_token", credentials.token), - ("aws_region_name", aws_region_name), - ) - if value is not None - }, + **_sigv4_principal(credentials), + "aws_region_name": aws_region_name, } serves_via_rust: Final = rust_chat_completions_accepts( model=model, diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index b437e25d24b..5363c3c0366 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -7,7 +7,7 @@ import json import time import types from collections.abc import Mapping -from typing import Final, Literal, cast, overload +from typing import TYPE_CHECKING, Final, Literal, cast, overload import httpx @@ -65,6 +65,7 @@ from litellm.types.llms.openai import ( OpenAIMessageContentListBlock, ) from litellm.types.utils import ( + CacheCreationTokenDetails, ChatCompletionMessageToolCall, CompletionTokensDetailsWrapper, Function, @@ -86,6 +87,7 @@ from ..common_utils import ( BedrockError, BedrockModelInfo, bedrock_converse_supports_parallel_tool_use_config, + bedrock_model_accepts_cache_points, get_anthropic_beta_from_headers, get_bedrock_tool_name, is_bedrock_application_inference_profile_arn, @@ -93,6 +95,9 @@ from ..common_utils import ( normalize_bedrock_opus_output_config_effort, ) +if TYPE_CHECKING: + import tiktoken + # Computer use tool prefixes supported by Bedrock BEDROCK_COMPUTER_USE_TOOLS: Final = [ "computer_use_preview", @@ -418,12 +423,16 @@ class AmazonConverseConfig(BaseConfig): Handle the reasoning_effort parameter based on the model type. - GPT-OSS models: passed through unchanged via additionalModelRequestFields. + - OpenAI GPT-5.x models: mapped to ``reasoning.effort`` via additionalModelRequestFields. - Nova 2 models: transformed to reasoningConfig. - Anthropic models: mapped to ``thinking`` (and ``output_config.effort`` on adaptive Claude 4.6 / 4.7). """ if "gpt-oss" in model: optional_params["reasoning_effort"] = reasoning_effort + elif "openai.gpt-5" in model: + reasoning: Final[BedrockConverseGptReasoningEffortBlock] = {"effort": reasoning_effort} + optional_params["reasoning"] = reasoning elif self._is_nova_2_model(model): reasoning_config: Final = self._transform_reasoning_effort_to_reasoning_config(reasoning_effort) optional_params.update(reasoning_config) @@ -555,7 +564,7 @@ class AmazonConverseConfig(BaseConfig): # only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html supported_params.append("tool_choice") - if "gpt-oss" in model: + if "gpt-oss" in model or "openai.gpt-5" in model or "openai.gpt-5" in base_model: supported_params.append("reasoning_effort") elif self._is_nova_2_model(model): # Nova 2 models support reasoning_effort (transformed to reasoningConfig) @@ -580,6 +589,10 @@ class AmazonConverseConfig(BaseConfig): supported_params.append("context_management") return supported_params + @staticmethod + def _auto_tool_choice() -> ToolChoiceValuesBlock: + return ToolChoiceValuesBlock(auto={}) + def map_tool_choice_values( self, model: str, tool_choice: str | dict, drop_params: bool ) -> ToolChoiceValuesBlock | None: @@ -592,10 +605,14 @@ class AmazonConverseConfig(BaseConfig): status_code=400, ) elif tool_choice == "required": + if AnthropicModelInfo.forced_tool_use_downgraded(model, drop_params): + return self._auto_tool_choice() return ToolChoiceValuesBlock(any={}) elif tool_choice == "auto": - return ToolChoiceValuesBlock(auto={}) + return self._auto_tool_choice() elif isinstance(tool_choice, dict): + if AnthropicModelInfo.forced_tool_use_downgraded(model, drop_params): + return self._auto_tool_choice() # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html specific_tool: Final = SpecificToolChoiceBlock( name=make_valid_bedrock_tool_name(tool_choice.get("function", {}).get("name", "")) @@ -903,7 +920,7 @@ class AmazonConverseConfig(BaseConfig): optional_params["_parallel_tool_use_config"] = { "tool_choice": {"type": "auto", "disable_parallel_tool_use": not value} } - if param == "thinking": + if param == "thinking" and "openai.gpt-5" not in model: if ( isinstance(value, dict) and value.get("type") == "adaptive" @@ -916,7 +933,7 @@ class AmazonConverseConfig(BaseConfig): custom_llm_provider="bedrock", ) capped = ( - AnthropicConfig._cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) + AnthropicConfig.cap_thinking_budget_to_max_tokens(legacy_thinking, max_tokens) if legacy_thinking is not None else None ) @@ -1057,6 +1074,7 @@ class AmazonConverseConfig(BaseConfig): if ( litellm.utils.supports_tool_choice(model=model, custom_llm_provider=self.custom_llm_provider) and not is_thinking_enabled + and not AnthropicModelInfo.forced_tool_use_unsupported(model) ): optional_params["tool_choice"] = ToolChoiceValuesBlock( tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) @@ -1132,7 +1150,7 @@ class AmazonConverseConfig(BaseConfig): model: str | None = None, ) -> SystemContentBlock | ContentBlock | None: cache_control: Final = message_block.get("cache_control", None) - if cache_control is None: + if cache_control is None or not bedrock_model_accepts_cache_points(model): return None cache_point: Final = self._build_cache_point_block(cache_control, model) @@ -1538,6 +1556,7 @@ class AmazonConverseConfig(BaseConfig): messages: list[AllMessageValues] | None = None, headers: dict | None = None, drop_params: bool = False, + litellm_params: Mapping[str, object] | None = None, ) -> CommonRequestObject: ## VALIDATE REQUEST """ @@ -1595,11 +1614,21 @@ class AmazonConverseConfig(BaseConfig): # Append cachePoint to tools if cache_control_injection_points has tool_config cache_injection_points: Final = additional_request_params.pop("cache_control_injection_points", None) - if cache_injection_points and len(bedrock_tools) > 0: + if cache_injection_points and len(bedrock_tools) > 0 and bedrock_model_accepts_cache_points(model): for point in cache_injection_points: if point.get("location") == "tool_config": cache_point = self._build_cache_point_block(point.get("control"), model) bedrock_tools.append(ToolBlock(cachePoint=cache_point)) + # Spend attribution credits the gateway only for breakpoints it placed, and + # this is the one place a tool_config point becomes one. The hook that reads + # the configuration cannot record it: whether a cachePoint lands depends on + # this provider and on the request carrying tools, neither of which it sees. + if litellm_params is not None: + from litellm.integrations.anthropic_cache_control_hook import ( + AnthropicCacheControlHook, + ) + + AnthropicCacheControlHook.record_gateway_injection(litellm_params, 1) break bedrock_tool_config: ToolConfigBlock | None = None @@ -1612,17 +1641,22 @@ class AmazonConverseConfig(BaseConfig): bedrock_tool_config["toolChoice"] = tool_choice_values self._drop_tool_choice_type_conflicting_with_tool_config(additional_request_params) + config_block_entries: Final = tuple( + (config_name, config_class, inference_params.pop(config_name, None)) + for config_name, config_class in self.get_config_blocks().items() + ) + data: Final[CommonRequestObject] = { "inferenceConfig": self._transform_inference_params(inference_params=inference_params), } if additional_request_params: data["additionalModelRequestFields"] = additional_request_params + if "thinking" in additional_request_params: + data["additionalModelResponseFieldPaths"] = ("/usage/output_tokens_details",) if system_content_blocks: data["system"] = system_content_blocks - # Handle all config blocks - for config_name, config_class in self.get_config_blocks().items(): - config_value = inference_params.pop(config_name, None) + for config_name, config_class, config_value in config_block_entries: if config_value is not None: data[config_name] = config_class(**config_value) @@ -1660,6 +1694,7 @@ class AmazonConverseConfig(BaseConfig): messages=messages, headers=headers, drop_params=litellm_params.get("drop_params") is True, + litellm_params=litellm_params, ) bedrock_messages: Final = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async( @@ -1719,6 +1754,7 @@ class AmazonConverseConfig(BaseConfig): messages=messages, headers=headers, drop_params=litellm_params.get("drop_params") is True, + litellm_params=litellm_params, ) ## TRANSFORMATION ## @@ -1750,7 +1786,7 @@ class AmazonConverseConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -1801,6 +1837,37 @@ class AmazonConverseConfig(BaseConfig): thinking_blocks_list.append(_redacted_block) return thinking_blocks_list + @staticmethod + def _parse_cache_details(usage: ConverseTokenUsageBlock) -> "CacheCreationTokenDetails | None": + """Split ``cacheDetails`` into 5m/1h buckets, or ``None`` unless the split fully + accounts for ``cacheWriteInputTokens``, since a partial or unrecognized-ttl + breakdown would understate the cache-write cost. + + https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_CacheDetail.html + """ + cache_details: Final = usage.get("cacheDetails") + if not cache_details: + return None + tokens_5m: Final = sum(d["inputTokens"] for d in cache_details if d.get("ttl") == "5m") + tokens_1h: Final = sum(d["inputTokens"] for d in cache_details if d.get("ttl") == "1h") + if tokens_5m + tokens_1h != usage.get("cacheWriteInputTokens", 0): + return None + return CacheCreationTokenDetails( + ephemeral_5m_input_tokens=tokens_5m, + ephemeral_1h_input_tokens=tokens_1h, + ) + + @staticmethod + def thinking_tokens_from_additional_fields(additional_fields: object) -> int | None: + """Converse omits thinking tokens from its usage block; they only arrive under + ``additionalModelResponseFields`` when ``/usage/output_tokens_details`` is requested.""" + if not isinstance(additional_fields, Mapping): + return None + usage: Final = additional_fields.get("usage") + if not isinstance(usage, Mapping): + return None + return AnthropicConfig.thinking_tokens_from_usage(usage) + @staticmethod def is_converse_usage_shape(usage_object: Mapping[str, object]) -> bool: """Converse-family models report camelCase token counts, not Anthropic's snake_case.""" @@ -1842,6 +1909,7 @@ class AmazonConverseConfig(BaseConfig): usage: ConverseTokenUsageBlock, reasoning_content: str | None = None, thinking_ran: bool = False, + provider_reasoning_tokens: int | None = None, ) -> Usage: input_tokens = usage["inputTokens"] output_tokens: Final = usage["outputTokens"] @@ -1860,11 +1928,17 @@ class AmazonConverseConfig(BaseConfig): prompt_tokens_details: Final = PromptTokensDetailsWrapper( cached_tokens=cache_read_input_tokens, cache_creation_tokens=cache_creation_input_tokens, + cache_creation_token_details=self._parse_cache_details(usage), text_tokens=raw_input_tokens, ) - reasoning_tokens: Final = ( + estimated_reasoning_tokens: Final = ( token_counter(text=reasoning_content, count_response_tokens=True) if reasoning_content else 0 ) + reasoning_tokens: Final = ( + min(max(0, provider_reasoning_tokens), output_tokens) + if provider_reasoning_tokens is not None + else estimated_reasoning_tokens + ) completion_tokens_details: Final = ( CompletionTokensDetailsWrapper( reasoning_tokens=reasoning_tokens, @@ -2272,6 +2346,9 @@ class AmazonConverseConfig(BaseConfig): completion_response["usage"], reasoning_content=chat_completion_message.get("reasoning_content"), thinking_ran=reasoningContentBlocks is not None, + provider_reasoning_tokens=self.thinking_tokens_from_additional_fields( + completion_response.get("additionalModelResponseFields") + ), ) ## HANDLE TOOL CALLS diff --git a/litellm/llms/bedrock/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py index 2198e19cd7e..e30ec731d8c 100644 --- a/litellm/llms/bedrock/chat/invoke_agent/transformation.py +++ b/litellm/llms/bedrock/chat/invoke_agent/transformation.py @@ -37,6 +37,8 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import Choices, Message, ModelResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -436,7 +438,7 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index ce89c6c23e2..fc34e403beb 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -331,6 +331,7 @@ class AWSEventStreamDecoder: self.json_mode = json_mode self._current_tool_name: str | None = None self._thinking_ran = False + self._provider_reasoning_tokens: int | None = None def check_empty_tool_call_args(self) -> bool: """ @@ -559,14 +560,22 @@ class AWSEventStreamDecoder: tool_use = self._handle_converse_stop_event(content_block_index) elif "stopReason" in chunk_data: finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop")) + self._provider_reasoning_tokens = AmazonConverseConfig.thinking_tokens_from_additional_fields( + chunk_data.get("additionalModelResponseFields") + ) elif "usage" in chunk_data: usage = converse_config.transform_usage( chunk_data.get("usage", {}), thinking_ran=self._thinking_ran, + provider_reasoning_tokens=self._provider_reasoning_tokens, ) if thinking_blocks: self._thinking_ran = True + carries_message_content: Final = any( + key in chunk_data for key in ("start", "delta", "contentBlockIndex", "stopReason", "trace") + ) + model_response_provider_specific_fields: Final = {} if "trace" in chunk_data: trace: Final = chunk_data.get("trace") @@ -577,8 +586,8 @@ class AWSEventStreamDecoder: finish_reason=finish_reason, index=0, # Always 0 - Bedrock never returns multiple choices delta=Delta( - content=text, - role="assistant", + content=text if carries_message_content else None, + role="assistant" if carries_message_content else None, tool_calls=[tool_use] if tool_use else None, provider_specific_fields=(provider_specific_fields if provider_specific_fields else None), thinking_blocks=thinking_blocks, diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py index d86c756ca99..5a3f4f17b8b 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py @@ -1,4 +1,4 @@ -from typing import Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, cast from httpx import Response @@ -24,6 +24,9 @@ from litellm.types.utils import ( from .amazon_llama_transformation import AmazonLlamaConfig +if TYPE_CHECKING: + import tiktoken + class AmazonDeepSeekR1Config(AmazonLlamaConfig): def transform_response( @@ -36,7 +39,7 @@ class AmazonDeepSeekR1Config(AmazonLlamaConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_moonshot_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_moonshot_transformation.py index a8275f1d35f..91c3a363c31 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_moonshot_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_moonshot_transformation.py @@ -21,6 +21,8 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import Choices if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.types.utils import ModelResponse @@ -200,7 +202,7 @@ class AmazonMoonshotConfig(AmazonInvokeConfig, MoonshotChatConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> "ModelResponse": diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py index 361f53d6ace..5f8ab94b00c 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py @@ -6,7 +6,7 @@ Inherits from `AmazonConverseConfig` Nova + Invoke API Tutorial: https://docs.aws.amazon.com/nova/latest/userguide/using-invoke-api.html """ -from typing import Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -18,6 +18,9 @@ from litellm.types.utils import ModelResponse from ..converse_transformation import AmazonConverseConfig from .base_invoke_transformation import AmazonInvokeConfig +if TYPE_CHECKING: + import tiktoken + class AmazonInvokeNovaConfig(AmazonInvokeConfig, AmazonConverseConfig): """ @@ -70,7 +73,7 @@ class AmazonInvokeNovaConfig(AmazonInvokeConfig, AmazonConverseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen2_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen2_transformation.py index a775db2ebc7..c78375c37bb 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen2_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen2_transformation.py @@ -7,7 +7,7 @@ The main difference is in the response format: Qwen2 uses "text" field while Qwe Qwen2 + Invoke API Tutorial: https://docs.aws.amazon.com/bedrock/latest/userguide/invoke-imported-model.html """ -from typing import Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -20,6 +20,9 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse, Usage +if TYPE_CHECKING: + import tiktoken + class AmazonQwen2Config(AmazonQwen3Config): """ @@ -41,7 +44,7 @@ class AmazonQwen2Config(AmazonQwen3Config): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen3_transformation.py index 7db8d77ff84..e251fb15725 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen3_transformation.py @@ -6,7 +6,7 @@ Inherits from `AmazonInvokeConfig` Qwen3 + Invoke API Tutorial: https://docs.aws.amazon.com/bedrock/latest/userguide/invoke-imported-model.html """ -from typing import Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -18,6 +18,9 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse, Usage +if TYPE_CHECKING: + import tiktoken + class AmazonQwen3Config(AmazonInvokeConfig, BaseConfig): """ @@ -167,7 +170,7 @@ class AmazonQwen3Config(AmazonInvokeConfig, BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py index 591de36dc18..cd8066cda4d 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py @@ -25,6 +25,8 @@ from litellm.types.utils import ModelResponse, Usage from litellm.utils import get_base64_str if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -188,7 +190,7 @@ class AmazonTwelveLabsPegasusConfig(AmazonInvokeConfig, BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index b8b07af59c6..2a4c38e71ea 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -3,7 +3,7 @@ from typing import TYPE_CHECKING, Any, Final import httpx from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers -from litellm.litellm_core_utils.litellm_logging import verbose_logger +from litellm.constants import RESPONSE_FORMAT_TOOL_NAME from litellm.litellm_core_utils.prompt_templates.factory import ( convert_to_anthropic_image_obj, ) @@ -12,23 +12,25 @@ from litellm.litellm_core_utils.prompt_templates.image_handling import ( convert_url_to_base64, ) from litellm.llms.anthropic.chat.transformation import AnthropicConfig +from litellm.llms.anthropic.common_utils import AnthropicModelInfo from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) from litellm.llms.bedrock.common_utils import ( - convert_bedrock_invoke_output_format_to_inline_schema, + apply_bedrock_invoke_structured_output, get_anthropic_beta_from_headers, normalize_bedrock_opus_output_config_effort, normalize_custom_field_on_tools, normalize_tool_input_schema_types_for_bedrock_invoke, - pop_bedrock_invoke_output_config_format, + strip_unsupported_bedrock_invoke_output_config_keys, ) from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse -from litellm.utils import _supports_factory if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -74,10 +76,14 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): drop_params: bool, ) -> dict: # Force tool-based structured outputs for Bedrock Invoke - # (similar to VertexAI fix in #19201) - # Bedrock Invoke doesn't support output_format parameter + # (similar to VertexAI fix in #19201) unless the model map advertises + # native structured output + from litellm.utils import supports_native_structured_output + original_model: Final = model - if "response_format" in non_default_params: + if "response_format" in non_default_params and not supports_native_structured_output( + model=model, custom_llm_provider="bedrock" + ): # Use a model name that forces tool-based approach model = "claude-3-sonnet-20240229" @@ -101,6 +107,16 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): # Restore original model name model = original_model + # The stub model hides the original model from the parent's forced-tool-use backstop + response_format_tool_choice: Final = optional_params.get("tool_choice") + if ( + "response_format" in non_default_params + and isinstance(response_format_tool_choice, dict) + and response_format_tool_choice.get("name") == RESPONSE_FORMAT_TOOL_NAME + and AnthropicModelInfo.forced_tool_use_unsupported(original_model) + ): + optional_params.pop("tool_choice") + return optional_params @staticmethod @@ -210,36 +226,14 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): anthropic_request.pop("model", None) anthropic_request.pop("stream", None) anthropic_request.pop("stream_chunk_size", None) - output_format: Final = anthropic_request.pop("output_format", None) - output_config_format: Final = pop_bedrock_invoke_output_config_format(anthropic_request) - if output_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_format, - request_body=anthropic_request, - ) - elif output_config_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_config_format, - request_body=anthropic_request, - ) - if not ( - _supports_factory( - model=model, - custom_llm_provider="bedrock", - key="supports_output_config", - ) - or AnthropicConfig._model_supports_effort_param(model, "bedrock") - ): - if anthropic_request.pop("output_config", None) is not None: - verbose_logger.warning( - "Bedrock Invoke: stripping unsupported `output_config` for " - "model=%s — neither `supports_output_config` nor any " - "`supports_*_reasoning_effort` flag is set in " - "model_prices_and_context_window.json. Add the capability " - "flag to the model JSON entry if this model accepts " - "`output_config`.", - model, - ) + apply_bedrock_invoke_structured_output( + model=model, + request_body=anthropic_request, + ) + strip_unsupported_bedrock_invoke_output_config_keys( + model=model, + request_body=anthropic_request, + ) if "anthropic_version" not in anthropic_request: anthropic_request["anthropic_version"] = self.anthropic_version @@ -397,7 +391,7 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py index 333326a766b..37121d2ece7 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py @@ -34,6 +34,8 @@ from litellm.types.utils import ModelResponse, Usage from litellm.utils import CustomStreamWrapper if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -286,7 +288,7 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 4ad20772ed0..66ee5f10679 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -21,6 +21,7 @@ import httpx import litellm from litellm import verbose_logger +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, ) @@ -176,6 +177,95 @@ def convert_bedrock_invoke_output_format_to_inline_schema( request_body["messages"] = new_messages +def _bedrock_model_supports(model: str, key: str) -> bool: + from litellm.utils import _supports_factory + + return _supports_factory(model=model, custom_llm_provider="bedrock", key=key) + + +def apply_bedrock_invoke_structured_output( + model: str, + request_body: dict[str, object], # mutable-ok: edited in place like siblings +) -> None: + """ + Route Anthropic structured-output params to what the Bedrock model supports. + + Consumes the legacy top-level ``output_format`` and the newer + ``output_config.format``, keeping the pre-existing precedence of the legacy + field when a request carries both. Models flagged + ``supports_native_structured_output`` in the model map get the schema + forwarded as ``output_config.format``, which Bedrock relays to the model for + enforced structured output. For every other model the schema is inlined into + the last user message as best-effort text, with a warning because nothing + enforces it. + """ + legacy_output_format: Final = request_body.pop("output_format", None) + output_config_format: Final = pop_bedrock_invoke_output_config_format(request_body) + schema_format: Final = legacy_output_format if isinstance(legacy_output_format, dict) else output_config_format + if schema_format is None: + return + + if _bedrock_model_supports(model, "supports_native_structured_output"): + existing_output_config: Final = request_body.get("output_config") + if isinstance(existing_output_config, dict): + existing_output_config["format"] = schema_format + else: + request_body["output_config"] = {"format": schema_format} # rebind-ok: out-param # mutable-ok: json + return + + verbose_logger.warning( + "Bedrock Invoke: model=%s does not advertise `supports_native_structured_output` " + "in model_prices_and_context_window.json, so the JSON schema was inlined into " + "the last user message and is NOT enforced by the model.", + model, + ) + convert_bedrock_invoke_output_format_to_inline_schema( + output_format=schema_format, + request_body=request_body, + ) + + +def strip_unsupported_bedrock_invoke_output_config_keys( + model: str, + request_body: dict[str, object], # mutable-ok: edited in place like siblings +) -> None: + """ + Drop ``output_config`` keys the Bedrock model does not accept. + + ``format`` survives unconditionally: it is only attached for models whose map + entry advertises ``supports_native_structured_output``. Effort-bearing keys + survive only when the map flags ``supports_output_config`` or a + ``supports_*_reasoning_effort`` tier; otherwise they are dropped with a + warning so Bedrock does not reject the request. + """ + from litellm.llms.anthropic.chat.transformation import AnthropicConfig + + output_config: Final = request_body.get("output_config") + if not isinstance(output_config, dict): + return + if all(key == "format" for key in output_config): + return + if _bedrock_model_supports(model, "supports_output_config") or AnthropicConfig._model_supports_effort_param( + model, "bedrock" + ): + return + + verbose_logger.warning( + "Bedrock Invoke: stripping unsupported `output_config` keys for " + "model=%s: neither `supports_output_config` nor any " + "`supports_*_reasoning_effort` flag is set in " + "model_prices_and_context_window.json. Add the capability " + "flag to the model JSON entry if this model accepts " + "`output_config`.", + model, + ) + preserved_format: Final = output_config.get("format") + if preserved_format is None: + request_body.pop("output_config", None) + else: + request_body["output_config"] = {"format": preserved_format} # rebind-ok: out-param # mutable-ok: json + + def normalize_custom_field_on_tools(request_body: dict) -> None: """ Drop the ``custom`` field from each tool, first hoisting a boolean @@ -434,15 +524,15 @@ def init_bedrock_client( ssl_verify: Final = _get_bedrock_client_ssl_verify() ### SET REGION NAME - if region_name: - pass - elif aws_region_name: - region_name = aws_region_name - elif litellm_aws_region_name: - region_name = litellm_aws_region_name - elif standard_aws_region_name: - region_name = standard_aws_region_name - else: + resolved_region_name: Final = next( + ( + candidate + for candidate in (region_name, aws_region_name, litellm_aws_region_name, standard_aws_region_name) + if isinstance(candidate, str) and candidate + ), + None, + ) + if resolved_region_name is None: raise BedrockError( message="AWS region not set: set AWS_REGION_NAME or AWS_REGION env variable or in .env file", status_code=401, @@ -455,7 +545,7 @@ def init_bedrock_client( elif env_aws_bedrock_runtime_endpoint: endpoint_url = env_aws_bedrock_runtime_endpoint else: - endpoint_url = f"https://bedrock-runtime.{region_name}.amazonaws.com" + endpoint_url = f"https://bedrock-runtime.{resolved_region_name}.{get_aws_dns_suffix(resolved_region_name)}" import boto3 @@ -492,7 +582,7 @@ def init_bedrock_client( aws_access_key_id=sts_response["Credentials"]["AccessKeyId"], aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"], aws_session_token=sts_response["Credentials"]["SessionToken"], - region_name=region_name, + region_name=resolved_region_name, endpoint_url=endpoint_url, config=config, verify=ssl_verify, @@ -513,7 +603,7 @@ def init_bedrock_client( aws_access_key_id=sts_response["Credentials"]["AccessKeyId"], aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"], aws_session_token=sts_response["Credentials"]["SessionToken"], - region_name=region_name, + region_name=resolved_region_name, endpoint_url=endpoint_url, config=config, verify=ssl_verify, @@ -526,7 +616,7 @@ def init_bedrock_client( service_name="bedrock-runtime", aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, - region_name=region_name, + region_name=resolved_region_name, endpoint_url=endpoint_url, config=config, verify=ssl_verify, @@ -536,7 +626,7 @@ def init_bedrock_client( client = boto3.Session(profile_name=aws_profile_name).client( service_name="bedrock-runtime", - region_name=region_name, + region_name=resolved_region_name, endpoint_url=endpoint_url, config=config, verify=ssl_verify, @@ -547,7 +637,7 @@ def init_bedrock_client( client = boto3.client( service_name="bedrock-runtime", - region_name=region_name, + region_name=resolved_region_name, endpoint_url=endpoint_url, config=config, verify=ssl_verify, @@ -726,6 +816,30 @@ def bedrock_converse_supports_parallel_tool_use_config(model: str) -> bool: ) +def bedrock_model_accepts_cache_points(model: str | None) -> bool: + """ + Whether Converse ``cachePoint`` blocks may be sent to this model. + + Bedrock rejects requests carrying cachePoint blocks for models without prompt + caching support ("You invoked an unsupported model or your request did not allow + prompt caching"), so a model whose cost-map entry does not declare + ``supports_prompt_caching`` must not receive them. A model absent from the map + (an application inference profile ARN, a model newer than the map) keeps emitting + so existing caching setups never silently degrade. ``litellm.utils.supports_prompt_caching`` + is not reusable here: it returns False for unmapped models, the opposite polarity. + """ + if model is None: + return True + entries: Final = tuple( + entry + for candidate in (model, get_bedrock_base_model(model)) + if (entry := litellm.model_cost.get(candidate)) is not None + ) + if not entries: + return True + return any(entry.get("supports_prompt_caching") is True for entry in entries) + + def is_claude_4_5_on_bedrock(model: str) -> bool: """ Check if the model supports Bedrock prompt caching with an extended '1h' TTL @@ -1486,6 +1600,7 @@ class CommonBatchFilesUtils: aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Prepare the request data diff --git a/litellm/llms/bedrock/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py index f87a3bc3452..48fc41ed12b 100644 --- a/litellm/llms/bedrock/count_tokens/transformation.py +++ b/litellm/llms/bedrock/count_tokens/transformation.py @@ -6,7 +6,10 @@ to AWS Bedrock's CountTokens API format and vice versa. """ import re -from typing import Any, Final +from collections.abc import Mapping +from typing import Final, Literal + +from pydantic import JsonValue from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.llms.bedrock.common_utils import get_bedrock_base_model @@ -17,6 +20,48 @@ from litellm.llms.bedrock.common_utils import get_bedrock_base_model DEFAULT_ANTHROPIC_INVOKE_MODEL_MAX_TOKENS: Final = 1024 +def _json_dict(value: JsonValue) -> dict[str, JsonValue]: + return value if isinstance(value, dict) else {} + + +def _json_list(value: JsonValue) -> list[JsonValue]: + return value if isinstance(value, list) else [] + + +def _to_converse_content(content: JsonValue) -> list[JsonValue]: + if isinstance(content, str): + return [{"text": content}] + if isinstance(content, list): + return content + return [] + + +def _to_converse_message(message: JsonValue) -> dict[str, JsonValue]: + fields: Final = _json_dict(message) + return { + "role": fields.get("role"), + "content": _to_converse_content(fields.get("content", "")), + } + + +def _sanitized_bedrock_tool_name(raw_name: JsonValue) -> str: + name: Final = re.sub(r"[^a-zA-Z0-9_]", "_", raw_name if isinstance(raw_name, str) else "") + prefixed: Final = name if not name or name[0].isalpha() else f"t_{name}" + return prefixed[:64] + + +def _to_bedrock_tool_spec(tool: JsonValue) -> dict[str, JsonValue]: + fields: Final = _json_dict(tool) + name: Final = _sanitized_bedrock_tool_name(fields.get("name", "")) + return { + "toolSpec": { + "name": name, + "description": fields.get("description") or name, + "inputSchema": {"json": fields.get("input_schema", {"type": "object", "properties": {}})}, + } + } + + class BedrockCountTokensConfig(BaseAWSLLM): """ Configuration and transformation logic for AWS Bedrock CountTokens API. @@ -27,7 +72,7 @@ class BedrockCountTokensConfig(BaseAWSLLM): - Response: {"inputTokens": } """ - def _detect_input_type(self, request_data: dict[str, Any]) -> str: + def _detect_input_type(self, request_data: Mapping[str, JsonValue]) -> Literal["converse", "invokeModel"]: """ Detect whether to use 'converse' or 'invokeModel' input format. @@ -57,8 +102,8 @@ class BedrockCountTokensConfig(BaseAWSLLM): def transform_anthropic_to_bedrock_count_tokens( self, - request_data: dict[str, Any], - ) -> dict[str, Any]: + request_data: Mapping[str, JsonValue], + ) -> dict[str, JsonValue]: """ Transform request to Bedrock CountTokens format. Supports both Converse and InvokeModel input types. @@ -95,27 +140,16 @@ class BedrockCountTokensConfig(BaseAWSLLM): else: return self._transform_to_invoke_model_format(request_data) - def _transform_to_converse_format(self, request_data: dict[str, Any]) -> dict[str, Any]: + def _transform_to_converse_format(self, request_data: Mapping[str, JsonValue]) -> dict[str, JsonValue]: """Transform to Converse input format, including system and tools.""" - messages: Final = request_data.get("messages", []) + messages: Final = _json_list(request_data.get("messages")) system: Final = request_data.get("system") tools: Final = request_data.get("tools") # Transform messages - user_messages: Final = [] - for message in messages: - transformed_message: dict[str, Any] = { - "role": message.get("role"), - "content": [], - } - content = message.get("content", "") - if isinstance(content, str): - transformed_message["content"].append({"text": content}) - elif isinstance(content, list): - transformed_message["content"] = content - user_messages.append(transformed_message) + user_messages: Final[list[JsonValue]] = [_to_converse_message(message) for message in messages] - converse_input: Final[dict[str, Any]] = {"messages": user_messages} + converse_input: Final[dict[str, JsonValue]] = {"messages": user_messages} # Transform system prompt (string or list of blocks → Bedrock format) system_blocks: Final = self._transform_system(system) @@ -129,7 +163,7 @@ class BedrockCountTokensConfig(BaseAWSLLM): return {"input": {"converse": converse_input}} - def _transform_system(self, system: Any | None) -> list[dict[str, Any]]: + def _transform_system(self, system: JsonValue) -> list[JsonValue]: """Transform Anthropic system prompt to Bedrock system blocks.""" if system is None: return [] @@ -140,36 +174,16 @@ class BedrockCountTokensConfig(BaseAWSLLM): return [{"text": block.get("text", "")} for block in system if isinstance(block, dict)] return [] - def _transform_tools(self, tools: list[dict[str, Any]] | None) -> dict[str, Any] | None: + def _transform_tools(self, tools: JsonValue) -> dict[str, JsonValue] | None: """Transform Anthropic tools to Bedrock toolConfig format.""" if not tools: return None - bedrock_tools: Final = [] - for tool in tools: - name = tool.get("name", "") - # Bedrock tool names must match [a-zA-Z][a-zA-Z0-9_]* and max 64 chars - name = re.sub(r"[^a-zA-Z0-9_]", "_", name) - if name and not name[0].isalpha(): - name = "t_" + name - name = name[:64] - - description = tool.get("description") or name - input_schema = tool.get("input_schema", {"type": "object", "properties": {}}) - - bedrock_tools.append( - { - "toolSpec": { - "name": name, - "description": description, - "inputSchema": {"json": input_schema}, - } - } - ) + bedrock_tools: Final[list[JsonValue]] = [_to_bedrock_tool_spec(tool) for tool in _json_list(tools)] return {"tools": bedrock_tools} - def _transform_to_invoke_model_format(self, request_data: dict[str, Any]) -> dict[str, Any]: + def _transform_to_invoke_model_format(self, request_data: Mapping[str, JsonValue]) -> dict[str, JsonValue]: """Transform to InvokeModel input format.""" import base64 import json @@ -223,7 +237,9 @@ class BedrockCountTokensConfig(BaseAWSLLM): return endpoint - def transform_bedrock_response_to_anthropic(self, bedrock_response: dict[str, Any]) -> dict[str, Any]: + def transform_bedrock_response_to_anthropic( + self, bedrock_response: Mapping[str, JsonValue] + ) -> dict[str, JsonValue]: """ Transform Bedrock CountTokens response to Anthropic format. @@ -241,7 +257,7 @@ class BedrockCountTokensConfig(BaseAWSLLM): return {"input_tokens": input_tokens} - def validate_count_tokens_request(self, request_data: dict[str, Any]) -> None: + def validate_count_tokens_request(self, request_data: Mapping[str, JsonValue]) -> None: """ Validate the incoming count tokens request. Supports both Converse and InvokeModel input formats. diff --git a/litellm/llms/bedrock/embed/cohere_transformation.py b/litellm/llms/bedrock/embed/cohere_transformation.py index d1c9ceb99d1..8a17bb9d595 100644 --- a/litellm/llms/bedrock/embed/cohere_transformation.py +++ b/litellm/llms/bedrock/embed/cohere_transformation.py @@ -20,7 +20,9 @@ class BedrockCohereEmbeddingConfig: def map_openai_params(self, non_default_params: dict, optional_params: dict) -> dict: for k, v in non_default_params.items(): if k == "encoding_format": - optional_params["embedding_types"] = v if isinstance(v, list) else [v] + optional_params["embedding_types"] = [ + "float" if fmt == "base64" else fmt for fmt in (tuple(v) if isinstance(v, list) else (v,)) + ] elif k == "dimensions": optional_params["output_dimension"] = v return optional_params diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index 082bf7ee2d9..c34ca7750e2 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -6,7 +6,7 @@ import copy import json import urllib.parse from collections.abc import Callable -from typing import Any, Final, get_args +from typing import TYPE_CHECKING, Any, Final, get_args import httpx @@ -37,6 +37,9 @@ from .amazon_titan_v2_transformation import AmazonTitanV2Config from .cohere_transformation import BedrockCohereEmbeddingConfig from .twelvelabs_marengo_transformation import TwelveLabsMarengoEmbeddingConfig +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class BedrockEmbedding(BaseAWSLLM): def _load_credentials( @@ -58,6 +61,7 @@ class BedrockEmbedding(BaseAWSLLM): aws_profile_name: Final = optional_params.pop("aws_profile_name", None) aws_web_identity_token: Final = optional_params.pop("aws_web_identity_token", None) aws_sts_endpoint: Final = optional_params.pop("aws_sts_endpoint", None) + aws_external_id: Final = optional_params.pop("aws_external_id", None) ### SET REGION NAME ### if aws_region_name is None: @@ -84,6 +88,7 @@ class BedrockEmbedding(BaseAWSLLM): aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) return credentials, aws_region_name @@ -233,7 +238,7 @@ class BedrockEmbedding(BaseAWSLLM): endpoint_url: str, aws_region_name: str, model: str, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL, api_key: str | None = None, is_async_invoke: bool | None = False, @@ -301,7 +306,7 @@ class BedrockEmbedding(BaseAWSLLM): endpoint_url: str, aws_region_name: str, model: str, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL, api_key: str | None = None, is_async_invoke: bool | None = False, diff --git a/litellm/llms/bedrock/files/handler.py b/litellm/llms/bedrock/files/handler.py index 13718d41cc1..e74c3802d20 100644 --- a/litellm/llms/bedrock/files/handler.py +++ b/litellm/llms/bedrock/files/handler.py @@ -113,6 +113,7 @@ class BedrockFilesHandler(BaseAWSLLM): aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Create S3 client diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index b034696594a..33b27943ad8 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -20,6 +20,7 @@ from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.constants import BEDROCK_INVOKE_PROVIDERS_LITERAL from litellm.files.utils import FilesAPIUtils +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.cloud_storage_security import ( BEDROCK_MANAGED_S3_BATCH_PREFIX, BEDROCK_MANAGED_S3_PREFIXES, @@ -145,6 +146,7 @@ class _BedrockS3RequestParams(BaseModel): aws_role_name: str | None = None aws_web_identity_token: str | None = None aws_sts_endpoint: str | None = None + aws_external_id: str | None = None s3_region_name: str | None = None s3_endpoint_url: str | None = None @@ -413,7 +415,8 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): # S3 endpoint URL format s3_endpoint_url: Final = ( - request_params.get("s3_endpoint_url") or f"https://s3.{aws_region_name}.amazonaws.com" + request_params.get("s3_endpoint_url") + or f"https://s3.{aws_region_name}.{get_aws_dns_suffix(aws_region_name)}" ).rstrip("/") return f"{s3_endpoint_url}/{bucket_name}/{encoded_object_name}" @@ -1027,6 +1030,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): aws_role_name=optional_params.get("aws_role_name"), aws_web_identity_token=optional_params.get("aws_web_identity_token"), aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + aws_external_id=optional_params.get("aws_external_id"), ) # Calculate SHA256 hash of the content (REQUIRED for S3) @@ -1249,7 +1253,9 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): region_params: Final[dict[str, str | None]] = {"aws_region_name": region_preference} aws_region_name: Final = self._get_aws_region_name(optional_params=region_params, model="") - s3_endpoint_url = (request_params.s3_endpoint_url or f"https://s3.{aws_region_name}.amazonaws.com").rstrip("/") + s3_endpoint_url = ( + request_params.s3_endpoint_url or f"https://s3.{aws_region_name}.{get_aws_dns_suffix(aws_region_name)}" + ).rstrip("/") url: Final = f"{s3_endpoint_url}/{bucket_name}/{encode_s3_object_key_for_url(object_key)}" litellm_params[S3_SIGNED_GET_HEADERS_PARAM] = self._sign_s3_get_request( @@ -1286,6 +1292,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): aws_role_name=request_params.aws_role_name, aws_web_identity_token=request_params.aws_web_identity_token, aws_sts_endpoint=request_params.aws_sts_endpoint, + aws_external_id=request_params.aws_external_id, ) empty_body_hash: Final = hashlib.sha256(b"").hexdigest() diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index f74a290d773..6ff9f0155f9 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -29,14 +29,14 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation AmazonInvokeConfig, ) from litellm.llms.bedrock.common_utils import ( - convert_bedrock_invoke_output_format_to_inline_schema, + apply_bedrock_invoke_structured_output, ensure_bedrock_anthropic_messages_tool_names, get_anthropic_beta_from_headers, is_claude_4_5_on_bedrock, normalize_bedrock_opus_output_config_effort, normalize_custom_field_on_tools, normalize_tool_input_schema_types_for_bedrock_invoke, - pop_bedrock_invoke_output_config_format, + strip_unsupported_bedrock_invoke_output_config_keys, ) from litellm.llms.bedrock.request_metadata import ( bedrock_request_metadata_headers, @@ -51,7 +51,6 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import GenericStreamingChunk, ModelResponseStream from litellm.types.utils import GenericStreamingChunk as GChunk -from litellm.utils import _supports_factory if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -708,52 +707,25 @@ class AmazonAnthropicClaudeMessagesConfig( # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it for older models) self._remove_ttl_from_cache_control(anthropic_messages_request=anthropic_messages_request, model=model) - # 5. Convert structured-output params to inline schema. - # Bedrock Invoke doesn't support top-level `output_format`; its - # accepted `output_config` subset is also narrower than Anthropic's, so - # consume the newer `output_config.format` shape here instead of - # forwarding it as an unknown nested key. + # 5. Route structured-output params (`output_format` / + # `output_config.format`) to native enforcement or the inline-schema + # fallback, then strip `output_config` keys the model does not accept. + # Ref: https://github.com/BerriAI/litellm/issues/22797 existing_output_config: Final = anthropic_messages_request.get("output_config") if isinstance(existing_output_config, dict): anthropic_messages_request["output_config"] = dict(existing_output_config) - output_format: Final = anthropic_messages_request.pop("output_format", None) - output_config_format: Final = pop_bedrock_invoke_output_config_format(anthropic_messages_request) - if output_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_format, - request_body=anthropic_messages_request, - ) - elif output_config_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_config_format, - request_body=anthropic_messages_request, - ) + apply_bedrock_invoke_structured_output( + model=model, + request_body=anthropic_messages_request, + ) normalize_bedrock_opus_output_config_effort( model=model, output_config=anthropic_messages_request.get("output_config"), ) - - # 5a. Bedrock Invoke supports output_config (effort) for Claude 4.6+ models, - # but older models do not — strip it to avoid request rejection. - # Ref: https://github.com/BerriAI/litellm/issues/22797 - if not ( - _supports_factory( - model=model, - custom_llm_provider="bedrock", - key="supports_output_config", - ) - or AnthropicConfig._model_supports_effort_param(model, "bedrock") - ): - if anthropic_messages_request.pop("output_config", None) is not None: - verbose_logger.warning( - "Bedrock Invoke: stripping unsupported `output_config` for " - "model=%s — neither `supports_output_config` nor any " - "`supports_*_reasoning_effort` flag is set in " - "model_prices_and_context_window.json. Add the capability " - "flag to the model JSON entry if this model accepts " - "`output_config`.", - model, - ) + strip_unsupported_bedrock_invoke_output_config_keys( + model=model, + request_body=anthropic_messages_request, + ) # 5b. Hoist `custom.defer_loading` then drop `custom` (Bedrock doesn't support it) # Ref: https://github.com/BerriAI/litellm/issues/22847 @@ -774,9 +746,11 @@ class AmazonAnthropicClaudeMessagesConfig( if filtered_betas: anthropic_messages_request["anthropic_beta"] = filtered_betas + remaining_output_config: Final = anthropic_messages_request.get("output_config") if ( litellm.drop_params is True - and "output_config" in anthropic_messages_request + and isinstance(remaining_output_config, dict) + and any(key != "format" for key in remaining_output_config) and not AnthropicConfig._model_supports_effort_param(model, "bedrock") ): verbose_logger.warning( diff --git a/litellm/llms/bedrock/passthrough/transformation.py b/litellm/llms/bedrock/passthrough/transformation.py index 0ce2e6f60d3..d0a3c37ffb3 100644 --- a/litellm/llms/bedrock/passthrough/transformation.py +++ b/litellm/llms/bedrock/passthrough/transformation.py @@ -1,4 +1,5 @@ import json +from collections.abc import Mapping from typing import TYPE_CHECKING, Final, Optional, cast from httpx import Response @@ -93,6 +94,9 @@ class BedrockPassthroughConfig(BaseAWSLLM, BedrockModelInfo, BedrockEventStreamD endpoint_url, ) + def get_bedrock_bearer_token(self, litellm_params: Mapping[str, object]) -> str | None: + return None + def sign_request( self, headers: dict, @@ -109,6 +113,7 @@ class BedrockPassthroughConfig(BaseAWSLLM, BedrockModelInfo, BedrockEventStreamD request_data=request_data or {}, api_base=api_base, model=model, + api_key=self.get_bedrock_bearer_token(optional_params), ) def logging_non_streaming_response( diff --git a/litellm/llms/bedrock/realtime/handler.py b/litellm/llms/bedrock/realtime/handler.py index 3eeb3cb9fc6..42fe8941443 100644 --- a/litellm/llms/bedrock/realtime/handler.py +++ b/litellm/llms/bedrock/realtime/handler.py @@ -7,18 +7,82 @@ This uses aws_sdk_bedrock_runtime for bidirectional streaming with Nova Sonic. import asyncio import contextlib import json -from typing import Any, Final +from collections.abc import AsyncIterator, Mapping +from typing import Final, Protocol -from pydantic import TypeAdapter +from pydantic import JsonValue, TypeAdapter +import litellm from litellm._logging import _redact_string, verbose_proxy_logger +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging +from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER +from litellm.litellm_core_utils.realtime_streaming import DefaultLoggedRealTimeEventTypes +from litellm.types.llms.openai import OpenAIRealtimeEvents +from litellm.types.realtime import RealtimeResponseTransformInput from ..base_aws_llm import BaseAWSLLM from ..common_utils import BedrockError from .transformation import BedrockRealtimeConfig _CLIENT_MODALITIES_ADAPTER: Final[TypeAdapter["list[str] | None"]] = TypeAdapter(list[str] | None) +_CLIENT_MESSAGE_ADAPTER: Final[TypeAdapter[JsonValue]] = TypeAdapter(JsonValue) + + +def _json_dict(value: JsonValue) -> dict[str, JsonValue]: + return value if isinstance(value, dict) else {} + + +def _json_str(value: JsonValue) -> str | None: + return value if isinstance(value, str) else None + + +def _should_log_event(openai_message: Mapping[str, object]) -> bool: + logged_types: Final = ( + litellm.logged_real_time_event_types + if litellm.logged_real_time_event_types is not None + else DefaultLoggedRealTimeEventTypes + ) + if logged_types == "*": + return True + return openai_message.get("type") in logged_types + + +class RealtimeClientWebSocket(Protocol): + """The client-facing websocket surface the realtime bridge talks to.""" + + async def receive_text(self) -> str: ... + + async def send_text(self, data: str) -> None: ... + + async def close(self, code: int = 1000, reason: str | None = None) -> None: ... + + +class BedrockInputStream(Protocol): + async def send(self, event: object) -> None: ... + + async def close(self) -> None: ... + + +class BedrockPayloadPart(Protocol): + @property + def bytes_(self) -> bytes | None: ... + + +class BedrockOutputChunk(Protocol): + @property + def value(self) -> BedrockPayloadPart | None: ... + + +class BedrockOutputStream(Protocol): + async def receive(self) -> BedrockOutputChunk | None: ... + + +class BedrockBidirectionalStream(Protocol): + @property + def input_stream(self) -> BedrockInputStream: ... + + async def await_output(self) -> tuple[object, BedrockOutputStream]: ... class BedrockRealtime(BaseAWSLLM): @@ -30,7 +94,7 @@ class BedrockRealtime(BaseAWSLLM): async def async_realtime( self, model: str, - websocket: Any, + websocket: RealtimeClientWebSocket, logging_obj: LiteLLMLogging, api_base: str | None = None, api_key: str | None = None, @@ -46,7 +110,7 @@ class BedrockRealtime(BaseAWSLLM): aws_sts_endpoint: str | None = None, aws_bedrock_runtime_endpoint: str | None = None, aws_external_id: str | None = None, - **kwargs, + **kwargs: object, ): """ Establish bidirectional streaming connection with Bedrock Nova Sonic. @@ -81,7 +145,7 @@ class BedrockRealtime(BaseAWSLLM): elif aws_bedrock_runtime_endpoint is not None: endpoint_uri = aws_bedrock_runtime_endpoint else: - endpoint_uri = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" + endpoint_uri = f"https://bedrock-runtime.{aws_region_name}.{get_aws_dns_suffix(aws_region_name)}" verbose_proxy_logger.debug("Bedrock Realtime: Connecting to %s with model %s", endpoint_uri, model) @@ -118,13 +182,16 @@ class BedrockRealtime(BaseAWSLLM): ) bedrock_client: Final = BedrockRuntimeClient(config=config) + async def open_bidirectional_stream() -> BedrockBidirectionalStream: + return await bedrock_client.invoke_model_with_bidirectional_stream( + InvokeModelWithBidirectionalStreamOperationInput(model_id=model) + ) + transformation_config: Final = BedrockRealtimeConfig() try: # Initialize the bidirectional stream - bedrock_stream: Final = await bedrock_client.invoke_model_with_bidirectional_stream( - InvokeModelWithBidirectionalStreamOperationInput(model_id=model) - ) + bedrock_stream: Final = await open_bidirectional_stream() verbose_proxy_logger.debug("Bedrock Realtime: Bidirectional stream established") @@ -132,7 +199,7 @@ class BedrockRealtime(BaseAWSLLM): verbose_proxy_logger.debug("Bedrock Realtime: sent session.created to client on connect") # Track state for transformation - session_state: Final = { + session_state: Final[RealtimeResponseTransformInput] = { "current_output_item_id": None, "current_response_id": None, "current_conversation_id": None, @@ -154,16 +221,22 @@ class BedrockRealtime(BaseAWSLLM): ) ) - bedrock_to_client_task: Final = asyncio.create_task( - self._forward_bedrock_to_client( - bedrock_stream, - websocket, - transformation_config, - model, - logging_obj, - session_state, + async def forward_bedrock_and_collect_logged_events() -> tuple[OpenAIRealtimeEvents, ...]: + return tuple( + [ + event + async for event in self._forward_bedrock_to_client( + bedrock_stream, + websocket, + transformation_config, + model, + logging_obj, + session_state, + ) + ] ) - ) + + bedrock_to_client_task: Final = asyncio.create_task(forward_bedrock_and_collect_logged_events()) # Wait for both tasks to complete await asyncio.gather( @@ -172,6 +245,27 @@ class BedrockRealtime(BaseAWSLLM): return_exceptions=True, ) + forwarded_logged_events: Final = ( + bedrock_to_client_task.result() + if not bedrock_to_client_task.cancelled() and bedrock_to_client_task.exception() is None + else () + ) + logged_events: Final = ( + *forwarded_logged_events, + *( + leftover_event + for leftover_event in transformation_config.leftover_usage_done_events() + if _should_log_event(leftover_event) + ), + ) + if logged_events: + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue( + logging_obj.dispatch_success_handlers( + list(logged_events), # mutable-ok: realtime spend logging requires a list result + prefer_async_handlers=True, + ) + ) + except Exception as e: verbose_proxy_logger.exception("Error in BedrockRealtime.async_realtime: %s", e) try: @@ -182,11 +276,11 @@ class BedrockRealtime(BaseAWSLLM): async def _forward_client_to_bedrock( self, - client_ws: Any, - bedrock_stream: Any, + client_ws: RealtimeClientWebSocket, + bedrock_stream: BedrockBidirectionalStream, transformation_config: BedrockRealtimeConfig, model: str, - session_state: dict, + session_state: RealtimeResponseTransformInput, logging_obj: LiteLLMLogging | None = None, ): """Forward messages from client WebSocket to Bedrock stream.""" @@ -195,10 +289,11 @@ class BedrockRealtime(BaseAWSLLM): InvokeModelWithBidirectionalStreamInputChunk, ) + def build_input_chunk(payload: bytes) -> object: + return InvokeModelWithBidirectionalStreamInputChunk(value=BidirectionalInputPayloadPart(bytes_=payload)) + async def send_to_bedrock(bedrock_message: str) -> None: - event: Final = InvokeModelWithBidirectionalStreamInputChunk( - value=BidirectionalInputPayloadPart(bytes_=bedrock_message.encode("utf-8")) - ) + event: Final = build_input_chunk(bedrock_message.encode("utf-8")) await bedrock_stream.input_stream.send(event) verbose_proxy_logger.debug("Bedrock Realtime: Sent to Bedrock: %s", bedrock_message[:200]) @@ -223,11 +318,11 @@ class BedrockRealtime(BaseAWSLLM): client_message_type: str | None = None requested_modalities: list[str] | None = None with contextlib.suppress(Exception): - parsed_client_message = json.loads(message) - client_message_type = parsed_client_message.get("type") + parsed_client_message = _json_dict(_CLIENT_MESSAGE_ADAPTER.validate_json(message)) + client_message_type = _json_str(parsed_client_message.get("type")) if client_message_type == "session.update": requested_modalities = _CLIENT_MODALITIES_ADAPTER.validate_python( - parsed_client_message.get("session", {}).get("modalities") + _json_dict(parsed_client_message.get("session")).get("modalities") ) if client_message_type == "session.update": await client_ws.send_text( @@ -246,14 +341,14 @@ class BedrockRealtime(BaseAWSLLM): async def _forward_bedrock_to_client( self, - bedrock_stream: Any, - client_ws: Any, + bedrock_stream: BedrockBidirectionalStream, + client_ws: RealtimeClientWebSocket, transformation_config: BedrockRealtimeConfig, model: str, logging_obj: LiteLLMLogging, - session_state: dict, - ): - """Forward messages from Bedrock stream to client WebSocket.""" + session_state: RealtimeResponseTransformInput, + ) -> AsyncIterator[OpenAIRealtimeEvents]: + """Forward messages from Bedrock to the client, yielding the ones to record for spend logging.""" try: while True: # Receive from Bedrock @@ -264,13 +359,12 @@ class BedrockRealtime(BaseAWSLLM): verbose_proxy_logger.debug("Bedrock Realtime: Bedrock stream ended") break - if result.value and result.value.bytes_: - bedrock_response = result.value.bytes_.decode("utf-8") + payload_bytes = result.value.bytes_ if result.value else None + if payload_bytes: + bedrock_response = payload_bytes.decode("utf-8") verbose_proxy_logger.debug("Bedrock Realtime: Received from Bedrock: %s", bedrock_response[:200]) # Transform Bedrock format to OpenAI format - from litellm.types.realtime import RealtimeResponseTransformInput - realtime_response_transform_input: RealtimeResponseTransformInput = { "current_output_item_id": session_state.get("current_output_item_id"), "current_response_id": session_state.get("current_response_id"), @@ -302,11 +396,14 @@ class BedrockRealtime(BaseAWSLLM): ) # Send transformed messages to client - openai_messages = transformed_response.get("response", []) + response_value = transformed_response["response"] + openai_messages = response_value if isinstance(response_value, list) else (response_value,) for openai_message in openai_messages: message_json = json.dumps(openai_message) await client_ws.send_text(message_json) verbose_proxy_logger.debug("Bedrock Realtime: Sent to client: %s", message_json[:200]) + if _should_log_event(openai_message): + yield openai_message except Exception as e: verbose_proxy_logger.debug("Bedrock to client forwarding ended: %s", e, exc_info=True) diff --git a/litellm/llms/bedrock/realtime/transformation.py b/litellm/llms/bedrock/realtime/transformation.py index 951bf636b2f..1f4c81d6491 100644 --- a/litellm/llms/bedrock/realtime/transformation.py +++ b/litellm/llms/bedrock/realtime/transformation.py @@ -7,7 +7,7 @@ Transforms between OpenAI Realtime API format and Bedrock Nova Sonic format. import base64 import json import uuid as uuid_lib -from typing import Any, Final +from typing import Final, cast from pydantic import BaseModel @@ -20,29 +20,54 @@ from litellm.types.llms.openai import ( OpenAIRealtimeContentPartDone, OpenAIRealtimeDoneEvent, OpenAIRealtimeEvents, + OpenAIRealtimeInputAudioBufferSpeechEvent, + OpenAIRealtimeInputAudioTranscriptionCompleted, + OpenAIRealtimeInputAudioTranscriptionDelta, OpenAIRealtimeOutputItemDone, OpenAIRealtimeResponseAudioDone, OpenAIRealtimeResponseContentPartAdded, OpenAIRealtimeResponseDelta, OpenAIRealtimeResponseDoneObject, OpenAIRealtimeResponseTextDone, + OpenAIRealtimeResponseUsage, OpenAIRealtimeStreamResponseBaseObject, OpenAIRealtimeStreamResponseOutputItemAdded, OpenAIRealtimeStreamSession, OpenAIRealtimeStreamSessionEvents, + OpenAIRealtimeUsageTokenDetails, ) from litellm.types.realtime import ( ALL_DELTA_TYPES, RealtimeResponseTransformInput, RealtimeResponseTypedDict, ) -from litellm.utils import get_empty_usage class BedrockContentEnd(BaseModel): stopReason: str | None = None +class BedrockUsageTokenDetails(BaseModel): + speechTokens: int = 0 + textTokens: int = 0 + + +class BedrockUsageDetailsTotal(BaseModel): + input: BedrockUsageTokenDetails = BedrockUsageTokenDetails() + output: BedrockUsageTokenDetails = BedrockUsageTokenDetails() + + +class BedrockUsageDetails(BaseModel): + total: BedrockUsageDetailsTotal = BedrockUsageDetailsTotal() + + +class BedrockUsageEvent(BaseModel): + totalInputTokens: int = 0 + totalOutputTokens: int = 0 + totalTokens: int = 0 + details: BedrockUsageDetails = BedrockUsageDetails() + + TRIGGER_AUDIO_SAMPLE_RATE_HERTZ: Final = 16000 TRIGGER_AUDIO_BYTES_PER_SECOND: Final = TRIGGER_AUDIO_SAMPLE_RATE_HERTZ * 2 TRIGGER_LEADING_SILENCE: Final = bytes(TRIGGER_AUDIO_BYTES_PER_SECOND // 2) @@ -87,6 +112,15 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): # Text configuration self.text_media_type = "text/plain" + # Response-stream state (Bedrock events carry no role on textOutput, + # so the USER/ASSISTANT split from contentStart is tracked here) + self._user_transcript_active = False + self._user_transcript_generation_stage: str | None = None + self._user_item_id: str | None = None + self._user_transcript_buffer = "" + self._cumulative_usage = BedrockUsageEvent() + self._reported_usage = BedrockUsageEvent() + def validate_environment(self, headers: dict, model: str, api_key: str | None = None) -> dict: """Validate environment - no special validation needed for Bedrock.""" return headers @@ -599,7 +633,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): List of Bedrock format messages (JSON strings) """ try: - json_message: Final = json.loads(message) + json_message: Final[dict[str, object]] = json.loads(message) except json.JSONDecodeError: verbose_logger.warning("Invalid JSON message: %s", message[:200]) return [] @@ -691,6 +725,11 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): role: Final = content_start.get("role") if role != "ASSISTANT": + if role == "USER" and content_start.get("type") == "TEXT": + self._user_transcript_active = True + self._user_transcript_generation_stage = self._parse_generation_stage( + content_start.get("additionalModelFields") + ) return ( [], current_response_id, @@ -700,6 +739,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): ) verbose_logger.debug("Handling ASSISTANT contentStart") + is_new_response: Final = current_response_id is None # Initialize IDs if needed if not current_response_id: @@ -715,7 +755,8 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): returned_messages: Final[list[OpenAIRealtimeEvents]] = [] - # Send response.created + # Send response.created only once per response (a response can contain + # multiple content blocks, e.g. TEXT then AUDIO) response_created: Final = OpenAIRealtimeStreamResponseBaseObject( type="response.created", event_id=f"event_{uuid.uuid4()}", @@ -727,7 +768,8 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): "conversation_id": current_conversation_id, }, ) - returned_messages.append(response_created) + if is_new_response: + returned_messages.append(response_created) # Send response.output_item.added output_item_added: Final = OpenAIRealtimeStreamResponseOutputItemAdded( @@ -767,6 +809,108 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): current_delta_type, ) + @staticmethod + def _parse_generation_stage(additional_model_fields: object) -> str | None: + if not isinstance(additional_model_fields, str): + return None + try: + parsed: Final = json.loads(additional_model_fields) + except json.JSONDecodeError: + return None + stage: Final = parsed.get("generationStage") if isinstance(parsed, dict) else None + return stage if isinstance(stage, str) else None + + def _current_user_item_id(self, new_utterance: bool = False) -> str: + """Item id shared by all events of one user utterance (speech boundaries and transcript).""" + if new_utterance or self._user_item_id is None: + self._user_item_id = f"item_{uuid.uuid4()}" + return self._user_item_id + + def transform_user_speech_event(self, is_speech_start: bool) -> tuple[OpenAIRealtimeEvents, ...]: + """Transform Bedrock userSpeechStart/userSpeechEnd to OpenAI speech boundary events.""" + verbose_logger.debug("Handling userSpeech%s", "Start" if is_speech_start else "End") + speech_event: Final[OpenAIRealtimeInputAudioBufferSpeechEvent] = { + "type": "input_audio_buffer.speech_started" if is_speech_start else "input_audio_buffer.speech_stopped", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(new_utterance=is_speech_start), + } + return (speech_event,) + + def transform_usage_event(self, usage_event: BedrockUsageEvent) -> None: + """Record Bedrock's session-cumulative usage totals for the next response.done.""" + verbose_logger.debug("Handling usageEvent") + self._cumulative_usage = usage_event + + def _take_usage_delta(self) -> OpenAIRealtimeResponseUsage: + """Usage for the response now completing: cumulative totals minus what prior response.done events reported.""" + prior: Final = self._reported_usage + latest: Final = self._cumulative_usage + self._reported_usage = latest + input_details: Final[OpenAIRealtimeUsageTokenDetails] = { + "audio_tokens": latest.details.total.input.speechTokens - prior.details.total.input.speechTokens, + "text_tokens": latest.details.total.input.textTokens - prior.details.total.input.textTokens, + "cached_tokens": 0, + } + output_details: Final[OpenAIRealtimeUsageTokenDetails] = { + "audio_tokens": latest.details.total.output.speechTokens - prior.details.total.output.speechTokens, + "text_tokens": latest.details.total.output.textTokens - prior.details.total.output.textTokens, + } + usage_delta: Final[OpenAIRealtimeResponseUsage] = { + "input_tokens": latest.totalInputTokens - prior.totalInputTokens, + "output_tokens": latest.totalOutputTokens - prior.totalOutputTokens, + "total_tokens": latest.totalTokens - prior.totalTokens, + "input_token_details": input_details, + "output_token_details": output_details, + } + return usage_delta + + def leftover_usage_done_events(self) -> tuple[OpenAIRealtimeEvents, ...]: + """Logged-only response.done for usage Bedrock reports after the final turn's contentEnd.""" + if self._cumulative_usage == self._reported_usage: + return () + usage: Final = self._take_usage_delta() + leftover_done: Final = OpenAIRealtimeDoneEvent( + type="response.done", + event_id=f"event_{uuid.uuid4()}", + response=OpenAIRealtimeResponseDoneObject( + object="realtime.response", + id=f"resp_{uuid.uuid4()}", + status="completed", + conversation_id=f"conv_{uuid.uuid4()}", + usage=dict(usage), # mutable-ok: OpenAIRealtimeResponseDoneObject types usage as plain dict + ), + ) + return (leftover_done,) + + def transform_user_transcript_event(self, transcript: str) -> tuple[OpenAIRealtimeEvents, ...]: + """Transform a USER-role Bedrock textOutput (ASR transcript) to an OpenAI transcription delta.""" + verbose_logger.debug("Handling USER textOutput (ASR transcript)") + delta_event: Final[OpenAIRealtimeInputAudioTranscriptionDelta] = { + "type": "conversation.item.input_audio_transcription.delta", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(), + "content_index": 0, + "delta": transcript, + } + if self._user_transcript_generation_stage != "SPECULATIVE": + self._user_transcript_buffer += transcript + return (delta_event,) + + def user_transcript_completed_events(self) -> tuple[OpenAIRealtimeEvents, ...]: + """One completed event with the full transcript once the FINAL user content block ends.""" + transcript: Final = self._user_transcript_buffer + if not transcript: + return () + self._user_transcript_buffer = "" + completed_event: Final[OpenAIRealtimeInputAudioTranscriptionCompleted] = { + "type": "conversation.item.input_audio_transcription.completed", + "event_id": f"event_{uuid.uuid4()}", + "item_id": self._current_user_item_id(), + "content_index": 0, + "transcript": transcript, + } + return (completed_event,) + def transform_text_output_event( self, event: dict, @@ -985,7 +1129,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): if not current_response_id or not current_conversation_id: return [], None, None, None - usage_obj: Final = get_empty_usage() + usage: Final = self._take_usage_delta() response_done: Final = OpenAIRealtimeDoneEvent( type="response.done", event_id=f"event_{uuid.uuid4()}", @@ -995,11 +1139,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): status="completed", output=[], conversation_id=current_conversation_id, - usage={ - "prompt_tokens": usage_obj.prompt_tokens, - "completion_tokens": usage_obj.completion_tokens, - "total_tokens": usage_obj.total_tokens, - }, + usage=dict(usage), ), ) @@ -1042,9 +1182,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): # Create a function call arguments done event # This is a custom event format that matches what clients expect - from typing import cast - - function_call_event: Final[dict[str, Any]] = { + function_call_event: Final[dict[str, object]] = { "type": "response.function_call_arguments.done", "event_id": f"event_{uuid.uuid4()}", "response_id": current_response_id, @@ -1194,18 +1332,26 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): returned_messages.extend(events) elif "textOutput" in event: - events, current_delta_chunks = self.transform_text_output_event( - event, - current_output_item_id, - current_response_id, - current_delta_chunks, - ) - returned_messages.extend(events) + if self._user_transcript_active: + returned_messages.extend(self.transform_user_transcript_event(event["textOutput"].get("content", ""))) + else: + events, current_delta_chunks = self.transform_text_output_event( + event, + current_output_item_id, + current_response_id, + current_delta_chunks, + ) + returned_messages.extend(events) elif "audioOutput" in event: events = self.transform_audio_output_event(event, current_output_item_id, current_response_id) returned_messages.extend(events) + elif "contentEnd" in event and self._user_transcript_active: + self._user_transcript_active = False + self._user_transcript_generation_stage = None + returned_messages.extend(self.user_transcript_completed_events()) + elif "contentEnd" in event: events, current_delta_chunks = self.transform_content_end_event( event, @@ -1224,6 +1370,12 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): ) = self._response_done_events(current_response_id, current_conversation_id) returned_messages.extend(done_events) + elif "userSpeechStart" in event or "userSpeechEnd" in event: + returned_messages.extend(self.transform_user_speech_event("userSpeechStart" in event)) + + elif "usageEvent" in event: + self.transform_usage_event(BedrockUsageEvent.model_validate(event["usageEvent"])) + elif "toolUse" in event: events, tool_call_id, tool_name = self.transform_tool_use_event( event, current_output_item_id, current_response_id diff --git a/litellm/llms/bedrock/rerank/handler.py b/litellm/llms/bedrock/rerank/handler.py index 1cc72f265eb..4860c99268e 100644 --- a/litellm/llms/bedrock/rerank/handler.py +++ b/litellm/llms/bedrock/rerank/handler.py @@ -29,6 +29,7 @@ class BedrockRerankHandler(BaseAWSLLM): async def arerank( self, prepared_request: BedrockPreparedRequest, + logging_obj: LitellmLogging, timeout: float | httpx.Timeout | None = None, client: AsyncHTTPHandler | None = None, ): @@ -40,6 +41,7 @@ class BedrockRerankHandler(BaseAWSLLM): headers=dict(prepared_request["prepped"].headers), data=prepared_request["body"], timeout=timeout, + logging_obj=logging_obj, ) response.raise_for_status() except httpx.HTTPStatusError as err: @@ -98,6 +100,7 @@ class BedrockRerankHandler(BaseAWSLLM): if _is_async: return self.arerank( prepared_request, + logging_obj=logging_obj, timeout=timeout, client=client if client is not None and isinstance(client, AsyncHTTPHandler) else None, ) @@ -135,11 +138,6 @@ class BedrockRerankHandler(BaseAWSLLM): data: dict, optional_params: dict, ) -> BedrockPreparedRequest: - try: - from botocore.auth import SigV4Auth - from botocore.awsrequest import AWSRequest - except ImportError: - raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") boto3_credentials_info: Final = self._get_boto_credentials_from_optional_params(optional_params, model) ### SET RUNTIME ENDPOINT ### @@ -150,24 +148,21 @@ class BedrockRerankHandler(BaseAWSLLM): ) proxy_endpoint_url = proxy_endpoint_url.replace("bedrock-runtime", "bedrock-agent-runtime") proxy_endpoint_url = f"{proxy_endpoint_url}/rerank" - sigv4: Final = SigV4Auth( - boto3_credentials_info.credentials, - "bedrock", - boto3_credentials_info.aws_region_name, - ) - # Make POST Request - body: Final = json.dumps(data).encode("utf-8") + body: Final = json.dumps(data).encode("utf-8") headers = {"Content-Type": "application/json"} if extra_headers is not None: headers = {"Content-Type": "application/json", **extra_headers} - request: Final = AWSRequest(method="POST", url=proxy_endpoint_url, data=body, headers=headers) - sigv4.add_auth(request) - if ( - extra_headers is not None and "Authorization" in extra_headers - ): # prevent sigv4 from overwriting the auth header - request.headers["Authorization"] = extra_headers["Authorization"] - prepped: Final = request.prepare() + + prepped: Final = self.get_request_headers( + credentials=boto3_credentials_info.credentials, + aws_region_name=boto3_credentials_info.aws_region_name, + extra_headers=extra_headers, + endpoint_url=proxy_endpoint_url, + data=body, + headers=headers, + supports_bearer_token=False, + ) return BedrockPreparedRequest( endpoint_url=proxy_endpoint_url, diff --git a/litellm/llms/bedrock/vector_stores/transformation.py b/litellm/llms/bedrock/vector_stores/transformation.py index 2d72db0cdba..bad17a2181d 100644 --- a/litellm/llms/bedrock/vector_stores/transformation.py +++ b/litellm/llms/bedrock/vector_stores/transformation.py @@ -27,6 +27,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -196,6 +197,7 @@ class BedrockVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: if isinstance(query, list): query = " ".join(query) diff --git a/litellm/llms/bedrock_mantle/common_utils.py b/litellm/llms/bedrock_mantle/common_utils.py index 889361cd808..d877fbb4e09 100644 --- a/litellm/llms/bedrock_mantle/common_utils.py +++ b/litellm/llms/bedrock_mantle/common_utils.py @@ -13,6 +13,7 @@ global state. """ import re +from collections.abc import Mapping from typing import Final from botocore.exceptions import ( @@ -31,30 +32,39 @@ BEDROCK_MANTLE_DEFAULT_REGION: Final = "us-east-1" MANTLE_HOST_RE: Final = re.compile(r"^https?://bedrock-mantle\.([^/.]+)\.api\.aws", re.IGNORECASE) +def resolve_mantle_bearer_token(api_key: str | None) -> str | None: + return api_key or get_secret_str("BEDROCK_MANTLE_API_KEY") or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") + + +def resolve_mantle_region(params: Mapping[str, object]) -> str: + region: Final = params.get("aws_region_name") + if isinstance(region, str) and region: + BaseAWSLLM._validate_aws_region_name(region) + return region + api_base: Final = params.get("api_base") + base: Final = (api_base if isinstance(api_base, str) else None) or get_secret_str("BEDROCK_MANTLE_API_BASE") + if base: + match: Final = MANTLE_HOST_RE.match(base.rstrip("/")) + if match: + return match.group(1) + return ( + get_secret_str("BEDROCK_MANTLE_REGION") + or get_secret_str("AWS_REGION_NAME") + or get_secret_str("AWS_REGION") + or BEDROCK_MANTLE_DEFAULT_REGION + ) + + class BedrockMantleAuthMixin: _aws_signer: BaseAWSLLM @staticmethod def _resolve_bearer_token(api_key: str | None) -> str | None: - return api_key or get_secret_str("BEDROCK_MANTLE_API_KEY") or get_secret_str("AWS_BEARER_TOKEN_BEDROCK") + return resolve_mantle_bearer_token(api_key) @staticmethod def _resolve_region(params: dict) -> str: - region: Final = params.get("aws_region_name") - if region: - BaseAWSLLM._validate_aws_region_name(region) - return region - base: Final = params.get("api_base") or get_secret_str("BEDROCK_MANTLE_API_BASE") - if base: - match: Final = MANTLE_HOST_RE.match(base.rstrip("/")) - if match: - return match.group(1) - return ( - get_secret_str("BEDROCK_MANTLE_REGION") - or get_secret_str("AWS_REGION_NAME") - or get_secret_str("AWS_REGION") - or BEDROCK_MANTLE_DEFAULT_REGION - ) + return resolve_mantle_region(params) def sign_request( self, diff --git a/litellm/llms/bedrock_mantle/passthrough/transformation.py b/litellm/llms/bedrock_mantle/passthrough/transformation.py new file mode 100644 index 00000000000..e6b831efa57 --- /dev/null +++ b/litellm/llms/bedrock_mantle/passthrough/transformation.py @@ -0,0 +1,71 @@ +from collections.abc import Mapping +from typing import TYPE_CHECKING, Final, Literal, Optional + +from httpx import Response + +from litellm.litellm_core_utils.litellm_logging import Logging +from litellm.llms.bedrock.passthrough.transformation import BedrockPassthroughConfig +from litellm.llms.bedrock_mantle.common_utils import ( + MANTLE_HOST_RE, + resolve_mantle_bearer_token, + resolve_mantle_region, +) +from litellm.types.utils import LlmProviders + +if TYPE_CHECKING: + from litellm.types.utils import CostResponseTypes + + +class BedrockMantlePassthroughConfig(BedrockPassthroughConfig): + """Native Bedrock runtime passthrough (InvokeModel, Converse) for deployments declared as bedrock_mantle. + + The Mantle host only serves the OpenAI-compatible surface, so a Mantle api_base lends its region and the + request itself goes to bedrock-runtime, signed with the deployment's Bearer token or SigV4 credentials. + """ + + def _get_aws_region_name( + self, + optional_params: Mapping[str, object], + model: str | None = None, + model_id: str | None = None, + ) -> str: + return resolve_mantle_region(optional_params) + + def get_runtime_endpoint( + self, + api_base: str | None, + aws_bedrock_runtime_endpoint: str | None, + aws_region_name: str, + endpoint_type: Literal["runtime", "agent", "agentcore"] | None = "runtime", + ) -> tuple[str, str]: + is_mantle_host: Final = api_base is not None and MANTLE_HOST_RE.match(api_base.rstrip("/")) is not None + return super().get_runtime_endpoint( + api_base=None if is_mantle_host else api_base, + aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + aws_region_name=aws_region_name, + endpoint_type=endpoint_type, + ) + + def get_bedrock_bearer_token(self, litellm_params: Mapping[str, object]) -> str | None: + api_key: Final = litellm_params.get("api_key") + return resolve_mantle_bearer_token(api_key if isinstance(api_key, str) else None) + + def logging_non_streaming_response( + self, + model: str, + custom_llm_provider: str, + httpx_response: Response, + request_data: dict, # mutable-ok: mirrors the inherited BedrockPassthroughConfig signature + logging_obj: Logging, + endpoint: str, + ) -> Optional["CostResponseTypes"]: + is_converse: Final = "invoke" not in endpoint and "converse" in endpoint + shape_provider: Final = LlmProviders.BEDROCK.value if is_converse else custom_llm_provider + return super().logging_non_streaming_response( + model=model, + custom_llm_provider=shape_provider, + httpx_response=httpx_response, + request_data=request_data, + logging_obj=logging_obj, + endpoint=endpoint, + ) diff --git a/litellm/llms/bedrock_mantle/responses/transformation.py b/litellm/llms/bedrock_mantle/responses/transformation.py index 92da5835b2d..2ea355fd369 100644 --- a/litellm/llms/bedrock_mantle/responses/transformation.py +++ b/litellm/llms/bedrock_mantle/responses/transformation.py @@ -15,8 +15,12 @@ role / access key / profile / web identity), signed via the shared BaseAWSLLM._sign_request after the request body is finalized. """ +import json +from collections.abc import Mapping from typing import Any, Final +from typing_extensions import ReadOnly, TypedDict + import litellm from litellm._logging import verbose_logger from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM @@ -50,6 +54,33 @@ _BEDROCK_MANTLE_SUPPORTED_SERVICE_TIERS: Final = frozenset({"auto", "default"}) _CODEX_ADDITIONAL_TOOLS_INPUT_ITEM_TYPE: Final = "additional_tools" +_CODEX_AGENT_MESSAGE_INPUT_ITEM_TYPE: Final = "agent_message" +_CODEX_CONTEXT_COMPACTION_INPUT_ITEM_TYPE: Final = "context_compaction" +_CODEX_LOCAL_SHELL_CALL_INPUT_ITEM_TYPE: Final = "local_shell_call" + + +class _RewrittenOutputTextBlock(TypedDict): + type: ReadOnly[str] + text: ReadOnly[str] + + +class _RewrittenAssistantMessageItem(TypedDict): + type: ReadOnly[str] + role: ReadOnly[str] + content: ReadOnly[tuple[_RewrittenOutputTextBlock, ...]] + + +class _RewrittenCompactionItem(TypedDict): + type: ReadOnly[str] + encrypted_content: ReadOnly[str] + + +class _RewrittenFunctionCallItem(TypedDict): + type: ReadOnly[str] + call_id: ReadOnly[str] + name: ReadOnly[str] + arguments: ReadOnly[str] + class BedrockMantleResponsesAPIConfig(BedrockMantleAuthMixin, OpenAIResponsesAPIConfig): def __init__( @@ -155,6 +186,7 @@ class BedrockMantleResponsesAPIConfig(BedrockMantleAuthMixin, OpenAIResponsesAPI headers: dict, ) -> dict: remaining_input, hoisted_tools = self._hoist_codex_additional_tools(input) + normalized_input: Final = self._normalize_codex_input_items(remaining_input) request_params: Final = ( { **response_api_optional_request_params, @@ -168,7 +200,7 @@ class BedrockMantleResponsesAPIConfig(BedrockMantleAuthMixin, OpenAIResponsesAPI ) return super().transform_responses_api_request( model=model, - input=remaining_input, + input=normalized_input, response_api_optional_request_params=request_params, litellm_params=litellm_params, headers=headers, @@ -210,6 +242,91 @@ class BedrockMantleResponsesAPIConfig(BedrockMantleAuthMixin, OpenAIResponsesAPI ) return remaining_input, cls._filter_unsupported_tools(hoisted_tools) + @staticmethod + def _agent_message_text(item: "Mapping[str, object]") -> str: + content: Final = item.get("content") + if not isinstance(content, list): + return "" + return "".join( + str(block.get("text") or block.get("encrypted_content") or "") + for block in content + if isinstance(block, dict) + ) + + @classmethod + def _normalize_agent_message_item(cls, item: "Mapping[str, object]") -> "_RewrittenAssistantMessageItem | None": + text: Final = cls._agent_message_text(item) + if not text: + return None + rewritten: Final[_RewrittenAssistantMessageItem] = { + "type": "message", + "role": "assistant", + "content": ({"type": "output_text", "text": text},), + } + return rewritten + + @staticmethod + def _normalize_context_compaction_item(item: "Mapping[str, object]") -> "_RewrittenCompactionItem | None": + encrypted_content: Final = item.get("encrypted_content") + if not isinstance(encrypted_content, str) or not encrypted_content: + return None + rewritten: Final[_RewrittenCompactionItem] = {"type": "compaction", "encrypted_content": encrypted_content} + return rewritten + + @staticmethod + def _normalize_local_shell_call_item(item: "Mapping[str, object]") -> "_RewrittenFunctionCallItem | None": + call_id: Final = item.get("call_id") + if not isinstance(call_id, str) or not call_id: + return None + action: Final = item.get("action") + rewritten: Final[_RewrittenFunctionCallItem] = { + "type": "function_call", + "call_id": call_id, + "name": "local_shell", + "arguments": json.dumps(action) if isinstance(action, dict) else "{}", + } + return rewritten + + @classmethod + def _normalize_codex_input_item(cls, item: object) -> "tuple[object, str | None]": + """Returns (normalized item or None to drop it, original type when rewritten).""" + if not isinstance(item, dict): + return item, None + item_type: Final = item.get("type") + if item_type == _CODEX_AGENT_MESSAGE_INPUT_ITEM_TYPE: + return cls._normalize_agent_message_item(item), item_type + if item_type == _CODEX_CONTEXT_COMPACTION_INPUT_ITEM_TYPE: + return cls._normalize_context_compaction_item(item), item_type + if item_type == _CODEX_LOCAL_SHELL_CALL_INPUT_ITEM_TYPE: + return cls._normalize_local_shell_call_item(item), item_type + return item, None + + @classmethod + def _normalize_codex_input_items( + cls, + input: "str | ResponseInputParam", + ) -> "str | ResponseInputParam": + """Rewrite Codex history item types Mantle rejects with 400 "Invalid + 'input': value did not match any expected variant" into supported + equivalents. `agent_message` (Codex multi-agent traffic; its + encrypted_content slot carries the plaintext payload when the model + never issued encrypted args) becomes an assistant message, + `context_compaction` becomes the `compaction` spelling Mantle accepts, + and `local_shell_call` becomes the function_call its recorded + function_call_output already pairs with. + """ + if not isinstance(input, list): + return input + normalized: Final = tuple(cls._normalize_codex_input_item(item) for item in input) + rewritten_types: Final = sorted(frozenset(item_type for _, item_type in normalized if item_type is not None)) + if rewritten_types: + verbose_logger.warning( + "Bedrock Mantle Responses API: rewrote Codex input item type(s) %s that Mantle rejects.", + rewritten_types, + ) + kept: Final = [item for item, _ in normalized if item is not None] # mutable-ok: ResponseInputParam is a list + return kept # pyright: ignore[reportReturnType] # Codex passthrough items sit outside the OpenAI input union + def map_openai_params( self, response_api_optional_params: ResponsesAPIOptionalRequestParams, diff --git a/litellm/llms/black_forest_labs/image_edit/handler.py b/litellm/llms/black_forest_labs/image_edit/handler.py index 1ff02a6f8d9..178acb0de0d 100644 --- a/litellm/llms/black_forest_labs/image_edit/handler.py +++ b/litellm/llms/black_forest_labs/image_edit/handler.py @@ -8,9 +8,11 @@ then we poll until the result is ready. import asyncio import time -from typing import Any, Final +from collections.abc import Coroutine, Mapping +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -33,6 +35,42 @@ from ..common_utils import ( from .transformation import BlackForestLabsImageEditConfig +class _BFLSubmitBody(TypedDict, total=False): + """Decoded body of the BFL submit response, which hands back a polling URL.""" + + errors: ReadOnly[object] + polling_url: ReadOnly[str] + + +class _BFLPollBody(TypedDict, total=False): + """Decoded body of a BFL polling response.""" + + status: ReadOnly[str] + + +class _BFLSubmitResponse(Protocol): + """The submit call's HTTP response, read for its status, body text and decoded body.""" + + @property + def status_code(self) -> int: ... + + @property + def text(self) -> str: ... + + def json(self) -> _BFLSubmitBody: ... + + +class _BFLPollResponse(Protocol): + """A polling call's HTTP response, read only for the task status it carries.""" + + def json(self) -> _BFLPollBody: ... + + +def _poll_status(response: _BFLPollResponse) -> str | None: + """Read the task status out of a BFL polling response body.""" + return response.json().get("status") + + class BlackForestLabsImageEdit: """ Black Forest Labs Image Edit handler. @@ -53,10 +91,10 @@ class BlackForestLabsImageEdit: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, object] | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, aimage_edit: bool = False, - ) -> ImageResponse | Any: + ) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Main entry point for image edit requests. @@ -185,7 +223,7 @@ class BlackForestLabsImageEdit: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, object] | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: """ @@ -281,7 +319,7 @@ class BlackForestLabsImageEdit: def _poll_for_result_sync( self, - initial_response: httpx.Response, + initial_response: _BFLSubmitResponse, headers: dict, sync_client: HTTPHandler, max_wait: float = DEFAULT_MAX_POLLING_TIME, @@ -356,8 +394,7 @@ class BlackForestLabsImageEdit: message=f"Polling failed: {response.text}", ) - data = response.json() - status = data.get("status") + status = _poll_status(response) verbose_logger.debug("BFL poll status: %s", status) @@ -383,7 +420,7 @@ class BlackForestLabsImageEdit: async def _poll_for_result_async( self, - initial_response: httpx.Response, + initial_response: _BFLSubmitResponse, headers: dict, async_client: AsyncHTTPHandler, max_wait: float = DEFAULT_MAX_POLLING_TIME, @@ -447,8 +484,7 @@ class BlackForestLabsImageEdit: message=f"Polling failed: {response.text}", ) - data = response.json() - status = data.get("status") + status = _poll_status(response) verbose_logger.debug("BFL poll status: %s", status) diff --git a/litellm/llms/black_forest_labs/image_generation/handler.py b/litellm/llms/black_forest_labs/image_generation/handler.py index 03e4999c5aa..879bef37b58 100644 --- a/litellm/llms/black_forest_labs/image_generation/handler.py +++ b/litellm/llms/black_forest_labs/image_generation/handler.py @@ -8,9 +8,11 @@ then we poll until the result is ready. import asyncio import time -from typing import Any, Final +from collections.abc import Coroutine, Mapping +from typing import Final, Protocol, TypedDict import httpx +from typing_extensions import ReadOnly import litellm from litellm._logging import verbose_logger @@ -33,6 +35,23 @@ from ..common_utils import ( from .transformation import BlackForestLabsImageGenerationConfig +class _BFLTaskPayload(TypedDict, total=False): + """The body BFL returns for a submitted or polled generation task.""" + + errors: ReadOnly[object] + polling_url: ReadOnly[str] + status: ReadOnly[str] + + +class _TaskJsonResponse(Protocol): + def json(self) -> _BFLTaskPayload: ... + + +def _task_payload(response: _TaskJsonResponse) -> _BFLTaskPayload: + """The JSON body of a BFL task submission or poll response.""" + return response.json() + + class BlackForestLabsImageGeneration: """ Black Forest Labs Image Generation handler. @@ -53,10 +72,10 @@ class BlackForestLabsImageGeneration: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, str] | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, aimg_generation: bool = False, - ) -> ImageResponse | Any: + ) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Main entry point for image generation requests. @@ -187,7 +206,7 @@ class BlackForestLabsImageGeneration: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, str] | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: """ @@ -305,7 +324,7 @@ class BlackForestLabsImageGeneration: # Parse initial response to get polling URL try: - response_data: Final = initial_response.json() + response_data: Final = _task_payload(initial_response) except Exception as e: raise BlackForestLabsError( status_code=initial_response.status_code, @@ -350,7 +369,7 @@ class BlackForestLabsImageGeneration: message=f"Polling failed: {response.text}", ) - data = response.json() + data = _task_payload(response) status = data.get("status") verbose_logger.debug("BFL poll status: %s", status) @@ -396,7 +415,7 @@ class BlackForestLabsImageGeneration: # Parse initial response to get polling URL try: - response_data: Final = initial_response.json() + response_data: Final = _task_payload(initial_response) except Exception as e: raise BlackForestLabsError( status_code=initial_response.status_code, @@ -441,7 +460,7 @@ class BlackForestLabsImageGeneration: message=f"Polling failed: {response.text}", ) - data = response.json() + data = _task_payload(response) status = data.get("status") verbose_logger.debug("BFL poll status: %s", status) diff --git a/litellm/llms/black_forest_labs/image_generation/transformation.py b/litellm/llms/black_forest_labs/image_generation/transformation.py index 5953ad1996b..119ffff1c34 100644 --- a/litellm/llms/black_forest_labs/image_generation/transformation.py +++ b/litellm/llms/black_forest_labs/image_generation/transformation.py @@ -29,6 +29,8 @@ from ..common_utils import ( ) if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -256,7 +258,7 @@ class BlackForestLabsImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/bytez/chat/transformation.py b/litellm/llms/bytez/chat/transformation.py index becd3f2d67e..d9a0c98b6db 100644 --- a/litellm/llms/bytez/chat/transformation.py +++ b/litellm/llms/bytez/chat/transformation.py @@ -23,6 +23,8 @@ from litellm.utils import CustomStreamWrapper, ModelResponse, Usage from ..common_utils import API_BASE, BytezError if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -185,7 +187,7 @@ class BytezChatConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/cerebras/chat.py b/litellm/llms/cerebras/chat.py index 8827b0afd87..c3aa26ade35 100644 --- a/litellm/llms/cerebras/chat.py +++ b/litellm/llms/cerebras/chat.py @@ -68,6 +68,8 @@ class CerebrasConfig(OpenAIGPTConfig): "tool_choice", "tools", "user", + "max_retries", + "extra_headers", ] # Only add reasoning_effort for models that support it diff --git a/litellm/llms/chatgpt/authenticator.py b/litellm/llms/chatgpt/authenticator.py index 6a3d278a74c..563826c2b93 100644 --- a/litellm/llms/chatgpt/authenticator.py +++ b/litellm/llms/chatgpt/authenticator.py @@ -2,9 +2,11 @@ import base64 import json import os import time -from typing import Any, Final +from collections.abc import Mapping +from typing import Final, TypeAlias import httpx +from pydantic import JsonValue, TypeAdapter, ValidationError from litellm._logging import verbose_logger from litellm.llms.custom_httpx.http_handler import _get_httpx_client @@ -27,6 +29,16 @@ DEVICE_CODE_TIMEOUT_SECONDS: Final = 15 * 60 DEVICE_CODE_COOLDOWN_SECONDS: Final = 5 * 60 DEVICE_CODE_POLL_SLEEP_SECONDS: Final = 5 +OPENAI_AUTH_CLAIM_KEY: Final = "https://api.openai.com/auth" + +JsonObject: TypeAlias = Mapping[str, JsonValue] + +_JSON_OBJECT_ADAPTER: Final = TypeAdapter(JsonObject) + + +def _optional_str(value: JsonValue | None) -> str | None: + return value if isinstance(value, str) else None + class Authenticator: def __init__(self) -> None: @@ -43,10 +55,10 @@ class Authenticator: def get_access_token(self) -> str: auth_data: Final = self._read_auth_file() if auth_data: - access_token: Final = auth_data.get("access_token") + access_token: Final = _optional_str(auth_data.get("access_token")) if access_token and not self._is_token_expired(auth_data, access_token): return access_token - refresh_token: Final = auth_data.get("refresh_token") + refresh_token: Final = _optional_str(auth_data.get("refresh_token")) if refresh_token: try: refreshed: Final = self._refresh_tokens(refresh_token) @@ -67,48 +79,47 @@ class Authenticator: auth_data: Final = self._read_auth_file() if not auth_data: return None - account_id: Final = auth_data.get("account_id") + account_id: Final = _optional_str(auth_data.get("account_id")) if account_id: return account_id id_token: Final = auth_data.get("id_token") access_token: Final = auth_data.get("access_token") - derived: Final = self._extract_account_id(id_token or access_token) + derived: Final = self._extract_account_id(_optional_str(id_token or access_token)) if derived: - auth_data["account_id"] = derived - self._write_auth_file(auth_data) + self._write_auth_file({**auth_data, "account_id": derived}) return derived def _ensure_token_dir(self) -> None: if not os.path.exists(self.token_dir): os.makedirs(self.token_dir, exist_ok=True) - def _read_auth_file(self) -> dict[str, Any] | None: + def _read_auth_file(self) -> JsonObject | None: try: with open(self.auth_file, "r") as f: - return json.load(f) + return _JSON_OBJECT_ADAPTER.validate_python(json.load(f)) except OSError: return None - except json.JSONDecodeError as exc: + except (json.JSONDecodeError, ValidationError) as exc: verbose_logger.warning("Invalid ChatGPT auth file: %s", exc) return None - def _write_auth_file(self, data: dict[str, Any]) -> None: + def _write_auth_file(self, data: JsonObject) -> None: try: with open(self.auth_file, "w") as f: json.dump(data, f) except OSError as exc: verbose_logger.error("Failed to write ChatGPT auth file: %s", exc) - def _is_token_expired(self, auth_data: dict[str, Any], access_token: str) -> bool: - expires_at = auth_data.get("expires_at") - if expires_at is None: - expires_at = self._get_expires_at(access_token) - if expires_at: - auth_data["expires_at"] = expires_at - self._write_auth_file(auth_data) - if expires_at is None: + def _is_token_expired(self, auth_data: JsonObject, access_token: str) -> bool: + stored_expires_at: Final = auth_data.get("expires_at") + if isinstance(stored_expires_at, (int, float)): + return time.time() >= float(stored_expires_at) - TOKEN_EXPIRY_SKEW_SECONDS + derived_expires_at: Final = self._get_expires_at(access_token) + if derived_expires_at: + self._write_auth_file({**auth_data, "expires_at": derived_expires_at}) + if derived_expires_at is None: return True - return time.time() >= float(expires_at) - TOKEN_EXPIRY_SKEW_SECONDS + return time.time() >= float(derived_expires_at) - TOKEN_EXPIRY_SKEW_SECONDS def _get_expires_at(self, token: str) -> int | None: claims: Final = self._decode_jwt_claims(token) @@ -117,15 +128,14 @@ class Authenticator: return int(exp) return None - def _decode_jwt_claims(self, token: str) -> dict[str, Any]: + def _decode_jwt_claims(self, token: str) -> JsonObject: try: parts: Final = token.split(".") if len(parts) < 2: return {} - payload_b64 = parts[1] - payload_b64 += "=" * (-len(payload_b64) % 4) + payload_b64: Final = parts[1] + "=" * (-len(parts[1]) % 4) payload_bytes: Final = base64.urlsafe_b64decode(payload_b64) - return json.loads(payload_bytes.decode("utf-8")) + return _JSON_OBJECT_ADAPTER.validate_python(json.loads(payload_bytes.decode("utf-8"))) except Exception: return {} @@ -133,7 +143,7 @@ class Authenticator: if not token: return None claims: Final = self._decode_jwt_claims(token) - auth_claims: Final = claims.get("https://api.openai.com/auth") + auth_claims: Final = claims.get(OPENAI_AUTH_CLAIM_KEY) if isinstance(auth_claims, dict): account_id: Final = auth_claims.get("chatgpt_account_id") if isinstance(account_id, str) and account_id: @@ -170,7 +180,7 @@ class Authenticator: json={"client_id": CHATGPT_CLIENT_ID}, ) resp.raise_for_status() - data: Final = resp.json() + data: Final = _JSON_OBJECT_ADAPTER.validate_python(resp.json()) except httpx.HTTPStatusError as exc: raise GetDeviceCodeError( message=f"Failed to request device code: {exc}", @@ -182,8 +192,8 @@ class Authenticator: status_code=400, ) - device_auth_id: Final = data.get("device_auth_id") - user_code: Final = data.get("user_code") or data.get("usercode") + device_auth_id: Final = _optional_str(data.get("device_auth_id")) + user_code: Final = _optional_str(data.get("user_code") or data.get("usercode")) interval: Final = data.get("interval") if not device_auth_id or not user_code: raise GetDeviceCodeError( @@ -210,16 +220,16 @@ class Authenticator: }, ) if resp.status_code == 200: - data = resp.json() - if all( - key in data - for key in ( - "authorization_code", - "code_challenge", - "code_verifier", - ) - ): - return data + data = _JSON_OBJECT_ADAPTER.validate_python(resp.json()) + authorization_code = _optional_str(data.get("authorization_code")) + code_challenge = _optional_str(data.get("code_challenge")) + code_verifier = _optional_str(data.get("code_verifier")) + if authorization_code and code_challenge and code_verifier: + return { + "authorization_code": authorization_code, + "code_challenge": code_challenge, + "code_verifier": code_verifier, + } if resp.status_code in (403, 404): time.sleep(max(interval, DEVICE_CODE_POLL_SLEEP_SECONDS)) continue @@ -262,7 +272,7 @@ class Authenticator: content=body, ) resp.raise_for_status() - data: Final = resp.json() + data: Final = _JSON_OBJECT_ADAPTER.validate_python(resp.json()) except httpx.HTTPStatusError as exc: raise GetAccessTokenError( message=f"Token exchange failed: {exc}", @@ -274,15 +284,18 @@ class Authenticator: status_code=400, ) - if not all(key in data for key in ("access_token", "refresh_token", "id_token")): + access_token: Final = _optional_str(data.get("access_token")) + refresh_token: Final = _optional_str(data.get("refresh_token")) + id_token: Final = _optional_str(data.get("id_token")) + if not access_token or not refresh_token or not id_token: raise GetAccessTokenError( message=f"Token exchange response missing fields: {data}", status_code=400, ) return { - "access_token": data["access_token"], - "refresh_token": data["refresh_token"], - "id_token": data["id_token"], + "access_token": access_token, + "refresh_token": refresh_token, + "id_token": id_token, } def _refresh_tokens(self, refresh_token: str) -> dict[str, str]: @@ -298,7 +311,7 @@ class Authenticator: }, ) resp.raise_for_status() - data: Final = resp.json() + data: Final = _JSON_OBJECT_ADAPTER.validate_python(resp.json()) except httpx.HTTPStatusError as exc: raise RefreshAccessTokenError( message=f"Refresh token failed: {exc}", @@ -310,8 +323,8 @@ class Authenticator: status_code=400, ) - access_token: Final = data.get("access_token") - id_token: Final = data.get("id_token") + access_token: Final = _optional_str(data.get("access_token")) + id_token: Final = _optional_str(data.get("id_token")) if not access_token or not id_token: raise RefreshAccessTokenError( message=f"Refresh response missing fields: {data}", @@ -320,14 +333,14 @@ class Authenticator: refreshed: Final = { "access_token": access_token, - "refresh_token": data.get("refresh_token", refresh_token), + "refresh_token": _optional_str(data.get("refresh_token")) or refresh_token, "id_token": id_token, } auth_data: Final = self._build_auth_record(refreshed) self._write_auth_file(auth_data) return refreshed - def _build_auth_record(self, tokens: dict[str, str]) -> dict[str, Any]: + def _build_auth_record(self, tokens: dict[str, str]) -> JsonObject: access_token: Final = tokens.get("access_token") id_token: Final = tokens.get("id_token") expires_at: Final = self._get_expires_at(access_token) if access_token else None @@ -340,31 +353,30 @@ class Authenticator: "account_id": account_id, } - def _get_device_code_cooldown_remaining(self, auth_data: dict[str, Any] | None) -> float: + def _get_device_code_cooldown_remaining(self, auth_data: JsonObject | None) -> float: if not auth_data: return 0.0 - requested_at = auth_data.get("device_code_requested_at") + requested_at: Final = auth_data.get("device_code_requested_at") if not isinstance(requested_at, (int, float, str)): return 0.0 try: - requested_at = float(requested_at) + requested_seconds: Final = float(requested_at) except (TypeError, ValueError): return 0.0 - elapsed: Final = time.time() - requested_at + elapsed: Final = time.time() - requested_seconds remaining: Final = DEVICE_CODE_COOLDOWN_SECONDS - elapsed return max(0.0, remaining) def _record_device_code_request(self) -> None: auth_data: Final = self._read_auth_file() or {} - auth_data["device_code_requested_at"] = time.time() - self._write_auth_file(auth_data) + self._write_auth_file({**auth_data, "device_code_requested_at": time.time()}) def _wait_for_access_token(self, timeout_seconds: float) -> str | None: deadline: Final = time.time() + timeout_seconds while time.time() < deadline: auth_data = self._read_auth_file() if auth_data: - access_token = auth_data.get("access_token") + access_token = _optional_str(auth_data.get("access_token")) if access_token and not self._is_token_expired(auth_data, access_token): return access_token sleep_for = min(DEVICE_CODE_POLL_SLEEP_SECONDS, max(0.0, deadline - time.time())) diff --git a/litellm/llms/chatgpt/chat/streaming_utils.py b/litellm/llms/chatgpt/chat/streaming_utils.py index 57f679947f6..ee3120bacea 100644 --- a/litellm/llms/chatgpt/chat/streaming_utils.py +++ b/litellm/llms/chatgpt/chat/streaming_utils.py @@ -4,7 +4,27 @@ Streaming utilities for ChatGPT provider. Normalizes non-spec-compliant tool_call chunks from the ChatGPT backend API. """ -from typing import Any, Final +from collections.abc import Awaitable +from typing import Final, Protocol + +from litellm.types.utils import ( + ChatCompletionDeltaCustomToolCall, + ChatCompletionDeltaToolCall, + Delta, + ModelResponseStream, +) + + +class ChatGPTChunkStream(Protocol): + """A ChatGPT chunk source driven either synchronously or asynchronously.""" + + def __next__(self) -> ModelResponseStream: ... + + def __anext__(self) -> Awaitable[ModelResponseStream]: ... + + +def _first_choice_delta(chunk: ModelResponseStream) -> Delta | None: + return chunk.choices[0].delta class ChatGPTToolCallNormalizer: @@ -20,45 +40,45 @@ class ChatGPTToolCallNormalizer: chunks to the consumer. """ - def __init__(self, stream: Any): - self._stream = stream + def __init__(self, stream: ChatGPTChunkStream): + self._stream: Final = stream self._seen_ids: dict[str, int] = {} # tool_call_id -> assigned_index self._next_index: int = 0 self._last_id: str | None = None # tracks which tool call the next delta belongs to - def __getattr__(self, name: str) -> Any: + def __getattr__(self, name: str) -> object: return getattr(self._stream, name) - def __iter__(self): + def __iter__(self) -> "ChatGPTToolCallNormalizer": return self - def __aiter__(self): + def __aiter__(self) -> "ChatGPTToolCallNormalizer": return self - def __next__(self): + def __next__(self) -> ModelResponseStream: while True: chunk = next(self._stream) result = self._normalize(chunk) if result is not None: return result - async def __anext__(self): + async def __anext__(self) -> ModelResponseStream: while True: chunk = await self._stream.__anext__() result = self._normalize(chunk) if result is not None: return result - def _normalize(self, chunk: Any) -> Any: + def _normalize(self, chunk: ModelResponseStream) -> ModelResponseStream | None: """Fix tool_calls in the chunk. Returns None to skip duplicate chunks.""" if not chunk.choices: return chunk - delta: Final = chunk.choices[0].delta + delta: Final = _first_choice_delta(chunk) if delta is None or not delta.tool_calls: return chunk - normalized: Final = [] + normalized: Final[list[ChatCompletionDeltaToolCall | ChatCompletionDeltaCustomToolCall]] = [] for tc in delta.tool_calls: if tc.id and tc.id not in self._seen_ids: # New tool call — assign correct index diff --git a/litellm/llms/chatgpt/responses/transformation.py b/litellm/llms/chatgpt/responses/transformation.py index 8e4bbf1d3c9..b96e06be3d8 100644 --- a/litellm/llms/chatgpt/responses/transformation.py +++ b/litellm/llms/chatgpt/responses/transformation.py @@ -1,4 +1,4 @@ -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final from litellm.exceptions import AuthenticationError from litellm.litellm_core_utils.core_helpers import process_response_headers @@ -28,6 +28,9 @@ from ..common_utils import ( get_chatgpt_default_instructions, ) +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class ChatGPTResponsesAPIConfig(OpenAIResponsesAPIConfig): def __init__(self) -> None: @@ -107,7 +110,7 @@ class ChatGPTResponsesAPIConfig(OpenAIResponsesAPIConfig): self, model: str, raw_response: Any, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", ): body_text: Final = raw_response.text or "" if not self._should_parse_as_sse(raw_response=raw_response, body_text=body_text): diff --git a/litellm/llms/clarifai/chat/transformation.py b/litellm/llms/clarifai/chat/transformation.py index f5227966aef..76d35467497 100644 --- a/litellm/llms/clarifai/chat/transformation.py +++ b/litellm/llms/clarifai/chat/transformation.py @@ -13,6 +13,8 @@ from litellm.types.utils import ModelResponse from ...openai.chat.gpt_transformation import OpenAIGPTConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -85,7 +87,7 @@ class ClarifaiConfig(OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/codestral/completion/handler.py b/litellm/llms/codestral/completion/handler.py index 8c08b2bc33c..f8486d3b274 100644 --- a/litellm/llms/codestral/completion/handler.py +++ b/litellm/llms/codestral/completion/handler.py @@ -4,9 +4,10 @@ import json from collections.abc import Callable from functools import partial -from typing import Final +from typing import Final, Protocol import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging @@ -23,6 +24,53 @@ from litellm.types.utils import TextChoices from litellm.utils import CustomStreamWrapper, TextCompletionResponse +class _CodestralChoiceMessage(TypedDict): + """`choices[].message` of a Codestral FIM completion.""" + + role: ReadOnly[NotRequired[str]] + content: ReadOnly[NotRequired[str | None]] + + +class _CodestralChoice(TypedDict): + """One entry of `choices` in a Codestral FIM completion.""" + + index: ReadOnly[int] + message: ReadOnly[NotRequired[_CodestralChoiceMessage]] + finish_reason: ReadOnly[NotRequired[str | None]] + logprobs: ReadOnly[NotRequired[dict[str, object] | None]] + + +class _CodestralUsage(TypedDict): + """Token accounting returned alongside a Codestral FIM completion.""" + + prompt_tokens: ReadOnly[NotRequired[int]] + completion_tokens: ReadOnly[NotRequired[int]] + total_tokens: ReadOnly[NotRequired[int]] + + +class _CodestralCompletionResponse(TypedDict): + """Body returned by the Codestral `/v1/fim/completions` endpoint.""" + + id: ReadOnly[NotRequired[str]] + created: ReadOnly[NotRequired[int]] + model: ReadOnly[NotRequired[str]] + object: ReadOnly[NotRequired[str]] + usage: ReadOnly[NotRequired[_CodestralUsage]] + choices: ReadOnly[NotRequired[list[_CodestralChoice]]] + + +class _CodestralHTTPResponse(Protocol): + """The Codestral completion response as this handler reads it.""" + + @property + def status_code(self) -> int: ... + + @property + def text(self) -> str: ... + + def json(self) -> _CodestralCompletionResponse: ... + + class TextCompletionCodestralError(Exception): def __init__( self, @@ -115,7 +163,7 @@ class CodestralTextCompletion: def process_text_completion_response( self, model: str, - response: httpx.Response, + response: _CodestralHTTPResponse, model_response: TextCompletionResponse, stream: bool, logging_obj: LiteLLMLogging, diff --git a/litellm/llms/cohere/chat/transformation.py b/litellm/llms/cohere/chat/transformation.py index 3560683c49b..319603b0dad 100644 --- a/litellm/llms/cohere/chat/transformation.py +++ b/litellm/llms/cohere/chat/transformation.py @@ -15,6 +15,8 @@ from ..common_utils import ModelResponseIterator as CohereModelResponseIterator from ..common_utils import validate_environment as cohere_validate_environment if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -225,7 +227,7 @@ class CohereChatConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/cohere/chat/v2_transformation.py b/litellm/llms/cohere/chat/v2_transformation.py index a7db03924b6..4252e7d02e9 100644 --- a/litellm/llms/cohere/chat/v2_transformation.py +++ b/litellm/llms/cohere/chat/v2_transformation.py @@ -20,6 +20,8 @@ from ..common_utils import CohereError, CohereV2ModelResponseIterator from ..common_utils import validate_environment as cohere_validate_environment if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -189,7 +191,7 @@ class CohereV2ChatConfig(OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/cohere/embed/handler.py b/litellm/llms/cohere/embed/handler.py index 3384839da85..3cebf6b9a90 100644 --- a/litellm/llms/cohere/embed/handler.py +++ b/litellm/llms/cohere/embed/handler.py @@ -3,8 +3,7 @@ Legacy /v1/embedding handler for Bedrock Cohere. """ import json -from collections.abc import Callable -from typing import Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -20,6 +19,9 @@ from litellm.types.utils import EmbeddingResponse from .v1_transformation import CohereEmbeddingConfig +if TYPE_CHECKING: + import tiktoken + def validate_environment(api_key, headers: dict): # Create a lowercase key lookup to avoid duplicate headers with different cases @@ -58,7 +60,7 @@ async def async_embedding( api_base: str, api_key: str | None, headers: dict, - encoding: Callable, + encoding: "tiktoken.Encoding | None", client: AsyncHTTPHandler | None = None, ): ## LOGGING @@ -120,7 +122,7 @@ def embedding( logging_obj: LiteLLMLoggingObj, optional_params: dict, headers: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", data: dict | CohereEmbeddingRequest | None = None, complete_api_base: str | None = None, api_key: str | None = None, diff --git a/litellm/llms/cohere/rerank/guardrail_translation/handler.py b/litellm/llms/cohere/rerank/guardrail_translation/handler.py index b5e49bd922e..84cb551190a 100644 --- a/litellm/llms/cohere/rerank/guardrail_translation/handler.py +++ b/litellm/llms/cohere/rerank/guardrail_translation/handler.py @@ -13,6 +13,7 @@ from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.rerank import RerankResponse @@ -42,7 +43,7 @@ class CohereRerankHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, ) -> Any: """ Process input text fields ('query' and 'instruction') by applying @@ -94,7 +95,7 @@ class CohereRerankHandler(BaseTranslation): self, response: "RerankResponse", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: Any | None = None, request_data: dict | None = None, ) -> Any: diff --git a/litellm/llms/cohere/rerank/transformation.py b/litellm/llms/cohere/rerank/transformation.py index 76386252b79..a8e755406d8 100644 --- a/litellm/llms/cohere/rerank/transformation.py +++ b/litellm/llms/cohere/rerank/transformation.py @@ -1,3 +1,4 @@ +from collections.abc import Mapping from typing import Any, Final import httpx @@ -81,6 +82,7 @@ class CohereRerankConfig(BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: if api_key is None: api_key = get_secret_str("COHERE_API_KEY") or get_secret_str("CO_API_KEY") or litellm.cohere_key diff --git a/litellm/llms/cometapi/image_generation/transformation.py b/litellm/llms/cometapi/image_generation/transformation.py index 3c643f5ce36..03c820de198 100644 --- a/litellm/llms/cometapi/image_generation/transformation.py +++ b/litellm/llms/cometapi/image_generation/transformation.py @@ -13,6 +13,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -130,7 +132,7 @@ class CometAPIImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/compactifai/chat/transformation.py b/litellm/llms/compactifai/chat/transformation.py index 44e1ab15801..3c5a889ce63 100644 --- a/litellm/llms/compactifai/chat/transformation.py +++ b/litellm/llms/compactifai/chat/transformation.py @@ -2,18 +2,23 @@ CompactifAI chat completion transformation """ +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx +from typing_extensions import ReadOnly, TypedDict from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.openai.common_utils import OpenAIError from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse from ...openai.chat.gpt_transformation import OpenAIGPTConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -21,6 +26,18 @@ else: LiteLLMLoggingObj = Any +class CompactifAIResponseFields(TypedDict, total=False): + """The chat completion fields of a CompactifAI response body.""" + + id: ReadOnly[str] + choices: ReadOnly[Sequence[Mapping[str, object]]] + created: ReadOnly[int] + model: ReadOnly[str] + system_fingerprint: ReadOnly[str | None] + usage: ReadOnly[Mapping[str, object]] + object: ReadOnly[str] + + class CompactifAIChatConfig(OpenAIGPTConfig): """ Configuration class for CompactifAI chat completions. @@ -45,11 +62,11 @@ class CompactifAIChatConfig(OpenAIGPTConfig): raw_response: httpx.Response, model_response: ModelResponse, logging_obj: LiteLLMLoggingObj, - request_data: dict, - messages: list, - optional_params: dict, - litellm_params: dict, - encoding: Any, + request_data: Mapping[str, object], + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -79,14 +96,18 @@ class CompactifAIChatConfig(OpenAIGPTConfig): message["content"] = tool_calls[0]["function"].get("arguments", "") message["tool_calls"] = None - returned_response: Final = ModelResponse(**response_json) + response_fields: Final[CompactifAIResponseFields] = response_json + + returned_response: Final = ModelResponse(**response_fields) # Set model name with provider prefix returned_response.model = f"compactifai/{model}" return returned_response - def get_error_class(self, error_message: str, status_code: int, headers: dict | httpx.Headers) -> BaseLLMException: + def get_error_class( + self, error_message: str, status_code: int, headers: dict[str, str] | httpx.Headers + ) -> BaseLLMException: """ Get the appropriate error class for CompactifAI errors. Since CompactifAI is OpenAI-compatible, we use OpenAI error handling. diff --git a/litellm/llms/custom_httpx/aiohttp_handler.py b/litellm/llms/custom_httpx/aiohttp_handler.py index 9f579fd6f55..7035ce58ae1 100644 --- a/litellm/llms/custom_httpx/aiohttp_handler.py +++ b/litellm/llms/custom_httpx/aiohttp_handler.py @@ -1,3 +1,4 @@ +import ssl from collections.abc import Callable from typing import TYPE_CHECKING, Any, Final, cast @@ -18,12 +19,15 @@ from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, HTTPHandler, _get_httpx_client, + get_ssl_configuration, ) from litellm.types.llms.openai import FileTypes from litellm.types.utils import HttpHandlerRequestFields, ImageResponse, LlmProviders from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -56,7 +60,11 @@ class BaseLLMAIOHTTPHandler: # Create a transport using AsyncHTTPHandler's logic try: - self.transport = AsyncHTTPHandler._create_aiohttp_transport() + ssl_config: Final = get_ssl_configuration() + self.transport = AsyncHTTPHandler._create_aiohttp_transport( + ssl_verify=ssl_config if isinstance(ssl_config, bool) else None, + ssl_context=ssl_config if isinstance(ssl_config, ssl.SSLContext) else None, + ) self._owns_transport = True return self.transport except Exception: @@ -79,20 +87,19 @@ class BaseLLMAIOHTTPHandler: def _create_client_session_with_transport(self) -> ClientSession: """Create a new client session using transport or connector configuration.""" - connector: Final = self._get_connector() + if self.transport is None: + connector: Final = self._get_connector() + if connector: + return aiohttp.ClientSession(connector=connector) - if self.transport and hasattr(self.transport, "_get_valid_client_session"): - # Use transport's session creation if available - session = self.transport._get_valid_client_session() - return session - elif connector: - # Use provided connector - session = aiohttp.ClientSession(connector=connector) - return session - else: - # Default session creation - session = aiohttp.ClientSession() - return session + transport: Final = self.transport or self._get_or_create_transport() + if transport is not None and hasattr(transport, "_get_valid_client_session"): + try: + return transport._get_valid_client_session() + except RuntimeError: + pass + + return aiohttp.ClientSession() def _get_async_client_session(self, dynamic_client_session: ClientSession | None = None) -> ClientSession: if dynamic_client_session: @@ -261,7 +268,7 @@ class BaseLLMAIOHTTPHandler: messages: list, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, client: ClientSession | None = None, ): diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index 344a53d87f6..73adf9c7455 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -3,22 +3,41 @@ import concurrent.futures import contextlib import os import ssl +import sys import typing import urllib.request -from collections.abc import Callable -from typing import Any, ClassVar, Final +from collections.abc import Callable, Generator +from typing import ClassVar, Final import aiohttp import aiohttp.client_exceptions import aiohttp.http_exceptions import httpx from aiohttp.client import ClientResponse, ClientSession +from pydantic import BaseModel, TypeAdapter +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger from litellm.secret_managers.main import str_to_bool -AIOHTTP_EXC_MAP: Final[dict] = { + +class HttpxTimeoutExtension(BaseModel): + connect: float | None = None + read: float | None = None + write: float | None = None + pool: float | None = None + + +class AiohttpSslRequestOption(TypedDict, total=False): + ssl: ReadOnly[bool | ssl.SSLContext] + + +_TIMEOUT_EXTENSION: Final = TypeAdapter(HttpxTimeoutExtension) +_EMPTY_TIMEOUT: Final[HttpxTimeoutExtension] = HttpxTimeoutExtension() +_NO_SSL_OVERRIDE: Final[AiohttpSslRequestOption] = {} + +AIOHTTP_EXC_MAP: Final[dict[type[BaseException], type[Exception]]] = { # Order matters here, most specific exception first # Timeout related exceptions asyncio.TimeoutError: httpx.TimeoutException, @@ -57,12 +76,24 @@ except ImportError: pass +def _current_task_is_cancelling() -> bool: + task: Final = asyncio.current_task() + if task is None or sys.version_info < (3, 11): + return True + return task.cancelling() > 0 + + @contextlib.contextmanager -def map_aiohttp_exceptions() -> typing.Iterator[None]: +def map_aiohttp_exceptions() -> Generator[None, None, None]: try: yield + except asyncio.CancelledError as exc: + # a closing connector cancels its shielded DNS task; that surfaces here without the request task being cancelled + if _current_task_is_cancelling(): + raise + raise httpx.ConnectError("aiohttp transport cancelled the request internally") from exc except Exception as exc: - mapped_exc = None + mapped_exc: type[Exception] | None = None for from_exc, to_exc in AIOHTTP_EXC_MAP.items(): if not isinstance(exc, from_exc): @@ -222,7 +253,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): if session.closed: return - session_loop: Final = getattr(session, "_loop", None) + session_loop: Final[asyncio.AbstractEventLoop | None] = getattr(session, "_loop", None) try: current_loop: asyncio.AbstractEventLoop | None = asyncio.get_running_loop() except RuntimeError: @@ -278,7 +309,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): # Check if the existing session is still valid for the current event loop try: - session_loop: Final = getattr(self.client, "_loop", None) + session_loop: Final[asyncio.AbstractEventLoop | None] = getattr(self.client, "_loop", None) current_loop: Final = asyncio.get_running_loop() # If session is from a different or closed loop, recreate it @@ -312,7 +343,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): self, client_session: ClientSession, request: httpx.Request, - timeout: dict, + timeout: HttpxTimeoutExtension, proxy: str | None, sni_hostname: str | None, ssl_verify: bool | ssl.SSLContext | None = None, @@ -323,7 +354,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): Args: client_session: The aiohttp ClientSession to use request: The httpx Request to send - timeout: Timeout settings dict with 'connect', 'read', 'pool' keys + timeout: Timeout settings with 'connect', 'read', 'pool' fields proxy: Optional proxy URL sni_hostname: Optional SNI hostname for SSL ssl_verify: Optional SSL verification setting (False to disable, SSLContext for custom) @@ -346,25 +377,24 @@ class LiteLLMAiohttpTransport(AiohttpTransport): # Only pass ssl kwarg when explicitly configured, to avoid # overriding the session/connector defaults with None (which is # not a valid value for aiohttp's ssl parameter). - request_kwargs: Final[dict[str, Any]] = { - "method": request.method, - "url": YarlURL(str(request.url), encoded=True), - "headers": request.headers, - "data": data, - "allow_redirects": False, - "auto_decompress": False, - "timeout": ClientTimeout( - sock_connect=timeout.get("connect"), - sock_read=timeout.get("read"), - connect=timeout.get("pool"), - ), - "proxy": proxy, - "server_hostname": sni_hostname, - } - if ssl_verify is not None: - request_kwargs["ssl"] = ssl_verify + ssl_option: Final[AiohttpSslRequestOption] = _NO_SSL_OVERRIDE if ssl_verify is None else {"ssl": ssl_verify} - response: Final = await client_session.request(**request_kwargs).__aenter__() + response: Final = await client_session.request( + method=request.method, + url=YarlURL(str(request.url), encoded=True), + headers=request.headers, + data=data, + allow_redirects=False, + auto_decompress=False, + timeout=ClientTimeout( + sock_connect=timeout.connect, + sock_read=timeout.read, + connect=timeout.pool, + ), + proxy=proxy, + server_hostname=sni_hostname, + **ssl_option, + ).__aenter__() return response @@ -372,8 +402,8 @@ class LiteLLMAiohttpTransport(AiohttpTransport): self, request: httpx.Request, ) -> httpx.Response: - timeout: Final = request.extensions.get("timeout", {}) - sni_hostname: Final = request.extensions.get("sni_hostname") + timeout: Final = _TIMEOUT_EXTENSION.validate_python(request.extensions.get("timeout", _EMPTY_TIMEOUT)) + sni_hostname: Final[str | None] = request.extensions.get("sni_hostname") # Use helper to ensure we have a valid session for the current event loop client_session = self._get_valid_client_session() diff --git a/litellm/llms/custom_httpx/container_handler.py b/litellm/llms/custom_httpx/container_handler.py index 91d68aa3bfb..dd20a8c2ed4 100644 --- a/litellm/llms/custom_httpx/container_handler.py +++ b/litellm/llms/custom_httpx/container_handler.py @@ -6,11 +6,12 @@ endpoint defined in endpoints.json, eliminating the need for individual handler """ import json -from collections.abc import Coroutine +from collections.abc import Coroutine, Mapping, Sequence from pathlib import Path from typing import TYPE_CHECKING, Any, Final import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.url_utils import encode_url_path_segment @@ -32,26 +33,58 @@ if TYPE_CHECKING: from litellm.llms.base_llm.containers.transformation import BaseContainerConfig +class EndpointConfig(TypedDict): + """One endpoint entry of ``litellm/containers/endpoints.json``.""" + + name: ReadOnly[str] + async_name: ReadOnly[str] + path: ReadOnly[str] + method: ReadOnly[str] + path_params: ReadOnly[Sequence[str]] + query_params: ReadOnly[Sequence[str]] + response_type: ReadOnly[str] + is_multipart: NotRequired[ReadOnly[bool]] + returns_binary: NotRequired[ReadOnly[bool]] + + +class EndpointsConfig(TypedDict): + """The parsed ``litellm/containers/endpoints.json`` document.""" + + endpoints: ReadOnly[Sequence[EndpointConfig]] + + +class ContainerErrorDetail(TypedDict, total=False): + """The ``error`` object of a container API error body.""" + + message: ReadOnly[str] + + +class ContainerResponseBody(TypedDict, total=False): + """The fields this handler reads off a container API JSON body.""" + + error: ReadOnly[ContainerErrorDetail] + + +_ContainerResponseModel = ContainerFileListResponse | ContainerFileObject | DeleteContainerFileResponse + # Response type mapping -RESPONSE_TYPES: Final[dict[str, type]] = { +RESPONSE_TYPES: Final[Mapping[str, type[_ContainerResponseModel]]] = { "ContainerFileListResponse": ContainerFileListResponse, "ContainerFileObject": ContainerFileObject, "DeleteContainerFileResponse": DeleteContainerFileResponse, } -ContainerEndpointResponse = ( - ContainerFileListResponse | ContainerFileObject | DeleteContainerFileResponse | bytes | dict[str, object] -) +ContainerEndpointResponse = _ContainerResponseModel | bytes | ContainerResponseBody -def _load_endpoints_config() -> dict: +def _load_endpoints_config() -> EndpointsConfig: """Load the endpoints configuration from JSON file.""" config_path: Final = Path(__file__).parent.parent.parent / "containers" / "endpoints.json" with open(config_path) as f: return json.load(f) -def _get_endpoint_config(endpoint_name: str) -> dict | None: +def _get_endpoint_config(endpoint_name: str) -> EndpointConfig | None: """Get config for a specific endpoint by name.""" config: Final = _load_endpoints_config() for endpoint in config["endpoints"]: @@ -60,10 +93,15 @@ def _get_endpoint_config(endpoint_name: str) -> dict | None: return None +def _response_model(response_type_name: str) -> type[_ContainerResponseModel] | None: + """The pydantic model a container endpoint's ``response_type`` names.""" + return RESPONSE_TYPES.get(response_type_name) + + def _build_url( api_base: str, path_template: str, - path_params: dict[str, str], + path_params: Mapping[str, object], ) -> str: """Build the full URL by substituting path parameters. @@ -93,16 +131,12 @@ def _build_url( def _build_query_params( - query_param_names: list, - kwargs: dict[str, Any], -) -> dict[str, str]: + query_param_names: Sequence[str], + kwargs: Mapping[str, object], +) -> dict[str, object]: """Build query parameters from kwargs.""" - params: Final = {} - for param_name in query_param_names: - value = kwargs.get(param_name) - if value is not None: - params[param_name] = str(value) if not isinstance(value, str) else value - return params + supplied: Final = ((param_name, kwargs.get(param_name)) for param_name in query_param_names) + return {name: value if isinstance(value, str) else str(value) for name, value in supplied if value is not None} def _error_message_from_response(response: httpx.Response) -> str: @@ -136,24 +170,24 @@ def _transform_response( if returns_binary: return response.content - response_json: Final = response.json() + response_json: Final[ContainerResponseBody] = response.json() if "error" in response_json: raise BaseLLMException( status_code=response.status_code, - message=response_json.get("error", {}).get("message", str(response_json)), + message=response_json["error"].get("message", str(response_json)), headers=dict(response.headers), ) - response_type: Final = RESPONSE_TYPES.get(response_type_name) + response_type: Final = _response_model(response_type_name) if response_type: - return response_type(**response_json) + return response_type.model_validate(response_json) return response_json def _prepare_multipart_file_upload( file: Any, - headers: dict[str, Any], -) -> tuple: + headers: dict[str, object], +) -> tuple[dict[str, tuple[str, bytes, str]], dict[str, object]]: """ Prepare file and headers for multipart upload. @@ -178,6 +212,52 @@ def _prepare_multipart_file_upload( return files, headers_copy +def _request_headers( + container_provider_config: "BaseContainerConfig", + extra_headers: dict[str, object] | None, + litellm_params: GenericLiteLLMParams, +) -> dict[str, object]: + """The provider auth headers for a container request.""" + return container_provider_config.validate_environment( + headers=extra_headers or {}, + api_key=litellm_params.get("api_key", None), + ) + + +def _request_api_base( + container_provider_config: "BaseContainerConfig", + litellm_params: GenericLiteLLMParams, +) -> str: + """The provider base URL for a container request.""" + return container_provider_config.get_complete_url( + api_base=litellm_params.get("api_base", None), + litellm_params=dict(litellm_params), + ) + + +def _sync_http_client( + client: HTTPHandler | AsyncHTTPHandler | None, + litellm_params: GenericLiteLLMParams, +) -> HTTPHandler: + """The sync HTTP client for a container request, reusing the caller's when usable.""" + if client is None or not isinstance(client, HTTPHandler): + return _get_httpx_client(params={"ssl_verify": litellm_params.get("ssl_verify", None)}) + return client + + +def _async_http_client( + client: HTTPHandler | AsyncHTTPHandler | None, + litellm_params: GenericLiteLLMParams, +) -> AsyncHTTPHandler: + """The async HTTP client for a container request, reusing the caller's when usable.""" + if client is None or not isinstance(client, AsyncHTTPHandler): + return get_async_httpx_client( + llm_provider=litellm.LlmProviders.OPENAI, + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + return client + + class GenericContainerHandler: """ Generic handler for container file API endpoints. @@ -192,13 +272,13 @@ class GenericContainerHandler: container_provider_config: "BaseContainerConfig", litellm_params: GenericLiteLLMParams, logging_obj: "LiteLLMLoggingObj", - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout = 600, _is_async: bool = False, client: HTTPHandler | AsyncHTTPHandler | None = None, - **kwargs, - ) -> Any | Coroutine[Any, Any, Any]: + **kwargs: object, + ) -> Any | Coroutine[object, object, Any]: """ Generic handler for any container file endpoint. @@ -245,11 +325,11 @@ class GenericContainerHandler: container_provider_config: "BaseContainerConfig", litellm_params: GenericLiteLLMParams, logging_obj: "LiteLLMLoggingObj", - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout = 600, client: HTTPHandler | AsyncHTTPHandler | None = None, - **kwargs, + **kwargs: object, ) -> Any: """Synchronous request handler.""" endpoint_config: Final = _get_endpoint_config(endpoint_name) @@ -257,23 +337,14 @@ class GenericContainerHandler: raise ValueError(f"Unknown endpoint: {endpoint_name}") # Get HTTP client - if client is None or not isinstance(client, HTTPHandler): - http_client = _get_httpx_client(params={"ssl_verify": litellm_params.get("ssl_verify", None)}) - else: - http_client = client + http_client: Final = _sync_http_client(client, litellm_params) # Build request - headers = container_provider_config.validate_environment( - headers=extra_headers or {}, - api_key=litellm_params.get("api_key", None), - ) + headers = _request_headers(container_provider_config, extra_headers, litellm_params) if extra_headers: headers.update(extra_headers) - api_base: Final = container_provider_config.get_complete_url( - api_base=litellm_params.get("api_base", None), - litellm_params=dict(litellm_params), - ) + api_base: Final = _request_api_base(container_provider_config, litellm_params) # Build URL with path params path_params: Final = {p: kwargs.get(p, "") for p in endpoint_config.get("path_params", [])} @@ -334,11 +405,11 @@ class GenericContainerHandler: container_provider_config: "BaseContainerConfig", litellm_params: GenericLiteLLMParams, logging_obj: "LiteLLMLoggingObj", - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, timeout: float | httpx.Timeout = 600, client: HTTPHandler | AsyncHTTPHandler | None = None, - **kwargs, + **kwargs: object, ) -> Any: """Asynchronous request handler.""" endpoint_config: Final = _get_endpoint_config(endpoint_name) @@ -346,26 +417,14 @@ class GenericContainerHandler: raise ValueError(f"Unknown endpoint: {endpoint_name}") # Get HTTP client - if client is None or not isinstance(client, AsyncHTTPHandler): - http_client = get_async_httpx_client( - llm_provider=litellm.LlmProviders.OPENAI, - params={"ssl_verify": litellm_params.get("ssl_verify", None)}, - ) - else: - http_client = client + http_client: Final = _async_http_client(client, litellm_params) # Build request - headers = container_provider_config.validate_environment( - headers=extra_headers or {}, - api_key=litellm_params.get("api_key", None), - ) + headers = _request_headers(container_provider_config, extra_headers, litellm_params) if extra_headers: headers.update(extra_headers) - api_base: Final = container_provider_config.get_complete_url( - api_base=litellm_params.get("api_base", None), - litellm_params=dict(litellm_params), - ) + api_base: Final = _request_api_base(container_provider_config, litellm_params) # Build URL with path params path_params: Final = {p: kwargs.get(p, "") for p in endpoint_config.get("path_params", [])} diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 52f30e31641..b6e93f590ca 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -9,7 +9,7 @@ import threading import time from collections.abc import AsyncIterable, Callable, Iterable, Mapping from http.cookiejar import CookieJar, DefaultCookiePolicy -from typing import TYPE_CHECKING, Any, Final, Optional, TypeAlias, TypedDict +from typing import TYPE_CHECKING, Any, ClassVar, Final, NoReturn, Optional, TypeAlias, TypedDict import certifi import httpx @@ -447,7 +447,7 @@ def _safe_read_response(response: httpx.Response, timeout: float | None = None) return b"" -def _raise_masked_sync_error(e: httpx.HTTPStatusError, stream: bool) -> None: +def _raise_masked_sync_error(e: httpx.HTTPStatusError, stream: bool) -> NoReturn: """Raise a MaskedHTTPStatusError for sync HTTP handlers.""" if stream: try: @@ -467,7 +467,7 @@ def _raise_masked_sync_error(e: httpx.HTTPStatusError, stream: bool) -> None: raise MaskedHTTPStatusError(e, message=_text, text=_text) from None -async def _raise_masked_async_error(e: httpx.HTTPStatusError, stream: bool) -> None: +async def _raise_masked_async_error(e: httpx.HTTPStatusError, stream: bool) -> NoReturn: """Raise a MaskedHTTPStatusError for async HTTP handlers.""" if stream: try: @@ -933,11 +933,83 @@ class AsyncHTTPHandler: response.raise_for_status() return response + # Strong references to finalizer-scheduled client-close tasks. A bare + # create_task() result may be garbage-collected before it runs, leaving + # the underlying aiohttp session unclosed ("Unclosed client session"). + # Mirrors LiteLLMAiohttpTransport._background_close_tasks. + _finalizer_close_tasks: ClassVar[set["asyncio.Task[None]"]] = set() # mutable-ok: strong refs for pending closes + + @classmethod + def _on_finalizer_close_done(cls, task: "asyncio.Task[None]") -> None: + cls._finalizer_close_tasks.discard(task) + if task.cancelled(): + return + exc: Final = task.exception() + if exc is not None: + verbose_logger.debug("Error closing client at finalization: %s", exc) + + def _aiohttp_session_bound_elsewhere(self, loop: asyncio.AbstractEventLoop) -> bool: + """True when the wrapped aiohttp session is bound to a loop other than + ``loop`` — awaiting ``aclose()`` here would touch that loop's internals.""" + from litellm.llms.custom_httpx.aiohttp_transport import ( + LiteLLMAiohttpTransport, + ) + + transport: Final = getattr(self._client, "_transport", None) + if not isinstance(transport, LiteLLMAiohttpTransport): + return False + session: Final = transport.client + if not isinstance(session, ClientSession) or session.closed: + return False + return getattr(session, "_loop", None) is not loop + + def _dispose_wrapped_aiohttp_session(self) -> None: + """Dispose the wrapped aiohttp session when ``aclose()`` cannot run here. + + Finalization either has no running loop, or a loop the session is not + bound to. Delegating to the transport's lifecycle-aware disposal picks + the safe path per session state (async close on its own loop, threadsafe + handoff to a loop running elsewhere, or the synchronous connector + teardown that flips the flags ``ClientSession.__del__`` checks), so no + "Unclosed client session" / "Unclosed connector" warnings fire at + garbage collection. + """ + from litellm.llms.custom_httpx.aiohttp_transport import ( + LiteLLMAiohttpTransport, + ) + + transport: Final = getattr(self._client, "_transport", None) + if not isinstance(transport, LiteLLMAiohttpTransport): + return + # A shared session (e.g. the proxy's) is never this handler's to close. + if not getattr(transport, "_owns_session", False): + return + session: Final = transport.client + if isinstance(session, ClientSession) and not session.closed: + transport._close_recycled_session(session) # pyright: ignore[reportPrivateUsage] # deliberate reuse of the transport's lifecycle-aware disposal; an async close can never run in this context + def __del__(self) -> None: try: if not _handler_may_close_client(sys.getrefcount(self._client), self._owns_client): return - asyncio.get_running_loop().create_task(self._client.aclose()) + try: + loop: Final = asyncio.get_running_loop() + except RuntimeError: + # No running loop at finalization time (worker threads after + # their loop closed, interpreter/worker shutdown, GC in a + # sync context). An async close can never run here. + self._dispose_wrapped_aiohttp_session() + return + if self._aiohttp_session_bound_elsewhere(loop): + # GC ran on a live loop (e.g. the app's) but the session + # belongs to another, possibly dead, loop — awaiting aclose() + # here is the cross-loop path the transport refuses. + self._dispose_wrapped_aiohttp_session() + return + task: Final = loop.create_task(self._client.aclose()) + cls: Final = type(self) + cls._finalizer_close_tasks.add(task) + task.add_done_callback(cls._on_finalizer_close_done) except Exception: pass diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 369e150f6bd..6f42d42de00 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -10,6 +10,7 @@ from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict, Type from urllib.parse import parse_qs, urlencode, urlparse, urlunparse import httpx +from httpx._types import FileContent from openai.types.file_deleted import FileDeleted import litellm @@ -19,7 +20,16 @@ import litellm.types.utils from litellm._logging import _redact_string, verbose_logger from litellm.anthropic_beta_headers_manager import update_headers_with_filtered_beta from litellm.constants import REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES +from litellm.litellm_core_utils.agentic_loop_settings import ( + DEFAULT_MAX_AGENTIC_LOOPS, + validated_max_agentic_loops, +) from litellm.litellm_core_utils.asyncify import run_async_function +from litellm.litellm_core_utils.audio_utils.subtitle_utils import ( + SUBTITLE_RESPONSE_FORMATS, + synthesize_subtitle_document, +) +from litellm.litellm_core_utils.llm_request_utils import serialize_multipart_form_fields from litellm.litellm_core_utils.realtime_errors import realtime_error_event, websocket_close_reason from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming from litellm.litellm_core_utils.url_utils import encode_url_path_segment @@ -89,6 +99,7 @@ from litellm.types.files import StreamingMediaUploadConfig, TwoStepFileUploadCon from litellm.types.integrations.custom_logger import ( AgenticLoopPlan, AgenticLoopRequestPatch, + AgenticLoopSafetyError, ) from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, @@ -154,6 +165,7 @@ def _rust_responses_websocket_enabled( from .http_handler import get_shared_realtime_ssl_context if TYPE_CHECKING: + import tiktoken from aiohttp import ClientSession from websockets.asyncio.client import ClientConnection @@ -166,6 +178,7 @@ if TYPE_CHECKING: AnthropicMessagesStreamingResponse, ) from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig + from litellm.router import Router from litellm.types.llms.openai_evals import ( CancelEvalResponse, CancelRunResponse, @@ -394,7 +407,7 @@ class BaseLLMHTTPHandler: messages: list, optional_params: dict, litellm_params: dict, - encoding: object, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, client: AsyncHTTPHandler | None = None, json_mode: bool = False, @@ -460,7 +473,7 @@ class BaseLLMHTTPHandler: api_base: str | None, custom_llm_provider: str, model_response: ModelResponse, - encoding: object, + encoding: "tiktoken.Encoding | None", logging_obj: LiteLLMLoggingObj, optional_params: dict, timeout: float | httpx.Timeout, @@ -1103,6 +1116,7 @@ class BaseLLMHTTPHandler: headers=headers or {}, model=model, optional_params=optional_rerank_params, + litellm_params=litellm_params, ) api_base = provider_config.get_complete_url( @@ -1195,6 +1209,7 @@ class BaseLLMHTTPHandler: headers=headers, data=json.dumps(request_data), timeout=timeout, + logging_obj=logging_obj, ) except Exception as e: raise self._handle_error(e=e, provider_config=provider_config) @@ -1287,9 +1302,23 @@ class BaseLLMHTTPHandler: api_key: str | None, ) -> TranscriptionResponse: """Shared logic for transforming audio transcription responses.""" - return provider_config.transform_audio_transcription_response( + transformed: Final = provider_config.transform_audio_transcription_response( raw_response=response, ) + if not provider_config.supports_subtitle_synthesis: + return transformed + requested_format: Final = optional_params.get("response_format") + if not isinstance(requested_format, str) or requested_format not in SUBTITLE_RESPONSE_FORMATS: + return transformed + document: Final = synthesize_subtitle_document( + words=transformed.get("words"), + response_format=requested_format, + ) + if document is not None: + transformed.text = document + if "words" in transformed: + delattr(transformed, "words") + return transformed def audio_transcriptions( self, @@ -1839,6 +1868,7 @@ class BaseLLMHTTPHandler: return provider_config.transform_search_response( raw_response=response, logging_obj=logging_obj, + optional_params=optional_params, ) async def async_search( @@ -1937,6 +1967,7 @@ class BaseLLMHTTPHandler: return provider_config.transform_search_response( raw_response=response, logging_obj=logging_obj, + optional_params=optional_params, ) async def _async_post_anthropic_messages_with_http_error_retry( @@ -2257,6 +2288,10 @@ class BaseLLMHTTPHandler: AgenticAnthropicStreamingIterator, ) + held_back_tool_names: Final = self._server_fulfilled_tools_in_request( + logging_obj=logging_obj, + tools=anthropic_messages_optional_request_params.get("tools"), + ) initial_response = AgenticAnthropicStreamingIterator( completion_stream=completion_stream, http_handler=self, @@ -2267,6 +2302,8 @@ class BaseLLMHTTPHandler: logging_obj=logging_obj, custom_llm_provider=custom_llm_provider, kwargs={**kwargs, "api_key": api_key} if api_key else kwargs, + hold_back=bool(held_back_tool_names), + server_fulfilled_tool_names=held_back_tool_names, ) return AnthropicMessagesStreamingResponse( completion_stream=initial_response, @@ -2836,6 +2873,7 @@ class BaseLLMHTTPHandler: headers=headers, timeout=timeout or float(response_api_optional_request_params.get("timeout", 0)), stream=stream, + logging_obj=logging_obj, **body_kwargs, ) @@ -2867,6 +2905,7 @@ class BaseLLMHTTPHandler: url=api_base, headers=headers, timeout=timeout or float(response_api_optional_request_params.get("timeout", 0)), + logging_obj=logging_obj, **body_kwargs, ) @@ -2885,7 +2924,7 @@ class BaseLLMHTTPHandler: final_response: Final = await self._call_agentic_completion_hooks( response=initial_response, model=model, - messages=(input if isinstance(input, list) else [{"role": "user", "content": input}]), + messages=(input if isinstance(input, list) else [{"role": "user", "content": input}]), # pyright: ignore[reportArgumentType] # pre-existing mismatch surfaced by the Router import; the hook accepts response input items at runtime anthropic_messages_provider_config=responses_api_provider_config, anthropic_messages_optional_request_params=response_api_optional_request_params, logging_obj=logging_obj, @@ -5077,9 +5116,12 @@ class BaseLLMHTTPHandler: @staticmethod def _get_agentic_loop_settings(kwargs: dict) -> tuple[int, int, list[str]]: depth: Final = int(kwargs.get("_agentic_loop_depth", 0) or 0) - max_loops: Final = int(kwargs.get("max_agentic_loops", 3) or 3) + configured: Final = validated_max_agentic_loops( + kwargs.get("max_agentic_loops"), field="litellm_params.max_agentic_loops" + ) + max_loops: Final = DEFAULT_MAX_AGENTIC_LOOPS if configured is None else configured fingerprints: Final = list(kwargs.get("_agentic_loop_fingerprints", []) or []) - return depth, max(max_loops, 1), fingerprints + return depth, max_loops, fingerprints @staticmethod def _has_agentic_completion_hook(logging_obj: LiteLLMLoggingObj) -> bool: @@ -5111,6 +5153,20 @@ class BaseLLMHTTPHandler: return True return False + @staticmethod + def _server_fulfilled_tools_in_request(logging_obj: LiteLLMLoggingObj, tools: object) -> frozenset[str]: + """The request's tools that a registered callback fulfills server-side (e.g. ``headroom_retrieve``).""" + if not isinstance(tools, list) or not tools: + return frozenset() + from litellm.litellm_core_utils.prompt_templates.factory import has_tool_with_name + + return frozenset( + name + for cb in _custom_logger_callbacks(logging_obj) + for name in getattr(cb, "server_fulfilled_tool_names", frozenset()) + if has_tool_with_name(tools, name) + ) + @staticmethod def _check_agentic_loop_safety( tool_calls: object, @@ -5122,7 +5178,8 @@ class BaseLLMHTTPHandler: """ Evaluate agentic-loop safety guards (fingerprint cycle / max depth). - Raises ValueError on abort. Returns the current fingerprint on success. + Raises AgenticLoopSafetyError on abort. Returns the current fingerprint + on success. These checks must not be swallowed by the per-callback ``except Exception`` block that wraps callback dispatch — they are bounded-loop / cycle-break @@ -5130,9 +5187,9 @@ class BaseLLMHTTPHandler: """ fingerprint: Final = BaseLLMHTTPHandler._fingerprint_agentic_tools(tool_calls) if fingerprint in fingerprints: - raise ValueError("Agentic loop detected repeated tool-call fingerprint; aborting rerun") + raise AgenticLoopSafetyError("Agentic loop detected repeated tool-call fingerprint; aborting rerun") if depth >= max_loops: - raise ValueError(f"Exceeded max_agentic_loops={max_loops} for model={model}") + raise AgenticLoopSafetyError(f"Exceeded max_agentic_loops={max_loops} for model={model}") return fingerprint @staticmethod @@ -5142,6 +5199,97 @@ class BaseLLMHTTPHandler: except Exception: return str(tools) + @staticmethod + def _refused_agentic_tool_identifiers(tool_calls: object) -> tuple[frozenset[str], frozenset[str]]: + """ + Collect the ids and names of the tool calls a safety rail just refused. + + Callbacks hand back either a bare list of tool calls or a dict wrapping + that list under ``tool_calls``, and both the anthropic and responses + shapes carry an ``id`` (or ``call_id``) plus a ``name``. + """ + calls: Final = tool_calls.get("tool_calls") if isinstance(tool_calls, dict) else tool_calls + if not isinstance(calls, list): + return frozenset(), frozenset() + dict_calls: Final = (call for call in calls if isinstance(call, dict)) + fields: Final = tuple((call.get("id"), call.get("call_id"), call.get("name")) for call in dict_calls) + ids: Final = frozenset( + value for call_id, caller_id, _ in fields for value in (call_id, caller_id) if isinstance(value, str) + ) + names: Final = frozenset(name for _, _, name in fields if isinstance(name, str)) + return ids, names + + @staticmethod + def _is_refused_tool_use_block(block: object, refused_ids: frozenset[str], refused_names: frozenset[str]) -> bool: + """ + Whether this response block belongs to a tool call the rail refused. + + An id settles it on its own, so a block carrying one is matched on the id + alone and a client's own tool call survives even where it happens to + share a name with a refused one. The name is only consulted for tool call + shapes that arrive without an id. + """ + if not isinstance(block, dict) or block.get("type") != "tool_use": + return False + block_id: Final = block.get("id") + if isinstance(block_id, str) and refused_ids: + return block_id in refused_ids + return block.get("name") in refused_names + + @staticmethod + def _can_replace_turn_with_terminal_response(stream: bool, api_surface: str) -> bool: + """ + Whether a refused rerun can still be answered with a finalized turn. + + Only the anthropic messages surface can. The responses surface carries a + pydantic model the finalizer does not rewrite, so it keeps raising, which + is what every surface did before this path learned to end the turn. + + The messages and responses call sites pass ``stream=False``, because + interception converts an intercepted stream to non-streaming before the + loop runs and rebuilds the SSE stream from the finalized turn + afterwards. ``AgenticStreamingIterator`` passes ``stream=True``, and + that path keeps raising: its events are already on the wire, so a + finalized turn would reach the client as a second message rather than + as a replacement. + """ + return not stream and api_surface == "anthropic_messages" + + @staticmethod + def _finalize_refused_agentic_response(response: object, tool_calls: object) -> object: + """ + Turn the response into a terminal turn after a safety rail refused the rerun. + + The refused tool calls target tools LiteLLM injected on the client's + behalf, so a client that never declared them cannot send back a matching + ``tool_result``. Their blocks are dropped and a ``tool_use`` stop reason + is closed out as ``end_turn``, which is what a provider-native web search + turn returns once it stops calling tools. + + A ``tool_use`` block the client itself declared is left alone, and while + one is still in the response the stop reason stays ``tool_use`` so the + client knows to answer it. + """ + if not isinstance(response, dict): + return response + + refused_ids, refused_names = BaseLLMHTTPHandler._refused_agentic_tool_identifiers(tool_calls) + finalized: Final = dict(response) + content: Final = finalized.get("content") + if isinstance(content, list): + kept_blocks: Final = [ + block + for block in content + if not BaseLLMHTTPHandler._is_refused_tool_use_block(block, refused_ids, refused_names) + ] + finalized["content"] = kept_blocks + client_tool_use_remains: Final = any( + isinstance(block, dict) and block.get("type") == "tool_use" for block in kept_blocks + ) + if not client_tool_use_remains and finalized.get("stop_reason") == "tool_use": + finalized["stop_reason"] = "end_turn" + return finalized + async def _execute_anthropic_agentic_plan( self, plan: AgenticLoopPlan, @@ -5268,7 +5416,7 @@ class BaseLLMHTTPHandler: try: response: ResponsesAPIResponse | BaseResponsesAPIStreamingIterator = await litellm.aresponses( model=patch.model or model, - input=patch.messages, + input=patch.messages, # pyright: ignore[reportArgumentType] # pre-existing mismatch surfaced by the Router import; patch messages are valid response input at runtime **optional_params, **kwargs_for_followup, ) @@ -5494,10 +5642,9 @@ class BaseLLMHTTPHandler: kwargs=hook_kwargs, ) except Exception as e: - _call_id = getattr(logging_obj, "litellm_call_id", "unknown") verbose_logger.exception( "LiteLLM.AgenticHookError: Exception in async_should_run_agentic_loop [call_id=%s model=%s]: %s", - _call_id, + logging_obj.litellm_call_id, model, str(e), ) @@ -5507,14 +5654,29 @@ class BaseLLMHTTPHandler: continue # Safety guards must run OUTSIDE the callback try/except — they are - # bounded-loop / cycle-break rails that must propagate to the caller. - fingerprint = self._check_agentic_loop_safety( - tool_calls=tool_calls, - fingerprints=fingerprints, - depth=depth, - max_loops=max_loops, - model=model, - ) + # bounded-loop / cycle-break rails, not callback bugs. + try: + fingerprint = self._check_agentic_loop_safety( + tool_calls=tool_calls, + fingerprints=fingerprints, + depth=depth, + max_loops=max_loops, + model=model, + ) + except AgenticLoopSafetyError as e: + if not self._can_replace_turn_with_terminal_response(stream, api_surface): + raise + verbose_logger.warning( + "LiteLLM.AgenticLoopRefused: ending turn [call_id=%s model=%s]: %s", + logging_obj.litellm_call_id, + model, + str(e), + ) + return self._maybe_wrap_in_fake_stream( + self._finalize_refused_agentic_response(response=response, tool_calls=tool_calls), + logging_obj, + api_surface, + ) try: kwargs_with_provider = hook_kwargs.copy() @@ -5789,11 +5951,13 @@ class BaseLLMHTTPHandler: BaseEvalsAPIConfig, ], ): - status_code = getattr(e, "status_code", 500) + received_status_code: Final = ( + e.response.status_code if isinstance(e, httpx.HTTPStatusError) else getattr(e, "status_code", None) + ) + status_code = received_status_code if isinstance(received_status_code, int) else 500 error_headers = getattr(e, "headers", None) if isinstance(e, httpx.HTTPStatusError): error_text = e.response.text - status_code = e.response.status_code else: error_text = getattr(e, "text", str(e)) error_response: Final = getattr(e, "response", None) @@ -5813,13 +5977,17 @@ class BaseLLMHTTPHandler: status_code=status_code, message=error_text, headers=error_headers, + status_code_is_synthesized=not isinstance(received_status_code, int), ) - raise provider_config.get_error_class( + provider_error: Final = provider_config.get_error_class( error_message=error_text, status_code=status_code, headers=error_headers, ) + if not isinstance(received_status_code, int): + provider_error.status_code_is_synthesized = True + raise provider_error @staticmethod def _append_query_params(url: str, query_params: RealtimeQueryParams | None) -> str: @@ -6934,9 +7102,7 @@ class BaseLLMHTTPHandler: ) try: - # Use JSON when no files, otherwise use form data with files if files and len(files) > 0: - # Use multipart/form-data when files are present response = sync_httpx_client.post( url=api_base, headers=headers, @@ -6944,9 +7110,14 @@ class BaseLLMHTTPHandler: files=files, timeout=timeout, ) - + elif video_generation_provider_config.use_multipart_form_data(): + response = sync_httpx_client.post( # rebind-ok: one of three mutually-exclusive branches + url=api_base, + headers=headers, + files=serialize_multipart_form_fields(data), + timeout=timeout, + ) else: - # Use JSON content type for POST requests without files response = sync_httpx_client.post( url=api_base, headers=headers, @@ -7038,20 +7209,26 @@ class BaseLLMHTTPHandler: ) try: - # Use JSON when no files, otherwise use form data with files - if files is None or len(files) == 0: + if files and len(files) > 0: response = await async_httpx_client.post( url=api_base, headers=headers, - json=data, + data=data, + files=files, + timeout=timeout, + ) + elif video_generation_provider_config.use_multipart_form_data(): + response = await async_httpx_client.post( # rebind-ok: one of three mutually-exclusive branches + url=api_base, + headers=headers, + files=serialize_multipart_form_fields(data), timeout=timeout, ) else: response = await async_httpx_client.post( url=api_base, headers=headers, - data=data, - files=files, + json=data, timeout=timeout, ) @@ -7711,6 +7888,7 @@ class BaseLLMHTTPHandler: custom_llm_provider: str, litellm_params, logging_obj, + video_file: FileContent | None = None, extra_headers: dict[str, object] | None = None, extra_body: dict[str, object] | None = None, timeout: float | None = None, @@ -7722,6 +7900,7 @@ class BaseLLMHTTPHandler: return self.async_video_edit_handler( prompt=prompt, video_id=video_id, + video_file=video_file, video_provider_config=video_provider_config, custom_llm_provider=custom_llm_provider, litellm_params=litellm_params, @@ -7775,9 +7954,10 @@ class BaseLLMHTTPHandler: prefetched_source_data = prefetch_resp.json() try: - url, data = video_provider_config.transform_video_edit_request( + url, data, files = video_provider_config.transform_video_edit_request( prompt=prompt, video_id=video_id, + video_file=video_file, api_base=api_base, litellm_params=litellm_params, headers=headers, @@ -7796,11 +7976,10 @@ class BaseLLMHTTPHandler: }, ) - response: Final = sync_httpx_client.post( - url=url, - headers=headers, - json=data, - timeout=timeout, + response: Final = ( + sync_httpx_client.post(url=url, headers=headers, data=data, files=files, timeout=timeout) + if files + else sync_httpx_client.post(url=url, headers=headers, json=data, timeout=timeout) ) response.raise_for_status() return video_provider_config.transform_video_edit_response( @@ -7820,6 +7999,7 @@ class BaseLLMHTTPHandler: custom_llm_provider: str, litellm_params, logging_obj, + video_file: FileContent | None = None, extra_headers: dict[str, object] | None = None, extra_body: dict[str, object] | None = None, timeout: float | None = None, @@ -7871,9 +8051,10 @@ class BaseLLMHTTPHandler: prefetched_source_data = prefetch_resp.json() try: - url, data = video_provider_config.transform_video_edit_request( + url, data, files = video_provider_config.transform_video_edit_request( prompt=prompt, video_id=video_id, + video_file=video_file, api_base=api_base, litellm_params=litellm_params, headers=headers, @@ -7892,11 +8073,10 @@ class BaseLLMHTTPHandler: }, ) - response: Final = await async_httpx_client.post( - url=url, - headers=headers, - json=data, - timeout=timeout, + response: Final = await ( + async_httpx_client.post(url=url, headers=headers, data=data, files=files, timeout=timeout) + if files + else async_httpx_client.post(url=url, headers=headers, json=data, timeout=timeout) ) response.raise_for_status() return video_provider_config.transform_video_edit_response( @@ -9509,6 +9689,7 @@ class BaseLLMHTTPHandler: timeout: float | httpx.Timeout | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, _is_async: bool = False, + router: "Router | None" = None, ) -> VectorStoreSearchResponse: if isinstance(vector_store_provider_config, BaseDirectVectorStoreConfig): self._pre_call_direct_vector_store_search( @@ -9559,6 +9740,7 @@ class BaseLLMHTTPHandler: litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), extra_body=extra_body, + router=router, ) else: ( @@ -9572,6 +9754,7 @@ class BaseLLMHTTPHandler: litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), extra_body=extra_body, + router=router, ) all_optional_params: Final[dict[str, object]] = dict(litellm_params) all_optional_params.update(vector_store_search_optional_params or {}) @@ -9623,6 +9806,7 @@ class BaseLLMHTTPHandler: timeout: float | httpx.Timeout | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, _is_async: bool = False, + router: "Router | None" = None, ) -> VectorStoreSearchResponse | Coroutine[object, object, VectorStoreSearchResponse]: if _is_async: return self.async_vector_store_search_handler( @@ -9637,6 +9821,7 @@ class BaseLLMHTTPHandler: extra_body=extra_body, timeout=timeout, client=client, + router=router, ) if isinstance(vector_store_provider_config, BaseDirectVectorStoreConfig): @@ -9683,6 +9868,7 @@ class BaseLLMHTTPHandler: litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), extra_body=extra_body, + router=router, ) all_optional_params: Final[dict[str, object]] = dict(litellm_params) diff --git a/litellm/llms/custom_llm.py b/litellm/llms/custom_llm.py index fcd41d11499..c70b9b81b42 100644 --- a/litellm/llms/custom_llm.py +++ b/litellm/llms/custom_llm.py @@ -25,6 +25,7 @@ from .base import BaseLLM if TYPE_CHECKING: from litellm import CustomStreamWrapper + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj class CustomLLMError(Exception): # use this for all your exceptions @@ -134,7 +135,7 @@ class CustomLLM(BaseLLM): api_base: str | None, model_response: ImageResponse, optional_params: dict, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", timeout: float | httpx.Timeout | None = None, client: HTTPHandler | None = None, ) -> ImageResponse: @@ -148,7 +149,7 @@ class CustomLLM(BaseLLM): api_key: str | None, # dynamically set api_key - https://docs.litellm.ai/docs/set_keys#api_key api_base: str | None, # dynamically set api_base - https://docs.litellm.ai/docs/set_keys#api_base optional_params: dict, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", timeout: float | httpx.Timeout | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: @@ -160,7 +161,7 @@ class CustomLLM(BaseLLM): input: list, model_response: EmbeddingResponse, print_verbose: Callable, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", optional_params: dict, api_key: str | None = None, api_base: str | None = None, @@ -175,7 +176,7 @@ class CustomLLM(BaseLLM): input: list, model_response: EmbeddingResponse, print_verbose: Callable, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", optional_params: dict, api_key: str | None = None, api_base: str | None = None, @@ -193,7 +194,7 @@ class CustomLLM(BaseLLM): api_key: str | None, api_base: str | None, optional_params: dict, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", timeout: float | httpx.Timeout | None = None, client: HTTPHandler | None = None, ) -> ImageResponse: @@ -208,7 +209,7 @@ class CustomLLM(BaseLLM): api_key: str | None, api_base: str | None, optional_params: dict, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", timeout: float | httpx.Timeout | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: diff --git a/litellm/llms/dashscope/chat/transformation.py b/litellm/llms/dashscope/chat/transformation.py index 5ab7fbf3658..26e60fa959d 100644 --- a/litellm/llms/dashscope/chat/transformation.py +++ b/litellm/llms/dashscope/chat/transformation.py @@ -54,6 +54,9 @@ class DashScopeChatConfig(OpenAIGPTConfig): dynamic_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") return api_base, dynamic_api_key + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or "https://dashscope.aliyuncs.com/compatible-mode/v1" + def get_complete_url( self, api_base: str | None, @@ -66,10 +69,7 @@ class DashScopeChatConfig(OpenAIGPTConfig): """ If api_base is not provided, use the default DashScope /chat/completions endpoint. """ - if not api_base: - api_base = "https://dashscope.aliyuncs.com/compatible-mode/v1" - - if not api_base.endswith("/chat/completions"): - api_base = f"{api_base}/chat/completions" - - return api_base + resolved_api_base: Final = self._resolve_chat_api_base(api_base) + if resolved_api_base.endswith("/chat/completions"): + return resolved_api_base + return f"{resolved_api_base}/chat/completions" diff --git a/litellm/llms/dashscope/common_utils.py b/litellm/llms/dashscope/common_utils.py index 9a7dd4da8d3..b7c97893a15 100644 --- a/litellm/llms/dashscope/common_utils.py +++ b/litellm/llms/dashscope/common_utils.py @@ -2,9 +2,89 @@ Common utilities for the DashScope LLM provider. """ +from typing import TYPE_CHECKING + import httpx from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.secret_managers.main import get_secret_str + +if TYPE_CHECKING: + from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig + from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, + ) + from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig + + +def get_dashscope_family_embedding_config(custom_llm_provider: str) -> "BaseEmbeddingConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudEmbeddingConfig + + return QwenCloudEmbeddingConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformEmbeddingConfig, + ) + + return QwenAIPlatformEmbeddingConfig() + from litellm.llms.dashscope.embed.transformation import DashScopeEmbeddingConfig + + return DashScopeEmbeddingConfig() + + +def get_dashscope_family_rerank_config(custom_llm_provider: str) -> "BaseRerankConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudRerankConfig + + return QwenCloudRerankConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import QwenAIPlatformRerankConfig + + return QwenAIPlatformRerankConfig() + from litellm.llms.dashscope.rerank.transformation import DashScopeRerankConfig + + return DashScopeRerankConfig() + + +def get_dashscope_family_image_generation_config( + custom_llm_provider: str, +) -> "BaseImageGenerationConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudImageGenerationConfig + + return QwenCloudImageGenerationConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformImageGenerationConfig, + ) + + return QwenAIPlatformImageGenerationConfig() + from litellm.llms.dashscope.image_generation.transformation import ( + DashScopeImageGenerationConfig, + ) + + return DashScopeImageGenerationConfig() + + +def resolve_dashscope_family_api_key(custom_llm_provider: str, api_key: str | None) -> str | None: + if custom_llm_provider == "dashscope": + return api_key or get_secret_str("DASHSCOPE_API_KEY") + return api_key or get_secret_str(f"{custom_llm_provider.upper()}_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def missing_dashscope_family_key_message(custom_llm_provider: str) -> str: + if custom_llm_provider == "qwencloud": + return ( + "Missing API key for QwenCloud. Set QWENCLOUD_API_KEY or " + "DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + if custom_llm_provider == "qwen_ai_platform": + return ( + "Missing API key for Qwen AI Platform. Set QWEN_AI_PLATFORM_API_KEY or " + "DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + return "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." class DashScopeError(BaseLLMException): diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py index 771ce140f66..dd5bee1fe8b 100644 --- a/litellm/llms/dashscope/cost_calculator.py +++ b/litellm/llms/dashscope/cost_calculator.py @@ -110,7 +110,7 @@ def _calculate_completion_cost( return (breakdown.completion_tokens * output_cost) + (breakdown.reasoning_tokens * reasoning_cost) -def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: +def cost_per_token(model: str, usage: Usage, custom_llm_provider: str = "dashscope") -> tuple[float, float]: """ Calculate cost per token for Dashscope models. @@ -119,11 +119,12 @@ def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: Args: model: Model name without provider prefix usage: LiteLLM Usage block + custom_llm_provider: The provider id the request resolved to; dashscope or one of its brand aliases Returns: Tuple[float, float] - (prompt_cost_in_usd, completion_cost_in_usd) """ - model_info: Final = get_model_info(model=model, custom_llm_provider="dashscope") + model_info: Final = get_model_info(model=model, custom_llm_provider=custom_llm_provider) breakdown: Final = _extract_token_breakdown(usage) raw_tiers: Final = model_info.get("tiered_pricing") tiered_pricing: Final = raw_tiers if isinstance(raw_tiers, list) else None diff --git a/litellm/llms/dashscope/embed/transformation.py b/litellm/llms/dashscope/embed/transformation.py index 6d13f1e53f7..63ee984a65c 100644 --- a/litellm/llms/dashscope/embed/transformation.py +++ b/litellm/llms/dashscope/embed/transformation.py @@ -62,6 +62,17 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): # for drop_params=False before this method is called. return optional_params + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key if api_key is not None else get_secret_str("DASHSCOPE_API_KEY") + if resolved_api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + return resolved_api_key + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE + def validate_environment( self, headers: dict, @@ -72,17 +83,11 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): api_key: str | None = None, api_base: str | None = None, ) -> dict: - if api_key is None: - api_key = get_secret_str("DASHSCOPE_API_KEY") - if api_key is None: - raise ValueError( - "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." - ) - default_headers: Final = { + return { "Content-Type": "application/json", - "Authorization": f"Bearer {api_key}", + "Authorization": f"Bearer {self._resolve_api_key(api_key)}", + **headers, } - return {**default_headers, **headers} def get_complete_url( self, @@ -93,8 +98,7 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): litellm_params: dict, stream: bool | None = None, ) -> str: - base = api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE - base = base.rstrip("/") + base: Final = self._resolve_embedding_api_base(api_base).rstrip("/") if base.endswith("/embeddings"): return base return f"{base}/embeddings" diff --git a/litellm/llms/dashscope/image_generation/transformation.py b/litellm/llms/dashscope/image_generation/transformation.py index 9652a5738c8..c0e278a96ef 100644 --- a/litellm/llms/dashscope/image_generation/transformation.py +++ b/litellm/llms/dashscope/image_generation/transformation.py @@ -11,7 +11,7 @@ Request format: "input": { "messages": [{"role": "user", "content": [{"text": ""}]}] }, - "parameters": {"size": "1024*1024", ...} + "parameters": {"size": "1024*1024", "n": 1, ...} } Response format: @@ -19,7 +19,7 @@ Response format: "output": { "choices": [{"message": {"content": [{"image": ""}]}}] }, - "usage": {"input_tokens": 0, "output_tokens": 0, "width": 1024, "height": 1024, "image_count": 1} + "usage": {"output_width": 1024, "output_height": 1024, "output_image_count": 1} } """ @@ -38,6 +38,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -46,6 +48,8 @@ else: DEFAULT_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +CHAT_COMPATIBLE_MODE_PATH: Final = "/compatible-mode/v1" + # Maps OpenAI size strings (WxH) to DashScope size strings (W*H) OPENAI_TO_DASHSCOPE_SIZE: Final[dict] = { "256x256": "256*256", @@ -59,7 +63,8 @@ OPENAI_TO_DASHSCOPE_SIZE: Final[dict] = { class DashScopeImageGenerationConfig(BaseImageGenerationConfig): """ - Configuration for DashScope image generation (qwen-image-2.0, qwen-image-2.0-pro). + Configuration for DashScope image generation (qwen-image-2.0, qwen-image-2.0-pro, + qwen-image-3.0, qwen-image-3.0-pro). """ def get_supported_openai_params(self, model: str) -> list[OpenAIImageGenerationOptionalParams]: @@ -82,10 +87,19 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): if k == "size": # Convert "WxH" → "W*H" mapped["size"] = OPENAI_TO_DASHSCOPE_SIZE.get(v, v.replace("x", "*")) - elif k == "n": - mapped["image_count"] = v + else: + mapped[k] = v return mapped + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") + if not resolved_api_key: + raise ValueError("DASHSCOPE_API_KEY is not set") + return resolved_api_key + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE + def get_complete_url( self, api_base: str | None, @@ -95,7 +109,10 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): litellm_params: dict, stream: bool | None = None, ) -> str: - return api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE + image_api_base: Final = ( + api_base if api_base and not api_base.rstrip("/").endswith(CHAT_COMPATIBLE_MODE_PATH) else None + ) + return self._resolve_image_api_base(image_api_base) def validate_environment( self, @@ -107,10 +124,7 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): api_key: str | None = None, api_base: str | None = None, ) -> dict: - final_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") - if not final_api_key: - raise ValueError("DASHSCOPE_API_KEY is not set") - headers["Authorization"] = f"Bearer {final_api_key}" + headers["Authorization"] = f"Bearer {self._resolve_api_key(api_key)}" headers["Content-Type"] = "application/json" return headers @@ -151,7 +165,7 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/dashscope/qwen_ai_platform.py b/litellm/llms/dashscope/qwen_ai_platform.py new file mode 100644 index 00000000000..9a44eaf574a --- /dev/null +++ b/litellm/llms/dashscope/qwen_ai_platform.py @@ -0,0 +1,62 @@ +from typing import Final + +from litellm.secret_managers.main import get_secret_str + +from .chat.transformation import DashScopeChatConfig +from .embed.transformation import DashScopeEmbeddingConfig +from .image_generation.transformation import DashScopeImageGenerationConfig +from .rerank.transformation import DashScopeRerankConfig + +QWEN_AI_PLATFORM_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-mode/v1" +QWEN_AI_PLATFORM_RERANK_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" +QWEN_AI_PLATFORM_IMAGE_API_BASE: Final = ( + "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +) + + +def _resolve_qwen_ai_platform_api_key(api_key: str | None) -> str | None: + return api_key or get_secret_str("QWEN_AI_PLATFORM_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def _require_qwen_ai_platform_api_key(api_key: str | None) -> str: + resolved: Final = _resolve_qwen_ai_platform_api_key(api_key) + if resolved is None: + raise ValueError( + "Qwen AI Platform API key is required. Set 'QWEN_AI_PLATFORM_API_KEY' or 'DASHSCOPE_API_KEY' env var " + "or pass api_key explicitly." + ) + return resolved + + +class QwenAIPlatformChatConfig(DashScopeChatConfig): + def _get_openai_compatible_provider_info( + self, api_base: str | None, api_key: str | None + ) -> tuple[str | None, str | None]: + return self._resolve_chat_api_base(api_base), _resolve_qwen_ai_platform_api_key(api_key) + + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE + + +class QwenAIPlatformEmbeddingConfig(DashScopeEmbeddingConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE + + +class QwenAIPlatformRerankConfig(DashScopeRerankConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_RERANK") or QWEN_AI_PLATFORM_RERANK_API_BASE + + +class QwenAIPlatformImageGenerationConfig(DashScopeImageGenerationConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_IMAGE") or QWEN_AI_PLATFORM_IMAGE_API_BASE diff --git a/litellm/llms/dashscope/qwencloud.py b/litellm/llms/dashscope/qwencloud.py new file mode 100644 index 00000000000..d8d53e340ef --- /dev/null +++ b/litellm/llms/dashscope/qwencloud.py @@ -0,0 +1,62 @@ +from typing import Final + +from litellm.secret_managers.main import get_secret_str + +from .chat.transformation import DashScopeChatConfig +from .embed.transformation import DashScopeEmbeddingConfig +from .image_generation.transformation import DashScopeImageGenerationConfig +from .rerank.transformation import DashScopeRerankConfig + +QWENCLOUD_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" +QWENCLOUD_RERANK_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/compatible-api/v1/reranks" +QWENCLOUD_IMAGE_API_BASE: Final = ( + "https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +) + + +def _resolve_qwencloud_api_key(api_key: str | None) -> str | None: + return api_key or get_secret_str("QWENCLOUD_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def _require_qwencloud_api_key(api_key: str | None) -> str: + resolved: Final = _resolve_qwencloud_api_key(api_key) + if resolved is None: + raise ValueError( + "QwenCloud API key is required. Set 'QWENCLOUD_API_KEY' or 'DASHSCOPE_API_KEY' env var " + "or pass api_key explicitly." + ) + return resolved + + +class QwenCloudChatConfig(DashScopeChatConfig): + def _get_openai_compatible_provider_info( + self, api_base: str | None, api_key: str | None + ) -> tuple[str | None, str | None]: + return self._resolve_chat_api_base(api_base), _resolve_qwencloud_api_key(api_key) + + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE + + +class QwenCloudEmbeddingConfig(DashScopeEmbeddingConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE + + +class QwenCloudRerankConfig(DashScopeRerankConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE_RERANK") or QWENCLOUD_RERANK_API_BASE + + +class QwenCloudImageGenerationConfig(DashScopeImageGenerationConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("QWENCLOUD_API_BASE_IMAGE") or QWENCLOUD_IMAGE_API_BASE diff --git a/litellm/llms/dashscope/rerank/transformation.py b/litellm/llms/dashscope/rerank/transformation.py index 3c7801d4d3c..3dd3996b2ee 100644 --- a/litellm/llms/dashscope/rerank/transformation.py +++ b/litellm/llms/dashscope/rerank/transformation.py @@ -22,6 +22,7 @@ as supported only for gte-rerank-v2 / qwen3-vl-rerank. Docs - https://help.aliyun.com/zh/model-studio/text-rerank-api """ +from collections.abc import Mapping from typing import Any, Final import httpx @@ -57,19 +58,30 @@ class DashScopeRerankConfig(BaseRerankConfig): def __init__(self) -> None: pass + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key if api_key is not None else get_secret_str("DASHSCOPE_API_KEY") + if resolved_api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + return resolved_api_key + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + if api_base is not None: + return api_base + return get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + def get_complete_url( self, api_base: str | None, model: str, optional_params: dict | None = None, ) -> str: - if api_base is None: - api_base = get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + resolved_api_base: Final = self._resolve_rerank_api_base(api_base) + if resolved_api_base == DEFAULT_RERANK_URL: + return resolved_api_base - if api_base == DEFAULT_RERANK_URL: - return DEFAULT_RERANK_URL - - cleaned: Final = api_base.rstrip("/") + cleaned: Final = resolved_api_base.rstrip("/") if cleaned.endswith("/reranks") or cleaned.endswith("/rerank"): return cleaned @@ -85,20 +97,14 @@ class DashScopeRerankConfig(BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: - if api_key is None: - api_key = get_secret_str("DASHSCOPE_API_KEY") - if api_key is None: - raise ValueError( - "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." - ) - - default_headers: Final = { - "Authorization": f"Bearer {api_key}", + return { + "Authorization": f"Bearer {self._resolve_api_key(api_key)}", "accept": "application/json", "content-type": "application/json", + **headers, } - return {**default_headers, **headers} def get_supported_cohere_rerank_params(self, model: str) -> list: return ["query", "documents", "top_n", "return_documents"] diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 8a625569cfa..c587146005f 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -136,6 +136,8 @@ def _split_parallel_tool_calls(messages: list[AllMessageValues]) -> list[AllMess if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -603,7 +605,7 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/databricks/common_utils.py b/litellm/llms/databricks/common_utils.py index b8dc98f2582..7695b1cb35e 100644 --- a/litellm/llms/databricks/common_utils.py +++ b/litellm/llms/databricks/common_utils.py @@ -13,6 +13,7 @@ Authentication priority: import os import re from typing import Any, Final, Literal +from urllib.parse import urlsplit, urlunsplit from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -224,11 +225,8 @@ class DatabricksBase: """ import requests - # Extract workspace URL from api_base - workspace_url = api_base.rstrip("/") - if "/serving-endpoints" in workspace_url: - workspace_url = workspace_url.replace("/serving-endpoints", "") - + api_base_parts: Final = urlsplit(api_base) + workspace_url: Final = urlunsplit((api_base_parts.scheme, api_base_parts.netloc, "", "", "")) token_url: Final = f"{workspace_url}/oidc/v1/token" try: diff --git a/litellm/llms/databricks/cost_calculator.py b/litellm/llms/databricks/cost_calculator.py index 05647883ebf..64166e6fc11 100644 --- a/litellm/llms/databricks/cost_calculator.py +++ b/litellm/llms/databricks/cost_calculator.py @@ -3,10 +3,31 @@ Helper util for handling databricks-specific cost calculation - e.g.: handling 'dbrx-instruct-*' """ +from types import MappingProxyType from typing import Final +from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token from litellm.types.utils import Usage -from litellm.utils import get_model_info + +_LEGACY_ENDPOINT_NAMES: Final = MappingProxyType( + { + "dbrx-instruct": "databricks-dbrx-instruct", + "meta-llama-3.1-70b-instruct": "databricks-meta-llama-3-1-70b-instruct", + "meta-llama-3.1-405b-instruct": "databricks-meta-llama-3-1-405b-instruct", + "mixtral-8x7b-instruct-v0.1": "databricks-mixtral-8x7b-instruct", + "bge-large-en": "databricks-bge-large-en", + "gte-large-en": "databricks-gte-large-en", + "llama-2-70b-chat": "databricks-llama-2-70b-chat", + } +) + + +def _registry_key(model: str) -> str: + name: Final = model.removeprefix("databricks/") + return next( + (key for prefix, key in _LEGACY_ENDPOINT_NAMES.items() if name.startswith(prefix)), + name, + ) def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: @@ -20,36 +41,8 @@ def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ - base_model = model - if model.startswith("databricks/dbrx-instruct") or model.startswith("dbrx-instruct"): - base_model = "databricks-dbrx-instruct" - elif model.startswith("databricks/meta-llama-3.1-70b-instruct") or model.startswith("meta-llama-3.1-70b-instruct"): - base_model = "databricks-meta-llama-3-1-70b-instruct" - elif model.startswith("databricks/meta-llama-3.1-405b-instruct") or model.startswith( - "meta-llama-3.1-405b-instruct" - ): - base_model = "databricks-meta-llama-3-1-405b-instruct" - elif ( - model.startswith("databricks/mixtral-8x7b-instruct-v0.1") - or model.startswith("mixtral-8x7b-instruct-v0.1") - or model.startswith("databricks/mixtral-8x7b-instruct-v0.1") - or model.startswith("mixtral-8x7b-instruct-v0.1") - ): - base_model = "databricks-mixtral-8x7b-instruct" - elif model.startswith("databricks/bge-large-en") or model.startswith("bge-large-en"): - base_model = "databricks-bge-large-en" - elif model.startswith("databricks/gte-large-en") or model.startswith("gte-large-en"): - base_model = "databricks-gte-large-en" - elif model.startswith("databricks/llama-2-70b-chat") or model.startswith("llama-2-70b-chat"): - base_model = "databricks-llama-2-70b-chat" - ## GET MODEL INFO - model_info: Final = get_model_info(model=base_model, custom_llm_provider="databricks") - - ## CALCULATE INPUT COST - - prompt_cost: Final[float] = usage["prompt_tokens"] * model_info["input_cost_per_token"] - - ## CALCULATE OUTPUT COST - completion_cost: Final = usage["completion_tokens"] * model_info["output_cost_per_token"] - - return prompt_cost, completion_cost + return generic_cost_per_token( + model=_registry_key(model), + usage=usage, + custom_llm_provider="databricks", + ) diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py index 366b82e1dcf..a3d0482af0a 100644 --- a/litellm/llms/deepinfra/rerank/transformation.py +++ b/litellm/llms/deepinfra/rerank/transformation.py @@ -2,9 +2,11 @@ Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict from litellm._uuid import uuid from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj @@ -23,6 +25,36 @@ from litellm.types.rerank import ( ) +class _DeepinfraInferenceStatus(TypedDict, total=False): + """The ``inference_status`` block of a DeepInfra rerank response.""" + + status: ReadOnly[str] + runtime_ms: ReadOnly[float] + cost: ReadOnly[float] + tokens_generated: ReadOnly[int] + tokens_input: ReadOnly[int] + + +class _DeepinfraRerankResponse(TypedDict, total=False): + """Body of a DeepInfra ``/rerank`` response.""" + + scores: ReadOnly[Sequence[float]] + input_tokens: ReadOnly[int] + request_id: ReadOnly[str | None] + inference_status: ReadOnly[_DeepinfraInferenceStatus] + + +class _DeepinfraRerankResponseSource(Protocol): + """The DeepInfra ``/rerank`` HTTP response, read for the body it decodes to.""" + + def json(self) -> _DeepinfraRerankResponse: ... + + +def _deepinfra_rerank_body(response: _DeepinfraRerankResponseSource) -> _DeepinfraRerankResponse: + """Decode the body of a DeepInfra ``/rerank`` response.""" + return response.json() + + class DeepinfraRerankConfig(BaseRerankConfig): """ Deepinfra Rerank - Follows the same Spec as Cohere Rerank @@ -67,6 +99,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: if api_key is None: api_key = get_secret_str("DEEPINFRA_API_KEY") @@ -93,7 +126,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): model: str, drop_params: bool, query: str, - documents: list[str | dict[str, Any]], + documents: list[str | dict[str, object]], custom_llm_provider: str | None = None, top_n: int | None = None, rank_fields: list[str] | None = None, @@ -148,7 +181,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): litellm_params: dict = {}, ) -> RerankResponse: try: - response_json: Final = raw_response.json() + response_json: Final = _deepinfra_rerank_body(raw_response) logging_obj.post_call(original_response=raw_response.text) # Extract the scores from the response diff --git a/litellm/llms/deepseek/chat/transformation.py b/litellm/llms/deepseek/chat/transformation.py index 566c960333a..ea19a7c7ddf 100644 --- a/litellm/llms/deepseek/chat/transformation.py +++ b/litellm/llms/deepseek/chat/transformation.py @@ -2,16 +2,17 @@ Translates from OpenAI's `/v1/chat/completions` to DeepSeek's `/v1/chat/completions` """ -from collections.abc import Coroutine +from collections.abc import Coroutine, Mapping, Sequence from typing import Any, Final, Literal, cast, overload import litellm from litellm.litellm_core_utils.prompt_templates.common_utils import ( - handle_messages_with_content_list_to_str_conversion, + convert_content_list_to_str, + extract_search_results_text, ) from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues -from litellm.utils import supports_reasoning +from litellm.utils import supports_reasoning, supports_vision from ...openai.chat.gpt_transformation import OpenAIGPTConfig @@ -117,13 +118,98 @@ class DeepSeekChatConfig(OpenAIGPTConfig): self, messages: list[AllMessageValues], model: str, is_async: bool = False ) -> list[AllMessageValues] | Coroutine[Any, Any, list[AllMessageValues]]: """ - DeepSeek does not support content in list format. + DeepSeek vision models accept image_url content blocks in user + messages (https://api-docs.deepseek.com/guides/vision), so those + content lists are forwarded as-is, with any search_results text + appended as a trailing text block. Every other message keeps the + historical string collapse (which also folds search_results text + into string content); a list with no extractable text stays + unchanged, matching what DeepSeek historically received. """ - messages = handle_messages_with_content_list_to_str_conversion(messages) + forward_images: Final = any( + isinstance(message.get("content"), list) for message in messages + ) and supports_vision(model=model, custom_llm_provider="deepseek") + transformed: Final = [ # mutable-ok: provider messages must stay JSON-array lists the base transform mutates + self._forward_or_collapse_content(message=message, forward_images=forward_images) for message in messages + ] + if is_async: - return super()._transform_messages(messages=messages, model=model, is_async=True) + return super()._transform_messages(messages=transformed, model=model, is_async=True) else: - return super()._transform_messages(messages=messages, model=model, is_async=False) + return super()._transform_messages(messages=transformed, model=model, is_async=False) + + def _forward_or_collapse_content(self, message: AllMessageValues, forward_images: bool) -> AllMessageValues: + """ + Returns the vision-forwardable message with any search_results text + appended as a text block; every other message keeps the historical + string collapse, which extracts the text from a content list and + folds search_results text into string content. + """ + content: Final = message.get("content") + if ( + forward_images + and isinstance(content, list) + and self._is_vision_forwardable_content(message=message, content=content) + ): + return self._with_search_results_text_block(message=message, content=content) + collapsed: Final = convert_content_list_to_str(message=message) + if not collapsed or collapsed == content: + return message + collapsed_message: Final = {**message, "content": collapsed} # mutable-ok: wire messages are plain JSON dicts + return cast(AllMessageValues, collapsed_message) # cast-ok: TypedDict spread narrows to dict + + def _is_vision_forwardable_content(self, message: AllMessageValues, content: Sequence[object]) -> bool: + """ + True only for a user message whose content list holds well-formed + text and image_url blocks with at least one image; a block missing + its payload falls back to the string collapse instead of crashing + or reaching the wire malformed. The model capability gate lives in + the caller. + """ + if message.get("role") != "user": + return False + if not all(self._is_forwardable_block(block) for block in content): + return False + return any(isinstance(block, dict) and block.get("type") == "image_url" for block in content) + + @staticmethod + def _is_forwardable_block(block: object) -> bool: + """A dict block typed text or image_url that carries its payload.""" + if not isinstance(block, dict): + return False + block_type: Final = block.get("type") + if block_type == "image_url": + return DeepSeekChatConfig._is_image_url_payload(block.get("image_url")) + if block_type == "text": + return isinstance(block.get("text"), str) + return False + + @staticmethod + def _is_image_url_payload(payload: object) -> bool: + """A url string or an object carrying one, per the OpenAI image_url shape.""" + if isinstance(payload, str): + return bool(payload) + if not isinstance(payload, Mapping): + return False + url: Final = payload.get("url") + return isinstance(url, str) and bool(url) + + def _with_search_results_text_block(self, message: AllMessageValues, content: Sequence[object]) -> AllMessageValues: + """ + Appends the message's search_results text as a trailing text block, + keeping the context that the string collapse used to fold in, and + drops the non-OpenAI search_results key from the wire message. + """ + message_fields: Final = cast(Mapping[str, object], message) # cast-ok: search_results is not on the TypedDicts + search_text: Final = extract_search_results_text(message_fields.get("search_results")) + if not search_text: + return message + forwarded_content: Final = [*content, {"type": "text", "text": search_text}] # mutable-ok: JSON-array content + forwarded: Final = { # mutable-ok: wire messages are plain JSON dicts + **{key: value for key, value in message_fields.items() if key != "search_results"}, + "content": forwarded_content, + } + return cast(AllMessageValues, forwarded) # cast-ok: TypedDict spread narrows to dict def _thinking_mode_active(self, model: str, optional_params: dict) -> bool: """ diff --git a/litellm/llms/e2b/sandbox/transformation.py b/litellm/llms/e2b/sandbox/transformation.py index 9cd9ade77a4..4928ca0c092 100644 --- a/litellm/llms/e2b/sandbox/transformation.py +++ b/litellm/llms/e2b/sandbox/transformation.py @@ -8,7 +8,7 @@ Talks to e2b's REST API directly over httpx (no e2b SDK dependency): """ import json -from typing import Final, cast +from typing import Final import httpx @@ -68,13 +68,10 @@ class E2BSandboxConfig(BaseSandboxConfig): if metadata: body["metadata"] = metadata - response: Final = cast( - httpx.Response, - await self._http(client).post( - url=f"{base}/sandboxes", - headers={"X-API-Key": key, "Content-Type": "application/json"}, - json=body, - ), + response: Final = await self._http(client).post( + url=f"{base}/sandboxes", + headers={"X-API-Key": key, "Content-Type": "application/json"}, + json=body, ) data: Final = response.json() @@ -117,14 +114,11 @@ class E2BSandboxConfig(BaseSandboxConfig): headers["E2B-Traffic-Access-Token"] = traffic_token url: Final = f"https://{JUPYTER_PORT}-{handle.id}.{handle.domain}/execute" - response: Final = cast( - httpx.Response, - await self._http(client).post( - url=url, - headers=headers, - json={"code": code, "context_id": None, "env_vars": env_vars}, - stream=True, - ), + response: Final = await self._http(client).post( + url=url, + headers=headers, + json={"code": code, "context_id": None, "env_vars": env_vars}, + stream=True, ) lines: Final = await self._read_capped_lines(response) return self._parse_lines(lines) @@ -142,12 +136,9 @@ class E2BSandboxConfig(BaseSandboxConfig): key: Final = api_key or handle._hidden_params.get("api_key") or self.validate_environment() base: Final = api_base or handle._hidden_params.get("api_base") or E2B_API_BASE try: - response: Final = cast( - httpx.Response, - await self._http(client).delete( - url=f"{base}/sandboxes/{handle.id}", - headers={"X-API-Key": key}, - ), + response: Final = await self._http(client).delete( + url=f"{base}/sandboxes/{handle.id}", + headers={"X-API-Key": key}, ) except httpx.HTTPStatusError as e: if e.response.status_code == 404: diff --git a/litellm/llms/fal_ai/image_generation/bria_transformation.py b/litellm/llms/fal_ai/image_generation/bria_transformation.py index 5cfe6a67523..c528550811a 100644 --- a/litellm/llms/fal_ai/image_generation/bria_transformation.py +++ b/litellm/llms/fal_ai/image_generation/bria_transformation.py @@ -8,6 +8,8 @@ from litellm.types.utils import ImageObject, ImageResponse from .transformation import FalAIBaseConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -185,7 +187,7 @@ class FalAIBriaConfig(FalAIBaseConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/fal_ai/image_generation/flux_pro_v11_ultra_transformation.py b/litellm/llms/fal_ai/image_generation/flux_pro_v11_ultra_transformation.py index 6e962978a43..228dd9257ce 100644 --- a/litellm/llms/fal_ai/image_generation/flux_pro_v11_ultra_transformation.py +++ b/litellm/llms/fal_ai/image_generation/flux_pro_v11_ultra_transformation.py @@ -8,6 +8,8 @@ from litellm.types.utils import ImageObject, ImageResponse from .transformation import FalAIBaseConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -192,7 +194,7 @@ class FalAIFluxProV11UltraConfig(FalAIBaseConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/fal_ai/image_generation/ideogram_v3_transformation.py b/litellm/llms/fal_ai/image_generation/ideogram_v3_transformation.py index 2c6716f1365..04b4f426878 100644 --- a/litellm/llms/fal_ai/image_generation/ideogram_v3_transformation.py +++ b/litellm/llms/fal_ai/image_generation/ideogram_v3_transformation.py @@ -8,6 +8,8 @@ from litellm.types.utils import ImageObject, ImageResponse from .transformation import FalAIBaseConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -148,7 +150,7 @@ class FalAIIdeogramV3Config(FalAIBaseConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/fal_ai/image_generation/imagen4_transformation.py b/litellm/llms/fal_ai/image_generation/imagen4_transformation.py index 28332a1f867..8a6665b2585 100644 --- a/litellm/llms/fal_ai/image_generation/imagen4_transformation.py +++ b/litellm/llms/fal_ai/image_generation/imagen4_transformation.py @@ -8,6 +8,8 @@ from litellm.types.utils import ImageObject, ImageResponse from .transformation import FalAIBaseConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -180,7 +182,7 @@ class FalAIImagen4Config(FalAIBaseConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/fal_ai/image_generation/recraft_v3_transformation.py b/litellm/llms/fal_ai/image_generation/recraft_v3_transformation.py index a5f0c086379..4880dfec7e3 100644 --- a/litellm/llms/fal_ai/image_generation/recraft_v3_transformation.py +++ b/litellm/llms/fal_ai/image_generation/recraft_v3_transformation.py @@ -8,6 +8,8 @@ from litellm.types.utils import ImageObject, ImageResponse from .transformation import FalAIBaseConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -170,7 +172,7 @@ class FalAIRecraftV3Config(FalAIBaseConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/fal_ai/image_generation/stable_diffusion_transformation.py b/litellm/llms/fal_ai/image_generation/stable_diffusion_transformation.py index 500aa859fe8..bc3a4d07282 100644 --- a/litellm/llms/fal_ai/image_generation/stable_diffusion_transformation.py +++ b/litellm/llms/fal_ai/image_generation/stable_diffusion_transformation.py @@ -8,6 +8,8 @@ from litellm.types.utils import ImageObject, ImageResponse from .transformation import FalAIBaseConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -206,7 +208,7 @@ class FalAIStableDiffusionConfig(FalAIBaseConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/fal_ai/image_generation/transformation.py b/litellm/llms/fal_ai/image_generation/transformation.py index b65f9585730..7a114677b2d 100644 --- a/litellm/llms/fal_ai/image_generation/transformation.py +++ b/litellm/llms/fal_ai/image_generation/transformation.py @@ -13,6 +13,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -76,7 +78,7 @@ class FalAIBaseConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index e64237da978..b6a5ee40672 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -1,6 +1,6 @@ import json from collections.abc import AsyncIterator, Iterator, Mapping -from typing import Any, Final, Literal, cast +from typing import TYPE_CHECKING, Any, Final, Literal, cast import httpx @@ -45,6 +45,9 @@ from ..common_utils import ( resolve_fireworks_resource_name, ) +if TYPE_CHECKING: + import tiktoken + def _extract_fireworks_hidden_params(payload: dict) -> dict: """ @@ -504,6 +507,7 @@ class FireworksAIConfig(FireworksAIMixin, OpenAIGPTConfig): m = cast(dict, message) m.pop("provider_specific_fields", None) m.pop("thinking_blocks", None) + m.pop("reasoning_content", None) return messages @@ -690,7 +694,7 @@ class FireworksAIConfig(FireworksAIMixin, OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/fireworks_ai/common_utils.py b/litellm/llms/fireworks_ai/common_utils.py index 8e35cfebc5b..8c306faa036 100644 --- a/litellm/llms/fireworks_ai/common_utils.py +++ b/litellm/llms/fireworks_ai/common_utils.py @@ -13,6 +13,13 @@ class FireworksAIException(BaseLLMException): def get_fireworks_session_id(litellm_params: dict) -> str | None: + """ + Session id to send as `x-session-affinity`, or None when the caller gave none. + + Deliberately does not fall back to `litellm_trace_id`: that is generated per + request (`str(uuid.uuid4())` when absent), so using it pins every request to a + different Fireworks node and prompt caching never hits. + """ params: Final = litellm_params for key in ("litellm_session_id", "session_id"): value = params.get(key) @@ -23,9 +30,6 @@ def get_fireworks_session_id(litellm_params: dict) -> str | None: value = metadata.get("session_id") if value: return str(value) - value = params.get("litellm_trace_id") - if value: - return str(value) return None diff --git a/litellm/llms/fireworks_ai/rerank/transformation.py b/litellm/llms/fireworks_ai/rerank/transformation.py index fde4f55e75b..8ef2c9acccb 100644 --- a/litellm/llms/fireworks_ai/rerank/transformation.py +++ b/litellm/llms/fireworks_ai/rerank/transformation.py @@ -4,6 +4,7 @@ Fireworks AI Rerank API transformation Reference: https://docs.fireworks.ai/inference-api-reference/rerank """ +from collections.abc import Mapping from typing import Any, Final import httpx @@ -102,6 +103,7 @@ class FireworksAIRerankConfig(FireworksAIMixin, BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: api_key = self._get_api_key(api_key) if api_key is None: diff --git a/litellm/llms/gdc/chat/transformation.py b/litellm/llms/gdc/chat/transformation.py index 2d0322bf10f..03037512551 100644 --- a/litellm/llms/gdc/chat/transformation.py +++ b/litellm/llms/gdc/chat/transformation.py @@ -6,11 +6,25 @@ import json import os import re import threading -from typing import Any, Final +from collections.abc import Callable +from typing import Any, Final, Protocol from urllib.parse import urlsplit import litellm from litellm.llms.openai_like.chat.transformation import OpenAILikeChatConfig +from litellm.types.llms.openai import AllMessageValues + + +class _GDCHAudienceCredentials(Protocol): + """A GDCH service account credential already bound to an audience, ready to mint a bearer token.""" + + @property + def valid(self) -> bool: ... + + @property + def token(self) -> str: ... + + def refresh(self, request: object) -> None: ... class GDCGeminiConfig(OpenAILikeChatConfig): @@ -21,7 +35,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): def __init__(self, **kwargs: Any) -> None: super().__init__(**kwargs) self._creds_lock = threading.Lock() - self._gdch_creds_cache: dict = {} + self._gdch_creds_cache: dict[tuple[str, str], _GDCHAudienceCredentials] = {} def get_supported_openai_params(self, model: str) -> list: return [ @@ -110,7 +124,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): return f"{api_base}/v1/projects/{project}/locations/{location}/chat/completions" - def _read_env_bool(self, val: Any, env_var: str, default: bool = True) -> bool | str: + def _read_env_bool(self, val: bool | str | None, env_var: str, default: bool = True) -> bool | str: def _parse(s: str) -> bool | str: cleaned: Final = s.strip().lower() if cleaned in ("false", "0", "no", "off"): @@ -129,7 +143,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): return default return _parse(_env_val) - def _fetch_auth(self, gdch_creds: Any, ssl_verify: bool | str) -> None: + def _fetch_auth(self, gdch_creds: _GDCHAudienceCredentials, ssl_verify: bool | str) -> None: import requests from google.auth.transport import requests as auth_requests @@ -138,13 +152,24 @@ class GDCGeminiConfig(OpenAILikeChatConfig): auth_request: Final = auth_requests.Request(session=auth_session) gdch_creds.refresh(auth_request) - def _cached_fetch_token(self, creds: Any, audience: str, ssl_verify: bool | str, api_key: str | None = None) -> str: + def _with_gdch_audience(self, creds: object, audience: str) -> _GDCHAudienceCredentials: + """The credential rebound to ``audience``, which GDCH requires before a token refresh.""" + bind_audience: Final[Callable[[str], _GDCHAudienceCredentials] | None] = getattr( + creds, "with_gdch_audience", None + ) + if bind_audience is None: + raise AttributeError("GDC credentials must expose with_gdch_audience to be bound to a request audience") + return bind_audience(audience) + + def _cached_fetch_token( + self, creds: object, audience: str, ssl_verify: bool | str, api_key: str | None = None + ) -> str: # Key cache by both audience and credential identity to prevent cross-caller contamination cache_key: Final = (audience.rstrip("/"), api_key or str(id(creds))) with self._creds_lock: if cache_key not in self._gdch_creds_cache: - self._gdch_creds_cache[cache_key] = creds.with_gdch_audience(audience.rstrip("/")) + self._gdch_creds_cache[cache_key] = self._with_gdch_audience(creds, audience.rstrip("/")) gdch_creds: Final = self._gdch_creds_cache[cache_key] @@ -155,7 +180,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): return token - def _load_creds_from_key(self, api_key: str) -> tuple[Any, bool]: + def _load_creds_from_key(self, api_key: str) -> tuple[object | None, bool]: import google.auth try: @@ -175,7 +200,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): self, headers: dict, model: str, - messages: list[Any], + messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, api_key: str | None = None, @@ -230,7 +255,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): if self._read_env_bool(litellm_params.get("gdc_token_caching"), "GDC_TOKEN_CACHING", default=False): token = self._cached_fetch_token(creds, audience, ssl_verify, api_key) else: - gdch_creds: Final = creds.with_gdch_audience(audience) + gdch_creds: Final = self._with_gdch_audience(creds, audience) self._fetch_auth(gdch_creds, ssl_verify) token = gdch_creds.token headers["Authorization"] = f"Bearer {token}" @@ -252,7 +277,7 @@ class GDCGeminiConfig(OpenAILikeChatConfig): def transform_request( self, model: str, - messages: list[Any], + messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, headers: dict, diff --git a/litellm/llms/gemini/audio_transcription/__init__.py b/litellm/llms/gemini/audio_transcription/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/gemini/audio_transcription/transformation.py b/litellm/llms/gemini/audio_transcription/transformation.py new file mode 100644 index 00000000000..c8dd7a9a5ff --- /dev/null +++ b/litellm/llms/gemini/audio_transcription/transformation.py @@ -0,0 +1,256 @@ +import base64 +from collections.abc import Mapping, Sequence +from typing import Final + +from httpx import Headers, Response + +from litellm.litellm_core_utils.audio_utils.subtitle_utils import SUBTITLE_RESPONSE_FORMATS +from litellm.litellm_core_utils.audio_utils.utils import ( + normalize_transcription_language_to_bcp47, + process_audio_file, +) +from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, + BaseAudioTranscriptionConfig, +) +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.gemini.common_utils import GeminiError, GeminiModelInfo +from litellm.types.llms.gemini_audio_transcription import ( + GeminiTranscriptionAudioInput, + GeminiTranscriptionConfig, + GeminiTranscriptionInteractionRequest, + GeminiTranscriptionInteractionResponse, + GeminiTranscriptionWordAnnotation, +) +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIAudioTranscriptionOptionalParams, +) +from litellm.types.utils import ( + FileTypes, + TranscriptionResponse, + TranscriptionUsageInputTokenDetailsObject, + TranscriptionUsageTokensObject, +) + +INTERACTIONS_API_REVISION: Final = "2026-05-20" +WORD_INFO_ANNOTATION_TYPE: Final = "word_info" + + +class GeminiAudioTranscriptionConfig(BaseAudioTranscriptionConfig): + """ + Maps OpenAI /v1/audio/transcriptions onto the Gemini Interactions API + (POST /v1beta/interactions) for transcription models like + gemini-3.5-transcribe. https://ai.google.dev/gemini-api/docs/transcribe + """ + + def get_supported_openai_params( + self, model: str + ) -> list[OpenAIAudioTranscriptionOptionalParams]: # mutable-ok: BaseAudioTranscriptionConfig signature + return ["language", "response_format", "timestamp_granularities"] # mutable-ok: base contract returns a list + + @property + def supports_subtitle_synthesis(self) -> bool: + return True + + def map_openai_params( + self, + non_default_params: Mapping[str, object], + optional_params: Mapping[str, object], + model: str, + drop_params: bool, + ) -> dict: # mutable-ok: BaseAudioTranscriptionConfig signature + supported_params: Final = frozenset(self.get_supported_openai_params(model)) + accepted: Final = tuple((k, v) for k, v in non_default_params.items() if k in supported_params) + return dict((*optional_params.items(), *accepted)) # mutable-ok: base contract returns a plain dict + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: dict | Headers, # mutable-ok: base signature and BaseLLMException take dict | Headers + ) -> BaseLLMException: + return GeminiError(status_code=status_code, message=error_message, headers=headers) + + def validate_environment( + self, + headers: Mapping[str, str], + model: str, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + api_key: str | None = None, + api_base: str | None = None, + ) -> dict: # mutable-ok: BaseAudioTranscriptionConfig signature + resolved_api_key: Final = GeminiModelInfo.get_api_key(api_key) + if not resolved_api_key: + raise GeminiError( + status_code=401, + message="Google API key is required. Set GOOGLE_API_KEY or GEMINI_API_KEY environment variable.", + ) + return { # mutable-ok: the http handler passes these headers straight to httpx + **headers, + "Content-Type": "application/json", + "x-goog-api-key": resolved_api_key, + "Api-Revision": INTERACTIONS_API_REVISION, + } + + def get_complete_url( + self, + api_base: str | None, + api_key: str | None, + model: str, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + stream: bool | None = None, + ) -> str: + resolved_api_base: Final = GeminiModelInfo.get_api_base(api_base) + return f"{resolved_api_base}/v1beta/interactions" + + def transform_audio_transcription_request( + self, + model: str, + audio_file: FileTypes, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + ) -> AudioTranscriptionRequestData: + processed_audio: Final = process_audio_file(audio_file) + audio_input: Final = GeminiTranscriptionAudioInput( + type="audio", + data=base64.b64encode(processed_audio.file_content).decode("utf-8"), + mime_type=processed_audio.content_type, + ) + request: Final = _build_interaction_request( + model=model, + audio_input=audio_input, + transcription_config=_build_transcription_config(optional_params), + ) + return AudioTranscriptionRequestData(data=dict(request)) # mutable-ok: AudioTranscriptionRequestData wants dict + + def transform_audio_transcription_response( + self, + raw_response: Response, + ) -> TranscriptionResponse: + try: + response_json: Final = raw_response.json() + except ValueError: + raise GeminiError( + status_code=raw_response.status_code, + message=f"Received non-JSON response from Gemini Interactions API: {raw_response.text}", + ) + parsed: Final = GeminiTranscriptionInteractionResponse.model_validate(response_json) + if parsed.status != "completed": + raise GeminiError( + status_code=raw_response.status_code, + message=f"Gemini transcription interaction did not complete (status={parsed.status}): {raw_response.text}", + ) + text_contents: Final = tuple( + content + for step in parsed.steps + for content in step.content + if content.type == "text" and content.text is not None + ) + response: Final = TranscriptionResponse(text=" ".join(content.text or "" for content in text_contents)) + response["task"] = "transcribe" + words: Final = tuple( + word + for content in text_contents + for annotation in content.annotations + if (word := _annotation_to_word(annotation)) is not None + ) + if words: + response["words"] = list(words) # mutable-ok: verbose_json words is a JSON array + last_word_end: Final = words[-1].get("end") + if last_word_end is not None: + response["duration"] = last_word_end + if parsed.usage is not None: + audio_tokens: Final = sum( + by_modality.tokens + for by_modality in parsed.usage.input_tokens_by_modality + if by_modality.modality == "audio" + ) + response.usage = TranscriptionUsageTokensObject( + type="tokens", + input_tokens=parsed.usage.total_input_tokens, + output_tokens=parsed.usage.total_output_tokens, + total_tokens=parsed.usage.total_tokens, + input_token_details=TranscriptionUsageInputTokenDetailsObject( + audio_tokens=audio_tokens, + text_tokens=parsed.usage.total_input_tokens - audio_tokens, + ), + ) + return response + + +_EMPTY_TRANSCRIPTION_CONFIG: Final[GeminiTranscriptionConfig] = {} +_WORD_TIMESTAMP_CONFIG: Final[GeminiTranscriptionConfig] = { + "mode": { + "type": "verbatim", + "timestamp_granularities": ("word",), + "diarization_mode": "speaker", + }, +} + + +def _build_interaction_request( + model: str, + audio_input: GeminiTranscriptionAudioInput, + transcription_config: GeminiTranscriptionConfig, +) -> GeminiTranscriptionInteractionRequest: + if not transcription_config: + bare_request: Final[GeminiTranscriptionInteractionRequest] = { + "model": model.removeprefix("gemini/"), + "input": (audio_input,), + } + return bare_request + configured_request: Final[GeminiTranscriptionInteractionRequest] = { + "model": model.removeprefix("gemini/"), + "input": (audio_input,), + "generation_config": {"transcription_config": transcription_config}, + } + return configured_request + + +def _language_config(language: object) -> GeminiTranscriptionConfig: + if not isinstance(language, str) or not language: + return _EMPTY_TRANSCRIPTION_CONFIG + language_config: Final[GeminiTranscriptionConfig] = { + "language_codes": (normalize_transcription_language_to_bcp47(language),), + } + return language_config + + +def _timestamp_config(timestamp_granularities: object, response_format: object) -> GeminiTranscriptionConfig: + wants_word_timestamps: Final = ( + isinstance(timestamp_granularities, list) and "word" in timestamp_granularities + ) or (isinstance(response_format, str) and response_format in SUBTITLE_RESPONSE_FORMATS) + return _WORD_TIMESTAMP_CONFIG if wants_word_timestamps else _EMPTY_TRANSCRIPTION_CONFIG + + +def _build_transcription_config(optional_params: Mapping[str, object]) -> GeminiTranscriptionConfig: + transcription_config: Final[GeminiTranscriptionConfig] = { + **_language_config(optional_params.get("language")), + **_timestamp_config(optional_params.get("timestamp_granularities"), optional_params.get("response_format")), + } + return transcription_config + + +def _annotation_to_word(annotation: GeminiTranscriptionWordAnnotation) -> Mapping[str, str | float] | None: + if annotation.type != WORD_INFO_ANNOTATION_TYPE or annotation.text is None: + return None + entries: Final = ( + ("word", annotation.text), + ("start", _parse_offset_seconds(annotation.start_offset)), + ("end", _parse_offset_seconds(annotation.end_offset)), + ("speaker", annotation.speaker), + ) + return {key: value for key, value in entries if value is not None} # mutable-ok: word entries serialize to JSON + + +def _parse_offset_seconds(offset: str | None) -> float | None: + if offset is None or not offset.endswith("s"): + return None + try: + return float(offset[:-1]) + except ValueError: + return None diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py index bc12995057e..1a67b33665b 100644 --- a/litellm/llms/gemini/chat/transformation.py +++ b/litellm/llms/gemini/chat/transformation.py @@ -8,7 +8,7 @@ from litellm.litellm_core_utils.prompt_templates.factory import ( from litellm.litellm_core_utils.prompt_templates.image_handling import ( convert_url_to_base64, ) -from litellm.types.llms.openai import AllMessageValues, ChatCompletionFileObject +from litellm.types.llms.openai import AllMessageValues, ChatCompletionFileObject, ChatCompletionImageObject from litellm.types.llms.vertex_ai import ContentType, PartType from litellm.utils import supports_reasoning @@ -16,6 +16,13 @@ from ...vertex_ai.gemini.transformation import _gemini_convert_messages_with_his from ...vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig +def _image_url_fields(img_element: ChatCompletionImageObject) -> tuple[str | None, str | None, str | None]: + image_value: Final = img_element.get("image_url") + if isinstance(image_value, dict): + return image_value.get("url"), image_value.get("format"), image_value.get("detail") + return image_value, None, None + + class GoogleAIStudioGeminiConfig(VertexGeminiConfig): """ Reference: https://ai.google.dev/api/rest/v1beta/GenerationConfig @@ -118,16 +125,8 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): _parts: list[PartType] = [] for element in _message_content: if element.get("type") == "image_url": - img_element = element - _image_url: str | None = None - format: str | None = None - detail: str | None = None - if isinstance(img_element.get("image_url"), dict): - _image_url = img_element["image_url"].get("url") - format = img_element["image_url"].get("format") - detail = img_element["image_url"].get("detail") - else: - _image_url = img_element.get("image_url") + img_element = cast(ChatCompletionImageObject, element) # cast-ok: runtime type tag checked + _image_url, format, detail = _image_url_fields(img_element) if _image_url and "https://" in _image_url: image_obj = convert_to_anthropic_image_obj(_image_url, format=format) converted_image_url = convert_generic_image_chunk_to_openai_image_obj(image_obj) diff --git a/litellm/llms/gemini/common_utils.py b/litellm/llms/gemini/common_utils.py index bd2b124605c..78e6e6aaf82 100644 --- a/litellm/llms/gemini/common_utils.py +++ b/litellm/llms/gemini/common_utils.py @@ -2,7 +2,7 @@ import base64 import datetime import json import math -from collections.abc import Sequence +from collections.abc import Mapping, Sequence from typing import Any, Final import httpx @@ -128,24 +128,35 @@ def is_gemini_image_model(model: str) -> bool: return "gemini" in base_model +def _parse_image_config_string(raw_image_config: str, model: str) -> object: + try: + return json.loads(raw_image_config) + except json.JSONDecodeError as exc: + raise litellm.UnsupportedParamsError( + model=model, + message="`imageConfig` must be valid JSON when provided as a string.", + ) from exc + + def map_openai_image_params_to_gemini( - params: dict[str, Any], + params: Mapping[str, object], model: str, supported_params: Sequence[str], - optional_params: dict[str, Any] | None = None, + optional_params: Mapping[str, object] | None = None, parse_image_config_string: bool = False, -) -> dict[str, Any]: - optional_params = optional_params or {} +) -> dict[str, object]: + already_mapped: Final[Mapping[str, object]] = optional_params or {} filtered_params: Final = {key: value for key, value in params.items() if key in supported_params} - mapped_params: Final[dict[str, Any]] = {} + mapped_params: Final[dict[str, object]] = {} - if "n" in filtered_params and "n" not in optional_params: + if "n" in filtered_params and "n" not in already_mapped: mapped_params["sampleCount"] = filtered_params["n"] - if "size" in filtered_params and "size" not in optional_params: + size_param: Final = filtered_params.get("size") + if isinstance(size_param, str) and "size" not in already_mapped: image_config: Final = map_openai_size_to_gemini_image_config( - filtered_params["size"], + size_param, model, ) if image_config is not None: @@ -156,33 +167,30 @@ def map_openai_image_params_to_gemini( if "imageSize" in image_config: mapped_params["imageSize"] = image_config["imageSize"] - image_config_param = filtered_params.get("imageConfig") - if isinstance(image_config_param, str) and parse_image_config_string: - try: - image_config_param = json.loads(image_config_param) - except json.JSONDecodeError as exc: - raise litellm.UnsupportedParamsError( - model=model, - message="`imageConfig` must be valid JSON when provided as a string.", - ) from exc + raw_image_config: Final = filtered_params.get("imageConfig") + image_config_param: Final[object] = ( + _parse_image_config_string(raw_image_config, model) + if isinstance(raw_image_config, str) and parse_image_config_string + else raw_image_config + ) if isinstance(image_config_param, dict): mapped_params["imageConfig"] = image_config_param for key, value in filtered_params.items(): - if key not in ("n", "size", "imageConfig", "tools", "web_search_options") and key not in optional_params: + if key not in ("n", "size", "imageConfig", "tools", "web_search_options") and key not in already_mapped: mapped_params[key] = value return mapped_params -def _dedupe_gemini_search_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]: +def _dedupe_gemini_search_tools(tools: list[dict[str, object]]) -> list[dict[str, object]]: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) search_tool_keys: Final = VertexGeminiConfig._search_tool_keys() seen_search_keys: Final[set[str]] = set() - deduped_tools: Final[list[dict[str, Any]]] = [] + deduped_tools: Final[list[dict[str, object]]] = [] for tool in tools: if not isinstance(tool, dict): @@ -203,7 +211,7 @@ def _dedupe_gemini_search_tools(tools: list[dict[str, Any]]) -> list[dict[str, A return deduped_tools -def _has_gemini_search_tool(tools: list[Any]) -> bool: +def _has_gemini_search_tool(tools: list[object]) -> bool: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) @@ -213,9 +221,9 @@ def _has_gemini_search_tool(tools: list[Any]) -> bool: def map_gemini_image_tools_params( - non_default_params: dict[str, Any], - mapped_params: dict[str, Any], -) -> dict[str, Any]: + non_default_params: Mapping[str, object], + mapped_params: Mapping[str, object], +) -> dict[str, object]: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) @@ -239,21 +247,24 @@ def map_gemini_image_tools_params( gemini_config._drop_search_tools_mixed_with_functions(result) - if isinstance(result.get("tools"), list): - result["tools"] = _dedupe_gemini_search_tools(result["tools"]) + resolved_tools: Final = result.get("tools") + if isinstance(resolved_tools, list): + result["tools"] = _dedupe_gemini_search_tools(resolved_tools) return result def get_gemini_image_web_search_requests( - response_data: dict[str, Any], + response_data: Mapping[str, object], ) -> int | None: from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig, ) - grounding_metadata: Final[list[dict[str, Any]]] = [] - for candidate in response_data.get("candidates", []): + raw_candidates: Final = response_data.get("candidates") + candidates: Final[list[object]] = raw_candidates if isinstance(raw_candidates, list) else [] + grounding_metadata: Final[list[dict[str, object]]] = [] + for candidate in candidates: if not isinstance(candidate, dict): continue candidate_grounding = candidate.get("groundingMetadata") @@ -267,13 +278,14 @@ def get_gemini_image_web_search_requests( def get_gemini_image_generation_config( model: str, - optional_params: dict[str, Any], -) -> dict[str, Any]: - generation_config: Final[dict[str, Any]] = {"response_modalities": ["IMAGE", "TEXT"]} + optional_params: Mapping[str, object], +) -> dict[str, object]: + generation_config: Final[dict[str, object]] = {"response_modalities": ["IMAGE", "TEXT"]} - image_config: Final[dict[str, Any]] = {} - if isinstance(optional_params.get("imageConfig"), dict): - image_config.update(optional_params["imageConfig"]) + raw_image_config: Final = optional_params.get("imageConfig") + image_config: Final[dict[str, object]] = {} + if isinstance(raw_image_config, dict): + image_config.update(raw_image_config) if not supports_gemini_image_size(model): image_config.pop("imageSize", None) @@ -398,7 +410,7 @@ class GeminiModelInfo(BaseLLMModelInfo): f"Failed to fetch models from Gemini. Status code: {response.status_code}, Response: {response.json()}" ) - models: Final = response.json()["models"] + models: Final[list[dict[str, str]]] = response.json()["models"] litellm_model_names: Final = self.process_model_name(models) return litellm_model_names @@ -473,12 +485,12 @@ class GoogleAIStudioTokenCounter(BaseTokenCounter): async def count_tokens( self, model_to_use: str, - messages: list[dict[str, Any]] | None, - contents: list[dict[str, Any]] | None, + messages: list[dict[str, object]] | None, + contents: list[dict[str, object]] | None, deployment: dict[str, Any] | None = None, request_model: str = "", - tools: list[dict[str, Any]] | None = None, - system: Any | None = None, + tools: list[dict[str, object]] | None = None, + system: object | None = None, ) -> TokenCountResponse | None: import copy diff --git a/litellm/llms/gemini/cost_calculator.py b/litellm/llms/gemini/cost_calculator.py index a041ef40622..b82103b0ff8 100644 --- a/litellm/llms/gemini/cost_calculator.py +++ b/litellm/llms/gemini/cost_calculator.py @@ -39,25 +39,71 @@ def cost_per_web_search_request(usage: "Usage", model_info: "ModelInfo") -> floa ``model_info`` when available, falling back to $0.035 for models not yet updated in the pricing JSON. """ + from litellm.litellm_core_utils.llm_cost_calc.utils import ( + get_web_search_requests_from_usage, + ) from litellm.types.utils import PromptTokensDetailsWrapper _DEFAULT_COST: Final = 35e-3 search_costs: Final = model_info.get("search_context_cost_per_query") or {} _cost: Final = search_costs.get("search_context_size_medium", _DEFAULT_COST) - number_of_web_search_requests = 0 - if ( - usage is not None - and usage.prompt_tokens_details is not None - and isinstance(usage.prompt_tokens_details, PromptTokensDetailsWrapper) - and hasattr(usage.prompt_tokens_details, "web_search_requests") - and usage.prompt_tokens_details.web_search_requests is not None - ): - number_of_web_search_requests = usage.prompt_tokens_details.web_search_requests + requests_from_prompt_details: Final = ( + usage.prompt_tokens_details.web_search_requests + if ( + usage is not None + and usage.prompt_tokens_details is not None + and isinstance(usage.prompt_tokens_details, PromptTokensDetailsWrapper) + and hasattr(usage.prompt_tokens_details, "web_search_requests") + and usage.prompt_tokens_details.web_search_requests is not None + ) + else None + ) + requests_from_server_tool_use: Final = get_web_search_requests_from_usage(usage) + number_of_web_search_requests: Final = requests_from_prompt_details or requests_from_server_tool_use or 0 - # per_prompt billing: clamp to 1 (flat fee per grounded API call) billing_mode: Final = model_info.get("web_search_billing_unit") or "per_prompt" - if number_of_web_search_requests > 0 and billing_mode == "per_prompt": - number_of_web_search_requests = 1 + billable_requests: Final = ( + 1 if (number_of_web_search_requests > 0 and billing_mode == "per_prompt") else number_of_web_search_requests + ) - return _cost * number_of_web_search_requests + return _cost * billable_requests + + +GOOGLE_MAPS_GROUNDING_DEFAULT_COST_PER_QUERY: Final = 14e-3 +GOOGLE_MAPS_GROUNDING_DEFAULT_COST_PER_PROMPT: Final = 25e-3 + + +def google_maps_grounding_requests(usage: "Usage | None") -> int | None: + from litellm.types.utils import PromptTokensDetailsWrapper + + details: Final = usage.prompt_tokens_details if usage is not None else None + if not isinstance(details, PromptTokensDetailsWrapper) or not hasattr(details, "google_maps_grounding_requests"): + return None + return details.google_maps_grounding_requests + + +def cost_per_google_maps_grounding_request(usage: "Usage", model_info: "ModelInfo") -> float: + """ + Calculates the cost of Grounding with Google Maps. + + Billing follows ``web_search_billing_unit`` in model_info the same way Google Search grounding + does: ``"per_query"`` (Gemini 3.x) multiplies the executed Maps queries, ``"per_prompt"`` + (default, Gemini 2.x) charges one flat fee per grounded prompt. + + The rate comes from ``google_maps_grounding_cost_per_query`` in ``model_info``, falling back + to Google's list price for that billing unit when the pricing JSON has no entry yet. + """ + requests: Final = google_maps_grounding_requests(usage) + if not requests or requests <= 0: + return 0.0 + billing_mode: Final = model_info.get("web_search_billing_unit") or "per_prompt" + default_cost: Final = ( + GOOGLE_MAPS_GROUNDING_DEFAULT_COST_PER_QUERY + if billing_mode == "per_query" + else GOOGLE_MAPS_GROUNDING_DEFAULT_COST_PER_PROMPT + ) + configured_cost: Final = model_info.get("google_maps_grounding_cost_per_query") + cost: Final = default_cost if configured_cost is None else configured_cost + billed_requests: Final = requests if billing_mode == "per_query" else 1 + return cost * billed_requests diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py index dee83407cb5..2c62e04c5a3 100644 --- a/litellm/llms/gemini/files/transformation.py +++ b/litellm/llms/gemini/files/transformation.py @@ -5,11 +5,13 @@ For vertex ai, check out the vertex_ai/files/handler.py file. """ import time -from typing import Any, Final, Literal +from collections.abc import Mapping +from typing import Final, Literal, TypedDict from urllib.parse import urlparse import httpx from openai.types.file_deleted import FileDeleted +from typing_extensions import ReadOnly, Required from litellm._logging import verbose_logger from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data @@ -18,7 +20,6 @@ from litellm.llms.base_llm.files.transformation import ( BaseFilesConfig, LiteLLMLoggingObj, ) -from litellm.types.llms.gemini import GeminiCreateFilesResponseObject from litellm.types.llms.openai import ( AllMessageValues, CreateFileRequest, @@ -31,6 +32,25 @@ from litellm.types.utils import LlmProviders from ..common_utils import GeminiModelInfo +class _GeminiFileMetadata(TypedDict, total=False): + name: ReadOnly[str] + uri: ReadOnly[Required[str]] + displayName: ReadOnly[Required[str]] + mimeType: ReadOnly[str] + sizeBytes: ReadOnly[Required[str]] + createTime: ReadOnly[Required[str]] + updateTime: ReadOnly[str] + expirationTime: ReadOnly[str] + sha256Hash: ReadOnly[str] + state: ReadOnly[str] + source: ReadOnly[str] + error: ReadOnly[Mapping[str, object]] + + +class _GeminiCreateFileResponse(TypedDict): + file: ReadOnly[_GeminiFileMetadata] + + class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): def __init__(self): pass @@ -41,14 +61,14 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): def validate_environment( self, - headers: dict[Any, Any], + headers: dict[str, str], model: str, messages: list[AllMessageValues], - optional_params: dict[Any, Any], - litellm_params: dict[Any, Any], + optional_params: dict[str, object], + litellm_params: dict[str, object], api_key: str | None = None, api_base: str | None = None, - ) -> dict[Any, Any]: + ) -> dict[str, str]: """ Validate environment and add Gemini API key to headers. Google AI Studio uses x-goog-api-key header for authentication. @@ -164,9 +184,9 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): Transform Gemini's file upload response into OpenAI-style FileObject """ try: - response_json: Final = raw_response.json() + response_json: Final[_GeminiCreateFileResponse] = raw_response.json() - response_object: Final = GeminiCreateFilesResponseObject(**response_json.get("file", {})) + response_object: Final = response_json["file"] # Extract file information from Gemini response @@ -262,7 +282,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): """ try: verbose_logger.debug("Retrieve file response: %s", raw_response.text) - response_json: Final = raw_response.json() + response_json: Final[_GeminiFileMetadata] = raw_response.json() verbose_logger.debug("Response JSON: %s", response_json) # Map Gemini state to OpenAI status gemini_state: Final = response_json.get("state", "STATE_UNSPECIFIED") diff --git a/litellm/llms/gemini/image_edit/transformation.py b/litellm/llms/gemini/image_edit/transformation.py index 67b1f97a3a2..e6c22dc60b4 100644 --- a/litellm/llms/gemini/image_edit/transformation.py +++ b/litellm/llms/gemini/image_edit/transformation.py @@ -120,7 +120,7 @@ class GeminiImageEditConfig(BaseImageEditConfig): self, model: str, raw_response: httpx.Response, - logging_obj: Any, + logging_obj: LiteLLMLoggingObj, ) -> ImageResponse: model_response: Final = ImageResponse() try: diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py index 3943c0a7dae..d009fe4cd72 100644 --- a/litellm/llms/gemini/image_generation/transformation.py +++ b/litellm/llms/gemini/image_generation/transformation.py @@ -24,6 +24,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -171,7 +173,7 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/gemini/interactions/transformation.py b/litellm/llms/gemini/interactions/transformation.py index dcd2e4e3471..6d0f211ed7b 100644 --- a/litellm/llms/gemini/interactions/transformation.py +++ b/litellm/llms/gemini/interactions/transformation.py @@ -12,9 +12,10 @@ Schema versioning: litellm.use_legacy_interactions_schema = True. Remove flag after June 8, 2026. """ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol, TypeAlias import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -41,6 +42,53 @@ else: LiteLLMLoggingObj = Any +_JsonObject: TypeAlias = dict[str, object] + + +class _InteractionPayload(TypedDict, total=False): + """JSON body of an Interactions API interaction, keyed as ``InteractionsAPIResponse`` fields.""" + + id: ReadOnly[str | None] + object: ReadOnly[str | None] + model: ReadOnly[str | None] + agent: ReadOnly[str | None] + status: ReadOnly[str | None] + created: ReadOnly[str | None] + updated: ReadOnly[str | None] + outputs: ReadOnly[list[_JsonObject] | None] + steps: ReadOnly[list[_JsonObject] | None] + usage: ReadOnly[_JsonObject | None] + + +class _CancelPayload(TypedDict, total=False): + """JSON body of an Interactions API cancel response.""" + + id: ReadOnly[str | None] + status: ReadOnly[str | None] + + +class _InteractionPayloadSource(Protocol): + """An Interactions API HTTP response, read for the interaction body it decodes to.""" + + def json(self) -> _InteractionPayload: ... + + +class _CancelPayloadSource(Protocol): + """An Interactions API cancel HTTP response, read for the body it decodes to.""" + + def json(self) -> _CancelPayload: ... + + +def _interaction_body(response: _InteractionPayloadSource) -> _InteractionPayload: + """Decode the body of an Interactions API interaction response.""" + return response.json() + + +def _cancel_body(response: _CancelPayloadSource) -> _CancelPayload: + """Decode the body of an Interactions API cancel response.""" + return response.json() + + class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): """ Configuration for Google AI Studio Interactions API. @@ -143,7 +191,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): """ use_legacy: Final[bool] = litellm.use_legacy_interactions_schema - request_body: Final[dict[str, Any]] = {} + request_body: Final[dict[str, object]] = {} # Model or Agent (one required) if model: @@ -189,7 +237,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): and (not isinstance(response_format, dict) or "mime_type" not in response_format) ): # Wrap the legacy schema into the new polymorphic format. - new_rf: Final[dict[str, Any]] = { + new_rf: Final[dict[str, object]] = { "type": "text", "mime_type": response_mime_type, } @@ -215,7 +263,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): if image_config is not None: # Move image_config to response_format with type=image. - image_rf: Final[dict[str, Any]] = {"type": "image", **image_config} + image_rf: Final[_JsonObject] = {"type": "image", **image_config} existing_rf: Final = request_body.get("response_format") if existing_rf is None: request_body["response_format"] = image_rf @@ -239,7 +287,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): original_response=raw_response.text, additional_args={"complete_input_dict": {}}, ) - raw_json: Final = raw_response.json() + raw_json: Final = _interaction_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, @@ -290,7 +338,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> InteractionsAPIResponse: try: - raw_json: Final = raw_response.json() + raw_json: Final = _interaction_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, @@ -355,7 +403,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> CancelInteractionResult: try: - raw_json: Final = raw_response.json() + raw_json: Final = _cancel_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, diff --git a/litellm/llms/gemini/realtime/transformation.py b/litellm/llms/gemini/realtime/transformation.py index ea576750cf3..c92af7de145 100644 --- a/litellm/llms/gemini/realtime/transformation.py +++ b/litellm/llms/gemini/realtime/transformation.py @@ -4,8 +4,11 @@ This file contains the transformation logic for the Gemini realtime API. import json from collections import OrderedDict +from collections.abc import Mapping, Sequence from typing import Any, Final, cast +from typing_extensions import ReadOnly, Required, TypedDict + import litellm from litellm import verbose_logger from litellm._uuid import uuid @@ -52,6 +55,7 @@ from litellm.types.llms.vertex_ai import ( ) from litellm.types.realtime import ( ALL_DELTA_TYPES, + RealtimeInputAudioTranscriptionUsage, RealtimeModalityResponseTransformOutput, RealtimeResponseTransformInput, RealtimeResponseTypedDict, @@ -72,6 +76,57 @@ MAP_GEMINI_FIELD_TO_OPENAI_EVENT: Final[dict[str, OpenAIRealtimeEventTypes | Res _KNOWN_GEMINI_TOP_LEVEL_KEYS: Final[set] = {map_key.split(".", 1)[0] for map_key in MAP_GEMINI_FIELD_TO_OPENAI_EVENT} +OPENAI_STOCK_REALTIME_VOICES: Final[frozenset[str]] = frozenset( + {"alloy", "ash", "ballad", "cedar", "coral", "echo", "marin", "sage", "shimmer", "verse"} +) + + +def _gemini_live_speech_config(voice: object) -> Mapping[str, object] | None: + """Build the Gemini Live speechConfig for a client-requested voice. + + OpenAI stock voice names have no Gemini equivalent and Gemini Live closes + the session on an unknown voice, so they are dropped with a warning and + the model keeps its default voice. Every other name is forwarded verbatim. + """ + if isinstance(voice, str) and voice.lower() in OPENAI_STOCK_REALTIME_VOICES: + verbose_logger.warning( + "Gemini Realtime: voice %s is an OpenAI voice with no Gemini equivalent; " + "dropping it so the session keeps the model's default voice.", + voice, + ) + return None + return VertexGeminiConfig()._map_audio_params({"voice": voice}) + + +class _GeminiLiveSetupEnvelope(TypedDict, total=False): + setup: ReadOnly[BidiGenerateContentSetup] + + +class _OpenAIRealtimeClientEvent(TypedDict, total=False): + type: ReadOnly[str] + audio: ReadOnly[Required[str]] + session: ReadOnly[dict[str, object]] + item: ReadOnly[dict[str, object]] + + +def _parse_setup(session_configuration_request: str) -> BidiGenerateContentSetup: + envelope: Final[_GeminiLiveSetupEnvelope] = json.loads(session_configuration_request) + empty_setup: Final[BidiGenerateContentSetup] = {} + return envelope.get("setup", empty_setup) + + +# Google bills Live transcription at an estimated 25 audio tokens/sec of input and +# 175 text tokens/min of output (ai.google.dev/gemini-api/docs/pricing). +GEMINI_LIVE_TRANSCRIBE_AUDIO_TOKENS_PER_SECOND: Final = 25 +GEMINI_LIVE_TRANSCRIBE_OUTPUT_TEXT_TOKENS_PER_MINUTE: Final = 175 +PCM16_INPUT_AUDIO_BYTES_PER_SECOND: Final = 48000 + + +def _base64_decoded_byte_count(data: str) -> int: + padding: Final = 2 if data.endswith("==") else 1 if data.endswith("=") else 0 + return max(len(data) * 3 // 4 - padding, 0) + + class GeminiRealtimeConfig(BaseRealtimeConfig): _TOOL_CALL_ID_TO_NAME_MAX = 256 # LRU cap for call_id→name mapping @@ -81,6 +136,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): # Gemini Live sometimes emits usageMetadata in a standalone frame between # turns; buffer it here so the next response.done carries the token counts. self._pending_usage_metadata: dict | None = None + self._unbilled_input_audio_bytes: int = 0 def is_setup_message(self, msg_obj: dict) -> bool: return "setup" in msg_obj @@ -93,7 +149,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return True @staticmethod - def _usage_detail_alias(details: Any, defaults: dict[str, int]) -> dict[str, Any]: + def _usage_detail_alias(details: Mapping[str, int | None] | None, defaults: dict[str, int]) -> dict[str, int]: if not isinstance(details, dict): return dict(defaults) return { @@ -102,7 +158,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): } @staticmethod - def _add_pipecat_usage_detail_aliases(usage_dict: dict[str, Any]) -> dict[str, Any]: + def _add_pipecat_usage_detail_aliases(usage_dict: dict[str, Any]) -> dict[str, object]: usage_dict.setdefault( "input_token_details", GeminiRealtimeConfig._usage_detail_alias( @@ -185,8 +241,10 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): if not session_configuration_request: return False try: - setup: Final = json.loads(session_configuration_request).get("setup", {}) - automatic_detection: Final = setup.get("realtimeInputConfig", {}).get("automaticActivityDetection", {}) + setup: Final = _parse_setup(session_configuration_request) + automatic_detection: Final[object] = setup.get("realtimeInputConfig", {}).get( + "automaticActivityDetection", {} + ) return isinstance(automatic_detection, dict) and automatic_detection.get("disabled") is True except (json.JSONDecodeError, TypeError, AttributeError): return False @@ -282,12 +340,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): automaticActivityDetection=transformed_audio_activity_config ) elif key == "voice": - from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( - VertexGeminiConfig, - ) - - vertex_gemini_config = VertexGeminiConfig() - speech_config = vertex_gemini_config._map_audio_params({"voice": value}) + speech_config = _gemini_live_speech_config(value) if speech_config: optional_params["generationConfig"]["speechConfig"] = speech_config if len(optional_params["generationConfig"]) == 0: @@ -366,25 +419,28 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return bool(entry.get("gemini_native_audio") or entry.get("gemini_audio_only_live")) @staticmethod - def _is_native_audio_model(model: str) -> bool: - return bool(GeminiRealtimeConfig._model_cost_entry(model).get("gemini_native_audio")) + def _is_text_only_live_model(model: str) -> bool: + return GeminiRealtimeConfig._model_cost_entry(model).get("mode") == "audio_transcription" @staticmethod - def _coerce_response_modalities(model: str, modalities: list[Any]) -> list[str]: - """Map unsupported TEXT responseModalities to AUDIO for audio-only Live models.""" - normalized: Final = [ + def _default_response_modality(model: str) -> GeminiResponseModalities: + return "TEXT" if GeminiRealtimeConfig._is_text_only_live_model(model) else "AUDIO" + + @staticmethod + def _coerce_response_modalities(model: str, modalities: Sequence[object]) -> tuple[str, ...]: + """Swap responseModalities a Live model cannot produce: TEXT to AUDIO for + audio-only models, AUDIO to TEXT for text-only ones (e.g. transcribe-live).""" + normalized: Final = tuple( modality.upper() if isinstance(modality, str) else str(modality).upper() for modality in modalities - ] - if not GeminiRealtimeConfig._is_audio_only_live_model(model): - return normalized - if "TEXT" not in normalized: - return normalized - without_text: Final = [modality for modality in normalized if modality != "TEXT"] - return without_text if without_text else ["AUDIO"] + ) + if GeminiRealtimeConfig._is_audio_only_live_model(model) and "TEXT" in normalized: + return tuple(modality for modality in normalized if modality != "TEXT") or ("AUDIO",) + if GeminiRealtimeConfig._is_text_only_live_model(model) and "AUDIO" in normalized: + return tuple(modality for modality in normalized if modality != "AUDIO") or ("TEXT",) + return normalized @staticmethod def _finalize_gemini_live_setup(model: str, setup: dict[str, Any]) -> dict[str, Any]: - """Drop fields Gemini Live native-audio rejects on ``setup``.""" generation_config: Final = setup.get("generationConfig") if isinstance(generation_config, dict): modalities: Final = generation_config.get("responseModalities") @@ -392,13 +448,11 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): generation_config["responseModalities"] = GeminiRealtimeConfig._coerce_response_modalities( model, modalities ) - if GeminiRealtimeConfig._is_native_audio_model(model): - generation_config.pop("speechConfig", None) return setup def _handle_session_update( self, - json_message: dict, + json_message: _OpenAIRealtimeClientEvent, model: str, session_configuration_request: str | None, ) -> list[str]: @@ -412,7 +466,8 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): with a 1007, tearing the session down). To carry tools/instructions, send them on the first session.update before any conversation content. """ - session_payload = json_message.get("session") or {} + empty_session: Final[dict[str, object]] = {} + session_payload = json_message.get("session") or empty_session # Normalize GA-remapped fields (``output_modalities``, # nested ``audio.input.transcription``, # ``audio.input.turn_detection``) back to their flat beta keys so @@ -425,7 +480,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): if session_configuration_request is None: generation_config: Final = new_overrides.setdefault("generationConfig", {}) - generation_config.setdefault("responseModalities", ["AUDIO"]) + generation_config.setdefault("responseModalities", [GeminiRealtimeConfig._default_response_modality(model)]) new_overrides.setdefault("inputAudioTranscription", {}) new_overrides["model"] = f"models/{model}" verbose_logger.debug("Gemini Realtime: Sending initial setup with tools to backend") @@ -453,14 +508,15 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): verbose_logger.debug("Gemini Realtime: Ignoring session.update (setup already sent)") return [] - def _handle_conversation_item(self, json_message: dict) -> list[str]: + def _handle_conversation_item(self, json_message: _OpenAIRealtimeClientEvent) -> list[str]: """ Handle conversation.item.create for user text or function call output. Converts OpenAI format to Gemini's clientContent (for user text) or toolResponse (for function outputs). """ - item: Final = json_message.get("item", {}) + empty_item: Final[dict[str, object]] = {} + item: Final = json_message.get("item", empty_item) item_type: Final = item.get("type") if item_type == "function_call_output": @@ -491,7 +547,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): call_id, ) - function_response: Final[dict[str, Any]] = {"response": output_dict} + function_response: Final[dict[str, object]] = {"response": output_dict} if self._include_function_response_id() and call_id: function_response["id"] = call_id if function_name: @@ -526,7 +582,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): ) -> list[str]: realtime_input_dict: BidiGenerateContentRealtimeInput = {} try: - json_message: Final = json.loads(message) + json_message: Final[_OpenAIRealtimeClientEvent] = json.loads(message) except json.JSONDecodeError: if isinstance(message, bytes): message_str = message.decode("utf-8", errors="replace") @@ -547,9 +603,10 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return self._handle_conversation_item(json_message) if msg_type == "input_audio_buffer.append": - realtime_input_dict["audio"] = HttpxBlobType( - mimeType=self.get_audio_mime_type(), data=json_message["audio"] - ) + audio_b64: Final = json_message["audio"] + if isinstance(audio_b64, str): + self._unbilled_input_audio_bytes += _base64_decoded_byte_count(audio_b64) + realtime_input_dict["audio"] = HttpxBlobType(mimeType=self.get_audio_mime_type(), data=audio_b64) realtime_input_dict = cast( BidiGenerateContentRealtimeInput, @@ -576,9 +633,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_configuration_request: str | None = None, ) -> OpenAIRealtimeStreamSessionEvents: if session_configuration_request: - session_configuration_request_dict: BidiGenerateContentSetup = json.loads( - session_configuration_request - ).get("setup", {}) + session_configuration_request_dict: BidiGenerateContentSetup = _parse_setup(session_configuration_request) else: session_configuration_request_dict = {} @@ -629,7 +684,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_configuration_request_dict: BidiGenerateContentSetup = {} if session_configuration_request is not None: try: - session_configuration_request_dict = json.loads(session_configuration_request).get("setup", {}) + session_configuration_request_dict = _parse_setup(session_configuration_request) except json.JSONDecodeError: session_configuration_request_dict = {} generation_config: Final = session_configuration_request_dict.get("generationConfig", {}) @@ -897,9 +952,9 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): return events @staticmethod - def get_nested_value(obj: dict, path: str) -> Any: + def get_nested_value(obj: dict, path: str) -> object | None: keys: Final = path.split(".") - current = obj + current: object = obj for key in keys: if isinstance(current, dict) and key in current: current = current[key] @@ -977,9 +1032,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): current_response_id = f"resp_{uuid.uuid4()}" if session_configuration_request: - session_configuration_request_dict: BidiGenerateContentSetup = json.loads( - session_configuration_request - ).get("setup", {}) + session_configuration_request_dict: BidiGenerateContentSetup = _parse_setup(session_configuration_request) else: session_configuration_request_dict = {} @@ -1140,6 +1193,26 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): raise ValueError(f"Unknown openai event: {key}, value: {value}") return openai_event + def _consume_input_transcription_usage_estimate(self, model: str) -> RealtimeInputAudioTranscriptionUsage | None: + """Gemini Live sends no usageMetadata for transcribe sessions; estimate billing from streamed audio duration.""" + if self._unbilled_input_audio_bytes <= 0 or not self._is_text_only_live_model(model): + return None + audio_seconds: Final = self._unbilled_input_audio_bytes / PCM16_INPUT_AUDIO_BYTES_PER_SECOND + self._unbilled_input_audio_bytes = 0 + audio_tokens: Final = round(audio_seconds * GEMINI_LIVE_TRANSCRIBE_AUDIO_TOKENS_PER_SECOND) + output_tokens: Final = round(audio_seconds * GEMINI_LIVE_TRANSCRIBE_OUTPUT_TEXT_TOKENS_PER_MINUTE / 60) + usage: Final[RealtimeInputAudioTranscriptionUsage] = { + "type": "tokens", + "input_tokens": audio_tokens, + "output_tokens": output_tokens, + "total_tokens": audio_tokens + output_tokens, + "input_token_details": {"text_tokens": 0, "audio_tokens": audio_tokens}, + } + return usage + + def unbilled_usage_on_session_close(self, model: str) -> RealtimeInputAudioTranscriptionUsage | None: + return self._consume_input_transcription_usage_estimate(model) + def transform_realtime_response( self, message: str | bytes, @@ -1179,6 +1252,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): if isinstance(server_content, dict): input_tx: Final = server_content.get("inputTranscription") if isinstance(input_tx, dict) and input_tx.get("text"): + transcription_usage: Final = self._consume_input_transcription_usage_estimate(model) returned_message.append( cast( OpenAIRealtimeEvents, @@ -1188,6 +1262,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): "transcript": input_tx["text"], "item_id": f"item_{uuid.uuid4()}", "content_index": 0, + **({} if transcription_usage is None else {"usage": transcription_usage}), }, ) ) @@ -1224,6 +1299,12 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): ) ) + # Transcription-only models emit generationComplete with no prior + # modelTurn delta; there is no started OpenAI response to close, so + # drop it and let siblings (turnComplete, usageMetadata) process. + if current_delta_type is None and "modelTurn" not in server_content: + server_content.pop("generationComplete", None) + # Mark transcription-only serverContent as handled so the main loop # skips it; sibling keys like toolCall are still processed below. _model_content_keys: Final = { @@ -1275,7 +1356,7 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): session_setup: BidiGenerateContentSetup = {} if session_configuration_request is not None: try: - session_setup = json.loads(session_configuration_request).get("setup", {}) + session_setup = _parse_setup(session_configuration_request) except (json.JSONDecodeError, TypeError): session_setup = {} tool_call_generation_config = session_setup.get("generationConfig", {}) or {} @@ -1572,7 +1653,9 @@ class GeminiRealtimeConfig(BaseRealtimeConfig): ``` """ - response_modalities: Final[list[GeminiResponseModalities]] = ["AUDIO"] + response_modalities: Final[list[GeminiResponseModalities]] = [ + GeminiRealtimeConfig._default_response_modality(model) + ] output_audio_transcription: Final = False # if "audio" in model: ## UNCOMMENT THIS WHEN AUDIO IS SUPPORTED # output_audio_transcription = True diff --git a/litellm/llms/gemini/vector_stores/transformation.py b/litellm/llms/gemini/vector_stores/transformation.py index f6525a449b6..82586b1f638 100644 --- a/litellm/llms/gemini/vector_stores/transformation.py +++ b/litellm/llms/gemini/vector_stores/transformation.py @@ -33,6 +33,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -168,6 +169,7 @@ class GeminiVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: Mapping[str, object] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """ Transform search request to Gemini's generateContent format. diff --git a/litellm/llms/gemini/videos/transformation.py b/litellm/llms/gemini/videos/transformation.py index db75be3b6a2..ff4c675b02f 100644 --- a/litellm/llms/gemini/videos/transformation.py +++ b/litellm/llms/gemini/videos/transformation.py @@ -1,4 +1,5 @@ import base64 +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import httpx @@ -54,8 +55,13 @@ def _convert_image_to_gemini_format(image_file) -> dict[str, str]: return {"bytesBase64Encoded": base64_encoded, "mimeType": mime_type} +def _json_payload(raw_response: httpx.Response) -> object: + """Read an HTTP response body as an opaque JSON payload.""" + return raw_response.json() + + def _usage_video_resolution_from_parameters( - parameters: dict[str, Any], + parameters: Mapping[str, object], ) -> str | None: """Normalize Veo ``parameters.resolution`` for usage and cost tracking.""" res: Final = parameters.get("resolution") @@ -97,7 +103,7 @@ class GeminiVideoConfig(BaseVideoConfig): video_create_optional_params: VideoCreateOptionalRequestParams, model: str, drop_params: bool, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Map OpenAI-style parameters to Veo format. @@ -111,7 +117,7 @@ class GeminiVideoConfig(BaseVideoConfig): All other params are passed through as-is to support Gemini-specific parameters. """ - mapped_params: Final[dict[str, Any]] = {} + mapped_params: Final[dict[str, object]] = {} # Get supported OpenAI params (exclude "model" and "prompt" which are handled separately) supported_openai_params: Final = self.get_supported_openai_params(model) @@ -312,11 +318,11 @@ class GeminiVideoConfig(BaseVideoConfig): - status: "processing" - usage: includes duration_seconds and optional video_resolution for cost calculation """ - response_data: Final = raw_response.json() + response_data: Final = _json_payload(raw_response) # Parse response using Pydantic model for type safety try: - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) except Exception as e: raise ValueError(f"Failed to parse operation response: {e}") @@ -336,7 +342,7 @@ class GeminiVideoConfig(BaseVideoConfig): model=model, ) - usage_data: Final[dict[str, Any]] = {} + usage_data: Final[dict[str, float | str]] = {} if request_data: parameters: Final = request_data.get("parameters", {}) duration: Final = parameters.get("durationSeconds") or DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS @@ -367,7 +373,7 @@ class GeminiVideoConfig(BaseVideoConfig): """ operation_name: Final = extract_original_video_id(video_id) url: Final = f"{api_base.rstrip('/')}/v1beta/{operation_name}" - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} return url, params @@ -403,9 +409,9 @@ class GeminiVideoConfig(BaseVideoConfig): } } """ - response_data: Final = raw_response.json() + response_data: Final = _json_payload(raw_response) # Parse response using Pydantic model for type safety - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) operation_name: Final = operation_response.name is_done: Final = operation_response.done @@ -443,9 +449,9 @@ class GeminiVideoConfig(BaseVideoConfig): client: Final = litellm.module_level_client status_response: Final = client.get(url=status_url, headers=headers) status_response.raise_for_status() - response_data: Final = status_response.json() + response_data: Final = _json_payload(status_response) - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) if not operation_response.done: raise ValueError( @@ -458,7 +464,7 @@ class GeminiVideoConfig(BaseVideoConfig): generated_samples: Final = operation_response.response.generateVideoResponse.generatedSamples download_url: Final = generated_samples[0].video.uri - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} return download_url, params @@ -480,7 +486,7 @@ class GeminiVideoConfig(BaseVideoConfig): api_base: str, litellm_params: GenericLiteLLMParams, headers: dict, - extra_body: dict[str, Any] | None = None, + extra_body: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """ Video remix is not supported by Veo API. @@ -506,7 +512,7 @@ class GeminiVideoConfig(BaseVideoConfig): after: str | None = None, limit: int | None = None, order: str | None = None, - extra_query: dict[str, Any] | None = None, + extra_query: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """ Video list is not supported by Veo API. @@ -547,7 +553,7 @@ class GeminiVideoConfig(BaseVideoConfig): """Video delete is not supported.""" raise NotImplementedError("Video delete is not supported by Google Veo.") - def transform_video_create_character_request(self, name, video, api_base, litellm_params, headers): + def transform_video_create_character_request(self, name, video: object, api_base, litellm_params, headers): raise NotImplementedError("video create character is not supported for Gemini") def transform_video_create_character_response(self, raw_response, logging_obj): @@ -566,6 +572,7 @@ class GeminiVideoConfig(BaseVideoConfig): api_base, litellm_params, headers, + video_file=None, extra_body=None, prefetched_source_data=None, ): diff --git a/litellm/llms/gigachat/__init__.py b/litellm/llms/gigachat/__init__.py index 3ddbd7864d9..e7c2206ffaa 100644 --- a/litellm/llms/gigachat/__init__.py +++ b/litellm/llms/gigachat/__init__.py @@ -15,9 +15,11 @@ API Documentation: https://developers.sber.ru/docs/ru/gigachat/api/overview from .chat.transformation import GigaChatConfig, GigaChatError from .embedding.transformation import GigaChatEmbeddingConfig +from .passthrough.transformation import GigaChatPassthroughConfig -__all__ = [ +__all__ = ( "GigaChatConfig", "GigaChatEmbeddingConfig", "GigaChatError", -] + "GigaChatPassthroughConfig", +) diff --git a/litellm/llms/gigachat/authenticator.py b/litellm/llms/gigachat/authenticator.py index 9ef6fe7a93c..d6b217d5746 100644 --- a/litellm/llms/gigachat/authenticator.py +++ b/litellm/llms/gigachat/authenticator.py @@ -7,6 +7,7 @@ Based on official GigaChat SDK authentication flow. import time import uuid +from collections.abc import Mapping from typing import Final import httpx @@ -16,7 +17,7 @@ from litellm.caching.caching import InMemoryCache from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, - _get_httpx_client, + _get_httpx_client, # pyright: ignore[reportPrivateUsage] # house cached-client factory has no public alias get_async_httpx_client, ) from litellm.secret_managers.main import get_secret_str @@ -63,6 +64,7 @@ def get_access_token( credentials: str | None = None, scope: str | None = None, auth_url: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str: """ Get valid access token, using cache if available. @@ -78,71 +80,88 @@ def get_access_token( Raises: GigaChatAuthError: If authentication fails """ - credentials = credentials or _get_credentials() - if not credentials: + if not litellm_params: + litellm_params = {} # mutable-ok: empty dict default; rebind-ok: provide default + + access_token: Final = litellm_params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN") + if access_token: + return access_token + + effective_credentials: Final = credentials or _get_credentials() + if not effective_credentials: raise GigaChatAuthError( status_code=401, message="GigaChat credentials not provided. Set GIGACHAT_CREDENTIALS or GIGACHAT_API_KEY environment variable.", ) - scope = scope or _get_scope() - auth_url = auth_url or _get_auth_url() + effective_scope: Final = scope or litellm_params.get("gigachat_scope") or _get_scope() + effective_auth_url: Final = auth_url or litellm_params.get("gigachat_auth_url") or _get_auth_url() # Check cache - cache_key: Final = f"gigachat_token:{credentials[:16]}" + cache_key: Final = f"gigachat_token:{effective_credentials[:16]}" cached: Final = _token_cache.get_cache(cache_key) if cached: - token, expires_at = cached + _token, _expires_at = cached # Check if token is still valid (with buffer) - if time.time() * 1000 < expires_at - TOKEN_EXPIRY_BUFFER_MS: + if time.time() * 1000 < _expires_at - TOKEN_EXPIRY_BUFFER_MS: verbose_logger.debug("Using cached GigaChat access token") - return token + return _token # Request new token - token, expires_at = _request_token_sync(credentials, scope, auth_url) + new_token, new_expires_at = _request_token_sync(effective_credentials, effective_scope, effective_auth_url) # pyright: ignore[reportArgumentType] # credential keys may be broader than str - # Cache token - ttl_seconds: Final = max(0, (expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) - if ttl_seconds > 0: - _token_cache.set_cache(cache_key, (token, expires_at), ttl=ttl_seconds) + if new_expires_at: + # Cache token + ttl_seconds: Final = max(0, (new_expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) + if ttl_seconds > 0: + _token_cache.set_cache(cache_key, (new_token, new_expires_at), ttl=ttl_seconds) - return token + return new_token async def get_access_token_async( credentials: str | None = None, scope: str | None = None, auth_url: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str: """Async version of get_access_token.""" - credentials = credentials or _get_credentials() - if not credentials: + if not litellm_params: + litellm_params = {} # mutable-ok: empty dict default; rebind-ok: provide default + + access_token: Final = litellm_params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN") + if access_token: + return access_token + + effective_credentials: Final = credentials or _get_credentials() + if not effective_credentials: raise GigaChatAuthError( status_code=401, message="GigaChat credentials not provided. Set GIGACHAT_CREDENTIALS or GIGACHAT_API_KEY environment variable.", ) - scope = scope or _get_scope() - auth_url = auth_url or _get_auth_url() + effective_scope: Final = scope or litellm_params.get("gigachat_scope") or _get_scope() + effective_auth_url: Final = auth_url or litellm_params.get("gigachat_auth_url") or _get_auth_url() # Check cache - cache_key: Final = f"gigachat_token:{credentials[:16]}" + cache_key: Final = f"gigachat_token:{effective_credentials[:16]}" cached: Final = _token_cache.get_cache(cache_key) if cached: - token, expires_at = cached - if time.time() * 1000 < expires_at - TOKEN_EXPIRY_BUFFER_MS: + _token, _expires_at = cached + if time.time() * 1000 < _expires_at - TOKEN_EXPIRY_BUFFER_MS: verbose_logger.debug("Using cached GigaChat access token") - return token + return _token # Request new token - token, expires_at = await _request_token_async(credentials, scope, auth_url) + new_token, new_expires_at = await _request_token_async(effective_credentials, effective_scope, effective_auth_url) # pyright: ignore[reportArgumentType] # credential keys may be broader than str - # Cache token - ttl_seconds: Final = max(0, (expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) - if ttl_seconds > 0: - _token_cache.set_cache(cache_key, (token, expires_at), ttl=ttl_seconds) + if new_expires_at: + # Cache token + ttl_seconds: Final = max(0, (new_expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000) + if ttl_seconds > 0: + _token_cache.set_cache(cache_key, (new_token, new_expires_at), ttl=ttl_seconds) - return token + return new_token def _request_token_sync( @@ -154,7 +173,7 @@ def _request_token_sync( Request new access token from GigaChat OAuth endpoint (sync). Returns: - Tuple of (access_token, expires_at_ms) + tuple of (access_token, expires_at_ms) """ headers: Final = { "Authorization": f"Basic {credentials}", @@ -169,7 +188,7 @@ def _request_token_sync( client: Final = _get_http_client() response: Final = client.post(auth_url, headers=headers, data=data, timeout=30) response.raise_for_status() - return _parse_token_response(response) + return _parse_token_response(response) # pyright: ignore[reportArgumentType] # httpx Response may be None at type level except httpx.HTTPStatusError as e: raise GigaChatAuthError( status_code=e.response.status_code, @@ -204,7 +223,7 @@ async def _request_token_async( ) response: Final = await client.post(auth_url, headers=headers, data=data, timeout=30) response.raise_for_status() - return _parse_token_response(response) + return _parse_token_response(response) # pyright: ignore[reportArgumentType] # httpx Response may be None at type level except httpx.HTTPStatusError as e: raise GigaChatAuthError( status_code=e.response.status_code, @@ -223,7 +242,7 @@ def _parse_token_response(response: httpx.Response) -> tuple[str, int]: # GigaChat returns either 'tok'/'exp' or 'access_token'/'expires_at' access_token: Final = data.get("tok") or data.get("access_token") - expires_at = data.get("exp") or data.get("expires_at") + expires_at_raw: Final = data.get("exp") or data.get("expires_at") if not access_token: raise GigaChatAuthError( @@ -232,8 +251,11 @@ def _parse_token_response(response: httpx.Response) -> tuple[str, int]: ) # expires_at is in milliseconds - if isinstance(expires_at, str): - expires_at = int(expires_at) + expires_at: int # rebind-ok: conditionally assigned from str or int + if isinstance(expires_at_raw, str): + expires_at = int(expires_at_raw) # rebind-ok: conditionally assigned from str or int + else: + expires_at = expires_at_raw # pyright: ignore[reportAssignmentType] # raw value is int or str; converted above; rebind-ok: conditionally assigned from str or int verbose_logger.debug("GigaChat access token obtained successfully") return access_token, expires_at diff --git a/litellm/llms/gigachat/chat/__init__.py b/litellm/llms/gigachat/chat/__init__.py index eb9492b90b3..0f9be19fedd 100644 --- a/litellm/llms/gigachat/chat/__init__.py +++ b/litellm/llms/gigachat/chat/__init__.py @@ -5,8 +5,8 @@ GigaChat Chat Module from .streaming import GigaChatModelResponseIterator from .transformation import GigaChatConfig, GigaChatError -__all__ = [ +__all__ = ( "GigaChatConfig", "GigaChatError", "GigaChatModelResponseIterator", -] +) diff --git a/litellm/llms/gigachat/chat/streaming.py b/litellm/llms/gigachat/chat/streaming.py index 219209773ea..2875b30232e 100644 --- a/litellm/llms/gigachat/chat/streaming.py +++ b/litellm/llms/gigachat/chat/streaming.py @@ -4,13 +4,15 @@ GigaChat Streaming Response Handler import json import uuid +from collections.abc import Mapping, Sequence from typing import Any, Final +from litellm.llms.gigachat.utils import convert_usage from litellm.types.llms.openai import ( ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, ) -from litellm.types.utils import GenericStreamingChunk +from litellm.types.utils import ChatCompletionUsageBlock, GenericStreamingChunk class GigaChatModelResponseIterator: @@ -26,14 +28,9 @@ class GigaChatModelResponseIterator: self.response_iterator = self.streaming_response self.json_mode = json_mode - def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: + def chunk_parser(self, chunk: Mapping[str, object]) -> GenericStreamingChunk: """Parse a single streaming chunk from GigaChat.""" - text = "" - tool_use: ChatCompletionToolCallChunk | None = None - is_finished = False - finish_reason: str | None = None - - choices: Final = chunk.get("choices", []) + choices: Sequence = chunk.get("choices") or () # mutable-ok: tuple literal as default if not choices: return GenericStreamingChunk( text="", @@ -45,40 +42,63 @@ class GigaChatModelResponseIterator: ) choice: Final = choices[0] - delta: Final = choice.get("delta", {}) - finish_reason = choice.get("finish_reason") + delta: Mapping[str, object] = choice.get("delta") or {} # mutable-ok: empty dict default for get + chunk_finish_reason: Final = choice.get("finish_reason") # Extract text content - text = delta.get("content", "") or "" + text: Final = delta.get("content", "") or "" + + usage_block: ChatCompletionUsageBlock | None = None # rebind-ok: conditionally assigned after stop detection + tool_use: ChatCompletionToolCallChunk | None = None # rebind-ok: conditionally assigned on function_call + finish_reason: str | None = chunk_finish_reason # Handle function_call in stream - if finish_reason == "function_call" and delta.get("function_call"): - func_call: Final = delta["function_call"] - args = func_call.get("arguments", {}) - - if isinstance(args, dict): - args = json.dumps(args, ensure_ascii=False) + raw_function_call: Final = delta.get("function_call") + if chunk_finish_reason == "function_call" and isinstance(raw_function_call, Mapping) and raw_function_call: + func_call: Final[Mapping[str, object]] = raw_function_call + args_raw: Final[object] = func_call.get("arguments") or {} + args_str: str # rebind-ok: conditionally assigned from dict or str + if isinstance(args_raw, dict): + args_str = json.dumps(args_raw, ensure_ascii=False) # rebind-ok: build from dict + else: + args_str = str(args_raw) + name_raw: Final = func_call.get("name") tool_use = ChatCompletionToolCallChunk( id=f"call_{uuid.uuid4().hex[:24]}", type="function", function=ChatCompletionToolCallFunctionChunk( - name=func_call.get("name", ""), - arguments=args, + name=name_raw if isinstance(name_raw, str) else "", + arguments=args_str, ), index=0, ) finish_reason = "tool_calls" - if finish_reason is not None: - is_finished = True + usage_data: Final = chunk.get("usage") or {} # mutable-ok: empty dict default + if usage_data and isinstance(usage_data, dict): + validated_usage: Final = {k: int(v) for k, v in usage_data.items()} + usage = convert_usage(validated_usage) + _prompt_details: dict | None = ( + usage.prompt_tokens_details.model_dump() if usage.prompt_tokens_details else None + ) # rebind-ok: conditional + _completion_details: dict | None = ( + usage.completion_tokens_details.model_dump() if usage.completion_tokens_details else None + ) # rebind-ok: conditional + usage_block = ChatCompletionUsageBlock( # pyright: ignore[reportCallIssue] # TypedDict kwarg constructor + prompt_tokens=usage.prompt_tokens, + completion_tokens=usage.completion_tokens, + total_tokens=usage.total_tokens, + prompt_tokens_details=_prompt_details, + completion_tokens_details=_completion_details, + ) return GenericStreamingChunk( - text=text, + text=str(text), tool_use=tool_use, - is_finished=is_finished, + is_finished=chunk_finish_reason is not None, finish_reason=finish_reason or "", - usage=None, + usage=usage_block, index=choice.get("index", 0), ) diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py index 6d75c311084..8f23c5175ec 100644 --- a/litellm/llms/gigachat/chat/transformation.py +++ b/litellm/llms/gigachat/chat/transformation.py @@ -4,33 +4,35 @@ GigaChat Chat Transformation Transforms OpenAI-format requests to GigaChat format and back. """ +from __future__ import annotations + import json import time import uuid -from collections.abc import AsyncIterator, Iterator +from collections.abc import AsyncIterator, Iterator, Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx from litellm._logging import verbose_logger from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException +from litellm.llms.gigachat.utils import convert_usage, get_api_base from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import Choices, Message, ModelResponse, Usage +from litellm.types.utils import Choices, Message, ModelResponse from ..authenticator import get_access_token from ..file_handler import upload_file_sync if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj else: LiteLLMLoggingObj = Any -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - def is_valid_json(value: str) -> bool: """Checks whether the value passed is a valid serialized JSON string""" @@ -88,30 +90,30 @@ class GigaChatConfig(BaseConfig): api_base: str | None, api_key: str | None, model: str, - optional_params: dict, - litellm_params: dict, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], stream: bool | None = None, ) -> str: """Get complete API URL for chat completions.""" - base: Final = api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL + base: Final = get_api_base(api_base) return f"{base}/chat/completions" def validate_environment( self, - headers: dict, + headers: dict, # mutable-ok: mutates in place per GigaChat OAuth setup model: str, - messages: list[AllMessageValues], - optional_params: dict, - litellm_params: dict, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], api_key: str | None = None, api_base: str | None = None, - ) -> dict: + ) -> dict: # mutable-ok: base class contract returns dict for httpx """ Set up headers with OAuth token. """ # Get access token credentials: Final = api_key or get_secret_str("GIGACHAT_CREDENTIALS") or get_secret_str("GIGACHAT_API_KEY") - access_token: Final = get_access_token(credentials=credentials) + access_token: Final = get_access_token(credentials=credentials, litellm_params=litellm_params) # Store credentials for image uploads self._current_credentials = credentials @@ -123,9 +125,9 @@ class GigaChatConfig(BaseConfig): return headers - def get_supported_openai_params(self, model: str) -> list[str]: + def get_supported_openai_params(self, model: str) -> list[str]: # mutable-ok: base class contract returns list """Return list of supported OpenAI parameters.""" - return [ + return [ # mutable-ok: base class contract returns list "stream", "temperature", "top_p", @@ -141,11 +143,11 @@ class GigaChatConfig(BaseConfig): def map_openai_params( self, - non_default_params: dict, - optional_params: dict, + non_default_params: Mapping[str, object], + optional_params: dict, # mutable-ok: mutated in place per GigaChat mapping model: str, drop_params: bool, - ) -> dict: + ) -> dict: # mutable-ok: base class contract returns dict """Map OpenAI parameters to GigaChat parameters.""" for param, value in non_default_params.items(): if param == "stream": @@ -165,42 +167,50 @@ class GigaChatConfig(BaseConfig): pass elif param == "tools": # Convert tools to functions format - optional_params["functions"] = self._convert_tools_to_functions(value) + if isinstance(value, Sequence): + optional_params["functions"] = self._convert_tools_to_functions(value) elif param == "tool_choice": # Map OpenAI tool_choice to GigaChat function_call - mapped_choice = self._map_tool_choice(value) - if mapped_choice is not None: - optional_params["function_call"] = mapped_choice + if isinstance(value, (str, Mapping)): + mapped_choice = self._map_tool_choice(value) + if mapped_choice is not None: + optional_params["function_call"] = mapped_choice elif param == "functions": optional_params["functions"] = value elif param == "function_call": optional_params["function_call"] = value elif param == "response_format": # Handle structured output via function calling - if value.get("type") == "json_schema": + if isinstance(value, Mapping) and value.get("type") == "json_schema": json_schema = value.get("json_schema", {}) schema_name = json_schema.get("name", "structured_output") schema = json_schema.get("schema", {}) - function_def = { + function_def = { # mutable-ok: request payload for httpx "name": schema_name, "description": f"Output structured response: {schema_name}", "parameters": schema, } - if "functions" not in optional_params: - optional_params["functions"] = [] - optional_params["functions"].append(function_def) - optional_params["function_call"] = {"name": schema_name} + existing_functions = optional_params.get("functions") + optional_params["functions"] = [ + *( + existing_functions + if isinstance(existing_functions, Sequence) and not isinstance(existing_functions, str) + else () + ), + function_def, + ] + optional_params["function_call"] = {"name": schema_name} # mutable-ok: request payload optional_params["_structured_output"] = True return optional_params - def _convert_tools_to_functions(self, tools: list[dict]) -> list[dict]: + def _convert_tools_to_functions(self, tools: Sequence) -> Sequence[dict]: """Convert OpenAI tools format to GigaChat functions format.""" - functions: Final = [] + functions: Final[list[dict]] = [] # mutable-ok: accumulator for building functions list for tool in tools: - if tool.get("type") == "function": + if isinstance(tool, dict) and tool.get("type") == "function": func = tool.get("function", {}) functions.append( { @@ -211,7 +221,7 @@ class GigaChatConfig(BaseConfig): ) return functions - def _map_tool_choice(self, tool_choice: str | dict) -> str | dict | None: + def _map_tool_choice(self, tool_choice: str | Mapping[str, object]) -> str | Mapping[str, object] | None: """ Map OpenAI tool_choice to GigaChat function_call format. @@ -244,8 +254,9 @@ class GigaChatConfig(BaseConfig): # OpenAI format: {"type": "function", "function": {"name": "func_name"}} # GigaChat format: {"name": "func_name"} if tool_choice.get("type") == "function": - func_name: Final = tool_choice.get("function", {}).get("name") - if func_name: + function_spec: Final = tool_choice.get("function") + func_name: Final = function_spec.get("name") if isinstance(function_spec, Mapping) else None + if isinstance(func_name, str) and func_name: return {"name": func_name} # Default to None (don't set function_call) @@ -271,20 +282,51 @@ class GigaChatConfig(BaseConfig): verbose_logger.error("Failed to upload image: %s", e) return None + def _transform_list_content(self, content: Sequence) -> tuple[str, Sequence[str]]: + """ + Extract text and image attachments from a multimodal message content list. + + Args: + content: List of content parts (OpenAI multimodal format) + + Returns: + Tuple of (combined text, list of attachment file ids) + """ + texts: Final[list[str]] = [] # mutable-ok: accumulator + attachments: Final[list[str]] = [] # mutable-ok: accumulator + for part in content: + if isinstance(part, dict): + if part.get("type") == "text": + texts.append(part.get("text", "")) + elif part.get("type") == "image_url": + # Extract image URL and upload to GigaChat + image_url: object = part.get("image_url", {}) + upload_url: str + if isinstance(image_url, str): + upload_url = image_url + else: + upload_url = str(image_url.get("url", "")) if isinstance(image_url, dict) else "" + if upload_url: + file_id = self._upload_image(upload_url) + if file_id: + attachments.append(file_id) + text: Final = "\n".join(texts) if texts else "" + return text, attachments + def transform_request( self, model: str, - messages: list[AllMessageValues], - optional_params: dict, - litellm_params: dict, - headers: dict, - ) -> dict: + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + headers: Mapping[str, object], + ) -> dict: # mutable-ok: request payload sent to httpx """Transform OpenAI request to GigaChat format.""" # Transform messages giga_messages: Final = self._transform_messages(messages) # Build request - request_data: Final = { + request_data: Final[dict[str, object]] = { "model": model.replace("gigachat/", ""), "messages": giga_messages, } @@ -309,9 +351,9 @@ class GigaChatConfig(BaseConfig): return request_data - def _transform_messages(self, messages: list[AllMessageValues]) -> list[dict]: + def _transform_messages(self, messages: Sequence[AllMessageValues]) -> Sequence[dict]: """Transform OpenAI messages to GigaChat format.""" - transformed: Final = [] + transformed: Final[list[dict]] = [] # mutable-ok: accumulator for building transformed messages for i, msg in enumerate(messages): message = dict(msg) @@ -339,24 +381,7 @@ class GigaChatConfig(BaseConfig): # Handle list content (multimodal) - extract text and images content = message.get("content") if isinstance(content, list): - texts = [] - attachments = [] - for part in content: - if isinstance(part, dict): - if part.get("type") == "text": - texts.append(part.get("text", "")) - elif part.get("type") == "image_url": - # Extract image URL and upload to GigaChat - image_url = part.get("image_url", {}) - if isinstance(image_url, str): - url = image_url - else: - url = image_url.get("url", "") - if url: - file_id = self._upload_image(url) - if file_id: - attachments.append(file_id) - message["content"] = "\n".join(texts) if texts else "" + message["content"], attachments = self._transform_list_content(content) if attachments: message["attachments"] = attachments @@ -391,7 +416,7 @@ class GigaChatConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: tiktoken.Encoding | None, api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -406,7 +431,7 @@ class GigaChatConfig(BaseConfig): is_structured_output: Final = optional_params.get("_structured_output", False) - choices: Final = [] + choices: Final[list[Choices]] = [] # mutable-ok: accumulator for building response choices for choice in response_json.get("choices", []): message_data = choice.get("message", {}) finish_reason = choice.get("finish_reason", "stop") @@ -460,11 +485,7 @@ class GigaChatConfig(BaseConfig): # Build usage usage_data: Final = response_json.get("usage", {}) - usage: Final = Usage( - prompt_tokens=usage_data.get("prompt_tokens", 0), - completion_tokens=usage_data.get("completion_tokens", 0), - total_tokens=usage_data.get("total_tokens", 0), - ) + usage: Final = convert_usage(usage_data) model_response.id = response_json.get("id", f"chatcmpl-{uuid.uuid4().hex[:12]}") model_response.created = response_json.get("created", int(time.time())) diff --git a/litellm/llms/gigachat/embedding/transformation.py b/litellm/llms/gigachat/embedding/transformation.py index bb495cea423..2ec8324e33c 100644 --- a/litellm/llms/gigachat/embedding/transformation.py +++ b/litellm/llms/gigachat/embedding/transformation.py @@ -5,6 +5,8 @@ Transforms OpenAI /v1/embeddings format to GigaChat format. API Documentation: https://developers.sber.ru/docs/ru/gigachat/api/reference/rest/post-embeddings """ +from __future__ import annotations + import types from typing import Final @@ -14,14 +16,12 @@ from litellm import LlmProviders from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.llms.gigachat.utils import get_api_base from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues from litellm.types.utils import EmbeddingResponse from ..authenticator import get_access_token -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - class GigaChatEmbeddingError(BaseLLMException): """GigaChat Embedding API error.""" @@ -78,9 +78,9 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): Returns provider info for GigaChat. Returns: - Tuple of (custom_llm_provider, api_base, dynamic_api_key) + tuple of (custom_llm_provider, api_base, dynamic_api_key) """ - api_base = api_base or GIGACHAT_BASE_URL + api_base = get_api_base(api_base) return LlmProviders.GIGACHAT.value, api_base, api_key def get_complete_url( @@ -93,7 +93,7 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): stream: bool | None = None, ) -> str: """Get the complete URL for embeddings endpoint.""" - base: Final = api_base or GIGACHAT_BASE_URL + base: Final = get_api_base(api_base) return f"{base}/embeddings" def transform_embedding_request( @@ -114,14 +114,12 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): """ # Normalize input to list if isinstance(input, str): - input_list: list = [input] - elif isinstance(input, list): - input_list = input + input_list: list = [input] # rebind-ok: locally scoped conversion else: - input_list = [input] + input_list = input # Remove gigachat/ prefix from model if present - model = model.removeprefix("gigachat/") + model = model.removeprefix("gigachat/") # rebind-ok: parameter reassignment for normalization return { "model": model, @@ -191,7 +189,7 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig): Set up headers with OAuth token for GigaChat. """ # Get access token via OAuth - access_token: Final = get_access_token(api_key) + access_token: Final = get_access_token(credentials=api_key, litellm_params=litellm_params) default_headers: Final = { "Content-Type": "application/json", diff --git a/litellm/llms/gigachat/file_handler.py b/litellm/llms/gigachat/file_handler.py index 4cbde551fa2..163e944f124 100644 --- a/litellm/llms/gigachat/file_handler.py +++ b/litellm/llms/gigachat/file_handler.py @@ -9,6 +9,7 @@ import base64 import hashlib import re import uuid +from collections.abc import Mapping from typing import Final from litellm._logging import verbose_logger @@ -16,13 +17,11 @@ from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, get_async_httpx_client, ) +from litellm.llms.gigachat.utils import get_api_base from litellm.types.utils import LlmProviders from .authenticator import get_access_token, get_access_token_async -# GigaChat API endpoint -GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" - # Simple in-memory cache for file IDs _file_cache: Final[dict[str, str]] = {} @@ -82,6 +81,7 @@ def upload_file_sync( image_url: str, credentials: str | None = None, api_base: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str | None: """ Upload file to GigaChat and return file_id (sync). @@ -114,10 +114,10 @@ def upload_file_sync( filename: Final = f"{uuid.uuid4()}.{ext}" # Get access token - access_token: Final = get_access_token(credentials) + access_token: Final = get_access_token(credentials=credentials, litellm_params=litellm_params) # Upload to GigaChat - base_url: Final = api_base or GIGACHAT_BASE_URL + base_url: Final = get_api_base(api_base) upload_url: Final = f"{base_url}/files" client: Final = _get_httpx_client(params={"ssl_verify": False}) @@ -147,6 +147,7 @@ async def upload_file_async( image_url: str, credentials: str | None = None, api_base: str | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> str | None: """ Upload file to GigaChat and return file_id (async). @@ -179,10 +180,10 @@ async def upload_file_async( filename: Final = f"{uuid.uuid4()}.{ext}" # Get access token - access_token: Final = await get_access_token_async(credentials) + access_token: Final = await get_access_token_async(credentials=credentials, litellm_params=litellm_params) # Upload to GigaChat - base_url: Final = api_base or GIGACHAT_BASE_URL + base_url: Final = get_api_base(api_base) upload_url: Final = f"{base_url}/files" client: Final = get_async_httpx_client( diff --git a/litellm/llms/gigachat/passthrough/__init__.py b/litellm/llms/gigachat/passthrough/__init__.py new file mode 100644 index 00000000000..a66a078dbeb --- /dev/null +++ b/litellm/llms/gigachat/passthrough/__init__.py @@ -0,0 +1,7 @@ +""" +GigaChat passthrough Module +""" + +from .transformation import GigaChatPassthroughConfig + +__all__ = ("GigaChatPassthroughConfig",) diff --git a/litellm/llms/gigachat/passthrough/transformation.py b/litellm/llms/gigachat/passthrough/transformation.py new file mode 100644 index 00000000000..a0edc6f5682 --- /dev/null +++ b/litellm/llms/gigachat/passthrough/transformation.py @@ -0,0 +1,213 @@ +from __future__ import annotations + +import json +from collections.abc import Mapping, Sequence +from typing import TYPE_CHECKING, Final + +import httpx + +from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig +from litellm.llms.gigachat.authenticator import get_access_token +from litellm.llms.gigachat.chat.streaming import GigaChatModelResponseIterator +from litellm.llms.gigachat.utils import GIGACHAT_BASE_URL +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import EmbeddingResponse + +if TYPE_CHECKING: + from httpx import URL, Response + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.types.utils import CostResponseTypes + + +class GigaChatPassthroughConfig(BasePassthroughConfig): + def is_streaming_request(self, endpoint: str, request_data: Mapping[str, object]) -> bool: + return request_data.get("stream", False) + + def get_complete_url( + self, + api_base: str | None, + api_key: str | None, + model: str, + endpoint: str, + request_query_params: Mapping[str, object] | None, + litellm_params: Mapping[str, object], + ) -> tuple[URL, str]: + """Get complete API URL for chat completions.""" + base_target_url: Final = self.get_api_base(api_base) + + if base_target_url is None: + raise Exception("GigaChat api base not found") + + complete_url: Final = f"{base_target_url}/{endpoint.lstrip('/')}" + + return ( + httpx.URL(complete_url), + base_target_url, + ) + + def validate_environment( + self, + headers: dict, # mutable-ok: mutates in place to set OAuth headers + model: str, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + api_key: str | None = None, + api_base: str | None = None, + ) -> dict: # mutable-ok: base class contract returns dict for httpx + """ + Set up headers with OAuth token. + """ + # Get access token + access_token: Final = get_access_token(credentials=api_key, litellm_params=litellm_params) + + headers["Authorization"] = f"Bearer {access_token}" # rebind-ok: mutating for OAuth setup + headers["Content-Type"] = "application/json" # rebind-ok: mutating for OAuth setup + headers["Accept"] = "application/json" # rebind-ok: mutating for OAuth setup + + return headers + + def logging_non_streaming_response( + self, + model: str, + custom_llm_provider: str, + httpx_response: Response, + request_data: Mapping[str, object], + logging_obj: LiteLLMLoggingObj, + endpoint: str, + ) -> CostResponseTypes | None: + from litellm import encoding + from litellm.types.utils import LlmProviders, ModelResponse + from litellm.utils import ProviderConfigManager + + # cost tracking only for completions and embeddings + if "completions" in endpoint: + provider_chat_config: Final = ProviderConfigManager.get_provider_chat_config( + provider=LlmProviders(custom_llm_provider), + model=model, + ) + + if provider_chat_config is None: + raise ValueError(f"No provider config found for model: {model}") + + raw_messages: Final = request_data.get("messages") + litellm_model_response: Final = provider_chat_config.transform_response( + model=model, + messages=list(raw_messages) + if isinstance(raw_messages, list) + else [], # mutable-ok: transform_response wants a list + raw_response=httpx_response, + model_response=ModelResponse(), + logging_obj=logging_obj, + optional_params={}, # mutable-ok: empty dict kwarg for transform_response + litellm_params={}, # mutable-ok: empty dict kwarg for transform_response + api_key="", + request_data=dict(request_data), # mutable-ok: transform_response wants a dict + encoding=encoding, + ) + + return litellm_model_response + + if "embeddings" in endpoint: + provider_embedding_config: Final = ProviderConfigManager.get_provider_embedding_config( + provider=LlmProviders(custom_llm_provider), + model=model, + ) + + if provider_embedding_config is None: + raise ValueError(f"No provider config found for model: {model}") + + litellm_embedding_response: Final[EmbeddingResponse] = ( + provider_embedding_config.transform_embedding_response( + model=model, + raw_response=httpx_response, + model_response=EmbeddingResponse(), + logging_obj=logging_obj, + optional_params={}, # mutable-ok: empty dict kwarg for transform_embedding_response + api_key="", + request_data=dict(request_data), # mutable-ok: transform_embedding_response wants a dict + litellm_params={}, # mutable-ok: empty dict kwarg for transform_embedding_response + ) + ) + + return litellm_embedding_response + + return None + + def handle_logging_collected_chunks( + self, + all_chunks: Sequence[str], + litellm_logging_obj: LiteLLMLoggingObj, + model: str, + custom_llm_provider: str, + endpoint: str, + ) -> CostResponseTypes | None: + """ + 1. Convert all_chunks to a ModelResponseStream + 2. combine model_response_stream to model_response + 3. Return the model_response + """ + + from litellm.litellm_core_utils.streaming_handler import ( + convert_generic_chunk_to_model_response_stream, + generic_chunk_has_all_required_fields, + ) + from litellm.main import stream_chunk_builder + from litellm.types.utils import ModelResponseStream + + all_translated_chunks: Final[list[object]] = [] # mutable-ok: accumulator + + for chunk in all_chunks: + chunk = chunk.strip() + if not chunk or chunk == "[DONE]": + continue + chunk = chunk.removeprefix("data: ") + try: + message = json.loads(chunk) + except json.JSONDecodeError: + continue + + gigachat_iterator = GigaChatModelResponseIterator( + streaming_response=None, + sync_stream=False, + ) + translated_chunk = gigachat_iterator.chunk_parser(chunk=message) + + if isinstance(translated_chunk, dict) and generic_chunk_has_all_required_fields( # pyright: ignore[reportUnnecessaryIsInstance] # runtime guard for patched chunk_parser + dict(translated_chunk) + ): + chunk_obj = convert_generic_chunk_to_model_response_stream( + translated_chunk # pyright: ignore[reportArgumentType] # validated TypedDict + ) + elif isinstance(translated_chunk, ModelResponseStream): + chunk_obj = translated_chunk + else: + continue + + all_translated_chunks.append(chunk_obj) + + if len(all_translated_chunks) > 0: + return stream_chunk_builder( + chunks=all_translated_chunks, + logging_obj=litellm_logging_obj, + ) + return None + + @staticmethod + def get_api_base(api_base: str | None = None) -> str | None: + return api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL + + @staticmethod + def get_api_key( + api_key: str | None = None, + ) -> str | None: + return api_key or get_secret_str("GIGACHAT_API_KEY") + + @staticmethod + def get_base_model(model: str) -> str | None: + return model + + def get_models(self, api_key: str | None = None, api_base: str | None = None) -> list[str]: + return list(super().get_models(api_key, api_base)) diff --git a/litellm/llms/gigachat/utils.py b/litellm/llms/gigachat/utils.py new file mode 100644 index 00000000000..cbb35cd1b57 --- /dev/null +++ b/litellm/llms/gigachat/utils.py @@ -0,0 +1,26 @@ +from collections.abc import Mapping +from typing import Final + +from litellm.secret_managers.main import get_secret_str +from litellm.types.utils import PromptTokensDetailsWrapper, Usage + +# GigaChat API endpoint +GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1" + + +def convert_usage(usage_data: Mapping[str, int]) -> Usage: + precached_prompt_tokens: Final = usage_data.get("precached_prompt_tokens", 0) + prompt_tokens_details: Final = ( + PromptTokensDetailsWrapper(cached_tokens=precached_prompt_tokens) if precached_prompt_tokens > 0 else None + ) + + return Usage( + prompt_tokens=usage_data.get("prompt_tokens", 0) + precached_prompt_tokens, + completion_tokens=usage_data.get("completion_tokens", 0), + prompt_tokens_details=prompt_tokens_details, + total_tokens=usage_data.get("total_tokens", 0) + precached_prompt_tokens, + ) + + +def get_api_base(api_base: str | None = None) -> str | None: + return api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL diff --git a/litellm/llms/github_copilot/chat/transformation.py b/litellm/llms/github_copilot/chat/transformation.py index 27a0028ce4a..8634b374f1b 100644 --- a/litellm/llms/github_copilot/chat/transformation.py +++ b/litellm/llms/github_copilot/chat/transformation.py @@ -1,6 +1,6 @@ import json import os -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx @@ -17,6 +17,9 @@ from ..common_utils import ( get_copilot_default_headers, ) +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class GithubCopilotConfig(OpenAIConfig): def __init__( @@ -272,7 +275,7 @@ class GithubCopilotConfig(OpenAIConfig): model: str, raw_response: httpx.Response, model_response: "ModelResponse", - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", request_data: dict, messages: list[AllMessageValues], optional_params: dict, diff --git a/litellm/llms/groq/chat/transformation.py b/litellm/llms/groq/chat/transformation.py index c5e6bc13153..41a2df17c6f 100644 --- a/litellm/llms/groq/chat/transformation.py +++ b/litellm/llms/groq/chat/transformation.py @@ -3,7 +3,7 @@ Translate from OpenAI's `/v1/chat/completions` to Groq's `/v1/chat/completions` """ from collections.abc import AsyncIterator, Coroutine, Iterator -from typing import Any, Final, Literal, cast, overload +from typing import TYPE_CHECKING, Any, Final, Literal, cast, overload import httpx from pydantic import BaseModel, TypeAdapter, ValidationError @@ -26,6 +26,9 @@ from litellm.types.utils import ModelResponse, ModelResponseStream, ServerToolUs from ...openai_like.chat.transformation import OpenAILikeChatConfig +if TYPE_CHECKING: + import tiktoken + GROQ_COMPOUND_MODELS: Final = frozenset({"compound", "compound-mini"}) @@ -283,7 +286,7 @@ class GroqChatConfig(OpenAILikeChatConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py index 46a2320b655..29dc485732f 100644 --- a/litellm/llms/hosted_vllm/chat/transformation.py +++ b/litellm/llms/hosted_vllm/chat/transformation.py @@ -164,12 +164,13 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): """ Support translating: - video files from file_id or file_data to video_url - - thinking_blocks on assistant messages are removed, and content lists - are converted to strings for vLLM compatibility + - thinking_blocks and reasoning_content on assistant messages are removed, + and content lists are converted to strings for vLLM compatibility """ for message in messages: if message["role"] == "assistant": message.pop("thinking_blocks", None) + message.pop("reasoning_content", None) existing_content = message.get("content") if isinstance(existing_content, list): text_parts = [] diff --git a/litellm/llms/hosted_vllm/embedding/README.md b/litellm/llms/hosted_vllm/embedding/README.md index 2c58e16fc23..50474aabdeb 100644 --- a/litellm/llms/hosted_vllm/embedding/README.md +++ b/litellm/llms/hosted_vllm/embedding/README.md @@ -4,13 +4,12 @@ VLLM is a superset of OpenAI's `embedding` endpoint. ## `encoding_format` -For OpenAI-compatible embedding calls (including `openai/...` with a custom `api_base` pointing at vLLM), LiteLLM resolves `encoding_format` when it is not set on the request: +For OpenAI-compatible embedding calls (including `openai/...` with a custom `api_base` pointing at vLLM), LiteLLM resolves `encoding_format` when it is not set on the request. `hosted_vllm/...` models use a separate handler that never adds the field on its own, so this resolution applies to the `openai/...`-style routes only: 1. Explicit value on the embedding call (`encoding_format=...`). 2. Model config (`litellm_params.encoding_format` on the proxy `model_list` entry). 3. Environment variable `LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT` (e.g. in `.env` or container env). -4. Default **`float`**. -That avoids forwarding `encoding_format=None` to the provider/SDK where some servers behave poorly. +If none of those is set, or the winning value is the literal string `none`, the field is omitted from the upstream request entirely (LiteLLM also bypasses the OpenAI SDK's own base64 default), so OpenAI-compatible servers that reject `encoding_format` keep working. -To pass provider-specific parameters, see [provider-specific params](https://docs.litellm.ai/docs/completion/provider_specific_params). \ No newline at end of file +To pass provider-specific parameters, see [provider-specific params](https://docs.litellm.ai/docs/completion/provider_specific_params). diff --git a/litellm/llms/hosted_vllm/rerank/transformation.py b/litellm/llms/hosted_vllm/rerank/transformation.py index 74c13b450f5..0e8fa294f5d 100644 --- a/litellm/llms/hosted_vllm/rerank/transformation.py +++ b/litellm/llms/hosted_vllm/rerank/transformation.py @@ -2,6 +2,7 @@ Transformation logic for Hosted VLLM rerank """ +from collections.abc import Mapping from typing import Any, Final import httpx @@ -107,6 +108,7 @@ class HostedVLLMRerankConfig(BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: if api_key is None: api_key = get_secret_str("HOSTED_VLLM_API_KEY") or "fake-api-key" diff --git a/litellm/llms/hosted_vllm/videos/__init__.py b/litellm/llms/hosted_vllm/videos/__init__.py new file mode 100644 index 00000000000..89aa5ef2e8b --- /dev/null +++ b/litellm/llms/hosted_vllm/videos/__init__.py @@ -0,0 +1,9 @@ +from litellm.llms.base_llm.videos.transformation import BaseVideoConfig + +from .transformation import HostedVLLMVideoConfig + +__all__ = ("HostedVLLMVideoConfig",) + + +def get_hosted_vllm_video_config(model: str | None) -> BaseVideoConfig: + return HostedVLLMVideoConfig() diff --git a/litellm/llms/hosted_vllm/videos/transformation.py b/litellm/llms/hosted_vllm/videos/transformation.py new file mode 100644 index 00000000000..96cbfc3cf70 --- /dev/null +++ b/litellm/llms/hosted_vllm/videos/transformation.py @@ -0,0 +1,206 @@ +"""Video generation for Hosted VLLM (vLLM-Omni OpenAI-compatible /v1/videos).""" + +import json +from collections.abc import Mapping +from io import BufferedReader +from types import MappingProxyType +from typing import Final +from urllib.parse import urlparse + +from httpx._types import FileTypes, RequestFiles + +from litellm.images.utils import ImageEditRequestUtils +from litellm.litellm_core_utils.url_utils import SSRFError, validate_url +from litellm.llms.openai.videos.transformation import OpenAIVideoConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.router import GenericLiteLLMParams +from litellm.types.videos.main import VideoCreateOptionalRequestParams + +_EXCLUDED_FORM_KEYS: Final = frozenset( + { + "model", + "prompt", + "extra_headers", + "extra_query", + "extra_body", + "timeout", + "custom_llm_provider", + "input_reference", + "characters", + } +) + +_VLLM_OMNI_VIDEO_PARAMS: Final = ( + "image_reference", + "video_reference", + "audio_reference", + "width", + "height", + "num_frames", + "fps", + "num_inference_steps", + "guidance_scale", + "guidance_scale_2", + "boundary_ratio", + "flow_shift", + "true_cfg_scale", + "seed", + "generate_sound", + "sound_duration", + "negative_prompt", + "enable_frame_interpolation", + "frame_interpolation_exp", + "frame_interpolation_scale", + "frame_interpolation_model_path", + "lora", + "extra_params", + "aspect_ratio", +) + +_REFERENCE_URL_KEYS: Final = MappingProxyType( + { + "image_reference": "image_url", + "video_reference": "video_url", + "audio_reference": "audio_url", + } +) + + +def _serialize_form_value(value: object) -> str: + if isinstance(value, str): + return value + if isinstance(value, bool): + return "true" if value else "false" + if isinstance(value, (Mapping, list, tuple)): + return json.dumps(value) + return str(value) + + +def _maybe_json(value: object) -> object: + if not isinstance(value, str): + return value + stripped: Final = value.strip() + if not stripped or stripped[0] not in "{[": + return value + return json.loads(stripped) + + +def _reject_unsafe_media_url(url: str) -> None: + scheme: Final = urlparse(url).scheme.lower() + if scheme in ("", "data"): + return + if scheme not in ("http", "https"): + raise SSRFError(f"URL scheme '{scheme}' is not allowed") + validate_url(url) + + +def _reject_unsafe_urls_in_item(url_key: str, item: object) -> None: + if not isinstance(item, Mapping): + return + url: Final = item.get(url_key) + if isinstance(url, str): + _reject_unsafe_media_url(url) + + +def _reject_unsafe_media_urls(field_name: str, value: object) -> None: + url_key: Final = _REFERENCE_URL_KEYS.get(field_name) + if url_key is None: + return + parsed: Final = _maybe_json(value) + if isinstance(parsed, list): + for item in parsed: + _reject_unsafe_urls_in_item(url_key, item) + return + if isinstance(parsed, Mapping): + _reject_unsafe_urls_in_item(url_key, parsed) + + +def _form_value(key: str, value: object) -> str: + _reject_unsafe_media_urls(key, value) + return _serialize_form_value(value) + + +def _input_reference_file(reference: object) -> tuple[str, FileTypes]: + content_type: Final = ImageEditRequestUtils.get_image_content_type(reference) + if isinstance(reference, BufferedReader): + return ("input_reference", (reference.name, reference, content_type)) + return ("input_reference", ("input_reference.png", reference, content_type)) + + +class HostedVLLMVideoConfig(OpenAIVideoConfig): + """ + vLLM-Omni videos API is OpenAI-compatible but requires multipart/form-data. + + https://docs.vllm.ai/projects/vllm-omni/en/latest/serving/videos_api/ + """ + + def get_supported_openai_params(self, model: str) -> list: # mutable-ok: BaseVideoConfig contract + return [ # mutable-ok: BaseVideoConfig returns list + *super().get_supported_openai_params(model), + *_VLLM_OMNI_VIDEO_PARAMS, + ] + + def map_openai_params( + self, + video_create_optional_params: VideoCreateOptionalRequestParams, + model: str, + drop_params: bool, + ) -> dict: # mutable-ok: BaseVideoConfig contract; extra_body merge mutates this dict + return { # mutable-ok: VideoGenerationRequestUtils.update/pop extra_body onto this mapping + key: value for key, value in video_create_optional_params.items() if value is not None + } + + def validate_environment( + self, + headers: dict, # mutable-ok: BaseVideoConfig contract + model: str, + api_key: str | None = None, + litellm_params: GenericLiteLLMParams | None = None, + ) -> dict: # mutable-ok: BaseVideoConfig contract + resolved_key: Final = ( + (litellm_params.api_key if litellm_params is not None else None) + or api_key + or get_secret_str("HOSTED_VLLM_API_KEY") + or "fake-api-key" + ) + return {**headers, "Authorization": f"Bearer {resolved_key}"} # mutable-ok: httpx headers are a dict + + def get_complete_url( + self, + model: str, + api_base: str | None, + litellm_params: dict, # mutable-ok: BaseVideoConfig contract + ) -> str: + resolved_api_base: Final = api_base or get_secret_str("HOSTED_VLLM_API_BASE") + if resolved_api_base is None: + raise ValueError( + "api_base not set for Hosted VLLM videos API. " + "Set via api_base parameter or HOSTED_VLLM_API_BASE environment variable" + ) + trimmed: Final = resolved_api_base.rstrip("/") + if trimmed.endswith("/v1"): + return f"{trimmed}/videos" + return f"{trimmed}/v1/videos" + + def transform_video_create_request( + self, + model: str, + prompt: str, + api_base: str, + video_create_optional_request_params: dict, # mutable-ok: BaseVideoConfig contract + litellm_params: GenericLiteLLMParams, + headers: dict, # mutable-ok: BaseVideoConfig contract + ) -> tuple[dict, RequestFiles, str]: # mutable-ok: BaseVideoConfig contract + data: Final = { # mutable-ok: BaseVideoConfig contract returns a data dict + "model": model, + "prompt": prompt, + **{ # mutable-ok: spread remaining Omni form fields into that data dict + key: _form_value(key, value) + for key, value in video_create_optional_request_params.items() + if key not in _EXCLUDED_FORM_KEYS and value is not None + }, + } + input_reference: Final = video_create_optional_request_params.get("input_reference") + if input_reference is None: + return data, (), api_base + return data, (_input_reference_file(input_reference),), api_base diff --git a/litellm/llms/huggingface/embedding/transformation.py b/litellm/llms/huggingface/embedding/transformation.py index d3db3530109..f6fe7f2fa10 100644 --- a/litellm/llms/huggingface/embedding/transformation.py +++ b/litellm/llms/huggingface/embedding/transformation.py @@ -1,8 +1,9 @@ import json import os import time +from collections.abc import Sequence from copy import deepcopy -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol import httpx @@ -24,6 +25,8 @@ from litellm.utils import token_counter from ..common_utils import HuggingFaceError, hf_task_list, hf_tasks, output_parser if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -31,6 +34,12 @@ else: LoggingClass = Any +class _TokenEncoding(Protocol): + """Tokenizer handle the caller passes in; only `encode` is used, to count completion tokens.""" + + def encode(self, text: str, /) -> Sequence[object]: ... + + tgi_models_cache = None conv_models_cache = None @@ -369,7 +378,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): model_response: ModelResponse, task: hf_tasks | None, optional_params: dict, - encoding: Any, + encoding: "_TokenEncoding | None", messages: list[AllMessageValues], model: str, ): @@ -439,9 +448,10 @@ class HuggingFaceEmbeddingConfig(BaseConfig): if output_text is not None and len(output_text) > 0: completion_tokens = 0 try: - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) ##[TODO] use the llama2 tokenizer here + if encoding is not None: + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) ##[TODO] use the llama2 tokenizer here except Exception: # this should remain non blocking we should not block a response returning if calculating usage fails pass @@ -469,7 +479,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/huggingface/rerank/transformation.py b/litellm/llms/huggingface/rerank/transformation.py index d56a76c933f..334c60ee848 100644 --- a/litellm/llms/huggingface/rerank/transformation.py +++ b/litellm/llms/huggingface/rerank/transformation.py @@ -1,4 +1,5 @@ import os +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import httpx @@ -123,6 +124,7 @@ class HuggingFaceRerankConfig(BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, api_base: str | None = None, ) -> dict: # Get API credentials diff --git a/litellm/llms/infinity/rerank/transformation.py b/litellm/llms/infinity/rerank/transformation.py index ebcbf1b5a07..e089b3fecbe 100644 --- a/litellm/llms/infinity/rerank/transformation.py +++ b/litellm/llms/infinity/rerank/transformation.py @@ -4,9 +4,11 @@ Transformation logic from Cohere's /v1/rerank format to Infinity's `/v1/rerank` Why separate file? Make it easy to see how transformation works """ +from collections.abc import Mapping, Sequence from typing import Final import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._uuid import uuid @@ -25,6 +27,31 @@ from litellm.types.rerank import ( from ..common_utils import InfinityError +class _InfinityRerankUsage(TypedDict, extra_items=ReadOnly[int]): + """The token counters Infinity reports in the ``usage`` block of a rerank response.""" + + +class _InfinityRerankResult(TypedDict): + """One scored document in an Infinity ``/v1/rerank`` response.""" + + index: ReadOnly[int] + relevance_score: ReadOnly[float] + document: ReadOnly[str] + + +class _InfinityRerankResponse(TypedDict): + """The JSON body returned by Infinity's ``/v1/rerank`` endpoint.""" + + id: ReadOnly[NotRequired[str]] + usage: ReadOnly[NotRequired[_InfinityRerankUsage]] + results: ReadOnly[Sequence[_InfinityRerankResult]] + + +def _parse_rerank_response(raw_response: httpx.Response) -> _InfinityRerankResponse: + """Read the untyped JSON body of an Infinity rerank response.""" + return raw_response.json() + + class InfinityRerankConfig(CohereRerankConfig): def get_complete_url( self, @@ -46,6 +73,7 @@ class InfinityRerankConfig(CohereRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: if api_key is None: api_key = get_secret_str("INFINITY_API_KEY") or get_secret_str("INFINITY_API_KEY") or litellm.infinity_key @@ -80,7 +108,7 @@ class InfinityRerankConfig(CohereRerankConfig): No transformation required, Infinity follows Cohere API response format """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _parse_rerank_response(raw_response) except Exception: raise InfinityError(message=raw_response.text, status_code=raw_response.status_code) diff --git a/litellm/llms/jina_ai/rerank/transformation.py b/litellm/llms/jina_ai/rerank/transformation.py index 25607443292..199599d6b9c 100644 --- a/litellm/llms/jina_ai/rerank/transformation.py +++ b/litellm/llms/jina_ai/rerank/transformation.py @@ -6,6 +6,7 @@ Why separate file? Make it easy to see how transformation works Docs - https://jina.ai/reranker """ +from collections.abc import Mapping from typing import Any, Final from httpx import URL, Response @@ -139,6 +140,7 @@ class JinaAIRerankConfig(BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: if api_key is None: raise ValueError("api_key is required. Set via `api_key` parameter or `JINA_API_KEY` environment variable.") diff --git a/litellm/llms/langflow/a2a.py b/litellm/llms/langflow/a2a.py index cae750d586e..060dc0a4d05 100644 --- a/litellm/llms/langflow/a2a.py +++ b/litellm/llms/langflow/a2a.py @@ -1,28 +1,9 @@ -import hashlib from typing import Any, Final - -def get_session_id_from_a2a_params(params: dict[str, Any]) -> str | None: - message: Final = params.get("message", {}) - if isinstance(message, dict): - return message.get("contextId") - return getattr(message, "contextId", None) - - -def scope_session_to_principal(session_id: str, principal: str | None) -> str: - """ - Bind a client-supplied A2A contextId to the authenticated principal. - - Without this, two distinct keys authorized for the same LangFlow agent could - set the same contextId and read/append to each other's LangFlow memory. The - principal is hashed (it is already a hashed token) so the raw value is never - sent to the LangFlow backend, while the original contextId is kept as a - suffix for operator-side correlation. - """ - if not principal: - return session_id - principal_prefix: Final = hashlib.sha256(principal.encode("utf-8")).hexdigest()[:16] - return f"{principal_prefix}-{session_id}" +from litellm.a2a_protocol.utils import ( + get_session_id_from_a2a_params, + scope_session_to_principal, +) def merge_a2a_session_into_litellm_params( diff --git a/litellm/llms/langflow/chat/transformation.py b/litellm/llms/langflow/chat/transformation.py index 6b837007f21..17ae7017cf6 100644 --- a/litellm/llms/langflow/chat/transformation.py +++ b/litellm/llms/langflow/chat/transformation.py @@ -14,6 +14,8 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import Choices, Message, ModelResponse, Usage if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.utils import CustomStreamWrapper @@ -223,7 +225,7 @@ class LangFlowConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/langgraph/chat/transformation.py b/litellm/llms/langgraph/chat/transformation.py index c72246114b8..84d79e6bd31 100644 --- a/litellm/llms/langgraph/chat/transformation.py +++ b/litellm/llms/langgraph/chat/transformation.py @@ -23,6 +23,8 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import Choices, Message, ModelResponse, Usage if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.utils import CustomStreamWrapper @@ -413,7 +415,7 @@ class LangGraphConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/lemonade/chat/transformation.py b/litellm/llms/lemonade/chat/transformation.py index 4ea96df0ac4..553478aec16 100644 --- a/litellm/llms/lemonade/chat/transformation.py +++ b/litellm/llms/lemonade/chat/transformation.py @@ -2,7 +2,7 @@ Translate from OpenAI's `/v1/chat/completions` to Lemonade's `/v1/chat/completions` """ -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final from urllib.parse import quote import httpx @@ -18,6 +18,9 @@ from litellm.types.utils import ModelResponse from ...openai_like.chat.transformation import OpenAILikeChatConfig +if TYPE_CHECKING: + import tiktoken + class LemonadeChatConfig(OpenAILikeChatConfig): _DEFAULT_API_KEY = "lemonade" @@ -228,7 +231,7 @@ class LemonadeChatConfig(OpenAILikeChatConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/litellm_proxy/skills/code_execution.py b/litellm/llms/litellm_proxy/skills/code_execution.py index d435994ce20..fbc287589b3 100644 --- a/litellm/llms/litellm_proxy/skills/code_execution.py +++ b/litellm/llms/litellm_proxy/skills/code_execution.py @@ -13,12 +13,111 @@ Generated files are returned directly in the response - no separate storage need import base64 import json +from collections.abc import Mapping, Sequence from enum import Enum -from typing import Any, Final +from typing import Any, Final, Protocol, TypedDict + +from typing_extensions import ReadOnly from litellm._logging import verbose_logger +class _ToolParameterSchema(TypedDict, total=False): + type: ReadOnly[str] + description: ReadOnly[str] + + +class _ToolArgumentSchema(TypedDict, total=False): + type: ReadOnly[str] + properties: ReadOnly[Mapping[str, _ToolParameterSchema]] + required: ReadOnly[Sequence[str]] + + +class _OpenAIToolFunction(TypedDict, total=False): + name: ReadOnly[str] + description: ReadOnly[str] + parameters: ReadOnly[_ToolArgumentSchema] + + +class _OpenAIToolSpec(TypedDict, total=False): + type: ReadOnly[str] + function: ReadOnly[_OpenAIToolFunction] + + +class _AnthropicToolSpec(TypedDict, total=False): + name: ReadOnly[str] + description: ReadOnly[str] + input_schema: ReadOnly[_ToolArgumentSchema] + + +class _CodeExecutionArguments(TypedDict, total=False): + code: ReadOnly[str] + + +class _GeneratedFile(TypedDict, total=False): + name: ReadOnly[str] + mime_type: ReadOnly[str] + content_base64: ReadOnly[str] + size: ReadOnly[int] + + +class _SandboxGeneratedFile(TypedDict): + name: ReadOnly[str] + mime_type: ReadOnly[str] + content_base64: ReadOnly[str] + + +class _SandboxExecutionResult(TypedDict): + success: ReadOnly[bool] + output: ReadOnly[str] + error: ReadOnly[str] + files: ReadOnly[Sequence[_SandboxGeneratedFile]] + + +class _ExecutionResult(TypedDict, total=False): + iteration: ReadOnly[int] + success: ReadOnly[bool] + output: ReadOnly[str] + error: ReadOnly[str] + files: ReadOnly[Sequence[str]] + + +class _ToolCallFunction(Protocol): + name: str + arguments: str + + +class _ToolCall(Protocol): + id: str + function: _ToolCallFunction + + +class _AssistantMessage(Protocol): + content: str | None + tool_calls: Sequence[_ToolCall] | None + + +class _ResponseChoice(Protocol): + message: _AssistantMessage + finish_reason: str | None + + +class _CompletionResponse(Protocol): + choices: Sequence[_ResponseChoice] + + +class _CodeExecutionOutcome(TypedDict, total=False): + response: ReadOnly[_CompletionResponse | None] + files: ReadOnly[Sequence[_GeneratedFile]] + execution_results: ReadOnly[Sequence[_ExecutionResult]] + messages: ReadOnly[Sequence[dict[str, object]]] + max_iterations_reached: ReadOnly[bool] + + +def _parse_code_execution_arguments(serialized_arguments: str) -> _CodeExecutionArguments: + return json.loads(serialized_arguments) + + class LiteLLMInternalTools(str, Enum): """ Enum for internal LiteLLM tools that are injected into requests. @@ -30,7 +129,7 @@ class LiteLLMInternalTools(str, Enum): CODE_EXECUTION = "litellm_code_execution" -def get_litellm_code_execution_tool() -> dict[str, Any]: +def get_litellm_code_execution_tool() -> _OpenAIToolSpec: """ Returns the litellm_code_execution tool definition in OpenAI format. @@ -51,7 +150,7 @@ def get_litellm_code_execution_tool() -> dict[str, Any]: } -def get_litellm_code_execution_tool_anthropic() -> dict[str, Any]: +def get_litellm_code_execution_tool_anthropic() -> _AnthropicToolSpec: """ Returns the litellm_code_execution tool definition in Anthropic/messages API format. @@ -98,12 +197,12 @@ class CodeExecutionHandler: async def execute_with_code_execution( self, model: str, - messages: list[dict], - tools: list[dict], + messages: list[dict[str, object]], + tools: list[_OpenAIToolSpec], skill_files: dict[str, bytes], skill_id: str | None = None, **kwargs, - ) -> dict[str, Any]: + ) -> _CodeExecutionOutcome: """ Execute an LLM call with automatic code execution handling. @@ -134,8 +233,8 @@ class CodeExecutionHandler: ) current_messages: Final = list(messages) - generated_files: Final[list[dict[str, Any]]] = [] # Files returned directly - execution_results: Final[list[dict]] = [] + generated_files: Final[list[_GeneratedFile]] = [] # Files returned directly + execution_results: Final[list[_ExecutionResult]] = [] executor: Final = SkillsSandboxExecutor(timeout=self.sandbox_timeout) response: Any = None # Initialize to avoid possibly unbound error @@ -151,11 +250,12 @@ class CodeExecutionHandler: **kwargs, ) - assistant_message = response.choices[0].message - stop_reason = response.choices[0].finish_reason + choice: _ResponseChoice = response.choices[0] + assistant_message = choice.message + stop_reason = choice.finish_reason # Build assistant message for conversation history - assistant_msg_dict: dict[str, Any] = { + assistant_msg_dict: dict[str, object] = { "role": "assistant", "content": assistant_message.content, } @@ -190,25 +290,27 @@ class CodeExecutionHandler: if tool_name == LiteLLMInternalTools.CODE_EXECUTION.value: # Execute code in sandbox try: - args = json.loads(tool_call.function.arguments) + args = _parse_code_execution_arguments(tool_call.function.arguments) code = args.get("code", "") verbose_logger.debug("CodeExecutionHandler: Executing code (%s chars)", len(code)) - exec_result = executor.execute( + exec_result: _SandboxExecutionResult = executor.execute( code=code, skill_files=skill_files, ) verbose_logger.debug("CodeExecutionHandler: Execution result: %s", exec_result) + sandbox_files: Sequence[_SandboxGeneratedFile] = exec_result["files"] + execution_results.append( { "iteration": iteration, "success": exec_result["success"], "output": exec_result["output"], "error": exec_result["error"], - "files": [f["name"] for f in exec_result["files"]], + "files": [f["name"] for f in sandbox_files], } ) @@ -216,9 +318,9 @@ class CodeExecutionHandler: tool_result = exec_result["output"] or "" # Collect generated files (returned directly, no storage) - if exec_result["files"]: + if sandbox_files: tool_result += "\n\nGenerated files:" - for f in exec_result["files"]: + for f in sandbox_files: file_content = base64.b64decode(f["content_base64"]) # Add to generated files list (returned in response) generated_files.append( @@ -278,7 +380,7 @@ class CodeExecutionHandler: } -def has_code_execution_tool(tools: list[dict] | None) -> bool: +def has_code_execution_tool(tools: list[_OpenAIToolSpec] | None) -> bool: """Check if litellm_code_execution tool is in the tools list.""" if not tools: return False @@ -289,7 +391,7 @@ def has_code_execution_tool(tools: list[dict] | None) -> bool: return False -def add_code_execution_tool(tools: list[dict] | None) -> list[dict]: +def add_code_execution_tool(tools: list[_OpenAIToolSpec] | None) -> list[_OpenAIToolSpec]: """Add litellm_code_execution tool if not already present.""" tools = tools or [] if not has_code_execution_tool(tools): diff --git a/litellm/llms/litellm_proxy/skills/transformation.py b/litellm/llms/litellm_proxy/skills/transformation.py index 33c26801617..c972dc349c9 100644 --- a/litellm/llms/litellm_proxy/skills/transformation.py +++ b/litellm/llms/litellm_proxy/skills/transformation.py @@ -7,8 +7,10 @@ API requests to database operations via LiteLLMSkillsHandler. Pattern follows litellm/llms/litellm_proxy/responses/transformation.py """ -from collections.abc import Coroutine -from typing import TYPE_CHECKING, Any, Final, Optional +from collections.abc import Coroutine, Sequence +from typing import TYPE_CHECKING, Final, Optional + +from pydantic import JsonValue from litellm.types.llms.anthropic_skills import ( DeleteSkillResponse, @@ -19,7 +21,7 @@ from litellm.types.utils import LlmProviders if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj - from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy._types import LiteLLM_SkillsTable, UserAPIKeyAuth class LiteLLMSkillsTransformationHandler: @@ -40,18 +42,18 @@ class LiteLLMSkillsTransformationHandler: display_title: str | None = None, description: str | None = None, instructions: str | None = None, - files: list[Any] | None = None, + files: Sequence[object] | None = None, file_content: bytes | None = None, file_name: str | None = None, file_type: str | None = None, - metadata: dict[str, Any] | None = None, + metadata: dict[str, JsonValue] | None = None, user_id: str | None = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, _is_async: bool = False, logging_obj: Optional["LiteLLMLoggingObj"] = None, litellm_call_id: str | None = None, **kwargs, - ) -> Skill | Coroutine[Any, Any, Skill]: + ) -> Skill | Coroutine[object, object, Skill]: """ Create a skill in LiteLLM database. @@ -127,7 +129,7 @@ class LiteLLMSkillsTransformationHandler: file_content: bytes | None = None, file_name: str | None = None, file_type: str | None = None, - metadata: dict[str, Any] | None = None, + metadata: dict[str, JsonValue] | None = None, user_id: str | None = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, ) -> Skill: @@ -163,7 +165,7 @@ class LiteLLMSkillsTransformationHandler: litellm_call_id: str | None = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, **kwargs, - ) -> ListSkillsResponse | Coroutine[Any, Any, ListSkillsResponse]: + ) -> ListSkillsResponse | Coroutine[object, object, ListSkillsResponse]: """ List skills from LiteLLM database. @@ -235,7 +237,7 @@ class LiteLLMSkillsTransformationHandler: litellm_call_id: str | None = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, **kwargs, - ) -> Skill | Coroutine[Any, Any, Skill]: + ) -> Skill | Coroutine[object, object, Skill]: """ Get a skill from LiteLLM database. @@ -296,7 +298,7 @@ class LiteLLMSkillsTransformationHandler: litellm_call_id: str | None = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, **kwargs, - ) -> DeleteSkillResponse | Coroutine[Any, Any, DeleteSkillResponse]: + ) -> DeleteSkillResponse | Coroutine[object, object, DeleteSkillResponse]: """ Delete a skill from LiteLLM database. @@ -352,7 +354,7 @@ class LiteLLMSkillsTransformationHandler: type=result.get("type", "skill_deleted"), ) - def _db_skill_to_response(self, db_skill: Any) -> Skill: + def _db_skill_to_response(self, db_skill: "LiteLLM_SkillsTable") -> Skill: """ Convert a database skill record to Anthropic-compatible Skill response. @@ -362,21 +364,8 @@ class LiteLLMSkillsTransformationHandler: Returns: Skill object """ - created_at = "" - updated_at = "" - - if hasattr(db_skill, "created_at") and db_skill.created_at: - created_at = ( - db_skill.created_at.isoformat() - if hasattr(db_skill.created_at, "isoformat") - else str(db_skill.created_at) - ) - if hasattr(db_skill, "updated_at") and db_skill.updated_at: - updated_at = ( - db_skill.updated_at.isoformat() - if hasattr(db_skill.updated_at, "isoformat") - else str(db_skill.updated_at) - ) + created_at: Final = db_skill.created_at.isoformat() if db_skill.created_at else "" + updated_at: Final = db_skill.updated_at.isoformat() if db_skill.updated_at else "" return Skill( id=db_skill.skill_id, diff --git a/litellm/llms/milvus/vector_stores/transformation.py b/litellm/llms/milvus/vector_stores/transformation.py index 34f0cd854c4..c3581abfbcc 100644 --- a/litellm/llms/milvus/vector_stores/transformation.py +++ b/litellm/llms/milvus/vector_stores/transformation.py @@ -19,6 +19,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -123,6 +124,7 @@ class MilvusVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict[str, Any]]: """ Transform search request for Azure AI Search API diff --git a/litellm/llms/minimax/messages/transformation.py b/litellm/llms/minimax/messages/transformation.py index 095b6c0c4b6..d4c24c65cfa 100644 --- a/litellm/llms/minimax/messages/transformation.py +++ b/litellm/llms/minimax/messages/transformation.py @@ -2,7 +2,7 @@ MiniMax Anthropic transformation config - extends AnthropicConfig for MiniMax's Anthropic-compatible API """ -from typing import Final +from typing import Any, Final # noqa: TID251 # override below must mirror the legacy base signature import litellm from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( @@ -49,6 +49,26 @@ class MinimaxMessagesConfig(AnthropicMessagesConfig): """ return api_base or get_secret_str("MINIMAX_API_BASE") or "https://api.minimax.io/anthropic/v1/messages" + def validate_anthropic_messages_environment( + self, + headers: dict, # mutable-ok: mirrors the legacy base override signature + model: str, + messages: list[Any], # mutable-ok: mirrors the legacy base override signature + optional_params: dict, # mutable-ok: mirrors the legacy base override signature + litellm_params: dict, # mutable-ok: mirrors the legacy base override signature + api_key: str | None = None, + api_base: str | None = None, + ) -> tuple[dict, str | None]: # mutable-ok: mirrors the legacy base override signature + return super().validate_anthropic_messages_environment( + headers=headers, + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + api_key=self.get_api_key(api_key=api_key), + api_base=api_base, + ) + def get_complete_url( self, api_base: str | None, diff --git a/litellm/llms/mistral/chat/transformation.py b/litellm/llms/mistral/chat/transformation.py index 0d9577669a4..a76a8a3e98c 100644 --- a/litellm/llms/mistral/chat/transformation.py +++ b/litellm/llms/mistral/chat/transformation.py @@ -7,7 +7,7 @@ Docs - https://docs.mistral.ai/api/ """ from collections.abc import AsyncIterator, Coroutine, Iterator -from typing import Any, Final, Literal, cast, get_type_hints, overload +from typing import TYPE_CHECKING, Any, Final, Literal, cast, get_type_hints, overload import httpx @@ -26,6 +26,9 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse, ModelResponseStream from litellm.utils import convert_to_model_response_object +if TYPE_CHECKING: + import tiktoken + class MistralConfig(OpenAIGPTConfig): """ @@ -292,7 +295,7 @@ class MistralConfig(OpenAIGPTConfig): file_id = file_content.get("file", {}).get("file_id") if file_id: # Replace 'file' with 'file_id' - file_content["file_id"] = file_id + file_content["file_id"] = file_id # pyright: ignore[reportGeneralTypeIssues] # legacy in-place rewrite of the block shape file_content.pop("file", None) return messages @@ -550,7 +553,7 @@ class MistralConfig(OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/mistral/ocr/guardrail_translation/handler.py b/litellm/llms/mistral/ocr/guardrail_translation/handler.py index 303e212e888..2af8172c992 100644 --- a/litellm/llms/mistral/ocr/guardrail_translation/handler.py +++ b/litellm/llms/mistral/ocr/guardrail_translation/handler.py @@ -13,6 +13,7 @@ from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.ocr.transformation import OCRResponse @@ -33,7 +34,7 @@ class OCRHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, ) -> Any: """ Process OCR input by applying guardrails to the document reference. @@ -87,7 +88,7 @@ class OCRHandler(BaseTranslation): self, response: "OCRResponse", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: Any | None = None, request_data: dict | None = None, ) -> Any: diff --git a/litellm/llms/mistral/ocr/transformation.py b/litellm/llms/mistral/ocr/transformation.py index 354e41c61bf..78c8dd11171 100644 --- a/litellm/llms/mistral/ocr/transformation.py +++ b/litellm/llms/mistral/ocr/transformation.py @@ -2,7 +2,7 @@ Mistral OCR transformation implementation. """ -from typing import Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -15,6 +15,9 @@ from litellm.llms.base_llm.ocr.transformation import ( ) from litellm.secret_managers.main import get_secret_str +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + MISTRAL_OCR_API_KEY_ENV_VAR: Final = "MISTRAL_API_KEY" @@ -198,7 +201,7 @@ class MistralOCRConfig(BaseOCRConfig): self, model: str, raw_response: httpx.Response, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", **kwargs, ) -> OCRResponse: """ diff --git a/litellm/llms/moonshot/chat/transformation.py b/litellm/llms/moonshot/chat/transformation.py index 8e4b116d79f..7b0fcd24770 100644 --- a/litellm/llms/moonshot/chat/transformation.py +++ b/litellm/llms/moonshot/chat/transformation.py @@ -2,7 +2,7 @@ Translates from OpenAI's `/v1/chat/completions` to Moonshot AI's `/v1/chat/completions` """ -from collections.abc import Coroutine +from collections.abc import Coroutine, Mapping from typing import Any, Final, Literal, cast, overload import litellm @@ -16,6 +16,15 @@ from litellm.utils import supports_reasoning from ...openai.chat.gpt_transformation import OpenAIGPTConfig +def _reasoning_effort_string(value: object) -> str | None: + """The /v1/messages and /v1/responses bridges wrap the level as {"effort", "summary"} for + providers with a reasoning-summary surface. Moonshot's API takes only the bare string and 400s + on an object, so the level is unwrapped and the summary, which has no Moonshot equivalent, is + dropped.""" + effort: Final = value.get("effort") if isinstance(value, Mapping) else value + return effort if isinstance(effort, str) else None + + class MoonshotChatConfig(OpenAIGPTConfig): @overload def _transform_messages( @@ -93,20 +102,18 @@ class MoonshotChatConfig(OpenAIGPTConfig): - functions parameter is not supported (use tools instead) - tool_choice doesn't support "required" value - kimi-thinking-preview doesn't support tool calls at all + + A reasoning model additionally takes `reasoning_effort`, which the OpenAI base list this + subtracts from does not carry, so it has to be added back rather than merely kept. """ - excluded_params: Final[list[str]] = ["functions"] - - # kimi-thinking-preview has additional limitations - if "kimi-thinking-preview" in model: - excluded_params.extend(["tools", "tool_choice"]) - + excluded_params: Final = frozenset( + ("functions", "tools", "tool_choice") if "kimi-thinking-preview" in model else ("functions",) + ) base_openai_params: Final = super().get_supported_openai_params(model=model) - final_params: Final[list[str]] = [] - for param in base_openai_params: - if param not in excluded_params: - final_params.append(param) - - return final_params + supported: Final = [param for param in base_openai_params if param not in excluded_params] + if supports_reasoning(model=model, custom_llm_provider="moonshot"): + return [*supported, "reasoning_effort"] + return supported def map_openai_params( self, @@ -126,7 +133,12 @@ class MoonshotChatConfig(OpenAIGPTConfig): for param, value in non_default_params.items(): if param == "max_completion_tokens": optional_params["max_tokens"] = value - elif param in supported_openai_params: + elif param not in supported_openai_params: + continue + elif param == "reasoning_effort": + if (effort := _reasoning_effort_string(value)) is not None: + optional_params["reasoning_effort"] = effort + else: optional_params[param] = value ########################################## diff --git a/litellm/llms/nlp_cloud/chat/transformation.py b/litellm/llms/nlp_cloud/chat/transformation.py index a06786d2163..17c547618d3 100644 --- a/litellm/llms/nlp_cloud/chat/transformation.py +++ b/litellm/llms/nlp_cloud/chat/transformation.py @@ -14,6 +14,8 @@ from litellm.utils import ModelResponse, Usage from ..common_utils import NLPCloudError if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -173,7 +175,7 @@ class NLPCloudConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/nvidia_nim/rerank/transformation.py b/litellm/llms/nvidia_nim/rerank/transformation.py index bb07f9ec74f..93e00dad9a1 100644 --- a/litellm/llms/nvidia_nim/rerank/transformation.py +++ b/litellm/llms/nvidia_nim/rerank/transformation.py @@ -1,3 +1,4 @@ +from collections.abc import Mapping from typing import Any, Final, Literal import httpx @@ -152,6 +153,7 @@ class NvidiaNimRerankConfig(BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: """ Validate that the Nvidia NIM API key is present. diff --git a/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py b/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py index 008a5a5780f..046b4e29a0a 100644 --- a/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py +++ b/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py @@ -16,7 +16,7 @@ import io import os import tempfile from dataclasses import dataclass -from typing import Any, Final, cast +from typing import Final, Protocol, cast from litellm.llms.nvidia_riva.audio_transcription.transformation import ( RIVA_TARGET_NUM_CHANNELS, @@ -24,10 +24,30 @@ from litellm.llms.nvidia_riva.audio_transcription.transformation import ( ) from litellm.llms.nvidia_riva.common_utils import NvidiaRivaException -# Keep this as Any: the module intentionally avoids importing numpy at module -# import time (optional dependency), and project-wide mypy config evaluates this -# file in contexts where conditional type aliases can degrade to "FloatArray?". -FloatArray = Any + +class FloatArray(Protocol): + """Structural view of the ``numpy.ndarray`` surface this module relies on.""" + + @property + def ndim(self) -> int: ... + + @property + def shape(self) -> tuple[int, ...]: ... + + @property + def size(self) -> int: ... + + def mean(self, axis: int) -> "FloatArray": ... + + def ravel(self) -> "FloatArray": ... + + def astype(self, dtype: object) -> "FloatArray": ... + + def tobytes(self) -> bytes: ... + + def __getitem__(self, key: object) -> "FloatArray": ... + + def __mul__(self, other: float) -> "FloatArray": ... _INSTALL_HINT = "Install Riva STT extras to enable automatic audio resampling: `pip install 'litellm[stt-nvidia-riva]'`" diff --git a/litellm/llms/oci/chat/cohere.py b/litellm/llms/oci/chat/cohere.py index 7ae438fd4cd..6e9bb83b0a0 100644 --- a/litellm/llms/oci/chat/cohere.py +++ b/litellm/llms/oci/chat/cohere.py @@ -8,10 +8,11 @@ response parsing, and streaming chunk parsing for models served with import datetime import json -from typing import Any, Final +from collections.abc import Iterable, Mapping, Sequence +from typing import Final import httpx -from pydantic import ValidationError +from pydantic import JsonValue, TypeAdapter, ValidationError from litellm.llms.oci.chat.generic import ( _normalize_oci_finish_reason, @@ -35,7 +36,7 @@ from litellm.types.llms.oci import ( CohereToolMessage, CohereToolResult, ) -from litellm.types.llms.openai import AllMessageValues +from litellm.types.llms.openai import AllMessageValues, ChatCompletionAssistantToolCall from litellm.types.utils import ( Choices, Delta, @@ -46,19 +47,60 @@ from litellm.types.utils import ( ) -def _extract_text_content(content: Any) -> str: - """Return the plain-text representation of a message content value.""" +def _json_dict(value: JsonValue) -> dict[str, JsonValue]: + return value if isinstance(value, dict) else {} + + +def _json_list(value: JsonValue) -> list[JsonValue]: + return value if isinstance(value, list) else [] + + +def _json_str(value: JsonValue) -> str: + return value if isinstance(value, str) else "" + + +def _content_block_text(block: Mapping[str, object]) -> str: + if not isinstance(block, dict) or block.get("type") != "text": + return "" + text: Final = block.get("text", "") + return text if isinstance(text, str) else "" + + +def _content_text(content: str | Iterable[Mapping[str, object]] | None) -> str: if content is None: return "" if isinstance(content, str): return content if isinstance(content, list): - return "".join( - item.get("text", "") for item in content if isinstance(item, dict) and item.get("type") == "text" - ) + return "".join(_content_block_text(block) for block in content) return str(content) +def _extract_text_content(content: str | Iterable[Mapping[str, object]] | None) -> str: + """Return the plain-text representation of a message content value.""" + return _content_text(content) + + +_TOOL_ARGUMENTS_ADAPTER: Final = TypeAdapter(dict[str, object]) + + +def _parsed_tool_arguments(raw_arguments: str | dict[str, object]) -> dict[str, object]: + if not isinstance(raw_arguments, str): + return raw_arguments + try: + return _TOOL_ARGUMENTS_ADAPTER.validate_json(raw_arguments) + except ValidationError: + return {} + + +def _to_cohere_tool_call(tool_call: ChatCompletionAssistantToolCall) -> CohereToolCall: + function_fields: Final = tool_call.get("function", {}) + return CohereToolCall( + name=str(function_fields.get("name", "")), + parameters=_parsed_tool_arguments(function_fields.get("arguments", "{}")), + ) + + def adapt_messages_to_cohere_standard( messages: list[AllMessageValues], ) -> list[CohereMessage]: @@ -78,21 +120,12 @@ def adapt_messages_to_cohere_standard( """ # First pass: build tool_call_id → CohereToolCall so tool-result messages can # reference the originating call by name and parameters. - tool_call_lookup: Final[dict[str, CohereToolCall]] = {} - for msg in messages: - if msg.get("role") == "assistant": - tool_calls_raw: Any = msg.get("tool_calls") or [] - for tc in tool_calls_raw: - tc_id = tc.get("id", "") - raw_args = tc.get("function", {}).get("arguments", "{}") - try: - params: dict[str, object] = json.loads(raw_args) if isinstance(raw_args, str) else raw_args - except json.JSONDecodeError: - params = {} - tool_call_lookup[tc_id] = CohereToolCall( - name=str(tc.get("function", {}).get("name", "")), - parameters=params, - ) + tool_call_lookup: Final = { + tool_call.get("id", ""): _to_cohere_tool_call(tool_call) + for msg in messages + if msg.get("role") == "assistant" and "tool_calls" in msg + for tool_call in msg["tool_calls"] or [] + } last_user_index: Final = next( (i for i in range(len(messages) - 1, -1, -1) if messages[i].get("role") == "user"), @@ -107,24 +140,11 @@ def adapt_messages_to_cohere_standard( role = msg.get("role") content = _extract_text_content(msg.get("content")) - tool_calls: list[CohereToolCall] | None = None - if role == "assistant" and msg.get("tool_calls"): - tool_calls = [] - for tc in msg["tool_calls"]: # pyright: ignore[reportOptionalIterable] # truthiness check above rules out None - raw_arguments = tc.get("function", {}).get("arguments", {}) - if isinstance(raw_arguments, str): - try: - arguments: dict[str, object] = json.loads(raw_arguments) - except json.JSONDecodeError: - arguments = {} - else: - arguments = raw_arguments - tool_calls.append( - CohereToolCall( - name=str(tc.get("function", {}).get("name", "")), - parameters=arguments, - ) - ) + tool_calls = ( + [_to_cohere_tool_call(tool_call) for tool_call in msg["tool_calls"]] + if role == "assistant" and "tool_calls" in msg and msg["tool_calls"] + else None + ) if role == "user": chat_history.append(CohereMessage(role="USER", message=content)) @@ -150,8 +170,41 @@ def adapt_messages_to_cohere_standard( return chat_history +def _resolved_oci_parameter_schema(raw_parameters: dict[str, JsonValue]) -> JsonValue: + return sanitize_oci_schema(resolve_oci_schema_anyof(resolve_oci_schema_refs(raw_parameters))) + + +def _cohere_parameter_definition(param_schema: dict[str, JsonValue], is_required: bool) -> CohereParameterDefinition: + json_type: Final = _json_str(param_schema.get("type")) or "string" + return CohereParameterDefinition( + description=enrich_cohere_param_description(_json_str(param_schema.get("description")), param_schema), + type=OCI_JSON_TO_PYTHON_TYPES.get(json_type, json_type), + isRequired=is_required, + ) + + +def _cohere_parameter_definitions(resolved_schema: JsonValue) -> dict[str, CohereParameterDefinition]: + schema_fields: Final = _json_dict(resolved_schema) + required: Final = _json_list(schema_fields.get("required")) + return { + param_name: _cohere_parameter_definition(_json_dict(param_schema), param_name in required) + for param_name, param_schema in _json_dict(schema_fields.get("properties")).items() + } + + +def _to_cohere_tool(tool: Mapping[str, JsonValue]) -> CohereTool: + function_def: Final = _json_dict(tool.get("function")) + return CohereTool( + name=_json_str(function_def.get("name")), + description=_json_str(function_def.get("description")), + parameterDefinitions=_cohere_parameter_definitions( + _resolved_oci_parameter_schema(_json_dict(function_def.get("parameters"))) + ), + ) + + def adapt_tool_definitions_to_cohere_standard( - tools: list[dict[str, Any]], + tools: Sequence[Mapping[str, JsonValue]], ) -> list[CohereTool]: """Adapt OpenAI-format tool definitions to the OCI Cohere format. @@ -160,45 +213,18 @@ def adapt_tool_definitions_to_cohere_standard( - Embeds unsupported constraints (enum, format, range, pattern) into the parameter description so the model can still see them. """ - cohere_tools: Final = [] - for tool in tools: - function_def = tool.get("function", {}) - raw_params = function_def.get("parameters", {}) - - resolved = sanitize_oci_schema(resolve_oci_schema_anyof(resolve_oci_schema_refs(raw_params))) - properties = resolved.get("properties", {}) - required = resolved.get("required", []) - - parameter_definitions = {} - for param_name, param_schema in properties.items(): - json_type = param_schema.get("type", "string") - python_type = OCI_JSON_TO_PYTHON_TYPES.get(json_type, json_type) - parameter_definitions[param_name] = CohereParameterDefinition( - description=enrich_cohere_param_description(param_schema.get("description", ""), param_schema), - type=python_type, - isRequired=param_name in required, - ) - - cohere_tools.append( - CohereTool( - name=function_def.get("name", ""), - description=function_def.get("description", ""), - parameterDefinitions=parameter_definitions, - ) - ) - - return cohere_tools + return [_to_cohere_tool(tool) for tool in tools] def handle_cohere_response( - json_response: dict, + json_response: Mapping[str, JsonValue], model: str, model_response: ModelResponse, raw_response: httpx.Response, ) -> ModelResponse: """Parse a non-streaming Cohere OCI response into a LiteLLM ModelResponse.""" try: - cohere_response: Final = CohereChatResult(**json_response) + cohere_response: Final = CohereChatResult.model_validate(json_response) except (TypeError, ValidationError) as e: raise OCIError( message=f"Response cannot be casted to CohereChatResult: {e}", @@ -258,7 +284,7 @@ def handle_cohere_response( def handle_cohere_stream_chunk( - dict_chunk: dict, + dict_chunk: Mapping[str, JsonValue], prior_tool_calls_emitted: bool = False, prior_text_emitted: bool = False, ) -> ModelResponseStream: @@ -279,7 +305,7 @@ def handle_cohere_stream_chunk( the text is passed through so the response content isn't silently lost. """ try: - typed_chunk: Final = CohereStreamChunk(**dict_chunk) + typed_chunk: Final = CohereStreamChunk.model_validate(dict_chunk) except (TypeError, ValidationError) as e: raise OCIError( status_code=500, diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 94494a87bba..98e23a59eea 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -65,6 +65,8 @@ from litellm.types.utils import ( from litellm.utils import supports_reasoning if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -601,7 +603,7 @@ class OCIChatConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/oci/common_utils.py b/litellm/llms/oci/common_utils.py index 5c3962bc05d..3f703564b5a 100644 --- a/litellm/llms/oci/common_utils.py +++ b/litellm/llms/oci/common_utils.py @@ -5,10 +5,11 @@ import os import re from dataclasses import dataclass from email.utils import formatdate -from typing import Any, Final, Protocol +from typing import Final, Protocol from urllib.parse import urlparse import httpx +from pydantic import JsonValue from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -64,7 +65,7 @@ class OCISignerProtocol(Protocol): See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html """ - def do_request_sign(self, request: Any, *, enforce_content_headers: bool = False) -> None: + def do_request_sign(self, request: "OCIRequestWrapper", *, enforce_content_headers: bool = False) -> None: pass @@ -113,7 +114,7 @@ def build_signature_string(method: str, path: str, headers: dict, signed_headers return "\n".join(lines) -def load_private_key_from_str(key_str: str) -> Any: +def load_private_key_from_str(key_str: str) -> "rsa.RSAPrivateKey": _require_cryptography() key: Final = serialization.load_pem_private_key( key_str.encode("utf-8"), @@ -124,7 +125,7 @@ def load_private_key_from_str(key_str: str) -> Any: return key -def load_private_key_from_file(file_path: str) -> Any: +def load_private_key_from_file(file_path: str) -> "rsa.RSAPrivateKey": """Loads a private key from a file path.""" try: with open(file_path, "r", encoding="utf-8") as f: @@ -421,16 +422,17 @@ OCI_JSON_TO_PYTHON_TYPES: Final[dict[str, str]] = { } -def resolve_oci_schema_refs(schema: dict[str, Any]) -> dict[str, Any]: +def resolve_oci_schema_refs(schema: JsonValue) -> JsonValue: """Inline all ``$ref``/``$defs`` references — OCI does not support JSON Schema ``$ref``.""" - defs: Final = schema.get("$defs", {}) - resolving_stack: Final[set] = set() + raw_defs: Final = schema.get("$defs") if isinstance(schema, dict) else None + defs: Final[dict[str, JsonValue]] = raw_defs if isinstance(raw_defs, dict) else {} + resolving_stack: Final[set[str]] = set() - def _resolve(obj: Any) -> Any: + def _resolve(obj: JsonValue) -> JsonValue: if isinstance(obj, dict): - if "$ref" in obj: - ref: Final = obj["$ref"] - if ref.startswith("#/$defs/"): + ref: Final = obj.get("$ref") + if ref is not None: + if isinstance(ref, str) and ref.startswith("#/$defs/"): key: Final = ref.split("/")[-1] if key in resolving_stack: return {"type": "object"} # break cycles @@ -451,7 +453,7 @@ def resolve_oci_schema_refs(schema: dict[str, Any]) -> dict[str, Any]: return resolved -def resolve_oci_schema_anyof(obj: Any) -> Any: +def resolve_oci_schema_anyof(obj: JsonValue) -> JsonValue: """Resolve Pydantic v2 ``Optional[T]`` → ``anyOf`` patterns. Pydantic v2 emits ``{"anyOf": [{"type": "T"}, {"type": "null"}]}`` for @@ -459,10 +461,13 @@ def resolve_oci_schema_anyof(obj: Any) -> Any: first non-null branch and merge top-level metadata into it. """ if isinstance(obj, dict): - if "anyOf" in obj and "type" not in obj: - non_null: Final = [t for t in obj["anyOf"] if not (isinstance(t, dict) and t.get("type") == "null")] + raw_any_of: Final = obj.get("anyOf") + if raw_any_of is not None and "type" not in obj: + branches: Final = raw_any_of if isinstance(raw_any_of, list) else [] + non_null: Final = [t for t in branches if not (isinstance(t, dict) and t.get("type") == "null")] if non_null: - resolved: Final = {**obj, **non_null[0]} + first: Final = non_null[0] + resolved: Final[dict[str, JsonValue]] = {**obj, **first} if isinstance(first, dict) else {**obj} resolved.pop("anyOf", None) return resolve_oci_schema_anyof(resolved) return {k: resolve_oci_schema_anyof(v) for k, v in obj.items()} @@ -471,7 +476,7 @@ def resolve_oci_schema_anyof(obj: Any) -> Any: return obj -def sanitize_oci_schema(schema: Any) -> Any: +def sanitize_oci_schema(schema: JsonValue) -> JsonValue: """Recursively remove OCI-incompatible fields from a JSON schema. Strips ``title`` keys, removes ``None``-valued ``default`` entries, @@ -483,7 +488,7 @@ def sanitize_oci_schema(schema: Any) -> Any: if not isinstance(schema, dict): return schema - sanitized: Final[dict[str, Any]] = {} + sanitized: Final[dict[str, JsonValue]] = {} for key, value in schema.items(): if key == "title": continue @@ -513,7 +518,7 @@ def sanitize_oci_schema(schema: Any) -> Any: return sanitized -def enrich_cohere_param_description(description: str, param_schema: dict[str, Any]) -> str: +def enrich_cohere_param_description(description: str, param_schema: dict[str, JsonValue]) -> str: """Embed schema constraints into a Cohere parameter description. ``CohereParameterDefinition`` only has ``type``, ``description``, and diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index d6aa1f1743b..de626b468f0 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -31,6 +31,8 @@ from litellm.types.utils import ModelResponse, ModelResponseStream from ..common_utils import OllamaError if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -319,7 +321,7 @@ class OllamaChatConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: str, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/ollama/completion/handler.py b/litellm/llms/ollama/completion/handler.py index 6e490f3ff15..449952217b5 100644 --- a/litellm/llms/ollama/completion/handler.py +++ b/litellm/llms/ollama/completion/handler.py @@ -4,16 +4,32 @@ Ollama /chat/completion calls handled in llm_http_handler.py [TODO]: migrate embeddings to a base handler as well. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Protocol, TypedDict + +from typing_extensions import NotRequired, ReadOnly import litellm from litellm.types.utils import EmbeddingResponse +class TokenEncoder(Protocol): + """The tokenizer surface used to estimate prompt tokens.""" + + def encode(self, text: str, /) -> Sequence[int]: ... + + +class OllamaEmbeddingResponse(TypedDict): + """Body of an Ollama ``/api/embed`` response.""" + + embeddings: ReadOnly[list[list[float]]] + prompt_eval_count: ReadOnly[NotRequired[int]] + + def _prepare_ollama_embedding_payload( - model: str, prompts: list[str], optional_params: dict[str, Any] -) -> dict[str, Any]: - data: Final[dict[str, Any]] = {"model": model, "input": prompts} + model: str, prompts: list[str], optional_params: Mapping[str, object] +) -> dict[str, object]: + data: Final[dict[str, object]] = {"model": model, "input": prompts} special_optional_params: Final = ["truncate", "options", "keep_alive", "dimensions"] for k, v in optional_params.items(): @@ -27,12 +43,12 @@ def _prepare_ollama_embedding_payload( def _process_ollama_embedding_response( - response_json: dict, + response_json: OllamaEmbeddingResponse, prompts: list[str], model: str, model_response: EmbeddingResponse, logging_obj: Any, - encoding: Any, + encoding: TokenEncoder | None, ) -> EmbeddingResponse: output_data: Final = [] embeddings: Final[list[list[float]]] = response_json["embeddings"] @@ -72,7 +88,7 @@ async def ollama_aembeddings( model_response: EmbeddingResponse, optional_params: dict, logging_obj: Any, - encoding: Any, + encoding: TokenEncoder | None, ): if not api_base.endswith("/api/embed"): api_base += "/api/embed" @@ -80,7 +96,7 @@ async def ollama_aembeddings( data: Final = _prepare_ollama_embedding_payload(model, prompts, optional_params) response: Final = await litellm.module_level_aclient.post(url=api_base, json=data) - response_json: Final = response.json() + response_json: Final[OllamaEmbeddingResponse] = response.json() return _process_ollama_embedding_response( response_json=response_json, @@ -99,7 +115,7 @@ def ollama_embeddings( optional_params: dict, model_response: EmbeddingResponse, logging_obj: Any, - encoding: Any = None, + encoding: TokenEncoder | None = None, ): if not api_base.endswith("/api/embed"): api_base += "/api/embed" @@ -107,7 +123,7 @@ def ollama_embeddings( data: Final = _prepare_ollama_embedding_payload(model, prompts, optional_params) response: Final = litellm.module_level_client.post(url=api_base, json=data) - response_json: Final = response.json() + response_json: Final[OllamaEmbeddingResponse] = response.json() return _process_ollama_embedding_response( response_json=response_json, diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 65edd5cb718..dccc83efed4 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -31,6 +31,8 @@ from litellm.types.utils import ( from ..common_utils import OllamaError, OllamaModelInfo, _convert_image if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -246,7 +248,7 @@ class OllamaConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: str, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -323,9 +325,10 @@ class OllamaConfig(BaseConfig): model_response.created = int(time.time()) model_response.model = "ollama/" + model _prompt: Final = request_data.get("prompt", "") + tokenizer: Final = encoding if encoding is not None else litellm.encoding prompt_tokens: Final = response_json.get( "prompt_eval_count", - len(encoding.encode(_prompt, disallowed_special=())), + len(tokenizer.encode(_prompt, disallowed_special=())), ) completion_tokens: Final = response_json.get( "eval_count", len(response_json.get("message", dict()).get("content", "")) diff --git a/litellm/llms/oobabooga/chat/oobabooga.py b/litellm/llms/oobabooga/chat/oobabooga.py index 8655d8c28c8..cd118a0af29 100644 --- a/litellm/llms/oobabooga/chat/oobabooga.py +++ b/litellm/llms/oobabooga/chat/oobabooga.py @@ -1,6 +1,6 @@ import json from collections.abc import Callable -from typing import Any, Final +from typing import TYPE_CHECKING, Final import litellm from litellm.llms.custom_httpx.http_handler import _get_httpx_client @@ -9,6 +9,9 @@ from litellm.utils import EmbeddingResponse, ModelResponse, Usage from ..common_utils import OobaboogaError from .transformation import OobaboogaConfig +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + oobabooga_config: Final = OobaboogaConfig() @@ -92,7 +95,7 @@ def embedding( model_response: EmbeddingResponse, api_key: str | None, api_base: str | None, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", optional_params: dict, encoding=None, ): diff --git a/litellm/llms/oobabooga/chat/transformation.py b/litellm/llms/oobabooga/chat/transformation.py index f695b2226e3..43d627102b6 100644 --- a/litellm/llms/oobabooga/chat/transformation.py +++ b/litellm/llms/oobabooga/chat/transformation.py @@ -11,6 +11,8 @@ from litellm.types.utils import ModelResponse, Usage from ..common_utils import OobaboogaError if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -37,7 +39,7 @@ class OobaboogaConfig(OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index ffa3de0d5c6..0223be300b0 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -6,17 +6,34 @@ import litellm from litellm.utils import ( _is_explicitly_disabled_factory, _supports_factory, + declared_value_factory, ) from .gpt_transformation import OpenAIGPTConfig +def _catalogue_declares_default_effort() -> bool: + """Whether the loaded cost map carries default_reasoning_effort for ANY entry. + + The map is fetched from the published branch at import time, so it can be OLDER than the + code reading it. On such a map every model looks undeclared, and treating that as "reasoning + is active" would silently strip temperature from the gpt-5.1/5.2/5.4 deployments that accept + it - a regression caused purely by data lag rather than by anything about the model. + + So the absence of the key is only meaningful once the catalogue is known to carry it at all. + A map that has never heard of the key predates the feature, and the honest answer there is + the one litellm gave before it existed. Scanning costs ~80us on the largest published map and + only on the fallback path, which is noise beside the request it precedes. + """ + return any(isinstance(entry, dict) and "default_reasoning_effort" in entry for entry in litellm.model_cost.values()) + + def _normalize_reasoning_effort_for_chat_completion( value: str | dict | None, ) -> str | None: """Convert reasoning_effort to the string format expected by OpenAI chat completion API. - The chat completion API expects a simple string: 'none', 'low', 'medium', 'high', or 'xhigh'. + The chat completion API expects an effort string such as 'low' or 'high'. Config/deployments may pass the Responses API format: {'effort': 'high', 'summary': 'detailed'}. """ if value is None: @@ -114,6 +131,17 @@ class OpenAIGPT5Config(OpenAIGPTConfig): except (ValueError, IndexError): return False + @classmethod + def _model_map_lookup_name(cls, model: str) -> str: + """The name this model is looked up by in the cost map. + + Identity here, because an OpenAI model name is already its map key. Azure overrides + it: its routing prefixes are not map keys, so every capability lookup has to + normalise the name the same way, and doing that in ONE place is what keeps the + supports/disabled/default answers from disagreeing about which entry they read. + """ + return model + @classmethod def _supports_reasoning_effort_level(cls, model: str, level: str) -> bool: """Check if the model supports a specific reasoning_effort level. @@ -123,11 +151,40 @@ class OpenAIGPT5Config(OpenAIGPTConfig): Returns False for unknown models (safe fallback). """ return _supports_factory( - model=model, + model=cls._model_map_lookup_name(model), custom_llm_provider=None, key=f"supports_{level}_reasoning_effort", ) + @classmethod + def effort_resolves_to_none(cls, model: str, effective_effort: str | None) -> bool: + """Whether this request's reasoning effort ends up as "none", which is the single + condition under which the provider accepts a non-default temperature or the + top_p/logprobs sampling params. + + An explicit reasoning_effort answers outright. When the request omits it the answer + is the model's DEFAULT effort, which only the map can state: supporting "none" is a + different fact from defaulting to it, and reading the former as the latter is what + forwarded temperature=0 to every gpt-5.5/5.6 deployment. + + An undeclared default resolves to False. The map not saying is not the model + saying no, so the gate takes the conservative branch: a param the provider would + have rejected gets dropped or refused with an actionable error, and a model + released before its map entry declares a default needs no code change to be safe. + """ + if effective_effort is not None: + return effective_effort == "none" + declared: Final = declared_value_factory( + model=cls._model_map_lookup_name(model), + custom_llm_provider=None, + key="default_reasoning_effort", + ) + if declared is not None: + return declared == "none" + if not _catalogue_declares_default_effort(): + return cls._supports_reasoning_effort_level(model, "none") + return False + @classmethod def _is_reasoning_effort_level_explicitly_disabled(cls, model: str, level: str) -> bool: """Return True only when the model map explicitly sets the capability to False. @@ -140,7 +197,7 @@ class OpenAIGPT5Config(OpenAIGPTConfig): Use this for opt-out checks where unknown models should be allowed through. """ return _is_explicitly_disabled_factory( - model=model, + model=cls._model_map_lookup_name(model), custom_llm_provider=None, key=f"supports_{level}_reasoning_effort", ) @@ -260,15 +317,16 @@ class OpenAIGPT5Config(OpenAIGPTConfig): if supports_none: sampling_params: Final = ["logprobs", "top_logprobs", "top_p"] has_sampling: Final = any(p in non_default_params for p in sampling_params) - if has_sampling and effective_effort not in (None, "none"): + if has_sampling and not self.effort_resolves_to_none(model, effective_effort): if litellm.drop_params or drop_params: for p in sampling_params: non_default_params.pop(p, None) else: raise litellm.utils.UnsupportedParamsError( message=( - "gpt-5.1/5.2/5.4 only support logprobs, top_p, top_logprobs when " - f"reasoning_effort='none'. Current reasoning_effort='{effective_effort}'. " + f"{model} only supports logprobs, top_p, top_logprobs when reasoning_effort " + "resolves to 'none', either set explicitly on the request or declared as the " + f"model's default_reasoning_effort. Current reasoning_effort={effective_effort!r}. " "To drop unsupported params set `litellm.drop_params = True`" ), status_code=400, @@ -277,17 +335,19 @@ class OpenAIGPT5Config(OpenAIGPTConfig): if "temperature" in non_default_params: temperature_value: Final[float | None] = non_default_params.pop("temperature") if temperature_value is not None: - # models supporting reasoning_effort="none" also support flexible temperature - if supports_none and (effective_effort == "none" or effective_effort is None) or temperature_value == 1: + # a non-default temperature rides on the effort resolving to "none", not on + # the model merely supporting it + if (supports_none and self.effort_resolves_to_none(model, effective_effort)) or temperature_value == 1: optional_params["temperature"] = temperature_value elif litellm.drop_params or drop_params: pass else: raise litellm.utils.UnsupportedParamsError( message=( - f"gpt-5 models (including gpt-5-codex) don't support temperature={temperature_value}. " - "Only temperature=1 is supported. " - "For gpt-5.1, temperature is supported when reasoning_effort='none' (or not specified, as it defaults to 'none'). " + f"{model} doesn't support temperature={temperature_value} while reasoning is " + "active. Only temperature=1 is supported unless reasoning_effort resolves to " + "'none', either set explicitly on the request or declared as the model's " + "default_reasoning_effort. " "To drop unsupported params set `litellm.drop_params = True`" ), status_code=400, diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 16fd042cb2f..9afc6331d96 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -4,7 +4,8 @@ Support for gpt model family import json import os -from collections.abc import AsyncIterator, Coroutine, Iterator +from collections.abc import AsyncIterator, Coroutine, Iterator, Mapping +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast, overload from urllib.parse import urlparse @@ -18,8 +19,10 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo _should_convert_tool_call_to_json_mode, ) from litellm.litellm_core_utils.prompt_templates.common_utils import ( + drop_tool_reference_parts_from_tool_messages, get_tool_call_names, hoist_images_from_tool_messages, + tool_with_flattened_parameters, ) from litellm.litellm_core_utils.prompt_templates.image_handling import ( async_convert_url_to_base64, @@ -53,6 +56,8 @@ from litellm.utils import convert_to_model_response_object from ..common_utils import OpenAIError if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.base_llm.base_utils import BaseTokenCounter from litellm.types.llms.openai import ChatCompletionToolParam @@ -62,6 +67,9 @@ else: LiteLLMLoggingObj = Any +_NO_TOOLS_UPDATE: Final[Mapping[str, object]] = MappingProxyType({}) + + class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): """ Reference: https://platform.openai.com/docs/api-reference/chat/create @@ -167,16 +175,20 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): if model != "gpt-3.5-turbo-16k" and model != "gpt-4": # gpt-4 does not support 'response_format' model_specific_params.append("response_format") - # Normalize model name for responses API (e.g., "responses/gpt-4.1" -> "gpt-4.1") - model_for_check: Final = model.split("responses/", 1)[1] if "responses/" in model else model - if ( - model_for_check in litellm.open_ai_chat_completion_models - ) or model_for_check in litellm.open_ai_text_completion_models: + if OpenAIGPTConfig.is_openai_catalog_model(model): model_specific_params.append( "user" ) # user is not a param supported by all openai-compatible endpoints - e.g. azure ai return base_params + model_specific_params + @staticmethod + def is_openai_catalog_model(model: str) -> bool: + model_for_check: Final = model.split("responses/", 1)[1] if "responses/" in model else model + return ( + model_for_check in litellm.open_ai_chat_completion_models + or model_for_check in litellm.open_ai_text_completion_models + ) + def _map_openai_params( self, non_default_params: dict, @@ -318,7 +330,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): @overload def _transform_messages( self, messages: list[AllMessageValues], model: str, is_async: Literal[True] - ) -> Coroutine[Any, Any, list[AllMessageValues]]: + ) -> Coroutine[object, object, list[AllMessageValues]]: ... @overload @@ -334,9 +346,10 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): def _transform_messages( self, messages: list[AllMessageValues], model: str, is_async: bool = False - ) -> list[AllMessageValues] | Coroutine[Any, Any, list[AllMessageValues]]: + ) -> list[AllMessageValues] | Coroutine[object, object, list[AllMessageValues]]: """OpenAI no longer supports image_url as a string, so we need to convert it to a dict""" - hoisted_messages: Final = hoist_images_from_tool_messages(messages) + stripped_messages: Final = drop_tool_reference_parts_from_tool_messages(messages) + hoisted_messages: Final = hoist_images_from_tool_messages(stripped_messages) async def _async_transform(): for message in hoisted_messages: @@ -389,6 +402,21 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ) return messages, tools + def _targets_openai_hosted_endpoint( + self, + custom_llm_provider: str | None, + api_base: str | None, + ) -> bool: + if custom_llm_provider != "openai": + return False + resolved_api_base = api_base or litellm.api_base or os.getenv("OPENAI_BASE_URL") or os.getenv("OPENAI_API_BASE") + if not resolved_api_base: + return True + hostname: Final = urlparse(resolved_api_base).hostname + if hostname is None: + return True + return hostname == "openai.com" or hostname.endswith(".openai.com") + def _should_preserve_cache_control_for_endpoint( self, custom_llm_provider: str | None, @@ -400,15 +428,34 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): api_base. Those can understand cache_control, so it must survive there. Real OpenAI cannot, so it is still stripped for an openai.com host. """ - if custom_llm_provider != "openai": - return False - resolved_api_base = api_base or litellm.api_base or os.getenv("OPENAI_BASE_URL") or os.getenv("OPENAI_API_BASE") - if not resolved_api_base: - return False - hostname: Final = urlparse(resolved_api_base).hostname - if hostname is None: - return False - return hostname != "openai.com" and not hostname.endswith(".openai.com") + return custom_llm_provider == "openai" and not self._targets_openai_hosted_endpoint( + custom_llm_provider, api_base + ) + + def _flattened_tools_update_for_openai( + self, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + ) -> Mapping[str, object]: + """ + OpenAI's chat completions validator rejects tool `parameters` carrying + 'oneOf'/'anyOf'/'allOf'/'enum'/'const'/'not' at the top level for every + model family, unlike the Responses API, where GPT-5+ accepts them. + """ + tools: Final = optional_params.get("tools") + if not isinstance(tools, list): + return _NO_TOOLS_UPDATE + provider: Final = litellm_params.get("custom_llm_provider") + raw_api_base: Final = litellm_params.get("api_base") + if not self._targets_openai_hosted_endpoint( + provider if isinstance(provider, str) else None, + raw_api_base if isinstance(raw_api_base, str) else None, + ): + return _NO_TOOLS_UPDATE + flattened: Final = [ # mutable-ok: request tools are a JSON list + tool_with_flattened_parameters(tool) if isinstance(tool, dict) else tool for tool in tools + ] + return MappingProxyType({"tools": flattened}) def transform_request( self, @@ -435,11 +482,14 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): optional_params["tools"] = tools optional_params.pop("max_retries", None) + if not optional_params.get("tools") and not optional_params.get("functions"): + optional_params.pop("tool_choice", None) return { "model": model, "messages": messages, **optional_params, + **self._flattened_tools_update_for_openai(optional_params, litellm_params), } async def async_transform_request( @@ -465,10 +515,13 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): if tools is not None and len(tools) > 0: optional_params["tools"] = tools if self.__class__._is_base_class: + if not optional_params.get("tools") and not optional_params.get("functions"): + optional_params.pop("tool_choice", None) return { "model": model, "messages": transformed_messages, **optional_params, + **self._flattened_tools_update_for_openai(optional_params, litellm_params), } else: ## allow for any object specific behaviour to be handled @@ -489,8 +542,12 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): return None tool_call_names: Final = get_tool_call_names(optional_params.get("tools", [])) try: - json_content: Final = json.loads(content) - if json_content.get("type") == "function" and json_content.get("name") in tool_call_names: + json_content: Final[object] = json.loads(content) + if ( + isinstance(json_content, dict) + and json_content.get("type") == "function" + and json_content.get("name") in tool_call_names + ): return ChatCompletionMessageToolCall( function=Function( name=json_content.get("name"), @@ -593,7 +650,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -614,7 +671,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ## RESPONSE OBJECT try: - completion_response: Final = raw_response.json() + completion_response: Final[dict[str, object]] = raw_response.json() except Exception as e: response_headers: Final = getattr(raw_response, "headers", None) raise OpenAIError( @@ -751,6 +808,14 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ) +class OpenAIUnknownModelConfig(OpenAIGPTConfig): + """A model the openai provider does not recognize is typically a LiteLLM proxy alias, so + forward reasoning_effort and let the server decide whether it is supported.""" + + def get_supported_openai_params(self, model: str) -> list: # mutable-ok: inherited contract + return super().get_supported_openai_params(model) + ["reasoning_effort"] # mutable-ok: inherited contract + + class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator): def _map_reasoning_to_reasoning_content(self, choices: list) -> list: """ diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index e411dc497fc..96a5ed663fc 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -14,8 +14,14 @@ Pattern Overview: This pattern can be replicated for other message formats (e.g., Anthropic). """ +import json +import time +import uuid +from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Final, Union, cast +from typing_extensions import NotRequired, ReadOnly, TypedDict + import litellm from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import ( @@ -23,6 +29,7 @@ from litellm.llms.base_llm.guardrail_translation.base_translation import ( StreamTransformSink, ) from litellm.llms.base_llm.guardrail_translation.utils import ( + blocked_chat_stream_usage, effective_scan_only_tool_results_for_guardrail, effective_skip_system_message_for_guardrail, effective_skip_tool_message_for_guardrail, @@ -31,6 +38,7 @@ from litellm.llms.base_llm.guardrail_translation.utils import ( openai_tool_name, role_out_of_guardrail_scope, scoped_structured_message_indices, + stream_item_field, ) from litellm.main import stream_chunk_builder from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam @@ -46,7 +54,14 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: - from litellm.integrations.custom_guardrail import CustomGuardrail + from fastapi import HTTPException + + from litellm.integrations.custom_guardrail import ( + CustomGuardrail, + ModifyResponseException, + ) + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth class OpenAIChatCompletionsHandler(BaseTranslation): @@ -75,8 +90,8 @@ class OpenAIChatCompletionsHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, - ) -> Any: + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, + ) -> dict: """ Process input messages by applying guardrails to text content. """ @@ -324,10 +339,10 @@ class OpenAIChatCompletionsHandler(BaseTranslation): self, response: "ModelResponse", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, - user_api_key_dict: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, + user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, - ) -> Any: + ) -> ModelResponse: """ Process output response by applying guardrails to text content. @@ -381,11 +396,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): if "response" not in request_data: request_data["response"] = response - # Add user API key metadata with prefixed keys - if "litellm_metadata" not in request_data: - user_metadata: Final = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + self.merge_user_api_key_metadata_into_request(request_data, user_api_key_dict) inputs: Final = GenericGuardrailAPIInputs(texts=texts_to_check) if images_to_check: @@ -435,8 +446,8 @@ class OpenAIChatCompletionsHandler(BaseTranslation): self, responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, - user_api_key_dict: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, + user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, stream_transform_sink: StreamTransformSink | None = None, ) -> list["ModelResponseStream"]: @@ -485,8 +496,8 @@ class OpenAIChatCompletionsHandler(BaseTranslation): *, responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None, - user_api_key_dict: Any | None, + litellm_logging_obj: "LiteLLMLoggingObj | None", + user_api_key_dict: "UserAPIKeyAuth | None", request_data: dict | None, ) -> list["ModelResponseStream"]: """Block-only streaming path: run the guardrail so an in-flight BLOCK can @@ -554,11 +565,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): if "responses" not in request_data: request_data["responses"] = responses_so_far - # Add user API key metadata with prefixed keys - if "litellm_metadata" not in request_data: - user_metadata: Final = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + self.merge_user_api_key_metadata_into_request(request_data, user_api_key_dict) inputs: Final = GenericGuardrailAPIInputs(texts=texts_to_check) if images_to_check: @@ -590,6 +597,18 @@ class OpenAIChatCompletionsHandler(BaseTranslation): return responses_so_far + def build_stream_error_items( + self, + exc: "HTTPException", + responses_so_far: Sequence[object] | None = None, + ) -> Sequence[bytes] | None: + import json + + from litellm.proxy.common_request_processing import sse_error_payload + + _, error_obj = sse_error_payload(exc) + return (f'data: {{"error": {json.dumps(error_obj)}}}\n\n'.encode(),) + @staticmethod def _accumulate_string_content_by_choice_index( responses_so_far: list["ModelResponseStream"], @@ -621,8 +640,8 @@ class OpenAIChatCompletionsHandler(BaseTranslation): *, responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None, - user_api_key_dict: Any | None, + litellm_logging_obj: "LiteLLMLoggingObj | None", + user_api_key_dict: "UserAPIKeyAuth | None", request_data: dict | None, sink: StreamTransformSink, ) -> None: @@ -652,10 +671,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): request_data = {"responses": responses_so_far} elif "responses" not in request_data: request_data["responses"] = responses_so_far - if "litellm_metadata" not in request_data: - user_metadata: Final = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) - if user_metadata: - request_data["litellm_metadata"] = user_metadata + self.merge_user_api_key_metadata_into_request(request_data, user_api_key_dict) inputs: Final = GenericGuardrailAPIInputs(texts=texts_to_check) if responses_so_far and getattr(responses_so_far[0], "model", None): @@ -789,7 +805,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): # Determine content source and tool calls based on choice type content = None - tool_calls: list[Any] | None = None + tool_calls: Sequence[object] | None = None if isinstance(choice, litellm.Choices): content = choice.message.content tool_calls = choice.message.tool_calls @@ -999,3 +1015,129 @@ class OpenAIChatCompletionsHandler(BaseTranslation): else: # Subsequent chunks - clear the text content_item["text"] = "" + + def _check_streaming_has_ended(self, responses_so_far: Sequence[object]) -> bool: + """ + True once any relayed chunk carries a non-null ``finish_reason``. + + The unified guardrail's ``end_of_stream_only`` streaming path probes + this via ``hasattr`` to withhold the terminal chunks until + end-of-stream moderation runs, so a block can replace the finish + instead of trailing after a ``finish_reason`` the client already saw. + """ + return any( + stream_item_field(choice, "finish_reason") is not None + for item in responses_so_far + for choice in _stream_chunk_choices(item) + ) + + def build_block_sse_chunks( + self, + exc: "ModifyResponseException", + stream_started: bool = False, + responses_so_far: Sequence[object] | None = None, + ) -> Sequence[bytes]: + """ + Build OpenAI chat-completions SSE chunks that deliver the guardrail + block message and terminate the stream cleanly, mirroring the + non-streaming block response: ``finish_reason`` ``content_filter`` plus + the real usage the upstream call consumed. + + - ``stream_started`` False (buffered / pre-stream): nothing has been + sent, so open a standalone completion with a ``role`` delta. + - ``stream_started`` True (sampling / mid-stream): chunks already + reached the client, so continue the in-progress completion (reuse its + id/created/model, content-only delta). + + The proxy's data generator appends ``data: [DONE]`` itself. + """ + chunk_id, created, model = _blocked_stream_identity(exc, responses_so_far or ()) + prompt_tokens, completion_tokens = blocked_chat_stream_usage(exc.original_response) + continuation_delta: Final[_BlockedChunkDelta] = {"content": exc.message} + standalone_delta: Final[_BlockedChunkDelta] = {"role": "assistant", "content": exc.message} + message_chunk: Final[_BlockedChunk] = { + "id": chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": ( + { + "index": 0, + "delta": continuation_delta if stream_started else standalone_delta, + "finish_reason": None, + }, + ), + } + final_chunk: Final[_BlockedChunk] = { + "id": chunk_id, + "object": "chat.completion.chunk", + "created": created, + "model": model, + "choices": ({"index": 0, "delta": {}, "finish_reason": "content_filter"},), + "usage": { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": prompt_tokens + completion_tokens, + }, + } + return _chat_sse_chunk(message_chunk), _chat_sse_chunk(final_chunk) + + +class _BlockedChunkDelta(TypedDict, total=False): + role: ReadOnly[str] + content: ReadOnly[str] + + +class _BlockedChunkChoice(TypedDict): + index: ReadOnly[int] + delta: ReadOnly[_BlockedChunkDelta] + finish_reason: ReadOnly[str | None] + + +class _BlockedChunkUsage(TypedDict): + prompt_tokens: ReadOnly[int] + completion_tokens: ReadOnly[int] + total_tokens: ReadOnly[int] + + +class _BlockedChunk(TypedDict): + id: ReadOnly[str] + object: ReadOnly[str] + created: ReadOnly[int] + model: ReadOnly[str] + choices: ReadOnly[tuple[_BlockedChunkChoice, ...]] + usage: NotRequired[ReadOnly[_BlockedChunkUsage]] + + +def _chat_sse_chunk(payload: _BlockedChunk) -> bytes: + return f"data: {json.dumps(payload)}\n\n".encode() + + +def _stream_chunk_choices(item: object) -> Sequence[object]: + choices: Final = stream_item_field(item, "choices") + if isinstance(choices, Sequence) and not isinstance(choices, (str, bytes)): + return choices + return () + + +def _blocked_stream_identity( + exc: "ModifyResponseException", responses_so_far: Sequence[object] +) -> tuple[str, int, str]: + identified: Final = next( + ( + (chunk_id, item) + for item in responses_so_far + if isinstance(chunk_id := stream_item_field(item, "id"), str) and chunk_id + ), + None, + ) + if identified is None: + return f"chatcmpl-{uuid.uuid4()}", int(time.time()), exc.model + chunk_id, source = identified + created: Final = stream_item_field(source, "created") + model: Final = stream_item_field(source, "model") + return ( + chunk_id, + created if isinstance(created, int) else int(time.time()), + model if isinstance(model, str) and model else exc.model, + ) diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index 1b1ab80e85d..4d774f6f165 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -268,6 +268,7 @@ class BaseOpenAILLM: "max_retries", "organization", "api_base", + "workload_identity_config", ) openai_client_fields: Final = ( BaseOpenAILLM.get_openai_client_initialization_param_fields(client_type=client_type) diff --git a/litellm/llms/openai/completion/guardrail_translation/handler.py b/litellm/llms/openai/completion/guardrail_translation/handler.py index 2c8c61ebf4e..f3557d4017e 100644 --- a/litellm/llms/openai/completion/guardrail_translation/handler.py +++ b/litellm/llms/openai/completion/guardrail_translation/handler.py @@ -13,6 +13,7 @@ from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.utils import TextCompletionResponse @@ -33,7 +34,7 @@ class OpenAITextCompletionHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, ) -> Any: """ Process input prompt by applying guardrails to text content. @@ -120,7 +121,7 @@ class OpenAITextCompletionHandler(BaseTranslation): self, response: "TextCompletionResponse", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: Any | None = None, request_data: dict | None = None, ) -> Any: diff --git a/litellm/llms/openai/containers/transformation.py b/litellm/llms/openai/containers/transformation.py index 6fc50458aa3..1a5211d5ff5 100644 --- a/litellm/llms/openai/containers/transformation.py +++ b/litellm/llms/openai/containers/transformation.py @@ -1,6 +1,8 @@ -from typing import TYPE_CHECKING, Any, Final +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, Literal import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( @@ -11,9 +13,11 @@ from litellm.secret_managers.main import get_secret_str from litellm.types.containers.main import ( ContainerCreateOptionalRequestParams, ContainerFileListResponse, + ContainerFileObject, ContainerListResponse, ContainerObject, DeleteContainerResult, + ExpiresAfter, ) from litellm.types.router import GenericLiteLLMParams @@ -32,6 +36,46 @@ else: BaseLLMException = Any +class OpenAIContainerPayload(TypedDict): + """The JSON body OpenAI returns for a single container.""" + + id: ReadOnly[str] + object: ReadOnly[Literal["container"]] + created_at: ReadOnly[int] + status: ReadOnly[str] + expires_after: ReadOnly[ExpiresAfter | None] + last_active_at: ReadOnly[int | None] + name: ReadOnly[str | None] + + +class OpenAIContainerListPayload(TypedDict): + """The JSON body OpenAI returns for a page of containers.""" + + object: ReadOnly[Literal["list"]] + data: ReadOnly[list[ContainerObject]] + first_id: ReadOnly[str | None] + last_id: ReadOnly[str | None] + has_more: ReadOnly[bool] + + +class OpenAIContainerDeletedPayload(TypedDict): + """The JSON body OpenAI returns for a deleted container.""" + + id: ReadOnly[str] + object: ReadOnly[Literal["container.deleted"]] + deleted: ReadOnly[bool] + + +class OpenAIContainerFileListPayload(TypedDict): + """The JSON body OpenAI returns for a page of container files.""" + + object: ReadOnly[Literal["list"]] + data: ReadOnly[list[ContainerFileObject]] + first_id: ReadOnly[str | None] + last_id: ReadOnly[str | None] + has_more: ReadOnly[bool] + + class OpenAIContainerConfig(BaseContainerConfig): """Configuration class for OpenAI container API.""" @@ -87,7 +131,7 @@ class OpenAIContainerConfig(BaseContainerConfig): def transform_container_create_request( self, name: str, - container_create_optional_request_params: dict, + container_create_optional_request_params: Mapping[str, object], litellm_params: GenericLiteLLMParams, headers: dict, ) -> dict: @@ -111,10 +155,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerObject: """Transform the OpenAI container creation response.""" - response_data: Final = raw_response.json() - - # Transform the response data - container_obj: Final = ContainerObject(**response_data) + container_obj: Final = ContainerObject.model_validate(raw_response.json()) # Add cost for container creation (OpenAI containers are code interpreter sessions) # https://platform.openai.com/docs/pricing @@ -140,7 +181,7 @@ class OpenAIContainerConfig(BaseContainerConfig): after: str | None = None, limit: int | None = None, order: str | None = None, - extra_query: dict[str, Any] | None = None, + extra_query: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """Transform the container list request for OpenAI API. @@ -151,7 +192,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = api_base # Prepare query parameters - params: Final = {} + params: Final[dict[str, object]] = {} if after is not None: params["after"] = after if limit is not None: @@ -171,10 +212,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerListResponse: """Transform the OpenAI container list response.""" - response_data: Final = raw_response.json() - - # Transform the response data - container_list: Final = ContainerListResponse(**response_data) + container_list: Final = ContainerListResponse.model_validate(raw_response.json()) return container_list @@ -191,7 +229,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}") # No additional data needed for GET request - data: Final[dict[str, Any]] = {} + data: Final[dict[str, str]] = {} return url, data @@ -201,9 +239,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerObject: """Transform the OpenAI container retrieve response.""" - response_data: Final = raw_response.json() - # Transform the response data - container_obj: Final = ContainerObject(**response_data) + container_obj: Final = ContainerObject.model_validate(raw_response.json()) return container_obj @@ -224,7 +260,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}") # No data needed for DELETE request - data: Final[dict[str, Any]] = {} + data: Final[dict[str, str]] = {} return url, data @@ -234,10 +270,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> DeleteContainerResult: """Transform the OpenAI container delete response.""" - response_data: Final = raw_response.json() - - # Transform the response data - delete_result: Final = DeleteContainerResult(**response_data) + delete_result: Final = DeleteContainerResult.model_validate(raw_response.json()) return delete_result @@ -250,7 +283,7 @@ class OpenAIContainerConfig(BaseContainerConfig): after: str | None = None, limit: int | None = None, order: str | None = None, - extra_query: dict[str, Any] | None = None, + extra_query: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """Transform the container file list request for OpenAI API. @@ -262,7 +295,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}/files") # Prepare query parameters - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} if after is not None: params["after"] = after if limit is not None: @@ -282,10 +315,7 @@ class OpenAIContainerConfig(BaseContainerConfig): logging_obj: LiteLLMLoggingObj, ) -> ContainerFileListResponse: """Transform the OpenAI container file list response.""" - response_data: Final = raw_response.json() - - # Transform the response data - file_list: Final = ContainerFileListResponse(**response_data) + file_list: Final = ContainerFileListResponse.model_validate(raw_response.json()) return file_list @@ -308,7 +338,7 @@ class OpenAIContainerConfig(BaseContainerConfig): url: Final = join_container_api_base_path(api_base, f"/{encoded_container_id}/files/{encoded_file_id}/content") # No query parameters needed - params: Final[dict[str, Any]] = {} + params: Final[dict[str, str]] = {} return url, params diff --git a/litellm/llms/openai/cost_calculation.py b/litellm/llms/openai/cost_calculation.py index 0352d246c09..115b2e27983 100644 --- a/litellm/llms/openai/cost_calculation.py +++ b/litellm/llms/openai/cost_calculation.py @@ -134,14 +134,7 @@ def cost_per_second(model: str, custom_llm_provider: str | None, duration: float def _video_resolution_to_cost_field_suffix(resolution: str) -> str | None: - """ - Map usage resolution to a safe suffix for ``output_cost_per_second_`` keys. - - Note: Currently only ``output_cost_per_second_1080p`` is explicitly declared in - ModelInfo (types/utils.py). Other resolution tiers (e.g., 720p, 4k) can be added - to model_prices_and_context_window.json but are not exposed via get_model_info() - until added to the ModelInfo TypedDict. - """ + """Map usage resolution to a safe suffix for ``output_cost_per_second_`` keys.""" r: Final = resolution.strip().lower() if not r: return None diff --git a/litellm/llms/openai/embeddings/guardrail_translation/handler.py b/litellm/llms/openai/embeddings/guardrail_translation/handler.py index 280b0783e52..ef464e8a849 100644 --- a/litellm/llms/openai/embeddings/guardrail_translation/handler.py +++ b/litellm/llms/openai/embeddings/guardrail_translation/handler.py @@ -13,6 +13,7 @@ from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.utils import EmbeddingResponse @@ -35,7 +36,7 @@ class OpenAIEmbeddingsHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, ) -> Any: """ Process input text by applying guardrails to text content. @@ -70,7 +71,7 @@ class OpenAIEmbeddingsHandler(BaseTranslation): data: dict, input_data: str, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None, + litellm_logging_obj: "LiteLLMLoggingObj | None", ) -> dict: """Process a single string input through the guardrail.""" inputs: Final = GenericGuardrailAPIInputs(texts=[input_data]) @@ -99,7 +100,7 @@ class OpenAIEmbeddingsHandler(BaseTranslation): data: dict, input_data: list[str | int | list[int]], guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None, + litellm_logging_obj: "LiteLLMLoggingObj | None", ) -> dict: """Process a list input through the guardrail (if it contains strings).""" if len(input_data) == 0: @@ -144,7 +145,7 @@ class OpenAIEmbeddingsHandler(BaseTranslation): self, response: "EmbeddingResponse", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: Any | None = None, request_data: dict | None = None, ) -> Any: diff --git a/litellm/llms/openai/image_generation/dall_e_2_transformation.py b/litellm/llms/openai/image_generation/dall_e_2_transformation.py index accdbf29efa..74936cf1895 100644 --- a/litellm/llms/openai/image_generation/dall_e_2_transformation.py +++ b/litellm/llms/openai/image_generation/dall_e_2_transformation.py @@ -1,4 +1,4 @@ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -10,6 +10,7 @@ from litellm.types.utils import ImageResponse from litellm.utils import convert_to_model_response_object if TYPE_CHECKING: + import tiktoken from litellm.litellm_core_utils.logging import Logging as LiteLLMLoggingObj @@ -51,7 +52,7 @@ class DallE2ImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/openai/image_generation/dall_e_3_transformation.py b/litellm/llms/openai/image_generation/dall_e_3_transformation.py index 02a287d375a..5c561d011a9 100644 --- a/litellm/llms/openai/image_generation/dall_e_3_transformation.py +++ b/litellm/llms/openai/image_generation/dall_e_3_transformation.py @@ -1,4 +1,4 @@ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -10,6 +10,7 @@ from litellm.types.utils import ImageResponse from litellm.utils import convert_to_model_response_object if TYPE_CHECKING: + import tiktoken from litellm.litellm_core_utils.logging import Logging as LiteLLMLoggingObj @@ -51,7 +52,7 @@ class DallE3ImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/openai/image_generation/gpt_transformation.py b/litellm/llms/openai/image_generation/gpt_transformation.py index 28abb136557..05494c497ca 100644 --- a/litellm/llms/openai/image_generation/gpt_transformation.py +++ b/litellm/llms/openai/image_generation/gpt_transformation.py @@ -1,4 +1,4 @@ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -10,6 +10,7 @@ from litellm.types.utils import ImageResponse from litellm.utils import convert_to_model_response_object if TYPE_CHECKING: + import tiktoken from litellm.litellm_core_utils.logging import Logging as LiteLLMLoggingObj @@ -60,7 +61,7 @@ class GPTImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/openai/image_generation/guardrail_translation/handler.py b/litellm/llms/openai/image_generation/guardrail_translation/handler.py index e6f1c7efc31..b1d64fb1c09 100644 --- a/litellm/llms/openai/image_generation/guardrail_translation/handler.py +++ b/litellm/llms/openai/image_generation/guardrail_translation/handler.py @@ -13,6 +13,7 @@ from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.utils import ImageResponse @@ -32,7 +33,7 @@ class OpenAIImageGenerationHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, ) -> Any: """ Process input prompt by applying guardrails to text content. @@ -82,7 +83,7 @@ class OpenAIImageGenerationHandler(BaseTranslation): self, response: "ImageResponse", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: Any | None = None, request_data: dict | None = None, ) -> Any: diff --git a/litellm/llms/openai/image_variations/transformation.py b/litellm/llms/openai/image_variations/transformation.py index be171bb3522..afd2909b697 100644 --- a/litellm/llms/openai/image_variations/transformation.py +++ b/litellm/llms/openai/image_variations/transformation.py @@ -1,4 +1,4 @@ -from typing import Any +from typing import TYPE_CHECKING from aiohttp import ClientResponse from httpx import Headers, Response @@ -11,6 +11,9 @@ from litellm.types.utils import FileTypes, HttpHandlerRequestFields, ImageRespon from ...base_llm.image_variations.transformation import BaseImageVariationConfig from ..common_utils import OpenAIError +if TYPE_CHECKING: + import tiktoken + class OpenAIImageVariationConfig(BaseImageVariationConfig): def get_supported_openai_params(self, model: str) -> list[OpenAIImageVariationOptionalParams]: @@ -50,7 +53,7 @@ class OpenAIImageVariationConfig(BaseImageVariationConfig): image: FileTypes, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, ) -> ImageResponse: return model_response @@ -65,7 +68,7 @@ class OpenAIImageVariationConfig(BaseImageVariationConfig): image: FileTypes, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, ) -> ImageResponse: return model_response diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 4fc6655ca54..1cfc6e06ee9 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -7,13 +7,19 @@ from urllib.parse import urlparse import httpx if TYPE_CHECKING: + import tiktoken from aiohttp import ClientSession import openai from openai import AsyncOpenAI, OpenAI +from openai._base_client import make_request_options +from openai._constants import RAW_RESPONSE_HEADER +from openai._legacy_response import LegacyAPIResponse +from openai._types import RequestOptions +from openai.types import CreateEmbeddingResponse from openai.types.beta.assistant_deleted import AssistantDeleted from openai.types.file_deleted import FileDeleted -from pydantic import BaseModel +from pydantic import BaseModel, TypeAdapter from typing_extensions import overload import litellm @@ -22,7 +28,7 @@ from litellm._logging import verbose_logger from litellm.constants import DEFAULT_MAX_RETRIES from litellm.files.types import FileContentStreamingResult from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj -from litellm.litellm_core_utils.logging_utils import track_llm_api_timing +from litellm.litellm_core_utils.logging_utils import speech_request_body, track_llm_api_timing from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.llms.bedrock.chat.invoke_handler import MockResponseIterator @@ -42,6 +48,7 @@ from litellm.utils import ( from ...types.llms.openai import * from ..base import BaseLLM from .chat.gpt_5_transformation import OpenAIGPT5Config +from .chat.gpt_transformation import OpenAIGPTConfig, OpenAIUnknownModelConfig from .chat.o_series_transformation import OpenAIOSeriesConfig from .common_utils import ( BaseOpenAILLM, @@ -50,6 +57,7 @@ from .common_utils import ( drop_params_from_unprocessable_entity_error, is_output_token_limit_error, ) +from .workload_identity import resolve_openai_workload_identity_config openaiOSeriesConfig: Final = OpenAIOSeriesConfig() openAIGPT5Config: Final = OpenAIGPT5Config() @@ -187,7 +195,12 @@ class OpenAIConfig(BaseConfig): elif litellm.openAIGPTAudioConfig.is_model_gpt_audio_model(model=model): return litellm.openAIGPTAudioConfig.get_supported_openai_params(model=model) else: - return litellm.openAIGPTConfig.get_supported_openai_params(model=model) + return self._gpt_config_for_model(model).get_supported_openai_params(model=model) + + def _gpt_config_for_model(self, model: str) -> OpenAIGPTConfig: + if type(self) is OpenAIConfig and not OpenAIGPTConfig.is_openai_catalog_model(model): + return OpenAIUnknownModelConfig() + return litellm.openAIGPTConfig def _map_openai_params(self, non_default_params: dict, optional_params: dict, model: str) -> dict: supported_openai_params: Final = self.get_supported_openai_params(model) @@ -229,7 +242,7 @@ class OpenAIConfig(BaseConfig): drop_params=drop_params, ) - return litellm.openAIGPTConfig.map_openai_params( + return self._gpt_config_for_model(model).map_openai_params( non_default_params=non_default_params, optional_params=optional_params, model=model, @@ -264,7 +277,7 @@ class OpenAIConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: object, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -321,6 +334,28 @@ class OpenAIChatCompletionResponseIterator(BaseModelResponseIterator): raise e +_EXTRA_HEADERS_ADAPTER: Final = TypeAdapter(dict[str, str] | None) +_EXTRA_QUERY_ADAPTER: Final = TypeAdapter(dict[str, object] | None) +_NO_EXTRA_HEADERS: Final[Mapping[str, str]] = types.MappingProxyType({}) +_SDK_OPTION_KEYS: Final = frozenset(("extra_headers", "extra_query", "extra_body")) + + +def _embedding_request_without_sdk_defaults( + data: Mapping[str, object], timeout: float | httpx.Timeout +) -> tuple[Mapping[str, object], RequestOptions]: + body: Final = { # mutable-ok: the SDK json-encodes the body and needs a plain dict + k: v for k, v in data.items() if k not in _SDK_OPTION_KEYS + } + extra_headers: Final = _EXTRA_HEADERS_ADAPTER.validate_python(data.get("extra_headers")) or _NO_EXTRA_HEADERS + options: Final = make_request_options( + extra_headers=types.MappingProxyType({**extra_headers, RAW_RESPONSE_HEADER: "true"}), + extra_query=_EXTRA_QUERY_ADAPTER.validate_python(data.get("extra_query")), + extra_body=data.get("extra_body"), + timeout=timeout, + ) + return body, options + + class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): def __init__(self) -> None: super().__init__() @@ -348,6 +383,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): client: OpenAI | AsyncOpenAI | None = None, shared_session: Optional["ClientSession"] = None, ) -> OpenAI | AsyncOpenAI | None: + workload_identity_config: Final = resolve_openai_workload_identity_config(api_key=api_key, api_base=api_base) client_initialization_params: Final[dict] = locals() if client is None: if not isinstance(max_retries, int): @@ -363,28 +399,49 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): if cached_client: if isinstance(cached_client, OpenAI) or isinstance(cached_client, AsyncOpenAI): return cached_client - http_client: Final[httpx.Client | httpx.AsyncClient | None] = ( - OpenAIChatCompletion._get_async_http_client(shared_session=shared_session) - if is_async - else OpenAIChatCompletion._get_sync_http_client() - ) if is_async: - _new_client: OpenAI | AsyncOpenAI = AsyncOpenAI( - api_key=api_key, - base_url=api_base, - http_client=http_client, - timeout=timeout, - max_retries=max_retries, - organization=organization, + async_http_client: Final = OpenAIChatCompletion._get_async_http_client(shared_session=shared_session) + http_client: httpx.Client | httpx.AsyncClient | None = async_http_client + _new_client: OpenAI | AsyncOpenAI = ( + AsyncOpenAI( + workload_identity=workload_identity_config.to_sdk_workload_identity(), + base_url=api_base, + http_client=async_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) + if workload_identity_config is not None + else AsyncOpenAI( + api_key=api_key, + base_url=api_base, + http_client=async_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) ) else: - _new_client = OpenAI( - api_key=api_key, - base_url=api_base, - http_client=http_client, - timeout=timeout, - max_retries=max_retries, - organization=organization, + sync_http_client: Final = OpenAIChatCompletion._get_sync_http_client() + http_client = sync_http_client + _new_client = ( + OpenAI( + workload_identity=workload_identity_config.to_sdk_workload_identity(), + base_url=api_base, + http_client=sync_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) + if workload_identity_config is not None + else OpenAI( + api_key=api_key, + base_url=api_base, + http_client=sync_http_client, + timeout=timeout, + max_retries=max_retries, + organization=organization, + ) ) ## SAVE CACHE KEY @@ -1147,19 +1204,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): data: dict, timeout: float | httpx.Timeout, logging_obj: LiteLLMLoggingObj, - ): - """ - Helper to: - - call embeddings.create.with_raw_response when litellm.return_response_headers is True - - call embeddings.create by default - """ - try: - raw_response = await openai_aclient.embeddings.with_raw_response.create(**data, timeout=timeout) - headers: Final = dict(raw_response.headers) - response: Final = raw_response.parse() - return headers, response - except Exception as e: - raise e + ) -> LegacyAPIResponse[CreateEmbeddingResponse]: + if "encoding_format" not in data: + body, options = _embedding_request_without_sdk_defaults(data, timeout) + bypass_response: Final = await openai_aclient.post( + "/embeddings", body=body, options=options, cast_to=CreateEmbeddingResponse + ) + assert isinstance(bypass_response, LegacyAPIResponse) + return bypass_response + return await openai_aclient.embeddings.with_raw_response.create(**data, timeout=timeout) @track_llm_api_timing() def make_sync_openai_embedding_request( @@ -1168,20 +1221,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): data: dict, timeout: float | httpx.Timeout, logging_obj: LiteLLMLoggingObj, - ): - """ - Helper to: - - call embeddings.create.with_raw_response when litellm.return_response_headers is True - - call embeddings.create by default - """ - try: - raw_response = openai_client.embeddings.with_raw_response.create(**data, timeout=timeout) - - headers: Final = dict(raw_response.headers) - response: Final = raw_response.parse() - return headers, response - except Exception as e: - raise e + ) -> LegacyAPIResponse[CreateEmbeddingResponse]: + if "encoding_format" not in data: + body, options = _embedding_request_without_sdk_defaults(data, timeout) + bypass_response: Final = openai_client.post( + "/embeddings", body=body, options=options, cast_to=CreateEmbeddingResponse + ) + assert isinstance(bypass_response, LegacyAPIResponse) + return bypass_response + return openai_client.embeddings.with_raw_response.create(**data, timeout=timeout) async def aembedding( self, @@ -1206,14 +1254,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): client=client, shared_session=shared_session, ) - headers, response = await self.make_openai_embedding_request( + raw_response: Final = await self.make_openai_embedding_request( openai_aclient=openai_aclient, data=data, timeout=timeout, logging_obj=logging_obj, ) + headers: Final = dict(raw_response.headers) logging_obj.model_call_details["response_headers"] = headers - stringified_response: Final = response.model_dump() + stringified_response: Final = raw_response.parse().model_dump() ## LOGGING logging_obj.post_call( input=input, @@ -1305,13 +1354,14 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): ) ## embedding CALL - headers: dict | None = None - headers, sync_embedding_response = self.make_sync_openai_embedding_request( + raw_response: Final = self.make_sync_openai_embedding_request( openai_client=openai_client, data=data, timeout=timeout, logging_obj=logging_obj, ) + headers: Final = dict(raw_response.headers) + sync_embedding_response: Final = raw_response.parse() ## LOGGING logging_obj.model_call_details["response_headers"] = headers @@ -1345,7 +1395,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): data: dict, model_response: ModelResponse, timeout: float, - logging_obj: Any, + logging_obj: LiteLLMLoggingObj, api_key: str | None = None, api_base: str | None = None, client=None, @@ -1365,9 +1415,21 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): client=client, ) - if headers: - data["extra_headers"] = headers - response = await openai_aclient.images.generate(**data, timeout=timeout) + logging_obj.pre_call( + input=prompt, + api_key=openai_aclient.api_key, + additional_args={ # mutable-ok: loggers isinstance-check this payload as a dict + "headers": {"Authorization": f"Bearer {openai_aclient.api_key}"}, # mutable-ok: logged header map + "api_base": str(openai_aclient.base_url), + "acompletion": True, + "complete_input_dict": data, + }, + ) + + request_data: Final = ( # mutable-ok: the OpenAI SDK takes the request body as a dict + {**data, "extra_headers": headers} if headers else data + ) + response = await openai_aclient.images.generate(**request_data, timeout=timeout) stringified_response: Final = response.model_dump() ## LOGGING logging_obj.post_call( @@ -1396,7 +1458,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): prompt: str, timeout: float, optional_params: dict, - logging_obj: Any, + logging_obj: LiteLLMLoggingObj, api_key: str | None = None, api_base: str | None = None, model_response: ImageResponse | None = None, @@ -1450,9 +1512,10 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): ) ## COMPLETION CALL - if headers: - data["extra_headers"] = headers - _response: Final = openai_client.images.generate(**data, timeout=timeout) + request_data: Final = ( # mutable-ok: the OpenAI SDK takes the request body as a dict + {**data, "extra_headers": headers} if headers else data + ) + _response: Final = openai_client.images.generate(**request_data, timeout=timeout) response: Final = _response.model_dump() ## LOGGING @@ -1501,6 +1564,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): project: str | None, max_retries: int, timeout: float | httpx.Timeout, + logging_obj: LiteLLMLoggingObj, aspeech: bool | None = None, client=None, shared_session: Optional["ClientSession"] = None, @@ -1517,6 +1581,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): project=project, max_retries=max_retries, timeout=timeout, + logging_obj=logging_obj, client=client, shared_session=shared_session, ) @@ -1531,7 +1596,17 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): shared_session=shared_session, ) - response: Final = cast(OpenAI, openai_client).audio.speech.create( + sync_client: Final = cast(OpenAI, openai_client) + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ # mutable-ok: loggers isinstance-check this payload as a dict + "complete_input_dict": speech_request_body(model, voice, optional_params), + "api_base": str(sync_client.base_url), + }, + ) + + response: Final = sync_client.audio.speech.create( model=model, voice=voice, input=input, @@ -1551,6 +1626,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): project: str | None, max_retries: int, timeout: float | httpx.Timeout, + logging_obj: LiteLLMLoggingObj, client=None, shared_session: Optional["ClientSession"] = None, ) -> HttpxBinaryResponseContent: @@ -1567,6 +1643,15 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): ), ) + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ # mutable-ok: loggers isinstance-check this payload as a dict + "complete_input_dict": speech_request_body(model, voice, optional_params), + "api_base": str(openai_client.base_url), + }, + ) + response: Final = await openai_client.audio.speech.create( model=model, voice=voice, diff --git a/litellm/llms/openai/responses/count_tokens/transformation.py b/litellm/llms/openai/responses/count_tokens/transformation.py index 6b2f4535df1..88f04c59e01 100644 --- a/litellm/llms/openai/responses/count_tokens/transformation.py +++ b/litellm/llms/openai/responses/count_tokens/transformation.py @@ -4,7 +4,100 @@ OpenAI Responses API token counting transformation logic. This module handles the transformation of requests to OpenAI's /v1/responses/input_tokens endpoint. """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Literal + +from typing_extensions import ReadOnly, TypedDict + + +class ResponsesInputTextPart(TypedDict): + type: ReadOnly[Literal["input_text"]] + text: ReadOnly[str] + + +class ResponsesInputImagePart(TypedDict): + type: ReadOnly[Literal["input_image"]] + image_url: ReadOnly[str] + detail: ReadOnly[str] + + +class ResponsesInputFilePart(TypedDict): + type: ReadOnly[Literal["input_file"]] + filename: ReadOnly[str] + file_data: ReadOnly[str] + + +ResponsesInputPart = ResponsesInputTextPart | ResponsesInputImagePart | ResponsesInputFilePart + +ResponsesContentRole = Literal["user", "assistant"] + + +def _chat_image_block_to_responses_part(image_url: object) -> ResponsesInputImagePart | None: + url: Final = image_url.get("url") if isinstance(image_url, Mapping) else image_url + if not isinstance(url, str) or not url: + return None + detail: Final = image_url.get("detail") if isinstance(image_url, Mapping) else None + part: Final[ResponsesInputImagePart] = { + "type": "input_image", + "image_url": url, + "detail": detail if isinstance(detail, str) and detail else "auto", + } + return part + + +def _chat_file_block_to_responses_part(file_value: object) -> ResponsesInputFilePart | None: + """Only an inline file round trips: OpenAI rejects `file_data` without the `filename` beside it.""" + if not isinstance(file_value, Mapping): + return None + filename: Final = file_value.get("filename") + file_data: Final = file_value.get("file_data") + if not isinstance(filename, str) or not filename or not isinstance(file_data, str) or not file_data: + return None + part: Final[ResponsesInputFilePart] = { + "type": "input_file", + "filename": filename, + "file_data": file_data, + } + return part + + +def _chat_block_to_responses_part(block: object, role: ResponsesContentRole) -> ResponsesInputPart | None: + if isinstance(block, str): + bare: Final[ResponsesInputTextPart] = {"type": "input_text", "text": block} + return bare + if not isinstance(block, Mapping): + return None + match block.get("type"): + case "text": + text_value: Final = block.get("text") + text: Final[ResponsesInputTextPart] = { + "type": "input_text", + "text": text_value if isinstance(text_value, str) else "", + } + return text + case "image_url" if role == "user": + return _chat_image_block_to_responses_part(block.get("image_url")) + case "file" if role == "user": + return _chat_file_block_to_responses_part(block.get("file")) + case _: + return None + + +def chat_content_blocks_to_responses_content( + content: Sequence[object], + role: ResponsesContentRole, +) -> str | tuple[ResponsesInputPart, ...]: + """Text-only content collapses to a joined string, which every role accepts and counts identically. + + Only a user turn may carry an image or file part: the Responses API rejects any part but + output_text and refusal inside an assistant turn. + """ + parts: Final = tuple( + part for part in (_chat_block_to_responses_part(block, role) for block in content) if part is not None + ) + if any(part["type"] != "input_text" for part in parts): + return parts + return "\n".join(part["text"] for part in parts if part["type"] == "input_text") class OpenAICountTokensConfig: @@ -120,18 +213,13 @@ class OpenAICountTokensConfig: instructions_parts.append("\n".join(text_parts)) elif role == "user": if isinstance(content, list): - # Extract text from content blocks for Responses API - text_parts = [] - for block in content: - if isinstance(block, dict) and block.get("type") == "text": - text_parts.append(block.get("text", "")) - elif isinstance(block, str): - text_parts.append(block) - content = "\n".join(text_parts) + content = chat_content_blocks_to_responses_content(content, "user") input_items.append({"role": "user", "content": content}) elif role == "assistant": # Map tool_calls to Responses API function_call items tool_calls = msg.get("tool_calls") + if isinstance(content, list): + content = chat_content_blocks_to_responses_content(content, "assistant") if content: input_items.append({"role": "assistant", "content": content}) if tool_calls: diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index 7c5d8ac99ad..1530c154e93 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -28,12 +28,16 @@ Output: response.output is List[GenericResponseOutputItem] where each has: - text: str """ -from collections.abc import Sequence +import time +import uuid +from collections.abc import Mapping, Sequence +from dataclasses import dataclass +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Union, cast from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall from openai.types.responses.tool_param import FunctionToolParam -from pydantic import BaseModel +from pydantic import BaseModel, TypeAdapter from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger @@ -41,15 +45,33 @@ from litellm.completion_extras.litellm_responses_transformation.transformation i OpenAiResponsesToChatCompletionStreamIterator, ) from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.llms.base_llm.guardrail_translation.utils import ( + blocked_responses_stream_usage, + stream_item_field, +) from litellm.responses.litellm_completion_transformation.transformation import ( LiteLLMCompletionResponsesConfig, ) from litellm.types.llms.openai import ( AllMessageValues, + BaseLiteLLMOpenAIResponseObject, ChatCompletionToolCallChunk, ChatCompletionToolParam, + ContentPartAddedEvent, + ContentPartDoneEvent, + ContentPartDonePartOutputText, + ErrorEvent, + ErrorEventError, OpenAIMcpServerTool, + OutputItemAddedEvent, + OutputItemDoneEvent, + OutputTextDeltaEvent, + OutputTextDoneEvent, + ResponseAPIUsage, + ResponseCompletedEvent, + ResponsesAPIResponse, ResponsesAPIStreamEvents, + ResponsesAPIStreamingResponse, ) from litellm.types.responses.main import ( GenericResponseOutputItem, @@ -59,11 +81,15 @@ from litellm.types.responses.main import ( from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: - from litellm.integrations.custom_guardrail import CustomGuardrail + from fastapi import HTTPException + + from litellm.integrations.custom_guardrail import ( + CustomGuardrail, + ModifyResponseException, + ) from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.proxy._types import UserAPIKeyAuth from litellm.types.llms.openai import ResponseInputParam - from litellm.types.utils import ResponsesAPIResponse class ResponseOutputEnvelope(TypedDict, total=False): @@ -78,6 +104,18 @@ class ResponsesStreamChunk(TypedDict, total=False): type: ReadOnly[str] text: ReadOnly[str] + delta: ReadOnly[str] + item_id: ReadOnly[str] + output_index: ReadOnly[int] + content_index: ReadOnly[int] + + +def _next_stream_sequence_number(responses_so_far: Sequence[Any] | None) -> int: + sequence_numbers: Final = ( + item.get("sequence_number") if isinstance(item, dict) else getattr(item, "sequence_number", None) + for item in reversed(responses_so_far or ()) + ) + return next((n + 1 for n in sequence_numbers if isinstance(n, int)), 0) class OpenAIResponsesHandler(BaseTranslation): @@ -620,11 +658,58 @@ class OpenAIResponsesHandler(BaseTranslation): } return responses_so_far[-1].get("type") in terminal_types + def build_stream_error_items( + self, + exc: "HTTPException", + responses_so_far: Sequence[Any] | None = None, + ) -> Sequence[Any] | None: + from litellm.proxy.common_request_processing import ( + serialize_http_exception_detail, + ) + + message, _ = serialize_http_exception_detail(exc.detail) + return ( + ErrorEvent( + type=ResponsesAPIStreamEvents.ERROR, + sequence_number=_next_stream_sequence_number(responses_so_far), + error=ErrorEventError( + type="guardrail_error", + code=str(exc.status_code), + message=message, + param=None, + ), + ), + ) + def get_streaming_string_so_far(self, responses_so_far: Sequence[ResponsesStreamChunk]) -> str: """ Get the string so far from the responses so far. + + ``response.output_text.done`` events carry the whole part in ``text``, while + ``response.output_text.delta`` events carry fragments in ``delta``. A stream + that dies before its done event (``response.failed`` / ``response.incomplete``) + has text only in deltas, so per content part the done text wins when present + and the joined deltas fill in otherwise, never both. """ - return "".join([response.get("text", "") for response in responses_so_far]) + keyed_events: Final = tuple( + ( + (event.get("item_id"), event.get("output_index"), event.get("content_index")), + event.get("text"), + event.get("delta"), + ) + for event in responses_so_far + if isinstance(event.get("text"), str) or isinstance(event.get("delta"), str) + ) + + def part_text(part_key: tuple[object, object, object]) -> str: + done_texts: Final = tuple( + text for key, text, _ in keyed_events if key == part_key and isinstance(text, str) + ) + if done_texts: + return done_texts[-1] + return "".join(delta for key, _, delta in keyed_events if key == part_key and isinstance(delta, str)) + + return "".join(part_text(key) for key in dict.fromkeys(key for key, _, _ in keyed_events)) def _has_text_content(self, response: "ResponsesAPIResponse") -> bool: """ @@ -802,3 +887,331 @@ class OpenAIResponsesHandler(BaseTranslation): content[content_idx]["text"] = guardrail_response elif hasattr(content[content_idx], "text"): content[content_idx].text = guardrail_response + + def build_block_sse_chunks( + self, + exc: "ModifyResponseException", + stream_started: bool = False, + responses_so_far: Sequence[object] | None = None, + ) -> Sequence[bytes]: + """ + Build Responses API SSE events that deliver the guardrail block message + and terminate the stream cleanly, mirroring the non-streaming block + response: a completed response whose only output is the violation text, + with the real usage the upstream call consumed. + + - ``stream_started`` False (buffered / pre-stream): nothing has been + sent, so emit the full synthetic sequence (``response.created`` + through ``response.completed``). + - ``stream_started`` True (sampling / mid-stream): events already + reached the client, so continue the in-progress response: close the + output item still open on the wire, deliver the block message as a + new output item under the same response id, and close with a + ``response.completed`` carrying only the replacement item. + + The proxy's data generator appends ``data: [DONE]`` itself. + """ + events: Final = ( + self._block_continuation_events(exc, responses_so_far or ()) + if stream_started + else self._standalone_block_events(exc) + ) + return tuple( + f"data: {event.model_dump_json(exclude_none=True, exclude_unset=True, serialize_as_any=True)}\n\n".encode() + for event in events + ) + + @staticmethod + def _standalone_block_events(exc: "ModifyResponseException") -> Sequence[ResponsesAPIStreamingResponse]: + from litellm.responses.streaming_iterator import build_synthetic_response_events + + return build_synthetic_response_events( + transformed=_blocked_response(exc, response_id=f"resp_{uuid.uuid4()}", model=exc.model), + logging_obj=None, + chunk_size=max(len(exc.message), 1), + ) + + @staticmethod + def _block_continuation_events( + exc: "ModifyResponseException", responses_so_far: Sequence[object] + ) -> Sequence[ResponsesAPIStreamingResponse]: + response_id, model, output_index = _continuation_identity(exc, responses_so_far) + item: Final = _blocked_output_item(exc) + item_id: Final = item.id + part: Final[_BlockedContentPart] = {"type": "output_text", "text": exc.message, "annotations": ()} + done_part: Final[_BlockedDoneContentPart] = { + "type": "output_text", + "text": exc.message, + "annotations": (), + "logprobs": None, + } + return ( + *_open_item_closing_events(responses_so_far), + OutputItemAddedEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED, + output_index=output_index, + item=item, + ), + ContentPartAddedEvent( + type=ResponsesAPIStreamEvents.CONTENT_PART_ADDED, + item_id=item_id, + output_index=output_index, + content_index=0, + part=BaseLiteLLMOpenAIResponseObject.model_validate(part), + ), + OutputTextDeltaEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA, + item_id=item_id, + output_index=output_index, + content_index=0, + delta=exc.message, + ), + OutputTextDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DONE, + item_id=item_id, + output_index=output_index, + content_index=0, + text=exc.message, + ), + ContentPartDoneEvent( + type=ResponsesAPIStreamEvents.CONTENT_PART_DONE, + item_id=item_id, + output_index=output_index, + content_index=0, + part=ContentPartDonePartOutputText.model_validate(done_part), + ), + OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=output_index, + item=item, + ), + ResponseCompletedEvent( + type=ResponsesAPIStreamEvents.RESPONSE_COMPLETED, + response=_blocked_response(exc, response_id=response_id, model=model, output_item=item), + ), + ) + + +class _BlockedContentPart(TypedDict): + type: ReadOnly[str] + text: ReadOnly[str] + annotations: ReadOnly[tuple[object, ...]] + + +class _BlockedDoneContentPart(TypedDict): + type: ReadOnly[str] + text: ReadOnly[str] + annotations: ReadOnly[tuple[object, ...]] + logprobs: ReadOnly[None] + + +class _BlockedItemPayload(TypedDict): + type: ReadOnly[str] + id: ReadOnly[str] + status: ReadOnly[str] + role: ReadOnly[str] + content: ReadOnly[tuple[_BlockedContentPart, ...]] + + +class _BlockedResponsePayload(TypedDict): + id: ReadOnly[str] + object: ReadOnly[str] + created_at: ReadOnly[int] + model: ReadOnly[str] + output: ReadOnly[tuple[GenericResponseOutputItem, ...]] + status: ReadOnly[str] + usage: ReadOnly[ResponseAPIUsage] + + +def _blocked_output_item(exc: "ModifyResponseException") -> GenericResponseOutputItem: + payload: Final[_BlockedItemPayload] = { + "type": "message", + "id": f"msg_{uuid.uuid4()}", + "status": "completed", + "role": "assistant", + "content": ({"type": "output_text", "text": exc.message, "annotations": ()},), + } + return GenericResponseOutputItem.model_validate(payload) + + +def _blocked_response( + exc: "ModifyResponseException", + response_id: str, + model: str, + output_item: GenericResponseOutputItem | None = None, +) -> ResponsesAPIResponse: + payload: Final[_BlockedResponsePayload] = { + "id": response_id, + "object": "response", + "created_at": int(time.time()), + "model": model, + "output": (output_item if output_item is not None else _blocked_output_item(exc),), + "status": "completed", + "usage": blocked_responses_stream_usage(exc.original_response), + } + return ResponsesAPIResponse.model_validate(payload) + + +def _continuation_identity(exc: "ModifyResponseException", responses_so_far: Sequence[object]) -> tuple[str, str, int]: + responses: Final = tuple( + response for item in responses_so_far if (response := stream_item_field(item, "response")) is not None + ) + response_id: Final = next( + (rid for response in responses if isinstance(rid := stream_item_field(response, "id"), str) and rid), + f"resp_{uuid.uuid4()}", + ) + model: Final = next( + (m for response in responses if isinstance(m := stream_item_field(response, "model"), str) and m), + exc.model, + ) + indices: Final = tuple( + index for item in responses_so_far if isinstance(index := stream_item_field(item, "output_index"), int) + ) + return response_id, model, max(indices) + 1 if indices else 0 + + +@dataclass(frozen=True, slots=True) +class _OpenItemState: + item_id: str + item_type: str + role: str + output_index: int + content_index: int + text: str + part_open: bool + payload: object + + +def _open_item_state(responses_so_far: Sequence[object]) -> _OpenItemState | None: + typed: Final = tuple((stream_item_field(event, "type"), event) for event in responses_so_far) + added: Final = tuple( + (added_index, stream_item_field(event, "item")) + for event_type, event in typed + if event_type == "response.output_item.added" + and isinstance(added_index := stream_item_field(event, "output_index"), int) + ) + done_indices: Final = frozenset( + done_index + for event_type, event in typed + if event_type == "response.output_item.done" + and isinstance(done_index := stream_item_field(event, "output_index"), int) + ) + open_added: Final = tuple((index, payload) for index, payload in added if index not in done_indices) + if not open_added: + return None + output_index, item_payload = open_added[-1] + if item_payload is None: + return None + item_id: Final = stream_item_field(item_payload, "id") + if not isinstance(item_id, str) or not item_id: + return None + raw_type: Final = stream_item_field(item_payload, "type") + raw_role: Final = stream_item_field(item_payload, "role") + part_added: Final = tuple( + part_index + for event_type, event in typed + if event_type == "response.content_part.added" + and stream_item_field(event, "item_id") == item_id + and isinstance(part_index := stream_item_field(event, "content_index"), int) + ) + part_done: Final = frozenset( + part_done_index + for event_type, event in typed + if event_type == "response.content_part.done" + and stream_item_field(event, "item_id") == item_id + and isinstance(part_done_index := stream_item_field(event, "content_index"), int) + ) + open_parts: Final = tuple(index for index in part_added if index not in part_done) + text: Final = "".join( + delta + for event_type, event in typed + if event_type == "response.output_text.delta" + and stream_item_field(event, "item_id") == item_id + and isinstance(delta := stream_item_field(event, "delta"), str) + ) + return _OpenItemState( + item_id=item_id, + item_type=raw_type if isinstance(raw_type, str) and raw_type else "message", + role=raw_role if isinstance(raw_role, str) and raw_role else "assistant", + output_index=output_index, + content_index=open_parts[-1] if open_parts else 0, + text=text, + part_open=bool(open_parts), + payload=item_payload, + ) + + +_item_fields_adapter: Final = TypeAdapter(Mapping[str, object]) +_no_item_fields: Final[Mapping[str, object]] = MappingProxyType({}) + + +def _incomplete_item_fields(payload: object) -> Mapping[str, object]: + raw: Final = payload.model_dump() if isinstance(payload, BaseModel) else payload + if not isinstance(raw, dict): + return _no_item_fields + return _item_fields_adapter.validate_python(raw) + + +def _open_item_closing_events(responses_so_far: Sequence[object]) -> Sequence[ResponsesAPIStreamingResponse]: + """Close the output item still in progress on the relayed stream before the + block item is appended: strict Responses clients reject a + ``response.completed`` that arrives while an earlier ``output_item.added`` + was never closed. A message item closes ``completed`` with exactly the text + the client has received so far; any other item type (a function call the + guardrail rejected, for instance) closes ``incomplete`` so the synthetic + done event can never authorize acting on it.""" + open_item: Final = _open_item_state(responses_so_far) + if open_item is None: + return () + if open_item.item_type != "message": + return ( + OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=open_item.output_index, + item=BaseLiteLLMOpenAIResponseObject.model_validate( + MappingProxyType({**_incomplete_item_fields(open_item.payload), "status": "incomplete"}) + ), + ), + ) + partial_part: Final[_BlockedContentPart] = { + "type": "output_text", + "text": open_item.text, + "annotations": (), + } + closed_payload: Final[_BlockedItemPayload] = { + "type": open_item.item_type, + "id": open_item.item_id, + "status": "completed", + "role": open_item.role, + "content": (partial_part,), + } + item_done: Final = OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=open_item.output_index, + item=GenericResponseOutputItem.model_validate(closed_payload), + ) + if not open_item.part_open: + return (item_done,) + partial_done_part: Final[_BlockedDoneContentPart] = { + "type": "output_text", + "text": open_item.text, + "annotations": (), + "logprobs": None, + } + return ( + OutputTextDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_TEXT_DONE, + item_id=open_item.item_id, + output_index=open_item.output_index, + content_index=open_item.content_index, + text=open_item.text, + ), + ContentPartDoneEvent( + type=ResponsesAPIStreamEvents.CONTENT_PART_DONE, + item_id=open_item.item_id, + output_index=open_item.output_index, + content_index=open_item.content_index, + part=ContentPartDonePartOutputText.model_validate(partial_done_part), + ), + item_done, + ) diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index b2a69564908..01313e95878 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -1,8 +1,11 @@ -from typing import TYPE_CHECKING, Any, Final, cast, get_type_hints +from collections.abc import Mapping, Sequence +from types import MappingProxyType +from typing import TYPE_CHECKING, Any, Final, Protocol, cast, get_type_hints import httpx from openai.types.responses import ResponseReasoningItem from pydantic import BaseModel, ValidationError +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -12,6 +15,7 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo ) from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig +from litellm.responses.litellm_completion_transformation.custom_tools import TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import * from litellm.types.responses.main import * @@ -19,6 +23,7 @@ from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import LlmProviders from ..common_utils import OpenAIError +from ..workload_identity import get_workload_identity_bearer_token, resolve_openai_workload_identity_config OPENAI_RESPONSES_API_MIN_MAX_OUTPUT_TOKENS: Final = 16 @@ -29,6 +34,41 @@ if TYPE_CHECKING: else: LiteLLMLoggingObj = Any +_NO_TOOL_UPDATE: Final[Mapping[str, object]] = MappingProxyType({}) +_MODEL_FAMILIES_REJECTING_TOP_LEVEL_SCHEMA_COMBINATORS: Final = ("gpt-4", "gpt-3.5", "chatgpt-4o", "o1", "o3", "o4") +_PROVIDERS_WITH_COMBINATOR_REJECTING_VALIDATOR: Final = frozenset({LlmProviders.AZURE, LlmProviders.OPENAI}) +_PROVIDERS_VALIDATING_TOOL_CALL_ITEM_IDS: Final = frozenset({LlmProviders.AZURE, LlmProviders.OPENAI}) + + +class _DeleteResponseBody(TypedDict): + """Decoded body of the Responses API delete call.""" + + id: ReadOnly[str | None] + object: ReadOnly[str | None] + deleted: ReadOnly[bool | None] + + +class _DeleteResponse(Protocol): + """The delete call's HTTP response, read for the decoded body it carries.""" + + def json(self) -> _DeleteResponseBody: ... + + +class _JsonObjectResponse(Protocol): + """A Responses API HTTP response, read for the JSON object it decodes to.""" + + def json(self) -> dict[str, object]: ... + + +def _delete_response_body(response: _DeleteResponse) -> _DeleteResponseBody: + """Decode a delete response body into the id, object and deleted fields it carries.""" + return response.json() + + +def _json_object_body(response: _JsonObjectResponse) -> dict[str, object]: + """Decode a Responses API response body into its JSON object form.""" + return response.json() + class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): @property @@ -61,6 +101,20 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): key="supports_none_reasoning_effort", ) + @staticmethod + def _effort_resolves_to_none(model: str, effort: str | None) -> bool: + """Whether this request's reasoning effort ends up as "none", the one condition + under which a non-default temperature is accepted. + + Delegates to the chat-completions gpt-5 config so both surfaces answer from one + rule: the Responses API reaches the same models over a different wire, and a second + copy of the rule here is what let this surface keep forwarding temperature after the + chat surface stopped. + """ + from litellm.llms.openai.chat.gpt_5_transformation import OpenAIGPT5Config + + return OpenAIGPT5Config.effort_resolves_to_none(model, effort) + @staticmethod def _enforce_min_max_output_tokens(max_output_tokens: "int | None") -> "int | None": """Raise sub-minimum max_output_tokens up to the OpenAI Responses API minimum. @@ -116,17 +170,17 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): reasoning: Final = params.get("reasoning") or {} effort: Final = reasoning.get("effort") if isinstance(reasoning, dict) else None supports_none: Final = self._supports_reasoning_effort_none(model=model) - if supports_none and (effort == "none" or effort is None): + if supports_none and self._effort_resolves_to_none(model, effort): pass # flexible temperature allowed elif drop_params or litellm.drop_params: params.pop("temperature", None) else: raise litellm.UnsupportedParamsError( message=( - f"gpt-5 models don't support temperature={temperature}. " - "Only temperature=1 is supported. " - "For models like gpt-5.1/5.4, temperature is supported " - "when reasoning.effort='none' (or not specified). " + f"{model} doesn't support temperature={temperature} while reasoning is " + "active. Only temperature=1 is supported unless reasoning.effort resolves " + "to 'none', either set explicitly on the request or declared as the " + "model's default_reasoning_effort. " "To drop unsupported params set `litellm.drop_params = True`" ), status_code=400, @@ -153,10 +207,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): input = self._validate_input_param(input) tools = response_api_optional_request_params.get("tools") input, tools = self.remove_cache_control_flag_from_input_and_tools(model=model, input=input, tools=tools) - if tools is not None: - response_api_optional_request_params["tools"] = tools + sanitized_tools: Final = self._flatten_tool_schema_combinators_for_openai( + model=model, tools=tools, litellm_params=litellm_params + ) + if sanitized_tools is not None: + response_api_optional_request_params["tools"] = sanitized_tools + replay_safe_input: Final = self._drop_foreign_tool_call_item_ids(input) final_request_params: Final = dict( - ResponsesAPIRequestParams(model=model, input=input, **response_api_optional_request_params) + ResponsesAPIRequestParams(model=model, input=replay_safe_input, **response_api_optional_request_params) ) return final_request_params @@ -193,6 +251,96 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): return input, tools + def _drop_foreign_tool_call_item_ids(self, input: str | ResponseInputParam) -> str | ResponseInputParam: + if self.custom_llm_provider not in _PROVIDERS_VALIDATING_TOOL_CALL_ITEM_IDS or not isinstance(input, list): + return input + sanitized_items: Final = [self._without_foreign_tool_call_item_id(item) for item in input] + return cast("ResponseInputParam", sanitized_items) # cast-ok: items keep their shape, minus a rejected id + + @staticmethod + def _without_foreign_tool_call_item_id(item: object) -> object: + if not isinstance(item, dict): + return item + item_type: Final = item.get("type") + item_id: Final = item.get("id") + genuine_prefix: Final = TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE.get(item_type) if isinstance(item_type, str) else None + if genuine_prefix is None or not isinstance(item_id, str) or item_id.startswith(genuine_prefix): + return item + return {key: value for key, value in item.items() if key != "id"} # mutable-ok: outgoing JSON request item + + def _flatten_tool_schema_combinators_for_openai( + self, + model: str, + tools: list[ALL_RESPONSES_API_TOOL_PARAMS] | None, # mutable-ok: request tools are a JSON list + litellm_params: GenericLiteLLMParams, + ) -> list[ALL_RESPONSES_API_TOOL_PARAMS] | None: # mutable-ok: request tools are a JSON list + """Flatten top-level schema combinators only where OpenAI's validator rejects them. + + OpenAI-compatible backends reusing this config (and the ChatGPT backend + Codex talks to natively) accept them, and so do GPT-5 and later models, + which also call tools better with the union intact. Codex wraps MCP tools + inside namespace entries, so nested ``tools`` arrays are walked too. + Azure OpenAI shares the validator but names deployments arbitrarily, so + the router's declared ``model_info.base_model`` wins over the deployment + name and an unrecognized name without one is left untouched. + """ + if tools is None or self.custom_llm_provider not in _PROVIDERS_WITH_COMBINATOR_REJECTING_VALIDATOR: + return tools + gate_model: Final = self._combinator_gate_model(model=model, litellm_params=litellm_params) + if not self._rejects_top_level_schema_combinators(gate_model): + return tools + flattened: Final = [ # mutable-ok: request tools are a JSON list + self._flattened_tool_or_passthrough(tool) for tool in tools + ] + return cast("list[ALL_RESPONSES_API_TOOL_PARAMS]", flattened) # cast-ok: dict spread keeps each tool's shape + + @staticmethod + def _flattened_tool_or_passthrough(tool: object) -> object: + return OpenAIResponsesAPIConfig._flattened_tool_entry(tool) if isinstance(tool, dict) else tool + + @staticmethod + def _rejects_top_level_schema_combinators(model: str) -> bool: + bare_model: Final = model.split("/")[-1] + base_model: Final = bare_model.split(":")[1] if bare_model.startswith("ft:") else bare_model + return base_model.startswith(_MODEL_FAMILIES_REJECTING_TOP_LEVEL_SCHEMA_COMBINATORS) + + @staticmethod + def _combinator_gate_model(model: str, litellm_params: GenericLiteLLMParams) -> str: + model_info: Final[object] = getattr(litellm_params, "model_info", None) + base_model: Final[object] = model_info.get("base_model") if isinstance(model_info, dict) else None + return base_model if isinstance(base_model, str) and base_model else model + + @staticmethod + def _flattened_tool_entry( + entry: Mapping[str, object], + ) -> dict[str, object]: # mutable-ok: request tools are JSON dicts + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + flatten_top_level_schema_combinators, + ) + + parameters: Final = entry.get("parameters") + nested_tools: Final = entry.get("tools") + parameters_update: Final = ( + MappingProxyType({"parameters": flatten_top_level_schema_combinators(parameters)}) + if isinstance(parameters, dict) + else _NO_TOOL_UPDATE + ) + tools_update: Final = ( + MappingProxyType({"tools": OpenAIResponsesAPIConfig._flattened_nested_tools(nested_tools)}) + if isinstance(nested_tools, list) + else _NO_TOOL_UPDATE + ) + return {**entry, **parameters_update, **tools_update} # mutable-ok: request tools are JSON dicts + + @staticmethod + def _flattened_nested_tools( + nested_tools: Sequence[object], + ) -> list[object]: # mutable-ok: namespace tools are a JSON list + return [ # mutable-ok: namespace tools are a JSON list + OpenAIResponsesAPIConfig._flattened_tool_entry(item) if isinstance(item, dict) else item + for item in nested_tools + ] + def _validate_input_param(self, input: str | ResponseInputParam) -> str | ResponseInputParam: """ Ensure all input fields if pydantic are converted to dict @@ -296,6 +444,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): litellm_params = litellm_params or GenericLiteLLMParams() api_key = litellm_params.api_key or litellm.api_key or litellm.openai_key or get_secret_str("OPENAI_API_KEY") headers.setdefault("Content-Type", "application/json") + workload_identity_config: Final = ( + resolve_openai_workload_identity_config(api_key=api_key, api_base=litellm_params.api_base) + if self.custom_llm_provider is LlmProviders.OPENAI + else None + ) + if workload_identity_config is not None: + headers["Authorization"] = f"Bearer {get_workload_identity_bearer_token(workload_identity_config)}" + return headers headers["Authorization"] = f"Bearer {api_key}" return headers @@ -364,7 +520,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): return None @staticmethod - def get_event_model_class(event_type: str) -> Any: + def get_event_model_class(event_type: str) -> type[BaseLiteLLMOpenAIResponseObject]: """ Returns the appropriate event model class based on the event type. @@ -478,7 +634,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the delete response API response into a DeleteResponseResult """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _delete_response_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) return DeleteResponseResult(**raw_response_json) @@ -513,7 +669,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the get response API response into a ResponsesAPIResponse """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) raw_response_headers: Final = dict(raw_response.headers) @@ -541,7 +697,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ) -> tuple[str, dict]: encoded_response_id: Final = encode_url_path_segment(response_id, field_name="response_id") url: Final = f"{api_base}/{encoded_response_id}/input_items" - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} if after is not None: params["after"] = after if before is not None: @@ -560,7 +716,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> dict: try: - return raw_response.json() + return _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) @@ -594,7 +750,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the cancel response API response into a ResponsesAPIResponse """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) raw_response_headers: Final = dict(raw_response.headers) @@ -632,9 +788,15 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): input = self._validate_input_param(input) tools = response_api_optional_request_params.get("tools") input, tools = self.remove_cache_control_flag_from_input_and_tools(model=model, input=input, tools=tools) - if tools is not None: - response_api_optional_request_params["tools"] = tools - data: Final = dict(ResponsesAPIRequestParams(model=model, input=input, **response_api_optional_request_params)) + sanitized_tools: Final = self._flatten_tool_schema_combinators_for_openai( + model=model, tools=tools, litellm_params=litellm_params + ) + if sanitized_tools is not None: + response_api_optional_request_params["tools"] = sanitized_tools + replay_safe_input: Final = self._drop_foreign_tool_call_item_ids(input) + data: Final = dict( + ResponsesAPIRequestParams(model=model, input=replay_safe_input, **response_api_optional_request_params) + ) return url, data diff --git a/litellm/llms/openai/speech/guardrail_translation/handler.py b/litellm/llms/openai/speech/guardrail_translation/handler.py index ea3bd6e6c53..9e338e80632 100644 --- a/litellm/llms/openai/speech/guardrail_translation/handler.py +++ b/litellm/llms/openai/speech/guardrail_translation/handler.py @@ -13,6 +13,7 @@ from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.llms.openai import HttpxBinaryResponseContent @@ -31,7 +32,7 @@ class OpenAITextToSpeechHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, ) -> Any: """ Process input text by applying guardrails. @@ -80,7 +81,7 @@ class OpenAITextToSpeechHandler(BaseTranslation): self, response: "HttpxBinaryResponseContent", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: Any | None = None, request_data: dict | None = None, ) -> Any: diff --git a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py index 0b8a88d64b0..97fd1038d35 100644 --- a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py +++ b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py @@ -13,6 +13,7 @@ from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.utils import TranscriptionResponse @@ -31,7 +32,7 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, ) -> Any: """ Process input - not applicable for audio transcription. @@ -55,7 +56,7 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): self, response: "TranscriptionResponse", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: Any | None = None, request_data: dict | None = None, ) -> Any: diff --git a/litellm/llms/openai/vector_stores/transformation.py b/litellm/llms/openai/vector_stores/transformation.py index f6c093f2e2a..4e925494039 100644 --- a/litellm/llms/openai/vector_stores/transformation.py +++ b/litellm/llms/openai/vector_stores/transformation.py @@ -21,6 +21,7 @@ from litellm.utils import add_openai_metadata if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -99,6 +100,7 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: encoded_vector_store_id: Final = encode_url_path_segment(vector_store_id, field_name="vector_store_id") url: Final = f"{api_base}/{encoded_vector_store_id}/search" diff --git a/litellm/llms/openai/videos/transformation.py b/litellm/llms/openai/videos/transformation.py index 50b466ae996..f1b6dcb330a 100644 --- a/litellm/llms/openai/videos/transformation.py +++ b/litellm/llms/openai/videos/transformation.py @@ -1,5 +1,7 @@ import mimetypes +from collections.abc import Mapping from io import BufferedReader, BytesIO +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, cast from urllib.parse import quote @@ -101,6 +103,9 @@ class OpenAIVideoConfig(BaseVideoConfig): return f"{api_base.rstrip('/')}/videos" + def use_multipart_form_data(self) -> bool: + return True + def transform_video_create_request( self, model: str, @@ -499,15 +504,26 @@ class OpenAIVideoConfig(BaseVideoConfig): api_base: str, litellm_params: GenericLiteLLMParams, headers: dict, + video_file: FileContent | None = None, extra_body: dict[str, object] | None = None, prefetched_source_data: dict[str, object] | None = None, - ) -> tuple[str, dict]: - original_video_id: Final = extract_original_video_id(video_id) + ) -> tuple[str, Mapping[str, object], RequestFiles | None]: url: Final = f"{api_base.rstrip('/')}/edits" + + if video_file is not None: + files: Final[RequestFiles] = (self._video_file_tuple(video_file, "video"),) + form_data: Final = ( + MappingProxyType({"prompt": prompt, **extra_body}) + if extra_body + else MappingProxyType({"prompt": prompt}) + ) + return url, form_data, files + + original_video_id: Final = extract_original_video_id(video_id) data: Final[dict[str, object]] = {"prompt": prompt, "video": {"id": original_video_id}} if extra_body: data.update(extra_body) - return url, data + return url, data, None def transform_video_edit_response( self, @@ -567,21 +583,22 @@ class OpenAIVideoConfig(BaseVideoConfig): else: files_list.append((field_name, ("input_reference.png", image, image_content_type))) + def _video_file_tuple(self, video: FileContent, field_name: str) -> tuple[str, FileTypes]: + """ + Build a multipart field tuple for a video upload with proper video MIME + type detection: these paths must send video/mp4, not image/* content types. + """ + filename: Final = getattr(video, "name", None) or "input_video.mp4" + content_type: Final = self._get_video_content_type(video=video, filename=filename) + return (field_name, (filename, video, content_type)) + def _add_video_to_files( self, files_list: list[tuple[str, FileTypes]], video: FileContent, field_name: str, ) -> None: - """ - Add a video to files with proper video MIME type detection. - - This path is used by POST /videos/characters and must send video/mp4, - not image/* content types. - """ - filename: Final = getattr(video, "name", None) or "input_video.mp4" - content_type: Final = self._get_video_content_type(video=video, filename=filename) - files_list.append((field_name, (filename, video, content_type))) + files_list.append(self._video_file_tuple(video, field_name)) def _get_video_content_type(self, video: FileContent, filename: str) -> str: guessed_content_type, _ = mimetypes.guess_type(filename) diff --git a/litellm/llms/openai/workload_identity.py b/litellm/llms/openai/workload_identity.py new file mode 100644 index 00000000000..ecec161ed46 --- /dev/null +++ b/litellm/llms/openai/workload_identity.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +from dataclasses import dataclass +from functools import lru_cache +from typing import TYPE_CHECKING, Final +from urllib.parse import urlparse + +import litellm +from litellm.secret_managers.main import get_secret_str, normalize_nonempty_secret_str + +from .common_utils import OpenAIError + +if TYPE_CHECKING: + from collections.abc import Callable + + from openai.auth import SubjectTokenProvider, WorkloadIdentity, WorkloadIdentityAuth + +OPENAI_WIF_CLIENT_ID: Final = "litellm" +_OPENAI_API_HOST: Final = "api.openai.com" +_SDK_UPGRADE_MESSAGE: Final = ( + "OpenAI workload identity federation requires openai>=2.32.0. " + "Upgrade the installed openai package to use OPENAI_IDENTITY_PROVIDER_ID / " + "OPENAI_SERVICE_ACCOUNT_ID / OPENAI_IDENTITY_TOKEN_FILE." +) + + +@dataclass(frozen=True, slots=True) +class OpenAIWorkloadIdentityConfig: + identity_provider_id: str + service_account_id: str + token_file: str + + def to_sdk_workload_identity(self) -> WorkloadIdentity: + k8s_token_provider: Final = _load_sdk_k8s_token_provider() + workload_identity: Final[WorkloadIdentity] = { + "client_id": OPENAI_WIF_CLIENT_ID, + "identity_provider_id": self.identity_provider_id, + "service_account_id": self.service_account_id, + "provider": k8s_token_provider(self.token_file), + } + return workload_identity + + +def resolve_openai_workload_identity_config( + api_key: str | None, + api_base: str | None, +) -> OpenAIWorkloadIdentityConfig | None: + static_api_key: Final = normalize_nonempty_secret_str(api_key) or normalize_nonempty_secret_str( + get_secret_str("OPENAI_API_KEY") + ) + if static_api_key is not None: + return None + effective_api_base: Final = ( + api_base or litellm.api_base or get_secret_str("OPENAI_BASE_URL") or get_secret_str("OPENAI_API_BASE") + ) + if not _targets_openai_api(effective_api_base): + return None + identity_provider_id: Final = get_secret_str("OPENAI_IDENTITY_PROVIDER_ID") + service_account_id: Final = get_secret_str("OPENAI_SERVICE_ACCOUNT_ID") + token_file: Final = get_secret_str("OPENAI_IDENTITY_TOKEN_FILE") + if not identity_provider_id or not service_account_id or not token_file: + return None + return OpenAIWorkloadIdentityConfig( + identity_provider_id=identity_provider_id, + service_account_id=service_account_id, + token_file=token_file, + ) + + +def get_workload_identity_bearer_token(config: OpenAIWorkloadIdentityConfig) -> str: + return _workload_identity_auth(config).get_token() + + +def _targets_openai_api(api_base: str | None) -> bool: + if api_base is None: + return True + parsed: Final = urlparse(api_base) + return parsed.scheme == "https" and parsed.hostname == _OPENAI_API_HOST + + +@lru_cache(maxsize=16) +def _workload_identity_auth(config: OpenAIWorkloadIdentityConfig) -> WorkloadIdentityAuth: + sdk_workload_identity_auth: Final = _load_sdk_workload_identity_auth() + return sdk_workload_identity_auth(workload_identity=config.to_sdk_workload_identity()) + + +def _load_sdk_workload_identity_auth() -> type[WorkloadIdentityAuth]: + try: + from openai.auth import WorkloadIdentityAuth as sdk_workload_identity_auth + except ImportError as e: + raise OpenAIError(status_code=500, message=_SDK_UPGRADE_MESSAGE) from e + return sdk_workload_identity_auth + + +def _load_sdk_k8s_token_provider() -> Callable[[str], SubjectTokenProvider]: + try: + from openai.auth import k8s_service_account_token_provider + except ImportError as e: + raise OpenAIError(status_code=500, message=_SDK_UPGRADE_MESSAGE) from e + return k8s_service_account_token_provider diff --git a/litellm/llms/openai_like/chat/handler.py b/litellm/llms/openai_like/chat/handler.py index 8c548b6b0d6..855c49c320b 100644 --- a/litellm/llms/openai_like/chat/handler.py +++ b/litellm/llms/openai_like/chat/handler.py @@ -5,10 +5,11 @@ For handling OpenAI-like chat completions, like IBM WatsonX, etc. """ import json -from collections.abc import Callable -from typing import Any, Final +from collections.abc import Callable, Mapping, Sequence +from typing import Final, TypedDict import httpx +from typing_extensions import ReadOnly import litellm from litellm import LlmProviders @@ -25,6 +26,23 @@ from ..common_utils import OpenAILikeBase, OpenAILikeError from .transformation import OpenAILikeChatConfig +class _OpenAILikeChatCompletion(TypedDict, total=False): + """The chat-completion JSON body an OpenAI-like provider returns for a non-streamed call.""" + + id: ReadOnly[str] + choices: ReadOnly[Sequence[Mapping[str, object]]] + created: ReadOnly[int] + model: ReadOnly[str] + system_fingerprint: ReadOnly[str] + usage: ReadOnly[Mapping[str, object]] + object: ReadOnly[str] + + +def _fake_streamed_model_response(payload: _OpenAILikeChatCompletion) -> ModelResponse: + """Build the single response a fake-streamed provider call replays as one chunk.""" + return ModelResponse(**payload) + + async def make_call( client: AsyncHTTPHandler | None, api_base: str, @@ -42,9 +60,9 @@ async def make_call( response: Final = await client.post(api_base, headers=headers, data=data, stream=not fake_stream) if streaming_decoder is not None: - completion_stream: Any = streaming_decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) + completion_stream = streaming_decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) elif fake_stream: - model_response: Final = ModelResponse(**response.json()) + model_response: Final = _fake_streamed_model_response(response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: completion_stream = ModelResponseIterator(streaming_response=response.aiter_lines(), sync_stream=False) @@ -82,7 +100,7 @@ def make_sync_call( if streaming_decoder is not None: completion_stream = streaming_decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) elif fake_stream: - model_response: Final = ModelResponse(**response.json()) + model_response: Final = _fake_streamed_model_response(response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: completion_stream = ModelResponseIterator(streaming_response=response.iter_lines(), sync_stream=True) diff --git a/litellm/llms/openai_like/chat/transformation.py b/litellm/llms/openai_like/chat/transformation.py index f0fd7db7f9f..030710c8b2d 100644 --- a/litellm/llms/openai_like/chat/transformation.py +++ b/litellm/llms/openai_like/chat/transformation.py @@ -13,6 +13,8 @@ from litellm.types.utils import ModelResponse from ...openai.chat.gpt_transformation import OpenAIGPTConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -129,7 +131,7 @@ class OpenAILikeChatConfig(OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py index 71c21f14351..77a902149d9 100644 --- a/litellm/llms/openrouter/chat/transformation.py +++ b/litellm/llms/openrouter/chat/transformation.py @@ -8,7 +8,7 @@ Docs: https://openrouter.ai/docs/parameters from collections.abc import AsyncIterator, Iterator from enum import Enum -from typing import Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, cast import httpx @@ -22,6 +22,11 @@ from litellm.types.utils import ModelResponse, ModelResponseStream from ...openai.chat.gpt_transformation import OpenAIGPTConfig from ..common_utils import OpenRouterException +if TYPE_CHECKING: + import tiktoken + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class CacheControlSupportedModels(str, Enum): """Models that support cache_control in content blocks.""" @@ -172,12 +177,12 @@ class OpenrouterConfig(OpenAIGPTConfig): model: str, raw_response: httpx.Response, model_response: ModelResponse, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", request_data: dict, messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/openrouter/image_generation/transformation.py b/litellm/llms/openrouter/image_generation/transformation.py index 3342a6e4c71..6bbda324336 100644 --- a/litellm/llms/openrouter/image_generation/transformation.py +++ b/litellm/llms/openrouter/image_generation/transformation.py @@ -50,6 +50,8 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj else: LiteLLMLoggingObj = Any @@ -317,7 +319,7 @@ class OpenRouterImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/opensandbox/sandbox/transformation.py b/litellm/llms/opensandbox/sandbox/transformation.py index 49a7fb08c4a..5126db9bbc9 100644 --- a/litellm/llms/opensandbox/sandbox/transformation.py +++ b/litellm/llms/opensandbox/sandbox/transformation.py @@ -1,7 +1,7 @@ import asyncio import json import time -from typing import Final, cast +from typing import Final import httpx @@ -86,13 +86,10 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): secure_access=secure_access, ) - response: Final = cast( - httpx.Response, - await self._http(client).post( - url=f"{base}/sandboxes", - headers=self._lifecycle_headers(key), - json=body, - ), + response: Final = await self._http(client).post( + url=f"{base}/sandboxes", + headers=self._lifecycle_headers(key), + json=body, ) data: Final = response.json() sandbox_id: Final = str(data["id"]) @@ -182,12 +179,9 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): base: Final = str(handle._hidden_params.get("api_base") or self._api_base(api_base)) key: Final = self._api_key(api_key=api_key, handle=handle) try: - response: Final = cast( - httpx.Response, - await self._http(client).delete( - url=f"{base}/sandboxes/{handle.id}", - headers=self._lifecycle_headers(key), - ), + response: Final = await self._http(client).delete( + url=f"{base}/sandboxes/{handle.id}", + headers=self._lifecycle_headers(key), ) except httpx.HTTPStatusError as e: if e.response.status_code == 404: @@ -245,12 +239,9 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): ) -> None: deadline: Final = time.monotonic() + ready_timeout while True: - response = cast( - httpx.Response, - await self._http(client).get( - url=f"{api_base}/sandboxes/{sandbox_id}", - headers=headers, - ), + response = await self._http(client).get( + url=f"{api_base}/sandboxes/{sandbox_id}", + headers=headers, ) data = response.json() state = self._sandbox_state(data) @@ -306,13 +297,10 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): use_server_proxy: bool, client: AsyncHTTPHandler | None, ) -> tuple[str, dict[str, str]]: - response: Final = cast( - httpx.Response, - await self._http(client).get( - url=f"{api_base}/sandboxes/{sandbox_id}/endpoints/{OPEN_SANDBOX_EXECD_PORT}", - headers=headers, - params={"use_server_proxy": use_server_proxy}, - ), + response: Final = await self._http(client).get( + url=f"{api_base}/sandboxes/{sandbox_id}/endpoints/{OPEN_SANDBOX_EXECD_PORT}", + headers=headers, + params={"use_server_proxy": use_server_proxy}, ) data: Final = response.json() endpoint: Final = data.get("endpoint") @@ -329,15 +317,12 @@ class OpenSandboxSandboxConfig(BaseSandboxConfig): client: AsyncHTTPHandler | None, ) -> list[str]: timeout: Final = httpx.Timeout(connect=30.0, read=None, write=30.0, pool=None) - response: Final = cast( - httpx.Response, - await self._http(client).post( - url=url, - headers=headers, - timeout=timeout, - json=body, - stream=True, - ), + response: Final = await self._http(client).post( + url=url, + headers=headers, + timeout=timeout, + json=body, + stream=True, ) return await self._read_capped_lines(response) diff --git a/litellm/llms/parallel_ai/search/cost_calculator.py b/litellm/llms/parallel_ai/search/cost_calculator.py new file mode 100644 index 00000000000..809cd280cc8 --- /dev/null +++ b/litellm/llms/parallel_ai/search/cost_calculator.py @@ -0,0 +1,90 @@ +from collections.abc import Mapping, Sequence +from types import MappingProxyType +from typing import Final + +from pydantic import TypeAdapter, ValidationError + +from litellm.utils import get_model_info + +PARALLEL_AI_DEFAULT_RESULTS: Final = 10 +PARALLEL_AI_ADDITIONAL_RESULT_COST: Final = 0.001 +PARALLEL_AI_USAGE_PARAM: Final = "_parallel_ai_usage" +PARALLEL_AI_STANDARD_SEARCH_MODEL: Final = "parallel_ai/search" +PARALLEL_AI_FAST_SEARCH_MODEL: Final = "parallel_ai/search-fast" +PARALLEL_AI_TURBO_SEARCH_MODEL: Final = "parallel_ai/search-turbo" +PARALLEL_AI_PRICING_MODEL_BY_MODE: Final[Mapping[str, str]] = MappingProxyType( + { + "fast": PARALLEL_AI_FAST_SEARCH_MODEL, + "turbo": PARALLEL_AI_TURBO_SEARCH_MODEL, + } +) +ADVANCED_SETTINGS_ADAPTER: Final[TypeAdapter[Mapping[str, object]]] = TypeAdapter(Mapping[str, object]) + + +def _non_negative_int(value: object) -> int | None: + if isinstance(value, bool) or not isinstance(value, int) or value < 0: + return None + return value + + +def _usage_count(usage: Sequence[Mapping[str, object]], sku: str) -> int | None: + counts: Final = tuple( + count + for item in usage + if item.get("name") == sku + if (count := _non_negative_int(item.get("count"))) is not None + ) + return sum(counts) if counts else None + + +def _effective_mode(optional_params: Mapping[str, object]) -> str: + mode: Final = optional_params.get("mode") + if isinstance(mode, str): + return mode + + processor: Final = optional_params.get("processor") + if processor == "pro": + return "advanced" + return "basic" + + +def _effective_max_results(optional_params: Mapping[str, object]) -> int: + try: + advanced_settings: Final = ADVANCED_SETTINGS_ADAPTER.validate_python(optional_params.get("advanced_settings")) + advanced_max_results: Final = _non_negative_int(advanced_settings.get("max_results")) + if advanced_max_results is not None: + return advanced_max_results + except ValidationError: + pass + + max_results: Final = _non_negative_int(optional_params.get("max_results")) + return max_results if max_results is not None else PARALLEL_AI_DEFAULT_RESULTS + + +def _request_cost(mode: str) -> float: + pricing_model: Final = PARALLEL_AI_PRICING_MODEL_BY_MODE.get(mode, PARALLEL_AI_STANDARD_SEARCH_MODEL) + model_info: Final = get_model_info(model=pricing_model, custom_llm_provider="parallel_ai") + return float(model_info.get("input_cost_per_query") or 0.0) + + +def _additional_results( + optional_params: Mapping[str, object], + usage: Sequence[Mapping[str, object]] | None, +) -> int: + usage_count: Final = _usage_count(usage, "sku_search_additional_results") if usage is not None else None + if usage_count is not None: + return usage_count + if usage is not None: + return 0 + return max(_effective_max_results(optional_params) - PARALLEL_AI_DEFAULT_RESULTS, 0) + + +def parallel_ai_search_cost( + optional_params: Mapping[str, object], + usage: Sequence[Mapping[str, object]] | None, +) -> float: + request_cost: Final = _request_cost(_effective_mode(optional_params)) + request_count_from_usage: Final = _usage_count(usage, "sku_search") if usage is not None else None + request_count: Final = request_count_from_usage if request_count_from_usage is not None else 1 + additional_results: Final = _additional_results(optional_params, usage) + return request_count * request_cost + additional_results * PARALLEL_AI_ADDITIONAL_RESULT_COST diff --git a/litellm/llms/parallel_ai/search/transformation.py b/litellm/llms/parallel_ai/search/transformation.py index ea21d1153fe..bde7b7b86db 100644 --- a/litellm/llms/parallel_ai/search/transformation.py +++ b/litellm/llms/parallel_ai/search/transformation.py @@ -4,9 +4,13 @@ Calls Parallel AI's /v1/search endpoint to search the web. Parallel AI API Reference: https://docs.parallel.ai/api-reference/search/search """ +from collections.abc import Mapping, Sequence +from types import MappingProxyType from typing import Final, TypedDict import httpx +from pydantic import BaseModel, ConfigDict +from typing_extensions import ReadOnly from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.search.transformation import ( @@ -14,9 +18,29 @@ from litellm.llms.base_llm.search.transformation import ( SearchResponse, SearchResult, ) +from litellm.llms.parallel_ai.search.cost_calculator import PARALLEL_AI_USAGE_PARAM from litellm.secret_managers.main import get_secret_str +class _ParallelAIV1SearchResult(BaseModel): + model_config = ConfigDict(extra="ignore") + + url: str | None = None + title: str | None = None + publish_date: str | None = None + excerpts: Sequence[str] | None = None + + +class _ParallelAIV1SearchResponse(BaseModel): + model_config = ConfigDict(extra="ignore") + + search_id: str | None = None + session_id: str | None = None + results: Sequence[_ParallelAIV1SearchResult] = () + usage: Sequence[Mapping[str, object]] | None = None + warnings: Sequence[Mapping[str, object]] | None = None + + class _ParallelAISourcePolicy(TypedDict, total=False): include_domains: list[str] exclude_domains: list[str] @@ -27,10 +51,16 @@ class _ParallelAIExcerptSettings(TypedDict, total=False): max_chars_per_result: int +class _ParallelAIFetchPolicy(TypedDict, total=False): + max_age_seconds: ReadOnly[int] + timeout_seconds: ReadOnly[float] + disable_cache_fallback: ReadOnly[bool] + + class _ParallelAIAdvancedSettings(TypedDict, total=False): source_policy: _ParallelAISourcePolicy excerpt_settings: _ParallelAIExcerptSettings - fetch_policy: dict + fetch_policy: _ParallelAIFetchPolicy location: str max_results: int @@ -43,14 +73,14 @@ class ParallelAISearchRequest(TypedDict, total=False): search_queries: list[str] # Required - at least one keyword search query objective: str # Optional - natural-language description of search goal - mode: str # Optional - 'turbo', 'basic', or 'advanced' (default 'advanced') + mode: str # Optional - 'turbo', 'fast', 'basic', or 'advanced' (default 'advanced') max_chars_total: int # Optional - upper bound on total excerpt characters session_id: str # Optional - tracks calls across search/extract requests client_model: str # Optional - model consuming the results advanced_settings: _ParallelAIAdvancedSettings -LEGACY_PROCESSOR_TO_MODE: Final = {"base": "basic", "pro": "advanced"} +LEGACY_PROCESSOR_TO_MODE: Final = MappingProxyType({"base": "basic", "pro": "advanced"}) class ParallelAISearchConfig(BaseSearchConfig): @@ -67,16 +97,16 @@ class ParallelAISearchConfig(BaseSearchConfig): api_base: str | None = None, **kwargs, ) -> dict: - api_key = self.resolve_server_api_key( + resolved_api_key: Final = self.resolve_server_api_key( caller_api_key=api_key, caller_api_base=api_base, key_env_vars=("PARALLEL_AI_API_KEY", "PARALLEL_API_KEY"), base_env_var="PARALLEL_AI_API_BASE", default_api_base=self.PARALLEL_AI_API_BASE, ) - if not api_key: + if not resolved_api_key: raise ValueError("PARALLEL_API_KEY is not set. Set `PARALLEL_API_KEY` environment variable.") - headers["x-api-key"] = api_key + headers["x-api-key"] = resolved_api_key headers["Content-Type"] = "application/json" return headers @@ -87,13 +117,12 @@ class ParallelAISearchConfig(BaseSearchConfig): data: dict | list[dict] | None = None, **kwargs, ) -> str: - api_base = api_base or get_secret_str("PARALLEL_AI_API_BASE") or self.PARALLEL_AI_API_BASE + resolved_api_base: Final = api_base or get_secret_str("PARALLEL_AI_API_BASE") or self.PARALLEL_AI_API_BASE - api_base = api_base.rstrip("/") - if not api_base.endswith("/v1/search"): - api_base = f"{api_base.removesuffix('/v1')}/v1/search" - - return api_base + trimmed: Final = resolved_api_base.rstrip("/") + if trimmed.endswith("/v1/search"): + return trimmed + return f"{trimmed.removesuffix('/v1')}/v1/search" def transform_search_request( self, @@ -109,14 +138,17 @@ class ParallelAISearchConfig(BaseSearchConfig): - If string: maps to `search_queries` (single item) and `objective` - If list: maps to `search_queries` (keyword queries) optional_params: Optional parameters for the request - - mode: Search mode ('turbo', 'basic', 'advanced'); defaults to 'basic' + - mode: Search mode ('turbo', 'fast', 'basic', 'advanced'); defaults to 'basic' - processor: Legacy v1beta param; 'base' maps to mode 'basic', 'pro' to 'advanced' - max_results: Maximum number of search results -> `advanced_settings.max_results` - - search_domain_filter: Domains to include -> `advanced_settings.source_policy.include_domains` + - search_domain_filter / include_domains: Domains to include -> `advanced_settings.source_policy.include_domains` - exclude_domains: Domains to exclude -> `advanced_settings.source_policy.exclude_domains` - - country: ISO 3166-1 alpha-2 code -> `advanced_settings.location` + - after_date: RFC 3339 date (YYYY-MM-DD) -> `advanced_settings.source_policy.after_date` + - country / location: ISO 3166-1 alpha-2 code -> `advanced_settings.location` - max_chars_per_result: -> `advanced_settings.excerpt_settings.max_chars_per_result` - - Any other params are passed through to the request body as-is + - fetch_policy: Cache vs live-fetch policy -> `advanced_settings.fetch_policy` + - Any other params (objective, max_chars_total, session_id, client_model, ...) + are passed through to the request body as-is Returns: Dict with request data following the v1 search request spec @@ -137,7 +169,7 @@ class ParallelAISearchConfig(BaseSearchConfig): mode = LEGACY_PROCESSOR_TO_MODE.get(processor, processor) # the v1 API defaults to 'advanced' when mode is omitted; default to 'basic' # instead to keep v1beta's default tier (processor 'base') and litellm's - # $0.004/query cost map entry for `parallel_ai/search` accurate + # cost map entry for `parallel_ai/search` accurate request_data["mode"] = mode or "basic" advanced_settings: Final[_ParallelAIAdvancedSettings] = {} @@ -148,17 +180,29 @@ class ParallelAISearchConfig(BaseSearchConfig): if "country" in params: advanced_settings["location"] = params.pop("country") + if "location" in params: + advanced_settings["location"] = params.pop("location") + if "max_chars_per_result" in params: advanced_settings["excerpt_settings"] = {"max_chars_per_result": params.pop("max_chars_per_result")} + if "fetch_policy" in params: + advanced_settings["fetch_policy"] = params.pop("fetch_policy") + source_policy: Final[_ParallelAISourcePolicy] = {} if "search_domain_filter" in params: source_policy["include_domains"] = params.pop("search_domain_filter") + if "include_domains" in params: + source_policy["include_domains"] = params.pop("include_domains") + if "exclude_domains" in params: source_policy["exclude_domains"] = params.pop("exclude_domains") + if "after_date" in params: + source_policy["after_date"] = params.pop("after_date") + if source_policy: advanced_settings["source_policy"] = source_policy @@ -170,9 +214,11 @@ class ParallelAISearchConfig(BaseSearchConfig): # unified-spec param with no v1 equivalent params.pop("max_tokens_per_page", None) - result_data: Final[dict] = dict(request_data) - result_data.update(params) - return result_data + # reserved for the provider's own reported usage, which prices the request; + # a caller-supplied value would otherwise set its own cost + params.pop(PARALLEL_AI_USAGE_PARAM, None) + + return {**request_data, **params} def transform_search_response( self, @@ -186,26 +232,49 @@ class ParallelAISearchConfig(BaseSearchConfig): Parallel AI -> LiteLLM mappings: - results[].title -> SearchResult.title - results[].url -> SearchResult.url - - results[].excerpts (array) -> SearchResult.snippet (joined string) + - results[].excerpts (array) -> SearchResult.snippet (joined string); the raw + array is preserved as an extra `excerpts` field on each result - results[].publish_date -> SearchResult.date + - search_id / session_id / warnings are preserved as extra fields on the + response; usage is preserved as `parallel_usage` (the `usage` name is + reserved for LiteLLM's token-usage object) """ - response_json: Final = raw_response.json() + parsed: Final = _ParallelAIV1SearchResponse.model_validate(raw_response.json()) - results: Final = [] - for result in response_json.get("results", []): - excerpts = result.get("excerpts") or [] - snippet = " ... ".join(excerpts) if excerpts else "" + # written unconditionally: leaving a caller-supplied value in place when the + # provider reports no usage would let the caller price its own request + logging_obj.optional_params = { + **logging_obj.optional_params, + PARALLEL_AI_USAGE_PARAM: parsed.usage, + } - search_result = SearchResult( - title=result.get("title") or "", - url=result.get("url") or "", - snippet=snippet, - date=result.get("publish_date"), - last_updated=None, + results: Final = tuple( + SearchResult.model_validate( + MappingProxyType( + { + "title": result.title or "", + "url": result.url or "", + "snippet": " ... ".join(result.excerpts or ()), + "date": result.publish_date, + "last_updated": None, + "excerpts": result.excerpts or (), + } + ) ) - results.append(search_result) - - return SearchResponse( - results=results, - object="search", + for result in parsed.results ) + + extra_fields: Final = MappingProxyType( + { + key: value + for key, value in ( + ("search_id", parsed.search_id), + ("session_id", parsed.session_id), + ("parallel_usage", parsed.usage), + ("warnings", parsed.warnings), + ) + if value is not None + } + ) + + return SearchResponse.model_validate(MappingProxyType({"results": results, "object": "search", **extra_fields})) diff --git a/litellm/llms/perplexity/chat/transformation.py b/litellm/llms/perplexity/chat/transformation.py index bf33103b480..354f7692fd5 100644 --- a/litellm/llms/perplexity/chat/transformation.py +++ b/litellm/llms/perplexity/chat/transformation.py @@ -2,7 +2,7 @@ Translate from OpenAI's `/v1/chat/completions` to Perplexity's `/v1/chat/completions` """ -from typing import Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -14,6 +14,9 @@ from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues, ChatCompletionAnnotation, ChatCompletionAnnotationURLCitation from litellm.types.utils import ModelResponse, PromptTokensDetailsWrapper, Usage +if TYPE_CHECKING: + import tiktoken + class PerplexityChatConfig(OpenAIGPTConfig): @property @@ -72,7 +75,7 @@ class PerplexityChatConfig(OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/petals/completion/transformation.py b/litellm/llms/petals/completion/transformation.py index 97b021bb119..3e0de14a7b2 100644 --- a/litellm/llms/petals/completion/transformation.py +++ b/litellm/llms/petals/completion/transformation.py @@ -1,4 +1,4 @@ -from typing import Any, Final +from typing import TYPE_CHECKING, Final from httpx import Headers, Response @@ -13,6 +13,9 @@ from litellm.types.utils import ModelResponse from ..common_utils import PetalsError +if TYPE_CHECKING: + import tiktoken + class PetalsConfig(BaseConfig): """ @@ -109,7 +112,7 @@ class PetalsConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/pg_vector/vector_stores/transformation.py b/litellm/llms/pg_vector/vector_stores/transformation.py index e4b06c36bf4..9de1f589ae4 100644 --- a/litellm/llms/pg_vector/vector_stores/transformation.py +++ b/litellm/llms/pg_vector/vector_stores/transformation.py @@ -8,6 +8,7 @@ from litellm.types.vector_stores import VectorStoreSearchOptionalRequestParams if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -80,6 +81,7 @@ class PGVectorStoreConfig(OpenAIVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: encoded_vector_store_id: Final = encode_url_path_segment(vector_store_id, field_name="vector_store_id") url: Final = f"{api_base}/{encoded_vector_store_id}/search" diff --git a/litellm/llms/ragflow/vector_stores/transformation.py b/litellm/llms/ragflow/vector_stores/transformation.py index 282cb7a92a7..ffa6c9e1076 100644 --- a/litellm/llms/ragflow/vector_stores/transformation.py +++ b/litellm/llms/ragflow/vector_stores/transformation.py @@ -17,6 +17,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -92,6 +93,7 @@ class RAGFlowVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """RAGFlow vector stores are management-only, search is not supported.""" raise NotImplementedError("RAGFlow vector stores support dataset management only, not search/retrieval") diff --git a/litellm/llms/recraft/image_generation/transformation.py b/litellm/llms/recraft/image_generation/transformation.py index 2b7b44c7233..3a04e0a62b4 100644 --- a/litellm/llms/recraft/image_generation/transformation.py +++ b/litellm/llms/recraft/image_generation/transformation.py @@ -14,6 +14,8 @@ from litellm.types.llms.recraft import RecraftImageGenerationRequestParams from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -120,7 +122,7 @@ class RecraftImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/reducto/ocr/transformation.py b/litellm/llms/reducto/ocr/transformation.py index 84d5164cf87..a7216e4ec40 100644 --- a/litellm/llms/reducto/ocr/transformation.py +++ b/litellm/llms/reducto/ocr/transformation.py @@ -1,4 +1,4 @@ -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx @@ -17,6 +17,9 @@ from litellm.llms.reducto.common import ( upload_bytes_sync, ) +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class _BaseReductoOCRConfig(BaseOCRConfig): def map_ocr_params( @@ -127,7 +130,7 @@ class _BaseReductoOCRConfig(BaseOCRConfig): self, model: str, raw_response: httpx.Response, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", **kwargs, ) -> OCRResponse: response_json: Final = raw_response.json() diff --git a/litellm/llms/replicate/chat/transformation.py b/litellm/llms/replicate/chat/transformation.py index 4cee5489fe0..769160c6ced 100644 --- a/litellm/llms/replicate/chat/transformation.py +++ b/litellm/llms/replicate/chat/transformation.py @@ -19,6 +19,8 @@ from litellm.utils import token_counter from ..common_utils import ReplicateError if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -235,7 +237,7 @@ class ReplicateConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/runwayml/image_generation/transformation.py b/litellm/llms/runwayml/image_generation/transformation.py index 344c8ae2d7c..5913709c8a0 100644 --- a/litellm/llms/runwayml/image_generation/transformation.py +++ b/litellm/llms/runwayml/image_generation/transformation.py @@ -1,8 +1,10 @@ import asyncio import time +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm.constants import ( @@ -20,6 +22,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -27,6 +31,16 @@ else: LiteLLMLoggingObj = Any +class _RunwayMLTask(TypedDict, total=False): + """The RunwayML task payload returned by POST /v1/text_to_image and GET /v1/tasks/{id}.""" + + id: ReadOnly[str] + status: ReadOnly[str] + output: ReadOnly[Sequence[str | Mapping[str, str]]] + failure: ReadOnly[str] + failureCode: ReadOnly[str] + + class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): """ Configuration for RunwayML image generation models. @@ -78,7 +92,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): @staticmethod def _transform_runwayml_response_to_openai( - response_data: dict[str, Any], + response_data: _RunwayMLTask, model_response: ImageResponse, ) -> ImageResponse: """ @@ -153,7 +167,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): raise TimeoutError(f"RunwayML task polling timed out after {timeout_secs} seconds") @staticmethod - def _check_task_status(response_data: dict[str, Any]) -> str: + def _check_task_status(response_data: _RunwayMLTask) -> str: """ Check RunwayML task status from response. @@ -225,7 +239,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): response = client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayMLTask = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -274,7 +288,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): response = await client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayMLTask = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -294,7 +308,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: @@ -320,7 +334,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): } """ try: - response_data = raw_response.json() + response_data: _RunwayMLTask = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error transforming image generation response: {e}", @@ -369,7 +383,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: @@ -380,7 +394,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): We need to poll the task until it completes (status SUCCEEDED) using async polling. """ try: - response_data = raw_response.json() + response_data: _RunwayMLTask = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error transforming image generation response: {e}", diff --git a/litellm/llms/runwayml/text_to_speech/transformation.py b/litellm/llms/runwayml/text_to_speech/transformation.py index 1da8f0c66f0..19e6d8ff494 100644 --- a/litellm/llms/runwayml/text_to_speech/transformation.py +++ b/litellm/llms/runwayml/text_to_speech/transformation.py @@ -6,10 +6,11 @@ Maps OpenAI TTS spec to RunwayML Text-to-Speech API import asyncio import time -from collections.abc import Coroutine -from typing import TYPE_CHECKING, Any, Final, Union +from collections.abc import Coroutine, Sequence +from typing import TYPE_CHECKING, Any, Final, TypedDict, Union import httpx +from typing_extensions import ReadOnly import litellm from litellm._logging import verbose_logger @@ -31,6 +32,14 @@ else: HttpxBinaryResponseContent = Any +class _RunwayTtsTaskResponse(TypedDict, total=False): + id: ReadOnly[str] + status: ReadOnly[str] + output: ReadOnly[Sequence[object]] + failure: ReadOnly[str] + failureCode: ReadOnly[str] + + class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): """ Configuration for RunwayML Text-to-Speech @@ -64,7 +73,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): litellm_params_dict: dict, logging_obj: "LiteLLMLoggingObj", timeout: float | httpx.Timeout, - extra_headers: dict[str, Any] | None, + extra_headers: dict[str, object] | None, base_llm_http_handler: Any, aspeech: bool, api_base: str | None, @@ -72,7 +81,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): **kwargs: Any, ) -> Union[ "HttpxBinaryResponseContent", - Coroutine[Any, Any, "HttpxBinaryResponseContent"], + Coroutine[object, object, "HttpxBinaryResponseContent"], ]: """ Dispatch method to handle RunwayML TTS requests @@ -242,7 +251,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): raise TimeoutError(f"RunwayML TTS task polling timed out after {timeout_secs} seconds") @staticmethod - def _check_task_status(response_data: dict[str, Any]) -> str: + def _check_task_status(response_data: _RunwayTtsTaskResponse) -> str: """ Check RunwayML task status from response. @@ -314,7 +323,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): response = client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayTtsTaskResponse = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -362,7 +371,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): response = await client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayTtsTaskResponse = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -453,7 +462,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): from litellm.types.llms.openai import HttpxBinaryResponseContent try: - response_data: Final = raw_response.json() + response_data: Final[_RunwayTtsTaskResponse] = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error parsing RunwayML TTS response: {e}", @@ -483,7 +492,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): ) # Get the completed task data - task_data: Final = polled_response.json() + task_data: Final[_RunwayTtsTaskResponse] = polled_response.json() verbose_logger.debug("RunwayML TTS polling complete, downloading audio") @@ -522,7 +531,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): from litellm.types.llms.openai import HttpxBinaryResponseContent try: - response_data: Final = raw_response.json() + response_data: Final[_RunwayTtsTaskResponse] = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error parsing RunwayML TTS response: {e}", @@ -552,7 +561,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig): ) # Get the completed task data - task_data: Final = polled_response.json() + task_data: Final[_RunwayTtsTaskResponse] = polled_response.json() verbose_logger.debug("RunwayML TTS polling complete (async), downloading audio") diff --git a/litellm/llms/runwayml/videos/transformation.py b/litellm/llms/runwayml/videos/transformation.py index b8e57fa7cc0..c7696a1cb29 100644 --- a/litellm/llms/runwayml/videos/transformation.py +++ b/litellm/llms/runwayml/videos/transformation.py @@ -1,5 +1,6 @@ from collections.abc import Mapping, Sequence from datetime import datetime +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal import httpx @@ -33,6 +34,10 @@ else: LiteLLMLoggingObj = Any +class RunwayMLError(BaseLLMException): + pass + + class _RunwayTaskResponse(TypedDict, total=False): id: ReadOnly[str] status: ReadOnly[str] @@ -41,7 +46,8 @@ class _RunwayTaskResponse(TypedDict, total=False): output: ReadOnly[Sequence[str] | str] failureCode: ReadOnly[str] failure: ReadOnly[str] - progress: ReadOnly[int] + progress: ReadOnly[float] + estimatedCost: ReadOnly[Mapping[str, float]] class _VideoObjectData(TypedDict, extra_items=object): @@ -56,12 +62,54 @@ def _parse_runway_task_response(raw_response: httpx.Response) -> _RunwayTaskResp return response_data +_USD_PER_CREDIT: Final = 0.01 + +_RESOLUTION_AREA_TIERS: Final[tuple[tuple[int, str], ...]] = ( + (600_000, "480p"), + (1_500_000, "720p"), + (4_000_000, "1080p"), +) + + +def _ratio_to_resolution(ratio: object) -> str | None: + if not isinstance(ratio, str) or ":" not in ratio: + return None + width_str, _, height_str = ratio.partition(":") + if not (width_str.isdigit() and height_str.isdigit()): + return None + area: Final = int(width_str) * int(height_str) + return next((label for threshold, label in _RESOLUTION_AREA_TIERS if area < threshold), "4k") + + +def _duration_seconds(seconds: str | None) -> float | None: + if not seconds: + return None + try: + return float(seconds) + except ValueError: + return None + + +def _estimated_cost_usd(response_data: _RunwayTaskResponse) -> float | None: + estimated_cost: Final = response_data.get("estimatedCost") + if not isinstance(estimated_cost, Mapping): + return None + credits: Final = estimated_cost.get("credits") + if not isinstance(credits, (int, float)): + return None + return float(credits) * _USD_PER_CREDIT + + +def _progress_percent(progress: float) -> int: + return min(100, max(0, round(float(progress) * 100))) + + class RunwayMLVideoConfig(BaseVideoConfig): """ Configuration class for RunwayML video generation. RunwayML uses a task-based API where: - 1. POST /v1/image_to_video creates a task + 1. POST /v1/text_to_video, /v1/image_to_video, or /v1/video_to_video creates a task 2. The task returns immediately with a task ID 3. Client must poll or wait for task completion """ @@ -69,6 +117,10 @@ class RunwayMLVideoConfig(BaseVideoConfig): def __init__(self): super().__init__() + @staticmethod + def _parse_task_response(raw_response: httpx.Response) -> _RunwayTaskResponse: + return raw_response.json() + def get_supported_openai_params(self, model: str) -> list: """ Get the list of supported OpenAI parameters for video generation. @@ -93,7 +145,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): video_create_optional_params: VideoCreateOptionalRequestParams, model: str, drop_params: bool, - ) -> dict: + ) -> dict[str, object]: """ Map OpenAI parameters to RunwayML format. @@ -103,37 +155,42 @@ class RunwayMLVideoConfig(BaseVideoConfig): - size -> ratio (convert "WIDTHxHEIGHT" to "WIDTH:HEIGHT") - seconds -> duration (convert to integer) """ - mapped_params: Final[dict[str, object]] = {} + supported_openai_params: Final = self.get_supported_openai_params(model) + return { + **self._prompt_image_param(video_create_optional_params), + **self._ratio_param(video_create_optional_params), + **self._duration_param(video_create_optional_params), + **{key: value for key, value in video_create_optional_params.items() if key not in supported_openai_params}, + } + @staticmethod + def _prompt_image_param(video_create_optional_params: VideoCreateOptionalRequestParams) -> Mapping[str, object]: # Handle input_reference parameter - map to promptImage if "input_reference" in video_create_optional_params: - input_reference: Final = video_create_optional_params["input_reference"] - # RunwayML supports URLs and data URIs directly - mapped_params["promptImage"] = input_reference + return {"promptImage": video_create_optional_params["input_reference"]} + return {} + @staticmethod + def _ratio_param(video_create_optional_params: VideoCreateOptionalRequestParams) -> Mapping[str, str]: # Handle size parameter - convert "1280x720" to "1280:720" if "size" in video_create_optional_params: size: Final = video_create_optional_params["size"] if isinstance(size, str) and "x" in size: - mapped_params["ratio"] = size.replace("x", ":") + return {"ratio": size.replace("x", ":")} + return {} + @staticmethod + def _duration_param(video_create_optional_params: VideoCreateOptionalRequestParams) -> Mapping[str, int]: # Handle seconds parameter - convert to integer if "seconds" in video_create_optional_params: seconds: Final = video_create_optional_params["seconds"] if seconds is not None: try: - mapped_params["duration"] = int(float(seconds)) if isinstance(seconds, str) else int(seconds) + return {"duration": int(float(seconds)) if isinstance(seconds, str) else int(seconds)} except (ValueError, TypeError): # If conversion fails, use default duration pass - - # Pass through other parameters that aren't OpenAI-specific - supported_openai_params: Final = self.get_supported_openai_params(model) - for key, value in video_create_optional_params.items(): - if key not in supported_openai_params: - mapped_params[key] = value - - return mapped_params + return {} def validate_environment( self, @@ -188,38 +245,43 @@ class RunwayMLVideoConfig(BaseVideoConfig): model: str, prompt: str, api_base: str, - video_create_optional_request_params: dict, + video_create_optional_request_params: dict[str, object], litellm_params: GenericLiteLLMParams, headers: dict, ) -> tuple[dict, RequestFiles, str]: """ Transform the video creation request for RunwayML API. - RunwayML expects: - { - "model": "gen4_turbo", - "promptImage": "https://... or data:image/...", - "promptText": "description", - "ratio": "1280:720", - "duration": 5 - } + RunwayML has three generation endpoints discriminated by which input is + present, and each request body rejects unknown fields: + - /text_to_video: promptText only (rejects promptImage) + - /image_to_video: promptImage (+ optional promptText) + - /video_to_video: promptVideo or videoUri (rejects promptImage) """ - # Build the request data + merged_params: Final = MappingProxyType( + { + "model": model, + "promptText": prompt, + **video_create_optional_request_params, + } + ) + + endpoint: Final = self._select_generation_endpoint(merged_params) + request_data: Final[dict[str, object]] = { - "model": model, - "promptText": prompt, + key: value for key, value in merged_params.items() if endpoint == "image_to_video" or key != "promptImage" } - # Add mapped parameters - request_data.update(video_create_optional_request_params) - - # RunwayML uses JSON body, no files multipart files_list: Final[RequestFiles] = [] - # Append the specific endpoint for video generation - full_api_base: Final = f"{api_base}/image_to_video" + return request_data, files_list, f"{api_base}/{endpoint}" - return request_data, files_list, full_api_base + def _select_generation_endpoint(self, request_data: Mapping[str, object]) -> str: + if request_data.get("promptVideo") is not None or request_data.get("videoUri") is not None: + return "video_to_video" + if request_data.get("promptImage") is not None: + return "image_to_video" + return "text_to_video" def transform_video_create_response( self, @@ -285,13 +347,15 @@ class RunwayMLVideoConfig(BaseVideoConfig): video_obj.id = encode_video_id_with_provider(video_obj.id, custom_llm_provider, model) # Add usage data for cost tracking - usage_data: Final = {} - if video_obj and hasattr(video_obj, "seconds") and video_obj.seconds: - try: - usage_data["duration_seconds"] = float(video_obj.seconds) - except (ValueError, TypeError): - pass - video_obj.usage = usage_data + video_obj.usage = { + key: value + for key, value in ( + ("duration_seconds", _duration_seconds(video_obj.seconds)), + ("video_resolution", _ratio_to_resolution(request_data.get("ratio") if request_data else None)), + ("provider_reported_cost_usd", _estimated_cost_usd(response_data)), + ) + if value is not None + } return video_obj @@ -351,20 +415,18 @@ class RunwayMLVideoConfig(BaseVideoConfig): # Get task status to retrieve video URL url: Final = f"{api_base}/tasks/{encoded_video_id}" - params: Final[dict[str, str]] = {} + return url, dict[str, str]() - return url, params - - def _extract_video_url_from_response(self, response_data: dict[str, Any]) -> str: + def _extract_video_url_from_response(self, response_data: _RunwayTaskResponse) -> str: """ Helper method to extract video URL from RunwayML response. Shared between sync and async transforms. """ # Extract video URL from the output field video_url = None - if "output" in response_data and response_data["output"]: - output: Final = response_data["output"] - video_url = output[0] if isinstance(output, list) else output + raw_output: Final = response_data.get("output") + if raw_output: + video_url = raw_output if isinstance(raw_output, str) else raw_output[0] if not video_url: # Check if the video generation failed or is still processing @@ -398,7 +460,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): "output":["https://dnznrvs05pmza.cloudfront.net/.../video.mp4?_jwt=..."] } """ - response_data: Final = raw_response.json() + response_data: Final[_RunwayTaskResponse] = self._parse_task_response(raw_response) video_url: Final = self._extract_video_url_from_response(response_data) # Download the video from the CloudFront URL synchronously @@ -427,7 +489,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): "output":["https://dnznrvs05pmza.cloudfront.net/.../video.mp4?_jwt=..."] } """ - response_data: Final = raw_response.json() + response_data: Final[_RunwayTaskResponse] = self._parse_task_response(raw_response) video_url: Final = self._extract_video_url_from_response(response_data) # Download the video from the CloudFront URL asynchronously @@ -509,9 +571,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): # Construct the URL for task cancellation url: Final = f"{api_base}/tasks/{encoded_video_id}/cancel" - data: Final[dict[str, str]] = {} - - return url, data + return url, dict[str, str]() def transform_video_delete_response( self, @@ -549,9 +609,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): url: Final = f"{api_base}/tasks/{encoded_video_id}" # Empty dict for GET request (no body) - data: Final[dict[str, str]] = {} - - return url, data + return url, dict[str, str]() def transform_video_status_retrieve_response( self, @@ -581,8 +639,9 @@ class RunwayMLVideoConfig(BaseVideoConfig): if "completedAt" in response_data: video_data["completed_at"] = self._parse_runway_timestamp(response_data.get("completedAt")) - if "progress" in response_data: - video_data["progress"] = response_data["progress"] + progress_value: Final = response_data.get("progress") + if progress_value is not None: + video_data["progress"] = _progress_percent(progress_value) if "failureCode" in response_data or "failure" in response_data: video_data["error"] = { @@ -616,6 +675,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): api_base, litellm_params, headers, + video_file=None, extra_body=None, prefetched_source_data=None, ): @@ -646,9 +706,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): raise NotImplementedError("video extension is not supported for RunwayML") def get_error_class(self, error_message: str, status_code: int, headers: dict | httpx.Headers) -> BaseLLMException: - from ...base_llm.chat.transformation import BaseLLMException - - raise BaseLLMException( + return RunwayMLError( status_code=status_code, message=error_message, headers=headers, diff --git a/litellm/llms/s3_vectors/vector_stores/transformation.py b/litellm/llms/s3_vectors/vector_stores/transformation.py index 5be35ae4148..733358381fe 100644 --- a/litellm/llms/s3_vectors/vector_stores/transformation.py +++ b/litellm/llms/s3_vectors/vector_stores/transformation.py @@ -1,8 +1,8 @@ -import re from typing import TYPE_CHECKING, Any, Final import httpx +from litellm.caching._embedding_router import resolve_embedding_router from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.types.router import GenericLiteLLMParams @@ -18,6 +18,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -58,13 +59,20 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): return headers def get_complete_url(self, api_base: str | None, litellm_params: dict) -> str: - aws_region_name: Final = litellm_params.get("aws_region_name") - if not aws_region_name: - raise ValueError("aws_region_name is required for S3 Vectors") - if not re.match(r"^[a-z][a-z0-9-]*$", aws_region_name): - raise ValueError("Invalid aws_region_name format") + # Resolve region the same way the ingestion path does: + # dynamic param -> AWS_REGION_NAME -> AWS_REGION -> default (us-west-2) + aws_region_name: Final = self.get_aws_region_name_for_non_llm_api_calls(litellm_params.get("aws_region_name")) return f"https://s3vectors.{aws_region_name}.api.aws" + def _resolve_query_embedding_router(self, embedding_model: str, router: "Router | None") -> "Router | None": + """Return the router iff it serves ``embedding_model`` as a deployment.""" + if router is None: + return None + model_list: Final = [ + dict(m) for m in (router.get_model_list() or ()) + ] # mutable-ok: resolve_embedding_router requires list[dict] + return resolve_embedding_router(embedding_model=embedding_model, llm_router=router, llm_model_list=model_list) + def transform_search_vector_store_request( self, vector_store_id: str, @@ -74,6 +82,7 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """Sync version - generates embedding synchronously.""" # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name @@ -99,10 +108,16 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): # Generate embedding for the query embedding_model: Final = litellm_params.get("embedding_model", "text-embedding-3-small") + embedding_router: Final = self._resolve_query_embedding_router(embedding_model=embedding_model, router=router) import litellm as litellm_module - embedding_response: Final = litellm_module.embedding(model=embedding_model, input=[query]) + embedding_input: Final = [query] # mutable-ok: the embedding API takes list input + embedding_response: Final = ( + embedding_router.embedding(model=embedding_model, input=embedding_input) + if embedding_router is not None + else litellm_module.embedding(model=embedding_model, input=embedding_input) + ) query_embedding: Final = embedding_response.data[0]["embedding"] url: Final = f"{api_base}/QueryVectors" @@ -128,6 +143,7 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """Async version - generates embedding asynchronously.""" # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name @@ -153,10 +169,16 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): # Generate embedding for the query asynchronously embedding_model: Final = litellm_params.get("embedding_model", "text-embedding-3-small") + embedding_router: Final = self._resolve_query_embedding_router(embedding_model=embedding_model, router=router) import litellm as litellm_module - embedding_response: Final = await litellm_module.aembedding(model=embedding_model, input=[query]) + embedding_input: Final = [query] # mutable-ok: the embedding API takes list input + embedding_response: Final = ( + await embedding_router.aembedding(model=embedding_model, input=embedding_input) + if embedding_router is not None + else await litellm_module.aembedding(model=embedding_model, input=embedding_input) + ) query_embedding: Final = embedding_response.data[0]["embedding"] url: Final = f"{api_base}/QueryVectors" diff --git a/litellm/llms/sagemaker/chat/handler.py b/litellm/llms/sagemaker/chat/handler.py index b3e9ed671fc..3f62b7276df 100644 --- a/litellm/llms/sagemaker/chat/handler.py +++ b/litellm/llms/sagemaker/chat/handler.py @@ -5,6 +5,7 @@ from typing import Final import httpx +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.utils import ModelResponse, get_secret @@ -34,6 +35,7 @@ class SagemakerChatHandler(BaseAWSLLM): optional_params.pop("aws_bedrock_runtime_endpoint", None) # https://bedrock-runtime.{region_name}.amazonaws.com aws_web_identity_token: Final = optional_params.pop("aws_web_identity_token", None) aws_sts_endpoint: Final = optional_params.pop("aws_sts_endpoint", None) + aws_external_id: Final = optional_params.pop("aws_external_id", None) ### SET REGION NAME ### if aws_region_name is None: @@ -60,6 +62,7 @@ class SagemakerChatHandler(BaseAWSLLM): aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) return credentials, aws_region_name @@ -79,10 +82,11 @@ class SagemakerChatHandler(BaseAWSLLM): raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") sigv4: Final = SigV4Auth(credentials, "sagemaker", aws_region_name) + dns_suffix: Final = get_aws_dns_suffix(aws_region_name) if optional_params.get("stream") is True: - api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations-response-stream" + api_base = f"https://runtime.sagemaker.{aws_region_name}.{dns_suffix}/endpoints/{model}/invocations-response-stream" else: - api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations" + api_base = f"https://runtime.sagemaker.{aws_region_name}.{dns_suffix}/endpoints/{model}/invocations" sagemaker_base_url: Final = optional_params.get("sagemaker_base_url", None) if sagemaker_base_url is not None: diff --git a/litellm/llms/sagemaker/chat/transformation.py b/litellm/llms/sagemaker/chat/transformation.py index 37ddd813d6f..04995f32d97 100644 --- a/litellm/llms/sagemaker/chat/transformation.py +++ b/litellm/llms/sagemaker/chat/transformation.py @@ -12,6 +12,7 @@ from typing import TYPE_CHECKING, Any, Final, cast import httpx from httpx._models import Headers +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.logging_utils import track_llm_api_timing from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -93,10 +94,11 @@ class SagemakerChatConfig(OpenAIGPTConfig, BaseAWSLLM): model=model, model_id=None, ) + dns_suffix: Final = get_aws_dns_suffix(aws_region_name) if stream is True: - api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations-response-stream" + api_base = f"https://runtime.sagemaker.{aws_region_name}.{dns_suffix}/endpoints/{model}/invocations-response-stream" else: - api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations" + api_base = f"https://runtime.sagemaker.{aws_region_name}.{dns_suffix}/endpoints/{model}/invocations" sagemaker_base_url: Final = cast(str | None, optional_params.get("sagemaker_base_url")) if sagemaker_base_url is not None: diff --git a/litellm/llms/sagemaker/completion/handler.py b/litellm/llms/sagemaker/completion/handler.py index 84cad56f0d4..fb8074d3682 100644 --- a/litellm/llms/sagemaker/completion/handler.py +++ b/litellm/llms/sagemaker/completion/handler.py @@ -1,13 +1,14 @@ import json from collections.abc import Callable from copy import deepcopy -from typing import Any, Final, cast +from typing import Final, cast import httpx import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.asyncify import asyncify +from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.llms.custom_httpx.http_handler import ( @@ -57,6 +58,7 @@ class SagemakerLLM(BaseAWSLLM): optional_params.pop("aws_bedrock_runtime_endpoint", None) # https://bedrock-runtime.{region_name}.amazonaws.com aws_web_identity_token: Final = optional_params.pop("aws_web_identity_token", None) aws_sts_endpoint: Final = optional_params.pop("aws_sts_endpoint", None) + aws_external_id: Final = optional_params.pop("aws_external_id", None) ### SET REGION NAME ### if aws_region_name is None: @@ -83,6 +85,7 @@ class SagemakerLLM(BaseAWSLLM): aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) return credentials, aws_region_name @@ -104,10 +107,11 @@ class SagemakerLLM(BaseAWSLLM): raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") sigv4: Final = SigV4Auth(credentials, "sagemaker", aws_region_name) + dns_suffix: Final = get_aws_dns_suffix(aws_region_name) if optional_params.get("stream") is True: - api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations-response-stream" + api_base = f"https://runtime.sagemaker.{aws_region_name}.{dns_suffix}/endpoints/{model}/invocations-response-stream" else: - api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations" + api_base = f"https://runtime.sagemaker.{aws_region_name}.{dns_suffix}/endpoints/{model}/invocations" sagemaker_base_url: Final = optional_params.get("sagemaker_base_url", None) if sagemaker_base_url is not None: @@ -404,7 +408,7 @@ class SagemakerLLM(BaseAWSLLM): encoding, model_response: ModelResponse, model_id: str | None, - logging_obj: Any, + logging_obj: LiteLLMLoggingObj, litellm_params: dict, headers: dict, ): @@ -467,7 +471,7 @@ class SagemakerLLM(BaseAWSLLM): encoding, model_response: ModelResponse, optional_params: dict, - logging_obj: Any, + logging_obj: LiteLLMLoggingObj, model_id: str | None, headers: dict, litellm_params: dict, diff --git a/litellm/llms/sagemaker/completion/transformation.py b/litellm/llms/sagemaker/completion/transformation.py index f0962a8eb66..576018f0046 100644 --- a/litellm/llms/sagemaker/completion/transformation.py +++ b/litellm/llms/sagemaker/completion/transformation.py @@ -24,6 +24,8 @@ from litellm.utils import token_counter from ..common_utils import SagemakerError if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -196,7 +198,7 @@ class SagemakerConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: str, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/sagemaker/embedding/cohere_transformation.py b/litellm/llms/sagemaker/embedding/cohere_transformation.py index b05e146a966..4687ff6b3f4 100644 --- a/litellm/llms/sagemaker/embedding/cohere_transformation.py +++ b/litellm/llms/sagemaker/embedding/cohere_transformation.py @@ -13,6 +13,7 @@ Reference: https://docs.cohere.com/v2/reference/embed from typing import TYPE_CHECKING, Any, cast if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.llms.openai import AllEmbeddingInputValues from httpx._models import Headers, Response @@ -90,7 +91,7 @@ class SagemakerCohereEmbeddingConfig(BaseEmbeddingConfig): model: str, raw_response: Response, model_response: "EmbeddingResponse", - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", api_key: str | None = None, request_data: dict = {}, optional_params: dict = {}, diff --git a/litellm/llms/sagemaker/embedding/transformation.py b/litellm/llms/sagemaker/embedding/transformation.py index 04bf040098e..97940929b09 100644 --- a/litellm/llms/sagemaker/embedding/transformation.py +++ b/litellm/llms/sagemaker/embedding/transformation.py @@ -7,6 +7,7 @@ In the Huggingface TGI format. from typing import TYPE_CHECKING, Any, Final if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.llms.openai import AllEmbeddingInputValues from httpx._models import Headers, Response @@ -84,7 +85,7 @@ class SagemakerEmbeddingConfig(BaseEmbeddingConfig): model: str, raw_response: Response, model_response: "EmbeddingResponse", - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", api_key: str | None = None, request_data: dict = {}, optional_params: dict = {}, diff --git a/litellm/llms/sap/chat/transformation.py b/litellm/llms/sap/chat/transformation.py index a376e9c60b3..d64d7a57281 100755 --- a/litellm/llms/sap/chat/transformation.py +++ b/litellm/llms/sap/chat/transformation.py @@ -15,6 +15,8 @@ from litellm.types.utils import ModelResponse from ...openai.chat.gpt_transformation import OpenAIGPTConfig if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -381,7 +383,7 @@ class GenAIHubOrchestrationConfig(OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/sap/credentials.py b/litellm/llms/sap/credentials.py index d7743d4d337..a2a93b6114a 100644 --- a/litellm/llms/sap/credentials.py +++ b/litellm/llms/sap/credentials.py @@ -8,9 +8,10 @@ from dataclasses import dataclass from datetime import datetime, timedelta, timezone from pathlib import Path from threading import Lock -from typing import Any, Final +from typing import Any, Final, Protocol import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -33,8 +34,8 @@ def _get_home() -> str: return os.getenv(HOME_PATH_ENV_VAR, DEFAULT_HOME_PATH) -def _get_nested(d: dict[str, Any] | str, path: Sequence[str]) -> Any: - cur: Any = d +def _get_nested(d: object, path: Sequence[str]) -> object: + cur: object = d if isinstance(cur, str): # This shouldn't happen if service keys are pre-parsed correctly try: @@ -54,7 +55,7 @@ def _get_nested(d: dict[str, Any] | str, path: Sequence[str]) -> Any: return cur -def _load_json_env(var_name: str) -> dict[str, Any] | None: +def _load_json_env(var_name: str) -> dict[str, object] | None: raw: Final = os.environ.get(var_name) if not raw: return None @@ -64,7 +65,7 @@ def _load_json_env(var_name: str) -> dict[str, Any] | None: return None -def _str_or_none(value) -> str | None: +def _str_or_none(value: object) -> str | None: try: return str(value) if value is not None else None except Exception: @@ -124,7 +125,7 @@ CREDENTIAL_VALUES: Final[list[CredentialsValue]] = [ ] -def init_conf(profile: str | None = None) -> dict[str, Any]: +def init_conf(profile: str | None = None) -> dict[str, object]: """ Loads config JSON from: 1) $AICORE_CONFIG if set, otherwise @@ -191,7 +192,7 @@ def resolve_resource_group(sources: list[Source]) -> str | None: def _parse_service_key_once( service_key: str | dict | None, -) -> dict[str, Any] | None: +) -> dict[str, object] | None: """ Pre-parse service_key if it's a string to avoid repeated JSON parsing. @@ -348,8 +349,33 @@ def validate_credentials( ) +class _TokenBody(TypedDict): + """Decoded body of the SAP AI Core OAuth2 token response.""" + + access_token: ReadOnly[str] + expires_in: ReadOnly[NotRequired[int]] + + +class _TokenResponse(Protocol): + """The token endpoint's HTTP response, read for the decoded token body it carries.""" + + def json(self) -> _TokenBody: ... + + +def _bearer_token_and_expiry(response: _TokenResponse) -> tuple[str, datetime]: + """Read a token response into the Authorization header value and the token's absolute expiry.""" + payload: Final = response.json() + expires_in: Final = int(payload.get("expires_in", 3600)) + access_token: Final = payload["access_token"] + return f"Bearer {access_token}", datetime.now(timezone.utc) + timedelta(seconds=expires_in) + + def _request_token( - client_id: str, auth_url: str, timeout: float, cert_pair=None, client_secret=None + client_id: str, + auth_url: str, + timeout: float, + cert_pair: tuple[str, str] | None = None, + client_secret: str | None = None, ) -> tuple[str, datetime]: data: Final = {"grant_type": "client_credentials", "client_id": client_id} if client_secret: @@ -361,15 +387,10 @@ def _request_token( with httpx.Client(cert=cert_pair) as raw_client: handler = HTTPHandler(client=raw_client) resp = handler.post(auth_url, data=data, timeout=timeout) - payload = resp.json() - else: - handler = _get_httpx_client() - resp = handler.post(auth_url, data=data, timeout=timeout) - payload = resp.json() - access_token: Final = payload["access_token"] - expires_in: Final = int(payload.get("expires_in", 3600)) - expiry_date: Final = datetime.now(timezone.utc) + timedelta(seconds=expires_in) - return f"Bearer {access_token}", expiry_date + return _bearer_token_and_expiry(resp) + handler = _get_httpx_client() + resp = handler.post(auth_url, data=data, timeout=timeout) + return _bearer_token_and_expiry(resp) except Exception as e: msg: Final = resp.text if resp is not None else getattr(e, "text", str(e)) raise RuntimeError(f"Token request failed: {msg}") from e diff --git a/litellm/llms/soniox/common_utils.py b/litellm/llms/soniox/common_utils.py index 90be94b8133..8d83b4c9218 100644 --- a/litellm/llms/soniox/common_utils.py +++ b/litellm/llms/soniox/common_utils.py @@ -2,10 +2,18 @@ Shared utilities for the Soniox provider (https://soniox.com). """ -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Final, TypeAlias +from litellm.litellm_core_utils.audio_utils.subtitle_utils import ( + SubtitleToken, + render_subtitle_tokens_as_srt, + render_subtitle_tokens_as_vtt, +) from litellm.llms.base_llm.chat.transformation import BaseLLMException +SonioxToken: TypeAlias = Mapping[str, object] + # Soniox API base URL. SONIOX_API_BASE: Final[str] = "https://api.soniox.com" @@ -63,7 +71,15 @@ def get_soniox_api_base(api_base: str | None = None) -> str: return base.rstrip("/") -def render_soniox_tokens(tokens: list[dict[str, Any]]) -> str: +def _token_text(value: object) -> str: + return value if isinstance(value, str) else "" + + +def _token_milliseconds(value: object) -> int | None: + return value if isinstance(value, int) else None + + +def render_soniox_tokens(tokens: Sequence[SonioxToken]) -> str: """ Render a list of Soniox tokens to a readable transcript string. @@ -80,11 +96,11 @@ def render_soniox_tokens(tokens: list[dict[str, Any]]) -> str: return "" text_parts: Final[list[str]] = [] - current_speaker: Any | None = None - current_language: Any | None = None + current_speaker: object = None + current_language: object = None for token in tokens: - text = token.get("text", "") + text = _token_text(token.get("text", "")) speaker = token.get("speaker") language = token.get("language") is_translation = token.get("translation_status") == "translation" @@ -102,166 +118,51 @@ def render_soniox_tokens(tokens: list[dict[str, Any]]) -> str: current_language = language prefix = "[Translation] " if is_translation else "" text_parts.append(f"\n{prefix}[{current_language}] ") - text = text.lstrip() if isinstance(text, str) else text + text = text.lstrip() text_parts.append(text) return "".join(text_parts) -# --------------------------------------------------------------------------- -# SRT / VTT subtitle rendering -# --------------------------------------------------------------------------- - -# Maximum number of tokens to group into a single subtitle cue. -_CUE_MAX_TOKENS: Final[int] = 15 - -# Maximum duration (in ms) for a single cue before forcing a break. -_CUE_MAX_DURATION_MS: Final[int] = 5000 +def _token_speaker(value: object) -> str | int | None: + return value if isinstance(value, str | int) else None -def _format_timestamp_srt(ms: int) -> str: - """Format milliseconds as SRT timestamp: HH:MM:SS,mmm""" - ms = max(ms, 0) - hours: Final = ms // 3_600_000 - ms %= 3_600_000 - minutes: Final = ms // 60_000 - ms %= 60_000 - seconds: Final = ms // 1_000 - millis: Final = ms % 1_000 - return f"{hours:02d}:{minutes:02d}:{seconds:02d},{millis:03d}" +def _soniox_token_to_subtitle_token(token: SonioxToken) -> SubtitleToken: + return SubtitleToken( + text=_token_text(token.get("text", "")), + start_ms=_token_milliseconds(token.get("start_ms")), + end_ms=_token_milliseconds(token.get("end_ms")), + speaker=_token_speaker(token.get("speaker")), + ) -def _format_timestamp_vtt(ms: int) -> str: - """Format milliseconds as VTT timestamp: HH:MM:SS.mmm""" - ms = max(ms, 0) - hours: Final = ms // 3_600_000 - ms %= 3_600_000 - minutes: Final = ms // 60_000 - ms %= 60_000 - seconds: Final = ms // 1_000 - millis: Final = ms % 1_000 - return f"{hours:02d}:{minutes:02d}:{seconds:02d}.{millis:03d}" - - -def _group_tokens_into_cues( - tokens: list[dict[str, Any]], -) -> list[dict[str, Any]]: +def _subtitle_tokens(tokens: Sequence[SonioxToken]) -> tuple[SubtitleToken, ...]: """ - Group Soniox tokens into subtitle cues. - - Each cue has: - - start_ms: int - - end_ms: int - - text: str - - Grouping heuristics: - - A new cue starts when token count exceeds _CUE_MAX_TOKENS. - - A new cue starts when duration exceeds _CUE_MAX_DURATION_MS. - - A new cue starts when the speaker changes (if diarization is on). - - Tokens without timestamps are appended to the current cue. + Convert Soniox tokens for subtitle rendering, excluding translation tokens + (``translation_status == "translation"``): Soniox does not timestamp them, + so they cannot be aligned to the audio and would otherwise mix translated + text into original-language cues. """ - cues: Final[list[dict[str, Any]]] = [] - current_tokens: list[str] = [] - current_start: int | None = None - current_end: int | None = None - current_speaker: Any | None = None - - def _flush() -> None: - if current_tokens and current_start is not None: - text: Final = "".join(current_tokens).strip() - if text: - cues.append( - { - "start_ms": current_start, - "end_ms": (current_end if current_end is not None else current_start), - "text": text, - } - ) - - for token in tokens: - start_ms = token.get("start_ms") - end_ms = token.get("end_ms") - text = token.get("text", "") - speaker = token.get("speaker") - - # Skip tokens with no timestamp data entirely if we have no cue started - if start_ms is None and current_start is None: - continue - - # Speaker change forces a new cue - if speaker is not None and speaker != current_speaker: - _flush() - current_tokens = [] - current_start = start_ms - current_end = end_ms - current_speaker = speaker - current_tokens.append(text) - continue - - # Duration or token count exceeded -> flush - should_break = False - if ( - len(current_tokens) >= _CUE_MAX_TOKENS - or current_start is not None - and start_ms is not None - and (start_ms - current_start) >= _CUE_MAX_DURATION_MS - ): - should_break = True - - if should_break: - _flush() - current_tokens = [] - current_start = start_ms - current_end = end_ms - current_tokens.append(text) - else: - if current_start is None: - current_start = start_ms - if end_ms is not None: - current_end = end_ms - current_tokens.append(text) - - _flush() - return cues + return tuple( + _soniox_token_to_subtitle_token(token) for token in tokens if token.get("translation_status") != "translation" + ) -def render_soniox_tokens_as_srt(tokens: list[dict[str, Any]]) -> str: +def render_soniox_tokens_as_srt(tokens: Sequence[SonioxToken]) -> str: """ Render Soniox tokens as SRT (SubRip) subtitle format. Returns an empty string if no tokens have timestamp data. """ - cues: Final = _group_tokens_into_cues(tokens) - if not cues: - return "" - - lines: Final[list[str]] = [] - for idx, cue in enumerate(cues, start=1): - start = _format_timestamp_srt(cue["start_ms"]) - end = _format_timestamp_srt(cue["end_ms"]) - lines.append(str(idx)) - lines.append(f"{start} --> {end}") - lines.append(cue["text"]) - lines.append("") # blank line between cues - - return "\n".join(lines) + return render_subtitle_tokens_as_srt(_subtitle_tokens(tokens)) -def render_soniox_tokens_as_vtt(tokens: list[dict[str, Any]]) -> str: +def render_soniox_tokens_as_vtt(tokens: Sequence[SonioxToken]) -> str: """ Render Soniox tokens as WebVTT subtitle format. Returns the VTT header even if no cues are present. """ - cues: Final = _group_tokens_into_cues(tokens) - - lines: Final[list[str]] = ["WEBVTT", ""] - for cue in cues: - start = _format_timestamp_vtt(cue["start_ms"]) - end = _format_timestamp_vtt(cue["end_ms"]) - lines.append(f"{start} --> {end}") - lines.append(cue["text"]) - lines.append("") # blank line between cues - - return "\n".join(lines) + return render_subtitle_tokens_as_vtt(_subtitle_tokens(tokens)) diff --git a/litellm/llms/stability/image_generation/transformation.py b/litellm/llms/stability/image_generation/transformation.py index 804613ea161..cf3576a9404 100644 --- a/litellm/llms/stability/image_generation/transformation.py +++ b/litellm/llms/stability/image_generation/transformation.py @@ -26,6 +26,8 @@ from litellm.types.llms.stability import ( from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -205,7 +207,7 @@ class StabilityImageGenerationConfig(BaseImageGenerationConfig): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/tencent/chat/transformation.py b/litellm/llms/tencent/chat/transformation.py index b1672d93542..7e80b0012df 100644 --- a/litellm/llms/tencent/chat/transformation.py +++ b/litellm/llms/tencent/chat/transformation.py @@ -3,14 +3,36 @@ Translates from OpenAI's `/v1/chat/completions` to Tencent TokenHub's OpenAI-compatible endpoint. """ -from typing import Final +from collections.abc import Mapping +from typing import Final, TypedDict +from typing_extensions import ReadOnly + +import litellm from litellm.secret_managers.main import get_secret_str from litellm.utils import supports_reasoning from ...openai.chat.gpt_transformation import OpenAIGPTConfig +class ThinkingPayload(TypedDict, total=False): + """Tencent TokenHub `thinking` object. + + `type` ("enabled"/"disabled"/"adaptive") is required by TokenHub when the + object is passed; `budget_tokens` is auto-filled server-side when omitted. + Ref: https://www.tencentcloud.com/document/product/1300/82345 + """ + + type: ReadOnly[str] + budget_tokens: ReadOnly[int] + + +class ThinkingExtraBody(TypedDict, total=False): + """`extra_body` payload carrying TokenHub's `thinking` object.""" + + thinking: ReadOnly[Mapping[str, object]] + + class TencentChatConfig(OpenAIGPTConfig): def get_supported_openai_params(self, model: str) -> list: params: Final = super().get_supported_openai_params(model) @@ -25,18 +47,71 @@ class TencentChatConfig(OpenAIGPTConfig): model: str, drop_params: bool, ) -> dict: - optional_params = super().map_openai_params(non_default_params, optional_params, model, drop_params) + mapped_params: Final = super().map_openai_params(non_default_params, optional_params, model, drop_params) - thinking_value: Final = optional_params.pop("thinking", None) - reasoning_effort: Final = optional_params.pop("reasoning_effort", None) + thinking_value: Final = mapped_params.pop("thinking", None) + reasoning_effort: Final = mapped_params.pop("reasoning_effort", None) - if thinking_value is not None: - if isinstance(thinking_value, dict): - optional_params["thinking"] = thinking_value - elif reasoning_effort is not None and reasoning_effort != "none": - optional_params["thinking"] = {"type": "enabled"} + thinking: Final = self._resolve_thinking_payload( + model=model, + thinking_value=thinking_value, # pyright: ignore[reportUnknownArgumentType] # value popped from the untyped provider params dict + reasoning_effort=reasoning_effort, # pyright: ignore[reportUnknownArgumentType] # value popped from the untyped provider params dict + ) + if thinking is not None: + # TokenHub expects `thinking` in the request JSON body, but the + # OpenAI SDK's chat.completions.create() rejects unknown top-level + # kwargs, so it travels via `extra_body`, which the SDK merges into + # the payload. A plain assignment is merge-safe: get_optional_params + # spreads this dict into its own extra_body assembly downstream. + extra_body: Final[ThinkingExtraBody] = {"thinking": thinking} + mapped_params["extra_body"] = extra_body + return mapped_params - return optional_params + @classmethod + def _resolve_thinking_payload( + cls, + model: str, + thinking_value: object, + reasoning_effort: object, + ) -> Mapping[str, object] | None: + if isinstance(thinking_value, dict): + return cls._coerce_thinking_type_for_model(model=model, thinking=thinking_value) # pyright: ignore[reportUnknownArgumentType] # isinstance narrows to dict[Unknown, Unknown] out of the untyped provider params dict + if isinstance(reasoning_effort, str): + # TokenHub recommends explicitly disabling thinking rather than + # relying on per-model defaults (deepseek-v4-* default to enabled). + payload: Final[ThinkingPayload] = {"type": "disabled" if reasoning_effort == "none" else "enabled"} + return cls._coerce_thinking_type_for_model(model=model, thinking=payload) + return None + + @staticmethod + def _coerce_thinking_type_for_model(model: str, thinking: Mapping[str, object]) -> Mapping[str, object]: + """Coerce `thinking.type` to a value the model accepts. + + MiniMax models on TokenHub only accept "adaptive"/"disabled" and reject + "enabled" with a 400; "adaptive" (the model decides when to think) is + the closest semantic, so "enabled" is coerced for them. The capability + is read from the model map's `supports_adaptive_thinking` flag, so + aliases and newly onboarded adaptive-only models need no code change. + Ref: https://www.tencentcloud.com/document/product/1300/82345 + """ + if thinking.get("type") != "enabled" or not TencentChatConfig._is_adaptive_thinking_model(model): + return thinking + + budget: Final[object] = thinking.get("budget_tokens") + if isinstance(budget, int): + coerced_with_budget: Final[ThinkingPayload] = {"type": "adaptive", "budget_tokens": budget} + return coerced_with_budget + coerced: Final[ThinkingPayload] = {"type": "adaptive"} + return coerced + + @staticmethod + def _is_adaptive_thinking_model(model: str) -> bool: + """Read `supports_adaptive_thinking` from the model map under tencent.""" + try: + model_info: Final[Mapping[str, object]] = litellm.get_model_info(model=model, custom_llm_provider="tencent") + except Exception: # noqa: BLE001 # get_model_info raises a bare Exception for unmapped models + return False + return model_info.get("supports_adaptive_thinking") is True def _get_openai_compatible_provider_info( self, api_base: str | None, api_key: str | None diff --git a/litellm/llms/together_ai/chat.py b/litellm/llms/together_ai/chat.py deleted file mode 100644 index 58d47e45faa..00000000000 --- a/litellm/llms/together_ai/chat.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -Support for OpenAI's `/v1/chat/completions` endpoint. - -Calls done in OpenAI/openai.py as TogetherAI is openai-compatible. - -Docs: https://docs.together.ai/reference/completions-1 -""" - -from typing import Final - -from litellm._logging import verbose_logger -from litellm.utils import supports_function_calling - -from ..openai.chat.gpt_transformation import OpenAIGPTConfig - - -class TogetherAIConfig(OpenAIGPTConfig): - def get_supported_openai_params(self, model: str) -> list: - """ - Only some together models support response_format / tool calling - - Docs: https://docs.together.ai/docs/json-mode - """ - # Use supports_function_calling() — which reads _get_model_info_helper - # directly — instead of get_model_info(). get_model_info() calls - # get_supported_openai_params() as its first step, which routes back - # into this method for together_ai models, creating a recursion that - # only terminates when Python's recursion limit or the "not mapped" - # exception in _get_model_info_helper is hit (~332 deep calls). - supports_fc: bool | None = None - try: - supports_fc = supports_function_calling(model, custom_llm_provider="together_ai") - except Exception as e: - verbose_logger.debug("Error getting supported openai params: %s", e) - - optional_params: Final = super().get_supported_openai_params(model) - if supports_fc is not True: - verbose_logger.debug( - "Only some together models support function calling/response_format. Docs - https://docs.together.ai/docs/function-calling" - ) - optional_params.remove("tools") - optional_params.remove("tool_choice") - optional_params.remove("function_call") - optional_params.remove("response_format") - return optional_params - - def map_openai_params( - self, - non_default_params: dict, - optional_params: dict, - model: str, - drop_params: bool, - ) -> dict: - mapped_openai_params: Final = super().map_openai_params(non_default_params, optional_params, model, drop_params) - - if "response_format" in mapped_openai_params and mapped_openai_params["response_format"] == {"type": "text"}: - mapped_openai_params.pop("response_format") - return mapped_openai_params diff --git a/litellm/llms/together_ai/chat/__init__.py b/litellm/llms/together_ai/chat/__init__.py new file mode 100644 index 00000000000..f260d9126d7 --- /dev/null +++ b/litellm/llms/together_ai/chat/__init__.py @@ -0,0 +1,3 @@ +from .transformation import TogetherAIChatConfig as TogetherAIChatConfig + +TogetherAIConfig = TogetherAIChatConfig diff --git a/litellm/llms/together_ai/chat/transformation.py b/litellm/llms/together_ai/chat/transformation.py new file mode 100644 index 00000000000..449cd3ecbc5 --- /dev/null +++ b/litellm/llms/together_ai/chat/transformation.py @@ -0,0 +1,247 @@ +""" +Translates from OpenAI's `/v1/chat/completions` to Together AI's `/v1/chat/completions`. + +Docs: https://docs.together.ai/docs/chat-overview +""" + +from collections.abc import Callable, Container, Coroutine, Mapping +from types import MappingProxyType +from typing import ( + Final, + Literal, + cast, # noqa: TID251 # rebuilding a TypedDict minus keys has no checked spelling + overload, +) + +from typing_extensions import ReadOnly, TypedDict + +import litellm +from litellm._logging import verbose_logger +from litellm.exceptions import UnsupportedParamsError +from litellm.router_utils.reasoning_effort_capability import declared_reasoning_efforts_for_model +from litellm.types.llms.openai import AllMessageValues +from litellm.utils import supports_function_calling, supports_reasoning, supports_response_schema + +from ...openai.chat.gpt_transformation import OpenAIGPTConfig + +TOOL_CALLING_PARAMS: Final = ("tools", "tool_choice", "function_call") +LITELLM_INTERNAL_ASSISTANT_FIELDS: Final = frozenset({"thinking_blocks", "provider_specific_fields"}) +FUNCTION_CALLING_DOCS_URL: Final = "https://docs.together.ai/docs/function-calling" +STRUCTURED_OUTPUTS_DOCS_URL: Final = "https://docs.together.ai/docs/inference/chat/structured-outputs" + + +def _registry_verdict(model: str, flag: str, check: Callable[[str], bool]) -> bool | None: + try: + if check(model): + return True + except Exception as e: + verbose_logger.debug("Error checking together_ai %s for %s: %s", flag, model, e) + registry_entry: Final = litellm.model_cost.get(f"together_ai/{model}") + if isinstance(registry_entry, dict) and registry_entry.get(flag) is False: + return False + return None + + +ADJUSTABLE_EFFORT_REASONING_MODELS: Final = frozenset( + { + "openai/gpt-oss-120b", + "openai/gpt-oss-20b", + } +) +HYBRID_REASONING_MODELS: Final = frozenset( + { + "MiniMaxAI/MiniMax-M3", + "Qwen/Qwen3.5-9B", + "Qwen/Qwen3.6-Plus", + "deepseek-ai/DeepSeek-V4-Pro", + "moonshotai/Kimi-K3", + "nvidia/nemotron-3-ultra-550b-a55b", + "zai-org/GLM-5.2", + } +) +HIGH_MAX_EFFORT_MODEL_PREFIX: Final = "deepseek-ai/DeepSeek-V4-Pro" +EFFORT_TRANSLATION: Final = MappingProxyType({"minimal": "low", "xhigh": "high", "max": "high"}) +HIGH_MAX_EFFORT_TRANSLATION: Final = MappingProxyType( + {"minimal": "high", "low": "high", "medium": "high", "xhigh": "max"} +) + + +class TogetherReasoningToggle(TypedDict): + enabled: ReadOnly[bool] + + +def _function_calling_verdict(model: str) -> bool | None: + return _registry_verdict( + model, + "supports_function_calling", + lambda checked_model: supports_function_calling(checked_model, custom_llm_provider="together_ai"), + ) + + +def _response_schema_verdict(model: str) -> bool | None: + return _registry_verdict( + model, + "supports_response_schema", + lambda checked_model: supports_response_schema(checked_model, custom_llm_provider="together_ai"), + ) + + +def _tool_params_to_drop(passed_params: Container[str], model: str, drop_params: bool) -> tuple[str, ...]: + passed_tool_params: Final = tuple(param for param in TOOL_CALLING_PARAMS if param in passed_params) + if not passed_tool_params: + return () + verdict: Final = _function_calling_verdict(model) + if verdict is True: + return () + if verdict is None: + verbose_logger.warning( + "together_ai model %s has no function calling entry in the model registry; passing %s through for Together to validate. Docs - %s", + model, + ", ".join(passed_tool_params), + FUNCTION_CALLING_DOCS_URL, + ) + return () + if drop_params or litellm.drop_params: + verbose_logger.warning( + "together_ai model %s does not support function calling per the model registry; dropping %s. Docs - %s", + model, + ", ".join(passed_tool_params), + FUNCTION_CALLING_DOCS_URL, + ) + return passed_tool_params + raise UnsupportedParamsError( + status_code=500, + message=f"together_ai does not support parameters: {', '.join(passed_tool_params)}, for model={model}. To drop it from the call, set `litellm.drop_params = True`.", + ) + + +def _supports_together_reasoning(model: str) -> bool: + if model in ADJUSTABLE_EFFORT_REASONING_MODELS or model in HYBRID_REASONING_MODELS: + return True + if model.startswith(HIGH_MAX_EFFORT_MODEL_PREFIX): + return True + return supports_reasoning(model, custom_llm_provider="together_ai") + + +def _adjustable_effort(effort: str, model: str) -> str: + if effort == "none": + verbose_logger.debug( + "together_ai model %s cannot disable reasoning; mapping reasoning_effort=none to low", model + ) + return "low" + return EFFORT_TRANSLATION.get(effort, effort) + + +def _reasoning_effort_payload(effort: str, model: str) -> Mapping[str, object]: + if effort == "default": + return MappingProxyType({}) + if model in ADJUSTABLE_EFFORT_REASONING_MODELS: + return MappingProxyType({"reasoning_effort": _adjustable_effort(effort, model)}) + if effort == "none": + disable_reasoning: Final[TogetherReasoningToggle] = {"enabled": False} + return MappingProxyType({"reasoning": disable_reasoning}) + if effort in (declared_reasoning_efforts_for_model(model, "together_ai") or ()): + return MappingProxyType({"reasoning_effort": effort}) + if model.startswith(HIGH_MAX_EFFORT_MODEL_PREFIX): + return MappingProxyType({"reasoning_effort": HIGH_MAX_EFFORT_TRANSLATION.get(effort, effort)}) + return MappingProxyType({"reasoning_effort": EFFORT_TRANSLATION.get(effort, effort)}) + + +def _drop_response_format(passed_params: Container[str], model: str, drop_params: bool) -> bool: + if "response_format" not in passed_params: + return False + verdict: Final = _response_schema_verdict(model) + if verdict is True: + return False + if verdict is None: + verbose_logger.warning( + "together_ai model %s has no structured outputs entry in the model registry; passing response_format through for Together to validate. Docs - %s", + model, + STRUCTURED_OUTPUTS_DOCS_URL, + ) + return False + if drop_params or litellm.drop_params: + verbose_logger.warning( + "together_ai model %s does not support structured outputs per the model registry; dropping response_format. Docs - %s", + model, + STRUCTURED_OUTPUTS_DOCS_URL, + ) + return True + raise UnsupportedParamsError( + status_code=500, + message=f"together_ai does not support parameters: response_format, for model={model}. To drop it from the call, set `litellm.drop_params = True`.", + ) + + +def _without_litellm_internal_fields(message: AllMessageValues) -> AllMessageValues: + if message["role"] != "assistant" or LITELLM_INTERNAL_ASSISTANT_FIELDS.isdisjoint(message): + return message + return cast( # cast-ok: rebuilding the same TypedDict minus internal keys loses the narrowed type + "AllMessageValues", + { # mutable-ok: TypedDict rebuild minus internal keys + key: value for key, value in message.items() if key not in LITELLM_INTERNAL_ASSISTANT_FIELDS + }, + ) + + +class TogetherAIChatConfig(OpenAIGPTConfig): + @overload + def _transform_messages( + self, + messages: list[AllMessageValues], # mutable-ok: inherited contract + model: str, + is_async: Literal[True], + ) -> Coroutine[object, object, list[AllMessageValues]]: ... # mutable-ok: inherited contract + + @overload + def _transform_messages( + self, + messages: list[AllMessageValues], # mutable-ok: inherited contract + model: str, + is_async: Literal[False] = False, + ) -> list[AllMessageValues]: ... # mutable-ok: inherited contract + + def _transform_messages( + self, + messages: list[AllMessageValues], # mutable-ok: inherited contract + model: str, + is_async: bool = False, + ) -> list[AllMessageValues] | Coroutine[object, object, list[AllMessageValues]]: # mutable-ok: inherited contract + """Together consumes replayed assistant `reasoning_content` (preserved thinking via + `chat_template_kwargs: {"clear_thinking": false}`), so it must stay in the payload; + only litellm-internal fields are stripped before sending.""" + stripped: Final = [ # mutable-ok: super() requires a list + _without_litellm_internal_fields(message) for message in messages + ] + if is_async: + return super()._transform_messages(stripped, model, is_async=True) + return super()._transform_messages(stripped, model, is_async=False) + + def get_supported_openai_params(self, model: str) -> list: # mutable-ok: inherited contract + supported_params: Final = super().get_supported_openai_params(model) + if not _supports_together_reasoning(model): + return supported_params + return [ # mutable-ok: the inherited contract returns a plain list; building fresh avoids mutating the base class's value + *supported_params, + "reasoning_effort", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + mapped_openai_params: Final = super().map_openai_params(non_default_params, optional_params, model, drop_params) + for param in _tool_params_to_drop(mapped_openai_params, model, drop_params): + mapped_openai_params.pop(param) + if _drop_response_format(mapped_openai_params, model, drop_params): + mapped_openai_params.pop("response_format") + effort: Final = mapped_openai_params.get("reasoning_effort") + if not isinstance(effort, str): + return mapped_openai_params + mapped_openai_params.pop("reasoning_effort") + for key, value in _reasoning_effort_payload(effort, model).items(): + mapped_openai_params.setdefault(key, value) + return mapped_openai_params diff --git a/litellm/llms/together_ai/cost_calculator.py b/litellm/llms/together_ai/cost_calculator.py index 431e94f1442..6fc2c949fa6 100644 --- a/litellm/llms/together_ai/cost_calculator.py +++ b/litellm/llms/together_ai/cost_calculator.py @@ -3,6 +3,7 @@ Handles calculating cost for together ai models """ import re +from collections.abc import Mapping from typing import Final from litellm.constants import ( @@ -18,6 +19,12 @@ from litellm.constants import ( from litellm.types.utils import CallTypes +def has_together_registry_pricing(model: str, cost_map: Mapping[str, object]) -> bool: + stripped: Final = model.removeprefix("together_ai/") + entry: Final = cost_map.get(f"together_ai/{stripped}") + return isinstance(entry, Mapping) and "input_cost_per_token" in entry + + # Extract the number of billion parameters from the model name # only used for together_computer LLMs def get_model_params_and_category(model_name, call_type: CallTypes) -> str: diff --git a/litellm/llms/together_ai/rerank/handler.py b/litellm/llms/together_ai/rerank/handler.py index 10246451a9d..b8079e52c97 100644 --- a/litellm/llms/together_ai/rerank/handler.py +++ b/litellm/llms/together_ai/rerank/handler.py @@ -16,11 +16,16 @@ from litellm.llms.together_ai.rerank.transformation import TogetherAIRerankConfi from litellm.types.rerank import RerankRequest, RerankResponse +def _rerank_url(api_base: str) -> str: + return f"{api_base.rstrip('/')}/rerank" + + class TogetherAIRerank(BaseLLM): def rerank( self, model: str, api_key: str, + api_base: str, query: str, documents: list[str | dict[str, Any]], top_n: int | None = None, @@ -46,10 +51,10 @@ class TogetherAIRerank(BaseLLM): raise ValueError("TogetherAI does not support max_chunks_per_doc") if _is_async: - return self.async_rerank(request_data_dict, api_key) # Call async method + return self.async_rerank(request_data_dict, api_key, api_base) response: Final = client.post( - "https://api.together.xyz/v1/rerank", + _rerank_url(api_base), headers={ "accept": "application/json", "content-type": "application/json", @@ -69,11 +74,12 @@ class TogetherAIRerank(BaseLLM): self, request_data_dict: dict[str, Any], api_key: str, + api_base: str, ) -> RerankResponse: client: Final = get_async_httpx_client(llm_provider=litellm.LlmProviders.TOGETHER_AI) # Use async client response: Final = await client.post( - "https://api.together.xyz/v1/rerank", + _rerank_url(api_base), headers={ "accept": "application/json", "content-type": "application/json", diff --git a/litellm/llms/topaz/image_variations/transformation.py b/litellm/llms/topaz/image_variations/transformation.py index 3c914eb6a4c..f4753c8ba17 100644 --- a/litellm/llms/topaz/image_variations/transformation.py +++ b/litellm/llms/topaz/image_variations/transformation.py @@ -2,7 +2,7 @@ import base64 import time from collections.abc import Mapping from io import BytesIO -from typing import Any, Final +from typing import TYPE_CHECKING, Final from aiohttp import ClientResponse from httpx import Headers, Response @@ -22,6 +22,9 @@ from litellm.types.utils import ( from ...base_llm.image_variations.transformation import BaseImageVariationConfig from ..common_utils import TopazException, TopazModelInfo +if TYPE_CHECKING: + import tiktoken + class TopazImageVariationConfig(TopazModelInfo, BaseImageVariationConfig): def get_supported_openai_params(self, model: str) -> list[OpenAIImageVariationOptionalParams]: @@ -136,7 +139,7 @@ class TopazImageVariationConfig(TopazModelInfo, BaseImageVariationConfig): image: FileTypes, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, ) -> ImageResponse: image_content: Final = await raw_response.read() @@ -155,7 +158,7 @@ class TopazImageVariationConfig(TopazModelInfo, BaseImageVariationConfig): image: FileTypes, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, ) -> ImageResponse: image_content: Final = raw_response.content diff --git a/litellm/llms/triton/completion/transformation.py b/litellm/llms/triton/completion/transformation.py index 5f1986c6124..98a68ba2c36 100644 --- a/litellm/llms/triton/completion/transformation.py +++ b/litellm/llms/triton/completion/transformation.py @@ -4,7 +4,7 @@ Translates from OpenAI's `/v1/chat/completions` endpoint to Triton's `/generate` import json from collections.abc import AsyncIterator, Iterator -from typing import Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal from httpx import Headers, Response @@ -28,6 +28,9 @@ from litellm.types.utils import ( from ..common_utils import TritonError +if TYPE_CHECKING: + import tiktoken + class TritonConfig(BaseConfig): """ @@ -92,7 +95,7 @@ class TritonConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -212,7 +215,7 @@ class TritonGenerateConfig(TritonConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: @@ -277,7 +280,7 @@ class TritonInferConfig(TritonConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/vertex_ai/agent_engine/transformation.py b/litellm/llms/vertex_ai/agent_engine/transformation.py index 76aaa4895e2..e430d9e2280 100644 --- a/litellm/llms/vertex_ai/agent_engine/transformation.py +++ b/litellm/llms/vertex_ai/agent_engine/transformation.py @@ -29,6 +29,8 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import Choices, Message, ModelResponse, Usage if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.utils import CustomStreamWrapper @@ -283,7 +285,7 @@ class VertexAgentEngineConfig(BaseConfig, VertexBase): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/vertex_ai/audio_transcription/gemini_transcribe_transformation.py b/litellm/llms/vertex_ai/audio_transcription/gemini_transcribe_transformation.py new file mode 100644 index 00000000000..f4db5eb110c --- /dev/null +++ b/litellm/llms/vertex_ai/audio_transcription/gemini_transcribe_transformation.py @@ -0,0 +1,216 @@ +import base64 +from collections.abc import Mapping, Sequence +from typing import Final + +from httpx import Headers, Response + +import litellm +from litellm.exceptions import UnsupportedParamsError +from litellm.litellm_core_utils.audio_utils.utils import ( + normalize_transcription_language_to_bcp47, + process_audio_file, +) +from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, + BaseAudioTranscriptionConfig, +) +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.vertex_ai.audio_transcription.transformation import ( + SUPPORTED_RESPONSE_FORMATS, + validate_vertex_transcription_location, + validate_vertex_transcription_project_id, +) +from litellm.llms.vertex_ai.common_utils import VertexAIError, get_vertex_base_url +from litellm.llms.vertex_ai.vertex_llm_base import VertexBase +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIAudioTranscriptionOptionalParams, +) +from litellm.types.llms.vertex_ai_gemini_transcription import ( + VertexGeminiTranscriptionAudioConfig, + VertexGeminiTranscriptionContent, + VertexGeminiTranscriptionGenerationConfig, + VertexGeminiTranscriptionInlineData, + VertexGeminiTranscriptionPart, + VertexGeminiTranscriptionRequest, + VertexGeminiTranscriptionResponse, +) +from litellm.types.utils import ( + FileTypes, + TranscriptionResponse, + TranscriptionUsageInputTokenDetailsObject, + TranscriptionUsageTokensObject, +) + +DEFAULT_GEMINI_TRANSCRIBE_LOCATION: Final = "global" +AUDIO_MODALITY: Final = "AUDIO" + + +class VertexGeminiAudioTranscriptionConfig(BaseAudioTranscriptionConfig, VertexBase): + def __init__(self) -> None: + BaseAudioTranscriptionConfig.__init__(self) + VertexBase.__init__(self) + + def get_supported_openai_params( + self, model: str + ) -> list[OpenAIAudioTranscriptionOptionalParams]: # mutable-ok: BaseAudioTranscriptionConfig signature + return ["language", "response_format"] + + def map_openai_params( + self, + non_default_params: Mapping[str, object], + optional_params: Mapping[str, object], + model: str, + drop_params: bool, + ) -> dict[str, object]: # mutable-ok: BaseAudioTranscriptionConfig signature + supported_params: Final = frozenset(self.get_supported_openai_params(model)) + mapped: Final = { + **optional_params, + **{k: v for k, v in non_default_params.items() if k in supported_params}, + } + response_format: Final = mapped.get("response_format") + if response_format is None or response_format in SUPPORTED_RESPONSE_FORMATS: + return mapped + if drop_params or litellm.drop_params: + return {k: v for k, v in mapped.items() if k != "response_format"} + raise UnsupportedParamsError( + status_code=400, + message=( + f"Vertex AI Gemini transcription does not support response_format={response_format!r}. " + f"Supported values: {', '.join(SUPPORTED_RESPONSE_FORMATS)}. " + "To drop unsupported openai params from the call, set `litellm.drop_params = True`" + ), + ) + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: dict | Headers, # mutable-ok: base signature and VertexAIError take dict | Headers + ) -> BaseLLMException: + return VertexAIError(status_code=status_code, message=error_message, headers=headers) + + def validate_environment( + self, + headers: Mapping[str, str], + model: str, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + api_key: str | None = None, + api_base: str | None = None, + ) -> dict[str, str]: # mutable-ok: BaseAudioTranscriptionConfig signature + vertex_params: Final = dict(litellm_params) + access_token, project_id = self._ensure_access_token( + credentials=self.safe_get_vertex_ai_credentials(vertex_params), + project_id=self.safe_get_vertex_ai_project(vertex_params), + custom_llm_provider="vertex_ai", + ) + return { + **headers, + "Authorization": f"Bearer {access_token}", + "x-goog-user-project": project_id, + "Content-Type": "application/json", + } + + def get_complete_url( + self, + api_base: str | None, + api_key: str | None, + model: str, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + stream: bool | None = None, + ) -> str: + vertex_params: Final = dict(litellm_params) + location: Final = validate_vertex_transcription_location( + self.safe_get_vertex_ai_location(vertex_params), default_location=DEFAULT_GEMINI_TRANSCRIBE_LOCATION + ) + project_id: Final = validate_vertex_transcription_project_id( + self.safe_get_vertex_ai_project(vertex_params) or self._resolve_project_id_from_credentials(vertex_params) + ) + base_url: Final = (api_base or get_vertex_base_url(location)).rstrip("/") + bare_model: Final = model.removeprefix("vertex_ai/") + model_path: Final = f"projects/{project_id}/locations/{location}/publishers/google/models/{bare_model}" + return f"{base_url}/v1/{model_path}:generateContent" + + def _resolve_project_id_from_credentials(self, litellm_params: Mapping[str, object]) -> str: + vertex_params: Final = dict(litellm_params) + _, project_id = self._ensure_access_token( + credentials=self.safe_get_vertex_ai_credentials(vertex_params), + project_id=None, + custom_llm_provider="vertex_ai", + ) + return project_id + + def transform_audio_transcription_request( + self, + model: str, + audio_file: FileTypes, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + ) -> AudioTranscriptionRequestData: + processed_audio: Final = process_audio_file(audio_file) + request_body: Final = VertexGeminiTranscriptionRequest( + contents=( + VertexGeminiTranscriptionContent( + role="user", + parts=( + VertexGeminiTranscriptionPart( + inlineData=VertexGeminiTranscriptionInlineData( + mimeType=processed_audio.content_type, + data=base64.b64encode(processed_audio.file_content).decode("utf-8"), + ) + ), + ), + ), + ), + generationConfig=VertexGeminiTranscriptionGenerationConfig( + audioTranscriptionConfig=_audio_transcription_config(optional_params.get("language")) + ), + ) + return AudioTranscriptionRequestData(data=dict(request_body)) + + def transform_audio_transcription_response( + self, + raw_response: Response, + ) -> TranscriptionResponse: + try: + response_json: Final = raw_response.json() + except ValueError: + raise VertexAIError( + status_code=raw_response.status_code, + message=f"Received non-JSON response from Vertex AI Gemini transcription: {raw_response.text}", + ) + parsed: Final = VertexGeminiTranscriptionResponse.model_validate(response_json) + texts: Final = tuple( + part.text + for candidate in parsed.candidates + if candidate.content is not None + for part in candidate.content.parts + if part.text + ) + response: Final = TranscriptionResponse(text=" ".join(texts)) + response["task"] = "transcribe" + usage: Final = parsed.usageMetadata + if usage is not None: + audio_tokens: Final = sum( + detail.tokenCount for detail in usage.promptTokensDetails if detail.modality == AUDIO_MODALITY + ) + response.usage = TranscriptionUsageTokensObject( + type="tokens", + input_tokens=usage.promptTokenCount, + output_tokens=usage.candidatesTokenCount, + total_tokens=usage.totalTokenCount, + input_token_details=TranscriptionUsageInputTokenDetailsObject( + audio_tokens=audio_tokens, + text_tokens=usage.promptTokenCount - audio_tokens, + ), + ) + return response + + +def _audio_transcription_config(language: object) -> VertexGeminiTranscriptionAudioConfig: + if not isinstance(language, str) or not language: + return VertexGeminiTranscriptionAudioConfig() + return VertexGeminiTranscriptionAudioConfig(languageCodes=(normalize_transcription_language_to_bcp47(language),)) diff --git a/litellm/llms/vertex_ai/audio_transcription/transformation.py b/litellm/llms/vertex_ai/audio_transcription/transformation.py index a352b2a34ca..db3504c9a6a 100644 --- a/litellm/llms/vertex_ai/audio_transcription/transformation.py +++ b/litellm/llms/vertex_ai/audio_transcription/transformation.py @@ -35,6 +35,19 @@ SUPPORTED_RESPONSE_FORMATS: Final = ("json", "text") _URL_UNSAFE_PROJECT_CHARS: Final = ("/", "?", "#", "\\", ":", " ", "\t", "\n", "\r") +def validate_vertex_transcription_location(location: str | None, default_location: str) -> str: + try: + return validate_vertex_location(location or default_location) + except ValueError as e: + raise VertexAIError(status_code=400, message=str(e)) from e + + +def validate_vertex_transcription_project_id(project_id: str) -> str: + if not project_id or ".." in project_id or any(c in project_id for c in _URL_UNSAFE_PROJECT_CHARS): + raise VertexAIError(status_code=400, message=f"Invalid vertex_project format: {project_id!r}") + return project_id + + class VertexAIAudioTranscriptionConfig(BaseAudioTranscriptionConfig, VertexBase): def __init__(self) -> None: BaseAudioTranscriptionConfig.__init__(self) @@ -103,27 +116,16 @@ class VertexAIAudioTranscriptionConfig(BaseAudioTranscriptionConfig, VertexBase) litellm_params: dict, stream: bool | None = None, ) -> str: - location: Final = self._validate_location(self.safe_get_vertex_ai_location(litellm_params)) - project_id: Final = self._validate_project_id( + location: Final = validate_vertex_transcription_location( + self.safe_get_vertex_ai_location(litellm_params), default_location=DEFAULT_SPEECH_TO_TEXT_LOCATION + ) + project_id: Final = validate_vertex_transcription_project_id( self.safe_get_vertex_ai_project(litellm_params) or self._resolve_project_id_from_credentials(litellm_params) ) host: Final = "speech.googleapis.com" if location == "global" else f"{location}-speech.googleapis.com" base_url: Final = (api_base or f"https://{host}").rstrip("/") return f"{base_url}/v2/projects/{project_id}/locations/{location}/recognizers/_:recognize" - @staticmethod - def _validate_location(location: str | None) -> str: - try: - return validate_vertex_location(location or DEFAULT_SPEECH_TO_TEXT_LOCATION) - except ValueError as e: - raise VertexAIError(status_code=400, message=str(e)) from e - - @staticmethod - def _validate_project_id(project_id: str) -> str: - if not project_id or ".." in project_id or any(c in project_id for c in _URL_UNSAFE_PROJECT_CHARS): - raise VertexAIError(status_code=400, message=f"Invalid vertex_project format: {project_id!r}") - return project_id - def _resolve_project_id_from_credentials(self, litellm_params: dict) -> str: _, project_id = self._ensure_access_token( credentials=self.safe_get_vertex_ai_credentials(litellm_params), diff --git a/litellm/llms/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py index 6481b67fad7..377cd9f3437 100644 --- a/litellm/llms/vertex_ai/batches/handler.py +++ b/litellm/llms/vertex_ai/batches/handler.py @@ -1,8 +1,9 @@ import json from collections.abc import Coroutine -from typing import Any, Final +from typing import TYPE_CHECKING, Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.url_utils import ( @@ -20,11 +21,47 @@ from litellm.types.llms.openai import CreateBatchRequest from litellm.types.llms.vertex_ai import ( VERTEX_CREDENTIALS_TYPES, VertexAIBatchPredictionJob, + VertexBatchPredictionResponse, ) from litellm.types.utils import LiteLLMBatch from .transformation import VertexAIBatchTransformation +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + + +class _VertexBatchJsonSource(Protocol): + """An HTTP response whose JSON body is a single Vertex AI batch prediction job.""" + + def json(self) -> VertexBatchPredictionResponse: ... + + +class _VertexBatchListJsonSource(Protocol): + """An HTTP response whose JSON body is a page of Vertex AI batch prediction jobs.""" + + def json(self) -> dict[str, object]: ... + + +class _VertexBatchPayloadView(TypedDict): + """Holds one decoded batch prediction job so the payload reads back typed.""" + + payload: ReadOnly[VertexBatchPredictionResponse] + + +class _FetchedResponseView(TypedDict): + """Holds one ``safe_get`` result so the response reads back as ``httpx.Response``.""" + + response: ReadOnly[httpx.Response] + + +def _vertex_batch_payload(response: _VertexBatchJsonSource) -> VertexBatchPredictionResponse: + return response.json() + + +def _vertex_batch_list_payload(response: _VertexBatchListJsonSource) -> dict[str, object]: + return response.json() + class VertexAIBatchPrediction(VertexLLM): def __init__(self, gcs_bucket_name: str, *args, **kwargs): @@ -41,7 +78,7 @@ class VertexAIBatchPrediction(VertexLLM): vertex_location: str | None, timeout: float | httpx.Timeout, max_retries: int | None, - ) -> LiteLLMBatch | Coroutine[Any, Any, LiteLLMBatch]: + ) -> LiteLLMBatch | Coroutine[object, object, LiteLLMBatch]: sync_handler: Final = _get_httpx_client() access_token, project_id = self._ensure_access_token( @@ -98,7 +135,8 @@ class VertexAIBatchPrediction(VertexLLM): data=json.dumps(vertex_batch_request), ) - _json_response: Final = response.json() + payload_view: Final[_VertexBatchPayloadView] = {"payload": response.json()} + _json_response: Final = payload_view["payload"] vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( response=_json_response ) @@ -128,7 +166,8 @@ class VertexAIBatchPrediction(VertexLLM): ) raise - _json_response: Final = response.json() + payload_view: Final[_VertexBatchPayloadView] = {"payload": response.json()} + _json_response: Final = payload_view["payload"] vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( response=_json_response ) @@ -154,8 +193,8 @@ class VertexAIBatchPrediction(VertexLLM): vertex_location: str | None, timeout: float | httpx.Timeout, max_retries: int | None, - logging_obj: Any | None = None, - ) -> LiteLLMBatch | Coroutine[Any, Any, LiteLLMBatch]: + logging_obj: "LiteLLMLoggingObj | None" = None, + ) -> LiteLLMBatch | Coroutine[object, object, LiteLLMBatch]: sync_handler: Final = _get_httpx_client() access_token, project_id = self._ensure_access_token( @@ -231,20 +270,22 @@ class VertexAIBatchPrediction(VertexLLM): # rebind / private / cloud-metadata targets are rejected; the # proxy auth gate already blocks malicious clientside ``api_base`` # at the boundary — this is defense-in-depth for SDK callers. - response: Final = safe_get( - sync_handler, - api_base, - headers=headers, - ) + fetched: Final[_FetchedResponseView] = { + "response": safe_get( + sync_handler, + api_base, + headers=headers, + ) + } + response: Final = fetched["response"] if response.status_code != 200: raise VertexAIError( status_code=response.status_code, message=f"Error: {response.status_code} {response.text}" ) - _json_response: Final = response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( - response=_json_response + response=_vertex_batch_payload(response) ) return vertex_batch_response @@ -252,7 +293,7 @@ class VertexAIBatchPrediction(VertexLLM): self, api_base: str, headers: dict[str, str], - logging_obj: Any | None = None, + logging_obj: "LiteLLMLoggingObj | None" = None, ) -> LiteLLMBatch: client: Final = get_async_httpx_client( llm_provider=litellm.LlmProviders.VERTEX_AI, @@ -284,19 +325,21 @@ class VertexAIBatchPrediction(VertexLLM): # request kwargs, so wrap the fetch in ``async_safe_get`` to reject # DNS-rebind / private / cloud-metadata targets. Defense-in-depth # behind the proxy auth gate's clientside ``api_base`` check. - response: Final = await async_safe_get( - client, - api_base, - headers=headers, - ) + fetched: Final[_FetchedResponseView] = { + "response": await async_safe_get( + client, + api_base, + headers=headers, + ) + } + response: Final = fetched["response"] if response.status_code != 200: raise VertexAIError( status_code=response.status_code, message=f"Error: {response.status_code} {response.text}" ) - _json_response: Final = response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( - response=_json_response + response=_vertex_batch_payload(response) ) return vertex_batch_response @@ -345,11 +388,9 @@ class VertexAIBatchPrediction(VertexLLM): "Authorization": f"Bearer {access_token}", } - params: Final[dict[str, Any]] = {} - if limit is not None: - params["pageSize"] = str(limit) - if after is not None: - params["pageToken"] = after + limit_params: Final[dict[str, str]] = {"pageSize": str(limit)} if limit is not None else {} + after_params: Final[dict[str, str]] = {"pageToken": after} if after is not None else {} + params: Final = {**limit_params, **after_params} if _is_async is True: return self._async_list_batches( @@ -369,7 +410,7 @@ class VertexAIBatchPrediction(VertexLLM): status_code=response.status_code, message=f"Error: {response.status_code} {response.text}" ) - _json_response: Final = response.json() + _json_response: Final = _vertex_batch_list_payload(response) vertex_batch_response: Final = ( VertexAIBatchTransformation.transform_vertex_ai_batch_list_response_to_openai_list_response( response=_json_response @@ -381,7 +422,7 @@ class VertexAIBatchPrediction(VertexLLM): self, api_base: str, headers: dict[str, str], - params: dict[str, Any], + params: dict[str, str], ): client: Final = get_async_httpx_client( llm_provider=litellm.LlmProviders.VERTEX_AI, @@ -396,7 +437,7 @@ class VertexAIBatchPrediction(VertexLLM): status_code=response.status_code, message=f"Error: {response.status_code} {response.text}" ) - _json_response: Final = response.json() + _json_response: Final = _vertex_batch_list_payload(response) vertex_batch_response: Final = ( VertexAIBatchTransformation.transform_vertex_ai_batch_list_response_to_openai_list_response( response=_json_response @@ -414,7 +455,7 @@ class VertexAIBatchPrediction(VertexLLM): vertex_location: str | None, timeout: float | httpx.Timeout, max_retries: int | None, - ) -> LiteLLMBatch | Coroutine[Any, Any, LiteLLMBatch]: + ) -> LiteLLMBatch | Coroutine[object, object, LiteLLMBatch]: access_token, project_id = self._ensure_access_token( credentials=vertex_credentials, project_id=vertex_project, @@ -494,9 +535,8 @@ class VertexAIBatchPrediction(VertexLLM): message=f"Error: {retrieve_response.status_code} {retrieve_response.text}", ) - _json_response: Final = retrieve_response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( - response=_json_response + response=_vertex_batch_payload(retrieve_response) ) return vertex_batch_response @@ -541,8 +581,7 @@ class VertexAIBatchPrediction(VertexLLM): message=f"Error: {retrieve_response.status_code} {retrieve_response.text}", ) - _json_response: Final = retrieve_response.json() vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( - response=_json_response + response=_vertex_batch_payload(retrieve_response) ) return vertex_batch_response diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 1de2337d8eb..a36c920dda0 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -1,7 +1,7 @@ import re from copy import deepcopy from enum import Enum -from typing import Any, Final, Literal, get_type_hints +from typing import Any, Final, Literal, cast, get_type_hints import httpx @@ -31,7 +31,7 @@ class VertexAIError(BaseLLMException): super().__init__(message=message, status_code=status_code, headers=headers) -def redact_vertex_ai_metadata_from_logged_object(obj: Any) -> None: +def redact_vertex_ai_metadata_from_logged_object(obj: object) -> None: if isinstance(obj, dict): for field in VERTEX_AI_PROVIDER_METADATA_FIELDS: if field in obj: @@ -651,7 +651,7 @@ def _build_json_schema(parameters: dict) -> dict: return parameters -def _filter_anyof_fields(schema_dict: dict[str, Any]) -> dict[str, Any]: +def _filter_anyof_fields(schema_dict: dict[str, object]) -> dict[str, object]: """ When anyof is present, only keep the anyof field and its contents - otherwise VertexAI will throw an error - https://github.com/BerriAI/litellm/issues/11164 Filter out other fields in the same dict. @@ -704,7 +704,7 @@ def process_items(schema, depth=0): process_items(item, depth + 1) -def set_schema_property_ordering(schema: dict[str, Any], depth: int = 0) -> dict[str, Any]: +def set_schema_property_ordering(schema: dict[str, object], depth: int = 0) -> dict[str, object]: """ vertex ai and generativeai apis order output of fields alphabetically, unless you specify the order. python dicts retain order, so we just use that. Note that this field only applies to structured outputs, and not tools. @@ -724,14 +724,16 @@ def set_schema_property_ordering(schema: dict[str, Any], depth: int = 0) -> dict # retain propertyOrdering as an escape hatch if user already specifies it if "propertyOrdering" not in schema: schema["propertyOrdering"] = [k for k, v in schema["properties"].items()] - for k, v in schema["properties"].items(): - set_schema_property_ordering(v, depth + 1) - if "items" in schema: - set_schema_property_ordering(schema["items"], depth + 1) + for v in schema["properties"].values(): + if isinstance(v, dict): + set_schema_property_ordering(cast("dict[str, object]", v), depth + 1) # cast-ok: JSON Schema child + items: Final = schema.get("items") + if isinstance(items, dict): + set_schema_property_ordering(cast("dict[str, object]", items), depth + 1) # cast-ok: JSON Schema child return schema -def filter_schema_fields(schema_dict: dict[str, Any], valid_fields: set[str], processed=None) -> dict[str, Any]: +def filter_schema_fields(schema_dict: dict[str, object], valid_fields: set[str], processed=None) -> dict[str, object]: """ Recursively filter a schema dictionary to keep only valid fields. """ @@ -905,7 +907,7 @@ def _convert_schema_types(schema, depth=0): "maxProperties", } - any_of: Final[list[dict[str, Any]]] = [] + any_of: Final[list[dict[str, object]]] = [] for t in type_val: if not isinstance(t, str): continue @@ -916,7 +918,7 @@ def _convert_schema_types(schema, depth=0): # For object/array types, include type-specific fields if t in ("object", "array"): - item_schema = {"type": t} + item_schema: dict[str, object] = {"type": t} # Move type-specific fields into this anyOf item for field in type_specific_fields: if field in schema: @@ -1110,11 +1112,11 @@ class VertexAITokenCounter(BaseTokenCounter): self, model_to_use: str, messages: list[dict[str, Any]] | None, - contents: list[dict[str, Any]] | None, + contents: list[dict[str, object]] | None, deployment: dict[str, Any] | None = None, request_model: str = "", - tools: list[dict[str, Any]] | None = None, - system: Any | None = None, + tools: list[dict[str, object]] | None = None, + system: object | None = None, ) -> TokenCountResponse | None: import copy @@ -1131,25 +1133,26 @@ class VertexAITokenCounter(BaseTokenCounter): partner_models_handler: Final = VertexAIPartnerModels() # Extract vertex-specific params from litellm_params - vertex_project = count_tokens_params_request.get("vertex_project") or count_tokens_params_request.get( + partner_litellm_params: Final[dict[str, object]] = count_tokens_params_request + vertex_project = partner_litellm_params.get("vertex_project") or partner_litellm_params.get( "vertex_ai_project" ) - vertex_location = count_tokens_params_request.get("vertex_location") or count_tokens_params_request.get( + vertex_location = partner_litellm_params.get("vertex_location") or partner_litellm_params.get( "vertex_ai_location" ) # Count tokens not available on global location: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/count-tokens - vertex_location = count_tokens_params_request.get("vertex_count_tokens_location") or vertex_location + vertex_location = partner_litellm_params.get("vertex_count_tokens_location") or vertex_location - vertex_credentials: Final = count_tokens_params_request.get( - "vertex_credentials" - ) or count_tokens_params_request.get("vertex_ai_credentials") + vertex_credentials: Final = partner_litellm_params.get("vertex_credentials") or partner_litellm_params.get( + "vertex_ai_credentials" + ) result = await partner_models_handler.count_tokens( model=model_to_use, messages=messages or [], - litellm_params=count_tokens_params_request, + litellm_params=partner_litellm_params, vertex_project=vertex_project, vertex_location=vertex_location, vertex_credentials=vertex_credentials, diff --git a/litellm/llms/vertex_ai/cost_calculator.py b/litellm/llms/vertex_ai/cost_calculator.py index 23cb1e5b580..8b00fc2e925 100644 --- a/litellm/llms/vertex_ai/cost_calculator.py +++ b/litellm/llms/vertex_ai/cost_calculator.py @@ -64,6 +64,7 @@ def cost_per_character( usage: Usage, prompt_characters: float | None = None, completion_characters: float | None = None, + service_tier: str | None = None, vertex_location: str | None = None, ) -> tuple[float, float]: """ @@ -74,6 +75,8 @@ def cost_per_character( - custom_llm_provider: str, "vertex_ai-*" - prompt_characters: float, the number of input characters - completion_characters: float, the number of output characters + - service_tier: optional tier derived from Gemini trafficType + ("priority" for ON_DEMAND_PRIORITY, "flex" for FLEX/batch). - vertex_location: the Vertex AI location serving the request; non-global locations apply the model's regional-endpoint uplift multiplier @@ -92,6 +95,7 @@ def cost_per_character( model=model, custom_llm_provider=custom_llm_provider, usage=usage, + service_tier=service_tier, ) else: try: @@ -123,6 +127,7 @@ def cost_per_character( model=model, custom_llm_provider=custom_llm_provider, usage=usage, + service_tier=service_tier, ) ## CALCULATE OUTPUT COST @@ -131,6 +136,7 @@ def cost_per_character( model=model, custom_llm_provider=custom_llm_provider, usage=usage, + service_tier=service_tier, ) else: completion_tokens: Final = usage.completion_tokens @@ -162,6 +168,7 @@ def cost_per_character( model=model, custom_llm_provider=custom_llm_provider, usage=usage, + service_tier=service_tier, ) vertex_uplift: Final = get_vertex_regional_endpoint_uplift(model_info, vertex_location) diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index b7f91bfba0d..b6ad9fbcc04 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -12,7 +12,7 @@ from urllib.parse import quote, unquote import httpx from httpx import Headers, Response from openai.types.file_deleted import FileDeleted -from typing_extensions import ReadOnly +from typing_extensions import ReadOnly, Required import litellm from litellm._uuid import uuid @@ -104,6 +104,27 @@ class _VertexBatchRow(TypedDict, total=False): processed_time: ReadOnly[str] +class _VertexEmbeddingVector(TypedDict): + values: ReadOnly[list[float]] + + +class _VertexEmbeddingUsageMetadata(TypedDict, total=False): + promptTokenCount: ReadOnly[int] + + +class _VertexEmbeddingResponse(TypedDict, total=False): + embedding: ReadOnly[Required[_VertexEmbeddingVector]] + usageMetadata: ReadOnly[_VertexEmbeddingUsageMetadata] + tokenCount: ReadOnly[int] + + +class _VertexEmbeddingBatchRow(TypedDict, total=False): + key: ReadOnly[str] + request: ReadOnly[Mapping[str, object]] + status: ReadOnly[Required[str]] + response: ReadOnly[Required[_VertexEmbeddingResponse]] + + class _OpenAIBatchOutputError(TypedDict): code: ReadOnly[str] message: ReadOnly[str] @@ -111,7 +132,7 @@ class _OpenAIBatchOutputError(TypedDict): class _OpenAIBatchOutputResponse(TypedDict): status_code: ReadOnly[int] - request_id: ReadOnly[str] + request_id: ReadOnly[object] body: ReadOnly[Mapping[str, object]] @@ -218,7 +239,7 @@ def _get_litellm_batch_custom_id_from_labels(labels: Mapping[str, object] | None return str(labels.get("litellm_custom_id", "unknown")) -def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, Any]) -> bool: +def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, object]) -> bool: """ Whether a Vertex batch output row came from an `EmbedContentRequest`. @@ -237,7 +258,7 @@ def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, Any]) def _openai_batch_output_row( custom_id: str, - body: Mapping[str, Any] | None = None, + body: Mapping[str, object] | None = None, error_code: str | None = None, error_message: str = "", ) -> _OpenAIBatchOutputRow: @@ -259,7 +280,7 @@ def _openai_batch_output_row( } -def _split_vertex_batch_key(vertex_output_row: Mapping[str, Any]) -> tuple[str, int, int]: +def _split_vertex_batch_key(vertex_output_row: Mapping[str, object]) -> tuple[str, int, int]: """ Resolve `(custom_id, index within that custom_id, group size)` for a Vertex batch output row. @@ -278,7 +299,7 @@ def _split_vertex_batch_key(vertex_output_row: Mapping[str, Any]) -> tuple[str, return unquote(match["custom_id"]), int(match["index"]), int(match["total"]) -def _embedding_prompt_token_count(vertex_response: Mapping[str, Any]) -> int: +def _embedding_prompt_token_count(vertex_response: _VertexEmbeddingResponse) -> int: """ Prompt tokens billed for one Vertex Gemini Embedding batch row. @@ -293,7 +314,7 @@ def _embedding_prompt_token_count(vertex_response: Mapping[str, Any]) -> int: def _vertex_embeddings_rows_to_openai_batch_output_row( custom_id: str, - vertex_output_rows: tuple[Mapping[str, Any], ...], + vertex_output_rows: tuple[_VertexEmbeddingBatchRow, ...], element_indices: tuple[int, ...], element_count: int, model: str | None, @@ -348,7 +369,7 @@ def _vertex_embeddings_rows_to_openai_batch_output_row( def _transform_vertex_embeddings_batch_output_to_openai( - vertex_output_rows: Iterable[Mapping[str, Any]], + vertex_output_rows: Iterable[_VertexEmbeddingBatchRow], model: str | None, ) -> tuple[_OpenAIBatchOutputRow, ...]: """ @@ -388,7 +409,7 @@ def _model_from_managed_gcs_url(url: str) -> str | None: return match.group(1) if match else None -def _is_embeddings_batch_entry(openai_entry: Mapping[str, Any]) -> bool: +def _is_embeddings_batch_entry(openai_entry: Mapping[str, object]) -> bool: """ Whether an OpenAI batch JSONL line targets the embeddings endpoint. @@ -431,7 +452,7 @@ def _vertex_batch_embeddings_key(custom_id: str, index: int, total: int) -> str: return encoded_custom_id if total < 2 else f"{encoded_custom_id}#{index}/{total}" -def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, Any]) -> Mapping[str, Any]: +def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, object]) -> Mapping[str, object]: """ One Vertex Gemini Embedding batch input row. @@ -453,8 +474,8 @@ def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, def _openai_batch_jsonl_entry_to_vertex_embeddings_rows( - openai_entry: Mapping[str, Any], -) -> tuple[Mapping[str, Any], ...]: + openai_entry: Mapping[str, object], +) -> tuple[Mapping[str, object], ...]: """ Transforms a single OpenAI `/v1/embeddings` batch entry into Vertex Gemini Embedding batch rows, one per requested embedding. @@ -512,7 +533,7 @@ def _openai_batch_jsonl_entry_to_vertex_embeddings_rows( def _openai_batch_jsonl_entry_to_vertex_rows( openai_entry: dict[str, Any], map_openai_to_vertex_params: Callable[[dict[str, Any]], dict[str, Any]], -) -> tuple[Mapping[str, Any], ...]: +) -> tuple[Mapping[str, object], ...]: """ Transforms a single OpenAI JSONL batch entry into the Vertex rows it maps to. @@ -533,7 +554,7 @@ def _openai_batch_jsonl_entry_to_vertex_rows( cached_content=None, ) - custom_id: Final = openai_entry.get("custom_id") + custom_id: Final[object] = openai_entry.get("custom_id") if custom_id is not None: if "labels" not in vertex_request_body: vertex_request_body["labels"] = {} diff --git a/litellm/llms/vertex_ai/gemini/grounding_requests.py b/litellm/llms/vertex_ai/gemini/grounding_requests.py new file mode 100644 index 00000000000..40acd9378df --- /dev/null +++ b/litellm/llms/vertex_ai/gemini/grounding_requests.py @@ -0,0 +1,56 @@ +from collections.abc import Mapping, Sequence +from dataclasses import dataclass +from typing import Final + + +@dataclass(frozen=True, slots=True) +class GroundingRequests: + web_search_requests: int | None + google_maps_grounding_requests: int | None + + def has_billable_grounding(self) -> bool: + return bool(self.web_search_requests or self.google_maps_grounding_requests) + + +def _chunk_kinds(item: Mapping[str, object]) -> frozenset[str]: + chunks: Final = item.get("groundingChunks") + if not isinstance(chunks, list): + return frozenset() + return frozenset(kind for chunk in chunks if isinstance(chunk, Mapping) for kind in chunk) + + +def _queries(item: Mapping[str, object]) -> frozenset[str]: + queries: Final = item.get("webSearchQueries") + if not isinstance(queries, list): + return frozenset() + return frozenset(query for query in queries if isinstance(query, str) and query) + + +def _is_maps_item(item: Mapping[str, object]) -> bool: + return "maps" in _chunk_kinds(item) or bool(item.get("googleMapsWidgetContextToken")) + + +def _attributes_queries_to_maps(item: Mapping[str, object]) -> bool: + return _is_maps_item(item) and "web" not in _chunk_kinds(item) + + +def calculate_grounding_requests(grounding_metadata: Sequence[Mapping[str, object]]) -> GroundingRequests: + """Billable grounding requests across candidates, counting each distinct query once. + + Duplicate queries within and across grounding metadata items collapse to the + distinct-query count (#36377), and empty strings are ignored. Maps grounding is + floored at one request whenever a candidate carries maps chunks or a widget token, + since per-prompt billing charges the prompt even when no query is reported. + """ + items: Final = tuple(item for item in grounding_metadata if isinstance(item, Mapping)) + web_queries: Final = frozenset( + query for item in items if not _attributes_queries_to_maps(item) for query in _queries(item) + ) + maps_queries: Final = frozenset( + query for item in items if _attributes_queries_to_maps(item) for query in _queries(item) + ) + has_maps: Final = any(_is_maps_item(item) for item in items) + return GroundingRequests( + web_search_requests=len(web_queries) or None, + google_maps_grounding_requests=max(len(maps_queries), 1) if has_maps else None, + ) diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 11c026010ee..e2d62be6a69 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -250,7 +250,7 @@ def _gs_uri_requires_content_type_metadata(url: str) -> bool: def _image_url_payload_may_need_sync_gcs_metadata_fetch( - raw_image_url: Any, + raw_image_url: object, ) -> bool: """ True when this image_url value (content-part image_url or assistant ``images[]`` @@ -326,7 +326,7 @@ def _openai_messages_may_need_sync_gcs_metadata_fetch( def _get_gcs_object_content_type( image_url: str, vertex_project: str | None = None, - vertex_credentials: Any | None = None, + vertex_credentials: object = None, ) -> str | None: """ Resolve content type from GCS object metadata. @@ -479,7 +479,7 @@ def _process_gemini_media( model: str | None = None, video_metadata: dict[str, Any] | None = None, vertex_project: str | None = None, - vertex_credentials: Any | None = None, + vertex_credentials: object = None, ) -> PartType: """ Given a media URL (image, audio, or video), return the appropriate PartType for Gemini @@ -1002,7 +1002,7 @@ def _gemini_convert_messages_with_history( if isinstance(_ss_invocations, list): for invocation in _ss_invocations: # Re-inject toolCall part - tc_part: dict[str, Any] = { + tc_part: dict[str, object] = { "toolCall": { "toolType": invocation.get("tool_type"), "id": invocation.get("id"), @@ -1015,13 +1015,13 @@ def _gemini_convert_messages_with_history( # Re-inject toolResponse part if response is present if "response" in invocation: - tr_dict: dict[str, Any] = { + tr_dict: dict[str, object] = { "id": invocation.get("id"), "response": invocation.get("response"), } if invocation.get("tool_type"): tr_dict["toolType"] = invocation["tool_type"] - tr_part: dict[str, Any] = {"toolResponse": tr_dict} + tr_part: dict[str, object] = {"toolResponse": tr_dict} if "response_thought_signature" in invocation: tr_part["thoughtSignature"] = invocation["response_thought_signature"] assistant_content.append(tr_part) @@ -1090,7 +1090,7 @@ def _pop_and_merge_extra_body(data: RequestBody, optional_params: dict) -> None: data_dict[k] = v -def _has_google_maps_tool(tools: Any | None) -> bool: +def _has_google_maps_tool(tools: object) -> bool: """Return True if any tool object in the list has a 'googleMaps' key.""" if not isinstance(tools, list): return False @@ -1127,7 +1127,7 @@ def _rewrite_mime_type_to_response_format(generation_config: GenerationConfig) - schema = generation_config.pop("response_schema", None) generation_config.pop("response_mime_type", None) - response_format: Final[dict[str, Any]] = {"text": {"mimeType": "APPLICATION_JSON"}} + response_format: Final[dict[str, dict[str, object]]] = {"text": {"mimeType": "APPLICATION_JSON"}} if schema is not None: response_format["text"]["schema"] = schema generation_config["responseFormat"] = response_format @@ -1316,7 +1316,7 @@ async def async_transform_request_body( timeout: float | httpx.Timeout | None, extra_headers: dict | None, optional_params: dict, - logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, + logging_obj: LiteLLMLoggingObj, custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], litellm_params: dict, vertex_project: str | None, diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index d298670aa7a..69fe5678de9 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -23,6 +23,7 @@ from litellm.constants import ( DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE, DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO, ) +from litellm.litellm_core_utils.json_fragment_accumulator import JSONFragmentAccumulator from litellm.litellm_core_utils.prompt_templates.factory import ( _encode_tool_call_id_with_signature, ) @@ -88,6 +89,7 @@ from ..common_utils import ( supports_response_json_schema, ) from ..vertex_llm_base import VertexBase +from .grounding_requests import calculate_grounding_requests from .transformation import ( _gemini_convert_messages_with_history, async_transform_request_body, @@ -947,7 +949,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): # For Gemini 3+ models, use thinkingLevel instead of thinkingBudget if model and VertexGeminiConfig._is_gemini_3_or_newer(model): if thinking_enabled: - if thinking_budget is None or thinking_budget == 0: + if thinking_budget == 0: params["includeThoughts"] = False else: params["includeThoughts"] = True @@ -1716,14 +1718,15 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): completion_response: GenerateContentResponseBody | BidiGenerateContentServerMessage, ) -> bool: """ - Whether the response used Grounding with Google Search, detected via - groundingMetadata.webSearchQueries (an actual web search was performed). + Whether the response used Grounding with Google Search or Grounding with Google Maps, + detected via groundingMetadata.webSearchQueries (an actual web search was performed) or + groundingMetadata.groundingChunks[].maps (a Maps lookup was performed). - Google bills grounding-with-Google-Search retrieved tokens separately (a per-request / - per-query search fee) and excludes them from input token billing, unlike URL context / - File Search / code execution whose tool-use tokens are charged at the input token rate. - URL context also emits groundingMetadata (with groundingChunks but no webSearchQueries), - so presence of groundingMetadata alone is not a sufficient signal. + Google bills both groundings separately (a per-request / per-query fee) and excludes their + retrieved tokens from input token billing, unlike URL context / File Search / code execution + whose tool-use tokens are charged at the input token rate. URL context also emits + groundingMetadata (with web groundingChunks but no webSearchQueries), so presence of + groundingMetadata alone is not a sufficient signal. See https://ai.google.dev/gemini-api/docs/pricing and https://github.com/BerriAI/litellm/discussions/33198 """ @@ -1731,7 +1734,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return False for candidate in completion_response["candidates"] or []: grounding_metadata, _, _, _ = VertexGeminiConfig._extract_candidate_metadata(candidate) - if VertexGeminiConfig._calculate_web_search_requests(grounding_metadata): + if calculate_grounding_requests(grounding_metadata).has_billable_grounding(): return True return False @@ -1978,16 +1981,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): @staticmethod def _calculate_web_search_requests(grounding_metadata: list[dict]) -> int | None: - web_search_requests: int | None = None + return calculate_grounding_requests(grounding_metadata).web_search_requests - if grounding_metadata and isinstance(grounding_metadata, list) and len(grounding_metadata) > 0: - for grounding_metadata_item in grounding_metadata: - web_search_queries = grounding_metadata_item.get("webSearchQueries") - if web_search_queries and web_search_requests: - web_search_requests += len([q for q in web_search_queries if q]) - elif web_search_queries: - web_search_requests = len([q for q in web_search_queries if q]) - return web_search_requests + @staticmethod + def _set_grounding_usage_counters(usage: Usage, grounding_metadata: Sequence[Mapping[str, object]]) -> None: + grounding_requests: Final = calculate_grounding_requests(grounding_metadata) + details: Final = cast(PromptTokensDetailsWrapper, usage.prompt_tokens_details) + if grounding_requests.web_search_requests is not None: + details.web_search_requests = grounding_requests.web_search_requests + if grounding_requests.google_maps_grounding_requests is not None: + details.google_maps_grounding_requests = grounding_requests.google_maps_grounding_requests @staticmethod def _create_streaming_choice( @@ -2453,9 +2456,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): usage: Final = VertexGeminiConfig._calculate_usage(completion_response=completion_response) - web_search_requests: Final = VertexGeminiConfig._calculate_web_search_requests(grounding_metadata) - if web_search_requests is not None: - cast(PromptTokensDetailsWrapper, usage.prompt_tokens_details).web_search_requests = web_search_requests + VertexGeminiConfig._set_grounding_usage_counters(usage, grounding_metadata) setattr(model_response, "usage", usage) @@ -3087,7 +3088,7 @@ class ModelResponseIterator: self.streaming_response = streaming_response self.response = response self.chunk_type: Literal["valid_json", "accumulated_json"] = "valid_json" - self.accumulated_json = "" + self._json_buffer = JSONFragmentAccumulator() self.sent_first_chunk = False self.logging_obj = logging_obj self.response_headers = response_headers or {} @@ -3095,6 +3096,14 @@ class ModelResponseIterator: self.cumulative_tool_call_index: int = 0 self.has_seen_tool_calls: bool = False + @property + def accumulated_json(self) -> str: + return self._json_buffer.snapshot() + + @accumulated_json.setter + def accumulated_json(self, value: str) -> None: + self._json_buffer.set(value) + @staticmethod def _check_streaming_error(chunk: dict) -> None: """Detect embedded errors (e.g. 429 RESOURCE_EXHAUSTED) in streaming chunks and raise VertexAIError.""" @@ -3212,9 +3221,7 @@ class ModelResponseIterator: completion_response=processed_chunk, ) - web_search_requests: Final = VertexGeminiConfig._calculate_web_search_requests(grounding_metadata) - if web_search_requests is not None: - cast(PromptTokensDetailsWrapper, usage.prompt_tokens_details).web_search_requests = web_search_requests + VertexGeminiConfig._set_grounding_usage_counters(usage, grounding_metadata) traffic_type: Final = processed_chunk.get("usageMetadata", {}).get("trafficType") if traffic_type: @@ -3298,8 +3305,8 @@ class ModelResponseIterator: return self.chunk_parser(chunk=json_chunk) def handle_accumulated_json_chunk(self, chunk: str, is_final: bool = False) -> Optional["ModelResponseStream"]: - message: Final = litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or "" - self.accumulated_json = (self.accumulated_json + message.replace("\n\n", "")).strip() + message: Final = (litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or "").replace("\n\n", "") + self._json_buffer.append(message) # Mid-stream, defer parsing until the buffer's last byte can close a value: # attempting a parse after every fragment of one large object is O(n^2) and @@ -3307,27 +3314,23 @@ class ModelResponseIterator: # data is coming, so drain whatever complete values remain regardless of the # trailing byte, otherwise a complete leading value sitting behind a truncated # trailing one would be silently dropped. - if not is_final and (not self.accumulated_json or self.accumulated_json[-1] not in "}]"): + if not is_final and not self._json_buffer.could_close_json(): return None # Peel one complete JSON value from the front of the buffer and keep the # unconsumed tail. Running json.loads over the whole buffer would fail # forever once it held more than one concatenated value ("Extra data") while # never resetting the buffer, so the buffer grew without bound and pinned the - # core. raw_decode reports where the value ended, so concatenated values drain - # one call at a time. A leading non-dict value (never emitted by Gemini in - # practice) is consumed and skipped so it cannot block the dict values behind it. - decoder: Final = json.JSONDecoder() - while self.accumulated_json: - try: - raw_value = decoder.raw_decode(self.accumulated_json) - except json.JSONDecodeError: + # core. pop_next_value reports where the value ended, so concatenated values + # drain one call at a time. A leading non-dict value (never emitted by Gemini + # in practice) is consumed and skipped so it cannot block the dict values + # behind it. + while True: + found, decoded = self._json_buffer.pop_next_value() + if not found: return None - decoded, end_index = cast("tuple[object, int]", raw_value) # cast-ok: raw_decode -> tuple[Any,int] - self.accumulated_json = self.accumulated_json[end_index:].strip() if isinstance(decoded, dict): return self.chunk_parser(chunk=decoded) - return None def _common_chunk_parsing_logic(self, chunk: str) -> Optional["ModelResponseStream"]: try: @@ -3351,7 +3354,7 @@ class ModelResponseIterator: try: chunk: Final = self.response_iterator.__next__() except StopIteration: - if self.chunk_type == "accumulated_json" and self.accumulated_json: + if self.chunk_type == "accumulated_json" and self._json_buffer: result: Final = self.handle_accumulated_json_chunk(chunk="", is_final=True) if result is not None: return result @@ -3375,7 +3378,7 @@ class ModelResponseIterator: try: chunk: Final = await self.async_response_iterator.__anext__() except StopAsyncIteration: - if self.chunk_type == "accumulated_json" and self.accumulated_json: + if self.chunk_type == "accumulated_json" and self._json_buffer: result: Final = self.handle_accumulated_json_chunk(chunk="", is_final=True) if result is not None: return result diff --git a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py index 13c1ba5a697..f81d4ca777e 100644 --- a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py +++ b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py @@ -3,7 +3,7 @@ Google AI Studio /batchEmbedContents Embeddings Endpoint """ import json -from typing import Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal import httpx @@ -29,6 +29,9 @@ from .batch_embed_content_transformation import ( transform_openai_input_gemini_embed_content, ) +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class GoogleBatchEmbeddings(VertexLLM): @staticmethod @@ -125,7 +128,7 @@ class GoogleBatchEmbeddings(VertexLLM): model_response: EmbeddingResponse, custom_llm_provider: Literal["gemini", "vertex_ai"], optional_params: dict, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", api_key: str | None = None, api_base: str | None = None, encoding=None, @@ -290,7 +293,7 @@ class GoogleBatchEmbeddings(VertexLLM): use_embed_content: bool = False, api_key: str | None = None, optional_params: dict | None = None, - logging_obj: Any | None = None, + logging_obj: "LiteLLMLoggingObj | None" = None, ) -> EmbeddingResponse: if client is None: _params: Final = {} diff --git a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py index 67c6bff4381..725a7f39917 100644 --- a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py +++ b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py @@ -2,7 +2,7 @@ import base64 import json import os from io import BufferedReader, BytesIO -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, cast import httpx from httpx._types import RequestFiles @@ -14,6 +14,11 @@ from litellm.llms.vertex_ai.common_utils import get_vertex_base_url from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM from litellm.secret_managers.main import get_secret_str from litellm.types.images.main import ImageEditOptionalRequestParams +from litellm.types.llms.vertex_ai import ( + GenerateContentResponseBody, + HttpxContentType, + HttpxPartType, +) from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import FileTypes, ImageObject, ImageResponse, OpenAIImage @@ -25,6 +30,16 @@ else: LiteLLMLoggingObj = Any +class _GenerateContentSource(Protocol): + """An HTTP response whose JSON body is a Gemini ``generateContent`` result.""" + + def json(self) -> GenerateContentResponseBody: ... + + +def _generate_content_payload(response: _GenerateContentSource) -> GenerateContentResponseBody: + return response.json() + + class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): """ Vertex AI Gemini Image Edit Configuration @@ -46,16 +61,13 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): image_edit_optional_params: ImageEditOptionalRequestParams, model: str, drop_params: bool, - ) -> dict[str, Any]: + ) -> dict[str, str]: supported_params: Final = self.get_supported_openai_params(model) - filtered_params = {key: value for key, value in image_edit_optional_params.items() if key in supported_params} + if "size" not in supported_params or "size" not in image_edit_optional_params: + return {} - mapped_params: Final[dict[str, Any]] = {} - - if "size" in filtered_params: - mapped_params["aspectRatio"] = self._map_size_to_aspect_ratio(filtered_params["size"]) - - return mapped_params + size: Final = image_edit_optional_params.get("size") + return {"aspectRatio": self._map_size_to_aspect_ratio(size or "")} def _resolve_vertex_project(self) -> str | None: return ( @@ -86,12 +98,12 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): def validate_environment( self, - headers: dict, + headers: dict[str, str], model: str, api_key: str | None = None, - litellm_params: dict | None = None, + litellm_params: dict[str, object] | None = None, api_base: str | None = None, - ) -> dict: + ) -> dict[str, str]: headers = headers or {} litellm_params = litellm_params or {} @@ -116,7 +128,7 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): self, model: str, api_base: str | None, - litellm_params: dict, + litellm_params: dict[str, object], ) -> str: """ Get the complete URL for Vertex AI Gemini generateContent API @@ -148,38 +160,35 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): model: str, prompt: str | None, image: FileTypes | None, - image_edit_optional_request_params: dict[str, Any], + image_edit_optional_request_params: dict[str, object], litellm_params: GenericLiteLLMParams, - headers: dict, - ) -> tuple[dict[str, Any], RequestFiles | None]: + headers: dict[str, str], + ) -> tuple[dict[str, object], RequestFiles | None]: inline_parts: Final = self._prepare_inline_image_parts(image) if image else [] if not inline_parts: raise ValueError("Vertex AI Gemini image edit requires at least one image.") # Build parts list with image and prompt (if provided) - parts: Final = inline_parts.copy() - if prompt is not None and prompt != "": - parts.append({"text": prompt}) + text_parts: Final[list[HttpxPartType]] = [{"text": prompt}] if prompt is not None and prompt != "" else [] + parts: Final[list[HttpxPartType]] = [*inline_parts, *text_parts] # Correct format for Vertex AI Gemini image editing - contents: Final = {"role": "USER", "parts": parts} - - request_body: Final[dict[str, Any]] = {"contents": contents} - - # Generation config with proper structure for image editing - generation_config: Final[dict[str, Any]] = {"response_modalities": ["IMAGE"]} + contents: Final[dict[str, object]] = {"role": "USER", "parts": parts} # Add image-specific configuration - image_config: Final[dict[str, Any]] = {} - if "aspectRatio" in image_edit_optional_request_params: - image_config["aspect_ratio"] = image_edit_optional_request_params["aspectRatio"] + image_config: Final = ( + {"aspect_ratio": image_edit_optional_request_params["aspectRatio"]} + if "aspectRatio" in image_edit_optional_request_params + else None + ) - if image_config: - generation_config["image_config"] = image_config + generation_config: Final[dict[str, object]] = { + key: value for key, value in (("response_modalities", ["IMAGE"]), ("image_config", image_config)) if value + } - request_body["generationConfig"] = generation_config + request_body: Final[dict[str, object]] = {"contents": contents, "generationConfig": generation_config} - payload: Final[Any] = json.dumps(request_body) + payload: Final = json.dumps(request_body) empty_files: Final = cast(RequestFiles, []) return cast(tuple[dict[str, Any], RequestFiles | None], (payload, empty_files)) @@ -187,11 +196,11 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): self, model: str, raw_response: httpx.Response, - logging_obj: Any, + logging_obj: LiteLLMLoggingObj, ) -> ImageResponse: model_response: Final = ImageResponse() try: - response_json: Final = raw_response.json() + response_json: Final = _generate_content_payload(raw_response) except Exception as exc: raise self.get_error_class( error_message=f"Error transforming image edit response: {exc}", @@ -200,20 +209,15 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): ) candidates: Final = response_json.get("candidates", []) - data_list: Final[list[ImageObject]] = [] - - for candidate in candidates: - content = candidate.get("content", {}) - parts = content.get("parts", []) - for part in parts: - inline_data = part.get("inlineData") - if inline_data and inline_data.get("data"): - data_list.append( - ImageObject( - b64_json=inline_data["data"], - url=None, - ) - ) + contents: Final[list[HttpxContentType]] = [ + candidate["content"] for candidate in candidates if "content" in candidate + ] + parts: Final[list[HttpxPartType]] = [part for content in contents for part in content.get("parts", [])] + data_list: Final[list[ImageObject]] = [ + ImageObject(b64_json=b64_json, url=None) + for part in parts + if (inline_data := part.get("inlineData")) and (b64_json := inline_data.get("data")) + ] model_response.data = cast(list[OpenAIImage], data_list) return model_response @@ -229,30 +233,18 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM): } return aspect_ratio_map.get(size, "1:1") - def _prepare_inline_image_parts(self, image: FileTypes | list[FileTypes]) -> list[dict[str, Any]]: - images: list[FileTypes] - if isinstance(image, list): - images = image - else: - images = [image] - - inline_parts: Final[list[dict[str, Any]]] = [] - for img in images: - if img is None: - continue - - mime_type = ImageEditRequestUtils.get_image_content_type(img) - image_bytes = self._read_all_bytes(img) - inline_parts.append( - { - "inlineData": { - "mimeType": mime_type, - "data": base64.b64encode(image_bytes).decode("utf-8"), - } + def _prepare_inline_image_parts(self, image: FileTypes | list[FileTypes]) -> list[HttpxPartType]: + images: Final[list[FileTypes]] = image if isinstance(image, list) else [image] + return [ + { + "inlineData": { + "mimeType": ImageEditRequestUtils.get_image_content_type(img), + "data": base64.b64encode(self._read_all_bytes(img)).decode("utf-8"), } - ) - - return inline_parts + } + for img in images + if img is not None + ] def _read_all_bytes(self, image: FileTypes) -> bytes: if isinstance(image, bytes): diff --git a/litellm/llms/vertex_ai/image_edit/vertex_imagen_transformation.py b/litellm/llms/vertex_ai/image_edit/vertex_imagen_transformation.py index 9c6e943dc04..c6ad5928b74 100644 --- a/litellm/llms/vertex_ai/image_edit/vertex_imagen_transformation.py +++ b/litellm/llms/vertex_ai/image_edit/vertex_imagen_transformation.py @@ -195,7 +195,7 @@ class VertexAIImagenImageEditConfig(BaseImageEditConfig, VertexLLM): self, model: str, raw_response: httpx.Response, - logging_obj: Any, + logging_obj: LiteLLMLoggingObj, ) -> ImageResponse: model_response: Final = ImageResponse() try: diff --git a/litellm/llms/vertex_ai/image_generation/image_generation_handler.py b/litellm/llms/vertex_ai/image_generation/image_generation_handler.py index 2d7d78efa48..6a5bb484540 100644 --- a/litellm/llms/vertex_ai/image_generation/image_generation_handler.py +++ b/litellm/llms/vertex_ai/image_generation/image_generation_handler.py @@ -1,5 +1,5 @@ import json -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx from openai.types.image import Image @@ -14,6 +14,9 @@ from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import Ver from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES from litellm.types.utils import ImageResponse +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class VertexImageGeneration(VertexLLM): def process_image_generation_response( @@ -74,7 +77,7 @@ class VertexImageGeneration(VertexLLM): vertex_location: str | None, vertex_credentials: VERTEX_CREDENTIALS_TYPES | None, model_response: ImageResponse, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", model: str = "imagegeneration", # vertex ai uses imagegeneration as the default model client: Any | None = None, optional_params: dict | None = None, @@ -173,7 +176,7 @@ class VertexImageGeneration(VertexLLM): vertex_location: str | None, vertex_credentials: VERTEX_CREDENTIALS_TYPES | None, model_response: ImageResponse, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", model: str = "imagegeneration", # vertex ai uses imagegeneration as the default model client: AsyncHTTPHandler | None = None, optional_params: dict | None = None, diff --git a/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py b/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py index 799307f98c7..d7a2491c04a 100644 --- a/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py +++ b/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py @@ -24,6 +24,8 @@ from litellm.types.utils import ( ) if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -282,7 +284,7 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py b/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py index 64d5b55d3f4..8faf7b0d484 100644 --- a/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py +++ b/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py @@ -20,6 +20,8 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ImageObject, ImageResponse if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -212,7 +214,7 @@ class VertexAIImagenImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): request_data: dict, optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ImageResponse: diff --git a/litellm/llms/vertex_ai/interactions/__init__.py b/litellm/llms/vertex_ai/interactions/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/vertex_ai/interactions/transformation.py b/litellm/llms/vertex_ai/interactions/transformation.py new file mode 100644 index 00000000000..0764a8bea62 --- /dev/null +++ b/litellm/llms/vertex_ai/interactions/transformation.py @@ -0,0 +1,149 @@ +from collections.abc import Callable, Mapping +from dataclasses import dataclass +from typing import Final + +from litellm.litellm_core_utils.url_utils import encode_url_path_segment +from litellm.llms.gemini.interactions.transformation import GoogleAIStudioInteractionsConfig +from litellm.llms.vertex_ai.common_utils import validate_vertex_location +from litellm.llms.vertex_ai.vertex_llm_base import VertexBase +from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES +from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders + +VERTEX_INTERACTIONS_API_VERSION: Final = "v1beta1" +VERTEX_INTERACTIONS_DEFAULT_LOCATION: Final = "global" + + +@dataclass(frozen=True, slots=True) +class VertexInteractionsTarget: + base_url: str + project_id: str + location: str + + @property + def collection_url(self) -> str: + return ( + f"{self.base_url}/{VERTEX_INTERACTIONS_API_VERSION}" + f"/projects/{self.project_id}/locations/{self.location}/interactions" + ) + + def interaction_url(self, interaction_id: str) -> str: + encoded_interaction_id: Final = encode_url_path_segment(interaction_id, field_name="interaction_id") + return f"{self.collection_url}/{encoded_interaction_id}" + + +class VertexAIInteractionsConfig(VertexBase, GoogleAIStudioInteractionsConfig): + def __init__( + self, + mint_access_token: Callable[[VERTEX_CREDENTIALS_TYPES | None, str | None], tuple[str, str]] | None = None, + ) -> None: + super().__init__() + self._mint_access_token: Final[Callable[[VERTEX_CREDENTIALS_TYPES | None, str | None], tuple[str, str]]] = ( + mint_access_token or self._mint_access_token_with_vertex_base + ) + + def _mint_access_token_with_vertex_base( + self, + credentials: VERTEX_CREDENTIALS_TYPES | None, + project_id: str | None, + ) -> tuple[str, str]: + return self._ensure_access_token( + credentials=credentials, project_id=project_id, custom_llm_provider="vertex_ai" + ) + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.VERTEX_AI + + @property + def api_version(self) -> str: + return VERTEX_INTERACTIONS_API_VERSION + + def get_default_vertex_location(self) -> str: + return VERTEX_INTERACTIONS_DEFAULT_LOCATION + + def _mint(self, litellm_params: GenericLiteLLMParams) -> tuple[str, str]: + raw_params: Final = litellm_params.model_dump() + return self._mint_access_token( + self.safe_get_vertex_ai_credentials(raw_params), + self.safe_get_vertex_ai_project(raw_params), + ) + + def _target(self, api_base: str | None, litellm_params: GenericLiteLLMParams) -> VertexInteractionsTarget: + _, project_id = self._mint(litellm_params) + if not project_id: + raise ValueError( + "Vertex AI project is required. Set vertex_project, litellm.vertex_project, or VERTEXAI_PROJECT" + ) + location: Final = validate_vertex_location( + self.explicit_vertex_ai_location(litellm_params.model_dump()) or VERTEX_INTERACTIONS_DEFAULT_LOCATION + ) + return VertexInteractionsTarget( + base_url=self.get_api_base(api_base or None, location), + project_id=project_id, + location=location, + ) + + def validate_environment( + self, + headers: Mapping[str, str], + model: str, + litellm_params: GenericLiteLLMParams | None, + ) -> dict: # mutable-ok: BaseInteractionsAPIConfig declares plain-dict headers + access_token, _ = self._mint(litellm_params or GenericLiteLLMParams()) + return { # mutable-ok: BaseInteractionsAPIConfig declares plain-dict headers + "Content-Type": "application/json", + "Authorization": f"Bearer {access_token}", + **headers, + } + + def get_complete_url( + self, + api_base: str | None, + model: str | None, + agent: str | None = None, + litellm_params: Mapping[str, object] | None = None, + stream: bool | None = None, + ) -> str: + params: Final = ( + GenericLiteLLMParams.model_validate(litellm_params) if litellm_params else GenericLiteLLMParams() + ) + collection_url: Final = self._target(api_base, params).collection_url + return f"{collection_url}?alt=sse" if stream else collection_url + + def _interaction_by_id_request( + self, + interaction_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + url_suffix: str = "", + ) -> tuple[str, dict]: # mutable-ok: BaseInteractionsAPIConfig declares a plain-dict request body + target: Final = self._target(api_base or None, litellm_params) + return f"{target.interaction_url(interaction_id)}{url_suffix}", {} # mutable-ok: same base contract + + def transform_get_interaction_request( + self, + interaction_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: Mapping[str, str], + ) -> tuple[str, dict]: # mutable-ok: BaseInteractionsAPIConfig declares a plain-dict request body + return self._interaction_by_id_request(interaction_id, api_base, litellm_params) + + def transform_delete_interaction_request( + self, + interaction_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: Mapping[str, str], + ) -> tuple[str, dict]: # mutable-ok: BaseInteractionsAPIConfig declares a plain-dict request body + return self._interaction_by_id_request(interaction_id, api_base, litellm_params) + + def transform_cancel_interaction_request( + self, + interaction_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: Mapping[str, str], + ) -> tuple[str, dict]: # mutable-ok: BaseInteractionsAPIConfig declares a plain-dict request body + return self._interaction_by_id_request(interaction_id, api_base, litellm_params, url_suffix=":cancel") diff --git a/litellm/llms/vertex_ai/ocr/deepseek_transformation.py b/litellm/llms/vertex_ai/ocr/deepseek_transformation.py index 2603552152d..b57a87c3325 100644 --- a/litellm/llms/vertex_ai/ocr/deepseek_transformation.py +++ b/litellm/llms/vertex_ai/ocr/deepseek_transformation.py @@ -177,8 +177,9 @@ class VertexAIDeepSeekOCRConfig(BaseOCRConfig): content_item = {"type": "image_url", "image_url": document_url} # Build DeepSeek OCR request + provider_model: Final = model if model.startswith("deepseek-ai/") else f"deepseek-ai/{model}" data: Final = { - "model": "deepseek-ai/" + model, + "model": provider_model, "messages": [{"role": "user", "content": [content_item]}], } diff --git a/litellm/llms/vertex_ai/rerank/transformation.py b/litellm/llms/vertex_ai/rerank/transformation.py index 36876f67afc..2ec4f2da79b 100644 --- a/litellm/llms/vertex_ai/rerank/transformation.py +++ b/litellm/llms/vertex_ai/rerank/transformation.py @@ -4,6 +4,7 @@ Translates from Cohere's `/v1/rerank` input format to Vertex AI Discovery Engine Why separate file? Make it easy to see how transformation works """ +from collections.abc import Mapping from typing import Any, Final import httpx @@ -74,14 +75,15 @@ class VertexAIRerankConfig(BaseRerankConfig, VertexBase): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: """ Validate and set up authentication for Vertex AI Discovery Engine API """ # Get credentials and project info from optional_params (which contains vertex_credentials, etc.) - litellm_params: Final = optional_params.copy() if optional_params else {} - vertex_credentials: Final = self.safe_get_vertex_ai_credentials(litellm_params) - vertex_project: Final = self.safe_get_vertex_ai_project(litellm_params) + vertex_params: Final = optional_params.copy() if optional_params else {} + vertex_credentials: Final = self.safe_get_vertex_ai_credentials(vertex_params) + vertex_project: Final = self.safe_get_vertex_ai_project(vertex_params) # Get access token using the base class method access_token, project_id = self._ensure_access_token( diff --git a/litellm/llms/vertex_ai/text_to_speech/transformation.py b/litellm/llms/vertex_ai/text_to_speech/transformation.py index cf14ab88751..332f892ae6b 100644 --- a/litellm/llms/vertex_ai/text_to_speech/transformation.py +++ b/litellm/llms/vertex_ai/text_to_speech/transformation.py @@ -7,10 +7,14 @@ Reference: https://cloud.google.com/text-to-speech/docs/reference/rest/v1/text/s import base64 from collections.abc import Coroutine +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Union import httpx +from litellm.litellm_core_utils.audio_utils.utils import ( + speech_media_type_from_audio_bytes, +) from litellm.llms.base_llm.text_to_speech.transformation import ( BaseTextToSpeechConfig, TextToSpeechRequestData, @@ -457,12 +461,11 @@ class VertexAITextToSpeechConfig(BaseTextToSpeechConfig, VertexBase): if not response_content: raise ValueError("No audioContent in Vertex AI TTS response") - # Decode base64 to get binary content binary_data: Final = base64.b64decode(response_content) - - # Create an httpx.Response object with the binary data + media_type: Final = speech_media_type_from_audio_bytes(binary_data) response: Final = httpx.Response( status_code=200, + headers=None if media_type is None else MappingProxyType({"content-type": media_type}), content=binary_data, ) diff --git a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py index 962dfe52c0a..36b57e7c995 100644 --- a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py @@ -1,6 +1,8 @@ -from typing import TYPE_CHECKING, Any, Final +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig from litellm.llms.vertex_ai.common_utils import get_vertex_base_url @@ -19,12 +21,73 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: LiteLLMLoggingObj = Any +class VertexRagPageSpan(TypedDict, total=False): + """Page range a retrieved chunk came from, as ``:retrieveContexts`` returns it.""" + + firstPage: ReadOnly[int] + lastPage: ReadOnly[int] + + +class VertexRagContext(TypedDict, total=False): + """One retrieved chunk in a Vertex AI RAG ``:retrieveContexts`` response.""" + + text: ReadOnly[str] + sourceUri: ReadOnly[str] + sourceDisplayName: ReadOnly[str] + score: ReadOnly[float] + pageSpan: ReadOnly[VertexRagPageSpan] + + +class VertexRagContextGroup(TypedDict, total=False): + contexts: ReadOnly[list[VertexRagContext]] + + +class VertexRagRetrieveContextsResponse(TypedDict, total=False): + contexts: ReadOnly[VertexRagContextGroup] + + +class VertexRagCorpusResponse(TypedDict, total=False): + """A RAG corpus resource, as ``POST /ragCorpora`` returns it.""" + + name: ReadOnly[str] + display_name: ReadOnly[str] + createTime: ReadOnly[object] + labels: ReadOnly[object] + + +class _SearchQueryView(TypedDict): + """Holds the logged search query so the model call detail reads back as ``str``.""" + + query: ReadOnly[str] + + +class _RetrieveContextsSource(Protocol): + """An HTTP response whose JSON body is a Vertex AI RAG ``:retrieveContexts`` result.""" + + def json(self) -> VertexRagRetrieveContextsResponse: ... + + +class _RagCorpusSource(Protocol): + """An HTTP response whose JSON body is a Vertex AI RAG corpus resource.""" + + def json(self) -> VertexRagCorpusResponse: ... + + +def _retrieve_contexts_payload(response: _RetrieveContextsSource) -> VertexRagRetrieveContextsResponse: + return response.json() + + +def _rag_corpus_payload(response: _RagCorpusSource) -> VertexRagCorpusResponse: + return response.json() + + class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): """ Configuration for Vertex AI Vector Store RAG API @@ -35,7 +98,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): def __init__(self): super().__init__() - def get_auth_credentials(self, litellm_params: dict) -> BaseVectorStoreAuthCredentials: + def get_auth_credentials(self, litellm_params: Mapping[str, object]) -> BaseVectorStoreAuthCredentials: # Get credentials and project info vertex_credentials: Final = self.get_vertex_ai_credentials(dict(litellm_params)) vertex_project: Final = self.get_vertex_ai_project(dict(litellm_params)) @@ -60,7 +123,9 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): "write": [("POST", "/ragCorpora")], } - def validate_environment(self, headers: dict, litellm_params: GenericLiteLLMParams | None) -> dict: + def validate_environment( + self, headers: dict[str, str], litellm_params: GenericLiteLLMParams | None + ) -> dict[str, str]: """ Validate and set up authentication for Vertex AI RAG API """ @@ -73,7 +138,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): def get_complete_url( self, api_base: str | None, - litellm_params: dict, + litellm_params: dict[str, object], ) -> str: """ Get the Base endpoint for Vertex AI RAG API @@ -96,8 +161,9 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, - extra_body: dict[str, Any] | None = None, - ) -> tuple[str, dict[str, Any]]: + extra_body: Mapping[str, object] | None = None, + router: "Router | None" = None, + ) -> tuple[str, dict[str, object]]: """ Transform search request for Vertex AI RAG API """ @@ -120,12 +186,6 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): # Just the corpus ID, construct full path full_rag_corpus = f"projects/{vertex_project}/locations/{vertex_location}/ragCorpora/{vector_store_id}" - # Build the request body for Vertex AI RAG API - request_body: Final[dict[str, Any]] = { - "vertex_rag_store": {"rag_resources": [{"rag_corpus": full_rag_corpus}]}, - "query": {"text": query}, - } - ######################################################### # Update logging object with details of the request ######################################################### @@ -133,22 +193,27 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): # Add optional parameters max_num_results: Final = vector_store_search_optional_params.get("max_num_results") - if max_num_results is not None: - request_body["query"]["rag_retrieval_config"] = {"top_k": max_num_results} - - # Add filters if provided filters: Final = vector_store_search_optional_params.get("filters") - if filters is not None: - if "rag_retrieval_config" not in request_body["query"]: - request_body["query"]["rag_retrieval_config"] = {} - request_body["query"]["rag_retrieval_config"]["filter"] = filters - - # Add ranking options if provided ranking_options: Final = vector_store_search_optional_params.get("ranking_options") - if ranking_options is not None: - if "rag_retrieval_config" not in request_body["query"]: - request_body["query"]["rag_retrieval_config"] = {} - request_body["query"]["rag_retrieval_config"]["ranking"] = ranking_options + rag_retrieval_config: Final[Mapping[str, object]] = { + key: value + for key, value in ( + ("top_k", max_num_results), + ("filter", filters), + ("ranking", ranking_options), + ) + if value is not None + } + + query_body: Final[Mapping[str, object]] = { + key: value + for key, value in (("text", query), ("rag_retrieval_config", rag_retrieval_config or None)) + if value is not None + } + request_body: Final[dict[str, object]] = { + "vertex_rag_store": {"rag_resources": [{"rag_corpus": full_rag_corpus}]}, + "query": query_body, + } return url, request_body @@ -159,12 +224,13 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): Transform Vertex AI RAG API response to standard vector store search response """ try: - response_json: Final = response.json() + response_json: Final = _retrieve_contexts_payload(response) # Extract contexts from Vertex AI response - handle nested structure - contexts: Final = response_json.get("contexts", {}).get("contexts", []) + context_group: Final[VertexRagContextGroup] = response_json.get("contexts", {}) + contexts: Final = context_group.get("contexts", []) # Transform contexts to standard format - search_results: Final = [] + search_results: Final[list[VectorStoreSearchResult]] = [] for context in contexts: content = [ VectorStoreResultContent( @@ -182,7 +248,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): filename = source_display_name if source_display_name else "Unknown Document" # Build attributes with available metadata - attributes = {} + attributes: dict[str, object] = {} if source_uri: attributes["sourceUri"] = source_uri if source_display_name: @@ -202,9 +268,10 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): ) search_results.append(result) + query_view: Final[_SearchQueryView] = {"query": litellm_logging_obj.model_call_details.get("query", "")} return VectorStoreSearchResponse( object="vector_store.search_results.page", - search_query=litellm_logging_obj.model_call_details.get("query", ""), + search_query=query_view["query"], data=search_results, ) @@ -219,22 +286,24 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): self, vector_store_create_optional_params: VectorStoreCreateOptionalRequestParams, api_base: str, - ) -> tuple[str, dict[str, Any]]: + ) -> tuple[str, dict[str, object]]: """ Transform create request for Vertex AI RAG Corpus """ url: Final = f"{api_base}/ragCorpora" # Base URL for creating RAG corpus - # Build the request body for Vertex AI RAG Corpus creation - request_body: Final[dict[str, Any]] = { - "display_name": vector_store_create_optional_params.get("name", "litellm-vector-store"), - "description": "Vector store created via LiteLLM", - } - # Add metadata if provided metadata: Final = vector_store_create_optional_params.get("metadata") - if metadata is not None: - request_body["labels"] = metadata + + request_body: Final[dict[str, object]] = { + key: value + for key, value in ( + ("display_name", vector_store_create_optional_params.get("name", "litellm-vector-store")), + ("description", "Vector store created via LiteLLM"), + ("labels", metadata), + ) + if value is not None + } return url, request_body @@ -243,7 +312,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): Transform Vertex AI RAG Corpus creation response to standard vector store response """ try: - response_json: Final = response.json() + response_json: Final = _rag_corpus_payload(response) # Extract the corpus ID from the response name corpus_name: Final = response_json.get("name", "") diff --git a/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py index a0597769f7b..f0812e3ed9f 100644 --- a/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py @@ -1,6 +1,8 @@ -from typing import TYPE_CHECKING, Any, Final +from collections.abc import Mapping +from typing import TYPE_CHECKING, Any, Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict from litellm import get_model_info from litellm.exceptions import BadRequestError @@ -23,6 +25,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -50,6 +53,52 @@ VERTEX_SEARCH_DATASTORE_EXTRA_BODY_FIELDS: Final = frozenset(VertexSearchDataSto VERTEX_SEARCH_ENGINE_EXTRA_BODY_FIELDS: Final = frozenset(VertexSearchEngineExtraBody.__annotations__) +class VertexSearchSnippet(TypedDict, total=False): + snippet: ReadOnly[str] + htmlSnippet: ReadOnly[str] + + +class VertexSearchDerivedStructData(TypedDict, total=False): + """The ``derivedStructData`` blob Discovery Engine attaches to each search hit.""" + + title: ReadOnly[str] + link: ReadOnly[str] + displayLink: ReadOnly[str] + formattedUrl: ReadOnly[str] + snippets: ReadOnly[list[VertexSearchSnippet]] + + +class VertexSearchDocument(TypedDict, total=False): + derivedStructData: ReadOnly[VertexSearchDerivedStructData] + + +class VertexSearchHit(TypedDict, total=False): + id: ReadOnly[str] + document: ReadOnly[VertexSearchDocument] + + +class VertexSearchApiResponse(TypedDict, total=False): + """Body of a Discovery Engine ``:search`` response.""" + + results: ReadOnly[list[VertexSearchHit]] + + +class _SearchQueryView(TypedDict): + """Holds the logged search query so the model call detail reads back as ``str``.""" + + query: ReadOnly[str] + + +class _VertexSearchApiSource(Protocol): + """An HTTP response whose JSON body is a Discovery Engine ``:search`` result.""" + + def json(self) -> VertexSearchApiResponse: ... + + +def _vertex_search_payload(response: _VertexSearchApiSource) -> VertexSearchApiResponse: + return response.json() + + class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): """ Configuration for Vertex AI Search API Vector Store @@ -61,7 +110,7 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): super().__init__() @staticmethod - def get_supported_extra_body_fields(is_engine: bool = False) -> frozenset: + def get_supported_extra_body_fields(is_engine: bool = False) -> frozenset[str]: """ Native SearchRequest fields callers may forward via ``extra_body``. @@ -75,7 +124,7 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): return VERTEX_SEARCH_DATASTORE_EXTRA_BODY_FIELDS @classmethod - def _filter_extra_body(cls, extra_body: dict[str, Any], is_engine: bool = False) -> dict[str, Any]: + def _filter_extra_body(cls, extra_body: Mapping[str, object], is_engine: bool = False) -> dict[str, object]: """ Validate ``extra_body`` against the supported-field allowlist for the active serving config (engine/app vs data store). @@ -196,8 +245,9 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, - extra_body: dict[str, Any] | None = None, - ) -> tuple[str, dict[str, Any]]: + extra_body: Mapping[str, object] | None = None, + router: "Router | None" = None, + ) -> tuple[str, dict[str, object]]: """ Transform a search request for the Vertex AI Search (Discovery Engine) API. @@ -222,7 +272,7 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): is_engine: Final = bool(litellm_params.get("vertex_engine_id")) - request_body: Final[dict[str, Any]] = {"query": query, "pageSize": 10} + request_body: Final[dict[str, object]] = {"query": query, "pageSize": 10} max_num_results: Final = vector_store_search_optional_params.get("max_num_results") if max_num_results is not None: request_body["pageSize"] = max_num_results @@ -256,7 +306,7 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): } """ try: - response_json: Final = response.json() + response_json: Final = _vertex_search_payload(response) # Extract results from Vertex AI Search API response results: Final = response_json.get("results", []) @@ -264,8 +314,8 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): # Transform results to standard format search_results: Final[list[VectorStoreSearchResult]] = [] for result in results: - document = result.get("document", {}) - derived_data = document.get("derivedStructData", {}) + document: VertexSearchDocument = result.get("document", {}) + derived_data: VertexSearchDerivedStructData = document.get("derivedStructData", {}) # Extract text content from snippets snippets = derived_data.get("snippets", []) @@ -329,9 +379,10 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): ) search_results.append(result_obj) + query_view: Final[_SearchQueryView] = {"query": litellm_logging_obj.model_call_details.get("query", "")} return VectorStoreSearchResponse( object="vector_store.search_results.page", - search_query=litellm_logging_obj.model_call_details.get("query", ""), + search_query=query_view["query"], data=search_results, ) diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py index a8430455323..ef03e61a858 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py @@ -1,6 +1,6 @@ # What is this? ## Handler file for calling claude-3 on vertex ai -from typing import Any, Final +from typing import TYPE_CHECKING, Final import httpx @@ -12,6 +12,9 @@ from litellm.types.utils import ModelResponse from ....anthropic.chat.transformation import AnthropicConfig from .output_params_utils import sanitize_vertex_anthropic_output_params +if TYPE_CHECKING: + import tiktoken + class VertexAIError(Exception): def __init__(self, status_code, message): @@ -150,14 +153,17 @@ class VertexAIAnthropicConfig(AnthropicConfig): drop_params: bool, ) -> dict: """ - Override parent method to ensure VertexAI always uses tool-based structured outputs. - VertexAI doesn't support the output_format parameter, so we force all models - to use the tool-based approach for structured outputs. + Override parent method so VertexAI uses tool-based structured outputs + unless the vertex map entry advertises native structured output + (``output_format``, which Vertex AI Claude forwards for those models). """ - # Temporarily override model name to force tool-based approach - # This ensures Claude Sonnet 4.5 uses tools instead of output_format + from litellm.llms.anthropic.common_utils import AnthropicModelInfo + original_model: Final = model - if "response_format" in non_default_params: + native_structured_output: Final = AnthropicModelInfo._get_provider_resolved_capability( + model, "supports_native_structured_output", "vertex_ai" + ) + if "response_format" in non_default_params and native_structured_output is not True: model = "claude-3-sonnet-20240229" # Use a model that will use tool-based approach # Call parent method with potentially modified model name @@ -183,7 +189,7 @@ class VertexAIAnthropicConfig(AnthropicConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py index 7b0c26f5881..279035c455d 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/llama3/transformation.py @@ -1,6 +1,6 @@ import types from collections.abc import AsyncIterator, Iterator -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx @@ -20,6 +20,9 @@ from litellm.types.utils import ( from ...common_utils import VertexAIError +if TYPE_CHECKING: + import tiktoken + class VertexAILlama3Config(OpenAIGPTConfig): """ @@ -109,7 +112,7 @@ class VertexAILlama3Config(OpenAIGPTConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py b/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py index 6c955d9bab1..58cf7c7e702 100644 --- a/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py +++ b/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py @@ -9,7 +9,7 @@ The actual message transformation reuses OpenAIGPTConfig since Gemma uses OpenAI """ from collections.abc import Callable -from typing import Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, cast import httpx @@ -23,6 +23,11 @@ from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse +if TYPE_CHECKING: + import tiktoken + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + class VertexGemmaConfig(OpenAIGPTConfig): """ @@ -210,7 +215,7 @@ class VertexGemmaConfig(OpenAIGPTConfig): custom_prompt_dict: dict, model_response: ModelResponse, print_verbose: Callable, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", optional_params: dict, acompletion: bool, litellm_params: dict, @@ -265,12 +270,12 @@ class VertexGemmaConfig(OpenAIGPTConfig): api_key: str, model_response: ModelResponse, print_verbose: Callable, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", optional_params: dict, litellm_params: dict, client: HTTPHandler | httpx.Client | None = None, timeout: float | httpx.Timeout | None = None, - encoding: Any = None, + encoding: "tiktoken.Encoding | None" = None, ): """Synchronous completion request""" from litellm.utils import convert_to_model_response_object @@ -355,12 +360,12 @@ class VertexGemmaConfig(OpenAIGPTConfig): api_key: str, model_response: ModelResponse, print_verbose: Callable, - logging_obj: Any, + logging_obj: "LiteLLMLoggingObj", optional_params: dict, litellm_params: dict, client: AsyncHTTPHandler | httpx.AsyncClient | None = None, timeout: float | httpx.Timeout | None = None, - encoding: Any = None, + encoding: "tiktoken.Encoding | None" = None, ): """Asynchronous completion request""" from litellm.utils import convert_to_model_response_object diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py index 75098515deb..1942bc850f1 100644 --- a/litellm/llms/vertex_ai/vertex_llm_base.py +++ b/litellm/llms/vertex_ai/vertex_llm_base.py @@ -9,7 +9,7 @@ import json import os import threading from collections.abc import Mapping -from typing import TYPE_CHECKING, Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol from urllib.parse import urlparse import litellm @@ -27,6 +27,15 @@ from .common_utils import ( get_vertex_base_url, ) + +def _graft_default_vertex_path(api_base: str, default_url: str) -> str: + parsed_api_base: Final = urlparse(api_base) + default_segments: Final = urlparse(default_url).path.lstrip("/").split("/") + graft_segments: Final = default_segments[1:] if default_segments[0] in ("v1", "v1beta1") else default_segments + grafted_path: Final = parsed_api_base.path.rstrip("/") + "/" + "/".join(graft_segments) + return parsed_api_base._replace(path=grafted_path).geturl() + + GOOGLE_IMPORT_ERROR_MESSAGE: Final = ( "Google Cloud SDK not found. Install it with: pip install 'litellm[google]' or pip install google-cloud-aiplatform" ) @@ -38,6 +47,21 @@ else: GoogleCredentialsObject = Any +class _VertexCredentialsObject(Protocol): + """Structural view of the google-auth credentials handle that this class caches and refreshes.""" + + @property + def token(self) -> object: ... + + @property + def quota_project_id(self) -> str | None: ... + + @property + def expired(self) -> object: ... + + def refresh(self, request: object) -> None: ... + + class VertexBase: def __init__(self) -> None: super().__init__() @@ -46,7 +70,7 @@ class VertexBase: self._credentials: GoogleCredentialsObject | None = None self._credentials_project_mapping: dict[ tuple[VERTEX_CREDENTIALS_TYPES | None, str | None], - tuple[GoogleCredentialsObject, str | None], + tuple[_VertexCredentialsObject, str | None], ] = {} self.project_id: str | None = None self.async_handler: AsyncHTTPHandler | None = None @@ -100,7 +124,7 @@ class VertexBase: self, credentials: VERTEX_CREDENTIALS_TYPES | None, project_id: str | None, - ) -> tuple[Any, str]: + ) -> tuple[_VertexCredentialsObject | None, str]: if credentials is not None: if isinstance(credentials, str): _is_path: Final = os.path.exists( @@ -200,7 +224,7 @@ class VertexBase: return creds, project_id # Google Auth Helpers -- extracted for mocking purposes in tests - def _credentials_from_identity_pool(self, json_obj, scopes): + def _credentials_from_identity_pool(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import identity_pool except ImportError: @@ -211,7 +235,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_pluggable(self, json_obj, scopes): + def _credentials_from_pluggable(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import pluggable except ImportError: @@ -222,7 +246,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_identity_pool_with_aws(self, json_obj, scopes): + def _credentials_from_identity_pool_with_aws(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import aws except ImportError: @@ -233,7 +257,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_authorized_user(self, json_obj, scopes): + def _credentials_from_authorized_user(self, json_obj, scopes) -> _VertexCredentialsObject: try: import google.oauth2.credentials except ImportError: @@ -241,7 +265,7 @@ class VertexBase: return google.oauth2.credentials.Credentials.from_authorized_user_info(json_obj, scopes=scopes) - def _credentials_from_service_account(self, json_obj, scopes): + def _credentials_from_service_account(self, json_obj, scopes) -> _VertexCredentialsObject: try: import google.oauth2.service_account except ImportError: @@ -249,7 +273,7 @@ class VertexBase: return google.oauth2.service_account.Credentials.from_service_account_info(json_obj, scopes=scopes) - def _credentials_from_default_auth(self, scopes): + def _credentials_from_default_auth(self, scopes) -> tuple[_VertexCredentialsObject, str | None]: try: import google.auth as google_auth except ImportError: @@ -341,7 +365,7 @@ class VertexBase: ) return api_base - def refresh_auth(self, credentials: Any) -> None: + def refresh_auth(self, credentials: _VertexCredentialsObject) -> None: try: from google.auth.transport.requests import ( Request, @@ -417,7 +441,7 @@ class VertexBase: self, credential_cache_key: tuple, project_id: str | None, - ) -> tuple[str, str, "TokenState", Any, str | None] | None: + ) -> tuple[str, str, "TokenState", _VertexCredentialsObject, str | None] | None: """ Look up cached credentials and return usable token info for FRESH or STALE tokens (both are still valid for outbound requests). STALE @@ -440,7 +464,9 @@ class VertexBase: return None return creds.token, resolved_project, token_state, creds, cached_project_id - def _unpack_cached_credentials(self, credential_cache_key: tuple) -> tuple[Any, str | None]: + def _unpack_cached_credentials( + self, credential_cache_key: tuple + ) -> tuple[_VertexCredentialsObject | None, str | None]: """ Return (credentials, project_id) from the cache, or (None, None) if not cached. Handles both tuple and legacy cache formats. @@ -452,7 +478,7 @@ class VertexBase: return cached_entry return cached_entry, cached_entry.quota_project_id or getattr(cached_entry, "project_id", None) - def _get_token_state(self, credentials: Any) -> "TokenState": + def _get_token_state(self, credentials: _VertexCredentialsObject) -> "TokenState": """ Return the token state using google-auth's TokenState enum. @@ -476,7 +502,7 @@ class VertexBase: credentials: VERTEX_CREDENTIALS_TYPES | None, project_id: str | None, credential_cache_key: tuple, - ) -> tuple[Any, str | None]: + ) -> tuple[_VertexCredentialsObject, str | None]: """Load credentials via load_auth (in thread) and cache the result.""" try: _credentials, credential_project_id = await asyncify(self.load_auth)( @@ -496,7 +522,7 @@ class VertexBase: async def _background_refresh_credentials( self, - credentials: Any, + credentials: _VertexCredentialsObject, credential_cache_key: tuple, credential_project_id: str | None, ) -> None: @@ -548,7 +574,7 @@ class VertexBase: def _schedule_background_refresh( self, - credentials: Any, + credentials: _VertexCredentialsObject, credential_cache_key: tuple, credential_project_id: str | None, ) -> None: @@ -566,7 +592,7 @@ class VertexBase: self._background_refresh_credentials(credentials, credential_cache_key, credential_project_id) ) - def _drop_background_refresh_task(_fut: asyncio.Future[Any]) -> None: + def _drop_background_refresh_task(_fut: asyncio.Future[None]) -> None: if self._background_refresh_tasks.get(credential_cache_key) is _fut: self._background_refresh_tasks.pop(credential_cache_key, None) @@ -621,8 +647,9 @@ class VertexBase: Handles custom api_base for: 1. Gemini (Google AI Studio) - constructs /models/{model}:{endpoint} - 2. Vertex AI with standard proxies - constructs {api_base}:{endpoint}; - if api_base has no path (bare host), grafts the default vertex URL path onto it + 2. Vertex AI with standard proxies - grafts the default vertex URL path onto the + api_base when its path is empty or only an API version (/v1, /v1beta1); + otherwise constructs {api_base}:{endpoint} 3. Vertex AI with PSC endpoints - constructs full path structure {api_base}/v1/projects/{project}/locations/{location}/endpoints/{model}:{endpoint} (only when use_psc_endpoint_format=True) @@ -669,10 +696,14 @@ class VertexBase: ) elif urlparse(api_base).path in ("", "/"): url = api_base.rstrip("/") + urlparse(url).path + elif urlparse(api_base).path.rstrip("/") in ("/v1", "/v1beta1") and "/projects/" in urlparse(url).path: + url = _graft_default_vertex_path(api_base=api_base, default_url=url) else: url = f"{api_base}:{endpoint}" if stream is True: - url = url + "?alt=sse" + parsed_stream_url: Final = urlparse(url) + stream_query: Final = f"{parsed_stream_url.query}&alt=sse" if parsed_stream_url.query else "alt=sse" + url = parsed_stream_url._replace(query=stream_query).geturl() return auth_header, url def _get_token_and_url( @@ -874,7 +905,7 @@ class VertexBase: # Convert dict credentials to string for caching cache_credentials: Final = json.dumps(credentials) if isinstance(credentials, dict) else credentials credential_cache_key: Final = (cache_credentials, project_id) - _credentials: GoogleCredentialsObject | None = None + _credentials: _VertexCredentialsObject | None = None verbose_logger.debug("Checking cached credentials for project_id: %s", project_id) diff --git a/litellm/llms/vertex_ai/videos/transformation.py b/litellm/llms/vertex_ai/videos/transformation.py index 16e72e3062d..e6e3c2739c1 100644 --- a/litellm/llms/vertex_ai/videos/transformation.py +++ b/litellm/llms/vertex_ai/videos/transformation.py @@ -7,13 +7,15 @@ Based on: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-refer import base64 import time -from collections.abc import Sequence -from typing import TYPE_CHECKING, Any, Final, TypedDict, cast +from collections.abc import Mapping, Sequence +from types import MappingProxyType +from typing import TYPE_CHECKING, Any, ClassVar, Final, TypedDict, cast import httpx -from httpx._types import RequestFiles +from httpx._types import FileContent, RequestFiles from typing_extensions import ReadOnly +import litellm from litellm.constants import DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS from litellm.images.utils import ImageEditRequestUtils from litellm.llms.base_llm.videos.transformation import BaseVideoConfig @@ -119,6 +121,23 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): 3. Extract video data (base64) from response """ + _OPENAI_VIDEO_SIZE_TO_ASPECT_RATIO: ClassVar[Mapping[str, str]] = MappingProxyType( + { + "1280x720": "16:9", + "1920x1080": "16:9", + "720x1280": "9:16", + "1080x1920": "9:16", + } + ) + _OPENAI_VIDEO_SIZE_TO_RESOLUTION: ClassVar[Mapping[str, str]] = MappingProxyType( + { + "1280x720": "720p", + "1920x1080": "1080p", + "720x1280": "720p", + "1080x1920": "1080p", + } + ) + def __init__(self): BaseVideoConfig.__init__(self) VertexBase.__init__(self) @@ -161,6 +180,9 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): - prompt → prompt (in instances) - input_reference → image (in instances) - size → aspectRatio (e.g., "1280x720" → "16:9") + - size → resolution for models with resolution-tier pricing when inferable + ("1280x720"/"720x1280" → "720p", "1920x1080"/"1080x1920" → "1080p"); + skipped if ``resolution`` is already set - seconds → durationSeconds (defaults to 4 seconds if not provided) """ mapped_params: Final[dict[str, object]] = {} @@ -175,6 +197,9 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): if "parameters" in video_create_optional_params: mapped_params["parameters"] = video_create_optional_params["parameters"] + if "resolution" in video_create_optional_params: + mapped_params["resolution"] = video_create_optional_params["resolution"] + # Map size to aspectRatio if "size" in video_create_optional_params: size: Final = video_create_optional_params["size"] @@ -182,6 +207,15 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): aspect_ratio: Final = self._convert_size_to_aspect_ratio(size) if aspect_ratio: mapped_params["aspectRatio"] = aspect_ratio + nested_params: Final = video_create_optional_params.get("parameters") + has_resolution = "resolution" in mapped_params or ( + isinstance(nested_params, dict) and nested_params.get("resolution") is not None + ) + supports_resolution = self._supports_resolution_inference(model) + if supports_resolution and not has_resolution: + inferred_resolution = self._convert_size_to_resolution(size) + if inferred_resolution is not None: + mapped_params["resolution"] = inferred_resolution # Map seconds to durationSeconds, default to 4 seconds (matching OpenAI) if "seconds" in video_create_optional_params: @@ -205,14 +239,16 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): if not size: return None - aspect_ratio_map: Final = { - "1280x720": "16:9", - "1920x1080": "16:9", - "720x1280": "9:16", - "1080x1920": "9:16", - } + return self._OPENAI_VIDEO_SIZE_TO_ASPECT_RATIO.get(size, "16:9") - return aspect_ratio_map.get(size, "16:9") + def _convert_size_to_resolution(self, size: str) -> str | None: + return self._OPENAI_VIDEO_SIZE_TO_RESOLUTION.get(size) + + @staticmethod + def _supports_resolution_inference(model: str) -> bool: + model_key: Final = model if model.startswith("vertex_ai/") else f"vertex_ai/{model}" + model_info: Final = litellm.model_cost.get(model_key) + return model_info is not None and model_info.get("output_cost_per_second_1080p") is not None def validate_environment( self, @@ -677,9 +713,10 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): api_base: str, litellm_params: GenericLiteLLMParams, headers: dict, + video_file: FileContent | None = None, extra_body: dict[str, object] | None = None, prefetched_source_data: dict[str, Any] | None = None, - ) -> tuple[str, dict]: + ) -> tuple[str, Mapping[str, object], RequestFiles | None]: """ Build a predictLongRunning edit request from the pre-fetched source video. @@ -727,7 +764,7 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): request_data["parameters"] = vertex_params edit_url: Final = f"{api_base.rstrip('/')}/{model}:predictLongRunning" - return edit_url, request_data + return edit_url, request_data, None def transform_video_edit_response( self, diff --git a/litellm/llms/voyage/rerank/transformation.py b/litellm/llms/voyage/rerank/transformation.py index 497b2f62a97..ee330c92f1a 100644 --- a/litellm/llms/voyage/rerank/transformation.py +++ b/litellm/llms/voyage/rerank/transformation.py @@ -4,6 +4,7 @@ Transformation logic for Voyage AI's /v1/rerank endpoint. Docs - https://docs.voyageai.com/docs/reranker """ +from collections.abc import Mapping from typing import Any, Final import httpx @@ -137,6 +138,7 @@ class VoyageRerankConfig(BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: if api_key is None: api_key = get_secret_str("VOYAGE_API_KEY") or get_secret_str("VOYAGE_AI_API_KEY") diff --git a/litellm/llms/watsonx/completion/transformation.py b/litellm/llms/watsonx/completion/transformation.py index 2645d099ee4..0b4c9ae917a 100644 --- a/litellm/llms/watsonx/completion/transformation.py +++ b/litellm/llms/watsonx/completion/transformation.py @@ -20,6 +20,8 @@ from ..common_utils import ( ) if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj LiteLLMLoggingObj = _LiteLLMLoggingObj @@ -278,7 +280,7 @@ class IBMWatsonXAIConfig(IBMWatsonXMixin, BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: str, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/watsonx/rerank/transformation.py b/litellm/llms/watsonx/rerank/transformation.py index 32b96db2817..293880b188d 100644 --- a/litellm/llms/watsonx/rerank/transformation.py +++ b/litellm/llms/watsonx/rerank/transformation.py @@ -5,6 +5,7 @@ Docs - https://cloud.ibm.com/apidocs/watsonx-ai#text-rerank """ import uuid +from collections.abc import Mapping from typing import Any, Final, cast import httpx @@ -60,6 +61,7 @@ class IBMWatsonXRerankConfig(IBMWatsonXMixin, BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: optional_params = optional_params or {} diff --git a/litellm/llms/xai/oauth.py b/litellm/llms/xai/oauth.py index 37dae93a725..e8196ec6cb9 100644 --- a/litellm/llms/xai/oauth.py +++ b/litellm/llms/xai/oauth.py @@ -8,11 +8,13 @@ import threading import time import uuid import webbrowser +from collections.abc import Mapping from http.server import BaseHTTPRequestHandler, HTTPServer -from typing import Any, Final +from typing import Final from urllib.parse import parse_qs, urlencode, urlparse import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm.constants import XAI_API_BASE @@ -31,6 +33,40 @@ XAI_OAUTH_CALLBACK_TIMEOUT_SECONDS: Final = 180 _XAI_OAUTH_REFRESH_LOCK: Final = threading.Lock() +class XAIOAuthRecord(TypedDict): + access_token: ReadOnly[str] + refresh_token: ReadOnly[str] + id_token: ReadOnly[str | None] + token_type: ReadOnly[str] + token_endpoint: ReadOnly[str] + expires_at: ReadOnly[float | None] + + +class _TokenPayload(TypedDict): + access_token: NotRequired[ReadOnly[str]] + refresh_token: NotRequired[ReadOnly[str]] + id_token: NotRequired[ReadOnly[str | None]] + token_type: NotRequired[ReadOnly[str]] + expires_in: NotRequired[ReadOnly[float]] + + +class _DiscoveryDocument(TypedDict): + authorization_endpoint: NotRequired[ReadOnly[str]] + token_endpoint: NotRequired[ReadOnly[str]] + + +class _AuthFileView(TypedDict): + record: ReadOnly[XAIOAuthRecord | None] + + +class _TokenPayloadView(TypedDict): + payload: ReadOnly[_TokenPayload | None] + + +class _DiscoveryView(TypedDict): + document: ReadOnly[_DiscoveryDocument] + + class XAIOAuthError(Exception): pass @@ -75,7 +111,7 @@ class _CallbackHandler(BaseHTTPRequestHandler): ) self.wfile.write(body) - def log_message(self, format: str, *args: Any) -> None: + def log_message(self, format: str, *args: object) -> None: return @@ -115,7 +151,7 @@ class XAIOAuthAuthenticator: refreshed: Final = self._refresh_tokens(locked_auth_data) return refreshed["access_token"] - def login(self, force: bool = False, no_browser: bool = False) -> dict[str, Any]: + def login(self, force: bool = False, no_browser: bool = False) -> XAIOAuthRecord: existing: Final = self._read_auth_file() if existing and not force and existing.get("access_token"): if not self._is_expired(existing): @@ -177,15 +213,16 @@ class XAIOAuthAuthenticator: except OSError: verbose_logger.debug("Could not chmod xAI OAuth token directory") - def _read_auth_file(self) -> dict[str, Any] | None: + def _read_auth_file(self) -> XAIOAuthRecord | None: try: with open(self.auth_file, "r") as f: - data: Final = json.load(f) + loaded: Final[_AuthFileView] = {"record": json.load(f)} + data: Final = loaded["record"] return data if isinstance(data, dict) else None except (OSError, json.JSONDecodeError): return None - def _write_auth_file(self, data: dict[str, Any]) -> None: + def _write_auth_file(self, data: XAIOAuthRecord) -> None: self._ensure_token_dir() tmp_file: Final = os.path.join( self.token_dir, @@ -216,7 +253,7 @@ class XAIOAuthAuthenticator: pass raise - def _is_expired(self, auth_data: dict[str, Any]) -> bool: + def _is_expired(self, auth_data: XAIOAuthRecord) -> bool: expires_at: Final = auth_data.get("expires_at") if expires_at is None: return True @@ -234,9 +271,10 @@ class XAIOAuthAuthenticator: f"xAI OAuth discovery request failed: {exc.response.status_code} {exc.response.text}" ) from exc try: - data: Final = response.json() + discovered: Final[_DiscoveryView] = {"document": response.json()} except ValueError as exc: raise XAIOAuthError("xAI OAuth discovery response was not valid JSON") from exc + data: Final = discovered["document"] authorization_endpoint: Final = data.get("authorization_endpoint") token_endpoint: Final = data.get("token_endpoint") if not authorization_endpoint or not token_endpoint: @@ -304,7 +342,7 @@ class XAIOAuthAuthenticator: server.server_close() raise XAIOAuthError("Timed out waiting for xAI OAuth callback") - def _exchange_token(self, token_endpoint: str, data: dict[str, str]) -> dict[str, Any]: + def _exchange_token(self, token_endpoint: str, data: dict[str, str]) -> _TokenPayload: try: response: Final = self._client().post( token_endpoint, @@ -320,19 +358,20 @@ class XAIOAuthAuthenticator: f"xAI OAuth token request failed: {exc.response.status_code} {exc.response.text}" ) from exc try: - body: Final = response.json() + exchanged: Final[_TokenPayloadView] = {"payload": response.json()} except ValueError as exc: raise XAIOAuthError("xAI OAuth token response was not valid JSON") from exc + body: Final = exchanged["payload"] if not isinstance(body, dict): raise XAIOAuthError("xAI OAuth token response was not an object") return body def _build_auth_record( self, - token_payload: dict[str, Any], + token_payload: _TokenPayload, token_endpoint: str, fallback_refresh_token: str | None = None, - ) -> dict[str, Any]: + ) -> XAIOAuthRecord: access_token: Final = token_payload.get("access_token") refresh_token: Final = token_payload.get("refresh_token") or fallback_refresh_token if not access_token: @@ -353,7 +392,7 @@ class XAIOAuthAuthenticator: "expires_at": expires_at, } - def _refresh_tokens(self, auth_data: dict[str, Any]) -> dict[str, Any]: + def _refresh_tokens(self, auth_data: XAIOAuthRecord) -> XAIOAuthRecord: token_endpoint = auth_data.get("token_endpoint") if not token_endpoint: token_endpoint = self._discover()["token_endpoint"] @@ -379,5 +418,5 @@ class XAIOAuthAuthenticator: return refreshed -def should_use_xai_oauth(litellm_params: dict[str, Any] | None) -> bool: +def should_use_xai_oauth(litellm_params: Mapping[str, object] | None) -> bool: return bool((litellm_params or {}).get("use_xai_oauth")) diff --git a/litellm/main.py b/litellm/main.py index 52785e7a393..01c106adc7c 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -24,6 +24,7 @@ from concurrent import futures from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait from copy import deepcopy from functools import partial +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol, Union, cast, get_args from litellm._logging import _redact_string @@ -416,7 +417,7 @@ async def acompletion( logprobs: bool | None = None, top_logprobs: int | None = None, deployment_id=None, - reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh", "default"] | None = None, + reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh", "max", "default"] | None = None, verbosity: Literal["low", "medium", "high"] | None = None, safety_identifier: str | None = None, service_tier: str | None = None, @@ -530,6 +531,7 @@ async def acompletion( tools=tools, prompt_label=kwargs.get("prompt_label", None), prompt_version=kwargs.get("prompt_version", None), + request_kwargs=kwargs, ) ######################################################### # if the chat completion logging hook removed all tools, @@ -602,7 +604,7 @@ async def acompletion( _, custom_llm_provider, _, _ = get_llm_provider( model=model, custom_llm_provider=custom_llm_provider, - api_base=base_url, + api_base=kwargs.get("api_base") or base_url, ) fallbacks = fallbacks or litellm.model_fallbacks @@ -1218,6 +1220,7 @@ def _register_custom_pricing_for_request( shared_key: CustomPricingLiteLLMParams.strip_custom_pricing_fields(entry), }, persist_across_reloads=False, + warning_display_name=shared_key, ) @@ -1811,6 +1814,56 @@ def _complete_fireworks_ai( return response +def _complete_together_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion: Final = ctx.acompletion + api_base: Final = ctx.api_base + api_key: Final = ctx.api_key + client: Final = _dispatch_client_http(ctx) + custom_llm_provider: Final = ctx.custom_llm_provider + headers: Final = ctx.headers + litellm_params: Final = ctx.litellm_params + logging: Final = ctx.logging + messages: Final = ctx.messages + model: Final = ctx.model + model_response: Final = ctx.model_response + optional_params: Final = ctx.optional_params + provider_config: Final = ctx.provider_config + shared_session: Final = ctx.shared_session + stream: Final = ctx.stream + timeout: Final = ctx.timeout + + try: + response: Final = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args=MappingProxyType({"headers": headers}), + ) + raise + + return response + + def _complete_heroku(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: acompletion: Final = ctx.acompletion api_base: Final = ctx.api_base @@ -4920,7 +4973,7 @@ def completion( logit_bias: dict | None = None, user: str | None = None, # openai v1.0+ new params - reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh", "default"] | None = None, + reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh", "max", "default"] | None = None, verbosity: Literal["low", "medium", "high"] | None = None, response_format: dict | type[BaseModel] | None = None, seed: int | None = None, @@ -5194,6 +5247,7 @@ def completion( prompt_variables=prompt_variables, prompt_label=kwargs.get("prompt_label", None), prompt_version=kwargs.get("prompt_version", None), + request_kwargs=kwargs, ) ### LITELLM SYSTEM PROMPT ### @@ -5453,6 +5507,9 @@ def completion( tpm=kwargs.get("tpm"), rpm=kwargs.get("rpm"), use_xai_oauth=kwargs.get("use_xai_oauth", False), + gigachat_scope=kwargs.get("gigachat_scope"), + gigachat_auth_url=kwargs.get("gigachat_auth_url"), + gigachat_access_token=kwargs.get("gigachat_access_token"), **{key: kwargs[key] for key in FORWARDED_KWARGS_KEYS if key in kwargs}, ) cast(LiteLLMLoggingObj, logging).update_environment_variables( @@ -5600,6 +5657,8 @@ def completion( elif custom_llm_provider == "fireworks_ai": ## COMPLETION CALL response = _complete_fireworks_ai(_dispatch_ctx) + elif custom_llm_provider == "together_ai": + response = _complete_together_ai(_dispatch_ctx) elif custom_llm_provider == "heroku": response = _complete_heroku(_dispatch_ctx) @@ -5649,7 +5708,6 @@ def completion( or custom_llm_provider == "volcengine" or custom_llm_provider == "anyscale" or custom_llm_provider == "openai" - or custom_llm_provider == "together_ai" or custom_llm_provider == "nebius" or custom_llm_provider == "wandb" or custom_llm_provider == "clarifai" @@ -5699,14 +5757,6 @@ def completion( response = _complete_openrouter(_dispatch_ctx) elif custom_llm_provider == "vercel_ai_gateway": response = _complete_vercel_ai_gateway(_dispatch_ctx) - elif ( - custom_llm_provider == "together_ai" - or ("togethercomputer" in model) - or (model in litellm.together_ai_models) - ): - """ - Deprecated. We now do together ai calls via the openai client - https://docs.together.ai/docs/openai-api-compatibility - """ elif custom_llm_provider == "palm": raise ValueError( "Palm was decommisioned on October 2024. Please use the `gemini/` route for Gemini Google AI Studio Models. Announcement: https://ai.google.dev/palm_docs/palm?hl=en" @@ -6242,18 +6292,15 @@ def embedding( if headers is not None and headers != {}: optional_params["extra_headers"] = headers - if encoding_format is not None: - optional_params["encoding_format"] = encoding_format + requested_encoding_format: Final = ( + encoding_format + or optional_params.get("encoding_format") + or get_secret_str("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT") + ) + if requested_encoding_format is None or requested_encoding_format.strip().lower() == "none": + optional_params.pop("encoding_format", None) else: - env_fmt: Final = get_secret_str("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT") - if env_fmt is not None and env_fmt.strip().lower() == "none": - optional_params.pop("encoding_format", None) - else: - _default_fmt: Final = optional_params.get("encoding_format") or env_fmt or "float" - if _default_fmt.strip().lower() == "none": - optional_params.pop("encoding_format", None) - else: - optional_params["encoding_format"] = _default_fmt + optional_params["encoding_format"] = requested_encoding_format api_version = None @@ -6828,6 +6875,8 @@ def embedding( aembedding=aembedding, ) elif custom_llm_provider == "azure_ai": + from litellm.llms.azure_ai.common_utils import get_azure_ai_entra_token + api_base = ( api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there or litellm.api_base @@ -6837,8 +6886,8 @@ def embedding( api_key = ( api_key or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there - or litellm.openai_key or get_secret_str("AZURE_AI_API_KEY") + or get_azure_ai_entra_token(litellm_params=litellm_params_dict) ) ## EMBEDDING CALL @@ -6900,12 +6949,18 @@ def embedding( aembedding=aembedding, headers=headers, ) - elif custom_llm_provider == "dashscope": - dashscope_key: Final = api_key or litellm.api_key or get_secret_str("DASHSCOPE_API_KEY") + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): + from litellm.llms.dashscope.common_utils import ( + missing_dashscope_family_key_message, + resolve_dashscope_family_api_key, + ) + + dashscope_key: Final = resolve_dashscope_family_api_key( + custom_llm_provider=custom_llm_provider, + api_key=api_key or litellm.api_key, + ) if dashscope_key is None: - raise ValueError( - "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." - ) + raise ValueError(missing_dashscope_family_key_message(custom_llm_provider)) if extra_headers is not None and isinstance(extra_headers, dict): headers = extra_headers else: @@ -7537,6 +7592,15 @@ async def amoderation( }, custom_llm_provider=custom_llm_provider, ) + moderation_request: Final = {"input": input, "model": model} # mutable-ok: logged as the raw request body + litellm_logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ # mutable-ok: loggers isinstance-check this payload as a dict + "complete_input_dict": moderation_request, + "api_base": str(_openai_client.base_url), + }, + ) if model is not None: response = await _openai_client.moderations.create(input=input, model=model) @@ -7958,7 +8022,7 @@ def speech( if max_retries is None: max_retries = litellm.num_retries or openai.DEFAULT_MAX_RETRIES - litellm_params_dict: Final = get_litellm_params(**kwargs) + litellm_params_dict: Final = get_litellm_params(metadata=metadata, api_key=api_key or dynamic_api_key, **kwargs) # Get provider-specific text-to-speech config and map parameters text_to_speech_provider_config = ProviderConfigManager.get_provider_text_to_speech_config( @@ -8042,6 +8106,7 @@ def speech( project=project, max_retries=max_retries, timeout=timeout, + logging_obj=logging_obj, client=client, # pass AsyncOpenAI, OpenAI client aspeech=aspeech, shared_session=shared_session, @@ -8120,6 +8185,7 @@ def speech( organization=organization, max_retries=max_retries, timeout=timeout, + logging_obj=logging_obj, client=client, # pass AsyncOpenAI, OpenAI client aspeech=aspeech, litellm_params=litellm_params_dict, @@ -8530,6 +8596,57 @@ def stream_chunk_builder_text_completion(chunks: list, messages: list | None = N return TextCompletionResponse(**response) +def _stream_builder_response_cost(response: ModelResponse, logging_obj: Optional["Logging"]) -> float | None: + usage_cost: Final = getattr(getattr(response, "usage", None), "cost", None) + if isinstance(usage_cost, (int, float)): + return float(usage_cost) + if logging_obj is not None: + return None + provider_hint: Final = response._hidden_params.get( # pyright: ignore[reportPrivateUsage] # no public accessor + "custom_llm_provider" + ) + try: + return litellm.completion_cost(completion_response=response, custom_llm_provider=provider_hint) + except Exception: + return _stream_builder_model_map_cost(response) + + +def _joined_streamed_citations(streamed_citations: "tuple[object, ...]") -> "list[object]": + if all(isinstance(citation, list) for citation in streamed_citations): + return list(streamed_citations) # mutable-ok: JSON list field + return [list(streamed_citations)] # mutable-ok: JSON list field + + +def _stream_builder_model_map_cost(response: ModelResponse) -> float | None: + model_name: Final = response.model + usage: Final = getattr(response, "usage", None) + if not model_name or not isinstance(usage, Usage): + return None + try: + prompt_cost, completion_tokens_cost = litellm.cost_per_token(model=model_name, usage_object=usage) + return prompt_cost + completion_tokens_cost + except Exception: # noqa: BLE001 # cost_per_token raises bare Exception for unpriceable models + return None + + +def _set_stream_builder_response_cost(response: ModelResponse, logging_obj: Optional["Logging"]) -> None: + response_cost: Final = _stream_builder_response_cost(response, logging_obj) + if response_cost is None: + return + hidden_params: Final = response._hidden_params # pyright: ignore[reportPrivateUsage] # no public accessor + hidden_params["response_cost"] = response_cost + + +def _stamp_streaming_usage_cost(usage: Usage, response: ModelResponse, logging_obj: Optional["Logging"]) -> None: + if logging_obj is None: + return + if isinstance(getattr(usage, "cost", None), (int, float)): + return + computed_cost: Final = logging_obj._response_cost_calculator(result=response) + if isinstance(computed_cost, (int, float)) and computed_cost > 0: + setattr(usage, "cost", computed_cost) + + def stream_chunk_builder( chunks: list, messages: list | None = None, @@ -8555,7 +8672,7 @@ def stream_chunk_builder( if len(chunks) == 0: return None ## Route to the text completion logic - first_chunk_with_choices: Final = next((c for c in chunks if c["choices"]), None) + first_chunk_with_choices: Final = next((c for c in chunks if c.get("choices")), None) if first_chunk_with_choices is not None and isinstance( first_chunk_with_choices["choices"][0], litellm.utils.TextChoices ): # route to the text completion logic @@ -8570,7 +8687,7 @@ def stream_chunk_builder( simple_content_parts: Final[list[str]] = [] is_simple_text_stream = True for chunk in chunks: - if len(chunk["choices"]) == 0: + if not chunk.get("choices"): continue choice = chunk["choices"][0] @@ -8624,19 +8741,16 @@ def stream_chunk_builder( ) break - if litellm.include_cost_in_streaming_usage and logging_obj is not None: - setattr( - usage, - "cost", - logging_obj._response_cost_calculator(result=response), - ) + _stamp_streaming_usage_cost(usage, response, logging_obj) + _set_stream_builder_response_cost(response, logging_obj) + processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj) return response tool_call_chunks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "tool_calls" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["tool_calls"] is not None ] @@ -8650,7 +8764,7 @@ def stream_chunk_builder( function_call_chunks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "function_call" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["function_call"] is not None ] @@ -8663,7 +8777,7 @@ def stream_chunk_builder( content_chunks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "content" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["content"] is not None ] @@ -8674,7 +8788,7 @@ def stream_chunk_builder( thinking_blocks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "thinking_blocks" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["thinking_blocks"] is not None ] @@ -8687,7 +8801,7 @@ def stream_chunk_builder( reasoning_chunks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "reasoning_content" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["reasoning_content"] is not None ] @@ -8700,7 +8814,7 @@ def stream_chunk_builder( annotation_chunks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "annotations" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["annotations"] is not None ] @@ -8717,7 +8831,7 @@ def stream_chunk_builder( audio_chunks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "audio" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["audio"] is not None ] @@ -8731,7 +8845,7 @@ def stream_chunk_builder( image_chunks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "images" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["images"] is not None ] @@ -8748,24 +8862,32 @@ def stream_chunk_builder( provider_specific_chunks: Final = [ chunk for chunk in chunks - if len(chunk["choices"]) > 0 + if chunk.get("choices") and "provider_specific_fields" in chunk["choices"][0]["delta"] and chunk["choices"][0]["delta"]["provider_specific_fields"] is not None ] if len(provider_specific_chunks) > 0: - combined_provider_fields: Final[dict[str, object]] = {} - for chunk in provider_specific_chunks: - fields = chunk["choices"][0]["delta"]["provider_specific_fields"] - if isinstance(fields, dict): - for key, value in fields.items(): - if key not in combined_provider_fields: - combined_provider_fields[key] = value - elif isinstance(value, list) and isinstance(combined_provider_fields[key], list): - # For lists like web_search_results, take the last (most complete) one - combined_provider_fields[key] = value - else: - combined_provider_fields[key] = value + provider_field_dicts: Final = tuple( + fields + for chunk in provider_specific_chunks + for fields in (chunk["choices"][0]["delta"]["provider_specific_fields"],) + if isinstance(fields, dict) + ) + streamed_citations: Final = tuple( + fields["citation"] for fields in provider_field_dicts if fields.get("citation") is not None + ) + citation_fields: Final = ( + {"citations": _joined_streamed_citations(streamed_citations)} # mutable-ok: JSON dict field + if streamed_citations + else {} # mutable-ok: JSON dict field + ) + combined_provider_fields: Final = { # mutable-ok: Message.provider_specific_fields is a plain dict field + key: value + for fields in (citation_fields, *provider_field_dicts) + for key, value in fields.items() + if key != "citation" + } if combined_provider_fields: _choice = cast(Choices, response.choices[0]) @@ -8798,9 +8920,8 @@ def stream_chunk_builder( ) break - # Add cost to usage object if include_cost_in_streaming_usage is True - if litellm.include_cost_in_streaming_usage and logging_obj is not None: - setattr(usage, "cost", logging_obj._response_cost_calculator(result=response)) + _stamp_streaming_usage_cost(usage, response, logging_obj) + _set_stream_builder_response_cost(response, logging_obj) processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj) return response diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 3af7d9e5019..2846d12db6e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -553,6 +553,27 @@ "supports_response_schema": true, "supports_vision": true }, + "amazon.nova-sonic-v1:0": { + "deprecation_date": "2026-09-14", + "input_cost_per_audio_token": 3.4e-06, + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.36e-05, + "output_cost_per_token": 2.4e-07, + "supports_audio_input": true, + "supports_audio_output": true + }, + "amazon.nova-2-sonic-v1:0": { + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 3.3e-07, + "litellm_provider": "bedrock", + "mode": "realtime", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2.75e-06, + "supports_audio_input": true, + "supports_audio_output": true + }, "amazon.rerank-v1:0": { "input_cost_per_query": 0.001, "input_cost_per_token": 0.0, @@ -1019,6 +1040,7 @@ }, "anthropic.claude-opus-4-6-v1": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, @@ -1053,6 +1075,7 @@ }, "global.anthropic.claude-opus-4-6-v1": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, @@ -1087,6 +1110,7 @@ }, "us.anthropic.claude-opus-4-6-v1": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 6.875e-06, "cache_creation_input_token_cost_above_1hr": 1.1e-05, "cache_read_input_token_cost": 5.5e-07, @@ -1121,6 +1145,7 @@ }, "eu.anthropic.claude-opus-4-6-v1": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 6.875e-06, "cache_creation_input_token_cost_above_1hr": 1.1e-05, "cache_read_input_token_cost": 5.5e-07, @@ -1155,6 +1180,7 @@ }, "au.anthropic.claude-opus-4-6-v1": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 6.875e-06, "cache_creation_input_token_cost_above_1hr": 1.1e-05, "cache_read_input_token_cost": 5.5e-07, @@ -1423,7 +1449,45 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 512 + }, + "anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 }, "global.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.25e-05, @@ -1460,7 +1524,45 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 512 + }, + "global.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 }, "us.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, @@ -1497,7 +1599,45 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 512 + }, + "us.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 2.75e-07, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 }, "eu.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, @@ -1534,7 +1674,45 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 512 + }, + "eu.anthropic.claude-fable-5-1": { + "cache_creation_input_token_cost": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 2.2e-05, + "cache_read_input_token_cost": 2.75e-07, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": false, + "supports_max_reasoning_effort": true, + "supports_output_config": true, + "bedrock_output_config_effort_ceiling": "xhigh", + "supports_parallel_tool_use_config": true, + "prompt_cache_min_tokens": 512 }, "anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, @@ -1566,7 +1744,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1602,7 +1780,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1638,7 +1816,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1674,7 +1852,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1710,7 +1888,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1746,7 +1924,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -2039,7 +2217,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2076,7 +2254,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2113,7 +2291,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2150,7 +2328,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2187,7 +2365,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2224,7 +2402,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2233,6 +2411,7 @@ }, "anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -2266,6 +2445,7 @@ }, "global.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -2299,6 +2479,7 @@ }, "us.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 4.125e-06, "cache_creation_input_token_cost_above_1hr": 6.6e-06, "cache_read_input_token_cost": 3.3e-07, @@ -2332,6 +2513,7 @@ }, "eu.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 4.125e-06, "cache_creation_input_token_cost_above_1hr": 6.6e-06, "cache_read_input_token_cost": 3.3e-07, @@ -2365,6 +2547,7 @@ }, "au.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 4.125e-06, "cache_creation_input_token_cost_above_1hr": 6.6e-06, "cache_read_input_token_cost": 3.3e-07, @@ -2398,6 +2581,7 @@ }, "jp.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 4.125e-06, "cache_creation_input_token_cost_above_1hr": 6.6e-06, "cache_read_input_token_cost": 3.3e-07, @@ -2922,7 +3106,8 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 4096 }, "azure_ai/claude-opus-4-5": { "deprecation_date": "2026-10-19", @@ -2945,11 +3130,13 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "supports_output_config": true + "supports_output_config": true, + "prompt_cache_min_tokens": 4096 }, "azure_ai/claude-opus-4-6": { "deprecation_date": "2027-02-02", "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "input_cost_per_token": 5e-06, "output_cost_per_token": 2.5e-05, "litellm_provider": "azure_ai", @@ -2975,7 +3162,8 @@ "supports_tool_choice": true, "supports_vision": true, "supports_output_config": true, - "supports_max_reasoning_effort": true + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 4096 }, "azure_ai/claude-opus-4-7": { "deprecation_date": "2027-04-06", @@ -3006,9 +3194,11 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 2048 }, "azure_ai/claude-fable-5": { + "deprecation_date": "2027-12-05", "supports_mid_conversation_system": true, "input_cost_per_token": 1e-05, "output_cost_per_token": 5e-05, @@ -3038,9 +3228,46 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, + "azure_ai/claude-fable-5-1": { + "supports_mid_conversation_system": true, + "input_cost_per_token": 1e-05, + "output_cost_per_token": 5e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 }, "azure_ai/claude-opus-5": { + "deprecation_date": "2027-07-08", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3073,6 +3300,7 @@ "prompt_cache_min_tokens": 512 }, "azure_ai/claude-opus-4-8": { + "deprecation_date": "2027-09-01", "supports_mid_conversation_system": true, "supports_adaptive_thinking": true, "input_cost_per_token": 5e-06, @@ -3101,7 +3329,8 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 1024 }, "azure_ai/claude-opus-4-1": { "deprecation_date": "2026-08-05", @@ -3123,7 +3352,8 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "azure_ai/claude-sonnet-4-5": { "deprecation_date": "2026-10-19", @@ -3145,9 +3375,11 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "azure_ai/claude-sonnet-5": { + "deprecation_date": "2027-06-30", "supports_mid_conversation_system": true, "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_1hr": 4e-06, @@ -3176,11 +3408,13 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 1024 }, "azure_ai/claude-sonnet-4-6": { "deprecation_date": "2027-02-10", "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -3201,7 +3435,8 @@ "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, - "supports_output_config": true + "supports_output_config": true, + "prompt_cache_min_tokens": 1024 }, "azure/computer-use-preview": { "input_cost_per_token": 3e-06, @@ -3386,6 +3621,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -3433,6 +3669,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -3566,6 +3803,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false }, @@ -3607,6 +3845,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false }, @@ -3648,6 +3887,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false }, @@ -3689,6 +3929,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false }, @@ -3697,7 +3938,7 @@ "output_cost_per_token": 0, "litellm_provider": "azure_ai", "mode": "chat", - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-services/", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/aoai/", "comment": "Flat cost of $0.14 per M input tokens for Azure AI Foundry Model Router infrastructure. Use pattern: azure_ai/model_router/ where deployment-name is your Azure deployment (e.g., azure-model-router)" }, "azure/eu/gpt-4o-2024-08-06": { @@ -3914,7 +4155,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/eu/gpt-5.1-chat": { "cache_read_input_token_cost": 1.4e-07, @@ -3949,7 +4191,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/eu/gpt-5.1-codex": { "deprecation_date": "2027-05-15", @@ -4224,7 +4467,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/global/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, @@ -4259,7 +4503,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/global/gpt-5.1-codex": { "deprecation_date": "2027-05-15", @@ -4668,7 +4913,7 @@ "supports_web_search": false }, "azure/gpt-4.1-nano": { - "deprecation_date": "2026-10-14", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, @@ -4701,7 +4946,7 @@ "supports_vision": true }, "azure/gpt-4.1-nano-2025-04-14": { - "deprecation_date": "2026-10-14", + "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, @@ -5295,7 +5540,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "azure", "mode": "audio_transcription", - "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/gpt-realtime-whisper", + "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/gpt-realtime-whisper", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -5344,6 +5589,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_minimal_reasoning_effort": true }, "azure/gpt-5.1-chat-2025-11-13": { @@ -5381,7 +5627,8 @@ "supports_system_messages": true, "supports_tool_choice": false, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/gpt-5.1-codex-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, @@ -5810,7 +6057,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, @@ -5845,7 +6093,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/gpt-5.1-codex": { "deprecation_date": "2027-05-15", @@ -6292,6 +6541,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -6331,6 +6581,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -6370,6 +6621,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -6415,6 +6667,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -6454,6 +6707,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -6493,6 +6747,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -6579,6 +6834,10 @@ "supports_web_search": true }, "azure/gpt-5.6": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.25e-05, + "cache_creation_input_token_cost_priority": 1.25e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.5e-05, "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1e-06, @@ -6629,6 +6888,10 @@ "supports_minimal_reasoning_effort": false }, "azure/gpt-5.6-sol": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.25e-05, + "cache_creation_input_token_cost_priority": 1.25e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.5e-05, "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, "cache_read_input_token_cost_priority": 1e-06, @@ -6680,6 +6943,10 @@ "supports_minimal_reasoning_effort": false }, "azure/gpt-5.6-terra": { + "cache_creation_input_token_cost": 2.5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 5e-06, + "cache_creation_input_token_cost_priority": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05, "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_272k_tokens": 4e-07, "cache_read_input_token_cost_priority": 4e-07, @@ -6731,6 +6998,10 @@ "supports_minimal_reasoning_effort": false }, "azure/gpt-5.6-luna": { + "cache_creation_input_token_cost": 2.5e-07, + "cache_creation_input_token_cost_above_272k_tokens": 5e-07, + "cache_creation_input_token_cost_priority": 5e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06, "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_above_272k_tokens": 4e-08, "cache_read_input_token_cost_priority": 4e-08, @@ -6782,12 +7053,18 @@ "supports_minimal_reasoning_effort": false }, "azure/us/gpt-5.6": { + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.75e-05, + "cache_creation_input_token_cost_priority": 1.375e-05, "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.375e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2.2e-06, + "cache_read_input_token_cost_priority": 1.1e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, - "input_cost_per_token_priority": 1.375e-05, + "input_cost_per_token_above_272k_tokens_priority": 2.2e-05, + "input_cost_per_token_priority": 1.1e-05, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -6795,7 +7072,8 @@ "mode": "chat", "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, - "output_cost_per_token_priority": 8.25e-05, + "output_cost_per_token_above_272k_tokens_priority": 9.9e-05, + "output_cost_per_token_priority": 6.6e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -6829,13 +7107,19 @@ "supports_minimal_reasoning_effort": false }, "azure/us/gpt-5.6-sol": { + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.75e-05, + "cache_creation_input_token_cost_priority": 1.375e-05, "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.375e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2.2e-06, + "cache_read_input_token_cost_priority": 1.1e-06, "deprecation_date": "2028-01-11", "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, - "input_cost_per_token_priority": 1.375e-05, + "input_cost_per_token_above_272k_tokens_priority": 2.2e-05, + "input_cost_per_token_priority": 1.1e-05, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -6843,7 +7127,8 @@ "mode": "chat", "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, - "output_cost_per_token_priority": 8.25e-05, + "output_cost_per_token_above_272k_tokens_priority": 9.9e-05, + "output_cost_per_token_priority": 6.6e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -6877,13 +7162,19 @@ "supports_minimal_reasoning_effort": false }, "azure/us/gpt-5.6-terra": { + "cache_creation_input_token_cost": 2.75e-06, + "cache_creation_input_token_cost_above_272k_tokens": 5.5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1.1e-05, + "cache_creation_input_token_cost_priority": 5.5e-06, "cache_read_input_token_cost": 2.2e-07, "cache_read_input_token_cost_above_272k_tokens": 4.4e-07, - "cache_read_input_token_cost_priority": 5.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-07, + "cache_read_input_token_cost_priority": 4.4e-07, "deprecation_date": "2028-01-11", "input_cost_per_token": 2.2e-06, "input_cost_per_token_above_272k_tokens": 4.4e-06, - "input_cost_per_token_priority": 5.5e-06, + "input_cost_per_token_above_272k_tokens_priority": 8.8e-06, + "input_cost_per_token_priority": 4.4e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -6891,7 +7182,8 @@ "mode": "chat", "output_cost_per_token": 1.32e-05, "output_cost_per_token_above_272k_tokens": 1.98e-05, - "output_cost_per_token_priority": 3.3e-05, + "output_cost_per_token_above_272k_tokens_priority": 3.96e-05, + "output_cost_per_token_priority": 2.64e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -6925,13 +7217,19 @@ "supports_minimal_reasoning_effort": false }, "azure/us/gpt-5.6-luna": { + "cache_creation_input_token_cost": 2.75e-07, + "cache_creation_input_token_cost_above_272k_tokens": 5.5e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1.1e-06, + "cache_creation_input_token_cost_priority": 5.5e-07, "cache_read_input_token_cost": 2.2e-08, "cache_read_input_token_cost_above_272k_tokens": 4.4e-08, - "cache_read_input_token_cost_priority": 5.5e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-08, + "cache_read_input_token_cost_priority": 4.4e-08, "deprecation_date": "2028-01-11", "input_cost_per_token": 2.2e-07, "input_cost_per_token_above_272k_tokens": 4.4e-07, - "input_cost_per_token_priority": 5.5e-07, + "input_cost_per_token_above_272k_tokens_priority": 8.8e-07, + "input_cost_per_token_priority": 4.4e-07, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -6939,7 +7237,8 @@ "mode": "chat", "output_cost_per_token": 1.32e-06, "output_cost_per_token_above_272k_tokens": 1.98e-06, - "output_cost_per_token_priority": 3.3e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.96e-06, + "output_cost_per_token_priority": 2.64e-06, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -6973,12 +7272,18 @@ "supports_minimal_reasoning_effort": false }, "azure/eu/gpt-5.6": { + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.75e-05, + "cache_creation_input_token_cost_priority": 1.375e-05, "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.375e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2.2e-06, + "cache_read_input_token_cost_priority": 1.1e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, - "input_cost_per_token_priority": 1.375e-05, + "input_cost_per_token_above_272k_tokens_priority": 2.2e-05, + "input_cost_per_token_priority": 1.1e-05, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -6986,7 +7291,8 @@ "mode": "chat", "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, - "output_cost_per_token_priority": 8.25e-05, + "output_cost_per_token_above_272k_tokens_priority": 9.9e-05, + "output_cost_per_token_priority": 6.6e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -7020,13 +7326,19 @@ "supports_minimal_reasoning_effort": false }, "azure/eu/gpt-5.6-sol": { + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.75e-05, + "cache_creation_input_token_cost_priority": 1.375e-05, "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.375e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 2.2e-06, + "cache_read_input_token_cost_priority": 1.1e-06, "deprecation_date": "2028-01-11", "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, - "input_cost_per_token_priority": 1.375e-05, + "input_cost_per_token_above_272k_tokens_priority": 2.2e-05, + "input_cost_per_token_priority": 1.1e-05, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -7034,7 +7346,8 @@ "mode": "chat", "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, - "output_cost_per_token_priority": 8.25e-05, + "output_cost_per_token_above_272k_tokens_priority": 9.9e-05, + "output_cost_per_token_priority": 6.6e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -7068,13 +7381,19 @@ "supports_minimal_reasoning_effort": false }, "azure/eu/gpt-5.6-terra": { + "cache_creation_input_token_cost": 2.75e-06, + "cache_creation_input_token_cost_above_272k_tokens": 5.5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1.1e-05, + "cache_creation_input_token_cost_priority": 5.5e-06, "cache_read_input_token_cost": 2.2e-07, "cache_read_input_token_cost_above_272k_tokens": 4.4e-07, - "cache_read_input_token_cost_priority": 5.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-07, + "cache_read_input_token_cost_priority": 4.4e-07, "deprecation_date": "2028-01-11", "input_cost_per_token": 2.2e-06, "input_cost_per_token_above_272k_tokens": 4.4e-06, - "input_cost_per_token_priority": 5.5e-06, + "input_cost_per_token_above_272k_tokens_priority": 8.8e-06, + "input_cost_per_token_priority": 4.4e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -7082,7 +7401,8 @@ "mode": "chat", "output_cost_per_token": 1.32e-05, "output_cost_per_token_above_272k_tokens": 1.98e-05, - "output_cost_per_token_priority": 3.3e-05, + "output_cost_per_token_above_272k_tokens_priority": 3.96e-05, + "output_cost_per_token_priority": 2.64e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -7116,13 +7436,19 @@ "supports_minimal_reasoning_effort": false }, "azure/eu/gpt-5.6-luna": { + "cache_creation_input_token_cost": 2.75e-07, + "cache_creation_input_token_cost_above_272k_tokens": 5.5e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1.1e-06, + "cache_creation_input_token_cost_priority": 5.5e-07, "cache_read_input_token_cost": 2.2e-08, "cache_read_input_token_cost_above_272k_tokens": 4.4e-08, - "cache_read_input_token_cost_priority": 5.5e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8.8e-08, + "cache_read_input_token_cost_priority": 4.4e-08, "deprecation_date": "2028-01-11", "input_cost_per_token": 2.2e-07, "input_cost_per_token_above_272k_tokens": 4.4e-07, - "input_cost_per_token_priority": 5.5e-07, + "input_cost_per_token_above_272k_tokens_priority": 8.8e-07, + "input_cost_per_token_priority": 4.4e-07, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -7130,7 +7456,8 @@ "mode": "chat", "output_cost_per_token": 1.32e-06, "output_cost_per_token_above_272k_tokens": 1.98e-06, - "output_cost_per_token_priority": 3.3e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.96e-06, + "output_cost_per_token_priority": 2.64e-06, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, @@ -7568,6 +7895,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-mini-2026-03-17": { @@ -7609,6 +7937,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-nano": { @@ -7650,6 +7979,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-nano-2026-03-17": { @@ -7691,6 +8021,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true }, "azure/gpt-image-1": { @@ -8761,7 +9092,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/us/gpt-5.1-chat": { "cache_read_input_token_cost": 1.4e-07, @@ -8796,7 +9128,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_vision": true, - "supports_none_reasoning_effort": true + "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none" }, "azure/us/gpt-5.1-codex": { "deprecation_date": "2027-05-15", @@ -8986,7 +9319,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 1024, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", "supports_embedding_image_input": true }, "azure_ai/Cohere-embed-v3-multilingual": { @@ -8997,7 +9330,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 1024, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/cohere.cohere-embed-v3-english-offer?tab=PlansAndPrice", "supports_embedding_image_input": true }, "azure_ai/FLUX-1.1-pro": { @@ -9013,7 +9346,7 @@ "litellm_provider": "azure_ai", "mode": "image_generation", "output_cost_per_image": 0.04, - "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", "supported_endpoints": [ "/v1/images/generations" ] @@ -9220,6 +9553,11 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.65e-05, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-kimi-k3-through-fireworks-ai-on-microsoft-foundry/4540187", "supported_modalities": [ "text", @@ -9305,7 +9643,8 @@ "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" - ] + ], + "deprecation_date": "2026-10-01" }, "azure_ai/MAI-Image-2.5-Flash": { "input_cost_per_image_token": 1.75e-06, @@ -9318,7 +9657,8 @@ "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" - ] + ], + "deprecation_date": "2026-10-01" }, "azure_ai/MAI-Image-2e": { "deprecation_date": "2026-08-15", @@ -9341,7 +9681,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 3.7e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-11b-vision-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -9355,7 +9695,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 2.04e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.meta-llama-3-2-90b-vision-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -9368,7 +9708,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 7.1e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -9417,7 +9757,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 1.6e-05, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Meta-Llama-3.1-70B-Instruct": { @@ -9428,7 +9768,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 3.54e-06, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Meta-Llama-3.1-8B-Instruct": { @@ -9440,7 +9780,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 6.1e-07, - "source": "https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice", "supports_tool_choice": true }, "azure_ai/Phi-3-medium-128k-instruct": { @@ -9630,7 +9970,7 @@ "supported_endpoints": [ "/v1/ocr" ], - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/" + "source": "https://ai.azure.com/catalog/models/mistral-document-ai-2512" }, "azure_ai/doc-intelligence/prebuilt-read": { "litellm_provider": "azure_ai", @@ -9817,7 +10157,9 @@ "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "cache_read_input_token_cost": 1.45e-07, + "supports_prompt_caching": true }, "azure_ai/deepseek-v4-flash": { "deprecation_date": "2028-02-20", @@ -9831,6 +10173,24 @@ "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", "supports_function_calling": true, "supports_reasoning": true, + "supports_tool_choice": true, + "cache_read_input_token_cost": 2.8e-08, + "supports_prompt_caching": true + }, + "azure_ai/DeepSeek-V4-Flash-0731": { + "cache_read_input_token_cost": 1.4e-08, + "deprecation_date": "2026-12-03", + "input_cost_per_token": 4.4e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.32e-06, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, "supports_tool_choice": true }, "azure_ai/embed-v-4-0": { @@ -9841,7 +10201,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 3072, - "source": "https://azuremarketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", "supported_endpoints": [ "/v1/embeddings" ], @@ -10025,7 +10385,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 0.00971, - "source": "https://azure.microsoft.com/en-us/products/ai-services/ai-foundry/models/jais-30b-chat" + "source": "https://ai.azure.com/catalog/models/jais-30b-chat" }, "azure_ai/jamba-instruct": { "input_cost_per_token": 5e-07, @@ -10046,11 +10406,13 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/kimi-k2-5-now-in-microsoft-foundry/4492321", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", "supports_function_calling": true, "supports_tool_choice": true, "supports_video_input": true, - "supports_vision": true + "supports_vision": true, + "cache_read_input_token_cost": 1e-07, + "supports_prompt_caching": true }, "azure_ai/kimi-k2.6": { "deprecation_date": "2027-04-16", @@ -10061,7 +10423,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-kimi-k2-6-in-microsoft-foundry/4513125", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", "supported_modalities": [ "text", "image" @@ -10072,7 +10434,9 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "cache_read_input_token_cost": 1.6e-07, + "supports_prompt_caching": true }, "azure_ai/ministral-3b": { "input_cost_per_token": 4e-08, @@ -10082,7 +10446,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 4e-08, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.ministral-3b-2410-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10105,7 +10469,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10117,7 +10481,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-ai-large-2407-offer?tab=Overview", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10154,7 +10518,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-07, - "source": "https://azuremarketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice", + "source": "https://marketplace.microsoft.com/en/marketplace/apps/000-000.mistral-nemo-12b-2407?tab=PlansAndPrice", "supports_function_calling": true }, "azure_ai/mistral-small": { @@ -11756,7 +12120,7 @@ "max_output_tokens": 2048, "max_tokens": 2048, "mode": "chat", - "output_cost_per_token": 2.65e-06, + "output_cost_per_token": 6e-07, "supports_pdf_input": true }, "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0": { @@ -12141,6 +12505,7 @@ "supports_tool_choice": true }, "cerebras/zai-glm-4.7": { + "deprecation_date": "2026-08-17", "input_cost_per_token": 2.25e-06, "litellm_provider": "cerebras", "max_input_tokens": 128000, @@ -12189,7 +12554,8 @@ "output_cost_per_token": 1e-05, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "claude-haiku-4-5-20251001": { "deprecation_date": "2026-10-15", @@ -12269,7 +12635,7 @@ }, "claude-3-haiku-20240307": { "cache_creation_input_token_cost": 3e-07, - "cache_creation_input_token_cost_above_1hr": 6e-06, + "cache_creation_input_token_cost_above_1hr": 5e-07, "cache_read_input_token_cost": 3e-08, "deprecation_date": "2026-04-20", "input_cost_per_token": 2.5e-07, @@ -12288,7 +12654,7 @@ }, "claude-3-opus-20240229": { "cache_creation_input_token_cost": 1.875e-05, - "cache_creation_input_token_cost_above_1hr": 6e-06, + "cache_creation_input_token_cost_above_1hr": 3e-05, "cache_read_input_token_cost": 1.5e-06, "deprecation_date": "2026-01-05", "input_cost_per_token": 1.5e-05, @@ -12469,7 +12835,8 @@ "us": 1.1 }, "supports_output_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://docs.anthropic.com/en/docs/about-claude/models/overview" }, "claude-sonnet-4-6": { "deprecation_date": "2027-02-17", @@ -12489,6 +12856,7 @@ "search_context_size_medium": 0.01 }, "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, @@ -12501,7 +12869,10 @@ "supports_tool_choice": true, "supports_vision": true, "supports_output_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "provider_specific_entry": { + "us": 1.1 + } }, "claude-sonnet-4-5-20250929-v1:0": { "cache_creation_input_token_cost": 3.75e-06, @@ -12698,6 +13069,7 @@ "search_context_size_medium": 0.01 }, "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -12709,8 +13081,7 @@ "supports_tool_choice": true, "supports_vision": true, "provider_specific_entry": { - "us": 1.1, - "fast": 6.0 + "us": 1.1 }, "supports_output_config": true, "supports_max_reasoning_effort": true, @@ -12735,6 +13106,7 @@ "search_context_size_medium": 0.01 }, "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -12746,8 +13118,7 @@ "supports_tool_choice": true, "supports_vision": true, "provider_specific_entry": { - "us": 1.1, - "fast": 6.0 + "us": 1.1 }, "supports_max_reasoning_effort": true, "supports_output_config": true, @@ -12786,8 +13157,7 @@ "supports_xhigh_reasoning_effort": true, "supports_max_reasoning_effort": true, "provider_specific_entry": { - "us": 1.1, - "fast": 6.0 + "us": 1.1 }, "supports_output_config": true, "supports_speed": true, @@ -12825,8 +13195,7 @@ "supports_xhigh_reasoning_effort": true, "supports_max_reasoning_effort": true, "provider_specific_entry": { - "us": 1.1, - "fast": 6.0 + "us": 1.1 }, "supports_output_config": true, "supports_speed": true, @@ -12869,7 +13238,49 @@ }, "supports_output_config": true, "prompt_cache_min_tokens": 512, - "supports_native_structured_output": true + "supports_native_structured_output": true, + "source": "https://docs.anthropic.com/en/docs/about-claude/models/overview" + }, + "claude-fable-5-1": { + "deprecation_date": "2027-09-01", + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "provider_specific_entry": { + "us": 1.1 + }, + "supports_output_config": true, + "prompt_cache_min_tokens": 512, + "supports_native_structured_output": true, + "source": "https://platform.claude.com/docs/en/models/fable-5-1/overview" }, "claude-opus-5": { "deprecation_date": "2027-07-24", @@ -12909,7 +13320,8 @@ }, "supports_output_config": true, "supports_speed": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://docs.anthropic.com/en/docs/about-claude/models/overview" }, "claude-opus-4-8": { "deprecation_date": "2027-05-28", @@ -14550,7 +14962,1929 @@ "/v1/images/generations" ] }, + "dashscope/qwen-image-3.0": { + "litellm_provider": "dashscope", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "dashscope/qwen-image-3.0-pro": { + "litellm_provider": "dashscope", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwencloud/deepseek-v4-flash": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/deepseek-v4-pro": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4.8e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/glm-5.1": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 202745, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/glm-5.2": { + "cache_read_input_token_cost": 2.8e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/kimi-k2.7-code": { + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 229376, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "qwencloud/qwen-coder": { + "input_cost_per_token": 3e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "qwencloud/qwen-flash": { + "litellm_provider": "qwencloud", + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", 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true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "qwen_ai_platform/qwq-plus": { + "input_cost_per_token": 8e-07, + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 98304, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.4e-06, + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "qwen_ai_platform/qwen-image-2.0": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-2.0-pro": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-3.0": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-3.0-pro": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, "databricks/databricks-bge-large-en": { + "cache_creation_input_token_cost": 1.0003e-07, + "cache_read_input_token_cost": 1.0003e-07, "input_cost_per_token": 1.0003e-07, "input_dbu_cost_per_token": 1.429e-06, "litellm_provider": "databricks", @@ -14566,6 +16900,8 @@ "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, "databricks/databricks-claude-3-7-sonnet": { + "cache_creation_input_token_cost": 3.74997e-06, + "cache_read_input_token_cost": 3.0002e-07, "input_cost_per_token": 2.9999900000000002e-06, "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", @@ -14581,10 +16917,41 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, + "databricks/databricks-claude-fable-5": { + "cache_creation_input_token_cost": 1.250004e-05, + "cache_read_input_token_cost": 1.00002e-06, + "input_cost_per_token": 1.000006e-05, + "input_dbu_cost_per_token": 0.000142858, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Costs per token are the published Global DBU rates times $0.070 per DBU. The '*_dbu_cost_per_token' fields are provided for reference; cost calculation reads the dollar '*_cost_per_token' fields." + }, + "mode": "chat", + "output_cost_per_token": 5.000002e-05, + "output_dbu_cost_per_token": 0.000714286, + "prompt_cache_min_tokens": 512, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_function_calling": true, + "supports_mid_conversation_system": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": false, + "thinking_always_on": true + }, "databricks/databricks-claude-haiku-4-5": { + "cache_creation_input_token_cost": 1.24999e-06, + "cache_read_input_token_cost": 1.0003e-07, "input_cost_per_token": 1.00002e-06, "input_dbu_cost_per_token": 1.4286e-05, "litellm_provider": "databricks", @@ -14600,10 +16967,14 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "prompt_cache_min_tokens": 4096 }, "databricks/databricks-claude-opus-4": { + "cache_creation_input_token_cost": 1.874999e-05, + "cache_read_input_token_cost": 1.50003e-06, "input_cost_per_token": 1.5000020000000002e-05, "input_dbu_cost_per_token": 0.000214286, "litellm_provider": "databricks", @@ -14619,10 +16990,14 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "prompt_cache_min_tokens": 1024 }, "databricks/databricks-claude-opus-4-1": { + "cache_creation_input_token_cost": 1.874999e-05, + "cache_read_input_token_cost": 1.50003e-06, "input_cost_per_token": 1.5000020000000002e-05, "input_dbu_cost_per_token": 0.000214286, "litellm_provider": "databricks", @@ -14638,10 +17013,14 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "prompt_cache_min_tokens": 1024 }, "databricks/databricks-claude-opus-4-5": { + "cache_creation_input_token_cost": 6.25002e-06, + "cache_read_input_token_cost": 5.0001e-07, "input_cost_per_token": 5.00003e-06, "input_dbu_cost_per_token": 7.1429e-05, "litellm_provider": "databricks", @@ -14657,11 +17036,15 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_output_config": true + "supports_output_config": true, + "prompt_cache_min_tokens": 4096 }, "databricks/databricks-claude-opus-4-6": { + "cache_creation_input_token_cost": 6.25002e-06, + "cache_read_input_token_cost": 5.0001e-07, "input_cost_per_token": 5.00003e-06, "input_dbu_cost_per_token": 7.1429e-05, "litellm_provider": "databricks", @@ -14677,10 +17060,95 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_legacy_thinking": true, + "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "prompt_cache_min_tokens": 4096 + }, + "databricks/databricks-claude-opus-4-7": { + "cache_creation_input_token_cost": 6.25002e-06, + "cache_read_input_token_cost": 5.0001e-07, + "input_cost_per_token": 5.00003e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Costs per token are the published Global DBU rates times $0.070 per DBU. The '*_dbu_cost_per_token' fields are provided for reference; cost calculation reads the dollar '*_cost_per_token' fields." + }, + "mode": "chat", + "output_cost_per_token": 2.500001e-05, + "output_dbu_cost_per_token": 0.000357143, + "prompt_cache_min_tokens": 2048, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true + }, + "databricks/databricks-claude-opus-4-8": { + "cache_creation_input_token_cost": 6.25002e-06, + "cache_read_input_token_cost": 5.0001e-07, + "input_cost_per_token": 5.00003e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Costs per token are the published Global DBU rates times $0.070 per DBU. The '*_dbu_cost_per_token' fields are provided for reference; cost calculation reads the dollar '*_cost_per_token' fields." + }, + "mode": "chat", + "output_cost_per_token": 2.500001e-05, + "output_dbu_cost_per_token": 0.000357143, + "prompt_cache_min_tokens": 1024, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_function_calling": true, + "supports_mid_conversation_system": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true + }, + "databricks/databricks-claude-opus-5": { + "cache_creation_input_token_cost": 6.25002e-06, + "cache_read_input_token_cost": 5.0001e-07, + "input_cost_per_token": 5.00003e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Costs per token are the published Global DBU rates times $0.070 per DBU. The '*_dbu_cost_per_token' fields are provided for reference; cost calculation reads the dollar '*_cost_per_token' fields." + }, + "mode": "chat", + "output_cost_per_token": 2.500001e-05, + "output_dbu_cost_per_token": 0.000357143, + "prompt_cache_min_tokens": 512, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_function_calling": true, + "supports_mid_conversation_system": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true }, "databricks/databricks-claude-sonnet-4": { + "cache_creation_input_token_cost": 3.74997e-06, + "cache_read_input_token_cost": 3.0002e-07, "input_cost_per_token": 2.9999900000000002e-06, "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", @@ -14696,10 +17164,14 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "prompt_cache_min_tokens": 1024 }, "databricks/databricks-claude-sonnet-4-1": { + "cache_creation_input_token_cost": 3.74997e-06, + "cache_read_input_token_cost": 3.0002e-07, "input_cost_per_token": 2.9999900000000002e-06, "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", @@ -14715,10 +17187,13 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, "databricks/databricks-claude-sonnet-4-5": { + "cache_creation_input_token_cost": 3.74997e-06, + "cache_read_input_token_cost": 3.0002e-07, "input_cost_per_token": 2.9999900000000002e-06, "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", @@ -14734,10 +17209,14 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "prompt_cache_min_tokens": 1024 }, "databricks/databricks-claude-sonnet-4-6": { + "cache_creation_input_token_cost": 3.74997e-06, + "cache_read_input_token_cost": 3.0002e-07, "input_cost_per_token": 2.9999900000000002e-06, "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", @@ -14753,10 +17232,98 @@ "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_legacy_thinking": true, + "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "prompt_cache_min_tokens": 1024 + }, + "databricks/databricks-claude-sonnet-5": { + "cache_creation_input_token_cost": 3.74997e-06, + "cache_read_input_token_cost": 3.0002e-07, + "input_cost_per_token": 2.99999e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Costs per token are the published Global DBU rates times $0.070 per DBU. The '*_dbu_cost_per_token' fields are provided for reference; cost calculation reads the dollar '*_cost_per_token' fields. Introductory launch rates of 28.571 input / 142.857 output / 35.714 cache write / 2.857 cache read DBU run through 2026-08-31; the standard rates are listed here because entries carry no expiry date." + }, + "mode": "chat", + "output_cost_per_token": 1.500002e-05, + "output_dbu_cost_per_token": 0.000214286, + "prompt_cache_min_tokens": 1024, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_function_calling": true, + "supports_mid_conversation_system": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true + }, + "databricks/databricks-deepseek-v4-flash-0731": { + "cache_creation_input_token_cost": 1.4e-07, + "cache_read_input_token_cost": 2.8e-08, + "input_cost_per_token": 1.4e-07, + "input_dbu_cost_per_token": 2e-06, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 2.8e-07, + "output_dbu_cost_per_token": 4e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "databricks/databricks-deepseek-v4-pro-0813": { + "cache_creation_input_token_cost": 1.31999e-06, + "cache_read_input_token_cost": 1.3202e-07, + "input_cost_per_token": 1.31999e-06, + "input_dbu_cost_per_token": 1.8857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference. Context/max output are the DeepSeek-published model limits (1M context, 384K max output)." + }, + "mode": "chat", + "output_cost_per_token": 3.95997e-06, + "output_dbu_cost_per_token": 5.6571e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false }, "databricks/databricks-gemini-2-5-flash": { + "cache_creation_input_token_cost": 3.0002e-07, + "cache_read_input_token_cost": 3.0002e-08, "input_cost_per_token": 3.0001999999999996e-07, "input_dbu_cost_per_token": 4.285999999999999e-06, "litellm_provider": "databricks", @@ -14771,9 +17338,12 @@ "output_dbu_cost_per_token": 3.5714e-05, "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true }, "databricks/databricks-gemini-2-5-pro": { + "cache_creation_input_token_cost": 1.24999e-06, + "cache_read_input_token_cost": 1.24999e-07, "input_cost_per_token": 1.24999e-06, "input_dbu_cost_per_token": 1.7857e-05, "litellm_provider": "databricks", @@ -14788,9 +17358,12 @@ "output_dbu_cost_per_token": 0.000142857, "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true }, "databricks/databricks-gemini-3-1-flash-lite": { + "cache_creation_input_token_cost": 3.1248e-07, + "cache_read_input_token_cost": 3.122e-08, "input_cost_per_token": 3.1248e-07, "input_dbu_cost_per_token": 4.464e-06, "litellm_provider": "databricks", @@ -14805,9 +17378,12 @@ "output_dbu_cost_per_token": 2.6786e-05, "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true }, "databricks/databricks-gemini-3-1-pro": { + "cache_creation_input_token_cost": 2.49998e-06, + "cache_read_input_token_cost": 2.4997e-07, "input_cost_per_token": 2.49998e-06, "input_dbu_cost_per_token": 3.5714e-05, "litellm_provider": "databricks", @@ -14822,9 +17398,12 @@ "output_dbu_cost_per_token": 0.000214286, "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true }, "databricks/databricks-gemini-3-flash": { + "cache_creation_input_token_cost": 6.2503e-07, + "cache_read_input_token_cost": 6.251e-08, "input_cost_per_token": 6.2503e-07, "input_dbu_cost_per_token": 8.929e-06, "litellm_provider": "databricks", @@ -14839,9 +17418,12 @@ "output_dbu_cost_per_token": 5.3571e-05, "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true }, "databricks/databricks-gemini-3-pro": { + "cache_creation_input_token_cost": 2.49998e-06, + "cache_read_input_token_cost": 2.4997e-07, "input_cost_per_token": 2.49998e-06, "input_dbu_cost_per_token": 3.5714e-05, "litellm_provider": "databricks", @@ -14856,9 +17438,12 @@ "output_dbu_cost_per_token": 0.000214286, "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true }, "databricks/databricks-gemma-3-12b": { + "cache_creation_input_token_cost": 1.5001e-07, + "cache_read_input_token_cost": 1.5001e-07, "input_cost_per_token": 1.5000999999999998e-07, "input_dbu_cost_per_token": 2.1429999999999996e-06, "litellm_provider": "databricks", @@ -14873,7 +17458,60 @@ "output_dbu_cost_per_token": 7.143e-06, "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, + "databricks/databricks-glm-5-2": { + "cache_creation_input_token_cost": 1.4e-06, + "cache_read_input_token_cost": 2.5998e-07, + "input_cost_per_token": 1.4e-06, + "input_dbu_cost_per_token": 2e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference." + }, + "mode": "chat", + "output_cost_per_token": 4.39999e-06, + "output_dbu_cost_per_token": 6.2857e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "databricks/databricks-glm-5-3-flash": { + "litellm_provider": "databricks", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "Databricks has not published pay-per-token DBU rates for this model yet (not on the foundation-model-serving pricing page as of 2026-08-27), so cost fields are omitted until rates are published." + }, + "mode": "chat", + "source": "https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, "databricks/databricks-gpt-5": { + "cache_creation_input_token_cost": 1.24999e-06, + "cache_read_input_token_cost": 1.2502e-07, "input_cost_per_token": 1.24999e-06, "input_dbu_cost_per_token": 1.7857e-05, "litellm_provider": "databricks", @@ -14886,9 +17524,12 @@ "mode": "chat", "output_cost_per_token": 9.999990000000002e-06, "output_dbu_cost_per_token": 0.000142857, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-1": { + "cache_creation_input_token_cost": 1.24999e-06, + "cache_read_input_token_cost": 1.2502e-07, "input_cost_per_token": 1.24999e-06, "input_dbu_cost_per_token": 1.7857e-05, "litellm_provider": "databricks", @@ -14901,9 +17542,12 @@ "mode": "chat", "output_cost_per_token": 9.999990000000002e-06, "output_dbu_cost_per_token": 0.000142857, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-1-codex-max": { + "cache_creation_input_token_cost": 1.24999e-06, + "cache_read_input_token_cost": 1.2502e-07, "input_cost_per_token": 1.24999e-06, "input_dbu_cost_per_token": 1.7857e-05, "litellm_provider": "databricks", @@ -14916,9 +17560,12 @@ "mode": "chat", "output_cost_per_token": 9.999990000000002e-06, "output_dbu_cost_per_token": 0.000142857, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-1-codex-mini": { + "cache_creation_input_token_cost": 2.4997e-07, + "cache_read_input_token_cost": 2.499e-08, "input_cost_per_token": 2.4997e-07, "input_dbu_cost_per_token": 3.571e-06, "litellm_provider": "databricks", @@ -14931,9 +17578,12 @@ "mode": "chat", "output_cost_per_token": 1.99997e-06, "output_dbu_cost_per_token": 2.8571e-05, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-2": { + "cache_creation_input_token_cost": 1.75e-06, + "cache_read_input_token_cost": 1.75e-07, "input_cost_per_token": 1.75e-06, "input_dbu_cost_per_token": 2.5e-05, "litellm_provider": "databricks", @@ -14946,9 +17596,12 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_dbu_cost_per_token": 0.0002, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-2-codex": { + "cache_creation_input_token_cost": 1.75e-06, + "cache_read_input_token_cost": 1.75e-07, "input_cost_per_token": 1.75e-06, "input_dbu_cost_per_token": 2.5e-05, "litellm_provider": "databricks", @@ -14961,9 +17614,12 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_dbu_cost_per_token": 0.0002, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-3-codex": { + "cache_creation_input_token_cost": 1.75e-06, + "cache_read_input_token_cost": 1.75e-07, "input_cost_per_token": 1.75e-06, "input_dbu_cost_per_token": 2.5e-05, "litellm_provider": "databricks", @@ -14976,9 +17632,12 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_dbu_cost_per_token": 0.0002, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-4": { + "cache_creation_input_token_cost": 2.49998e-06, + "cache_read_input_token_cost": 2.4997e-07, "input_cost_per_token": 2.49998e-06, "input_dbu_cost_per_token": 3.5714e-05, "litellm_provider": "databricks", @@ -14991,9 +17650,12 @@ "mode": "chat", "output_cost_per_token": 1.5000020000000002e-05, "output_dbu_cost_per_token": 0.000214286, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-4-mini": { + "cache_creation_input_token_cost": 7.4998e-07, + "cache_read_input_token_cost": 7.497e-08, "input_cost_per_token": 7.4998e-07, "input_dbu_cost_per_token": 1.0714e-05, "litellm_provider": "databricks", @@ -15006,9 +17668,12 @@ "mode": "chat", "output_cost_per_token": 4.50002e-06, "output_dbu_cost_per_token": 6.4286e-05, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-4-nano": { + "cache_creation_input_token_cost": 1.9999e-07, + "cache_read_input_token_cost": 2.002e-08, "input_cost_per_token": 1.9999e-07, "input_dbu_cost_per_token": 2.857e-06, "litellm_provider": "databricks", @@ -15021,9 +17686,12 @@ "mode": "chat", "output_cost_per_token": 1.24999e-06, "output_dbu_cost_per_token": 1.7857e-05, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-mini": { + "cache_creation_input_token_cost": 2.4997e-07, + "cache_read_input_token_cost": 2.499e-08, "input_cost_per_token": 2.4997000000000006e-07, "input_dbu_cost_per_token": 3.571e-06, "litellm_provider": "databricks", @@ -15036,9 +17704,12 @@ "mode": "chat", "output_cost_per_token": 1.9999700000000004e-06, "output_dbu_cost_per_token": 2.8571e-05, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-5-nano": { + "cache_creation_input_token_cost": 4.998e-08, + "cache_read_input_token_cost": 4.97e-09, "input_cost_per_token": 4.998e-08, "input_dbu_cost_per_token": 7.14e-07, "litellm_provider": "databricks", @@ -15051,9 +17722,12 @@ "mode": "chat", "output_cost_per_token": 3.9998000000000007e-07, "output_dbu_cost_per_token": 5.714000000000001e-06, - "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_prompt_caching": true }, "databricks/databricks-gpt-oss-120b": { + "cache_creation_input_token_cost": 1.5001e-07, + "cache_read_input_token_cost": 1.5001e-07, "input_cost_per_token": 1.5000999999999998e-07, "input_dbu_cost_per_token": 2.1429999999999996e-06, "litellm_provider": "databricks", @@ -15069,6 +17743,8 @@ "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, "databricks/databricks-gpt-oss-20b": { + "cache_creation_input_token_cost": 7e-08, + "cache_read_input_token_cost": 7e-08, "input_cost_per_token": 7e-08, "input_dbu_cost_per_token": 1e-06, "litellm_provider": "databricks", @@ -15084,6 +17760,8 @@ "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, "databricks/databricks-gte-large-en": { + "cache_creation_input_token_cost": 1.2999e-07, + "cache_read_input_token_cost": 1.2999e-07, "input_cost_per_token": 1.2999000000000001e-07, "input_dbu_cost_per_token": 1.857e-06, "litellm_provider": "databricks", @@ -15098,7 +17776,38 @@ "output_vector_size": 1024, "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, + "databricks/databricks-kimi-k3": { + "cache_creation_input_token_cost": 2.99999e-06, + "cache_read_input_token_cost": 3.0002e-07, + "input_cost_per_token": 2.99999e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1000000, + "max_output_tokens": 1048576, + "max_tokens": 1048576, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Billing reads the per-token dollar fields; the '*_dbu_cost_per_token' fields are the published Databricks rates, kept for reference." + }, + "mode": "chat", + "output_cost_per_token": 1.500002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, "databricks/databricks-llama-2-70b-chat": { + "cache_creation_input_token_cost": 5.0001e-07, + "cache_read_input_token_cost": 5.0001e-07, "input_cost_per_token": 5.0001e-07, "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", @@ -15115,6 +17824,8 @@ "supports_tool_choice": true }, "databricks/databricks-llama-4-maverick": { + "cache_creation_input_token_cost": 5.0001e-07, + "cache_read_input_token_cost": 5.0001e-07, "input_cost_per_token": 5.0001e-07, "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", @@ -15131,6 +17842,8 @@ "supports_tool_choice": true }, "databricks/databricks-meta-llama-3-1-405b-instruct": { + "cache_creation_input_token_cost": 5.00003e-06, + "cache_read_input_token_cost": 5.00003e-06, "input_cost_per_token": 5.00003e-06, "input_dbu_cost_per_token": 7.1429e-05, "litellm_provider": "databricks", @@ -15147,6 +17860,8 @@ "supports_tool_choice": true }, "databricks/databricks-meta-llama-3-1-8b-instruct": { + "cache_creation_input_token_cost": 1.5001e-07, + "cache_read_input_token_cost": 1.5001e-07, "input_cost_per_token": 1.5000999999999998e-07, "input_dbu_cost_per_token": 2.1429999999999996e-06, "litellm_provider": "databricks", @@ -15162,6 +17877,8 @@ "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, "databricks/databricks-meta-llama-3-3-70b-instruct": { + "cache_creation_input_token_cost": 5.0001e-07, + "cache_read_input_token_cost": 5.0001e-07, "input_cost_per_token": 5.0001e-07, "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", @@ -15178,6 +17895,8 @@ "supports_tool_choice": true }, "databricks/databricks-meta-llama-3-70b-instruct": { + "cache_creation_input_token_cost": 1.00002e-06, + "cache_read_input_token_cost": 1.00002e-06, "input_cost_per_token": 1.00002e-06, "input_dbu_cost_per_token": 1.4286e-05, "litellm_provider": "databricks", @@ -15194,6 +17913,8 @@ "supports_tool_choice": true }, "databricks/databricks-mixtral-8x7b-instruct": { + "cache_creation_input_token_cost": 5.0001e-07, + "cache_read_input_token_cost": 5.0001e-07, "input_cost_per_token": 5.0001e-07, "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", @@ -15210,6 +17931,8 @@ "supports_tool_choice": true }, "databricks/databricks-mpt-30b-instruct": { + "cache_creation_input_token_cost": 1.00002e-06, + "cache_read_input_token_cost": 1.00002e-06, "input_cost_per_token": 1.00002e-06, "input_dbu_cost_per_token": 1.4286e-05, "litellm_provider": "databricks", @@ -15226,6 +17949,8 @@ "supports_tool_choice": true }, "databricks/databricks-mpt-7b-instruct": { + "cache_creation_input_token_cost": 5.0001e-07, + "cache_read_input_token_cost": 5.0001e-07, "input_cost_per_token": 5.0001e-07, "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", @@ -15758,12 +18483,13 @@ "max_tokens": 4096, "max_input_tokens": 4096, "max_output_tokens": 4096, - "input_cost_per_token": 8e-08, - "output_cost_per_token": 9e-08, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/NousResearch/Hermes-3-Llama-3.1-405B": { "max_tokens": 131072, @@ -15780,11 +18506,12 @@ "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 3e-07, - "output_cost_per_token": 3e-07, + "input_cost_per_token": 7e-07, + "output_cost_per_token": 7e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": false + "supports_tool_choice": false, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Qwen/QwQ-32B": { "max_tokens": 131072, @@ -15801,12 +18528,13 @@ "max_tokens": 32768, "max_input_tokens": 32768, "max_output_tokens": 32768, - "input_cost_per_token": 1.2e-07, - "output_cost_per_token": 3.9e-07, + "input_cost_per_token": 3.6e-07, + "output_cost_per_token": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Qwen/Qwen2.5-7B-Instruct": { "max_tokens": 32768, @@ -15834,12 +18562,13 @@ "max_tokens": 40960, "max_input_tokens": 40960, "max_output_tokens": 40960, - "input_cost_per_token": 6e-08, + "input_cost_per_token": 1.2e-07, "output_cost_per_token": 2.4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Qwen/Qwen3-235B-A22B": { "max_tokens": 40960, @@ -15857,11 +18586,12 @@ "max_input_tokens": 262144, "max_output_tokens": 262144, "input_cost_per_token": 9e-08, - "output_cost_per_token": 6e-07, + "output_cost_per_token": 5.5e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Qwen/Qwen3-235B-A22B-Thinking-2507": { "max_tokens": 262144, @@ -15878,23 +18608,25 @@ "max_tokens": 40960, "max_input_tokens": 40960, "max_output_tokens": 40960, - "input_cost_per_token": 8e-08, - "output_cost_per_token": 2.9e-07, + "input_cost_per_token": 1.2e-07, + "output_cost_per_token": 5e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Qwen/Qwen3-32B": { "max_tokens": 40960, "max_input_tokens": 40960, "max_output_tokens": 40960, - "input_cost_per_token": 1e-07, + "input_cost_per_token": 8e-08, "output_cost_per_token": 2.8e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Qwen/Qwen3-Coder-480B-A35B-Instruct": { "max_tokens": 262144, @@ -15911,23 +18643,27 @@ "max_tokens": 262144, "max_input_tokens": 262144, "max_output_tokens": 262144, - "input_cost_per_token": 2.9e-07, - "output_cost_per_token": 1.2e-06, + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1e-06, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "cache_read_input_token_cost": 1e-07, + "supports_prompt_caching": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Qwen/Qwen3-Next-80B-A3B-Instruct": { "max_tokens": 262144, "max_input_tokens": 262144, "max_output_tokens": 262144, - "input_cost_per_token": 1.4e-07, - "output_cost_per_token": 1.4e-06, + "input_cost_per_token": 9e-08, + "output_cost_per_token": 1.1e-06, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Qwen/Qwen3-Next-80B-A3B-Thinking": { "max_tokens": 262144, @@ -15954,11 +18690,12 @@ "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 6.5e-07, - "output_cost_per_token": 7.5e-07, + "input_cost_per_token": 8.5e-07, + "output_cost_per_token": 8.5e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": false + "supports_tool_choice": false, + "source": "https://deepinfra.com/pricing" }, "deepinfra/Sao10K/L3.3-70B-Euryale-v2.3": { "max_tokens": 131072, @@ -16084,36 +18821,41 @@ "max_tokens": 163840, "max_input_tokens": 163840, "max_output_tokens": 163840, - "input_cost_per_token": 3.8e-07, + "input_cost_per_token": 3.2e-07, "output_cost_per_token": 8.9e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/deepseek-ai/DeepSeek-V3-0324": { "max_tokens": 163840, "max_input_tokens": 163840, "max_output_tokens": 163840, - "input_cost_per_token": 2.5e-07, - "output_cost_per_token": 8.8e-07, + "input_cost_per_token": 2.4e-07, + "output_cost_per_token": 9e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "cache_read_input_token_cost": 1.35e-07, + "supports_prompt_caching": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/deepseek-ai/DeepSeek-V3.1": { "max_tokens": 163840, "max_input_tokens": 163840, "max_output_tokens": 163840, - "input_cost_per_token": 2.7e-07, - "output_cost_per_token": 1e-06, + "input_cost_per_token": 2.5e-07, + "output_cost_per_token": 9.5e-07, "cache_read_input_token_cost": 2.16e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, "supports_reasoning": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/deepseek-ai/DeepSeek-V3.1-Terminus": { "max_tokens": 163840, @@ -16167,33 +18909,36 @@ "max_input_tokens": 131072, "max_output_tokens": 131072, "input_cost_per_token": 5e-08, - "output_cost_per_token": 1e-07, + "output_cost_per_token": 1.5e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/google/gemma-3-27b-it": { "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 9e-08, + "input_cost_per_token": 8e-08, "output_cost_per_token": 1.6e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/google/gemma-3-4b-it": { "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 4e-08, - "output_cost_per_token": 8e-08, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 1e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/meta-llama/Llama-3.2-11B-Vision-Instruct": { "max_tokens": 131072, @@ -16231,34 +18976,37 @@ "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 3.9e-07, + "input_cost_per_token": 1e-07, + "output_cost_per_token": 3.2e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_function_calling": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8": { "max_tokens": 1048576, "max_input_tokens": 1048576, "max_output_tokens": 1048576, - "input_cost_per_token": 1.5e-07, - "output_cost_per_token": 6e-07, + "input_cost_per_token": 2e-07, + "output_cost_per_token": 8e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/meta-llama/Llama-4-Scout-17B-16E-Instruct": { "max_tokens": 327680, "max_input_tokens": 327680, "max_output_tokens": 327680, - "input_cost_per_token": 8e-08, + "input_cost_per_token": 1e-07, "output_cost_per_token": 3e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/meta-llama/Llama-Guard-3-8B": { "max_tokens": 131072, @@ -16306,12 +19054,13 @@ "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 2.8e-07, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/meta-llama/Meta-Llama-3.1-8B-Instruct": { "max_tokens": 131072, @@ -16329,11 +19078,12 @@ "max_input_tokens": 131072, "max_output_tokens": 131072, "input_cost_per_token": 2e-08, - "output_cost_per_token": 3e-08, + "output_cost_per_token": 4e-08, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/microsoft/WizardLM-2-8x22B": { "max_tokens": 65536, @@ -16360,12 +19110,13 @@ "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 2e-08, - "output_cost_per_token": 4e-08, + "input_cost_per_token": 1.9e-08, + "output_cost_per_token": 3e-08, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/mistralai/Mistral-Small-24B-Instruct-2501": { "max_tokens": 32768, @@ -16447,14 +19198,16 @@ }, "deepinfra/nvidia/NVIDIA-Nemotron-3.5-Lightning": { "max_input_tokens": 262144, - "input_cost_per_token": 5e-08, + "input_cost_per_token": 8e-08, "output_cost_per_token": 2e-07, "litellm_provider": "deepinfra", "mode": "chat", - "source": "https://deepinfra.com/nvidia/NVIDIA-Nemotron-3.5-Lightning", + "source": "https://deepinfra.com/pricing", "supports_tool_choice": true, "supports_function_calling": true, - "supports_reasoning": true + "supports_reasoning": true, + "cache_read_input_token_cost": 4e-08, + "supports_prompt_caching": true }, "deepinfra/nvidia/NVIDIA-Nemotron-Nano-9B-v2": { "max_tokens": 131072, @@ -16471,23 +19224,25 @@ "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 5e-08, - "output_cost_per_token": 4.5e-07, + "input_cost_per_token": 3.7e-08, + "output_cost_per_token": 1.7e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/openai/gpt-oss-20b": { "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 4e-08, - "output_cost_per_token": 1.5e-07, + "input_cost_per_token": 3e-08, + "output_cost_per_token": 1.4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true, - "supports_function_calling": true + "supports_function_calling": true, + "source": "https://deepinfra.com/pricing" }, "deepinfra/zai-org/GLM-4.5": { "max_tokens": 131072, @@ -16890,6 +19645,14 @@ "notes": "Web Search on Amazon Bedrock AgentCore, billed by AWS on the gateway" } }, + "bing_grounding/search": { + "input_cost_per_query": 0.035, + "litellm_provider": "bing_grounding", + "mode": "search", + "metadata": { + "notes": "Grounding with Bing Search (G1 SKU): $35 per 1,000 transactions. Tokens for the Foundry model deployment that runs the grounded search are billed separately on that deployment." + } + }, "tinyfish/search": { "input_cost_per_query": 0.0, "litellm_provider": "tinyfish", @@ -17245,7 +20008,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "deprecation_date": "2027-01-08" }, "eu.anthropic.claude-opus-4-20250514-v1:0": { "cache_creation_input_token_cost": 1.875e-05, @@ -18274,6 +21038,22 @@ "supports_tool_choice": true, "supports_vision": false }, + "fireworks_ai/accounts/fireworks/models/deepseek-v4-pro-0813": { + "cache_read_input_token_cost": 4.4e-08, + "input_cost_per_token": 1.32e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 3.96e-06, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, "fireworks_ai/accounts/fireworks/models/firefunction-v2": { "input_cost_per_token": 9e-07, "litellm_provider": "fireworks_ai", @@ -19026,6 +21806,61 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "friendliai/zai-org/GLM-5.3-Flash": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "cache_read_input_token_cost": 3e-08, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Native multimodal GLM model for efficient coding and long-horizon agent tasks", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": true, + "supports_image_input": true, + "supports_video_input": true + }, + "friendliai/zai-org/GLM-5.3": { + "litellm_provider": "friendliai", + "supports_reasoning": true, + "supports_function_calling": true, + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "max_output_tokens": 1048576, + "input_cost_per_token": 1.26e-06, + "output_cost_per_token": 3.96e-06, + "cache_read_input_token_cost": 2.34e-07, + "supports_prompt_caching": true, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], + "supports_parallel_function_calling": true, + "supports_response_schema": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "mode": "chat", + "comment": "Flagship GLM model for long-horizon coding, agents, and complex project delivery", + "source": "https://api.friendli.ai/serverless/v1/models", + "supports_vision": false, + "supports_image_input": false + }, "ft:babbage-002": { "deprecation_date": "2026-10-23", "input_cost_per_token": 1.6e-06, @@ -19434,6 +22269,7 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini-2.5-flash-image": { @@ -19723,7 +22559,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini-3.1-flash-lite": { "deprecation_date": "2027-05-07", @@ -19780,12 +22617,13 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", "cache_read_input_token_cost": 3e-08, - "cache_read_input_token_cost_flex": 2e-08, + "cache_read_input_token_cost_flex": 1.5e-08, "cache_read_input_token_cost_priority": 5e-08, "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, @@ -19836,7 +22674,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "deep-research-pro-preview-12-2025": { "input_cost_per_image": 0.0011, @@ -19916,6 +22755,7 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini-2.5-flash-lite-preview-09-2025": { @@ -19961,10 +22801,11 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini-2.5-flash-preview-09-2025": { - "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost": 3e-08, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "litellm_provider": "vertex_ai-language-models", @@ -19974,7 +22815,7 @@ "mode": "chat", "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, - "source": "https://developers.googleblog.com/en/continuing-to-bring-you-our-latest-models-with-an-improved-gemini-2-5-flash-and-flash-lite-release/", + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -20006,12 +22847,58 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, + "gemini-live-2.5-flash-native-audio": { + "deprecation_date": "2026-12-13", + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 65535, + "mode": "realtime", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supported_endpoints": [ + "/vertex_ai/live", + "/v1/realtime" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "audio" + ], + "supports_audio_input": true, + "supports_audio_output": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.035, + "search_context_size_medium": 0.035, + "search_context_size_high": 0.035 + }, + "gemini_native_audio": true + }, "gemini-live-2.5-flash-preview-native-audio-09-2025": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_audio_token": 3e-06, - "input_cost_per_token": 3e-07, + "input_cost_per_token": 5e-07, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65535, @@ -20055,7 +22942,7 @@ "gemini/gemini-live-2.5-flash-preview-native-audio-09-2025": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_audio_token": 3e-06, - "input_cost_per_token": 3e-07, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 65535, @@ -20100,7 +22987,7 @@ }, "gemini-2.5-flash-lite-preview-06-17": { "deprecation_date": "2025-11-18", - "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost": 1e-08, "input_cost_per_audio_token": 5e-07, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-language-models", @@ -20110,7 +22997,7 @@ "mode": "chat", "output_cost_per_reasoning_token": 4e-07, "output_cost_per_token": 4e-07, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-preview", + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -20142,6 +23029,7 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini-2.5-pro": { @@ -20187,7 +23075,8 @@ "search_context_size_low": 0.035, "search_context_size_medium": 0.035, "search_context_size_high": 0.035 - } + }, + "google_maps_grounding_cost_per_query": 0.025 }, "gemini-3-pro-preview": { "deprecation_date": "2026-03-26", @@ -20301,7 +23190,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini-3.1-pro-preview-customtools": { "prompt_cache_min_tokens": 4096, @@ -20353,7 +23243,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "vertex_ai/gemini-3-pro-preview": { "cache_read_input_token_cost": 2e-07, @@ -20456,7 +23347,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "vertex_ai/gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, @@ -20511,6 +23403,7 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_token_batches": 7.5e-07, "output_cost_per_token_batches": 4.5e-06, "input_cost_per_token_flex": 7.5e-07, @@ -20571,7 +23464,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "vertex_ai/gemini-3.7-flash": { "prompt_cache_min_tokens": 4096, @@ -20627,7 +23521,65 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, + "vertex_ai/gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "vertex_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "regional_endpoint_uplift_multiplier": 1.1, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "vertex_ai/gemini-3.1-pro-preview": { "prompt_cache_min_tokens": 4096, @@ -20685,7 +23637,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "vertex_ai/gemini-3.1-pro-preview-customtools": { "prompt_cache_min_tokens": 4096, @@ -20743,22 +23696,20 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini-2.5-pro-preview-tts": { "cache_read_input_token_cost": 1.25e-07, - "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, "input_cost_per_audio_token": 7e-07, - "input_cost_per_token": 1.25e-06, - "input_cost_per_token_above_200k_tokens": 2.5e-06, + "input_cost_per_token": 1e-06, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, "max_output_tokens": 65535, "max_tokens": 65535, "mode": "chat", - "output_cost_per_token": 1e-05, - "output_cost_per_token_above_200k_tokens": 1.5e-05, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-pro-preview", + "output_cost_per_token": 2e-05, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -21282,6 +24233,7 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini/gemini-2.5-flash-image": { @@ -21422,6 +24374,49 @@ }, "web_search_billing_unit": "per_query" }, + "gemini/nano-banana-pro-preview": { + "input_cost_per_image": 0.0011, + "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "gemini", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "image_generation", + "output_cost_per_image": 0.134, + "output_cost_per_image_token": 0.00012, + "output_cost_per_token": 1.2e-05, + "rpm": 1000, + "tpm": 4000000, + "output_cost_per_token_batches": 6e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": false, + "supports_prompt_caching": true, + "supports_reasoning": false, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini/gemini-3.1-flash-image": { "input_cost_per_token": 5e-07, "input_cost_per_token_batches": 2.5e-07, @@ -21629,6 +24624,7 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini/gemini-2.5-flash-lite-preview-09-2025": { @@ -21677,10 +24673,11 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini/gemini-2.5-flash-preview-09-2025": { - "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost": 3e-08, "deprecation_date": "2026-02-17", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, @@ -21692,7 +24689,7 @@ "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, "rpm": 15, - "source": "https://developers.googleblog.com/en/continuing-to-bring-you-our-latest-models-with-an-improved-gemini-2-5-flash-and-flash-lite-release/", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -21725,10 +24722,11 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini/gemini-flash-latest": { - "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost": 3e-08, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "litellm_provider": "gemini", @@ -21739,100 +24737,7 @@ "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, "rpm": 15, - "source": "https://developers.googleblog.com/en/continuing-to-bring-you-our-latest-models-with-an-improved-gemini-2-5-flash-and-flash-lite-release/", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions", - "/v1/batch" - ], - "supported_modalities": [ - "text", - "image", - "audio", - "video" - ], - "supported_output_modalities": [ - "text" - ], - "supports_audio_output": false, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_pdf_input": true, - "supports_prompt_caching": true, - "supports_reasoning": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_url_context": true, - "supports_vision": true, - "supports_web_search": true, - "tpm": 250000, - "search_context_cost_per_query": { - "search_context_size_low": 0.035, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.035 - } - }, - "gemini/gemini-flash-lite-latest": { - "cache_read_input_token_cost": 2.5e-08, - "input_cost_per_audio_token": 3e-07, - "input_cost_per_token": 1e-07, - "litellm_provider": "gemini", - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_tokens": 65535, - "mode": "chat", - "output_cost_per_reasoning_token": 4e-07, - "output_cost_per_token": 4e-07, - "rpm": 15, - "source": "https://developers.googleblog.com/en/continuing-to-bring-you-our-latest-models-with-an-improved-gemini-2-5-flash-and-flash-lite-release/", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions", - "/v1/batch" - ], - "supported_modalities": [ - "text", - "image", - "audio", - "video" - ], - "supported_output_modalities": [ - "text" - ], - "supports_audio_output": false, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_pdf_input": true, - "supports_prompt_caching": true, - "supports_reasoning": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_url_context": true, - "supports_vision": true, - "supports_web_search": true, - "tpm": 250000, - "search_context_cost_per_query": { - "search_context_size_low": 0.035, - "search_context_size_medium": 0.035, - "search_context_size_high": 0.035 - } - }, - "gemini/gemini-2.5-flash-lite-preview-06-17": { - "deprecation_date": "2025-11-18", - "cache_read_input_token_cost": 2.5e-08, - "input_cost_per_audio_token": 5e-07, - "input_cost_per_token": 1e-07, - "litellm_provider": "gemini", - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_tokens": 65535, - "mode": "chat", - "output_cost_per_reasoning_token": 4e-07, - "output_cost_per_token": 4e-07, - "rpm": 15, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-lite", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -21865,14 +24770,110 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, + "google_maps_grounding_cost_per_query": 0.025 + }, + "gemini/gemini-flash-lite-latest": { + "cache_read_input_token_cost": 1e-08, + "input_cost_per_audio_token": 3e-07, + "input_cost_per_token": 1e-07, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 65535, + "mode": "chat", + "output_cost_per_reasoning_token": 4e-07, + "output_cost_per_token": 4e-07, + "rpm": 15, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 250000, + "search_context_cost_per_query": { + "search_context_size_low": 0.035, + "search_context_size_medium": 0.035, + "search_context_size_high": 0.035 + }, + "google_maps_grounding_cost_per_query": 0.025 + }, + "gemini/gemini-2.5-flash-lite-preview-06-17": { + "deprecation_date": "2025-11-18", + "cache_read_input_token_cost": 1e-08, + "input_cost_per_audio_token": 5e-07, + "input_cost_per_token": 1e-07, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 65535, + "mode": "chat", + "output_cost_per_reasoning_token": 4e-07, + "output_cost_per_token": 4e-07, + "rpm": 15, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 250000, + "search_context_cost_per_query": { + "search_context_size_low": 0.035, + "search_context_size_medium": 0.035, + "search_context_size_high": 0.035 + }, + "google_maps_grounding_cost_per_query": 0.025, "supports_image_size": false }, "gemini/gemini-2.5-flash-preview-tts": { - "input_cost_per_token": 3e-07, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "mode": "audio_speech", - "output_cost_per_token": 2.5e-06, - "source": "https://ai.google.dev/pricing", + "output_cost_per_token": 1e-05, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/audio/speech" ], @@ -21926,7 +24927,8 @@ "search_context_size_low": 0.035, "search_context_size_medium": 0.035, "search_context_size_high": 0.035 - } + }, + "google_maps_grounding_cost_per_query": 0.025 }, "gemini/gemini-2.5-computer-use-preview-10-2025": { "input_cost_per_token": 1.25e-06, @@ -22063,7 +25065,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3.1-flash-lite": { "cache_read_input_token_cost": 2.5e-08, @@ -22122,7 +25125,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3.5-flash-lite": { "cache_read_input_token_cost": 3e-08, @@ -22179,7 +25183,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3-flash-preview": { "cache_read_input_token_cost": 5e-08, @@ -22193,7 +25198,7 @@ "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22231,7 +25236,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, @@ -22246,7 +25252,7 @@ "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22287,6 +25293,7 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_token_batches": 7.5e-07, "output_cost_per_token_batches": 4.5e-06, "input_cost_per_token_flex": 7.5e-07, @@ -22310,7 +25317,7 @@ "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22349,7 +25356,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3.7-flash": { "prompt_cache_min_tokens": 4096, @@ -22368,7 +25376,7 @@ "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22407,15 +25415,75 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, + "gemini/gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "rpm": 2000, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "tpm": 800000, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-omni-flash-preview": { "input_cost_per_audio_token": 1.5e-06, "input_cost_per_token": 1.5e-06, "litellm_provider": "gemini", - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_tokens": 65535, + "max_input_tokens": 131072, + "max_output_tokens": 65536, + "max_tokens": 65536, "mode": "chat", "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, @@ -22423,7 +25491,7 @@ "rpm": 2000, "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ - "/v1/chat/completions" + "/v1beta/interactions" ], "supported_modalities": [ "text", @@ -22440,7 +25508,8 @@ "supports_system_messages": true, "supports_video_input": true, "supports_vision": true, - "tpm": 800000 + "tpm": 800000, + "deprecation_date": "2026-09-30" }, "gemini/gemini-3.1-pro-preview": { "prompt_cache_min_tokens": 4096, @@ -22498,7 +25567,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3.1-pro-preview-customtools": { "prompt_cache_min_tokens": 4096, @@ -22556,7 +25626,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini-3-flash-preview": { "cache_read_input_token_cost": 5e-08, @@ -22569,7 +25640,7 @@ "mode": "chat", "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22606,7 +25677,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini-omni-flash-preview": { "input_cost_per_audio_token": 1.5e-06, @@ -22652,7 +25724,7 @@ "mode": "chat", "output_cost_per_reasoning_token": 9e-06, "output_cost_per_token": 9e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22692,6 +25764,7 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014, "input_cost_per_token_batches": 7.5e-07, "output_cost_per_token_batches": 4.5e-06, "input_cost_per_token_flex": 7.5e-07, @@ -22714,7 +25787,7 @@ "output_cost_per_token": 3.75e-06, "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22752,7 +25825,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini-3.7-flash": { "prompt_cache_min_tokens": 4096, @@ -22770,7 +25844,7 @@ "output_cost_per_token": 3.75e-06, "output_cost_per_token_batches": 1.875e-06, "output_cost_per_token_flex": 1.875e-06, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -22808,23 +25882,78 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, + "gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-2.5-pro-preview-tts": { "cache_read_input_token_cost": 1.25e-07, - "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, "input_cost_per_audio_token": 7e-07, - "input_cost_per_token": 1.25e-06, - "input_cost_per_token_above_200k_tokens": 2.5e-06, + "input_cost_per_token": 1e-06, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 65535, "max_tokens": 65535, "mode": "chat", - "output_cost_per_token": 1e-05, - "output_cost_per_token_above_200k_tokens": 1.5e-05, + "output_cost_per_token": 2e-05, "rpm": 10000, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-pro-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -22948,6 +26077,38 @@ "supports_tool_choice": true, "supports_vision": true }, + "gemini/gemma-4-26b-a4b-it": { + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "gemini", + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://ai.google.dev/gemini-api/docs/pricing" + }, + "gemini/gemma-4-31b-it": { + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "gemini", + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://ai.google.dev/gemini-api/docs/pricing" + }, "gemini/imagen-3.0-fast-generate-001": { "litellm_provider": "gemini", "mode": "image_generation", @@ -23085,8 +26246,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23100,7 +26263,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23128,8 +26292,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23143,7 +26309,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://ai.google.dev/gemini-api/docs/video", + "output_cost_per_second_4k": 0.6, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_modalities": [ "text" ], @@ -23180,6 +26347,7 @@ }, "github_copilot/claude-opus-4.6-fast": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "litellm_provider": "github_copilot", "max_input_tokens": 128000, "max_output_tokens": 16000, @@ -23651,7 +26819,7 @@ "supports_response_schema": true, "supports_vision": true }, - "gigachat/GigaChat-2-Lite": { + "gigachat/GigaChat-2": { "input_cost_per_token": 0.0, "litellm_provider": "gigachat", "max_input_tokens": 128000, @@ -23713,6 +26881,15 @@ "output_cost_per_token": 0.0, "output_vector_size": 2560 }, + "gigachat/GigaEmbeddings-3B-2025-09": { + "input_cost_per_token": 0.0, + "litellm_provider": "gigachat", + "max_input_tokens": 4096, + "max_tokens": 4096, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_vector_size": 2048 + }, "gmi/anthropic/claude-opus-4.5": { "input_cost_per_token": 5e-06, "litellm_provider": "gmi", @@ -24636,7 +27813,7 @@ "supports_vision": true }, "gpt-4o-audio-preview": { - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", @@ -24959,7 +28136,7 @@ "supports_vision": true }, "gpt-4o-mini-audio-preview": { - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 1.5e-07, "litellm_provider": "openai", @@ -24997,7 +28174,7 @@ "gpt-4o-mini-realtime-preview": { "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 3e-07, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", @@ -25096,7 +28273,8 @@ "output_cost_per_token": 5e-06, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "gpt-4o-mini-tts": { "input_cost_per_token": 2.5e-06, @@ -25118,7 +28296,7 @@ }, "gpt-4o-realtime-preview": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25137,7 +28315,7 @@ }, "gpt-4o-realtime-preview-2024-12-17": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25156,7 +28334,7 @@ }, "gpt-4o-realtime-preview-2025-06-03": { "cache_read_input_token_cost": 2.5e-06, - "deprecation_date": "2027-01-20", + "deprecation_date": "2026-05-07", "input_cost_per_audio_token": 4e-05, "input_cost_per_token": 5e-06, "litellm_provider": "openai", @@ -25235,7 +28413,8 @@ "output_cost_per_token": 1e-05, "supported_endpoints": [ "/v1/audio/transcriptions" - ] + ], + "deprecation_date": "2027-02-26" }, "gpt-image-1.5": { "cache_read_input_token_cost": 1.25e-06, @@ -25486,7 +28665,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "low/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -25497,7 +28677,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "low/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -25508,7 +28689,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.034, @@ -25519,7 +28701,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -25530,7 +28713,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "medium/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.05, @@ -25541,7 +28725,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.133, @@ -25552,7 +28737,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -25563,7 +28749,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "high/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.2, @@ -25574,7 +28761,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -25585,7 +28773,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -25596,7 +28785,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "standard/1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -25607,7 +28797,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1024-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.009, @@ -25618,7 +28809,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1024-x-1536/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -25629,7 +28821,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "1536-x-1024/gpt-image-1.5-2025-12-16": { "input_cost_per_image": 0.013, @@ -25640,7 +28833,8 @@ "/v1/images/edits" ], "supports_vision": true, - "supports_pdf_input": true + "supports_pdf_input": true, + "deprecation_date": "2026-12-01" }, "gpt-5": { "cache_read_input_token_cost": 1.25e-07, @@ -25730,6 +28924,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": false, "supports_minimal_reasoning_effort": true }, @@ -25774,6 +28969,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": false, "supports_minimal_reasoning_effort": true }, @@ -25819,6 +29015,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": false, "supports_minimal_reasoning_effort": true }, @@ -25864,6 +29061,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -25909,6 +29107,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -26082,16 +29281,19 @@ "cache_creation_input_token_cost": 5e-06, "cache_creation_input_token_cost_above_272k_tokens": 1e-05, "cache_creation_input_token_cost_above_272k_tokens_flex": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, "cache_creation_input_token_cost_flex": 2.5e-06, "cache_creation_input_token_cost_priority": 1e-05, "cache_read_input_token_cost": 4e-07, "cache_read_input_token_cost_above_272k_tokens": 8e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 4e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "cache_read_input_token_cost_priority": 8e-07, "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, "input_cost_per_token_batches": 2e-06, "input_cost_per_token_flex": 2e-06, "input_cost_per_token_priority": 8e-06, @@ -26103,6 +29305,7 @@ "output_cost_per_token": 2e-05, "output_cost_per_token_above_272k_tokens": 3e-05, "output_cost_per_token_above_272k_tokens_flex": 1.5e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, "output_cost_per_token_batches": 1e-05, "output_cost_per_token_flex": 1e-05, "output_cost_per_token_priority": 4e-05, @@ -26145,16 +29348,19 @@ "cache_creation_input_token_cost": 5e-06, "cache_creation_input_token_cost_above_272k_tokens": 1e-05, "cache_creation_input_token_cost_above_272k_tokens_flex": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, "cache_creation_input_token_cost_flex": 2.5e-06, "cache_creation_input_token_cost_priority": 1e-05, "cache_read_input_token_cost": 4e-07, "cache_read_input_token_cost_above_272k_tokens": 8e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 4e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "cache_read_input_token_cost_priority": 8e-07, "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, "input_cost_per_token_batches": 2e-06, "input_cost_per_token_flex": 2e-06, "input_cost_per_token_priority": 8e-06, @@ -26166,6 +29372,7 @@ "output_cost_per_token": 2e-05, "output_cost_per_token_above_272k_tokens": 3e-05, "output_cost_per_token_above_272k_tokens_flex": 1.5e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, "output_cost_per_token_batches": 1e-05, "output_cost_per_token_flex": 1e-05, "output_cost_per_token_priority": 4e-05, @@ -26209,16 +29416,19 @@ "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_272k_tokens": 5e-06, "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05, "cache_creation_input_token_cost_flex": 1.25e-06, "cache_creation_input_token_cost_priority": 5e-06, "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_272k_tokens": 4e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, "cache_read_input_token_cost_flex": 1e-07, "cache_read_input_token_cost_priority": 4e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "input_cost_per_token_above_272k_tokens_priority": 8e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, "input_cost_per_token_priority": 4e-06, @@ -26230,6 +29440,7 @@ "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_272k_tokens": 1.8e-05, "output_cost_per_token_above_272k_tokens_flex": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.6e-05, "output_cost_per_token_batches": 6e-06, "output_cost_per_token_flex": 6e-06, "output_cost_per_token_priority": 2.4e-05, @@ -26272,16 +29483,19 @@ "cache_creation_input_token_cost": 2.5e-07, "cache_creation_input_token_cost_above_272k_tokens": 5e-07, "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06, "cache_creation_input_token_cost_flex": 1.25e-07, "cache_creation_input_token_cost_priority": 5e-07, "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_above_272k_tokens": 4e-08, "cache_read_input_token_cost_above_272k_tokens_flex": 2e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, "cache_read_input_token_cost_flex": 1e-08, "cache_read_input_token_cost_priority": 4e-08, "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, "input_cost_per_token_above_272k_tokens_flex": 2e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, "input_cost_per_token_batches": 1e-07, "input_cost_per_token_flex": 1e-07, "input_cost_per_token_priority": 4e-07, @@ -26293,6 +29507,7 @@ "output_cost_per_token": 1.2e-06, "output_cost_per_token_above_272k_tokens": 1.8e-06, "output_cost_per_token_above_272k_tokens_flex": 9e-07, + "output_cost_per_token_above_272k_tokens_priority": 3.6e-06, "output_cost_per_token_batches": 6e-07, "output_cost_per_token_flex": 6e-07, "output_cost_per_token_priority": 2.4e-06, @@ -26366,7 +29581,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/gpt-5.6-cyber", + "source": "https://developers.openai.com/api/docs/models/gpt-5.6-cyber", "supports_computer_use": true, "supports_parallel_function_calling": true }, @@ -26405,7 +29620,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/daybreak-red-latest", + "source": "https://developers.openai.com/api/docs/models/daybreak-red-latest", "supports_computer_use": true, "supports_parallel_function_calling": true }, @@ -26445,7 +29660,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "source": "https://platform.openai.com/docs/models/daybreak-blue-latest", + "source": "https://developers.openai.com/api/docs/models/daybreak-blue-latest", "supports_parallel_function_calling": true }, "chat-latest": { @@ -26457,7 +29672,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 3e-05, - "source": "https://platform.openai.com/docs/models/chat-latest", + "source": "https://developers.openai.com/api/docs/models/chat-latest", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -26532,7 +29747,10 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": false + "supports_minimal_reasoning_effort": false, + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07 }, "gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5e-07, @@ -26586,7 +29804,10 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": false + "supports_minimal_reasoning_effort": false, + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07 }, "gpt-5.5-pro": { "cache_read_input_token_cost": 3e-06, @@ -26733,8 +29954,12 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07 }, "gpt-5.4-2026-03-05": { "cache_read_input_token_cost": 2.5e-07, @@ -26781,8 +30006,12 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07 }, "gpt-5.4-pro": { "cache_read_input_token_cost": 3e-06, @@ -26831,7 +30060,9 @@ "supports_web_search": true, "supports_none_reasoning_effort": false, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 0.000135 }, "gpt-5.4-pro-2026-03-05": { "cache_read_input_token_cost": 3e-06, @@ -26880,7 +30111,9 @@ "supports_web_search": true, "supports_none_reasoning_effort": false, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 0.000135 }, "gpt-5.4-mini": { "cache_read_input_token_cost": 7.5e-08, @@ -26930,6 +30163,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false }, @@ -26981,6 +30215,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false }, @@ -27029,6 +30264,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false }, @@ -27077,6 +30313,7 @@ "supports_vision": true, "supports_web_search": true, "supports_none_reasoning_effort": true, + "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": false }, @@ -27812,17 +31049,18 @@ }, "gpt-realtime-2": { "cache_creation_input_audio_token_cost": 4e-07, + "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image": 5e-06, "input_cost_per_token": 4e-06, "litellm_provider": "openai", - "max_input_tokens": 32000, - "max_output_tokens": 4096, - "max_tokens": 4096, + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "max_tokens": 32000, "mode": "realtime", "output_cost_per_audio_token": 6.4e-05, - "output_cost_per_token": 1.6e-05, + "output_cost_per_token": 2.4e-05, "supported_endpoints": [ "/v1/realtime" ], @@ -27886,8 +31124,8 @@ "input_cost_per_token": 6e-07, "litellm_provider": "openai", "max_input_tokens": 128000, - "max_output_tokens": 4096, - "max_tokens": 4096, + "max_output_tokens": 32000, + "max_tokens": 32000, "mode": "realtime", "output_cost_per_audio_token": 2e-05, "output_cost_per_token": 2.4e-06, @@ -27919,7 +31157,7 @@ "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", - "max_input_tokens": 128000, + "max_input_tokens": 32000, "max_output_tokens": 4096, "max_tokens": 4096, "mode": "realtime", @@ -29746,7 +32984,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -29787,7 +33025,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -29828,7 +33066,7 @@ "search_context_size_low": 0.0025, "search_context_size_medium": 0.0025 }, - "source": "https://dev.meta.ai/docs/getting-started/pricing-rate-limits", + "source": "https://ai.developer.meta.com/docs/pricing-rate-limits", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses", @@ -29861,7 +33099,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text" ], @@ -29877,7 +33115,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text" ], @@ -29893,7 +33131,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text", "image" @@ -29910,7 +33148,7 @@ "max_output_tokens": 4028, "max_tokens": 4028, "mode": "chat", - "source": "https://llama.developer.meta.com/docs/models", + "source": "https://ai.developer.meta.com/docs/models", "supported_modalities": [ "text", "image" @@ -30245,6 +33483,7 @@ "supports_tool_choice": true }, "mistral/codestral-2508": { + "cache_read_input_token_cost": 3e-08, "input_cost_per_token": 3e-07, "litellm_provider": "mistral", "max_input_tokens": 128000, @@ -30259,6 +33498,7 @@ "supports_tool_choice": true }, "mistral/codestral-latest": { + "cache_read_input_token_cost": 3e-08, "input_cost_per_token": 3e-07, "litellm_provider": "mistral", "max_input_tokens": 128000, @@ -30266,11 +33506,11 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 9e-07, - "supports_assistant_prefill": true, - "supports_response_schema": true, - "supports_tool_choice": true, "source": "https://docs.mistral.ai/models/model-cards/codestral-25-08", - "supports_function_calling": true + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true }, "mistral/codestral-mamba-latest": { "input_cost_per_token": 2.5e-07, @@ -30401,6 +33641,152 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "mistral/ministral-14b-2512": { + "input_cost_per_token": 2e-07, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2e-07, + "source": "https://docs.mistral.ai/models/ministral-3-14b-25-12", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/ministral-14b-latest": { + "input_cost_per_token": 2e-07, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2e-07, + "source": "https://docs.mistral.ai/models/ministral-3-14b-25-12", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/ministral-3b-2512": { + "input_cost_per_token": 1e-07, + "litellm_provider": "mistral", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1e-07, + "source": "https://docs.mistral.ai/models/ministral-3-3b-25-12", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/ministral-3b-latest": { + "input_cost_per_token": 1e-07, + "litellm_provider": "mistral", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1e-07, + "source": "https://docs.mistral.ai/models/ministral-3-3b-25-12", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/mistral-embed-2312": { + "input_cost_per_token": 1e-07, + "litellm_provider": "mistral", + "max_input_tokens": 8192, + "max_tokens": 8192, + "mode": "embedding", + "source": "https://docs.mistral.ai/models/mistral-embed-23-12" + }, + "mistral/mistral-medium-3": { + "input_cost_per_token": 1.5e-06, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "mistral/voxtral-mini-transcribe-realtime-latest": { + "input_cost_per_second": 0.0001, + "litellm_provider": "mistral", + "mode": "audio_transcription", + "source": "https://docs.mistral.ai/models/model-cards/voxtral-mini-transcribe-realtime-26-02", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "mistral/voxtral-mini-tts-latest": { + "litellm_provider": "mistral", + "mode": "audio_speech", + "output_cost_per_character": 1.6e-05, + "source": "https://docs.mistral.ai/models/model-cards/voxtral-tts-26-03", + "supported_endpoints": [ + "/v1/audio/speech" + ], + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "audio" + ], + "supports_audio_output": true + }, + "mistral/voxtral-small-2507": { + "input_cost_per_second": 6.666666666666667e-05, + "input_cost_per_token": 1e-07, + "litellm_provider": "mistral", + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://docs.mistral.ai/models/voxtral-small-25-07", + "supports_audio_input": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/voxtral-small-latest": { + "input_cost_per_second": 6.666666666666667e-05, + "input_cost_per_token": 1e-07, + "litellm_provider": "mistral", + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://docs.mistral.ai/models/voxtral-small-25-07", + "supports_audio_input": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "mistral/zai-glm-5-2": { "cache_read_input_token_cost": 1.4e-07, "input_cost_per_token": 1.4e-06, @@ -30535,19 +33921,21 @@ "source": "https://mistral.ai/pricing#api-pricing" }, "mistral/magistral-medium-latest": { - "input_cost_per_token": 2e-06, + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", - "max_input_tokens": 40000, - "max_output_tokens": 40000, - "max_tokens": 40000, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 5e-06, - "source": "https://mistral.ai/news/magistral", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/magistral-small-2506": { "deprecation_date": "2025-11-30", @@ -30566,19 +33954,21 @@ "supports_tool_choice": true }, "mistral/magistral-small-latest": { - "input_cost_per_token": 5e-07, + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", - "max_input_tokens": 40000, - "max_output_tokens": 40000, - "max_tokens": 40000, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 1.5e-06, - "source": "https://mistral.ai/pricing#api-pricing", + "output_cost_per_token": 6e-07, + "source": "https://docs.mistral.ai/models/model-cards/mistral-small-4-0-26-03", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/magistral-small-1-2-2509": { "deprecation_date": "2026-07-31", @@ -30604,6 +33994,7 @@ "mode": "embedding" }, "mistral/codestral-embed": { + "cache_read_input_token_cost": 1.5e-08, "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", "max_input_tokens": 8192, @@ -30611,6 +34002,7 @@ "mode": "embedding" }, "mistral/codestral-embed-2505": { + "cache_read_input_token_cost": 1.5e-08, "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", "max_input_tokens": 8192, @@ -30660,6 +34052,7 @@ "supports_tool_choice": true }, "mistral/mistral-large-latest": { + "cache_read_input_token_cost": 5e-08, "input_cost_per_token": 5e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30675,6 +34068,7 @@ "supports_vision": true }, "mistral/mistral-large-3": { + "cache_read_input_token_cost": 5e-08, "input_cost_per_token": 5e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30690,6 +34084,7 @@ "supports_vision": true }, "mistral/mistral-large-2512": { + "cache_read_input_token_cost": 5e-08, "input_cost_per_token": 5e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30705,16 +34100,21 @@ "supports_vision": true }, "mistral/mistral-medium": { - "input_cost_per_token": 2.7e-06, + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "max_tokens": 8191, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 8.1e-06, + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/mistral-medium-2312": { "deprecation_date": "2025-06-16", @@ -30760,6 +34160,7 @@ "supports_vision": true }, "mistral/mistral-medium-2604": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30776,6 +34177,7 @@ "supports_vision": true }, "mistral/mistral-medium-latest": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30808,6 +34210,7 @@ "supports_vision": true }, "mistral/mistral-medium-3-5": { + "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30837,6 +34240,7 @@ "supports_tool_choice": true }, "mistral/mistral-small-latest": { + "cache_read_input_token_cost": 1.5e-08, "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30847,9 +34251,9 @@ "source": "https://docs.mistral.ai/models/model-cards/mistral-small-4-0-26-03", "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_reasoning": true, "supports_vision": true }, "mistral/mistral-small-3-2-2506": { @@ -30869,6 +34273,7 @@ "supports_vision": true }, "mistral/ministral-3-3b-2512": { + "cache_read_input_token_cost": 1e-08, "input_cost_per_token": 1e-07, "litellm_provider": "mistral", "max_input_tokens": 131072, @@ -30884,6 +34289,7 @@ "supports_vision": true }, "mistral/ministral-3-8b-2512": { + "cache_read_input_token_cost": 1.5e-08, "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30899,6 +34305,7 @@ "supports_vision": true }, "mistral/ministral-3-14b-2512": { + "cache_read_input_token_cost": 2e-08, "input_cost_per_token": 2e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30914,6 +34321,7 @@ "supports_vision": true }, "mistral/ministral-8b-2512": { + "cache_read_input_token_cost": 1.5e-08, "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -30929,6 +34337,7 @@ "supports_vision": true }, "mistral/ministral-8b-latest": { + "cache_read_input_token_cost": 1.5e-08, "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -31136,6 +34545,24 @@ "supports_tool_choice": true, "supports_web_search": true }, + "moonshot/kimi-k2.7-code": { + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "moonshot", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://platform.kimi.ai/docs/pricing/chat-k27-code", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true + }, "moonshot/kimi-k2-turbo-preview": { "cache_read_input_token_cost": 1.5e-07, "deprecation_date": "2026-05-25", @@ -31194,6 +34621,11 @@ "max_tokens": 1048576, "mode": "chat", "output_cost_per_token": 1.5e-05, + "reasoning_effort_levels": [ + "low", + "high", + "max" + ], "source": "https://platform.kimi.ai/docs/pricing/chat-k3", "supports_function_calling": true, "supports_reasoning": true, @@ -31670,7 +35102,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-R1-0528": { "max_tokens": 164000, @@ -31682,7 +35114,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": { "max_tokens": 128000, @@ -31693,7 +35125,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-V3": { "max_tokens": 128000, @@ -31704,7 +35136,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/deepseek-ai/DeepSeek-V3-0324": { "max_tokens": 128000, @@ -31715,7 +35147,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/google/gemma-3-27b-it": { "max_tokens": 128000, @@ -31727,7 +35159,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Llama-3.3-70B-Instruct": { "max_tokens": 128000, @@ -31738,7 +35170,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Llama-Guard-3-8B": { "max_tokens": 128000, @@ -31748,7 +35180,7 @@ "output_cost_per_token": 6e-08, "litellm_provider": "nebius", "mode": "chat", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-8B-Instruct": { "max_tokens": 128000, @@ -31759,7 +35191,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-70B-Instruct": { "max_tokens": 128000, @@ -31770,7 +35202,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/meta-llama/Meta-Llama-3.1-405B-Instruct": { "max_tokens": 128000, @@ -31781,7 +35213,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/mistralai/Mistral-Nemo-Instruct-2407": { "max_tokens": 128000, @@ -31792,7 +35224,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/NousResearch/Hermes-3-Llama-3.1-405B": { "max_tokens": 128000, @@ -31803,7 +35235,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/nvidia/Llama-3.1-Nemotron-Ultra-253B-v1": { "max_tokens": 128000, @@ -31814,7 +35246,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/nvidia/Llama-3.3-Nemotron-Super-49B-v1": { "max_tokens": 131072, @@ -31825,7 +35257,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-235B-A22B": { "max_tokens": 262144, @@ -31836,7 +35268,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-32B": { "max_tokens": 32768, @@ -31847,7 +35279,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-30B-A3B": { "max_tokens": 32768, @@ -31858,7 +35290,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-14B": { "max_tokens": 32768, @@ -31869,7 +35301,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen3-4B": { "max_tokens": 32768, @@ -31880,7 +35312,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/QwQ-32B": { "max_tokens": 32768, @@ -31892,7 +35324,7 @@ "mode": "chat", "supports_function_calling": true, "supports_reasoning": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-72B-Instruct": { "max_tokens": 128000, @@ -31903,7 +35335,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-32B-Instruct": { "max_tokens": 128000, @@ -31914,7 +35346,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-Coder-7B": { "max_tokens": 32768, @@ -31925,7 +35357,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_function_calling": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2.5-VL-72B-Instruct": { "max_tokens": 131072, @@ -31937,7 +35369,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2-VL-72B-Instruct": { "max_tokens": 131072, @@ -31949,7 +35381,7 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/Qwen/Qwen2-VL-7B-Instruct": { "max_tokens": 131072, @@ -31960,7 +35392,7 @@ "litellm_provider": "nebius", "mode": "chat", "supports_vision": true, - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/BAAI/bge-en-icl": { "max_tokens": 32768, @@ -31969,7 +35401,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/BAAI/bge-multilingual-gemma2": { "max_tokens": 8192, @@ -31978,7 +35410,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nebius/intfloat/e5-mistral-7b-instruct": { "max_tokens": 32768, @@ -31987,7 +35419,7 @@ "output_cost_per_token": 0.0, "litellm_provider": "nebius", "mode": "embedding", - "source": "https://nebius.com/prices-ai-studio" + "source": "https://nebius.com/prices" }, "nvidia.nemotron-nano-12b-v2": { "input_cost_per_token": 2e-07, @@ -32728,7 +36160,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -32741,7 +36173,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -32754,7 +36186,7 @@ "max_tokens": 4000, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true @@ -32811,7 +36243,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": true, "supports_response_schema": false, "supports_native_streaming": true, @@ -32825,7 +36257,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 1.56e-06, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_function_calling": false, "supports_response_schema": false, "supports_native_streaming": true @@ -32836,7 +36268,7 @@ "max_input_tokens": 512, "mode": "embedding", "output_vector_size": 1024, - "source": "https://www.oracle.com/cloud/ai/generative-ai/pricing/", + "source": "https://www.oracle.com/artificial-intelligence/enterprise-ai/cost-estimator/", "supports_vision": true }, "oci/cohere.command-a-reasoning-08-2025": { @@ -33516,7 +36948,8 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "openrouter/anthropic/claude-opus-4.1": { "input_cost_per_image": 0.0048, @@ -33536,7 +36969,8 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "openrouter/anthropic/claude-sonnet-4": { "input_cost_per_image": 0.0048, @@ -33559,10 +36993,12 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "openrouter/anthropic/claude-sonnet-4.6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, "cache_read_input_token_cost": 3e-07, @@ -33584,7 +37020,8 @@ "supports_reasoning": true, "supports_max_reasoning_effort": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "openrouter/anthropic/claude-opus-4.5": { "cache_creation_input_token_cost": 6.25e-06, @@ -33603,10 +37040,12 @@ "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, - "supports_output_config": true + "supports_output_config": true, + "prompt_cache_min_tokens": 4096 }, "openrouter/anthropic/claude-opus-4.6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 6.25e-06, "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 5e-06, @@ -33623,7 +37062,8 @@ "supports_reasoning": true, "supports_max_reasoning_effort": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 4096 }, "openrouter/anthropic/claude-sonnet-4.5": { "input_cost_per_image": 0.0048, @@ -33646,7 +37086,8 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "openrouter/anthropic/claude-haiku-4.5": { "cache_creation_input_token_cost": 1.25e-06, @@ -33664,7 +37105,8 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 4096 }, "openrouter/anthropic/claude-opus-4.7": { "supports_adaptive_thinking": true, @@ -33687,7 +37129,8 @@ "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "prompt_cache_min_tokens": 2048 }, "openrouter/anthropic/claude-opus-5": { "prompt_cache_min_tokens": 512, @@ -33722,7 +37165,7 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 2e-07, - "source": "https://openrouter.ai/api/v1/models/bytedance/ui-tars-1.5-7b", + "source": "https://openrouter.ai/bytedance/ui-tars-1.5-7b", "supports_tool_choice": true }, "openrouter/deepseek/deepseek-chat": { @@ -33957,7 +37400,7 @@ "output_cost_per_reasoning_token": 3e-06, "output_cost_per_token": 3e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -33998,7 +37441,7 @@ "output_cost_per_reasoning_token": 1.5e-06, "output_cost_per_token": 1.5e-06, "rpm": 2000, - "source": "https://ai.google.dev/pricing/gemini-3", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -35083,7 +38526,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/deepseek-r1-distill-llama-70b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -35097,7 +38540,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/llama-3-1-8b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35110,7 +38553,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-1-70b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": false, "supports_tool_choice": false @@ -35123,7 +38566,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 6.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/meta-llama-3-3-70b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35136,7 +38579,7 @@ "max_tokens": 127000, "mode": "chat", "output_cost_per_token": 1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-7b-instruct-v0-3", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35149,7 +38592,7 @@ "max_tokens": 118000, "mode": "chat", "output_cost_per_token": 1.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-nemo-instruct-2407", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true @@ -35162,7 +38605,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.8e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mistral-small-3-2-24b-instruct-2506", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -35176,7 +38619,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 6.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mixtral-8x7b-instruct-v0-1", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -35189,7 +38632,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 8.7e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-coder-32b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -35202,7 +38645,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 9.1e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen2-5-vl-72b-instruct", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false, @@ -35216,7 +38659,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 2.3e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/qwen3-32b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -35230,7 +38673,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 4e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-120b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_reasoning": true, "supports_response_schema": true, @@ -35244,7 +38687,7 @@ "max_tokens": 131000, "mode": "chat", "output_cost_per_token": 1.5e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/gpt-oss-20b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_reasoning": true, "supports_response_schema": true, @@ -35258,7 +38701,7 @@ "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 2.9e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/llava-next-mistral-7b", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false, @@ -35272,7 +38715,7 @@ "max_tokens": 256000, "mode": "chat", "output_cost_per_token": 1.9e-07, - "source": "https://endpoints.ai.cloud.ovh.net/models/mamba-codestral-7b-v0-1", + "source": "https://www.ovhcloud.com/en/public-cloud/ai-endpoints/catalog/", "supports_function_calling": false, "supports_response_schema": true, "supports_tool_choice": false @@ -35338,12 +38781,22 @@ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" }, "parallel_ai/search": { - "input_cost_per_query": 0.004, + "input_cost_per_query": 0.005, + "litellm_provider": "parallel_ai", + "mode": "search" + }, + "parallel_ai/search-fast": { + "input_cost_per_query": 0.001, "litellm_provider": "parallel_ai", "mode": "search" }, "parallel_ai/search-pro": { - "input_cost_per_query": 0.009, + "input_cost_per_query": 0.005, + "litellm_provider": "parallel_ai", + "mode": "search" + }, + "parallel_ai/search-turbo": { + "input_cost_per_query": 0.001, "litellm_provider": "parallel_ai", "mode": "search" }, @@ -35681,6 +39134,7 @@ }, "perplexity/anthropic/claude-opus-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "litellm_provider": "perplexity", "mode": "responses", "supports_web_search": true, @@ -35790,6 +39244,14 @@ "litellm_provider": "perplexity", "mode": "responses", "output_cost_per_token": 1.5e-05, + "reasoning_effort_levels": [ + "minimal", + "low", + "medium", + "high", + "xhigh", + "max" + ], "source": "https://docs.perplexity.ai/docs/agent-api/models", "supports_web_search": true, "supports_reasoning": true, @@ -36153,7 +39615,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_response_schema": true, - "supports_prompt_caching": true + "supports_prompt_caching": true, + "prompt_cache_min_tokens": 4096 }, "replicate/ibm-granite/granite-3.3-8b-instruct": { "input_cost_per_token": 3e-08, @@ -36235,7 +39698,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_response_schema": true, - "supports_prompt_caching": true + "supports_prompt_caching": true, + "prompt_cache_min_tokens": 1024 }, "replicate/deepseek-ai/deepseek-v3": { "input_cost_per_token": 1.45e-06, @@ -36310,7 +39774,8 @@ "supports_system_messages": true, "supports_tool_choice": true, "supports_response_schema": true, - "supports_prompt_caching": true + "supports_prompt_caching": true, + "prompt_cache_min_tokens": 1024 }, "replicate/openai/gpt-4.1": { "input_cost_per_token": 2e-06, @@ -37396,7 +40861,7 @@ "source": "https://docs.mistral.ai/capabilities/code_generation/" }, "text-embedding-004": { - "deprecation_date": "2026-01-14", + "deprecation_date": "2027-04-01", "input_cost_per_character": 2.5e-08, "input_cost_per_token": 1e-07, "litellm_provider": "vertex_ai-embedding-models", @@ -37609,6 +41074,7 @@ "output_cost_per_token": 1e-07 }, "together_ai/Qwen/Qwen2.5-72B-Instruct-Turbo": { + "deprecation_date": "2026-02-06", "litellm_provider": "together_ai", "mode": "chat", "supports_function_calling": true, @@ -37625,6 +41091,7 @@ "supports_tool_choice": true }, "together_ai/Qwen/Qwen3-235B-A22B-Instruct-2507-tput": { + "deprecation_date": "2026-07-10", "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "max_input_tokens": 262000, @@ -37637,6 +41104,7 @@ "supports_tool_choice": true }, "together_ai/Qwen/Qwen3-235B-A22B-Thinking-2507": { + "deprecation_date": "2026-04-16", "input_cost_per_token": 6.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 256000, @@ -37649,6 +41117,7 @@ "supports_tool_choice": true }, "together_ai/Qwen/Qwen3-235B-A22B-fp8-tput": { + "deprecation_date": "2026-02-06", "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "max_input_tokens": 40000, @@ -37660,6 +41129,7 @@ "supports_tool_choice": false }, "together_ai/Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8": { + "deprecation_date": "2026-06-04", "input_cost_per_token": 2e-06, "litellm_provider": "together_ai", "max_input_tokens": 256000, @@ -37672,11 +41142,15 @@ "supports_tool_choice": true }, "together_ai/deepseek-ai/DeepSeek-R1": { + "deprecation_date": "2026-05-14", "input_cost_per_token": 3e-06, "litellm_provider": "together_ai", "max_input_tokens": 128000, "max_output_tokens": 20480, "max_tokens": 20480, + "metadata": { + "successor": "together_ai/deepseek-ai/DeepSeek-V4-Pro-0813" + }, "mode": "chat", "output_cost_per_token": 7e-06, "supports_function_calling": true, @@ -37685,6 +41159,7 @@ "supports_tool_choice": true }, "together_ai/deepseek-ai/DeepSeek-R1-0528-tput": { + "deprecation_date": "2026-02-03", "input_cost_per_token": 5.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 128000, @@ -37702,6 +41177,9 @@ "max_input_tokens": 65536, "max_output_tokens": 8192, "max_tokens": 8192, + "metadata": { + "successor": "together_ai/deepseek-ai/DeepSeek-V4-Pro-0813" + }, "mode": "chat", "output_cost_per_token": 1.25e-06, "supports_function_calling": true, @@ -37710,9 +41188,13 @@ "supports_tool_choice": true }, "together_ai/deepseek-ai/DeepSeek-V3.1": { + "deprecation_date": "2026-05-14", "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_tokens": 16384, + "metadata": { + "successor": "together_ai/deepseek-ai/DeepSeek-V4-Pro-0813" + }, "mode": "chat", "output_cost_per_token": 1.7e-06, "source": "https://www.together.ai/models/deepseek-v3-1", @@ -37724,6 +41206,7 @@ "max_output_tokens": 16384 }, "together_ai/meta-llama/Llama-3.2-3B-Instruct-Turbo": { + "deprecation_date": "2026-03-06", "litellm_provider": "together_ai", "mode": "chat", "supports_function_calling": true, @@ -37732,16 +41215,20 @@ "supports_tool_choice": true }, "together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo": { - "input_cost_per_token": 8.8e-07, + "input_cost_per_token": 1.04e-06, "litellm_provider": "together_ai", + "max_input_tokens": 131072, + "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 8.8e-07, + "output_cost_per_token": 1.04e-06, + "source": "https://docs.together.ai/docs/serverless-models", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true }, "together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free": { + "deprecation_date": "2025-11-13", "input_cost_per_token": 0, "litellm_provider": "together_ai", "mode": "chat", @@ -37752,6 +41239,7 @@ "supports_tool_choice": true }, "together_ai/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8": { + "deprecation_date": "2026-03-31", "input_cost_per_token": 2.7e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -37762,6 +41250,7 @@ "supports_tool_choice": true }, "together_ai/meta-llama/Llama-4-Scout-17B-16E-Instruct": { + "deprecation_date": "2026-02-06", "input_cost_per_token": 1.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -37772,6 +41261,7 @@ "supports_tool_choice": true }, "together_ai/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo": { + "deprecation_date": "2026-02-06", "input_cost_per_token": 3.5e-06, "litellm_provider": "together_ai", "mode": "chat", @@ -37782,6 +41272,7 @@ "supports_tool_choice": true }, "together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": { + "deprecation_date": "2026-02-25", "input_cost_per_token": 8.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -37792,6 +41283,7 @@ "supports_tool_choice": true }, "together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo": { + "deprecation_date": "2026-03-06", "input_cost_per_token": 1.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -37802,6 +41294,7 @@ "supports_tool_choice": true }, "together_ai/mistralai/Mistral-7B-Instruct-v0.1": { + "deprecation_date": "2025-11-13", "litellm_provider": "together_ai", "mode": "chat", "supports_function_calling": true, @@ -37810,6 +41303,7 @@ "supports_tool_choice": true }, "together_ai/mistralai/Mistral-Small-24B-Instruct-2501": { + "deprecation_date": "2026-04-02", "litellm_provider": "together_ai", "mode": "chat", "supports_function_calling": true, @@ -37817,6 +41311,7 @@ "supports_tool_choice": true }, "together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1": { + "deprecation_date": "2026-04-16", "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -37829,6 +41324,9 @@ "together_ai/moonshotai/Kimi-K2-Instruct": { "input_cost_per_token": 1e-06, "litellm_provider": "together_ai", + "metadata": { + "successor": "together_ai/moonshotai/Kimi-K3" + }, "mode": "chat", "output_cost_per_token": 3e-06, "source": "https://www.together.ai/models/kimi-k2-instruct", @@ -37841,7 +41339,6 @@ "input_cost_per_token": 1.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 131072, - "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 6e-07, @@ -37855,7 +41352,7 @@ "together_ai/openai/gpt-oss-20b": { "input_cost_per_token": 5e-08, "litellm_provider": "together_ai", - "max_input_tokens": 128000, + "max_input_tokens": 131072, "mode": "chat", "output_cost_per_token": 2e-07, "source": "https://www.together.ai/models/gpt-oss-20b", @@ -37872,6 +41369,7 @@ "supports_tool_choice": true }, "together_ai/zai-org/GLM-4.5-Air-FP8": { + "deprecation_date": "2026-04-02", "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "max_input_tokens": 128000, @@ -37887,8 +41385,10 @@ "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "max_input_tokens": 200000, - "max_output_tokens": 200000, "max_tokens": 200000, + "metadata": { + "successor": "together_ai/zai-org/GLM-5.2" + }, "mode": "chat", "output_cost_per_token": 2.2e-06, "source": "https://www.together.ai/models/glm-4-6", @@ -37898,11 +41398,14 @@ "supports_tool_choice": true }, "together_ai/zai-org/GLM-4.7": { + "deprecation_date": "2026-04-02", "input_cost_per_token": 4.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 200000, - "max_output_tokens": 200000, "max_tokens": 200000, + "metadata": { + "successor": "together_ai/zai-org/GLM-5.2" + }, "mode": "chat", "output_cost_per_token": 2e-06, "source": "https://www.together.ai/models/glm-4-7", @@ -37912,11 +41415,14 @@ "supports_tool_choice": true }, "together_ai/moonshotai/Kimi-K2.5": { + "deprecation_date": "2026-05-21", "input_cost_per_token": 5e-07, "litellm_provider": "together_ai", "max_input_tokens": 256000, - "max_output_tokens": 256000, "max_tokens": 256000, + "metadata": { + "successor": "together_ai/moonshotai/Kimi-K3" + }, "mode": "chat", "output_cost_per_token": 2.8e-06, "source": "https://www.together.ai/models/kimi-k2-5", @@ -37926,9 +41432,13 @@ "supports_reasoning": true }, "together_ai/moonshotai/Kimi-K2-Instruct-0905": { + "deprecation_date": "2026-03-06", "input_cost_per_token": 1e-06, "litellm_provider": "together_ai", "max_input_tokens": 262144, + "metadata": { + "successor": "together_ai/moonshotai/Kimi-K3" + }, "mode": "chat", "output_cost_per_token": 3e-06, "source": "https://www.together.ai/models/kimi-k2-0905", @@ -37937,9 +41447,13 @@ "supports_tool_choice": true }, 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"supports_output_config": true, + "prompt_cache_min_tokens": 4096 }, "vercel_ai_gateway/anthropic/claude-sonnet-4": { "cache_creation_input_token_cost": 3.75e-06, @@ -39092,7 +43020,8 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "vercel_ai_gateway/anthropic/claude-sonnet-4.5": { "cache_creation_input_token_cost": 3.75e-06, @@ -39110,7 +43039,8 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "vercel_ai_gateway/cohere/command-a": { "input_cost_per_token": 2.5e-06, @@ -40315,6 +44245,7 @@ "deprecation_date": "2027-02-05", "regional_endpoint_uplift_multiplier": 1.1, "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, @@ -40347,6 +44278,7 @@ "deprecation_date": "2027-02-05", "regional_endpoint_uplift_multiplier": 1.1, "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 6.25e-06, "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, @@ -40473,7 +44405,44 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, + "vertex_ai/claude-fable-5-1": { + "regional_endpoint_uplift_multiplier": 1.1, + "supports_mid_conversation_system": true, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 }, "vertex_ai/claude-fable-5@default": { "deprecation_date": "2027-06-08", @@ -40507,7 +44476,44 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_max_reasoning_effort": true + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 + }, + "vertex_ai/claude-fable-5-1@default": { + "regional_endpoint_uplift_multiplier": 1.1, + "supports_mid_conversation_system": true, + "cache_creation_input_token_cost": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 2e-05, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_forced_tool_use": false, + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "supports_max_reasoning_effort": true, + "prompt_cache_min_tokens": 512 }, "vertex_ai/claude-opus-5": { "deprecation_date": "2027-01-24", @@ -40712,6 +44718,7 @@ "vertex_ai/claude-sonnet-4-6": { "regional_endpoint_uplift_multiplier": 1.1, "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -41188,7 +45195,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "vertex_ai/gemini-3.1-flash-lite": { "deprecation_date": "2027-05-07", @@ -41246,12 +45254,13 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "vertex_ai/gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", "cache_read_input_token_cost": 3e-08, - "cache_read_input_token_cost_flex": 2e-08, + "cache_read_input_token_cost_flex": 1.5e-08, "cache_read_input_token_cost_priority": 5e-08, "input_cost_per_token": 3e-07, "input_cost_per_token_batches": 1.5e-07, @@ -41303,7 +45312,8 @@ "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, - "web_search_billing_unit": "per_query" + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 }, "vertex_ai/deep-research-pro-preview-12-2025": { "input_cost_per_image": 0.0011, @@ -41849,7 +45859,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -41865,7 +45875,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -41882,7 +45892,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -41898,7 +45908,7 @@ "max_tokens": 2000000, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -42018,7 +46028,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -42031,8 +46042,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -42047,7 +46060,8 @@ "max_tokens": 1024, "mode": "video_generation", "output_cost_per_second": 0.4, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second_4k": 0.6, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -42061,8 +46075,10 @@ "max_input_tokens": 1024, "max_tokens": 1024, "mode": "video_generation", - "output_cost_per_second": 0.15, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/veo", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.12, + "output_cost_per_second_4k": 0.3, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supported_modalities": [ "text" ], @@ -42070,6 +46086,22 @@ "video" ] }, + "vertex_ai/veo-3.1-lite-generate-001": { + "litellm_provider": "vertex_ai-video-models", + "max_input_tokens": 1024, + "max_tokens": 1024, + "mode": "video_generation", + "output_cost_per_second": 0.05, + "output_cost_per_second_1080p": 0.08, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing#veo", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "video" + ] + }, "voyage/rerank-2": { "input_cost_per_token": 5e-08, "litellm_provider": "voyage", @@ -42106,6 +46138,26 @@ "mode": "rerank", "output_cost_per_token": 0.0 }, + "voyage/rerank-3": { + "input_cost_per_token": 5e-08, + "litellm_provider": "voyage", + "max_input_tokens": 32000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "mode": "rerank", + "output_cost_per_token": 0.0, + "source": "https://docs.voyageai.com/docs/pricing" + }, + "voyage/rerank-3-lite": { + "input_cost_per_token": 2e-08, + "litellm_provider": "voyage", + "max_input_tokens": 32000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "mode": "rerank", + "output_cost_per_token": 0.0, + "source": "https://docs.voyageai.com/docs/pricing" + }, "voyage/voyage-2": { "input_cost_per_token": 1e-07, "litellm_provider": "voyage", @@ -42230,19 +46282,21 @@ "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 0.015, - "output_cost_per_token": 0.06, + "input_cost_per_token": 3e-08, + "output_cost_per_token": 1.7e-07, "litellm_provider": "wandb", - "mode": "chat" + "mode": "chat", + "source": "https://wandb.ai/site/pricing/tokens/" }, "wandb/openai/gpt-oss-20b": { "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 0.005, - "output_cost_per_token": 0.02, + "input_cost_per_token": 3e-08, + "output_cost_per_token": 1.3e-07, "litellm_provider": "wandb", - "mode": "chat" + "mode": "chat", + "source": "https://wandb.ai/site/pricing/tokens/" }, "wandb/zai-org/GLM-4.5": { "max_tokens": 131072, @@ -42266,10 +46320,11 @@ "max_tokens": 262144, "max_input_tokens": 262144, "max_output_tokens": 262144, - "input_cost_per_token": 0.1, - "output_cost_per_token": 0.15, + "input_cost_per_token": 1e-06, + "output_cost_per_token": 1.5e-06, "litellm_provider": "wandb", - "mode": "chat" + "mode": "chat", + "source": "https://wandb.ai/site/pricing/tokens/" }, "wandb/Qwen/Qwen3-235B-A22B-Thinking-2507": { "max_tokens": 262144, @@ -42321,19 +46376,21 @@ "max_tokens": 128000, "max_input_tokens": 128000, "max_output_tokens": 128000, - "input_cost_per_token": 0.022, - "output_cost_per_token": 0.022, + "input_cost_per_token": 2.2e-07, + "output_cost_per_token": 2.2e-07, "litellm_provider": "wandb", - "mode": "chat" + "mode": "chat", + "source": "https://wandb.ai/site/pricing/tokens/" }, "wandb/deepseek-ai/DeepSeek-V3.1": { "max_tokens": 128000, - "max_input_tokens": 128000, + "max_input_tokens": 161000, "max_output_tokens": 128000, - "input_cost_per_token": 0.055, - "output_cost_per_token": 0.165, + "input_cost_per_token": 5.5e-07, + "output_cost_per_token": 1.65e-06, "litellm_provider": "wandb", - "mode": "chat" + "mode": "chat", + "source": "https://wandb.ai/site/pricing/tokens/" }, "wandb/deepseek-ai/DeepSeek-R1-0528": { "max_tokens": 161000, @@ -42357,10 +46414,11 @@ "max_tokens": 128000, "max_input_tokens": 128000, "max_output_tokens": 128000, - "input_cost_per_token": 0.071, - "output_cost_per_token": 0.071, + "input_cost_per_token": 7.1e-07, + "output_cost_per_token": 7.1e-07, "litellm_provider": "wandb", - "mode": "chat" + "mode": "chat", + "source": "https://wandb.ai/site/pricing/tokens/" }, "wandb/meta-llama/Llama-4-Scout-17B-16E-Instruct": { "max_tokens": 64000, @@ -42744,369 +46802,339 @@ "output_cost_per_second": 0.0001, "supported_endpoints": [ "/v1/audio/transcriptions" - ] - }, - "xai/grok-2": { - "input_cost_per_token": 2e-06, - "litellm_provider": "xai", - "max_input_tokens": 131072, - "max_output_tokens": 131072, - "max_tokens": 131072, - "mode": "chat", - "output_cost_per_token": 1e-05, - 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"deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -43115,19 +47143,21 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-reasoning": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -43137,19 +47167,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-reasoning-latest": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -43159,19 +47190,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-non-reasoning": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-non-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -43180,19 +47212,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": "2026-05-15", + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07 }, "xai/grok-4-1-fast-non-reasoning-latest": { - "cache_read_input_token_cost": 5e-08, - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "output_cost_per_token": 5e-07, - "output_cost_per_token_above_128k_tokens": 1e-06, + "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models/grok-4-1-fast-non-reasoning", "supports_audio_input": true, "supports_function_calling": true, @@ -43201,7 +47234,10 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2026-05-15" + "deprecation_date": 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"cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "xai/grok-build-latest": { + "cache_read_input_token_cost": 3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_above_200k_tokens": 4e-06, "litellm_provider": "xai", @@ -43391,15 +47430,23 @@ "supports_vision": true, "supports_web_search": true }, - "xai/grok-beta": { - "input_cost_per_token": 5e-06, + "xai/grok-4.6": { + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, "litellm_provider": "xai", - "max_input_tokens": 131072, - "max_output_tokens": 131072, - "max_tokens": 131072, + "max_input_tokens": 500000, + "max_output_tokens": 500000, + "max_tokens": 500000, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 6e-06, + "output_cost_per_token_above_200k_tokens": 1.2e-05, + "source": "https://docs.x.ai/developers/models", "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true @@ -43467,20 +47514,6 @@ "supports_vision": true, "deprecation_date": "2026-05-15" }, - "xai/grok-vision-beta": { - "input_cost_per_image": 5e-06, - "input_cost_per_token": 5e-06, - "litellm_provider": "xai", - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "supports_web_search": true - }, "zai.glm-4.7": { "input_cost_per_token": 6e-07, "litellm_provider": "bedrock_converse", @@ -43539,6 +47572,37 @@ "supports_tool_choice": true, "source": "https://docs.z.ai/guides/overview/pricing" }, + "zai/glm-5.3": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "zai", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.z.ai/guides/overview/pricing", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "zai/glm-5.3-flash": { + "cache_creation_input_token_cost": 0, + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 1.5e-07, + "output_cost_per_token": 5e-07, + "litellm_provider": "zai", + "max_input_tokens": 1048576, + "max_output_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing", + "supports_vision": true + }, "zai/glm-5.1": { "cache_creation_input_token_cost": 0, "cache_read_input_token_cost": 2.6e-07, @@ -43744,10 +47808,11 @@ ] }, "azure/sora-2": { + "deprecation_date": "2026-10-15", "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2", "supported_modalities": [ "text" ], @@ -43759,7 +47824,7 @@ "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.3, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2-pro", "supported_modalities": [ "text" ], @@ -43771,7 +47836,7 @@ "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.5, - "source": "https://azure.microsoft.com/en-us/products/ai-services/video-generation", + "source": "https://ai.azure.com/catalog/models/sora-2-pro", "supported_modalities": [ "text" ], @@ -43795,10 +47860,10 @@ "comment": "5 credits per second @ $0.01 per credit = $0.05 per second" } }, - "runwayml/gen4_aleph": { + "runwayml/gen4.5": { "litellm_provider": "runwayml", "mode": "video_generation", - "output_cost_per_video_per_second": 0.15, + "output_cost_per_second": 0.12, "source": "https://docs.dev.runwayml.com/guides/pricing/", "supported_modalities": [ "text", @@ -43808,13 +47873,136 @@ "video" ], "metadata": { - "comment": "15 credits per second @ $0.01 per credit = $0.15 per second" + "comment": "12 credits per second @ $0.01 per credit = $0.12 per second" } }, - "runwayml/gen3a_turbo": { + "runwayml/aleph2": { "litellm_provider": "runwayml", "mode": "video_generation", - "output_cost_per_video_per_second": 0.05, + "output_cost_per_second": 0.28, + "source": "https://docs.dev.runwayml.com/guides/pricing/", + "supported_modalities": [ + "text", + "video" + ], + "supported_output_modalities": [ + "video" + ], + "metadata": { + "comment": "28 credits per second @ $0.01 per credit = $0.28 per second; 56 credit minimum per task not modeled" + } + }, + "runwayml/seedance2": { + "litellm_provider": "runwayml", + "mode": "video_generation", + "output_cost_per_second": 0.36, + "output_cost_per_second_1080p": 0.4, + "output_cost_per_second_4k": 1.5, + "source": "https://docs.dev.runwayml.com/guides/pricing/", + "supported_modalities": [ + "text", + "image", + "video" + ], + "supported_output_modalities": [ + "video" + ], + "metadata": { + "comment": "36 credits per second at 480p/720p, 40 at 1080p, 150 at 4K @ $0.01 per credit" + } + }, + "runwayml/seedance2_fast": { + "litellm_provider": "runwayml", + "mode": "video_generation", + "output_cost_per_second": 0.29, + "source": "https://docs.dev.runwayml.com/guides/pricing/", + "supported_modalities": [ + "text", + "image", + "video" + ], + "supported_output_modalities": [ + "video" + ], + "metadata": { + "comment": "29 credits per second at 480p/720p @ $0.01 per credit = $0.29 per second" + } + }, + "runwayml/seedance2_mini": { + "litellm_provider": "runwayml", + "mode": "video_generation", + "output_cost_per_second": 0.16, + "source": "https://docs.dev.runwayml.com/guides/pricing/", + "supported_modalities": [ + "text", + "image", + "video" + ], + "supported_output_modalities": [ + "video" + ], + "metadata": { + "comment": "16 credits per second @ $0.01 per credit = $0.16 per second; 64 credit minimum per task not modeled" + } + }, + "runwayml/seedance2_5": { + "litellm_provider": "runwayml", + "mode": "video_generation", + "output_cost_per_second": 0.3, + "output_cost_per_second_480p": 0.2, + "output_cost_per_second_1080p": 0.68, + "source": "https://docs.dev.runwayml.com/guides/pricing/", + "supported_modalities": [ + "text", + "image", + "video" + ], + "supported_output_modalities": [ + "video" + ], + "metadata": { + "comment": "Output: 20/30/68 credits per second at 480p/720p/1080p @ $0.01 per credit; input video billed additionally at 10/15/34 credits per input second and the 80 credit minimum per task are not modeled" + } + }, + "runwayml/hailuo3": { + "litellm_provider": "runwayml", + "mode": "video_generation", + "output_cost_per_second": 0.1, + "output_cost_per_second_1080p": 0.15, + "source": "https://docs.dev.runwayml.com/guides/pricing/", + "supported_modalities": [ + "text", + "image", + "video" + ], + "supported_output_modalities": [ + "video" + ], + "metadata": { + "comment": "10 credits per second at 768P, 15 at 2K (mapped to the 1080p tier) @ $0.01 per credit; 2 credits per reference image not modeled" + } + }, + "runwayml/gemini_omni_flash": { + "litellm_provider": "runwayml", + "mode": "video_generation", + "output_cost_per_second": 0.1, + "source": "https://docs.dev.runwayml.com/guides/pricing/", + "supported_modalities": [ + "text", + "image", + "video" + ], + "supported_output_modalities": [ + "video" + ], + "metadata": { + "comment": "10 credits per second @ $0.01 per credit = $0.10 per second" + } + }, + "runwayml/veo3.1": { + "litellm_provider": "runwayml", + "mode": "video_generation", + "output_cost_per_second": 0.4, "source": "https://docs.dev.runwayml.com/guides/pricing/", "supported_modalities": [ "text", @@ -43824,7 +48012,23 @@ "video" ], "metadata": { - "comment": "5 credits per second @ $0.01 per credit = $0.05 per second" + "comment": "40 credits per second with audio, 20 without @ $0.01 per credit; priced at the with-audio rate" + } + }, + "runwayml/veo3.1_fast": { + "litellm_provider": "runwayml", + "mode": "video_generation", + "output_cost_per_second": 0.15, + "source": "https://docs.dev.runwayml.com/guides/pricing/", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "video" + ], + "metadata": { + "comment": "15 credits per second with audio, 10 without @ $0.01 per credit; priced at the with-audio rate" } }, "runwayml/gen4_image": { @@ -46188,8 +50392,8 @@ "novita/xiaomimimo/mimo-v2-flash": { "litellm_provider": "novita", "mode": "chat", - "input_cost_per_token": 1e-07, - "output_cost_per_token": 3e-07, + "input_cost_per_token": 1.1e-07, + "output_cost_per_token": 3.3e-07, "max_input_tokens": 262144, "max_output_tokens": 32000, "max_tokens": 32000, @@ -46198,8 +50402,8 @@ "supports_tool_choice": true, "supports_system_messages": true, "supports_response_schema": true, - "cache_read_input_token_cost": 2e-08, - "input_cost_per_token_cache_hit": 2e-08, + "cache_read_input_token_cost": 2.4e-08, + "input_cost_per_token_cache_hit": 2.4e-08, "supports_reasoning": true }, "novita/zai-org/autoglm-phone-9b-multilingual": { @@ -46219,14 +50423,16 @@ "input_cost_per_token": 6e-07, "output_cost_per_token": 2.5e-06, "max_input_tokens": 262144, - "max_output_tokens": 262144, - "max_tokens": 262144, + "max_output_tokens": 100352, + "max_tokens": 100352, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_tool_choice": true, "supports_system_messages": true, "supports_response_schema": true, - "supports_reasoning": true + "supports_reasoning": true, + "cache_read_input_token_cost": 1.5e-07, + "supports_prompt_caching": true }, "novita/minimax/minimax-m2": { "litellm_provider": "novita", @@ -46242,7 +50448,8 @@ "supports_system_messages": true, "cache_read_input_token_cost": 3e-08, "input_cost_per_token_cache_hit": 3e-08, - "supports_reasoning": true + "supports_reasoning": true, + "supports_response_schema": true }, "novita/paddlepaddle/paddleocr-vl": { "litellm_provider": "novita", @@ -46280,7 +50487,9 @@ "max_tokens": 32768, "supports_vision": true, "supports_system_messages": true, - "supports_reasoning": true + "supports_reasoning": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "novita/zai-org/glm-4.6v": { "litellm_provider": "novita", @@ -46345,7 +50554,8 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true, "supports_system_messages": true, - "supports_response_schema": true + "supports_response_schema": true, + "supports_reasoning": true }, "novita/qwen/qwen3-next-80b-a3b-thinking": { "litellm_provider": "novita", @@ -46457,8 +50667,8 @@ "input_cost_per_token": 6e-07, "output_cost_per_token": 2.5e-06, "max_input_tokens": 262144, - "max_output_tokens": 262144, - "max_tokens": 262144, + "max_output_tokens": 100352, + "max_tokens": 100352, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_tool_choice": true, @@ -46468,8 +50678,8 @@ "novita/qwen/qwen3-coder-480b-a35b-instruct": { "litellm_provider": "novita", "mode": "chat", - "input_cost_per_token": 3e-07, - "output_cost_per_token": 1.3e-06, + "input_cost_per_token": 3.8e-07, + "output_cost_per_token": 1.55e-06, "max_input_tokens": 262144, "max_output_tokens": 65536, "max_tokens": 65536, @@ -46515,8 +50725,8 @@ "input_cost_per_token": 5.7e-07, "output_cost_per_token": 2.3e-06, "max_input_tokens": 131072, - "max_output_tokens": 131072, - "max_tokens": 131072, + "max_output_tokens": 100352, + "max_tokens": 100352, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_tool_choice": true, @@ -46529,8 +50739,8 @@ "input_cost_per_token": 2.7e-07, "output_cost_per_token": 1.12e-06, "max_input_tokens": 163840, - "max_output_tokens": 163840, - "max_tokens": 163840, + "max_output_tokens": 65536, + "max_tokens": 65536, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_tool_choice": true, @@ -46577,7 +50787,8 @@ "max_input_tokens": 16384, "max_output_tokens": 16384, "max_tokens": 16384, - "supports_system_messages": true + "supports_system_messages": true, + "supports_response_schema": true }, "novita/google/gemma-3-12b-it": { "litellm_provider": "novita", @@ -46656,13 +50867,14 @@ "mode": "chat", "input_cost_per_token": 1.35e-07, "output_cost_per_token": 4e-07, - "max_input_tokens": 131072, - "max_output_tokens": 120000, - "max_tokens": 120000, + "max_input_tokens": 12288, + "max_output_tokens": 12288, + "max_tokens": 12288, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_response_schema": true }, "novita/qwen/qwen-2.5-72b-instruct": { "litellm_provider": "novita", @@ -46702,7 +50914,8 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true, "supports_system_messages": true, - "supports_reasoning": true + "supports_reasoning": true, + "supports_response_schema": true }, "novita/deepseek/deepseek-r1-0528": { "litellm_provider": "novita", @@ -46742,7 +50955,8 @@ "max_input_tokens": 8192, "max_output_tokens": 8192, "max_tokens": 8192, - "supports_system_messages": true + "supports_system_messages": true, + "supports_response_schema": true }, "novita/microsoft/wizardlm-2-8x22b": { "litellm_provider": "novita", @@ -46752,7 +50966,8 @@ "max_input_tokens": 65535, "max_output_tokens": 8000, "max_tokens": 8000, - "supports_system_messages": true + "supports_system_messages": true, + "supports_response_schema": true }, "novita/deepseek/deepseek-r1-0528-qwen3-8b": { "litellm_provider": "novita", @@ -46799,7 +51014,8 @@ "max_output_tokens": 20000, "max_tokens": 20000, "supports_system_messages": true, - "supports_reasoning": true + "supports_reasoning": true, + "supports_response_schema": true }, "novita/meta-llama/llama-4-maverick-17b-128e-instruct-fp8": { "litellm_provider": "novita", @@ -46810,7 +51026,8 @@ "max_output_tokens": 8192, "max_tokens": 8192, "supports_vision": true, - "supports_system_messages": true + "supports_system_messages": true, + "supports_response_schema": true }, "novita/meta-llama/llama-4-scout-17b-16e-instruct": { "litellm_provider": "novita", @@ -46946,7 +51163,9 @@ "max_output_tokens": 20000, "max_tokens": 20000, "supports_system_messages": true, - "supports_reasoning": true + "supports_reasoning": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "novita/google/gemma-3-27b-it": { "litellm_provider": "novita", @@ -46984,7 +51203,8 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true, "supports_system_messages": true, - "supports_reasoning": true + "supports_reasoning": true, + "supports_response_schema": true }, "novita/Sao10K/L3-8B-Stheno-v3.2": { "litellm_provider": "novita", @@ -47052,7 +51272,9 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true, "supports_system_messages": true, - "supports_reasoning": true + "supports_reasoning": true, + "cache_read_input_token_cost": 2.5e-08, + "supports_prompt_caching": true }, "novita/qwen/qwen3-vl-30b-a3b-instruct": { "litellm_provider": "novita", @@ -47173,10 +51395,12 @@ "input_cost_per_token": 3e-08, "output_cost_per_token": 3e-08, "max_input_tokens": 128000, - "max_output_tokens": 20000, - "max_tokens": 20000, + "max_output_tokens": 8192, + "max_tokens": 8192, "supports_system_messages": true, - "supports_reasoning": true + "supports_reasoning": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "novita/qwen/qwen2.5-7b-instruct": { "litellm_provider": "novita", @@ -47184,8 +51408,8 @@ "input_cost_per_token": 7e-08, "output_cost_per_token": 7e-08, "max_input_tokens": 32000, - "max_output_tokens": 32000, - "max_tokens": 32000, + "max_output_tokens": 8192, + "max_tokens": 8192, "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_tool_choice": true, @@ -47822,7 +52046,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-realtime-whisper", + "source": "https://developers.openai.com/api/docs/models/gpt-realtime-whisper", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -47968,15 +52192,16 @@ } }, "gemini-2.5-flash-native-audio-latest": { - "input_cost_per_audio_token": 1e-06, - "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2.5e-06, - "source": "https://ai.google.dev/pricing", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -47993,15 +52218,16 @@ "gemini_native_audio": true }, "gemini-2.5-flash-native-audio-preview-09-2025": { - "input_cost_per_audio_token": 1e-06, - "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2.5e-06, - "source": "https://ai.google.dev/pricing", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -48018,15 +52244,16 @@ "gemini_native_audio": true }, "gemini-2.5-flash-native-audio-preview-12-2025": { - "input_cost_per_audio_token": 1e-06, - "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2.5e-06, - "source": "https://ai.google.dev/pricing", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -48076,15 +52303,16 @@ "gemini_audio_only_live": true }, "gemini/gemini-2.5-flash-native-audio-latest": { - "input_cost_per_audio_token": 1e-06, - "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2.5e-06, - "source": "https://ai.google.dev/pricing", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -48103,15 +52331,16 @@ "gemini_native_audio": true }, "gemini/gemini-2.5-flash-native-audio-preview-09-2025": { - "input_cost_per_audio_token": 1e-06, - "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2.5e-06, - "source": "https://ai.google.dev/pricing", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -48130,15 +52359,16 @@ "gemini_native_audio": true }, "gemini/gemini-2.5-flash-native-audio-preview-12-2025": { - "input_cost_per_audio_token": 1e-06, - "input_cost_per_token": 3e-07, + "input_cost_per_audio_token": 3e-06, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2.5e-06, - "source": "https://ai.google.dev/pricing", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_token": 2e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -48207,11 +52437,11 @@ "rpm": 10 }, "gemini-2.5-flash-preview-tts": { - "input_cost_per_token": 3e-07, + "input_cost_per_token": 5e-07, "litellm_provider": "gemini", "mode": "audio_speech", - "output_cost_per_token": 2.5e-06, - "source": "https://ai.google.dev/pricing", + "output_cost_per_token": 1e-05, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/audio/speech" ] @@ -48260,7 +52490,8 @@ "search_context_size_low": 0.035, "search_context_size_medium": 0.035, "search_context_size_high": 0.035 - } + }, + "google_maps_grounding_cost_per_query": 0.025 }, "gemini-flash-lite-latest": { "cache_read_input_token_cost": 1e-08, @@ -48306,7 +52537,8 @@ "search_context_size_low": 0.035, "search_context_size_medium": 0.035, "search_context_size_high": 0.035 - } + }, + "google_maps_grounding_cost_per_query": 0.025 }, "gemini-pro-latest": { "cache_read_input_token_cost": 1.25e-07, @@ -48351,7 +52583,8 @@ "search_context_size_low": 0.035, "search_context_size_medium": 0.035, "search_context_size_high": 0.035 - } + }, + "google_maps_grounding_cost_per_query": 0.025 }, "gemini/gemini-pro-latest": { "cache_read_input_token_cost": 1.25e-07, @@ -48396,7 +52629,8 @@ "search_context_size_low": 0.035, "search_context_size_medium": 0.035, "search_context_size_high": 0.035 - } + }, + "google_maps_grounding_cost_per_query": 0.025 }, "gemini-exp-1206": { "cache_read_input_token_cost": 3e-08, @@ -48481,6 +52715,7 @@ "vertex_ai/claude-sonnet-4-6@default": { "regional_endpoint_uplift_multiplier": 1.1, "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -48595,12 +52830,13 @@ "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, "litellm_provider": "bedrock_mantle", - "max_input_tokens": 1000000, + "max_input_tokens": 1050000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "use_openai_responses_path": true, "supported_endpoints": [ + "/v1/chat/completions", "/v1/responses" ], "supported_modalities": [ @@ -48627,7 +52863,36 @@ "output_cost_per_token": 1.32e-05, "output_cost_per_token_above_272k_tokens": 1.98e-05, "litellm_provider": "bedrock_mantle", - "max_input_tokens": 1000000, + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "bedrock_mantle/openai.gpt-5.6-cyber": { + "input_cost_per_token": 1.375e-05, + "cache_creation_input_token_cost": 1.71875e-05, + "cache_read_input_token_cost": 1.375e-06, + "output_cost_per_token": 8.25e-05, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", @@ -48659,12 +52924,13 @@ "output_cost_per_token": 1.32e-06, "output_cost_per_token_above_272k_tokens": 1.98e-06, "litellm_provider": "bedrock_mantle", - "max_input_tokens": 1000000, + "max_input_tokens": 1050000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "use_openai_responses_path": true, "supported_endpoints": [ + "/v1/chat/completions", "/v1/responses" ], "supported_modalities": [ @@ -48682,14 +52948,14 @@ "supports_vision": true }, "us.openai.gpt-5.6-sol": { - "input_cost_per_token": 5.5e-06, - "input_cost_per_token_above_272k_tokens": 1.1e-05, - "cache_creation_input_token_cost": 6.875e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, - "cache_read_input_token_cost": 5.5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "output_cost_per_token": 3.3e-05, - "output_cost_per_token_above_272k_tokens": 4.95e-05, + "input_cost_per_token": 4.4e-06, + "input_cost_per_token_above_272k_tokens": 8.8e-06, + "cache_creation_input_token_cost": 5.5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.1e-05, + "cache_read_input_token_cost": 4.4e-07, + "cache_read_input_token_cost_above_272k_tokens": 8.8e-07, + "output_cost_per_token": 2.2e-05, + "output_cost_per_token_above_272k_tokens": 3.3e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 1000000, "max_output_tokens": 128000, @@ -48704,17 +52970,18 @@ ], "supports_function_calling": true, "supports_tool_choice": true, + "supports_reasoning": true, "supports_vision": true }, "global.openai.gpt-5.6-sol": { - "input_cost_per_token": 5e-06, - "input_cost_per_token_above_272k_tokens": 1e-05, - "cache_creation_input_token_cost": 6.25e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.25e-05, - "cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "output_cost_per_token": 3e-05, - "output_cost_per_token_above_272k_tokens": 4.5e-05, + "input_cost_per_token": 4e-06, + "input_cost_per_token_above_272k_tokens": 8e-06, + "cache_creation_input_token_cost": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1e-05, + "cache_read_input_token_cost": 4e-07, + "cache_read_input_token_cost_above_272k_tokens": 8e-07, + "output_cost_per_token": 2e-05, + "output_cost_per_token_above_272k_tokens": 3e-05, "litellm_provider": "bedrock_converse", "max_input_tokens": 1000000, "max_output_tokens": 128000, @@ -48729,6 +52996,7 @@ ], "supports_function_calling": true, "supports_tool_choice": true, + "supports_reasoning": true, "supports_vision": true }, "us.openai.gpt-5.6-terra": { @@ -48754,6 +53022,7 @@ ], "supports_function_calling": true, "supports_tool_choice": true, + "supports_reasoning": true, "supports_vision": true }, "global.openai.gpt-5.6-terra": { @@ -48779,6 +53048,7 @@ ], "supports_function_calling": true, "supports_tool_choice": true, + "supports_reasoning": true, "supports_vision": true }, "us.openai.gpt-5.6-luna": { @@ -48804,6 +53074,7 @@ ], "supports_function_calling": true, "supports_tool_choice": true, + "supports_reasoning": true, "supports_vision": true }, "global.openai.gpt-5.6-luna": { @@ -48829,14 +53100,18 @@ ], "supports_function_calling": true, "supports_tool_choice": true, + "supports_reasoning": true, "supports_vision": true }, "bedrock_mantle/openai.gpt-5.5": { "input_cost_per_token": 5.5e-06, + "input_cost_per_token_above_272k_tokens": 1.1e-05, "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, "output_cost_per_token": 3.3e-05, + "output_cost_per_token_above_272k_tokens": 4.95e-05, "litellm_provider": "bedrock_mantle", - "max_input_tokens": 272000, + "max_input_tokens": 1050000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", @@ -48860,10 +53135,13 @@ }, "bedrock_mantle/openai.gpt-5.4": { "input_cost_per_token": 2.75e-06, + "input_cost_per_token_above_272k_tokens": 5.5e-06, "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "output_cost_per_token": 1.65e-05, + "output_cost_per_token_above_272k_tokens": 2.475e-05, "litellm_provider": "bedrock_mantle", - "max_input_tokens": 272000, + "max_input_tokens": 1050000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", @@ -48994,7 +53272,7 @@ "max_tokens": 500000, "mode": "chat", "supports_function_calling": true, - "supports_prompt_caching": true, + "supports_prompt_caching": false, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true @@ -49009,7 +53287,7 @@ "max_tokens": 500000, "mode": "chat", "supports_function_calling": true, - "supports_prompt_caching": true, + "supports_prompt_caching": false, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true @@ -49020,7 +53298,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -49058,7 +53336,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -49096,7 +53374,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -49134,7 +53412,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "source": "https://www.volcengine.com/docs/82379/1330310", + "source": "https://docs.volcengine.com/docs/82379/1330310", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": false, @@ -49261,10 +53539,12 @@ "supports_vision": true, "supports_prompt_caching": true, "supports_system_messages": true, - "supports_response_schema": true + "supports_response_schema": true, + "prompt_cache_min_tokens": 1024 }, "snowflake/claude-sonnet-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "max_tokens": 16384, "max_input_tokens": 200000, "max_output_tokens": 16384, @@ -49277,7 +53557,8 @@ "supports_vision": true, "supports_prompt_caching": true, "supports_system_messages": true, - "supports_response_schema": true + "supports_response_schema": true, + "prompt_cache_min_tokens": 1024 }, "snowflake/claude-4-sonnet": { "max_tokens": 16384, @@ -49292,7 +53573,8 @@ "supports_vision": true, "supports_prompt_caching": true, "supports_system_messages": true, - "supports_response_schema": true + "supports_response_schema": true, + "prompt_cache_min_tokens": 1024 }, "snowflake/claude-4-opus": { "max_tokens": 16384, @@ -49308,7 +53590,8 @@ "supports_prompt_caching": true, "supports_system_messages": true, "supports_reasoning": true, - "supports_response_schema": true + "supports_response_schema": true, + "prompt_cache_min_tokens": 1024 }, "snowflake/claude-haiku-4-5": { "max_tokens": 16384, @@ -49323,7 +53606,8 @@ "supports_vision": true, "supports_prompt_caching": true, "supports_system_messages": true, - "supports_response_schema": true + "supports_response_schema": true, + "prompt_cache_min_tokens": 4096 }, "snowflake/claude-3-7-sonnet": { "max_tokens": 16384, @@ -49634,6 +53918,32 @@ "supports_tool_choice": true, "supports_vision": false }, + "deepseek-v4-flash-vision-exp": { + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 1.4e-08, + "input_cost_per_token": 4.4e-07, + "input_cost_per_token_cache_hit": 1.4e-08, + "litellm_provider": "deepseek", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 1.32e-06, + "source": "https://api-docs.deepseek.com/quick_start/pricing", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, "deepseek-v4-pro": { "cache_creation_input_token_cost": 0.0, "cache_read_input_token_cost": 4.4e-08, @@ -49686,6 +53996,32 @@ "supports_tool_choice": true, "supports_vision": false }, + "deepseek/deepseek-v4-flash-vision-exp": { + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 1.4e-08, + "input_cost_per_token": 4.4e-07, + "input_cost_per_token_cache_hit": 1.4e-08, + "litellm_provider": "deepseek", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 1.32e-06, + "source": "https://api-docs.deepseek.com/quick_start/pricing", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, "deepseek/deepseek-v4-pro": { "cache_creation_input_token_cost": 0.0, "cache_read_input_token_cost": 4.4e-08, @@ -49764,6 +54100,26 @@ "supports_reasoning": true, "supports_vision": false }, + "tencent/minimax-m3": { + "cache_creation_input_token_cost": 0.0, + "cache_read_input_token_cost": 6e-08, + "input_cost_per_token": 3e-07, + "input_cost_per_token_cache_hit": 6e-08, + "litellm_provider": "tencent", + "max_input_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://www.tencentcloud.com/products/tokenhub", + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supports_adaptive_thinking": true, + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_vision": false + }, "cognition/swe-1.6": { "input_cost_per_token": 5e-07, "output_cost_per_token": 2.5e-06, @@ -49805,7 +54161,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3.6-35b-a3b": { "max_tokens": 131072, @@ -49818,7 +54174,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3-30b-a3b": { "max_tokens": 131072, @@ -49831,7 +54187,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/qwen3-coder-30b-a3b": { "max_tokens": 131072, @@ -49844,7 +54200,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": false, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/deepseek-v4-flash": { "max_tokens": 163840, @@ -49857,7 +54213,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": true, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "pinstripes/ps/minimax-m2.7": { "max_tokens": 1000192, @@ -49870,7 +54226,7 @@ "supports_function_calling": true, "supports_assistant_prefill": true, "supports_reasoning": false, - "source": "https://pinstripes.io/pricing" + "source": "https://pinstripes.io/" }, "darkbloom/gemma-4-26b": { "input_cost_per_token": 3e-08, @@ -49933,19 +54289,22 @@ "max_input_tokens": 1000000, "max_output_tokens": 1000000, "max_tokens": 1000000, - "mode": "chat", + "mode": "responses", "output_cost_per_token": 2.5e-06, "source": "https://docs.x.ai/docs/models", - "supports_function_calling": true, + "supports_function_calling": false, "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true, + "supports_tool_choice": false, "supports_vision": true, "supports_web_search": true, "input_cost_per_token_above_200k_tokens": 2.5e-06, "output_cost_per_token_above_200k_tokens": 5e-06, "cache_read_input_token_cost_above_200k_tokens": 4e-07, - "supports_response_schema": true + "supports_response_schema": true, + "supported_endpoints": [ + "/v1/responses" + ] }, "xai/grok-build-0.1": { "cache_read_input_token_cost": 2e-07, @@ -49971,7 +54330,7 @@ "input_cost_per_second": 7.5e-05, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-transcribe", + "source": "https://developers.openai.com/api/docs/models/gpt-transcribe", "supported_endpoints": [ "/v1/audio/transcriptions", "/v1/realtime/transcription_sessions" @@ -49989,7 +54348,7 @@ "input_cost_per_second": 0.0002833333333333333, "litellm_provider": "openai", "mode": "audio_transcription", - "source": "https://platform.openai.com/docs/models/gpt-live-transcribe", + "source": "https://developers.openai.com/api/docs/models/gpt-live-transcribe", "supported_endpoints": [ "/v1/realtime", "/v1/realtime/transcription_sessions" @@ -50010,7 +54369,7 @@ "max_output_tokens": 2000, "max_tokens": 2000, "mode": "realtime", - "source": "https://platform.openai.com/docs/models/gpt-realtime-translate", + "source": "https://developers.openai.com/api/docs/models/gpt-realtime-translate", "supported_modalities": [ "audio" ], @@ -50038,7 +54397,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, - "source": "https://docs.claude.com/en/docs/about-claude/models/overview", + "source": "https://platform.claude.com/docs/en/about-claude/models/overview", "supports_adaptive_thinking": true, "thinking_always_on": true, "supports_mid_conversation_system": true, @@ -50055,7 +54414,10 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true + "supports_native_structured_output": true, + "provider_specific_entry": { + "us": 1.1 + } }, "claude-mythos-preview": { "cache_creation_input_token_cost": 1.25e-05, @@ -50074,7 +54436,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, - "source": "https://docs.claude.com/en/docs/about-claude/models/overview", + "source": "https://platform.claude.com/docs/en/about-claude/models/overview", "supports_adaptive_thinking": true, "thinking_always_on": true, "supports_assistant_prefill": false, @@ -50090,7 +54452,10 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true + "supports_native_structured_output": true, + "provider_specific_entry": { + "us": 1.1 + } }, "gemini/gemini-robotics-er-2-streaming-preview": { "input_cost_per_audio_token": 2e-06, @@ -50124,6 +54489,7 @@ "web_search_billing_unit": "per_query" }, "mistral/mistral-small-2603": { + "cache_read_input_token_cost": 1.5e-08, "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", "max_input_tokens": 262144, @@ -50255,6 +54621,14 @@ "supports_adaptive_thinking": true } }, + { + "name": "claude-legacy-thinking", + "pattern": "claude-[a-z]+-4[-._]6(?!\\d)", + "description": "Claude at version 4.6 exactly, in any id shape that contains claude--4-6 (dotted and underscored minors included, dated releases such as claude-sonnet-4-6-20260219 too). The 4.6 family is adaptive-thinking yet still accepts legacy thinking.type=enabled with budget_tokens, so the caller's hard budget cap is forwarded verbatim instead of being rewritten to an uncapped output_config.effort. The lookahead keeps two-digit minors such as 4-60 from matching. 4.7+ and 5+ majors reject the legacy shape and stay on the adaptive translation.", + "model_info": { + "supports_legacy_thinking": true + } + }, { "name": "claude-always-on-thinking", "pattern": "claude-(?:fable|mythos)-", @@ -50295,6 +54669,84 @@ "supports_audio_output": true, "tpm": 250000 }, + "gemini/gemini-3.5-transcribe": { + "input_cost_per_audio_token": 2e-06, + "input_cost_per_token": 2e-06, + "litellm_provider": "gemini", + "mode": "audio_transcription", + "output_cost_per_token": 1.2e-05, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "tpm": 800000, + "rpm": 2000 + }, + "gemini/gemini-3.5-transcribe-live": { + "input_cost_per_audio_token": 3.5e-06, + "input_cost_per_token": 3.5e-06, + "litellm_provider": "gemini", + "mode": "audio_transcription", + "output_cost_per_token": 2.1e-05, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "tpm": 250000, + "rpm": 10 + }, + "vertex_ai/gemini-3.5-transcribe-preview": { + "input_cost_per_audio_token": 2.5e-06, + "input_cost_per_token": 2.5e-06, + "litellm_provider": "vertex_ai", + "mode": "audio_transcription", + "output_cost_per_token": 1.2e-05, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true + }, + "vertex_ai/gemini-3.5-transcribe-live-preview": { + "input_cost_per_audio_token": 3.5e-06, + "input_cost_per_token": 3.5e-06, + 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}, + "bedrock/us-gov-east-1/openai.gpt-oss-120b-1:0": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/anthropic.claude-sonnet-5": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 3e-06, + "cache_creation_input_token_cost_above_1hr": 4.8e-06, + "cache_read_input_token_cost": 2.4e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": false, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock/us-gov-east-1/anthropic.claude-opus-4-8": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 7.5e-06, + "cache_creation_input_token_cost_above_1hr": 1.2e-05, + "cache_read_input_token_cost": 6e-07, + "input_cost_per_token": 6e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": true, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.6-terra": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 2.64e-06, + "input_cost_per_token_above_272k_tokens": 5.28e-06, + "cache_creation_input_token_cost": 3.3e-06, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-06, + "cache_read_input_token_cost": 2.64e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-07, + "output_cost_per_token": 1.584e-05, + "output_cost_per_token_above_272k_tokens": 2.376e-05 + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.6-luna": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 2.64e-07, + "input_cost_per_token_above_272k_tokens": 5.28e-07, + "cache_creation_input_token_cost": 3.3e-07, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-07, + "cache_read_input_token_cost": 2.64e-08, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-08, + "output_cost_per_token": 1.584e-06, + "output_cost_per_token_above_272k_tokens": 2.376e-06 + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.4": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 3.3e-06, + "cache_read_input_token_cost": 3.3e-07, + "output_cost_per_token": 1.98e-05 + }, + "bedrock_mantle/us-gov-west-1/xai.grok-4.3": { + "use_openai_responses_path": true, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 131072, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/", + "input_cost_per_token": 1.5e-06, + "output_cost_per_token": 3e-06, + "cache_read_input_token_cost": 2.4e-07 + }, + "bedrock_mantle/us-gov-east-1/openai.gpt-5.4": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 3.3e-06, + "cache_read_input_token_cost": 3.3e-07, + "output_cost_per_token": 1.98e-05 + }, + "azure/us-gov/gpt-5.1": { + "cache_read_input_token_cost": 1.71875e-07, + "default_reasoning_effort": "none", + "input_cost_per_token": 1.71875e-06, + "litellm_provider": "azure", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.375e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_none_reasoning_effort": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "azure/us-gov/o3-mini": { + "cache_read_input_token_cost": 7.57e-07, + "input_cost_per_token": 1.513e-06, + "litellm_provider": "azure", + "max_input_tokens": 200000, + "max_output_tokens": 100000, + "max_tokens": 100000, + "mode": "chat", + "output_cost_per_token": 6.05e-06, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "azure/us-gov/text-embedding-3-large": { + "input_cost_per_token": 1.63e-07, + "litellm_provider": "azure", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0 + }, + "azure/us-gov/text-embedding-3-small": { + "input_cost_per_token": 2.5e-08, + "litellm_provider": "azure", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0 + }, + "cloudflare/@cf/openai/whisper": { + "input_cost_per_second": 7.5e-06, + "litellm_provider": "cloudflare", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://developers.cloudflare.com/workers-ai/models/whisper/", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "cloudflare/@cf/openai/whisper-large-v3-turbo": { + "input_cost_per_second": 8.5e-06, + "litellm_provider": "cloudflare", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://developers.cloudflare.com/workers-ai/models/whisper-large-v3-turbo/", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] } } diff --git a/litellm/models/base.py b/litellm/models/base.py index 7eedf10212e..8125bfd0205 100644 --- a/litellm/models/base.py +++ b/litellm/models/base.py @@ -33,6 +33,6 @@ class DomainModel(BaseModel): return cls(**record.dict()) return cls(**dict(record)) - def to_db_dict(self, exclude_unset: bool = False) -> dict[str, Any]: + def to_db_dict(self, exclude_unset: bool = False) -> dict[str, object]: """Convert domain model to a dictionary for database operations.""" return self.model_dump(exclude_none=True, exclude_unset=exclude_unset) diff --git a/litellm/models/model.py b/litellm/models/model.py index 209f26d4837..a0c840341ab 100644 --- a/litellm/models/model.py +++ b/litellm/models/model.py @@ -29,6 +29,8 @@ class LiteLLM_ProxyModelTable(LiteLLMPydanticObjectBase): @model_validator(mode="before") @classmethod def check_potential_json_str(cls, values): + if not isinstance(values, dict): + return values if isinstance(values.get("litellm_params"), str): try: values["litellm_params"] = json.loads(values["litellm_params"]) diff --git a/litellm/models/team.py b/litellm/models/team.py index 544e2cf5bbc..da526515e6e 100644 --- a/litellm/models/team.py +++ b/litellm/models/team.py @@ -64,8 +64,8 @@ class TeamBase(LiteLLMPydanticObjectBase): team_alias: str | None = None team_id: str | None = None organization_id: str | None = None - admins: list = [] - members: list = [] + admins: list[str] = [] + members: list[str] = [] members_with_roles: list[Member] = [] team_member_permissions: list[str] | None = None metadata: dict | None = None @@ -75,7 +75,7 @@ class TeamBase(LiteLLMPydanticObjectBase): soft_budget: float | None = None budget_duration: str | None = None budget_limits: list[BudgetLimitEntry] | None = None - models: list = [] + models: list[str] = [] blocked: bool = False router_settings: dict | None = None access_group_ids: list[str] | None = None diff --git a/litellm/models/user.py b/litellm/models/user.py index 259c3440d87..82f78c28078 100644 --- a/litellm/models/user.py +++ b/litellm/models/user.py @@ -7,7 +7,7 @@ Canonical definition for ``litellm_usertable``. Re-exported from from datetime import datetime -from pydantic import ConfigDict, Field, model_validator +from pydantic import BaseModel, ConfigDict, Field, model_validator from litellm.models.object_permission import LiteLLM_ObjectPermissionTable from litellm.models.organization_membership import ( @@ -67,3 +67,11 @@ class LiteLLM_UserTable(LiteLLMPydanticObjectBase): if not self.models: return True return model_name in self.models + + +class SCIMPlaceholder(BaseModel): + """A user row keyed by a value that names another account by SSO identity or email.""" + + placeholder_user_id: str + resolved_user_ids: tuple[str, ...] + team_ids: tuple[str, ...] diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index 8a2ee2a3af8..c4bd03fb1c3 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -2,17 +2,22 @@ This module is used to pass through requests to the LLM APIs. """ +from __future__ import annotations + import asyncio import contextvars -from collections.abc import AsyncGenerator, Coroutine, Generator +from collections.abc import AsyncGenerator, AsyncIterator, Awaitable, Coroutine, Generator, Iterator from functools import partial -from typing import TYPE_CHECKING, Any, Final, Optional, cast +from types import TracebackType +from typing import Any, Final, cast import httpx -from httpx._types import CookieTypes, QueryParamTypes, RequestFiles +from httpx._types import CookieTypes, QueryParamTypes, RequestContent, RequestFiles from litellm._logging import verbose_logger from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.passthrough.utils import CommonUtils @@ -21,9 +26,222 @@ from litellm.utils import client base_llm_http_handler = BaseLLMHTTPHandler() from .utils import BasePassthroughUtils -if TYPE_CHECKING: - from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj - from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig + +async def _as_async_generator(iterable: AsyncIterator[bytes]) -> AsyncGenerator[bytes, bytes]: + async for chunk in iterable: + yield chunk + + +def _as_generator(iterable: Iterator[bytes]) -> Generator[bytes, bytes, None]: + yield from iterable + + +class AsyncPassthroughStreamingResponse(AsyncGenerator[bytes, bytes]): + def __init__( + self, + response: Awaitable[httpx.Response], + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, + ) -> None: + self._initialized = False + self._status_code: int = 0 + self._headers = httpx.Headers() + self._response_coro = response + self._response: httpx.Response + self._iterator: AsyncGenerator[bytes, bytes] + self._litellm_logging_obj = litellm_logging_obj + self._provider_config = provider_config + self._raw_bytes: list[bytes] = [] # mutable-ok: instance buffer for streaming chunks + self._flush_scheduled = False + self._background_tasks: set[asyncio.Task] = set() # mutable-ok: instance set for background task tracking + self._hidden_params: dict[str, object] = {} # mutable-ok: router attaches response headers here in place + + @property + def status_code(self) -> int: + if not self._initialized: + raise RuntimeError("AsyncPassthroughStreamingResponse must be awaited before accessing status_code") + return self._status_code + + @status_code.setter + def status_code(self, value: int) -> None: + self._status_code = value + + @property + def headers(self) -> httpx.Headers: + if not self._initialized: + raise RuntimeError("AsyncPassthroughStreamingResponse must be awaited before accessing headers") + return self._headers + + @headers.setter + def headers(self, value: httpx.Headers) -> None: + self._headers = value + + def __await__(self) -> Iterator[Any]: + async def _init(): + if not self._initialized: + self._response = await self._response_coro + self.headers = self._response.headers + self.status_code = self._response.status_code + self._initialized = True + try: + self._response.raise_for_status() + self._iterator = _as_async_generator(self._response.aiter_bytes()) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + try: + await self._response.aread() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + try: + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + return self + + return _init().__await__() + + def _start_flush(self) -> None: + if self._flush_scheduled or not self._raw_bytes: + return + self._flush_scheduled = True + + try: + task: Final = asyncio.create_task( + self._litellm_logging_obj.async_flush_passthrough_collected_chunks( + raw_bytes=self._raw_bytes, + provider_config=self._provider_config, + ) + ) + + # Compliant: Save a strong reference to prevent GC + self._background_tasks.add(task) + + # Remove the task from the set when it finishes to avoid memory leaks + task.add_done_callback(self._background_tasks.discard) + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception( + "Failed to schedule passthrough spend-tracking flush; %d buffered chunks dropped: %s", + len(self._raw_bytes), + e, + ) + + def __aiter__(self) -> AsyncPassthroughStreamingResponse: + return self + + def aiter_bytes(self) -> AsyncPassthroughStreamingResponse: + return self + + async def __anext__(self) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + try: + chunk: Final = await anext(self._iterator) + self._raw_bytes.append(chunk) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + self._start_flush() + try: + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + else: + return chunk + + async def asend(self, value: bytes) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + return await self._iterator.asend(value) + + async def athrow( + self, + typ: BaseException | type[BaseException], + val: BaseException | object = None, + tb: TracebackType | None = None, + ) -> bytes: + if not self._initialized: + await self # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ + return await self._iterator.athrow(typ, val, tb) # pyright: ignore[reportCallIssue, reportArgumentType] # matches one of the athrow overloads + + async def aclose(self) -> None: + self._start_flush() + try: + if self._initialized: + await self._iterator.aclose() + await self._response.aclose() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + + +class PassthroughStreamingResponse(Generator[bytes, bytes, None]): + def __init__( + self, + response: httpx.Response, + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, + ) -> None: + self._response = response + self.headers = response.headers + self.status_code = response.status_code + self._litellm_logging_obj = litellm_logging_obj + self._provider_config = provider_config + self._iterator: Generator[bytes, bytes, None] = _as_generator(response.iter_bytes()) + self._raw_bytes: list[bytes] = [] # mutable-ok: instance buffer for streaming chunks + self._flush_scheduled = False + + def _start_flush(self) -> None: + if self._flush_scheduled or not self._raw_bytes: + return + self._flush_scheduled = True + + from litellm.utils import executor + + try: + executor.submit( + self._litellm_logging_obj.flush_passthrough_collected_chunks, + raw_bytes=self._raw_bytes, + provider_config=self._provider_config, + ) + except Exception as e: # noqa: BLE001 # Safe catch-all for verbose logging + verbose_logger.exception( + "Failed to schedule passthrough spend-tracking flush; %d buffered chunks dropped: %s", + len(self._raw_bytes), + e, + ) + + def __iter__(self) -> PassthroughStreamingResponse: + return self + + def __next__(self) -> bytes: + try: + chunk: Final = next(self._iterator) + self._raw_bytes.append(chunk) + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + self._start_flush() + try: + self._response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise + else: + return chunk + + def send(self, value: bytes) -> bytes: + return self._iterator.send(value) + + def throw( + self, + typ: BaseException | type[BaseException], + val: BaseException | object = None, + tb: TracebackType | None = None, + ) -> bytes: + return self._iterator.throw(typ, val, tb) # pyright: ignore[reportCallIssue, reportArgumentType] # matches one of the throw overloads + + def close(self) -> None: + self._start_flush() + try: + self._response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass @client @@ -37,15 +255,15 @@ async def allm_passthrough_route( api_key: str | None = None, request_query_params: dict | None = None, request_headers: dict | None = None, - content: Any | None = None, + content: RequestContent | None = None, data: dict | None = None, files: RequestFiles | None = None, - json: Any | None = None, + json: object | None = None, params: QueryParamTypes | None = None, cookies: CookieTypes | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, **kwargs, -) -> httpx.Response | AsyncGenerator[Any, Any]: +) -> httpx.Response | AsyncGenerator[bytes, bytes]: """ Async: Reranks a list of documents based on their relevance to the query """ @@ -64,7 +282,7 @@ async def allm_passthrough_route( from litellm.utils import ProviderConfigManager provider_config = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(custom_llm_provider), model=model, @@ -132,12 +350,12 @@ async def allm_passthrough_route( if resolved_custom_llm_provider: try: provider_config = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(resolved_custom_llm_provider), model=model, ) - except Exception: + except Exception: # noqa: BLE001 S110 # If we can't get provider config, pass None pass @@ -162,20 +380,20 @@ def llm_passthrough_route( api_key: str | None = None, request_query_params: dict | None = None, request_headers: dict | None = None, - content: Any | None = None, + content: RequestContent | None = None, data: dict | None = None, files: RequestFiles | None = None, - json: Any | None = None, + json: object | None = None, params: QueryParamTypes | None = None, cookies: CookieTypes | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, **kwargs, ) -> ( httpx.Response - | Coroutine[Any, Any, httpx.Response] - | Coroutine[Any, Any, httpx.Response | AsyncGenerator[Any, Any]] - | Generator[Any, Any, Any] - | AsyncGenerator[Any, Any] + | Coroutine[object, object, httpx.Response] + | Coroutine[object, object, httpx.Response | AsyncGenerator[bytes, bytes]] + | Generator[bytes, bytes, None] + | AsyncGenerator[bytes, bytes] ): """ Pass through requests to the LLM APIs. @@ -190,7 +408,9 @@ def llm_passthrough_route( _is_async: Final = bool(kwargs.get("allm_passthrough_route", False)) - litellm_logging_obj: Final = cast("LiteLLMLoggingObj", kwargs.get("litellm_logging_obj")) + litellm_logging_obj: Final = cast( + LiteLLMLoggingObj, kwargs.get("litellm_logging_obj") + ) # cast-ok: logging obj is constructed upstream; tests inject mocks model, custom_llm_provider, api_key, api_base = get_llm_provider( model=model, @@ -199,7 +419,7 @@ def llm_passthrough_route( api_key=api_key, ) - litellm_params_dict: Final = get_litellm_params(**kwargs) + litellm_params_dict: Final = get_litellm_params(api_key=api_key, api_base=api_base, **kwargs) if client is None: from litellm.llms.custom_httpx.http_handler import ( @@ -235,7 +455,7 @@ def llm_passthrough_route( ) provider_config: Final = cast( - Optional["BasePassthroughConfig"], kwargs.get("provider_config") + BasePassthroughConfig | None, kwargs.get("provider_config") ) or ProviderConfigManager.get_provider_passthrough_config( provider=LlmProviders(custom_llm_provider), model=model, @@ -276,10 +496,13 @@ def llm_passthrough_route( forward_headers=False, ) + _request_data: dict | None = ( + data if isinstance(data, dict) else (json if isinstance(json, dict) else None) + ) # rebind-ok: conditional headers, signed_json_body = provider_config.sign_request( headers=headers, litellm_params=litellm_params_dict, - request_data=data if data else json, + request_data=_request_data, api_base=str(updated_url), model=model, ) @@ -301,9 +524,12 @@ def llm_passthrough_route( ) ## IS STREAMING REQUEST + _streaming_request_data: dict = ( + data if isinstance(data, dict) else (json if isinstance(json, dict) else {}) + ) # rebind-ok: conditional is_streaming_request: Final = provider_config.is_streaming_request( endpoint=endpoint, - request_data=data or json or {}, + request_data=_streaming_request_data, ) # Update logging object with streaming status @@ -334,18 +560,26 @@ def llm_passthrough_route( else: # Sync path - client.client.send returns Response directly response: httpx.Response = client.client.send(request=request, stream=is_streaming_request) - response.raise_for_status() + try: + response.raise_for_status() + except Exception: # noqa: BLE001 # Safe catch-all for cleanup logic + try: + response.read() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + try: + response.close() + except Exception: # noqa: BLE001 S110 # Safe catch-all for cleanup logic + pass + raise - if ( - hasattr(response, "iter_bytes") and is_streaming_request - ): # yield the chunk, so we can store it in the logging object - return _sync_streaming(response, litellm_logging_obj, provider_config) + if hasattr(response, "iter_bytes") and is_streaming_request: + return PassthroughStreamingResponse(response, litellm_logging_obj, provider_config) else: - # For non-streaming responses, yield the entire response return response except Exception as e: - if provider_config is None: - raise e + # provider_config is guaranteed non-None here due to the earlier guard + assert provider_config is not None raise base_llm_http_handler._handle_error( e=e, provider_config=provider_config, @@ -356,9 +590,9 @@ async def _async_passthrough_request( client: HTTPHandler | AsyncHTTPHandler, request: httpx.Request, is_streaming_request: bool, - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -) -> httpx.Response | AsyncGenerator[Any, Any]: + litellm_logging_obj: LiteLLMLoggingObj, + provider_config: BasePassthroughConfig, +) -> httpx.Response | AsyncGenerator[bytes, bytes]: """ Handle async passthrough requests. Uses async client to send request and properly handles streaming. @@ -369,8 +603,7 @@ async def _async_passthrough_request( # Check if it's a coroutine and await it if asyncio.iscoroutine(response_result): if is_streaming_request: - # Pass the coroutine to _async_streaming which will await it - return _async_streaming( + return await AsyncPassthroughStreamingResponse( # pyright: ignore[reportGeneralTypeIssues] # structural type check misses __await__ response=response_result, litellm_logging_obj=litellm_logging_obj, provider_config=provider_config, @@ -383,84 +616,3 @@ async def _async_passthrough_request( else: # Fallback for sync-like behavior (shouldn't happen in async path) raise Exception("Expected coroutine from async client") - - -def _sync_streaming( - response: httpx.Response, - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -): - from litellm.utils import executor - - raw_bytes: Final[list[bytes]] = [] - flush_scheduled = False - try: - for chunk in response.iter_bytes(): - raw_bytes.append(chunk) - yield chunk - finally: - if not flush_scheduled and raw_bytes: - flush_scheduled = True - try: - executor.submit( - litellm_logging_obj.flush_passthrough_collected_chunks, - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - except Exception as e: - verbose_logger.exception( - "Failed to schedule passthrough spend-tracking flush " - "in _sync_streaming; %d buffered chunks dropped: %s", - len(raw_bytes), - e, - ) - - -async def _async_streaming( - response: Coroutine[Any, Any, httpx.Response], - litellm_logging_obj: "LiteLLMLoggingObj", - provider_config: "BasePassthroughConfig", -): - iter_response: Final = await response - - try: - iter_response.raise_for_status() - except Exception: - try: - await iter_response.aclose() - except Exception: - pass - raise - - raw_bytes: Final[list[bytes]] = [] - flush_scheduled = False - try: - async for chunk in iter_response.aiter_bytes(): - raw_bytes.append(chunk) - yield chunk - except Exception: - try: - await iter_response.aclose() - except Exception: - pass - raise - finally: - # GeneratorExit (raised on client disconnect) is not caught by - # `except Exception`; the finally block ensures partial usage - # still gets flushed for spend tracking. See LIT-2642. - if not flush_scheduled and raw_bytes: - flush_scheduled = True - try: - asyncio.create_task( - litellm_logging_obj.async_flush_passthrough_collected_chunks( - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - ) - except Exception as e: - verbose_logger.exception( - "Failed to schedule passthrough spend-tracking flush " - "in _async_streaming; %d buffered chunks dropped: %s", - len(raw_bytes), - e, - ) diff --git a/litellm/passthrough/utils.py b/litellm/passthrough/utils.py index df39b8fad48..7eb14fcc118 100644 --- a/litellm/passthrough/utils.py +++ b/litellm/passthrough/utils.py @@ -6,6 +6,7 @@ import httpx from litellm._logging import verbose_logger from litellm.constants import PASS_THROUGH_HEADER_PREFIX +from litellm.litellm_core_utils.aws_partition import contains_aws_arn # Headers that must not be overwritten via the x-pass- forwarding mechanism. # Includes standard credential/auth headers and protocol-level headers that @@ -126,7 +127,7 @@ class CommonUtils: import re # Early exit: if no ARN detected, return unchanged - if "arn:aws:" not in endpoint: + if not contains_aws_arn(endpoint): return endpoint # Handle all patterns in one go - more efficient and cleaner diff --git a/litellm/provider_endpoints_support_backup.json b/litellm/provider_endpoints_support_backup.json index 86c14fb4cd8..9d6b1e18f59 100644 --- a/litellm/provider_endpoints_support_backup.json +++ b/litellm/provider_endpoints_support_backup.json @@ -671,6 +671,42 @@ "interactions": true } }, + "qwencloud": { + "display_name": "QwenCloud (`qwencloud`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, + "qwen_ai_platform": { + "display_name": "Qwen AI Platform (`qwen_ai_platform`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, "databricks": { "display_name": "Databricks (`databricks`)", "url": "https://docs.litellm.ai/docs/providers/databricks", @@ -1180,7 +1216,8 @@ "files": true, "rerank": true, "a2a": true, - "interactions": true + "interactions": true, + "video_generations": true } }, "huggingface": { diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py index 7d85f3c4908..425f82794e6 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py +++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py @@ -1,6 +1,8 @@ import re -from collections.abc import Sequence +from collections.abc import Mapping, Sequence +from dataclasses import dataclass from datetime import datetime, timezone +from types import MappingProxyType from typing import TYPE_CHECKING, Final, cast from fastapi import HTTPException @@ -13,6 +15,7 @@ import litellm from litellm._logging import verbose_logger from litellm.proxy._experimental.mcp_server.oauth_utils import ( get_passthrough_resource_metadata_url, + get_passthrough_www_authenticate, get_request_base_url, well_known_root_suffix, ) @@ -43,6 +46,7 @@ from litellm.proxy._types import ( ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.auth.user_api_key_auth import ( + _get_bearer_token_or_received_api_key, # pyright: ignore[reportPrivateUsage] # shared x-litellm-api-key parser lives with user_api_key_auth _run_centralized_common_checks, user_api_key_auth, ) @@ -63,6 +67,9 @@ if TYPE_CHECKING: from litellm.proxy.utils import PrismaClient +_EMPTY_TOOLSET_GRANTS: Final[Mapping[str, Sequence[str]]] = MappingProxyType({}) + + def _as_list(values: Sequence[str] | None) -> list[str] | None: # mutable-ok: resolver returns a list """Widen a read-only allowlist back to the mutable list the resolver's own contract returns, preserving the ``None`` that means "no restriction".""" @@ -298,6 +305,16 @@ def _admission_failure_fallback( raise exc +@dataclass(frozen=True, slots=True) +class DcrBridgeTarget: + """The single DCR-bridge server a request targets, paired with the exact name the caller + used to reach it (alias or server_name, whichever they typed), which is the spelling an + ``invalid_token`` challenge must echo back.""" + + requested_name: str + server: MCPServer + + class MCPRequestHandler: """ Class to handle MCP request processing, including: @@ -416,7 +433,10 @@ class MCPRequestHandler: # An explicit x-litellm-api-key is always a LiteLLM credential, even # for a delegated server, so validate it: identity / spend / rate # limits resolve and any stored upstream token can be forwarded. - validated_user_api_key_auth = await user_api_key_auth(api_key=litellm_api_key, request=request) + validated_user_api_key_auth = await user_api_key_auth( + api_key=f"Bearer {_get_bearer_token_or_received_api_key(litellm_api_key)}", + request=request, + ) elif MCPRequestHandler._target_servers_delegate_auth_to_upstream( path=request_route, mcp_servers=mcp_servers, @@ -437,27 +457,33 @@ class MCPRequestHandler: path=request_route, mcp_servers=mcp_servers, client_ip=IPAddressUtils.get_mcp_client_ip(request), + ) or ( + MCPRequestHandler._single_dcr_bridge_delegate_target( + path=request_route, + mcp_servers=mcp_servers, + client_ip=IPAddressUtils.get_mcp_client_ip(request), + ) + is not None + and not oauth2_headers + and not mcp_server_auth_headers + and not mcp_auth_header ): validated_user_api_key_auth = UserAPIKeyAuth() elif ( - ( - bridge_delegate_target := MCPRequestHandler._single_dcr_bridge_delegate_target( - path=request_route, - mcp_servers=mcp_servers, - client_ip=IPAddressUtils.get_mcp_client_ip(request), - ) + bridge_delegate_target := MCPRequestHandler._single_dcr_bridge_delegate_target( + path=request_route, + mcp_servers=mcp_servers, + client_ip=IPAddressUtils.get_mcp_client_ip(request), ) - is not None - and oauth2_headers - and is_bridge_envelope_shaped(oauth2_headers["Authorization"]) - ): - # A single DCR-bridge oauth_delegate target carrying an envelope-shaped - # Authorization: open the envelope, admit under its recovered identity, and - # inject the inner upstream token for egress. A non-envelope bearer on the same - # server is NOT admitted here — it falls through to the oauth2 arm, which 401s. - validated_user_api_key_auth, mcp_server_auth_headers = await MCPRequestHandler._admit_dcr_bridge_delegate( - server=bridge_delegate_target, + ) is not None and oauth2_headers: + ( + validated_user_api_key_auth, + mcp_server_auth_headers, + ) = await MCPRequestHandler._admit_dcr_bridge_authorization( + server=bridge_delegate_target.server, + requested_name=bridge_delegate_target.requested_name, authorization_value=oauth2_headers["Authorization"], + litellm_api_key=litellm_api_key, mcp_server_auth_headers=mcp_server_auth_headers, request=request, route=request_route, @@ -723,10 +749,10 @@ class MCPRequestHandler: @staticmethod def _single_dcr_bridge_delegate_target( path: str, mcp_servers: list[str] | None, client_ip: str | None - ) -> MCPServer | None: + ) -> DcrBridgeTarget | None: """The one DCR-bridge ``oauth_delegate`` server this request targets, or ``None``. - Returns the server only when EXACTLY ONE target resolves and it is both + Returns the target only when EXACTLY ONE name resolves and its server is both ``is_oauth_delegate`` and ``is_dcr_bridge``. Fails closed (``None``) on a multi-target request, an unresolved target, or a non-matching server, so the envelope admission arm never fires for an aggregate scope or a server that did not @@ -740,17 +766,21 @@ class MCPRequestHandler: if len(target_names) != 1: return None server: Final = global_mcp_server_manager.get_mcp_server_by_name(target_names[0], client_ip=client_ip) - if server is None or not server.is_oauth_delegate or not server.is_dcr_bridge: + # Both flags are security-sensitive opt-ins. Require literal booleans so + # partially populated objects and truthy proxy values cannot enable bridge + # admission accidentally. + if server is None or server.is_oauth_delegate is not True or server.is_dcr_bridge is not True: return None # Egress resolves the injected per-server token only by alias / server_name; a server with # neither cannot receive the forwarded token, so fail closed rather than admit-and-drop. if not (server.server_name or server.alias): return None - return server + return DcrBridgeTarget(requested_name=target_names[0], server=server) @staticmethod async def _admit_dcr_bridge_delegate( server: MCPServer, + requested_name: str, authorization_value: str, mcp_server_auth_headers: dict[str, dict[str, str]] | None, request: Request, @@ -798,10 +828,62 @@ class MCPRequestHandler: new_headers: Final = {**(mcp_server_auth_headers or {}), **injected} return admitted, new_headers case BridgeEnvelopeInvalid() | NotBridgeEnvelope(): - raise HTTPException(status_code=401, detail="Invalid or expired credential") + raise MCPRequestHandler._dcr_bridge_invalid_token_challenge( + requested_name=requested_name, request=request + ) case _: assert_never(result) + @staticmethod + async def _admit_dcr_bridge_authorization( + server: MCPServer, + requested_name: str, + authorization_value: str, + litellm_api_key: str, + mcp_server_auth_headers: dict[str, dict[str, str]] | None, # mutable-ok: existing MCP sink shape + request: Request, + route: str, + ) -> tuple[UserAPIKeyAuth, dict[str, dict[str, str]] | None]: # mutable-ok: existing MCP sink shape + if is_bridge_envelope_shaped(authorization_value): + return await MCPRequestHandler._admit_dcr_bridge_delegate( + server=server, + requested_name=requested_name, + authorization_value=authorization_value, + mcp_server_auth_headers=mcp_server_auth_headers, + request=request, + route=route, + ) + try: + admitted: Final = await user_api_key_auth(api_key=litellm_api_key, request=request) + except (HTTPException, ProxyException) as exc: + if not _is_litellm_auth_admission_error(exc): + raise + raise MCPRequestHandler._dcr_bridge_invalid_token_challenge( + requested_name=requested_name, request=request + ) from exc + return admitted, mcp_server_auth_headers + + @staticmethod + def _dcr_bridge_invalid_token_challenge(requested_name: str, request: Request) -> HTTPException: + """The RFC 6750 ``invalid_token`` challenge for a failed bridge admission. + + Named by the exact spelling the caller requested, matching the per-server well-known + document and the other challenge emitters, so ``resource_metadata`` always points at the + resource the client actually asked for even when alias and server_name differ.""" + return HTTPException( + status_code=401, + detail="Invalid or expired credential", + headers=MappingProxyType( + { + "www-authenticate": get_passthrough_www_authenticate( + scope=request.scope, + server_name=requested_name, + invalid_token=True, + ) + } + ), + ) + @staticmethod async def _admit_gateway_session( authorization_value: str, @@ -821,8 +903,9 @@ class MCPRequestHandler: NotSessionBearer, SessionBearerAdmitted, SessionBearerInvalid, + SessionSigningConfigError, + active_session_signing_keys, resolve_session_bearer, - session_keys_from_master_key, ) from litellm.proxy.proxy_server import master_key @@ -831,7 +914,10 @@ class MCPRequestHandler: await MCPRequestHandler._run_pre_db_read_auth_checks(request=request, route=route) - keys: Final = session_keys_from_master_key(master_key) + keys: Final = active_session_signing_keys(master_key) + if isinstance(keys, SessionSigningConfigError): + verbose_logger.error("mcp gateway session admission rejected: %s", keys.detail) + raise HTTPException(status_code=500, detail="Server misconfigured: mcp_session_token_signing is invalid") result: Final = resolve_session_bearer(authorization_value, keys, datetime.now(timezone.utc)) match result: case SessionBearerAdmitted(): @@ -1418,7 +1504,11 @@ class MCPRequestHandler: team_set: Final = set(allowed_mcp_servers_for_team) grants_set: Final = set(key_access_group_grants) - has_lower_level_mcp_restrictions = bool(key_set or team_set or grants_set) + # A DECLARED toolset restricts even when it resolves to no servers: the org + # ceiling below may only cap it, never substitute the org's full server list. + has_lower_level_mcp_restrictions = bool(key_set or team_set or grants_set) or ( + await MCPRequestHandler._key_or_team_declares_toolsets(user_api_key_auth) + ) # 1. Key/team ceiling. An empty set means "this level does not restrict". if not team_set: @@ -1862,6 +1952,105 @@ class MCPRequestHandler: return team_obj.object_permission + @staticmethod + async def _toolset_tool_permissions( + object_permission: LiteLLM_ObjectPermissionTable | None, + ) -> Mapping[str, Sequence[str]]: + """The ``server_id -> tool names`` grants of this permission row's toolsets, empty when it + declares none. The shared resolver for the team, org, and internal-user levels, so a toolset + behaves identically wherever it is attached. + + RAISES ``UnloadableEntitlementError`` when the row DECLARES toolsets but resolution yields + nothing (deleted or unknown ids, a swallowed DB fault, or a toolset with no tools): that is a + KNOWN restriction with unknown contents, and every caller already turns this error into deny + rather than letting the level read as unrestricted.""" + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + + if object_permission is None or not object_permission.mcp_toolsets: + return _EMPTY_TOOLSET_GRANTS + resolved: Final = await global_mcp_server_manager.resolve_toolset_tool_permissions( + toolset_ids=object_permission.mcp_toolsets + ) + if not resolved: + raise UnloadableEntitlementError( + f"declared mcp_toolsets {object_permission.mcp_toolsets!r} resolved to no grants" + ) + return resolved + + @staticmethod + async def _toolset_tools_for_server( + object_permission: LiteLLM_ObjectPermissionTable | None, + server_id: str, + ) -> Sequence[str] | None: + """Tool names this row's toolsets grant on ``server_id``, ``None`` when its toolsets place + no restriction on that server (it declares no toolsets, or none of them name it).""" + return (await MCPRequestHandler._toolset_tool_permissions(object_permission)).get(server_id) + + @staticmethod + def _union_tool_grants( + direct: Sequence[str] | None, + via_toolsets: Sequence[str] | None, + ) -> Sequence[str] | None: + """Union of one level's direct tool grants and its toolset-granted tools on one server, + ``None`` when neither source restricts (allow-all from this level).""" + if direct is None and via_toolsets is None: + return None + return tuple({*(direct or ()), *(via_toolsets or ())}) + + @staticmethod + async def _key_object_permission_hydrated( + user_api_key_auth: UserAPIKeyAuth, + ) -> LiteLLM_ObjectPermissionTable | None: + """The key's object_permission, loading it by ``object_permission_id`` when the main auth + flow cached the key with the relation unhydrated (its loader swallows a failed read and + caches the partial object).""" + loaded: Final = MCPRequestHandler._get_key_object_permission(user_api_key_auth) + if loaded is not None or not user_api_key_auth.object_permission_id: + return loaded + from litellm.proxy.auth.auth_checks import get_object_permission + from litellm.proxy.proxy_server import ( + prisma_client, + proxy_logging_obj, + user_api_key_cache, + ) + + if prisma_client is None: + return None + return await get_object_permission( + object_permission_id=user_api_key_auth.object_permission_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=user_api_key_auth.parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ) + + @staticmethod + async def _key_or_team_declares_toolsets(user_api_key_auth: UserAPIKeyAuth | None) -> bool: + """Whether the key or its team GRANTS any toolset, resolvable or not. A declared toolset is + a lower-level restriction even when it resolves to no servers (deleted or unknown ids), so the + org ceiling may only cap it; reading an empty resolution as "no restriction" would substitute + the org's entire server list for the narrowest grant an operator can write. + + Falls back to the DB when the auth object carries ``object_permission_id`` unhydrated (the + main auth flow swallows a failed load and caches the partial object). An INDETERMINATE fault + answers False — no gate, org substitution as before the fault — mirroring how the org ceiling + keeps key auth open on a fault it cannot classify.""" + if user_api_key_auth is None: + return False + try: + key_obj_perm: Final = await MCPRequestHandler._key_object_permission_hydrated(user_api_key_auth) + if key_obj_perm is not None and key_obj_perm.mcp_toolsets: + return True + if not user_api_key_auth.team_id: + return False + team_obj_perm: Final = await MCPRequestHandler._get_team_object_permission(user_api_key_auth) + return bool(team_obj_perm is not None and team_obj_perm.mcp_toolsets) + except Exception as e: # noqa: BLE001 # indeterminate fault: no gate, as before this level existed + verbose_logger.warning("Failed to check declared MCP toolsets, org ceiling unchanged: %s", e) + return False + @staticmethod async def get_allowed_tools_for_server( server_id: str, @@ -1925,12 +2114,17 @@ class MCPRequestHandler: if key_direct_tools is not None or key_toolset_tools is not None else None ) - team_tools: Final = ( + team_direct_tools: Final = ( global_mcp_server_manager.expand_tool_permissions(team_obj_perm.mcp_tool_permissions).get(server_id) if team_obj_perm else None ) + # Tools granted through the team's toolsets restrict this server exactly + # as the team's direct tool permissions do, mirroring the key path above + team_toolset_tools: Final = await MCPRequestHandler._toolset_tools_for_server(team_obj_perm, server_id) + team_tools: Final = MCPRequestHandler._union_tool_grants(team_direct_tools, team_toolset_tools) + # Apply same inheritance logic as get_allowed_mcp_servers if team_tools: if key_tools: @@ -2015,11 +2209,13 @@ class MCPRequestHandler: e, ) return allowed_tools - org_tools: Final = ( + org_direct_tools: Final = ( global_mcp_server_manager.expand_tool_permissions(org_obj_perm.mcp_tool_permissions).get(server_id) if org_obj_perm and org_obj_perm.mcp_tool_permissions else None ) + org_toolset_tools: Final = await MCPRequestHandler._toolset_tools_for_server(org_obj_perm, server_id) + org_tools: Final = MCPRequestHandler._union_tool_grants(org_direct_tools, org_toolset_tools) if org_tools is not None: allowed_tools = ( list(set(allowed_tools) & set(org_tools)) if allowed_tools is not None else list(org_tools) @@ -2261,7 +2457,8 @@ class MCPRequestHandler: async def _team_granted_servers(team_obj: LiteLLM_TeamTable, team_access_group_servers: list[str]) -> set[str]: """The raw MCP-server set a team grants (before any org ceiling): its object_permission (direct ``mcp_servers``, the ``all_proxy_servers`` sentinel → the full registry, legacy access groups, - tool-perm-referenced servers) unioned with its unified ``access_group_ids`` servers.""" + tool-perm-referenced servers, toolset-referenced servers) unioned with its unified + ``access_group_ids`` servers.""" from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( global_mcp_server_manager, ) @@ -2278,6 +2475,7 @@ class MCPRequestHandler: set(global_mcp_server_manager.expand_permission_list(object_permissions.mcp_servers or [])) | set(legacy_access_group_servers) | set(global_mcp_server_manager.expand_tool_permissions(object_permissions.mcp_tool_permissions).keys()) + | (await MCPRequestHandler._toolset_tool_permissions(object_permissions)).keys() | set(team_access_group_servers) ) @@ -2336,6 +2534,8 @@ class MCPRequestHandler: servers: Final = await MCPRequestHandler._team_granted_servers(team_obj, team_access_group_servers) return list(servers) except Exception as e: + if isinstance(e, UnloadableEntitlementError): + raise verbose_logger.warning("Failed to get allowed MCP servers for team: %s", e) return [] @@ -2467,7 +2667,13 @@ class MCPRequestHandler: global_mcp_server_manager.expand_tool_permissions(object_permissions.mcp_tool_permissions).keys() ) - all_servers: Final = direct_mcp_servers + access_group_servers + tool_perm_servers + # servers referenced by the org's toolset grants are part of the org ceiling, + # exactly as servers referenced by its inline tool permissions are + toolset_grants: Final = await MCPRequestHandler._toolset_tool_permissions(object_permissions) + + all_servers: Final = tuple( + {*direct_mcp_servers, *access_group_servers, *tool_perm_servers, *toolset_grants} + ) return list(set(all_servers)) except Exception as e: # None = ceiling UNRESOLVED, distinct from [] = org places no restriction. Collapsing them @@ -2661,8 +2867,8 @@ class MCPRequestHandler: ``[]`` means this human places no restriction (allow-all from this level); ``None`` means the ceiling is UNRESOLVED, which the caller denies on. Servers named only under - ``mcp_tool_permissions`` count as entitled, exactly as they do for a key or a team, so - granting one tool never requires naming its server twice. + ``mcp_tool_permissions`` or reached through ``mcp_toolsets`` count as entitled, exactly as + they do for a key or a team, so granting one tool never requires naming its server twice. """ from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( global_mcp_server_manager, @@ -2680,7 +2886,8 @@ class MCPRequestHandler: tool_perm_servers: Final = list( global_mcp_server_manager.expand_tool_permissions(object_permissions.mcp_tool_permissions).keys() ) - return list(set(direct_mcp_servers + access_group_servers + tool_perm_servers)) + toolset_grants: Final = await MCPRequestHandler._toolset_tool_permissions(object_permissions) + return tuple({*direct_mcp_servers, *access_group_servers, *tool_perm_servers, *toolset_grants}) except Exception as e: # noqa: BLE001 # any resolution fault is an unresolved ceiling, never "no ceiling" verbose_logger.warning("Failed to get allowed MCP servers for user: %s", e) return None @@ -2781,12 +2988,14 @@ class MCPRequestHandler: verbose_logger.warning("MCP user tool ceiling unresolvable, denying tools on %r: %s", server_id, e) return [] - if object_permissions is None or not object_permissions.mcp_tool_permissions: + if object_permissions is None: return allowed_tools - user_tools = global_mcp_server_manager.expand_tool_permissions(object_permissions.mcp_tool_permissions).get( - server_id - ) + user_direct_tools: Final = global_mcp_server_manager.expand_tool_permissions( + object_permissions.mcp_tool_permissions + ).get(server_id) + user_toolset_tools: Final = await MCPRequestHandler._toolset_tools_for_server(object_permissions, server_id) + user_tools: Final = MCPRequestHandler._union_tool_grants(user_direct_tools, user_toolset_tools) if user_tools is None: return allowed_tools if allowed_tools is None: diff --git a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py index b8c25236b0d..09a3703e904 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -306,15 +306,15 @@ _UpstreamGrantRejection = Literal["no_access_token", "expired_lifetime"] - ``expired_lifetime``: the response reports a parseable, non-positive ``expires_in``, i.e. an upstream token that is already dead, so sealing it would forward a bearer the edge cannot use An absent or unparseable ``expires_in`` is NOT a rejection; the lifetime is merely unknown and the -envelope caps it, the by-design behaviour for an upstream that omits the field.""" +envelope uses its fallback lifetime, the by-design behaviour for an upstream that omits the field.""" def _classify_upstream_lifetime(raw_expires_in: object) -> "int | Literal['unspecified', 'expired']": """Classify an upstream ``expires_in`` into a positive number of seconds, ``"unspecified"`` (absent - or unparseable, so the envelope caps it), or ``"expired"`` (a non-positive value the upstream reports + or unparseable, so the envelope uses its fallback), or ``"expired"`` (a non-positive value the upstream reports as already elapsed). Telling "we do not know the lifetime" apart from "the upstream says it is - already dead" is what stops an explicitly-expired token from silently receiving the envelope's 1h - cap. The expired decision is made on the parsed numeric value, not on ``int(...)`` of it, so a + already dead" is what stops an explicitly-expired token from silently receiving the envelope's + one-hour fallback. The expired decision is made on the parsed numeric value, not on ``int(...)`` of it, so a positive sub-second lifetime in ``(0, 1)`` is not truncated to ``0`` and misread as elapsed; the envelope works in whole seconds, so such a lifetime clamps up to its 1s floor. ``bool`` is excluded (an ``int`` subclass but never a real lifetime), and the conversions can raise on ``NaN`` / @@ -335,7 +335,7 @@ def _bridge_grant_from_token_response(token_response: object) -> "UpstreamTokenG """Validate an upstream OAuth token response into a typed grant, or say why it cannot back an envelope. Each field is isinstance-checked so nothing untyped from ``response.json()`` reaches the grant. ``expires_in`` is read three ways (see :func:`_classify_upstream_lifetime`): an unknown - lifetime leaves the grant ``expires_in`` ``None`` for the envelope to cap, a positive value is + lifetime leaves the grant ``expires_in`` ``None`` for the envelope fallback, a positive value is honoured, and an explicit already-elapsed value is a rejection rather than a silent fall-through to the cap.""" from litellm.proxy._experimental.mcp_server.outbound_credentials.envelope import ( # noqa: PLC0415 # inline import avoids a module-load circular import @@ -357,8 +357,8 @@ def _bridge_grant_from_token_response(token_response: object) -> "UpstreamTokenG token_type=token_type if isinstance(token_type, str) and token_type else "Bearer", # The upstream refresh_token is deliberately NOT sealed: the edge never consumes it (it forwards # only token_type + access_token), so it would be dead weight embedding a long-lived upstream - # credential in the client-held bearer, and it enlarges the envelope. Refresh support is a - # follow-up (a dedicated refresh-envelope); the client re-runs authorization_code at the cap. + # credential in the client-held bearer, and it enlarges the envelope. The dedicated refresh + # envelope carries that credential separately. refresh_token=None, scope=scope if isinstance(scope, str) and scope else None, expires_in=lifetime if isinstance(lifetime, int) else None, @@ -387,6 +387,7 @@ _BridgeMintError = Literal[ "not_configured", "no_upstream_token", "upstream_token_expired", + "upstream_lifetime_unrepresentable", "too_large", ] @@ -456,6 +457,12 @@ def _bridge_mint_error_response(error: _BridgeMintError) -> JSONResponse: "server_error", "the upstream token response reports an already-expired lifetime", ) + case "upstream_lifetime_unrepresentable": + status, code, desc = ( + 502, + "server_error", + "the upstream token response reports an unrepresentable lifetime", + ) case "too_large": status, code, desc = ( 502, @@ -619,6 +626,7 @@ def _finish_bridge_mint( build_bridge_token_response, ) from litellm.proxy._experimental.mcp_server.outbound_credentials.envelope import ( # noqa: PLC0415 # inline import avoids a module-load circular import + EnvelopeLifetimeUnrepresentable, SealedEnvelope, UpstreamTokenGrant, ) @@ -627,6 +635,8 @@ def _finish_bridge_mint( if not isinstance(grant, UpstreamTokenGrant): return _upstream_rejection_to_mint_error(grant) sealed: Final = build_bridge_token_response(ready.identity, grant, ready.keys, now) + if isinstance(sealed, EnvelopeLifetimeUnrepresentable): + return "upstream_lifetime_unrepresentable" if not isinstance(sealed, SealedEnvelope): return "too_large" # Report expires_in from the JWT's own second-truncated exp, rounding the elapsed portion up, so the diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py index 28638ed9c77..41d0b78b555 100644 --- a/litellm/proxy/_experimental/mcp_server/db.py +++ b/litellm/proxy/_experimental/mcp_server/db.py @@ -4,7 +4,7 @@ import hashlib import json from collections.abc import Awaitable, Callable, Iterable, Mapping, Sequence from datetime import datetime, timedelta, timezone -from typing import TYPE_CHECKING, Any, Final, Protocol, TypedDict, TypeVar, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, TypedDict, cast from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -13,7 +13,6 @@ from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.proxy._experimental.mcp_server.oauth_utils import build_upstream_oauth2_token_request from litellm.proxy._types import ( LiteLLM_MCPServerTable, - LiteLLM_ObjectPermissionTable, MCPApprovalStatus, MCPEnvVar, MCPEnvVarScope, @@ -30,6 +29,7 @@ from litellm.proxy.common_utils.encrypt_decrypt_utils import ( ) from litellm.proxy.utils import PrismaClient from litellm.repositories.object_permission_repository import ObjectPermissionRepository +from litellm.repositories.prisma_protocols import TableActions from litellm.repositories.table_repositories import ( MCPServerOAuthClientRepository, MCPServerRepository, @@ -48,34 +48,9 @@ if TYPE_CHECKING: from litellm.types.mcp_server.mcp_server_manager import MCPServer -_RowT = TypeVar("_RowT") - - -class _TableActions(Protocol[_RowT]): - async def find_unique( - self, where: Mapping[str, object], include: Mapping[str, object] | None = None - ) -> _RowT | None: ... - - async def find_many( - self, - take: int | None = None, - where: Mapping[str, object] | None = None, - order: Mapping[str, object] | None = None, - ) -> list[_RowT]: ... - - async def create(self, data: Mapping[str, object]) -> _RowT: ... - - async def upsert(self, where: Mapping[str, object], data: Mapping[str, object]) -> _RowT: ... - - async def update(self, where: Mapping[str, object], data: Mapping[str, object]) -> _RowT | None: ... - - async def delete(self, where: Mapping[str, object]) -> _RowT | None: ... - - async def delete_many(self, where: Mapping[str, object] | None = None) -> int: ... - class _UserEnvVarsTransactionClient(Protocol): - litellm_mcpuserenvvars: "_TableActions[prisma_db_models.LiteLLM_MCPUserEnvVars]" + litellm_mcpuserenvvars: "TableActions[prisma_db_models.LiteLLM_MCPUserEnvVars]" async def execute_raw(self, query: str, *args: object) -> int: ... @@ -473,15 +448,15 @@ def _credentials_blob_to_mutable_dict(blob: str | Mapping[str, object]) -> dict[ def _mcp_server_table_actions( prisma_client: PrismaClient, -) -> "_TableActions[prisma_db_models.LiteLLM_MCPServerTable]": - table: Final[_TableActions[prisma_db_models.LiteLLM_MCPServerTable]] = MCPServerRepository(prisma_client).table +) -> "TableActions[prisma_db_models.LiteLLM_MCPServerTable]": + table: Final[TableActions[prisma_db_models.LiteLLM_MCPServerTable]] = MCPServerRepository(prisma_client).table return table def _verification_token_table_actions( prisma_client: PrismaClient, -) -> "_TableActions[prisma_db_models.LiteLLM_VerificationToken]": - table: Final[_TableActions[prisma_db_models.LiteLLM_VerificationToken]] = VerificationTokenRepository( +) -> "TableActions[prisma_db_models.LiteLLM_VerificationToken]": + table: Final[TableActions[prisma_db_models.LiteLLM_VerificationToken]] = VerificationTokenRepository( prisma_client ).table return table @@ -489,15 +464,15 @@ def _verification_token_table_actions( def _team_table_actions( prisma_client: PrismaClient, -) -> "_TableActions[prisma_db_models.LiteLLM_TeamTable]": - table: Final[_TableActions[prisma_db_models.LiteLLM_TeamTable]] = TeamRepository(prisma_client).table +) -> "TableActions[prisma_db_models.LiteLLM_TeamTable]": + table: Final[TableActions[prisma_db_models.LiteLLM_TeamTable]] = TeamRepository(prisma_client).table return table def _oauth_client_table_actions( prisma_client: PrismaClient, -) -> "_TableActions[prisma_db_models.LiteLLM_MCPServerOAuthClient]": - table: Final[_TableActions[prisma_db_models.LiteLLM_MCPServerOAuthClient]] = MCPServerOAuthClientRepository( +) -> "TableActions[prisma_db_models.LiteLLM_MCPServerOAuthClient]": + table: Final[TableActions[prisma_db_models.LiteLLM_MCPServerOAuthClient]] = MCPServerOAuthClientRepository( prisma_client ).table return table @@ -511,7 +486,7 @@ def _db_transaction_manager(prisma_client: PrismaClient) -> _UserEnvVarsTransact async def _db_find_mcp_server_rows( prisma_client: PrismaClient, where: "prisma_db_types.LiteLLM_MCPServerTableWhereInput | None" = None, -) -> "list[prisma_db_models.LiteLLM_MCPServerTable]": +) -> "Sequence[prisma_db_models.LiteLLM_MCPServerTable]": return await _mcp_server_table_actions(prisma_client).find_many(where=where) @@ -526,17 +501,19 @@ async def _db_update_mcp_server_row( server_id: str, data: "prisma_db_types.LiteLLM_MCPServerTableUpdateInput", ) -> "prisma_db_models.LiteLLM_MCPServerTable": - row: Final[prisma_db_models.LiteLLM_MCPServerTable] = await MCPServerRepository(prisma_client).table.update( + row: Final[prisma_db_models.LiteLLM_MCPServerTable | None] = await _mcp_server_table_actions(prisma_client).update( where={"server_id": server_id}, data=data, ) + if row is None: + raise ValueError(f"MCP server not found, passed server_id={server_id}") return row def _user_credential_actions( prisma_client: PrismaClient, -) -> "_TableActions[prisma_db_models.LiteLLM_MCPUserCredentials]": - table: Final[_TableActions[prisma_db_models.LiteLLM_MCPUserCredentials]] = MCPUserCredentialsRepository( +) -> "TableActions[prisma_db_models.LiteLLM_MCPUserCredentials]": + table: Final[TableActions[prisma_db_models.LiteLLM_MCPUserCredentials]] = MCPUserCredentialsRepository( prisma_client ).table return table @@ -544,8 +521,8 @@ def _user_credential_actions( def _user_env_var_actions( prisma_client: PrismaClient, -) -> "_TableActions[prisma_db_models.LiteLLM_MCPUserEnvVars]": - table: Final[_TableActions[prisma_db_models.LiteLLM_MCPUserEnvVars]] = prisma_client.db.litellm_mcpuserenvvars +) -> "TableActions[prisma_db_models.LiteLLM_MCPUserEnvVars]": + table: Final[TableActions[prisma_db_models.LiteLLM_MCPUserEnvVars]] = prisma_client.db.litellm_mcpuserenvvars return table @@ -560,7 +537,7 @@ async def _db_find_user_credential_row( async def _db_find_user_credential_rows( prisma_client: PrismaClient, where: "prisma_db_types.LiteLLM_MCPUserCredentialsWhereInput | None" = None, -) -> "list[prisma_db_models.LiteLLM_MCPUserCredentials]": +) -> "Sequence[prisma_db_models.LiteLLM_MCPUserCredentials]": return await _user_credential_actions(prisma_client).find_many(where=where) @@ -583,7 +560,7 @@ async def _db_upsert_user_credential_row( async def _db_find_user_env_var_rows( prisma_client: PrismaClient, where: "prisma_db_types.LiteLLM_MCPUserEnvVarsWhereInput | None" = None, -) -> "list[prisma_db_models.LiteLLM_MCPUserEnvVars]": +) -> "Sequence[prisma_db_models.LiteLLM_MCPUserEnvVars]": return await _user_env_var_actions(prisma_client).find_many(where=where) @@ -623,23 +600,19 @@ async def get_all_mcp_servers( NULL approval_status predates the approval workflow, so those rows are kept explicitly rather than dropped by a bare inequality, which SQL evaluates as NULL and would silently hide them. """ - try: - where: Final[prisma_db_types.LiteLLM_MCPServerTableWhereInput] = ( - {"approval_status": approval_status} - if approval_status is not None - # mutable-ok: prisma where-inputs must be plain dicts, and both `NOT` and `not` drop - # NULL rows (measured), so the OR is the only NULL-preserving way to exclude drafts - else {"OR": [{"approval_status": None}, {"approval_status": {"not": MCPApprovalStatus.draft}}]} - ) - mcp_servers: Final = await _db_find_mcp_server_rows(prisma_client, where) + where: Final[prisma_db_types.LiteLLM_MCPServerTableWhereInput] = ( + {"approval_status": approval_status} + if approval_status is not None + # mutable-ok: prisma where-inputs must be plain dicts, and both `NOT` and `not` drop + # NULL rows (measured), so the OR is the only NULL-preserving way to exclude drafts + else {"OR": [{"approval_status": None}, {"approval_status": {"not": MCPApprovalStatus.draft}}]} + ) + mcp_servers: Final = await _db_find_mcp_server_rows(prisma_client, where) - tables: Final = [LiteLLM_MCPServerTable.model_validate(mcp_server.model_dump()) for mcp_server in mcp_servers] - for table in tables: - decrypt_global_env_var_values(table.env_vars) - return tables - except Exception as e: - verbose_proxy_logger.debug("litellm.proxy._experimental.mcp_server.db.py::get_all_mcp_servers - %s", e) - return [] + tables: Final = [LiteLLM_MCPServerTable.model_validate(mcp_server.model_dump()) for mcp_server in mcp_servers] + for table in tables: + decrypt_global_env_var_values(table.env_vars) + return tables async def get_mcp_server(prisma_client: PrismaClient, server_id: str) -> LiteLLM_MCPServerTable | None: @@ -658,7 +631,7 @@ async def get_mcp_servers(prisma_client: PrismaClient, server_ids: Iterable[str] """ Returns the matching mcp servers from the db with the server_ids """ - _mcp_servers: Final[list[prisma_db_models.LiteLLM_MCPServerTable]] = await _mcp_server_table_actions( + _mcp_servers: Final[Sequence[prisma_db_models.LiteLLM_MCPServerTable]] = await _mcp_server_table_actions( prisma_client ).find_many( where={ @@ -745,13 +718,13 @@ async def get_all_mcp_servers_for_user( async def get_objectpermissions_for_mcp_server( prisma_client: PrismaClient, mcp_server_id: str -) -> list[LiteLLM_ObjectPermissionTable]: +) -> "Sequence[prisma_db_models.LiteLLM_ObjectPermissionTable]": """ Get all the object permissions records and the associated team and verficiationtoken records that have access to the mcp server """ - object_permission_records: Final[list[LiteLLM_ObjectPermissionTable]] = await ObjectPermissionRepository( - prisma_client - ).table.find_many( + object_permission_records: Final[ + Sequence[prisma_db_models.LiteLLM_ObjectPermissionTable] + ] = await ObjectPermissionRepository(prisma_client).table.find_many( where={ "mcp_servers": {"has": mcp_server_id}, }, @@ -766,19 +739,19 @@ async def get_objectpermissions_for_mcp_server( async def get_virtualkeys_for_mcp_server( prisma_client: PrismaClient, server_id: str -) -> "list[prisma_db_models.LiteLLM_VerificationToken]": +) -> "Sequence[prisma_db_models.LiteLLM_VerificationToken]": """ Get all the virtual keys that have access to the mcp server """ - virtual_keys: Final[list[prisma_db_models.LiteLLM_VerificationToken] | None] = await VerificationTokenRepository( - prisma_client - ).table.find_many( + virtual_keys: Final[ + Sequence[prisma_db_models.LiteLLM_VerificationToken] | None + ] = await VerificationTokenRepository(prisma_client).table.find_many( where={ "mcp_servers": {"has": server_id}, }, ) - if virtual_keys is None: + if virtual_keys is None: # pyright: ignore[reportUnnecessaryComparison] # unreachable per seam types; kept as-is return [] return virtual_keys @@ -860,7 +833,7 @@ async def delete_mcp_server( invalidate_token_cache = global_mcp_server_manager.invalidate_user_oauth_token_cache for user_id in credential_user_ids: await invalidate_token_cache(user_id, server_id) - return deleted_server + return deleted_server # pyright: ignore[reportReturnType] # prisma row, not domain LiteLLM_MCPServerTable async def create_mcp_server( @@ -880,7 +853,7 @@ async def create_mcp_server( data_dict["updated_by"] = touched_by new_mcp_server: Final[LiteLLM_MCPServerTable] = await MCPServerRepository(prisma_client).table.create( - data=data_dict + data=data_dict, # pyright: ignore[reportAssignmentType] # prisma row, not domain LiteLLM_MCPServerTable ) _decrypt_env_vars_on_returned_row(new_mcp_server) @@ -982,7 +955,7 @@ async def update_mcp_server( data: UpdateMCPServerRequest, touched_by: str, fields_set: set[str] | None = None, -) -> LiteLLM_MCPServerTable: +) -> LiteLLM_MCPServerTable | None: """ Update a new mcp server record in the db """ @@ -1093,9 +1066,9 @@ async def update_mcp_server( data_dict["credentials"] = Json(None) - updated_mcp_server: Final[LiteLLM_MCPServerTable] = await MCPServerRepository(prisma_client).table.update( + updated_mcp_server: Final[LiteLLM_MCPServerTable | None] = await MCPServerRepository(prisma_client).table.update( where={"server_id": data.server_id}, - data=data_dict, + data=data_dict, # pyright: ignore[reportAssignmentType] # prisma row, not domain LiteLLM_MCPServerTable ) _decrypt_env_vars_on_returned_row(updated_mcp_server) @@ -1181,7 +1154,7 @@ async def rotate_mcp_server_credentials_master_key(prisma_client: PrismaClient, ) updated += 1 - oauth_clients: Final[list[prisma_db_models.LiteLLM_MCPServerOAuthClient]] = await _oauth_client_table_actions( + oauth_clients: Final[Sequence[prisma_db_models.LiteLLM_MCPServerOAuthClient]] = await _oauth_client_table_actions( prisma_client ).find_many() oauth_updated = 0 @@ -1623,7 +1596,7 @@ async def refresh_user_oauth_token( ) -> OAuthCredentialPayload | None: """Attempt to refresh a per-user OAuth2 token using its stored refresh_token. - POSTs to ``server.token_url`` with ``grant_type=refresh_token``. + POSTs to ``server.effective_token_url`` with ``grant_type=refresh_token``. On success: persists the new credential via ``store_user_oauth_credential`` and returns the updated payload dict. @@ -1632,7 +1605,7 @@ async def refresh_user_oauth_token( stale credential and triggering re-authentication. """ refresh_token: Final[str | None] = cred.get("refresh_token") - token_url: Final[str | None] = getattr(server, "token_url", None) + token_url: Final[str | None] = getattr(server, "effective_token_url", None) or getattr(server, "token_url", None) server_id: Final[str] = getattr(server, "server_id", "") client_id: Final[str | None] = getattr(server, "client_id", None) client_secret: Final[str | None] = getattr(server, "client_secret", None) @@ -1914,7 +1887,7 @@ async def get_mcp_submissions( along with a summary count breakdown by approval_status. Mirrors get_guardrail_submissions() from guardrail_endpoints.py. """ - rows: Final[list[prisma_db_models.LiteLLM_MCPServerTable]] = await _mcp_server_table_actions( + rows: Final[Sequence[prisma_db_models.LiteLLM_MCPServerTable]] = await _mcp_server_table_actions( prisma_client ).find_many( where={"submitted_at": {"not": None}}, diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index aef4f5dc721..94bca9460dd 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -3,13 +3,13 @@ import html as _html import json import secrets import time -from collections.abc import Mapping +from collections.abc import Callable, Mapping from datetime import datetime, timezone from typing import TYPE_CHECKING, Any, Final, Literal, Optional from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse import httpx -from fastapi import APIRouter, Form, HTTPException, Request +from fastapi import APIRouter, Depends, Form, HTTPException, Request from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse, Response from pydantic import BaseModel, ConfigDict, Field, SecretStr, ValidationError @@ -46,6 +46,7 @@ from litellm.proxy._experimental.mcp_server.gateway_dcr_flow import ( aggregate_authorize, aggregate_token, complete_connect_flow, + introspect_gateway_token, is_gateway_dcr_client_id, is_proxy_api_resource, native_client_auth_contract, @@ -67,6 +68,7 @@ from litellm.proxy._experimental.mcp_server.proxy_api_credentials import ( mint_proxy_credential, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils +from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.common_utils.encrypt_decrypt_utils import ( decrypt_value_helper, encrypt_value_helper, @@ -663,6 +665,26 @@ def _endpoint_not_configured_detail( ) +async def _server_with_oauth_endpoints( + mcp_server: MCPServer, + needed_endpoint: Callable[[MCPServer], str | None], +) -> MCPServer: + """Join deferred OAuth discovery only when the endpoint this caller needs is still missing. + + Admin-entered endpoints live on ``configured_*`` after an anchored issuer empties the + resolved fields. A caller whose needed endpoint already resolves never awaits discovery + and cannot 503 over a leftover pin. A server still missing it joins the deferred task; + no slot is a no-op and the caller 400s. + """ + if needed_endpoint(mcp_server) is not None: + return mcp_server + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( # noqa: PLC0415 # circular import with mcp_server_manager at module load + global_mcp_server_manager, + ) + + return await global_mcp_server_manager.ensure_oauth_metadata_discovered(mcp_server) + + def _raise_unless_oauth2_discovery_server( mcp_server: MCPServer | None, mcp_server_name: str | None, @@ -697,7 +719,7 @@ def _dcr_bridge_relays_client_registration(mcp_server: MCPServer) -> bool: returns directly to the client's redirect URI without transiting the gateway. Gateway-side redirect trust and the ``/callback`` state relay therefore only apply to the short-circuit arm, where the upstream only knows the gateway's own callback.""" - return mcp_server.is_dcr_bridge and bool(mcp_server.registration_url) and not mcp_server.client_id + return mcp_server.is_dcr_bridge and bool(mcp_server.effective_registration_url) and not mcp_server.client_id def _require_s256_pkce( @@ -745,7 +767,7 @@ def _redirect_to_upstream_authorize( **({"scope": scope_value} if scope_value else {}), **({"resource": upstream_resource} if upstream_resource else {}), } - parsed_auth_url: Final = urlparse(mcp_server.authorization_url or "") + parsed_auth_url: Final = urlparse(mcp_server.effective_authorization_url or "") merged_params: Final = {**dict(parse_qsl(parsed_auth_url.query)), **passthrough_params} return RedirectResponse(urlunparse(parsed_auth_url._replace(query=urlencode(merged_params)))) @@ -812,18 +834,19 @@ async def authorize_with_server( ephemeral_dcr_client: "EphemeralDcrClient | None" = None, ): _raise_if_not_oauth2(mcp_server) - if mcp_server.authorization_url is None: + resolved_server: Final = await _server_with_oauth_endpoints(mcp_server, _register_flow_needed_endpoint) + if resolved_server.effective_authorization_url is None: raise HTTPException( status_code=400, detail=_endpoint_not_configured_detail( - mcp_server, + resolved_server, "authorization url", "set Authorization URL and Token URL manually", "set Issuer to discover them from the identity provider (RFC 8414)", ), ) - if mcp_server.is_dcr_bridge: + if resolved_server.is_dcr_bridge: # Enforce S256 PKCE on both bridge arms. The relay arm forwards the validated, # now-non-optional pair to the upstream authorize; the short-circuit arm keeps # calling this for its enforcement side effect, then falls through to the gateway @@ -832,9 +855,9 @@ async def authorize_with_server( # A gateway-minted ephemeral client is registered against {base}/callback, so its # flow must run the short-circuit arm; the relay arm is only for clients that # registered themselves through the front door and hold their own redirect binding. - if _dcr_bridge_relays_client_registration(mcp_server) and ephemeral_dcr_client is None: + if _dcr_bridge_relays_client_registration(resolved_server) and ephemeral_dcr_client is None: return _redirect_to_upstream_authorize( - mcp_server=mcp_server, + mcp_server=resolved_server, client_id=client_id, redirect_uri=redirect_uri, state=state, @@ -860,7 +883,7 @@ async def authorize_with_server( # litellm key, so the browser session is the only identity source; without one there is nothing to # bind, so send the user through login first. Every other oauth2 server keeps the identity-less state. litellm_user_id: str | None = None - if mcp_server.is_dcr_bridge and mcp_server.is_oauth_delegate: + if resolved_server.is_dcr_bridge and resolved_server.is_oauth_delegate: from litellm.proxy._experimental.mcp_server.byok_oauth_endpoints import ( # noqa: PLC0415 # inline import avoids a module-load circular import _user_id_from_session_cookie, ) @@ -870,7 +893,7 @@ async def authorize_with_server( return _redirect_to_litellm_login(request) denial: Final = await _bridge_authorize_access_denial( litellm_user_id=litellm_user_id, - mcp_server=mcp_server, + mcp_server=resolved_server, redirect_uri=redirect_uri, state=state, ) @@ -884,7 +907,7 @@ async def authorize_with_server( code_challenge_method=code_challenge_method, client_redirect_uri=redirect_uri, litellm_user_id=litellm_user_id, - mcp_server_id=mcp_server.server_id if (litellm_user_id or ephemeral_dcr_client) else None, + mcp_server_id=resolved_server.server_id if (litellm_user_id or ephemeral_dcr_client) else None, dcr_client_id=ephemeral_dcr_client.client_id if ephemeral_dcr_client else None, dcr_client_secret=ephemeral_dcr_client.client_secret if ephemeral_dcr_client else None, dcr_token_endpoint_auth_method=ephemeral_dcr_client.token_endpoint_auth_method @@ -894,26 +917,26 @@ async def authorize_with_server( relay_state: Final = secrets.token_urlsafe(_OAUTH_STATE_HANDLE_BYTES) params: Final = { - "client_id": mcp_server.client_id if mcp_server.client_id else client_id, + "client_id": resolved_server.client_id if resolved_server.client_id else client_id, "redirect_uri": f"{request_base_url}/callback", "state": relay_state, "response_type": response_type or "code", } if scope: params["scope"] = scope - elif mcp_server.scopes: - params["scope"] = " ".join(mcp_server.scopes) + elif resolved_server.scopes: + params["scope"] = " ".join(resolved_server.scopes) if code_challenge: params["code_challenge"] = code_challenge if code_challenge_method: params["code_challenge_method"] = code_challenge_method - upstream_resource: Final = resolve_upstream_resource(mcp_server) + upstream_resource: Final = resolve_upstream_resource(resolved_server) if upstream_resource: params["resource"] = upstream_resource - parsed_auth_url: Final = urlparse(mcp_server.authorization_url) + parsed_auth_url: Final = urlparse(resolved_server.effective_authorization_url) existing_params: Final = dict(parse_qsl(parsed_auth_url.query)) existing_params.update(params) final_url: Final = urlunparse(parsed_auth_url._replace(query=urlencode(existing_params))) @@ -946,11 +969,13 @@ async def exchange_token_with_server( if grant_type not in ("authorization_code", "refresh_token"): raise HTTPException(status_code=400, detail="Unsupported grant_type") - if mcp_server.token_url is None: + resolved_server: Final = await _server_with_oauth_endpoints(mcp_server, _token_flow_needed_endpoint) + token_url: Final = resolved_server.effective_token_url + if token_url is None: raise HTTPException( status_code=400, detail=_endpoint_not_configured_detail( - mcp_server, + resolved_server, "token url", "set Token URL manually", "set Issuer to discover it from the identity provider (RFC 8414)", @@ -965,16 +990,16 @@ async def exchange_token_with_server( # recovered from a sealed code) must authenticate the way its own registration was granted, # not the way the server row is configured; callers that carry no method keep the row's method # as before. - resolved_client_id: Final = mcp_server.client_id if mcp_server.client_id else client_id - resolved_client_secret: Final = mcp_server.client_secret if mcp_server.client_id else client_secret + resolved_client_id: Final = resolved_server.client_id if resolved_server.client_id else client_id + resolved_client_secret: Final = resolved_server.client_secret if resolved_server.client_id else client_secret resolved_auth_method: Final = ( - mcp_server.token_endpoint_auth_method - if mcp_server.client_id - else (client_token_endpoint_auth_method or mcp_server.token_endpoint_auth_method) + resolved_server.token_endpoint_auth_method + if resolved_server.client_id + else (client_token_endpoint_auth_method or resolved_server.token_endpoint_auth_method) ) try: token_request: Final = build_upstream_oauth2_token_request( - mcp_server, + resolved_server, auth_method=resolved_auth_method, client_id=resolved_client_id, client_secret=resolved_client_secret, @@ -987,14 +1012,14 @@ async def exchange_token_with_server( bridge_upstream_refresh: SecretStr | None = None bridge_upstream_scope: str | None = None refresh_request_scope: str | None = None - is_bridge: Final = mcp_server.is_oauth_delegate and mcp_server.is_dcr_bridge + is_bridge: Final = resolved_server.is_oauth_delegate and resolved_server.is_dcr_bridge if grant_type == "refresh_token": # Phase 1 for a bridge refresh: open the client's refresh envelope, re-validate the sealed # identity, and unwrap the real upstream refresh token BEFORE building token_data, so the exchange # sends the upstream token and never the envelope. A failure returns without touching the upstream. if is_bridge: - prepared_refresh: Final = await _prepare_bridge_refresh(mcp_server, refresh_token) + prepared_refresh: Final = await _prepare_bridge_refresh(resolved_server, refresh_token) if not isinstance(prepared_refresh, _BridgeRefreshReady): return _bridge_mint_error_response(prepared_refresh) bridge_mint_ready = prepared_refresh.ready @@ -1031,13 +1056,13 @@ async def exchange_token_with_server( # A raw upstream code (scripted path) opens to None and the code is used as-is. bridge_identity = open_bridge_authorization_code(code) if bridge_identity is not None: - if bridge_identity.mcp_server_id != mcp_server.server_id: + if bridge_identity.mcp_server_id != resolved_server.server_id: raise HTTPException( status_code=400, detail="Authorization code was issued for a different MCP server", ) code = bridge_identity.upstream_code - bridge_token_relay: Final = _dcr_bridge_relays_client_registration(mcp_server) + bridge_token_relay: Final = _dcr_bridge_relays_client_registration(resolved_server) if bridge_token_relay and not redirect_uri: raise HTTPException( status_code=400, @@ -1059,7 +1084,7 @@ async def exchange_token_with_server( # Phase 1 for a bridge authorization_code mint: resolve identity (the SSO user recovered above, or # the presented litellm key) and the envelope keys BEFORE the exchange consumes the single-use code. if is_bridge: - prepared: Final = await _prepare_bridge_mint(request, mcp_server, bridge_identity) + prepared: Final = await _prepare_bridge_mint(request, resolved_server, bridge_identity) if not isinstance(prepared, _BridgeMintReady): return _bridge_mint_error_response(prepared) bridge_mint_ready = prepared @@ -1067,17 +1092,16 @@ async def exchange_token_with_server( async_client: Final = get_async_httpx_client(llm_provider=httpxSpecialProvider.Oauth2Check) try: response: Final = await async_client.post( - mcp_server.token_url, + token_url, headers={"Accept": "application/json", **token_request.headers}, data=token_data, ) - if response is not None: - response.raise_for_status() + response.raise_for_status() except httpx.HTTPStatusError as exc: fault: Final = classify_upstream_token_rejection( exc.response, - credential_source=_token_credential_source(mcp_server), - log_context=mcp_server.server_id, + credential_source=_token_credential_source(resolved_server), + log_context=resolved_server.server_id, ) upstream_rejected_bridge_refresh: Final = ( is_bridge @@ -1090,35 +1114,30 @@ async def exchange_token_with_server( "bridge refresh: the upstream rejected the sealed refresh token for server=%s with " "invalid_grant (revoked or expired at the IdP); returning invalid_grant so the client " "re-runs authorization_code rather than an opaque upstream error", - mcp_server.server_id, + resolved_server.server_id, ) return _bridge_mint_error_response("invalid_refresh") return render_token_fault(fault) - if response is None: - raise HTTPException( - status_code=502, - detail="MCP upstream token endpoint returned no response", - ) token_response = response.json() # Validate token response against server-configured rules before any storage. # This rejects tokens from wrong Slack workspaces, Atlassian orgs, etc. - if mcp_server.token_validation and isinstance(mcp_server.token_validation, dict): + if resolved_server.token_validation and isinstance(resolved_server.token_validation, dict): _validate_token_response( token_response=token_response, - validation_rules=mcp_server.token_validation, - server_id=mcp_server.server_id, + validation_rules=resolved_server.token_validation, + server_id=resolved_server.server_id, ) # Store server-side when the server is configured for per-user OAuth and # the calling client has provided a valid LiteLLM identity. # Errors are non-fatal: the token is still returned to the client. - if mcp_server.needs_user_oauth_token: + if resolved_server.needs_user_oauth_token: user_id: Final = await _extract_user_id_from_request(request) if user_id: try: await _store_per_user_token_server_side( - server=mcp_server, + server=resolved_server, user_id=user_id, token_response=token_response, ) @@ -1126,7 +1145,7 @@ async def exchange_token_with_server( verbose_logger.warning( "exchange_token_with_server: server-side storage failed for user=%s server=%s: %s", user_id, - mcp_server.server_id, + resolved_server.server_id, exc, ) else: @@ -1136,7 +1155,7 @@ async def exchange_token_with_server( "requires the stored token, so the client will be challenged with 401 on reconnect. " "Ensure the request carries a valid LiteLLM key (x-litellm-api-key or Authorization), " "or store it via POST /mcp/server/{id}/oauth-user-credential.", - mcp_server.server_id, + resolved_server.server_id, ) # A DCR-bridge oauth_delegate server hands the client a gateway-bound envelope (identity plus the @@ -1147,7 +1166,9 @@ async def exchange_token_with_server( token_response = {**token_response, "scope": refresh_request_scope} # Phase 3: seal the upstream grant into the client-held envelope; failures map through the same # OAuth-shaped response as the phase-1 preconditions. - minted: Final = _finish_bridge_mint(bridge_mint_ready, mcp_server, token_response, datetime.now(timezone.utc)) + minted: Final = _finish_bridge_mint( + bridge_mint_ready, resolved_server, token_response, datetime.now(timezone.utc) + ) return minted if isinstance(minted, JSONResponse) else _bridge_mint_error_response(minted) raw_access_token: Final = token_response.get("access_token") if isinstance(token_response, dict) else None @@ -1509,16 +1530,10 @@ async def _post_dcr_registration( headers=headers, json=register_data, ) - if response is not None: - response.raise_for_status() + response.raise_for_status() except httpx.HTTPStatusError as exc: status_code, detail = dcr_fault_detail(classify_upstream_dcr_rejection(exc.response, log_context=server_id)) raise HTTPException(status_code=status_code, detail=detail) from exc - if response is None: - raise HTTPException( - status_code=502, - detail="MCP upstream registration endpoint returned no response", - ) return response @@ -1551,7 +1566,8 @@ async def mint_ephemeral_dcr_client(request: Request, mcp_server: MCPServer) -> bounded by the server count even when the request origin varies) so parallel authorize requests cannot each register an upstream client; the cache stamps nothing onto the server record and correctness never depends on it because the sealed state carries the client through the flow.""" - if mcp_server.registration_url is None: + registration_url: Final = mcp_server.effective_registration_url + if registration_url is None: return None request_base_url: Final = get_request_base_url(request) cache_key: Final = f"mcp_ephemeral_dcr_client:{mcp_server.server_id}:{request_base_url}" @@ -1571,7 +1587,7 @@ async def mint_ephemeral_dcr_client(request: Request, mcp_server: MCPServer) -> "token_endpoint_auth_method": "none", } response: Final = await _post_dcr_registration( - registration_url=mcp_server.registration_url, + registration_url=registration_url, register_data=register_data, server_id=mcp_server.server_id, ) @@ -1617,7 +1633,7 @@ async def resolve_ephemeral_dcr_client( usable to generate orphan IdP clients).""" if not (mcp_server.is_true_passthrough or (mcp_server.is_oauth_delegate and not mcp_server.is_dcr_bridge)): return None - if mcp_server.authorization_url is None: + if mcp_server.effective_authorization_url is None: raise HTTPException( status_code=400, detail="MCP server authorization url is not set", @@ -1627,6 +1643,29 @@ async def resolve_ephemeral_dcr_client( return await mint_ephemeral_dcr_client(request, mcp_server) +def _register_flow_needed_endpoint(mcp_server: MCPServer) -> str | None: + """The register flow's deferred-discovery join gate. A DCR bridge with no admin-configured + client can only register callers through the upstream's registration endpoint + (``_oauth_endpoints_unresolved`` keeps its discovery slot armed for exactly this shape), so + the flow must keep joining discovery while registration is still missing instead of silently + degrading to the dummy short-circuit. Every other shape only needs the authorization url.""" + if mcp_server.is_dcr_bridge and not mcp_server.client_id and mcp_server.effective_registration_url is None: + return None + return mcp_server.effective_authorization_url + + +def _token_flow_needed_endpoint(mcp_server: MCPServer) -> str | None: + """The token exchange's deferred-discovery join gate. The exchange's relay-vs-callback arm + (:func:`_dcr_bridge_relays_client_registration`) reads the registration url, so a clientless + DCR bridge rebuilt without its discovered registration endpoint must keep joining discovery + even when the token url already resolves; skipping it would select the gateway-callback arm + and the upstream would reject the code over a redirect_uri mismatch. Every other shape only + needs the token url.""" + if mcp_server.is_dcr_bridge and not mcp_server.client_id and mcp_server.effective_registration_url is None: + return None + return mcp_server.effective_token_url + + async def register_client_with_server( request: Request, mcp_server: MCPServer, @@ -1661,21 +1700,23 @@ async def register_client_with_server( ): return dummy_return - if mcp_server.authorization_url is None: + resolved_server: Final = await _server_with_oauth_endpoints(mcp_server, _register_flow_needed_endpoint) + if resolved_server.effective_authorization_url is None: raise HTTPException( status_code=400, detail=_endpoint_not_configured_detail( - mcp_server, + resolved_server, "authorization url", "set Authorization URL and Token URL manually", "set Issuer to discover them from the identity provider (RFC 8414)", ), ) - if mcp_server.registration_url is None: + registration_url: Final = resolved_server.effective_registration_url + if registration_url is None: return dummy_return - bridge_relay: Final = _dcr_bridge_relays_client_registration(mcp_server) + bridge_relay: Final = _dcr_bridge_relays_client_registration(resolved_server) if bridge_relay and not client_redirect_uris: raise HTTPException( status_code=400, @@ -1690,15 +1731,17 @@ async def register_client_with_server( "token_endpoint_auth_method": token_endpoint_auth_method or ("none" if bridge_relay else ""), } response: Final = await _post_dcr_registration( - registration_url=mcp_server.registration_url, + registration_url=registration_url, register_data=register_data, - server_id=mcp_server.server_id, + server_id=resolved_server.server_id, ) token_response = response.json() if persist_credentials and not bridge_relay: - persistence_result = await _persist_dcr_client_registration(mcp_server, token_response, current_redirect_uri) + persistence_result = await _persist_dcr_client_registration( + resolved_server, token_response, current_redirect_uri + ) if persistence_result == "reused": return dummy_return @@ -1755,17 +1798,10 @@ async def authorize( lookup_name: Final[str | None] = mcp_server_name or client_id client_ip: Final = IPAddressUtils.get_mcp_client_ip(request) mcp_server = ( - await global_mcp_server_manager.get_resolved_mcp_server_by_name(lookup_name, client_ip=client_ip) - if lookup_name - else None + global_mcp_server_manager.get_mcp_server_by_name(lookup_name, client_ip=client_ip) if lookup_name else None ) if mcp_server is None and mcp_server_name is None: - unresolved_server: Final = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) - mcp_server = ( - await global_mcp_server_manager.ensure_oauth_metadata_discovered(unresolved_server) - if unresolved_server is not None - else None - ) + mcp_server = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) if mcp_server is None: raise HTTPException(status_code=404, detail="MCP server not found") _raise_if_not_oauth2(mcp_server) @@ -1846,14 +1882,9 @@ async def token_endpoint( lookup_name: Final = mcp_server_name or client_id client_ip: Final = IPAddressUtils.get_mcp_client_ip(request) - mcp_server = await global_mcp_server_manager.get_resolved_mcp_server_by_name(lookup_name, client_ip=client_ip) + mcp_server = global_mcp_server_manager.get_mcp_server_by_name(lookup_name, client_ip=client_ip) if mcp_server is None and mcp_server_name is None: - unresolved_server: Final = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) - mcp_server = ( - await global_mcp_server_manager.ensure_oauth_metadata_discovered(unresolved_server) - if unresolved_server is not None - else None - ) + mcp_server = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) if mcp_server is None: raise HTTPException(status_code=404, detail="MCP server not found") return await exchange_token_with_server( @@ -1910,6 +1941,26 @@ async def revoke_endpoint(request: Request, token: str = Form(...), client_id: s return await revoke_refresh_token(token=token, client_id=client_id, master_key=master_key, cache=user_api_key_cache) +@router.post("/introspect", dependencies=[Depends(user_api_key_auth)]) +async def introspect_endpoint(token: str = Form(...)) -> Response: + """RFC 7662 introspection for gateway-issued session tokens (``llm_session_`` / + ``llm_srefresh_``), so an external gateway can validate them without the signing + secret. The caller authenticates with a LiteLLM virtual key (section 2.1, enforced by + the route dependency); any token the gateway cannot vouch for answers + ``{"active": false}`` with no further detail.""" + from litellm.proxy.proxy_server import ( # noqa: PLC0415 # circular import at module load + master_key, + user_api_key_cache, + ) + + return await introspect_gateway_token( + token=token, + master_key=master_key, + reload_user=_reload_active_user_by_id, + cache=user_api_key_cache, + ) + + @router.get("/.well-known/litellm-cli-auth") async def native_client_auth_discovery(request: Request) -> JSONResponse: """The versioned contract a native client (``lite login --pkce``, or a CLI in any other @@ -2415,6 +2466,7 @@ def _build_aggregate_authorization_server_response(request: Request) -> dict: "issuer": f"{request_base_url}/mcp", "authorization_endpoint": f"{request_base_url}/authorize", "token_endpoint": f"{request_base_url}/token", + "introspection_endpoint": f"{request_base_url}/introspect", "registration_endpoint": f"{request_base_url}/register", "response_types_supported": ["code"], "scopes_supported": [], @@ -2684,10 +2736,9 @@ async def register_client(request: Request, mcp_server_name: str | None = None): return await register_aggregate_client(request=request, request_body=data) resolved: Final = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) if resolved: - resolved_server: Final = await global_mcp_server_manager.ensure_oauth_metadata_discovered(resolved) return await register_client_with_server( request=request, - mcp_server=resolved_server, + mcp_server=resolved, client_name=data.get("client_name", ""), grant_types=data.get("grant_types", []), response_types=data.get("response_types", []), @@ -2697,10 +2748,7 @@ async def register_client(request: Request, mcp_server_name: str | None = None): ) return dummy_return - mcp_server: Final = await global_mcp_server_manager.get_resolved_mcp_server_by_name( - mcp_server_name, - client_ip=client_ip, - ) + mcp_server: Final = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name, client_ip=client_ip) if mcp_server is None: return dummy_return return await register_client_with_server( diff --git a/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py b/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py index 314c80adbc4..a43e762a456 100644 --- a/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py +++ b/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py @@ -65,17 +65,24 @@ from litellm.proxy._experimental.mcp_server.oauth_utils import ( ) from litellm.proxy._experimental.mcp_server.outbound_credentials.session_credentials import ( SessionRefreshOpened, + SessionSigningConfigError, + active_session_signing_keys, open_session_refresh_bearer, - session_keys_from_master_key, ) from litellm.proxy._experimental.mcp_server.outbound_credentials.session_token import ( + SESSION_ISSUER, SESSION_REFRESH_TTL_SECONDS, MintedSessionToken, + OpenedSessionToken, SessionAudience, - SessionKeys, SessionPrincipal, + SessionSigningKeys, + is_session_refresh_token, + is_session_token, mint_session_refresh_token, mint_session_token, + open_session_refresh_token, + open_session_token, ) from litellm.proxy.common_utils.encrypt_decrypt_utils import ( decrypt_value_helper, @@ -884,8 +891,25 @@ class _SingleUseGuard: count = await self._cache.async_increment_cache(key, 1, ttl=ttl_seconds, local_only=True) return "first" if count == 1 else "replayed" + async def peek(self, key: str) -> Literal["unclaimed", "claimed", "unavailable"]: + """Read-only view of a single-use marker, resolved against the same shared authority as + :meth:`claim` so introspection observes exactly the record redemption and revocation wrote. + A backend fault is ``"unavailable"`` (fail closed) rather than a guess either way.""" + from litellm.proxy.proxy_server import redis_usage_cache # noqa: PLC0415 # circular import at module load -def _session_token_pair(principal: SessionPrincipal, keys: SessionKeys, now: datetime) -> Response: + redis_cache: Final = redis_usage_cache or getattr(self._cache, "redis_cache", None) + if redis_cache is not None: + try: + value = await redis_cache.async_get_cache(key) + except Exception as e: # noqa: BLE001 # ANY Redis fault fails the read closed + verbose_logger.warning("mcp gateway single-use peek: shared cache backend unavailable: %s", e) + return "unavailable" + return "unclaimed" if value is None else "claimed" + local: Final = await self._cache.async_get_cache(key, local_only=True) + return "unclaimed" if local is None else "claimed" + + +def _session_token_pair(principal: SessionPrincipal, keys: SessionSigningKeys, now: datetime) -> Response: access: Final = mint_session_token(principal, keys, now) refresh: Final = mint_session_refresh_token(principal, keys, now) if not isinstance(access, MintedSessionToken) or not isinstance(refresh, MintedSessionToken): @@ -912,7 +936,7 @@ class _ProxyCredentialTokenResponse(TypedDict): def _proxy_credential_response( - minted: MintedProxyCredential, principal: SessionPrincipal, keys: SessionKeys, now: datetime + minted: MintedProxyCredential, principal: SessionPrincipal, keys: SessionSigningKeys, now: datetime ) -> Response: """The proxy-API token response: the access token is the very credential ``lite login`` stores (accepted on every proxy route with user and team attribution), and @@ -998,7 +1022,10 @@ async def aggregate_token( if master_key is None: verbose_logger.error("mcp_gateway_dcr token grant rejected: no master_key configured") return _oauth_error(500, "server_error", "the gateway has no master key configured") - keys: Final = session_keys_from_master_key(master_key) + keys: Final = active_session_signing_keys(master_key) + if isinstance(keys, SessionSigningConfigError): + verbose_logger.error("mcp_gateway_dcr token grant rejected: %s", keys.detail) + return _oauth_error(500, "server_error", "the gateway session signing configuration is invalid") now: Final = datetime.now(timezone.utc) issue: Final = _GrantIssuer( request=request, @@ -1043,7 +1070,7 @@ class _GrantIssuer: self, request: Request, resource: str | None, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, reload_user: ReloadUser, mint_proxy_credential: MintProxyCredential, @@ -1146,7 +1173,7 @@ async def _refresh_token_grant( refresh_token: str | None, client_id: str, resource: str | None, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, issue: _GrantIssuer, ) -> Response: @@ -1182,7 +1209,10 @@ async def revoke_refresh_token(token: str, client_id: str, master_key: str | Non if master_key is None: verbose_logger.error("mcp_gateway_dcr revoke rejected: no master_key configured") return _oauth_error(500, "server_error", "the gateway has no master key configured") - keys: Final = session_keys_from_master_key(master_key) + keys: Final = active_session_signing_keys(master_key) + if isinstance(keys, SessionSigningConfigError): + verbose_logger.error("mcp_gateway_dcr revoke rejected: %s", keys.detail) + return _oauth_error(500, "server_error", "the gateway session signing configuration is invalid") now: Final = datetime.now(timezone.utc) opened: Final = open_session_refresh_bearer(token, keys, now, expected_client_id=client_id) if isinstance(opened, SessionRefreshOpened): @@ -1192,3 +1222,83 @@ async def revoke_refresh_token(token: str, client_id: str, master_key: str | Non if burned == "unavailable": return _oauth_error(503, "temporarily_unavailable", _CLAIM_UNAVAILABLE_DESCRIPTION) return Response(content="{}", media_type="application/json", headers=TOKEN_NO_CACHE_HEADERS) + + +def _inactive_introspection_response() -> Response: + """RFC 7662 section 2.2: any token the gateway cannot vouch for, whatever the reason + (wrong family, bad signature, expired, revoked, or a deactivated user), answers 200 + with ``active: false`` and nothing else, so introspection is not a token oracle.""" + return JSONResponse(status_code=200, content={"active": False}, headers=TOKEN_NO_CACHE_HEADERS) + + +def _active_introspection_response(opened: OpenedSessionToken) -> Response: + principal: Final = opened.principal + optional_claims: Final = { + key: value + for key, value in ( + ("token_type", "Bearer" if opened.kind == "session" else None), + ("team_id", principal.team_id), + ("resource_server_id", principal.resource_server_id), + ("audience", principal.audience), + ) + if value is not None + } + return JSONResponse( + status_code=200, + content={ + "active": True, + "iss": SESSION_ISSUER, + "sub": principal.user_id, + "client_id": principal.client_id, + "jti": opened.jti, + "iat": opened.iat, + "exp": opened.exp, + "kind": opened.kind, + **optional_claims, + }, + headers=TOKEN_NO_CACHE_HEADERS, + ) + + +async def introspect_gateway_token( + token: str, + master_key: str | None, + reload_user: ReloadUser, + cache: DualCache, +) -> Response: + """RFC 7662 introspection for the gateway's session tokens, so an external gateway + (Kong, an API management layer) can validate a LiteLLM-issued MCP session credential + without holding the signing secret. The caller is already authenticated by the route + (section 2.1). Active means everything admission itself would require: valid signature + under the configured session signing keys, unexpired, not a revoked or rotated refresh + token, and a litellm user that is still live, so a deactivated user's outstanding + tokens introspect as inactive immediately. A shared-backend or DB outage answers 503 + rather than guessing in either direction.""" + if master_key is None: + verbose_logger.error("mcp_gateway_dcr introspect rejected: no master_key configured") + return _oauth_error(500, "server_error", "the gateway has no master key configured") + keys: Final = active_session_signing_keys(master_key) + if isinstance(keys, SessionSigningConfigError): + verbose_logger.error("mcp_gateway_dcr introspect rejected: %s", keys.detail) + return _oauth_error(500, "server_error", keys.detail) + now: Final = datetime.now(timezone.utc) + if is_session_token(token): + opened = open_session_token(token, keys, now) + elif is_session_refresh_token(token): + opened = open_session_refresh_token(token, keys, now) + else: + return _inactive_introspection_response() + if not isinstance(opened, OpenedSessionToken): + return _inactive_introspection_response() + if opened.kind == "session_refresh": + peeked: Final = await _SingleUseGuard(cache).peek(f"{_USED_REFRESH_CACHE_PREFIX}{opened.jti}") + if peeked == "unavailable": + return _oauth_error(503, "temporarily_unavailable", _CLAIM_UNAVAILABLE_DESCRIPTION) + if peeked == "claimed": + return _inactive_introspection_response() + failure: Final = await reload_user(opened.principal.user_id) + if failure == "unavailable": + return _oauth_error(503, "temporarily_unavailable", "the gateway database is unavailable; retry") + if failure is not None: + return _inactive_introspection_response() + return _active_introspection_response(opened) diff --git a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py index e836e2bd363..4918229c2b8 100644 --- a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py +++ b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py @@ -39,6 +39,7 @@ if TYPE_CHECKING: from mcp.types import CallToolResult from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj class MCPGuardrailTranslationHandler(BaseTranslation): @@ -48,7 +49,7 @@ class MCPGuardrailTranslationHandler(BaseTranslation): self, data: dict[str, Any], guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, ) -> dict[str, Any]: mcp_tool_name: Final = data.get("mcp_tool_name") or data.get("name") mcp_arguments = data.get("mcp_arguments") or data.get("arguments") @@ -99,7 +100,7 @@ class MCPGuardrailTranslationHandler(BaseTranslation): self, response: "CallToolResult", guardrail_to_apply: "CustomGuardrail", - litellm_logging_obj: Any | None = None, + litellm_logging_obj: "LiteLLMLoggingObj | None" = None, user_api_key_dict: Any | None = None, request_data: dict | None = None, ) -> Any: diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 7ab26db0f3e..1f552ff3e13 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -13,7 +13,7 @@ import json import os import re import time -from collections.abc import AsyncIterator, Callable, Mapping, Sequence +from collections.abc import AsyncIterator, Awaitable, Callable, Mapping, Sequence from contextlib import asynccontextmanager from dataclasses import dataclass, replace from typing import TYPE_CHECKING, Any, Final, Literal, TypeAlias, TypedDict, cast @@ -34,6 +34,7 @@ from mcp.types import ( ) from mcp.types import Tool as MCPTool from pydantic import AnyUrl, BaseModel +from typing_extensions import ReadOnly import litellm from litellm._logging import verbose_logger @@ -72,6 +73,7 @@ from litellm.proxy._experimental.mcp_server.oauth2_token_cache import ( MCPPerUserTokenCache, mcp_per_user_token_cache, resolve_mcp_auth, + resolved_token_header, ) from litellm.proxy._experimental.mcp_server.oauth_utils import ( _redact_mcp_resource_url, @@ -99,6 +101,7 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.token_exchange_ build_token_exchanger, ) from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( + DEFAULT_CREDENTIAL_HEADER, AuthorizationCodeConfig, ClientCredentialsConfig, CredError, @@ -153,6 +156,8 @@ from litellm.types.mcp import ( MCPAuth, MCPStdioConfig, MCPTokenEndpointAuthMethod, + has_header, + without_header, ) from litellm.types.mcp_server.mcp_server_manager import ( MCPInfo, @@ -349,6 +354,7 @@ class MCPServerConfig(TypedDict, total=False): audience: str subject_token_type: str upstream_resource: str + upstream_token_header: ReadOnly[str] id_jag_resource_token_endpoint: str id_jag_resource: str client_private_key: str @@ -523,7 +529,7 @@ def _oauth_endpoints_unresolved(server: MCPServer) -> bool: # can come from resource discovery, so a server that resolved its endpoints but no scopes is # still unresolved for its flow. return True - if server.is_dcr_bridge and not server.client_id and server.registration_url is None: + if server.is_dcr_bridge and not server.client_id and server.effective_registration_url is None: # A DCR bridge with no admin-configured client can only register callers through the # upstream's registration endpoint, so a build that resolved the authorize and token # endpoints but not registration_endpoint (partial metadata) is still unresolved for its @@ -535,8 +541,8 @@ def _oauth_endpoints_unresolved(server: MCPServer) -> bool: return _flow_endpoints_missing( server.auth_type, MCPServerManager.effective_oauth2_flow(server), - server.authorization_url, - server.token_url, + server.effective_authorization_url, + server.effective_token_url, server.token_exchange_endpoint, ) @@ -828,18 +834,6 @@ def _should_strip_caller_authorization( ) -def _without_authorization( - headers: dict[str, str] | None, -) -> dict[str, str] | None: - """A copy of ``headers`` with any ``Authorization`` key removed (case-insensitive), or - None if nothing remains. Drops only the credential, keeping other forwarded headers. - """ - if not headers: - return None - filtered: Final = {k: v for k, v in headers.items() if k.lower() != "authorization"} - return filtered or None - - def _format_byok_openapi_auth_header(mcp_server: MCPServer, mcp_auth_header: str) -> str: """Format a raw BYOK credential for OpenAPI tool ``Authorization`` injection. @@ -914,7 +908,9 @@ def _resolve_openapi_tool_auth( if isinstance(per_server, dict): authorization: Final = next((v for k, v in per_server.items() if k.lower() == "authorization"), None) - merged: Final = merge_mcp_headers(extra_headers=forwarded, static_headers=_without_authorization(per_server)) + merged: Final = merge_mcp_headers( + extra_headers=forwarded, static_headers=without_header(per_server, DEFAULT_CREDENTIAL_HEADER) + ) if authorization is None: byok: Final = _format_byok_openapi_auth_header(mcp_server, mcp_auth_header) if mcp_auth_header else None return byok, merged, mcp_auth_header @@ -981,7 +977,7 @@ def _client_forwarded_authorization_headers( raw_headers=raw_headers, user_api_key_auth=user_api_key_auth, ): - return _without_authorization(extra_headers) + return without_header(extra_headers, DEFAULT_CREDENTIAL_HEADER) return extra_headers @@ -994,7 +990,7 @@ def _take_forwarded_authorization( if not headers: return None, headers value: Final = next((v for k, v in headers.items() if k.lower() == "authorization"), None) - return value, _without_authorization(headers) + return value, without_header(headers, DEFAULT_CREDENTIAL_HEADER) def _passthrough_token_from_mcp_auth_header( @@ -1210,7 +1206,7 @@ def _deserialize_json_dict(data: str | _StringMap | None) -> dict[str, str] | No return data -def _deserialize_json_list(data: Any) -> list[dict[str, Any]] | None: +def _deserialize_json_list(data: object) -> list[dict[str, Any]] | None: """Deserialize a JSON array stored in the DB (``env_vars`` and friends). Returns ``None`` for empty / null / unparseable input. Accepts strings @@ -1223,7 +1219,7 @@ def _deserialize_json_list(data: Any) -> list[dict[str, Any]] | None: return None if isinstance(data, str): try: - parsed: Final = json.loads(data) + parsed: Final[object] = json.loads(data) except (json.JSONDecodeError, TypeError): return None data = parsed @@ -1918,7 +1914,7 @@ class MCPServerManager: async def load_servers_from_config( self, - mcp_servers_config: dict[str, Any], + mcp_servers_config: dict[str, MCPServerConfig], mcp_aliases: dict[str, str] | None = None, ): """ @@ -2166,6 +2162,7 @@ class MCPServerManager: DEFAULT_SUBJECT_TOKEN_TYPE, ), upstream_resource=server_config.get("upstream_resource", None), + upstream_token_header=server_config.get("upstream_token_header", None), # ID-JAG fields id_jag_resource_token_endpoint=server_config.get("id_jag_resource_token_endpoint", None), id_jag_resource=server_config.get("id_jag_resource", None), @@ -2698,6 +2695,7 @@ class MCPServerManager: or (credentials_dict.get("subject_token_type") if credentials_dict else None) or DEFAULT_SUBJECT_TOKEN_TYPE, upstream_resource=(credentials_dict.get("upstream_resource") if credentials_dict else None), + upstream_token_header=(credentials_dict.get("upstream_token_header") if credentials_dict else None), # ID-JAG fields — read from credentials JSON blob id_jag_resource_token_endpoint=( credentials_dict.get("id_jag_resource_token_endpoint") if credentials_dict else None @@ -3070,7 +3068,7 @@ class MCPServerManager: return {} cache_key: Final = "toolset_perms:" + ",".join(sorted(toolset_ids)) - cached: Final = await user_api_key_cache.async_get_cache(key=cache_key) + cached: Final[dict[str, list[str]] | None] = await user_api_key_cache.async_get_cache(key=cache_key) if cached is not None: return cached @@ -3525,10 +3523,9 @@ class MCPServerManager: case Ok(auth): # NoOpAuth has no header_name and so never conflicts. header_name: Final[str | None] = getattr(auth, "header_name", None) - conflicts: Final = bool( - header_name and extra_headers and any(key.lower() == header_name.lower() for key in extra_headers) - ) - if not conflicts: + if header_name is None or not extra_headers: + return auth, extra_headers + if not has_header(extra_headers, header_name): return auth, extra_headers if isinstance( spec.config, @@ -3540,9 +3537,10 @@ class MCPServerManager: # guardrail such as MCPJWTSigner, static_headers, or any other injected # Authorization must NOT shadow it (otherwise the upstream gets e.g. the # signer's JWT instead of the minted token and rejects it, and for M2M the - # one-shot 401 refetch is lost with it). Drop the conflicting header so the - # resolved token reaches upstream. - return auth, _without_authorization(extra_headers) + # one-shot 401 refetch is lost with it). Drop only the header the resolved + # credential is about to occupy, so a static credential the operator aimed at a + # DIFFERENT header still reaches upstream. + return auth, without_header(extra_headers, header_name) # Other modes: an Authorization already supplied via extra_headers (a forwarded caller # header or static_headers) is intentional and wins; v1 applies those last. return None, extra_headers @@ -3650,6 +3648,7 @@ class MCPServerManager: ): spec = None auth_value: Final = await resolve_mcp_auth(resolved_server, mcp_auth_header) if spec is None else None + auth_header_name: Final = resolved_token_header(resolved_server, mcp_auth_header) if spec is None else None # Create sampling and elicitation callbacks for this client sampling_cb = ( @@ -3758,6 +3757,7 @@ class MCPServerManager: transport_type=transport, auth_type=resolved_server.auth_type, auth_value=auth_value, + auth_header_name=auth_header_name, timeout=(resolved_server.timeout if resolved_server.timeout is not None else MCP_CLIENT_TIMEOUT), extra_headers=extra_headers, aws_auth=aws_auth, @@ -5154,7 +5154,7 @@ class MCPServerManager: # Wrapped so the bridge runs inside the task: the caller only holds the task and # gathers it later, so there is no other point that still sees a block here. - async def _run_during_call_hook() -> Mapping[str, Any] | None: + async def _run_during_call_hook() -> Mapping[str, object] | None: try: return await proxy_logging_obj.during_call_hook( user_api_key_dict=user_api_key_auth, @@ -5256,7 +5256,9 @@ class MCPServerManager: proxy_logging_obj: Optional ProxyLogging object for hook integration host_progress_callback: Optional callback for progress updates hook_extra_headers: Optional headers injected by pre_mcp_call guardrail - hooks. Merged last (highest priority) into outbound request headers. + hooks. Merged last into outbound request headers, except a hook + Authorization header is dropped when an upstream credential already + occupies the Authorization slot. Returns: CallToolResult from the MCP server @@ -5304,7 +5306,7 @@ class MCPServerManager: raw_headers=raw_headers, user_api_key_auth=user_api_key_auth, ): - extra_headers = _without_authorization(extra_headers) + extra_headers = without_header(extra_headers, DEFAULT_CREDENTIAL_HEADER) elif mcp_server.is_client_forwarded_token: extra_headers = _client_forwarded_authorization_headers( mcp_server=mcp_server, @@ -5347,27 +5349,26 @@ class MCPServerManager: if hook_extra_headers: if extra_headers is None: extra_headers = {} - if "Authorization" in hook_extra_headers: - if "Authorization" in extra_headers: - verbose_logger.warning( - "MCPServerManager: hook_extra_headers 'Authorization' will overwrite " - "the existing Authorization header from static_headers. " - "The hook JWT will take precedence." - ) - elif server_auth_header is not None: - # server_auth_header is passed separately to _create_mcp_client as - # auth_value. Both will reach the upstream server — warn so admins - # know two Authorization credentials are being sent. - verbose_logger.warning( - "MCPServerManager: hook_extra_headers injects 'Authorization' while " - "server '%s' already has a configured authentication_token. " - "Both credentials will be sent; the hook header is in extra_headers " - "and the server token is in auth_value — the upstream server decides " - "which one wins. Consider unsetting authentication_token if you want " - "the hook JWT to be the sole credential.", - mcp_server.server_name or mcp_server.name, - ) - extra_headers.update(hook_extra_headers) + hook_has_authorization: Final = any(k.lower() == "authorization" for k in hook_extra_headers) + existing_has_authorization: Final = any(k.lower() == "authorization" for k in extra_headers) + server_auth_occupies_authorization: Final = ( + any(k.lower() == "authorization" for k in server_auth_header) + if isinstance(server_auth_header, dict) + else server_auth_header is not None and mcp_server.auth_type != MCPAuth.api_key + ) + if hook_has_authorization and (existing_has_authorization or server_auth_occupies_authorization): + # Mirror the tools/list signer guard: an upstream credential (user OAuth, + # static header, or configured authentication_token) already occupies the + # Authorization slot, so the hook must not replace it. + verbose_logger.warning( + "MCPServerManager: dropping hook-injected 'Authorization' header for " + "server '%s' because an upstream credential already occupies the " + "Authorization slot; the existing credential is kept.", + mcp_server.server_name or mcp_server.name, + ) + extra_headers.update({k: v for k, v in hook_extra_headers.items() if k.lower() != "authorization"}) + else: + extra_headers.update(hook_extra_headers) # Reset to None if no headers were actually added if extra_headers is not None and len(extra_headers) == 0: @@ -5655,7 +5656,7 @@ class MCPServerManager: async def _gather_openapi_tool_tasks( self, - tasks: list[Any], + tasks: Sequence[Awaitable[object]], proxy_logging_obj: ProxyLogging | None, ) -> CallToolResult: """Await OpenAPI tool tasks and return the tool call result.""" @@ -6205,14 +6206,6 @@ class MCPServerManager: return server return None - async def get_resolved_mcp_server_by_name( - self, - server_name: str, - client_ip: str | None = None, - ) -> MCPServer | None: - server: Final = self.get_mcp_server_by_name(server_name, client_ip=client_ip) - return await self.ensure_oauth_metadata_discovered(server) if server is not None else None - def get_filtered_registry(self, client_ip: str | None = None) -> dict[str, MCPServer]: """ Get registry filtered by client IP access control. diff --git a/litellm/proxy/_experimental/mcp_server/oauth2_flow_backfill.py b/litellm/proxy/_experimental/mcp_server/oauth2_flow_backfill.py index c09106273e1..150900e7ff2 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth2_flow_backfill.py +++ b/litellm/proxy/_experimental/mcp_server/oauth2_flow_backfill.py @@ -36,7 +36,10 @@ a healed fleet has no null rows and the backfill exits after one query. import json from collections import Counter -from typing import Any, Final, Literal +from collections.abc import Mapping, Sequence +from typing import Final, Literal, Protocol + +from pydantic import JsonValue from litellm._logging import verbose_proxy_logger from litellm.proxy._experimental.mcp_server.db import _decode_oauth_payload, decrypt_credentials @@ -55,9 +58,59 @@ BackfillRule = Literal[ _BACKFILL_AUDIT_ACTOR: Final = "oauth2_flow_backfill" -def _decrypted_credentials(raw_credentials: Any) -> MCPCredentials | None: +class _MCPServerRow(Protocol): + """The ``LiteLLM_MCPServerTable`` columns this backfill reads.""" + + @property + def server_id(self) -> str: ... + + @property + def authorization_url(self) -> str | None: ... + + @property + def registration_url(self) -> str | None: ... + + @property + def token_url(self) -> str | None: ... + + @property + def credentials(self) -> str | Mapping[str, JsonValue] | None: ... + + +class _MCPUserCredentialRow(Protocol): + """The ``LiteLLM_MCPUserCredentials`` columns this backfill reads.""" + + @property + def server_id(self) -> str: ... + + @property + def credential_b64(self) -> str: ... + + +class _MCPServerTable(Protocol): + async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_MCPServerRow]: ... + + async def update_many(self, *, where: Mapping[str, object], data: Mapping[str, str]) -> object: ... + + +class _MCPUserCredentialsTable(Protocol): + async def find_many(self, *, where: Mapping[str, object]) -> Sequence[_MCPUserCredentialRow]: ... + + +def _mcp_server_table(prisma_client: PrismaClient) -> _MCPServerTable: + """The MCP server table, typed so the untyped prisma client surface stops here.""" + return prisma_client.db.litellm_mcpservertable + + +def _mcp_user_credentials_table(prisma_client: PrismaClient) -> _MCPUserCredentialsTable: + """The per-user MCP credential table, typed so the untyped prisma client surface stops here.""" + return prisma_client.db.litellm_mcpusercredentials + + +def _decrypted_credentials(raw_credentials: str | Mapping[str, JsonValue] | None) -> MCPCredentials | None: if raw_credentials is None: return None + parsed: JsonValue | Mapping[str, JsonValue] if isinstance(raw_credentials, str): try: parsed = json.loads(raw_credentials) @@ -92,14 +145,14 @@ def classify_null_flow_row( async def backfill_null_oauth2_flows(prisma_client: PrismaClient) -> dict[BackfillRule, int]: """Classify every ``auth_type=oauth2`` row whose ``oauth2_flow`` is null; stamp the provable ones, warn on the ambiguous ones, and return counts per rule.""" - null_rows: Final[list[Any]] = await prisma_client.db.litellm_mcpservertable.find_many( + null_rows: Final[Sequence[_MCPServerRow]] = await _mcp_server_table(prisma_client).find_many( where={"auth_type": "oauth2", "oauth2_flow": None}, ) if not null_rows: return {} server_ids: Final = [row.server_id for row in null_rows] - token_rows: Final[list[Any]] = await prisma_client.db.litellm_mcpusercredentials.find_many( + token_rows: Final[Sequence[_MCPUserCredentialRow]] = await _mcp_user_credentials_table(prisma_client).find_many( where={"server_id": {"in": server_ids}}, ) server_ids_with_oauth_tokens: Final[set[str]] = { @@ -141,7 +194,7 @@ async def backfill_null_oauth2_flows(prisma_client: PrismaClient) -> dict[Backfi stamped_flows: Final = {flow for _, (flow, _) in classified if flow is not None} for stamped_flow in stamped_flows: server_ids_for_flow = [row.server_id for row, (row_flow, _) in classified if row_flow == stamped_flow] - await prisma_client.db.litellm_mcpservertable.update_many( + await _mcp_server_table(prisma_client).update_many( where={"server_id": {"in": server_ids_for_flow}, "oauth2_flow": None}, data={"oauth2_flow": stamped_flow, "updated_by": _BACKFILL_AUDIT_ACTOR}, ) diff --git a/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py b/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py index c76c933c5b5..a4ef970b87a 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py +++ b/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py @@ -7,6 +7,7 @@ with ``client_id``, ``client_secret``, and ``token_url``. import asyncio import hashlib +from collections.abc import Mapping from typing import TYPE_CHECKING, Final import httpx @@ -67,7 +68,7 @@ class MCPOAuth2TokenCache(InMemoryCache): rest of the identity rather than stored in a key.""" material: Final = "\x00".join( ( - server.token_url or "", + server.effective_token_url or "", server.client_id or "", server.client_secret or "", " ".join(server.scopes or ()), @@ -82,7 +83,7 @@ class MCPOAuth2TokenCache(InMemoryCache): @staticmethod def _has_client_credentials_config(server: "MCPServer") -> bool: - return bool(server.client_id and server.client_secret and server.token_url) + return bool(server.client_id and server.client_secret and server.effective_token_url) async def async_get_token(self, server: "MCPServer") -> str | None: """Return a valid access token, fetching or refreshing as needed. @@ -112,19 +113,20 @@ class MCPOAuth2TokenCache(InMemoryCache): return token async def _fetch_token(self, server: "MCPServer") -> tuple[str, int]: - """POST to ``token_url`` with ``grant_type=client_credentials``. + """POST to ``effective_token_url`` with ``grant_type=client_credentials``. Returns ``(access_token, ttl_seconds)`` where ttl accounts for the expiry buffer so the cache entry expires before the real token does. """ client: Final = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) - if not server.client_id or not server.client_secret or not server.token_url: + token_url: Final = server.effective_token_url + if not server.client_id or not server.client_secret or not token_url: raise ValueError( f"MCP server '{server.server_id}' missing required OAuth2 fields: " f"client_id={bool(server.client_id)}, " f"client_secret={bool(server.client_secret)}, " - f"token_url={bool(server.token_url)}" + f"token_url={bool(token_url)}" ) token_request: Final = build_upstream_oauth2_token_request( @@ -146,7 +148,7 @@ class MCPOAuth2TokenCache(InMemoryCache): ) try: - response: Final = await client.post(server.token_url, data=data, headers=token_request.headers or None) + response: Final = await client.post(token_url, data=data, headers=token_request.headers or None) response.raise_for_status() except httpx.HTTPStatusError as exc: raise ValueError( @@ -312,9 +314,26 @@ async def resolve_mcp_auth( 1. ``mcp_auth_header`` — per-request/per-user override 2. OAuth2 client_credentials token — auto-fetched and cached 3. ``server.authentication_token`` — static token from config/DB + + ``resolved_token_header`` answers, for the same two inputs, which header the value belongs in. """ if mcp_auth_header: return mcp_auth_header if server.has_client_credentials: return await mcp_oauth2_token_cache.async_get_token(server) return server.authentication_token + + +def resolved_token_header( + server: "MCPServer", + mcp_auth_header: str | Mapping[str, str] | None = None, +) -> str | None: + """Which upstream header the value ``resolve_mcp_auth`` just returned belongs in. + + ``None`` means keep the auth_type default. A caller-supplied ``mcp_auth_header`` is the caller's + own credential aimed at the slot the upstream normally uses, so it never moves; only the values + the gateway resolved from its own config (the minted M2M token, the static token) follow + ``upstream_token_header``. Same inputs and same branch order as ``resolve_mcp_auth``, so the two + cannot disagree about which case they are in. + """ + return None if mcp_auth_header else server.upstream_token_header diff --git a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py index 083a98cdd36..16f58ef5b76 100644 --- a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py +++ b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py @@ -47,12 +47,14 @@ def sanitize_openapi_tool_name(raw_name: str) -> str: from litellm._logging import verbose_logger from litellm.litellm_core_utils.url_utils import async_safe_get from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, get_async_httpx_client, httpxSpecialProvider, ) from litellm.proxy._experimental.mcp_server.tool_registry import ( global_mcp_tool_registry, ) +from litellm.types.mcp import credential_redirect_hook, custom_credential_slot class _OpenAPIJSONSchema(TypedDict, total=False): @@ -119,6 +121,10 @@ _request_resolved_auth_headers: Final[contextvars.ContextVar[dict[str, str] | No "_request_resolved_auth_headers", default=None ) +_request_upstream_url: Final[contextvars.ContextVar[str | None]] = contextvars.ContextVar( + "_request_upstream_url", default=None +) + def _sanitize_path_parameter_value(param_value: object, param_name: str) -> str: """Ensure path params cannot introduce directory traversal.""" @@ -349,6 +355,35 @@ def build_input_schema(operation: _OpenAPIOperation) -> dict[str, object]: } +async def _drop_credential_across_origin(request: httpx.Request) -> None: + """Apply this request's cross-origin credential guard, if it needs one. + + Reads the per-request context rather than closing over it so the hook is one stable object, which + keeps the guarded client cacheable. A closure would key a new entry per call, and the handler it + built would never be closed. + """ + guard: Final = credential_redirect_hook( + _request_upstream_url.get() or "", custom_credential_slot(_request_resolved_auth_headers.get()) + ) + if guard is not None: + await guard(request) + + +def _upstream_client() -> AsyncHTTPHandler: + """The HTTP client for one upstream call, guarded when a credential rides a custom slot. + + A resolved credential outside ``Authorization`` is not stripped across origins by the client + itself, so this arm installs the same hook the MCP client uses. Both variants come from the + shared cache, so a guarded call reuses its connection pool like any other. + """ + if custom_credential_slot(_request_resolved_auth_headers.get()) is None: + return get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) + return get_async_httpx_client( + llm_provider=httpxSpecialProvider.MCP, + params={"event_hooks": {"request": [_drop_credential_across_origin]}}, + ) + + def _merge_openapi_tool_request_headers( static_headers: dict[str, str], ) -> dict[str, str]: @@ -510,8 +545,9 @@ def create_tool_function( except (json.JSONDecodeError, TypeError): json_body = {"data": body_value} - client: Final = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) + client: Final = _upstream_client() upstream: Final = server_label or f"{original_method.upper()} {path}" + url_token: Final = _request_upstream_url.set(url) try: if original_method == "get": @@ -529,6 +565,8 @@ def create_tool_function( except MaskedHTTPStatusError as e: _raise_for_upstream_failure(e.response, upstream, relays_upstream_auth) raise + finally: + _request_upstream_url.reset(url_token) _raise_for_upstream_failure(response, upstream, relays_upstream_auth) return response.text diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/__init__.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/__init__.py index a5dc75e3829..d61f8395677 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/__init__.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/__init__.py @@ -21,6 +21,7 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.result import ( Result, ) from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( + DEFAULT_CREDENTIAL_HEADER, Ambient, ApiKeyConfig, ApiKeySource, @@ -35,6 +36,7 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( ClientCredentialsConfig, ClientSecretAuth, CredError, + HeaderCarrier, IdJagConfig, NoneConfig, PassthroughConfig, @@ -45,9 +47,11 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( Subject, TokenExchangeConfig, parse_auth_spec_kind, + validate_header_name, ) __all__ = [ + "DEFAULT_CREDENTIAL_HEADER", "Ambient", "ApiKeyConfig", "ApiKeySource", @@ -63,6 +67,7 @@ __all__ = [ "ClientSecretAuth", "CredError", "Error", + "HeaderCarrier", "IdJagConfig", "NoOpAuth", "NoneConfig", @@ -78,4 +83,5 @@ __all__ = [ "TokenExchangeConfig", "UpstreamCredentialProvider", "parse_auth_spec_kind", + "validate_header_name", ] diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py index be8ec1b8eb3..4458ac7f190 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py @@ -20,6 +20,7 @@ from typing_extensions import assert_never from litellm.proxy._experimental.mcp_server.oauth_utils import resolve_upstream_resource from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( + DEFAULT_CREDENTIAL_HEADER, ApiKeyConfig, AuthorizationCodeConfig, ClientAuth, @@ -45,6 +46,15 @@ _TOKEN_EXCHANGE_SUBJECT_TOKEN_DEFAULT: Final = "urn:ietf:params:oauth:token-type _ID_JAG_SUBJECT_TOKEN_DEFAULT: Final = "urn:ietf:params:oauth:token-type:id_token" +def token_header(server: MCPServer) -> str: + """The upstream header this server's resolved credential occupies. + + One owner for every arm, so no spec builder spells the default itself and a server can never + hand two arms different answers. + """ + return server.upstream_token_header or DEFAULT_CREDENTIAL_HEADER + + def to_subject(user_api_key_auth: UserAPIKeyAuth | None, subject_token: str | None) -> Subject: """Map v1's authenticated principal onto the resolver's Subject. @@ -122,7 +132,7 @@ def _oauth2_spec(server: MCPServer, resource: str) -> ServerSpec | None: return ServerSpec( server_id=server.server_id, resource=resource, - config=AuthorizationCodeConfig(), + config=AuthorizationCodeConfig(header_name=token_header(server)), ) return None @@ -140,9 +150,10 @@ def _client_credentials_spec(server: MCPServer, resource: str) -> ServerSpec: server_id=server.server_id, resource=resource, config=ClientCredentialsConfig( + header_name=token_header(server), client_id=server.client_id, client_secret=SecretStr(server.client_secret) if server.client_secret else None, - token_url=server.token_url, + token_url=server.effective_token_url, scopes=tuple(server.scopes or ()), audience=server.audience, upstream_resource=resolve_upstream_resource(server), @@ -163,7 +174,7 @@ def _token_exchange_spec(server: MCPServer, resource: str) -> ServerSpec | None: normalizes to ``rfc8693`` so a bad config value cannot crash spec-building. ``audience`` is forwarded only when the operator set it; a missing one is omitted, not derived. """ - endpoint: Final = server.token_exchange_endpoint or server.token_url + endpoint: Final = server.token_exchange_endpoint or server.effective_token_url if not server.client_id or not server.client_secret: return None profile: Final[Literal["rfc8693", "entra_obo"]] = ( @@ -173,6 +184,7 @@ def _token_exchange_spec(server: MCPServer, resource: str) -> ServerSpec | None: server_id=server.server_id, resource=resource, config=TokenExchangeConfig( + header_name=token_header(server), profile=profile, subject_token_type=server.subject_token_type or DEFAULT_SUBJECT_TOKEN_TYPE, token_exchange_endpoint=endpoint, @@ -206,7 +218,7 @@ def _shared_key_spec( server_id=server.server_id, resource=resource, config=ApiKeyConfig( - header_name=header_name, + header_name=server.upstream_token_header or header_name, value_prefix=value_prefix, key_source=SharedKey(value=SecretStr(value)), ), @@ -231,6 +243,7 @@ def _id_jag_spec(server: MCPServer, resource: str) -> ServerSpec | None: server_id=server.server_id, resource=resource, config=IdJagConfig( + header_name=token_header(server), org_token_endpoint=org_token_endpoint, resource_token_endpoint=resource_token_endpoint, client_id=client_id, diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/authz_code_refresher.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/authz_code_refresher.py index 6ea5756d43d..92bd30694af 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/authz_code_refresher.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/authz_code_refresher.py @@ -88,7 +88,10 @@ class AuthorizationCodeRefresher: if token.refresh_token is None: return None server: Final = self._server_lookup(server_id) - if server is None or not server.token_url: + if server is None: + return None + token_url: Final = server.effective_token_url + if not token_url: return None try: @@ -106,7 +109,7 @@ class AuthorizationCodeRefresher: "refresh_token": token.refresh_token, **token_request.body, } - body: Final = await self._token_endpoint(server.token_url, form, token_request.headers) + body: Final = await self._token_endpoint(token_url, form, token_request.headers) if body is None: return None access_token: Final = body.get("access_token") diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/bridge_credentials.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/bridge_credentials.py index 69feaaff195..ab5fa65480e 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/bridge_credentials.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/bridge_credentials.py @@ -92,7 +92,7 @@ def build_bridge_token_response( The producer mirror of :func:`resolve_bridge_envelope`: a thin, pure wrapper over :func:`mint_envelope` that returns the sealed envelope, or the mint error as a value - (an oversized grant) for the caller to map onto an OAuth error response. + for the caller to map onto an OAuth error response. """ return mint_envelope(identity, grant, keys, now) @@ -239,5 +239,6 @@ def resolve_bridge_envelope( if opened.identity.server_id != expected_server_id: return BridgeEnvelopeInvalid() grant: Final = opened.grant - upstream_authorization: Final = f"{grant.token_type} {grant.access_token.get_secret_value()}" + authorization_scheme: Final = "Bearer" if grant.token_type.lower() == "bearer" else grant.token_type + upstream_authorization: Final = f"{authorization_scheme} {grant.access_token.get_secret_value()}" return BridgeEnvelopeAdmitted(identity=opened.identity, upstream_authorization=SecretStr(upstream_authorization)) diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py index d0053fbe0a8..ad18d1bb10f 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py @@ -19,9 +19,9 @@ Implements the client-credentials behavior contract for the v2 resolver: identity. The token-endpoint POST is injected (``M2MTokenEndpointPost``) so the grant orchestration is -testable without a live IdP; ``post_client_credentials_grant`` is the httpx edge and the one -place the untyped response boundary is contained. Failures are values: the source returns -``Result[OAuthToken, CredError]``; only the httpx edge touches exceptions. +testable without a live IdP; ``post_client_credentials_grant`` is the httpx edge. Failures are +values: the source returns ``Result[OAuthToken, CredError]``; only the httpx edge touches +exceptions. """ from __future__ import annotations @@ -50,6 +50,7 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.result import ( from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( ClientCredentialsConfig, CredError, + HeaderCarrier, ) @@ -94,18 +95,17 @@ async def post_client_credentials_grant( ) -> TokenEndpointOutcome: """POST the grant to the token endpoint and classify the transport outcome. - The httpx edge: litellm's handler is partially typed (and raises ``HTTPStatusError`` itself on - a 4xx/5xx), so the untyped boundary is contained here and every field the caller reads comes - out of a validated ``TokenEndpointOutcome``. + The httpx edge: litellm's handler raises ``HTTPStatusError`` itself on a 4xx/5xx, and every + field the caller reads comes out of a validated ``TokenEndpointOutcome``. """ from litellm.llms.custom_httpx.http_handler import ( # noqa: PLC0415 # defer heavy handler import to call time - get_async_httpx_client, # pyright: ignore[reportUnknownVariableType] # handler is partially typed + get_async_httpx_client, # pyright: ignore[reportUnknownVariableType] # handler factory params are coarsely typed ) from litellm.types.llms.custom_http import httpxSpecialProvider # noqa: PLC0415 # deferred with the handler import try: client: Final = get_async_httpx_client(llm_provider=httpxSpecialProvider.Oauth2Check) - response = await client.post( # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType] # handler is partially typed + response: Final = await client.post( # pyright: ignore[reportUnknownMemberType] # handler params are coarsely typed url, headers={"Accept": "application/json", **headers}, data=form ) except httpx.HTTPStatusError as status_err: @@ -113,8 +113,6 @@ async def post_client_credentials_grant( return TokenEndpointDenied(status_code=status_code, detail=f"token endpoint returned HTTP {status_code}") except Exception as exc: # noqa: BLE001 # any transport failure is the same outcome: unreachable return TokenEndpointUnreachable(detail=str(exc)) - if not isinstance(response, httpx.Response): - return TokenEndpointUnreachable(detail="token endpoint returned no response") try: body: Final = _TOKEN_BODY_ADAPTER.validate_json(response.content) except ValidationError: @@ -328,14 +326,21 @@ class ClientCredentialsBearerAuth(httpx.Auth): refetch fails, or the retried request 401s again, the upstream's response stands. """ - def __init__(self, access_token: str, refetch: Callable[[str], Awaitable[str | None]]) -> None: - self.header_name = "Authorization" + def __init__( + self, + access_token: str, + refetch: Callable[[str], Awaitable[str | None]], + carrier: HeaderCarrier, + ) -> None: + self._carrier = carrier + self.header_name = carrier.header_name self._access_token = SecretStr(access_token) self._refetch = refetch async def async_auth_flow(self, request: httpx.Request) -> AsyncGenerator[httpx.Request, httpx.Response]: token: Final = self._access_token.get_secret_value() - request.headers[self.header_name] = f"Bearer {token}" + name, value = self._carrier.header(token) + request.headers[name] = value response: Final = yield request if response.status_code != 401: return @@ -343,7 +348,8 @@ class ClientCredentialsBearerAuth(httpx.Auth): if fresh is None: return self._access_token = SecretStr(fresh) - request.headers[self.header_name] = f"Bearer {fresh}" + fresh_name, fresh_value = self._carrier.header(fresh) + request.headers[fresh_name] = fresh_value yield request def sync_auth_flow(self, request: httpx.Request) -> Generator[httpx.Request, httpx.Response, None]: diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/envelope.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/envelope.py index f91bdb9c9c2..df883d5a208 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/envelope.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/envelope.py @@ -19,17 +19,16 @@ in plaintext anywhere in the envelope. Failures are values: :func:`open_envelope` returns one of the frozen ``EnvelopeOpenError`` variants (discriminated on ``tag``) for invalid, expired, -tampered, or undecryptable input, and :func:`mint_envelope` returns -``EnvelopeTooLarge`` for oversized grants. Error values carry tags and sizes only, -never token material. +tampered, or undecryptable input, and :func:`mint_envelope` returns a typed error +for oversized grants or an unrepresentable provider lifetime. Error values carry +tags and metadata only, never token material. The pydantic input models reject programmer errors at construction (e.g. a non-positive ``expires_in`` or an empty required field). :func:`open_envelope` is additionally total over hostile, attacker-controlled input: it never raises, only returns an ``EnvelopeOpenError``. :func:`mint_envelope` operates on a gateway-supplied grant (an upstream IdP's UTF-8 JSON token response), so it does not -defend against non-UTF-8 field content that cannot survive JSON parsing; its only -value-typed failure is ``EnvelopeTooLarge``. +defend against non-UTF-8 field content that cannot survive JSON parsing. """ from __future__ import annotations @@ -57,10 +56,11 @@ ENVELOPE_ISSUER: Final = "litellm-mcp-bridge" """``iss`` claim stamped into every envelope and required back on open.""" MAX_ENVELOPE_TTL_SECONDS: Final = 3600 -"""Hard ceiling on ACCESS envelope lifetime. ``exp`` is ``min(upstream expires_in, this cap)`` -(the cap alone when the upstream omits ``expires_in``), matching the 1h lifetime of the -BYOK session bearer this module's signing approach is borrowed from: a client-held -credential should never outlive a bounded window even when the upstream token does.""" +"""Fallback ACCESS envelope lifetime when the upstream omits ``expires_in``. + +The historical exported name is retained for import compatibility. When the upstream +reports a positive lifetime, the envelope matches it so a renewal does not consume a +still-valid provider refresh grant.""" MAX_REFRESH_ENVELOPE_TTL_SECONDS: Final = 1209600 """Hard ceiling on REFRESH envelope lifetime (14 days). A refresh envelope only renews the short-lived @@ -202,7 +202,15 @@ class EnvelopeTooLarge(BaseModel): max_bytes: int -EnvelopeMintError: TypeAlias = EnvelopeTooLarge +class EnvelopeLifetimeUnrepresentable(BaseModel): + """A positive provider lifetime cannot be represented as a Python datetime.""" + + model_config = ConfigDict(frozen=True) + tag: Literal["envelope_lifetime_unrepresentable"] = "envelope_lifetime_unrepresentable" + expires_in: int + + +EnvelopeMintError: TypeAlias = EnvelopeTooLarge | EnvelopeLifetimeUnrepresentable class NotAnEnvelope(BaseModel): @@ -307,11 +315,17 @@ def mint_envelope( ) -> SealedEnvelope | EnvelopeMintError: """Seal ``grant`` for ``identity`` into a client-held envelope. - ``exp`` is ``min(grant.expires_in, MAX_ENVELOPE_TTL_SECONDS)`` seconds from ``now`` - (the cap alone when ``expires_in`` is absent). Returns ``EnvelopeTooLarge`` when the - serialized envelope exceeds ``MAX_ENVELOPE_BYTES``. + ``exp`` is ``grant.expires_in`` seconds from ``now`` when the upstream reports a + lifetime, or ``MAX_ENVELOPE_TTL_SECONDS`` when it does not. Returns + ``EnvelopeLifetimeUnrepresentable`` when that positive lifetime cannot be represented + as a Python datetime, or ``EnvelopeTooLarge`` when the serialized envelope exceeds + ``MAX_ENVELOPE_BYTES``. """ - expires_at: Final = now + timedelta(seconds=_envelope_ttl_seconds(grant.expires_in)) + ttl_seconds: Final = _envelope_ttl_seconds(grant.expires_in) + try: + expires_at: Final = now + timedelta(seconds=ttl_seconds) + except OverflowError: + return EnvelopeLifetimeUnrepresentable(expires_in=ttl_seconds) return _seal( kind="access", prefix=ENVELOPE_PREFIX, @@ -457,7 +471,7 @@ def _open_claims( def _envelope_ttl_seconds(upstream_expires_in: int | None) -> int: if upstream_expires_in is None: return MAX_ENVELOPE_TTL_SECONDS - return min(upstream_expires_in, MAX_ENVELOPE_TTL_SECONDS) + return upstream_expires_in def _refresh_ttl_seconds(upstream_refresh_expires_in: int | None) -> int: diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/resolver.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/resolver.py index 94c59962b70..3af7b51f432 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/resolver.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/resolver.py @@ -145,8 +145,8 @@ class UpstreamCredentialProvider: return await self._token_exchange(subject, server, config) case IdJagConfig() as config: return await self._id_jag(subject, server, config) - case AuthorizationCodeConfig(): - return await self._authorization_code(subject, server) + case AuthorizationCodeConfig() as config: + return await self._authorization_code(subject, server, config) case AwsSigV4Config(): return _not_implemented(AuthSpecKind.aws_sigv4) assert_never(server.config) @@ -284,15 +284,19 @@ class UpstreamCredentialProvider: match await self._exchanged_tokens.get_or_compute(slot, _exchange, fingerprint=fingerprint): case Ok(access_token): - return Ok(StaticHeaderAuth(f"Bearer {access_token}")) + header_name, header_value = config.header(access_token) + return Ok(StaticHeaderAuth(header_value, header_name=header_name)) case Error(err): return Error(err) - async def _authorization_code(self, subject: Subject, server: ServerSpec) -> Result[StaticHeaderAuth, CredError]: + async def _authorization_code( + self, subject: Subject, server: ServerSpec, config: AuthorizationCodeConfig + ) -> Result[StaticHeaderAuth, CredError]: token: Final = await self._authz_token(subject, server) if token is None: return Error(CredError.of_unauthorized("Authorization required: complete the OAuth flow for this server.")) - return Ok(StaticHeaderAuth(f"Bearer {token.access_token}", header_name="Authorization")) + header_name, header_value = config.header(token.access_token) + return Ok(StaticHeaderAuth(header_value, header_name=header_name)) async def _client_credentials( self, server_id: str, config: ClientCredentialsConfig @@ -307,7 +311,7 @@ class UpstreamCredentialProvider: match await self._client_credentials_source.get(server_id, config): case Ok(token): refetch: Final = partial(self._client_credentials_source.refetch, server_id, config) - return Ok(ClientCredentialsBearerAuth(token.access_token, refetch)) + return Ok(ClientCredentialsBearerAuth(token.access_token, refetch, config)) case Error(err): return Error(err) @@ -332,7 +336,8 @@ class UpstreamCredentialProvider: inbound.get_secret_value(), server, config, tenant_id=subject.tenant_id ): case Ok(token): - return Ok(StaticHeaderAuth(f"Bearer {token.access_token}", header_name="Authorization")) + header_name, header_value = config.header(token.access_token) + return Ok(StaticHeaderAuth(header_value, header_name=header_name)) case Error(err): return Error(err) diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_credentials.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_credentials.py index 70a04ac290a..df2bbdba345 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_credentials.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_credentials.py @@ -20,13 +20,16 @@ from datetime import datetime from functools import lru_cache from typing import Final, Literal, TypeAlias -from pydantic import BaseModel, ConfigDict, SecretStr +from pydantic import BaseModel, ConfigDict, Field, SecretStr, ValidationError from litellm.proxy._experimental.mcp_server.outbound_credentials.session_token import ( + AsymmetricSessionKeys, OpenedSessionToken, SessionExpired, SessionKeys, SessionPrincipal, + SessionRotatedPublicKey, + SessionSigningKeys, is_session_refresh_token, is_session_token, open_session_refresh_token, @@ -68,6 +71,99 @@ def session_keys_from_master_key(master_key: str) -> SessionKeys: return SessionKeys(signing_key=SecretStr(signing)) +class SessionSigningPreviousKey(BaseModel): + """One retired key in ``mcp_session_token_signing.previous_public_keys``: its ``kid`` + and the PEM public half (inline or an ``os.environ/`` reference).""" + + model_config = ConfigDict(frozen=True, extra="forbid") + kid: str = Field(min_length=1) + public_key: str = Field(min_length=1) + + +class MCPSessionTokenSigningSettings(BaseModel): + """The ``general_settings.mcp_session_token_signing`` block: opt-in asymmetric signing + for the gateway session tokens. Absent, the gateway keeps the backward-compatible + HS256 key derived from ``master_key``. ``private_key`` and each ``public_key`` accept + a PEM string inline or an ``os.environ/`` (or secret manager) reference.""" + + model_config = ConfigDict(frozen=True, extra="forbid") + algorithm: Literal["RS256"] + kid: str = Field(min_length=1) + private_key: str = Field(min_length=1) + previous_public_keys: tuple[SessionSigningPreviousKey, ...] = () + + +class SessionSigningConfigError(BaseModel): + """``mcp_session_token_signing`` is present but unusable (bad shape, unresolvable + secret reference, or a key that is not a loadable RSA PEM); the caller fails closed + with a server error instead of silently falling back to HS256.""" + + model_config = ConfigDict(frozen=True) + tag: Literal["session_signing_config_error"] = "session_signing_config_error" + detail: str + + +def _resolve_key_material(value: str) -> str | None: + if not value.startswith("os.environ/"): + return value + from litellm.secret_managers.main import get_secret_str # noqa: PLC0415 # heavy import kept off the pure path + + return get_secret_str(value) + + +def resolve_session_signing_keys( + master_key: str, + raw_settings: object | None, +) -> SessionSigningKeys | SessionSigningConfigError: + """Turn the operator's ``mcp_session_token_signing`` setting into signing key material. + + ``None`` (the setting absent) keeps the backward-compatible HS256 key derived from + ``master_key``. A present setting must fully validate into RS256 material; any defect + is a ``SessionSigningConfigError`` value so token issuance and admission fail closed + rather than minting under a key the operator did not intend. + """ + if raw_settings is None: + return session_keys_from_master_key(master_key) + try: + settings: Final = MCPSessionTokenSigningSettings.model_validate(raw_settings) + except ValidationError as exc: + return SessionSigningConfigError(detail=f"mcp_session_token_signing is malformed: {exc}") + private_pem: Final = _resolve_key_material(settings.private_key) + if private_pem is None: + return SessionSigningConfigError(detail="mcp_session_token_signing.private_key reference did not resolve") + resolved_previous: Final = tuple( + (previous.kid, _resolve_key_material(previous.public_key)) for previous in settings.previous_public_keys + ) + unresolved: Final = tuple(kid for kid, pem in resolved_previous if pem is None) + if unresolved: + return SessionSigningConfigError( + detail=f"mcp_session_token_signing.previous_public_keys reference did not resolve for kid(s): {', '.join(unresolved)}" + ) + try: + return AsymmetricSessionKeys( + private_key_pem=SecretStr(private_pem), + kid=settings.kid, + previous_public_keys=tuple( + SessionRotatedPublicKey(kid=kid, public_key_pem=pem) + for kid, pem in resolved_previous + if pem is not None + ), + ) + except ValidationError as exc: + return SessionSigningConfigError( + detail=f"mcp_session_token_signing keys are not usable RSA PEM material: {exc}" + ) + + +def active_session_signing_keys(master_key: str) -> SessionSigningKeys | SessionSigningConfigError: + """Wiring helper for the token endpoint and the admission edge: resolve the signing + keys from the live ``general_settings.mcp_session_token_signing`` block, or derive the + default HS256 key from ``master_key`` when the block is absent.""" + from litellm.proxy.proxy_server import general_settings # noqa: PLC0415 # circular import at module load + + return resolve_session_signing_keys(master_key, general_settings.get("mcp_session_token_signing")) + + class NotSessionBearer(BaseModel): """The bearer is not session-shaped; admission continues on its normal path.""" @@ -116,7 +212,7 @@ def is_session_bearer_shaped(authorization_value: str) -> bool: def resolve_session_bearer( authorization_value: str, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, ) -> SessionBearerResult: """Classify an ``Authorization`` value presented at the aggregate MCP edge. @@ -166,7 +262,7 @@ SessionRefreshResult: TypeAlias = SessionRefreshOpened | SessionRefreshInvalid def open_session_refresh_bearer( refresh_value: str, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, expected_client_id: str, ) -> SessionRefreshResult: diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py index d6b0a462062..0fa750a4c4a 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py @@ -8,8 +8,11 @@ is therefore a stable REFERENCE, not an authorization: admission reloads the liv record and policy on every request, so deactivating the user (or their team) kills outstanding sessions immediately without a revocation store. -Wire shape: ``llm_session_`` (access) / ``llm_srefresh_`` (refresh) + an HS256 JWT, -the same signing approach as :mod:`.envelope`. Claims are ``iss``/``iat``/``exp`` +Wire shape: ``llm_session_`` (access) / ``llm_srefresh_`` (refresh) + a JWT signed with +the injected key material: HS256 under the default master-key-derived secret (the same +signing approach as :mod:`.envelope`), or RS256 under an operator-provided RSA private +key (:class:`AsymmetricSessionKeys`) so downstream validators hold only the public half. +Claims are ``iss``/``iat``/``exp`` plus ``jti`` (per-mint uniqueness, so two tokens minted in the same second never collide and a future revocation list has a stable handle), ``kind``, ``user_id``, and ``client_id``; ``client_id`` binds the refresh token @@ -31,11 +34,16 @@ injected ``now``); the strict pydantic claims model is the sole, total type gate from __future__ import annotations import secrets +from collections import Counter from datetime import datetime, timedelta +from functools import lru_cache from typing import Final, Literal, TypeAlias import jwt -from pydantic import BaseModel, ConfigDict, Field, SecretStr, ValidationError +from cryptography.exceptions import UnsupportedAlgorithm +from cryptography.hazmat.primitives import serialization +from cryptography.hazmat.primitives.asymmetric import rsa +from pydantic import BaseModel, ConfigDict, Field, SecretStr, ValidationError, field_validator, model_validator SESSION_TOKEN_PREFIX: Final = "llm_session_" """Marker prefix on every serialized session ACCESS token so the admission edge can cheaply @@ -54,9 +62,9 @@ the envelope issuer so a token of one family can never validate in the other eve hypothetical shared signing key.""" SESSION_TTL_SECONDS: Final = 3600 -"""Session ACCESS token lifetime (1h), matching the access-envelope and BYOK session bearer -windows: a client-held credential never outlives a bounded window, and each refresh -re-validates the live user before re-minting.""" +"""Session ACCESS token lifetime (1h), matching the BYOK session bearer window: a +client-held credential never outlives a bounded window, and each refresh re-validates +the live user before re-minting.""" SESSION_REFRESH_TTL_SECONDS: Final = 1209600 """Session REFRESH token lifetime (14 days), matching the refresh-envelope bound. Each @@ -71,6 +79,11 @@ limits while bounding hostile input before JWT parsing.""" _SESSION_JWT_ALGORITHM: Final = "HS256" +_SESSION_RSA_ALGORITHM: Final = "RS256" + +_MIN_RSA_KEY_BITS: Final = 2048 +"""RFC 7518 section 3.3: RS256 requires a key of at least 2048 bits.""" + SessionTokenKind = Literal["session", "session_refresh"] """Which credential a session token is. Stamped into the signed claims and required to match on open, so a signature-valid token of one kind cannot be replayed as the other even if its @@ -120,6 +133,85 @@ class SessionKeys(BaseModel): signing_key: SecretStr = Field(min_length=32) +class SessionRotatedPublicKey(BaseModel): + """The public half of a retired signing key, kept verifiable under its ``kid`` during a + rotation window so tokens minted before the rotation stay valid until they expire.""" + + model_config = ConfigDict(frozen=True) + kid: str = Field(min_length=1) + public_key_pem: str = Field(min_length=1) + + @field_validator("public_key_pem") + @classmethod + def _pem_is_an_rsa_public_key(cls, value: str) -> str: + try: + loaded: Final = serialization.load_pem_public_key(value.encode()) + except (ValueError, TypeError, UnsupportedAlgorithm) as exc: + raise ValueError(f"public_key_pem is not a loadable PEM public key: {exc}") from exc + if not isinstance(loaded, rsa.RSAPublicKey): + raise ValueError("public_key_pem must be an RSA public key in PEM format") # noqa: TRY004 # pydantic validators must raise ValueError + if loaded.key_size < _MIN_RSA_KEY_BITS: + raise ValueError(f"public_key_pem must be an RSA key of at least {_MIN_RSA_KEY_BITS} bits") + return value + + +class AsymmetricSessionKeys(BaseModel): + """Injected RS256 key material: the issuer-held RSA private key and the stable ``kid`` + stamped into every minted token's JOSE header, plus the public halves of previously + rotated keys that verification still accepts while their tokens age out. Downstream + validators never need the private key: :func:`session_public_key_pem` yields the + public half to distribute.""" + + model_config = ConfigDict(frozen=True) + private_key_pem: SecretStr + kid: str = Field(min_length=1) + previous_public_keys: tuple[SessionRotatedPublicKey, ...] = () + + @field_validator("private_key_pem") + @classmethod + def _pem_is_a_strong_rsa_private_key(cls, value: SecretStr) -> SecretStr: + try: + loaded: Final = serialization.load_pem_private_key(value.get_secret_value().encode(), password=None) + except (ValueError, TypeError, UnsupportedAlgorithm) as exc: + raise ValueError(f"private_key_pem is not a loadable unencrypted PEM private key: {exc}") from exc + if not isinstance(loaded, rsa.RSAPrivateKey): + raise ValueError("private_key_pem must be an unencrypted RSA private key in PEM format") # noqa: TRY004 # pydantic validators must raise ValueError + if loaded.key_size < _MIN_RSA_KEY_BITS: + raise ValueError(f"private_key_pem must be an RSA key of at least {_MIN_RSA_KEY_BITS} bits") + return value + + @model_validator(mode="after") + def _kids_are_unique(self) -> AsymmetricSessionKeys: + kids: Final = (self.kid, *(previous.kid for previous in self.previous_public_keys)) + duplicates: Final = tuple(kid for kid, count in Counter(kids).items() if count > 1) + if duplicates: + raise ValueError( + f"every kid must be unique across the current and previous keys; duplicated: {', '.join(duplicates)}" + ) + return self + + +SessionSigningKeys: TypeAlias = SessionKeys | AsymmetricSessionKeys +"""Every key material shape the mints and openers accept: the default master-key-derived +HS256 secret, or operator-configured RS256 RSA keys.""" + + +@lru_cache(maxsize=8) +def _public_key_pem_from_private(private_key_pem: str) -> str: + loaded: Final = serialization.load_pem_private_key(private_key_pem.encode(), password=None) + return ( + loaded.public_key() + .public_bytes(serialization.Encoding.PEM, serialization.PublicFormat.SubjectPublicKeyInfo) + .decode() + ) + + +def session_public_key_pem(keys: AsymmetricSessionKeys) -> str: + """The PEM public half of the current RS256 signing key: the only material a downstream + validator (an external gateway verifying ``kid``-matched tokens) ever needs.""" + return _public_key_pem_from_private(keys.private_key_pem.get_secret_value()) + + class MintedSessionToken(BaseModel): """A minted session token: the client-held bearer value and when it expires.""" @@ -129,12 +221,17 @@ class MintedSessionToken(BaseModel): class OpenedSessionToken(BaseModel): - """A validated session token of either kind: the principal it was minted for, plus the - ``jti`` so the token endpoint can enforce single-use rotation on a refresh token.""" + """A validated session token of either kind: the principal it was minted for, the + ``jti`` so the token endpoint can enforce single-use rotation on a refresh token, and + the signed ``kind``/``iat``/``exp`` so an introspection response can report the + token's metadata without re-decoding.""" model_config = ConfigDict(frozen=True) principal: SessionPrincipal jti: str + kind: SessionTokenKind + iat: int + exp: int class SessionTokenTooLarge(BaseModel): @@ -221,7 +318,7 @@ def is_session_refresh_token(candidate: str) -> bool: def mint_session_token( principal: SessionPrincipal, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, ) -> MintedSessionToken | SessionTokenMintError: """Mint the short-lived session ACCESS token for ``principal``. @@ -241,7 +338,7 @@ def mint_session_token( def mint_session_refresh_token( principal: SessionPrincipal, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, ) -> MintedSessionToken | SessionTokenMintError: """Mint the long-lived session REFRESH token for ``principal``. @@ -262,7 +359,7 @@ def mint_session_refresh_token( def open_session_token( candidate: str, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, ) -> OpenedSessionToken | SessionTokenOpenError: """Validate a session ACCESS ``candidate`` and recover the principal. @@ -275,7 +372,7 @@ def open_session_token( def open_session_refresh_token( candidate: str, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, ) -> OpenedSessionToken | SessionTokenOpenError: """Validate a session REFRESH ``candidate`` and recover the principal. @@ -292,7 +389,7 @@ def _mint( prefix: str, principal: SessionPrincipal, expires_at: datetime, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, ) -> MintedSessionToken | SessionTokenTooLarge: """Sign the claims for either token kind and enforce the size cap. Shared by both mints @@ -309,20 +406,33 @@ def _mint( audience=principal.audience, team_id=principal.team_id, ) - token: Final = prefix + jwt.encode( - claims.model_dump(exclude_none=True), keys.signing_key.get_secret_value(), algorithm=_SESSION_JWT_ALGORITHM - ) + token: Final = prefix + _sign_claims(claims, keys) size_bytes: Final = len(token.encode("utf-8")) if size_bytes > MAX_SESSION_TOKEN_BYTES: return SessionTokenTooLarge(size_bytes=size_bytes, max_bytes=MAX_SESSION_TOKEN_BYTES) return MintedSessionToken(token=SecretStr(token), expires_at=expires_at) +def _sign_claims(claims: _SessionClaims, keys: SessionSigningKeys) -> str: + """Sign the claim set under whichever key material was injected: RS256 with the ``kid`` + in the JOSE header (so a validator can pick the right public key), or the default + HS256 secret with no header extras (byte-compatible with every pre-RS256 token).""" + payload: Final = claims.model_dump(exclude_none=True) + if isinstance(keys, AsymmetricSessionKeys): + return jwt.encode( + payload, + keys.private_key_pem.get_secret_value(), + algorithm=_SESSION_RSA_ALGORITHM, + headers={"kid": keys.kid}, + ) + return jwt.encode(payload, keys.signing_key.get_secret_value(), algorithm=_SESSION_JWT_ALGORITHM) + + def _open( candidate: str, prefix: str, expected_kind: SessionTokenKind, - keys: SessionKeys, + keys: SessionSigningKeys, now: datetime, ) -> OpenedSessionToken | SessionTokenOpenError: """Prefix-route, size-bound, signature-verify, kind-check, and expiry-check an @@ -337,7 +447,7 @@ def _open( return SessionMalformed() if len(candidate.encode("utf-8", "surrogatepass")) > MAX_SESSION_TOKEN_BYTES: return SessionMalformed() - claims: Final = _decode_claims(candidate.removeprefix(prefix), keys.signing_key) + claims: Final = _decode_claims(candidate.removeprefix(prefix), keys) if not isinstance(claims, _SessionClaims): return claims if claims.kind != expected_kind: @@ -353,17 +463,57 @@ def _open( team_id=claims.team_id, ), jti=claims.jti, + kind=claims.kind, + iat=claims.iat, + exp=claims.exp, ) +class _VerificationMaterial(BaseModel): + model_config = ConfigDict(frozen=True) + key: SecretStr + algorithm: Literal["HS256", "RS256"] + + +def _verification_material( + compact: str, + keys: SessionSigningKeys, +) -> _VerificationMaterial | SessionBadSignature | SessionMalformed: + """Pick the single key and algorithm the candidate is allowed to verify under. + + HS256 mode has exactly one secret. RS256 mode routes by the JOSE header ``kid``: the + current key's derived public half, or a retired key's stored public half during a + rotation window. An unknown or missing ``kid`` is ``SessionBadSignature`` (a foreign + key), and an undecodable header is ``SessionMalformed``. The algorithm is pinned per + key shape, never read from the header, so an HS256 token can never be verified + against a public key or vice versa. + """ + if isinstance(keys, SessionKeys): + return _VerificationMaterial(key=keys.signing_key, algorithm=_SESSION_JWT_ALGORITHM) + try: + header: Final = jwt.get_unverified_header(compact) + except jwt.InvalidTokenError: + return SessionMalformed() + kid: Final = header.get("kid") + if kid == keys.kid: + return _VerificationMaterial(key=SecretStr(session_public_key_pem(keys)), algorithm=_SESSION_RSA_ALGORITHM) + for previous in keys.previous_public_keys: + if previous.kid == kid: + return _VerificationMaterial(key=SecretStr(previous.public_key_pem), algorithm=_SESSION_RSA_ALGORITHM) + return SessionBadSignature() + + def _decode_claims( compact: str, - signing_key: SecretStr, + keys: SessionSigningKeys, ) -> _SessionClaims | SessionBadSignature | SessionMalformed: - """Verify the HS256 signature and shape of an attacker-controlled compact JWT. + """Verify the signature and shape of an attacker-controlled compact JWT. ``compact`` is fully hostile and bounded to ``MAX_SESSION_TOKEN_BYTES`` by the caller. - PyJWT's ``iat``/``nbf``/``exp`` validators are disabled: they raise on hostile claim + The accepted algorithm is pinned by :func:`_verification_material` from the injected + key shape, so ``alg`` confusion (``none``, or HS256 signed with a public key as the + secret) fails before or at signature verification. PyJWT's ``iat``/``nbf``/``exp`` + validators are disabled: they raise on hostile claim types and, for ``iat``/``nbf``, compare against the wall clock rather than the injected ``now`` (``exp`` is checked by the caller against ``now``). Apart from a signature mismatch, every decode failure is ``SessionMalformed``: a non-UTF-8 candidate surfaces @@ -371,11 +521,14 @@ def _decode_claims( ``TypeError`` from PyJWT's claim validators, and a wrong issuer or structurally invalid token as an ``InvalidTokenError``. ``_SessionClaims`` is the total type gate. """ + material: Final = _verification_material(compact, keys) + if not isinstance(material, _VerificationMaterial): + return material try: payload: Final = jwt.decode( compact, - signing_key.get_secret_value(), - algorithms=[_SESSION_JWT_ALGORITHM], + material.key.get_secret_value(), + algorithms=[material.algorithm], issuer=SESSION_ISSUER, options={ "verify_exp": False, diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py index f6d40b82eda..84f714db449 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py @@ -111,9 +111,6 @@ class TokenEndpointClient: return Error( CredError.of_upstream_unavailable("token exchange failed: token endpoint returned a non-JSON response") ) - if raw is None: - verbose_proxy_logger.warning("MCP token endpoint %s returned no response", endpoint) - return Error(CredError.of_upstream_unavailable("token exchange failed: no response from token endpoint")) try: parsed: Final = _TokenEndpointResponse.model_validate(raw) except ValidationError: @@ -199,7 +196,7 @@ def _cache_ttl_seconds(expires_in: int | None) -> int: ) -async def _post_form(endpoint: str, data: dict[str, str]) -> object | None: +async def _post_form(endpoint: str, data: dict[str, str]) -> object: # litellm's httpx handler and httpx.Response are only partially typed; the token endpoint # returns a JSON object that `_TokenEndpointResponse` validates, so the untyped boundary is # contained here. A non-2xx raises `httpx.HTTPStatusError`, an unreachable endpoint raises @@ -208,8 +205,6 @@ async def _post_form(endpoint: str, data: dict[str, str]) -> object | None: # each to a CredError. client = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) # pyright: ignore[reportUnknownVariableType] # litellm http handler is untyped response = await client.post(endpoint, data=data) # pyright: ignore[reportUnknownMemberType,reportUnknownVariableType] # litellm http handler is untyped - if response is None: - return None response.raise_for_status() return response.json() # pyright: ignore[reportAny] # untyped JSON; validated by _TokenEndpointResponse in fetch diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/types.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/types.py index ce9948f0448..67aad3e443e 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/types.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/types.py @@ -31,7 +31,7 @@ from enum import Enum from typing import Annotated, Final, Literal from expression import case, tag, tagged_union -from pydantic import BaseModel, ConfigDict, Field, SecretStr +from pydantic import BaseModel, ConfigDict, Field, SecretStr, field_validator from typing_extensions import assert_never from litellm.proxy._experimental.mcp_server.outbound_credentials.result import ( @@ -39,7 +39,11 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.result import ( Ok, Result, ) -from litellm.types.mcp import DEFAULT_SUBJECT_TOKEN_TYPE +from litellm.types.mcp import ( + DEFAULT_CREDENTIAL_HEADER, + DEFAULT_SUBJECT_TOKEN_TYPE, + normalize_upstream_header_name, +) class AuthSpecKind(str, Enum): @@ -161,7 +165,52 @@ class CredError: assert_never(self.tag) -class AuthorizationCodeConfig(BaseModel): +def validate_header_name(raw: str) -> Result[str, CredError]: + """``normalize_upstream_header_name`` with this package's error-as-value policy. + + The grammar itself lives in ``litellm.types.mcp`` so the v1 model, the management endpoint and + this vocabulary all judge a header name the same way while each keeps its own failure shape. + """ + normalized: Final = normalize_upstream_header_name(raw) + if normalized is None: + return Error(CredError.of_misconfigured(f"invalid upstream header name: {raw!r}")) + return Ok(normalized) + + +class HeaderCarrier(BaseModel): + """Where a resolved credential is written upstream, and how its value is formatted. + + ``Authorization: Bearer`` is only OAuth's *default* conveyance (RFC 6750 section 2.1), not its + only one: an ESB or API gateway commonly terminates its own credential in a private header while + a second credential passes through to the origin, so a credential has to be able to say which + slot it owns. Modeled like OpenAPI's apiKey scheme, so any upstream convention is expressible + (Authorization + Bearer, a raw value on X-API-Key, Ocp-Apim-Subscription-Key, esb-oauth, ...). + + Every config whose credential the gateway mints or holds inherits this, so no resolver arm names + a header itself and the conflict rule in ``_resolve_v2_auth`` can always ask the auth object + which slot it is about to occupy. ``passthrough`` deliberately does not: it forwards the + caller's own credential into the slot the caller used, and mints nothing to place. + """ + + model_config = ConfigDict(frozen=True) + header_name: str = DEFAULT_CREDENTIAL_HEADER + value_prefix: str = "Bearer" + + @field_validator("header_name") + @classmethod + def _check_header_name(cls, value: str) -> str: + match validate_header_name(value): + case Ok(name): + return name + case Error(err): + raise ValueError(err.summary) + + def header(self, value: str) -> tuple[str, str]: + formatted: Final = f"{self.value_prefix} {value}" if self.value_prefix else value + return self.header_name, formatted + + +class AuthorizationCodeConfig(HeaderCarrier): """Per-user 3LO; the gateway is the OAuth client and stores the user's token. Endpoints are discovered (RFC 9728 -> RFC 8414) and the client is registered via DCR @@ -179,7 +228,7 @@ class AuthorizationCodeConfig(BaseModel): token_url: str | None = None -class ClientCredentialsConfig(BaseModel): +class ClientCredentialsConfig(HeaderCarrier): """M2M service account; one upstream identity for every user. Fields are optional so the config can be built incomplete: a value may be supplied at @@ -203,7 +252,7 @@ class ClientCredentialsConfig(BaseModel): token_endpoint_auth_method: Literal["client_secret_post", "client_secret_basic"] | None = None -class TokenExchangeConfig(BaseModel): +class TokenExchangeConfig(HeaderCarrier): """OBO: swap the caller's live inbound token for a token bound to the upstream's audience. The gateway authenticates to the exchange endpoint as an OAuth client (`client_id`/`client_secret`); the inbound token is sent only to that endpoint, never to the upstream. @@ -255,7 +304,7 @@ class ClientSecretAuth(BaseModel): ClientAuth = Annotated[PrivateKeyJwtAuth | ClientSecretAuth, Field(discriminator="source")] -class IdJagConfig(BaseModel): +class IdJagConfig(HeaderCarrier): """draft-ietf-oauth-identity-assertion-authz-grant (Okta "AI agent token exchange"). Two legs: leg 1 is an RFC 8693 token exchange at the IdP org AS (`org_token_endpoint`) that @@ -297,23 +346,16 @@ class Byok(BaseModel): ApiKeySource = Annotated[SharedKey | Byok, Field(discriminator="source")] -class ApiKeyConfig(BaseModel): +class ApiKeyConfig(HeaderCarrier): """A fixed credential injected as a header. The value is shared (in config) or seeded - per-user (pulled from the store); `header_name` and `value_prefix` say where and how it is - written, modeled like OpenAPI's apiKey scheme so any upstream convention is expressible - (Authorization + Bearer, a raw value on X-API-Key, Ocp-Apim-Subscription-Key, etc.). + per-user (pulled from the store); the inherited `header_name` and `value_prefix` say where + and how it is written. """ model_config = ConfigDict(frozen=True) kind: Literal[AuthSpecKind.api_key] = AuthSpecKind.api_key - header_name: str = "Authorization" - value_prefix: str = "Bearer" key_source: ApiKeySource - def header(self, value: str) -> tuple[str, str]: - formatted: Final = f"{self.value_prefix} {value}" if self.value_prefix else value - return self.header_name, formatted - class PassthroughConfig(BaseModel): """Client-driven upstream OAuth; the gateway forwards the client's upstream token.""" diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index 3a8fd6de5e5..d1ef73a15cd 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -1,13 +1,16 @@ import asyncio import importlib -from collections.abc import Awaitable, Callable, Mapping +from collections.abc import Awaitable, Callable, Mapping, Sequence from datetime import datetime +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal +import anyio import httpx from fastapi import APIRouter, Depends, HTTPException, Query, Request, status from litellm._logging import verbose_logger +from litellm.constants import MCP_CLIENT_TIMEOUT, MCP_TOOL_LISTING_TIMEOUT from litellm.exceptions import ( BlockedPiiEntityError, GuardrailRaisedException, @@ -18,8 +21,11 @@ from litellm.proxy._experimental.mcp_server.exceptions import ( MCPUpstreamAuthError, ) from litellm.proxy._experimental.mcp_server.faults.list_outcomes import ( + ServerListOk, + ServerOutcome, classify_list_exception, list_fault_http_status, + outcome_wire_value, ) from litellm.proxy._experimental.mcp_server.ui_session_utils import ( acting_user_auth, @@ -86,8 +92,6 @@ def _connection_error_message(exc: BaseException) -> str: if MCP_AVAILABLE: - from mcp.types import Tool as MCPTool - from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( _UPSTREAM_OAUTH_DISCOVERY_AUTH_TYPES, global_mcp_server_manager, @@ -99,6 +103,7 @@ if MCP_AVAILABLE: ListMCPToolsRestAPIResponseObject, MCPInfo, MCPServer, + _aggregate_server_key, # pyright: ignore[reportPrivateUsage] # same per-server key as the tools/list _meta outcomes _apply_toolset_scope, _fire_mcp_tool_call_logging, execute_mcp_tool, @@ -168,8 +173,11 @@ if MCP_AVAILABLE: MCPRequestHandler, ) from litellm.proxy._experimental.mcp_server.tool_search import ( + AGENT_SEARCH_TOOL_NAME, + DEFAULT_AGENT_SEARCH_TOP_K, MCP_TOOL_SEARCH_TOOL_NAME, coerce_top_k, + handle_agent_search, handle_mcp_tool_call, handle_mcp_tool_search, ) @@ -182,6 +190,14 @@ if MCP_AVAILABLE: detail={"error": "forbidden", "message": f"{tool_name} requires mcp_tool_search_enabled on the key"}, ) tool_arguments: Final = data.get("arguments") or {} + if tool_name == AGENT_SEARCH_TOOL_NAME: + return await handle_agent_search( + query=str(tool_arguments.get("query", "")), + top_k=coerce_top_k( + tool_arguments.get("top_k", DEFAULT_AGENT_SEARCH_TOP_K), default=DEFAULT_AGENT_SEARCH_TOP_K + ), + user_api_key_dict=user_api_key_dict, + ) rest_client_ip: Final = IPAddressUtils.get_mcp_client_ip(request) ( virtual_mcp_auth_header, @@ -792,9 +808,6 @@ if MCP_AVAILABLE: list(allowed_server_ids_set), _rest_client_ip ) - list_tools_result: Final = [] - error_message = None - # If server_id is specified, only query that specific server if server_id: return await _list_tools_for_single_server( @@ -838,22 +851,19 @@ if MCP_AVAILABLE: else {} ) - # Query all servers the user has access to - errors: Final = [] - for allowed_server_id in allowed_server_ids: - server = global_mcp_server_manager.get_mcp_server_by_id(allowed_server_id) - if server is None: - continue - - server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header) - user_oauth_extra_headers = await _get_user_oauth_extra_headers( + async def list_server( + server: MCPServer, + ) -> tuple[Sequence[ListMCPToolsRestAPIResponseObject], ServerOutcome]: + server_auth_header: Final = _get_server_auth_header( + server, mcp_server_auth_headers, mcp_auth_header + ) + user_oauth_extra_headers: Final = await _get_user_oauth_extra_headers( server, user_api_key_dict, prefetched_creds=prefetched_oauth_creds, ) - try: - tools_result = await _get_tools_for_single_server( + tools_result: Final = await _get_tools_for_single_server( server, server_auth_header, raw_headers_from_request, @@ -861,24 +871,36 @@ if MCP_AVAILABLE: extra_headers=user_oauth_extra_headers, apply_tool_filters=apply_tool_filters, ) - list_tools_result.extend(tools_result) except Exception as e: verbose_logger.exception("Error getting tools from %s: %s", server.name, e) - errors.append( - f"{get_server_prefix(server)}: {classify_list_exception(e).tag}" - if isinstance(e, (MCPServerListError, MCPUpstreamAuthError)) - else f"{get_server_prefix(server)}: {e}" - ) - continue + return (), classify_list_exception(e) + return tools_result, ServerListOk(tool_count=len(tools_result)) - if errors and not list_tools_result: - error_message = "Failed to get tools from servers: " + "; ".join(errors) - - return { - "tools": list_tools_result, - "error": "partial_failure" if error_message else None, - "message": (error_message if error_message else "Successfully retrieved tools"), - } + # Query all servers the user has access to + queried_servers: Final = tuple( + server + for server in map(global_mcp_server_manager.get_mcp_server_by_id, allowed_server_ids) + if server is not None + ) + listings: Final = tuple([await list_server(server) for server in queried_servers]) + list_tools_result: Final = [tool for tools, _ in listings for tool in tools] + server_outcomes: Final = MappingProxyType( + {_aggregate_server_key(server): outcome for server, (_, outcome) in zip(queried_servers, listings)} + ) + errors: Final = tuple( + f"{key}: {outcome.tag}" for key, outcome in server_outcomes.items() if outcome.tag != "ok" + ) + error_message: Final = ( + "Failed to get tools from servers: " + "; ".join(errors) + if errors and not list_tools_result + else None + ) + return { + "tools": list_tools_result, + "error": "partial_failure" if error_message else None, + "message": (error_message if error_message else "Successfully retrieved tools"), + "server_outcomes": {key: outcome_wire_value(outcome) for key, outcome in server_outcomes.items()}, + } except MCPUpstreamAuthError as e: # Surface upstream pass-through 401/403 challenges to the client so @@ -939,12 +961,9 @@ if MCP_AVAILABLE: tool_name: Final[str | None] = data.get("name") tool_arguments: Final[dict[str, object]] = data.get("arguments") or {} - from litellm.proxy._experimental.mcp_server.tool_search import ( - MCP_TOOL_CALL_TOOL_NAME, - MCP_TOOL_SEARCH_TOOL_NAME, - ) + from litellm.proxy._experimental.mcp_server.tool_search import VIRTUAL_TOOL_NAMES - if tool_name in (MCP_TOOL_SEARCH_TOOL_NAME, MCP_TOOL_CALL_TOOL_NAME): + if tool_name in VIRTUAL_TOOL_NAMES: return await _handle_virtual_mcp_tool(request, data, tool_name, user_api_key_dict) # Validate required parameters early @@ -1165,6 +1184,7 @@ if MCP_AVAILABLE: transport=request.transport, auth_type=request.auth_type, mcp_info=request.mcp_info, + timeout=request.timeout, command=request.command, args=request.args, env=request.env, @@ -1394,11 +1414,28 @@ if MCP_AVAILABLE: oauth2_headers = MCPRequestHandler._get_oauth2_headers_from_headers(headers) async def _list_tools_operation(client): - async def _list_tools_session_operation(session): - return await session.list_tools() - - list_tools_response: Final = await client.run_with_session(_list_tools_session_operation) - list_tools_result: Final[list[MCPTool]] = list_tools_response.tools + # Bound the whole pagination walk: without this the preview is limited only by the + # per-request timeout times the page cap. max() keeps the pre-pagination guarantee + # that a single slow page within the client timeout still succeeds, and a + # per-server timeout above the global default extends the deadline with it. + listing_deadline: Final = max( + getattr(client, "timeout", MCP_CLIENT_TIMEOUT) or MCP_CLIENT_TIMEOUT, + MCP_TOOL_LISTING_TIMEOUT, + ) + list_tools_result = None # rebind-ok: set inside the timeout scope below + with anyio.move_on_after(listing_deadline): + list_tools_result = await client.list_tools(raise_on_error=True) # rebind-ok: fills the init above + if list_tools_result is None: + verbose_logger.warning( + "MCP tools/list preview timed out after %s seconds while paginating upstream tools", + listing_deadline, + ) + return { # mutable-ok: error response payload + "status": "error", + "error": True, + "message": f"Timed out listing tools after {listing_deadline} seconds. " + "The MCP server may be responding slowly or paginating excessively.", + } model_dumped_tools: Final[list[dict]] = [tool.model_dump() for tool in list_tools_result] return { "tools": model_dumped_tools, diff --git a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py index 7ec0f4b5192..dcf1b01bc25 100644 --- a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py +++ b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py @@ -5,6 +5,7 @@ Filters MCP tools semantically for /chat/completions and /responses endpoints. """ import asyncio +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final from litellm._logging import verbose_logger @@ -74,7 +75,7 @@ class SemanticMCPToolFilter: self.router_instance = litellm_router_instance self.tool_router: SemanticRouter | None = None self.context_window_error: str | None = None - self._tool_map: dict[str, Any] = {} # MCPTool objects or OpenAI function dicts + self._tool_map: dict[str, object] = {} # MCPTool objects or OpenAI function dicts self._index_sync_lock = asyncio.Lock() async def build_router_from_mcp_registry(self) -> None: @@ -182,11 +183,11 @@ class SemanticMCPToolFilter: return raise - def _has_tools_missing_from_index(self, tools: list[Any]) -> bool: + def _has_tools_missing_from_index(self, tools: Sequence[object]) -> bool: """Allocation-free check for any named tool not yet in the semantic index.""" return any(name and name not in self._tool_map for name in (self._extract_tool_info(t)[0] for t in tools)) - def _tools_missing_from_index(self, tools: list[Any]) -> dict[str, Any]: + def _tools_missing_from_index(self, tools: Sequence[object]) -> Mapping[str, object]: """Map name -> tool for every named tool not yet in the semantic index.""" return { name: tool @@ -194,7 +195,7 @@ class SemanticMCPToolFilter: if name and name not in self._tool_map } - async def _ensure_tools_indexed(self, available_tools: list[Any]) -> None: + async def _ensure_tools_indexed(self, available_tools: Sequence[object]) -> None: """ Index request-time tools the startup build never saw. @@ -385,7 +386,7 @@ class SemanticMCPToolFilter: separator: Final = client_name[-len(canonical) - 1] return separator in ("_", "-") - def _get_tools_by_names(self, tool_names: list[str], available_tools: list[Any]) -> list[Any]: + def _get_tools_by_names(self, tool_names: Sequence[str], available_tools: Sequence[object]) -> list[object]: """ Get tools from available_tools by their names, preserving the semantic router's ordering. @@ -401,14 +402,14 @@ class SemanticMCPToolFilter: # Exact matches win over suffix matches when both are present, and # each incoming tool is returned at most once even if two canonical # names happen to be tail-compatible with the same incoming name. - available_by_name: Final[dict[str, Any]] = {} + available_by_name: Final[dict[str, object]] = {} for tool in available_tools: client_name, _ = self._extract_tool_info(tool) if client_name and client_name not in available_by_name: available_by_name[client_name] = tool - matched: Final[list[Any]] = [] - used_ids: Final[set] = set() + matched: Final[list[object]] = [] + used_ids: Final[set[int]] = set() for canonical in tool_names: tool = available_by_name.get(canonical) if tool is None: @@ -430,7 +431,7 @@ class SemanticMCPToolFilter: used_ids.add(id(tool)) return matched - def extract_user_query(self, messages: list[dict[str, Any]]) -> str: + def extract_user_query(self, messages: Sequence[Mapping[str, object]]) -> str: """ Extract user query from messages for /chat/completions or /responses. diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 3c6eb06bc71..989b08b929a 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -246,11 +246,12 @@ def _mcp_meta_trace_carrier(req_ctx: object) -> dict[str, str] | None: """The W3C trace context (``traceparent``/``tracestate``) the MCP client propagated in the request's ``params._meta`` (SEP-414), or ``None``. - When present, per the OTel MCP semconv the MCP span parents to this propagated - context rather than to the HTTP transport (which is recorded as a link instead). - When absent, the span nests under the transport span of the request carrying - this specific message, so a streamable-HTTP session that multiplexes many - messages still does not glue every message under the session's first request; + When present, the MCP span records this propagated context as a span *link*, + never the parent — a remote parent would root the span in a trace whose root + never reaches the gateway's tracing backend. The span itself nests under the + transport span of the request carrying this specific message, so a + streamable-HTTP session that multiplexes many messages still does not glue + every message under the session's first request; see ``resolve_mcp_span_context``. The client's W3C Baggage is deliberately excluded: it is caller-controlled, and the otel baggage processor stamps allowlisted baggage keys (``litellm.team.id``, ``litellm.metadata.*``, @@ -432,7 +433,6 @@ if MCP_AVAILABLE: _client_forwarded_authorization_headers, _resolve_openapi_tool_auth, _should_strip_caller_authorization, - _without_authorization, global_mcp_server_manager, ) from litellm.proxy._experimental.mcp_server.openapi_to_mcp_generator import ( @@ -451,6 +451,7 @@ if MCP_AVAILABLE: split_server_prefix_from_name, strip_known_server_prefix, ) + from litellm.types.mcp import DEFAULT_CREDENTIAL_HEADER, without_header ###################################################### ############ MCP Tools List REST API Response Object # @@ -911,14 +912,17 @@ if MCP_AVAILABLE: the caller falls through to normal tool routing. """ from litellm.proxy._experimental.mcp_server.tool_search import ( - MCP_TOOL_CALL_TOOL_NAME, + AGENT_SEARCH_TOOL_NAME, + DEFAULT_AGENT_SEARCH_TOP_K, MCP_TOOL_SEARCH_TOOL_NAME, + VIRTUAL_TOOL_NAMES, coerce_top_k, + handle_agent_search, handle_mcp_tool_call, handle_mcp_tool_search, ) - if name not in (MCP_TOOL_SEARCH_TOOL_NAME, MCP_TOOL_CALL_TOOL_NAME): + if name not in VIRTUAL_TOOL_NAMES: return None if not getattr( @@ -951,6 +955,12 @@ if MCP_AVAILABLE: ) assert user_api_key_auth is not None # guaranteed by the flag check above + if name == AGENT_SEARCH_TOOL_NAME: + return await handle_agent_search( + query=str(args.get("query", "")), + top_k=coerce_top_k(args.get("top_k", DEFAULT_AGENT_SEARCH_TOP_K), default=DEFAULT_AGENT_SEARCH_TOP_K), + user_api_key_dict=user_api_key_auth, + ) virtual_logging_obj: Final = await _build_virtual_call_logging_obj( name=name, arguments=args, @@ -1732,7 +1742,7 @@ if MCP_AVAILABLE: raw_headers=raw_headers, user_api_key_auth=user_api_key_auth, ): - extra_headers = _without_authorization(extra_headers) + extra_headers = without_header(extra_headers, DEFAULT_CREDENTIAL_HEADER) elif is_client_forwarded_mode: if not withhold_forwarded_authorization: extra_headers = _client_forwarded_authorization_headers( diff --git a/litellm/proxy/_experimental/mcp_server/tool_search.py b/litellm/proxy/_experimental/mcp_server/tool_search.py index 3b0dd2071ae..f79765f6d01 100644 --- a/litellm/proxy/_experimental/mcp_server/tool_search.py +++ b/litellm/proxy/_experimental/mcp_server/tool_search.py @@ -1,8 +1,14 @@ from __future__ import annotations import json +from collections.abc import Mapping, Sequence from datetime import datetime -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, TypedDict, assert_never + +from typing_extensions import ReadOnly, Required + +import litellm +from litellm.proxy.agent_endpoints.agent_search import DEFAULT_AGENT_SEARCH_TOP_K if TYPE_CHECKING: from mcp.types import CallToolResult @@ -12,6 +18,8 @@ if TYPE_CHECKING: MCP_TOOL_SEARCH_TOOL_NAME: Final[str] = "mcp_tool_search" MCP_TOOL_CALL_TOOL_NAME: Final[str] = "mcp_tool_call" +AGENT_SEARCH_TOOL_NAME: Final[str] = "agent_search" +VIRTUAL_TOOL_NAMES: Final = frozenset((MCP_TOOL_SEARCH_TOOL_NAME, MCP_TOOL_CALL_TOOL_NAME, AGENT_SEARCH_TOOL_NAME)) def coerce_top_k(value: Any, default: int = 5) -> int: @@ -34,46 +42,116 @@ def search_tools(query: str, tools: list[dict[str, Any]], top_k: int = 5) -> lis return [tool for _, tool in sorted(scored, key=lambda x: x[0], reverse=True)[:top_k]] -def get_virtual_tool_definitions() -> list[dict[str, Any]]: - return [ - { - "name": MCP_TOOL_SEARCH_TOOL_NAME, - "description": "Search for MCP tools by keyword. Returns top matching tools with names, descriptions, and input schemas.", - "inputSchema": { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "Keywords to search for in tool names and descriptions.", - }, - "top_k": { - "type": "integer", - "description": "Maximum number of results to return.", - "default": 5, - }, - }, - "required": ["query"], +class _ToolParamSchema(TypedDict, total=False): + type: Required[ReadOnly[str]] + description: Required[ReadOnly[str]] + default: ReadOnly[int] + + +class _ToolInputSchema(TypedDict): + type: ReadOnly[str] + properties: ReadOnly[Mapping[str, _ToolParamSchema]] + required: ReadOnly[Sequence[str]] + + +class VirtualToolDefinition(TypedDict): + name: ReadOnly[str] + description: ReadOnly[str] + inputSchema: ReadOnly[_ToolInputSchema] + + +def _json_array(*items: str) -> Sequence[str]: + return list(items) # mutable-ok: jsonschema's metaschema only accepts a JSON array for required + + +_MCP_TOOL_SEARCH_DEFINITION: Final[VirtualToolDefinition] = { + "name": MCP_TOOL_SEARCH_TOOL_NAME, + "description": "Search for MCP tools by keyword. Returns top matching tools with names, descriptions, and input schemas.", + "inputSchema": { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Keywords to search for in tool names and descriptions."}, + "top_k": {"type": "integer", "description": "Maximum number of results to return.", "default": 5}, + }, + "required": _json_array("query"), + }, +} + +_MCP_TOOL_CALL_DEFINITION: Final[VirtualToolDefinition] = { + "name": MCP_TOOL_CALL_TOOL_NAME, + "description": "Call an MCP tool by name with the given arguments.", + "inputSchema": { + "type": "object", + "properties": { + "tool_name": {"type": "string", "description": "The exact name of the MCP tool to call."}, + "arguments": {"type": "object", "description": "Arguments to pass to the tool."}, + }, + "required": _json_array("tool_name"), + }, +} + +_AGENT_SEARCH_DEFINITION: Final[VirtualToolDefinition] = { + "name": AGENT_SEARCH_TOOL_NAME, + "description": "Find A2A agents by describing the task in natural language. Returns the best matching agents you can access, ranked by semantic similarity, each with its agent_id, name, description, skills, and score.", + "inputSchema": { + "type": "object", + "properties": { + "query": {"type": "string", "description": "The task the agent should be able to do, in natural language."}, + "top_k": { + "type": "integer", + "description": "Maximum number of agents to return.", + "default": DEFAULT_AGENT_SEARCH_TOP_K, }, }, - { - "name": MCP_TOOL_CALL_TOOL_NAME, - "description": "Call an MCP tool by name with the given arguments.", - "inputSchema": { - "type": "object", - "properties": { - "tool_name": { - "type": "string", - "description": "The exact name of the MCP tool to call.", - }, - "arguments": { - "type": "object", - "description": "Arguments to pass to the tool.", - }, - }, - "required": ["tool_name"], - }, - }, - ] + "required": _json_array("query"), + }, +} + + +def get_virtual_tool_definitions() -> tuple[VirtualToolDefinition, ...]: + return (_MCP_TOOL_SEARCH_DEFINITION, _MCP_TOOL_CALL_DEFINITION, _AGENT_SEARCH_DEFINITION) + + +def _text_tool_result(text: str, is_error: bool) -> CallToolResult: + from mcp.types import CallToolResult, TextContent + + return CallToolResult( + content=[TextContent(type="text", text=text)], # mutable-ok: CallToolResult accepts only list content + isError=is_error, + ) + + +async def handle_agent_search(query: str, top_k: int, user_api_key_dict: UserAPIKeyAuth) -> CallToolResult: + from litellm.proxy.agent_endpoints.agent_search import ( + AgentSearchEmbeddingFailed, + AgentSearchHits, + AgentSearchNotConfigured, + agent_search_result, + global_agent_search_index, + search_agents, + ) + from litellm.proxy.agent_endpoints.auth.agent_permission_handler import accessible_agents + from litellm.proxy.common_utils.rbac_utils import check_feature_access_for_user + from litellm.proxy.proxy_server import llm_router + + await check_feature_access_for_user(user_api_key_dict, "agents") + outcome: Final = await search_agents( + query=query, + agents=await accessible_agents(user_api_key_dict), + top_k=max(top_k, 1), + router=llm_router, + embedding_model=litellm.agent_search_embedding_model, + index=global_agent_search_index, + user_api_key_dict=user_api_key_dict, + ) + match outcome: + case AgentSearchHits(hits): + results: Final = tuple(agent_search_result(hit).model_dump() for hit in hits) + return _text_tool_result(json.dumps(results), is_error=False) + case AgentSearchNotConfigured(reason) | AgentSearchEmbeddingFailed(reason): + return _text_tool_result(reason, is_error=True) + case _: + assert_never(outcome) async def handle_mcp_tool_search( diff --git a/litellm/proxy/_experimental/mcp_server/toolset_db.py b/litellm/proxy/_experimental/mcp_server/toolset_db.py index 9672383a572..ecaaf35e817 100644 --- a/litellm/proxy/_experimental/mcp_server/toolset_db.py +++ b/litellm/proxy/_experimental/mcp_server/toolset_db.py @@ -1,5 +1,9 @@ import json -from typing import Final +from collections.abc import Mapping, Sequence +from datetime import datetime +from typing import Final, Protocol + +from typing_extensions import NotRequired, ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm._uuid import uuid @@ -7,18 +11,73 @@ from litellm.proxy.utils import PrismaClient from litellm.repositories.table_repositories import MCPToolsetRepository from litellm.types.mcp_server.mcp_toolset import ( MCPToolset, + MCPToolsetTool, NewMCPToolsetRequest, UpdateMCPToolsetRequest, ) -def _toolset_from_row(row) -> MCPToolset: +class MCPToolsetFields(TypedDict): + """The ``MCPToolset`` constructor keywords a toolset row expands into.""" + + toolset_id: ReadOnly[str] + toolset_name: ReadOnly[str] + description: NotRequired[ReadOnly[str | None]] + tools: NotRequired[ReadOnly[list[MCPToolsetTool]]] + created_at: NotRequired[ReadOnly[datetime | None]] + created_by: NotRequired[ReadOnly[str | None]] + updated_at: NotRequired[ReadOnly[datetime | None]] + updated_by: NotRequired[ReadOnly[str | None]] + + +class MCPToolsetRowData(TypedDict): + """A toolset table row, whose ``tools`` column is stored as JSON.""" + + toolset_id: ReadOnly[str] + toolset_name: ReadOnly[str] + description: NotRequired[ReadOnly[str | None]] + tools: NotRequired[ReadOnly[str | list[MCPToolsetTool]]] + created_at: NotRequired[ReadOnly[datetime | None]] + created_by: NotRequired[ReadOnly[str | None]] + updated_at: NotRequired[ReadOnly[datetime | None]] + updated_by: NotRequired[ReadOnly[str | None]] + + +class MCPToolsetRow(Protocol): + """A row of the toolset table, as the prisma client returns it.""" + + def model_dump(self) -> MCPToolsetRowData: ... + + +class MCPToolsetTable(Protocol): + """The prisma table actions this module runs against the toolset table.""" + + async def create(self, data: Mapping[str, object]) -> MCPToolsetRow: ... + + async def find_unique(self, where: Mapping[str, object]) -> MCPToolsetRow | None: ... + + async def find_first(self, where: Mapping[str, object]) -> MCPToolsetRow | None: ... + + async def find_many(self, where: Mapping[str, object]) -> Sequence[MCPToolsetRow]: ... + + async def update(self, where: Mapping[str, object], data: Mapping[str, object]) -> MCPToolsetRow: ... + + async def delete(self, where: Mapping[str, object]) -> MCPToolsetRow: ... + + +def _toolset_table(prisma_client: PrismaClient) -> MCPToolsetTable: + """The toolset table actions of the prisma client.""" + return MCPToolsetRepository(prisma_client).table + + +def _toolset_from_row(row: MCPToolsetRow) -> MCPToolset: data: Final = row.model_dump() - tools = data.get("tools") or [] - if isinstance(tools, str): - tools = json.loads(tools) - data["tools"] = tools - return MCPToolset(**data) + tools: Final = data.get("tools") or [] + resolved: Final[MCPToolsetFields] = { + **data, + "tools": json.loads(tools) if isinstance(tools, str) else tools, + } + return MCPToolset(**resolved) async def create_mcp_toolset( @@ -31,7 +90,7 @@ async def create_mcp_toolset( data_dict["tools"] = json.dumps(data_dict.get("tools", [])) data_dict["created_by"] = touched_by data_dict["updated_by"] = touched_by - row: Final = await MCPToolsetRepository(prisma_client).table.create(data=data_dict) + row: Final = await _toolset_table(prisma_client).create(data=data_dict) return _toolset_from_row(row) @@ -39,7 +98,7 @@ async def get_mcp_toolset( prisma_client: PrismaClient, toolset_id: str, ) -> MCPToolset | None: - row: Final = await MCPToolsetRepository(prisma_client).table.find_unique(where={"toolset_id": toolset_id}) + row: Final = await _toolset_table(prisma_client).find_unique(where={"toolset_id": toolset_id}) if row is None: return None return _toolset_from_row(row) @@ -47,13 +106,11 @@ async def get_mcp_toolset( async def list_mcp_toolsets( prisma_client: PrismaClient, - toolset_ids: list[str] | None = None, -) -> list[MCPToolset]: + toolset_ids: Sequence[str] | None = None, +) -> Sequence[MCPToolset]: try: - where = {} - if toolset_ids is not None: - where = {"toolset_id": {"in": toolset_ids}} - rows: Final = await MCPToolsetRepository(prisma_client).table.find_many(where=where) + where: Final[Mapping[str, object]] = {} if toolset_ids is None else {"toolset_id": {"in": toolset_ids}} + rows: Final = await _toolset_table(prisma_client).find_many(where=where) return [_toolset_from_row(r) for r in rows] except Exception as e: verbose_proxy_logger.warning("litellm.proxy._experimental.mcp_server.toolset_db::list_mcp_toolsets - %s", e) @@ -64,7 +121,7 @@ async def get_mcp_toolset_by_name( prisma_client: PrismaClient, toolset_name: str, ) -> MCPToolset | None: - row: Final = await MCPToolsetRepository(prisma_client).table.find_first(where={"toolset_name": toolset_name}) + row: Final = await _toolset_table(prisma_client).find_first(where={"toolset_name": toolset_name}) if row is None: return None return _toolset_from_row(row) @@ -80,7 +137,7 @@ async def update_mcp_toolset( data_dict["tools"] = json.dumps(data_dict["tools"]) data_dict["updated_by"] = touched_by try: - row: Final = await MCPToolsetRepository(prisma_client).table.update( + row: Final = await _toolset_table(prisma_client).update( where={"toolset_id": data.toolset_id}, data=data_dict, ) @@ -98,7 +155,7 @@ async def delete_mcp_toolset( toolset_id: str, ) -> MCPToolset | None: try: - row: Final = await MCPToolsetRepository(prisma_client).table.delete(where={"toolset_id": toolset_id}) + row: Final = await _toolset_table(prisma_client).delete(where={"toolset_id": toolset_id}) except Exception as e: from prisma.errors import RecordNotFoundError diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index a7d19a9e907..72dc4764ce4 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

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-result = client.images.generate( - model="${j}", - prompt="${n}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${v}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - 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Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case r.IMAGE_EDITS:t="azure"===g?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${v}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${v}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. 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* 2.5)}.w-2\/3{width:66.6667%}.w-2\/5{width:40%}.w-3{width:calc(var(--spacing) * 3)}.w-3\.5{width:calc(var(--spacing) * 3.5)}.w-3\/4{width:75%}.w-4{width:calc(var(--spacing) * 4)}.w-5{width:calc(var(--spacing) * 5)}.w-6{width:calc(var(--spacing) * 6)}.w-7{width:calc(var(--spacing) * 7)}.w-8{width:calc(var(--spacing) * 8)}.w-9{width:calc(var(--spacing) * 9)}.w-9\!{width:calc(var(--spacing) * 9)!important}.w-10{width:calc(var(--spacing) * 10)}.w-11{width:calc(var(--spacing) * 11)}.w-11\/12{width:91.6667%}.w-12{width:calc(var(--spacing) * 12)}.w-14{width:calc(var(--spacing) * 14)}.w-16{width:calc(var(--spacing) * 16)}.w-20{width:calc(var(--spacing) * 20)}.w-24{width:calc(var(--spacing) * 24)}.w-28{width:calc(var(--spacing) * 28)}.w-32{width:calc(var(--spacing) * 32)}.w-36{width:calc(var(--spacing) * 36)}.w-40{width:calc(var(--spacing) * 40)}.w-44{width:calc(var(--spacing) * 44)}.w-48{width:calc(var(--spacing) * 48)}.w-50{width:calc(var(--spacing) * 50)}.w-52{width:calc(var(--spacing) * 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(animation-timeline:scroll()){.scroll-fade-e{--scroll-fade-e:var(--_scroll-fade-size-e)}}.animate-bounce{animation:var(--animate-bounce)}.animate-pulse{animation:var(--animate-pulse)}.animate-spin{animation:var(--animate-spin)}.cursor-col-resize{cursor:col-resize}.cursor-default{cursor:default}.cursor-grab{cursor:grab}.cursor-help{cursor:help}.cursor-not-allowed{cursor:not-allowed}.cursor-pointer{cursor:pointer}.cursor-text{cursor:text}.touch-pinch-zoom{--tw-pinch-zoom:pinch-zoom;touch-action:var(--tw-pan-x,) var(--tw-pan-y,) 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auto}.grid-cols-\[80px_minmax\(0\,1fr\)\]{grid-template-columns:80px minmax(0,1fr)}.grid-cols-\[160px_minmax\(0\,1fr\)\]{grid-template-columns:160px minmax(0,1fr)}.grid-cols-\[auto\]{grid-template-columns:auto}.grid-cols-\[auto_1fr\]{grid-template-columns:auto 1fr}.grid-cols-\[auto_minmax\(0\,1fr\)\]{grid-template-columns:auto minmax(0,1fr)}.grid-cols-\[max-content_1fr\]{grid-template-columns:max-content 1fr}.grid-cols-\[repeat\(auto-fill\,minmax\(220px\,1fr\)\)\]{grid-template-columns:repeat(auto-fill,minmax(220px,1fr))}.grid-cols-\[repeat\(auto-fit\,minmax\(7rem\,1fr\)\)\]{grid-template-columns:repeat(auto-fit,minmax(7rem,1fr))}.grid-cols-none{grid-template-columns:none}.grid-rows-\[auto_1fr\]{grid-template-rows:auto 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var(--color-teal-300) 40%, transparent)}}.bg-cyan-500.bg-opacity-10{background-color:#00b7d71a}@supports (color:color-mix(in lab, red, red)){.bg-cyan-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-cyan-500) 10%, transparent)}}.bg-cyan-500.bg-opacity-20{background-color:#00b7d733}@supports (color:color-mix(in lab, red, red)){.bg-cyan-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-cyan-500) 20%, transparent)}}.bg-cyan-500.bg-opacity-40{background-color:#00b7d766}@supports (color:color-mix(in lab, red, red)){.bg-cyan-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-cyan-500) 40%, transparent)}}.hover\:bg-cyan-500.hover\:bg-opacity-20:hover{background-color:#00b7d733}@supports (color:color-mix(in lab, red, red)){.hover\:bg-cyan-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-cyan-500) 20%, transparent)}}.group:hover .bg-cyan-500.group-hover\:bg-opacity-30{background-color:#00b7d74d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-cyan-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-cyan-500) 30%, transparent)}}.ring-cyan-500.ring-opacity-20{--tw-ring-color:#00b7d733}@supports (color:color-mix(in lab, red, red)){.ring-cyan-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-cyan-500) 20%, transparent)}}.ring-cyan-300.ring-opacity-40{--tw-ring-color:#53eafd66}@supports (color:color-mix(in lab, red, red)){.ring-cyan-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-cyan-300) 40%, transparent)}}.bg-sky-500.bg-opacity-10{background-color:#00a5ef1a}@supports (color:color-mix(in lab, red, red)){.bg-sky-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-sky-500) 10%, transparent)}}.bg-sky-500.bg-opacity-20{background-color:#00a5ef33}@supports (color:color-mix(in lab, red, red)){.bg-sky-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-sky-500) 20%, transparent)}}.bg-sky-500.bg-opacity-40{background-color:#00a5ef66}@supports (color:color-mix(in lab, red, red)){.bg-sky-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-sky-500) 40%, transparent)}}.hover\:bg-sky-500.hover\:bg-opacity-20:hover{background-color:#00a5ef33}@supports (color:color-mix(in lab, red, red)){.hover\:bg-sky-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-sky-500) 20%, transparent)}}.group:hover .bg-sky-500.group-hover\:bg-opacity-30{background-color:#00a5ef4d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-sky-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-sky-500) 30%, transparent)}}.ring-sky-500.ring-opacity-20{--tw-ring-color:#00a5ef33}@supports (color:color-mix(in lab, red, red)){.ring-sky-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-sky-500) 20%, transparent)}}.ring-sky-300.ring-opacity-40{--tw-ring-color:#77d4ff66}@supports (color:color-mix(in lab, red, red)){.ring-sky-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-sky-300) 40%, transparent)}}.bg-blue-500.bg-opacity-10{background-color:#3080ff1a}@supports (color:color-mix(in lab, red, red)){.bg-blue-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-blue-500) 10%, transparent)}}.bg-blue-500.bg-opacity-20{background-color:#3080ff33}@supports (color:color-mix(in lab, red, red)){.bg-blue-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-blue-500) 20%, transparent)}}.bg-blue-500.bg-opacity-40{background-color:#3080ff66}@supports (color:color-mix(in lab, red, red)){.bg-blue-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-blue-500) 40%, transparent)}}.hover\:bg-blue-500.hover\:bg-opacity-20:hover{background-color:#3080ff33}@supports (color:color-mix(in lab, red, red)){.hover\:bg-blue-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-blue-500) 20%, transparent)}}.group:hover .bg-blue-500.group-hover\:bg-opacity-30{background-color:#3080ff4d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-blue-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-blue-500) 30%, transparent)}}.ring-blue-500.ring-opacity-20{--tw-ring-color:#3080ff33}@supports (color:color-mix(in lab, red, red)){.ring-blue-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-blue-500) 20%, transparent)}}.ring-blue-300.ring-opacity-40{--tw-ring-color:#90c5ff66}@supports (color:color-mix(in lab, red, red)){.ring-blue-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-blue-300) 40%, transparent)}}.bg-indigo-500.bg-opacity-10{background-color:#625fff1a}@supports (color:color-mix(in lab, red, red)){.bg-indigo-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-indigo-500) 10%, transparent)}}.bg-indigo-500.bg-opacity-20{background-color:#625fff33}@supports (color:color-mix(in lab, red, red)){.bg-indigo-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-indigo-500) 20%, transparent)}}.bg-indigo-500.bg-opacity-40{background-color:#625fff66}@supports (color:color-mix(in lab, red, red)){.bg-indigo-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-indigo-500) 40%, transparent)}}.hover\:bg-indigo-500.hover\:bg-opacity-20:hover{background-color:#625fff33}@supports (color:color-mix(in lab, red, red)){.hover\:bg-indigo-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-indigo-500) 20%, transparent)}}.group:hover .bg-indigo-500.group-hover\:bg-opacity-30{background-color:#625fff4d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-indigo-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-indigo-500) 30%, transparent)}}.ring-indigo-500.ring-opacity-20{--tw-ring-color:#625fff33}@supports (color:color-mix(in lab, red, red)){.ring-indigo-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-indigo-500) 20%, transparent)}}.ring-indigo-300.ring-opacity-40{--tw-ring-color:#a4b3ff66}@supports (color:color-mix(in lab, red, red)){.ring-indigo-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-indigo-300) 40%, transparent)}}.bg-violet-500.bg-opacity-10{background-color:#8d54ff1a}@supports (color:color-mix(in lab, red, red)){.bg-violet-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-violet-500) 10%, transparent)}}.bg-violet-500.bg-opacity-20{background-color:#8d54ff33}@supports (color:color-mix(in lab, red, red)){.bg-violet-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-violet-500) 20%, transparent)}}.bg-violet-500.bg-opacity-40{background-color:#8d54ff66}@supports (color:color-mix(in lab, red, red)){.bg-violet-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-violet-500) 40%, transparent)}}.hover\:bg-violet-500.hover\:bg-opacity-20:hover{background-color:#8d54ff33}@supports (color:color-mix(in lab, red, red)){.hover\:bg-violet-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-violet-500) 20%, transparent)}}.group:hover .bg-violet-500.group-hover\:bg-opacity-30{background-color:#8d54ff4d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-violet-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-violet-500) 30%, transparent)}}.ring-violet-500.ring-opacity-20{--tw-ring-color:#8d54ff33}@supports (color:color-mix(in lab, red, red)){.ring-violet-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-violet-500) 20%, transparent)}}.ring-violet-300.ring-opacity-40{--tw-ring-color:#c4b4ff66}@supports (color:color-mix(in lab, red, red)){.ring-violet-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-violet-300) 40%, transparent)}}.bg-purple-500.bg-opacity-10{background-color:#ac4bff1a}@supports (color:color-mix(in lab, red, red)){.bg-purple-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-purple-500) 10%, transparent)}}.bg-purple-500.bg-opacity-20{background-color:#ac4bff33}@supports (color:color-mix(in lab, red, red)){.bg-purple-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-purple-500) 20%, transparent)}}.bg-purple-500.bg-opacity-40{background-color:#ac4bff66}@supports (color:color-mix(in lab, red, red)){.bg-purple-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-purple-500) 40%, transparent)}}.hover\:bg-purple-500.hover\:bg-opacity-20:hover{background-color:#ac4bff33}@supports (color:color-mix(in lab, red, red)){.hover\:bg-purple-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-purple-500) 20%, transparent)}}.group:hover .bg-purple-500.group-hover\:bg-opacity-30{background-color:#ac4bff4d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-purple-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-purple-500) 30%, transparent)}}.ring-purple-500.ring-opacity-20{--tw-ring-color:#ac4bff33}@supports (color:color-mix(in lab, red, red)){.ring-purple-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-purple-500) 20%, transparent)}}.ring-purple-300.ring-opacity-40{--tw-ring-color:#d9b3ff66}@supports (color:color-mix(in lab, red, red)){.ring-purple-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-purple-300) 40%, transparent)}}.bg-fuchsia-500.bg-opacity-10{background-color:#e12afb1a}@supports (color:color-mix(in lab, red, red)){.bg-fuchsia-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-fuchsia-500) 10%, transparent)}}.bg-fuchsia-500.bg-opacity-20{background-color:#e12afb33}@supports (color:color-mix(in lab, red, red)){.bg-fuchsia-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-fuchsia-500) 20%, transparent)}}.bg-fuchsia-500.bg-opacity-40{background-color:#e12afb66}@supports (color:color-mix(in lab, red, red)){.bg-fuchsia-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-fuchsia-500) 40%, transparent)}}.hover\:bg-fuchsia-500.hover\:bg-opacity-20:hover{background-color:#e12afb33}@supports (color:color-mix(in lab, red, red)){.hover\:bg-fuchsia-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-fuchsia-500) 20%, transparent)}}.group:hover .bg-fuchsia-500.group-hover\:bg-opacity-30{background-color:#e12afb4d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-fuchsia-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-fuchsia-500) 30%, transparent)}}.ring-fuchsia-500.ring-opacity-20{--tw-ring-color:#e12afb33}@supports (color:color-mix(in lab, red, red)){.ring-fuchsia-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-fuchsia-500) 20%, transparent)}}.ring-fuchsia-300.ring-opacity-40{--tw-ring-color:#f2a9ff66}@supports (color:color-mix(in lab, red, red)){.ring-fuchsia-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-fuchsia-300) 40%, transparent)}}.bg-pink-500.bg-opacity-10{background-color:#f6339a1a}@supports (color:color-mix(in lab, red, red)){.bg-pink-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-pink-500) 10%, transparent)}}.bg-pink-500.bg-opacity-20{background-color:#f6339a33}@supports (color:color-mix(in lab, red, red)){.bg-pink-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-pink-500) 20%, transparent)}}.bg-pink-500.bg-opacity-40{background-color:#f6339a66}@supports (color:color-mix(in lab, red, red)){.bg-pink-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-pink-500) 40%, transparent)}}.hover\:bg-pink-500.hover\:bg-opacity-20:hover{background-color:#f6339a33}@supports (color:color-mix(in lab, red, red)){.hover\:bg-pink-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-pink-500) 20%, transparent)}}.group:hover .bg-pink-500.group-hover\:bg-opacity-30{background-color:#f6339a4d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-pink-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-pink-500) 30%, transparent)}}.ring-pink-500.ring-opacity-20{--tw-ring-color:#f6339a33}@supports (color:color-mix(in lab, red, red)){.ring-pink-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-pink-500) 20%, transparent)}}.ring-pink-300.ring-opacity-40{--tw-ring-color:#fda5d566}@supports (color:color-mix(in lab, red, red)){.ring-pink-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-pink-300) 40%, transparent)}}.bg-rose-500.bg-opacity-10{background-color:#ff23571a}@supports (color:color-mix(in lab, red, red)){.bg-rose-500.bg-opacity-10{background-color:color-mix(in oklab, var(--color-rose-500) 10%, transparent)}}.bg-rose-500.bg-opacity-20{background-color:#ff235733}@supports (color:color-mix(in lab, red, red)){.bg-rose-500.bg-opacity-20{background-color:color-mix(in oklab, var(--color-rose-500) 20%, transparent)}}.bg-rose-500.bg-opacity-40{background-color:#ff235766}@supports (color:color-mix(in lab, red, red)){.bg-rose-500.bg-opacity-40{background-color:color-mix(in oklab, var(--color-rose-500) 40%, transparent)}}.hover\:bg-rose-500.hover\:bg-opacity-20:hover{background-color:#ff235733}@supports (color:color-mix(in lab, red, red)){.hover\:bg-rose-500.hover\:bg-opacity-20:hover{background-color:color-mix(in oklab, var(--color-rose-500) 20%, transparent)}}.group:hover .bg-rose-500.group-hover\:bg-opacity-30{background-color:#ff23574d}@supports (color:color-mix(in lab, red, red)){.group:hover .bg-rose-500.group-hover\:bg-opacity-30{background-color:color-mix(in oklab, var(--color-rose-500) 30%, transparent)}}.ring-rose-500.ring-opacity-20{--tw-ring-color:#ff235733}@supports (color:color-mix(in lab, red, red)){.ring-rose-500.ring-opacity-20{--tw-ring-color:color-mix(in oklab, var(--color-rose-500) 20%, transparent)}}.ring-rose-300.ring-opacity-40{--tw-ring-color:#ffa2ae66}@supports (color:color-mix(in lab, red, red)){.ring-rose-300.ring-opacity-40{--tw-ring-color:color-mix(in oklab, var(--color-rose-300) 40%, transparent)}}}@property --tw-animation-delay{syntax:"*";inherits:false;initial-value:0s}@property --tw-animation-direction{syntax:"*";inherits:false;initial-value:normal}@property --tw-animation-duration{syntax:"*";inherits:false}@property --tw-animation-fill-mode{syntax:"*";inherits:false;initial-value:none}@property --tw-animation-iteration-count{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-blur{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-opacity{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-rotate{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-scale{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-translate-y{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-blur{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-opacity{syntax:"*";inherits:false;initial-value:1}@property --tw-exit-rotate{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-scale{syntax:"*";inherits:false;initial-value:1}@property --tw-exit-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-translate-y{syntax:"*";inherits:false;initial-value:0}@property --scroll-fade-e{syntax:"";inherits:false;initial-value:0}@property 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ef=((t={}).A2A_Agent="A2A Agent",t.AI21="Ai21",t.AI21_CHAT="Ai21 Chat",t.AIML="AI/ML API",t.AIOHTTP_OPENAI="Aiohttp Openai",t.Anthropic="Anthropic",t.ANTHROPIC_TEXT="Anthropic Text",t.AssemblyAI="AssemblyAI",t.AUTO_ROUTER="Auto Router",t.Bedrock="Amazon Bedrock",t.BedrockMantle="Amazon Bedrock Mantle",t.SageMaker="AWS SageMaker",t.Azure="Azure",t.Azure_AI_Studio="Azure AI Foundry (Studio)",t.AZURE_TEXT="Azure Text",t.BASETEN="Baseten",t.BYTEZ="Bytez",t.Cerebras="Cerebras",t.CLARIFAI="Clarifai",t.CLOUDFLARE="Cloudflare",t.CODESTRAL="Codestral",t.Cognition="Cognition",t.Cohere="Cohere",t.COHERE_CHAT="Cohere Chat",t.COMETAPI="Cometapi",t.COMPACTIFAI="Compactifai",t.Cursor="Cursor",t.Dashscope="Dashscope",t.Databricks="Databricks (Qwen API)",t.DATAROBOT="Datarobot",t.DeepInfra="DeepInfra",t.Deepgram="Deepgram",t.Deepseek="Deepseek",t.DOCKER_MODEL_RUNNER="Docker Model Runner",t.DOTPROMPT="Dotprompt",t.ElevenLabs="ElevenLabs",t.EMPOWER="Empower",t.FalAI="Fal AI",t.FEATHERLESS_AI="Featherless 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Context to Preserve -User preferences or style requirements -Domain-specific details that aren't obvious -Any promises made to the user -Be concise but complete—err on the side of including information that would prevent duplicate work or repeated mistakes. Write in a way that enables immediate resumption of the task. -Wrap your summary in tags.`;function rb(){let e,t;return{promise:new Promise((s,r)=>{e=s,t=r}),resolve:e,reject:t}}class rv{constructor(e,t,s){M.add(this),this.client=e,$.set(this,!1),O.set(this,!1),L.set(this,void 0),U.set(this,void 0),D.set(this,void 0),B.set(this,void 0),z.set(this,void 0),q.set(this,0),tK(this,L,{params:{...t,messages:structuredClone(t.messages)}},"f");const r=["BetaToolRunner",...s3(t.tools,t.messages)].join(", ");tK(this,U,{...s,headers:sQ([{"x-stainless-helper":r},s?.headers])},"f"),tK(this,z,rb(),"f"),t.compactionControl?.enabled&&console.warn('Anthropic: The `compactionControl` parameter is deprecated and will be removed in a future version. 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${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:sQ([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>rn.fromResponse(t.response,t.controller))}}class rq extends sX{constructor(){super(...arguments),this.batches=new rz(this._client)}create(e,t){e.model in rF&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${rF[e.model]} -Please migrate to a newer model. 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Use 'thinking.type=adaptive' instead which results in better model performance in our testing: https://platform.claude.com/docs/en/build-with-claude/adaptive-thinking`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=rl[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}let r=s6(e.tools,e.messages);return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,headers:sQ([r,t?.headers]),stream:e.stream??!1})}parse(e,t){return this.create(e,t).then(t=>rO(t,e,{logger:this._client.logger??console}))}stream(e,t){return rD.createMessage(this,e,t,{logger:this._client.logger??console})}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let rF={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-3-opus-20240229":"January 5th, 2026","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025","claude-3-7-sonnet-latest":"February 19th, 2026","claude-3-7-sonnet-20250219":"February 19th, 2026","claude-3-5-haiku-latest":"February 19th, 2026","claude-3-5-haiku-20241022":"February 19th, 2026","claude-opus-4-0":"June 15th, 2026","claude-opus-4-20250514":"June 15th, 2026","claude-sonnet-4-0":"June 15th, 2026","claude-sonnet-4-20250514":"June 15th, 2026"},rW=["claude-mythos-preview","claude-opus-4-6"];rq.Batches=rz;class rV extends sX{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s1`/v1/models/${e}`,{...s,headers:sQ([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",sL,{query:r,...t,headers:sQ([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}class rH extends sX{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:sQ([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let rG=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()||void 0:void 0!==globalThis.Deno&&globalThis.Deno.env?.get?.(e)?.trim()||void 0;class rJ{constructor({baseURL:e=rG("ANTHROPIC_BASE_URL"),apiKey:t=rG("ANTHROPIC_API_KEY")??null,authToken:s=rG("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){eg.add(this),ex.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new t0("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??ef.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=sj(a.logLevel,"ClientOptions.logLevel",this)??sj(rG("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),tK(this,ex,sf,"f");const i=rG("ANTHROPIC_CUSTOM_HEADERS");if(i){const e={};for(const t of i.split("\n")){const s=t.indexOf(":");s>=0&&(e[t.substring(0,s).trim()]=t.substring(s+1).trim())}a.defaultHeaders={...e,...a.defaultHeaders}}this._options=a,this.apiKey="string"==typeof t?t:null,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetch:this.fetch,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(e.get("x-api-key")||e.get("authorization")||this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. Expected either apiKey or authToken to be set. Or for one of the "X-Api-Key" or "Authorization" headers to be explicitly omitted')}async authHeaders(e){return sQ([await this.apiKeyAuth(e),await this.bearerAuth(e)])}async apiKeyAuth(e){if(null!=this.apiKey)return sQ([{"X-Api-Key":this.apiKey}])}async bearerAuth(e){if(null!=this.authToken)return sQ([{Authorization:`Bearer ${this.authToken}`}])}stringifyQuery(e){return Object.entries(e).filter(([e,t])=>void 0!==t).map(([e,t])=>{if("string"==typeof t||"number"==typeof t||"boolean"==typeof t)return`${encodeURIComponent(e)}=${encodeURIComponent(t)}`;if(null===t)return`${encodeURIComponent(e)}=`;throw new t0(`Cannot stringify type ${typeof t}; Expected string, number, boolean, or null. If you need to pass nested query parameters, you can manually encode them, e.g. { query: { 'foo[key1]': value1, 'foo[key2]': value2 } }, and please open a GitHub issue requesting better support for your use case.`)}).join("&")}getUserAgent(){return`${this.constructor.name}/JS ${sc}`}defaultIdempotencyKey(){return`stainless-node-retry-${tY()}`}makeStatusError(e,t,s,r){return t1.generate(e,t,s,r)}buildURL(e,t,s){let r=!tX(this,eg,"m",ey).call(this)&&s||this.baseURL,a=new URL(sr.test(e)?e:r+(r.endsWith("/")&&e.startsWith("/")?e.slice(1):e)),n=this.defaultQuery(),i=Object.fromEntries(a.searchParams);return sl(n)&&sl(i)||(t={...i,...n,...t}),"object"==typeof t&&t&&!Array.isArray(t)&&(a.search=this.stringifyQuery(t)),a.toString()}_calculateNonstreamingTimeout(e){if(3600*e/128e3>600)throw new t0("Streaming is required for operations that may take longer than 10 minutes. See https://github.com/anthropics/anthropic-sdk-typescript#streaming-responses for more details");return 6e5}async prepareOptions(e){}async prepareRequest(e,{url:t,options:s}){}get(e,t){return this.methodRequest("get",e,t)}post(e,t){return this.methodRequest("post",e,t)}patch(e,t){return this.methodRequest("patch",e,t)}put(e,t){return this.methodRequest("put",e,t)}delete(e,t){return this.methodRequest("delete",e,t)}methodRequest(e,t,s){return this.request(Promise.resolve(s).then(s=>({method:e,path:t,...s})))}request(e,t=null){return new sM(this,this.makeRequest(e,t,void 0))}async makeRequest(e,t,s){let r=await e,a=r.maxRetries??this.maxRetries;null==t&&(t=a),await this.prepareOptions(r);let{req:n,url:i,timeout:l}=await this.buildRequest(r,{retryCount:a-t});await this.prepareRequest(n,{url:i,options:r});let o="log_"+(0x1000000*Math.random()|0).toString(16).padStart(6,"0"),c=void 0===s?"":`, retryOf: ${s}`,d=Date.now();if(sk(this).debug(`[${o}] sending request`,sC({retryOfRequestLogID:s,method:r.method,url:i,options:r,headers:n.headers})),r.signal?.aborted)throw new t2;let u=new AbortController,m=await this.fetchWithTimeout(i,n,l,u).catch(tZ),h=Date.now();if(m instanceof globalThis.Error){let e=`retrying, ${t} attempts remaining`;if(r.signal?.aborted)throw new t2;let a=tQ(m)||/timed? ?out/i.test(String(m)+("cause"in m?String(m.cause):""));if(t)return sk(this).info(`[${o}] connection ${a?"timed out":"failed"} - ${e}`),sk(this).debug(`[${o}] connection ${a?"timed out":"failed"} (${e})`,sC({retryOfRequestLogID:s,url:i,durationMs:h-d,message:m.message})),this.retryRequest(r,t,s??o);if(sk(this).info(`[${o}] connection ${a?"timed out":"failed"} - error; no more retries left`),sk(this).debug(`[${o}] connection ${a?"timed out":"failed"} (error; no more retries left)`,sC({retryOfRequestLogID:s,url:i,durationMs:h-d,message:m.message})),a)throw new t4;throw new t5({cause:m})}let p=[...m.headers.entries()].filter(([e])=>"request-id"===e).map(([e,t])=>", "+e+": "+JSON.stringify(t)).join(""),g=`[${o}${c}${p}] ${n.method} ${i} ${m.ok?"succeeded":"failed"} with status ${m.status} in ${h-d}ms`;if(!m.ok){let e=await this.shouldRetry(m);if(t&&e){let e=`retrying, ${t} attempts remaining`;return await sg(m.body),sk(this).info(`${g} - ${e}`),sk(this).debug(`[${o}] response error (${e})`,sC({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,durationMs:h-d})),this.retryRequest(r,t,s??o,m.headers)}let a=e?"error; no more retries left":"error; not retryable";sk(this).info(`${g} - ${a}`);let n=await m.text().catch(e=>tZ(e).message),i=so(n),l=i?void 0:n;throw sk(this).debug(`[${o}] response error (${a})`,sC({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,message:l,durationMs:Date.now()-d})),this.makeStatusError(m.status,i,l,m.headers)}return sk(this).info(g),sk(this).debug(`[${o}] response start`,sC({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,durationMs:h-d})),{response:m,options:r,controller:u,requestLogID:o,retryOfRequestLogID:s,startTime:d}}getAPIList(e,t,s){return this.requestAPIList(t,s&&"then"in s?s.then(t=>({method:"get",path:e,...t})):{method:"get",path:e,...s})}requestAPIList(e,t){return new sO(this,this.makeRequest(t,null,void 0),e)}async fetchWithTimeout(e,t,s,r){let{signal:a,method:n,...i}=t||{},l=this._makeAbort(r);a&&a.addEventListener("abort",l,{once:!0});let o=setTimeout(l,s),c=globalThis.ReadableStream&&i.body instanceof globalThis.ReadableStream||"object"==typeof i.body&&null!==i.body&&Symbol.asyncIterator in i.body,d={signal:r.signal,...c?{duplex:"half"}:{},method:"GET",...i};n&&(d.method=n.toUpperCase());try{return await this.fetch.call(void 0,e,d)}finally{clearTimeout(o)}}async shouldRetry(e){let t=e.headers.get("x-should-retry");return"true"===t||"false"!==t&&(408===e.status||409===e.status||429===e.status||!!(e.status>=500))}async retryRequest(e,t,s,r){let a,n,i=r?.get("retry-after-ms");if(i){let e=parseFloat(i);Number.isNaN(e)||(a=e)}let l=r?.get("retry-after");if(l&&!a){let e=parseFloat(l);a=Number.isNaN(e)?Date.parse(l)-Date.now():1e3*e}if(void 0===a){let s=e.maxRetries??this.maxRetries;a=this.calculateDefaultRetryTimeoutMillis(t,s)}return await (n=a,new Promise(e=>setTimeout(e,n))),this.makeRequest(e,t-1,s)}calculateDefaultRetryTimeoutMillis(e,t){return Math.min(.5*Math.pow(2,t-e),8)*(1-.25*Math.random())*1e3}calculateNonstreamingTimeout(e,t){if(36e5*e/128e3>6e5||null!=t&&e>t)throw new t0("Streaming is required for operations that may take longer than 10 minutes. 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n={};this.idempotencyHeader&&"get"!==s&&(e.idempotencyKey||(e.idempotencyKey=this.defaultIdempotencyKey()),n[this.idempotencyHeader]=e.idempotencyKey);let i=sQ([n,{Accept:"application/json","User-Agent":this.getUserAgent(),"X-Stainless-Retry-Count":String(a),...e.timeout?{"X-Stainless-Timeout":String(Math.trunc(e.timeout/1e3))}:{},...t??(t=(()=>{let e="u">typeof Deno&&null!=Deno.build?"deno":"u">typeof EdgeRuntime?"edge":"[object process]"===Object.prototype.toString.call(void 0!==globalThis.process?globalThis.process:0)?"node":"unknown";if("deno"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sc,"X-Stainless-OS":su(Deno.build.os),"X-Stainless-Arch":sd(Deno.build.arch),"X-Stainless-Runtime":"deno","X-Stainless-Runtime-Version":"string"==typeof Deno.version?Deno.version:Deno.version?.deno??"unknown"};if("u">typeof EdgeRuntime)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sc,"X-Stainless-OS":"Unknown","X-Stainless-Arch":`other:${EdgeRuntime}`,"X-Stainless-Runtime":"edge","X-Stainless-Runtime-Version":globalThis.process.version};if("node"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sc,"X-Stainless-OS":su(globalThis.process.platform??"unknown"),"X-Stainless-Arch":sd(globalThis.process.arch??"unknown"),"X-Stainless-Runtime":"node","X-Stainless-Runtime-Version":globalThis.process.version??"unknown"};let t=function(){if("u"e.abort()}buildBody({options:{body:e,headers:t}}){if(!e)return{bodyHeaders:void 0,body:void 0};let s=sQ([t]);return ArrayBuffer.isView(e)||e instanceof ArrayBuffer||e instanceof DataView||"string"==typeof e&&s.values.has("content-type")||globalThis.Blob&&e instanceof globalThis.Blob||e instanceof FormData||e instanceof URLSearchParams||globalThis.ReadableStream&&e instanceof globalThis.ReadableStream?{bodyHeaders:void 0,body:e}:"object"==typeof e&&(Symbol.asyncIterator in e||Symbol.iterator in e&&"next"in e&&"function"==typeof e.next)?{bodyHeaders:void 0,body:sh(e)}:"object"==typeof e&&"application/x-www-form-urlencoded"===s.values.get("content-type")?{bodyHeaders:{"content-type":"application/x-www-form-urlencoded"},body:this.stringifyQuery(e)}:tX(this,ex,"f").call(this,{body:e,headers:s})}}ef=rJ,ex=new WeakMap,eg=new WeakSet,ey=function(){return"https://api.anthropic.com"!==this.baseURL},rJ.Anthropic=ef,rJ.HUMAN_PROMPT="\\n\\nHuman:",rJ.AI_PROMPT="\\n\\nAssistant:",rJ.DEFAULT_TIMEOUT=6e5,rJ.AnthropicError=t0,rJ.APIError=t1,rJ.APIConnectionError=t5,rJ.APIConnectionTimeoutError=t4,rJ.APIUserAbortError=t2,rJ.NotFoundError=t7,rJ.ConflictError=t9,rJ.RateLimitError=st,rJ.BadRequestError=t3,rJ.AuthenticationError=t6,rJ.InternalServerError=ss,rJ.PermissionDeniedError=t8,rJ.UnprocessableEntityError=se,rJ.toFile=sJ;class rK extends rJ{constructor(){super(...arguments),this.completions=new rH(this),this.messages=new rq(this),this.models=new rV(this),this.beta=new rR(this)}}rK.Completions=rH,rK.Messages=rq,rK.Models=rV,rK.Beta=rR;let rX="toolset:";async function rY(e,t,s,r,a=[],n,i,l,o,c,d,u,m,h,p,g,f,x){if(!r)throw Error("Virtual Key is required");console.log=function(){};let y=p||(0,eD.getProxyBaseUrl)(),b={};a&&a.length>0&&(b["x-litellm-tags"]=a.join(","));let v=new rK({apiKey:r,baseURL:y,dangerouslyAllowBrowser:!0,defaultHeaders:b});try{let r=Date.now(),a=!1,p={model:s,messages:e.map(e=>({role:e.role,content:e.content})),stream:!0,max_tokens:1024,litellm_trace_id:c},y=function({selectedMCPServers:e,mcpServers:t,mcpToolsets:s,mcpServerToolRestrictions:r}){return e&&0!==e.length?e.includes("__all__")?[{type:"mcp",server_label:"litellm",server_url:"litellm_proxy/mcp",require_approval:"never"}]:e.map(e=>{if(e.startsWith(rX)){let t=e.slice(rX.length),r=s?.find(e=>e.toolset_id===t),a=r?.toolset_name||t;return{type:"mcp",server_label:a,server_url:`litellm_proxy/mcp/${a}`,require_approval:"never"}}let a=t?.find(t=>t.server_id===e),n=a?.server_name||e,i=r?.[e]||[];return{type:"mcp",server_label:n,server_url:`litellm_proxy/mcp/${n}`,require_approval:"never",...i.length>0?{allowed_tools:i}:{}}}):[]}({selectedMCPServers:h,mcpServers:g,mcpToolsets:x,mcpServerToolRestrictions:f});for await(let e of(y.length>0&&(p.tools=y),d&&(p.vector_store_ids=d),u&&(p.guardrails=u),m&&(p.policies=m),v.messages.stream(p,{signal:n}))){if("content_block_delta"===e.type){let n=e.delta;if(!a){a=!0;let e=Date.now()-r;l&&l(e)}"text_delta"===n.type?t("assistant",n.text,s):"reasoning_delta"===n.type&&i&&i(n.text)}if("message_delta"===e.type&&e.usage&&o){let t=e.usage,s={completionTokens:t.output_tokens,promptTokens:t.input_tokens,totalTokens:t.input_tokens+t.output_tokens,...(0,eG.extractPromptCacheTokens)(t)};o(s)}}}catch(e){throw n?.aborted||eU.default.fromBackend(`Error occurred while generating model response. Please try again. Error: ${e}`),e}}async function rQ(e,t,s,r,a,n,i,l,o,c){console.log=function(){};let d=c||(0,eD.getProxyBaseUrl)(),u=new eH.default.OpenAI({apiKey:a,baseURL:d,dangerouslyAllowBrowser:!0,defaultHeaders:n&&n.length>0?{"x-litellm-tags":n.join(",")}:void 0});try{let a=await u.audio.speech.create({model:r,input:e,voice:t,...l?{response_format:l}:{},...o?{speed:o}:{}},{signal:i}),n=await a.blob(),c=URL.createObjectURL(n);s(c,r)}catch(e){throw i?.aborted||eU.default.fromBackend(`Error occurred while generating speech. Please try again. Error: ${e}`),e}}async function rZ(e,t,s,r,a,n,i,l,o,c,d){console.log=function(){};let u=d||(0,eD.getProxyBaseUrl)(),m=new eH.default.OpenAI({apiKey:r,baseURL:u,dangerouslyAllowBrowser:!0,defaultHeaders:a&&a.length>0?{"x-litellm-tags":a.join(",")}:void 0});try{let r=await m.audio.transcriptions.create({model:s,file:e,...i?{language:i}:{},...l?{prompt:l}:{},...o?{response_format:o}:{},...void 0!==c?{temperature:c}:{}},{signal:n});if(r&&r.text)t(r.text,s),eU.default.success("Audio transcribed successfully");else throw Error("No transcription text in response")}catch(e){if(console.error("Error making audio transcription request:",e),n?.aborted);else{let t="Failed to transcribe audio";e?.error?.message?t=e.error.message:e?.message&&(t=e.message),eU.default.fromBackend(`Audio transcription failed: ${t}`)}throw e}}async function r0(e,t,s,r,a,n){if(!r)throw Error("Virtual Key is required");console.log=function(){};let i=n||(0,eD.getProxyBaseUrl)(),l={};a&&a.length>0&&(l["x-litellm-tags"]=a.join(","));try{let a=i.endsWith("/")?i.slice(0,-1):i,n=`${a}/embeddings`,o=await fetch(n,{method:"POST",headers:{"Content-Type":"application/json",[(0,eD.getGlobalLitellmHeaderName)()]:`Bearer ${r}`,...l},body:JSON.stringify({model:s,input:e})});if(!o.ok){let e=await o.text();throw Error(e||`Request failed with status ${o.status}`)}let c=await o.json(),d=c?.data?.[0]?.embedding;if(!d)throw Error("No embedding returned from server");t(JSON.stringify(d),c?.model??s)}catch(e){throw eU.default.fromBackend(`Error occurred while making embeddings request. Please try again. 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tN=e.i(922143),tD=e.i(682358);function tE(e,t,i,r){let n=Math.abs(e),a=n%10,o=n%100;return o>=11&&o<=19?r:1===a?t:a>=2&&a<=4?i:r}e.s([],543365),e.i(543365);var tT=e.i(40824);function tA(e,t,i,r){let n=Math.abs(e),a=n%10,o=n%100;return o>=11&&o<=19?r:1===a?t:a>=2&&a<=4?i:r}e.s(["ar",0,function(){let e,t;return{localeError:(e={string:{unit:"حرف",verb:"أن يحوي"},file:{unit:"بايت",verb:"أن يحوي"},array:{unit:"عنصر",verb:"أن يحوي"},set:{unit:"عنصر",verb:"أن يحوي"}},t={regex:"مدخل",email:"بريد إلكتروني",url:"رابط",emoji:"إيموجي",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"تاريخ ووقت بمعيار ISO",date:"تاريخ بمعيار ISO",time:"وقت بمعيار ISO",duration:"مدة بمعيار ISO",ipv4:"عنوان IPv4",ipv6:"عنوان IPv6",cidrv4:"مدى عناوين بصيغة IPv4",cidrv6:"مدى عناوين بصيغة IPv6",base64:"نَص بترميز base64-encoded",base64url:"نَص بترميز base64url-encoded",json_string:"نَص على هيئة JSON",e164:"رقم هاتف بمعيار 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e,t;return{localeError:(e={string:{unit:{one:"сімвал",few:"сімвалы",many:"сімвалаў"},verb:"мець"},array:{unit:{one:"элемент",few:"элементы",many:"элементаў"},verb:"мець"},set:{unit:{one:"элемент",few:"элементы",many:"элементаў"},verb:"мець"},file:{unit:{one:"байт",few:"байты",many:"байтаў"},verb:"мець"}},t={regex:"увод",email:"email адрас",url:"URL",emoji:"эмодзі",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO дата і час",date:"ISO дата",time:"ISO час",duration:"ISO працягласць",ipv4:"IPv4 адрас",ipv6:"IPv6 адрас",cidrv4:"IPv4 дыяпазон",cidrv6:"IPv6 дыяпазон",base64:"радок у фармаце base64",base64url:"радок у фармаце base64url",json_string:"JSON радок",e164:"нумар E.164",jwt:"JWT",template_literal:"увод"},i=>{switch(i.code){case"invalid_type":return`Няправільны ўвод: чакаўся ${i.expected}, атрымана ${(e=>{let t=typeof e;switch(t){case"number":return 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e,t;return{localeError:(e={string:{unit:"caràcters",verb:"contenir"},file:{unit:"bytes",verb:"contenir"},array:{unit:"elements",verb:"contenir"},set:{unit:"elements",verb:"contenir"}},t={regex:"entrada",email:"adreça electrònica",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"data i hora ISO",date:"data ISO",time:"hora ISO",duration:"durada ISO",ipv4:"adreça IPv4",ipv6:"adreça IPv6",cidrv4:"rang IPv4",cidrv6:"rang IPv6",base64:"cadena codificada en base64",base64url:"cadena codificada en base64url",json_string:"cadena JSON",e164:"número E.164",jwt:"JWT",template_literal:"entrada"},i=>{switch(i.code){case"invalid_type":return`Tipus inv\xe0lid: s'esperava ${i.expected}, s'ha rebut ${(e=>{let t=typeof e;switch(t){case"number":return 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e,t;return{localeError:(e={string:{unit:"znaků",verb:"mít"},file:{unit:"bajtů",verb:"mít"},array:{unit:"prvků",verb:"mít"},set:{unit:"prvků",verb:"mít"}},t={regex:"regulární výraz",email:"e-mailová adresa",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"datum a čas ve formátu ISO",date:"datum ve formátu ISO",time:"čas ve formátu ISO",duration:"doba trvání ISO",ipv4:"IPv4 adresa",ipv6:"IPv6 adresa",cidrv4:"rozsah IPv4",cidrv6:"rozsah IPv6",base64:"řetězec zakódovaný ve formátu base64",base64url:"řetězec zakódovaný ve formátu base64url",json_string:"řetězec ve formátu JSON",e164:"číslo E.164",jwt:"JWT",template_literal:"vstup"},i=>{switch(i.code){case"invalid_type":return`Neplatn\xfd vstup: oček\xe1v\xe1no ${i.expected}, obdrženo ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"číslo";case"string":return"řetězec";case"boolean":return"boolean";case"bigint":return"bigint";case"function":return"funkce";case"symbol":return"symbol";case"undefined":return"undefined";case"object":if(Array.isArray(e))return"pole";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Neplatn\xfd vstup: oček\xe1v\xe1no ${V.stringifyPrimitive(i.values[0])}`;return`Neplatn\xe1 možnost: oček\xe1v\xe1na jedna z hodnot ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Hodnota je př\xedliš velk\xe1: ${i.origin??"hodnota"} mus\xed m\xedt ${t}${i.maximum.toString()} ${r.unit??"prvků"}`;return`Hodnota je př\xedliš velk\xe1: ${i.origin??"hodnota"} mus\xed b\xfdt ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Hodnota je př\xedliš 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${i.origin}`}})}},"de",0,function(){let e,t;return{localeError:(e={string:{unit:"Zeichen",verb:"zu haben"},file:{unit:"Bytes",verb:"zu haben"},array:{unit:"Elemente",verb:"zu haben"},set:{unit:"Elemente",verb:"zu haben"}},t={regex:"Eingabe",email:"E-Mail-Adresse",url:"URL",emoji:"Emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO-Datum und -Uhrzeit",date:"ISO-Datum",time:"ISO-Uhrzeit",duration:"ISO-Dauer",ipv4:"IPv4-Adresse",ipv6:"IPv6-Adresse",cidrv4:"IPv4-Bereich",cidrv6:"IPv6-Bereich",base64:"Base64-codierter String",base64url:"Base64-URL-codierter String",json_string:"JSON-String",e164:"E.164-Nummer",jwt:"JWT",template_literal:"Eingabe"},i=>{switch(i.code){case"invalid_type":return`Ung\xfcltige Eingabe: erwartet ${i.expected}, erhalten ${(e=>{let t=typeof e;switch(t){case"number":return 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"${i.prefix}" beginnen`;if("ends_with"===i.format)return`Ung\xfcltiger String: muss mit "${i.suffix}" enden`;if("includes"===i.format)return`Ung\xfcltiger String: muss "${i.includes}" enthalten`;if("regex"===i.format)return`Ung\xfcltiger String: muss dem Muster ${i.pattern} entsprechen`;return`Ung\xfcltig: ${t[i.format]??i.format}`;case"not_multiple_of":return`Ung\xfcltige Zahl: muss ein Vielfaches von ${i.divisor} sein`;case"unrecognized_keys":return`${i.keys.length>1?"Unbekannte Schlüssel":"Unbekannter Schlüssel"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Ung\xfcltiger Schl\xfcssel in ${i.origin}`;case"invalid_union":default:return"Ungültige Eingabe";case"invalid_element":return`Ung\xfcltiger Wert in ${i.origin}`}})}},"en",()=>tT.default,"eo",0,function(){let e,t;return{localeError:(e={string:{unit:"karaktrojn",verb:"havi"},file:{unit:"bajtojn",verb:"havi"},array:{unit:"elementojn",verb:"havi"},set:{unit:"elementojn",verb:"havi"}},t={regex:"enigo",email:"retadreso",url:"URL",emoji:"emoĝio",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO-datotempo",date:"ISO-dato",time:"ISO-tempo",duration:"ISO-daŭro",ipv4:"IPv4-adreso",ipv6:"IPv6-adreso",cidrv4:"IPv4-rango",cidrv6:"IPv6-rango",base64:"64-ume kodita karaktraro",base64url:"URL-64-ume kodita karaktraro",json_string:"JSON-karaktraro",e164:"E.164-nombro",jwt:"JWT",template_literal:"enigo"},i=>{switch(i.code){case"invalid_type":return`Nevalida enigo: atendiĝis ${i.expected}, riceviĝis ${(e=>{let t=typeof e;switch(t){case"number":return 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electrónico",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"fecha y hora ISO",date:"fecha ISO",time:"hora ISO",duration:"duración ISO",ipv4:"dirección IPv4",ipv6:"dirección IPv6",cidrv4:"rango IPv4",cidrv6:"rango IPv6",base64:"cadena codificada en base64",base64url:"URL codificada en base64",json_string:"cadena JSON",e164:"número E.164",jwt:"JWT",template_literal:"entrada"},i=>{switch(i.code){case"invalid_type":return`Entrada inv\xe1lida: se esperaba ${i.expected}, recibido ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"número";case"object":if(Array.isArray(e))return"arreglo";if(null===e)return"nulo";if(Object.getPrototypeOf(e)!==Object.prototype)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Entrada inv\xe1lida: se esperaba ${V.stringifyPrimitive(i.values[0])}`;return`Opci\xf3n 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${i.pattern}`;return`Inv\xe1lido ${t[i.format]??i.format}`;case"not_multiple_of":return`N\xfamero inv\xe1lido: debe ser m\xfaltiplo de ${i.divisor}`;case"unrecognized_keys":return`Llave${i.keys.length>1?"s":""} desconocida${i.keys.length>1?"s":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Llave inv\xe1lida en ${i.origin}`;case"invalid_union":default:return"Entrada inválida";case"invalid_element":return`Valor inv\xe1lido en ${i.origin}`}})}},"fa",0,function(){let e,t;return{localeError:(e={string:{unit:"کاراکتر",verb:"داشته باشد"},file:{unit:"بایت",verb:"داشته باشد"},array:{unit:"آیتم",verb:"داشته باشد"},set:{unit:"آیتم",verb:"داشته باشد"}},t={regex:"ورودی",email:"آدرس ایمیل",url:"URL",emoji:"ایموجی",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"تاریخ و زمان ایزو",date:"تاریخ ایزو",time:"زمان ایزو",duration:"مدت زمان ایزو",ipv4:"IPv4 آدرس",ipv6:"IPv6 آدرس",cidrv4:"IPv4 دامنه",cidrv6:"IPv6 دامنه",base64:"base64-encoded رشته",base64url:"base64url-encoded رشته",json_string:"JSON رشته",e164:"E.164 عدد",jwt:"JWT",template_literal:"ورودی"},i=>{switch(i.code){case"invalid_type":return`ورودی نامعتبر: می‌بایست ${i.expected} می‌بود، ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"عدد";case"object":if(Array.isArray(e))return"آرایه";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)} دریافت شد`;case"invalid_value":if(1===i.values.length)return`ورودی نامعتبر: می‌بایست ${V.stringifyPrimitive(i.values[0])} می‌بود`;return`گزینه نامعتبر: می‌بایست یکی از ${V.joinValues(i.values,"|")} می‌بود`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`خیلی بزرگ: ${i.origin??"مقدار"} باید ${t}${i.maximum.toString()} ${r.unit??"عنصر"} باشد`;return`خیلی بزرگ: ${i.origin??"مقدار"} باید ${t}${i.maximum.toString()} باشد`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`خیلی کوچک: ${i.origin} باید ${t}${i.minimum.toString()} ${r.unit} باشد`;return`خیلی کوچک: ${i.origin} باید ${t}${i.minimum.toString()} باشد`}case"invalid_format":if("starts_with"===i.format)return`رشته نامعتبر: باید با "${i.prefix}" شروع شود`;if("ends_with"===i.format)return`رشته نامعتبر: باید با "${i.suffix}" تمام شود`;if("includes"===i.format)return`رشته نامعتبر: باید شامل "${i.includes}" باشد`;if("regex"===i.format)return`رشته نامعتبر: باید با الگوی ${i.pattern} مطابقت داشته باشد`;return`${t[i.format]??i.format} نامعتبر`;case"not_multiple_of":return`عدد نامعتبر: باید مضرب ${i.divisor} باشد`;case"unrecognized_keys":return`کلید${i.keys.length>1?"های":""} ناشناس: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`کلید ناشناس در ${i.origin}`;case"invalid_union":default:return"ورودی نامعتبر";case"invalid_element":return`مقدار نامعتبر در ${i.origin}`}})}},"fi",0,function(){let e,t;return{localeError:(e={string:{unit:"merkkiä",subject:"merkkijonon"},file:{unit:"tavua",subject:"tiedoston"},array:{unit:"alkiota",subject:"listan"},set:{unit:"alkiota",subject:"joukon"},number:{unit:"",subject:"luvun"},bigint:{unit:"",subject:"suuren kokonaisluvun"},int:{unit:"",subject:"kokonaisluvun"},date:{unit:"",subject:"päivämäärän"}},t={regex:"säännöllinen lauseke",email:"sähköpostiosoite",url:"URL-osoite",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO-aikaleima",date:"ISO-päivämäärä",time:"ISO-aika",duration:"ISO-kesto",ipv4:"IPv4-osoite",ipv6:"IPv6-osoite",cidrv4:"IPv4-alue",cidrv6:"IPv6-alue",base64:"base64-koodattu merkkijono",base64url:"base64url-koodattu merkkijono",json_string:"JSON-merkkijono",e164:"E.164-luku",jwt:"JWT",template_literal:"templaattimerkkijono"},i=>{switch(i.code){case"invalid_type":return`Virheellinen tyyppi: odotettiin ${i.expected}, 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e,t;return{localeError:(e={string:{unit:"caractères",verb:"avoir"},file:{unit:"octets",verb:"avoir"},array:{unit:"éléments",verb:"avoir"},set:{unit:"éléments",verb:"avoir"}},t={regex:"entrée",email:"adresse e-mail",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"date et heure ISO",date:"date ISO",time:"heure ISO",duration:"durée ISO",ipv4:"adresse IPv4",ipv6:"adresse IPv6",cidrv4:"plage IPv4",cidrv6:"plage IPv6",base64:"chaîne encodée en base64",base64url:"chaîne encodée en base64url",json_string:"chaîne JSON",e164:"numéro E.164",jwt:"JWT",template_literal:"entrée"},i=>{switch(i.code){case"invalid_type":return`Entr\xe9e invalide : ${i.expected} attendu, ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"nombre";case"object":if(Array.isArray(e))return"tableau";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)} re\xe7u`;case"invalid_value":if(1===i.values.length)return`Entr\xe9e invalide : ${V.stringifyPrimitive(i.values[0])} attendu`;return`Option invalide : une valeur parmi ${V.joinValues(i.values,"|")} attendue`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Trop grand : ${i.origin??"valeur"} doit ${r.verb} ${t}${i.maximum.toString()} ${r.unit??"élément(s)"}`;return`Trop grand : ${i.origin??"valeur"} doit \xeatre ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Trop petit : ${i.origin} doit ${r.verb} ${t}${i.minimum.toString()} ${r.unit}`;return`Trop petit : ${i.origin} doit \xeatre ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Cha\xeene invalide : doit commencer par "${i.prefix}"`;if("ends_with"===i.format)return`Cha\xeene invalide : doit se terminer par "${i.suffix}"`;if("includes"===i.format)return`Cha\xeene invalide : doit inclure "${i.includes}"`;if("regex"===i.format)return`Cha\xeene invalide : doit correspondre au mod\xe8le ${i.pattern}`;return`${t[i.format]??i.format} invalide`;case"not_multiple_of":return`Nombre invalide : doit \xeatre un multiple de ${i.divisor}`;case"unrecognized_keys":return`Cl\xe9${i.keys.length>1?"s":""} non reconnue${i.keys.length>1?"s":""} : ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Cl\xe9 invalide dans ${i.origin}`;case"invalid_union":default:return"Entrée invalide";case"invalid_element":return`Valeur invalide dans ${i.origin}`}})}},"frCA",0,function(){let e,t;return{localeError:(e={string:{unit:"caractères",verb:"avoir"},file:{unit:"octets",verb:"avoir"},array:{unit:"éléments",verb:"avoir"},set:{unit:"éléments",verb:"avoir"}},t={regex:"entrée",email:"adresse courriel",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"date-heure ISO",date:"date ISO",time:"heure ISO",duration:"durée ISO",ipv4:"adresse IPv4",ipv6:"adresse IPv6",cidrv4:"plage IPv4",cidrv6:"plage IPv6",base64:"chaîne encodée en base64",base64url:"chaîne encodée en base64url",json_string:"chaîne JSON",e164:"numéro E.164",jwt:"JWT",template_literal:"entrée"},i=>{switch(i.code){case"invalid_type":return`Entr\xe9e invalide : attendu ${i.expected}, re\xe7u ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"number";case"object":if(Array.isArray(e))return"array";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Entr\xe9e invalide : attendu ${V.stringifyPrimitive(i.values[0])}`;return`Option invalide : attendu l'une des valeurs suivantes ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"≤":"<",r=e[i.origin]??null;if(r)return`Trop grand : attendu que ${i.origin??"la valeur"} ait ${t}${i.maximum.toString()} ${r.unit}`;return`Trop grand : attendu que ${i.origin??"la valeur"} soit ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?"≥":">",r=e[i.origin]??null;if(r)return`Trop petit : attendu que ${i.origin} ait ${t}${i.minimum.toString()} ${r.unit}`;return`Trop petit : attendu que ${i.origin} soit ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Cha\xeene invalide : doit commencer par "${i.prefix}"`;if("ends_with"===i.format)return`Cha\xeene invalide : doit se terminer par "${i.suffix}"`;if("includes"===i.format)return`Cha\xeene invalide : doit inclure "${i.includes}"`;if("regex"===i.format)return`Cha\xeene invalide : doit correspondre au motif ${i.pattern}`;return`${t[i.format]??i.format} invalide`;case"not_multiple_of":return`Nombre invalide : doit \xeatre un multiple de ${i.divisor}`;case"unrecognized_keys":return`Cl\xe9${i.keys.length>1?"s":""} non reconnue${i.keys.length>1?"s":""} : ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Cl\xe9 invalide dans ${i.origin}`;case"invalid_union":default:return"Entrée invalide";case"invalid_element":return`Valeur invalide dans ${i.origin}`}})}},"he",0,function(){let e,t;return{localeError:(e={string:{unit:"אותיות",verb:"לכלול"},file:{unit:"בייטים",verb:"לכלול"},array:{unit:"פריטים",verb:"לכלול"},set:{unit:"פריטים",verb:"לכלול"}},t={regex:"קלט",email:"כתובת אימייל",url:"כתובת רשת",emoji:"אימוג'י",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"תאריך וזמן ISO",date:"תאריך ISO",time:"זמן ISO",duration:"משך זמן ISO",ipv4:"כתובת IPv4",ipv6:"כתובת IPv6",cidrv4:"טווח IPv4",cidrv6:"טווח IPv6",base64:"מחרוזת בבסיס 64",base64url:"מחרוזת 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${t}${i.minimum.toString()} ${r.unit}`;return`קטן מדי: ${i.origin} צריך להיות ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`מחרוזת לא תקינה: חייבת להתחיל ב"${i.prefix}"`;if("ends_with"===i.format)return`מחרוזת לא תקינה: חייבת להסתיים ב "${i.suffix}"`;if("includes"===i.format)return`מחרוזת לא תקינה: חייבת לכלול "${i.includes}"`;if("regex"===i.format)return`מחרוזת לא תקינה: חייבת להתאים לתבנית ${i.pattern}`;return`${t[i.format]??i.format} לא תקין`;case"not_multiple_of":return`מספר לא תקין: חייב להיות מכפלה של ${i.divisor}`;case"unrecognized_keys":return`מפתח${i.keys.length>1?"ות":""} לא מזוה${i.keys.length>1?"ים":"ה"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`מפתח לא תקין ב${i.origin}`;case"invalid_union":default:return"קלט לא תקין";case"invalid_element":return`ערך לא תקין ב${i.origin}`}})}},"hu",0,function(){let e,t;return{localeError:(e={string:{unit:"karakter",verb:"legyen"},file:{unit:"byte",verb:"legyen"},array:{unit:"elem",verb:"legyen"},set:{unit:"elem",verb:"legyen"}},t={regex:"bemenet",email:"email cím",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO időbélyeg",date:"ISO dátum",time:"ISO idő",duration:"ISO időintervallum",ipv4:"IPv4 cím",ipv6:"IPv6 cím",cidrv4:"IPv4 tartomány",cidrv6:"IPv6 tartomány",base64:"base64-kódolt string",base64url:"base64url-kódolt string",json_string:"JSON string",e164:"E.164 szám",jwt:"JWT",template_literal:"bemenet"},i=>{switch(i.code){case"invalid_type":return`\xc9rv\xe9nytelen bemenet: a v\xe1rt \xe9rt\xe9k ${i.expected}, a kapott \xe9rt\xe9k ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"szám";case"object":if(Array.isArray(e))return"tömb";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`\xc9rv\xe9nytelen bemenet: a v\xe1rt \xe9rt\xe9k ${V.stringifyPrimitive(i.values[0])}`;return`\xc9rv\xe9nytelen opci\xf3: valamelyik \xe9rt\xe9k v\xe1rt ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`T\xfal nagy: ${i.origin??"érték"} m\xe9rete t\xfal nagy ${t}${i.maximum.toString()} ${r.unit??"elem"}`;return`T\xfal nagy: a bemeneti \xe9rt\xe9k ${i.origin??"érték"} t\xfal nagy: ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`T\xfal kicsi: a bemeneti \xe9rt\xe9k ${i.origin} m\xe9rete t\xfal kicsi ${t}${i.minimum.toString()} ${r.unit}`;return`T\xfal kicsi: a bemeneti \xe9rt\xe9k ${i.origin} t\xfal kicsi ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`\xc9rv\xe9nytelen string: "${i.prefix}" \xe9rt\xe9kkel kell kezdődnie`;if("ends_with"===i.format)return`\xc9rv\xe9nytelen string: "${i.suffix}" \xe9rt\xe9kkel kell v\xe9gződnie`;if("includes"===i.format)return`\xc9rv\xe9nytelen string: "${i.includes}" \xe9rt\xe9ket kell tartalmaznia`;if("regex"===i.format)return`\xc9rv\xe9nytelen string: ${i.pattern} mint\xe1nak kell megfelelnie`;return`\xc9rv\xe9nytelen ${t[i.format]??i.format}`;case"not_multiple_of":return`\xc9rv\xe9nytelen sz\xe1m: ${i.divisor} t\xf6bbsz\xf6r\xf6s\xe9nek kell lennie`;case"unrecognized_keys":return`Ismeretlen kulcs${i.keys.length>1?"s":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`\xc9rv\xe9nytelen kulcs ${i.origin}`;case"invalid_union":default:return"Érvénytelen bemenet";case"invalid_element":return`\xc9rv\xe9nytelen \xe9rt\xe9k: ${i.origin}`}})}},"id",0,function(){let e,t;return{localeError:(e={string:{unit:"karakter",verb:"memiliki"},file:{unit:"byte",verb:"memiliki"},array:{unit:"item",verb:"memiliki"},set:{unit:"item",verb:"memiliki"}},t={regex:"input",email:"alamat email",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"tanggal dan waktu format ISO",date:"tanggal format ISO",time:"jam format ISO",duration:"durasi format ISO",ipv4:"alamat IPv4",ipv6:"alamat IPv6",cidrv4:"rentang alamat IPv4",cidrv6:"rentang alamat IPv6",base64:"string dengan enkode base64",base64url:"string dengan enkode base64url",json_string:"string JSON",e164:"angka E.164",jwt:"JWT",template_literal:"input"},i=>{switch(i.code){case"invalid_type":return`Input tidak valid: diharapkan ${i.expected}, diterima ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"number";case"object":if(Array.isArray(e))return"array";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Input tidak valid: diharapkan ${V.stringifyPrimitive(i.values[0])}`;return`Pilihan tidak valid: diharapkan salah satu dari ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Terlalu besar: diharapkan ${i.origin??"value"} memiliki ${t}${i.maximum.toString()} ${r.unit??"elemen"}`;return`Terlalu besar: diharapkan ${i.origin??"value"} menjadi ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Terlalu kecil: diharapkan ${i.origin} memiliki ${t}${i.minimum.toString()} ${r.unit}`;return`Terlalu kecil: diharapkan ${i.origin} menjadi ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`String tidak valid: harus dimulai dengan "${i.prefix}"`;if("ends_with"===i.format)return`String tidak valid: harus berakhir dengan "${i.suffix}"`;if("includes"===i.format)return`String tidak valid: harus menyertakan "${i.includes}"`;if("regex"===i.format)return`String tidak valid: harus sesuai pola ${i.pattern}`;return`${t[i.format]??i.format} tidak valid`;case"not_multiple_of":return`Angka tidak valid: harus kelipatan dari ${i.divisor}`;case"unrecognized_keys":return`Kunci tidak dikenali ${i.keys.length>1?"s":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Kunci tidak valid di ${i.origin}`;case"invalid_union":default:return"Input tidak valid";case"invalid_element":return`Nilai tidak valid di ${i.origin}`}})}},"it",0,function(){let e,t;return{localeError:(e={string:{unit:"caratteri",verb:"avere"},file:{unit:"byte",verb:"avere"},array:{unit:"elementi",verb:"avere"},set:{unit:"elementi",verb:"avere"}},t={regex:"input",email:"indirizzo email",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"data e ora ISO",date:"data ISO",time:"ora ISO",duration:"durata ISO",ipv4:"indirizzo IPv4",ipv6:"indirizzo IPv6",cidrv4:"intervallo IPv4",cidrv6:"intervallo IPv6",base64:"stringa codificata in base64",base64url:"URL codificata in base64",json_string:"stringa JSON",e164:"numero E.164",jwt:"JWT",template_literal:"input"},i=>{switch(i.code){case"invalid_type":return`Input non valido: atteso ${i.expected}, ricevuto ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"numero";case"object":if(Array.isArray(e))return"vettore";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Input non valido: atteso ${V.stringifyPrimitive(i.values[0])}`;return`Opzione non valida: atteso uno tra ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Troppo grande: ${i.origin??"valore"} deve avere ${t}${i.maximum.toString()} ${r.unit??"elementi"}`;return`Troppo grande: ${i.origin??"valore"} deve essere ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Troppo piccolo: ${i.origin} deve avere ${t}${i.minimum.toString()} ${r.unit}`;return`Troppo piccolo: ${i.origin} deve essere ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Stringa non valida: deve iniziare con "${i.prefix}"`;if("ends_with"===i.format)return`Stringa non valida: deve terminare con "${i.suffix}"`;if("includes"===i.format)return`Stringa non valida: deve includere "${i.includes}"`;if("regex"===i.format)return`Stringa non valida: deve corrispondere al pattern ${i.pattern}`;return`Invalid ${t[i.format]??i.format}`;case"not_multiple_of":return`Numero non valido: deve essere un multiplo di ${i.divisor}`;case"unrecognized_keys":return`Chiav${i.keys.length>1?"i":"e"} non riconosciut${i.keys.length>1?"e":"a"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Chiave non valida in ${i.origin}`;case"invalid_union":default:return"Input non valido";case"invalid_element":return`Valore non valido in ${i.origin}`}})}},"ja",0,function(){let e,t;return{localeError:(e={string:{unit:"文字",verb:"である"},file:{unit:"バイト",verb:"である"},array:{unit:"要素",verb:"である"},set:{unit:"要素",verb:"である"}},t={regex:"入力値",email:"メールアドレス",url:"URL",emoji:"絵文字",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO日時",date:"ISO日付",time:"ISO時刻",duration:"ISO期間",ipv4:"IPv4アドレス",ipv6:"IPv6アドレス",cidrv4:"IPv4範囲",cidrv6:"IPv6範囲",base64:"base64エンコード文字列",base64url:"base64urlエンコード文字列",json_string:"JSON文字列",e164:"E.164番号",jwt:"JWT",template_literal:"入力値"},i=>{switch(i.code){case"invalid_type":return`無効な入力: ${i.expected}が期待されましたが、${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"数値";case"object":if(Array.isArray(e))return"配列";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}が入力されました`;case"invalid_value":if(1===i.values.length)return`無効な入力: ${V.stringifyPrimitive(i.values[0])}が期待されました`;return`無効な選択: ${V.joinValues(i.values,"、")}のいずれかである必要があります`;case"too_big":{let t=i.inclusive?"以下である":"より小さい",r=e[i.origin]??null;if(r)return`大きすぎる値: ${i.origin??"値"}は${i.maximum.toString()}${r.unit??"要素"}${t}必要があります`;return`大きすぎる値: ${i.origin??"値"}は${i.maximum.toString()}${t}必要があります`}case"too_small":{let t=i.inclusive?"以上である":"より大きい",r=e[i.origin]??null;if(r)return`小さすぎる値: ${i.origin}は${i.minimum.toString()}${r.unit}${t}必要があります`;return`小さすぎる値: ${i.origin}は${i.minimum.toString()}${t}必要があります`}case"invalid_format":if("starts_with"===i.format)return`無効な文字列: "${i.prefix}"で始まる必要があります`;if("ends_with"===i.format)return`無効な文字列: "${i.suffix}"で終わる必要があります`;if("includes"===i.format)return`無効な文字列: "${i.includes}"を含む必要があります`;if("regex"===i.format)return`無効な文字列: パターン${i.pattern}に一致する必要があります`;return`無効な${t[i.format]??i.format}`;case"not_multiple_of":return`無効な数値: ${i.divisor}の倍数である必要があります`;case"unrecognized_keys":return`認識されていないキー${i.keys.length>1?"群":""}: ${V.joinValues(i.keys,"、")}`;case"invalid_key":return`${i.origin}内の無効なキー`;case"invalid_union":default:return"無効な入力";case"invalid_element":return`${i.origin}内の無効な値`}})}},"kh",0,function(){let e,t;return{localeError:(e={string:{unit:"តួអក្សរ",verb:"គួរមាន"},file:{unit:"បៃ",verb:"គួរមាន"},array:{unit:"ធាតុ",verb:"គួរមាន"},set:{unit:"ធាតុ",verb:"គួរមាន"}},t={regex:"ទិន្នន័យបញ្ចូល",email:"អាសយដ្ឋានអ៊ីមែល",url:"URL",emoji:"សញ្ញាអារម្មណ៍",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"កាលបរិច្ឆេទ និងម៉ោង ISO",date:"កាលបរិច្ឆេទ ISO",time:"ម៉ោង ISO",duration:"រយៈពេល ISO",ipv4:"អាសយដ្ឋាន IPv4",ipv6:"អាសយដ្ឋាន IPv6",cidrv4:"ដែនអាសយដ្ឋាន IPv4",cidrv6:"ដែនអាសយដ្ឋាន IPv6",base64:"ខ្សែអក្សរអ៊ិកូដ base64",base64url:"ខ្សែអក្សរអ៊ិកូដ base64url",json_string:"ខ្សែអក្សរ JSON",e164:"លេខ E.164",jwt:"JWT",template_literal:"ទិន្នន័យបញ្ចូល"},i=>{switch(i.code){case"invalid_type":return`ទិន្នន័យបញ្ចូលមិនត្រឹមត្រូវ៖ ត្រូវការ ${i.expected} ប៉ុន្តែទទួលបាន ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"មិនមែនជាលេខ (NaN)":"លេខ";case"object":if(Array.isArray(e))return"អារេ (Array)";if(null===e)return"គ្មានតម្លៃ (null)";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`ទិន្នន័យបញ្ចូលមិនត្រឹមត្រូវ៖ ត្រូវការ ${V.stringifyPrimitive(i.values[0])}`;return`ជម្រើសមិនត្រឹមត្រូវ៖ ត្រូវជាមួយក្នុងចំណោម ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`ធំពេក៖ ត្រូវការ ${i.origin??"តម្លៃ"} ${t} ${i.maximum.toString()} ${r.unit??"ធាតុ"}`;return`ធំពេក៖ ត្រូវការ ${i.origin??"តម្លៃ"} ${t} ${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`តូចពេក៖ ត្រូវការ ${i.origin} ${t} ${i.minimum.toString()} ${r.unit}`;return`តូចពេក៖ ត្រូវការ ${i.origin} ${t} ${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`ខ្សែអក្សរមិនត្រឹមត្រូវ៖ ត្រូវចាប់ផ្តើមដោយ "${i.prefix}"`;if("ends_with"===i.format)return`ខ្សែអក្សរមិនត្រឹមត្រូវ៖ ត្រូវបញ្ចប់ដោយ "${i.suffix}"`;if("includes"===i.format)return`ខ្សែអក្សរមិនត្រឹមត្រូវ៖ ត្រូវមាន "${i.includes}"`;if("regex"===i.format)return`ខ្សែអក្សរមិនត្រឹមត្រូវ៖ ត្រូវតែផ្គូផ្គងនឹងទម្រង់ដែលបានកំណត់ ${i.pattern}`;return`មិនត្រឹមត្រូវ៖ ${t[i.format]??i.format}`;case"not_multiple_of":return`លេខមិនត្រឹមត្រូវ៖ ត្រូវតែជាពហុគុណនៃ ${i.divisor}`;case"unrecognized_keys":return`រកឃើញសោមិនស្គាល់៖ ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`សោមិនត្រឹមត្រូវនៅក្នុង ${i.origin}`;case"invalid_union":default:return"ទិន្នន័យមិនត្រឹមត្រូវ";case"invalid_element":return`ទិន្នន័យមិនត្រឹមត្រូវនៅក្នុង ${i.origin}`}})}},"ko",0,function(){let e,t;return{localeError:(e={string:{unit:"문자",verb:"to have"},file:{unit:"바이트",verb:"to have"},array:{unit:"개",verb:"to have"},set:{unit:"개",verb:"to have"}},t={regex:"입력",email:"이메일 주소",url:"URL",emoji:"이모지",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO 날짜시간",date:"ISO 날짜",time:"ISO 시간",duration:"ISO 기간",ipv4:"IPv4 주소",ipv6:"IPv6 주소",cidrv4:"IPv4 범위",cidrv6:"IPv6 범위",base64:"base64 인코딩 문자열",base64url:"base64url 인코딩 문자열",json_string:"JSON 문자열",e164:"E.164 번호",jwt:"JWT",template_literal:"입력"},i=>{switch(i.code){case"invalid_type":return`잘못된 입력: 예상 타입은 ${i.expected}, 받은 타입은 ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"number";case"object":if(Array.isArray(e))return"array";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}입니다`;case"invalid_value":if(1===i.values.length)return`잘못된 입력: 값은 ${V.stringifyPrimitive(i.values[0])} 이어야 합니다`;return`잘못된 옵션: ${V.joinValues(i.values,"또는 ")} 중 하나여야 합니다`;case"too_big":{let t=i.inclusive?"이하":"미만",r="미만"===t?"이어야 합니다":"여야 합니다",n=e[i.origin]??null,a=n?.unit??"요소";if(n)return`${i.origin??"값"}이 너무 큽니다: ${i.maximum.toString()}${a} ${t}${r}`;return`${i.origin??"값"}이 너무 큽니다: ${i.maximum.toString()} ${t}${r}`}case"too_small":{let t=i.inclusive?"이상":"초과",r="이상"===t?"이어야 합니다":"여야 합니다",n=e[i.origin]??null,a=n?.unit??"요소";if(n)return`${i.origin??"값"}이 너무 작습니다: ${i.minimum.toString()}${a} ${t}${r}`;return`${i.origin??"값"}이 너무 작습니다: ${i.minimum.toString()} ${t}${r}`}case"invalid_format":if("starts_with"===i.format)return`잘못된 문자열: "${i.prefix}"(으)로 시작해야 합니다`;if("ends_with"===i.format)return`잘못된 문자열: "${i.suffix}"(으)로 끝나야 합니다`;if("includes"===i.format)return`잘못된 문자열: "${i.includes}"을(를) 포함해야 합니다`;if("regex"===i.format)return`잘못된 문자열: 정규식 ${i.pattern} 패턴과 일치해야 합니다`;return`잘못된 ${t[i.format]??i.format}`;case"not_multiple_of":return`잘못된 숫자: ${i.divisor}의 배수여야 합니다`;case"unrecognized_keys":return`인식할 수 없는 키: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`잘못된 키: ${i.origin}`;case"invalid_union":default:return"잘못된 입력";case"invalid_element":return`잘못된 값: ${i.origin}`}})}},"mk",0,function(){let e,t;return{localeError:(e={string:{unit:"знаци",verb:"да имаат"},file:{unit:"бајти",verb:"да имаат"},array:{unit:"ставки",verb:"да имаат"},set:{unit:"ставки",verb:"да имаат"}},t={regex:"внес",email:"адреса на е-пошта",url:"URL",emoji:"емоџи",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO датум и време",date:"ISO датум",time:"ISO време",duration:"ISO времетраење",ipv4:"IPv4 адреса",ipv6:"IPv6 адреса",cidrv4:"IPv4 опсег",cidrv6:"IPv6 опсег",base64:"base64-енкодирана низа",base64url:"base64url-енкодирана низа",json_string:"JSON низа",e164:"E.164 број",jwt:"JWT",template_literal:"внес"},i=>{switch(i.code){case"invalid_type":return`Грешен внес: се очекува ${i.expected}, примено ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"број";case"object":if(Array.isArray(e))return"низа";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Invalid input: expected ${V.stringifyPrimitive(i.values[0])}`;return`Грешана опција: се очекува една ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Премногу голем: се очекува ${i.origin??"вредноста"} да има ${t}${i.maximum.toString()} ${r.unit??"елементи"}`;return`Премногу голем: се очекува ${i.origin??"вредноста"} да биде ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Премногу мал: се очекува ${i.origin} да има ${t}${i.minimum.toString()} ${r.unit}`;return`Премногу мал: се очекува ${i.origin} да биде ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Неважечка низа: мора да започнува со "${i.prefix}"`;if("ends_with"===i.format)return`Неважечка низа: мора да завршува со "${i.suffix}"`;if("includes"===i.format)return`Неважечка низа: мора да вклучува "${i.includes}"`;if("regex"===i.format)return`Неважечка низа: мора да одгоара на патернот ${i.pattern}`;return`Invalid ${t[i.format]??i.format}`;case"not_multiple_of":return`Грешен број: мора да биде делив со ${i.divisor}`;case"unrecognized_keys":return`${i.keys.length>1?"Непрепознаени клучеви":"Непрепознаен клуч"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Грешен клуч во ${i.origin}`;case"invalid_union":default:return"Грешен внес";case"invalid_element":return`Грешна вредност во ${i.origin}`}})}},"ms",0,function(){let e,t;return{localeError:(e={string:{unit:"aksara",verb:"mempunyai"},file:{unit:"bait",verb:"mempunyai"},array:{unit:"elemen",verb:"mempunyai"},set:{unit:"elemen",verb:"mempunyai"}},t={regex:"input",email:"alamat e-mel",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"tarikh masa ISO",date:"tarikh ISO",time:"masa ISO",duration:"tempoh ISO",ipv4:"alamat IPv4",ipv6:"alamat IPv6",cidrv4:"julat IPv4",cidrv6:"julat IPv6",base64:"string dikodkan base64",base64url:"string dikodkan base64url",json_string:"string JSON",e164:"nombor E.164",jwt:"JWT",template_literal:"input"},i=>{switch(i.code){case"invalid_type":return`Input tidak sah: dijangka ${i.expected}, diterima ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"nombor";case"object":if(Array.isArray(e))return"array";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Input tidak sah: dijangka ${V.stringifyPrimitive(i.values[0])}`;return`Pilihan tidak sah: dijangka salah satu daripada ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Terlalu besar: dijangka ${i.origin??"nilai"} ${r.verb} ${t}${i.maximum.toString()} ${r.unit??"elemen"}`;return`Terlalu besar: dijangka ${i.origin??"nilai"} adalah ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Terlalu kecil: dijangka ${i.origin} ${r.verb} ${t}${i.minimum.toString()} ${r.unit}`;return`Terlalu kecil: dijangka ${i.origin} adalah ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`String tidak sah: mesti bermula dengan "${i.prefix}"`;if("ends_with"===i.format)return`String tidak sah: mesti berakhir dengan "${i.suffix}"`;if("includes"===i.format)return`String tidak sah: mesti mengandungi "${i.includes}"`;if("regex"===i.format)return`String tidak sah: mesti sepadan dengan corak ${i.pattern}`;return`${t[i.format]??i.format} tidak sah`;case"not_multiple_of":return`Nombor tidak sah: perlu gandaan ${i.divisor}`;case"unrecognized_keys":return`Kunci tidak dikenali: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Kunci tidak sah dalam ${i.origin}`;case"invalid_union":default:return"Input tidak sah";case"invalid_element":return`Nilai tidak sah dalam ${i.origin}`}})}},"nl",0,function(){let e,t;return{localeError:(e={string:{unit:"tekens"},file:{unit:"bytes"},array:{unit:"elementen"},set:{unit:"elementen"}},t={regex:"invoer",email:"emailadres",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO datum en tijd",date:"ISO datum",time:"ISO tijd",duration:"ISO duur",ipv4:"IPv4-adres",ipv6:"IPv6-adres",cidrv4:"IPv4-bereik",cidrv6:"IPv6-bereik",base64:"base64-gecodeerde tekst",base64url:"base64 URL-gecodeerde tekst",json_string:"JSON string",e164:"E.164-nummer",jwt:"JWT",template_literal:"invoer"},i=>{switch(i.code){case"invalid_type":return`Ongeldige invoer: verwacht ${i.expected}, ontving ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"getal";case"object":if(Array.isArray(e))return"array";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Ongeldige invoer: verwacht ${V.stringifyPrimitive(i.values[0])}`;return`Ongeldige optie: verwacht \xe9\xe9n van ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Te lang: verwacht dat ${i.origin??"waarde"} ${t}${i.maximum.toString()} ${r.unit??"elementen"} bevat`;return`Te lang: verwacht dat ${i.origin??"waarde"} ${t}${i.maximum.toString()} is`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Te kort: verwacht dat ${i.origin} ${t}${i.minimum.toString()} ${r.unit} bevat`;return`Te kort: verwacht dat ${i.origin} ${t}${i.minimum.toString()} is`}case"invalid_format":if("starts_with"===i.format)return`Ongeldige tekst: moet met "${i.prefix}" beginnen`;if("ends_with"===i.format)return`Ongeldige tekst: moet op "${i.suffix}" eindigen`;if("includes"===i.format)return`Ongeldige tekst: moet "${i.includes}" bevatten`;if("regex"===i.format)return`Ongeldige tekst: moet overeenkomen met patroon ${i.pattern}`;return`Ongeldig: ${t[i.format]??i.format}`;case"not_multiple_of":return`Ongeldig getal: moet een veelvoud van ${i.divisor} zijn`;case"unrecognized_keys":return`Onbekende key${i.keys.length>1?"s":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Ongeldige key in ${i.origin}`;case"invalid_union":default:return"Ongeldige invoer";case"invalid_element":return`Ongeldige waarde in ${i.origin}`}})}},"no",0,function(){let e,t;return{localeError:(e={string:{unit:"tegn",verb:"å ha"},file:{unit:"bytes",verb:"å ha"},array:{unit:"elementer",verb:"å inneholde"},set:{unit:"elementer",verb:"å inneholde"}},t={regex:"input",email:"e-postadresse",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO dato- og klokkeslett",date:"ISO-dato",time:"ISO-klokkeslett",duration:"ISO-varighet",ipv4:"IPv4-område",ipv6:"IPv6-område",cidrv4:"IPv4-spekter",cidrv6:"IPv6-spekter",base64:"base64-enkodet streng",base64url:"base64url-enkodet streng",json_string:"JSON-streng",e164:"E.164-nummer",jwt:"JWT",template_literal:"input"},i=>{switch(i.code){case"invalid_type":return`Ugyldig input: forventet ${i.expected}, fikk ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"tall";case"object":if(Array.isArray(e))return"liste";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Ugyldig verdi: forventet ${V.stringifyPrimitive(i.values[0])}`;return`Ugyldig valg: forventet en av ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`For stor(t): forventet ${i.origin??"value"} til \xe5 ha ${t}${i.maximum.toString()} ${r.unit??"elementer"}`;return`For stor(t): forventet ${i.origin??"value"} til \xe5 ha ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`For lite(n): forventet ${i.origin} til \xe5 ha ${t}${i.minimum.toString()} ${r.unit}`;return`For lite(n): forventet ${i.origin} til \xe5 ha ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Ugyldig streng: m\xe5 starte med "${i.prefix}"`;if("ends_with"===i.format)return`Ugyldig streng: m\xe5 ende med "${i.suffix}"`;if("includes"===i.format)return`Ugyldig streng: m\xe5 inneholde "${i.includes}"`;if("regex"===i.format)return`Ugyldig streng: m\xe5 matche m\xf8nsteret ${i.pattern}`;return`Ugyldig ${t[i.format]??i.format}`;case"not_multiple_of":return`Ugyldig tall: m\xe5 v\xe6re et multiplum av ${i.divisor}`;case"unrecognized_keys":return`${i.keys.length>1?"Ukjente nøkler":"Ukjent nøkkel"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Ugyldig n\xf8kkel i ${i.origin}`;case"invalid_union":default:return"Ugyldig input";case"invalid_element":return`Ugyldig verdi i ${i.origin}`}})}},"ota",0,function(){let e,t;return{localeError:(e={string:{unit:"harf",verb:"olmalıdır"},file:{unit:"bayt",verb:"olmalıdır"},array:{unit:"unsur",verb:"olmalıdır"},set:{unit:"unsur",verb:"olmalıdır"}},t={regex:"giren",email:"epostagâh",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO hengâmı",date:"ISO tarihi",time:"ISO zamanı",duration:"ISO müddeti",ipv4:"IPv4 nişânı",ipv6:"IPv6 nişânı",cidrv4:"IPv4 menzili",cidrv6:"IPv6 menzili",base64:"base64-şifreli metin",base64url:"base64url-şifreli metin",json_string:"JSON metin",e164:"E.164 sayısı",jwt:"JWT",template_literal:"giren"},i=>{switch(i.code){case"invalid_type":return`F\xe2sit giren: umulan ${i.expected}, alınan ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"numara";case"object":if(Array.isArray(e))return"saf";if(null===e)return"gayb";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`F\xe2sit giren: umulan ${V.stringifyPrimitive(i.values[0])}`;return`F\xe2sit tercih: m\xfbteberler ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Fazla b\xfcy\xfck: ${i.origin??"value"}, ${t}${i.maximum.toString()} ${r.unit??"elements"} sahip olmalıydı.`;return`Fazla b\xfcy\xfck: ${i.origin??"value"}, ${t}${i.maximum.toString()} olmalıydı.`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Fazla k\xfc\xe7\xfck: ${i.origin}, ${t}${i.minimum.toString()} ${r.unit} sahip olmalıydı.`;return`Fazla k\xfc\xe7\xfck: ${i.origin}, ${t}${i.minimum.toString()} olmalıydı.`}case"invalid_format":if("starts_with"===i.format)return`F\xe2sit metin: "${i.prefix}" ile başlamalı.`;if("ends_with"===i.format)return`F\xe2sit metin: "${i.suffix}" ile bitmeli.`;if("includes"===i.format)return`F\xe2sit metin: "${i.includes}" ihtiv\xe2 etmeli.`;if("regex"===i.format)return`F\xe2sit metin: ${i.pattern} nakşına uymalı.`;return`F\xe2sit ${t[i.format]??i.format}`;case"not_multiple_of":return`F\xe2sit sayı: ${i.divisor} katı olmalıydı.`;case"unrecognized_keys":return`Tanınmayan anahtar ${i.keys.length>1?"s":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`${i.origin} i\xe7in tanınmayan anahtar var.`;case"invalid_union":return"Giren tanınamadı.";case"invalid_element":return`${i.origin} i\xe7in tanınmayan kıymet var.`;default:return"Kıymet tanınamadı."}})}},"pl",0,function(){let e,t;return{localeError:(e={string:{unit:"znaków",verb:"mieć"},file:{unit:"bajtów",verb:"mieć"},array:{unit:"elementów",verb:"mieć"},set:{unit:"elementów",verb:"mieć"}},t={regex:"wyrażenie",email:"adres email",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"data i godzina w formacie ISO",date:"data w formacie ISO",time:"godzina w formacie ISO",duration:"czas trwania ISO",ipv4:"adres IPv4",ipv6:"adres IPv6",cidrv4:"zakres IPv4",cidrv6:"zakres IPv6",base64:"ciąg znaków zakodowany w formacie base64",base64url:"ciąg znaków zakodowany w formacie base64url",json_string:"ciąg znaków w formacie JSON",e164:"liczba E.164",jwt:"JWT",template_literal:"wejście"},i=>{switch(i.code){case"invalid_type":return`Nieprawidłowe dane wejściowe: oczekiwano ${i.expected}, otrzymano ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"liczba";case"object":if(Array.isArray(e))return"tablica";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Nieprawidłowe dane wejściowe: oczekiwano ${V.stringifyPrimitive(i.values[0])}`;return`Nieprawidłowa opcja: oczekiwano jednej z wartości ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Za duża wartość: oczekiwano, że ${i.origin??"wartość"} będzie mieć ${t}${i.maximum.toString()} ${r.unit??"elementów"}`;return`Zbyt duż(y/a/e): oczekiwano, że ${i.origin??"wartość"} będzie wynosić ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Za mała wartość: oczekiwano, że ${i.origin??"wartość"} będzie mieć ${t}${i.minimum.toString()} ${r.unit??"elementów"}`;return`Zbyt mał(y/a/e): oczekiwano, że ${i.origin??"wartość"} będzie wynosić ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Nieprawidłowy ciąg znak\xf3w: musi zaczynać się od "${i.prefix}"`;if("ends_with"===i.format)return`Nieprawidłowy ciąg znak\xf3w: musi kończyć się na "${i.suffix}"`;if("includes"===i.format)return`Nieprawidłowy ciąg znak\xf3w: musi zawierać "${i.includes}"`;if("regex"===i.format)return`Nieprawidłowy ciąg znak\xf3w: musi odpowiadać wzorcowi ${i.pattern}`;return`Nieprawidłow(y/a/e) ${t[i.format]??i.format}`;case"not_multiple_of":return`Nieprawidłowa liczba: musi być wielokrotnością ${i.divisor}`;case"unrecognized_keys":return`Nierozpoznane klucze${i.keys.length>1?"s":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Nieprawidłowy klucz w ${i.origin}`;case"invalid_union":default:return"Nieprawidłowe dane wejściowe";case"invalid_element":return`Nieprawidłowa wartość w ${i.origin}`}})}},"ps",0,function(){let e,t;return{localeError:(e={string:{unit:"توکي",verb:"ولري"},file:{unit:"بایټس",verb:"ولري"},array:{unit:"توکي",verb:"ولري"},set:{unit:"توکي",verb:"ولري"}},t={regex:"ورودي",email:"بریښنالیک",url:"یو آر ال",emoji:"ایموجي",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"نیټه او وخت",date:"نېټه",time:"وخت",duration:"موده",ipv4:"د IPv4 پته",ipv6:"د IPv6 پته",cidrv4:"د IPv4 ساحه",cidrv6:"د IPv6 ساحه",base64:"base64-encoded متن",base64url:"base64url-encoded متن",json_string:"JSON متن",e164:"د E.164 شمېره",jwt:"JWT",template_literal:"ورودي"},i=>{switch(i.code){case"invalid_type":return`ناسم ورودي: باید ${i.expected} وای, مګر ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"عدد";case"object":if(Array.isArray(e))return"ارې";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)} ترلاسه شو`;case"invalid_value":if(1===i.values.length)return`ناسم ورودي: باید ${V.stringifyPrimitive(i.values[0])} وای`;return`ناسم انتخاب: باید یو له ${V.joinValues(i.values,"|")} څخه وای`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`ډیر لوی: ${i.origin??"ارزښت"} باید ${t}${i.maximum.toString()} ${r.unit??"عنصرونه"} ولري`;return`ډیر لوی: ${i.origin??"ارزښت"} باید ${t}${i.maximum.toString()} وي`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`ډیر کوچنی: ${i.origin} باید ${t}${i.minimum.toString()} ${r.unit} ولري`;return`ډیر کوچنی: ${i.origin} باید ${t}${i.minimum.toString()} وي`}case"invalid_format":if("starts_with"===i.format)return`ناسم متن: باید د "${i.prefix}" سره پیل شي`;if("ends_with"===i.format)return`ناسم متن: باید د "${i.suffix}" سره پای ته ورسيږي`;if("includes"===i.format)return`ناسم متن: باید "${i.includes}" ولري`;if("regex"===i.format)return`ناسم متن: باید د ${i.pattern} سره مطابقت ولري`;return`${t[i.format]??i.format} ناسم دی`;case"not_multiple_of":return`ناسم عدد: باید د ${i.divisor} مضرب وي`;case"unrecognized_keys":return`ناسم ${i.keys.length>1?"کلیډونه":"کلیډ"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`ناسم کلیډ په ${i.origin} کې`;case"invalid_union":default:return"ناسمه ورودي";case"invalid_element":return`ناسم عنصر په ${i.origin} کې`}})}},"pt",0,function(){let e,t;return{localeError:(e={string:{unit:"caracteres",verb:"ter"},file:{unit:"bytes",verb:"ter"},array:{unit:"itens",verb:"ter"},set:{unit:"itens",verb:"ter"}},t={regex:"padrão",email:"endereço de e-mail",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"data e hora ISO",date:"data ISO",time:"hora ISO",duration:"duração ISO",ipv4:"endereço IPv4",ipv6:"endereço IPv6",cidrv4:"faixa de IPv4",cidrv6:"faixa de IPv6",base64:"texto codificado em base64",base64url:"URL codificada em base64",json_string:"texto JSON",e164:"número E.164",jwt:"JWT",template_literal:"entrada"},i=>{switch(i.code){case"invalid_type":return`Tipo inv\xe1lido: esperado ${i.expected}, recebido ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"número";case"object":if(Array.isArray(e))return"array";if(null===e)return"nulo";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Entrada inv\xe1lida: esperado ${V.stringifyPrimitive(i.values[0])}`;return`Op\xe7\xe3o inv\xe1lida: esperada uma das ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Muito grande: esperado que ${i.origin??"valor"} tivesse ${t}${i.maximum.toString()} ${r.unit??"elementos"}`;return`Muito grande: esperado que ${i.origin??"valor"} fosse ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Muito pequeno: esperado que ${i.origin} tivesse ${t}${i.minimum.toString()} ${r.unit}`;return`Muito pequeno: esperado que ${i.origin} fosse ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Texto inv\xe1lido: deve come\xe7ar com "${i.prefix}"`;if("ends_with"===i.format)return`Texto inv\xe1lido: deve terminar com "${i.suffix}"`;if("includes"===i.format)return`Texto inv\xe1lido: deve incluir "${i.includes}"`;if("regex"===i.format)return`Texto inv\xe1lido: deve corresponder ao padr\xe3o ${i.pattern}`;return`${t[i.format]??i.format} inv\xe1lido`;case"not_multiple_of":return`N\xfamero inv\xe1lido: deve ser m\xfaltiplo de ${i.divisor}`;case"unrecognized_keys":return`Chave${i.keys.length>1?"s":""} desconhecida${i.keys.length>1?"s":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Chave inv\xe1lida em ${i.origin}`;case"invalid_union":return"Entrada inválida";case"invalid_element":return`Valor inv\xe1lido em ${i.origin}`;default:return"Campo inválido"}})}},"ru",0,function(){let e,t;return{localeError:(e={string:{unit:{one:"символ",few:"символа",many:"символов"},verb:"иметь"},file:{unit:{one:"байт",few:"байта",many:"байт"},verb:"иметь"},array:{unit:{one:"элемент",few:"элемента",many:"элементов"},verb:"иметь"},set:{unit:{one:"элемент",few:"элемента",many:"элементов"},verb:"иметь"}},t={regex:"ввод",email:"email адрес",url:"URL",emoji:"эмодзи",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO дата и время",date:"ISO дата",time:"ISO время",duration:"ISO длительность",ipv4:"IPv4 адрес",ipv6:"IPv6 адрес",cidrv4:"IPv4 диапазон",cidrv6:"IPv6 диапазон",base64:"строка в формате base64",base64url:"строка в формате base64url",json_string:"JSON строка",e164:"номер E.164",jwt:"JWT",template_literal:"ввод"},i=>{switch(i.code){case"invalid_type":return`Неверный ввод: ожидалось ${i.expected}, получено ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"число";case"object":if(Array.isArray(e))return"массив";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Неверный ввод: ожидалось ${V.stringifyPrimitive(i.values[0])}`;return`Неверный вариант: ожидалось одно из ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r){let e=tA(Number(i.maximum),r.unit.one,r.unit.few,r.unit.many);return`Слишком большое значение: ожидалось, что ${i.origin??"значение"} будет иметь ${t}${i.maximum.toString()} ${e}`}return`Слишком большое значение: ожидалось, что ${i.origin??"значение"} будет ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r){let e=tA(Number(i.minimum),r.unit.one,r.unit.few,r.unit.many);return`Слишком маленькое значение: ожидалось, что ${i.origin} будет иметь ${t}${i.minimum.toString()} ${e}`}return`Слишком маленькое значение: ожидалось, что ${i.origin} будет ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Неверная строка: должна начинаться с "${i.prefix}"`;if("ends_with"===i.format)return`Неверная строка: должна заканчиваться на "${i.suffix}"`;if("includes"===i.format)return`Неверная строка: должна содержать "${i.includes}"`;if("regex"===i.format)return`Неверная строка: должна соответствовать шаблону ${i.pattern}`;return`Неверный ${t[i.format]??i.format}`;case"not_multiple_of":return`Неверное число: должно быть кратным ${i.divisor}`;case"unrecognized_keys":return`Нераспознанн${i.keys.length>1?"ые":"ый"} ключ${i.keys.length>1?"и":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Неверный ключ в ${i.origin}`;case"invalid_union":default:return"Неверные входные данные";case"invalid_element":return`Неверное значение в ${i.origin}`}})}},"sl",0,function(){let e,t;return{localeError:(e={string:{unit:"znakov",verb:"imeti"},file:{unit:"bajtov",verb:"imeti"},array:{unit:"elementov",verb:"imeti"},set:{unit:"elementov",verb:"imeti"}},t={regex:"vnos",email:"e-poštni naslov",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO datum in čas",date:"ISO datum",time:"ISO čas",duration:"ISO trajanje",ipv4:"IPv4 naslov",ipv6:"IPv6 naslov",cidrv4:"obseg IPv4",cidrv6:"obseg IPv6",base64:"base64 kodiran niz",base64url:"base64url kodiran niz",json_string:"JSON niz",e164:"E.164 številka",jwt:"JWT",template_literal:"vnos"},i=>{switch(i.code){case"invalid_type":return`Neveljaven vnos: pričakovano ${i.expected}, prejeto ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"število";case"object":if(Array.isArray(e))return"tabela";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Neveljaven vnos: pričakovano ${V.stringifyPrimitive(i.values[0])}`;return`Neveljavna možnost: pričakovano eno izmed ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Preveliko: pričakovano, da bo ${i.origin??"vrednost"} imelo ${t}${i.maximum.toString()} ${r.unit??"elementov"}`;return`Preveliko: pričakovano, da bo ${i.origin??"vrednost"} ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Premajhno: pričakovano, da bo ${i.origin} imelo ${t}${i.minimum.toString()} ${r.unit}`;return`Premajhno: pričakovano, da bo ${i.origin} ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Neveljaven niz: mora se začeti z "${i.prefix}"`;if("ends_with"===i.format)return`Neveljaven niz: mora se končati z "${i.suffix}"`;if("includes"===i.format)return`Neveljaven niz: mora vsebovati "${i.includes}"`;if("regex"===i.format)return`Neveljaven niz: mora ustrezati vzorcu ${i.pattern}`;return`Neveljaven ${t[i.format]??i.format}`;case"not_multiple_of":return`Neveljavno število: mora biti večkratnik ${i.divisor}`;case"unrecognized_keys":return`Neprepoznan${i.keys.length>1?"i ključi":" ključ"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Neveljaven ključ v ${i.origin}`;case"invalid_union":default:return"Neveljaven vnos";case"invalid_element":return`Neveljavna vrednost v ${i.origin}`}})}},"sv",0,function(){let e,t;return{localeError:(e={string:{unit:"tecken",verb:"att ha"},file:{unit:"bytes",verb:"att ha"},array:{unit:"objekt",verb:"att innehålla"},set:{unit:"objekt",verb:"att innehålla"}},t={regex:"reguljärt uttryck",email:"e-postadress",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO-datum och tid",date:"ISO-datum",time:"ISO-tid",duration:"ISO-varaktighet",ipv4:"IPv4-intervall",ipv6:"IPv6-intervall",cidrv4:"IPv4-spektrum",cidrv6:"IPv6-spektrum",base64:"base64-kodad sträng",base64url:"base64url-kodad sträng",json_string:"JSON-sträng",e164:"E.164-nummer",jwt:"JWT",template_literal:"mall-literal"},i=>{switch(i.code){case"invalid_type":return`Ogiltig inmatning: f\xf6rv\xe4ntat ${i.expected}, fick ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"antal";case"object":if(Array.isArray(e))return"lista";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Ogiltig inmatning: f\xf6rv\xe4ntat ${V.stringifyPrimitive(i.values[0])}`;return`Ogiltigt val: f\xf6rv\xe4ntade en av ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`F\xf6r stor(t): f\xf6rv\xe4ntade ${i.origin??"värdet"} att ha ${t}${i.maximum.toString()} ${r.unit??"element"}`;return`F\xf6r stor(t): f\xf6rv\xe4ntat ${i.origin??"värdet"} att ha ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`F\xf6r lite(t): f\xf6rv\xe4ntade ${i.origin??"värdet"} att ha ${t}${i.minimum.toString()} ${r.unit}`;return`F\xf6r lite(t): f\xf6rv\xe4ntade ${i.origin??"värdet"} att ha ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Ogiltig str\xe4ng: m\xe5ste b\xf6rja med "${i.prefix}"`;if("ends_with"===i.format)return`Ogiltig str\xe4ng: m\xe5ste sluta med "${i.suffix}"`;if("includes"===i.format)return`Ogiltig str\xe4ng: m\xe5ste inneh\xe5lla "${i.includes}"`;if("regex"===i.format)return`Ogiltig str\xe4ng: m\xe5ste matcha m\xf6nstret "${i.pattern}"`;return`Ogiltig(t) ${t[i.format]??i.format}`;case"not_multiple_of":return`Ogiltigt tal: m\xe5ste vara en multipel av ${i.divisor}`;case"unrecognized_keys":return`${i.keys.length>1?"Okända nycklar":"Okänd nyckel"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Ogiltig nyckel i ${i.origin??"värdet"}`;case"invalid_union":default:return"Ogiltig input";case"invalid_element":return`Ogiltigt v\xe4rde i ${i.origin??"värdet"}`}})}},"ta",0,function(){let e,t;return{localeError:(e={string:{unit:"எழுத்துக்கள்",verb:"கொண்டிருக்க வேண்டும்"},file:{unit:"பைட்டுகள்",verb:"கொண்டிருக்க வேண்டும்"},array:{unit:"உறுப்புகள்",verb:"கொண்டிருக்க வேண்டும்"},set:{unit:"உறுப்புகள்",verb:"கொண்டிருக்க வேண்டும்"}},t={regex:"உள்ளீடு",email:"மின்னஞ்சல் முகவரி",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO தேதி நேரம்",date:"ISO தேதி",time:"ISO நேரம்",duration:"ISO கால அளவு",ipv4:"IPv4 முகவரி",ipv6:"IPv6 முகவரி",cidrv4:"IPv4 வரம்பு",cidrv6:"IPv6 வரம்பு",base64:"base64-encoded சரம்",base64url:"base64url-encoded சரம்",json_string:"JSON சரம்",e164:"E.164 எண்",jwt:"JWT",template_literal:"input"},i=>{switch(i.code){case"invalid_type":return`தவறான உள்ளீடு: எதிர்பார்க்கப்பட்டது ${i.expected}, பெறப்பட்டது ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"எண் அல்லாதது":"எண்";case"object":if(Array.isArray(e))return"அணி";if(null===e)return"வெறுமை";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`தவறான உள்ளீடு: எதிர்பார்க்கப்பட்டது ${V.stringifyPrimitive(i.values[0])}`;return`தவறான விருப்பம்: எதிர்பார்க்கப்பட்டது ${V.joinValues(i.values,"|")} இல் ஒன்று`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`மிக பெரியது: எதிர்பார்க்கப்பட்டது ${i.origin??"மதிப்பு"} ${t}${i.maximum.toString()} ${r.unit??"உறுப்புகள்"} ஆக இருக்க வேண்டும்`;return`மிக பெரியது: எதிர்பார்க்கப்பட்டது ${i.origin??"மதிப்பு"} ${t}${i.maximum.toString()} ஆக இருக்க வேண்டும்`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`மிகச் சிறியது: எதிர்பார்க்கப்பட்டது ${i.origin} ${t}${i.minimum.toString()} ${r.unit} ஆக இருக்க வேண்டும்`;return`மிகச் சிறியது: எதிர்பார்க்கப்பட்டது ${i.origin} ${t}${i.minimum.toString()} ஆக இருக்க வேண்டும்`}case"invalid_format":if("starts_with"===i.format)return`தவறான சரம்: "${i.prefix}" இல் தொடங்க வேண்டும்`;if("ends_with"===i.format)return`தவறான சரம்: "${i.suffix}" இல் முடிவடைய வேண்டும்`;if("includes"===i.format)return`தவறான சரம்: "${i.includes}" ஐ உள்ளடக்க வேண்டும்`;if("regex"===i.format)return`தவறான சரம்: ${i.pattern} முறைபாட்டுடன் பொருந்த வேண்டும்`;return`தவறான ${t[i.format]??i.format}`;case"not_multiple_of":return`தவறான எண்: ${i.divisor} இன் பலமாக இருக்க வேண்டும்`;case"unrecognized_keys":return`அடையாளம் தெரியாத விசை${i.keys.length>1?"கள்":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`${i.origin} இல் தவறான விசை`;case"invalid_union":default:return"தவறான உள்ளீடு";case"invalid_element":return`${i.origin} இல் தவறான மதிப்பு`}})}},"th",0,function(){let e,t;return{localeError:(e={string:{unit:"ตัวอักษร",verb:"ควรมี"},file:{unit:"ไบต์",verb:"ควรมี"},array:{unit:"รายการ",verb:"ควรมี"},set:{unit:"รายการ",verb:"ควรมี"}},t={regex:"ข้อมูลที่ป้อน",email:"ที่อยู่อีเมล",url:"URL",emoji:"อิโมจิ",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"วันที่เวลาแบบ ISO",date:"วันที่แบบ ISO",time:"เวลาแบบ ISO",duration:"ช่วงเวลาแบบ ISO",ipv4:"ที่อยู่ IPv4",ipv6:"ที่อยู่ IPv6",cidrv4:"ช่วง IP แบบ IPv4",cidrv6:"ช่วง IP แบบ IPv6",base64:"ข้อความแบบ Base64",base64url:"ข้อความแบบ Base64 สำหรับ URL",json_string:"ข้อความแบบ JSON",e164:"เบอร์โทรศัพท์ระหว่างประเทศ (E.164)",jwt:"โทเคน JWT",template_literal:"ข้อมูลที่ป้อน"},i=>{switch(i.code){case"invalid_type":return`ประเภทข้อมูลไม่ถูกต้อง: ควรเป็น ${i.expected} แต่ได้รับ ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"ไม่ใช่ตัวเลข (NaN)":"ตัวเลข";case"object":if(Array.isArray(e))return"อาร์เรย์ (Array)";if(null===e)return"ไม่มีค่า (null)";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`ค่าไม่ถูกต้อง: ควรเป็น ${V.stringifyPrimitive(i.values[0])}`;return`ตัวเลือกไม่ถูกต้อง: ควรเป็นหนึ่งใน ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"ไม่เกิน":"น้อยกว่า",r=e[i.origin]??null;if(r)return`เกินกำหนด: ${i.origin??"ค่า"} ควรมี${t} ${i.maximum.toString()} ${r.unit??"รายการ"}`;return`เกินกำหนด: ${i.origin??"ค่า"} ควรมี${t} ${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?"อย่างน้อย":"มากกว่า",r=e[i.origin]??null;if(r)return`น้อยกว่ากำหนด: ${i.origin} ควรมี${t} ${i.minimum.toString()} ${r.unit}`;return`น้อยกว่ากำหนด: ${i.origin} ควรมี${t} ${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`รูปแบบไม่ถูกต้อง: ข้อความต้องขึ้นต้นด้วย "${i.prefix}"`;if("ends_with"===i.format)return`รูปแบบไม่ถูกต้อง: ข้อความต้องลงท้ายด้วย "${i.suffix}"`;if("includes"===i.format)return`รูปแบบไม่ถูกต้อง: ข้อความต้องมี "${i.includes}" อยู่ในข้อความ`;if("regex"===i.format)return`รูปแบบไม่ถูกต้อง: ต้องตรงกับรูปแบบที่กำหนด ${i.pattern}`;return`รูปแบบไม่ถูกต้อง: ${t[i.format]??i.format}`;case"not_multiple_of":return`ตัวเลขไม่ถูกต้อง: ต้องเป็นจำนวนที่หารด้วย ${i.divisor} ได้ลงตัว`;case"unrecognized_keys":return`พบคีย์ที่ไม่รู้จัก: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`คีย์ไม่ถูกต้องใน ${i.origin}`;case"invalid_union":return"ข้อมูลไม่ถูกต้อง: ไม่ตรงกับรูปแบบยูเนียนที่กำหนดไว้";case"invalid_element":return`ข้อมูลไม่ถูกต้องใน ${i.origin}`;default:return"ข้อมูลไม่ถูกต้อง"}})}},"tr",0,function(){let e,t;return{localeError:(e={string:{unit:"karakter",verb:"olmalı"},file:{unit:"bayt",verb:"olmalı"},array:{unit:"öğe",verb:"olmalı"},set:{unit:"öğe",verb:"olmalı"}},t={regex:"girdi",email:"e-posta adresi",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO tarih ve saat",date:"ISO tarih",time:"ISO saat",duration:"ISO süre",ipv4:"IPv4 adresi",ipv6:"IPv6 adresi",cidrv4:"IPv4 aralığı",cidrv6:"IPv6 aralığı",base64:"base64 ile şifrelenmiş metin",base64url:"base64url ile şifrelenmiş metin",json_string:"JSON dizesi",e164:"E.164 sayısı",jwt:"JWT",template_literal:"Şablon dizesi"},i=>{switch(i.code){case"invalid_type":return`Ge\xe7ersiz değer: beklenen ${i.expected}, alınan ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"number";case"object":if(Array.isArray(e))return"array";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Ge\xe7ersiz değer: beklenen ${V.stringifyPrimitive(i.values[0])}`;return`Ge\xe7ersiz se\xe7enek: aşağıdakilerden biri olmalı: ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`\xc7ok b\xfcy\xfck: beklenen ${i.origin??"değer"} ${t}${i.maximum.toString()} ${r.unit??"öğe"}`;return`\xc7ok b\xfcy\xfck: beklenen ${i.origin??"değer"} ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`\xc7ok k\xfc\xe7\xfck: beklenen ${i.origin} ${t}${i.minimum.toString()} ${r.unit}`;return`\xc7ok k\xfc\xe7\xfck: beklenen ${i.origin} ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Ge\xe7ersiz metin: "${i.prefix}" ile başlamalı`;if("ends_with"===i.format)return`Ge\xe7ersiz metin: "${i.suffix}" ile bitmeli`;if("includes"===i.format)return`Ge\xe7ersiz metin: "${i.includes}" i\xe7ermeli`;if("regex"===i.format)return`Ge\xe7ersiz metin: ${i.pattern} desenine uymalı`;return`Ge\xe7ersiz ${t[i.format]??i.format}`;case"not_multiple_of":return`Ge\xe7ersiz sayı: ${i.divisor} ile tam b\xf6l\xfcnebilmeli`;case"unrecognized_keys":return`Tanınmayan anahtar${i.keys.length>1?"lar":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`${i.origin} i\xe7inde ge\xe7ersiz anahtar`;case"invalid_union":default:return"Geçersiz değer";case"invalid_element":return`${i.origin} i\xe7inde ge\xe7ersiz değer`}})}},"ua",0,function(){let e,t;return{localeError:(e={string:{unit:"символів",verb:"матиме"},file:{unit:"байтів",verb:"матиме"},array:{unit:"елементів",verb:"матиме"},set:{unit:"елементів",verb:"матиме"}},t={regex:"вхідні дані",email:"адреса електронної пошти",url:"URL",emoji:"емодзі",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"дата та час ISO",date:"дата ISO",time:"час ISO",duration:"тривалість ISO",ipv4:"адреса IPv4",ipv6:"адреса IPv6",cidrv4:"діапазон IPv4",cidrv6:"діапазон IPv6",base64:"рядок у кодуванні base64",base64url:"рядок у кодуванні base64url",json_string:"рядок JSON",e164:"номер E.164",jwt:"JWT",template_literal:"вхідні дані"},i=>{switch(i.code){case"invalid_type":return`Неправильні вхідні дані: очікується ${i.expected}, отримано ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"число";case"object":if(Array.isArray(e))return"масив";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Неправильні вхідні дані: очікується ${V.stringifyPrimitive(i.values[0])}`;return`Неправильна опція: очікується одне з ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Занадто велике: очікується, що ${i.origin??"значення"} ${r.verb} ${t}${i.maximum.toString()} ${r.unit??"елементів"}`;return`Занадто велике: очікується, що ${i.origin??"значення"} буде ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Занадто мале: очікується, що ${i.origin} ${r.verb} ${t}${i.minimum.toString()} ${r.unit}`;return`Занадто мале: очікується, що ${i.origin} буде ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Неправильний рядок: повинен починатися з "${i.prefix}"`;if("ends_with"===i.format)return`Неправильний рядок: повинен закінчуватися на "${i.suffix}"`;if("includes"===i.format)return`Неправильний рядок: повинен містити "${i.includes}"`;if("regex"===i.format)return`Неправильний рядок: повинен відповідати шаблону ${i.pattern}`;return`Неправильний ${t[i.format]??i.format}`;case"not_multiple_of":return`Неправильне число: повинно бути кратним ${i.divisor}`;case"unrecognized_keys":return`Нерозпізнаний ключ${i.keys.length>1?"і":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Неправильний ключ у ${i.origin}`;case"invalid_union":default:return"Неправильні вхідні дані";case"invalid_element":return`Неправильне значення у ${i.origin}`}})}},"ur",0,function(){let e,t;return{localeError:(e={string:{unit:"حروف",verb:"ہونا"},file:{unit:"بائٹس",verb:"ہونا"},array:{unit:"آئٹمز",verb:"ہونا"},set:{unit:"آئٹمز",verb:"ہونا"}},t={regex:"ان پٹ",email:"ای میل ایڈریس",url:"یو آر ایل",emoji:"ایموجی",uuid:"یو یو آئی ڈی",uuidv4:"یو یو آئی ڈی وی 4",uuidv6:"یو یو آئی ڈی وی 6",nanoid:"نینو آئی ڈی",guid:"جی یو آئی ڈی",cuid:"سی یو آئی ڈی",cuid2:"سی یو آئی ڈی 2",ulid:"یو ایل آئی ڈی",xid:"ایکس آئی ڈی",ksuid:"کے ایس یو آئی ڈی",datetime:"آئی ایس او ڈیٹ ٹائم",date:"آئی ایس او تاریخ",time:"آئی ایس او وقت",duration:"آئی ایس او مدت",ipv4:"آئی پی وی 4 ایڈریس",ipv6:"آئی پی وی 6 ایڈریس",cidrv4:"آئی پی وی 4 رینج",cidrv6:"آئی پی وی 6 رینج",base64:"بیس 64 ان کوڈڈ سٹرنگ",base64url:"بیس 64 یو آر ایل ان کوڈڈ سٹرنگ",json_string:"جے ایس او این سٹرنگ",e164:"ای 164 نمبر",jwt:"جے ڈبلیو ٹی",template_literal:"ان پٹ"},i=>{switch(i.code){case"invalid_type":return`غلط ان پٹ: ${i.expected} متوقع تھا، ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"نمبر";case"object":if(Array.isArray(e))return"آرے";if(null===e)return"نل";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)} موصول ہوا`;case"invalid_value":if(1===i.values.length)return`غلط ان پٹ: ${V.stringifyPrimitive(i.values[0])} متوقع تھا`;return`غلط آپشن: ${V.joinValues(i.values,"|")} میں سے ایک متوقع تھا`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`بہت بڑا: ${i.origin??"ویلیو"} کے ${t}${i.maximum.toString()} ${r.unit??"عناصر"} ہونے متوقع تھے`;return`بہت بڑا: ${i.origin??"ویلیو"} کا ${t}${i.maximum.toString()} ہونا متوقع تھا`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`بہت چھوٹا: ${i.origin} کے ${t}${i.minimum.toString()} ${r.unit} ہونے متوقع تھے`;return`بہت چھوٹا: ${i.origin} کا ${t}${i.minimum.toString()} ہونا متوقع تھا`}case"invalid_format":if("starts_with"===i.format)return`غلط سٹرنگ: "${i.prefix}" سے شروع ہونا چاہیے`;if("ends_with"===i.format)return`غلط سٹرنگ: "${i.suffix}" پر ختم ہونا چاہیے`;if("includes"===i.format)return`غلط سٹرنگ: "${i.includes}" شامل ہونا چاہیے`;if("regex"===i.format)return`غلط سٹرنگ: پیٹرن ${i.pattern} سے میچ ہونا چاہیے`;return`غلط ${t[i.format]??i.format}`;case"not_multiple_of":return`غلط نمبر: ${i.divisor} کا مضاعف ہونا چاہیے`;case"unrecognized_keys":return`غیر تسلیم شدہ کی${i.keys.length>1?"ز":""}: ${V.joinValues(i.keys,"، ")}`;case"invalid_key":return`${i.origin} میں غلط کی`;case"invalid_union":default:return"غلط ان پٹ";case"invalid_element":return`${i.origin} میں غلط ویلیو`}})}},"vi",0,function(){let e,t;return{localeError:(e={string:{unit:"ký tự",verb:"có"},file:{unit:"byte",verb:"có"},array:{unit:"phần tử",verb:"có"},set:{unit:"phần tử",verb:"có"}},t={regex:"đầu vào",email:"địa chỉ email",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ngày giờ ISO",date:"ngày ISO",time:"giờ ISO",duration:"khoảng thời gian ISO",ipv4:"địa chỉ IPv4",ipv6:"địa chỉ IPv6",cidrv4:"dải IPv4",cidrv6:"dải IPv6",base64:"chuỗi mã hóa base64",base64url:"chuỗi mã hóa base64url",json_string:"chuỗi JSON",e164:"số E.164",jwt:"JWT",template_literal:"đầu vào"},i=>{switch(i.code){case"invalid_type":return`Đầu v\xe0o kh\xf4ng hợp lệ: mong đợi ${i.expected}, nhận được ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"số";case"object":if(Array.isArray(e))return"mảng";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`Đầu v\xe0o kh\xf4ng hợp lệ: mong đợi ${V.stringifyPrimitive(i.values[0])}`;return`T\xf9y chọn kh\xf4ng hợp lệ: mong đợi một trong c\xe1c gi\xe1 trị ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Qu\xe1 lớn: mong đợi ${i.origin??"giá trị"} ${r.verb} ${t}${i.maximum.toString()} ${r.unit??"phần tử"}`;return`Qu\xe1 lớn: mong đợi ${i.origin??"giá trị"} ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Qu\xe1 nhỏ: mong đợi ${i.origin} ${r.verb} ${t}${i.minimum.toString()} ${r.unit}`;return`Qu\xe1 nhỏ: mong đợi ${i.origin} ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Chuỗi kh\xf4ng hợp lệ: phải bắt đầu bằng "${i.prefix}"`;if("ends_with"===i.format)return`Chuỗi kh\xf4ng hợp lệ: phải kết th\xfac bằng "${i.suffix}"`;if("includes"===i.format)return`Chuỗi kh\xf4ng hợp lệ: phải bao gồm "${i.includes}"`;if("regex"===i.format)return`Chuỗi kh\xf4ng hợp lệ: phải khớp với mẫu ${i.pattern}`;return`${t[i.format]??i.format} kh\xf4ng hợp lệ`;case"not_multiple_of":return`Số kh\xf4ng hợp lệ: phải l\xe0 bội số của ${i.divisor}`;case"unrecognized_keys":return`Kh\xf3a kh\xf4ng được nhận dạng: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Kh\xf3a kh\xf4ng hợp lệ trong ${i.origin}`;case"invalid_union":default:return"Đầu vào không hợp lệ";case"invalid_element":return`Gi\xe1 trị kh\xf4ng hợp lệ trong ${i.origin}`}})}},"zhCN",0,function(){let e,t;return{localeError:(e={string:{unit:"字符",verb:"包含"},file:{unit:"字节",verb:"包含"},array:{unit:"项",verb:"包含"},set:{unit:"项",verb:"包含"}},t={regex:"输入",email:"电子邮件",url:"URL",emoji:"表情符号",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO日期时间",date:"ISO日期",time:"ISO时间",duration:"ISO时长",ipv4:"IPv4地址",ipv6:"IPv6地址",cidrv4:"IPv4网段",cidrv6:"IPv6网段",base64:"base64编码字符串",base64url:"base64url编码字符串",json_string:"JSON字符串",e164:"E.164号码",jwt:"JWT",template_literal:"输入"},i=>{switch(i.code){case"invalid_type":return`无效输入：期望 ${i.expected}，实际接收 ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"非数字(NaN)":"数字";case"object":if(Array.isArray(e))return"数组";if(null===e)return"空值(null)";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`无效输入：期望 ${V.stringifyPrimitive(i.values[0])}`;return`无效选项：期望以下之一 ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`数值过大：期望 ${i.origin??"值"} ${t}${i.maximum.toString()} ${r.unit??"个元素"}`;return`数值过大：期望 ${i.origin??"值"} ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`数值过小：期望 ${i.origin} ${t}${i.minimum.toString()} ${r.unit}`;return`数值过小：期望 ${i.origin} ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`无效字符串：必须以 "${i.prefix}" 开头`;if("ends_with"===i.format)return`无效字符串：必须以 "${i.suffix}" 结尾`;if("includes"===i.format)return`无效字符串：必须包含 "${i.includes}"`;if("regex"===i.format)return`无效字符串：必须满足正则表达式 ${i.pattern}`;return`无效${t[i.format]??i.format}`;case"not_multiple_of":return`无效数字：必须是 ${i.divisor} 的倍数`;case"unrecognized_keys":return`出现未知的键(key): ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`${i.origin} 中的键(key)无效`;case"invalid_union":default:return"无效输入";case"invalid_element":return`${i.origin} 中包含无效值(value)`}})}},"zhTW",0,function(){let e,t;return{localeError:(e={string:{unit:"字元",verb:"擁有"},file:{unit:"位元組",verb:"擁有"},array:{unit:"項目",verb:"擁有"},set:{unit:"項目",verb:"擁有"}},t={regex:"輸入",email:"郵件地址",url:"URL",emoji:"emoji",uuid:"UUID",uuidv4:"UUIDv4",uuidv6:"UUIDv6",nanoid:"nanoid",guid:"GUID",cuid:"cuid",cuid2:"cuid2",ulid:"ULID",xid:"XID",ksuid:"KSUID",datetime:"ISO 日期時間",date:"ISO 日期",time:"ISO 時間",duration:"ISO 期間",ipv4:"IPv4 位址",ipv6:"IPv6 位址",cidrv4:"IPv4 範圍",cidrv6:"IPv6 範圍",base64:"base64 編碼字串",base64url:"base64url 編碼字串",json_string:"JSON 字串",e164:"E.164 數值",jwt:"JWT",template_literal:"輸入"},i=>{switch(i.code){case"invalid_type":return`無效的輸入值：預期為 ${i.expected}，但收到 ${(e=>{let t=typeof e;switch(t){case"number":return Number.isNaN(e)?"NaN":"number";case"object":if(Array.isArray(e))return"array";if(null===e)return"null";if(Object.getPrototypeOf(e)!==Object.prototype&&e.constructor)return e.constructor.name}return t})(i.input)}`;case"invalid_value":if(1===i.values.length)return`無效的輸入值：預期為 ${V.stringifyPrimitive(i.values[0])}`;return`無效的選項：預期為以下其中之一 ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`數值過大：預期 ${i.origin??"值"} 應為 ${t}${i.maximum.toString()} ${r.unit??"個元素"}`;return`數值過大：預期 ${i.origin??"值"} 應為 ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`數值過小：預期 ${i.origin} 應為 ${t}${i.minimum.toString()} ${r.unit}`;return`數值過小：預期 ${i.origin} 應為 ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`無效的字串：必須以 "${i.prefix}" 開頭`;if("ends_with"===i.format)return`無效的字串：必須以 "${i.suffix}" 結尾`;if("includes"===i.format)return`無效的字串：必須包含 "${i.includes}"`;if("regex"===i.format)return`無效的字串：必須符合格式 ${i.pattern}`;return`無效的 ${t[i.format]??i.format}`;case"not_multiple_of":return`無效的數字：必須為 ${i.divisor} 的倍數`;case"unrecognized_keys":return`無法識別的鍵值${i.keys.length>1?"們":""}：${V.joinValues(i.keys,"、")}`;case"invalid_key":return`${i.origin} 中有無效的鍵值`;case"invalid_union":default:return"無效的輸入值";case"invalid_element":return`${i.origin} 中有無效的值`}})}}],554580);var tL=e.i(554580);let tC=Symbol("ZodOutput"),tR=Symbol("ZodInput");class tV{constructor(){this._map=new Map,this._idmap=new Map}add(e,...t){let i=t[0];if(this._map.set(e,i),i&&"object"==typeof i&&"id"in i){if(this._idmap.has(i.id))throw Error(`ID ${i.id} already exists in the registry`);this._idmap.set(i.id,e)}return this}clear(){return this._map=new Map,this._idmap=new Map,this}remove(e){let t=this._map.get(e);return t&&"object"==typeof t&&"id"in t&&this._idmap.delete(t.id),this._map.delete(e),this}get(e){let t=e._zod.parent;if(t){let i={...this.get(t)??{}};return delete 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