diff --git a/.circleci/config.yml b/.circleci/config.yml index e8a8483781b..55fa9410845 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -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][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=20 \ -n 4 \ @@ -1025,12 +905,12 @@ jobs: name: Run tests command: | mkdir -p test-results - TEST_FILES=$(circleci tests glob "tests/agent_tests/**/test_*.py" | grep -v "^tests/agent_tests/local_only_agent_tests/") + TEST_FILES=$(circleci tests glob "tests/agent_tests/test_*.py") echo "$TEST_FILES" | circleci tests run \ --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5" no_output_timeout: 15m @@ -1074,7 +954,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); 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 118e5491939..cfa0390e836 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,5 +1,10 @@ -/ui/ @yuneng-jiang @ryan-crabbe-berri -/litellm/proxy/_experimental/out/ @yuneng-jiang @ryan-crabbe-berri +/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 +/.github/CODEOWNERS @yuneng-berri diff --git a/.github/actions/cache-cargo-build/action.yml b/.github/actions/cache-cargo-build/action.yml new file mode 100644 index 00000000000..36c6c790b84 --- /dev/null +++ b/.github/actions/cache-cargo-build/action.yml @@ -0,0 +1,31 @@ +name: "Cache the Rust build" +description: >- + Cache the Cargo registry and target directory the root package's build needs, + 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. + + 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. + +runs: + using: composite + steps: + - name: Restore the Cargo registry and target directory + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cargo/registry + ~/.cargo/git + litellm-rust/target + key: ${{ runner.os }}-cargo-release-${{ hashFiles('litellm-rust/Cargo.lock') }} + restore-keys: | + ${{ runner.os }}-cargo-release- diff --git a/.github/ci-coverage-allowlist.yml b/.github/ci-coverage-allowlist.yml index 1423228e725..f7a785b3b80 100644 --- a/.github/ci-coverage-allowlist.yml +++ b/.github/ci-coverage-allowlist.yml @@ -4,6 +4,22 @@ description: >- by a job nor listed here, so every entry below is a decision on the record. test_paths: + - reason: >- + 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_caching.py + - tests/local_testing/test_disk_cache_unit_tests.py + - tests/local_testing/test_gcs_cache_unit_tests.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 @@ -21,72 +37,14 @@ test_paths: - tests/documentation_tests/test_requests_lib_usage.py - tests/documentation_tests/test_standard_logging_payload.py - reason: >- - Sibling files here are executed by name from the code-quality workflow; this one is referenced - by no job + Named like a test but shaped like a benchmark: it fetches live image URLs, times aiohttp + against httpx, prints the ratio, and asserts nothing, so pytest cannot collect it (its + functions take arguments, not fixtures) and running it beside its siblings in the + code-quality workflow would add a network dependency for a number nothing reads. Exempt + as a script rather than as an unresolved gap; revisit by deleting it once the aiohttp + choice it informed is settled paths: - tests/code_coverage_tests/test_aio_http_image_conversion.py - - reason: >- - A second mirror of the package tree living beside tests/test_litellm, which is the mirror the - repo convention names; only test_no_hardcoded_secrets.py is invoked, from the linting - workflow, and whether this directory should exist at all is unresolved - paths: - - tests/litellm/a2a_protocol/providers/pydantic_ai_agents/test_pydantic_ai_agent_headers.py - - tests/litellm/a2a_protocol/providers/pydantic_ai_agents/test_pydantic_ai_agent_transformation.py - - tests/litellm/integrations/helicone/test_helicone_gemini.py - - tests/litellm/litellm_core_utils/test_json_schema_validation.py - - tests/litellm/llms/anthropic/test_anthropic_reasoning_effort.py - - tests/litellm/llms/anthropic/test_anthropic_schema_filter.py - - tests/litellm/llms/azure/test_azure_embedding.py - - tests/litellm/llms/bedrock/embed/test_embedding.py - - tests/litellm/llms/bedrock/test_nova_imported_models.py - - tests/litellm/llms/deepseek/chat/test_deepseek_chat_transformation.py - - tests/litellm/llms/gradient_ai/chat/test_gradient_ai_chat_transformation.py - - tests/litellm/llms/oci/chat/test_oci_chat_transformation.py - - tests/litellm/llms/openai_like/test_abliteration_provider.py - - tests/litellm/llms/openai_like/test_assemblyai_provider.py - - tests/litellm/llms/openai_like/test_empiriolabs_provider.py - - tests/litellm/llms/vertex_ai/agent_engine/test_transformation.py - - tests/litellm/llms/vertex_ai/gemini/test_transformation.py - - tests/litellm/llms/vertex_ai/text_to_speech/test_transformation.py - - tests/litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py - - tests/litellm/proxy/agent_endpoints/test_agent_rbac.py - - tests/litellm/proxy/common_utils/test_rbac_utils.py - - tests/litellm/proxy/management_endpoints/test_common_utils.py - - tests/litellm/proxy/management_endpoints/test_cost_estimate_endpoint.py - - tests/litellm/proxy/test_claude_code_marketplace.py - - tests/litellm/proxy/test_init_litellm_callbacks.py - - tests/litellm/proxy/test_prisma_engine_watchdog.py - - tests/litellm/proxy/vector_store_endpoints/test_vector_store_rbac.py - - tests/litellm/test_bedrock_extended_beta_models.py - - tests/litellm/test_bedrock_nemotron_super.py - - tests/litellm/test_proxy_auth.py - - tests/litellm/test_router_retry_backoff_headers.py - - tests/litellm/test_sambanova_model_metadata.py - - tests/litellm/test_stream_chunk_builder_images.py - - reason: >- - Legacy proxy suite superseded by the proxy shards; no job invokes it and whether it still - describes supported behaviour is unresolved - paths: - - tests/old_proxy_tests/tests/test_anthropic_context_caching.py - - tests/old_proxy_tests/tests/test_anthropic_sdk.py - - tests/old_proxy_tests/tests/test_async.py - - tests/old_proxy_tests/tests/test_gemini_context_caching.py - - tests/old_proxy_tests/tests/test_langchain_embedding.py - - tests/old_proxy_tests/tests/test_langchain_request.py - - tests/old_proxy_tests/tests/test_llamaindex.py - - tests/old_proxy_tests/tests/test_mistral_sdk.py - - tests/old_proxy_tests/tests/test_openai_embedding.py - - tests/old_proxy_tests/tests/test_openai_exception_request.py - - tests/old_proxy_tests/tests/test_openai_request.py - - tests/old_proxy_tests/tests/test_openai_request_with_traceparent.py - - tests/old_proxy_tests/tests/test_openai_simple_embedding.py - - tests/old_proxy_tests/tests/test_openai_tts_request.py - - tests/old_proxy_tests/tests/test_pass_through_langfuse.py - - tests/old_proxy_tests/tests/test_q.py - - tests/old_proxy_tests/tests/test_simple_traceparent_openai.py - - tests/old_proxy_tests/tests/test_vertex_sdk_forward_headers.py - - tests/old_proxy_tests/tests/test_vtx_embedding.py - - tests/old_proxy_tests/tests/test_vtx_sdk_embedding.py - reason: >- No job invokes this suite and its files mix pure transformation tests with ones driving live vendor vector stores, so assigning them needs a per-file decision @@ -116,6 +74,14 @@ test_paths: - tests/load_tests/test_otel_load_test.py - tests/load_tests/test_vertex_embeddings_load_test.py - tests/load_tests/test_vertex_load_tests.py + - reason: >- + A local-only agent rig: test_a2a_completion_bridge.py needs a LangGraph server on + localhost:2024 and test_a2a.py drives a live A2A endpoint, so neither can run in a + pull request job. Until 2026-08-20 the CircleCI agent job hid them behind a grep -v + that this census could not see; the glob now excludes them structurally and this entry + is the decision on the record. Revisit when the A2A bridge gets a recorded-wire fixture + paths: + - tests/agent_tests/local_only_agent_tests - reason: >- Third-party integration tests that skip themselves without OCI configuration or sandbox credentials, neither of which a pull request job holds @@ -123,17 +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: >- - Two prompt-factory tests sitting at the top level of tests/ instead of under the - tests/test_litellm mirror the shards enumerate; they need moving rather than a shard entry - paths: - - tests/litellm_core_utils/test_anthropic_dedup_factory.py - - tests/litellm_core_utils/test_bedrock_converse_dedup_factory.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 - 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 5b7ca9c7275..411852acb98 100644 --- a/.github/scripts/assert_ci_coverage.py +++ b/.github/scripts/assert_ci_coverage.py @@ -1,10 +1,14 @@ from __future__ import annotations +import ast +import operator import pathlib import re import sys -from collections.abc import Iterable, Mapping, Sequence +import warnings +from collections.abc import Callable, Iterable, Mapping, Sequence from dataclasses import dataclass +from typing import Final import yaml @@ -25,6 +29,15 @@ DOCKERFILE_TOKEN_RE = re.compile(r"[A-Za-z0-9_./-]*Dockerfile[A-Za-z0-9_.-]*") COMMENT_RE = re.compile(r"^\s*#.*$", re.MULTILINE) GLOB_CHARS = frozenset("*?") +# Trees whose jobs are sharded with no catch-all bucket, so every child that holds +# tests has to be named by some shard or it runs nowhere. A child listed here is +# itself decomposed one level deeper and is checked through its own entry. +SHARDED_ROOTS: tuple[str, ...] = ( + "tests/proxy_unit_tests", + "tests/test_litellm", + "tests/test_litellm/proxy", +) + @dataclass(frozen=True, slots=True) class AllowEntry: @@ -44,6 +57,14 @@ class Allowlist: return any(relative_path == path for entry in self.dockerfiles for path in entry.paths) +@dataclass(frozen=True, slots=True) +class Section: + name: str + entries: tuple[AllowEntry, ...] + candidates: tuple[str, ...] + matches: Callable[[str, str], bool] + + @dataclass(frozen=True, slots=True) class Scalar: key: str @@ -107,20 +128,34 @@ def _built_dockerfile_tokens(scalars: Iterable[Scalar]) -> frozenset[str]: ) -def _glob_to_regex(token: str) -> re.Pattern[str]: - parts = re.split(r"(\*\*/|\*\*|\*|\?)", token) +def _glob_to_regex(token: str, *, subtree: bool) -> re.Pattern[str]: + parts = re.split(r"(\*\*/|\*\*|\*|\?|\[[^\]]*\])", token) translated = "".join( - {"**/": r"(?:.*/)?", "**": r".*", "*": r"[^/]*", "?": r"[^/]"}.get(part, re.escape(part)) for part in parts + {"**/": r"(?:.*/)?", "**": r".*", "*": r"[^/]*", "?": r"[^/]"}.get(part) + or (part if part.startswith("[") and part.endswith("]") else re.escape(part)) + for part in parts ) - return re.compile(rf"{translated}(?:/.*)?$") + return re.compile(rf"{translated}(?:/.*)?$" if subtree else rf"{translated}$") def _token_covers(token: str, relative_path: str) -> bool: if GLOB_CHARS & set(token): - return _glob_to_regex(token).match(relative_path) is not None + return _glob_to_regex(token, subtree=True).match(relative_path) is not None return relative_path == token or relative_path.startswith(f"{token}/") +def _token_names(token: str, relative_path: str) -> bool: + """Whether the token names this path itself, rather than merely containing it. + + A sharded tree has no catch-all bucket, so the ancestor token the census is happy + with (`tests/x` standing in for everything below it) is exactly what would let a + newly added child ride along without a shard. + """ + if GLOB_CHARS & set(token): + return _glob_to_regex(token, subtree=False).match(relative_path) is not None + return token == relative_path + + def _test_files() -> tuple[str, ...]: return tuple( sorted( @@ -166,6 +201,190 @@ def _describe(paths: tuple[str, ...]) -> str: return f"{len(paths)} test file(s) invoked by no job: {names}{suffix}" +GLOB_CALL_RE = re.compile(r'circleci tests glob "([^"]+)"') +KEYWORD_RE = re.compile(r"-k\s+\\?[\"']([^\"'\\]+)") + + +@dataclass(frozen=True, slots=True) +class Slice: + """One job's selection: the files it globs, narrowed by its `-k` expression.""" + + job: str + globs: tuple[str, ...] + named: frozenset[str] + required: tuple[str, ...] + excluded: tuple[str, ...] + understood: bool + + def claims(self, relative_path: str, inner_names: frozenset[str]) -> bool: + """Whether this job runs any test in the file. + + The question is deliberately per-file, not per-test. An excluded term is only + honoured when it appears in the path, because that is the case where it takes + the whole module with it; a term matching one function inside drops that test + and leaves the file claimed. Losing a whole file is the failure worth a gate, + and answering per-test would mean a baseline of test ids that churns on every + rename. + """ + if relative_path in self.named: + return True + if not any(_token_covers(glob, relative_path) for glob in self.globs): + return False + if not self.understood: + return True # a `-k` this parser cannot model is assumed to claim everything + if any(term.lower() in relative_path.lower() for term in self.excluded): + return False + return not self.required or any( + term.lower() in name.lower() for term in self.required for name in inner_names + ) + + +def _strings(node: object) -> Iterable[str]: + if isinstance(node, str): + yield node + elif isinstance(node, dict): + for value in node.values(): + yield from _strings(value) + elif isinstance(node, list): + for value in node: + yield from _strings(value) + + +def _keyword_terms( + expressions: Sequence[str], *, attributable: bool = True +) -> tuple[tuple[str, ...], tuple[str, ...], bool]: + """A `-k` expression as (required, excluded, understood). + + Only flat `and` chains of bare terms are modelled. Anything with `or`, parentheses + or negation of a group is left unmodelled, and its job is then treated as claiming + every file it globs, so an unparsed selector can never raise a false alarm. + + `attributable` is False when a job runs several pytest commands, since a selector + read out of the job's text cannot then be tied to the glob it belongs to, and + pairing one command's exclusion with another's glob would invent a gap. + """ + terms: Final = tuple(part.strip() for expression in expressions for part in expression.split(" and ")) + if not attributable and terms: + return (), (), False + if any(("or " in term) or ("(" in term) or (term.startswith("not ") and " " in term[4:]) for term in terms): + return (), (), False + return ( + tuple(term for term in terms if term and not term.startswith("not ")), + tuple(term[4:].strip() for term in terms if term.startswith("not ")), + True, + ) + + +def _slices() -> tuple[Slice, ...]: + if not CIRCLECI_CONFIG.exists(): + return () + jobs: Final = yaml.safe_load(CIRCLECI_CONFIG.read_text()).get("jobs", {}) + return tuple( + Slice(job=job, globs=globs, named=named, required=required, excluded=excluded, understood=understood) + for job, body in jobs.items() + for text in ("\n".join(_strings(body)),) + if "pytest" in text + for globs in (tuple(GLOB_CALL_RE.findall(text)),) + for named in (frozenset(TEST_TOKEN_RE.findall(text)) & frozenset(_test_files()),) + for required, excluded, understood in ( + _keyword_terms(tuple(KEYWORD_RE.findall(text)), attributable=len(globs) < 2), + ) + if globs or named + ) + + +def _matchable_names(relative_path: str) -> frozenset[str]: + """Every name a `-k` term can match for this file: its path, plus the names inside it. + + pytest matches a keyword against an item's own name and each of its parents', so a + positive term hits a file when it appears in the path or in a class or function name. + """ + try: + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # test files carry stray escapes; their names still parse + tree: Final = ast.parse((REPO_ROOT / relative_path).read_text()) + except (OSError, SyntaxError): + return frozenset({relative_path}) + return frozenset({relative_path}) | frozenset( + node.name + for node in ast.walk(tree) + if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)) + ) + + +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() + if any(_token_covers(glob, path) for slice_ in slices for glob in slice_.globs) + ) + return tuple( + Finding( + subject=path, + detail="globbed by a job, then deselected by every one of their -k expressions", + ) + 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) + ) + + +def _holds_tests(directory: pathlib.Path) -> bool: + return any(directory.rglob("test_*.py")) + + +def _shard_children(root: str, repo_root: pathlib.Path = REPO_ROOT) -> tuple[str, ...]: + """Children of a sharded root that carry tests, so each one needs its own shard. + + A directory earns an entry by containing a test file rather than by being named + `test_*`, which is what keeps fixture directories (`test_configs`, `expected_*`) + out without a hand-maintained list of exceptions. + """ + return tuple( + sorted( + child.relative_to(repo_root).as_posix() + for child in (repo_root / root).iterdir() + if not child.name.startswith(".") + and ( + _holds_tests(child) + if child.is_dir() + else child.name.startswith("test_") and child.suffix == ".py" + ) + ) + ) + + +def _unassigned_shard_children( + tokens: frozenset[str], + roots: tuple[str, ...] = SHARDED_ROOTS, + repo_root: pathlib.Path = REPO_ROOT, +) -> tuple[Finding, ...]: + return tuple( + Finding(subject=child, detail=f"holds tests but no shard of {root} names it") + for root in roots + if (repo_root / root).is_dir() + for child in _shard_children(root, repo_root) + if child not in roots and not any(_token_names(token, child) for token in tokens) + ) + + def _uncovered_dockerfiles(allowlist: Allowlist, tokens: frozenset[str]) -> tuple[Finding, ...]: return tuple( Finding(subject=relative_path, detail="built by no job") @@ -174,6 +393,25 @@ def _uncovered_dockerfiles(allowlist: Allowlist, tokens: frozenset[str]) -> tupl ) +def _stale_allowlist_paths( + allowlist: Allowlist, + *, + test_files: tuple[str, ...], + dockerfiles: tuple[str, ...], +) -> tuple[Finding, ...]: + sections: Final[tuple[Section, ...]] = ( + Section("test_paths", allowlist.test_paths, test_files, _token_covers), + Section("dockerfiles", allowlist.dockerfiles, dockerfiles, operator.eq), + ) + return tuple( + Finding(subject=path, detail=f"listed under '{section.name}' but matches no file the census looks at") + for section in sections + for entry in section.entries + for path in entry.paths + if not any(section.matches(path, candidate) for candidate in section.candidates) + ) + + def _parse_entry(item: object, section: str) -> AllowEntry: if not isinstance(item, dict): raise SystemExit(f"{ALLOWLIST_FILE.name}: '{section}' entries must be mappings") @@ -229,13 +467,56 @@ def _report(title: str, findings: tuple[Finding, ...], remedy: str) -> None: _write("") +def _check_slices() -> int: + findings: Final = _deselected_everywhere(_load_allowlist()) + if findings: + _report( + "test files a -k expression removes from every job that globs them", + findings, + "Give each one a job whose -k keeps it, or list it in " + ".github/ci-coverage-allowlist.yml with the reason it may stay unrun.", + ) + return 1 + + _write(f"OK: no test file is globbed by a job and then deselected by every -k across {len(_slices())} slices.") + return 0 + + +def _check_shards() -> int: + findings = _unassigned_shard_children(_invoked_test_tokens(_all_scalars())) + if findings: + _report( + "test directories and files that no shard claims", + findings, + "Add each to the shard it belongs to. A directory that is itself split across " + "several shards belongs in SHARDED_ROOTS instead, so its own children get checked.", + ) + return 1 + + counted = sum(len(_shard_children(root)) for root in SHARDED_ROOTS if (REPO_ROOT / root).is_dir()) + _write(f"OK: all {counted} test children across {len(SHARDED_ROOTS)} sharded trees are assigned to a shard.") + return 0 + + def main() -> int: + if "--shards" in sys.argv[1:]: + return _check_shards() + if "--slices" in sys.argv[1:]: + return _check_slices() + allowlist = _load_allowlist() scalars = _all_scalars() test_findings = _uncovered_tests(allowlist, _invoked_test_tokens(scalars)) dockerfile_findings = _uncovered_dockerfiles(allowlist, _built_dockerfile_tokens(scalars)) + stale_findings = _stale_allowlist_paths(allowlist, test_files=_test_files(), dockerfiles=_dockerfiles()) + if stale_findings: + _report( + "allowlist entries that exempt nothing", + stale_findings, + "Delete each from .github/ci-coverage-allowlist.yml; the file it named is gone or was renamed.", + ) if test_findings: _report( "test files that no CI job invokes", @@ -248,7 +529,7 @@ def main() -> int: dockerfile_findings, "Build each in a workflow, or list it in .github/ci-coverage-allowlist.yml with a reason.", ) - if test_findings or dockerfile_findings: + if stale_findings or test_findings or dockerfile_findings: return 1 _write( diff --git a/.github/scripts/assert_workflow_dir_hygiene.py b/.github/scripts/assert_workflow_dir_hygiene.py new file mode 100644 index 00000000000..681a365b1ba --- /dev/null +++ b/.github/scripts/assert_workflow_dir_hygiene.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +"""Three invariants about what lives in .github/workflows/ and what its names mean. + +`.github/workflows/` is a directory GitHub reads, not a place to keep things. Every +file at its top level is parsed as a workflow, so a script or a data file parked there +is either an invalid workflow or an orphan nobody can find. A subdirectory is not read +at all, so helper files may live in one. GitHub accepts both `.yml` and `.yaml`, and +this repo spells them `.yml`, which is a naming rule rather than a validity one and is +reported separately. And the `_` prefix is the repo's only signal that a workflow is a +reusable building block rather than something that runs on its own, which is worth +nothing unless it is true both ways. + + WF001 a top-level file in .github/workflows/ that is not a workflow at all + WF002 a workflow whose only trigger is `workflow_call` but is not `_`-prefixed + WF003 a `_`-prefixed workflow that no other workflow can call + WF004 a real workflow spelled `.yaml` where this directory spells them `.yml` + +A workflow with `workflow_call` alongside a human trigger is deliberately dual-mode +and belongs under its plain name, so only the call-only ones are held to WF002. + +Usage +----- + python assert_workflow_dir_hygiene.py + +Exit code 1 if any violation is found. +""" + +from __future__ import annotations + +import pathlib +import sys +from dataclasses import dataclass +from typing import Final + +import yaml + +REPO_ROOT: Final = pathlib.Path(__file__).resolve().parents[2] +WORKFLOW_DIR: Final = REPO_ROOT / ".github" / "workflows" +SCRIPT_HOME: Final = ".github/scripts/" +REUSABLE_PREFIX: Final = "_" +CALL_TRIGGER: Final = "workflow_call" +CANONICAL_SUFFIX: Final = ".yml" +WORKFLOW_SUFFIXES: Final = frozenset((CANONICAL_SUFFIX, ".yaml")) + + +@dataclass(frozen=True, slots=True) +class Finding: + subject: str + code: str + detail: str + + def render(self) -> str: + return f" - {self.subject}: {self.code} {self.detail}" + + +def _triggers(document: object) -> frozenset[str]: + if not isinstance(document, dict): + return frozenset() + raw: Final = document.get("on", document.get(True)) + if isinstance(raw, str): + return frozenset({raw}) + if isinstance(raw, dict): + return frozenset(str(key) for key in raw) + if isinstance(raw, list): + return frozenset(str(item) for item in raw) + return frozenset() + + +def _workflows(directory: pathlib.Path) -> tuple[pathlib.Path, ...]: + return tuple( + path + for path in sorted(directory.iterdir()) + if path.is_file() and path.suffix in WORKFLOW_SUFFIXES + ) + + +def _strays(directory: pathlib.Path) -> tuple[Finding, ...]: + return tuple( + Finding( + path.name, + "WF001", + f"is not a workflow, and GitHub parses every top-level file here as one; " + f"move it to {SCRIPT_HOME} or into a subdirectory, which GitHub does not read", + ) + for path in sorted(directory.iterdir()) + if path.is_file() and path.suffix not in WORKFLOW_SUFFIXES + ) + + +def _misspelled(directory: pathlib.Path) -> tuple[Finding, ...]: + return tuple( + Finding( + path.name, + "WF004", + f"is a real workflow and GitHub reads it, but this directory spells them " + f"{CANONICAL_SUFFIX}; rename it to {path.stem}{CANONICAL_SUFFIX}", + ) + for path in _workflows(directory) + if path.suffix != CANONICAL_SUFFIX + ) + + +def _misnamed(directory: pathlib.Path) -> tuple[Finding, ...]: + return tuple( + finding + for path in _workflows(directory) + for finding in _naming_findings(path, _triggers(yaml.safe_load(path.read_text(encoding="utf-8")))) + ) + + +def _naming_findings(path: pathlib.Path, triggers: frozenset[str]) -> tuple[Finding, ...]: + underscored: Final = path.name.startswith(REUSABLE_PREFIX) + if triggers == frozenset({CALL_TRIGGER}) and not underscored: + return ( + Finding( + path.name, + "WF002", + f"is only callable by another workflow, so name it {REUSABLE_PREFIX}{path.name}", + ), + ) + if underscored and CALL_TRIGGER not in triggers: + return ( + Finding( + path.name, + "WF003", + f"is named as a reusable workflow but has no {CALL_TRIGGER} trigger; " + "add one or drop the prefix", + ), + ) + return () + + +def main() -> int: + findings: Final = _strays(WORKFLOW_DIR) + _misspelled(WORKFLOW_DIR) + _misnamed(WORKFLOW_DIR) + if not findings: + total: Final = len(_workflows(WORKFLOW_DIR)) + sys.stdout.write( + f"OK: {total} workflows, every file in .github/workflows/ is one, and the " + f"{REUSABLE_PREFIX} prefix means callable in both directions.\n" + ) + return 0 + sys.stdout.write("ERROR: .github/workflows/ holds files that break its own conventions\n") + for finding in findings: + sys.stdout.write(f"{finding.render()}\n") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.github/workflows/auto_update_price_and_context_window_file.py b/.github/scripts/auto_update_price_and_context_window_file.py similarity index 100% rename from .github/workflows/auto_update_price_and_context_window_file.py rename to .github/scripts/auto_update_price_and_context_window_file.py 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/workflows/run_llm_translation_tests.py b/.github/scripts/run_llm_translation_tests.py similarity index 100% rename from .github/workflows/run_llm_translation_tests.py rename to .github/scripts/run_llm_translation_tests.py diff --git a/.github/scripts/triage_rollout_heads_up.py b/.github/scripts/triage_rollout_heads_up.py deleted file mode 100644 index a5dedb1c9e7..00000000000 --- a/.github/scripts/triage_rollout_heads_up.py +++ /dev/null @@ -1,557 +0,0 @@ -#!/usr/bin/env python3 -"""One-shot 7-day heads-up sweep for the Agent Shin rollout. - -Posts a friendly "the OSS triage bot kicks in next Monday" comment on every -open external PR/issue that currently *would* fail the new rubric — i.e., -every PR/issue Agent Shin would close once the rollout completes. The point -is to give contributors a full week to fix their description before the bot -ever takes a destructive action, so nobody is surprised by an auto-close. - -The script is designed to run **exactly once** at rollout, fired by a manual -``workflow_dispatch`` (``dry_run=false``) on the heads-up workflow. Re-runs -are safe: every comment is stamped with the hidden ``HEADS_UP_MARKER`` and -PRs/issues that already carry the marker are skipped. - -Dry-run vs. real run --------------------- -Defaults to dry-run. Passing ``--close`` flips into real mode. Every GitHub -mutation goes through ``_agent_shin_actions``, which has a one-line -``if dry_run: log else: do_it`` per call, so the only difference between a -dry-run preview and the real run is the call site that actually hits the -GitHub API. - -Local preview:: - - python3 .github/scripts/triage_rollout_heads_up.py --repo BerriAI/litellm - -Real run (the manual rollout dispatch uses this):: - - python3 .github/scripts/triage_rollout_heads_up.py --repo BerriAI/litellm --close -""" - -from __future__ import annotations - -import argparse -import datetime as dt -import json -import os -import sys -from pathlib import Path -from typing import Any - -# Make the sibling triage_with_llm + _agent_shin_actions importable when this -# script is invoked directly (the GitHub workflow does `python3 .github/scripts/...`). -_SCRIPTS_DIR = Path(__file__).resolve().parent -if str(_SCRIPTS_DIR) not in sys.path: - sys.path.insert(0, str(_SCRIPTS_DIR)) - -from _agent_shin_actions import maybe_post_comment # noqa: E402 -from agent_shin_shared import ( # noqa: E402 - AGENT_SHIN_DEFAULT_BOT_LOGIN, - ALLOWLIST_LOGINS, - list_open_items, -) -from triage_with_llm import ( # noqa: E402 - DEFAULT_MODEL, - call_llm_judge, - fetch_issue, - fetch_pr, - gh, - is_internal_contributor, - review_gate, - triage, -) - -# Hidden marker so re-runs skip PRs/issues we've already notified. Distinct from -# the within-grace / ready / regressed markers so it can't be confused with the -# steady-state lifecycle comments. -HEADS_UP_MARKER = "" - -# Placeholder until the litellm-docs PR ships. The rollout blog post explains -# the new rubric, the 7-day grace, and how to recover after an auto-close. -# TODO(docs): replace with the canonical URL once the litellm-docs PR merges. -ROLLOUT_BLOG_URL = "https://docs.litellm.ai/docs/agent_shin_triage_rollout" - -# Default cutoff is one week from "now". Computed at runtime so the wording -# stays correct even if the rollout is merged later than planned. The user can -# override with --close-on YYYY-MM-DD when running the script manually. -DEFAULT_GRACE_DAYS = 7 - -# The daily auto-close sweeps (close_low_quality_prs.yml at 09:00 UTC and -# review_gate.yml at 09:30 UTC) are what actually close a still-failing item, -# so the deadline we promise contributors has to name that wall-clock moment. -ACTIVATION_TIME_UTC = "09:00 UTC" - - -def _format_cutoff(cutoff: dt.date) -> str: - """Human-readable, timezone-explicit cutoff, e.g. ``Monday, June 1, 2026 - (09:00 UTC)`` — the moment a still-failing PR/issue gets closed.""" - return ( - f"{cutoff.strftime('%A, %B')} {cutoff.day}, {cutoff.year} " - f"({ACTIVATION_TIME_UTC})" - ) - - -def _rubric_section_pr() -> str: - return ( - "**Going forward, every external PR needs ONE of:**\n" - "\n" - "- A linked GitHub issue using a closing keyword: " - "`Fixes #1234`, `Closes #1234`, or `Resolves #1234`, OR\n" - "- All three of: a clear **problem description**, **expected vs. " - "actual behavior**, and **end-to-end QA proof** (at least one of a " - "short screen recording / video, before/after screenshots, or the " - "exact commands you ran with their real output; mocked or stubbed " - "runs don't count).\n" - "\n" - "PRs also need a **Greptile confidence score of 4/5 or higher** before " - "the bot will tag them `ready for review`. You can `@greptileai` to " - "request a fresh review at any time, including after the PR is closed." - ) - - -def _rubric_section_issue() -> str: - return ( - "**Going forward, every external issue needs:**\n" - "\n" - "- For **bug reports**: end-to-end evidence of the bug (at least one " - "of a screen recording / video, a screenshot, or the exact commands " - "you ran with their real output / traceback) plus expected vs. actual " - "behavior. Written steps with no run output don't count, and mocked " - "or stubbed runs don't count.\n" - "- For **feature requests**: a clear description of the proposed " - "feature plus a use case + concrete example (config, API call, UI " - "flow, or scenario showing what's blocked today)." - ) - - -def _description_only_note(kind: str) -> str: - noun = "PR" if kind == "pr" else "issue" - return ( - f"⚠️ **The requirements must live in the {noun} *description*, not in " - "comments.** Some PRs/issues collect 100+ comments from humans and " - "bots; reading the entire thread on every triage run would balloon " - "GitHub API usage (we'd start getting 429'd) and blow out the LLM " - "judge's context. The bot only reads the description, so anything " - "you add as a comment will be invisible to it." - ) - - -def _missing_section(verdict: dict, greptile_score: int | None) -> str: - """Bullet list of what's currently missing on this PR/issue. - - Combines the LLM judge's `missing` list (rubric items) with a Greptile - shortfall (for PRs) so the contributor sees one list of things to fix. - """ - missing = list(verdict.get("missing") or []) - if greptile_score is not None and greptile_score < 4: - missing.insert( - 0, - f"Greptile's most recent review scored this PR {greptile_score}/5 " - "(below the 4/5 bar Agent Shin will require).", - ) - if not missing: - return ( - "_The bot couldn't articulate a specific missing piece; see the " - "rubric link above and double-check the description includes all " - "of it before the rollout._" - ) - bullets = "\n".join(f"- {m}" for m in missing) - return f"**What this one is currently missing:**\n\n{bullets}" - - -def _recovery_section(kind: str) -> str: - if kind == "pr": - return ( - "**If the bot closes this PR after the rollout:** update the " - "description with the missing pieces, then either open a fresh " - "PR or comment `@agent-shin reconsider` on the closed PR. If " - "Greptile re-scores you at 4/5 or higher I'll reopen and tag " - "the PR `ready for review`. (`@greptileai` works on closed PRs " - "too; a fresh review is one of the signals that lifts you back " - "into the queue.) This is **not** us losing interest in your " - "change; far from it. We just need open PRs to be a list of " - "things a maintainer can act on, so we can get to yours faster." - ) - return ( - "**If the bot closes this issue after the rollout:** edit the issue " - "description to add the missing pieces, then comment `@agent-shin " - "reconsider` on the closed issue. I'll re-evaluate and, if the rubric " - "is met, reopen it. (GitHub doesn't let external authors reopen an " - "issue a maintainer or bot closed, so the comment is the reliable " - "path.) This is **not** us saying the bug isn't real or the request " - "isn't useful; it's so the remaining open issues are a list of things " - "a maintainer can act on." - ) - - -def format_heads_up_comment( - *, kind: str, verdict: dict, greptile_score: int | None, cutoff: dt.date -) -> str: - """Compose the friendly 7-day heads-up comment posted on a failing PR/issue.""" - noun = "PR" if kind == "pr" else "issue" - rubric = _rubric_section_pr() if kind == "pr" else _rubric_section_issue() - cutoff_str = _format_cutoff(cutoff) - explanation = (verdict.get("explanation") or "").strip() - explanation_block = ( - f"> _(The judge's note for this one: {explanation})_\n\n" if explanation else "" - ) - - return ( - "🚅 **Heads-up: we're turning on the OSS triage bot in " - f"{DEFAULT_GRACE_DAYS} days, on {cutoff_str}.**\n" - "\n" - "We're rolling out **Agent Shin**, an LLM-as-judge triage bot for " - f"external {noun}s. Once it's live, the bot reads each open " - f"{noun}'s description, scores it against a small rubric, and " - f"auto-closes any {noun} that's missing the basics, with a single " - f"comment explaining what's missing and how to recover. Full " - f"context: [Agent Shin rollout blog post]({ROLLOUT_BLOG_URL}).\n" - "\n" - f"{rubric}\n" - "\n" - f"{_description_only_note(kind)}\n" - "\n" - f"{_missing_section(verdict, greptile_score)}\n" - "\n" - f"{explanation_block}" - "**Timeline (you have a week):**\n" - "\n" - f"- We turn the bot on in {DEFAULT_GRACE_DAYS} days, on " - f"**{cutoff_str}**. You have until then to update this {noun}'s " - "description with the missing pieces above.\n" - f"- If this {noun} still fails the rubric at **{cutoff_str}**, " - "we'll close it.\n" - f"- From then on the bot runs daily, and every {noun} that fails " - "the rubric gets a **2-hour lifetime**: one warning comment, then " - "auto-close 2 hours later.\n" - "\n" - f"{_recovery_section(kind)}\n" - "\n" - f"{HEADS_UP_MARKER}" - ) - - -def _list_open_numbers(repo: str, kind: str) -> list[int]: - """Return every open PR or issue number in ``repo``. - - Delegates to ``list_open_items`` so the full backlog is fetched (no cap) - and the `gh {pr,issue} list` invocation stays in one shared place. ``gh - issue list`` would include PRs, but ``list_open_items`` uses the dedicated - command per kind, so the two never mix. - """ - return [ - item["number"] for item in list_open_items(kind, repo=repo, fields="number") - ] - - -def _has_heads_up_marker(item: dict) -> bool: - """Cheap fast-path: check the PR/issue body itself for the marker. - - The marker is appended to the *comment* we post, not the body, so this - will only fire if the body literally contains the marker text. We still - do the comment-marker check separately below; this body check just lets - us short-circuit for PRs/issues that quote the marker for any reason. - """ - body = item.get("body") or "" - return HEADS_UP_MARKER in body - - -def _comments_have_marker(repo: str, number: int) -> bool: - """True if the bot already posted a comment carrying the marker. - - Used for idempotency: a re-run skips items the previous run notified. - Filters by author (matching the sibling marker-checks in - ``triage_with_llm._has_marker`` and - ``agent_shin_shared.seconds_since_latest_marker_comment``) so a - contributor who quotes the heads-up via GitHub's "Quote reply" — which - preserves HTML comments in the raw markdown — can't trick the - idempotency check into silently skipping a real heads-up. - - Comments live on the unified issues endpoint regardless of whether the - item is a PR or an issue, so no ``kind`` argument is required here. - """ - expected_login = ( - os.environ.get("AGENT_SHIN_BOT_LOGIN") or AGENT_SHIN_DEFAULT_BOT_LOGIN - ).lower() - raw = gh( - "api", - "--paginate", - f"repos/{repo}/issues/{number}/comments?per_page=100", - ) - for line in raw.splitlines(): - line = line.strip() - if not line: - continue - try: - payload = json.loads(line) - except json.JSONDecodeError: - continue - comments = payload if isinstance(payload, list) else [payload] - for comment in comments: - author = ((comment.get("user") or {}).get("login") or "").lower() - if author != expected_login: - continue - if HEADS_UP_MARKER in (comment.get("body") or ""): - return True - return False - - -def _evaluate_pr(*, repo: str, number: int, model: str, judge: Any = None) -> dict: - """Run the future PR rubric (review_gate) in dry-run and return the result.""" - return review_gate( - repo=repo, - number=number, - close=False, # we only want the verdict, never act here - model=model, - judge=judge, - ) - - -def _evaluate_issue(*, repo: str, number: int, model: str, judge: Any = None) -> dict: - """Run the future issue rubric (triage kind='issue') in dry-run.""" - return triage( - repo=repo, - kind="issue", - number=number, - close=False, - model=model, - judge=judge, - ) - - -def _would_be_closed(kind: str, result: dict) -> bool: - """True if the future triage would auto-close this PR/issue based on the - rubric (regardless of grace-period gating). - - For PRs we trust ``review_gate``'s ``passing`` field — it combines the LLM - verdict and the Greptile score. For issues we read the LLM verdict - directly. Both fields are ``None``/missing on skip paths - (skip-internal-author, skip-llm-error, etc.) where the future bot would - NOT close the item — those return False. - """ - if kind == "pr": - passing = result.get("passing") - if passing is None: - return False # skipped — nothing for the heads-up to warn about - return passing is False - verdict = result.get("verdict") or {} - return (verdict.get("verdict") or "").lower() == "fail" - - -def _process_one( - *, - repo: str, - kind: str, - number: int, - model: str, - cutoff: dt.date, - dry_run: bool, - judge: Any = None, - skip_marker_check: bool = False, - allowlist: frozenset[str] = ALLOWLIST_LOGINS, -) -> dict: - """Evaluate one PR/issue and post a heads-up if it would be auto-closed. - - Returns a per-item dict for the summary table. - """ - base = {"kind": kind, "number": number} - fetcher = fetch_pr if kind == "pr" else fetch_issue - item = fetcher(repo, number) - - if (item.get("state") or "") != "open": - return {**base, "action": "skip-not-open"} - if allowlist: - login = (item.get("user") or {}).get("login") or "" - if login.lower() not in allowlist: - return {**base, "action": "skip-not-allowlisted"} - elif is_internal_contributor(item): - return {**base, "action": "skip-internal-author"} - if not skip_marker_check and _has_heads_up_marker(item): - return {**base, "action": "skip-already-marked-in-body"} - if not skip_marker_check and _comments_have_marker(repo, number): - return {**base, "action": "skip-already-notified"} - - if kind == "pr": - result = _evaluate_pr(repo=repo, number=number, model=model, judge=judge) - else: - result = _evaluate_issue(repo=repo, number=number, model=model, judge=judge) - - if not _would_be_closed(kind, result): - return {**base, "action": "skip-passing", "evaluator": result.get("action")} - - verdict = result.get("verdict") or {} - greptile_score = result.get("greptile_score") if kind == "pr" else None - comment = format_heads_up_comment( - kind=kind, verdict=verdict, greptile_score=greptile_score, cutoff=cutoff - ) - maybe_post_comment(repo, number, comment, dry_run=dry_run) - return { - **base, - "action": "heads-up-posted" if not dry_run else "would-post-heads-up", - "verdict": (verdict.get("verdict") or "").lower(), - "greptile_score": greptile_score, - } - - -def _print_summary(results: list[dict]) -> None: - """Tally per-action counts so a dry-run preview tells you at a glance how - many comments the real run would post.""" - counts: dict[str, int] = {} - for r in results: - counts[r["action"]] = counts.get(r["action"], 0) + 1 - print("\n=== rollout heads-up summary ===") - for action in sorted(counts): - print(f" {action:35s} {counts[action]}") - print(f" total {len(results)}") - - -def run( - *, - repo: str, - close: bool, - cutoff: dt.date, - model: str, - kinds: tuple[str, ...] = ("pr", "issue"), - judge: Any = None, - only_numbers: dict[str, list[int]] | None = None, - skip_marker_check: bool = False, -) -> list[dict]: - """Sweep ``repo`` and post heads-up comments. Returns the per-item results.""" - dry_run = not close - if dry_run: - print( - f"[DRY RUN] sweeping {repo}; --close not passed, no comments will be posted." - ) - else: - print(f"[REAL RUN] sweeping {repo}; comments WILL be posted.") - print(f"Cutoff date in comment body: {cutoff.isoformat()}") - - results: list[dict] = [] - for kind in kinds: - if only_numbers and kind in only_numbers: - numbers = list(only_numbers[kind]) - else: - numbers = _list_open_numbers(repo, kind) - print(f"\n--- {kind}s: {len(numbers)} open ---") - for n in numbers: - try: - result = _process_one( - repo=repo, - kind=kind, - number=n, - model=model, - cutoff=cutoff, - dry_run=dry_run, - judge=judge, - skip_marker_check=skip_marker_check, - ) - except ( - Exception - ) as exc: # noqa: BLE001 - per-item errors don't abort the sweep - result = { - "kind": kind, - "number": n, - "action": "error", - "error": str(exc), - } - print(f"!! {kind}#{n}: {exc}", file=sys.stderr) - print(f" {kind}#{n}: {result['action']}") - results.append(result) - _print_summary(results) - return results - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--repo", required=True, help="owner/repo") - parser.add_argument( - "--close", - action="store_true", - help=( - "Actually post comments. Without this flag the script is in " - "dry-run mode and only logs what it would do." - ), - ) - parser.add_argument( - "--close-on", - type=dt.date.fromisoformat, - default=None, - help=( - "Cutoff date shown in the heads-up comment as the rollout date " - f"(default: today + {DEFAULT_GRACE_DAYS} days)." - ), - ) - parser.add_argument( - "--model", - default=os.environ.get("TRIAGE_MODEL") or DEFAULT_MODEL, - help=f"Model for the rubric LLM judge (default: {DEFAULT_MODEL}).", - ) - parser.add_argument( - "--kind", - choices=("pr", "issue", "both"), - default="both", - help="Restrict the sweep to PRs or issues only (default: both).", - ) - parser.add_argument( - "--only-pr", - type=int, - action="append", - default=[], - help="Limit the PR sweep to these PR numbers (repeat for several).", - ) - parser.add_argument( - "--only-issue", - type=int, - action="append", - default=[], - help="Limit the issue sweep to these issue numbers (repeat for several).", - ) - parser.add_argument( - "--ignore-existing-marker", - action="store_true", - help=( - "Re-post on PRs/issues that already carry the heads-up marker. " - "Useful for testing the comment wording on a known PR." - ), - ) - args = parser.parse_args() - - cutoff = args.close_on or ( - dt.datetime.now(dt.timezone.utc).date() + dt.timedelta(days=DEFAULT_GRACE_DAYS) - ) - - kinds: tuple[str, ...] - if args.kind == "pr": - kinds = ("pr",) - elif args.kind == "issue": - kinds = ("issue",) - else: - kinds = ("pr", "issue") - - only: dict[str, list[int]] = {} - if args.only_pr: - only["pr"] = args.only_pr - if args.only_issue: - only["issue"] = args.only_issue - - # The script must NOT hit the LLM in dry-run if no key is set — we still - # want a useful preview that says "skip-no-llm-key" for items that would - # have been judged. Production runs require OPENAI_API_KEY. - if args.close and not os.environ.get("OPENAI_API_KEY"): - parser.error("OPENAI_API_KEY must be set for --close (real-run) mode.") - - run( - repo=args.repo, - close=args.close, - cutoff=cutoff, - model=args.model, - kinds=kinds, - only_numbers=only or None, - skip_marker_check=args.ignore_existing_marker, - ) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/.github/workflows/_test-unit-base.yml b/.github/workflows/_test-unit-base.yml index 4f4339a360a..c4045a08ffb 100644 --- a/.github/workflows/_test-unit-base.yml +++ b/.github/workflows/_test-unit-base.yml @@ -27,7 +27,7 @@ on: default: 20 job-timeout-minutes: description: >- - Backstop for the whole job. Keep it >= `timeout-minutes` plus 35: 30 for + Backstop for the whole job. Keep it >= `timeout-minutes` plus 40: 35 for the per-step ceilings on the setup steps below, and 5 for the runner overhead the job clock charges but no step owns (job init, step transitions, post-job cleanup). That headroom is what makes the test @@ -36,7 +36,7 @@ on: arithmetic, so the sum is passed in rather than computed. required: false type: number - default: 55 + default: 60 max-failures: description: "Stop after this many failures" required: false @@ -103,6 +103,11 @@ jobs: restore-keys: | ${{ runner.os }}-uv- + - name: Cache the Rust build + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 5 + uses: ./.github/actions/cache-cargo-build + - name: Install dependencies if: steps.changes.outputs.decision != 'skip' timeout-minutes: 8 @@ -129,6 +134,13 @@ jobs: WORKERS: ${{ inputs.workers }} RERUNS: ${{ inputs.reruns }} DIST: ${{ inputs.dist }} + # coverage.py's sys.monitoring backend (PEP 669), the cheapest core it has. + # It is only the default from Python 3.14, and these shards run 3.12, so it + # has to be asked for. Coverage refuses it when branch measurement is on + # (`branch_right_left` needs > 3.14.0a5) and falls back to the slow core with + # a `no-sysmon` warning, so turning on `branch = true` here means giving this + # back until the runners move to 3.14. + COVERAGE_CORE: sysmon run: | if [ "${WORKERS}" = "0" ]; then uv run --no-sync pytest ${TEST_PATH:?} \ @@ -137,7 +149,7 @@ jobs: --reruns "${RERUNS}" \ --reruns-delay 1 \ --durations=20 \ - --cov=./litellm \ + --cov=./litellm --cov=./enterprise/litellm_enterprise \ --cov-report=xml:coverage.xml \ --cov-config=pyproject.toml else @@ -149,7 +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_update_price_and_context_window.yml b/.github/workflows/auto_update_price_and_context_window.yml index d391c0bd6ce..7e40a860ee9 100644 --- a/.github/workflows/auto_update_price_and_context_window.yml +++ b/.github/workflows/auto_update_price_and_context_window.yml @@ -23,7 +23,7 @@ jobs: version: "0.10.9" - name: Update JSON Data run: | - uv run --frozen --with 'aiohttp==3.13.3' python ".github/workflows/auto_update_price_and_context_window_file.py" + uv run --frozen --with 'aiohttp==3.13.3' python ".github/scripts/auto_update_price_and_context_window_file.py" - name: Regenerate JSON Schema run: | uv run --frozen python ci_cd/generate_model_prices_schema.py diff --git a/.github/workflows/check-ui-api-types.yml b/.github/workflows/check-ui-api-types.yml index dbd663a2efa..312a80103f8 100644 --- a/.github/workflows/check-ui-api-types.yml +++ b/.github/workflows/check-ui-api-types.yml @@ -67,6 +67,10 @@ jobs: restore-keys: | ${{ runner.os }}-uv- + - name: Cache the Rust build + if: steps.changes.outputs.relevant == 'true' + uses: ./.github/actions/cache-cargo-build + - name: Install backend dependencies if: steps.changes.outputs.relevant == 'true' run: .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router @@ -79,6 +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/ci-coverage.yml b/.github/workflows/ci-coverage.yml index c95921297a2..7bc476db134 100644 --- a/.github/workflows/ci-coverage.yml +++ b/.github/workflows/ci-coverage.yml @@ -40,3 +40,12 @@ jobs: run: | python -m pip install "pyyaml==6.0.3" python .github/scripts/assert_ci_coverage.py + + # The census asks whether a job names a file; this asks whether that job's -k + # then throws it back out. A file both globbed and deselected everywhere runs + # nowhere while counting as covered, which is how the caching suite went unrun. + - name: Assert no -k expression deselects a file from every job that globs it + run: python .github/scripts/assert_ci_coverage.py --slices + + - name: Assert .github/workflows/ holds only workflows, correctly named + run: python .github/scripts/assert_workflow_dir_hygiene.py 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..bb04563c1a8 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 * * *" @@ -183,6 +187,35 @@ jobs: python -m pip install "pytest==9.0.3" python -m pytest tests/proxy_migration_tests/test_component_image_serves_offline.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 runs-on: ubuntu-latest diff --git a/.github/workflows/mutation-test.yml b/.github/workflows/mutation-test.yml index 68317d5dd12..b7d28bcaae4 100644 --- a/.github/workflows/mutation-test.yml +++ b/.github/workflows/mutation-test.yml @@ -53,6 +53,9 @@ jobs: restore-keys: | ${{ runner.os }}-uv- + - 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 google --extra proxy --extra semantic-router --extra saml @@ -84,11 +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: ${{ github.workspace }}/.github/mutmut-coverage.rc run: | set -o pipefail mkdir -p mutants @@ -127,6 +139,7 @@ jobs: mutmut-run.log mutants/mutmut-stats.json mutants/mutmut-cicd-stats.json + mutants/**/*.meta mutants/litellm/proxy/management_endpoints/**/*.py if-no-files-found: warn retention-days: 14 diff --git a/.github/workflows/publish-basedpyright-base-counts.yml b/.github/workflows/publish-basedpyright-base-counts.yml index 71e196d8361..cd443a8e9db 100644 --- a/.github/workflows/publish-basedpyright-base-counts.yml +++ b/.github/workflows/publish-basedpyright-base-counts.yml @@ -43,6 +43,9 @@ jobs: with: version: "0.10.9" + - name: Cache the Rust build + uses: ./.github/actions/cache-cargo-build + - name: Cache Prisma binaries uses: ./.github/actions/cache-prisma-binaries diff --git a/.github/workflows/sync-together-ai-models.yml b/.github/workflows/sync-together-ai-models.yml new file mode 100644 index 00000000000..1daaadeabe2 --- /dev/null +++ b/.github/workflows/sync-together-ai-models.yml @@ -0,0 +1,68 @@ +name: Sync Together AI model registry + +on: + schedule: + - cron: "30 6 * * *" + workflow_dispatch: + +permissions: + contents: write + pull-requests: write + +jobs: + sync_together_ai_models: + if: github.repository == 'BerriAI/litellm' + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 + with: + ref: litellm_internal_staging + persist-credentials: false + - name: Set up uv + uses: ./.github/actions/setup-uv-with-retries + with: + version: "0.10.9" + - name: Look for an already-open sync PR + id: existing + run: | + open_pr="$(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 8f62837d29a..c112bf2bb22 100644 --- a/.github/workflows/test-code-quality.yml +++ b/.github/workflows/test-code-quality.yml @@ -56,6 +56,9 @@ jobs: restore-keys: | ${{ runner.os }}-uv- + - name: Cache the Rust build + uses: ./.github/actions/cache-cargo-build + - name: Install dependencies run: uv sync --frozen --all-groups --all-extras @@ -128,6 +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 7a0ae0faaa0..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: | + ${{ runner.os }}-uv-lint- + - name: Clean Python cache if: steps.changes.outputs.decision != 'skip' run: | @@ -78,6 +89,10 @@ jobs: run: | uv lock --check || (echo "❌ uv.lock is out of sync with pyproject.toml. Run 'uv lock' locally and commit the result." && exit 1) + - name: Cache the Rust build + if: steps.changes.outputs.decision != 'skip' + uses: ./.github/actions/cache-cargo-build + - name: Install dependencies if: steps.changes.outputs.decision != 'skip' run: | @@ -122,6 +137,11 @@ jobs: uv run --no-sync ruff check . cd .. + - name: Run Ruff linting (test tree) + if: steps.changes.outputs.decision != 'skip' + run: | + uv run --no-sync ruff check --config ruff-tests.toml tests + - name: Check strict-rule budget (delta vs base) if: steps.changes.outputs.decision != 'skip' run: | @@ -132,6 +152,11 @@ jobs: run: | uv run --no-sync python scripts/type_discipline_gate.py --base "$GATE_BASE_SHA" + - name: Check test-quality budget (zero-assert / mock-echo tests, sys.path.insert, raw env writes, litellm global mutation, credential-gated skips, conftest snapshot inventory, delta vs base) + if: steps.changes.outputs.decision != 'skip' + run: | + uv run --no-sync python scripts/test_quality_gate.py --base "$GATE_BASE_SHA" + - name: Print OpenAI version if: steps.changes.outputs.decision != 'skip' run: | @@ -223,7 +248,7 @@ jobs: - name: Run secret scan test run: | - uv run --no-project --with 'pytest==9.0.2' pytest tests/litellm/test_no_hardcoded_secrets.py -v + uv run --no-project --with 'pytest==9.0.2' pytest tests/code_coverage_tests/test_no_hardcoded_secrets.py -v - name: Run ggshield secret scan env: diff --git a/.github/workflows/test-mcp.yml b/.github/workflows/test-mcp.yml index 95187ef2835..93ffcbe0586 100644 --- a/.github/workflows/test-mcp.yml +++ b/.github/workflows/test-mcp.yml @@ -47,6 +47,10 @@ jobs: with: version: "0.10.9" + - name: Cache the Rust build + if: steps.changes.outputs.decision != 'skip' + uses: ./.github/actions/cache-cargo-build + - name: Install dependencies if: steps.changes.outputs.decision != 'skip' run: | @@ -56,4 +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-model-map.yaml b/.github/workflows/test-model-map.yml similarity index 100% rename from .github/workflows/test-model-map.yaml rename to .github/workflows/test-model-map.yml 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-terraform-provider.yml b/.github/workflows/test-terraform-provider.yml index 7ea22825f4f..eb7b299fd1f 100644 --- a/.github/workflows/test-terraform-provider.yml +++ b/.github/workflows/test-terraform-provider.yml @@ -88,6 +88,9 @@ jobs: restore-keys: | ${{ runner.os }}-uv- + - name: Cache the Rust build + uses: ./.github/actions/cache-cargo-build + - name: Install dependencies run: | .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router @@ -111,4 +114,4 @@ jobs: - name: Audit provider endpoints against the schema working-directory: terraform/provider - run: go run ./tools/endpointaudit -provider-dir ./litellm -spec "${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-core-utils.yml b/.github/workflows/test-unit-core-utils.yml deleted file mode 100644 index a01f09559c6..00000000000 --- a/.github/workflows/test-unit-core-utils.yml +++ /dev/null @@ -1,31 +0,0 @@ -name: "Unit Tests: Core Utilities" - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - litellm_oss_staging - - "litellm_**" - push: - branches: - - main - - litellm_internal_staging - -permissions: - contents: read - id-token: write - pull-requests: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - core-utils: - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: "tests/test_litellm/litellm_core_utils" - workers: 2 - reruns: 1 - artifact-name: core-utils diff --git a/.github/workflows/test-unit-documentation.yml b/.github/workflows/test-unit-documentation.yml index cb8035aafa1..90b6b28374e 100644 --- a/.github/workflows/test-unit-documentation.yml +++ b/.github/workflows/test-unit-documentation.yml @@ -67,6 +67,10 @@ jobs: restore-keys: | ${{ runner.os }}-uv- + - name: Cache the Rust build + if: steps.changes.outputs.decision != 'skip' + uses: ./.github/actions/cache-cargo-build + - name: Install dependencies if: steps.changes.outputs.decision != 'skip' run: | diff --git a/.github/workflows/test-unit-enterprise-routing.yml b/.github/workflows/test-unit-enterprise-routing.yml deleted file mode 100644 index a64f00f4744..00000000000 --- a/.github/workflows/test-unit-enterprise-routing.yml +++ /dev/null @@ -1,35 +0,0 @@ -name: "Unit Tests: Enterprise, Google GenAI & Routing" - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - litellm_oss_staging - - "litellm_**" - push: - branches: - - main - - litellm_internal_staging - -permissions: - contents: read - id-token: write - pull-requests: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - enterprise-routing: - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: >- - tests/test_litellm/enterprise - tests/test_litellm/google_genai - tests/test_litellm/router_utils - tests/test_litellm/router_strategy - workers: 2 - reruns: 2 - artifact-name: enterprise-routing diff --git a/.github/workflows/test-unit-integrations.yml b/.github/workflows/test-unit-integrations.yml deleted file mode 100644 index 39752cf8e5d..00000000000 --- a/.github/workflows/test-unit-integrations.yml +++ /dev/null @@ -1,31 +0,0 @@ -name: "Unit Tests: Integrations (Callbacks & Logging)" - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - litellm_oss_staging - - "litellm_**" - push: - branches: - - main - - litellm_internal_staging - -permissions: - contents: read - id-token: write - pull-requests: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - integrations: - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: "tests/test_litellm/integrations" - workers: 2 - reruns: 3 - artifact-name: integrations diff --git a/.github/workflows/test-unit-llm-providers.yml b/.github/workflows/test-unit-llm-providers.yml deleted file mode 100644 index 4d1c921f723..00000000000 --- a/.github/workflows/test-unit-llm-providers.yml +++ /dev/null @@ -1,47 +0,0 @@ -name: "Unit Tests: LLM Provider Transformations" - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - litellm_oss_staging - - "litellm_**" - push: - branches: - - main - - litellm_internal_staging - -permissions: - contents: read - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - vertex-ai: - name: Vertex AI - permissions: - contents: read - id-token: write - pull-requests: write - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: "tests/test_litellm/llms/vertex_ai" - workers: 1 - reruns: 2 - artifact-name: llm-vertex-ai - - other-providers: - name: All Other Providers - permissions: - contents: read - id-token: write - pull-requests: write - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: "tests/test_litellm/llms --ignore=tests/test_litellm/llms/vertex_ai" - workers: 2 - reruns: 2 - artifact-name: llm-other-providers diff --git a/.github/workflows/test-unit-misc.yml b/.github/workflows/test-unit-misc.yml deleted file mode 100644 index 123a31e23f7..00000000000 --- a/.github/workflows/test-unit-misc.yml +++ /dev/null @@ -1,53 +0,0 @@ -name: "Unit Tests: MCP, Secrets, Containers & Misc" - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - litellm_oss_staging - - "litellm_**" - push: - branches: - - main - - litellm_internal_staging - -permissions: - contents: read - id-token: write - pull-requests: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - misc: - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: >- - tests/test_litellm/batches - tests/test_litellm/secret_managers - tests/test_litellm/a2a_protocol - tests/test_litellm/anthropic_interface - tests/test_litellm/completion_extras - tests/test_litellm/compression - tests/test_litellm/containers - tests/test_litellm/experimental_mcp_client - tests/test_litellm/models - tests/test_litellm/repositories - tests/test_litellm/images - tests/test_litellm/interactions - tests/test_litellm/ocr - tests/test_litellm/passthrough - tests/test_litellm/rag - tests/test_litellm/realtime_api - tests/test_litellm/rerank_api - tests/test_litellm/sandbox - tests/test_litellm/test_router - tests/test_litellm/vector_stores - tests/test_litellm/videos - tests/test_litellm/test_*.py - workers: 2 - reruns: 2 - artifact-name: misc diff --git a/.github/workflows/test-unit-proxy-auth.yml b/.github/workflows/test-unit-proxy-auth.yml deleted file mode 100644 index c27fe16d611..00000000000 --- a/.github/workflows/test-unit-proxy-auth.yml +++ /dev/null @@ -1,31 +0,0 @@ -name: "Unit Tests: Proxy Auth & Key Management" - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - litellm_oss_staging - - "litellm_**" - push: - branches: - - main - - litellm_internal_staging - -permissions: - contents: read - id-token: write - pull-requests: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - proxy-auth: - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: "tests/test_litellm/proxy/auth tests/test_litellm/proxy/hooks tests/test_litellm/proxy/policy_engine tests/test_litellm/proxy/client" - workers: 2 - reruns: 2 - artifact-name: proxy-auth diff --git a/.github/workflows/test-unit-proxy-db.yml b/.github/workflows/test-unit-proxy-db.yml index 93fc314462e..3725e0f5805 100644 --- a/.github/workflows/test-unit-proxy-db.yml +++ b/.github/workflows/test-unit-proxy-db.yml @@ -42,11 +42,10 @@ concurrency: # pinning the whole file to one worker (the default --dist=loadscope # behavior for single-file targets). jobs: - # Fast guard — fails the workflow if a test_*.py file under - # tests/proxy_unit_tests/ is not referenced by any matrix entry below. - # The semantic-shard design (no catch-all "remaining" bucket) relies on - # every test file being explicitly assigned; this guard prevents a new - # file from silently dropping out of CI. + # Fast guard — fails the workflow when a test directory or file inside a sharded + # tree is claimed by no shard. The semantic-shard design has no catch-all bucket, + # so an unassigned child runs nowhere; assert_ci_coverage.py holds the tree list + # and reads the same test-path keys the coverage census does. assert-shard-coverage: runs-on: ubuntu-latest timeout-minutes: 2 @@ -56,31 +55,8 @@ jobs: - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 with: persist-credentials: false - - name: Assert every test_*.py is in a matrix shard - run: | - python3 - <<'PY' - import pathlib, sys, yaml - wf = yaml.safe_load(open(".github/workflows/test-unit-proxy-db.yml")) - matrix = wf["jobs"]["proxy-db"]["strategy"]["matrix"]["include"] - referenced = set() - for entry in matrix: - for token in entry["test-path"].split(): - if token.startswith("tests/proxy_unit_tests/"): - referenced.add(pathlib.PurePosixPath(token).name) - actual = {p.name for p in pathlib.Path("tests/proxy_unit_tests").iterdir() - if p.name.startswith("test_") and (p.suffix == ".py" or p.is_dir()) - and p.name != "test_configs"} - orphans = sorted(actual - referenced) - if orphans: - print("ERROR: the following files/dirs under tests/proxy_unit_tests/") - print(" are not assigned to any shard in test-unit-proxy-db.yml:") - for o in orphans: - print(f" - {o}") - print() - print("Add each to whichever semantic shard it belongs to.") - sys.exit(1) - print(f"OK: all {len(actual)} files assigned to a shard.") - PY + - name: Assert every test directory and file is claimed by a shard + run: python3 .github/scripts/assert_ci_coverage.py --shards proxy-db: needs: assert-shard-coverage diff --git a/.github/workflows/test-unit-proxy-endpoints.yml b/.github/workflows/test-unit-proxy-endpoints.yml deleted file mode 100644 index 3d1d0fcd6c3..00000000000 --- a/.github/workflows/test-unit-proxy-endpoints.yml +++ /dev/null @@ -1,81 +0,0 @@ -name: "Unit Tests: Proxy API Endpoints" - -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: - proxy-endpoints: - permissions: - contents: read - id-token: write - pull-requests: write - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: >- - tests/test_litellm/proxy/analytics_endpoints - tests/test_litellm/proxy/management_endpoints - tests/test_litellm/proxy/memory - tests/test_litellm/proxy/guardrails - tests/test_litellm/proxy/management_helpers - tests/test_litellm/proxy/anthropic_endpoints - tests/test_litellm/proxy/google_endpoints - tests/test_litellm/proxy/openai_files_endpoint - tests/test_litellm/proxy/batches_endpoints - tests/test_litellm/proxy/fine_tuning_endpoints - tests/test_litellm/proxy/vector_store_files_endpoints - tests/test_litellm/proxy/video_endpoints - tests/test_litellm/proxy/response_api_endpoints - tests/test_litellm/proxy/image_endpoints - tests/test_litellm/proxy/ocr_endpoints - tests/test_litellm/proxy/vector_store_endpoints - tests/test_litellm/proxy/agent_endpoints - tests/test_litellm/proxy/a2a - tests/test_litellm/proxy/credential_endpoints - tests/test_litellm/proxy/discovery_endpoints - tests/test_litellm/proxy/health_endpoints - tests/test_litellm/proxy/shutdown - tests/test_litellm/proxy/public_endpoints - tests/test_litellm/proxy/prompts - tests/test_litellm/proxy/rag_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 - reruns: 2 - artifact-name: proxy-endpoints - - # Behavior-pinning tests for litellm/proxy/proxy_server.py. Owns its - # own job (not a path on the proxy-endpoints job above) so its budget - # is independent and its coverage artifact is uploaded separately. - # See: https://www.notion.so/36c43b8acdab81ee845fd5365128a2fc - proxy-server: - permissions: - contents: read - id-token: write - pull-requests: write - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: tests/test_litellm/proxy/proxy_server - workers: 4 - reruns: 2 - timeout-minutes: 60 - job-timeout-minutes: 95 - artifact-name: proxy-server diff --git a/.github/workflows/test-unit-proxy-infra.yml b/.github/workflows/test-unit-proxy-infra.yml deleted file mode 100644 index 83d95463cdf..00000000000 --- a/.github/workflows/test-unit-proxy-infra.yml +++ /dev/null @@ -1,42 +0,0 @@ -name: "Unit Tests: Proxy Infrastructure" - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - litellm_oss_staging - - "litellm_**" - push: - branches: - - main - - litellm_internal_staging - -permissions: - contents: read - id-token: write - pull-requests: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - proxy-infra: - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: >- - tests/test_litellm/proxy/db - tests/test_litellm/proxy/middleware - tests/test_litellm/proxy/spend_tracking - tests/test_litellm/proxy/pass_through_endpoints - tests/test_litellm/proxy/_experimental - tests/test_litellm/proxy/experimental - tests/test_litellm/proxy/common_utils - tests/test_litellm/proxy/enterprise_billing - tests/test_litellm/proxy/types_utils - tests/test_litellm/proxy/logging_endpoints - tests/test_litellm/proxy/test_*.py - workers: 2 - reruns: 2 - artifact-name: proxy-infra diff --git a/.github/workflows/test-unit-responses-caching-types.yml b/.github/workflows/test-unit-responses-caching-types.yml deleted file mode 100644 index 5b336452069..00000000000 --- a/.github/workflows/test-unit-responses-caching-types.yml +++ /dev/null @@ -1,31 +0,0 @@ -name: "Unit Tests: Responses, Caching & Types" - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - litellm_oss_staging - - "litellm_**" - push: - branches: - - main - - litellm_internal_staging - -permissions: - contents: read - id-token: write - pull-requests: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: ${{ github.event_name == 'pull_request' }} - -jobs: - responses-caching-types: - uses: ./.github/workflows/_test-unit-base.yml - with: - test-path: "tests/test_litellm/responses tests/test_litellm/caching tests/test_litellm/types" - workers: 2 - reruns: 2 - artifact-name: responses-caching-types diff --git a/.github/workflows/test-unit.yml b/.github/workflows/test-unit.yml new file mode 100644 index 00000000000..ed9d8800202 --- /dev/null +++ b/.github/workflows/test-unit.yml @@ -0,0 +1,251 @@ +name: "Unit 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' }} + +# One caller for every tests/test_litellm shard, replacing the nine thin workflow +# files that each wrapped a single call to _test-unit-base.yml. Adding a shard is +# now one matrix entry rather than a new file. +# +# `name` is the shard id and nothing else, so each check reports as +# " / Run tests" exactly as it did when the shard had its own file. Those +# strings are the branch ruleset's required contexts, so they are load-bearing: +# renaming an entry renames a required check and the ruleset stops matching it. +# +# Every entry states its timeouts even when they equal the base workflow's +# defaults. An absent matrix key renders as an empty string, which is not a +# number, so a partially-specified entry would fail the call rather than fall +# back to the default. +# +# tests/proxy_unit_tests keeps its own caller (test-unit-proxy-db.yml): it is +# already a matrix and carries a shard-coverage guard that reads that file by +# name. Folding it in here is a follow-up, together with generalising that guard +# into assert_ci_coverage.py. +jobs: + unit: + name: ${{ matrix.shard }} + permissions: + contents: read + id-token: write + pull-requests: write + strategy: + fail-fast: false + matrix: + include: + - shard: core-utils + artifact-name: core-utils + test-path: "tests/test_litellm/litellm_core_utils" + workers: 2 + reruns: 1 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: enterprise-routing + artifact-name: enterprise-routing + test-path: >- + tests/test_litellm/enterprise + tests/test_litellm/google_genai + tests/test_litellm/router_utils + tests/test_litellm/router_strategy + workers: 2 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: integrations + artifact-name: integrations + test-path: "tests/test_litellm/integrations" + workers: 2 + reruns: 3 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: Vertex AI + artifact-name: llm-vertex-ai + test-path: "tests/test_litellm/llms/vertex_ai" + workers: 1 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: All Other Providers + artifact-name: llm-other-providers + test-path: "tests/test_litellm/llms --ignore=tests/test_litellm/llms/vertex_ai" + workers: 2 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: misc + artifact-name: misc + test-path: >- + tests/test_litellm/batches + tests/test_litellm/secret_managers + tests/test_litellm/a2a_protocol + tests/test_litellm/anthropic_interface + tests/test_litellm/completion_extras + tests/test_litellm/compression + tests/test_litellm/containers + tests/test_litellm/experimental_mcp_client + tests/test_litellm/models + tests/test_litellm/repositories + tests/test_litellm/images + tests/test_litellm/interactions + tests/test_litellm/ocr + tests/test_litellm/passthrough + tests/test_litellm/rag + tests/test_litellm/realtime_api + tests/test_litellm/rerank_api + tests/test_litellm/rust_bridge + tests/test_litellm/sandbox + tests/test_litellm/test_router + tests/test_litellm/vector_stores + tests/test_litellm/videos + tests/test_litellm/test_*.py + workers: 2 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: proxy-auth + artifact-name: proxy-auth + test-path: >- + tests/test_litellm/proxy/auth + tests/test_litellm/proxy/hooks + tests/test_litellm/proxy/policy_engine + tests/test_litellm/proxy/client + workers: 2 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: proxy-endpoints + artifact-name: proxy-endpoints + 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 + tests/test_litellm/proxy/anthropic_endpoints + tests/test_litellm/proxy/google_endpoints + tests/test_litellm/proxy/openai_files_endpoint + tests/test_litellm/proxy/batches_endpoints + tests/test_litellm/proxy/fine_tuning_endpoints + tests/test_litellm/proxy/vector_store_files_endpoints + tests/test_litellm/proxy/video_endpoints + tests/test_litellm/proxy/response_api_endpoints + tests/test_litellm/proxy/image_endpoints + tests/test_litellm/proxy/ocr_endpoints + tests/test_litellm/proxy/vector_store_endpoints + tests/test_litellm/proxy/agent_endpoints + tests/test_litellm/proxy/a2a + tests/test_litellm/proxy/credential_endpoints + tests/test_litellm/proxy/discovery_endpoints + tests/test_litellm/proxy/health_endpoints + tests/test_litellm/proxy/shutdown + 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: 4 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: proxy-server + artifact-name: proxy-server + test-path: "tests/test_litellm/proxy/proxy_server" + workers: 4 + reruns: 2 + timeout-minutes: 60 + job-timeout-minutes: 100 + + - shard: proxy-infra + artifact-name: proxy-infra + test-path: >- + tests/test_litellm/proxy/db + tests/test_litellm/proxy/middleware + tests/test_litellm/proxy/spend_tracking + tests/test_litellm/proxy/pass_through_endpoints + tests/test_litellm/proxy/_experimental + tests/test_litellm/proxy/experimental + tests/test_litellm/proxy/common_utils + tests/test_litellm/proxy/enterprise_billing + 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: >- + tests/test_litellm/responses + tests/test_litellm/caching + tests/test_litellm/types + workers: 2 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + uses: ./.github/workflows/_test-unit-base.yml + with: + test-path: ${{ matrix.test-path }} + workers: ${{ matrix.workers }} + reruns: ${{ matrix.reruns }} + timeout-minutes: ${{ matrix.timeout-minutes }} + job-timeout-minutes: ${{ matrix.job-timeout-minutes }} + artifact-name: ${{ matrix.artifact-name }} diff --git a/.github/workflows/triage_rollout_heads_up.yml b/.github/workflows/triage_rollout_heads_up.yml deleted file mode 100644 index 903960151e2..00000000000 --- a/.github/workflows/triage_rollout_heads_up.yml +++ /dev/null @@ -1,92 +0,0 @@ -name: Agent Shin — rollout heads-up (one-shot) - -# Fires the 7-day heads-up comment on every open external PR/issue that the -# new triage bot would auto-close. The real sweep is a deliberate one-shot: -# trigger it at rollout via a manual `workflow_dispatch` with `dry_run=false`. -# The script is idempotent (skips items that already carry the -# `` marker), so a re-run is harmless. -# -# The automatic push trigger runs DRY-RUN only, so merging the script to -# `litellm_internal_staging` never posts a comment; it just confirms the -# workflow is wired up. Posting real comments requires the manual dispatch, -# which is also the only trigger that exposes `OPENAI_API_KEY`. The heads-up -# is intentionally NOT gated on `AGENT_SHIN_ENABLED`: it has to warn -# contributors while that flag is still off, ahead of the flip that turns on -# auto-closing. -# -# The workflow is a thin shell over `.github/scripts/triage_rollout_heads_up.py`. -# Dry-run vs. real run differ in EXACTLY one CLI flag (`--close`), added only -# on a manual dispatch with `dry_run=false`. - -on: - push: - branches: - - litellm_internal_staging - paths: - # The presence of this script on staging IS the rollout merge marker. - # Editing the file later would re-fire the workflow; that's safe because - # the script skips PRs/issues that already have the heads-up marker. - - ".github/scripts/triage_rollout_heads_up.py" - workflow_dispatch: - inputs: - dry_run: - description: "Dry run (true = preview only, false = actually post comments)." - required: false - default: "true" - type: choice - options: - - "true" - - "false" - -permissions: - contents: read - issues: write - pull-requests: write - -jobs: - heads-up: - if: github.repository == 'BerriAI/litellm' - runs-on: ubuntu-latest - steps: - - name: Checkout triage scripts - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 - with: - sparse-checkout: .github/scripts - persist-credentials: false - - - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: "3.12" - - - name: Install LLM client - run: pip install --no-cache-dir --require-hashes -r .github/scripts/triage-requirements.txt - - - name: Run heads-up sweep - env: - GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} - # Only the manual dispatch (the real-run trigger) needs the LLM key. - # The automatic push trigger runs dry-run and never posts, so it gets - # no key. Mirrors the sibling triage workflows, which expose the key - # only on an enabled/dispatched run rather than unconditionally. - OPENAI_API_KEY: ${{ github.event_name == 'workflow_dispatch' && secrets.OPENAI_API_KEY || '' }} - OPENAI_BASE_URL: ${{ vars.OPENAI_BASE_URL }} - TRIAGE_MODEL: ${{ vars.TRIAGE_MODEL }} - # The real run is a deliberate manual dispatch with dry_run=false. - # Use the EXACT "false" comparison so any unexpected input value - # fail-closes to dry-run (mirrors the AGENT_SHIN_ENABLED pattern in - # the sibling workflows). The automatic push trigger always stays - # dry-run, so merging the script never posts. - DRY_RUN_INPUT: ${{ github.event.inputs.dry_run }} - run: | - set -euo pipefail - ARGS=(--repo "${{ github.repository }}") - if [ "${GITHUB_EVENT_NAME:-}" = "workflow_dispatch" ] && [ "${DRY_RUN_INPUT:-true}" = "false" ]; then - ARGS+=(--close) - echo "::notice::Manual rollout dispatch with dry_run=false -> heads-up comments WILL be posted." - elif [ "${GITHUB_EVENT_NAME:-}" = "workflow_dispatch" ]; then - echo "::notice::Manual dispatch in dry-run mode -> previewing only, no comments will be posted." - else - echo "::notice::Automatic push trigger -> dry-run preview only. Fire the real rollout sweep with a manual workflow_dispatch (dry_run=false)." - fi - python3 .github/scripts/triage_rollout_heads_up.py "${ARGS[@]}" diff --git a/.github/workflows/weekly_load_anomaly.yml b/.github/workflows/weekly_load_anomaly.yml index 2dffc889d0e..3e1fca89645 100644 --- a/.github/workflows/weekly_load_anomaly.yml +++ b/.github/workflows/weekly_load_anomaly.yml @@ -47,6 +47,9 @@ jobs: 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 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..930825aeb89 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -37,13 +37,14 @@ 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 +66,8 @@ 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 +82,8 @@ 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..700b0d6525e 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:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 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 diff --git a/Makefile b/Makefile index 5e5f7c80027..e17fdba3c85 100644 --- a/Makefile +++ b/Makefile @@ -7,6 +7,7 @@ info lint lint-inner lint-dev lint-checks format \ lint-basedpyright lint-e2e-basedpyright lint-basedpyright-budget-update lint-type-discipline lint-type-discipline-budget-update \ lint-ruff-budget lint-ruff-budget-update lint-budget-update lint-gate \ + lint-test-quality lint-test-quality-budget-update \ install-dev install-proxy-dev install-test-deps install-hooks \ install-helm-unittest check-circular-imports check-import-safety check check-inner pre-commit \ lint-install lint-fetch-base bootstrap @@ -35,7 +36,8 @@ help: @echo " make lint-ruff-budget - Gate the codebase total of each strict ruff rule against its limit" @echo " make lint-gate - Strict ruff gate in CI-parity mode (fetches staging, simulates the merge)" @echo " make lint-ruff-budget-update - Ratchet ruff-strict-budget.json limits down by what this branch fixed" - @echo " make lint-budget-update - Ratchet all budgets down (ruff + type-discipline + basedpyright)" + @echo " make lint-test-quality - Gate the test suite against test-quality-budget.json" + @echo " make lint-budget-update - Ratchet all budgets down (ruff + type-discipline + test quality + basedpyright)" @echo " make check-circular-imports - Check for circular imports" @echo " make check-import-safety - Check import safety" @echo " make test - Run all tests" @@ -142,11 +144,13 @@ lint-install: $(UV) sync --inexact --frozen --group proxy-dev --group e2e-dev $(UV_RUN) python scripts/prisma_generate_if_needed.py -# Diff-scoped format check, identical to test-linting.yml's "Check ruff format" step: +# Diff-scoped format check, mirroring test-linting.yml's "Check ruff format" step: # only the litellm Python files changed vs the base are checked, so a pre-existing -# format issue elsewhere doesn't block an unrelated commit. +# format issue elsewhere doesn't block an unrelated commit. Git pathspecs match +# recursively, so 'litellm/*.py' covers nested modules and the top-level files that +# CI's 'litellm/**/*.py' skips, which makes this target a superset of the CI step. lint-format-check-changed: $(LINT_DEP_INSTALL) $(LINT_DEP_BASE) - @files=$$(git diff --name-only origin/litellm_internal_staging...HEAD -- 'litellm/**/*.py' | grep -v '^litellm/enterprise/' || true); \ + @files=$$(git diff --name-only --diff-filter=ACMR origin/litellm_internal_staging...HEAD -- 'litellm/*.py' | grep -v '^litellm/enterprise/' || true); \ if [ -z "$$files" ]; then \ echo "No changed litellm Python files to format-check."; \ else \ @@ -156,6 +160,7 @@ lint-format-check-changed: $(LINT_DEP_INSTALL) $(LINT_DEP_BASE) # Linting targets lint-ruff: $(LINT_DEP_INSTALL) cd litellm && $(UV_RUN) ruff check . && cd .. + $(UV_RUN) ruff check --config ruff-tests.toml tests # faster linter for developing ... # inspiration from: @@ -200,6 +205,12 @@ lint-e2e-basedpyright: $(LINT_E2E_DEP_INSTALL) lint-type-discipline: $(LINT_DEP_INSTALL) $(LINT_DEP_BASE) $(UV_RUN) python scripts/type_discipline_gate.py --base origin/litellm_internal_staging +# Test-quality budget (zero-assert / mock-echo tests, sys.path.insert, raw env writes, +# litellm module-global mutation, credential-gated skips, conftest snapshot +# inventory), counted across tests/ the same delta-vs-base way. +lint-test-quality: $(LINT_DEP_INSTALL) $(LINT_DEP_BASE) + $(UV_RUN) python scripts/test_quality_gate.py --base origin/litellm_internal_staging + # --update lowers each limit by what this branch fixed since its branch point, so # it needs the base ref fetched to resolve the merge-base. lint-basedpyright-budget-update: install-dev lint-fetch-base @@ -221,8 +232,11 @@ lint-ruff-budget-update: install-dev lint-fetch-base lint-type-discipline-budget-update: install-dev lint-fetch-base $(UV_RUN) python scripts/type_discipline_gate.py --update -# Ratchet all budgets in one shot (ruff strict + type-discipline + basedpyright) -lint-budget-update: lint-ruff-budget-update lint-type-discipline-budget-update lint-basedpyright-budget-update +lint-test-quality-budget-update: install-dev lint-fetch-base + $(UV_RUN) python scripts/test_quality_gate.py --update + +# Ratchet all budgets in one shot (ruff strict + type-discipline + test quality + basedpyright) +lint-budget-update: lint-ruff-budget-update lint-type-discipline-budget-update lint-test-quality-budget-update lint-basedpyright-budget-update check-circular-imports: $(LINT_DEP_INSTALL) cd litellm && $(UV_RUN) python ../tests/documentation_tests/test_circular_imports.py && cd .. @@ -244,7 +258,7 @@ lint: lint-inner: lint-install lint-fetch-base $(MAKE) -j $(LINT_JOBS) $(LINT_OUTPUT_SYNC) LINT_DEP_INSTALL= LINT_E2E_DEP_INSTALL= LINT_DEP_BASE= lint-checks -lint-checks: lint-format-check-changed lint-ruff lint-gate lint-type-discipline lint-basedpyright lint-e2e-basedpyright check-circular-imports check-import-safety +lint-checks: lint-format-check-changed lint-ruff lint-gate lint-type-discipline lint-test-quality lint-basedpyright lint-e2e-basedpyright check-circular-imports check-import-safety # Faster linting for local development (only checks changed code) lint-dev: lint-format-changed check-circular-imports check-import-safety @@ -314,7 +328,7 @@ test-unit-helm: install-helm-unittest # LLM Translation testing targets test-llm-translation: install-test-deps @echo "Running LLM translation tests..." - @python .github/workflows/run_llm_translation_tests.py + @python .github/scripts/run_llm_translation_tests.py test-llm-translation-single: install-test-deps @echo "Running single LLM translation test file..." diff --git a/README.md b/README.md index 32b0160dbaa..68aaa09ec98 100644 --- a/README.md +++ b/README.md @@ -292,6 +292,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [Clarifai (`clarifai`)](https://docs.litellm.ai/docs/providers/clarifai) | ✅ | ✅ | ✅ | | | | | | | | | [Cloudflare AI Workers (`cloudflare`)](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | | | | | | | | | [Codestral (`codestral`)](https://docs.litellm.ai/docs/providers/codestral) | ✅ | ✅ | ✅ | | | | | | | | +| [Cognition (`cognition`)](https://docs.litellm.ai/docs/providers/cognition) | ✅ | ✅ | ✅ | | | | | | | | | [Cohere (`cohere`)](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | | | | | | ✅ | | [Cohere Chat (`cohere_chat`)](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | | | | | | | | | [CometAPI (`cometapi`)](https://docs.litellm.ai/docs/providers/cometapi) | ✅ | ✅ | ✅ | ✅ | | | | | | | diff --git a/backend/Dockerfile b/backend/Dockerfile index 853c74b05ca..4ca40944606 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:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json index 58b8ae01cb0..3fa30f96dea 100644 --- a/basedpyright-code-budget.json +++ b/basedpyright-code-budget.json @@ -1,9 +1,9 @@ { "reportAny": { - "limit": 18773 + "limit": 15800 }, "reportArgumentType": { - "limit": 2566 + "limit": 2262 }, "reportAssignmentType": { "limit": 320 @@ -24,7 +24,7 @@ "limit": 19 }, "reportExplicitAny": { - "limit": 5774 + "limit": 5174 }, "reportFunctionMemberAccess": { "limit": 7 @@ -54,10 +54,10 @@ "limit": 0 }, "reportMissingParameterType": { - "limit": 5640 + "limit": 5590 }, "reportMissingTypeArgument": { - "limit": 15498 + "limit": 15327 }, "reportMissingTypeStubs": { "limit": 40 @@ -72,7 +72,7 @@ "limit": 0 }, "reportOptionalMemberAccess": { - "limit": 1061 + "limit": 0 }, "reportOptionalOperand": { "limit": 0 @@ -90,7 +90,7 @@ "limit": 8 }, "reportReturnType": { - "limit": 213 + "limit": 181 }, "reportTypedDictNotRequiredAccess": { "limit": 26 @@ -99,31 +99,31 @@ "limit": 0 }, "reportUnknownArgumentType": { - "limit": 44589 + "limit": 44375 }, "reportUnknownLambdaType": { "limit": 109 }, "reportUnknownMemberType": { - "limit": 38882 + "limit": 38487 }, "reportUnknownParameterType": { - "limit": 19806 + "limit": 19590 }, "reportUnknownVariableType": { - "limit": 30456 + "limit": 30126 }, "reportUnnecessaryCast": { - "limit": 116 + "limit": 113 }, "reportUnnecessaryComparison": { - "limit": 698 + "limit": 697 }, "reportUnnecessaryContains": { "limit": 5 }, "reportUnnecessaryIsInstance": { - "limit": 835 + "limit": 834 }, "reportUntypedBaseClass": { "limit": 0 @@ -138,9 +138,9 @@ "limit": 139 }, "reportUnusedImport": { - "limit": 544 + "limit": 541 }, "reportUnusedVariable": { - "limit": 142 + "limit": 133 } } diff --git a/ci_cd/generate_model_prices_schema.py b/ci_cd/generate_model_prices_schema.py index 252e3675329..57cc742d5c4 100644 --- a/ci_cd/generate_model_prices_schema.py +++ b/ci_cd/generate_model_prices_schema.py @@ -27,6 +27,7 @@ EXTRA_BOOLEAN_KEYS = frozenset( "uses_embed_content", "use_openai_responses_path", "bedrock_converse_supports_strict_tools", + "thinking_always_on", } ) @@ -72,6 +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).", @@ -156,6 +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.", @@ -211,6 +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/cookbook/litellm_proxy_server/cli_token_usage.py b/cookbook/litellm_proxy_server/cli_token_usage.py index 6306970cdde..e6b3744019c 100644 --- a/cookbook/litellm_proxy_server/cli_token_usage.py +++ b/cookbook/litellm_proxy_server/cli_token_usage.py @@ -60,4 +60,4 @@ if __name__ == "__main__": print("\n💡 Tips:") print("1. Run 'litellm-proxy login' to authenticate first") print("2. Replace 'https://your-proxy.com' with your actual proxy URL") - print("3. The token is stored locally at ~/.litellm/token.json") + print("3. The token is stored in your OS keychain, or in ~/.litellm/token.json when there is none") 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..f0d6d02fccf 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:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:42df77a9974d6ec8b17a5ee8bc23b532600a44d705acef2409e0933c1251b45f +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 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 diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 7392cc09a0d..4a5df6ecd69 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:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 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. diff --git a/enterprise/enterprise_hooks/banned_keywords.py b/enterprise/enterprise_hooks/banned_keywords.py index 47421c96051..6f6a37b6c55 100644 --- a/enterprise/enterprise_hooks/banned_keywords.py +++ b/enterprise/enterprise_hooks/banned_keywords.py @@ -21,6 +21,7 @@ from fastapi import HTTPException class _ENTERPRISE_BannedKeywords(CustomLogger): + enforces_request_content: bool = True # Class variables or attributes def __init__(self): banned_keywords_list = litellm.banned_keywords_list diff --git a/enterprise/enterprise_hooks/blocked_user_list.py b/enterprise/enterprise_hooks/blocked_user_list.py index d34605b30ac..a032ea7662d 100644 --- a/enterprise/enterprise_hooks/blocked_user_list.py +++ b/enterprise/enterprise_hooks/blocked_user_list.py @@ -18,6 +18,7 @@ from fastapi import HTTPException class _ENTERPRISE_BlockedUserList(CustomLogger): + enforces_request_content: bool = True # Class variables or attributes def __init__(self, prisma_client: Optional[PrismaClient]): self.prisma_client = prisma_client 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 a8e46349917..aee3295d1da 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -14,6 +14,8 @@ 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 @@ -255,6 +257,52 @@ class CheckBatchCost: "so it will no longer be polled" ) + async def _claim_job_for_costing(self, job: "LiteLLM_ManagedObjectTable") -> bool: + """ + Atomically flip batch_processed from false to true, returning whether this pod won + the row. Every pod and uvicorn worker schedules its own poller against the shared + table, so without this compare-and-swap two of them can select the same completed + batch in one window and both emit an aretrieve_batch spend log for it. Schemas + without the column can't be claimed, so they keep the pre-existing behavior. + + Called immediately before the spend log is written rather than before the results + fetch, because batch_processed is also what holds off deletion of the files that + fetch reads and what keeps an unbilled row selectable by the next poll cycle. + """ + if not self._has_batch_processed_column: + return True + try: + claimed: Final = await self.prisma_client.db.litellm_managedobjecttable.update_many( + where={"id": job.id, "batch_processed": False}, + data={"batch_processed": True}, + ) + except Exception as db_err: + verbose_proxy_logger.error( + f"CheckBatchCost: failed to claim job {job.id} for cost tracking: {db_err}" + ) + return False + return claimed > 0 + + async def _release_job_claim(self, job: "LiteLLM_ManagedObjectTable") -> None: + """Give a claimed row back once billing it failed, so a later poll cycle retries it. + + Safe to match on batch_processed=True: while this poller is active the retrieve + path leaves the column alone (batch_cost_poller_is_active), so a true value here + is always this pod's own claim. + """ + if not self._has_batch_processed_column: + return + try: + await self.prisma_client.db.litellm_managedobjecttable.update_many( + where={"id": job.id, "batch_processed": True}, + data={"batch_processed": False}, + ) + except Exception as db_err: + verbose_proxy_logger.error( + f"CheckBatchCost: failed to release the claim on job {job.id}, " + f"so its cost will not be retried: {db_err}" + ) + @staticmethod def _has_unified_id_without_model(job: "LiteLLM_ManagedObjectTable") -> bool: """A unified id that decodes but carries no model_id can never be routed.""" @@ -305,7 +353,7 @@ 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.""" @@ -572,9 +620,10 @@ class CheckBatchCost: """ Fetch a completed batch's results, compute cost/usage, and emit the aretrieve_batch spend log. Returns (model_name, llm_provider) on - success, None when the job can't be routed to a deployment. Raises on - results-fetch or cost-computation failures so the caller can leave the - job unprocessed and retry it on a later poll. + success, None when the job can't be routed to a deployment or when + another pod claimed it. Raises on results-fetch or cost-computation + failures so the caller can leave the job unprocessed and retry it on a + later poll. """ from litellm.batches.batch_utils import ( _get_file_content_as_dictionary, @@ -743,12 +792,23 @@ class CheckBatchCost: optional_params={}, ) - await logging_obj.async_success_handler( - result=response, - batch_cost=batch_cost, - batch_usage=batch_usage, - batch_models=batch_models, - ) + if not await self._claim_job_for_costing(job): + verbose_proxy_logger.info( + f"CheckBatchCost: batch {batch_id} (job {job.id}) was claimed by another pod " + "in this window, so its cost is already being tracked there" + ) + return None + + try: + await logging_obj.async_success_handler( + result=response, + batch_cost=batch_cost, + batch_usage=batch_usage, + batch_models=batch_models, + ) + except Exception: + await self._release_job_claim(job) + raise # Record batch duration (completed_at - created_at) if prom_logger and response.completed_at and response.created_at: @@ -908,6 +968,16 @@ 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..39f8de0b0cc 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -45,6 +45,8 @@ 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 +56,8 @@ 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 +67,9 @@ from litellm.types.llms.openai import ( # pyright: ignore[reportAttributeAccess AsyncCursorPage, ChatCompletionFileObject, CreateFileRequest, + FileListPage, FileObject, OpenAIFileObject, - OpenAIFilesPurpose, ResponsesAPIResponse, ) from litellm.types.utils import ( @@ -144,7 +148,14 @@ 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: ... @@ -1365,12 +1376,76 @@ 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: """ diff --git a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py index 579f203554e..5ec482c385a 100644 --- a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py @@ -12,7 +12,7 @@ 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 @@ -35,6 +35,8 @@ if TYPE_CHECKING: LiteLLM_VerificationTokenActions, ) + from litellm import Router + router = APIRouter() @@ -205,6 +207,114 @@ 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, @@ -352,7 +462,9 @@ async def new_project( ``` """ from litellm.proxy.proxy_server import ( + general_settings, litellm_proxy_admin_name, + llm_router, premium_user, prisma_client, ) @@ -399,6 +511,10 @@ 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( @@ -538,7 +654,9 @@ 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 +760,12 @@ 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 diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index bb580c82760..3653aba67ef 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.57" +version = "0.1.61" 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 = "0.1.57" +version = "0.1.61" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/gateway/Dockerfile b/gateway/Dockerfile index 223df524d7c..4a2e32e186e 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:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:a31344ab2cb8618db84f535eec56f76f6178b142cb92cb2e48676cc2dcebea72 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin diff --git a/helm/litellm-helm/templates/deployment.yaml b/helm/litellm-helm/templates/deployment.yaml index 32bfa4b2647..52ffd117535 100644 --- a/helm/litellm-helm/templates/deployment.yaml +++ b/helm/litellm-helm/templates/deployment.yaml @@ -100,6 +100,13 @@ spec: - name: DATABASE_URL value: {{ .Values.db.url | quote }} {{- end }} + {{- if and .Values.db.useExisting .Values.db.readReplicaUrl .Values.db.secret.readReplicaEndpointKey (not .Values.db.secret.readReplicaUrlKey) }} + - name: DATABASE_READER_HOST + valueFrom: + secretKeyRef: + name: {{ .Values.db.secret.name }} + key: {{ .Values.db.secret.readReplicaEndpointKey }} + {{- end }} {{- if and .Values.db.useExisting .Values.db.secret.readReplicaUrlKey }} - name: DATABASE_URL_READ_REPLICA valueFrom: diff --git a/helm/litellm-helm/tests/deployment_tests.yaml b/helm/litellm-helm/tests/deployment_tests.yaml index b11c445889e..ee946038202 100644 --- a/helm/litellm-helm/tests/deployment_tests.yaml +++ b/helm/litellm-helm/tests/deployment_tests.yaml @@ -80,6 +80,96 @@ tests: secretKeyRef: name: my-secret key: my-key + - it: should inject DATABASE_READER_HOST from readReplicaEndpointKey before DATABASE_URL_READ_REPLICA + template: deployment.yaml + set: + db: + deployStandalone: false + useExisting: true + secret: + name: postgres + usernameKey: username + passwordKey: password + readReplicaEndpointKey: reader-host + readReplicaUrl: postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_READER_HOST):5432/$(DATABASE_NAME)?sslmode=require + asserts: + - contains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_READER_HOST + valueFrom: + secretKeyRef: + name: postgres + key: reader-host + - contains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_URL_READ_REPLICA + value: postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_READER_HOST):5432/$(DATABASE_NAME)?sslmode=require + # $(VAR) interpolation only resolves vars defined EARLIER in the env + # array, so the reader host must precede the composed URL + - equal: + path: spec.template.spec.containers[0].env[7].name + value: DATABASE_READER_HOST + - equal: + path: spec.template.spec.containers[0].env[8].name + value: DATABASE_URL_READ_REPLICA + - it: should omit reader host when readReplicaUrl is unset + template: deployment.yaml + set: + db: + deployStandalone: false + useExisting: true + secret: + name: postgres + usernameKey: username + passwordKey: password + readReplicaEndpointKey: reader-host + asserts: + - notContains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_READER_HOST + valueFrom: + secretKeyRef: + name: postgres + key: reader-host + - it: should prefer readReplicaUrlKey over readReplicaEndpointKey composition + template: deployment.yaml + set: + db: + useExisting: true + secret: + name: postgres + usernameKey: username + passwordKey: password + readReplicaUrlKey: reader-url + readReplicaEndpointKey: reader-host + readReplicaUrl: postgresql://ignored + asserts: + - contains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_URL_READ_REPLICA + valueFrom: + secretKeyRef: + name: postgres + key: reader-url + - notContains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_URL_READ_REPLICA + value: postgresql://ignored + # the unused reader-host secret ref must be suppressed so a missing + # key can't fail pod creation + - notContains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_READER_HOST + valueFrom: + secretKeyRef: + name: postgres + key: reader-host - it: should work with extraEnvVars template: deployment.yaml set: diff --git a/helm/litellm-helm/values.yaml b/helm/litellm-helm/values.yaml index 4ef8fc97b27..f8df98de102 100644 --- a/helm/litellm-helm/values.yaml +++ b/helm/litellm-helm/values.yaml @@ -277,6 +277,14 @@ db: # written to db.readReplicaUrl ends up visible in the rendered pod spec # and the Helm release secret. readReplicaUrlKey: "" + # Optional: when set, a DATABASE_READER_HOST env var is sourced from this + # secret key, so db.readReplicaUrl can compose the reader URL from + # individual secret components, e.g. + # postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_READER_HOST):5432/$(DATABASE_NAME) + # Use this when your secret store holds the bare reader hostname rather + # than a full connection URL. Only takes effect when readReplicaUrl is + # set; ignored when readReplicaUrlKey is set. + readReplicaEndpointKey: "" # Optional read-replica routing. When set, the proxy sends read-only # queries (find_*, count, group_by, query_raw/_first) to this URL while diff --git a/helm/litellm/templates/_helpers.tpl b/helm/litellm/templates/_helpers.tpl index bffd627393a..72f7f74bcf6 100644 --- a/helm/litellm/templates/_helpers.tpl +++ b/helm/litellm/templates/_helpers.tpl @@ -213,18 +213,21 @@ whenever the password contains a URL-reserved character (@, /, ?, %, +, When `database.writer.useIAMAuth: true`, the chart injects IAM_TOKEN_DB_AUTH=true and omits DATABASE_PASSWORD — the entrypoint mints -the URL from DATABASE_HOST/PORT/USER/NAME plus a short-lived IAM token -instead of a static password. +the URL from DATABASE_HOST/PORT/USER/NAME plus a short-lived AWS RDS IAM +token instead of a static password. `database.writer.useAzureEntraAuth: true` +does the same with AZURE_POSTGRESQL_AUTH=true and a Microsoft Entra ID token, +for Azure Database for PostgreSQL. The two are mutually exclusive. The read replica is opt-in via `database.reader.host`. The chart emits DATABASE_HOST_READ_REPLICA / DATABASE_PORT_READ_REPLICA / DATABASE_NAME_READ_REPLICA (+ DATABASE_SCHEMA_READ_REPLICA) for both auth modes, plus DATABASE_USER_READ_REPLICA / DATABASE_PASSWORD_READ_REPLICA for -password auth. When `database.reader.useIAMAuth: true` it omits +password auth. When `database.reader.useIAMAuth: true` (or +`database.reader.useAzureEntraAuth: true`) it omits DATABASE_PASSWORD_READ_REPLICA and the entrypoint mints the reader URL the -same way. Reader IAM only takes effect when the writer also uses IAM auth -(the proxy gates URL minting on IAM_TOKEN_DB_AUTH, which only the writer -sets). +same way. Reader token auth only takes effect when the writer uses the same +token source, since the proxy gates URL minting on the single global +IAM_TOKEN_DB_AUTH / AZURE_POSTGRESQL_AUTH toggle that only the writer sets. */}} {{- define "litellm.serverEnv" -}} {{- $root := .root -}} @@ -254,9 +257,15 @@ sets). - name: DATABASE_SCHEMA value: {{ .schema | quote }} {{- end }} +{{- if and .useIAMAuth .useAzureEntraAuth }} +{{- fail "database.writer.useIAMAuth and database.writer.useAzureEntraAuth are mutually exclusive: the database password can only come from one token source" }} +{{- end }} {{- if .useIAMAuth }} - name: IAM_TOKEN_DB_AUTH value: "true" +{{- else if .useAzureEntraAuth }} +- name: AZURE_POSTGRESQL_AUTH + value: "true" {{- else }} - name: DATABASE_PASSWORD valueFrom: @@ -270,6 +279,9 @@ sets). {{- if and .useIAMAuth (not $root.Values.database.writer.useIAMAuth) }} {{- fail "database.reader.useIAMAuth requires database.writer.useIAMAuth: true (the proxy gates IAM URL minting on IAM_TOKEN_DB_AUTH, which is only set by the writer)" }} {{- end }} +{{- if and .useAzureEntraAuth (not $root.Values.database.writer.useAzureEntraAuth) }} +{{- fail "database.reader.useAzureEntraAuth requires database.writer.useAzureEntraAuth: true (the proxy gates Entra URL minting on AZURE_POSTGRESQL_AUTH, which is only set by the writer)" }} +{{- end }} - name: DATABASE_HOST_READ_REPLICA value: {{ .host | quote }} - name: DATABASE_PORT_READ_REPLICA @@ -280,7 +292,7 @@ sets). - name: DATABASE_SCHEMA_READ_REPLICA value: {{ .schema | quote }} {{- end }} -{{- if .useIAMAuth }} +{{- if or .useIAMAuth .useAzureEntraAuth }} {{- if .passwordSecret.name }} - name: DATABASE_USER_READ_REPLICA valueFrom: diff --git a/helm/litellm/tests/database_auth_tests.yaml b/helm/litellm/tests/database_auth_tests.yaml new file mode 100644 index 00000000000..adbe14c59c2 --- /dev/null +++ b/helm/litellm/tests/database_auth_tests.yaml @@ -0,0 +1,116 @@ +suite: test database token auth env vars +templates: + - gateway/deployment.yaml + - gateway/configmap.yaml + - backend/deployment.yaml + - backend/configmap.yaml +values: + - ./values/required.yaml +tests: + - it: writer emits DATABASE_PASSWORD and no token toggle by default + template: gateway/deployment.yaml + asserts: + - contains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_PASSWORD + valueFrom: + secretKeyRef: + name: litellm-writer-secret + key: password + any: true + - notContains: + path: spec.template.spec.containers[0].env + content: + name: IAM_TOKEN_DB_AUTH + value: "true" + any: true + - notContains: + path: spec.template.spec.containers[0].env + content: + name: AZURE_POSTGRESQL_AUTH + value: "true" + any: true + + - it: writer emits AZURE_POSTGRESQL_AUTH and omits DATABASE_PASSWORD under Entra auth + template: gateway/deployment.yaml + set: + database.writer.useAzureEntraAuth: true + asserts: + - contains: + path: spec.template.spec.containers[0].env + content: + name: AZURE_POSTGRESQL_AUTH + value: "true" + any: true + - notContains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_PASSWORD + any: true + - notContains: + path: spec.template.spec.containers[0].env + content: + name: IAM_TOKEN_DB_AUTH + value: "true" + any: true + + - it: backend gets the same Entra toggle as the gateway + template: backend/deployment.yaml + set: + database.writer.useAzureEntraAuth: true + asserts: + - contains: + path: spec.template.spec.containers[0].env + content: + name: AZURE_POSTGRESQL_AUTH + value: "true" + any: true + + - it: writer rejects both token sources at once + template: gateway/deployment.yaml + set: + database.writer.useIAMAuth: true + database.writer.useAzureEntraAuth: true + asserts: + - failedTemplate: + errorMessage: "database.writer.useIAMAuth and database.writer.useAzureEntraAuth are mutually exclusive: the database password can only come from one token source" + + - it: reader Entra auth without writer Entra auth is rejected + template: gateway/deployment.yaml + set: + database.reader.host: reader.example.com + database.reader.dbname: litellm + database.reader.useAzureEntraAuth: true + asserts: + - failedTemplate: + errorMessage: "database.reader.useAzureEntraAuth requires database.writer.useAzureEntraAuth: true (the proxy gates Entra URL minting on AZURE_POSTGRESQL_AUTH, which is only set by the writer)" + + - it: reader under Entra auth omits DATABASE_PASSWORD_READ_REPLICA + template: gateway/deployment.yaml + set: + database.writer.useAzureEntraAuth: true + database.reader.host: reader.example.com + database.reader.dbname: litellm + database.reader.useAzureEntraAuth: true + asserts: + - contains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_HOST_READ_REPLICA + value: reader.example.com + any: true + - contains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_USER_READ_REPLICA + valueFrom: + secretKeyRef: + name: litellm-reader-secret + key: username + any: true + - notContains: + path: spec.template.spec.containers[0].env + content: + name: DATABASE_PASSWORD_READ_REPLICA + any: true diff --git a/helm/litellm/values.yaml b/helm/litellm/values.yaml index 3f8aacfce17..06ba72d84b3 100644 --- a/helm/litellm/values.yaml +++ b/helm/litellm/values.yaml @@ -145,6 +145,8 @@ database: dbname: "" schema: "" useIAMAuth: false + # Azure Database for PostgreSQL with a Microsoft Entra ID token; mutually exclusive with useIAMAuth + useAzureEntraAuth: false passwordSecret: name: litellm-writer-secret usernameKey: username @@ -159,6 +161,8 @@ database: dbname: "" schema: "" useIAMAuth: false + # Azure Database for PostgreSQL with a Microsoft Entra ID token; mutually exclusive with useIAMAuth + useAzureEntraAuth: false passwordSecret: name: litellm-reader-secret usernameKey: username @@ -424,9 +428,11 @@ 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/20260819000000_backfill_spend_log_timestamps/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260819000000_backfill_spend_log_timestamps/migration.sql deleted file mode 100644 index 10003afa9db..00000000000 --- a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260819000000_backfill_spend_log_timestamps/migration.sql +++ /dev/null @@ -1,4 +0,0 @@ -UPDATE "LiteLLM_SpendLogs" -SET "created_at" = "endTime", - "updated_at" = "endTime" -WHERE "created_at" > "endTime" + interval '1 hour'; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260819000000_shadow_eval_max_budget/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260819000000_shadow_eval_max_budget/migration.sql new file mode 100644 index 00000000000..7b60dca9415 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260819000000_shadow_eval_max_budget/migration.sql @@ -0,0 +1,5 @@ +-- AlterTable +ALTER TABLE "LiteLLM_ShadowEvalJob" ADD COLUMN "max_budget" DOUBLE PRECISION; + +-- AlterTable +ALTER TABLE "LiteLLM_ShadowEvalAttempt" ADD COLUMN "shadow_cost" DOUBLE PRECISION NOT NULL DEFAULT 0; 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/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 60058c777ca..2bb850139a2 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -754,6 +754,7 @@ 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 +790,7 @@ 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 +826,7 @@ 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 +861,7 @@ 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 +896,7 @@ 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 +934,7 @@ 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) @@ -1502,7 +1508,8 @@ model LiteLLM_ShadowEvalJob { baseline_model String? // reverse only: the fixed model the router is judged against judge_model String shadow_percentage Float - max_turns Int // this key's sample budget: judge at most this many turns + 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 created_at DateTime @default(now()) created_by String? ends_at DateTime @@ -1525,12 +1532,28 @@ model LiteLLM_ShadowEvalAttempt { 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..b2dc0a52c8f 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/utils.py +++ b/litellm-proxy-extras/litellm_proxy_extras/utils.py @@ -40,6 +40,65 @@ 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 +414,24 @@ 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 +452,7 @@ 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 +461,16 @@ 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 +497,55 @@ 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: + 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 +664,8 @@ 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: @@ -972,6 +1109,8 @@ 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"], diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index e1d62b70c29..0ef5cd1e856 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.87" +version = "0.4.90" 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 = "0.4.87" +version = "0.4.90" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", 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 8b6896f3846..0c9faeda6e7 100644 --- a/litellm-rust/crates/ai-gateway/src/audio_transcription/hooks.rs +++ b/litellm-rust/crates/ai-gateway/src/audio_transcription/hooks.rs @@ -295,6 +295,8 @@ fn core_error_kind(error: &CoreError) -> &'static str { CoreError::Http { .. } => "HttpError", CoreError::InvalidResponse(_) => "InvalidResponse", CoreError::Network(_) => "NetworkError", + CoreError::Connect(_) => "ConnectError", CoreError::Routing(_) => "RoutingError", + CoreError::Unsupported(_) => "UnsupportedRequest", } } diff --git a/litellm-rust/crates/ai-gateway/src/ocr/hooks.rs b/litellm-rust/crates/ai-gateway/src/ocr/hooks.rs index ffe2e0122c0..95df566dc53 100644 --- a/litellm-rust/crates/ai-gateway/src/ocr/hooks.rs +++ b/litellm-rust/crates/ai-gateway/src/ocr/hooks.rs @@ -324,6 +324,8 @@ fn core_error_kind(error: &CoreError) -> &'static str { CoreError::Http { .. } => "HttpError", CoreError::InvalidResponse(_) => "InvalidResponse", CoreError::Network(_) => "NetworkError", + CoreError::Connect(_) => "ConnectError", CoreError::Routing(_) => "RoutingError", + CoreError::Unsupported(_) => "UnsupportedRequest", } } 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 a34b2edd7b8..7e38d10c6ff 100644 --- a/litellm-rust/crates/ai-gateway/src/routes/messages/mod.rs +++ b/litellm-rust/crates/ai-gateway/src/routes/messages/mod.rs @@ -105,12 +105,20 @@ impl IntoResponse for MessagesRouteError { ), CoreError::Http { .. } | CoreError::Network(_) + | CoreError::Connect(_) | CoreError::InvalidResponse(_) | CoreError::InvalidType { .. } | CoreError::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) => ( + StatusCode::BAD_REQUEST, + format!("messages request is not supported: {reason}"), + ), }; ( status, diff --git a/litellm-rust/crates/core/src/chat_completions/client.rs b/litellm-rust/crates/core/src/chat_completions/client.rs new file mode 100644 index 00000000000..f2ef73ed030 --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/client.rs @@ -0,0 +1,15 @@ +use std::sync::OnceLock; +use std::time::Duration; + +use crate::constants::{CHAT_COMPLETIONS_CONNECT_TIMEOUT_SECS, CHAT_COMPLETIONS_TIMEOUT_SECS}; + +pub(super) fn http_client() -> &'static reqwest::Client { + static CLIENT: OnceLock = OnceLock::new(); + CLIENT.get_or_init(|| { + reqwest::Client::builder() + .timeout(Duration::from_secs(CHAT_COMPLETIONS_TIMEOUT_SECS)) + .connect_timeout(Duration::from_secs(CHAT_COMPLETIONS_CONNECT_TIMEOUT_SECS)) + .build() + .unwrap_or_else(|_| reqwest::Client::new()) + }) +} diff --git a/litellm-rust/crates/core/src/chat_completions/common_utils.rs b/litellm-rust/crates/core/src/chat_completions/common_utils.rs new file mode 100644 index 00000000000..36eaf242a5a --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/common_utils.rs @@ -0,0 +1,28 @@ +use serde_json::{Map, Value}; + +use crate::error::CoreResult; +use crate::http_utils::string_headers as shared_string_headers; +use crate::providers::anthropic::chat_completions::transformation::ANTHROPIC_CHAT_COMPLETIONS_CONFIG; + +use super::transformation::ChatCompletionsProviderConfig; + +const HEADER_CONTEXT: &str = "chat completions"; + +pub(super) fn chat_completions_provider_config( + provider: &str, +) -> Option<&'static dyn ChatCompletionsProviderConfig> { + match provider { + "anthropic" => Some(&ANTHROPIC_CHAT_COMPLETIONS_CONFIG), + #[cfg(feature = "bedrock-auth")] + "bedrock" => Some( + &crate::providers::bedrock::chat_completions::transformation::BEDROCK_CHAT_COMPLETIONS_CONFIG, + ), + _ => None, + } +} + +pub(super) fn string_headers( + extra_headers: Option>, +) -> CoreResult> { + shared_string_headers(HEADER_CONTEXT, extra_headers) +} diff --git a/litellm-rust/crates/core/src/chat_completions/conversation.rs b/litellm-rust/crates/core/src/chat_completions/conversation.rs new file mode 100644 index 00000000000..f7bdc60af37 --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/conversation.rs @@ -0,0 +1,254 @@ +//! Provider-neutral conversation shape. +//! +//! Both Anthropic Messages and Bedrock Converse want the same thing out of an +//! OpenAI message list: the system prompt lifted out, consecutive same-role +//! turns merged, and text blocks that are never empty. That normalization is +//! shared here so a provider config only renders the result into its own wire +//! shape. +//! +//! Mirrors Python's `anthropic_messages_pt` / +//! `_bedrock_converse_messages_pt` for the text-only surface this route +//! accepts; anything richer is declined upstream by the capability gate. + +use crate::constants::EMPTY_TEXT_PLACEHOLDER; + +use super::types::{ChatMessage, ChatMessageContent}; + +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub enum TurnRole { + User, + Assistant, +} + +impl TurnRole { + pub fn as_str(self) -> &'static str { + match self { + Self::User => "user", + Self::Assistant => "assistant", + } + } +} + +#[derive(Clone, Debug, PartialEq, Eq)] +pub struct Turn { + pub role: TurnRole, + pub texts: Vec, +} + +#[derive(Clone, Debug, Default, PartialEq, Eq)] +pub struct Conversation { + pub system: Vec, + pub turns: Vec, +} + +/// True when the conversation can be sent as-is. +/// +/// Python inserts a placeholder first user turn only under +/// `litellm.modify_params`, which the core cannot see, so a conversation that +/// does not open on a user turn is declined rather than guessed at. +impl Conversation { + pub fn opens_on_user_turn(&self) -> bool { + self.turns + .first() + .is_some_and(|turn| turn.role == TurnRole::User) + } +} + +fn message_texts(content: &ChatMessageContent) -> Vec { + match content { + ChatMessageContent::Text(text) => vec![text.clone()], + ChatMessageContent::Parts(parts) => parts + .iter() + .filter_map(|part| part.get("text").and_then(|text| text.as_str())) + .map(str::to_string) + .collect(), + } +} + +/// Python rewrites empty or whitespace-only text rather than dropping it, so an +/// entirely empty content list never reaches a provider that rejects one. +fn sanitize(text: String) -> String { + if text.trim().is_empty() { + return EMPTY_TEXT_PLACEHOLDER.to_string(); + } + text +} + +pub fn build_conversation(messages: &[ChatMessage]) -> Conversation { + let system = messages + .iter() + .filter(|message| message.role == "system") + .filter_map(|message| message.content.as_ref()) + .flat_map(message_texts) + .filter(|text| !text.is_empty()) + .collect(); + + let turns = messages + .iter() + .filter(|message| message.role != "system") + .fold(Vec::::new(), |mut turns, message| { + let role = if message.role == "assistant" { + TurnRole::Assistant + } else { + TurnRole::User + }; + let texts = message + .content + .as_ref() + .map(message_texts) + .unwrap_or_default() + .into_iter() + .map(sanitize); + match turns.last_mut() { + Some(last) if last.role == role => last.texts.extend(texts), + _ => turns.push(Turn { + role, + texts: texts.collect(), + }), + } + turns + }); + + // Anthropic and Bedrock both reject trailing whitespace on the final + // assistant turn, so Python right-strips it there; mirror that exactly. + let turns = match turns.split_last() { + Some((last, rest)) if last.role == TurnRole::Assistant => rest + .iter() + .cloned() + .chain([Turn { + role: last.role, + texts: last + .texts + .iter() + .map(|text| text.trim_end().to_string()) + .collect(), + }]) + .collect(), + _ => turns, + }; + + Conversation { system, turns } +} + +#[cfg(test)] +mod tests { + use super::*; + use serde_json::json; + + fn messages(value: serde_json::Value) -> Vec { + serde_json::from_value(value).expect("valid messages") + } + + #[test] + fn lifts_system_messages_out_of_the_turn_list() { + let conversation = build_conversation(&messages(json!([ + {"role": "system", "content": "be terse"}, + {"role": "user", "content": "hi"} + ]))); + assert_eq!(conversation.system, vec!["be terse".to_string()]); + assert_eq!( + conversation.turns, + vec![Turn { + role: TurnRole::User, + texts: vec!["hi".to_string()] + }] + ); + } + + #[test] + fn merges_consecutive_same_role_turns() { + let conversation = build_conversation(&messages(json!([ + {"role": "user", "content": "one"}, + {"role": "user", "content": "two"}, + {"role": "assistant", "content": "ack"}, + {"role": "user", "content": "three"} + ]))); + assert_eq!( + conversation.turns, + vec![ + Turn { + role: TurnRole::User, + texts: vec!["one".to_string(), "two".to_string()] + }, + Turn { + role: TurnRole::Assistant, + texts: vec!["ack".to_string()] + }, + Turn { + role: TurnRole::User, + texts: vec!["three".to_string()] + }, + ] + ); + } + + #[test] + fn flattens_text_parts_in_order() { + let conversation = build_conversation(&messages(json!([ + {"role": "user", "content": [ + {"type": "text", "text": "first"}, + {"type": "text", "text": "second"} + ]} + ]))); + assert_eq!( + conversation.turns[0].texts, + vec!["first".to_string(), "second".to_string()] + ); + } + + #[test] + fn rewrites_empty_and_whitespace_only_text_to_the_python_placeholder() { + let conversation = build_conversation(&messages(json!([ + {"role": "user", "content": ""}, + {"role": "assistant", "content": " "}, + {"role": "user", "content": "real"} + ]))); + assert_eq!(conversation.turns[0].texts, vec![EMPTY_TEXT_PLACEHOLDER]); + assert_eq!(conversation.turns[1].texts, vec![EMPTY_TEXT_PLACEHOLDER]); + } + + #[test] + fn right_strips_only_the_final_assistant_turn() { + let conversation = build_conversation(&messages(json!([ + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": "kept "}, + {"role": "user", "content": "more"}, + {"role": "assistant", "content": "stripped "} + ]))); + assert_eq!(conversation.turns[1].texts, vec!["kept ".to_string()]); + assert_eq!(conversation.turns[3].texts, vec!["stripped".to_string()]); + } + + #[test] + fn does_not_strip_when_the_last_turn_is_a_user_turn() { + let conversation = build_conversation(&messages(json!([ + {"role": "assistant", "content": "kept "}, + {"role": "user", "content": "hi "} + ]))); + assert_eq!(conversation.turns[0].texts, vec!["kept ".to_string()]); + assert_eq!(conversation.turns[1].texts, vec!["hi ".to_string()]); + } + + #[test] + fn reports_whether_the_conversation_opens_on_a_user_turn() { + assert!( + build_conversation(&messages(json!([{"role": "user", "content": "hi"}]))) + .opens_on_user_turn() + ); + assert!( + !build_conversation(&messages(json!([{"role": "assistant", "content": "hi"}]))) + .opens_on_user_turn() + ); + assert!(!Conversation::default().opens_on_user_turn()); + } + + #[test] + fn drops_empty_system_text_the_way_python_skips_empty_system_blocks() { + let conversation = build_conversation(&messages(json!([ + {"role": "system", "content": ""}, + {"role": "system", "content": "kept"}, + {"role": "user", "content": "hi"} + ]))); + assert_eq!(conversation.system, vec!["kept".to_string()]); + } +} diff --git a/litellm-rust/crates/core/src/chat_completions/handler.rs b/litellm-rust/crates/core/src/chat_completions/handler.rs new file mode 100644 index 00000000000..afc4529fd26 --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/handler.rs @@ -0,0 +1,147 @@ +use serde_json::Value; + +use crate::error::{CoreError, CoreResult}; +use crate::http_utils::truncate_error_body; + +use super::client::http_client; +use super::transformation::ChatCompletionsAuth; +use super::types::{ + ChatCompletionsResponse, ProviderChatCompletionsRequest, ProviderChatResponseData, +}; + +pub(super) async fn execute_chat_completions_provider_call( + request: ProviderChatCompletionsRequest, +) -> CoreResult { + let body = serde_json::to_vec(&request.body).map_err(|err| { + CoreError::InvalidRequest(format!( + "failed to serialize chat completions request: {err}" + )) + })?; + let headers = signed_headers(&request, &body).await?; + + let mut request_builder = http_client().post(&request.url).body(body); + for (key, value) in &headers { + request_builder = request_builder.header(key, value); + } + if let Some(duration) = request.timeout { + request_builder = request_builder.timeout(duration); + } + + let response = request_builder.send().await.map_err(|err| { + // Failing to establish the connection means the request never went out, + // 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()) + } else { + CoreError::Network(err.to_string()) + } + })?; + + let status = response.status(); + let text = response + .text() + .await + .map_err(|err| CoreError::Network(err.to_string()))?; + + if !status.is_success() { + return Err(CoreError::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}")) + })?; + request + .config + .transform_response(&request.model, ProviderChatResponseData { body }) + .map_err(as_response_error) +} + +/// Re-tag an error raised while normalizing a response the provider already +/// returned. +/// +/// A config reports the same variants on either side of the call: a missing +/// field or an unsupported block can mean "this request cannot be translated" +/// during prepare and "this response cannot be normalized" here. Only the +/// 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 { + match err { + already @ (CoreError::InvalidResponse(_) | CoreError::Http { .. }) => already, + other => CoreError::InvalidResponse(other.to_string()), + } +} + +#[cfg(feature = "bedrock-auth")] +pub(super) async fn signed_headers( + request: &ProviderChatCompletionsRequest, + body: &[u8], +) -> CoreResult> { + use std::collections::BTreeMap; + use std::time::SystemTime; + + use crate::providers::bedrock::aws_base::{ + aws_auth_config, aws_signature_headers, host_supplied_credentials, + is_sigv4_computed_header, resolve_credentials, sign_bedrock_post, + }; + + let ChatCompletionsAuth::AwsSigV4 { region } = &request.auth else { + return Ok(request.upstream_headers.clone()); + }; + // Reattaching a header the signer also emits would put both copies on the + // wire, and Bedrock rejects that pair. Python instead drops the caller's + // copy and prefers a forwarded Authorization over the signature, so leave + // the request to Python rather than serving it a different way here. + if request + .upstream_headers + .iter() + .any(|(name, _)| is_sigv4_computed_header(name)) + { + return Err(CoreError::Unsupported( + "request forwards a header AWS SigV4 computes", + )); + } + let env_lookup = |key: &str| std::env::var(key).ok(); + let unsigned: BTreeMap = request.upstream_headers.iter().cloned().collect(); + // A host with its own resolution chain hands the result down; only fall + // back to deriving credentials here when it supplied none. + let credentials = match host_supplied_credentials(&request.optional_params) { + Some(credentials) => credentials, + None => { + resolve_credentials( + aws_auth_config(&request.optional_params, &env_lookup), + &env_lookup, + ) + .await? + } + }; + let signature = sign_bedrock_post( + &request.url, + body, + &aws_signature_headers(&unsigned), + region, + &credentials, + SystemTime::now(), + )?; + // Every original header goes back on the wire alongside the computed ones, + // as Python reattaches them. The guard above already rejected the names + // that would collide, so no name appears twice. + Ok(unsigned.into_iter().chain(signature).collect()) +} + +#[cfg(not(feature = "bedrock-auth"))] +pub(super) async fn signed_headers( + request: &ProviderChatCompletionsRequest, + _body: &[u8], +) -> CoreResult> { + match &request.auth { + ChatCompletionsAuth::AwsSigV4 { .. } => Err(CoreError::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 new file mode 100644 index 00000000000..f30ac1a24bf --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/mod.rs @@ -0,0 +1,59 @@ +//! The `/chat/completions` call, the Rust equivalent of Python's +//! `litellm.completion()`. +//! +//! [`chat_completions`] is the top-level entrypoint: give it a model, the +//! OpenAI-shaped message list, the provider-mapped optional params, and +//! credentials, and it resolves the provider, translates the conversation, +//! calls the provider, and returns a typed OpenAI-shaped response. + +mod client; +mod common_utils; +pub mod conversation; +pub(crate) mod handler; +mod prepare; +pub mod response_utils; +pub mod transformation; +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 { + execute_chat_completions_provider_call(prepare_chat_completions_call(request)?).await +} + +/// Whether the core would accept this request, without resolving credentials or +/// touching the network. +/// +/// A host that keeps the Python implementation asks this first so it can emit +/// its pre-call logging exactly once, on whichever path is about to run. +/// Returns the decline reason, or `None` when the request is accepted. +pub fn chat_completions_decline_reason( + model: &str, + custom_llm_provider: Option<&str>, + messages: Value, + optional_params: &Map, +) -> Option<&'static str> { + let Ok((_, config)) = resolve_provider_config(model, custom_llm_provider) else { + return Some("provider is not on the rust chat completions path"); + }; + let Ok(messages) = parse_messages(messages) else { + return Some("unreadable message list"); + }; + if messages.is_empty() { + return Some("empty message list"); + } + config + .unsupported_reason(&messages, optional_params) + .map(|reason| reason.0) +} + +#[cfg(test)] +mod tests; diff --git a/litellm-rust/crates/core/src/chat_completions/prepare.rs b/litellm-rust/crates/core/src/chat_completions/prepare.rs new file mode 100644 index 00000000000..1e1c8d1bafd --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/prepare.rs @@ -0,0 +1,118 @@ +use serde_json::Value; + +use crate::error::{CoreError, CoreResult}; +use crate::http_utils::has_header; +use crate::routing_utils::provider::{CustomLlmProvider, get_custom_llm_provider}; + +use super::common_utils::{chat_completions_provider_config, string_headers}; +use super::transformation::{ChatCompletionsAuth, ChatCompletionsProviderConfig}; +use super::types::{ChatCompletionsRequest, ChatMessage, ProviderChatCompletionsRequest}; + +pub(super) fn resolve_provider_config<'a>( + model: &'a str, + custom_llm_provider: Option<&'a str>, +) -> CoreResult<(String, &'static dyn ChatCompletionsProviderConfig)> { + let provider_info = get_custom_llm_provider(model, custom_llm_provider) + .or_else(|| { + custom_llm_provider.map(|provider| CustomLlmProvider { + model, + custom_llm_provider: provider, + }) + }) + .ok_or_else(|| { + CoreError::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((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 prepare_chat_completions_call( + request: ChatCompletionsRequest<'_>, +) -> CoreResult { + 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( + "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)); + } + + let mut headers = string_headers(request.extra_headers)?; + let auth = config.auth( + request.api_key, + &model, + &request.optional_params, + &env_lookup, + )?; + match &auth { + ChatCompletionsAuth::Header { name, value } => { + // The deployment's credential replaces whatever the caller forwarded + // under the same name, mirroring Python's + // `{**headers, **anthropic_headers}`: letting a request header win + // would let its sender choose the principal the call bills to. + // + // The exception is a scheme the provider hands off to entirely, such + // as an Anthropic OAuth bearer, where Python drops `x-api-key` + // instead of resolving one. Re-adding it there would put the + // credential into a header the host removed on purpose. + if !config.defers_to_forwarded_auth(&headers) { + headers.retain(|(header, _)| !header.eq_ignore_ascii_case(name)); + headers.push(((*name).to_string(), value.clone())); + } + } + ChatCompletionsAuth::Bearer { token } => { + // Bedrock's `get_request_headers` assigns `headers["Authorization"]` + // unconditionally once a bearer token resolves, so the deployment's + // identity outranks whatever the caller forwarded. Keeping the + // caller's would bill and authorize the call as a different + // principal than the same deployment uses on Python. + // + // The `Header` arm below keeps the opposite precedence on purpose: + // Anthropic's transform honours a forwarded OAuth bearer. + headers.retain(|(name, _)| !name.eq_ignore_ascii_case("authorization")); + headers.push(("authorization".to_string(), format!("Bearer {token}"))); + } + // SigV4 signs the serialized body, so the handler adds its headers. + ChatCompletionsAuth::AwsSigV4 { .. } => {} + } + + for (name, value) in config.default_headers() { + if !has_header(&headers, name) { + headers.push(((*name).to_string(), (*value).to_string())); + } + } + + let url = config.complete_url( + request.api_base, + &model, + &request.optional_params, + &env_lookup, + )?; + let transformed = + config.transform_request(&model, messages, request.optional_params.clone())?; + + Ok(ProviderChatCompletionsRequest { + model, + config, + url, + body: transformed.body, + upstream_headers: headers, + auth, + optional_params: request.optional_params, + timeout: request.timeout, + }) +} diff --git a/litellm-rust/crates/core/src/chat_completions/response_utils.rs b/litellm-rust/crates/core/src/chat_completions/response_utils.rs new file mode 100644 index 00000000000..1ada5d43980 --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/response_utils.rs @@ -0,0 +1,101 @@ +//! Response normalization shared by every chat completions provider config. + +use std::time::{SystemTime, UNIX_EPOCH}; + +use super::types::{ChatCompletionsUsage, PromptTokensDetails}; + +/// OpenAI finish reasons, mirroring Python's `_FINISH_REASON_MAP` for the +/// reasons the providers on this route can emit. Python warns and falls back to +/// `stop` for anything unmapped, so do the same. +const FINISH_REASONS: &[(&str, &str)] = &[ + ("end_turn", "stop"), + ("stop_sequence", "stop"), + ("max_tokens", "length"), + ("refusal", "content_filter"), + ("compaction", "length"), + ("guardrail_intervened", "content_filter"), + ("content_filtered", "content_filter"), + ("content_filter", "content_filter"), + ("stop", "stop"), + ("length", "length"), +]; + +pub fn finish_reason_for(provider_reason: &str) -> &'static str { + FINISH_REASONS + .iter() + .find(|(reason, _)| *reason == provider_reason) + .map_or("stop", |(_, mapped)| *mapped) +} + +/// Python folds cache tokens into `prompt_tokens` and reports the split under +/// `prompt_tokens_details`; mirror that so cost tracking agrees on both paths. +pub fn usage_from_parts( + input_tokens: u64, + output_tokens: u64, + cache_read_tokens: u64, + cache_creation_tokens: u64, +) -> ChatCompletionsUsage { + let prompt_tokens = input_tokens + cache_read_tokens + cache_creation_tokens; + ChatCompletionsUsage { + prompt_tokens, + completion_tokens: output_tokens, + total_tokens: prompt_tokens + output_tokens, + prompt_tokens_details: PromptTokensDetails { + cached_tokens: cache_read_tokens, + cache_creation_tokens, + text_tokens: input_tokens, + }, + } +} + +pub fn unix_now() -> u64 { + SystemTime::now() + .duration_since(UNIX_EPOCH) + .map_or(0, |elapsed| elapsed.as_secs()) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn maps_every_reason_the_route_can_observe() { + assert_eq!(finish_reason_for("end_turn"), "stop"); + assert_eq!(finish_reason_for("stop_sequence"), "stop"); + assert_eq!(finish_reason_for("max_tokens"), "length"); + assert_eq!(finish_reason_for("refusal"), "content_filter"); + assert_eq!(finish_reason_for("guardrail_intervened"), "content_filter"); + // Converse emits these two, and folding them into `stop` would report a + // filtered completion as a normal one. + assert_eq!(finish_reason_for("content_filtered"), "content_filter"); + assert_eq!(finish_reason_for("content_filter"), "content_filter"); + } + + #[test] + fn defaults_an_unmapped_reason_to_stop_like_python() { + // Python warns and falls back to `stop` for a reason its own map does + // not carry, so only a reason absent from `_FINISH_REASON_MAP` belongs + // here. + assert_eq!(finish_reason_for("something_new"), "stop"); + assert_eq!(finish_reason_for(""), "stop"); + } + + #[test] + fn folds_cache_tokens_into_prompt_tokens() { + let usage = usage_from_parts(10, 4, 7, 3); + assert_eq!(usage.prompt_tokens, 20); + assert_eq!(usage.completion_tokens, 4); + assert_eq!(usage.total_tokens, 24); + assert_eq!(usage.prompt_tokens_details.cached_tokens, 7); + assert_eq!(usage.prompt_tokens_details.cache_creation_tokens, 3); + assert_eq!(usage.prompt_tokens_details.text_tokens, 10); + } + + #[test] + fn reports_raw_input_tokens_when_no_cache_is_involved() { + let usage = usage_from_parts(12, 5, 0, 0); + assert_eq!(usage.prompt_tokens, 12); + assert_eq!(usage.total_tokens, 17); + assert_eq!(usage.prompt_tokens_details.text_tokens, 12); + } +} diff --git a/litellm-rust/crates/core/src/chat_completions/tests.rs b/litellm-rust/crates/core/src/chat_completions/tests.rs new file mode 100644 index 00000000000..e2383723cb0 --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/tests.rs @@ -0,0 +1,820 @@ +use serde_json::{Map, Value, json}; + +use crate::error::CoreError; + +use super::prepare::prepare_chat_completions_call; +use super::transformation::ChatCompletionsAuth; +use super::types::ChatCompletionsRequest; + +fn request<'a>( + model: &'a str, + provider: Option<&'a str>, + messages: Value, + optional_params: Value, +) -> ChatCompletionsRequest<'a> { + ChatCompletionsRequest { + model, + messages, + optional_params: match optional_params { + Value::Object(map) => map, + other => panic!("params must be an object, got {other}"), + }, + api_key: Some("sk-test"), + api_base: None, + custom_llm_provider: provider, + extra_headers: None, + timeout: None, + } +} + +/// `ProviderChatCompletionsRequest` deliberately has no `Debug` (its headers +/// carry resolved credentials), so unwrap the failure case by hand. +fn decline(request: ChatCompletionsRequest<'_>) -> CoreError { + match prepare_chat_completions_call(request) { + Err(error) => error, + Ok(prepared) => panic!("expected a decline, prepared a call to {}", prepared.url), + } +} + +#[test] +fn resolves_the_provider_from_the_model_prefix() { + let prepared = prepare_chat_completions_call(request( + "anthropic/claude-sonnet-4-5", + None, + json!([{"role": "user", "content": "hi"}]), + json!({"max_tokens": 16}), + )) + .expect("prepares"); + assert_eq!(prepared.model, "claude-sonnet-4-5"); + assert_eq!(prepared.url, "https://api.anthropic.com/v1/messages"); + assert_eq!(prepared.body["model"], json!("claude-sonnet-4-5")); +} + +#[test] +fn strips_an_explicit_provider_prefix_from_the_model() { + let prepared = prepare_chat_completions_call(request( + "anthropic/claude-sonnet-4-5", + Some("anthropic"), + json!([{"role": "user", "content": "hi"}]), + json!({}), + )) + .expect("prepares"); + assert_eq!(prepared.model, "claude-sonnet-4-5"); +} + +#[test] +fn adds_the_auth_and_default_headers() { + let prepared = prepare_chat_completions_call(request( + "claude-sonnet-4-5", + Some("anthropic"), + json!([{"role": "user", "content": "hi"}]), + json!({}), + )) + .expect("prepares"); + assert!( + prepared + .upstream_headers + .contains(&("x-api-key".to_string(), "sk-test".to_string())) + ); + assert!( + prepared + .upstream_headers + .contains(&("anthropic-version".to_string(), "2023-06-01".to_string())) + ); + assert!(matches!( + prepared.auth, + ChatCompletionsAuth::Header { + name: "x-api-key", + .. + } + )); +} + +#[test] +fn the_deployment_credential_replaces_a_caller_supplied_auth_header() { + // Python builds `{**headers, **anthropic_headers}`, so the deployment's key + // overwrites a forwarded one. Honouring the caller's would let whoever sends + // the request choose the Anthropic principal it bills to. + let mut call = request( + "claude-sonnet-4-5", + Some("anthropic"), + json!([{"role": "user", "content": "hi"}]), + json!({}), + ); + call.extra_headers = Some(Map::from_iter([( + "X-Api-Key".to_string(), + json!("sk-caller"), + )])); + let prepared = prepare_chat_completions_call(call).expect("prepares"); + let keys: Vec<_> = prepared + .upstream_headers + .iter() + .filter(|(name, _)| name.eq_ignore_ascii_case("x-api-key")) + .collect(); + assert_eq!(keys.len(), 1, "got {:?}", prepared.upstream_headers); + assert_eq!(keys[0].1, "sk-test"); +} + +#[test] +fn a_forwarded_authorization_header_suppresses_the_resolved_api_key_header() { + // Anthropic's `validate_environment` pops `x-api-key` and sets `authorization` + // for an OAuth token, so re-adding the key here would put the credential into + // a header the host removed on purpose. + let mut call = request( + "claude-sonnet-4-5", + Some("anthropic"), + json!([{"role": "user", "content": "hi"}]), + json!({}), + ); + call.extra_headers = Some(Map::from_iter([ + ( + "Authorization".to_string(), + json!("Bearer sk-ant-oat01-token"), + ), + ("X-Api-Key".to_string(), json!("sk-caller")), + ])); + let prepared = prepare_chat_completions_call(call).expect("prepares"); + assert!( + !prepared + .upstream_headers + .iter() + .any(|(name, value)| name.eq_ignore_ascii_case("x-api-key") && value == "sk-test"), + "the resolved key must not be applied over an OAuth bearer, got {:?}", + prepared.upstream_headers + ); + assert!( + prepared + .upstream_headers + .iter() + .any(|(name, value)| name.eq_ignore_ascii_case("authorization") + && value == "Bearer sk-ant-oat01-token") + ); +} + +#[test] +fn an_unrelated_forwarded_authorization_does_not_defer_the_resolved_key() { + // Only an OAuth bearer replaces the credential. Python sends the deployment's + // `x-api-key` alongside any other forwarded `authorization`, so deferring on + // the mere presence of that header would drop the deployment's auth. + let mut call = request( + "claude-sonnet-4-5", + Some("anthropic"), + json!([{"role": "user", "content": "hi"}]), + json!({}), + ); + call.extra_headers = Some(Map::from_iter([ + ("Authorization".to_string(), json!("Bearer unrelated")), + ("X-Api-Key".to_string(), json!("sk-caller")), + ])); + let prepared = prepare_chat_completions_call(call).expect("prepares"); + let keys: Vec<_> = prepared + .upstream_headers + .iter() + .filter(|(name, _)| name.eq_ignore_ascii_case("x-api-key")) + .collect(); + assert_eq!(keys.len(), 1, "got {:?}", prepared.upstream_headers); + assert_eq!(keys[0].1, "sk-test"); + assert!( + prepared + .upstream_headers + .iter() + .any(|(name, value)| name.eq_ignore_ascii_case("authorization") + && value == "Bearer unrelated"), + "the unrelated authorization must survive, got {:?}", + prepared.upstream_headers + ); +} + +#[test] +fn declines_an_unsupported_request_before_resolving_credentials() { + let mut call = request( + "claude-sonnet-4-5", + Some("anthropic"), + json!([{"role": "user", "content": "hi"}]), + json!({"stream": true}), + ); + 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")); +} + +#[test] +fn rejects_an_unknown_provider() { + assert_eq!( + decline(request( + "openai/gpt-4o", + None, + json!([{"role": "user", "content": "hi"}]), + json!({}), + )), + CoreError::InvalidProvider("openai".to_string()) + ); +} + +#[test] +fn rejects_a_model_with_no_resolvable_provider() { + assert!(matches!( + decline(request( + "claude-sonnet-4-5", + None, + json!([{"role": "user", "content": "hi"}]), + json!({}), + )), + CoreError::InvalidProvider(_) + )); +} + +#[test] +fn rejects_an_empty_or_malformed_message_list() { + assert_eq!( + decline(request( + "anthropic/claude-sonnet-4-5", + None, + json!([]), + json!({}), + )), + CoreError::InvalidRequest("chat completions requires at least one message".to_string()) + ); + assert!(matches!( + decline(request( + "anthropic/claude-sonnet-4-5", + None, + json!("not a list"), + json!({}), + )), + CoreError::InvalidRequest(_) + )); +} + +#[test] +fn rejects_non_string_extra_headers() { + let mut call = request( + "anthropic/claude-sonnet-4-5", + None, + json!([{"role": "user", "content": "hi"}]), + json!({}), + ); + call.extra_headers = Some(Map::from_iter([("x-trace".to_string(), json!(7))])); + assert_eq!( + decline(call), + CoreError::InvalidRequest( + "chat completions extra_headers.x-trace must be a string, got number".to_string() + ) + ); +} + +#[cfg(feature = "bedrock-auth")] +#[test] +fn prepares_a_bedrock_call_without_resolving_credentials() { + let mut call = request( + "bedrock/us-east-1/anthropic.claude-v2", + None, + json!([{"role": "user", "content": "hi"}]), + json!({"maxTokens": 16}), + ); + call.api_key = None; + let prepared = prepare_chat_completions_call(call).expect("prepares"); + assert_eq!( + prepared.url, + "https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-v2/converse" + ); + assert_eq!( + prepared.auth, + ChatCompletionsAuth::AwsSigV4 { + region: "us-east-1".to_string() + } + ); + // SigV4 signs the serialized body, so prepare must not have added an + // Authorization header; the handler does it. + assert!( + !prepared + .upstream_headers + .iter() + .any(|(name, _)| name.eq_ignore_ascii_case("authorization")) + ); + assert_eq!(prepared.body["inferenceConfig"], json!({"maxTokens": 16})); +} + +#[cfg(feature = "bedrock-auth")] +#[tokio::test] +async fn a_forwarded_client_header_does_not_enter_the_bedrock_signature() { + // Python signs only the AWS header set and reattaches the rest, so a header + // the caller forwarded rides along without joining the canonical request. + // Signing it makes Converse 403 on a deployment that works on Python. + let mut call = request( + "bedrock/us-east-1/anthropic.claude-v2", + None, + json!([{"role": "user", "content": "hi"}]), + json!({ + "maxTokens": 16, + "aws_access_key_id": "AKIDEXAMPLE", + "aws_secret_access_key": "wJalrXUtnFEMI/K7MDENG+bPxRfiCYEXAMPLEKEY" + }), + ); + // A key would resolve to a bearer token and never reach the signer. + call.api_key = None; + call.extra_headers = Some(Map::from_iter([( + "x-request-id".to_string(), + json!("abc-123"), + )])); + let prepared = prepare_chat_completions_call(call).expect("prepares"); + let signed = super::handler::signed_headers(&prepared, br#"{"a":1}"#) + .await + .expect("signs"); + + let authorization = signed + .iter() + .find(|(name, _)| name.eq_ignore_ascii_case("authorization")) + .map(|(_, value)| value.clone()) + .expect("carries an authorization header"); + assert!( + authorization.starts_with("AWS4-HMAC-SHA256"), + "expected a SigV4 signature, got {authorization}" + ); + assert!( + !authorization.contains("x-request-id"), + "forwarded header reached SignedHeaders: {authorization}" + ); + // It still goes on the wire, it is just not part of the signature. + assert!( + signed + .iter() + .any(|(name, value)| name == "x-request-id" && value == "abc-123"), + "forwarded header was dropped instead of reattached" + ); +} + +#[cfg(feature = "bedrock-auth")] +#[tokio::test] +async fn a_forwarded_header_the_signer_computes_declines_to_python() { + // Reattaching the caller's copy next to the computed one puts the name on + // the wire twice and Bedrock rejects the pair, so a request carrying one + // has to go to Python instead of being signed here. + for forwarded in [ + "Authorization", + "x-amz-date", + "x-amz-security-token", + "Date", + ] { + let mut call = request( + "bedrock/us-east-1/anthropic.claude-v2", + None, + json!([{"role": "user", "content": "hi"}]), + json!({ + "maxTokens": 16, + "aws_access_key_id": "AKIDEXAMPLE", + "aws_secret_access_key": "wJalrXUtnFEMI/K7MDENG+bPxRfiCYEXAMPLEKEY" + }), + ); + call.api_key = None; + call.extra_headers = Some(Map::from_iter([(forwarded.to_string(), json!("forged"))])); + let prepared = prepare_chat_completions_call(call).expect("prepares"); + let error = super::handler::signed_headers(&prepared, br#"{"a":1}"#) + .await + .expect_err("{forwarded} should decline instead of being signed"); + assert!( + matches!(error, CoreError::Unsupported(_)), + "{forwarded} declined as {error:?}, which the host would not fall back on" + ); + } +} + +#[cfg(feature = "bedrock-auth")] +#[test] +fn a_bedrock_deployment_bearer_outranks_a_forwarded_authorization() { + // `get_request_headers` assigns `headers["Authorization"]` unconditionally + // once a bearer token resolves, so the deployment's identity wins on + // Python. Keeping the caller's would authorize and bill the call as a + // different principal, and only when the deployment carries `rust: true`. + let mut call = request( + "bedrock/us-east-1/anthropic.claude-v2", + None, + json!([{"role": "user", "content": "hi"}]), + json!({"maxTokens": 16}), + ); + call.extra_headers = Some(Map::from_iter([( + "Authorization".to_string(), + json!("Bearer caller-supplied"), + )])); + let prepared = prepare_chat_completions_call(call).expect("prepares"); + let authorizations: Vec<_> = prepared + .upstream_headers + .iter() + .filter(|(name, _)| name.eq_ignore_ascii_case("authorization")) + .map(|(_, value)| value.as_str()) + .collect(); + assert_eq!( + authorizations, + vec!["Bearer sk-test"], + "the deployment token must be the only authorization on the wire" + ); +} + +#[test] +fn an_anthropic_forwarded_oauth_bearer_still_outranks_the_resolved_key() { + // The opposite precedence, and deliberate: Anthropic's own transform + // honours a forwarded OAuth bearer, so the Bedrock fix above must not be + // generalized into a rule that the configured key always wins. + // + // An OAuth bearer is the whole of that exception. This forwarded a plain + // `x-api-key` until round 17, which read as the same claim and was not: + // Python overwrites a forwarded `x-api-key` with the deployment's. + let mut call = request( + "claude-sonnet-4-5", + Some("anthropic"), + json!([{"role": "user", "content": "hi"}]), + json!({}), + ); + call.extra_headers = Some(Map::from_iter([( + "authorization".to_string(), + json!("Bearer sk-ant-oat01-forwarded"), + )])); + let prepared = prepare_chat_completions_call(call).expect("prepares"); + let keys: Vec<_> = prepared + .upstream_headers + .iter() + .filter(|(name, _)| name.eq_ignore_ascii_case("x-api-key")) + .map(|(_, value)| value.as_str()) + .collect(); + assert!(keys.is_empty(), "got {:?}", prepared.upstream_headers); + assert!( + prepared + .upstream_headers + .iter() + .any(|(name, value)| name.eq_ignore_ascii_case("authorization") + && value == "Bearer sk-ant-oat01-forwarded") + ); +} + +#[cfg(feature = "bedrock-auth")] +#[test] +fn a_bedrock_api_key_is_sent_as_a_bearer_token_instead_of_being_signed() { + // The configured bearer identity has its own account and quota boundary, + // so a request carrying one must not be signed as whatever principal the + // host's AWS credentials resolve to. + let prepared = prepare_chat_completions_call(request( + "bedrock/us-east-1/anthropic.claude-v2", + None, + json!([{"role": "user", "content": "hi"}]), + json!({"maxTokens": 16}), + )) + .expect("prepares"); + assert_eq!( + prepared.auth, + ChatCompletionsAuth::Bearer { + token: "sk-test".to_string() + } + ); + assert!( + prepared + .upstream_headers + .iter() + .any(|(name, value)| name.eq_ignore_ascii_case("authorization") + && value == "Bearer sk-test"), + "prepare did not carry the bearer token" + ); +} + +fn decline_reason( + model: &str, + provider: Option<&str>, + messages: Value, + params: Value, +) -> Option<&'static str> { + let params = match params { + Value::Object(map) => map, + other => panic!("params must be an object, got {other}"), + }; + super::chat_completions_decline_reason(model, provider, messages, ¶ms) +} + +#[test] +fn the_gate_accepts_what_prepare_accepts() { + assert_eq!( + decline_reason( + "anthropic/claude-sonnet-4-5", + None, + json!([{"role": "user", "content": "hi"}]), + json!({"max_tokens": 16}), + ), + None + ); +} + +#[test] +fn the_gate_declines_without_resolving_credentials_or_calling_out() { + assert_eq!( + decline_reason( + "anthropic/claude-sonnet-4-5", + None, + json!([{"role": "user", "content": "hi"}]), + json!({"stream": true}), + ), + Some("streaming") + ); + assert_eq!( + decline_reason( + "openai/gpt-4o", + None, + json!([{"role": "user", "content": "hi"}]), + json!({}), + ), + Some("provider is not on the rust chat completions path") + ); + assert_eq!( + decline_reason( + "claude-sonnet-4-5", + None, + json!([{"role": "user", "content": "hi"}]), + json!({}), + ), + Some("provider is not on the rust chat completions path") + ); + assert_eq!( + decline_reason( + "anthropic/claude-sonnet-4-5", + None, + json!("nope"), + json!({}) + ), + Some("unreadable message list") + ); + assert_eq!( + decline_reason("anthropic/claude-sonnet-4-5", None, json!([]), json!({})), + Some("empty message list") + ); +} + +#[test] +fn the_gate_agrees_with_prepare_on_every_case_it_accepts() { + // A gate that accepts what prepare then declines would make the host emit + // its pre-call logging on a path that falls back, so pin the agreement. + for (messages, params) in [ + ( + json!([{"role": "user", "content": "hi"}]), + json!({"max_tokens": 8}), + ), + ( + json!([{"role": "system", "content": "s"}, {"role": "user", "content": "hi"}]), + json!({"temperature": 0.1}), + ), + ( + json!([{"role": "user", "content": "hi"}, {"role": "assistant", "content": "yo"}]), + json!({}), + ), + ] { + assert_eq!( + decline_reason( + "anthropic/claude-sonnet-4-5", + None, + messages.clone(), + params.clone() + ), + None, + "gate declined {messages}" + ); + prepare_chat_completions_call(request( + "anthropic/claude-sonnet-4-5", + None, + messages.clone(), + params, + )) + .unwrap_or_else(|error| panic!("prepare declined {messages}: {error}")); + } +} + +mod round_trip { + use super::*; + use tokio::io::{AsyncReadExt, AsyncWriteExt}; + use tokio::net::{TcpListener, TcpStream}; + + use crate::chat_completions::chat_completions; + + async fn read_http_request(socket: &mut TcpStream) -> String { + let mut request = Vec::new(); + let mut buffer = [0_u8; 1024]; + let header_end = loop { + let n = socket.read(&mut buffer).await.expect("reads request"); + if n == 0 { + break request.len(); + } + request.extend_from_slice(&buffer[..n]); + if let Some(position) = request.windows(4).position(|window| window == b"\r\n\r\n") { + break position + 4; + } + }; + let headers = String::from_utf8_lossy(&request[..header_end]); + let content_length = headers + .lines() + .find_map(|line| { + let (name, value) = line.split_once(':')?; + name.eq_ignore_ascii_case("content-length") + .then(|| value.trim().parse::().ok()) + .flatten() + }) + .unwrap_or(0); + while request.len().saturating_sub(header_end) < content_length { + let n = socket.read(&mut buffer).await.expect("reads body"); + if n == 0 { + break; + } + request.extend_from_slice(&buffer[..n]); + } + String::from_utf8(request).expect("request is utf8") + } + + fn http_response(status: &str, body: &str) -> String { + format!( + "HTTP/1.1 {status}\r\ncontent-type: application/json\r\ncontent-length: {}\r\nconnection: close\r\n\r\n{}", + body.len(), + body + ) + } + + /// Serve one request from a stub upstream and hand back what it received. + async fn serve_once( + status: &'static str, + body: &'static str, + ) -> (String, tokio::task::JoinHandle) { + let listener = TcpListener::bind("127.0.0.1:0").await.expect("binds"); + let port = listener.local_addr().expect("addr").port(); + let handle = tokio::spawn(async move { + let (mut socket, _) = listener.accept().await.expect("accepts"); + let received = read_http_request(&mut socket).await; + socket + .write_all(http_response(status, body).as_bytes()) + .await + .expect("writes response"); + socket.flush().await.expect("flushes"); + received + }); + (format!("http://127.0.0.1:{port}/v1/messages"), handle) + } + + fn call(api_base: &str, messages: Value, params: Value) -> ChatCompletionsRequest<'_> { + ChatCompletionsRequest { + model: "anthropic/claude-sonnet-4-5", + messages, + optional_params: match params { + Value::Object(map) => map, + other => panic!("params must be an object, got {other}"), + }, + api_key: Some("sk-test"), + api_base: Some(api_base), + custom_llm_provider: None, + extra_headers: None, + timeout: Some(std::time::Duration::from_secs(10)), + } + } + + const GOOD_BODY: &str = r#"{"id":"msg_1","type":"message","role":"assistant","model":"claude-sonnet-4-5-20260101","content":[{"type":"text","text":"hello"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":11,"output_tokens":4}}"#; + + #[tokio::test] + async fn round_trip_sends_the_translated_body_and_normalizes_the_response() { + let (api_base, handle) = serve_once("200 OK", GOOD_BODY).await; + let response = chat_completions(call( + &api_base, + json!([ + {"role": "system", "content": "be terse"}, + {"role": "user", "content": "hi"} + ]), + json!({"max_tokens": 16}), + )) + .await + .expect("call succeeds"); + + let received = handle.await.expect("server task"); + let sent: Value = serde_json::from_str( + received + .split_once("\r\n\r\n") + .expect("request has a body") + .1, + ) + .expect("body is json"); + assert_eq!( + sent["messages"], + json!([{"role": "user", "content": [{"type": "text", "text": "hi"}]}]) + ); + assert_eq!( + sent["system"], + json!([{"type": "text", "text": "be terse"}]) + ); + assert_eq!(sent["max_tokens"], json!(16)); + assert!(received.to_lowercase().contains("x-api-key: sk-test")); + + assert_eq!( + response.choices[0].message.content.as_deref(), + Some("hello") + ); + assert_eq!(response.usage.total_tokens, 15); + } + + #[tokio::test] + async fn a_response_it_cannot_normalize_is_reported_as_already_sent() { + // The provider was called and billed, so the host must not retry this + // on its own path. `MissingField` here would read as a pre-send + // decline and be retried; `InvalidResponse` cannot. + const NO_USAGE: &str = + r#"{"model":"m","content":[{"type":"text","text":"hi"}],"stop_reason":"end_turn"}"#; + let (api_base, handle) = serve_once("200 OK", NO_USAGE).await; + let err = chat_completions(call( + &api_base, + json!([{"role": "user", "content": "hi"}]), + json!({"max_tokens": 16}), + )) + .await + .expect_err("response cannot be normalized"); + handle.await.expect("server task"); + assert!( + matches!(err, CoreError::InvalidResponse(_)), + "expected a post-send error, got {err:?}" + ); + } + + #[tokio::test] + async fn a_tool_use_block_in_the_response_is_also_reported_as_already_sent() { + const TOOL_USE: &str = r#"{"model":"m","content":[{"type":"tool_use","id":"t","name":"f","input":{}}],"stop_reason":"tool_use","usage":{"input_tokens":1,"output_tokens":1}}"#; + let (api_base, handle) = serve_once("200 OK", TOOL_USE).await; + let err = chat_completions(call( + &api_base, + json!([{"role": "user", "content": "hi"}]), + json!({"max_tokens": 16}), + )) + .await + .expect_err("response cannot be normalized"); + handle.await.expect("server task"); + assert!( + matches!(err, CoreError::InvalidResponse(_)), + "expected a post-send error, got {err:?}" + ); + } + + #[tokio::test] + async fn an_upstream_error_status_keeps_its_code() { + let (api_base, handle) = + serve_once("429 Too Many Requests", r#"{"error":"slow down"}"#).await; + let err = chat_completions(call( + &api_base, + json!([{"role": "user", "content": "hi"}]), + json!({"max_tokens": 16}), + )) + .await + .expect_err("upstream rejects"); + handle.await.expect("server task"); + assert!( + matches!(err, CoreError::Http { status: 429, .. }), + "expected a 429, got {err:?}" + ); + } + + #[tokio::test] + async fn a_connection_that_is_never_established_declines_instead_of_failing() { + // Nothing was sent, so nothing was billed and the host can still serve + // the request. Classing this with the post-send failures would turn a + // recoverable fallback into a user-facing error on exactly the + // deployments whose transport is configured only on the Python client. + let port = { + let listener = TcpListener::bind("127.0.0.1:0").await.expect("binds"); + listener.local_addr().expect("has an address").port() + // Dropped here, so the port is closed and the connect is refused. + }; + let err = chat_completions(call( + &format!("http://127.0.0.1:{port}/v1/messages"), + json!([{"role": "user", "content": "hi"}]), + json!({"max_tokens": 16}), + )) + .await + .expect_err("nothing is listening"); + assert!( + matches!(err, CoreError::Connect(_)), + "expected a pre-send connect failure, got {err:?}" + ); + } + + #[test] + fn response_errors_collapse_to_one_variant_that_can_only_mean_already_sent() { + 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()), + ] { + let label = format!("{original:?}"); + assert!( + matches!(as_response_error(original), CoreError::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 { + status: 500, + body: "boom".to_string() + }), + CoreError::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 new file mode 100644 index 00000000000..a30ce9dc77c --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/transformation.rs @@ -0,0 +1,155 @@ +use serde_json::{Map, Value}; + +use crate::error::CoreResult; + +use super::types::{ + ChatCompletionsResponse, ChatMessage, ChatMessageContent, ProviderChatRequestData, + ProviderChatResponseData, +}; + +/// How the upstream call is authenticated. API-key strategies are resolved in +/// `prepare`; SigV4 needs the serialized body, so the handler signs it. +#[derive(Clone, Debug, PartialEq, Eq)] +pub enum ChatCompletionsAuth { + Header { name: &'static str, value: String }, + Bearer { token: String }, + AwsSigV4 { region: String }, +} + +/// Why a request cannot be served by the Rust path. +/// +/// The core declines rather than guessing: the host turns this into a +/// transparent fallback to the Python implementation, which covers the full +/// surface. Acceptance is an allowlist, so a parameter or message shape the +/// core has never seen declines by construction instead of being translated +/// wrong. +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +pub struct Unsupported(pub &'static str); + +pub const STREAM_PARAM: &str = "stream"; + +/// Message fields that carry no meaning for the upstream body, so their +/// presence does not make a request untranslatable. +const IGNORABLE_MESSAGE_FIELDS: &[&str] = &["name"]; + +pub trait ChatCompletionsProviderConfig: Sync { + fn complete_url( + &self, + api_base: Option<&str>, + model: &str, + optional_params: &Map, + env_lookup: &dyn Fn(&str) -> Option, + ) -> CoreResult; + + fn auth( + &self, + api_key: Option<&str>, + model: &str, + optional_params: &Map, + env_lookup: &dyn Fn(&str) -> Option, + ) -> CoreResult; + + fn default_headers(&self) -> &'static [(&'static str, &'static str)] { + &[("content-type", "application/json")] + } + + /// Whether an auth header the caller already supplied is the credential this + /// request should authenticate with, so the resolved one is not applied. + /// + /// Defaults to false: the deployment's credential outranks anything + /// forwarded, which is what every provider wants for its own auth header. + /// A provider overrides this only for a scheme it hands off to entirely. + fn defers_to_forwarded_auth(&self, _headers: &[(String, String)]) -> bool { + false + } + + /// Provider parameter names (post-mapping) the Rust path knows how to place + /// in the upstream body. Anything outside this set declines the request. + fn supported_params(&self) -> &'static [&'static str]; + + /// Parameters consumed as call configuration (credentials, endpoints) + /// rather than placed in the body. Accepted, never serialized. + fn config_params(&self) -> &'static [&'static str] { + &[] + } + + fn unsupported_reason( + &self, + messages: &[ChatMessage], + optional_params: &Map, + ) -> Option { + unsupported_param( + self.supported_params(), + self.config_params(), + optional_params, + ) + .or_else(|| messages.iter().find_map(unsupported_message)) + } + + fn transform_request( + &self, + model: &str, + messages: Vec, + optional_params: Map, + ) -> CoreResult; + + fn transform_response( + &self, + model: &str, + response: ProviderChatResponseData, + ) -> CoreResult; +} + +pub fn unsupported_param( + supported: &'static [&'static str], + config: &'static [&'static str], + optional_params: &Map, +) -> Option { + if optional_params + .get(STREAM_PARAM) + .and_then(Value::as_bool) + .unwrap_or(false) + { + return Some(Unsupported("streaming")); + } + optional_params + .keys() + .any(|key| { + key != STREAM_PARAM + && !supported.contains(&key.as_str()) + && !config.contains(&key.as_str()) + }) + .then_some(Unsupported("unrecognized request parameter")) +} + +/// Message shapes the core can translate faithfully: text content, either a +/// plain string or a non-empty list of parts that are all +/// `{"type": "text", "text": ...}`. Tool calls, tool results, and multimodal +/// parts decline so Python's fuller translation handles them. +pub fn unsupported_message(message: &ChatMessage) -> Option { + if message + .extra + .keys() + .any(|key| !IGNORABLE_MESSAGE_FIELDS.contains(&key.as_str())) + { + return Some(Unsupported("unrecognized message field")); + } + if !matches!(message.role.as_str(), "system" | "user" | "assistant") { + return Some(Unsupported("unrecognized message role")); + } + match &message.content { + None => Some(Unsupported("message without content")), + Some(ChatMessageContent::Text(_)) => None, + Some(ChatMessageContent::Parts(parts)) if parts.is_empty() => { + Some(Unsupported("message without content")) + } + Some(ChatMessageContent::Parts(parts)) => parts + .iter() + .any(|part| { + part.get("type").and_then(Value::as_str) != Some("text") + || part.get("text").and_then(Value::as_str).is_none() + || part.as_object().is_some_and(|object| object.len() != 2) + }) + .then_some(Unsupported("non-text message content")), + } +} diff --git a/litellm-rust/crates/core/src/chat_completions/types.rs b/litellm-rust/crates/core/src/chat_completions/types.rs new file mode 100644 index 00000000000..35dd543a986 --- /dev/null +++ b/litellm-rust/crates/core/src/chat_completions/types.rs @@ -0,0 +1,112 @@ +use std::time::Duration; + +use serde::{Deserialize, Serialize}; +use serde_json::{Map, Value}; + +use super::transformation::{ChatCompletionsAuth, ChatCompletionsProviderConfig}; + +/// A `/chat/completions` call as it crosses into the core. +/// +/// `optional_params` arrives already mapped to the provider's own parameter +/// names by the host, exactly as the messages route receives an already +/// Anthropic-shaped body. The core owns the conversation translation, the +/// provider call, and the response normalization. +pub struct ChatCompletionsRequest<'a> { + pub model: &'a str, + pub messages: Value, + pub optional_params: Map, + pub api_key: Option<&'a str>, + pub api_base: Option<&'a str>, + pub custom_llm_provider: Option<&'a str>, + pub extra_headers: Option>, + pub timeout: Option, +} + +pub(super) struct ProviderChatCompletionsRequest { + pub(super) model: String, + pub(super) config: &'static dyn ChatCompletionsProviderConfig, + pub(super) url: String, + pub(super) body: Value, + pub(super) upstream_headers: Vec<(String, String)>, + pub(super) auth: ChatCompletionsAuth, + #[cfg_attr(not(feature = "bedrock-auth"), allow(dead_code))] + pub(super) optional_params: Map, + pub(super) timeout: Option, +} + +/// The provider-shaped request body a config produces. Named rather than a bare +/// `Value` so the transform contract stays a typed one, mirroring +/// [`crate::audio_transcription::types::AudioTranscriptionRequestData`]. +pub struct ProviderChatRequestData { + pub body: Value, +} + +/// The raw provider response body handed back to a config for normalization. +pub struct ProviderChatResponseData { + pub body: Value, +} + +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +#[serde(untagged)] +pub enum ChatMessageContent { + Text(String), + Parts(Vec), +} + +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct ChatMessage { + pub role: String, + #[serde(default, skip_serializing_if = "Option::is_none")] + pub content: Option, + #[serde(default, skip_serializing_if = "Option::is_none")] + pub name: Option, + #[serde(flatten)] + pub extra: Map, +} + +/// OpenAI `usage`, including the `prompt_tokens_details` split LiteLLM's Python +/// path reports so cost tracking sees the same numbers on either path. +#[derive(Clone, Debug, Default, PartialEq, Serialize, Deserialize)] +pub struct PromptTokensDetails { + pub cached_tokens: u64, + pub cache_creation_tokens: u64, + pub text_tokens: u64, +} + +#[derive(Clone, Debug, Default, PartialEq, Serialize, Deserialize)] +pub struct ChatCompletionsUsage { + pub prompt_tokens: u64, + pub completion_tokens: u64, + pub total_tokens: u64, + pub prompt_tokens_details: PromptTokensDetails, +} + +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct ChatCompletionsChoiceMessage { + pub role: String, + // Whether an empty turn is `None` or `""` is the provider's choice, not a + // shared invariant: Anthropic's transform ends on `merged_text or None` + // while Converse assigns the joined string unconditionally. Each config + // mirrors its own, so keep this optional and serialize it even when None. + pub content: Option, +} + +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct ChatCompletionsChoice { + pub index: u64, + pub message: ChatCompletionsChoiceMessage, + pub finish_reason: String, +} + +/// The normalized response handed back to the host. +/// +/// There is deliberately no `id`: Python mints the `chatcmpl-…` id on the +/// `ModelResponse` it already created, and echoing the provider's own id here +/// would change it. Pinned by `response_carries_no_id` in `tests.rs`. +#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] +pub struct ChatCompletionsResponse { + pub created: u64, + pub model: String, + pub choices: Vec, + pub usage: ChatCompletionsUsage, +} diff --git a/litellm-rust/crates/core/src/constants.rs b/litellm-rust/crates/core/src/constants.rs index caada1d98b0..e1ac0a4fc8f 100644 --- a/litellm-rust/crates/core/src/constants.rs +++ b/litellm-rust/crates/core/src/constants.rs @@ -12,8 +12,30 @@ pub(crate) const MESSAGES_CONNECT_TIMEOUT_SECS: u64 = 10; /// Max characters of an upstream error body echoed across the call boundary /// before truncation, so provider bodies are bounded and data-minimized. -pub(crate) const MESSAGES_ERROR_BODY_MAX_CHARS: usize = 256; +pub(crate) const UPSTREAM_ERROR_BODY_MAX_CHARS: usize = 256; /// Provider name used for Anthropic Messages when a deployment's provider model /// does not carry an explicit provider prefix. pub const ANTHROPIC_MESSAGES_PROVIDER: &str = "anthropic"; + +/// Prefix identifying an Anthropic OAuth token. Mirrors Python's +/// `ANTHROPIC_OAUTH_TOKEN_PREFIX`, which is what makes `validate_environment` +/// authenticate with `authorization` and drop `x-api-key` entirely. +pub(crate) const ANTHROPIC_OAUTH_TOKEN_PREFIX: &str = "sk-ant-oat"; + +/// Full-request timeout ceiling for chat completions provider calls, in +/// seconds. Mirrors the Python chat completions default. +pub(crate) const CHAT_COMPLETIONS_TIMEOUT_SECS: u64 = 600; + +/// Connect timeout for chat completions provider calls, in seconds. +pub(crate) const CHAT_COMPLETIONS_CONNECT_TIMEOUT_SECS: u64 = 10; + +/// `object` field every non-streaming chat completion response carries. +pub const CHAT_COMPLETION_OBJECT: &str = "chat.completion"; + +/// Placeholder Python substitutes for empty or whitespace-only message text, +/// which Anthropic and Bedrock both reject. Must match +/// `_EMPTY_TEXT_PLACEHOLDER` in +/// `litellm/litellm_core_utils/prompt_templates/factory.py`. +pub const EMPTY_TEXT_PLACEHOLDER: &str = + "[System: Empty message content sanitised to satisfy protocol]"; diff --git a/litellm-rust/crates/core/src/error.rs b/litellm-rust/crates/core/src/error.rs index c2b08eee0c0..739532f8cb5 100644 --- a/litellm-rust/crates/core/src/error.rs +++ b/litellm-rust/crates/core/src/error.rs @@ -23,8 +23,19 @@ pub enum CoreError { Http { status: u16, body: String }, #[error("upstream network error: {0}")] Network(String), + /// The provider was never reached: DNS, TCP, TLS or proxy setup failed + /// before any byte of the request went out. Nothing was billed, so a host + /// that keeps a reference implementation can serve the request itself. + /// A timeout is deliberately not this, since the provider may have received + /// and answered the request already. + #[error("could not reach the provider: {0}")] + Connect(String), #[error("routing error: {0}")] Routing(String), + /// The request is outside the surface this route covers in Rust. Hosts that + /// keep a reference implementation treat this as "fall back", not "fail". + #[error("unsupported by the rust path: {0}")] + Unsupported(&'static str), } pub fn json_type_name(value: &serde_json::Value) -> &'static str { diff --git a/litellm-rust/crates/core/src/http_utils.rs b/litellm-rust/crates/core/src/http_utils.rs new file mode 100644 index 00000000000..c541f50275b --- /dev/null +++ b/litellm-rust/crates/core/src/http_utils.rs @@ -0,0 +1,112 @@ +//! Header and upstream-body helpers shared by every route module. + +use serde_json::{Map, Value}; + +use crate::constants::UPSTREAM_ERROR_BODY_MAX_CHARS; +use crate::error::{CoreError, CoreResult, json_type_name}; + +/// Bound an upstream error body before it crosses a host boundary, so provider +/// bodies stay data-minimized. +pub fn truncate_error_body(body: &str) -> String { + if body.chars().count() <= UPSTREAM_ERROR_BODY_MAX_CHARS { + return body.to_string(); + } + let truncated: String = body.chars().take(UPSTREAM_ERROR_BODY_MAX_CHARS).collect(); + format!("{truncated}... (truncated)") +} + +pub fn string_headers( + context: &'static str, + extra_headers: Option>, +) -> CoreResult> { + extra_headers + .unwrap_or_default() + .into_iter() + .map(|(key, value)| { + value + .as_str() + .map(|value| (key.clone(), value.to_string())) + .ok_or_else(|| { + CoreError::InvalidRequest(format!( + "{context} extra_headers.{key} must be a string, got {}", + json_type_name(&value) + )) + }) + }) + .collect() +} + +pub fn has_header(headers: &[(String, String)], name: &str) -> bool { + headers + .iter() + .any(|(key, _)| key.eq_ignore_ascii_case(name)) +} + +pub fn has_bearer_auth(headers: &[(String, String)]) -> bool { + headers.iter().any(|(name, value)| { + if !name.eq_ignore_ascii_case("authorization") { + return false; + } + let value = value.trim(); + value.len() > 7 + && value[..7].eq_ignore_ascii_case("bearer ") + && !value[7..].trim().is_empty() + }) +} + +#[cfg(test)] +mod tests { + use super::*; + use serde_json::json; + + #[test] + fn truncate_leaves_short_bodies_untouched() { + assert_eq!(truncate_error_body("short"), "short"); + } + + #[test] + fn truncate_bounds_long_bodies_by_characters() { + let body = "\u{00e9}".repeat(UPSTREAM_ERROR_BODY_MAX_CHARS + 10); + let truncated = truncate_error_body(&body); + assert!(truncated.ends_with("... (truncated)")); + assert_eq!( + truncated.chars().count(), + UPSTREAM_ERROR_BODY_MAX_CHARS + "... (truncated)".chars().count() + ); + } + + #[test] + fn string_headers_rejects_non_string_values() { + let headers = Map::from_iter([("x-trace".to_string(), json!(7))]); + let err = string_headers("chat completions", Some(headers)).expect_err("non-string value"); + assert_eq!( + err, + CoreError::InvalidRequest( + "chat completions extra_headers.x-trace must be a string, got number".to_string() + ) + ); + } + + #[test] + fn header_lookup_is_case_insensitive() { + let headers = vec![("X-Api-Key".to_string(), "k".to_string())]; + assert!(has_header(&headers, "x-api-key")); + assert!(!has_header(&headers, "authorization")); + } + + #[test] + fn bearer_detection_requires_a_non_empty_token() { + assert!(has_bearer_auth(&[( + "Authorization".to_string(), + "Bearer abc".to_string() + )])); + assert!(!has_bearer_auth(&[( + "Authorization".to_string(), + "Bearer ".to_string() + )])); + assert!(!has_bearer_auth(&[( + "Authorization".to_string(), + "Basic abc".to_string() + )])); + } +} diff --git a/litellm-rust/crates/core/src/lib.rs b/litellm-rust/crates/core/src/lib.rs index 51ea19750ea..dce4a425ea0 100644 --- a/litellm-rust/crates/core/src/lib.rs +++ b/litellm-rust/crates/core/src/lib.rs @@ -1,8 +1,10 @@ pub mod audio_transcription; pub mod caching; pub mod call_lifecycle; +pub mod chat_completions; pub mod constants; pub mod error; +pub mod http_utils; pub mod messages; pub mod ocr; pub mod providers; diff --git a/litellm-rust/crates/core/src/messages/common_utils.rs b/litellm-rust/crates/core/src/messages/common_utils.rs index 9dcfcaa71e3..a14dffbc1fe 100644 --- a/litellm-rust/crates/core/src/messages/common_utils.rs +++ b/litellm-rust/crates/core/src/messages/common_utils.rs @@ -1,19 +1,15 @@ use serde_json::{Map, Value}; -use crate::constants::MESSAGES_ERROR_BODY_MAX_CHARS; -use crate::error::{CoreError, CoreResult, json_type_name}; +use crate::error::CoreResult; +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 super::transformation::AnthropicMessagesProviderConfig; -pub(super) fn truncate_error_body(body: &str) -> String { - if body.chars().count() <= MESSAGES_ERROR_BODY_MAX_CHARS { - return body.to_string(); - } - let truncated: String = body.chars().take(MESSAGES_ERROR_BODY_MAX_CHARS).collect(); - format!("{truncated}... (truncated)") -} +pub(super) use crate::http_utils::{has_bearer_auth, has_header, truncate_error_body}; + +const HEADER_CONTEXT: &str = "messages"; pub(super) fn messages_provider_config( provider: &str, @@ -28,37 +24,5 @@ pub(super) fn messages_provider_config( pub(super) fn string_headers( extra_headers: Option>, ) -> CoreResult> { - extra_headers - .unwrap_or_default() - .into_iter() - .map(|(key, value)| { - value - .as_str() - .map(|value| (key.clone(), value.to_string())) - .ok_or_else(|| { - CoreError::InvalidRequest(format!( - "messages extra_headers.{key} must be a string, got {}", - json_type_name(&value) - )) - }) - }) - .collect() -} - -pub(super) fn has_header(headers: &[(String, String)], name: &str) -> bool { - headers - .iter() - .any(|(key, _)| key.eq_ignore_ascii_case(name)) -} - -pub(super) fn has_bearer_auth(headers: &[(String, String)]) -> bool { - headers.iter().any(|(name, value)| { - if !name.eq_ignore_ascii_case("authorization") { - return false; - } - let value = value.trim(); - value.len() > 7 - && value[..7].eq_ignore_ascii_case("bearer ") - && !value[7..].trim().is_empty() - }) + shared_string_headers(HEADER_CONTEXT, extra_headers) } diff --git a/litellm-rust/crates/core/src/providers/anthropic/chat_completions/mod.rs b/litellm-rust/crates/core/src/providers/anthropic/chat_completions/mod.rs new file mode 100644 index 00000000000..f239b6921fa --- /dev/null +++ b/litellm-rust/crates/core/src/providers/anthropic/chat_completions/mod.rs @@ -0,0 +1 @@ +pub mod transformation; 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 new file mode 100644 index 00000000000..4534ac0182c --- /dev/null +++ b/litellm-rust/crates/core/src/providers/anthropic/chat_completions/tests.rs @@ -0,0 +1,444 @@ +use super::*; +use serde_json::json; + +fn messages(value: Value) -> Vec { + serde_json::from_value(value).expect("valid messages") +} + +fn params(value: Value) -> Map { + match value { + Value::Object(map) => map, + other => panic!("params must be an object, got {other}"), + } +} + +fn transform(model: &str, msgs: Value, opts: Value) -> Value { + ANTHROPIC_CHAT_COMPLETIONS_CONFIG + .transform_request(model, messages(msgs), params(opts)) + .expect("request transforms") + .body +} + +fn transform_response(body: Value) -> CoreResult { + ANTHROPIC_CHAT_COMPLETIONS_CONFIG + .transform_response("claude-sonnet-4-5", ProviderChatResponseData { body }) +} + +fn reason(msgs: Value, opts: Value) -> Option { + ANTHROPIC_CHAT_COMPLETIONS_CONFIG.unsupported_reason(&messages(msgs), ¶ms(opts)) +} + +#[test] +fn builds_the_messages_body_python_builds() { + let body = transform( + "claude-sonnet-4-5", + json!([ + {"role": "system", "content": "be terse"}, + {"role": "user", "content": "hi"} + ]), + json!({"max_tokens": 128, "temperature": 0.2}), + ); + assert_eq!( + body, + json!({ + "model": "claude-sonnet-4-5", + "messages": [ + {"role": "user", "content": [{"type": "text", "text": "hi"}]} + ], + "system": [{"type": "text", "text": "be terse"}], + "max_tokens": 128, + "temperature": 0.2 + }) + ); +} + +#[test] +fn omits_system_when_no_system_message_is_present() { + let body = transform( + "claude-sonnet-4-5", + json!([{"role": "user", "content": "hi"}]), + json!({"max_tokens": 16}), + ); + assert!(body.get("system").is_none()); +} + +#[test] +fn merges_consecutive_turns_and_wraps_every_text_in_a_block() { + let body = transform( + "claude-sonnet-4-5", + json!([ + {"role": "user", "content": "one"}, + {"role": "user", "content": [{"type": "text", "text": "two"}]}, + {"role": "assistant", "content": "ack"} + ]), + json!({"max_tokens": 16}), + ); + assert_eq!( + body["messages"], + json!([ + {"role": "user", "content": [ + {"type": "text", "text": "one"}, + {"type": "text", "text": "two"} + ]}, + {"role": "assistant", "content": [{"type": "text", "text": "ack"}]} + ]) + ); +} + +#[test] +fn right_strips_a_trailing_assistant_prefill_like_python() { + let body = transform( + "claude-sonnet-4-5", + json!([ + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": "Argentina "} + ]), + json!({"max_tokens": 16}), + ); + assert_eq!( + body["messages"][1]["content"][0]["text"], + json!("Argentina") + ); +} + +#[test] +fn passes_every_supported_param_through_untouched() { + let body = transform( + "claude-sonnet-4-5", + json!([{"role": "user", "content": "hi"}]), + json!({ + "max_tokens": 64, + "temperature": 0.1, + "top_p": 0.9, + "stop_sequences": ["STOP"] + }), + ); + assert_eq!(body["max_tokens"], json!(64)); + assert_eq!(body["temperature"], json!(0.1)); + assert_eq!(body["top_p"], json!(0.9)); + assert_eq!(body["stop_sequences"], json!(["STOP"])); +} + +#[test] +fn declines_top_k_because_python_gates_it_by_model_below_this_point() { + // `temperature` and `top_p` arrive already resolved, because + // `map_openai_params` applies `_apply_sampling_param` to them before the + // gate runs. `top_k` bypasses that and is gated inside `transform_request`, + // the function this route replaces, so forwarding it would send `top_k` to + // a model that removed sampling params and take a 400 after the call, where + // Python drops it and succeeds. + assert_eq!( + reason( + json!([{"role": "user", "content": "hi"}]), + json!({"top_k": 40}) + ), + Some(Unsupported("unrecognized request parameter")) + ); +} + +#[test] +fn declines_streaming_before_anything_else() { + assert_eq!( + reason( + json!([{"role": "user", "content": "hi"}]), + json!({"stream": true, "max_tokens": 16}) + ), + Some(Unsupported("streaming")) + ); +} + +#[test] +fn accepts_an_explicit_stream_false() { + assert_eq!( + reason( + json!([{"role": "user", "content": "hi"}]), + json!({"stream": false, "max_tokens": 16}) + ), + None + ); +} + +#[test] +fn declines_any_param_outside_the_allowlist() { + for param in [ + json!({"tools": []}), + json!({"tool_choice": {"type": "auto"}}), + json!({"thinking": {"type": "enabled"}}), + json!({"system": "injected"}), + json!({"metadata": {"user_id": "u1"}}), + json!({"output_config": {"effort": "high"}}), + ] { + assert_eq!( + reason(json!([{"role": "user", "content": "hi"}]), param.clone()), + Some(Unsupported("unrecognized request parameter")), + "expected {param} to decline" + ); + } +} + +#[test] +fn declines_tool_calls_tool_results_and_multimodal_content() { + assert_eq!( + reason( + json!([ + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": null, "tool_calls": [ + {"id": "c1", "type": "function", + "function": {"name": "f", "arguments": "{}"}} + ]} + ]), + json!({}) + ), + Some(Unsupported("unrecognized message field")) + ); + assert_eq!( + reason( + json!([ + {"role": "user", "content": "hi"}, + {"role": "tool", "tool_call_id": "c1", "content": "ok"} + ]), + json!({}) + ), + Some(Unsupported("unrecognized message field")) + ); + assert_eq!( + reason( + json!([{"role": "user", "content": [ + {"type": "image_url", "image_url": {"url": "https://x/y.png"}} + ]}]), + json!({}) + ), + Some(Unsupported("non-text message content")) + ); + assert_eq!( + reason( + json!([{"role": "user", "content": [ + {"type": "text", "text": "hi", "cache_control": {"type": "ephemeral"}} + ]}]), + json!({}) + ), + Some(Unsupported("non-text message content")) + ); +} + +#[test] +fn declines_a_message_whose_content_list_is_empty() { + // An empty list passes every per-part check, so without this it would reach + // the provider as an empty `content` array and fail after the call rather + // than declining to Python before it. + assert_eq!( + reason(json!([{"role": "user", "content": []}]), json!({})), + Some(Unsupported("message without content")) + ); + assert_eq!( + reason( + json!([{"role": "user", "content": [{"type": "text", "text": "hi"}]}]), + json!({}) + ), + None + ); +} + +#[test] +fn declines_a_conversation_that_does_not_open_on_a_user_turn() { + assert_eq!( + reason( + json!([ + {"role": "system", "content": "be terse"}, + {"role": "assistant", "content": "prefill"} + ]), + json!({}) + ), + Some(Unsupported("conversation does not open on a user turn")) + ); +} + +#[test] +fn accepts_a_plain_text_conversation() { + assert_eq!( + reason( + json!([ + {"role": "system", "content": "be terse"}, + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": "hello"}, + {"role": "user", "content": [{"type": "text", "text": "again"}]} + ]), + json!({"max_tokens": 16, "temperature": 0.5}) + ), + None + ); +} + +#[test] +fn normalizes_a_text_response_into_openai_shape() { + let response = transform_response(json!({ + "id": "msg_123", + "type": "message", + "role": "assistant", + "model": "claude-sonnet-4-5-20260101", + "content": [{"type": "text", "text": "hello"}, {"type": "text", "text": " there"}], + "stop_reason": "end_turn", + "stop_sequence": null, + "usage": {"input_tokens": 11, "output_tokens": 4} + })) + .expect("response transforms"); + + assert_eq!(response.model, "claude-sonnet-4-5-20260101"); + assert_eq!(response.choices.len(), 1); + assert_eq!(response.choices[0].index, 0); + assert_eq!(response.choices[0].message.role, "assistant"); + assert_eq!( + response.choices[0].message.content.as_deref(), + Some("hello there") + ); + assert_eq!(response.choices[0].finish_reason, "stop"); + assert_eq!(response.usage.prompt_tokens, 11); + assert_eq!(response.usage.completion_tokens, 4); + assert_eq!(response.usage.total_tokens, 15); +} + +#[test] +fn folds_cache_tokens_into_prompt_tokens_like_python() { + let response = transform_response(json!({ + "model": "claude-sonnet-4-5", + "content": [{"type": "text", "text": "hi"}], + "stop_reason": "end_turn", + "usage": { + "input_tokens": 10, + "output_tokens": 2, + "cache_read_input_tokens": 5, + "cache_creation_input_tokens": 3 + } + })) + .expect("response transforms"); + assert_eq!(response.usage.prompt_tokens, 18); + assert_eq!(response.usage.total_tokens, 20); + assert_eq!(response.usage.prompt_tokens_details.cached_tokens, 5); + assert_eq!( + response.usage.prompt_tokens_details.cache_creation_tokens, + 3 + ); + assert_eq!(response.usage.prompt_tokens_details.text_tokens, 10); +} + +#[test] +fn maps_max_tokens_stop_reason_to_length() { + let response = transform_response(json!({ + "model": "claude-sonnet-4-5", + "content": [{"type": "text", "text": "hi"}], + "stop_reason": "max_tokens", + "usage": {"input_tokens": 1, "output_tokens": 1} + })) + .expect("response transforms"); + assert_eq!(response.choices[0].finish_reason, "length"); +} + +#[test] +fn a_refusal_returns_the_completion_python_returns() { + // `refusal` is a stop_reason, not a content block type, so the content is + // ordinary text and this normalizes rather than declining. Python maps it + // to content_filter in _FINISH_REASON_MAP and returns the completion. + let response = transform_response(json!({ + "model": "claude-sonnet-4-5", + "content": [{"type": "text", "text": "I can't help with that."}], + "stop_reason": "refusal", + "usage": {"input_tokens": 9, "output_tokens": 6} + })) + .expect("a refusal still transforms"); + assert_eq!(response.choices[0].finish_reason, "content_filter"); + assert_eq!( + response.choices[0].message.content.as_deref(), + Some("I can't help with that.") + ); +} + +#[test] +fn reports_no_content_rather_than_an_empty_string() { + let response = transform_response(json!({ + "model": "claude-sonnet-4-5", + "content": [], + "stop_reason": "end_turn", + "usage": {"input_tokens": 1, "output_tokens": 0} + })) + .expect("response transforms"); + assert_eq!(response.choices[0].message.content, None); +} + +#[test] +fn response_carries_no_id_so_python_keeps_its_chatcmpl_id() { + let response = transform_response(json!({ + "id": "msg_should_not_leak", + "model": "claude-sonnet-4-5", + "content": [{"type": "text", "text": "hi"}], + "stop_reason": "end_turn", + "usage": {"input_tokens": 1, "output_tokens": 1} + })) + .expect("response transforms"); + let value = serde_json::to_value(response).expect("serializable"); + assert!( + value.get("id").is_none(), + "the rust response must not carry an id, got {value}" + ); +} + +#[test] +fn declines_a_response_carrying_a_non_text_block() { + let err = transform_response(json!({ + "model": "claude-sonnet-4-5", + "content": [{"type": "tool_use", "id": "t1", "name": "f", "input": {}}], + "stop_reason": "tool_use", + "usage": {"input_tokens": 1, "output_tokens": 1} + })) + .expect_err("non-text block"); + assert_eq!( + err, + CoreError::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()) + ); + assert_eq!( + transform_response(json!({"model": "m", "usage": {}})).expect_err("no content"), + CoreError::MissingField("content") + ); + assert_eq!( + transform_response(json!({"model": "m", "content": []})).expect_err("no usage"), + CoreError::MissingField("usage") + ); + assert_eq!( + transform_response(json!({"content": [], "usage": {}})).expect_err("no model"), + CoreError::MissingField("model") + ); +} + +#[test] +fn resolves_the_messages_url_and_x_api_key_auth() { + let config = &ANTHROPIC_CHAT_COMPLETIONS_CONFIG; + assert_eq!( + config + .complete_url(None, "claude-sonnet-4-5", &Map::new(), &|_| None) + .expect("url builds"), + "https://api.anthropic.com/v1/messages" + ); + assert_eq!( + config + .auth(Some("sk-x"), "claude-sonnet-4-5", &Map::new(), &|_| None) + .expect("auth resolves"), + ChatCompletionsAuth::Header { + name: "x-api-key", + value: "sk-x".to_string() + } + ); + assert_eq!( + config.default_headers(), + &[ + ("anthropic-version", "2023-06-01"), + ("content-type", "application/json"), + ] + ); +} 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 new file mode 100644 index 00000000000..3658642b539 --- /dev/null +++ b/litellm-rust/crates/core/src/providers/anthropic/chat_completions/transformation.rs @@ -0,0 +1,211 @@ +use serde_json::{Map, Value, json}; + +use crate::chat_completions::conversation::{Conversation, build_conversation}; +use crate::chat_completions::transformation::{ + ChatCompletionsAuth, ChatCompletionsProviderConfig, Unsupported, unsupported_message, + unsupported_param, +}; +use crate::chat_completions::types::{ + ChatCompletionsChoice, ChatCompletionsChoiceMessage, ChatCompletionsResponse, ChatMessage, + ProviderChatRequestData, ProviderChatResponseData, +}; +use crate::constants::ANTHROPIC_OAUTH_TOKEN_PREFIX; +use crate::error::{CoreError, CoreResult}; +use crate::providers::anthropic::messages::transformation::{ + complete_anthropic_url, resolve_anthropic_api_key, +}; + +use crate::chat_completions::response_utils::{finish_reason_for, unix_now, usage_from_parts}; + +/// Anthropic parameter names, post `map_openai_params`, that the Rust path can +/// place verbatim in the Messages body. +/// +/// `top_k` is deliberately absent even though the Messages API takes it. +/// `temperature` and `top_p` reach this gate already resolved, because +/// `map_openai_params` runs first and applies `_apply_sampling_param` to them. +/// `top_k` bypasses `map_openai_params` entirely, so Python applies that same +/// per-model gate inside `transform_request`, the function this route replaces. +/// Forwarding it would send `top_k` to a model that removed sampling params and +/// take a 400 after the call, where Python drops it and succeeds. +const SUPPORTED_PARAMS: &[&str] = &["max_tokens", "temperature", "top_p", "stop_sequences"]; + +pub struct AnthropicChatCompletionsConfig; + +pub const ANTHROPIC_CHAT_COMPLETIONS_CONFIG: AnthropicChatCompletionsConfig = + AnthropicChatCompletionsConfig; + +fn text_block(text: &str) -> Value { + json!({"type": "text", "text": text}) +} + +fn anthropic_body(model: &str, conversation: &Conversation, params: Map) -> Value { + let messages: Vec = conversation + .turns + .iter() + .map(|turn| { + json!({ + "role": turn.role.as_str(), + "content": turn.texts.iter().map(|text| text_block(text)).collect::>(), + }) + }) + .collect(); + + let system: Vec = conversation.system.iter().map(|s| text_block(s)).collect(); + + let body = Map::from_iter( + [ + ("model".to_string(), json!(model)), + ("messages".to_string(), json!(messages)), + ] + .into_iter() + // Python builds `{"model", "messages", **optional_params}` with + // `system` already folded into optional_params, so a caller-supplied + // key of the same name wins here too. + .chain((!system.is_empty()).then(|| ("system".to_string(), json!(system)))) + .chain(params), + ); + Value::Object(body) +} + +impl ChatCompletionsProviderConfig for AnthropicChatCompletionsConfig { + fn complete_url( + &self, + api_base: Option<&str>, + _model: &str, + _optional_params: &Map, + env_lookup: &dyn Fn(&str) -> Option, + ) -> CoreResult { + Ok(complete_anthropic_url(api_base, env_lookup)) + } + + fn auth( + &self, + api_key: Option<&str>, + _model: &str, + _optional_params: &Map, + env_lookup: &dyn Fn(&str) -> Option, + ) -> CoreResult { + Ok(ChatCompletionsAuth::Header { + name: "x-api-key", + value: resolve_anthropic_api_key(api_key, env_lookup)?, + }) + } + + fn default_headers(&self) -> &'static [(&'static str, &'static str)] { + &[ + ("anthropic-version", "2023-06-01"), + ("content-type", "application/json"), + ] + } + + /// An OAuth bearer is the whole credential: Python's `validate_environment` + /// authenticates with it and drops `x-api-key` rather than resolving one, so + /// the resolved key must not be applied over the top. Any other forwarded + /// `authorization` is unrelated to this header and does not defer, which is + /// also what Python does: it sends the deployment's `x-api-key` alongside. + fn defers_to_forwarded_auth(&self, headers: &[(String, String)]) -> bool { + headers.iter().any(|(name, value)| { + name.eq_ignore_ascii_case("authorization") + && value + .strip_prefix("Bearer ") + .is_some_and(|token| token.starts_with(ANTHROPIC_OAUTH_TOKEN_PREFIX)) + }) + } + + fn supported_params(&self) -> &'static [&'static str] { + SUPPORTED_PARAMS + } + + fn unsupported_reason( + &self, + messages: &[ChatMessage], + optional_params: &Map, + ) -> Option { + unsupported_param(SUPPORTED_PARAMS, &[], optional_params) + .or_else(|| messages.iter().find_map(unsupported_message)) + // Anthropic rejects a request whose first turn is not a user turn. + // Python only repairs that under `litellm.modify_params`, which the + // core cannot observe, so decline instead of guessing. + .or_else(|| { + (!build_conversation(messages).opens_on_user_turn()) + .then_some(Unsupported("conversation does not open on a user turn")) + }) + } + + fn transform_request( + &self, + model: &str, + messages: Vec, + optional_params: Map, + ) -> CoreResult { + Ok(ProviderChatRequestData { + body: anthropic_body(model, &build_conversation(&messages), optional_params), + }) + } + + fn transform_response( + &self, + _model: &str, + response: ProviderChatResponseData, + ) -> CoreResult { + let body = response.body.as_object().ok_or_else(|| { + CoreError::InvalidResponse("messages response is not an object".into()) + })?; + + let content = body + .get("content") + .and_then(Value::as_array) + .ok_or(CoreError::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. + if content + .iter() + .any(|block| block.get("type").and_then(Value::as_str) != Some("text")) + { + return Err(CoreError::Unsupported("non-text response content block")); + } + let text: String = content + .iter() + .filter_map(|block| block.get("text").and_then(Value::as_str)) + .collect(); + + let usage = body + .get("usage") + .and_then(Value::as_object) + .ok_or(CoreError::MissingField("usage"))?; + let field = |name: &str| usage.get(name).and_then(Value::as_u64).unwrap_or(0); + + Ok(ChatCompletionsResponse { + created: unix_now(), + model: body + .get("model") + .and_then(Value::as_str) + .ok_or(CoreError::MissingField("model"))? + .to_string(), + choices: vec![ChatCompletionsChoice { + index: 0, + message: ChatCompletionsChoiceMessage { + role: "assistant".to_string(), + content: (!text.is_empty()).then_some(text), + }, + finish_reason: finish_reason_for( + body.get("stop_reason") + .and_then(Value::as_str) + .unwrap_or(""), + ) + .to_string(), + }], + usage: usage_from_parts( + field("input_tokens"), + field("output_tokens"), + field("cache_read_input_tokens"), + field("cache_creation_input_tokens"), + ), + }) + } +} + +#[cfg(test)] +#[path = "tests.rs"] +mod tests; diff --git a/litellm-rust/crates/core/src/providers/anthropic/mod.rs b/litellm-rust/crates/core/src/providers/anthropic/mod.rs index ba63992f3cb..0bb20991ff7 100644 --- a/litellm-rust/crates/core/src/providers/anthropic/mod.rs +++ b/litellm-rust/crates/core/src/providers/anthropic/mod.rs @@ -1 +1,2 @@ +pub mod chat_completions; pub mod messages; 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 86eb589e2c0..5e885734182 100644 --- a/litellm-rust/crates/core/src/providers/bedrock/audio_transcription.rs +++ b/litellm-rust/crates/core/src/providers/bedrock/audio_transcription.rs @@ -8,11 +8,8 @@ use crate::audio_transcription::types::{ }; use crate::error::{CoreError, CoreResult, json_type_name}; -use super::aws_base::AwsAuthConfig; -use super::constants::{ - AWS_REGION, AWS_REGION_NAME, BEDROCK_RUNTIME_ENDPOINT_TEMPLATE, BEDROCK_SERVICE, - DEFAULT_BEDROCK_REGION, -}; +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}; const SUPPORTED_PARAMS: &[&str] = &["language", "prompt", "temperature", "response_format"]; @@ -21,64 +18,6 @@ pub static BEDROCK_AUDIO_TRANSCRIPTION_CONFIG: BedrockAudioTranscriptionConfig = pub struct BedrockAudioTranscriptionConfig; -pub fn bedrock_model_id_and_region(model: &str) -> (String, Option) { - let mut stripped = model; - for prefix in ["bedrock/converse/", "bedrock/", "converse/"] { - if let Some(value) = stripped.strip_prefix(prefix) { - stripped = value; - break; - } - } - let mut region = None; - if let Some((candidate, remainder)) = stripped.split_once('/') - && is_bedrock_region(candidate) - { - region = Some(candidate.to_string()); - stripped = remainder; - } - for prefix in ["nova-2/", "nova/"] { - if let Some(value) = stripped.strip_prefix(prefix) { - stripped = value; - break; - } - } - if region.is_none() { - region = stripped - .strip_prefix("arn:") - .and_then(|value| value.split(':').nth(3)) - .filter(|value| !value.is_empty()) - .map(str::to_string); - } - (stripped.to_string(), region) -} - -fn is_bedrock_region(value: &str) -> bool { - value.len() > 3 - && value.contains('-') - && value - .chars() - .all(|char| char.is_ascii_alphanumeric() || char == '-') -} - -pub fn resolve_bedrock_region( - model_region: Option<&str>, - optional_params: &Map, - env_lookup: &dyn Fn(&str) -> Option, -) -> String { - if let Some(region) = optional_params - .get("aws_region_name") - .and_then(Value::as_str) - { - return region.to_string(); - } - if let Some(region) = model_region { - return region.to_string(); - } - env_lookup(AWS_REGION_NAME) - .or_else(|| env_lookup(AWS_REGION)) - .unwrap_or_else(|| DEFAULT_BEDROCK_REGION.to_string()) -} - fn audio_fields(audio: Value) -> CoreResult<(String, String)> { let object = audio.as_object().ok_or_else(|| CoreError::InvalidType { expected: "object", @@ -203,32 +142,6 @@ impl AudioTranscriptionProviderConfig for BedrockAudioTranscriptionConfig { } } -pub fn aws_auth_config( - optional_params: &Map, - env_lookup: &dyn Fn(&str) -> Option, -) -> AwsAuthConfig { - let value = |key: &str| { - optional_params - .get(key) - .and_then(Value::as_str) - .map(str::to_string) - }; - let env = |key: &str| env_lookup(key); - AwsAuthConfig { - access_key_id: value("aws_access_key_id").or_else(|| env("AWS_ACCESS_KEY_ID")), - secret_access_key: value("aws_secret_access_key").or_else(|| env("AWS_SECRET_ACCESS_KEY")), - session_token: value("aws_session_token").or_else(|| env("AWS_SESSION_TOKEN")), - region_name: value("aws_region_name").or_else(|| env(AWS_REGION_NAME)), - session_name: value("aws_session_name").or_else(|| env("AWS_SESSION_NAME")), - profile_name: value("aws_profile_name").or_else(|| env("AWS_PROFILE_NAME")), - role_name: value("aws_role_name").or_else(|| env("AWS_ROLE_NAME")), - web_identity_token: value("aws_web_identity_token") - .or_else(|| env("AWS_WEB_IDENTITY_TOKEN")), - sts_endpoint: value("aws_sts_endpoint").or_else(|| env("AWS_STS_ENDPOINT")), - external_id: value("aws_external_id").or_else(|| env("AWS_EXTERNAL_ID")), - } -} - #[cfg(test)] mod tests { use super::*; 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 dc036a3cf21..b11639aa09b 100644 --- a/litellm-rust/crates/core/src/providers/bedrock/aws_base.rs +++ b/litellm-rust/crates/core/src/providers/bedrock/aws_base.rs @@ -12,13 +12,15 @@ use aws_sigv4::http_request::{ }; use aws_sigv4::sign::v4; use aws_smithy_runtime_api::client::identity::Identity; +use serde_json::{Map, Value}; use sha2::{Digest, Sha256}; use super::constants::{ - AWS_ACCESS_KEY_ID, AWS_EXTERNAL_ID, AWS_PROFILE_NAME, AWS_REGION_NAME, AWS_ROLE_ARN, - AWS_ROLE_NAME, AWS_SECRET_ACCESS_KEY, AWS_SESSION_NAME, AWS_SESSION_TOKEN, AWS_STS_ENDPOINT, - AWS_WEB_IDENTITY_TOKEN, AWS_WEB_IDENTITY_TOKEN_FILE, BEDROCK_SERVICE, - DEFAULT_SESSION_NAME_PREFIX, + AWS_ACCESS_KEY_ID, AWS_EXTERNAL_ID, AWS_PROFILE_NAME, AWS_REGION, AWS_REGION_NAME, + AWS_ROLE_ARN, AWS_ROLE_NAME, AWS_SECRET_ACCESS_KEY, AWS_SESSION_NAME, AWS_SESSION_TOKEN, + AWS_SIGNED_HEADER_NAMES, AWS_STS_ENDPOINT, AWS_WEB_IDENTITY_TOKEN, AWS_WEB_IDENTITY_TOKEN_FILE, + BEDROCK_SERVICE, DEFAULT_BEDROCK_REGION, DEFAULT_SESSION_NAME_PREFIX, + SIGV4_COMPUTED_HEADER_NAMES, }; const STATIC_CREDENTIALS_TTL: Duration = Duration::from_secs(3600 - 60); @@ -401,6 +403,33 @@ fn default_session_name() -> String { format!("{DEFAULT_SESSION_NAME_PREFIX}-{seconds}") } +/// The subset of `headers` SigV4 should cover. +/// +/// Python signs only these and reattaches the rest afterwards, so a forwarded +/// client header cannot change the canonical request and invalidate the +/// signature. Signing everything instead makes the request 403 on a header the +/// caller supplied, on a deployment that works on the Python path. +pub fn aws_signature_headers(headers: &BTreeMap) -> BTreeMap { + headers + .iter() + .filter(|(name, _)| { + let name = name.to_ascii_lowercase(); + AWS_SIGNED_HEADER_NAMES.contains(&name.as_str()) + || name.starts_with("x-amz-") + || name.starts_with("x-amzn-") + }) + .map(|(name, value)| (name.clone(), value.clone())) + .collect() +} + +/// Whether the signer produces `name` itself. +/// +/// Python's reattach loop skips these, so a caller-supplied copy never reaches +/// the wire next to the computed one. +pub fn is_sigv4_computed_header(name: &str) -> bool { + SIGV4_COMPUTED_HEADER_NAMES.contains(&name.to_ascii_lowercase().as_str()) +} + pub fn sign_bedrock_post( url: &str, body: &[u8], @@ -441,6 +470,121 @@ pub fn sign_bedrock_post( .collect()) } +/// Model-id and region parsing shared by every Bedrock route. +pub fn bedrock_model_id_and_region(model: &str) -> (String, Option) { + let mut stripped = model; + for prefix in ["bedrock/converse/", "bedrock/", "converse/"] { + if let Some(value) = stripped.strip_prefix(prefix) { + stripped = value; + break; + } + } + let mut region = None; + if let Some((candidate, remainder)) = stripped.split_once('/') + && is_bedrock_region(candidate) + { + region = Some(candidate.to_string()); + stripped = remainder; + } + for prefix in ["nova-2/", "nova/"] { + if let Some(value) = stripped.strip_prefix(prefix) { + stripped = value; + break; + } + } + if region.is_none() { + // Python splits the whole ARN and takes field 3, the region. Stripping + // `arn:` first shifts every field down one, so the region is field 2 + // here; field 3 is the account id. + region = stripped + .strip_prefix("arn:") + .and_then(|value| value.split(':').nth(2)) + .filter(|value| !value.is_empty()) + .map(str::to_string); + } + (stripped.to_string(), region) +} + +fn is_bedrock_region(value: &str) -> bool { + value.len() > 3 + && value.contains('-') + && value + .chars() + .all(|char| char.is_ascii_alphanumeric() || char == '-') +} + +pub fn resolve_bedrock_region( + model_region: Option<&str>, + optional_params: &Map, + env_lookup: &dyn Fn(&str) -> Option, +) -> String { + if let Some(region) = optional_params + .get("aws_region_name") + .and_then(Value::as_str) + { + return region.to_string(); + } + if let Some(region) = model_region { + return region.to_string(); + } + env_lookup(AWS_REGION_NAME) + .or_else(|| env_lookup(AWS_REGION)) + .unwrap_or_else(|| DEFAULT_BEDROCK_REGION.to_string()) +} + +pub fn aws_auth_config( + optional_params: &Map, + env_lookup: &dyn Fn(&str) -> Option, +) -> AwsAuthConfig { + let value = |key: &str| { + optional_params + .get(key) + .and_then(Value::as_str) + .map(str::to_string) + }; + let env = |key: &str| env_lookup(key); + AwsAuthConfig { + access_key_id: value("aws_access_key_id").or_else(|| env("AWS_ACCESS_KEY_ID")), + secret_access_key: value("aws_secret_access_key").or_else(|| env("AWS_SECRET_ACCESS_KEY")), + session_token: value("aws_session_token").or_else(|| env("AWS_SESSION_TOKEN")), + region_name: value("aws_region_name").or_else(|| env(AWS_REGION_NAME)), + session_name: value("aws_session_name").or_else(|| env("AWS_SESSION_NAME")), + profile_name: value("aws_profile_name").or_else(|| env("AWS_PROFILE_NAME")), + role_name: value("aws_role_name").or_else(|| env("AWS_ROLE_NAME")), + web_identity_token: value("aws_web_identity_token") + .or_else(|| env("AWS_WEB_IDENTITY_TOKEN")), + sts_endpoint: value("aws_sts_endpoint").or_else(|| env("AWS_STS_ENDPOINT")), + external_id: value("aws_external_id").or_else(|| env("AWS_EXTERNAL_ID")), + } +} + +/// Credentials a host resolved through its own chain and handed down verbatim. +/// +/// A host with its own resolution (LiteLLM's Python `BaseAWSLLM`, which reads +/// profiles, STS and boto sessions) passes the result here so the core signs +/// with exactly those. Without this the core would re-derive from ambient +/// state, where an unrelated `AWS_ROLE_NAME` or `AWS_PROFILE_NAME` in the +/// environment outranks explicit keys in [`classify_auth`] and the two sides +/// would sign as different principals. +pub fn host_supplied_credentials(optional_params: &Map) -> Option { + let value = |key: &str| { + optional_params + .get(key) + .and_then(Value::as_str) + .map(str::trim) + .filter(|value| !value.is_empty()) + }; + let access_key_id = value("aws_access_key_id")?; + let secret_access_key = value("aws_secret_access_key")?; + Some(Credentials::new( + access_key_id, + secret_access_key, + value("aws_session_token").map(str::to_string), + None, + "litellm-host-supplied", + )) +} + #[cfg(test)] mod tests { use super::*; @@ -458,6 +602,18 @@ mod tests { ) } + #[test] + fn reads_the_region_field_of_a_model_arn_not_the_account_id() { + // Python's `_get_aws_region_from_model_arn` splits the whole ARN and + // takes field 3. Stripping `arn:` first shifts every field down one, so + // the region is field 2 here. Taking field 3 after the strip returns + // the account id, which is not a region at all. + let (_, region) = bedrock_model_id_and_region( + "bedrock/arn:aws:bedrock:us-west-2:123456789012:foundation-model/anthropic.claude-v2", + ); + assert_eq!(region.as_deref(), Some("us-west-2")); + } + #[test] fn classification_preserves_python_precedence() { let config = AwsAuthConfig { @@ -610,6 +766,52 @@ mod tests { )); } + #[test] + fn a_forwarded_client_header_is_not_folded_into_the_signature() { + // Python signs only the AWS header set, so a header a caller forwarded + // cannot change the canonical request. Signing it instead makes the + // request 403 the moment anything on the wire rewrites or drops it. + let (url, body, mut headers) = parity_inputs(); + headers.insert("x-request-id".to_string(), "abc-123".to_string()); + headers.insert("Accept-Encoding".to_string(), "gzip".to_string()); + headers.insert("x-amzn-trace-id".to_string(), "Root=1-abc".to_string()); + let signable = aws_signature_headers(&headers); + + assert!(!signable.contains_key("x-request-id")); + assert!(!signable.contains_key("Accept-Encoding")); + // The AWS-prefixed one is genuinely part of the signature. + assert!(signable.contains_key("x-amzn-trace-id")); + assert!(signable.contains_key("Content-Type")); + + let credentials = Credentials::new( + "AKIDEXAMPLE", + "wJalrXUtnFEMI/K7MDENG+bPxRfiCYEXAMPLEKEY", + None, + None, + "test", + ); + let signed = sign_bedrock_post( + &url, + &body, + &signable, + "us-east-1", + &credentials, + SystemTime::UNIX_EPOCH, + ) + .expect("signs"); + let authorization = signed + .get("Authorization") + .expect("carries an authorization header"); + assert!( + !authorization.contains("x-request-id"), + "forwarded header reached SignedHeaders: {authorization}" + ); + assert!( + !authorization.contains("accept-encoding"), + "forwarded header reached SignedHeaders: {authorization}" + ); + } + #[test] fn signing_matches_botocore_golden_vector() { let (url, body, headers) = parity_inputs(); diff --git a/litellm-rust/crates/core/src/providers/bedrock/chat_completions/mod.rs b/litellm-rust/crates/core/src/providers/bedrock/chat_completions/mod.rs new file mode 100644 index 00000000000..f239b6921fa --- /dev/null +++ b/litellm-rust/crates/core/src/providers/bedrock/chat_completions/mod.rs @@ -0,0 +1 @@ +pub mod transformation; 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 new file mode 100644 index 00000000000..4b75dcb8e9d --- /dev/null +++ b/litellm-rust/crates/core/src/providers/bedrock/chat_completions/tests.rs @@ -0,0 +1,580 @@ +use super::*; +use serde_json::json; + +fn messages(value: Value) -> Vec { + serde_json::from_value(value).expect("valid messages") +} + +fn params(value: Value) -> Map { + match value { + Value::Object(map) => map, + other => panic!("params must be an object, got {other}"), + } +} + +fn transform(msgs: Value, opts: Value) -> Value { + BEDROCK_CHAT_COMPLETIONS_CONFIG + .transform_request( + "anthropic.claude-sonnet-4-5-v1:0", + messages(msgs), + params(opts), + ) + .expect("request transforms") + .body +} + +fn transform_response(body: Value) -> CoreResult { + BEDROCK_CHAT_COMPLETIONS_CONFIG.transform_response( + "anthropic.claude-sonnet-4-5-v1:0", + ProviderChatResponseData { body }, + ) +} + +fn reason(msgs: Value, opts: Value) -> Option { + BEDROCK_CHAT_COMPLETIONS_CONFIG.unsupported_reason(&messages(msgs), ¶ms(opts)) +} + +#[test] +fn builds_the_converse_body_python_builds() { + let body = transform( + json!([ + {"role": "system", "content": "be terse"}, + {"role": "user", "content": "hi"} + ]), + json!({"maxTokens": 128, "temperature": 0.2}), + ); + assert_eq!( + body, + json!({ + "inferenceConfig": {"maxTokens": 128, "temperature": 0.2}, + "messages": [{"role": "user", "content": [{"text": "hi"}]}], + "system": [{"text": "be terse"}] + }) + ); +} + +#[test] +fn always_emits_inference_config_even_when_empty() { + let body = transform(json!([{"role": "user", "content": "hi"}]), json!({})); + assert_eq!(body["inferenceConfig"], json!({})); + assert!(body.get("system").is_none()); +} + +#[test] +fn places_only_inference_params_in_inference_config() { + let body = transform( + json!([{"role": "user", "content": "hi"}]), + json!({ + "maxTokens": 64, + "temperature": 0.1, + "topP": 0.9, + "stopSequences": ["STOP"] + }), + ); + assert_eq!( + body["inferenceConfig"], + json!({"maxTokens": 64, "temperature": 0.1, "topP": 0.9, "stopSequences": ["STOP"]}) + ); + assert!(body.get("additionalModelRequestFields").is_none()); +} + +#[test] +fn merges_consecutive_user_turns_into_one_message() { + let body = transform( + json!([ + {"role": "user", "content": "one"}, + {"role": "user", "content": [{"type": "text", "text": "two"}]}, + {"role": "assistant", "content": "ack"}, + {"role": "user", "content": "three"} + ]), + json!({}), + ); + assert_eq!( + body["messages"], + json!([ + {"role": "user", "content": [{"text": "one"}, {"text": "two"}]}, + {"role": "assistant", "content": [{"text": "ack"}]}, + {"role": "user", "content": [{"text": "three"}]} + ]) + ); +} + +#[test] +fn declines_streaming() { + assert_eq!( + reason( + json!([{"role": "user", "content": "hi"}]), + json!({"stream": true}) + ), + Some(Unsupported("streaming")) + ); +} + +#[test] +fn declines_top_k_because_python_routes_it_by_base_model() { + assert_eq!( + reason( + json!([{"role": "user", "content": "hi"}]), + json!({"topK": 40}) + ), + Some(Unsupported("unrecognized request parameter")) + ); +} + +#[test] +fn declines_tools_and_other_params_outside_the_allowlist() { + for param in [ + json!({"tools": []}), + json!({"tool_choice": {"auto": {}}}), + json!({"thinking": {"type": "enabled"}}), + json!({"requestMetadata": {"k": "v"}}), + json!({"outputConfig": {}}), + json!({"_parallel_tool_use_config": {}}), + ] { + assert_eq!( + reason(json!([{"role": "user", "content": "hi"}]), param.clone()), + Some(Unsupported("unrecognized request parameter")), + "expected {param} to decline" + ); + } +} + +#[test] +fn declines_blank_text_rather_than_substituting_the_anthropic_placeholder() { + for content in [ + json!(""), + json!(" "), + json!([{"type": "text", "text": " "}]), + ] { + assert_eq!( + reason( + json!([{"role": "user", "content": content}, {"role": "user", "content": "hi"}]), + json!({}) + ), + Some(Unsupported("blank message text")), + "expected blank content {content} to decline" + ); + } +} + +#[test] +fn declines_a_message_whose_content_list_is_empty() { + // The blank-text check scans parts, so an empty list clears it; Converse + // rejects an empty `content` array, which is a decline the core owes the + // host before the call rather than an error after it. + assert_eq!( + reason(json!([{"role": "user", "content": []}]), json!({})), + Some(Unsupported("message without content")) + ); + assert_eq!( + reason( + json!([{"role": "user", "content": [{"type": "text", "text": "hi"}]}]), + json!({}) + ), + None + ); +} + +#[test] +fn declines_a_conversation_that_opens_or_closes_on_an_assistant_turn() { + assert_eq!( + reason( + json!([ + {"role": "assistant", "content": "prefill"}, + {"role": "user", "content": "hi"} + ]), + json!({}) + ), + Some(Unsupported( + "conversation does not run user turn to user turn" + )) + ); + assert_eq!( + reason( + json!([ + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": "prefill"} + ]), + json!({}) + ), + Some(Unsupported( + "conversation does not run user turn to user turn" + )) + ); +} + +#[test] +fn accepts_a_user_to_user_text_conversation() { + assert_eq!( + reason( + json!([ + {"role": "system", "content": "be terse"}, + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": "hello"}, + {"role": "user", "content": "again"} + ]), + json!({"maxTokens": 16}) + ), + None + ); +} + +#[test] +fn builds_the_converse_url_from_the_region_in_the_model_id() { + let config = &BEDROCK_CHAT_COMPLETIONS_CONFIG; + assert_eq!( + config + .complete_url(None, "us-east-1/anthropic.claude-v2", &Map::new(), &|_| { + None + }) + .expect("url builds"), + "https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-v2/converse" + ); +} + +#[test] +fn falls_back_to_the_region_env_then_the_default_region() { + let config = &BEDROCK_CHAT_COMPLETIONS_CONFIG; + let with_env = |key: &str| (key == "AWS_REGION_NAME").then(|| "eu-west-1".to_string()); + assert_eq!( + config + .complete_url(None, "anthropic.claude-v2", &Map::new(), &with_env) + .expect("url builds"), + "https://bedrock-runtime.eu-west-1.amazonaws.com/model/anthropic.claude-v2/converse" + ); + assert_eq!( + config + .complete_url(None, "anthropic.claude-v2", &Map::new(), &|_| None) + .expect("url builds"), + "https://bedrock-runtime.us-west-2.amazonaws.com/model/anthropic.claude-v2/converse" + ); +} + +#[test] +fn prefers_an_explicit_runtime_endpoint_over_the_api_base() { + let config = &BEDROCK_CHAT_COMPLETIONS_CONFIG; + let overrides = params(json!({"aws_bedrock_runtime_endpoint": "https://vpce.internal/"})); + assert_eq!( + config + .complete_url( + Some("https://ignored.example"), + "anthropic.claude-v2", + &overrides, + &|_| None + ) + .expect("url builds"), + "https://vpce.internal/model/anthropic.claude-v2/converse" + ); +} + +#[test] +fn signs_with_sigv4_in_the_resolved_region() { + let config = &BEDROCK_CHAT_COMPLETIONS_CONFIG; + assert_eq!( + config + .auth( + None, + "eu-central-1/anthropic.claude-v2", + &Map::new(), + &|_| None + ) + .expect("auth resolves"), + ChatCompletionsAuth::AwsSigV4 { + region: "eu-central-1".to_string() + } + ); +} + +#[test] +fn a_bearer_token_outranks_sigv4_the_way_python_resolves_it() { + // Python's get_request_headers reads `api_key` as the Bedrock bearer token + // and only falls back to the env when the caller passed none, so each case + // pins one of its precedence rules. Signing as the host principal when a + // bearer identity is configured would cross an account and quota boundary. + let bedrock_env = + |key: &str| (key == "AWS_BEARER_TOKEN_BEDROCK").then(|| "from-env".to_string()); + let no_env = |_: &str| None; + let resolve = |api_key, env: &dyn Fn(&str) -> Option| { + BEDROCK_CHAT_COMPLETIONS_CONFIG + .auth( + api_key, + "eu-central-1/anthropic.claude-v2", + &Map::new(), + env, + ) + .expect("auth resolves") + }; + let bearer = |token: &str| ChatCompletionsAuth::Bearer { + token: token.to_string(), + }; + let sigv4 = ChatCompletionsAuth::AwsSigV4 { + region: "eu-central-1".to_string(), + }; + + // A caller-supplied key is the bearer token, and outranks the env. + assert_eq!( + resolve(Some("bedrock-api-key"), &bedrock_env), + bearer("bedrock-api-key") + ); + // No key, so the env supplies it. + assert_eq!(resolve(None, &bedrock_env), bearer("from-env")); + // An empty key is not a bearer token, and deliberately does NOT reach for + // the env, which is what Python's `is not None` check does. + assert_eq!(resolve(Some(""), &bedrock_env), sigv4); + // Whitespace is truthy in Python, so it stays a bearer token rather than + // silently becoming a host-credentialed SigV4 request. + assert_eq!(resolve(Some(" "), &no_env), bearer(" ")); + // Neither present, so SigV4 as before. + assert_eq!(resolve(None, &no_env), sigv4); +} + +#[test] +fn normalizes_a_converse_response_into_openai_shape() { + let response = transform_response(json!({ + "output": {"message": {"role": "assistant", "content": [ + {"text": "hello"}, {"text": " there"} + ]}}, + "stopReason": "end_turn", + "usage": {"inputTokens": 11, "outputTokens": 4, "totalTokens": 15} + })) + .expect("response transforms"); + + assert_eq!(response.model, "anthropic.claude-sonnet-4-5-v1:0"); + assert_eq!( + response.choices[0].message.content.as_deref(), + Some("hello there") + ); + assert_eq!(response.choices[0].finish_reason, "stop"); + assert_eq!(response.usage.prompt_tokens, 11); + assert_eq!(response.usage.completion_tokens, 4); + assert_eq!(response.usage.total_tokens, 15); +} + +#[test] +fn maps_converse_stop_reasons_python_maps() { + for (provider_reason, expected) in [ + ("end_turn", "stop"), + ("stop_sequence", "stop"), + ("max_tokens", "length"), + ("guardrail_intervened", "content_filter"), + // Converse emits this one, and Python's `_FINISH_REASON_MAP` carries + // it. Folding it into `stop` reports a filtered completion as a normal + // one to anything keying on the finish reason. + ("content_filtered", "content_filter"), + ("content_filter", "content_filter"), + ] { + let response = transform_response(json!({ + "output": {"message": {"content": [{"text": "x"}]}}, + "stopReason": provider_reason, + "usage": {"inputTokens": 1, "outputTokens": 1} + })) + .expect("response transforms"); + assert_eq!( + response.choices[0].finish_reason, expected, + "stopReason {provider_reason}" + ); + } +} + +#[test] +fn reports_an_empty_converse_answer_as_an_empty_string_not_null() { + // Converse assigns the joined text unconditionally + // (`chat_completion_message["content"] = content_str`), unlike Anthropic's + // `merged_text or None`, so an empty answer is `""` on both paths. A caller + // calling `.strip()` on it would break on the Rust path alone. Reachable + // through a filtered or guardrail-intervened response. + for content in [json!([]), json!([{"text": ""}])] { + let response = transform_response(json!({ + "output": {"message": {"content": content}}, + "stopReason": "content_filtered", + "usage": {"inputTokens": 1, "outputTokens": 0} + })) + .expect("response transforms"); + assert_eq!(response.choices[0].message.content, Some(String::new())); + } +} + +#[test] +fn reports_the_total_tokens_converse_sent_rather_than_recomputing_them() { + // Python reads `usage["totalTokens"]` straight through here, where Anthropic + // has no such field and adds the two counts instead. The two agree while the + // gate declines every cache_control request, so this is what keeps them + // agreeing if that ever widens. + let response = transform_response(json!({ + "output": {"message": {"content": [{"text": "x"}]}}, + "stopReason": "end_turn", + "usage": {"inputTokens": 10, "outputTokens": 4, "cacheReadInputTokens": 7, "totalTokens": 14} + })) + .expect("response transforms"); + assert_eq!( + response.usage.total_tokens, 14, + "provider total was recomputed" + ); + assert_eq!(response.usage.prompt_tokens, 17); + assert_eq!(response.usage.completion_tokens, 4); +} + +#[test] +fn falls_back_to_the_computed_total_when_converse_omits_it() { + // Python raises a KeyError on a body with no `totalTokens`. Reporting a zero + // instead would be a worse divergence than the one above, so the computed + // total stands in. + let response = transform_response(json!({ + "output": {"message": {"content": [{"text": "x"}]}}, + "stopReason": "end_turn", + "usage": {"inputTokens": 10, "outputTokens": 4} + })) + .expect("response transforms"); + assert_eq!(response.usage.total_tokens, 14); +} + +#[test] +fn declines_a_cache_control_message_so_widening_the_gate_is_a_red_test() { + // Converse only reports cache token counts when the request carries a + // cachePoint block, which is why the provider total and the computed one + // cannot disagree today. This is the tripwire: whoever widens the gate to + // admit prompt caching has to come back and re-check the usage mapping + // rather than discovering a silent number change in production. + assert_eq!( + reason( + json!([{"role": "user", "content": [ + {"type": "text", "text": "hi", "cache_control": {"type": "ephemeral"}} + ]}]), + json!({}) + ), + Some(Unsupported("non-text message content")) + ); +} + +#[test] +fn folds_converse_cache_tokens_into_prompt_tokens() { + let response = transform_response(json!({ + "output": {"message": {"content": [{"text": "x"}]}}, + "stopReason": "end_turn", + "usage": { + "inputTokens": 10, + "outputTokens": 2, + "cacheReadInputTokens": 5, + "cacheWriteInputTokens": 3 + } + })) + .expect("response transforms"); + assert_eq!(response.usage.prompt_tokens, 18); + assert_eq!(response.usage.prompt_tokens_details.cached_tokens, 5); + assert_eq!( + response.usage.prompt_tokens_details.cache_creation_tokens, + 3 + ); + assert_eq!(response.usage.prompt_tokens_details.text_tokens, 10); +} + +#[test] +fn declines_a_response_carrying_a_tool_use_block() { + let err = transform_response(json!({ + "output": {"message": {"content": [ + {"toolUse": {"toolUseId": "t1", "name": "f", "input": {}}} + ]}}, + "stopReason": "tool_use", + "usage": {"inputTokens": 1, "outputTokens": 1} + })) + .expect_err("tool use block"); + assert_eq!( + err, + CoreError::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()) + ); + assert_eq!( + transform_response(json!({"usage": {}})).expect_err("no output"), + CoreError::MissingField("output.message.content") + ); + assert_eq!( + transform_response(json!({"output": {"message": {"content": []}}})).expect_err("no usage"), + CoreError::MissingField("usage") + ); +} + +#[test] +fn accepts_aws_call_configuration_without_serializing_it() { + let call_config = json!({ + "maxTokens": 16, + "aws_access_key_id": "AKIA", + "aws_secret_access_key": "secret", + "aws_session_token": "token", + "aws_region_name": "us-east-1", + "aws_profile_name": "litellm-stage", + "aws_role_name": "role", + "aws_session_name": "session", + "aws_web_identity_token": "wit", + "aws_sts_endpoint": "https://sts.example", + "aws_external_id": "ext", + "aws_bedrock_runtime_endpoint": "https://vpce.internal" + }); + assert_eq!( + reason( + json!([{"role": "user", "content": "hi"}]), + call_config.clone() + ), + None + ); + let body = transform(json!([{"role": "user", "content": "hi"}]), call_config); + assert_eq!( + body, + json!({ + "inferenceConfig": {"maxTokens": 16}, + "messages": [{"role": "user", "content": [{"text": "hi"}]}] + }), + "aws call configuration must not reach the Converse body" + ); +} + +#[test] +fn leaves_a_complete_converse_url_untouched() { + let config = &BEDROCK_CHAT_COMPLETIONS_CONFIG; + let already_built = + "https://bedrock-runtime.us-east-1.amazonaws.com/model/us.anthropic.claude-v2%3A0/converse"; + assert_eq!( + config + .complete_url( + Some(already_built), + "anthropic.claude-v2", + &Map::new(), + &|_| None + ) + .expect("url builds"), + already_built, + "a host that encoded the model id itself must not have it re-derived" + ); +} + +#[test] +fn host_supplied_credentials_outrank_ambient_profile_and_role_state() { + use crate::providers::bedrock::aws_base::host_supplied_credentials; + + let supplied = params(json!({ + "aws_access_key_id": "AKIAHOST", + "aws_secret_access_key": "hostsecret", + "aws_session_token": "hosttoken" + })); + let credentials = host_supplied_credentials(&supplied).expect("host credentials"); + assert_eq!(credentials.access_key_id(), "AKIAHOST"); + assert_eq!(credentials.secret_access_key(), "hostsecret"); + assert_eq!(credentials.session_token(), Some("hosttoken")); + + // Without a full static pair there is nothing to honor, so the core falls + // back to deriving credentials itself. + assert!(host_supplied_credentials(¶ms(json!({"aws_access_key_id": "AKIA"}))).is_none()); + assert!( + host_supplied_credentials(¶ms( + json!({"aws_access_key_id": " ", "aws_secret_access_key": "s"}) + )) + .is_none() + ); + assert!(host_supplied_credentials(&Map::new()).is_none()); +} 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 new file mode 100644 index 00000000000..b107950748e --- /dev/null +++ b/litellm-rust/crates/core/src/providers/bedrock/chat_completions/transformation.rs @@ -0,0 +1,297 @@ +use serde_json::{Map, Value, json}; + +use crate::chat_completions::conversation::{Conversation, TurnRole, build_conversation}; +use crate::chat_completions::response_utils::{finish_reason_for, unix_now, usage_from_parts}; +use crate::chat_completions::transformation::{ + ChatCompletionsAuth, ChatCompletionsProviderConfig, Unsupported, unsupported_message, + unsupported_param, +}; +use crate::chat_completions::types::{ + ChatCompletionsChoice, ChatCompletionsChoiceMessage, ChatCompletionsResponse, + ChatCompletionsUsage, ChatMessage, ChatMessageContent, ProviderChatRequestData, + ProviderChatResponseData, +}; +use crate::error::{CoreError, CoreResult}; + +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}; + +/// Converse parameter names, post `map_openai_params`, that the Rust path can +/// place verbatim in `inferenceConfig`. +/// +/// `topK` is deliberately absent: Python routes it to +/// `additionalModelRequestFields` for Anthropic base models and to +/// `inferenceConfig` otherwise, and that branch reads the model catalog the +/// core cannot see. +const SUPPORTED_PARAMS: &[&str] = &["maxTokens", "temperature", "topP", "stopSequences"]; + +/// Params that belong in `inferenceConfig`, in the order Python's +/// `AmazonConverseConfig` declares them, so bodies compare cleanly. +const INFERENCE_CONFIG_PARAMS: &[&str] = SUPPORTED_PARAMS; + +const AWS_BEDROCK_RUNTIME_ENDPOINT: &str = "aws_bedrock_runtime_endpoint"; + +/// AWS call configuration a host passes down: consumed for signing and endpoint +/// resolution, never serialized into the Converse body. +const CONFIG_PARAMS: &[&str] = &[ + "aws_access_key_id", + "aws_secret_access_key", + "aws_session_token", + "aws_region_name", + "aws_session_name", + "aws_profile_name", + "aws_role_name", + "aws_web_identity_token", + "aws_sts_endpoint", + "aws_external_id", + AWS_BEDROCK_RUNTIME_ENDPOINT, +]; + +const CONVERSE_PATH_SUFFIX: &str = "/converse"; + +pub struct BedrockChatCompletionsConfig; + +pub const BEDROCK_CHAT_COMPLETIONS_CONFIG: BedrockChatCompletionsConfig = + BedrockChatCompletionsConfig; + +fn converse_body(conversation: &Conversation, params: &Map) -> Value { + let messages: Vec = conversation + .turns + .iter() + .map(|turn| { + json!({ + "role": turn.role.as_str(), + "content": turn.texts.iter().map(|text| json!({"text": text})).collect::>(), + }) + }) + .collect(); + + let inference_config = Map::from_iter(INFERENCE_CONFIG_PARAMS.iter().filter_map(|name| { + params + .get(*name) + .map(|value| ((*name).to_string(), value.clone())) + })); + + let system: Vec = conversation + .system + .iter() + .map(|text| json!({"text": text})) + .collect(); + + Value::Object(Map::from_iter( + [ + ( + "inferenceConfig".to_string(), + Value::Object(inference_config), + ), + ("messages".to_string(), json!(messages)), + ] + .into_iter() + .chain((!system.is_empty()).then(|| ("system".to_string(), json!(system)))), + )) +} + +fn has_blank_text(message: &ChatMessage) -> bool { + match &message.content { + None => false, + Some(ChatMessageContent::Text(text)) => text.trim().is_empty(), + Some(ChatMessageContent::Parts(parts)) => parts.iter().any(|part| { + part.get("text") + .and_then(Value::as_str) + .is_none_or(|text| text.trim().is_empty()) + }), + } +} + +impl ChatCompletionsProviderConfig for BedrockChatCompletionsConfig { + fn complete_url( + &self, + api_base: Option<&str>, + model: &str, + optional_params: &Map, + env_lookup: &dyn Fn(&str) -> Option, + ) -> CoreResult { + 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 + .get(AWS_BEDROCK_RUNTIME_ENDPOINT) + .and_then(Value::as_str) + .or(api_base) + .map(str::trim) + .filter(|value| !value.is_empty()) + .map(str::to_string) + .unwrap_or_else(|| BEDROCK_RUNTIME_ENDPOINT_TEMPLATE.replace("{region}", ®ion)); + let endpoint = endpoint.trim_end_matches('/'); + // A host that already built the full Converse URL (LiteLLM's Python + // path encodes the model id itself) passes it through untouched, the + // way the Anthropic config leaves a complete `/v1/messages` URL alone. + if endpoint.ends_with(CONVERSE_PATH_SUFFIX) { + return Ok(endpoint.to_string()); + } + Ok(format!("{endpoint}/model/{model_id}{CONVERSE_PATH_SUFFIX}")) + } + + fn auth( + &self, + api_key: Option<&str>, + model: &str, + optional_params: &Map, + env_lookup: &dyn Fn(&str) -> Option, + ) -> CoreResult { + // 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 + // all-whitespace token stays a bearer token here because Python sends + // it too: treating it as absent would sign as the host principal + // instead, which is the identity swap this branch exists to prevent. + let bearer = match api_key { + Some(key) => Some(key.to_string()), + None => env_lookup(AWS_BEARER_TOKEN_BEDROCK), + } + .filter(|token| !token.is_empty()); + if let Some(token) = bearer { + return Ok(ChatCompletionsAuth::Bearer { token }); + } + let (_, model_region) = bedrock_model_id_and_region(model); + Ok(ChatCompletionsAuth::AwsSigV4 { + region: resolve_bedrock_region(model_region.as_deref(), optional_params, env_lookup), + }) + } + + fn default_headers(&self) -> &'static [(&'static str, &'static str)] { + &[("Content-Type", "application/json")] + } + + fn supported_params(&self) -> &'static [&'static str] { + SUPPORTED_PARAMS + } + + fn config_params(&self) -> &'static [&'static str] { + CONFIG_PARAMS + } + + fn unsupported_reason( + &self, + messages: &[ChatMessage], + optional_params: &Map, + ) -> Option { + unsupported_param(SUPPORTED_PARAMS, CONFIG_PARAMS, optional_params) + .or_else(|| messages.iter().find_map(unsupported_message)) + // Python's Converse translation drops blank text blocks instead of + // substituting the placeholder the shared conversation builder + // applies, so decline blank text rather than diverge. + .or_else(|| { + messages + .iter() + .any(has_blank_text) + .then_some(Unsupported("blank message text")) + }) + // Converse has no assistant prefill: Python inserts a continue turn + // when a conversation opens or closes on an assistant message, and + // only under `litellm.modify_params`, which the core cannot see. + // Declining both ends also keeps the shared builder's final + // assistant right-strip (an Anthropic rule) unreachable here. + .or_else(|| { + let conversation = build_conversation(messages); + let ends_on_assistant = conversation + .turns + .last() + .is_some_and(|turn| turn.role == TurnRole::Assistant); + (!conversation.opens_on_user_turn() || ends_on_assistant).then_some(Unsupported( + "conversation does not run user turn to user turn", + )) + }) + } + + fn transform_request( + &self, + _model: &str, + messages: Vec, + optional_params: Map, + ) -> CoreResult { + Ok(ProviderChatRequestData { + body: converse_body(&build_conversation(&messages), &optional_params), + }) + } + + fn transform_response( + &self, + model: &str, + response: ProviderChatResponseData, + ) -> CoreResult { + let body = response.body.as_object().ok_or_else(|| { + CoreError::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"))?; + // 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| { + block + .as_object() + .is_none_or(|block| block.len() != 1 || !block.contains_key("text")) + }) { + return Err(CoreError::Unsupported("non-text response content block")); + } + let text: String = content + .iter() + .filter_map(|block| block.get("text").and_then(Value::as_str)) + .collect(); + + let usage = body + .get("usage") + .and_then(Value::as_object) + .ok_or(CoreError::MissingField("usage"))?; + let field = |name: &str| usage.get(name).and_then(Value::as_u64).unwrap_or(0); + let computed = usage_from_parts( + field("inputTokens"), + field("outputTokens"), + field("cacheReadInputTokens"), + field("cacheWriteInputTokens"), + ); + // Converse reports `totalTokens` and Python passes it straight through, + // where Anthropic has no such field and Python adds the two counts + // instead, so only this provider overrides the computed total. Python + // does a bare `usage["totalTokens"]` lookup, so a body without the key + // raises there rather than reporting a zero; fall back to the computed + // total, which is the closest thing to that without failing the call. + let usage = ChatCompletionsUsage { + total_tokens: usage + .get("totalTokens") + .and_then(Value::as_u64) + .unwrap_or(computed.total_tokens), + ..computed + }; + + Ok(ChatCompletionsResponse { + created: unix_now(), + // Converse echoes no model id, so Python reports the requested one. + model: model.to_string(), + choices: vec![ChatCompletionsChoice { + index: 0, + message: ChatCompletionsChoiceMessage { + role: "assistant".to_string(), + // Converse assigns the joined string unconditionally, so an + // empty response is `""` here and not `None` as it is on + // Anthropic. A caller calling `.strip()` on it would break + // on this path alone. + content: Some(text), + }, + finish_reason: finish_reason_for( + body.get("stopReason").and_then(Value::as_str).unwrap_or(""), + ) + .to_string(), + }], + usage, + }) + } +} + +#[cfg(test)] +#[path = "tests.rs"] +mod tests; diff --git a/litellm-rust/crates/core/src/providers/bedrock/constants.rs b/litellm-rust/crates/core/src/providers/bedrock/constants.rs index 785295207e7..be215cc9016 100644 --- a/litellm-rust/crates/core/src/providers/bedrock/constants.rs +++ b/litellm-rust/crates/core/src/providers/bedrock/constants.rs @@ -11,6 +11,31 @@ pub const AWS_ROLE_ARN: &str = "AWS_ROLE_ARN"; pub const AWS_WEB_IDENTITY_TOKEN_FILE: &str = "AWS_WEB_IDENTITY_TOKEN_FILE"; pub const AWS_STS_ENDPOINT: &str = "AWS_STS_ENDPOINT"; pub const AWS_EXTERNAL_ID: &str = "AWS_EXTERNAL_ID"; +pub const AWS_BEARER_TOKEN_BEDROCK: &str = "AWS_BEARER_TOKEN_BEDROCK"; + +/// Headers SigV4 covers, beyond the `x-amz-` / `x-amzn-` prefixes. Mirrors +/// Python's `_filter_headers_for_aws_signature` allowlist. +pub const AWS_SIGNED_HEADER_NAMES: &[&str] = &[ + "host", + "content-type", + "date", + "x-amz-date", + "x-amz-security-token", + "x-amz-content-sha256", + "x-amz-algorithm", + "x-amz-credential", + "x-amz-signedheaders", + "x-amz-signature", +]; +/// Headers the signer emits itself. Mirrors Python's `SIGV4_COMPUTED_HEADERS`, +/// which the reattach loop skips so a caller's copy cannot ride alongside the +/// computed one. +pub const SIGV4_COMPUTED_HEADER_NAMES: &[&str] = &[ + "authorization", + "x-amz-date", + "x-amz-security-token", + "date", +]; pub const BEDROCK_SERVICE: &str = "bedrock"; pub const DEFAULT_SESSION_NAME_PREFIX: &str = "litellm-session"; pub const DEFAULT_BEDROCK_REGION: &str = "us-west-2"; diff --git a/litellm-rust/crates/core/src/providers/bedrock/mod.rs b/litellm-rust/crates/core/src/providers/bedrock/mod.rs index b09675ad7dd..d9cd3efcb74 100644 --- a/litellm-rust/crates/core/src/providers/bedrock/mod.rs +++ b/litellm-rust/crates/core/src/providers/bedrock/mod.rs @@ -5,4 +5,5 @@ #[cfg(feature = "bedrock-auth")] pub mod audio_transcription; pub mod aws_base; +pub mod chat_completions; mod constants; diff --git a/litellm-rust/crates/python-bridge/src/lib.rs b/litellm-rust/crates/python-bridge/src/lib.rs index f0cc26a0cca..c6f81cf6916 100644 --- a/litellm-rust/crates/python-bridge/src/lib.rs +++ b/litellm-rust/crates/python-bridge/src/lib.rs @@ -6,6 +6,10 @@ use litellm_ai_gateway::io::audio_transcription::{ }; use litellm_ai_gateway::io::ocr::{OcrRequest, ocr as run_ocr}; use litellm_ai_gateway::io::responses_ws::ResponsesWebSocketConnection as RustResponsesWebSocketConnection; +use litellm_core::chat_completions::types::{ChatCompletionsRequest, ChatCompletionsResponse}; +use litellm_core::chat_completions::{ + chat_completions as run_chat_completions, chat_completions_decline_reason, +}; use litellm_core::error::CoreError; use litellm_core::messages::messages as run_messages; use litellm_core::messages::types::{AnthropicMessagesResponse, MessagesRequest}; @@ -16,6 +20,20 @@ use serde_json::{Map, Value}; mod gil; +pyo3::create_exception!( + _native, + RustBridgeDeclined, + pyo3::exceptions::PyException, + "The route declined before calling the provider, so the host may retry on its own path." +); + +pyo3::create_exception!( + _native, + RustUpstreamError, + pyo3::exceptions::PyException, + "The provider call was already issued and failed. Args are (status, message); status is 0 when there was no HTTP response." +); + type MarshaledOcrInputs = ( Value, Option>, @@ -45,6 +63,15 @@ fn messages_response_to_py( json_to_py(py, value) } +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) +} + fn core_error_to_pyerr(err: CoreError) -> PyErr { match err { CoreError::Auth(message) => PyValueError::new_err(message), @@ -56,6 +83,33 @@ fn core_error_to_pyerr(err: CoreError) -> PyErr { } } +/// Map a core error for a route whose host keeps a Python implementation. +/// +/// The distinction the host needs is whether the provider was already called. +/// 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 { + match err { + CoreError::Unsupported(_) + | CoreError::Auth(_) + | CoreError::InvalidProvider(_) + | CoreError::InvalidRequest(_) + | CoreError::InvalidType { .. } + | CoreError::MissingField(_) + | CoreError::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 } => { + RustUpstreamError::new_err((status, format!("{status}: {body}"))) + } + CoreError::Network(message) | CoreError::InvalidResponse(message) => { + RustUpstreamError::new_err((0u16, message)) + } + } +} + fn optional_object_to_map( py: Python<'_>, name: &'static str, @@ -430,6 +484,143 @@ fn amessages( }) } +type MarshaledChatCompletionsInputs = ( + Value, + Map, + Option>, + Option, +); + +fn marshal_chat_completions_inputs( + py: Python<'_>, + messages: Py, + optional_params: Option>, + extra_headers: Option>, + timeout_seconds: Option, +) -> PyResult { + let messages = py_to_json(py, messages.bind(py))?; + if !messages.is_array() { + return Err(PyValueError::new_err("messages must be a list")); + } + let optional_params = optional_object_to_map(py, "optional_params", optional_params)?; + let extra_headers = match extra_headers { + Some(headers) => Some(optional_object_to_map(py, "extra_headers", Some(headers))?), + None => None, + }; + Ok(( + messages, + optional_params, + extra_headers, + optional_timeout(timeout_seconds), + )) +} + +/// The decline reason for this request, or `None` when the Rust path accepts +/// it. Resolves no credentials and performs no I/O, so a host can ask before +/// committing to either path. +#[pyfunction] +#[pyo3(signature = (model, messages, optional_params=None, custom_llm_provider=None))] +fn chat_completions_decline( + py: Python<'_>, + model: String, + messages: Py, + optional_params: Option>, + custom_llm_provider: Option, +) -> PyResult> { + let messages = py_to_json(py, messages.bind(py))?; + let optional_params = optional_object_to_map(py, "optional_params", optional_params)?; + Ok(chat_completions_decline_reason( + &model, + custom_llm_provider.as_deref(), + messages, + &optional_params, + ) + .map(str::to_string)) +} + +#[pyfunction] +#[pyo3(signature = (model, messages, optional_params=None, api_key=None, api_base=None, custom_llm_provider=None, extra_headers=None, timeout_seconds=None))] +#[allow(clippy::too_many_arguments)] +fn chat_completions( + py: Python<'_>, + model: String, + messages: Py, + optional_params: Option>, + api_key: Option, + api_base: Option, + custom_llm_provider: Option, + extra_headers: Option>, + timeout_seconds: Option, +) -> PyResult> { + let (messages, optional_params, extra_headers, timeout) = marshal_chat_completions_inputs( + py, + messages, + optional_params, + extra_headers, + timeout_seconds, + )?; + + let result = gil::release_gil(py, || { + pyo3_async_runtimes::tokio::get_runtime().block_on(run_chat_completions( + ChatCompletionsRequest { + model: &model, + messages, + optional_params, + api_key: api_key.as_deref(), + api_base: api_base.as_deref(), + custom_llm_provider: custom_llm_provider.as_deref(), + extra_headers, + timeout, + }, + )) + }); + + match result { + Ok(response) => chat_completions_response_to_py(py, response), + Err(err) => Err(chat_completions_error_to_pyerr(err)), + } +} + +#[pyfunction] +#[pyo3(signature = (model, messages, optional_params=None, api_key=None, api_base=None, custom_llm_provider=None, extra_headers=None, timeout_seconds=None))] +#[allow(clippy::too_many_arguments)] +fn achat_completions( + py: Python<'_>, + model: String, + messages: Py, + optional_params: Option>, + api_key: Option, + api_base: Option, + custom_llm_provider: Option, + extra_headers: Option>, + timeout_seconds: Option, +) -> PyResult> { + let (messages, optional_params, extra_headers, timeout) = marshal_chat_completions_inputs( + py, + messages, + optional_params, + extra_headers, + timeout_seconds, + )?; + + pyo3_async_runtimes::tokio::future_into_py(py, async move { + let response = run_chat_completions(ChatCompletionsRequest { + model: &model, + messages, + optional_params, + api_key: api_key.as_deref(), + api_base: api_base.as_deref(), + custom_llm_provider: custom_llm_provider.as_deref(), + extra_headers, + timeout, + }) + .await + .map_err(chat_completions_error_to_pyerr)?; + + Python::attach(|py| chat_completions_response_to_py(py, response)) + }) +} + #[pyfunction] fn gil_stats(py: Python<'_>) -> PyResult> { let stats = PyDict::new(py); @@ -439,12 +630,18 @@ fn gil_stats(py: Python<'_>) -> PyResult> { #[pymodule] fn _native(module: &Bound<'_, PyModule>) -> PyResult<()> { + let py = module.py(); module.add_function(wrap_pyfunction!(ocr, module)?)?; module.add_function(wrap_pyfunction!(aocr, module)?)?; module.add_function(wrap_pyfunction!(transcription, module)?)?; module.add_function(wrap_pyfunction!(atranscription, module)?)?; module.add_function(wrap_pyfunction!(messages, module)?)?; module.add_function(wrap_pyfunction!(amessages, module)?)?; + module.add("RustBridgeDeclined", py.get_type::())?; + module.add("RustUpstreamError", py.get_type::())?; + module.add_function(wrap_pyfunction!(chat_completions_decline, module)?)?; + module.add_function(wrap_pyfunction!(chat_completions, module)?)?; + module.add_function(wrap_pyfunction!(achat_completions, module)?)?; module.add_class::()?; module.add_function(wrap_pyfunction!(gil_stats, module)?)?; Ok(()) diff --git a/litellm/__init__.py b/litellm/__init__.py index 00f67ea0ff5..c83e72a78b4 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -199,6 +199,7 @@ 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 +274,6 @@ 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 +444,7 @@ 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 @@ -453,6 +454,7 @@ max_end_user_budget_id: Optional[str] = None # backwards compatibility — arbitrary client-supplied identifiers still # pass through unchanged. validate_end_user_id_in_db: bool = False +block_requests_for_models_without_pricing: bool = False disable_end_user_cost_tracking: Optional[bool] = None disable_end_user_cost_tracking_prometheus_only: Optional[bool] = None enable_end_user_cost_tracking_prometheus_only: Optional[bool] = None @@ -462,6 +464,11 @@ 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 @@ -479,6 +486,7 @@ 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) @@ -1627,6 +1635,9 @@ 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, @@ -1800,6 +1811,9 @@ 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, diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 89c72acc06d..1c833256598 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", @@ -740,6 +742,10 @@ _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 +983,10 @@ _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", diff --git a/litellm/_logging.py b/litellm/_logging.py index 7d3a30c6d1a..fbb35b72be2 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -5,9 +5,15 @@ 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 ( + LITELLM_TRUNCATED_PAYLOAD_FIELD, + LITELLM_TRUNCATION_STDOUT_SAFEGUARD_NOTE, + MAX_STRING_LENGTH_STDOUT_LOG, +) +from litellm.litellm_core_utils.env_utils import get_env_int 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.litellm_core_utils.secret_redaction import redact_string, redact_structured_value @@ -82,6 +88,24 @@ def redact_secrets(value: str) -> str: return _redact_string(value) +def _substituted_color_message(record: logging.LogRecord) -> str | None: + """Render a record's ``color_message`` against its args, or None if absent. + + uvicorn's colorized formatter re-renders `color_message` against + record.args at emit time (see uvicorn.logging.ColourizedFormatter) instead + of using the already-formatted record.msg, so it has to be substituted + before args are cleared or it is later formatted with no args and prints + the raw "%s://%s:%d" placeholders instead of the URL. + """ + color_message: Final = record.__dict__.get("color_message") + if not isinstance(color_message, str) or not record.args: + return None + try: + return color_message % record.args + except TypeError: + return color_message + + class SecretRedactionFilter(logging.Filter): """Scrubs known secret/credential patterns from log records.""" @@ -91,6 +115,12 @@ class SecretRedactionFilter(logging.Filter): if not _ENABLE_SECRET_REDACTION: return True + # Runs before args are cleared, and before the extra-field loop below + # that redacts the substituted result. + substituted_color_message: Final = _substituted_color_message(record) + if substituted_color_message is not None: + record.color_message = substituted_color_message # rebind-ok: a Filter scrubs records in place + try: record.msg = _redact_string(record.getMessage()) record.args = None @@ -101,7 +131,7 @@ class SecretRedactionFilter(logging.Filter): # Redact exception tracebacks if record.exc_info and record.exc_info[1] is not None: try: - record.exc_text = _redact_string(self._formatter.formatException(record.exc_info)) + record.exc_text = _redact_string(record.exc_text or self._formatter.formatException(record.exc_info)) except Exception: pass @@ -116,6 +146,72 @@ class SecretRedactionFilter(logging.Filter): _secret_filter: Final = SecretRedactionFilter() +def _get_max_string_length_stdout_log() -> int: + """Read the limit per record so a value loaded later via proxy config + environment_variables is honored.""" + return get_env_int("MAX_STRING_LENGTH_STDOUT_LOG", MAX_STRING_LENGTH_STDOUT_LOG) + + +def _stdout_truncation_marker(skipped_chars: int) -> str: + return ( + f"... ({LITELLM_TRUNCATED_PAYLOAD_FIELD} skipped {skipped_chars} chars. " + f"{LITELLM_TRUNCATION_STDOUT_SAFEGUARD_NOTE}) ..." + ) + + +def _truncate_for_stdout_log(text: str, limit: int) -> str: + kept_chars: Final = limit - len(_stdout_truncation_marker(len(text))) + if kept_chars <= 0: + return text[:limit] + head_chars: Final = kept_chars // 2 + tail_chars: Final = kept_chars - head_chars + return f"{text[:head_chars]}{_stdout_truncation_marker(len(text) - kept_chars)}{text[-tail_chars:]}" + + +class StdoutLogTruncationFilter(logging.Filter): + """Bounds how much of an oversized log line reaches stdout. + + A provider error string can echo the whole request payload, so one failed agentic + request writes hundreds of KB to stdout, repeatedly as the exception propagates from + the router to the proxy handler and into its traceback, all inline on the event loop. + + DEBUG records pass through untouched, since dumping full payloads is the point of + `--detailed_debug`, and logging callbacks (OTEL, Datadog, etc.) don't run through + logging filters at all, so they still get the untruncated error. + """ + + _formatter = logging.Formatter() + + def filter(self, record: logging.LogRecord) -> bool: + if record.levelno < logging.INFO: + return True + + limit: Final = _get_max_string_length_stdout_log() + if limit <= 0: + return True + + try: + message: Final = record.getMessage() + except (TypeError, ValueError): + return True + + if len(message) > limit: + record.msg = _truncate_for_stdout_log(message, limit) # rebind-ok: the Filter interface mutates the record + record.args = None # rebind-ok: args are consumed by the truncated message above + + if isinstance(record.exc_info, tuple): + exc_text: Final = record.exc_text or self._formatter.formatException(record.exc_info) + if len(exc_text) > limit: + record.exc_text = _truncate_for_stdout_log( # rebind-ok: the Filter interface mutates the record + exc_text, limit + ) + + return True + + +_stdout_truncation_filter: Final = StdoutLogTruncationFilter() + + class CorrelationContextFilter(logging.Filter): """Stamps each log record with the current request's trace_id and session_id from contextvars. @@ -138,11 +234,65 @@ 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 to stdout and WARNING and above to stderr. + + Collectors that derive severity from the stream report every stderr line as an error. + """ + + def emit(self, record: logging.LogRecord) -> None: + preferred: Final = sys.stdout if record.levelno < logging.WARNING 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) @@ -301,6 +451,7 @@ def _setup_json_exception_handlers(formatter): error_handler: Final = logging.StreamHandler() error_handler.setFormatter(formatter) error_handler.addFilter(_secret_filter) + error_handler.addFilter(_stdout_truncation_filter) error_handler.addFilter(_correlation_filter) # Setup excepthook for uncaught exceptions @@ -350,7 +501,7 @@ 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", ) @@ -365,6 +516,12 @@ verbose_router_logger.addHandler(handler) verbose_proxy_logger.addHandler(handler) verbose_logger.addHandler(handler) +# Filters attached to the logger, not the handler, survive callers swapping in their own +# 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_logger.addFilter(_stdout_truncation_filter) + def _suppress_loggers(): """Suppress noisy loggers at INFO level""" @@ -525,7 +682,7 @@ def _turn_on_json(): - Adds a JSON formatter to all loggers """ - handler: Final = logging.StreamHandler() + handler: Final = LevelRoutingStreamHandler() handler.setFormatter(JsonFormatter()) _initialize_loggers_with_handler(handler) # Set up exception handlers diff --git a/litellm/_redis.py b/litellm/_redis.py index 0acc01fa14f..9381357931e 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -12,11 +12,13 @@ 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 typing import Final +from urllib.parse import urlsplit, urlunsplit import redis import redis.asyncio as async_redis +from redis.credentials import CredentialProvider from litellm import get_secret, get_secret_str from litellm._redis_credential_provider import ( @@ -49,6 +51,7 @@ def _get_redis_kwargs(): include_args: Final = { "url", "redis_connect_func", + "credential_provider", "gcp_service_account", "gcp_ssl_ca_certs", "azure_redis_ad_token", @@ -134,6 +137,7 @@ def _get_redis_cluster_kwargs(client=None): "ssl_check_hostname", "ssl_ca_certs", "redis_connect_func", # Needed for sync clusters and IAM detection + "credential_provider", "gcp_service_account", "gcp_ssl_ca_certs", "azure_redis_ad_token", @@ -153,7 +157,8 @@ 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 _redis_kwargs_from_environment(): @@ -351,6 +356,12 @@ 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 @@ -408,54 +419,58 @@ 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) @@ -463,6 +478,13 @@ 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 @@ -530,8 +552,7 @@ 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.") @@ -549,14 +570,22 @@ def _init_redis_sentinel(redis_kwargs) -> redis.Redis: return sentinel.master_for(service_name, **connection_kwargs) +def _sentinel_auth_kwargs(connection_kwargs: dict, sentinel_password: str | None) -> dict: + """The Sentinel monitors are separate servers that authenticate with their own password, so the + data node's credential provider never belongs on them: leaving it there makes redis-py send the + data node's token to a monitor, which fails whether the monitor is unauthenticated or has its + own password.""" + kept: Final = ((k, v) for k, v in connection_kwargs.items() if k != "credential_provider") + return dict(kept, password=sentinel_password) + + def _init_async_redis_sentinel(redis_kwargs) -> async_redis.Redis: sentinel_nodes: Final = redis_kwargs.get("sentinel_nodes") sentinel_password: Final = redis_kwargs.get("sentinel_password") 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.") @@ -574,6 +603,41 @@ def _init_async_redis_sentinel(redis_kwargs) -> async_redis.Redis: return sentinel.master_for(service_name, **connection_kwargs) +def _async_credential_provider(redis_connect_func: object | None) -> CredentialProvider | None: + """The Azure AD and GCP IAM connect funcs run their AUTH exchange with the blocking client + API, so on an async connection their ``send_command``/``read_response`` calls return + coroutines nobody awaits and every connect fails. Async paths authenticate through a + ``CredentialProvider`` instead, which redis-py consults per connection so the token stays + fresh. Any other ``redis_connect_func`` is left where it is, since redis-py awaits it + itself when it is a coroutine function.""" + gcp_service_account: Final = getattr(redis_connect_func, "_gcp_service_account", None) + if gcp_service_account is not None: + return GCPIAMCredentialProvider(gcp_service_account) + + azure_credential: Final = getattr(redis_connect_func, "_azure_credential", None) + if azure_credential is not None: + return AzureADCredentialProvider(azure_credential, username=os.environ.get("REDIS_USERNAME") or None) + + return None + + +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.""" + 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 + + superseded: Final = frozenset({"redis_connect_func", "username", "password"}) + kept: Final = ((k, v) for k, v in redis_kwargs.items() if k not in superseded) + return dict(kept, credential_provider=credential_provider) # mutable-ok: the branches below mutate these kwargs + + def get_redis_client(**env_overrides): redis_kwargs: Final = _get_redis_client_logic(**env_overrides) @@ -600,7 +664,7 @@ def get_redis_async_client( connection_pool: async_redis.BlockingConnectionPool | None = None, **env_overrides, ) -> async_redis.Redis | async_redis.RedisCluster: - redis_kwargs: Final = _get_redis_client_logic(**env_overrides) + redis_kwargs: Final = _async_auth_kwargs(_get_redis_client_logic(**env_overrides)) if "startup_nodes" in redis_kwargs: from redis.cluster import ClusterNode @@ -611,28 +675,12 @@ def get_redis_async_client( if arg in args: cluster_kwargs[arg] = redis_kwargs[arg] - # Handle GCP IAM authentication for async clusters - redis_connect_func = cluster_kwargs.pop("redis_connect_func", None) - - # Use a CredentialProvider so the IAM token is regenerated on every new - # connection — mirrors the sync path where redis_connect_func is invoked - # per connection. Without this, the token would expire after ~1 hour. - if redis_connect_func and hasattr(redis_connect_func, "_gcp_service_account"): - cluster_kwargs["credential_provider"] = GCPIAMCredentialProvider(redis_connect_func._gcp_service_account) - # Handle Azure AD authentication for async clusters via CredentialProvider - # so the credential's internal cache + silent refresh runs per connection - # (mirrors GCP IAM above; avoids static-token-baked-in-pool expiry). - elif redis_connect_func and hasattr(redis_connect_func, "_azure_credential"): - cluster_kwargs["credential_provider"] = AzureADCredentialProvider( - redis_connect_func._azure_credential, - username=os.environ.get("REDIS_USERNAME") or None, - ) - new_startup_nodes: Final[list[ClusterNode]] = [] for item in redis_kwargs["startup_nodes"]: new_startup_nodes.append(ClusterNode(**item)) cluster_kwargs.pop("startup_nodes", None) + cluster_kwargs.pop("redis_connect_func", None) # Default to a periodic health check + TCP keepalive so a connection silently dropped # by a cluster restart (e.g. ElastiCache Serverless maintenance) is revalidated and @@ -641,8 +689,16 @@ def get_redis_async_client( cluster_kwargs.setdefault("health_check_interval", REDIS_CLUSTER_HEALTH_CHECK_INTERVAL) cluster_kwargs.setdefault("socket_keepalive", True) + # A single node's client-side timeout must reset only that node's connections, + # not tear down the whole cluster client for every concurrent caller. + from litellm.caching.redis_cluster_node_isolation import ( + get_litellm_async_redis_cluster_class, + ) + + async_redis_cluster_class: Final = get_litellm_async_redis_cluster_class() + # Create async RedisCluster with IAM token as password if available - cluster_client: Final = async_redis.RedisCluster( + cluster_client: Final = async_redis_cluster_class( startup_nodes=new_startup_nodes, **cluster_kwargs, ) @@ -667,19 +723,6 @@ def get_redis_async_client( if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs: return _init_async_redis_sentinel(redis_kwargs) - # Wrap GCP / Azure AD auth in a CredentialProvider for the standard async - # Redis client. The async client doesn't support redis_connect_func, but it - # does honour credential_provider — which is called per connection, so the - # underlying SDK can refresh tokens silently before they expire. - redis_connect_func = redis_kwargs.pop("redis_connect_func", None) - if redis_connect_func and hasattr(redis_connect_func, "_azure_credential"): - redis_kwargs["credential_provider"] = AzureADCredentialProvider( - redis_connect_func._azure_credential, - username=os.environ.get("REDIS_USERNAME") or None, - ) - elif redis_connect_func and hasattr(redis_connect_func, "_gcp_service_account"): - redis_kwargs["credential_provider"] = GCPIAMCredentialProvider(redis_connect_func._gcp_service_account) - _pretty_print_redis_config(redis_kwargs=redis_kwargs) if connection_pool is not None: @@ -693,7 +736,7 @@ def get_redis_async_client( def get_redis_connection_pool( **env_overrides, ) -> async_redis.BlockingConnectionPool | None: - redis_kwargs: Final = _get_redis_client_logic(**env_overrides) + redis_kwargs: Final = _async_auth_kwargs(_get_redis_client_logic(**env_overrides)) verbose_logger.debug("get_redis_connection_pool: redis_kwargs", redis_kwargs) if "startup_nodes" in redis_kwargs: @@ -714,25 +757,25 @@ def get_redis_connection_pool( ) return async_redis.BlockingConnectionPool.from_url(**pool_kwargs) - # Wrap GCP / Azure AD auth in a CredentialProvider so pool-managed - # connections re-fetch tokens via the SDK's internal cache + silent refresh - # rather than reusing a single token captured at pool creation. - redis_connect_func: Final = redis_kwargs.pop("redis_connect_func", None) - if redis_connect_func and hasattr(redis_connect_func, "_azure_credential"): - redis_kwargs["credential_provider"] = AzureADCredentialProvider( - redis_connect_func._azure_credential, - username=os.environ.get("REDIS_USERNAME") or None, - ) - elif redis_connect_func and hasattr(redis_connect_func, "_gcp_service_account"): - redis_kwargs["credential_provider"] = GCPIAMCredentialProvider(redis_connect_func._gcp_service_account) - if redis_kwargs.pop("ssl", None): redis_kwargs["connection_class"] = async_redis.SSLConnection 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 @@ -750,7 +793,7 @@ 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") @@ -813,7 +856,7 @@ 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/main.py b/litellm/a2a_protocol/main.py index 1c6ebf0b95c..56b8089b0af 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, @@ -782,13 +783,17 @@ 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 +804,7 @@ 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/batches/batch_utils.py b/litellm/batches/batch_utils.py index 0cf22d82ca6..2bc61aed771 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -296,6 +296,32 @@ def calculate_vertex_ai_batch_cost_and_usage( ) +def _provider_output_file_id(output_file_id: str) -> str: + """ + Resolve the file id the provider actually knows: unified ids yield their embedded + llm_output_file_id, model-encoded ids decode to the raw provider id, raw ids pass through. + """ + from litellm.proxy.openai_files_endpoints.common_utils import ( + _is_base64_encoded_unified_file_id, + get_original_file_id, + ) + + unified_file_id: Final = _is_base64_encoded_unified_file_id(output_file_id) + if not unified_file_id: + return get_original_file_id(output_file_id) + try: + extracted: Final = unified_file_id.split("llm_output_file_id,")[1].split(";")[0] + except (IndexError, AttributeError) as e: + verbose_logger.error( + "Failed to extract LLM output file ID from unified file ID: %s, error: %s", + output_file_id, + e, + ) + return output_file_id + verbose_logger.debug("Extracted LLM output file ID from unified file ID: %s", extracted) + return extracted + + async def _fetch_batch_output_file_content( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic"] = "openai", @@ -311,23 +337,11 @@ async def _fetch_batch_output_file_content( Required for Azure and other providers that need authentication """ from litellm.files.main import afile_content - from litellm.proxy.openai_files_endpoints.common_utils import ( - _is_base64_encoded_unified_file_id, - ) if batch.output_file_id is None: raise ValueError("Output file id is None cannot retrieve file content") - file_id = batch.output_file_id - is_base64_unified_file_id: Final = _is_base64_encoded_unified_file_id(file_id) - if is_base64_unified_file_id: - try: - file_id = is_base64_unified_file_id.split("llm_output_file_id,")[1].split(";")[0] - verbose_logger.debug("Extracted LLM output file ID from unified file ID: %s", file_id) - except (IndexError, AttributeError) as e: - verbose_logger.error( - "Failed to extract LLM output file ID from unified file ID: %s, error: %s", batch.output_file_id, e - ) + file_id: Final = _provider_output_file_id(batch.output_file_id) # Build kwargs for afile_content with credentials from litellm_params file_content_kwargs: Final = { @@ -537,7 +551,7 @@ 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. @@ -549,7 +563,7 @@ 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/caching/_embedding_router.py b/litellm/caching/_embedding_router.py index 8dfcddf158a..cec25634bb8 100644 --- a/litellm/caching/_embedding_router.py +++ b/litellm/caching/_embedding_router.py @@ -16,6 +16,7 @@ from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Final import litellm +from litellm.constants import SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS if TYPE_CHECKING: from litellm.router import Router @@ -60,6 +61,13 @@ def resolve_embedding_max_input_tokens( return deployment_max_input_tokens +def resolve_embedding_timeout(configured_timeout: float | None) -> float: + """Explicit cache setting first, else the short semantic-cache default.""" + if configured_timeout is not None: + return configured_timeout + return SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS + + def truncate_embedding_input(prompt: str, embedding_model: str, max_input_tokens: int | None) -> str: """Keep only the first ``max_input_tokens`` tokens of ``prompt`` for the embedding call.""" if max_input_tokens is None: diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index 6b68ae98111..cefe6aae9ed 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -98,6 +98,7 @@ class Cache: qdrant_semantic_cache_embedding_model: str = "text-embedding-ada-002", qdrant_semantic_cache_vector_size: int | None = None, semantic_cache_embedding_max_input_tokens: int | None = None, + semantic_cache_embedding_timeout: float | None = None, # GCP IAM authentication parameters gcp_service_account: str | None = None, gcp_ssl_ca_certs: str | None = None, @@ -124,6 +125,7 @@ class Cache: qdrant_collection_name (str, optional): The name for your qdrant collection. Required if type is "qdrant-semantic". similarity_threshold (float, optional): The similarity threshold for semantic-caching, Required if type is "redis-semantic" or "qdrant-semantic". semantic_cache_embedding_max_input_tokens (int, optional): Truncate prompts to this many tokens before embedding them for semantic caching. Defaults to the embedding deployment's configured max_input_tokens. + semantic_cache_embedding_timeout (float, optional): Seconds a semantic-cache lookup may spend embedding the prompt before it gives up and lets the request continue to the LLM. Defaults to SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS. # Disk Cache Args disk_cache_dir (str, optional): The directory for the disk cache. Defaults to None. @@ -195,6 +197,7 @@ class Cache: embedding_model=redis_semantic_cache_embedding_model, index_name=redis_semantic_cache_index_name, embedding_max_input_tokens=semantic_cache_embedding_max_input_tokens, + embedding_timeout=semantic_cache_embedding_timeout, **kwargs, ) elif type == LiteLLMCacheType.VALKEY_SEMANTIC: @@ -211,6 +214,7 @@ class Cache: index_name=valkey_semantic_cache_index_name, startup_nodes=redis_startup_nodes, embedding_max_input_tokens=semantic_cache_embedding_max_input_tokens, + embedding_timeout=semantic_cache_embedding_timeout, **kwargs, ) elif type == LiteLLMCacheType.QDRANT_SEMANTIC: @@ -223,6 +227,7 @@ class Cache: embedding_model=qdrant_semantic_cache_embedding_model, vector_size=qdrant_semantic_cache_vector_size, embedding_max_input_tokens=semantic_cache_embedding_max_input_tokens, + embedding_timeout=semantic_cache_embedding_timeout, ) elif type == LiteLLMCacheType.LOCAL: self.cache = InMemoryCache() 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 8270c655d82..4898700c403 100644 --- a/litellm/caching/qdrant_semantic_cache.py +++ b/litellm/caching/qdrant_semantic_cache.py @@ -16,7 +16,11 @@ from typing import TYPE_CHECKING, Any, Final, cast import litellm from litellm._logging import print_verbose -from litellm.constants import QDRANT_SCALAR_QUANTILE, QDRANT_VECTOR_SIZE +from litellm.constants import ( + QDRANT_SCALAR_QUANTILE, + QDRANT_VECTOR_SIZE, + SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS, +) from litellm.litellm_core_utils.prompt_templates.common_utils import ( get_str_from_messages, ) @@ -26,6 +30,7 @@ from ._embedding_router import ( build_router_embedding_metadata, resolve_embedding_max_input_tokens, resolve_embedding_router, + resolve_embedding_timeout, truncate_embedding_input, ) from .base_cache import BaseCache @@ -37,6 +42,7 @@ if TYPE_CHECKING: class QdrantSemanticCache(BaseCache): CACHE_KEY_FIELD_NAME = "litellm_cache_key" embedding_max_input_tokens: int | None = None + embedding_timeout: float = SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS def __init__( self, @@ -49,6 +55,7 @@ class QdrantSemanticCache(BaseCache): host_type=None, vector_size=None, embedding_max_input_tokens: int | None = None, + embedding_timeout: float | None = None, ): from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, @@ -68,6 +75,7 @@ class QdrantSemanticCache(BaseCache): self.similarity_threshold = similarity_threshold self.embedding_model = embedding_model self.embedding_max_input_tokens = embedding_max_input_tokens + self.embedding_timeout = resolve_embedding_timeout(embedding_timeout) self.vector_size = vector_size if vector_size is not None else QDRANT_VECTOR_SIZE headers = {} @@ -222,11 +230,15 @@ class QdrantSemanticCache(BaseCache): input=embedding_input, cache={"no-store": True, "no-cache": True}, metadata=build_router_embedding_metadata(metadata), + timeout=self.embedding_timeout, + num_retries=0, ) return litellm.embedding( model=self.embedding_model, input=embedding_input, cache={"no-store": True, "no-cache": True}, + timeout=self.embedding_timeout, + num_retries=0, ) async def _get_async_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: @@ -238,19 +250,25 @@ class QdrantSemanticCache(BaseCache): router: Final = resolve_embedding_router(self.embedding_model, llm_router, llm_model_list) embedding_input: Final = self._embedding_input(prompt, router) - if router is not None: - return await router.aembedding( + embedding_call: Final = ( + router.aembedding( model=self.embedding_model, input=embedding_input, cache={"no-store": True, "no-cache": True}, metadata=build_router_embedding_metadata(metadata), + timeout=self.embedding_timeout, + num_retries=0, + ) + if router is not None + else litellm.aembedding( + model=self.embedding_model, + input=embedding_input, + cache={"no-store": True, "no-cache": True}, + timeout=self.embedding_timeout, + num_retries=0, ) - - return await litellm.aembedding( - model=self.embedding_model, - input=embedding_input, - cache={"no-store": True, "no-cache": True}, ) + return await asyncio.wait_for(embedding_call, self.embedding_timeout) def set_cache(self, key, value, **kwargs): print_verbose(f"qdrant semantic-cache set_cache, kwargs: {kwargs}") diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index 934ba500ef9..f1c80eaacbe 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -175,6 +175,10 @@ _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 +403,9 @@ 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}" @@ -432,7 +435,7 @@ class RedisCache(BaseCache): """ 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 @@ -1384,10 +1387,10 @@ 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() 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 new file mode 100644 index 00000000000..ae8c78709d9 --- /dev/null +++ b/litellm/caching/redis_cluster_node_isolation.py @@ -0,0 +1,201 @@ +"""Bounds the blast radius of a single node's transient connection error on the async +Redis Cluster client. + +redis-py's ``RedisCluster._execute_command`` responds to a ``ConnectionError`` or +``TimeoutError`` on ANY one node by tearing down every node's connections and flipping +the client into "needs reinitialization", which forces every other concurrent caller +sharing this client through one reinit lock until the whole cluster topology is +re-walked. Under real proxy load, a client-side socket timeout on a single node is a +routine event (the event loop was too busy to read the response before ``socket_timeout`` +elapsed) and does not mean the cluster's topology moved, so treating it as a full-cluster +event turns one slow node into a proxy-wide latency spike while Redis itself stays +healthy -- confirmed live: pausing one of three local cluster nodes made every concurrent +command against the other two, untouched nodes stall for the full pause duration too. + +``get_litellm_async_redis_cluster_class`` returns a ``RedisCluster`` subclass that resets +only the node that actually failed (mirroring what a plain, non-cluster Redis client +already does when one of its pooled connections errors), leaving every other node's +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 +from typing import TYPE_CHECKING, Final, Protocol + +from litellm._logging import verbose_logger + +if TYPE_CHECKING: + from redis.asyncio.cluster import RedisCluster as _AsyncRedisClusterType + + +class _ClusterNodeAttrs(Protocol): + """The subset of ``redis.asyncio.cluster.ClusterNode`` this override reads. redis-py + ships no resolvable stub for these members under the repo's current types-redis pin, + so a plain attribute access resolves every downstream use to ``Unknown`` under strict + mode; typing ``target_node`` as this Protocol at the one boundary keeps the override's + own logic fully typed without a banned ``typing.cast``.""" + + async def execute_command( + self, + *args: object, + **kwargs: object, # kwargs-ok: mirrors redis-py's own ClusterNode.execute_command signature, a raw command dispatch with no fixed keyword contract + ) -> object: ... + async def disconnect(self) -> None: ... + + +class _NodesManagerAttrs(Protocol): + _moved_exception: object + + def get_node_from_slot( + self, slot: int, read_from_replicas: bool, load_balancing_strategy: object + ) -> _ClusterNodeAttrs: ... + + +class _ClusterAttrs(Protocol): + RedisClusterRequestTTL: int + reinitialize_counter: int + reinitialize_steps: int + read_from_replicas: bool + load_balancing_strategy: object + nodes_manager: _NodesManagerAttrs + + def get_node(self, node_name: str) -> _ClusterNodeAttrs: ... + async def _determine_slot(self, *args: object) -> int: ... + async def aclose(self) -> None: ... + + +#: redis-py versions this override's copied ``_execute_command`` body has been verified +#: against. A version outside this set may have changed the method's structure in a way +#: this override can't see (Python won't error -- it'll just run our now-stale copy), so +#: construction logs a loud warning rather than silently trusting an unverified copy. +_VERIFIED_REDIS_VERSIONS: Final = frozenset({"5.3.1"}) + + +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 + submodules are cached in ``sys.modules`` after the first import. + """ + import redis + from redis.asyncio.cluster import ( + 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 + from redis.exceptions import ( + AskError, + BusyLoadingError, + ClusterDownError, + ClusterError, + MaxConnectionsError, + MovedError, + SlotNotCoveredError, + TryAgainError, + ) + 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 " + "against (%s). The per-node-isolation override may not match the installed library's " + "real _execute_command behavior.", + redis.__version__, + sorted(_VERIFIED_REDIS_VERSIONS), + ) + + class LiteLLMAsyncRedisCluster( + _BaseAsyncRedisCluster # pyright: ignore[reportUntypedBaseClass] # same stale-stub gap as the import above; the base class itself is unresolvable, not this subclass's own code + ): + async def _execute_command( + self, + target_node: _ClusterNodeAttrs, + *args: object, + **kwargs: object, # kwargs-ok: overrides redis-py's own **kwargs signature; the keyword contract is defined by the Redis command being dispatched, not by this method + ) -> object: + cluster: _ClusterAttrs = self + node = target_node + + asking = moved = False + redirect_addr: str | None = None + ttl = cluster.RedisClusterRequestTTL + + while ttl > 0: + ttl -= 1 + try: + if asking: + assert redirect_addr is not None + node = cluster.get_node(node_name=redirect_addr) + await node.execute_command("ASKING") + asking = False + elif moved: + slot = await cluster._determine_slot(*args) # pyright: ignore[reportPrivateUsage] # mirrors upstream's own un-overridden branch, which makes this identical private call from the same subclass + node = cluster.nodes_manager.get_node_from_slot( + slot, + cluster.read_from_replicas and args[0] in READ_COMMANDS, + (cluster.load_balancing_strategy if args[0] in READ_COMMANDS else None), + ) + moved = False + + return await node.execute_command(*args, **kwargs) + except (BusyLoadingError, MaxConnectionsError): + raise + except (_RedisConnectionError, _RedisTimeoutError): + # Reset only the node that actually failed instead of the upstream + # default (`await self.aclose()`, a full-cluster teardown that forces + # every other concurrent caller through the shared reinit lock). + await node.disconnect() + raise + except (ClusterDownError, SlotNotCoveredError): + await cluster.aclose() + await asyncio.sleep(0.25) + raise + except MovedError as e: + cluster.reinitialize_counter += 1 + if cluster.reinitialize_steps and cluster.reinitialize_counter % cluster.reinitialize_steps == 0: + await cluster.aclose() + cluster.reinitialize_counter = 0 + else: + cluster.nodes_manager._moved_exception = e # pyright: ignore[reportPrivateUsage] # mirrors upstream's own un-overridden branch; redis-py exposes no public setter for this + moved = True + except AskError as e: + redirect_addr = get_node_name(host=e.host, port=e.port) + asking = True + except TryAgainError: + if ttl < cluster.RedisClusterRequestTTL / 2: + await asyncio.sleep(0.05) + + raise ClusterError("TTL exhausted.") + + return LiteLLMAsyncRedisCluster diff --git a/litellm/caching/redis_semantic_cache.py b/litellm/caching/redis_semantic_cache.py index d91260f4d9c..f5264e28124 100644 --- a/litellm/caching/redis_semantic_cache.py +++ b/litellm/caching/redis_semantic_cache.py @@ -18,6 +18,7 @@ from typing import TYPE_CHECKING, Any, Final, cast import litellm from litellm._logging import print_verbose, verbose_logger +from litellm.constants import SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS from litellm.litellm_core_utils.prompt_templates.common_utils import ( get_str_from_messages, ) @@ -27,6 +28,7 @@ from ._embedding_router import ( build_router_embedding_metadata, resolve_embedding_max_input_tokens, resolve_embedding_router, + resolve_embedding_timeout, truncate_embedding_input, ) from .base_cache import BaseCache @@ -47,6 +49,7 @@ class RedisSemanticCache(BaseCache): DEFAULT_REDIS_INDEX_NAME: str = "litellm_semantic_cache_index" CACHE_KEY_FIELD_NAME: str = "litellm_cache_key" embedding_max_input_tokens: int | None = None + embedding_timeout: float = SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS def __init__( self, @@ -58,6 +61,7 @@ class RedisSemanticCache(BaseCache): embedding_model: str = "text-embedding-ada-002", index_name: str | None = None, embedding_max_input_tokens: int | None = None, + embedding_timeout: float | None = None, **kwargs: object, ): """ @@ -74,6 +78,8 @@ class RedisSemanticCache(BaseCache): index_name: Name for the Redis index embedding_max_input_tokens: Truncate prompts to this many tokens before embedding; defaults to the Router deployment's configured max_input_tokens + embedding_timeout: Seconds a cache lookup may spend embedding the prompt before it + gives up and lets the request continue to the LLM ttl: Default time-to-live for cache entries in seconds **kwargs: Additional arguments passed to the Redis client @@ -99,6 +105,7 @@ class RedisSemanticCache(BaseCache): self.distance_threshold = 1 - similarity_threshold self.embedding_model = embedding_model self.embedding_max_input_tokens = embedding_max_input_tokens + self.embedding_timeout = resolve_embedding_timeout(embedding_timeout) # Set up Redis connection if redis_url is None: @@ -349,6 +356,8 @@ class RedisSemanticCache(BaseCache): input=embedding_input, cache={"no-store": True, "no-cache": True}, metadata=build_router_embedding_metadata(metadata), + timeout=self.embedding_timeout, + num_retries=0, ), ) else: @@ -358,6 +367,8 @@ class RedisSemanticCache(BaseCache): model=self.embedding_model, input=embedding_input, cache={"no-store": True, "no-cache": True}, + timeout=self.embedding_timeout, + num_retries=0, ), ) return embedding_response["data"][0]["embedding"] @@ -512,20 +523,26 @@ class RedisSemanticCache(BaseCache): router: Final = resolve_embedding_router(self.embedding_model, llm_router, llm_model_list) embedding_input: Final = self._embedding_input(prompt, router) + embedding_call: Final = ( + router.aembedding( + model=self.embedding_model, + input=embedding_input, + cache={"no-store": True, "no-cache": True}, + metadata=build_router_embedding_metadata(metadata), + timeout=self.embedding_timeout, + num_retries=0, + ) + if router is not None + else litellm.aembedding( + model=self.embedding_model, + input=embedding_input, + cache={"no-store": True, "no-cache": True}, + timeout=self.embedding_timeout, + num_retries=0, + ) + ) try: - if router is not None: - embedding_response = await router.aembedding( - model=self.embedding_model, - input=embedding_input, - cache={"no-store": True, "no-cache": True}, - metadata=build_router_embedding_metadata(metadata), - ) - else: - embedding_response = await litellm.aembedding( - model=self.embedding_model, - input=embedding_input, - cache={"no-store": True, "no-cache": True}, - ) + embedding_response: Final = await asyncio.wait_for(embedding_call, self.embedding_timeout) return embedding_response["data"][0]["embedding"] except Exception as e: print_verbose(f"Error generating async embedding: {e}") diff --git a/litellm/caching/valkey_semantic_cache.py b/litellm/caching/valkey_semantic_cache.py index ac4d4033546..58b76d98d6d 100644 --- a/litellm/caching/valkey_semantic_cache.py +++ b/litellm/caching/valkey_semantic_cache.py @@ -31,6 +31,7 @@ from litellm._logging import print_verbose from litellm._uuid import uuid from litellm.llms.valkey.common_utils import build_valkey_url, pack_vector +from ._embedding_router import resolve_embedding_timeout from .redis_semantic_cache import RedisSemanticCache @@ -63,6 +64,7 @@ class ValkeySemanticCache(RedisSemanticCache): sync_client: Redis | None = None, async_client: AsyncRedis | None = None, embedding_max_input_tokens: int | None = None, + embedding_timeout: float | None = None, **kwargs: object, ): if similarity_threshold is None: @@ -81,15 +83,18 @@ class ValkeySemanticCache(RedisSemanticCache): self.similarity_threshold = similarity_threshold self.embedding_model = embedding_model self.embedding_max_input_tokens = embedding_max_input_tokens + self.embedding_timeout = resolve_embedding_timeout(embedding_timeout) self.index_name = index_name or self.DEFAULT_VALKEY_INDEX_NAME 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}") @@ -212,7 +217,7 @@ class ValkeySemanticCache(RedisSemanticCache): """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)}, + query_params={"vec": self._embedding_to_bytes(embedding)}, # pyright: ignore[reportArgumentType] # redis stubs omit bytes; KNN vectors are raw bytes at runtime ) @classmethod @@ -282,7 +287,7 @@ 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: diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index 33206629b41..727c39c16ec 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -25,6 +25,11 @@ class ResponsesToCompletionBridgeHandlerInputKwargs(TypedDict): encoding: object +def _restore_routing_prefix(model: str, custom_llm_provider: str) -> str: + """`responses()` runs `get_llm_provider()` itself, so hand back the prefixed model `completion()` started from.""" + return f"{custom_llm_provider}/{model}" + + class ResponsesToCompletionBridgeHandler: def __init__(self): from .transformation import LiteLLMResponsesTransformationHandler @@ -184,14 +189,11 @@ class ResponsesToCompletionBridgeHandler: client=kwargs.get("client"), ) - # Pin the resolved provider so `responses()` doesn't re-run - # `get_llm_provider()` on the model string and strip a second - # provider prefix (see GitHub issue #28505). request_data already - # carries `custom_llm_provider` via the spread of - # `sanitized_litellm_params`; overwriting it on the dict (rather - # than adding an explicit kwarg) avoids the duplicate-keyword - # TypeError that would otherwise fire on the real bridge path. + # Set on request_data rather than passed as explicit kwargs: the spread of + # `sanitized_litellm_params` already carries both, so passing them again + # would raise a duplicate-keyword TypeError. request_data["custom_llm_provider"] = custom_llm_provider + request_data["model"] = _restore_routing_prefix(model, custom_llm_provider) result: Final = responses( **request_data, ) @@ -282,13 +284,11 @@ class ResponsesToCompletionBridgeHandler: except Exception as e: raise e - # Pin the resolved provider so `aresponses()` doesn't re-run - # `get_llm_provider()` on the model string and strip a second - # provider prefix (see GitHub issue #28505). Set on request_data - # rather than passed as a separate kwarg to avoid the duplicate- - # keyword TypeError when `sanitized_litellm_params` already - # carries `custom_llm_provider`. + # Set on request_data rather than passed as explicit kwargs: the spread of + # `sanitized_litellm_params` already carries both, so passing them again + # would raise a duplicate-keyword TypeError. request_data["custom_llm_provider"] = custom_llm_provider + request_data["model"] = _restore_routing_prefix(model, custom_llm_provider) result: Final = await aresponses( **request_data, aresponses=True, diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 5f3e9ac753c..85fb0bc8dc6 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, @@ -113,6 +135,68 @@ def _build_reasoning_item( } +def _reasoning_item_from_output_item(item: object) -> _BuiltReasoningItem | None: + from openai.types.responses import ResponseReasoningItem + + if isinstance(item, ResponseReasoningItem): + return _build_reasoning_item( + item_id=item.id, + encrypted_content=getattr(item, "encrypted_content", None), + summary_raw=item.summary, + ) + if isinstance(item, dict) and item.get("type") == "reasoning": + return _build_reasoning_item( + item_id=item.get("id", ""), + encrypted_content=item.get("encrypted_content"), + summary_raw=item.get("summary"), + ) + return None + + +def _reasoning_items_from_output_items(output_items: Sequence[object]) -> tuple[_BuiltReasoningItem, ...]: + return tuple( + reasoning_item + for reasoning_item in (_reasoning_item_from_output_item(item) for item in output_items) + if reasoning_item is not None + ) + + +def _as_chat_reasoning_items( + reasoning_items: Sequence[_BuiltReasoningItem], +) -> list[ChatCompletionReasoningItem] | None: + if not reasoning_items: + return None + # cast-ok: _BuiltReasoningItem is the structural shape ChatCompletionReasoningItem + # describes, and TypedDict invariance is what stops the two from unifying here. + return cast(list[ChatCompletionReasoningItem], list(reasoning_items)) + + +def _map_incomplete_reason_to_finish_reason(incomplete_reason: str | None) -> Literal["length", "content_filter"]: + if incomplete_reason == "content_filter": + return "content_filter" + 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 + incomplete_details: Final = response_payload.get("incomplete_details") + if not isinstance(incomplete_details, Mapping): + return None + reason: Final = incomplete_details.get("reason") + return reason if isinstance(reason, str) else None + + class _ChatToolCallDict(ChatCompletionToolCallChunk, total=False): provider_specific_fields: Mapping[str, object] @@ -320,8 +404,15 @@ 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") @@ -348,15 +439,16 @@ 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 @@ -657,6 +749,27 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): return choices + @staticmethod + def _build_empty_incomplete_choice( + output_items: Sequence[object], + finish_reason: Literal["length", "content_filter"], + ) -> "Choices": + from litellm.types.utils import Choices, Message + + reasoning_items: Final = _reasoning_items_from_output_items(output_items) + reasoning_content: Final = " ".join( + summary_block["text"] + for reasoning_item in reasoning_items + for summary_block in reasoning_item["summary"] + if summary_block.get("text") + ) + message: Final = Message( + content="", + reasoning_content=reasoning_content if reasoning_content else None, + reasoning_items=_as_chat_reasoning_items(reasoning_items), + ) + return Choices(message=message, finish_reason=finish_reason, index=0) + @classmethod def _extract_output_from_completed_event(cls, parsed_chunk: Mapping[str, object]) -> list[dict[str, object]] | None: response_payload: Final = parsed_chunk.get("response") @@ -763,11 +876,22 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): handle_raw_dict_callback=self._handle_raw_dict_response_item, ) - if len(choices) == 0: - if raw_response.incomplete_details is not None and raw_response.incomplete_details.reason is not None: - raise ValueError(f"{model} unable to complete request: {raw_response.incomplete_details.reason}") + response_is_incomplete: Final = raw_response.status == "incomplete" or ( + raw_response.incomplete_details is not None and raw_response.incomplete_details.reason is not None + ) + + if len(choices) == 0 and not response_is_incomplete: + raise ValueError(f"Unknown items in responses API response: {output_items}") + + if response_is_incomplete: + incomplete_finish_reason: Final = _map_incomplete_reason_to_finish_reason( + raw_response.incomplete_details.reason if raw_response.incomplete_details is not None else None + ) + if len(choices) == 0: + choices.append(self._build_empty_incomplete_choice(output_items, incomplete_finish_reason)) else: - raise ValueError(f"Unknown items in responses API response: {output_items}") + for choice in choices: + choice.finish_reason = incomplete_finish_reason setattr(model_response, "choices", choices) @@ -845,7 +969,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): content: str | list[object] | Iterable[ - Union["OpenAIMessageContentListBlock", "ChatCompletionThinkingBlock", "ChatCompletionRedactedThinkingBlock"] + Union[ + "OpenAIMessageContentListBlock", + "ChatCompletionThinkingBlock", + "ChatCompletionRedactedThinkingBlock", + "ChatCompletionToolReferenceObject", + ] ] | None, role: str, @@ -894,17 +1023,15 @@ 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", @@ -1002,22 +1129,11 @@ 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 @@ -1392,12 +1508,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): ) ] ) - elif event_type == "response.completed": - # Response is fully complete - now we can signal is_finished=True - # This ensures we don't prematurely end the stream before tool_calls arrive - - # Check if response contains function_call items in output - # to determine correct finish_reason + elif event_type in ("response.completed", "response.incomplete"): response_data: Final = parsed_chunk.get("response", {}) output_items: Final = response_data.get("output", []) if response_data else [] @@ -1407,25 +1518,14 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): if isinstance(item, dict) ) - finish_reason: Final = "tool_calls" if has_function_calls else "stop" + finish_reason: Final = ( + _map_incomplete_reason_to_finish_reason(_incomplete_reason_from_response_payload(response_data)) + if event_type == "response.incomplete" + else ("tool_calls" if has_function_calls else "stop") + ) - # Extract reasoning items with encrypted_content for round-tripping - completed_reasoning_items: list[_BuiltReasoningItem] | None = None - for item in output_items: - if not isinstance(item, dict) or item.get("type") != "reasoning": - continue - if completed_reasoning_items is None: - completed_reasoning_items = [] - completed_reasoning_items.append( - _build_reasoning_item( - item_id=item.get("id", ""), - encrypted_content=item.get("encrypted_content"), - summary_raw=item.get("summary"), - ) - ) - completed_reasoning_items_typed: Final = cast( - list[ChatCompletionReasoningItem] | None, - completed_reasoning_items, + terminal_reasoning_items_typed: Final = _as_chat_reasoning_items( + _reasoning_items_from_output_items(output_items) ) usage = None @@ -1439,7 +1539,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): index=0, delta=Delta( content="", - reasoning_items=completed_reasoning_items_typed, + reasoning_items=terminal_reasoning_items_typed, ), finish_reason=finish_reason, ) diff --git a/litellm/constants.py b/litellm/constants.py index facfc6f7c19..fc88086805f 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -3,7 +3,7 @@ import sys from types import MappingProxyType from typing import Final, Literal -from litellm.litellm_core_utils.env_utils import get_env_int, get_env_int_or_none +from litellm.litellm_core_utils.env_utils import get_env_int, get_env_int_in_range, get_env_int_or_none DEFAULT_HEALTH_CHECK_PROMPT: Final = str(os.getenv("DEFAULT_HEALTH_CHECK_PROMPT", "test from litellm")) AZURE_DEFAULT_RESPONSES_API_VERSION: Final = str(os.getenv("AZURE_DEFAULT_RESPONSES_API_VERSION", "preview")) @@ -48,6 +48,11 @@ 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) # When true, adds detailed per-phase timing breakdown headers to responses. # Headers: x-litellm-timing-{pre-processing,llm-api,post-processing,message-copy}-ms @@ -144,6 +149,7 @@ 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 @@ -243,6 +249,12 @@ AIOHTTP_NEEDS_CLEANUP_CLOSED: Final = (3, 13, 0) <= sys.version_info < ( # https://github.com/openai/openai-agents-python/blob/cf1b933660e44fd37b4350c41febab8221801409/src/agents/realtime/openai_realtime.py#L235 _max_size_env: Final = os.getenv("REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES") REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES: Final = int(_max_size_env) if _max_size_env is not None else None +REALTIME_CREDENTIAL_RESOLUTION_TIMEOUT_SECONDS: Final = float( + os.getenv("REALTIME_CREDENTIAL_RESOLUTION_TIMEOUT_SECONDS", "20.0") +) + +# RFC 6455 caps the close frame payload at 125 bytes, 2 of which carry the status code +WEBSOCKET_CLOSE_REASON_MAX_BYTES: Final = 123 # SSL/TLS cipher configuration for faster handshakes # Strategy: Strongly prefer fast modern ciphers, but allow fallback to commonly supported ones @@ -284,6 +296,9 @@ 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))) @@ -317,6 +332,17 @@ DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT: Final = int(os.getenv("DEFAULT_MOCK_RE DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT: Final = int(os.getenv("DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT", 20)) MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES: Final = int(os.getenv("MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES", 768)) MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES: Final = int(os.getenv("MAX_LONG_SIDE_FOR_IMAGE_HIGH_RES", 2000)) +# tiktoken's BPE merge loop is quadratic in the length of a single regex piece, so a long run of one +# repeated character (dot leaders, whitespace, zero-padded base64) can take minutes on a multi-MB payload. +# Encoding in chunks makes the cost linear, at a drift of at most ~1 token per chunk boundary. The upper +# bound keeps a misconfigured chunk size from restoring the quadratic cost this exists to remove. +TIKTOKEN_ENCODE_MAX_CHUNK_SIZE_CHARS: Final = 4096 +TIKTOKEN_ENCODE_CHUNK_SIZE_CHARS: Final = get_env_int_in_range( + "TIKTOKEN_ENCODE_CHUNK_SIZE_CHARS", + default=1024, + minimum=1, + maximum=TIKTOKEN_ENCODE_MAX_CHUNK_SIZE_CHARS, +) MAX_TILE_WIDTH: Final = int(os.getenv("MAX_TILE_WIDTH", 512)) MAX_TILE_HEIGHT: Final = int(os.getenv("MAX_TILE_HEIGHT", 512)) OPENAI_FILE_SEARCH_COST_PER_1K_CALLS: Final = float(os.getenv("OPENAI_FILE_SEARCH_COST_PER_1K_CALLS", 2.5 / 1000)) @@ -358,6 +384,7 @@ 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)) @@ -417,6 +444,9 @@ DEFAULT_REQUEST_TIMEOUT_SECONDS: Final[float] = 6000.0 # deadline and connect handshake (see ``http_handler`` cached handler paths). COMPLETION_HTTP_FALLBACK_SECONDS: Final[float] = 600.0 HTTP_HANDLER_CONNECT_TIMEOUT_SECONDS: Final[float] = 5.0 +SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS: Final[float] = float( + os.getenv("SEMANTIC_CACHE_EMBEDDING_TIMEOUT_SECONDS", "5.0") +) request_timeout: float = float(os.getenv("REQUEST_TIMEOUT", str(int(DEFAULT_REQUEST_TIMEOUT_SECONDS)))) request_timeout_explicitly_set: bool = "REQUEST_TIMEOUT" in os.environ DEFAULT_A2A_AGENT_TIMEOUT: Final[float] = float(os.getenv("DEFAULT_A2A_AGENT_TIMEOUT", 6000)) # 10 minutes @@ -438,6 +468,8 @@ 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) @@ -461,6 +493,9 @@ MAX_TIME_TO_CLEAR_QUEUE: Final = float(os.getenv("MAX_TIME_TO_CLEAR_QUEUE", 5.0) LOGGING_WORKER_AGGRESSIVE_CLEAR_COOLDOWN_SECONDS: Final = float( os.getenv("LOGGING_WORKER_AGGRESSIVE_CLEAR_COOLDOWN_SECONDS", 0.5) ) # Cooldown time in seconds before allowing another aggressive clear (default: 0.5s) +LOGGING_EXECUTOR_MAX_THREADS: Final = get_env_int("LOGGING_EXECUTOR_MAX_THREADS", 100) +LOGGING_EXECUTOR_MAX_PENDING_TASKS: Final = get_env_int("LOGGING_EXECUTOR_MAX_PENDING_TASKS", 10_000) +LOGGING_EXECUTOR_DROPPED_TASK_LOG_INTERVAL_SECONDS: Final = 30.0 DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE: Final = os.getenv( "DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE", "streaming.chunk.yield" ) @@ -594,6 +629,15 @@ 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", @@ -724,6 +768,7 @@ 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", @@ -757,6 +802,8 @@ openai_compatible_endpoints: Final[list] = [ "https://api.libertai.io/v1", "https://pinstripes.io/v1", "https://api.meta.ai/v1", + "https://api.cognition.ai/v1", + "https://api.scx.ai/v1", ] @@ -824,6 +871,8 @@ openai_compatible_providers: Final[list] = [ "pinstripes", # Pinstripes - JSON-configured provider "darkbloom", "meta", # Meta Model API (Muse Spark) - JSON-configured provider + "cognition", + "scx-ai", ] openai_text_completion_compatible_providers: Final[list] = [ # providers that support `/v1/completions` "together_ai", @@ -1336,6 +1385,11 @@ LITELLM_TRUNCATION_DB_SAFEGUARD_NOTE: Final = ( "Full, untruncated data is logged to logging callbacks (OTEL, Datadog, etc.). " "To increase the truncation limit, set `MAX_STRING_LENGTH_PROMPT_IN_DB` in your env." ) +LITELLM_TRUNCATION_STDOUT_SAFEGUARD_NOTE: Final = ( + "Truncation is a stdout logging safeguard. " + "Full, untruncated data is logged to logging callbacks (OTEL, Datadog, etc.) and at DEBUG level. " + "To increase the truncation limit, set `MAX_STRING_LENGTH_STDOUT_LOG` in your env." +) ########################### LiteLLM Proxy Specific Constants ########################### ######################################################################################## @@ -1431,6 +1485,12 @@ 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( @@ -1502,14 +1562,22 @@ TOOL_SPEND_TOP_TOOLS: Final = 100 SPEND_LOG_PARTITION_INTERVAL: Final = os.getenv("SPEND_LOG_PARTITION_INTERVAL", "day") SPEND_LOG_PARTITION_PRECREATE_AHEAD: Final = int(os.getenv("SPEND_LOG_PARTITION_PRECREATE_AHEAD", 7)) SPEND_LOG_WRITE_BATCH_MAX_BYTES: Final = max(1, int(os.getenv("SPEND_LOG_WRITE_BATCH_MAX_BYTES", 2_000_000))) +SPEND_LOG_WRITE_BATCH_MAX_ROWS: Final = max(1, int(os.getenv("SPEND_LOG_WRITE_BATCH_MAX_ROWS", "100"))) 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)) RESET_BUDGET_JOB_BATCH_SIZE: Final = max(1, int(os.getenv("RESET_BUDGET_JOB_BATCH_SIZE", "500"))) RESET_BUDGET_JOB_MAX_CHUNKS_PER_RUN: Final = max(1, int(os.getenv("RESET_BUDGET_JOB_MAX_CHUNKS_PER_RUN", "100"))) +RESET_BUDGET_JOB_NAME: Final = "reset_budget_job" +# Comfortably longer than one PROXY_BUDGET_RESCHEDULER_MIN_TIME tick, so a healthy +# leader keeps the lease across its own run, and a crashed one strands the sweep for +# at most a single tick. +RESET_BUDGET_JOB_LOCK_TTL_SECONDS: Final[int] = 900 PROXY_BATCH_POLLING_INTERVAL: Final = int(os.getenv("PROXY_BATCH_POLLING_INTERVAL", 3600)) MAX_OBJECTS_PER_POLL_CYCLE: Final = max(1, int(os.getenv("MAX_OBJECTS_PER_POLL_CYCLE", 50))) MANAGED_OBJECT_STALENESS_CUTOFF_DAYS: Final = max(1, int(os.getenv("MANAGED_OBJECT_STALENESS_CUTOFF_DAYS", 7))) @@ -1519,6 +1587,19 @@ 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) @@ -1574,6 +1655,7 @@ LITELLM_SETTINGS_SAFE_DB_OVERRIDES: Final = [ "public_model_groups_links", "cost_discount_config", "cost_margin_config", + "block_requests_for_models_without_pricing", "budget_exceeded_throttle_percentage", # Every field editable from the Admin UI (proxy_server._GENERAL_SETTINGS_UI_LITELLM_FIELDS) # must be listed here so a DB write from one worker overrides the live litellm attribute on @@ -1582,6 +1664,7 @@ 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)) @@ -1748,6 +1831,43 @@ 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/cost_calculator.py b/litellm/cost_calculator.py index 8f7cd09d364..3adc1c25dfd 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": @@ -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 @@ -2336,6 +2389,64 @@ 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 +2471,12 @@ 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/transformation.py b/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py index a9429b673e4..fb66edbf272 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/transformation.py @@ -8,6 +8,14 @@ if TYPE_CHECKING: 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 + + class SpeechToCompletionBridgeTransformationHandler: def transform_request( self, @@ -123,4 +131,6 @@ class SpeechToCompletionBridgeTransformationHandler: # Create an httpx.Response object response: Final = httpx.Response(status_code=200, content=binary_data, headers=headers) - return HttpxBinaryResponseContent(response) + binary_response: Final = HttpxBinaryResponseContent(response) + binary_response.set_response_cost(_completion_response_cost(model_response)) + return binary_response diff --git a/litellm/exceptions.py b/litellm/exceptions.py index 2eb4232fef9..286f7528896 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -1039,16 +1039,29 @@ class LiteLLMUnknownProvider(BadRequestError): class GuardrailRaisedException(Exception): + """ + Raised both when a guardrail judged content and when it could not judge it at all, since a + guardrail that fails closed refuses the request the same way a policy violation does. + + ``blocked_content`` separates the two. Set it only where the guardrail actually reached a + verdict on the payload; leave it alone for an unreachable backend, a timeout, or a response + the integration could not parse. Callers that treat a block as something other than a plain + failure, such as the batch path dropping one record and submitting the rest, must gate on it, + because dropping a record no guardrail ever inspected is a silent loss of enforcement. + """ + def __init__( self, guardrail_name: str | None = None, message: str = "", should_wrap_with_default_message: bool = True, status_code: int = 400, + blocked_content: bool = False, ): default_message: Final = f"Guardrail raised an exception, Guardrail: {guardrail_name}, Message: {message}" self.guardrail_name = guardrail_name self.status_code = status_code + self.blocked_content = blocked_content self.message = default_message if should_wrap_with_default_message else message super().__init__(self.message) diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index 7bd0a847ad8..f0a1bff8fdc 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -7,6 +7,7 @@ import base64 import os from collections.abc import Awaitable, Callable, Generator from datetime import timedelta +from importlib import metadata from typing import Any, Final, TypeVar import httpx @@ -21,6 +22,18 @@ 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 ( @@ -43,6 +56,9 @@ from litellm.types.mcp import ( MCPStdioConfig, MCPTransport, MCPTransportType, + credential_redirect_hook, + has_header, + without_header, ) @@ -51,6 +67,42 @@ def to_basic_auth(auth_value: str) -> str: return base64.b64encode(auth_value.encode("utf-8")).decode() +def strip_auth_scheme(auth_value: str, scheme: str) -> str: + """Return ``auth_value`` with a leading `` `` removed, or unchanged when absent. + + Callers supply both a bare credential and a complete header value, so prefixing + unconditionally yields ``Bearer Bearer ``. Scheme names are case-insensitive per + RFC 7235. A credential is required after the scheme, so both a token that merely begins + with the scheme text and a scheme with nothing behind it are returned untouched. + Surrounding whitespace is left to ``_strip_header_whitespace`` at header-build time. + """ + scheme_name, _, remainder = auth_value.lstrip().partition(" ") + credential: Final = remainder.lstrip() + if credential and scheme_name.lower() == scheme.lower(): + return credential + return auth_value + + +def to_basic_credentials(auth_value: str) -> str: + """Return the base64 credentials for a ``Basic`` header, encoding only when needed. + + ``Basic `` carries credentials that are already encoded, so encoding the whole + value again would bury the scheme inside the payload. This has to run before + :func:`to_basic_auth` rather than at header-build time, where no prefix is left to find. + A schemed value whose remainder does not decode is the bare ``username:password`` shape with + the scheme written in front of it, and is encoded rather than forwarded as an invalid header; + a pair always contains ``:``, which is outside the base64 alphabet, so the two never collide. + """ + credentials: Final = strip_auth_scheme(auth_value, "Basic") + if credentials == auth_value: + return to_basic_auth(auth_value) + try: + base64.b64decode(credentials, validate=True) + except ValueError: + return to_basic_auth(credentials) + return credentials + + def _strip_header_whitespace(headers: dict[str, str]) -> dict[str, str]: return { (key.strip() if isinstance(key, str) else key): (value.strip() if isinstance(value, str) else value) @@ -224,6 +276,7 @@ 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, @@ -239,6 +292,11 @@ 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 @@ -287,7 +345,7 @@ 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) @@ -441,37 +499,44 @@ class MCPClient: except BaseException as e: verbose_logger.debug("Error during http_client cleanup: %s", e) - def update_auth_value(self, mcp_auth_value: str | dict[str, str]): + def update_auth_value(self, mcp_auth_value: str | dict[str, str]) -> None: """ Set the authentication header for the MCP client. """ if isinstance(mcp_auth_value, dict): self._mcp_auth_value = mcp_auth_value + elif self.auth_type == MCPAuth.basic: + self._mcp_auth_value = to_basic_credentials(mcp_auth_value) else: - if self.auth_type == MCPAuth.basic: - # Assuming mcp_auth_value is in format "username:password", convert it when updating - mcp_auth_value = to_basic_auth(mcp_auth_value) 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 {self._mcp_auth_value}" + 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: - headers["Authorization"] = self._mcp_auth_value + # This auth type means the caller owns the whole header value. + headers[self._header_slot("Authorization")] = self._mcp_auth_value elif self.auth_type == MCPAuth.oauth2: - headers["Authorization"] = f"Bearer {self._mcp_auth_value}" + 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 {self._mcp_auth_value}" + 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 {self._mcp_auth_value}" + 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 @@ -479,7 +544,14 @@ 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]: @@ -507,12 +579,14 @@ 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 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..1688087c2da 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -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 @@ -422,24 +423,32 @@ 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 +464,7 @@ def image_generation( api_version=api_version, aimg_generation=aimg_generation, client=client, - headers=headers, + headers=request_headers, litellm_params=litellm_params_dict, ) elif ( @@ -846,6 +855,18 @@ 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, @@ -995,6 +1016,9 @@ 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, 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..d7d06387d85 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -57,6 +57,13 @@ 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: @@ -1431,13 +1438,43 @@ 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) @@ -1473,28 +1510,6 @@ 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] @@ -1531,6 +1546,24 @@ 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 diff --git a/litellm/integrations/anthropic_cache_control_hook.py b/litellm/integrations/anthropic_cache_control_hook.py index 4df6fce74c0..545b0f40018 100644 --- a/litellm/integrations/anthropic_cache_control_hook.py +++ b/litellm/integrations/anthropic_cache_control_hook.py @@ -10,17 +10,37 @@ Supported for both `v1/chat/completions` (via the prompt-management hook) and """ import copy +import os +import re +from collections.abc import Iterable, Mapping, Sequence from typing import TYPE_CHECKING, Any, Final, cast +from urllib.parse import urlparse from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.custom_prompt_management import CustomPromptManagement from litellm.integrations.prompt_management_base import PromptManagementClient +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, ) -from litellm.types.llms.openai import AllMessageValues, ChatCompletionCachedContent +from litellm.types.llms.anthropic import ( + AllAnthropicToolsValues, + AnthropicSystemMessageContent, +) +from litellm.types.llms.openai import ( + AllMessageValues, + ChatCompletionCachedContent, + ChatCompletionTextObject, + ChatCompletionToolParam, + PromptCacheBreakpoint, + PromptCacheOptions, +) from litellm.types.prompts.init_prompts import PromptSpec from litellm.types.utils import StandardCallbackDynamicParams @@ -34,6 +54,64 @@ else: # breakpoints: "A maximum of 4 blocks with cache_control may be provided." MAX_CACHE_CONTROL_BLOCKS: Final = 4 +CACHE_BREAKPOINT_KEYS: Final = ("cache_control", "prompt_cache_breakpoint") +OPENAI_PROMPT_CACHE_BREAKPOINT_MIN_GPT_VERSION: Final = (5, 6) +_GPT_VERSION_PATTERN: Final = re.compile(r"^gpt-(\d+)(?:\.(\d+))?") +OPENAI_PROMPT_CACHE_BREAKPOINT_BLOCK_TYPES: Final = frozenset( + {"text", "image", "image_url", "file", "input_audio", "input_text", "input_image", "input_file"} +) +OPENAI_API_HOST: Final = "api.openai.com" +OPENAI_API_BASE_ENV_VARS: Final = ("OPENAI_BASE_URL", "OPENAI_API_BASE") + +AllToolParamValues = ChatCompletionToolParam | AllAnthropicToolsValues + + +def supports_openai_prompt_cache_breakpoint(model: str) -> bool: + model_map_flag: Final = _model_map_prompt_cache_breakpoint_flag(model) + if model_map_flag is not None: + return model_map_flag + version_match: Final = _GPT_VERSION_PATTERN.match(model.rsplit("/", 1)[-1].lower()) + if version_match is None: + return False + version: Final = (int(version_match.group(1)), int(version_match.group(2) or 0)) + return version >= OPENAI_PROMPT_CACHE_BREAKPOINT_MIN_GPT_VERSION + + +def _model_map_prompt_cache_breakpoint_flag(model: str) -> bool | None: + import litellm + + entries: Final = (litellm.model_cost.get(key) for key in (model, model.rsplit("/", 1)[-1])) + flags: Final = (entry.get("supports_prompt_cache_breakpoint") for entry in entries if isinstance(entry, dict)) + return next((bool(flag) for flag in flags if flag is not None), None) + + +def targets_openai_api(api_base: object) -> bool: + import litellm + + resolved: Final = next( + (value for value in (api_base, litellm.api_base, *map(os.getenv, OPENAI_API_BASE_ENV_VARS)) if value), + None, + ) + if not isinstance(resolved, str): + return True + host: Final = urlparse(resolved).hostname + return host is not None and (host == OPENAI_API_HOST or host.endswith(f".{OPENAI_API_HOST}")) + + +def _carries_cache_breakpoint(block: object) -> bool: + return isinstance(block, dict) and any(block.get(key) is not None for key in CACHE_BREAKPOINT_KEYS) + + +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( @@ -59,6 +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", [] ) @@ -81,27 +160,96 @@ class AnthropicCacheControlHook(CustomPromptManagement): # provider transform, where each tool_config point appends at most one # cachePoint to the tools. That block also counts toward Anthropic's # limit, so reserve a slot for it here to leave room. - reserved_blocks: Final = 1 if any(p.get("location") == "tool_config" for p in remaining_points) else 0 - + stamped_dialect: Final = injection_points[0].get("_litellm_openai_dialect") + openai_dialect: Final = ( + stamped_dialect + if isinstance(stamped_dialect, bool) + else AnthropicCacheControlHook._targets_openai_prompt_cache_breakpoint( + model, + non_default_params.get("custom_llm_provider"), + non_default_params.get("api_base") or non_default_params.get("base_url"), + 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) 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 + ): + 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 + @staticmethod + def _targets_openai_prompt_cache_breakpoint( + model: str | None, + custom_llm_provider: str | None, + api_base: object = None, + prompt_cache_options: object = None, + ) -> bool: + if model is None or not supports_openai_prompt_cache_breakpoint(model): + return False + if (custom_llm_provider or AnthropicCacheControlHook._resolve_provider(model)) != "openai": + return False + return prompt_cache_options is not None or targets_openai_api(api_base) + + @staticmethod + def _resolve_provider(model: str) -> str | None: + from litellm.exceptions import BadRequestError + from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + + try: + _, provider, _, _ = get_llm_provider(model=model) + except BadRequestError: + return None + return provider + + @staticmethod + 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 + ) + return system_blocks + sum(AnthropicCacheControlHook._count_cache_control_blocks(msg) for msg in messages) + @staticmethod def _apply_message_injections( - points: list[CacheControlMessageInjectionPoint], + points: Sequence[CacheControlMessageInjectionPoint], messages: list[AllMessageValues], max_blocks: int, + openai_dialect: bool = False, ) -> list[AllMessageValues]: """Apply message-level cache control injection points in order. @@ -112,7 +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 = sum(AnthropicCacheControlHook._count_cache_control_blocks(msg) for msg in messages) + used_blocks = AnthropicCacheControlHook.count_request_cache_breakpoints(messages) limit_reached = False for point in points: @@ -134,16 +282,17 @@ class AnthropicCacheControlHook(CustomPromptManagement): continue messages[target_index] = AnthropicCacheControlHook._safe_insert_cache_control_in_message( - messages[target_index], control + messages[target_index], control, openai_dialect ) - used_blocks += 1 + if AnthropicCacheControlHook._message_has_cache_control(messages[target_index]): + used_blocks += 1 if limit_reached: break if limit_reached: verbose_logger.warning( - "AnthropicCacheControlHook: Reached the Anthropic limit of %s cache_control blocks. Skipping further injection.", + "AnthropicCacheControlHook: Reached the provider limit of %s cache breakpoints. Skipping further injection.", MAX_CACHE_CONTROL_BLOCKS, ) @@ -189,16 +338,13 @@ class AnthropicCacheControlHook(CustomPromptManagement): return [] @staticmethod - def _count_cache_control_blocks(message: AllMessageValues) -> int: - """Count cache_control breakpoints on a message (message + content level).""" - count = 0 - if message.get("cache_control") is not None: - count += 1 + def _count_cache_control_blocks(message: object) -> int: + if not isinstance(message, dict): + return 0 + count = 1 if _carries_cache_breakpoint(message) else 0 content: Final = message.get("content") if isinstance(content, list): - for block in content: - if isinstance(block, dict) and block.get("cache_control") is not None: - count += 1 + count += sum(1 for block in content if _carries_cache_breakpoint(block)) return count @staticmethod @@ -208,7 +354,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): @staticmethod def _safe_insert_cache_control_in_message( - message: AllMessageValues, control: ChatCompletionCachedContent + message: AllMessageValues, control: ChatCompletionCachedContent, openai_dialect: bool = False ) -> AllMessageValues: """ Safe way to insert cache control in a message @@ -221,6 +367,9 @@ class AnthropicCacheControlHook(CustomPromptManagement): Per Anthropic's API specification, when using multiple content blocks, only the last content block can have cache_control. """ + if openai_dialect: + return AnthropicCacheControlHook._insert_prompt_cache_breakpoint_in_message(message) + message_content: Final = message.get("content", None) # 1. if string, insert cache control in the message @@ -229,14 +378,54 @@ 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 + def _insert_prompt_cache_breakpoint_in_message(message: AllMessageValues) -> AllMessageValues: + if message.get("role") == "assistant": + return message + message_content: Final = message.get("content", None) + if isinstance(message_content, str): + marked: Final = copy.copy(message) + marked["content"] = [ + with_prompt_cache_breakpoint( + ChatCompletionTextObject(type="text", text=message_content), PromptCacheBreakpoint(mode="explicit") + ) + ] + return marked + if isinstance(message_content, list): + target_index: Final = next( + ( + index + for index in range(len(message_content) - 1, -1, -1) + if _accepts_prompt_cache_breakpoint(message_content[index]) + ), + None, + ) + if target_index is not None: + message_content[target_index] = with_prompt_cache_breakpoint( + message_content[target_index], PromptCacheBreakpoint(mode="explicit") + ) + return message + + @staticmethod + def _system_block_with_breakpoint( + block: Mapping[str, object], control: ChatCompletionCachedContent, openai_dialect: bool + ) -> Mapping[str, object]: + marker: Final = ( + ("prompt_cache_breakpoint", PromptCacheBreakpoint(mode="explicit")) + if openai_dialect + else ("cache_control", control) + ) + return {**block, marker[0]: marker[1]} + @staticmethod def apply_to_anthropic_messages_request( messages: list[dict], system: str | list | None, injection_points: list[CacheControlInjectionPoint], + openai_dialect: bool = False, ) -> tuple[list[dict], str | list | None, list[CacheControlInjectionPoint]]: """Apply cache control injection for the Anthropic-native v1/messages endpoint. @@ -262,30 +451,32 @@ class AnthropicCacheControlHook(CustomPromptManagement): else: remaining_points.append(point) - reserved_blocks: Final = 1 if any(p.get("location") == "tool_config" for p in remaining_points) else 0 + reserved_blocks: Final = ( + 1 if not openai_dialect and any(p.get("location") == "tool_config" for p in remaining_points) else 0 + ) max_blocks: Final = MAX_CACHE_CONTROL_BLOCKS - reserved_blocks - used_blocks = sum( - AnthropicCacheControlHook._count_cache_control_blocks(cast(AllMessageValues, msg)) - for msg in processed_messages - ) - if isinstance(processed_system, list): - used_blocks += sum( - 1 for b in processed_system if isinstance(b, dict) and b.get("cache_control") is not None - ) + 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 used_blocks < max_blocks: + 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( - isinstance(b, dict) and b.get("cache_control") is not None for b in processed_system + _carries_cache_breakpoint(b) for b in processed_system ) if not system_already_has_cc: control: Final = system_points[0].get("control") or ChatCompletionCachedContent(type="ephemeral") if isinstance(processed_system, str): - processed_system = [{"type": "text", "text": processed_system, "cache_control": control}] - used_blocks += 1 + processed_system = [ + AnthropicCacheControlHook._system_block_with_breakpoint( + AnthropicSystemMessageContent(type="text", text=processed_system), control, openai_dialect + ) + ] + system_blocks += 1 elif len(processed_system) > 0 and isinstance(processed_system[-1], dict): - processed_system[-1] = {**processed_system[-1], "cache_control": control} - used_blocks += 1 + processed_system[-1] = AnthropicCacheControlHook._system_block_with_breakpoint( + processed_system[-1], control, openai_dialect + ) + system_blocks += 1 for i, msg in enumerate(processed_messages): content = msg.get("content") @@ -295,7 +486,8 @@ class AnthropicCacheControlHook(CustomPromptManagement): processed_messages = AnthropicCacheControlHook._apply_message_injections( points=message_points, messages=cast(list[AllMessageValues], processed_messages), - max_blocks=max_blocks - used_blocks, + max_blocks=max_blocks - system_blocks, + openai_dialect=openai_dialect, ) return processed_messages, processed_system, remaining_points @@ -315,17 +507,57 @@ class AnthropicCacheControlHook(CustomPromptManagement): return ChatCompletionCachedContent(type="ephemeral") @staticmethod - def _stamped_as_judged(points: list[CacheControlInjectionPoint]) -> list[dict[str, object]]: + def _stamped_as_judged(points: Sequence[CacheControlInjectionPoint]) -> Sequence[Mapping[str, object]]: """Mark written-back points as having passed the client cache_control judgment. Builds copies because config-owned point dicts are shared across requests; mutating them would leak the stamp into future requests. """ - return [{**point, "_litellm_judged": True} for point in points] + return AnthropicCacheControlHook._stamped(points, "_litellm_judged", True) + + @staticmethod + def _judged_configured_points( + points: Sequence[CacheControlInjectionPoint], + messages: list[AllMessageValues], + tools: list[object] | None, + model: str, + custom_llm_provider: str | None, + api_base: object, + prompt_cache_options: object, + ) -> Sequence[Mapping[str, object]] | None: + if AnthropicCacheControlHook._should_stand_down(points, messages, None, tools): + return None + return AnthropicCacheControlHook._stamped_with_dialect( + points, model, custom_llm_provider, api_base, prompt_cache_options + ) + + @staticmethod + def _stamped_with_dialect( + points: Sequence[CacheControlInjectionPoint], + model: str, + custom_llm_provider: str | None, + api_base: object, + prompt_cache_options: object, + ) -> Sequence[Mapping[str, object]]: + if not supports_openai_prompt_cache_breakpoint(model): + return points + return AnthropicCacheControlHook._stamped( + points, + "_litellm_openai_dialect", + AnthropicCacheControlHook._targets_openai_prompt_cache_breakpoint( + model, custom_llm_provider, api_base, prompt_cache_options + ), + ) + + @staticmethod + def _stamped( + points: Sequence[CacheControlInjectionPoint], key: str, value: object + ) -> Sequence[Mapping[str, object]]: + return [{**point, key: value} for point in points] @staticmethod def _should_stand_down( - points: list[CacheControlInjectionPoint], + points: Sequence[CacheControlInjectionPoint], messages: list[AllMessageValues], system: str | list | None, tools: list | None, @@ -359,11 +591,8 @@ 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 any(AnthropicCacheControlHook._count_cache_control_blocks(msg) for msg in messages): + if AnthropicCacheControlHook.count_request_cache_breakpoints(messages, system) > 0: return True - if isinstance(system, list): - if any(isinstance(block, dict) and block.get("cache_control") is not None for block in system): - return True if tools is not None: return any( isinstance(tool, dict) @@ -430,6 +659,50 @@ class AnthropicCacheControlHook(CustomPromptManagement): ] return points + @staticmethod + def messages_with_default_injections( + messages: list[AllMessageValues], + models: Iterable[str], + tools: list[AllToolParamValues] | None = None, + enable_prompt_caching: bool | None = None, + ) -> list[AllMessageValues]: + """Return the messages auto prompt caching will send, default breakpoints included. + + Router cache affinity depends on this. Deployment selection runs before the injection in + `litellm.acompletion`, so it has to reproduce the markers to derive the same cache key the + success event later writes from the sent messages. `models` is every candidate model of the + group: the first that would auto-inject decides, since the default breakpoints (system + prompt and trailing turn) do not depend on which deployment serves the call. Returns the + input list itself when auto-injection would not apply + """ + points: Final = next( + ( + candidate + for candidate in ( + AnthropicCacheControlHook.get_default_injection_points( + messages=messages, + system=None, + model=model, + custom_llm_provider=None, + tools=tools, + enable_prompt_caching=enable_prompt_caching, + ) + for model in models + ) + if candidate + ), + None, + ) + if not points: + return messages + return AnthropicCacheControlHook._apply_message_injections( + points=cast( # cast-ok: the default points are all message-location points + list[CacheControlMessageInjectionPoint], points + ), + messages=copy.deepcopy(messages), + max_blocks=MAX_CACHE_CONTROL_BLOCKS, + ) + @staticmethod def maybe_seed_default_injection_points( non_default_params: dict[str, Any], @@ -438,6 +711,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): custom_llm_provider: str | None, tools: list | None = None, enable_prompt_caching: bool | None = None, + api_base: object = None, ) -> None: """For /chat/completions: resolve the injection points the request should carry. @@ -452,10 +726,19 @@ class AnthropicCacheControlHook(CustomPromptManagement): unchanged. """ if non_default_params.get("cache_control_injection_points"): - if AnthropicCacheControlHook._should_stand_down( - non_default_params["cache_control_injection_points"], messages, None, tools - ): + judged: Final = AnthropicCacheControlHook._judged_configured_points( + non_default_params["cache_control_injection_points"], + messages, + tools, + model, + custom_llm_provider, + api_base, + non_default_params.get("prompt_cache_options"), + ) + if judged is None: non_default_params.pop("cache_control_injection_points") + else: + non_default_params["cache_control_injection_points"] = judged return points: Final = AnthropicCacheControlHook.get_default_injection_points( messages=messages, @@ -468,6 +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], @@ -476,6 +817,7 @@ class AnthropicCacheControlHook(CustomPromptManagement): model: str | None = None, custom_llm_provider: str | None = None, tools: list[dict] | None = None, + api_base: str | None = None, ) -> tuple[list[dict], str | list | None]: """Extract cache_control_injection_points from kwargs and apply if present. @@ -513,11 +855,22 @@ class AnthropicCacheControlHook(CustomPromptManagement): if not injection_points: return messages, system + 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) messages, system, remaining = AnthropicCacheControlHook.apply_to_anthropic_messages_request( messages=messages, system=system, injection_points=injection_points, + openai_dialect=openai_dialect, ) + 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) return messages, system diff --git a/litellm/integrations/arize/arize_phoenix_prompt_manager.py b/litellm/integrations/arize/arize_phoenix_prompt_manager.py index fa178a02752..71f4902bbe5 100644 --- a/litellm/integrations/arize/arize_phoenix_prompt_manager.py +++ b/litellm/integrations/arize/arize_phoenix_prompt_manager.py @@ -359,10 +359,10 @@ class ArizePhoenixPromptManager(CustomPromptManagement): """ Determine if prompt management should run based on the prompt_id. - For Arize Phoenix, we always return True and handle the prompt loading - in the _compile_prompt_helper method. + Arize Phoenix needs a prompt_id to compile, so it declines requests without one; + prompt loading itself happens in the _compile_prompt_helper method. """ - return True + return prompt_id is not None def _compile_prompt_helper( self, 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/compression_interception/handler.py b/litellm/integrations/compression_interception/handler.py index 219718f6771..1be7a01ba3a 100644 --- a/litellm/integrations/compression_interception/handler.py +++ b/litellm/integrations/compression_interception/handler.py @@ -8,7 +8,7 @@ litellm_content_retrieve tool calls server-side via the typed agentic loop plan. import time import uuid from collections.abc import Mapping, Sequence -from typing import Any, Final, Protocol, cast +from typing import Any, ClassVar, Final, Protocol, cast from typing_extensions import ReadOnly, TypedDict @@ -88,6 +88,8 @@ 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, 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 0172c789d1e..8dc6881d23e 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -60,11 +60,52 @@ _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 ) +def is_guardrail_intervention(e: Exception) -> bool: + """ + Returns True if the exception represents an intentional guardrail block + (this was logged previously as an API failure - guardrail_failed_to_respond). + + Guardrails signal intentional blocks by raising: + - GuardrailRaisedException (generic guardrail API, tool permission) + - BlockedPiiEntityError (Presidio PII detection) + - SensitiveDataRouteException (sensitive-data reroute to on-premise model) + - HTTPException with a block-signalling status (400, 403, 422) + - ModifyResponseException (passthrough mode violation) + + Only the statuses guardrails use in-tree to signal a deliberate rejection + count as an intervention: 400 (content policy), 403 (e.g. akto) and 422 + (e.g. llm_as_a_judge). Other 4xx codes are commonly propagated from an + upstream guardrail provider response (401 bad key, 408 timeout, 429 rate + limit, or a raw upstream status), which are technical failures, not + blocks, so they stay guardrail_failed_to_respond. + """ + if isinstance(e, ModifyResponseException): + return True + if isinstance( + e, + ( + GuardrailRaisedException, + BlockedPiiEntityError, + SensitiveDataRouteException, + ), + ): + return True + if HTTPException is not None and isinstance(e, HTTPException) and e.status_code in _GUARDRAIL_BLOCK_STATUS_CODES: + return True + return False + + def _strict_guardrail_modes_enabled() -> bool: """Whether guardrail-mode validation raises (default) or logs a warning. @@ -123,6 +164,7 @@ 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, ): @@ -145,6 +187,13 @@ 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 @@ -160,6 +209,7 @@ 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: @@ -246,6 +296,82 @@ 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, @@ -429,11 +555,13 @@ class CustomGuardrail(CustomLogger): f"Sensitive data detected by {self.guardrail_name} (routing skipped: request has no session_id)" ), guardrail_name=self.guardrail_name, + blocked_content=True, ) else: raise GuardrailRaisedException( message=f"Sensitive data detected by {self.guardrail_name}", guardrail_name=self.guardrail_name, + blocked_content=True, ) @staticmethod @@ -1068,42 +1196,8 @@ class CustomGuardrail(CustomLogger): @staticmethod def _is_guardrail_intervention(e: Exception) -> bool: - """ - Returns True if the exception represents an intentional guardrail block - (this was logged previously as an API failure - guardrail_failed_to_respond). - - Guardrails signal intentional blocks by raising: - - GuardrailRaisedException (generic guardrail API, tool permission) - - BlockedPiiEntityError (Presidio PII detection) - - SensitiveDataRouteException (sensitive-data reroute to on-premise model) - - HTTPException with a block-signalling status (400, 403, 422) - - ModifyResponseException (passthrough mode violation) - - Only the statuses guardrails use in-tree to signal a deliberate rejection - count as an intervention: 400 (content policy), 403 (e.g. akto) and 422 - (e.g. llm_as_a_judge). Other 4xx codes are commonly propagated from an - upstream guardrail provider response (401 bad key, 408 timeout, 429 rate - limit, or a raw upstream status), which are technical failures, not - blocks, so they stay guardrail_failed_to_respond. - """ - if isinstance(e, ModifyResponseException): - return True - if isinstance( - e, - ( - GuardrailRaisedException, - BlockedPiiEntityError, - SensitiveDataRouteException, - ), - ): - return True - if ( - HTTPException is not None - and isinstance(e, HTTPException) - and e.status_code in _GUARDRAIL_BLOCK_STATUS_CODES - ): - return True - return False + """Retained spelling for existing callers; prefer ``is_guardrail_intervention``.""" + return is_guardrail_intervention(e) def _process_error( self, diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index 417edc77e9c..f177076d8fe 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, Sequence -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 @@ -60,6 +60,26 @@ _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 + """ + Whether this hook's ``async_pre_call_hook`` judges the request payload itself. + + False for the accounting hooks, which count a request rather than read it: rate limits, + parallel slots, budgets, cache lookups. Those must run once per request and never once per + record of a batch upload, which would charge a caller once for every line of their file. + + Set it to True on a hook that inspects or rejects content, so that scanning a payload which + is not itself a request, such as one record of a batch input file, still reaches it. A + ``CustomGuardrail`` does not need it; guardrails are dispatched by their own branch. + + Judging content is necessary but not sufficient. A hook that also rewrites the payload for + routing, as the managed-files and managed-vector-store hooks do, stays False: a per-record + rewrite would read as a redaction and ship embedded in the record. Only the leaf class is + consulted, so a subclass that does not override ``async_pre_call_hook`` inherits nothing. + """ + def __init__( self, turn_off_message_logging: bool = False, @@ -273,6 +293,54 @@ 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, diff --git a/litellm/integrations/datadog/datadog_cost_management.py b/litellm/integrations/datadog/datadog_cost_management.py index b30700e98f2..7255c9c761c 100644 --- a/litellm/integrations/datadog/datadog_cost_management.py +++ b/litellm/integrations/datadog/datadog_cost_management.py @@ -11,6 +11,7 @@ from litellm.integrations.datadog.datadog_handler import ( get_datadog_hostname, get_datadog_pod_name, get_datadog_service, + normalize_datadog_tag_value, ) from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.llms.custom_httpx.http_handler import ( @@ -184,7 +185,7 @@ class DatadogCostManagementLogger(CustomBatchLogger): # Backwards-compat: team/user/model_group preserved regardless of allowlist. if metadata.get("user_api_key_alias"): - tags["user"] = str(metadata["user_api_key_alias"]) + tags["user"] = normalize_datadog_tag_value(metadata["user_api_key_alias"]) team_tag: Final = ( metadata.get("user_api_key_team_alias") or metadata.get("team_alias") @@ -192,7 +193,7 @@ class DatadogCostManagementLogger(CustomBatchLogger): or metadata.get("team_id") ) if team_tag: - tags["team"] = str(team_tag) + tags["team"] = normalize_datadog_tag_value(team_tag) if metadata.get("model_group"): tags["model_group"] = str(metadata["model_group"]) @@ -229,7 +230,7 @@ class DatadogCostManagementLogger(CustomBatchLogger): value, ) return - tags[key] = value + tags[key] = normalize_datadog_tag_value(value) @staticmethod def _add_tag(tags: dict[str, str], key: str, value: Any) -> None: diff --git a/litellm/integrations/datadog/datadog_handler.py b/litellm/integrations/datadog/datadog_handler.py index 2450382a192..d360dac121c 100644 --- a/litellm/integrations/datadog/datadog_handler.py +++ b/litellm/integrations/datadog/datadog_handler.py @@ -3,6 +3,7 @@ from __future__ import annotations import os +import re from typing import Final from litellm.types.utils import StandardLoggingPayload @@ -36,6 +37,13 @@ def get_datadog_pod_name() -> str: return os.getenv("POD_NAME", "unknown") +def normalize_datadog_tag_value(value: object) -> str: + normalized_value: Final = "".join( + character if character.isalnum() or character in "_-:./" else "_" for character in str(value).lower() + ) + return re.sub(r"_+", "_", normalized_value).strip("_") + + def get_datadog_tags( standard_logging_object: StandardLoggingPayload | None = None, ) -> list[str]: @@ -58,7 +66,7 @@ def get_datadog_tags( if standard_logging_object: request_tags: Final = standard_logging_object.get("request_tags", []) or [] - tags.extend(f"request_tag:{tag}" for tag in request_tags) + tags.extend(f"request_tag:{normalize_datadog_tag_value(tag)}" for tag in request_tags) # Add Team Tag metadata: Final = standard_logging_object.get("metadata", {}) or {} @@ -69,6 +77,6 @@ def get_datadog_tags( or metadata.get("team_id") ) if team_tag: - tags.append(f"team:{team_tag}") + tags.append(f"team:{normalize_datadog_tag_value(team_tag)}") return tags diff --git a/litellm/integrations/datadog/datadog_metrics.py b/litellm/integrations/datadog/datadog_metrics.py index 89f990cf661..5dda336dc94 100644 --- a/litellm/integrations/datadog/datadog_metrics.py +++ b/litellm/integrations/datadog/datadog_metrics.py @@ -12,6 +12,7 @@ from litellm.integrations.datadog.datadog_handler import ( get_datadog_hostname, get_datadog_pod_name, get_datadog_service, + normalize_datadog_tag_value, ) from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.llms.custom_httpx.http_handler import ( @@ -97,7 +98,7 @@ class DatadogMetricsLogger(CustomBatchLogger): ) if team_tag: - tags.append(f"team:{team_tag}") + tags.append(f"team:{normalize_datadog_tag_value(team_tag)}") return tags 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..fd0b17ba746 100644 --- a/litellm/integrations/dotprompt/prompt_manager.py +++ b/litellm/integrations/dotprompt/prompt_manager.py @@ -11,6 +11,13 @@ from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +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: """Represents a single prompt template with metadata and content.""" @@ -124,11 +131,13 @@ 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 +145,10 @@ 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: @@ -272,8 +280,12 @@ 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.""" 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/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index da924a81e0c..296c2b5714e 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 @@ -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) @@ -942,6 +966,20 @@ 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: """ 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/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 a0b5aff559f..d2a32ef73b6 100644 --- a/litellm/integrations/otel/logger.py +++ b/litellm/integrations/otel/logger.py @@ -9,7 +9,15 @@ from typing import TYPE_CHECKING, Any, Final, cast from opentelemetry.context import Context, attach, get_current from opentelemetry.sdk._logs import LoggerProvider from opentelemetry.sdk.trace import TracerProvider -from opentelemetry.trace import Span, Tracer, get_current_span, use_span +from opentelemetry.trace import ( + INVALID_SPAN, + Link, + Span, + Tracer, + get_current_span, + set_span_in_context, + use_span, +) import litellm from litellm._logging import verbose_logger @@ -21,6 +29,7 @@ from litellm.integrations.otel.model.config import OpenTelemetryV2Config from litellm.integrations.otel.model.metadata import ( LLMCallEvent, RequestIdentity, + auth_metadata, model_from_request_data, ) from litellm.integrations.otel.model.payloads import ( @@ -118,6 +127,12 @@ _OTEL_MODULES: Final = ( _OPEN_CALLS_MAX: Final = 10_000 +def _request_trace_links(context: Context | None) -> tuple[Link, ...] | None: + """A link back to the request trace, for a span detached into its own trace.""" + anchor: Final = get_current_span(context).get_span_context() + return (Link(anchor),) if anchor.is_valid else None + + class _LLMCallSpan: """The state carried from the ``pre_call`` boundary to span close. @@ -127,13 +142,24 @@ class _LLMCallSpan: own (worker-copied) ambient context using ``start_time_ns``. The presence of a carrier for a call at all is the proof that ``pre_call`` ran, i.e. that an upstream call was actually attempted. + + ``provider`` is the routed provider the live span was opened on (``None`` on + the default route or when creation was deferred). It is held in the tenant + cache while the span is open so LRU eviction can't shut the provider down + under it, and must be released exactly once when the carrier is removed. """ - __slots__ = ("span", "start_time_ns") + __slots__ = ("provider", "span", "start_time_ns") - def __init__(self, span: "Span | None", start_time_ns: int | None) -> None: + def __init__( + self, + span: "Span | None", + start_time_ns: int | None, + provider: "TracerProvider | None" = None, + ) -> None: self.span = span self.start_time_ns = start_time_ns + self.provider = provider class OpenTelemetryV2(CustomLogger): @@ -258,26 +284,37 @@ class OpenTelemetryV2(CustomLogger): if call_id in self._open_llm_calls: return start_time_ns: Final = to_ns(datetime.now()) - span: Span | None = None # Parent to the request's anchored root span (stable across the request), # falling back to ambient on the SDK path. Open the span live only when # that resolves to a recordable parent; otherwise defer to the close # callback (the thread-pool case, where the anchor isn't visible here). + # Do not route on the deferred path: creating or LRU-touching a tenant + # provider here would evict idle ones even though close re-routes. parent_context: Final = resolve_request_span_context() - if is_recordable_span(get_current_span(parent_context)): - span = self._emitter.start_span( + if not is_recordable_span(get_current_span(parent_context)): + self._store_open_call(call_id, _LLMCallSpan(span=None, start_time_ns=start_time_ns)) + return + # A detached route roots its own trace instead (linked to the request + # trace) — see ``TenantRoute.detached``. + route: Final = self._tenant_tracers.route_for(self.tracer, call.dynamic_params, call.auth_metadata) + try: + span: Final = self._emitter.start_span( SpanRole.LLM_CALL, call.provisional_span_name, - parent_context=parent_context, + parent_context=( + set_span_in_context(INVALID_SPAN, parent_context) if route.detached else parent_context + ), start_time_ns=start_time_ns, - tracer=self._tenant_tracers.tracer_for(self.tracer, call.dynamic_params), + tracer=route.tracer, + links=_request_trace_links(parent_context) if route.detached else None, ) - self._open_llm_calls[call_id] = _LLMCallSpan(span=span, start_time_ns=start_time_ns) - # Evict the oldest open call if the map is 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). - if len(self._open_llm_calls) > _OPEN_CALLS_MAX: - self._open_llm_calls.popitem(last=False) + except BaseException: + self._tenant_tracers.release(route.provider) + raise + self._store_open_call( + call_id, + _LLMCallSpan(span=span, start_time_ns=start_time_ns, provider=route.provider), + ) async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): if self._emit_mcp_tool_call(kwargs, start_time, end_time): @@ -353,10 +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. """ @@ -371,17 +408,22 @@ class OpenTelemetryV2(CustomLogger): # otherwise linger until evicted; drop it so it's neither leaked nor closed # as a phantom LLM span. if data.identity.call_id: - self._open_llm_calls.pop(data.identity.call_id, None) - parent_context, links = resolve_mcp_span_context() - parent_context = self._seed_identity_baggage(data.identity, None, parent_context) - self._emitter.emit( - SpanRole.MCP_TOOL_CALL, - data, - parent_context=parent_context, - start_time_ns=to_ns(start_time), - end_time_ns=to_ns(end_time), - links=links, - ) + self._release_carrier(self._open_llm_calls.pop(data.identity.call_id, None)) + route: Final = self._tenant_tracers.route_for(self.tracer, None, auth_metadata(payload, kwargs)) + try: + parent_context, links = resolve_mcp_span_context() + seeded: Final = self._seed_identity_baggage(data.identity, None, parent_context) + self._emitter.emit( + SpanRole.MCP_TOOL_CALL, + data, + parent_context=(set_span_in_context(INVALID_SPAN, seeded) if route.detached else seeded), + start_time_ns=to_ns(start_time), + end_time_ns=to_ns(end_time), + links=((*(links or ()), *(_request_trace_links(seeded) or ())) if route.detached else links), + tracer=route.tracer, + ) + finally: + self._tenant_tracers.release(route.provider) return True def _emit_mcp_list_tools( @@ -394,9 +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") @@ -407,17 +449,22 @@ class OpenTelemetryV2(CustomLogger): payload, capture_content=self.config.capture_span_content ) if data.identity.call_id: - self._open_llm_calls.pop(data.identity.call_id, None) - parent_context, links = resolve_mcp_span_context() - parent_context = self._seed_identity_baggage(data.identity, None, parent_context) - self._emitter.emit( - SpanRole.MCP_LIST_TOOLS, - data, - parent_context=parent_context, - start_time_ns=to_ns(start_time), - end_time_ns=to_ns(end_time), - links=links, - ) + self._release_carrier(self._open_llm_calls.pop(data.identity.call_id, None)) + route: Final = self._tenant_tracers.route_for(self.tracer, None, auth_metadata(payload, kwargs)) + try: + parent_context, links = resolve_mcp_span_context() + seeded: Final = self._seed_identity_baggage(data.identity, None, parent_context) + self._emitter.emit( + SpanRole.MCP_LIST_TOOLS, + data, + parent_context=(set_span_in_context(INVALID_SPAN, seeded) if route.detached else seeded), + start_time_ns=to_ns(start_time), + end_time_ns=to_ns(end_time), + links=((*(links or ()), *(_request_trace_links(seeded) or ())) if route.detached else links), + tracer=route.tracer, + ) + finally: + self._tenant_tracers.release(route.provider) return True def _close_llm_call( @@ -437,13 +484,56 @@ 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, 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. + self._release_carrier(carrier) + + def _store_open_call(self, call_id: str, carrier: _LLMCallSpan) -> None: + """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. 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: + _, evicted = self._open_llm_calls.popitem(last=False) + self._release_carrier(evicted) + + def _release_carrier(self, carrier: "_LLMCallSpan | None") -> None: + """Release the routed provider a removed carrier was holding open.""" + if carrier is not None: + self._tenant_tracers.release(carrier.provider) + + def _finish_carrier( + self, + 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( @@ -452,26 +542,36 @@ 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. # The worker copied the request task's context, which carries the anchored # root span — parent to it (ambient fallback on the SDK path). Seed identity # Baggage so the span — and the SDK path, which has none — is labeled - # consistently. - parent_ctx = self._seed_identity_baggage(data.identity, data.request_model, resolve_request_span_context()) - return self._emitter.emit( - SpanRole.LLM_CALL, - data, - parent_context=parent_ctx, - start_time_ns=carrier.start_time_ns, - end_time_ns=end_time_ns, - tracer=self._tenant_tracers.tracer_for(self.tracer, call.dynamic_params), - ) + # consistently. A detached route roots its own trace instead, linked back. + route: Final = self._tenant_tracers.route_for(self.tracer, call.dynamic_params, call.auth_metadata) + try: + parent_ctx: Final = self._seed_identity_baggage( + data.identity, data.request_model, resolve_request_span_context() + ) + return self._emitter.emit( + 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 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, + ) + finally: + self._tenant_tracers.release(route.provider) # ====================================================================== # # Service hooks diff --git a/litellm/integrations/otel/mappers/genai.py b/litellm/integrations/otel/mappers/genai.py index 79487e69ac4..b09498f9292 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, @@ -65,6 +66,7 @@ class GenAIMapper: 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 +136,9 @@ 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 c7cdaae0417..062b2ca20b4 100644 --- a/litellm/integrations/otel/model/metadata.py +++ b/litellm/integrations/otel/model/metadata.py @@ -36,8 +36,9 @@ model. They coincide on the SDK path, which is correct. from __future__ import annotations -from collections.abc import Mapping +from collections.abc import Iterator, Mapping from dataclasses import dataclass, field +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, cast from litellm.constants import LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL @@ -195,9 +196,18 @@ class LLMCallEvent: # The ``standard_callback_dynamic_params`` routing the call to a per-tenant # tracer (its own exporter/endpoint), or ``None`` when the call isn't scoped. dynamic_params: Any + # The key/team config the proxy resolved at auth (``user_api_key_auth_metadata``), + # routing the call to that tenant's telemetry project. Server-set and so + # trusted, unlike ``dynamic_params``, which carries client-supplied metadata. + auth_metadata: Mapping[str, str] | None # 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). @@ -214,7 +224,9 @@ class LLMCallEvent: call_id=_call_id(payload, kwargs), payload=payload, 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), ) @@ -235,6 +247,64 @@ def time_to_first_chunk_seconds(kwargs: Mapping[str, Any]) -> float | None: return completion_start - api_call_start +def auth_metadata(payload: StandardLoggingPayload | None, kwargs: Mapping[str, object]) -> Mapping[str, str] | None: + """The key/team config the proxy resolved at auth, or ``None`` off the proxy. + + Read from the payload once the call closes and from ``litellm_params`` at + ``pre_call``, where no payload exists yet — the LLM-call span is *created* at + ``pre_call``, so the tracer (and therefore the destination) must be + resolvable there. Values arrive untyped, so non-string entries are dropped + rather than passed on to header builders. + """ + return next( + ( + typed + for metadata in _metadata_dicts(payload, kwargs) + if (typed := _string_entries(metadata.get("user_api_key_auth_metadata"))) + ), + None, + ) + + +def _as_str_mapping(value: object) -> Mapping[str, object] | None: + """A read-only view of ``value`` when it is a mapping, else ``None``.""" + if not isinstance(value, Mapping): + return None + return cast("Mapping[str, object]", value) # cast-ok: isinstance-guarded, JSON metadata has str keys + + +def _string_entries(value: object) -> Mapping[str, str] | None: + entries: Final = _as_str_mapping(value) + if entries is None: + return None + typed: Final = MappingProxyType({key: item for key, item in entries.items() if isinstance(item, str)}) + return typed or None + + +def _metadata_dicts( + payload: StandardLoggingPayload | None, kwargs: Mapping[str, object] +) -> Iterator[Mapping[str, object]]: + """Request metadata dicts, closed-call payload first then the live kwargs. + + ``litellm_metadata`` is the metadata field on the Anthropic-shaped routes; + litellm copies it onto ``metadata``, but both are yielded so a route that + populates only one is still covered. + """ + payload_view: Final = _as_str_mapping(payload) + if payload_view is not None: + payload_metadata: Final = _as_str_mapping(payload_view.get("metadata")) + if payload_metadata is not None: + yield payload_metadata + params: Final = _as_str_mapping(kwargs.get("litellm_params")) + if params is None: + return + yield from ( + metadata + for key in ("metadata", "litellm_metadata") + if (metadata := _as_str_mapping(params.get(key))) is not None + ) + + def _call_id(payload: StandardLoggingPayload | None, kwargs: Mapping[str, Any]) -> str | None: """The call id from the payload (when closed) or the bare kwargs (at pre_call).""" if payload is not None: diff --git a/litellm/integrations/otel/model/payloads.py b/litellm/integrations/otel/model/payloads.py index aba9cc80240..f70c777e1a7 100644 --- a/litellm/integrations/otel/model/payloads.py +++ b/litellm/integrations/otel/model/payloads.py @@ -15,8 +15,10 @@ 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 ( @@ -188,6 +190,15 @@ 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 +218,8 @@ 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 +242,9 @@ 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 +326,11 @@ 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( @@ -334,8 +355,9 @@ 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, @@ -358,6 +380,8 @@ 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..4ad0cb5d1b4 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): @@ -258,6 +276,11 @@ 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 +308,15 @@ 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 +384,24 @@ _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 +435,23 @@ _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 +483,11 @@ 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..c7e491c002a 100644 --- a/litellm/integrations/otel/plumbing/metrics.py +++ b/litellm/integrations/otel/plumbing/metrics.py @@ -32,6 +32,7 @@ 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 @@ -198,16 +199,21 @@ class GenAIMetricRecorder: ) -> 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( diff --git a/litellm/integrations/otel/plumbing/providers.py b/litellm/integrations/otel/plumbing/providers.py index 80a8d01c061..81b00f788ee 100644 --- a/litellm/integrations/otel/plumbing/providers.py +++ b/litellm/integrations/otel/plumbing/providers.py @@ -436,6 +436,8 @@ 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 d9e9e84364a..dc2db823a8d 100644 --- a/litellm/integrations/otel/plumbing/routing.py +++ b/litellm/integrations/otel/plumbing/routing.py @@ -1,31 +1,49 @@ """Per-request multi-tenant tracer routing. When a request carries team/key vendor credentials in -``standard_callback_dynamic_params``, its spans must export through a -``TracerProvider`` whose OTLP headers carry those credentials. -``TenantTracerCache`` builds and caches one provider per distinct credential -set, 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. +``standard_callback_dynamic_params``, or the key/team config resolved at auth +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 from collections import OrderedDict from collections.abc import Mapping -from typing import Any, Final +from dataclasses import dataclass +from types import MappingProxyType +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.integrations.otel.model.config import OpenTelemetryV2Config +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, get_tracer, ) -from litellm.integrations.otel.presets import dynamic_otlp_headers +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") +# gRPC exporters still take dynamic credentials (as gRPC metadata) but not +# project headers: the routing headers backends read (Phoenix's +# ``x-project-name``) are only honored on the OTLP/HTTP endpoint. +_GRPC_KINDS: Final = ("otlp_grpc", "grpc") + # Cap on distinct credential-scoped providers held at once. ``dynamic_params`` # can be populated from request metadata, so an unbounded cache lets a caller # spawn one ``TracerProvider`` (plus its ``BatchSpanProcessor`` background @@ -34,6 +52,45 @@ _NON_OTLP_KINDS: Final = ("console", "in_memory", "inmemory", "memory") # evicted providers so their threads are reclaimed. _MAX_CACHED_PROVIDERS: Final = 256 +# Cap on providers evicted from the cache while still holding open spans, which +# are kept alive to drain instead of being shut down under them. Their only +# other bound is the logger's open-call map (10k), so without this a caller +# cycling unique credential sets across long-lived calls could pin far more +# live providers, and exporter threads, than the cache cap allows. Past this +# many, the stalest retiree is shut down and whatever it was draining is +# dropped (a shut-down ``BatchSpanProcessor`` discards spans handed to it after +# the fact), which by then means a span on a route evicted long ago. A quarter +# of the cache cap: enough that a burst of tenant churn during long-lived calls +# still drains normally, small enough that the worst case is a bounded 320 +# providers rather than one per concurrent call. +_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). @@ -49,8 +106,41 @@ def _shutdown_provider(provider: TracerProvider) -> None: verbose_logger.debug("OTel V2: error shutting down evicted provider: %s", e) +def _plain_header_string(headers: Mapping[str, str]) -> str: + return ",".join(f"{key}={value}" for key, value in headers.items()) + + +def _encoded_header_string(headers: Mapping[str, str]) -> str: + """Percent-encode values so one containing the ``k=v,k=v`` separators (e.g. + a project name with a comma) survives; ``parse_env_headers`` decodes it back. + """ + return ",".join(f"{key}={quote(value, safe='')}" for key, value in headers.items()) + + +@dataclass(frozen=True, slots=True) +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 + 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. + """ + + tracer: Tracer + detached: bool + #: The provider ``tracer`` came from, or ``None`` on the default route. It + #: is returned already held (counted as an open span, atomically with the + #: cache update), so LRU eviction can't shut it down before the caller's + #: span lands; the caller must ``release`` it exactly once when done. + provider: TracerProvider | None = None + + class TenantTracerCache: - """Credential-scoped ``TracerProvider`` cache keyed by the dynamic headers.""" + """Tenant-scoped ``TracerProvider`` cache keyed by routing headers and service name.""" def __init__( self, @@ -61,49 +151,198 @@ class TenantTracerCache: self._config = config self._callback_name = callback_name self._tracer_name = tracer_name - self._providers: OrderedDict[tuple[tuple[str, str], ...], TracerProvider] = OrderedDict() + # Guards the three mutable structures below: ``pre_call`` can run on + # 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[_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 + ) + self._warned_project_unroutable = False - def tracer_for(self, default: Tracer, dynamic_params: Any) -> Tracer: - """Return the tracer for this request. + def release(self, provider: TracerProvider | None) -> None: + """Drop one open-span count; shut a retired provider down once drained. - Use ``default`` unless the request's dynamic credentials require a - credential-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. + ``None`` (the default route) is a no-op so callers can release a + ``TenantRoute.provider`` unconditionally. The shutdown itself runs + outside the lock: it force-flushes over the network and must not stall + every concurrently routing request. """ - headers: Final = dynamic_otlp_headers(self._callback_name, dynamic_params) - if not headers: - return default - cache_key: Final = tuple(sorted(headers.items())) - provider = self._providers.get(cache_key) - if provider is not None: - self._providers.move_to_end(cache_key) - else: - provider = build_tracer_provider(self._config_with_headers(headers)) - self._providers[cache_key] = provider - if len(self._providers) > _MAX_CACHED_PROVIDERS: - _, evicted = self._providers.popitem(last=False) - _shutdown_provider(evicted) - return get_tracer(provider, self._tracer_name) + if provider is None: + return + with self._lock: + remaining: Final = self._open_span_counts.get(provider, 0) - 1 + if remaining > 0: + self._open_span_counts[provider] = remaining + return + self._open_span_counts.pop(provider, None) + drained: Final = provider in self._retired + self._retired.pop(provider, None) + if drained: + _shutdown_provider(provider) - def _config_with_headers(self, headers: Mapping[str, str]) -> OpenTelemetryV2Config: - """Clone the config, stamping ``headers`` onto the credential's own exporter. + def route_for( + self, + default: Tracer, + dynamic_params: StandardCallbackDynamicParams | None, + auth_metadata: Mapping[str, str] | None = None, + ) -> TenantRoute: + """Return the tracer (and trace-detachment flag) for this request. - ``headers`` are the per-request credentials of ``self._callback_name`` (the - integration that built this cache), so they apply only to the exporter that - integration contributed (``spec.owner``). A request that carries one - tenant's Arize key must never rewrite the headers of a co-configured - Langfuse or self-hosted collector exporter, which would leak that key to a - different backend. + 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. """ - header_str: Final = ",".join(f"{key}={value}" for key, value in headers.items()) - header_update: Final[dict[str, str]] = {"headers": header_str} - exporters: Final = [ - ( - spec.model_copy(update=header_update) - if spec.owner == self._callback_name and spec.kind.lower() not in _NON_OTLP_KINDS - else spec + credential_headers: Final = dynamic_otlp_headers(self._callback_name, dynamic_params) or _NO_HEADERS + project_headers: Final = self._project_headers(auth_metadata) + 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, 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), + provider=provider, + ) + + def _cached_provider_locked( + self, + 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, endpoint, service_name) + ) + self._providers[cache_key] = built + return built + + def _evicted_on_overflow_locked(self) -> TracerProvider | None: + """Pop the LRU provider past the cap; return it if the caller must shut it down. + + A provider with open spans is retired to drain instead: stopping its + processors while a span opened at ``pre_call`` is still live would + silently drop that span at end instead of exporting it. Retirees are + themselves capped, so the stalest one is shut down (and its open-span + count dropped, making its eventual ``release`` a no-op) once too many + pile up rather than letting them accumulate a thread each. + """ + if len(self._providers) <= _MAX_CACHED_PROVIDERS: + return None + _, evicted = self._providers.popitem(last=False) + if self._open_span_counts.get(evicted, 0) == 0: + return evicted + self._retired[evicted] = None + if len(self._retired) <= _MAX_RETIRED_PROVIDERS: + return None + overflowed, _ = self._retired.popitem(last=False) + self._open_span_counts.pop(overflowed, None) + return overflowed + + 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. + + A gRPC-only exporter can't (the project header route is HTTP-only), so + the request warns once and stays on the env-configured default project. + """ + requested: Final = project_routing_headers(self._callback_name, auth_metadata) + if not requested or self._project_routable: + return requested + if not self._warned_project_unroutable: + self._warned_project_unroutable = True + verbose_logger.warning( + "OTel V2: %s key/team config names a per-request project, but its exporter " + "is not OTLP/HTTP and the project header is HTTP-only; spans stay in the " + "default project.", + self._callback_name, + ) + return _NO_HEADERS + + def _routed_config( + 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. + + Both header sets apply only to the exporter ``self._callback_name`` + contributed (``spec.owner``). A request that carries one tenant's Arize + key must never rewrite the headers of a co-configured Langfuse or + self-hosted collector exporter, which would leak that key to a + different backend. + + Dynamic credentials REPLACE the exporter's headers — they are the + tenant's complete credential set. Project headers APPEND instead: the + preset's static headers carry the backend auth (Phoenix's + ``Authorization``), which must survive routing to a project. + """ + exporters: Final = [ + 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: + return spec + base: Final = _plain_header_string(credential_headers) if credential_headers else spec.headers + routed: Final = ( + ",".join(part for part in (base, _encoded_header_string(project_headers)) if part) + if project_headers and kind not in _GRPC_KINDS + else base + ) + 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 95ac2783325..a0cd5b3fd98 100644 --- a/litellm/integrations/otel/presets/__init__.py +++ b/litellm/integrations/otel/presets/__init__.py @@ -8,7 +8,8 @@ the factory in ``litellm_logging`` can resolve a name and build a single ``OpenTelemetryV2`` instance from the result. """ -from collections.abc import Callable +from collections.abc import Callable, Mapping +from types import MappingProxyType from typing import Final from litellm.integrations.otel.presets.agentops import agentops_preset @@ -20,31 +21,76 @@ 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.phoenix import phoenix_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, +) from litellm.integrations.otel.presets.weave import weave_dynamic_headers, weave_preset 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 +#: config the proxy resolved at auth. Deliberately separate from +#: ``DYNAMIC_HEADERS_BY_CALLBACK``: that one is fed +#: ``StandardCallbackDynamicParams``, which is populated from client-supplied +#: request metadata. Naming a destination project is a data-exfiltration +#: primitive, so it must only ever come from server-set key/team config. +PROJECT_HEADERS_BY_CALLBACK: Final[Mapping[str, Callable[[Mapping[str, str] | None], Mapping[str, str]]]] = ( + MappingProxyType( + { + "arize_phoenix": phoenix_project_headers, + } + ) +) + +_NO_PROJECT_HEADERS: Final[Mapping[str, str]] = MappingProxyType({}) def dynamic_otlp_headers( @@ -62,16 +108,50 @@ def dynamic_otlp_headers( return headers or None +def project_routing_headers( + callback_name: str | None, + auth_metadata: Mapping[str, str] | None, +) -> Mapping[str, str]: + """Per-request project-routing headers from trusted key/team config. + + Empty means "no per-request project" — the caller keeps its default tracer, + whose resource attributes carry the env-configured project. + """ + builder: Final = PROJECT_HEADERS_BY_CALLBACK.get(callback_name or "") + if builder is None: + return _NO_PROJECT_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/otel/presets/phoenix.py b/litellm/integrations/otel/presets/phoenix.py index fc1eb9f748f..eef407b6c1b 100644 --- a/litellm/integrations/otel/presets/phoenix.py +++ b/litellm/integrations/otel/presets/phoenix.py @@ -1,5 +1,7 @@ """Arize-Phoenix preset.""" +from collections.abc import Mapping +from types import MappingProxyType from typing import Final from pydantic import AliasChoices, Field @@ -25,6 +27,36 @@ class _PhoenixSettings(BaseSettings): ) +#: Phoenix routes an OTLP/HTTP export to a project by this header, which takes +#: precedence over the ``openinference.project.name`` resource attribute the env +#: var sets. Requires arize-phoenix 15.5.0+; older collectors ignore it and the +#: spans land in the resource attribute's project. +PHOENIX_PROJECT_HEADER: Final = "x-project-name" + +#: Key/team config fields naming the target project, highest precedence first. +_PROJECT_KEYS: Final = ("phoenix_project_name_override", "phoenix_project_name") + +_NO_PROJECT: Final[Mapping[str, str]] = MappingProxyType({}) + + +def phoenix_project_headers(auth_metadata: Mapping[str, str] | None) -> Mapping[str, str]: + """The per-request Phoenix project header for this key/team, if any. + + ``auth_metadata`` must be the key/team config the proxy resolved at auth + (``user_api_key_auth_metadata``), never client-supplied request metadata: + choosing the destination project is a data-exfiltration primitive, so a + caller must not be able to name one. Returns an empty mapping when the key + and team name no project, leaving the request on the env-configured default. + """ + if not auth_metadata: + return _NO_PROJECT + project: Final = next( + (stripped for key in _PROJECT_KEYS if (stripped := (auth_metadata.get(key) or "").strip())), + "", + ) + return MappingProxyType({PHOENIX_PROJECT_HEADER: project}) if project else _NO_PROJECT + + def phoenix_preset( *, config_overrides: OpenTelemetryV2Config | None = None, diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 6df04ff622d..467ec72dc4a 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -49,6 +49,7 @@ 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, @@ -96,7 +97,10 @@ 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): @@ -172,6 +176,11 @@ 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 +224,9 @@ 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, ) @@ -458,7 +469,8 @@ 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, ) @@ -2078,27 +2090,37 @@ 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"), @@ -2449,6 +2471,7 @@ 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 +2494,17 @@ 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 +2540,7 @@ 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 +2598,7 @@ 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") @@ -2603,6 +2639,7 @@ 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", []), @@ -3539,7 +3576,9 @@ 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): @@ -4067,9 +4106,10 @@ class PrometheusLogger(CustomLogger): require_auth (bool, optional): Whether to require authentication for the metrics endpoint. Defaults to False. """ - from prometheus_client import make_asgi_app + from prometheus_client import REGISTRY from litellm._logging import verbose_proxy_logger + from litellm.integrations.prometheus_metrics_endpoint import make_metrics_asgi_app from litellm.proxy.proxy_server import app # Create metrics ASGI app @@ -4078,9 +4118,9 @@ class PrometheusLogger(CustomLogger): registry: Final = CollectorRegistry() multiprocess.MultiProcessCollector(registry) - metrics_app = make_asgi_app(registry) + metrics_app = make_metrics_asgi_app(registry) else: - metrics_app = make_asgi_app() + metrics_app = make_metrics_asgi_app(REGISTRY) # Mount the metrics app to the app app.mount("/metrics", metrics_app) diff --git a/litellm/integrations/prometheus_metrics_endpoint.py b/litellm/integrations/prometheus_metrics_endpoint.py new file mode 100644 index 00000000000..b41cc13a04f --- /dev/null +++ b/litellm/integrations/prometheus_metrics_endpoint.py @@ -0,0 +1,100 @@ +"""ASGI app for `/metrics` that keeps registry rendering off the event loop. + +``prometheus_client.make_asgi_app`` collects and serializes the whole registry +inline in the coroutine, so a large scrape (tens of MB on high cardinality +deployments) blocks every other request on the loop for its whole duration. This +app renders in a worker thread instead, shares one render across concurrent +scrapes that want the same output, and streams the payload back in chunks. +""" + +from __future__ import annotations + +import asyncio +import gzip +from collections.abc import Callable, Iterator, Mapping +from dataclasses import dataclass +from types import MappingProxyType +from typing import Final + +from prometheus_client import CollectorRegistry +from prometheus_client.exposition import choose_encoder, gzip_accepted +from starlette.requests import Request +from starlette.responses import StreamingResponse +from starlette.types import ASGIApp, Receive, Scope, Send + +RESPONSE_CHUNK_SIZE_BYTES: Final = 64 * 1024 + +_GZIP_HEADERS: Final = MappingProxyType({"Content-Encoding": "gzip"}) + + +@dataclass(frozen=True, slots=True) +class ScrapeRequest: + """What a scrape asks for, normalized so that header spellings sharing an output share a render.""" + + encoder: Callable[[CollectorRegistry], bytes] + content_type: str + gzipped: bool + metric_names: tuple[str, ...] + + +def parse_scrape_request(accept: str, accept_encoding: str, metric_names: tuple[str, ...]) -> ScrapeRequest: + encoder, content_type = choose_encoder(accept) + return ScrapeRequest( + encoder=encoder, + content_type=content_type, + gzipped=gzip_accepted(accept_encoding), + metric_names=metric_names, + ) + + +def render_scrape(registry: CollectorRegistry, request: ScrapeRequest) -> bytes: + rendered: Final = request.encoder( + registry.restricted_registry(request.metric_names) if request.metric_names else registry # pyright: ignore[reportArgumentType] # RestrictedRegistry is registry-shaped but not a subclass + ) + return gzip.compress(rendered) if request.gzipped else rendered + + +class CoalescedScrapeRenderer: + """Renders the registry in a worker thread, sharing one render per distinct output across concurrent scrapes.""" + + def __init__(self, registry: CollectorRegistry) -> None: + self._registry = registry + self._inflight: Mapping[ScrapeRequest, asyncio.Task[bytes]] = MappingProxyType({}) + + def _forget(self, finished: asyncio.Task[bytes]) -> None: + self._inflight = MappingProxyType({key: task for key, task in self._inflight.items() if task is not finished}) + + async def render(self, request: ScrapeRequest) -> bytes: + inflight: Final = self._inflight.get(request) + if inflight is not None: + return await asyncio.shield(inflight) + + task: Final = asyncio.create_task(asyncio.to_thread(render_scrape, self._registry, request)) + self._inflight = MappingProxyType({**self._inflight, request: task}) + task.add_done_callback(self._forget) + return await asyncio.shield(task) + + +def _chunks(body: bytes) -> Iterator[bytes]: + return (body[start : start + RESPONSE_CHUNK_SIZE_BYTES] for start in range(0, len(body), RESPONSE_CHUNK_SIZE_BYTES)) + + +def make_metrics_asgi_app(registry: CollectorRegistry) -> ASGIApp: + renderer: Final = CoalescedScrapeRenderer(registry) + + async def metrics_app(scope: Scope, receive: Receive, send: Send) -> None: + request: Final = Request(scope, receive) + scrape: Final = parse_scrape_request( + accept=request.headers.get("accept", ""), + accept_encoding=request.headers.get("accept-encoding", ""), + metric_names=tuple(request.query_params.getlist("name[]")), + ) + body: Final = await renderer.render(scrape) + response: Final = StreamingResponse( + _chunks(body), + media_type=scrape.content_type, + headers=_GZIP_HEADERS if scrape.gzipped else None, + ) + await response(scope, receive, send) + + return metrics_app diff --git a/litellm/integrations/prompt_management_base.py b/litellm/integrations/prompt_management_base.py index d16afa92ec2..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 @@ -165,7 +178,7 @@ class PromptManagementBase(ABC): ignore_prompt_manager_optional_params: bool | None = False, ) -> tuple[str, list[AllMessageValues], dict]: if prompt_id is None: - raise ValueError("prompt_id is required for Prompt Management Base class") + return model, messages, non_default_params if not self.should_run_prompt_management( prompt_id=prompt_id, prompt_spec=prompt_spec, @@ -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/s3_v2.py b/litellm/integrations/s3_v2.py index d52bcda525f..10b4c0dd433 100644 --- a/litellm/integrations/s3_v2.py +++ b/litellm/integrations/s3_v2.py @@ -11,6 +11,7 @@ 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 @@ -206,6 +207,23 @@ 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}.amazonaws.com/{encoded_key}" + def _sse_headers(self) -> Mapping[str, str]: candidates: Final = { "x-amz-server-side-encryption": self.s3_server_side_encryption, @@ -292,7 +310,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): import base64 import hashlib - import requests from botocore.auth import S3SigV4Auth from botocore.awsrequest import AWSRequest except ImportError: @@ -316,18 +333,7 @@ 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 +354,19 @@ 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( @@ -478,7 +474,6 @@ 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 +488,7 @@ 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 +509,22 @@ 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 +556,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): try: import hashlib - import requests from botocore.auth import S3SigV4Auth from botocore.awsrequest import AWSRequest except ImportError: @@ -607,18 +580,7 @@ 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 +588,15 @@ 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 da02db4e44b..cf8aa38d86e 100644 --- a/litellm/integrations/shadow_eval_logger.py +++ b/litellm/integrations/shadow_eval_logger.py @@ -10,7 +10,7 @@ import asyncio import hashlib import random import traceback -from collections.abc import Callable, Mapping, Sequence +from collections.abc import Awaitable, Callable, Mapping, Sequence from dataclasses import dataclass from datetime import datetime, timezone from itertools import groupby @@ -38,12 +38,14 @@ 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 -# A job starting, stopping, or hitting its turn budget propagates to sampling within one -# TTL; the turn budget can overshoot by at most one TTL of in-flight samples per pod. +# A job starting, stopping, or hitting a budget propagates to sampling within one TTL; +# the spend gate re-checks the cross-pod counter at pipeline entry, so it overshoots +# only by the samples already in flight when the cap is crossed. _JOBS_CACHE_TTL_SECONDS: Final = 10 # Concurrent shadow+judge pipelines per pod: a traffic spike turns into skipped samples @@ -340,13 +342,24 @@ def _failure_detail(e: BaseException) -> str: return f"{type(e).__name__}{location}: {e}" -def _judge_call_cost(response: object) -> float: - """Price a judge call, treating an unmapped judge model as free rather than fatal.""" +def _call_cost(response: object) -> float: + """Price one eval-arm call with the figure the spend pipeline bills: the router client + stamps _hidden_params.response_cost from the deployment's own pricing, which the public + price map lookup below cannot see (it reads 0 for deployment-priced models).""" + getter: Final = getattr(getattr(response, "_hidden_params", None), "get", None) + stamped: Final = getter("response_cost") if callable(getter) else None + if isinstance(stamped, (int, float)): + return float(stamped) + return _price_map_cost(response) + + +def _price_map_cost(response: object) -> float: + """Public price map fallback, treating an unmapped model as free rather than fatal.""" import litellm try: return litellm.completion_cost(completion_response=response) or 0.0 - except Exception: # noqa: BLE001 # unmapped judge model: the verdict still counts, cost stays 0 + except Exception: # noqa: BLE001 # unmapped model: the attempt still counts, cost stays 0 return 0.0 @@ -374,6 +387,48 @@ 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}" + + +async def _job_spend_from_counter(counter_key: str, fallback_spend: float, max_budget: float) -> float: + """The leg's spend through the cross-pod counter the key budget gates read. The owner + degrades internally to the fill-time DB floor and raises only under fail-closed + enforcement, which the caller honors by skipping the sample.""" + from litellm.proxy.proxy_server import get_current_spend + + return await get_current_spend(counter_key=counter_key, fallback_spend=fallback_spend, max_budget=max_budget) + + +async def _add_job_spend_to_counter(counter_key: str, cost: float) -> None: + """Advance the counter the moment a cost is known, so even a lost row closes the gate. + Known failure mode: a Redis outage freezes the counter (the owner invalidates it), the + gate degrades to the fill floor, and overshoot grows to in-flight plus one TTL of + samples, the same degradation the key budget counters accept.""" + try: + from litellm.proxy.proxy_server import increment_spend_counter + + await increment_spend_counter(counter_key=counter_key, increment=cost) + except Exception as e: # noqa: BLE001 # attempt recording must proceed; the row stays truth and the fill floor gates + 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. @@ -414,6 +469,14 @@ 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 @@ -428,6 +491,13 @@ def _routed_tier(metadata: Mapping[str, object]) -> str | None: return str(raw) if raw is not None else None +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 _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 @@ -438,11 +508,12 @@ def _request_was_routed_by(request_metadata: Mapping[str, object], router_name: @dataclass(frozen=True, slots=True) class _CallFailure: - """A shadow or judge call that produced no usable response. cost carries any judge - spend the failed attempt still billed, so job-level judge_spend never undercounts.""" + """A shadow or judge call that produced no usable response. cost carries any spend + the failed call still billed, so job-level spend figures never undercount.""" error: str cost: float = 0.0 + classifier_cost: float = 0.0 @dataclass(frozen=True, slots=True) @@ -452,6 +523,8 @@ class _ShadowResponse: text: str model: str tier: str | None + cost: float + classifier_cost: float @dataclass(frozen=True, slots=True) @@ -478,8 +551,10 @@ class ActiveShadowEvalJob(BaseModel): shadow_percentage: float judge_model: str max_turns: int + max_budget: float | None = None ends_at: datetime attempts: int = 0 + spend: float = 0.0 @field_validator("ends_at") @classmethod @@ -500,7 +575,7 @@ class ActiveShadowEvalJob(BaseModel): return self.baseline_model or self.router_name -def _as_active_job(record: object, attempts: int) -> ActiveShadowEvalJob | None: +def _as_active_job(record: object, attempts: int, spend: float) -> ActiveShadowEvalJob | None: """The sampling path's view of one job row, or None for a row it cannot sample: an unknown direction, or a reverse job with no baseline model to duplicate against. Failing closed here is what keeps the dispatch path total.""" @@ -509,7 +584,7 @@ def _as_active_job(record: object, attempts: int) -> ActiveShadowEvalJob | None: except ValidationError as e: verbose_logger.debug("shadow_eval: skipping unsamplable job row: %s", e) return None - return job.model_copy(update={"attempts": attempts}) + return job.model_copy(update={"attempts": attempts, "spend": spend}) # mutable-ok: pydantic update payload _jobs_cache: Final = InMemoryCache(max_size_in_memory=4, default_ttl=_JOBS_CACHE_TTL_SECONDS) @@ -524,12 +599,19 @@ class ShadowEvalLogger(CustomLogger): router_provider: Callable[[], "Router | None"] | None = None, prisma_provider: Callable[[], "PrismaClient | None"] | None = None, 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.""" + at call time, not at logger construction. The spend reader and writer wrap the + proxy's cross-pod spend counter; tests inject a plain in-memory pair.""" self._router_provider = router_provider or default_router_provider self._prisma_provider = prisma_provider or _default_prisma_provider 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. @@ -556,18 +638,26 @@ class ShadowEvalLogger(CustomLogger): await prisma.db.litellm_shadowevalattempt.group_by( by=["job_id"], count=True, + # 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 else () ) - attempt_counts: Final = {str(row["job_id"]): int(row["_count"]["_all"]) for row in grouped or []} + attempt_stats: Final = { # mutable-ok: frozen snapshot of the grouped read + str(row["job_id"]): ( + int(row["_count"]["_all"]), + _leg_eval_spend(row["_sum"] or _EMPTY_METADATA), + ) + for row in grouped or [] + } by_key: Final = tuple( sorted( ( (str(record.api_key_id), job) for record in records or [] - if (job := _as_active_job(record, attempt_counts.get(str(record.id), 0))) is not None + if (job := _as_active_job(record, *attempt_stats.get(str(record.id), (0, 0.0)))) is not None ), key=itemgetter(0), ) @@ -584,6 +674,32 @@ 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 _request_was_routed_by(request_metadata, job.router_name) != (job.direction == "reverse") + ): + 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], @@ -615,17 +731,8 @@ 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 _sample_hits(request_id, job.id, job.shadow_percentage) - and _request_was_routed_by(request_metadata, job.router_name) == (job.direction == "reverse") + eligible: Final = self._sampled_jobs( + (await self._active_jobs()).get(str(api_key_hash), ()), request_metadata, request_id ) if not eligible: return @@ -636,12 +743,18 @@ 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._record_funnel(job.id, "shed") + continue self._job_starts[job.id] = self._job_starts.get(job.id, 0) + 1 self._inflight_shadow_tasks += 1 asyncio.create_task( @@ -651,6 +764,9 @@ 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 @@ -671,25 +787,70 @@ 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 gate -> shadow call -> blind judge -> one attempt row, and every exit + in exactly one coverage bucket: 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 it withheld, so eligible traffic still reconciles as + not_sampled + unjudgeable + shed + withheld + attempt rows. 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() try: 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 shadow: Final = await self._call_router_shadow(job.shadow_target, messages, shadow_params, parent_metadata) - if isinstance(shadow, _CallFailure): - await self._record_attempt(prisma, job, request_id, control_tier, outcome="error", error=shadow.error) - return - + 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}", + 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, + 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. + try: verdict: Final = await self._call_judge( judge_model=job.judge_model, messages=messages, @@ -707,6 +868,11 @@ class ShadowEvalLogger(CustomLogger): 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( @@ -719,27 +885,51 @@ class ShadowEvalLogger(CustomLogger): 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: record what happened, never raise + 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) await self._record_attempt( - prisma, job, request_id, control_tier, outcome="error", error=f"pipeline error: {e}" + prisma, + job, + request_id, + control_tier, + 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, ) - @staticmethod async def _record_attempt( + self, prisma: "PrismaClient | None", job: ActiveShadowEvalJob, request_id: str, control_tier: str | None, *, 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: + 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: @@ -753,6 +943,11 @@ class ShadowEvalLogger(CustomLogger): "shadow_model": shadow.model if shadow else None, "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, } ) @@ -789,14 +984,23 @@ 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") + 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( @@ -830,6 +1034,7 @@ 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, @@ -843,11 +1048,11 @@ class ShadowEvalLogger(CustomLogger): verdict: Final = PairwiseVerdict.model_validate(parse_json_verdict(raw)) except Exception as e: # noqa: BLE001 # malformed verdicts become error rows verbose_logger.debug("shadow_eval: unparseable judge verdict: %s", e) - return _CallFailure(f"unparseable judge verdict: {e}", cost=_judge_call_cost(response)) + return _CallFailure(f"unparseable judge verdict: {e}", cost=_call_cost(response)) return _JudgeVerdict( preference=_unmask_preference(verdict.preference, real_is_a), confidence=max(0.0, min(1.0, verdict.confidence)), - cost=_judge_call_cost(response), + cost=_call_cost(response), ) 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 e1ec3360351..af82cf33796 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -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, @@ -126,6 +129,7 @@ class WebSearchInterceptionLogger(CustomLogger): self, enabled_providers: list[LlmProviders | str] | None = None, search_tool_name: str | None = None, + max_agentic_loops: int | None = None, ): """ Args: @@ -135,6 +139,9 @@ 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 @@ -143,8 +150,16 @@ 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, @@ -402,6 +417,7 @@ 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", {}) @@ -410,6 +426,7 @@ 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 @@ -427,6 +444,7 @@ class WebSearchInterceptionLogger(CustomLogger): return cls( enabled_providers=enabled_providers, search_tool_name=search_tool_name, + max_agentic_loops=max_agentic_loops, ) @staticmethod @@ -497,6 +515,10 @@ 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 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..615873e295d --- /dev/null +++ b/litellm/litellm_core_utils/audio_utils/subtitle_utils.py @@ -0,0 +1,193 @@ +"""Provider-agnostic SRT/WebVTT subtitle synthesis from timestamped transcription tokens.""" + +from collections.abc import Sequence +from dataclasses import dataclass +from itertools import accumulate, chain +from typing import Final + +from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError + +CUE_MAX_TOKENS: Final = 15 +CUE_MAX_DURATION_MS: Final = 5000 + +SRT_RESPONSE_FORMAT: Final = "srt" +VTT_RESPONSE_FORMAT: Final = "vtt" +SUBTITLE_RESPONSE_FORMATS: Final = frozenset((SRT_RESPONSE_FORMAT, VTT_RESPONSE_FORMAT)) + + +@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 _CueAccumulator: + texts: tuple[str, ...] = () + start_ms: int | None = None + end_ms: int | None = None + speaker: str | int | None = None + + +def _completed_cue(accumulator: _CueAccumulator) -> tuple[SubtitleCue, ...]: + if not accumulator.texts or accumulator.start_ms is None: + return () + text: Final = "".join(accumulator.texts).strip() + if not text: + return () + end_ms: Final = accumulator.end_ms if accumulator.end_ms is not None else accumulator.start_ms + return (SubtitleCue(start_ms=accumulator.start_ms, end_ms=end_ms, text=text),) + + +def _cue_break_reached(accumulator: _CueAccumulator, token: SubtitleToken) -> bool: + if len(accumulator.texts) >= CUE_MAX_TOKENS: + return True + return ( + accumulator.start_ms is not None + and token.start_ms is not None + and token.start_ms - accumulator.start_ms >= CUE_MAX_DURATION_MS + ) + + +_AbsorbStep = tuple[tuple[SubtitleCue, ...], _CueAccumulator] + + +def _absorb_token(accumulator: _CueAccumulator, token: SubtitleToken) -> _AbsorbStep: + if token.start_ms is None and accumulator.start_ms is None: + return (), accumulator + if token.speaker is not None and token.speaker != accumulator.speaker: + return _completed_cue(accumulator), _CueAccumulator( + texts=(token.text,), + start_ms=token.start_ms, + end_ms=token.end_ms, + speaker=token.speaker, + ) + if _cue_break_reached(accumulator, token): + return _completed_cue(accumulator), _CueAccumulator( + texts=(token.text,), + start_ms=token.start_ms, + end_ms=token.end_ms, + speaker=accumulator.speaker, + ) + return (), _CueAccumulator( + texts=(*accumulator.texts, token.text), + start_ms=accumulator.start_ms if accumulator.start_ms is not None else token.start_ms, + end_ms=token.end_ms if token.end_ms is not None else accumulator.end_ms, + speaker=accumulator.speaker, + ) + + +def _absorb_step(carry: _AbsorbStep, token: SubtitleToken) -> _AbsorbStep: + return _absorb_token(carry[1], token) + + +def group_subtitle_tokens_into_cues(tokens: Sequence[SubtitleToken]) -> tuple[SubtitleCue, ...]: + steps: Final = tuple(accumulate(tokens, _absorb_step, initial=((), _CueAccumulator()))) + completed: Final = chain.from_iterable(emitted for emitted, _ in steps) + return (*completed, *_completed_cue(steps[-1][1])) + + +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/chat_completion_agentic_loop.py b/litellm/litellm_core_utils/chat_completion_agentic_loop.py index b91c1785a54..07bed1f88ad 100644 --- a/litellm/litellm_core_utils/chat_completion_agentic_loop.py +++ b/litellm/litellm_core_utils/chat_completion_agentic_loop.py @@ -5,6 +5,10 @@ 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.types.integrations.custom_logger import ( CHAT_COMPLETION_AGENTIC_SURFACE, NON_CODE_INTERPRETER_INTERCEPTION_INTERNAL_PREFIXES, @@ -52,7 +56,10 @@ def _coerce_int(value: object, default: int) -> int: 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 diff --git a/litellm/litellm_core_utils/cli_keyring.py b/litellm/litellm_core_utils/cli_keyring.py new file mode 100644 index 00000000000..70b1773739d --- /dev/null +++ b/litellm/litellm_core_utils/cli_keyring.py @@ -0,0 +1,231 @@ +""" +CLI Keyring Access + +SDK-level access to the OS keychain (macOS Keychain, Windows Credential Manager, +Linux Secret Service) that holds the credential minted by `lite login`. + +The `keyring` package is optional and imported lazily, so importing this module +never pulls it in. Every failure is returned as a value, naming which of the +ways the keychain can be out of reach applies, so callers can degrade to the +token file and tell the user what to do about it. + +A write is only reported as stored once it has been read back, because keyring's +null backend, which `keyring --disable` and headless CI images both select, +accepts every write and keeps nothing. Writes are also pre-flighted with a +throwaway value, because a keychain can answer neither way and block forever. +""" + +import os +import threading +from contextlib import suppress +from dataclasses import dataclass, field +from typing import Final, Protocol, TypeAlias + +KEYRING_SERVICE: Final = "litellm-cli" +KEYRING_ACCOUNT: Final = "credential" +KEYRING_PREFLIGHT_ACCOUNT: Final = "credential-preflight" +DISABLE_KEYRING_ENV_VAR: Final = "LITELLM_CLI_DISABLE_KEYRING" + +_DISABLED_VALUES: Final = frozenset(("1", "true", "yes", "on")) +_PREFLIGHT_VALUE: Final = "preflight" +_PREFLIGHT_TIMEOUT_SECONDS: Final = 5.0 + + +@dataclass(frozen=True, slots=True) +class SecretFound: + blob: str + + +@dataclass(frozen=True, slots=True) +class SecretMissing: + pass + + +@dataclass(frozen=True, slots=True) +class SecretStored: + pass + + +@dataclass(frozen=True, slots=True) +class SecretErased: + pass + + +@dataclass(frozen=True, slots=True) +class SecretStranded: + pass + + +@dataclass(frozen=True, slots=True) +class KeyringNotInstalled: + pass + + +@dataclass(frozen=True, slots=True) +class KeyringDisabled: + pass + + +@dataclass(frozen=True, slots=True) +class KeyringUnreachable: + pass + + +@dataclass(frozen=True, slots=True) +class KeyringDiscardsWrites: + pass + + +KeyringUnusable: TypeAlias = KeyringNotInstalled | KeyringDisabled | KeyringUnreachable +SecretRead: TypeAlias = SecretFound | SecretMissing | KeyringUnusable +SecretWrite: TypeAlias = SecretStored | KeyringUnusable | KeyringDiscardsWrites +SecretErase: TypeAlias = SecretErased | SecretStranded | KeyringUnusable + + +class SecretVault(Protocol): + """The single slot holding the CLI credential's secret material.""" + + def read(self) -> SecretRead: ... + + def write(self, blob: str) -> SecretWrite: ... + + def erase(self) -> SecretErase: ... + + +class KeyringApi(Protocol): + def get_password(self, service_name: str, username: str) -> str | None: ... + + def set_password(self, service_name: str, username: str, password: str) -> None: ... + + def delete_password(self, service_name: str, username: str) -> None: ... + + +def _keyring_disabled() -> bool: + return os.getenv(DISABLE_KEYRING_ENV_VAR, "").strip().lower() in _DISABLED_VALUES + + +def _import_keyring() -> KeyringApi | None: + try: + import keyring + except ImportError: + return None + return keyring + + +def _keyring_api() -> KeyringApi | KeyringNotInstalled | KeyringDisabled: + if _keyring_disabled(): + return KeyringDisabled() + api: Final = _import_keyring() + return KeyringNotInstalled() if api is None else api + + +def _answers_a_write(api: KeyringApi, timeout_seconds: float) -> bool: + """Whether the keychain answers a write at all, asked with a value worth nothing. + + macOS derives the login keychain from `$HOME`, and `set_password` against a HOME with no usable + one blocks forever with no timeout of its own. Containers, CI images, `sudo -H`, and service + accounts all run there, and reads answer normally, so nothing cheaper tells them apart. Asking + with a throwaway value keeps a keychain that never answers from taking `lite login` down with + it, and keeps the real credential out of a store that might accept it long after we gave up. + A keychain that refuses the probe outright still answered it, so only silence counts against it. + """ + answered: Final = threading.Event() + + def ask() -> None: + with suppress(Exception): + api.set_password(KEYRING_SERVICE, KEYRING_PREFLIGHT_ACCOUNT, _PREFLIGHT_VALUE) + answered.set() + + threading.Thread(target=ask, daemon=True, name="litellm-cli-keyring-preflight").start() + return answered.wait(timeout_seconds) + + +def _forget_the_preflight(api: KeyringApi) -> None: + """Take the throwaway probe back out. + + A backend that kept nothing has nothing to remove, and the probe is worth nothing either way, + so a keychain that refuses to give it up costs the caller nothing. + """ + with suppress(Exception): + api.delete_password(KEYRING_SERVICE, KEYRING_PREFLIGHT_ACCOUNT) + + +@dataclass(frozen=True, slots=True) +class KeyringVault: + """The OS keychain, reached through the optional `keyring` package. + + A keychain that let the pre-flight time out is not asked anything else for the rest of the + process. The probe that timed out is still sitting in the keychain on a thread of its own, and + it holds the keychain against every later call, so the read after it would block on the main + thread with no timeout to save it. One silence is answer enough. + """ + + preflight_timeout_seconds: float = _PREFLIGHT_TIMEOUT_SECONDS + stopped_answering: threading.Event = field(default_factory=threading.Event, compare=False, repr=False) + + def read(self) -> SecretRead: + if self.stopped_answering.is_set(): + return KeyringUnreachable() + api: Final = _keyring_api() + if isinstance(api, (KeyringNotInstalled, KeyringDisabled)): + return api + try: + blob: Final = api.get_password(KEYRING_SERVICE, KEYRING_ACCOUNT) + except Exception: # noqa: BLE001 # backends raise outside keyring.errors; never break the SDK + return KeyringUnreachable() + return SecretMissing() if blob is None else SecretFound(blob) + + def write(self, blob: str) -> SecretWrite: + """Store the secret, reporting stored only once the keychain hands the same bytes back. + + A backend that accepts writes and keeps nothing, which is exactly what `keyring --disable` + and `PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring` select, raises nothing to + distinguish itself. Reading the value back is the only way to tell it apart from a keychain + that really stored the credential, and the caller is about to drop its own copy on our word. + + The keychain is pre-flighted first, because one that blocks rather than answering would + otherwise hang `lite login` outright. + """ + if self.stopped_answering.is_set(): + return KeyringUnreachable() + api: Final = _keyring_api() + if isinstance(api, (KeyringNotInstalled, KeyringDisabled)): + return api + if not _answers_a_write(api, self.preflight_timeout_seconds): + self.stopped_answering.set() + return KeyringUnreachable() + _forget_the_preflight(api) + try: + api.set_password(KEYRING_SERVICE, KEYRING_ACCOUNT, blob) + except Exception: # noqa: BLE001 # a keychain that refuses the write falls back to the token file + return KeyringUnreachable() + return SecretStored() if self.read() == SecretFound(blob) else KeyringDiscardsWrites() + + def erase(self) -> SecretErase: + """Remove our entry, reporting whether the keychain is guaranteed to be free of it. + + A keychain out of reach is never an erasure: the entry belongs to the OS, not to this + install, so it outlives an uninstalled `keyring` package and a kill switch set after login. + Those cases are reported apart from a confirmed entry that would not delete, because only + the caller knows whether this machine ever put a secret in a keychain. + """ + match self.read(): + case KeyringNotInstalled() | KeyringDisabled() | KeyringUnreachable() as unusable: + return unusable + case SecretMissing(): + return SecretErased() + case SecretFound(): + return self._delete() + + def _delete(self) -> SecretErase: + api: Final = _keyring_api() + if isinstance(api, (KeyringNotInstalled, KeyringDisabled)): + return api + try: + api.delete_password(KEYRING_SERVICE, KEYRING_ACCOUNT) + except Exception: # noqa: BLE001 # report the failure as a value so `lite logout` can warn + return SecretStranded() + return SecretErased() + + +SYSTEM_KEYRING: Final[SecretVault] = KeyringVault() diff --git a/litellm/litellm_core_utils/cli_token_utils.py b/litellm/litellm_core_utils/cli_token_utils.py index a44ce431f4e..ee506a69ef9 100644 --- a/litellm/litellm_core_utils/cli_token_utils.py +++ b/litellm/litellm_core_utils/cli_token_utils.py @@ -1,16 +1,134 @@ """ CLI Token Utilities -SDK-level utilities for reading CLI authentication tokens. +SDK-level utilities for reading the credential minted by `lite login`. + +Non-secret metadata lives in ~/.litellm/token.json. The secret material (the +bearer key, the refresh token that renews it, and a JWT when one is issued) +lives in the OS keychain when the machine has one, and in that same 0600 file +otherwise. This module hides the split from callers, and migrates a plaintext +file into the keychain the first time it reads one. + This module has no dependencies on proxy code and can be safely imported at the SDK level. """ -import json -import os +import math import time from collections.abc import Mapping +from dataclasses import dataclass from pathlib import Path -from typing import Final +from types import MappingProxyType +from typing import Final, TypeAlias + +from pydantic import BaseModel, ConfigDict, ValidationError + +from litellm.litellm_core_utils.cli_keyring import ( + SYSTEM_KEYRING, + KeyringDisabled, + KeyringNotInstalled, + KeyringUnreachable, + SecretErase, + SecretErased, + SecretFound, + SecretMissing, + SecretStored, + SecretStranded, + SecretVault, + SecretWrite, +) +from litellm.litellm_core_utils.private_json import ( + commit_staged_json, + discard_staged_json, + ensure_private_dir, + overwrite_private_json, + stage_private_json, + write_private_json, +) + + +@dataclass(frozen=True, slots=True) +class CredentialNotSaved: + """The credential was minted but no store would keep it, so this machine has none. + + Nothing was touched on the way to this, so a login that already worked still does. + """ + + detail: str + + +@dataclass(frozen=True, slots=True) +class CredentialNotRecorded: + """The keychain took the credential, but the file that names it could not be replaced. + + The keychain holds one entry, so the secret that was there is already gone and no rollback + brings it back. Removing the new one as well would only turn a login this machine may still + be able to use into no login at all, so it stays, and the user is told what is where. + """ + + +@dataclass(frozen=True, slots=True) +class CredentialNotCleared: + """The token file still holds the secret, because it could not be removed or rewritten. + + Logging out of the keychain is only half of it. A `~/.litellm` that refuses both the scrubbed + rewrite and the removal leaves the credential readable on disk, which is the one thing a logout + is for, so it is reported instead of being counted as a clean sweep. + """ + + detail: str + + +SecretSave: TypeAlias = SecretWrite | CredentialNotSaved | CredentialNotRecorded + +SecretClear: TypeAlias = SecretErase | CredentialNotCleared + + +class CliTokenRecord(BaseModel): + """A stored CLI credential. + + `key is None` means the metadata was found but the secret could not be + produced: the keychain holds nothing for us, or we could not reach it. + """ + + model_config = ConfigDict(frozen=True, extra="allow") + + base_url: str = "" + key: str | None = None + user_id: str = "" + user_email: str = "" + user_role: str = "" + auth_header_name: str = "Authorization" + jwt_token: str = "" + timestamp: float = 0.0 + expires_at: float | None = None + refresh_token: str | None = None + + +class CliTokenSecret(BaseModel): + """The secret material as stored in the OS keychain. + + `base_url` is duplicated from the metadata file purely as a pairing tag: a + secret minted for one server is never handed to another, even if the + metadata file is edited underneath us. `timestamp` is the sign-in this + secret came from, which is what decides it against a secret still on disk. + + Every field a thief could sign in with belongs here, which is why the + refresh token is one of them: it buys a fresh key from the proxy on demand, + so leaving it on disk would leave the login readable there. `key` is + optional because the file can hold a refresh token without one, and moving + that into the keychain must not invent a key to go with it. + """ + + model_config = ConfigDict(frozen=True) + + base_url: str + key: str | None = None + jwt_token: str = "" + refresh_token: str | None = None + timestamp: float = 0.0 + + +CLI_TOKEN_FRESHNESS_BUFFER_SECONDS: Final = 360 def get_cli_token_file_path() -> str: @@ -20,26 +138,183 @@ def get_cli_token_file_path() -> str: return str(config_dir / "token.json") -def load_cli_token() -> dict | None: - """Load CLI token data from file""" - token_file: Final = get_cli_token_file_path() - if not os.path.exists(token_file): +def load_cli_token(*, vault: SecretVault = SYSTEM_KEYRING) -> CliTokenRecord | None: + """Load the stored CLI credential, or None when this machine has none""" + record: Final = _read_token_file() + if record is None: return None + return _resolve_secret(record, vault) + +def save_cli_token(record: CliTokenRecord, *, vault: SecretVault = SYSTEM_KEYRING) -> SecretSave: + """Store a freshly minted credential. Reports where its secret material ended up, and why. + + The token file is what makes a keychain-backed credential findable again, and it is also the + half that a read-only or full directory refuses, so it is staged before the keychain is handed + anything. A save that cannot land then leaves both stores exactly as it found them, which + matters most when the login it failed to replace is still perfectly good. + + Staging can still succeed and the replacement fail afterwards. That is the one case where the + keychain has already taken the new secret, and it reports itself as such rather than claiming + the previous login survived. + """ + stamped: Final = _stamped_past_every_stored_login(record, vault) + staged: Final = _stage_token_file(_without_secret(stamped)) + if isinstance(staged, CredentialNotSaved): + return staged + outcome: Final = vault.write(_encode_secret(stamped)) if _holds_a_secret(stamped) else SecretStored() + if isinstance(outcome, SecretStored): + return outcome if _commit_token_file(staged) else CredentialNotRecorded() + discard_staged_json(staged) + return _keep_the_secret_in_the_file(stamped, outcome) + + +def _stamped_past_every_stored_login(record: CliTokenRecord, vault: SecretVault) -> CliTokenRecord: + """Keep a sign-in's stamp ahead of every login already stored, whatever the clock did in between. + + The stamp is what decides a keychain secret against one still on disk, so a clock that stepped + backwards between two logins would hand the older of them the win and put a superseded + credential back in use. Pinning the new stamp just past the highest one either store holds costs + one read each and changes nothing on a clock that only moves forwards. + """ + highest: Final = _highest_stamp_already_stored(record.base_url, vault) + if highest < record.timestamp: + return record + return record.model_copy(update=MappingProxyType({"timestamp": math.nextafter(highest, math.inf)})) + + +def _highest_stamp_already_stored(base_url: str, vault: SecretVault) -> float: + """When the latest login either store still holds was made, or minus infinity when neither has one. + + Both are asked because the file names the login being replaced only while the two agree. A login + the keychain took but the file could not record afterwards leaves the keychain holding the later + of the two, and reading only the file would stamp the next sign-in below it. + """ + previous: Final = _read_token_file() + secret: Final = _stored_secret(base_url, vault) + return max( + -math.inf if previous is None else previous.timestamp, + -math.inf if secret is None else secret.timestamp, + ) + + +def _stored_secret(base_url: str, vault: SecretVault) -> CliTokenSecret | None: + """The keychain's secret for this server, when it holds one this login may be compared against""" + match vault.read(): + case SecretFound(blob=blob): + return _decode_secret(blob, base_url) + case SecretMissing() | KeyringNotInstalled() | KeyringDisabled() | KeyringUnreachable(): + return None + + +def _keep_the_secret_in_the_file(record: CliTokenRecord, outcome: SecretWrite) -> SecretSave: + """Fall back to the owner-only file, which is all that is left when no keychain took the secret""" try: - with open(token_file, "r") as f: - return json.load(f) - except (OSError, json.JSONDecodeError): - return None + _write_token_file(record) + except OSError as error: + return CredentialNotSaved(str(error)) + return outcome + + +def clear_cli_token(*, vault: SecretVault = SYSTEM_KEYRING) -> SecretClear: + """Remove the credential from both stores. Reports whether the keychain is now free of it. + + A logout the keychain never answered keeps the token file, with its secret taken out, because + that file is the only remaining record that something may still be in there to remove. It is + what lets a later run tell a machine with a credential it cannot reach apart from one that never + had a login at all, and taking it away would leave the next logout answering the warning this + one just issued with a false all-clear. The secret goes either way, and a file that will give up + neither its copy nor itself is removed rather than kept, with the note written again afterwards + so the warning still outlives this run. + """ + outcome: Final = vault.erase() + record: Final = _read_token_file() + settled: Final = _nothing_left_behind(outcome, record) + if not settled and _keep_the_unchecked_keychain_on_record(outcome, record): + return outcome + removal: Final = _remove_token_file() + if removal is not None and record is not None and not _scrub_file_secret(record): + return removal + if removal is None and record is not None and _the_keychain_went_unchecked(outcome): + _write_the_note_the_removal_took_with_it(record) + return SecretErased() if settled else outcome + + +def _remove_token_file() -> CredentialNotCleared | None: + try: + Path(get_cli_token_file_path()).unlink(missing_ok=True) + except OSError as error: + return CredentialNotCleared(str(error)) + return None + + +def _write_the_note_the_removal_took_with_it(record: CliTokenRecord) -> None: + """Put the secret-free note back after the file carrying it had to go to get the secret off disk. + + Reaching here means neither rewrite would take, so the file went instead, and its absence is + what the next logout would read as a keychain already known to be clean. Removing it is also + what frees the room the rewrite was refused for, so the note usually lands on this second try. + When it does not, the warning this logout printed is the only one the user gets. + """ + staged: Final = _stage_scrubbed_file(record) + if staged is not None: + _commit_token_file(staged) + + +def _keep_the_unchecked_keychain_on_record(outcome: SecretErase, record: CliTokenRecord | None) -> bool: + """Whether the token file, stripped of its secret, is worth keeping as the note that says so. + + Only a keychain that could not be reached leaves the question open. One that answered for itself + is remembered without any help from the file, and a file it can still pair a live entry with + would leave the machine signed in to the login that was just ended. A copy that will give up + its secret neither to a staged replacement nor to an overwrite is not kept either, because the + secret goes first. + """ + if record is None or not _the_keychain_went_unchecked(outcome): + return False + return _scrub_file_secret(record) + + +def _the_keychain_went_unchecked(outcome: SecretErase) -> bool: + """Whether the keychain neither confirmed the erase nor answered that it still holds the secret""" + match outcome: + case SecretErased() | SecretStranded(): + return False + case KeyringDisabled() | KeyringNotInstalled() | KeyringUnreachable(): + return True + + +def _nothing_left_behind(outcome: SecretErase, record: CliTokenRecord | None) -> bool: + """Whether the keychain can be trusted to hold no credential of ours once the file is gone. + + A machine with no token file has no stored login to end, and `clear_cli_token` keeps one behind + whenever the keychain is left unconfirmed, taking the secret out in place when it cannot stage a + replacement and writing the note again when the file holding it had to go, so a missing file is + real evidence rather than the absence of it. Past that, a + keychain that could not be reached is never trusted, whatever the file looks like. Even a file + holding its own secret says only that the login which wrote it had no keychain to write to, and + the login before it may well have had one: the entry that login left outlives both the + uninstalled package and the file that replaced it. `SecretStranded` is + the keychain answering for itself and outranks the file. + """ + match outcome: + case SecretErased(): + return True + case SecretStranded(): + return False + case KeyringDisabled() | KeyringNotInstalled() | KeyringUnreachable(): + return record is None def get_litellm_gateway_api_key( expected_base_url: str | None = None, + *, + vault: SecretVault = SYSTEM_KEYRING, ) -> str | None: """ Get the stored CLI API key for use with LiteLLM SDK. - This function reads the token file created by `lite login` + This function reads the credential created by `lite login` and returns the API key for use in Python scripts. Args: @@ -47,6 +322,7 @@ def get_litellm_gateway_api_key( originally issued for this URL. Pass the target server URL to prevent credential leakage when the client is pointed at a different (possibly malicious) server. + vault: Where the secret material is stored. Defaults to the OS keychain. Returns: str: The API key if found (and origin matches), None otherwise @@ -62,25 +338,222 @@ def get_litellm_gateway_api_key( >>> base_url="https://your-proxy.com/v1" >>> ) """ - token_data: Final = load_cli_token() - if not token_data or "key" not in token_data: + record: Final = _read_token_file() + if record is None: return None - if expected_base_url is not None: - stored_url: Final = token_data.get("base_url") - if stored_url != expected_base_url.rstrip("/"): - return None - return token_data["key"] + if expected_base_url is not None and record.base_url != expected_base_url.rstrip("/"): + return None + resolved: Final = _resolve_secret(record, vault) + return None if resolved is None else resolved.key -def is_cli_token_fresh(token_data: Mapping[str, object], buffer_hours: float = 0.1) -> bool: - """Check whether a cached CLI token (as stored in token.json) is still - within its expiration window. Used by `lite auth print-token` to fail - fast, without a network round trip, once the cached token is past - `LITELLM_CLI_JWT_EXPIRATION_HOURS`.""" +def is_cli_token_fresh( + token_data: CliTokenRecord | Mapping[str, object], + buffer_hours: float = CLI_TOKEN_FRESHNESS_BUFFER_SECONDS / 3600, +) -> bool: + """Check whether a cached CLI token is still within its expiration window. + Used by `lite auth print-token` to fail fast, without a network round trip, + once the cached token is past `LITELLM_CLI_JWT_EXPIRATION_HOURS`. A `--pkce` + credential carries its own `expires_at`, which is authoritative when present.""" from litellm.constants import CLI_JWT_EXPIRATION_HOURS - timestamp: Final = token_data.get("timestamp") + expires_at: Final = ( + token_data.expires_at if isinstance(token_data, CliTokenRecord) else token_data.get("expires_at") + ) + if isinstance(expires_at, (int, float)): + return time.time() < expires_at - buffer_hours * 3600 + timestamp: Final = token_data.timestamp if isinstance(token_data, CliTokenRecord) else token_data.get("timestamp") if not isinstance(timestamp, (int, float)): return False age_hours: Final = (time.time() - timestamp) / 3600 return age_hours < (CLI_JWT_EXPIRATION_HOURS - buffer_hours) + + +def _read_token_file() -> CliTokenRecord | None: + try: + raw: Final = Path(get_cli_token_file_path()).read_text() + except (OSError, ValueError): + return None + try: + return CliTokenRecord.model_validate_json(raw) + except ValidationError: + return None + + +def _resolve_secret(record: CliTokenRecord, vault: SecretVault) -> CliTokenRecord | None: + match vault.read(): + case SecretFound(blob=blob): + return _apply_vault_secret(record, blob, vault) + case SecretMissing(): + return _migrate_file_secret(record, vault) + case KeyringNotInstalled() | KeyringDisabled() | KeyringUnreachable(): + return record + + +def _apply_vault_secret(record: CliTokenRecord, blob: str, vault: SecretVault) -> CliTokenRecord | None: + """Resolve the credential when both stores hold one. + + The sign-in each secret came from decides it, because either store can be the stale one. A + secret is usually left on disk by a keychain that would not take it, which makes the file the + fresher of the two. It is the older one when a login the keychain did take could not replace + the file afterwards, and serving that one would put a superseded credential back in use. Equal + stamps are one login sitting in both stores, left by a migration whose scrub was refused or by + an upgrade that took the key into the keychain and left the refresh token behind, so that branch + rejoins the halves and retries the migration rather than trading one credential for another. + + A scrub the file refuses leaves that superseded secret where it lies, which is the state the + login already named when it could not replace the file, and which `lite logout` reports rather + than counting as a clean sweep. Rolling the vault back the way a migration does is not the + answer here, because the two stores hold different credentials and the rollback would hand the + superseded one back out. + """ + secret: Final = _decode_secret(blob, record.base_url) + if secret is None or (_holds_a_secret(record) and secret.timestamp <= record.timestamp): + return _migrate_file_secret(_rejoined(record, secret), vault, replacing=secret) + _scrub_file_secret(record) + return record.model_copy( + update=MappingProxyType( + { + "key": secret.key, + "jwt_token": secret.jwt_token, + "refresh_token": secret.refresh_token, + "timestamp": max(secret.timestamp, record.timestamp), + } + ) + ) + + +def _rejoined(record: CliTokenRecord, secret: CliTokenSecret | None) -> CliTokenRecord: + """Put one sign-in's secret material back together when each store holds part of it. + + Upgrading from the release that kept only the key in the keychain leaves the refresh token + behind in the file, so a single login sits across both stores. Filling in whatever the file is + missing before the migration writes its entry is what stops that write from replacing a live key + with nothing. Only a matching stamp is one login. Two stamps are two logins, and pairing one's + key with the other's refresh token would build a credential neither store ever held. + """ + if secret is None or secret.timestamp != record.timestamp: + return record + return record.model_copy( + update=MappingProxyType( + { + "key": record.key if record.key is not None else secret.key, + "jwt_token": record.jwt_token or secret.jwt_token, + "refresh_token": record.refresh_token if record.refresh_token is not None else secret.refresh_token, + } + ) + ) + + +def _migrate_file_secret( + record: CliTokenRecord, vault: SecretVault, *, replacing: CliTokenSecret | None = None +) -> CliTokenRecord | None: + """Move a file-held secret into the vault, but only once the file's copy can be taken away. + + The scrubbed file is staged first so a directory that will not accept it stops the migration + before the keychain is handed anything. Copying the credential into a second store and only + then discovering the first one cannot be cleaned would widen exposure instead of narrowing it, + which is the opposite of what moving it into the keychain is for. + + A staged file that will not go into place is overwritten where it lies before the keychain is + asked to take the new entry back, so the migration finishes on a directory that would only ever + have refused it. Rolling back is the last resort, and a rollback the keychain also refuses + leaves the secret in both stores until the next read, which retries this same migration. + + Only an entry this migration put there is taken back. `replacing` names one that was already in + the keychain, whose material the new entry carries forward, so erasing it would take away the + half the file never had, and a machine that refuses the scrub is exactly the one with nowhere + else to keep it. The next read finds the same two halves and tries the move again. + """ + if not _holds_a_secret(record): + return None + staged: Final = _stage_scrubbed_file(record) + if staged is None: + return record + if not isinstance(vault.write(_encode_secret(record)), SecretStored): + discard_staged_json(staged) + return record + if not _commit_token_file(staged) and not _overwrite_file_secret(record) and replacing is None: + vault.erase() + return record + + +def _scrub_file_secret(record: CliTokenRecord) -> bool: + """Leave no secret material in the token file once the vault holds it""" + if not _holds_a_secret(record): + return True + staged: Final = _stage_scrubbed_file(record) + if staged is not None and _commit_token_file(staged): + return True + return _overwrite_file_secret(record) + + +def _overwrite_file_secret(record: CliTokenRecord) -> bool: + """Take the secret out of the token file where it lies, when no replacement can be put in place. + + The atomic rewrite wants room for a second file and a directory that will accept it. A full disk + refuses the first and a read-only `~/.litellm` the second, and neither stands in the way of + shortening the file that is already there. It is worth the loss of atomicity because a partial + write reads as no login at all, which is where the refused rewrite left the next run anyway. + """ + try: + overwrite_private_json(get_cli_token_file_path(), _without_secret(record).model_dump(exclude_none=True)) + except OSError: + return False + return True + + +def _stage_scrubbed_file(record: CliTokenRecord) -> str | None: + staged: Final = _stage_token_file(_without_secret(record)) + return None if isinstance(staged, CredentialNotSaved) else staged + + +def _stage_token_file(record: CliTokenRecord) -> str | CredentialNotSaved: + path: Final = Path(get_cli_token_file_path()) + try: + ensure_private_dir(path.parent) + return stage_private_json(str(path), record.model_dump(exclude_none=True)) + except OSError as error: + return CredentialNotSaved(str(error)) + + +def _commit_token_file(staged: str) -> bool: + try: + commit_staged_json(staged, get_cli_token_file_path()) + except OSError: + return False + return True + + +def _holds_a_secret(record: CliTokenRecord) -> bool: + """Whether the record carries anything that would sign someone in as this user""" + return record.key is not None or bool(record.jwt_token) or record.refresh_token is not None + + +def _without_secret(record: CliTokenRecord) -> CliTokenRecord: + return record.model_copy(update=MappingProxyType({"key": None, "jwt_token": "", "refresh_token": None})) + + +def _encode_secret(record: CliTokenRecord) -> str: + return CliTokenSecret( + base_url=record.base_url, + key=record.key, + jwt_token=record.jwt_token, + refresh_token=record.refresh_token, + timestamp=record.timestamp, + ).model_dump_json() + + +def _decode_secret(blob: str, base_url: str) -> CliTokenSecret | None: + """The keychain entry, when it is one this metadata file may be paired with""" + try: + secret: Final = CliTokenSecret.model_validate_json(blob) + except ValidationError: + return None + return secret if secret.base_url == base_url else None + + +def _write_token_file(record: CliTokenRecord) -> None: + path: Final = Path(get_cli_token_file_path()) + ensure_private_dir(path.parent) + write_private_json(str(path), record.model_dump(exclude_none=True)) 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/env_utils.py b/litellm/litellm_core_utils/env_utils.py index af0520eaf31..d641884b4cd 100644 --- a/litellm/litellm_core_utils/env_utils.py +++ b/litellm/litellm_core_utils/env_utils.py @@ -2,6 +2,7 @@ Utility helpers for reading and parsing environment variables. """ +import logging import os from typing import Final @@ -22,6 +23,26 @@ def get_env_int(env_var: str, default: int) -> int: return default +def get_env_int_in_range(env_var: str, default: int, minimum: int, maximum: int) -> int: + """Parse an environment variable as an integer constrained to ``[minimum, maximum]``. + + Values outside the range fall back to the default and warn, so a misconfigured knob can + neither crash the caller nor silently change the meaning of what it computes. + """ + value: Final = get_env_int(env_var, default) + if minimum <= value <= maximum: + return value + logging.getLogger("LiteLLM").warning( + "%s=%s is outside the supported range [%s, %s]. Falling back to %s.", + env_var, + value, + minimum, + maximum, + default, + ) + return default + + def get_env_int_or_none(env_var: str) -> int | None: """Parse an environment variable as an integer, returning None when it is unset or unusable. diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index d23466938f2..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}", @@ -811,6 +825,24 @@ def _map_openai_like_exception( ) +_BEDROCK_MANTLE_CONTEXT_WINDOW_PATTERN: Final = re.compile(r"prompt tokens \((\d+)\) exceed model maximum \((\d+)\)") + + +def _get_bedrock_mantle_context_window_message(error_str: str) -> str | None: + """ + Mantle reports context overflow as a structured validation error rather than + the plain-text patterns Bedrock itself uses, so it needs its own detection and a + message clients recognize as context overflow (litellm/litellm#36546). + """ + if "invalid_request_error" not in error_str and "validation_error" not in error_str: + return None + match = _BEDROCK_MANTLE_CONTEXT_WINDOW_PATTERN.search(error_str) + if match is None: + return None + prompt_tokens, max_tokens = match.groups() + return f"prompt is too long: {prompt_tokens} tokens > {max_tokens} maximum" + + def _map_bedrock_exception( *, model: str, @@ -821,6 +853,14 @@ def _map_bedrock_exception( exception_provider: str, extra_information: str, ) -> None: + if custom_llm_provider == "bedrock_mantle": + mantle_context_window_message = _get_bedrock_mantle_context_window_message(error_str) + if mantle_context_window_message is not None: + raise ContextWindowExceededError( + message=mantle_context_window_message, + model=model, + llm_provider=custom_llm_provider, + ) if ( "too many tokens" in error_str or "expected maxLength:" in error_str @@ -2161,6 +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, @@ -2187,6 +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 = ( @@ -2203,7 +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) @@ -2275,6 +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, @@ -2315,7 +2472,7 @@ def exception_type( exception_provider=exception_provider, extra_information=extra_information, ) - elif custom_llm_provider == "bedrock": + elif custom_llm_provider in ("bedrock", "bedrock_mantle"): _map_bedrock_exception( model=model, original_exception=mappable_exception, @@ -2474,6 +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 3eb8c163d5c..b12c715c9f5 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -21,6 +21,14 @@ AWS_CREDENTIAL_KWARGS_KEYS: Final = frozenset( } ) +# The per-deployment Rust opt-in. +RUST_KWARG_KEY: Final = "rust" + +# Keys `completion()` forwards from its own kwargs into `get_litellm_params`, +# which are otherwise invisible to it because that call site passes explicit +# named arguments rather than `**kwargs`. +FORWARDED_KWARGS_KEYS: Final = AWS_CREDENTIAL_KWARGS_KEYS | frozenset({RUST_KWARG_KEY}) + # Pre-define optional kwargs keys as frozenset for O(1) lookups # These are extracted from kwargs only if present, avoiding unnecessary .get() calls OPTIONAL_KWARGS_KEYS: Final = ( @@ -47,6 +55,10 @@ OPTIONAL_KWARGS_KEYS: Final = ( "itpm", "otpm", "use_xai_oauth", + # The per-deployment Rust opt-in. `all_litellm_params` keeps it out + # of the provider body; this keeps it *in* litellm_params, which is + # where the chat completions handlers read it from. + RUST_KWARG_KEY, } ) | AWS_CREDENTIAL_KWARGS_KEYS diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index dbb40913e14..74a1d3e5008 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, 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] + 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 (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) 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}") @@ -704,7 +727,7 @@ 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") diff --git a/litellm/litellm_core_utils/get_provider_specific_headers.py b/litellm/litellm_core_utils/get_provider_specific_headers.py index ab07a6af1b3..2618aee9afa 100644 --- a/litellm/litellm_core_utils/get_provider_specific_headers.py +++ b/litellm/litellm_core_utils/get_provider_specific_headers.py @@ -1,3 +1,4 @@ +from collections.abc import Sequence from typing import Final from litellm.types.utils import ProviderSpecificHeader @@ -6,13 +7,17 @@ from litellm.types.utils import ProviderSpecificHeader class ProviderSpecificHeaderUtils: @staticmethod def get_provider_specific_headers( - provider_specific_header: ProviderSpecificHeader | None, + provider_specific_header: ProviderSpecificHeader | Sequence[ProviderSpecificHeader] | None, custom_llm_provider: str | None, ) -> dict: """ Get the provider specific headers for the given custom llm provider. - Supports comma-separated provider lists for headers that work across multiple providers. + Accepts either a single ProviderSpecificHeader or a sequence of them. Each entry + carries its own comma-separated provider list, so headers that are safe for several + providers and headers that are safe for exactly one can travel on the same request + without sharing a scope. Entries whose provider list does not contain + `custom_llm_provider` contribute nothing. Returns: Dict: The provider specific headers for the given custom llm provider @@ -20,10 +25,15 @@ class ProviderSpecificHeaderUtils: if provider_specific_header is None or custom_llm_provider is None: return {} - stored_providers: Final = provider_specific_header.get("custom_llm_provider", "") - provider_list: Final = [p.strip() for p in stored_providers.split(",")] + scoped_headers: Final = ( + (provider_specific_header,) if isinstance(provider_specific_header, dict) else provider_specific_header + ) - if custom_llm_provider in provider_list: - return provider_specific_header.get("extra_headers", {}) + matched_headers: Final = {} + for scoped_header in scoped_headers: + stored_providers = scoped_header.get("custom_llm_provider", "") + provider_list = [p.strip() for p in stored_providers.split(",")] + if custom_llm_provider in provider_list: + matched_headers.update(scoped_header.get("extra_headers", {})) - return {} + return matched_headers 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..34d5797a6d8 100644 --- a/litellm/litellm_core_utils/internal_call_metadata.py +++ b/litellm/litellm_core_utils/internal_call_metadata.py @@ -20,8 +20,8 @@ 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"}) @@ -45,6 +45,60 @@ 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 91a312b4f45..a8672b5c112 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -62,8 +62,9 @@ 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 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 +72,9 @@ 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 +87,10 @@ 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, @@ -605,37 +613,60 @@ 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 @@ -832,8 +863,8 @@ class Logging(LiteLLMLoggingBaseClass): eg. AnthropicCacheControlHook and BedrockKnowledgeBaseHook both don't require a `prompt_id` to be passed in, they are triggered by dynamic params """ - for param in non_default_params: - if param in DynamicPromptManagementParamLiteral.list_all_params(): + for param in DynamicPromptManagementParamLiteral.list_all_params(): + if non_default_params.get(param): return True ############################################################################# @@ -857,7 +888,10 @@ 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 +901,7 @@ class Logging(LiteLLMLoggingBaseClass): ) if custom_logger: + breakpoints_before: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(messages) ( model, messages, @@ -882,6 +917,11 @@ 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 +937,10 @@ 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 +951,7 @@ class Logging(LiteLLMLoggingBaseClass): ) if custom_logger: + breakpoints_before: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(messages) ( model, messages, @@ -925,6 +969,11 @@ 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 @@ -966,6 +1015,23 @@ class Logging(LiteLLMLoggingBaseClass): return None + @staticmethod + def _prompt_manager_runs_without_prompt_id( + logger: CustomLogger, + prompt_spec: PromptSpec | None, + dynamic_callback_params: StandardCallbackDynamicParams | None, + ) -> bool: + if not isinstance(logger, CustomPromptManagement): + return False + try: + return logger.should_run_prompt_management( + prompt_id=None, + prompt_spec=prompt_spec, + dynamic_callback_params=dynamic_callback_params or StandardCallbackDynamicParams(), + ) + except Exception: + return False + def get_custom_logger_for_prompt_management( self, model: str, @@ -1016,8 +1082,13 @@ class Logging(LiteLLMLoggingBaseClass): callback_type=CustomPromptManagement ) - if prompt_management_loggers: - logger: Final = prompt_management_loggers[0] + for logger in prompt_management_loggers: + if prompt_id is None and not self._prompt_manager_runs_without_prompt_id( + logger=logger, + prompt_spec=prompt_spec, + dynamic_callback_params=dynamic_callback_params, + ): + continue self.model_call_details["prompt_integration"] = logger.__class__.__name__ return logger @@ -1557,11 +1628,16 @@ 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 @@ -2123,6 +2199,11 @@ 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) @@ -2135,6 +2216,87 @@ 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], @@ -2260,7 +2422,9 @@ 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( @@ -2746,14 +2910,14 @@ 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: @@ -3007,6 +3171,13 @@ 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"] = ( @@ -3536,7 +3707,7 @@ 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): @@ -3561,9 +3732,40 @@ 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. @@ -4481,6 +4683,22 @@ 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 @@ -4767,7 +4985,11 @@ 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 @@ -4895,7 +5117,7 @@ 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. @@ -5063,6 +5285,8 @@ 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)}") @@ -5089,6 +5313,8 @@ 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() @@ -5296,9 +5522,10 @@ 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) @@ -5593,6 +5820,37 @@ def _extract_response_obj_and_hidden_params( return response_obj, hidden_params +def _autorouter_savings_for_payload( + request_metadata: Mapping[str, object], + model: str | None, + custom_llm_provider: str | None, + model_id: str | None, + usage_object: Mapping[str, object] | None, + cost_breakdown: Mapping[str, object] | None, +) -> float | None: + """The auto-router savings figure for the payload, or ``None`` when there is none. + + Lazy proxy import: the savings module lives with the spend trackers that own the + math, and SDK-only installs have no proxy package to import. + """ + try: + from litellm.proxy.spend_tracking.savings import autorouter_savings_for_logging_payload + except Exception: # noqa: BLE001 # SDK-only install: no savings driver to run + return None + try: + return autorouter_savings_for_logging_payload( + request_metadata=request_metadata, + model=model, + custom_llm_provider=custom_llm_provider, + model_id=model_id, + usage_object=usage_object, + cost_breakdown=cost_breakdown, + ) + except Exception as e: # noqa: BLE001 # a savings figure must never fail request logging + verbose_logger.debug("autorouter savings skipped on logging payload: %s", e) + return None + + def get_standard_logging_object_payload( kwargs: dict | None, init_response_obj: Any | BaseModel | dict, @@ -5621,7 +5879,7 @@ 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 = ( @@ -5740,16 +5998,30 @@ 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 ): model_name = response_model_name + request_cost_breakdown: Final = cost_breakdown_with_guardrail(logging_obj.cost_breakdown, guardrail_cost) + autorouter_savings: Final = _autorouter_savings_for_payload( + request_metadata=metadata, + model=model_name, + custom_llm_provider=custom_llm_provider, + model_id=_model_id, + usage_object=usage_dict, + cost_breakdown=request_cost_breakdown, + ) + payload: Final[StandardLoggingPayload] = StandardLoggingPayload( id=str(id), litellm_call_id=kwargs.get("litellm_call_id") or litellm_params.get("litellm_call_id"), @@ -5780,12 +6052,13 @@ def get_standard_logging_object_payload( metadata=clean_metadata, cache_key=clean_hidden_params["cache_key"], response_cost=response_cost, - cost_breakdown=cost_breakdown_with_guardrail(logging_obj.cost_breakdown, guardrail_cost), + cost_breakdown=request_cost_breakdown, + autorouter_savings=autorouter_savings, total_tokens=usage_dict.get("total_tokens", 0), 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 "", + 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, @@ -5976,6 +6249,7 @@ def create_dummy_standard_logging_payload() -> StandardLoggingPayload: call_type="completion", stream=False, response_cost=response_cost, + autorouter_savings=None, response_cost_failure_debug_info=None, status="success", total_tokens=int(DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT + DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT), 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..9250b92e268 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 @@ -7,7 +7,9 @@ from typing import Any, 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, @@ -64,11 +66,17 @@ 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 +86,56 @@ 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 +147,21 @@ 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, ) @@ -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: @@ -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): 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 0793fe20b21..19e3f624268 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1,6 +1,7 @@ # What is this? ## Helper utilities for cost_per_token() +import re from collections.abc import Mapping from dataclasses import dataclass from types import MappingProxyType @@ -72,7 +73,20 @@ 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 +106,16 @@ 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 @@ -889,11 +913,22 @@ 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 +1098,17 @@ 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 +1325,13 @@ 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 +1343,7 @@ 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, ) @@ -1371,6 +1409,7 @@ class CostCalculatorUtils: return fal_ai_image_cost_calculator( model=model, image_response=completion_response, + optional_params=optional_params, ) elif custom_llm_provider == litellm.LlmProviders.RUNWAYML.value: from litellm.llms.runwayml.cost_calculator import ( @@ -1400,19 +1439,19 @@ 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..c833d57b6a9 100644 --- a/litellm/litellm_core_utils/llm_request_utils.py +++ b/litellm/litellm_core_utils/llm_request_utils.py @@ -1,8 +1,88 @@ +from collections.abc import Mapping from typing import Final import litellm +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], ...]: + if isinstance(value, Mapping): + return tuple( + item for subkey, subvalue in value.items() for item in _flatten_form_field(f"{key}[{subkey}]", subvalue) + ) + if isinstance(value, (list, tuple)): + return tuple(item for entry in value for item in _flatten_form_field(f"{key}[]", entry)) + if value is None: + return () + serialized: Final = _form_field_value(value) + if not serialized: + return () + return ((key, serialized),) + + +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, ...]], ...]: + if isinstance(value, Mapping): + return tuple( + item + for subkey, subvalue in value.items() + for item in _flatten_form_data_field(f"{key}[{subkey}]", subvalue) + ) + if isinstance(value, (list, tuple)): + if all(_is_form_scalar(entry) for entry in value): + serialized_fields: Final = tuple(field for entry in value if (field := _form_field_value(entry))) + return ((key, serialized_fields),) if serialized_fields else () + return tuple(item for entry in value for item in _flatten_form_data_field(f"{key}[]", entry)) + if value is None: + return () + serialized: Final = _form_field_value(value) + if not serialized: + return () + return ((key, serialized),) + + +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: """ Ensure that the extra_body sent in the request is safe, otherwise users will see this error 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 1737e2b8cb0..ea97b90c61f 100644 --- a/litellm/litellm_core_utils/llm_response_utils/response_metadata.py +++ b/litellm/litellm_core_utils/llm_response_utils/response_metadata.py @@ -177,7 +177,7 @@ def update_response_metadata( - response._hidden_params["litellm_overhead_time_ms"] - response.response_time_ms """ - if result is None: + if result is None or not hasattr(result, "_hidden_params"): return metadata: Final = ResponseMetadata(result) 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/private_json.py b/litellm/litellm_core_utils/private_json.py new file mode 100644 index 00000000000..30f64c8fc27 --- /dev/null +++ b/litellm/litellm_core_utils/private_json.py @@ -0,0 +1,70 @@ +import json +import os +import stat +import tempfile +from collections.abc import Mapping +from pathlib import Path +from typing import Final + +PRIVATE_DIR_MODE: Final = 0o700 + + +def ensure_private_dir(directory: Path) -> None: + """Create directory (and parents) owner-only, tightening it if it already exists group/world readable""" + directory.mkdir(mode=PRIVATE_DIR_MODE, parents=True, exist_ok=True) + if stat.S_IMODE(directory.stat().st_mode) & 0o077: + directory.chmod(PRIVATE_DIR_MODE) + + +def stage_private_json(path: str, data: Mapping[str, object]) -> str: + """Write JSON to a private temp file beside `path`, ready for `commit_staged_json`. + + Staging is the half that can fail on a read-only or full directory, so callers with something + to lose can find that out before they act on the assumption that the rewrite will land. + """ + parent: Final = Path(path).parent + parent.mkdir(parents=True, exist_ok=True) + fd, tmp_path = tempfile.mkstemp(dir=str(parent), prefix=".tmp-", suffix=".json") + try: + with os.fdopen(fd, "w") as f: + json.dump(data, f, indent=2) + f.flush() + os.fsync(f.fileno()) + except BaseException: + Path(tmp_path).unlink(missing_ok=True) + raise + return tmp_path + + +def commit_staged_json(staged: str, path: str) -> None: + """Move a staged file into place, replacing whatever is there in one step""" + try: + os.replace(staged, path) + except OSError: + Path(staged).unlink(missing_ok=True) + raise + + +def overwrite_private_json(path: str, data: Mapping[str, object]) -> None: + """Rewrite a file that is already there, in place, keeping the mode it was created with. + + `write_private_json` needs room for a second file and a directory that will accept it, which is + what a full disk and a read-only `~/.litellm` respectively refuse. Shortening the file already + in place needs neither. It is not atomic, so an interrupted write leaves a partial file, and it + never creates one, so it cannot put a world-readable file where a private one was. + """ + fd: Final = os.open(path, os.O_WRONLY | os.O_TRUNC) + with os.fdopen(fd, "w") as f: + json.dump(data, f, indent=2) + f.flush() + os.fsync(f.fileno()) + + +def discard_staged_json(staged: str) -> None: + """Throw a staged file away when the change it was part of is abandoned""" + Path(staged).unlink(missing_ok=True) + + +def write_private_json(path: str, data: Mapping[str, object]) -> None: + """Atomically write JSON to path with owner-only permissions (0600)""" + commit_staged_json(stage_private_json(path, data), path) diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index e4c1c9fc5cf..f0e5086b660 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -10,7 +10,7 @@ from collections.abc import Iterable, Mapping, Sequence from itertools import groupby from os import PathLike from pathlib import Path -from typing import TYPE_CHECKING, Any, Final, Literal, cast +from typing import TYPE_CHECKING, Any, Final, Literal, TypeVar, cast from openai.types.chat.chat_completion_custom_tool_param import ( CustomFormatGrammar, @@ -28,8 +28,12 @@ from litellm.types.llms.openai import ( ChatCompletionAssistantMessage, ChatCompletionFileObject, ChatCompletionImageObject, + ChatCompletionReasoningItem, + ChatCompletionReasoningSummaryTextBlock, + ChatCompletionRedactedThinkingBlock, ChatCompletionResponseMessage, ChatCompletionTextObject, + ChatCompletionThinkingBlock, ChatCompletionToolParam, ChatCompletionUserMessage, ) @@ -466,6 +470,8 @@ 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 +510,8 @@ 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 @@ -531,6 +539,8 @@ 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 +584,10 @@ 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) @@ -1325,6 +1339,16 @@ def check_is_function_call(logging_obj: "LoggingClass") -> bool: return False +_MarkedT: Final = TypeVar("_MarkedT", bound=Mapping[str, object]) + + +def with_prompt_cache_breakpoint(target: _MarkedT, marker: object) -> _MarkedT: + if marker is None: + return target + marked: Final = {**target, "prompt_cache_breakpoint": marker} # mutable-ok: API message payload + return cast(_MarkedT, marked) # cast-ok: same block shape as the input plus the marker key + + def filter_value_from_dict(dictionary: dict, key: str, depth: int = 0) -> Any: """ Filters a value from a dictionary @@ -1539,6 +1563,44 @@ 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]: @@ -1685,6 +1747,46 @@ def hoist_images_from_tool_messages( ] +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) -> Any | None: """ Attempt to repair truncated JSON produced by LLM tool calls. diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 0ed15c43ccf..795fb36961e 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 @@ -1200,13 +1158,14 @@ def _encode_tool_call_id_with_signature(tool_call_id: str, thought_signature: st return tool_call_id -def _get_thought_signature_from_tool(tool: dict, model: str | None = None) -> str | None: +def _get_thought_signature_from_tool(tool: dict) -> str | None: """Extract thought signature from tool call's provider_specific_fields. If not provided try to extract thought signature from tool call id Checks both tool.provider_specific_fields and tool.function.provider_specific_fields. - If no signature is found and model is gemini-3, returns a dummy signature. + Returns None when the tool call carries no signature; callers decide whether a + placeholder signature is needed. """ # First check tool's provider_specific_fields provider_fields: Final = tool.get("provider_specific_fields") or {} @@ -1236,13 +1195,6 @@ def _get_thought_signature_from_tool(tool: dict, model: str | None = None) -> st if len(parts) == 2: _, signature = parts return signature - # If no signature found and model is gemini-3, return dummy signature - from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( - VertexGeminiConfig, - ) - - if model and VertexGeminiConfig._is_gemini_3_or_newer(model): - return _get_dummy_thought_signature() return None @@ -1251,10 +1203,14 @@ def _get_dummy_thought_signature() -> str: This is used when transferring conversation history from older models (like gemini-2.5-flash) to gemini-3, which requires thought_signature - for strict validation. + for strict validation. Google documents it as a last resort that "will + negatively impact model performance", so callers must only fall back to it + when no real signature is available. + + See: + https://ai.google.dev/gemini-api/docs/thought-signatures#faqs + https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/thinking/thought-signatures """ - # Return a base64-encoded dummy signature string - # Below dummy signature is recommended by google - https://ai.google.dev/gemini-api/docs/thought-signatures#faqs dummy_data: Final = b"skip_thought_signature_validator" return base64.b64encode(dummy_data).decode("utf-8") @@ -1312,8 +1268,10 @@ def convert_to_gemini_tool_call_invoke( VertexGeminiConfig, ) + needs_dummy_signature: Final = model is not None and VertexGeminiConfig._is_gemini_3_or_newer(model) + if tool_calls is not None: - for idx, tool in enumerate(tool_calls): + for tool in tool_calls: if "function" in tool: gemini_function_call: VertexFunctionCall | None = _gemini_tool_call_invoke_helper( function_call_params=tool["function"], @@ -1321,7 +1279,13 @@ def convert_to_gemini_tool_call_invoke( ) if gemini_function_call is not None: part_dict: VertexPartType = {"function_call": gemini_function_call} - thought_signature = _get_thought_signature_from_tool(dict(tool), model=model) + thought_signature = _get_thought_signature_from_tool(dict(tool)) + # Gemini signs only the first functionCall part of a parallel batch, so scope the + # placeholder fallback to that part instead of fabricating one per sibling call: + # https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/thinking/thought-signatures#parallel_function_calling_example + is_first_function_call = len(_parts_list) == 0 + if not thought_signature and is_first_function_call and needs_dummy_signature: + thought_signature = _get_dummy_thought_signature() if thought_signature: part_dict["thoughtSignature"] = thought_signature @@ -1344,7 +1308,7 @@ def convert_to_gemini_tool_call_invoke( thought_signature = provider_fields.get("thought_signature") # If no signature found and model is gemini-3, use dummy signature - if not thought_signature and model and VertexGeminiConfig._is_gemini_3_or_newer(model): + if not thought_signature and needs_dummy_signature: thought_signature = _get_dummy_thought_signature() if thought_signature: @@ -1448,7 +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: @@ -1600,14 +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": @@ -1650,6 +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) @@ -5377,12 +5352,13 @@ 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 a1f8bb36e27..f545ba4aa3b 100644 --- a/litellm/litellm_core_utils/ptu_pricing.py +++ b/litellm/litellm_core_utils/ptu_pricing.py @@ -8,7 +8,7 @@ router prices at zero serves its traffic for free. from collections.abc import Mapping from dataclasses import dataclass -from datetime import datetime, timezone +from datetime import date, datetime, time, timezone from types import MappingProxyType from typing import Final @@ -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 @@ -68,9 +69,17 @@ def _to_utc(parsed: datetime) -> datetime: def _as_utc(value: object) -> datetime | None: - """A model_info datetime as UTC, parsing an ISO string, else None.""" + """A model_info datetime as UTC, parsing an ISO string, else None. + + An unquoted ``2027-01-01`` in config.yaml is loaded as a ``date``, not a string, and a + reservation bound that fails to parse takes the whole deployment out of PTU handling, + so the day is read as its opening midnight rather than discarded. ``datetime`` derives + from ``date``, so it has to be matched first. + """ if isinstance(value, datetime): return _to_utc(value) + if isinstance(value, date): + return datetime.combine(value, time.min, tzinfo=timezone.utc) if not isinstance(value, str): return None try: @@ -79,6 +88,85 @@ def _as_utc(value: object) -> datetime | None: return None +def _named(reason: str, model_name: str | None) -> str: + """The reason on its own for a caller that already has the deployment in hand, else named.""" + return reason if model_name is None else f"PTU configuration on model '{model_name}' is invalid: {reason}" + + +def ptu_identity_error( + *, declared_id: str | None, taken: bool, current_id: str | None = None, model_name: str | None = None +) -> str | None: + """Why this config-declared reservation cannot be identified, else None. + + A deployment declared in config.yaml is otherwise keyed by a hash of its resolved + ``litellm_params``, so rotating a credential or editing an endpoint mints a second + identity and the reservation is charged again under it. The flat cost is keyed by that + id, and a charge already written is never retracted, so the duplicate is permanent. + + ``current_id`` is what the deployment is keyed by today. Naming it is the difference + between an operator carrying their history forward and an operator inventing a fresh + id, which starts a second identity beside the charges already written. + """ + if not declared_id: + return _named( + "model_info.id is required when PTU fields are set. Without one the deployment is " + "identified by a hash of its litellm_params, so rotating a credential bills the " + "reservation a second time under the new identity. Set it to the id this deployment " + f"already uses, {current_id or 'shown by GET /model/info'}, so the flat cost already " + "written stays under one identity; any other value starts a second one", + model_name, + ) + if taken: + return _named( + f"model_info.id '{declared_id}' is declared on more than one deployment. Each would key " + "the same flat-cost row, so one reservation would go unbilled", + model_name, + ) + return None + + +PTU_MODEL_INFO_FIELDS: Final = ("ptu_count", "cost_per_ptu_per_hour", "ptu_effective_from", "ptu_effective_to") + + +def declares_ptu(model_info: Mapping[str, object]) -> bool: + """Whether any PTU field is set here, including one too malformed to charge.""" + return any(model_info.get(field) is not None for field in PTU_MODEL_INFO_FIELDS) + + +def ptu_config_error(model_info: Mapping[str, object], *, model_name: str | None = None) -> str | None: + """Why this PTU configuration cannot be honoured, else None. + + Both the model endpoints and config.yaml registration ask this, so a deployment that + one refuses is refused by the other for the same stated reason. + + Window ordering is checked before the count/rate gate. A patch that touches only one end + of the window carries no count or rate, so leaving the order to that gate would let an + inverted window reach the row; the next load then fails to parse it and drops the + deployment out of the router, where no further patch can repair it. + """ + effective_from: Final = _as_utc(model_info.get("ptu_effective_from")) + effective_to: Final = _as_utc(model_info.get("ptu_effective_to")) + if effective_from is not None and effective_to is not None and effective_to <= effective_from: + return _named("ptu_effective_to must be after ptu_effective_from", model_name) + + has_count: Final = model_info.get("ptu_count") is not None + has_rate: Final = model_info.get("cost_per_ptu_per_hour") is not None + if not has_count and not has_rate: + return None + if has_count != has_rate: + return _named("ptu_count and cost_per_ptu_per_hour must be set together", model_name) + if effective_from is None: + return _named( + "ptu_effective_from is required when PTU fields are set. Flat cost accrues from that " + "instant, so without it the start would have to be inferred and a deployment configured " + "today could be billed for days it did not exist", + model_name, + ) + if not model_info.get("team_id"): + return _named("team_id is required when PTU fields are set (one model maps to one team)", model_name) + return None + + def ptu_terms(model_info: Mapping[str, object]) -> PTUTerms | None: """The reservation this deployment accrues flat cost for, else None. diff --git a/litellm/litellm_core_utils/realtime_errors.py b/litellm/litellm_core_utils/realtime_errors.py new file mode 100644 index 00000000000..e1b957f4325 --- /dev/null +++ b/litellm/litellm_core_utils/realtime_errors.py @@ -0,0 +1,31 @@ +"""Loud-failure helpers for the realtime WebSocket paths. + +A realtime caller that only gets a bare close frame has nothing to act on, so +every failure surfaces as an OpenAI-style ``error`` event plus a close frame +whose reason names the failure. Close reasons are capped at +``WEBSOCKET_CLOSE_REASON_MAX_BYTES``: RFC 6455 control frames carry at most 125 +bytes, two of which hold the status code, and a longer reason makes the close +frame itself fail, which is how a loud failure turns back into a silent one. +""" + +import json +from typing import Final + +from litellm.types.realtime import RealtimeErrorDetail, RealtimeErrorEvent + +WEBSOCKET_CLOSE_REASON_MAX_BYTES: Final = 123 + + +def realtime_error_event(message: str, error_type: str) -> str: + detail: Final[RealtimeErrorDetail] = {"type": error_type, "message": message} + event: Final[RealtimeErrorEvent] = {"type": "error", "error": detail} + return json.dumps(event) + + +def websocket_close_reason(message: str, fallback: str) -> str: + encoded: Final = message.encode("utf-8") + if not encoded: + return fallback + if len(encoded) <= WEBSOCKET_CLOSE_REASON_MAX_BYTES: + return message + return encoded[:WEBSOCKET_CLOSE_REASON_MAX_BYTES].decode("utf-8", errors="ignore") diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 10056d64a20..9125ed6e70a 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 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..0e2139d688b 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -173,6 +173,27 @@ 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 +239,22 @@ 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 +376,15 @@ 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 @@ -778,6 +824,7 @@ 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 +874,13 @@ 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 +906,7 @@ 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 +994,7 @@ 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 +1054,11 @@ 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 300ba427cda..f5f671e7585 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 @@ -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, @@ -191,7 +234,7 @@ class CustomStreamWrapper: custom_llm_provider: str | None = None, stream_options=None, make_call: Callable | None = None, - _response_headers: dict | None = None, + _response_headers: dict | httpx.Headers | None = None, ): self.model = model self.make_call = make_call @@ -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 @@ -1504,7 +1550,12 @@ 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 @@ -2315,10 +2366,19 @@ class CustomStreamWrapper: if self.logging_obj is None or not self.chunks: return try: - partial_response: Final = litellm.stream_chunk_builder(chunks=self.chunks) + 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 usage: Final = cast(Usage | None, getattr(partial_response, "usage", None)) if usage is None: return + if self.model: + partial_response.model = self.model + backfill_missing_cache_usage_fields(usage) self.logging_obj.model_call_details["combined_usage_object"] = usage self.logging_obj.model_call_details["response_cost"] = ( self.logging_obj._response_cost_calculator(result=partial_response) or 0.0 @@ -2439,6 +2499,35 @@ class CustomStreamWrapper: return chunk +def _cache_token_count(details: PromptTokensDetailsWrapper | None, keys: tuple[str, ...]) -> int: + for key in keys: + value = getattr(details, key, None) + if isinstance(value, int) and not isinstance(value, bool) and value: + return value + return 0 + + +def backfill_missing_cache_usage_fields(usage: Usage) -> None: + """Give partial-stream usage the same cache fields a complete stream reports. + + Carries OpenAI-style ``prompt_tokens_details`` counts up to the Anthropic-style + top-level keys, defaulting to zero. It must carry the real count rather than a + flat zero: downstream readers treat these keys as authoritative once present and + skip their own normalization, so a zero here would overwrite a real cache read. + """ + details: Final = usage.prompt_tokens_details + if getattr(usage, "cache_read_input_tokens", None) is None: + usage.cache_read_input_tokens = _cache_token_count( # rebind-ok: in-place backfill is the contract + details, ("cached_tokens",) + ) + if getattr(usage, "cache_creation_input_tokens", None) is None: + usage.cache_creation_input_tokens = _cache_token_count( # rebind-ok: in-place backfill is the contract + details, ("cache_write_tokens", "cache_creation_tokens") + ) + if usage.prompt_tokens_details is None: + usage.prompt_tokens_details = PromptTokensDetailsWrapper(cached_tokens=0) # rebind-ok: backfill in place + + _TokenDetails = TypeVar("_TokenDetails", PromptTokensDetailsWrapper, CompletionTokensDetailsWrapper) diff --git a/litellm/litellm_core_utils/thread_pool_executor.py b/litellm/litellm_core_utils/thread_pool_executor.py index 881a91400df..f989f20247f 100644 --- a/litellm/litellm_core_utils/thread_pool_executor.py +++ b/litellm/litellm_core_utils/thread_pool_executor.py @@ -1,6 +1,82 @@ -from concurrent.futures import ThreadPoolExecutor -from typing import Final +import logging +import threading +import time +from collections.abc import Callable +from concurrent.futures import Future, ThreadPoolExecutor +from typing import Final, ParamSpec, TypeVar -MAX_THREADS: Final = 100 -# Create a ThreadPoolExecutor -executor: Final = ThreadPoolExecutor(max_workers=MAX_THREADS) +from litellm._logging import verbose_logger +from litellm.constants import ( + LOGGING_EXECUTOR_DROPPED_TASK_LOG_INTERVAL_SECONDS, + LOGGING_EXECUTOR_MAX_PENDING_TASKS, + LOGGING_EXECUTOR_MAX_THREADS, +) + +MAX_THREADS: Final = LOGGING_EXECUTOR_MAX_THREADS + +_P = ParamSpec("_P") +_T = TypeVar("_T") + + +class BoundedLoggingThreadPoolExecutor(ThreadPoolExecutor): + """ThreadPoolExecutor with a cap on queued-plus-running tasks. + + The default ThreadPoolExecutor work queue is unbounded, and every queued + logging task pins its request/response payload in memory, so a sustained + burst of sync callbacks slower than request arrival grows memory without + bound. Logging is best-effort: once the cap is reached, new submissions + are dropped with a rate-limited warning instead of queueing forever. + """ + + def __init__( + self, + max_workers: int, + max_pending_tasks: int, + drop_log_interval_seconds: float = LOGGING_EXECUTOR_DROPPED_TASK_LOG_INTERVAL_SECONDS, + logger: logging.Logger = verbose_logger, + ) -> None: + super().__init__(max_workers=max_workers, thread_name_prefix="litellm-logging") + self._max_pending_tasks: Final = max_pending_tasks + self._drop_log_interval_seconds: Final = drop_log_interval_seconds + self._logger: Final = logger + self._pending_slots: Final = threading.Semaphore(max_pending_tasks) + self._drop_lock: Final = threading.Lock() + self._dropped_since_last_log = 0 + self._last_drop_log_time = 0.0 + + def submit(self, fn: Callable[_P, _T], /, *args: _P.args, **kwargs: _P.kwargs) -> Future[_T]: + if not self._pending_slots.acquire(blocking=False): + self._record_drop() + dropped_future: Final[Future[_T]] = Future() + dropped_future.cancel() + return dropped_future + try: + future: Final = super().submit(fn, *args, **kwargs) + except BaseException: + self._pending_slots.release() + raise + future.add_done_callback(lambda _: self._pending_slots.release()) + return future + + def _record_drop(self) -> None: + with self._drop_lock: + self._dropped_since_last_log += 1 + now: Final = time.monotonic() + if now - self._last_drop_log_time < self._drop_log_interval_seconds: + return + dropped_count: Final = self._dropped_since_last_log + self._dropped_since_last_log = 0 + self._last_drop_log_time = now + + self._logger.warning( + "litellm logging executor backlog is full (max_pending_tasks=%s); dropped %s logging task(s) " + "since the last warning. Set LOGGING_EXECUTOR_MAX_PENDING_TASKS to raise the cap.", + self._max_pending_tasks, + dropped_count, + ) + + +executor: Final = BoundedLoggingThreadPoolExecutor( + max_workers=MAX_THREADS, + max_pending_tasks=LOGGING_EXECUTOR_MAX_PENDING_TASKS, +) diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index 17f3dea72ec..256bee7b348 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -3,7 +3,7 @@ import base64 import io import struct -from collections.abc import Callable, Mapping +from collections.abc import Callable, Iterable, Mapping, Sequence from typing import Any, Final, Literal, cast import tiktoken @@ -19,19 +19,27 @@ from litellm.constants import ( MAX_SHORT_SIDE_FOR_IMAGE_HIGH_RES, MAX_TILE_HEIGHT, MAX_TILE_WIDTH, + TIKTOKEN_ENCODE_CHUNK_SIZE_CHARS, ) from litellm.litellm_core_utils.default_encoding import encoding as default_encoding 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 @@ -305,6 +313,16 @@ Type for a function that counts tokens in a string. """ +def _get_tiktoken_count_function( + encode_length: Callable[[str], int], + chunk_size: int = TIKTOKEN_ENCODE_CHUNK_SIZE_CHARS, +) -> TokenCounterFunction: + def count_tokens(text: str) -> int: + return sum(encode_length(text[start : start + chunk_size]) for start in range(0, len(text), chunk_size)) + + return count_tokens + + class _MessageCountParams: """ A class to hold the parameters for counting tokens in messages. @@ -335,7 +353,7 @@ 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, @@ -531,6 +549,7 @@ def _get_count_function( enc: Final = tokenizer_json["tokenizer"].encode(text) return len(enc.ids) + return count_tokens elif tokenizer_json["type"] == "openai_tokenizer": model_to_use: Final = _fix_model_name(model) try: @@ -542,17 +561,18 @@ def _get_count_function( print_verbose("Warning: model not found. Using cl100k_base encoding.") encoding = tiktoken.get_encoding("cl100k_base") - def count_tokens(text: str) -> int: + def encode_length(text: str) -> int: return len(encoding.encode(text, disallowed_special=())) + return _get_tiktoken_count_function(encode_length) else: raise ValueError("Unsupported tokenizer type") else: - def count_tokens(text: str) -> int: + def encode_length(text: str) -> int: return len(default_encoding.encode(text, disallowed_special=())) - return count_tokens + return _get_tiktoken_count_function(encode_length) def _fix_model_name(model: str) -> str: @@ -633,6 +653,46 @@ 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_anthropic_content( content: Mapping[str, Any], count_function: TokenCounterFunction, @@ -684,13 +744,17 @@ 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: @@ -701,6 +765,19 @@ 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"] in ("tool_use", "tool_result"): num_tokens += _count_anthropic_content( c, @@ -729,7 +806,8 @@ 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, tool_use, tool_result, thinking, tool_reference)." ) return num_tokens except Exception as e: 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/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 721a6653597..b9ca18c7843 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -24,10 +24,12 @@ 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, @@ -360,7 +362,13 @@ 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 +427,10 @@ 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") @@ -677,12 +688,9 @@ 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( diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index 39d3947c07c..cd47cdd57d6 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -18,12 +18,15 @@ 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, _get_httpx_client, get_async_httpx_client, ) +from litellm.rust_bridge import chat_completions as rust_chat_completions_bridge +from litellm.rust_bridge.chat_completions import rust_chat_completions_accepts from litellm.types.llms.anthropic import ( ContentBlockDelta, ContentBlockStart, @@ -361,30 +364,135 @@ class AnthropicChatCompletion(BaseLLM): if config is None: raise ValueError(f"Provider config not found for model: {model} and provider: {custom_llm_provider}") - data = config.transform_request( + def build_request() -> tuple[dict, dict]: # mutable-ok: rewritten in place downstream + """Translate the request the Python way, returning `(headers, data)`. + + The pair stays mutable because the streaming path rewrites it in + place (`data["stream"] = True`) before sending. + + Shared by the normal path and by the Rust path's fallback, which + builds it only when the Rust call did not serve the request. + """ + request_data: Final = config.transform_request( + model=model, + messages=messages, + optional_params={**optional_params, "is_vertex_request": is_vertex_request}, + litellm_params=litellm_params, + headers=headers, + ) + return update_request_with_filtered_beta( + headers=headers, + request_data=request_data, + provider=custom_llm_provider, + ) + + # The Rust core owns the whole call for the subset it accepts, so ask + # before transforming: whichever path runs emits pre_call exactly once. + # `get_config` merges the class-level defaults (Anthropic's required + # `max_tokens` among them) that `transform_request` would have applied. + rust_optional_params: Final = { # mutable-ok: json.dumps in the bridge rejects a mappingproxy + **AnthropicConfig.get_config(model=model), + **optional_params, + } + serves_via_rust: Final = rust_chat_completions_accepts( model=model, messages=messages, - optional_params={**optional_params, "is_vertex_request": is_vertex_request}, + optional_params=rust_optional_params, + custom_llm_provider=custom_llm_provider, litellm_params=litellm_params, - headers=headers, + stream=stream, ) - - headers, data = update_request_with_filtered_beta( - headers=headers, - request_data=data, - provider=custom_llm_provider, - ) - - ## LOGGING - logging_obj.pre_call( - input=messages, - api_key=api_key, - additional_args={ - "complete_input_dict": data, + if serves_via_rust: + rust_logging_args: Final = { # mutable-ok: logging callbacks read additional_args as a plain dict + "complete_input_dict": { # mutable-ok: same, and it is serialized alongside its parent + "model": model, + "messages": messages, + **rust_optional_params, + }, "api_base": api_base, "headers": headers, - }, - ) + } + logging_obj.pre_call(input=messages, api_key=api_key, additional_args=rust_logging_args) + log_rust_post_call: Final = rust_chat_completions_bridge.response_logger( + logging_obj=logging_obj, + messages=messages, + api_key=api_key, + additional_args=rust_logging_args, + ) + if acompletion is True: + + async def python_fallback() -> "ModelResponse | CustomStreamWrapper": + # pre_call already fired for this request above. The Rust + # path only declines before the provider is called, so this + # is the same attempt continuing, not a second one. + fallback_headers, fallback_data = build_request() + return await self.acompletion_function( + model=model, + messages=messages, + data=fallback_data, + api_base=api_base, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + encoding=encoding, + api_key=api_key, + provider_config=config, + logging_obj=logging_obj, + optional_params=optional_params, + stream=stream, + _is_function_call=_is_function_call, + litellm_params=litellm_params, + logger_fn=logger_fn, + headers=fallback_headers, + client=client, + json_mode=json_mode, + timeout=timeout, + ) + + return rust_chat_completions_bridge.achat_completions_or_fallback( + model=model, + messages=messages, + optional_params=rust_optional_params, + model_response=model_response, + api_key=api_key, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + extra_headers=headers, + timeout=timeout, + on_response=log_rust_post_call, + python_fallback=python_fallback, + ) + rust_response: Final = rust_chat_completions_bridge.chat_completions( + model=model, + messages=messages, + optional_params=rust_optional_params, + model_response=model_response, + api_key=api_key, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + extra_headers=headers, + timeout=timeout, + on_response=log_rust_post_call, + ) + if rust_response is not None: + return rust_response + + headers, data = build_request() + + ## LOGGING + # Reaching here with `serves_via_rust` set means the Rust attempt + # declined at call time, before the provider was called, and already + # logged this request. That is the same attempt continuing. + if not serves_via_rust: + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "api_base": api_base, + "headers": headers, + }, + ) print_verbose(f"_is_function_call: {_is_function_call}") if acompletion is True: if ( @@ -547,7 +655,7 @@ 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 @@ -571,6 +679,14 @@ 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 @@ -596,11 +712,14 @@ 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 @@ -1042,31 +1161,39 @@ 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. @@ -1102,13 +1229,10 @@ 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}") @@ -1151,13 +1275,10 @@ 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}") diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 414fd23381a..15fc482b34e 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -1,7 +1,8 @@ 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 @@ -121,6 +122,32 @@ else: # response side. _ANTHROPIC_TOOL_NAME_INVALID_CHARS: Final = re.compile(r"[^a-zA-Z0-9_-]") _ANTHROPIC_TOOL_NAME_MAX_LEN: Final = 128 + +_ENUM_TYPE_CHECKS: Final[Mapping[str, Callable[[Any], 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_ @@ -565,9 +592,13 @@ 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 +1215,11 @@ 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( @@ -1827,6 +1861,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): custom_llm_provider=self.custom_llm_provider, ) + AnthropicModelInfo.maybe_drop_disabled_thinking( + model=model, + optional_params=optional_params, + custom_llm_provider=self._resolved_provider, + ) + headers = self.update_headers_with_optional_anthropic_beta(headers=headers, optional_params=optional_params) # === Tool-name sanitization (single chokepoint) === @@ -2107,7 +2147,7 @@ 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 @@ -2139,7 +2179,7 @@ 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) @@ -2162,7 +2202,7 @@ 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) @@ -2216,7 +2256,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def calculate_usage( self, - usage_object: dict, + usage_object: Mapping[str, Any], reasoning_content: str | None, completion_response: dict | None = None, speed: str | None = None, @@ -2239,6 +2279,8 @@ 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") if iterations: @@ -2313,7 +2355,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): else None ), inference_geo=inference_geo, - speed=speed, + speed=resolved_speed, service_tier=service_tier, ) return usage diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 1cdbd60f943..681a8397f66 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 @@ -32,6 +32,27 @@ from litellm.types.llms.anthropic import ( from litellm.types.llms.openai import AllMessageValues from litellm.types.proxy.model_listing import ModelInfoResponse +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+$") _DATED_RELEASE_SUFFIX_RE: Final = re.compile(r"-\d{8}$") @@ -72,8 +93,8 @@ 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 @@ -83,16 +104,21 @@ 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" @@ -425,6 +451,45 @@ class AnthropicModelInfo(BaseLLMModelInfo): """ return AnthropicModelInfo._supports_model_capability(model, "supports_adaptive_thinking", custom_llm_provider) + @staticmethod + def _is_always_on_thinking_model(model: str, custom_llm_provider: str) -> bool: + """Whether ``model`` always thinks and rejects ``thinking.type=disabled`` + (Fable 5 / Mythos 5 generation). The model cost map is authoritative: an + explicit ``thinking_always_on`` entry resolved under ``custom_llm_provider``, + or a ``fallback_generalizations`` rule for unmapped ids of those families. + """ + 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: 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 + (Fable 5 / Mythos 5), which 400 on it; omission is the API-documented + remedy and yields the model's default adaptive thinking.""" + thinking: Final = optional_params.get("thinking") + if not isinstance(thinking, dict) or thinking.get("type") != "disabled": + return + if not AnthropicModelInfo._is_always_on_thinking_model(model, custom_llm_provider): + return + litellm.verbose_logger.warning( + DROP_DISABLED_THINKING_WARNING, + model, + ) + optional_params.pop("thinking", None) + def is_effort_used( self, optional_params: dict | None, @@ -909,19 +974,25 @@ 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]: @@ -963,22 +1034,29 @@ 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`. @@ -991,7 +1069,7 @@ 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: @@ -1006,6 +1084,21 @@ def _is_empty_text_block(block: Any) -> bool: 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 normalize_anthropic_tool_use_id(raw_id: str) -> str: """ Normalize a tool_use / tool_result id for Anthropic's ``^[a-zA-Z0-9_-]+$`` 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/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index 48d8a03d549..9d61701d26d 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py @@ -21,6 +21,7 @@ from litellm.llms.anthropic.experimental_pass_through.context_management import ) from litellm.llms.anthropic.experimental_pass_through.utils import ( is_reasoning_auto_summary_enabled, + local_model_name, ) from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, @@ -358,9 +359,9 @@ class LiteLLMMessagesToCompletionTransformationHandler: except Exception: pass - if isinstance(model, str) and model and not model.startswith("responses/"): - # Prefix model with "responses/" to route to OpenAI Responses API - completion_kwargs["model"] = f"responses/{model}" + if isinstance(model, str) and model and "responses/" not in model: + local_model: Final = model.removeprefix(f"{custom_llm_provider}/") + completion_kwargs["model"] = f"{custom_llm_provider}/responses/{local_model}" auto_summary: Final = is_reasoning_auto_summary_enabled() @@ -484,10 +485,14 @@ class LiteLLMMessagesToCompletionTransformationHandler: if "output_config" in extra_kwargs: request_data["output_config"] = extra_kwargs["output_config"] + custom_llm_provider: Final = extra_kwargs.get("custom_llm_provider") ( openai_request, tool_name_mapping, - ) = ANTHROPIC_ADAPTER.translate_completion_input_params_with_tool_mapping(request_data) + ) = ANTHROPIC_ADAPTER.translate_completion_input_params_with_tool_mapping( + request_data, + custom_llm_provider=custom_llm_provider if isinstance(custom_llm_provider, str) else None, + ) if openai_request is None: raise ValueError("Failed to translate request to OpenAI format") @@ -526,6 +531,10 @@ class LiteLLMMessagesToCompletionTransformationHandler: if key not in excluded_keys and key not in completion_kwargs and value is not None: completion_kwargs[key] = value + explicit_prompt_cache_key: Final = extra_kwargs.get("prompt_cache_key") + if explicit_prompt_cache_key is not None: + completion_kwargs["prompt_cache_key"] = explicit_prompt_cache_key + # Normalize reasoning_effort based on model capabilities # (e.g. "max" → "xhigh"/"high", "minimal" → "low" if unsupported) # Must run BEFORE _route_openai_thinking, which prepends "responses/" @@ -608,7 +617,7 @@ class LiteLLMMessagesToCompletionTransformationHandler: if stream: transformed_stream: Final = ANTHROPIC_ADAPTER.translate_completion_output_params_streaming( completion_response, - model=model, + model=local_model_name(model, kwargs.get("custom_llm_provider")), tool_name_mapping=tool_name_mapping, polyfill_result=polyfill_result, is_async=True, @@ -742,7 +751,7 @@ class LiteLLMMessagesToCompletionTransformationHandler: if stream: transformed_stream: Final = ANTHROPIC_ADAPTER.translate_completion_output_params_streaming( completion_response, - model=model, + model=local_model_name(model, kwargs.get("custom_llm_provider")), tool_name_mapping=tool_name_mapping, polyfill_result=polyfill_result, is_async=False, 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..cefd4aa2d77 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_thinking_block + choice: Final = chunk.choices[0] if choice.finish_reason is not None: return False @@ -1039,7 +1041,11 @@ 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 (even if signed) must not + # open a block: the emitted {"type": "thinking", "thinking": ""} gets + # replayed as history and Anthropic rejects it (LIT-6357). + thinking_blocks: Final = getattr(delta, "thinking_blocks", None) + if thinking_blocks and any(isinstance(b, dict) and not is_empty_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 e45414b4a73..a9fa00c827a 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -1,11 +1,13 @@ import copy import hashlib import json -from collections.abc import AsyncIterator, Iterator, Mapping -from typing import TYPE_CHECKING, Any, Final, Literal, 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 ( is_reasoning_auto_summary_enabled, + prompt_cache_key_from_user_id, ) # OpenAI has a 64-character limit for function/tool names @@ -13,6 +15,23 @@ from litellm.llms.anthropic.experimental_pass_through.utils import ( OPENAI_MAX_TOOL_NAME_LENGTH: Final = 64 TOOL_NAME_HASH_LENGTH: Final = 8 TOOL_NAME_PREFIX_LENGTH: Final = OPENAI_MAX_TOOL_NAME_LENGTH - TOOL_NAME_HASH_LENGTH - 1 # 55 +PROVIDERS_PROXYING_AN_UNKNOWN_BACKEND: Final = frozenset({"litellm_proxy"}) + + +_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: @@ -61,6 +80,8 @@ 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 ( THOUGHT_SIGNATURE_SEPARATOR, @@ -68,7 +89,10 @@ 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_thinking_block, + normalize_anthropic_tool_use_id, +) from litellm.llms.anthropic.experimental_pass_through.context_management import ( PolyfillResult, ) @@ -94,6 +118,7 @@ from litellm.types.llms.anthropic import ( ContextManagementResponse, MessageBlockDelta, MessageDelta, + ServerToolUsage, StreamingContentBlockDeltaType, UsageDelta, UsageIteration, @@ -120,7 +145,9 @@ from litellm.types.llms.openai import ( ChatCompletionToolMessage, ChatCompletionToolParam, ChatCompletionToolParamFunctionChunk, + ChatCompletionToolReferenceObject, ChatCompletionUserMessage, + ToolMessageContentPart, ) from litellm.types.utils import Choices, ModelResponse, StreamingChoices, Usage @@ -129,6 +156,8 @@ 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: @@ -148,7 +177,7 @@ class AnthropicAdapter: return result def translate_completion_input_params_with_tool_mapping( - self, kwargs + self, kwargs, *, custom_llm_provider: str | None = None ) -> tuple[ChatCompletionRequest | None, dict[str, str]]: """ Translate Anthropic request params to OpenAI format, returning tool name mapping. @@ -179,7 +208,10 @@ class AnthropicAdapter: ( translated_body, tool_name_mapping, - ) = LiteLLMAnthropicMessagesAdapter().translate_anthropic_to_openai(anthropic_message_request=request_body) + ) = LiteLLMAnthropicMessagesAdapter().translate_anthropic_to_openai( + anthropic_message_request=request_body, + custom_llm_provider=custom_llm_provider, + ) return translated_body, tool_name_mapping @@ -245,6 +277,9 @@ class AnthropicAdapter: return anthropic_wrapper.anthropic_sse_wrapper() +_BlockT: Final = TypeVar("_BlockT", bound=Mapping[str, object]) + + class LiteLLMAnthropicMessagesAdapter: def __init__(self): pass @@ -308,6 +343,12 @@ class LiteLLMAnthropicMessagesAdapter: # Fallback for non-dict objects (shouldn't happen in practice) cast(dict[str, object], target)["cache_control"] = cache_control + @staticmethod + def _add_prompt_cache_breakpoint_if_present(source: object, target: _BlockT) -> _BlockT: + if isinstance(source, dict) and "prompt_cache_breakpoint" in source: + return with_prompt_cache_breakpoint(target, source["prompt_cache_breakpoint"]) + return target + def translatable_anthropic_params(self) -> list[str]: """ Which anthropic params, we need to translate to the openai format. @@ -368,7 +409,9 @@ class LiteLLMAnthropicMessagesAdapter: if content.get("type") == "text": text_obj = ChatCompletionTextObject(type="text", text=content.get("text", "")) self._add_cache_control_if_applicable(content, text_obj, model) - new_user_content_list.append(text_obj) + new_user_content_list.append( + self._add_prompt_cache_breakpoint_if_present(content, text_obj) + ) elif content.get("type") == "image": # Convert Anthropic image format to OpenAI format source = content.get("source", {}) @@ -378,7 +421,9 @@ class LiteLLMAnthropicMessagesAdapter: image_url_obj = ChatCompletionImageUrlObject(url=openai_image_url) image_obj = ChatCompletionImageObject(type="image_url", image_url=image_url_obj) self._add_cache_control_if_applicable(content, image_obj, model) - new_user_content_list.append(image_obj) + new_user_content_list.append( + self._add_prompt_cache_breakpoint_if_present(content, image_obj) + ) elif content.get("type") == "document": # Convert Anthropic document format (PDF, etc.) to OpenAI format source = content.get("source", {}) @@ -390,90 +435,13 @@ 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) @@ -572,6 +540,9 @@ 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 @@ -746,6 +717,10 @@ 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}" @@ -869,7 +844,7 @@ class LiteLLMAnthropicMessagesAdapter: continue text_obj = ChatCompletionTextObject(type="text", text=text) self._add_cache_control_if_applicable(block, text_obj, model) - text_parts.append(text_obj) + text_parts.append(self._add_prompt_cache_breakpoint_if_present(block, text_obj)) return ChatCompletionSystemMessage(role="system", content=text_parts) if text_parts else None def _add_system_message_to_messages( @@ -900,23 +875,66 @@ class LiteLLMAnthropicMessagesAdapter: "text": block.get("text", ""), } self._add_cache_control_if_applicable(block, text_block, model_name) - openai_system_content.append(text_block) + openai_system_content.append(self._add_prompt_cache_breakpoint_if_present(block, text_block)) if openai_system_content: new_messages.insert( 0, ChatCompletionSystemMessage(role="system", content=openai_system_content), ) + @staticmethod + def _supports_prompt_cache_key(model: str | None, custom_llm_provider: str | None) -> bool: + if not model or not custom_llm_provider: + return False + if custom_llm_provider in PROVIDERS_PROXYING_AN_UNKNOWN_BACKEND: + return False + supported_params: Final = litellm.get_supported_openai_params( + model=model, custom_llm_provider=custom_llm_provider + ) + 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, new_kwargs: ChatCompletionRequest, + *, + custom_llm_provider: str | None = None, ) -> None: """Translate metadata fields from Anthropic request to OpenAI request.""" if "metadata" in anthropic_message_request: metadata: Final = anthropic_message_request["metadata"] if metadata and "user_id" in metadata: new_kwargs["user"] = metadata["user_id"] + prompt_cache_key: Final = prompt_cache_key_from_user_id(metadata["user_id"]) + if prompt_cache_key is not None and self._supports_prompt_cache_key( + anthropic_message_request.get("model"), custom_llm_provider + ): + new_kwargs["prompt_cache_key"] = prompt_cache_key if "litellm_metadata" in anthropic_message_request: # metadata will be passed to litellm.acompletion(), it's a litellm_param @@ -988,8 +1006,32 @@ 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 @@ -998,35 +1040,40 @@ 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( @@ -1069,7 +1116,10 @@ class LiteLLMAnthropicMessagesAdapter: new_kwargs[k] = v def translate_anthropic_to_openai( - self, anthropic_message_request: AnthropicMessagesRequest + self, + anthropic_message_request: AnthropicMessagesRequest, + *, + custom_llm_provider: str | None = None, ) -> tuple[ChatCompletionRequest, dict[str, str]]: """ This is used by the beta Anthropic Adapter, for translating anthropic `/v1/messages` requests to the openai format. @@ -1103,6 +1153,7 @@ class LiteLLMAnthropicMessagesAdapter: self._translate_metadata_to_openai( anthropic_message_request=anthropic_message_request, new_kwargs=new_kwargs, + custom_llm_provider=custom_llm_provider, ) ## CONVERT TOOL CHOICE self._translate_tool_choice_to_openai( @@ -1118,6 +1169,7 @@ 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( @@ -1163,6 +1215,39 @@ 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 @@ -1182,6 +1267,8 @@ 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_thinking_block(thinking_block): + continue thinking_value = thinking_block.get("thinking", "") signature_value = thinking_block.get("signature", "") new_content.append( @@ -1308,10 +1395,22 @@ 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, @@ -1325,6 +1424,11 @@ 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/editors/compact.py b/litellm/llms/anthropic/experimental_pass_through/context_management/editors/compact.py index a7c462a8fb0..c8cbbba8784 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 @@ -56,7 +56,7 @@ from ..result import PolyfillResult # so the summary's spend is attributed to the same scopes. The list mirrors the # fields populated by # ``LiteLLMProxyRequestSetup.add_user_api_key_auth_to_request_metadata``. -# ``user_api_key_model_max_budget`` / ``user_api_key_end_user_model_max_budget`` +# The three ``*_model_max_budget`` fields # are what ``_PROXY_VirtualKeyModelMaxBudgetLimiter`` reads post-call to update # the per-model spend caches, so without them the summary spend would never # count against the caller's model budget. ``user_api_key_end_user_id`` / @@ -76,6 +76,7 @@ _PROPAGATED_METADATA_KEYS: Final = ( "user_api_key_end_user_id", "user_api_end_user_max_budget", "user_api_key_model_max_budget", + "user_api_key_user_model_max_budget", "user_api_key_end_user_model_max_budget", "litellm_call_id", "litellm_parent_otel_span", @@ -317,10 +318,14 @@ async def _check_summary_model_budget( The summary subrequest never passes back through ``user_api_key_auth``, so without this gate a caller whose ``model_max_budget`` for ``context_management_summary_model`` is exhausted could keep consuming that - model via compaction. Mirrors the ``model_max_budget`` / - ``end_user_model_max_budget`` enforcement that ``user_api_key_auth`` runs for - the client-requested model. Returns True outside the proxy or when no + model via compaction. Mirrors the per-model budget enforcement that + ``user_api_key_auth`` runs for the client-requested model. Returns True outside the proxy or when no per-model budget is configured. + + All three scopes are checked because the summary's spend is charged to all + three: this file propagates the key, user and end-user budgets into the + subrequest's metadata, so enforcing only two of them would let compaction + increment a counter it can never be refused by. """ if user_api_key_auth is None: return True @@ -347,6 +352,25 @@ async def _check_summary_model_budget( ) return False + 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( + user_id=user_id, + user_model_max_budget=user_model_max_budget, + model=summary_model, + ) + except litellm.BudgetExceededError: + return False + except Exception as e: # noqa: BLE001 # a budget gate denies on any failure, as the key and end-user scopes do + verbose_logger.warning( + "compact_20260112: unexpected error during user model-budget check for summary_model=%s; denying: %s", + summary_model, + e, + ) + 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) if isinstance(end_user_model_max_budget, dict) and end_user_model_max_budget and end_user_id is not None: 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..55a85c011d0 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 +6,41 @@ 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 litellm._logging import verbose_logger +from litellm.constants import STREAM_SSE_KEEPALIVE_PING_BYTES + +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) @@ -156,6 +184,9 @@ class AgenticAnthropicStreamingIterator: logging_obj: Any, 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 +197,32 @@ 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 +241,102 @@ 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 +355,6 @@ 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 c4b5cc628e2..3e314f76a3e 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 @@ -42,15 +44,46 @@ from .utils import AnthropicMessagesRequestUtils, mock_response _RESPONSES_API_PROVIDERS: Final = frozenset({"openai"}) -def _should_route_to_responses_api(custom_llm_provider: str | None) -> bool: - """Return True when the provider should use the Responses API path. +def _bridges_to_responses_api(model: str, custom_llm_provider: str) -> bool: + from litellm.main import responses_api_bridge_check + + model_info, _ = responses_api_bridge_check(model=model, custom_llm_provider=custom_llm_provider) + return model_info.get("mode") == "responses" + + +def _responses_mode_is_lost_by_prefix_strip( + requested_model: str, resolved_model: str, custom_llm_provider: str +) -> bool: + """Whether a Responses-only deployment stops looking like one once its provider prefix is stripped. + + ``litellm.completion`` re-derives the Responses bridge from the stripped id alone, so a + deployment id such as ``perplexity/perplexity/sonar`` (mode ``responses``) is shadowed by the + chat entry ``perplexity/sonar`` and would otherwise be sent to chat/completions. + """ + if requested_model == resolved_model: + return False + return _bridges_to_responses_api(requested_model, custom_llm_provider) and not _bridges_to_responses_api( + resolved_model, custom_llm_provider + ) + + +def _should_route_to_responses_api( + custom_llm_provider: str | None, + requested_model: str | None = None, + resolved_model: str | None = None, +) -> bool: + """Return True when the request should use the Responses API path. Set ``litellm.use_chat_completions_url_for_anthropic_messages = True`` to opt out and route OpenAI/Azure requests through chat/completions instead. """ if litellm.use_chat_completions_url_for_anthropic_messages: return False - return custom_llm_provider in _RESPONSES_API_PROVIDERS + if custom_llm_provider in _RESPONSES_API_PROVIDERS: + return True + if custom_llm_provider is None or requested_model is None or resolved_model is None: + return False + return _responses_mode_is_lost_by_prefix_strip(requested_model, resolved_model, custom_llm_provider) def _deployment_passes_through_anthropic_messages(model_info: object) -> bool: @@ -209,17 +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) @@ -230,7 +266,7 @@ async def anthropic_messages( ) messages, system = AnthropicCacheControlHook.maybe_inject_cache_control( - messages, system, kwargs, model=model, custom_llm_provider=custom_llm_provider, tools=tools + messages, system, kwargs, model=model, custom_llm_provider=custom_llm_provider, tools=tools, api_base=api_base ) original_stream: Final = stream or kwargs.get("_websearch_interception_converted_stream", False) @@ -341,7 +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 @@ -351,13 +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: @@ -413,7 +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) @@ -422,7 +463,7 @@ def anthropic_messages_handler( ) messages, system = AnthropicCacheControlHook.maybe_inject_cache_control( - messages, system, kwargs, model=model, custom_llm_provider=custom_llm_provider, tools=tools + messages, system, kwargs, model=model, custom_llm_provider=custom_llm_provider, tools=tools, api_base=api_base ) metadata = validate_anthropic_api_metadata(metadata) @@ -533,7 +574,7 @@ def anthropic_messages_handler( _shared_kwargs: Final = dict( max_tokens=max_tokens, messages=messages, - model=model, + model=original_model, metadata=metadata, stop_sequences=stop_sequences, stream=stream, @@ -551,7 +592,7 @@ def anthropic_messages_handler( custom_llm_provider=custom_llm_provider, **kwargs, ) - if _should_route_to_responses_api(custom_llm_provider): + 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 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..a282d5f4d4f 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 @@ -11,9 +11,11 @@ from typing_extensions import TypedDict 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 @@ -33,26 +35,239 @@ 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_error_event_payload(chunk: object) -> Mapping[str, object] | None: + if isinstance(chunk, dict): + return chunk if chunk.get("type") == "error" 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") == "error" + ), + None, + ) + return 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 _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 +312,10 @@ 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 +342,7 @@ 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 +354,26 @@ 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, @@ -214,7 +438,7 @@ class BaseAnthropicMessagesStreamingIterator: # 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) + await self._handle_streaming_logging(collected_chunks, stream_teardown=True) raise if not saw_terminal_event: diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index 4d3354c58b7..ebd514c2605 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -1,4 +1,4 @@ -from collections.abc import AsyncIterator +from collections.abc import AsyncIterator, Mapping, Sequence from typing import Any, Final import httpx @@ -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 ( @@ -159,8 +160,61 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): def _is_system_role_message(message: Any) -> bool: return isinstance(message, dict) and message.get("role") == "system" + _CONVERTED_SYSTEM_NOTE: Final = ( + "Operator note (not from the user): the following was originally a mid-conversation system-role reminder." + ) + + def _system_role_message_as_user(self, message: Mapping) -> Mapping: + return { + "role": "user", + "content": self._as_system_content_blocks(self._CONVERTED_SYSTEM_NOTE) + + self._as_system_content_blocks(message.get("content")), + } + + @staticmethod + def _opens_with_tool_results(message: object) -> bool: + if not isinstance(message, dict) or message.get("role") != "user": + return False + content: Final = message.get("content") + return ( + isinstance(content, list) + and len(content) > 0 + and isinstance(content[0], dict) + and content[0].get("type") == "tool_result" + ) + + def _system_run_before(self, messages: Sequence, index: int) -> Sequence: + start: Final = next( + (j + 1 for j in range(index - 1, -1, -1) if not self._is_system_role_message(messages[j])), + 0, + ) + return messages[start:index] + + def _system_run_end(self, messages: Sequence, index: int) -> int: + return next( + (j for j in range(index, len(messages)) if not self._is_system_role_message(messages[j])), + len(messages), + ) + + def _reordered_around_tool_results(self, messages: Sequence, index: int) -> tuple: + message: Final = messages[index] + if self._opens_with_tool_results(message): + return (message, *self._system_run_before(messages, index)) + if not self._is_system_role_message(message): + return (message,) + run_end: Final = self._system_run_end(messages, index) + follower: Final = messages[run_end] if run_end < len(messages) else None + return () if self._opens_with_tool_results(follower) else (message,) + + def _system_turns_after_tool_results(self, messages: Sequence) -> tuple: + return tuple( + message + for index in range(len(messages)) + for message in self._reordered_around_tool_results(messages, index) + ) + def _normalize_system_role_messages(self, anthropic_messages_request: dict, model: str) -> None: - """Move ``role: "system"`` entries out of ``messages`` per the Anthropic + """Normalize ``role: "system"`` entries in ``messages`` per the Anthropic ``/v1/messages`` contract, which the first-party API, Bedrock Invoke, Vertex, and Azure Foundry all enforce identically. @@ -173,9 +227,18 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): stay: hoisting one mutates the ``system`` prefix and invalidates the prompt cache for the whole message history. Older Claude models reject the role in every position ("role 'system' is not supported on this model"), - so without the flag every system entry is hoisted to keep the request from - 400-ing. Billing-header system blocks are stripped from the top-level - ``system`` field regardless of whether anything was hoisted. + so without the flag a mid-conversation entry is converted to a user turn + in place (prefixed with an operator note) rather than hoisted: hoisting + would mutate the ``system`` prefix and likewise collapse the cache, while + the in-place conversion keeps everything before it byte-identical. Like + the hoist, the conversion carries only the entry's content. A run of + entries wedged between an assistant ``tool_use`` turn and its + ``tool_result`` turn is placed after that turn instead, since a user + turn in between would split the tool call from its result ("tool_use + ids were found without tool_result blocks immediately after") while + consecutive user turns merge upstream. + Billing-header system blocks are stripped from the top-level ``system`` + field regardless of whether anything was hoisted. Subclasses whose upstream rejects the role opt in by calling this from their ``transform_anthropic_messages_request``; the first-party Anthropic @@ -185,21 +248,24 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): messages: Final = anthropic_messages_request.get("messages") if not isinstance(messages, list): return - if _supports_factory( - model=model, - custom_llm_provider=self.custom_llm_provider, - key="supports_mid_conversation_system", - ): - leading_count: Final = next( - (i for i, m in enumerate(messages) if not self._is_system_role_message(m)), - len(messages), + leading_count: Final = next( + (i for i, m in enumerate(messages) if not self._is_system_role_message(m)), + len(messages), + ) + hoisted: Final = messages[:leading_count] + remaining: Final = ( + messages[leading_count:] + if _supports_factory( + model=model, + custom_llm_provider=self.custom_llm_provider, + key="supports_mid_conversation_system", ) - hoisted = messages[:leading_count] - remaining = messages[leading_count:] - else: - hoisted = [m for m in messages if self._is_system_role_message(m)] - remaining = [m for m in messages if not self._is_system_role_message(m)] - if hoisted: + else [ + self._system_role_message_as_user(m) if self._is_system_role_message(m) else m + for m in self._system_turns_after_tool_results(messages[leading_count:]) + ] + ) + if hoisted or remaining != messages: anthropic_messages_request["messages"] = remaining system_content: Final = [ block @@ -242,10 +308,20 @@ 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: @@ -314,13 +390,19 @@ 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 @@ -503,6 +585,12 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): custom_llm_provider=self._resolved_provider, ) + AnthropicModelInfo.maybe_drop_disabled_thinking( + model=model, + optional_params=anthropic_messages_optional_request_params, + custom_llm_provider=self._resolved_provider, + ) + self._translate_legacy_thinking_for_adaptive_model( model=model, optional_params=anthropic_messages_optional_request_params, diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py index d0dc5d527fe..02d82887dde 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/utils.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/utils.py @@ -44,6 +44,7 @@ class AnthropicMessagesRequestUtils: filtered_params: Final = {k: v for k, v in params.items() if k in valid_keys and v is not None} if model is not None: from litellm.llms.anthropic.chat.transformation import AnthropicConfig + from litellm.llms.anthropic.common_utils import AnthropicModelInfo AnthropicConfig._maybe_drop_speed_param( model=model, @@ -51,6 +52,16 @@ class AnthropicMessagesRequestUtils: drop_params=drop_params, custom_llm_provider=custom_llm_provider, ) + for param in ("temperature", "top_p", "top_k"): + if param in filtered_params: + AnthropicModelInfo._apply_sampling_param( # pyright: ignore[reportPrivateUsage] # same gating the /chat/completions path applies; forking it would drift + optional_params=filtered_params, + model=model, + param=param, + value=filtered_params.pop(param), + drop_params=drop_params, + output_key=param, + ) return cast(AnthropicMessagesRequestOptionalParams, filtered_params) 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 d99c7f556fb..6e5ac7221d0 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py @@ -19,6 +19,7 @@ from litellm.types.llms.anthropic_messages.anthropic_response import ( ) from litellm.types.llms.openai import ResponsesAPIResponse +from ..utils import local_model_name from .streaming_iterator import AnthropicResponsesStreamWrapper from .transformation import LiteLLMAnthropicToResponsesAPIAdapter @@ -105,7 +106,8 @@ def _build_responses_kwargs( # Forward litellm-specific kwargs (api_key, api_base, logging obj, etc.) excluded: Final = {"anthropic_messages"} - for key, value in _forwarded_kwargs(extra_kwargs).items(): + 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 ( Logging as LiteLLMLoggingObject, @@ -121,6 +123,10 @@ def _build_responses_kwargs( elif key not in excluded and key not in responses_kwargs and value is not None: responses_kwargs[key] = value + explicit_prompt_cache_key: Final = forwarded_kwargs.get("prompt_cache_key") + if explicit_prompt_cache_key is not None: + responses_kwargs["prompt_cache_key"] = explicit_prompt_cache_key + return responses_kwargs @@ -174,7 +180,9 @@ class LiteLLMMessagesToResponsesAPIHandler: result: Final = await litellm.aresponses(**responses_kwargs) if stream: - wrapper: Final = AnthropicResponsesStreamWrapper(responses_stream=result, model=model) + wrapper: Final = AnthropicResponsesStreamWrapper( + responses_stream=result, model=local_model_name(model, kwargs.get("custom_llm_provider")) + ) return wrapper.async_anthropic_sse_wrapper() if not isinstance(result, ResponsesAPIResponse): @@ -252,7 +260,9 @@ class LiteLLMMessagesToResponsesAPIHandler: result: Final = litellm.responses(**responses_kwargs) if stream: - wrapper: Final = AnthropicResponsesStreamWrapper(responses_stream=result, model=model) + wrapper: Final = AnthropicResponsesStreamWrapper( + responses_stream=result, model=local_model_name(model, kwargs.get("custom_llm_provider")) + ) return wrapper.async_anthropic_sse_wrapper() if not isinstance(result, ResponsesAPIResponse): 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 21a8cb9501e..ace7fc25dc9 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -6,18 +6,22 @@ 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 ( reasoning_effort_from_thinking_budget, ) from litellm.llms.anthropic.experimental_pass_through.utils import ( is_reasoning_auto_summary_enabled, + prompt_cache_key_from_user_id, ) from litellm.types.llms.anthropic import ( AllAnthropicPassThroughMessageValues, @@ -34,7 +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: @@ -79,10 +87,55 @@ 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], - ) -> list[dict[str, str]]: # mutable-ok: API message payload + ) -> list[dict[str, object]]: # mutable-ok: API message payload """Convert in-sequence system content to Responses input-text parts.""" if isinstance(content, str): return ( @@ -91,11 +144,65 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if not isinstance(content, list): return [] # mutable-ok: API message payload return [ # mutable-ok: API message payload - {"type": "input_text", "text": text} # mutable-ok: API message payload + with_prompt_cache_breakpoint( + {"type": "input_text", "text": text}, block.get("prompt_cache_breakpoint") + ) # mutable-ok: API message payload for block in content 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, Any]]) -> 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, Any], ...] + ) -> 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], @@ -107,8 +214,10 @@ 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]]] = [] @@ -146,14 +255,39 @@ class LiteLLMAnthropicToResponsesAPIAdapter: continue btype = block.get("type") if btype == "text": - user_parts.append({"type": "input_text", "text": block.get("text", "")}) + user_parts.append( + with_prompt_cache_breakpoint( + {"type": "input_text", "text": block.get("text", "")}, + block.get("prompt_cache_breakpoint"), + ) + ) elif btype == "image": url = self._translate_anthropic_image_source_to_url(cast(dict, block.get("source", {}))) if url: - user_parts.append({"type": "input_image", "image_url": url}) + user_parts.append( + with_prompt_cache_breakpoint( + {"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): @@ -186,7 +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: @@ -220,27 +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( { @@ -376,19 +500,36 @@ class LiteLLMAnthropicToResponsesAPIAdapter: anthropic_request["messages"], ) + input_items: Final = self.translate_messages_to_responses_input(messages_list) + system: Final = anthropic_request.get("system") + developer_parts: Final = ( + self._translate_midturn_system_content_to_responses(system) + if isinstance(system, list) + and any(isinstance(block, dict) and block.get("prompt_cache_breakpoint") is not None for block in system) + else () + ) + if developer_parts: + input_items.insert( + 0, + { # mutable-ok: API message payload + "type": "message", + "role": "developer", + "content": developer_parts, + }, + ) + responses_kwargs: Final[dict[str, Any]] = { "model": model, - "input": self.translate_messages_to_responses_input(messages_list), + "input": input_items, } - # system -> instructions - system: Final = anthropic_request.get("system") - if system: + if system and not developer_parts: if isinstance(system, str): responses_kwargs["instructions"] = system elif isinstance(system, list): - text_parts = [b.get("text", "") for b in system if isinstance(b, dict) and b.get("type") == "text"] - responses_kwargs["instructions"] = "\n".join(filter(None, text_parts)) + responses_kwargs["instructions"] = "\n".join( + filter(None, (b.get("text", "") for b in system if isinstance(b, dict) and b.get("type") == "text")) + ) # max_tokens -> max_output_tokens max_tokens: Final = anthropic_request.get("max_tokens") @@ -441,7 +582,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: "type": "json_schema", "name": "structured_output", "schema": schema, - "strict": True, + "strict": output_format.get("strict", False), } } @@ -452,10 +593,13 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if openai_cm is not None: responses_kwargs["context_management"] = openai_cm - # metadata user_id -> user + # metadata user_id -> user and prompt_cache_key metadata: Final = anthropic_request.get("metadata") if isinstance(metadata, dict) and "user_id" in metadata: responses_kwargs["user"] = str(metadata["user_id"])[:64] + prompt_cache_key: Final = prompt_cache_key_from_user_id(metadata["user_id"]) + if prompt_cache_key is not None: + responses_kwargs["prompt_cache_key"] = prompt_cache_key return responses_kwargs @@ -481,16 +625,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: 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: @@ -522,6 +657,12 @@ 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 46091cd89a2..716a4f54778 100644 --- a/litellm/llms/anthropic/experimental_pass_through/utils.py +++ b/litellm/llms/anthropic/experimental_pass_through/utils.py @@ -1,8 +1,33 @@ import os +from collections.abc import Mapping +from types import MappingProxyType +from typing import Final import litellm 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: + return None + return str(user_id)[:OPENAI_MAX_PROMPT_CACHE_KEY_LENGTH] or None + + +def local_model_name(model: str, custom_llm_provider: object) -> str: + """The id the provider itself knows, for reporting back to the caller in ``message_start``.""" + return model.removeprefix(f"{custom_llm_provider}/") if isinstance(custom_llm_provider, str) else model + def is_reasoning_auto_summary_enabled() -> bool: """Check whether the default 'summary: detailed' injection is enabled (opt-in).""" @@ -14,38 +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..5fdf2ceff7f 100644 --- a/litellm/llms/anthropic/files/handler.py +++ b/litellm/llms/anthropic/files/handler.py @@ -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: diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index c8f94b575ad..980b27cda55 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -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, @@ -1352,6 +1352,7 @@ 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 +1374,7 @@ 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 +1389,15 @@ 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 +1419,7 @@ 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 +1433,15 @@ 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..30fc3635d9d 100644 --- a/litellm/llms/azure/chat/gpt_transformation.py +++ b/litellm/llms/azure/chat/gpt_transformation.py @@ -4,6 +4,7 @@ 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, ) from litellm.litellm_core_utils.prompt_templates.factory import ( @@ -252,7 +253,8 @@ 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, diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py index b77ba2f9460..2c34851d275 100644 --- a/litellm/llms/azure/common_utils.py +++ b/litellm/llms/azure/common_utils.py @@ -3,6 +3,7 @@ import hashlib import json import os from collections.abc import Callable, Mapping +from functools import lru_cache from typing import Any, Final, Literal, NamedTuple, cast import httpx @@ -75,6 +76,24 @@ 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 +112,6 @@ 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 +137,13 @@ 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) 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/azure_model_router/transformation.py b/litellm/llms/azure_ai/azure_model_router/transformation.py index 1a924088390..61cbc213b11 100644 --- a/litellm/llms/azure_ai/azure_model_router/transformation.py +++ b/litellm/llms/azure_ai/azure_model_router/transformation.py @@ -65,15 +65,24 @@ 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 +95,15 @@ 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..9e7161120cc 100644 --- a/litellm/llms/azure_ai/chat/transformation.py +++ b/litellm/llms/azure_ai/chat/transformation.py @@ -30,7 +30,12 @@ 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 +178,8 @@ 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. 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/ocr/document_intelligence/transformation.py b/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py index e7b94b3812b..a0e427eab9a 100644 --- a/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py +++ b/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py @@ -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, @@ -236,17 +237,13 @@ 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 +254,12 @@ 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, } 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/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..da1776d8dc7 100644 --- a/litellm/llms/base_llm/audio_transcription/transformation.py +++ b/litellm/llms/base_llm/audio_transcription/transformation.py @@ -40,6 +40,16 @@ 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, diff --git a/litellm/llms/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py index d147063df73..11c763ceb9a 100644 --- a/litellm/llms/base_llm/chat/transformation.py +++ b/litellm/llms/base_llm/chat/transformation.py @@ -46,8 +46,10 @@ 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: diff --git a/litellm/llms/base_llm/files/transformation.py b/litellm/llms/base_llm/files/transformation.py index 174be93448b..b20fe0f1560 100644 --- a/litellm/llms/base_llm/files/transformation.py +++ b/litellm/llms/base_llm/files/transformation.py @@ -11,9 +11,9 @@ from litellm.types.llms.openai import ( AllMessageValues, CreateFileRequest, FileContentRequest, + FileListPage, OpenAICreateFileRequestOptionalParams, OpenAIFileObject, - OpenAIFilesPurpose, ) from litellm.types.utils import LlmProviders, ModelResponse @@ -240,10 +240,13 @@ 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/utils.py b/litellm/llms/base_llm/guardrail_translation/utils.py index 1546adbb0bd..f09ee210e6c 100644 --- a/litellm/llms/base_llm/guardrail_translation/utils.py +++ b/litellm/llms/base_llm/guardrail_translation/utils.py @@ -158,6 +158,22 @@ 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 +225,20 @@ 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/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/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..4332848e545 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -1434,9 +1434,12 @@ class BaseAWSLLM: 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") diff --git a/litellm/llms/bedrock/batches/handler.py b/litellm/llms/bedrock/batches/handler.py index 6efdd17f98d..60d5c9533a1 100644 --- a/litellm/llms/bedrock/batches/handler.py +++ b/litellm/llms/bedrock/batches/handler.py @@ -1,3 +1,4 @@ +from collections.abc import Mapping from datetime import datetime from typing import TYPE_CHECKING, Any, Final, cast @@ -68,6 +69,19 @@ 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 +285,11 @@ 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 @@ -386,7 +400,7 @@ 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/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index 25e544f4521..ca5f1298360 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -14,6 +14,8 @@ from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, get_async_httpx_client, ) +from litellm.rust_bridge import chat_completions as rust_chat_completions_bridge +from litellm.rust_bridge.chat_completions import rust_chat_completions_accepts from litellm.types.utils import ModelResponse from litellm.utils import CustomStreamWrapper @@ -33,7 +35,7 @@ def make_sync_call( json_mode: bool | None = False, fake_stream: bool = False, stream_chunk_size: int | None = None, -): +) -> tuple[Any, httpx.Headers]: if client is None: client = _get_httpx_client() # Create a new client if none provided @@ -74,7 +76,7 @@ def make_sync_call( additional_args={"complete_input_dict": data}, ) - return completion_stream + return completion_stream, response.headers class BedrockConverseLLM(BaseAWSLLM): @@ -132,7 +134,7 @@ class BedrockConverseLLM(BaseAWSLLM): }, ) - completion_stream: Final = await make_call( + completion_stream, response_headers = await make_call( client=client, api_base=api_base, headers=dict(prepped.headers), @@ -149,6 +151,7 @@ class BedrockConverseLLM(BaseAWSLLM): model=model, custom_llm_provider="bedrock", logging_obj=logging_obj, + _response_headers=response_headers, ) return streaming_response @@ -169,6 +172,7 @@ class BedrockConverseLLM(BaseAWSLLM): headers: dict = {}, client: AsyncHTTPHandler | None = None, api_key: str | None = None, + skip_pre_call_logging: bool = False, ) -> ModelResponse | CustomStreamWrapper: request_data: Final = await litellm.AmazonConverseConfig()._async_transform_request( model=model, @@ -190,15 +194,19 @@ class BedrockConverseLLM(BaseAWSLLM): ) ## LOGGING - logging_obj.pre_call( - input=messages, - api_key="", - additional_args={ - "complete_input_dict": data, - "api_base": api_base, - "headers": prepped.headers, - }, - ) + # The Rust path already logged this request's pre_call before handing + # it here, and it only declines before the provider is called, so this + # is the same attempt continuing rather than a second one. + if not skip_pre_call_logging: + logging_obj.pre_call( + input=messages, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": api_base, + "headers": prepped.headers, + }, + ) headers = dict(prepped.headers) if client is None or not isinstance(client, AsyncHTTPHandler): @@ -225,7 +233,7 @@ class BedrockConverseLLM(BaseAWSLLM): except httpx.TimeoutException: raise BedrockError(status_code=408, message="Timeout error occurred.") - return litellm.AmazonConverseConfig()._transform_response( + transformed_response: Final = litellm.AmazonConverseConfig()._transform_response( model=model, response=response, model_response=model_response, @@ -237,6 +245,8 @@ class BedrockConverseLLM(BaseAWSLLM): optional_params=optional_params, encoding=encoding, ) + transformed_response.set_provider_response_headers(response.headers) + return transformed_response def completion( self, @@ -354,6 +364,94 @@ class BedrockConverseLLM(BaseAWSLLM): # Filter beta headers in HTTP headers before making the request headers = update_headers_with_filtered_beta(headers=headers, provider="bedrock_converse") + + # 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. + 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 + }, + } + serves_via_rust: Final = rust_chat_completions_accepts( + model=model, + messages=messages, + optional_params=rust_optional_params, + custom_llm_provider="bedrock", + litellm_params=litellm_params, + stream=stream, + ) + if serves_via_rust: + rust_logging_args: Final = { # mutable-ok: logging callbacks read additional_args as a plain dict + "complete_input_dict": { # mutable-ok: same, and it is serialized alongside its parent + "messages": messages, + **optional_params, + }, + "api_base": proxy_endpoint_url, + "headers": headers, + } + logging_obj.pre_call(input=messages, api_key="", additional_args=rust_logging_args) + log_rust_post_call: Final = rust_chat_completions_bridge.response_logger( + logging_obj=logging_obj, + messages=messages, + api_key="", + additional_args=rust_logging_args, + ) + if acompletion: + return rust_chat_completions_bridge.achat_completions_or_fallback( + model=model, + messages=messages, + optional_params=rust_optional_params, + model_response=model_response, + api_key=api_key, + api_base=proxy_endpoint_url, + custom_llm_provider="bedrock", + extra_headers=headers, + timeout=timeout, + on_response=log_rust_post_call, + python_fallback=lambda: self.async_completion( + model=model, + messages=messages, + api_base=proxy_endpoint_url, + model_response=model_response, + encoding=encoding, + logging_obj=logging_obj, + optional_params=optional_params, + stream=stream, + litellm_params=litellm_params, + logger_fn=logger_fn, + headers=headers, + timeout=timeout, + client=client, + credentials=credentials, + api_key=api_key, + skip_pre_call_logging=True, + ), + ) + rust_response: Final = rust_chat_completions_bridge.chat_completions( + model=model, + messages=messages, + optional_params=rust_optional_params, + model_response=model_response, + api_key=api_key, + api_base=proxy_endpoint_url, + custom_llm_provider="bedrock", + extra_headers=headers, + timeout=timeout, + on_response=log_rust_post_call, + ) + if rust_response is not None: + return rust_response + ### ROUTING (ASYNC, STREAMING, SYNC) if acompletion: if isinstance(client, HTTPHandler): @@ -420,15 +518,21 @@ class BedrockConverseLLM(BaseAWSLLM): ) ## LOGGING - logging_obj.pre_call( - input=messages, - api_key="", - additional_args={ - "complete_input_dict": data, - "api_base": proxy_endpoint_url, - "headers": prepped.headers, - }, - ) + # Reaching here with `serves_via_rust` set means the synchronous Rust + # attempt declined at call time, before the provider was called, and + # already logged this request. That is the same attempt continuing. + # The asynchronous branch above returns before this point, and hands + # its own fallback `skip_pre_call_logging=True` for the same reason. + if not serves_via_rust: + logging_obj.pre_call( + input=messages, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": proxy_endpoint_url, + "headers": prepped.headers, + }, + ) if client is None or isinstance(client, AsyncHTTPHandler): _params: Final = {} if timeout is not None: @@ -440,7 +544,7 @@ class BedrockConverseLLM(BaseAWSLLM): client = client if stream is not None and stream is True: - completion_stream: Final = make_sync_call( + completion_stream, response_headers = make_sync_call( client=(client if client is not None and isinstance(client, HTTPHandler) else None), api_base=proxy_endpoint_url, headers=prepped.headers, @@ -457,6 +561,7 @@ class BedrockConverseLLM(BaseAWSLLM): model=model, custom_llm_provider="bedrock", logging_obj=logging_obj, + _response_headers=response_headers, ) return streaming_response @@ -477,7 +582,7 @@ class BedrockConverseLLM(BaseAWSLLM): except httpx.TimeoutException: raise BedrockError(status_code=408, message="Timeout error occurred.") - return litellm.AmazonConverseConfig()._transform_response( + sync_transformed_response: Final = litellm.AmazonConverseConfig()._transform_response( model=model, response=response, model_response=model_response, @@ -489,3 +594,5 @@ class BedrockConverseLLM(BaseAWSLLM): optional_params=optional_params, encoding=encoding, ) + sync_transformed_response.set_provider_response_headers(response.headers) + return sync_transformed_response diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 4dd3f802638..d0d97b41692 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -39,6 +39,7 @@ from litellm.llms.anthropic.chat.transformation import ( REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT, AnthropicConfig, ) +from litellm.llms.anthropic.common_utils import AnthropicModelInfo from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.llms.bedrock.request_metadata import ( bedrock_request_metadata_headers, @@ -64,6 +65,7 @@ from litellm.types.llms.openai import ( OpenAIMessageContentListBlock, ) from litellm.types.utils import ( + CacheCreationTokenDetails, ChatCompletionMessageToolCall, CompletionTokensDetailsWrapper, Function, @@ -417,12 +419,16 @@ 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) @@ -554,7 +560,7 @@ 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) @@ -902,7 +908,7 @@ 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" @@ -1537,6 +1543,7 @@ 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 """ @@ -1571,6 +1578,12 @@ class AmazonConverseConfig(BaseConfig): "has no thinking_blocks. The model won't use extended thinking for this turn." ) + AnthropicModelInfo.maybe_drop_disabled_thinking( + model=model, + optional_params=optional_params, + custom_llm_provider="bedrock", + ) + # Prepare and separate parameters ( inference_params, @@ -1593,6 +1606,16 @@ class AmazonConverseConfig(BaseConfig): 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 @@ -1610,6 +1633,8 @@ class AmazonConverseConfig(BaseConfig): } 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 @@ -1653,6 +1678,7 @@ 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( @@ -1712,6 +1738,7 @@ class AmazonConverseConfig(BaseConfig): messages=messages, headers=headers, drop_params=litellm_params.get("drop_params") is True, + litellm_params=litellm_params, ) ## TRANSFORMATION ## @@ -1794,6 +1821,37 @@ 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.""" @@ -1835,6 +1893,7 @@ 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"] @@ -1853,11 +1912,17 @@ 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, @@ -2265,6 +2330,9 @@ 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_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 2a125e38a82..fc34e403beb 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -163,7 +163,7 @@ async def make_call( json_mode: bool | None = False, bedrock_invoke_provider: litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL | None = None, stream_chunk_size: int | None = None, -): +) -> tuple[Any, httpx.Headers]: try: if client is None: client = get_async_httpx_client( @@ -225,7 +225,7 @@ async def make_call( additional_args={"complete_input_dict": data}, ) - return completion_stream + return completion_stream, response.headers except httpx.HTTPStatusError as err: error_code: Final = err.response.status_code raise BedrockError(status_code=error_code, message=err.response.text) @@ -248,7 +248,7 @@ def make_sync_call( json_mode: bool | None = False, bedrock_invoke_provider: litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL | None = None, stream_chunk_size: int | None = None, -): +) -> tuple[Any, httpx.Headers]: try: if client is None: client = _get_httpx_client( @@ -309,7 +309,7 @@ def make_sync_call( additional_args={"complete_input_dict": data}, ) - return completion_stream + return completion_stream, response.headers except httpx.HTTPStatusError as err: error_code: Final = err.response.status_code raise BedrockError(status_code=error_code, message=err.response.text) @@ -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/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py index 76f91aa9115..333326a766b 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py @@ -1,7 +1,6 @@ import copy import json import time -from functools import partial from typing import TYPE_CHECKING, Any, Final, cast, get_args import httpx @@ -446,24 +445,24 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): json_mode: bool | None = None, signed_json_body: bytes | None = None, ) -> CustomStreamWrapper: + completion_stream, response_headers = await make_call( + client=client, + api_base=api_base, + headers=headers, + data=json.dumps(data), + model=model, + messages=messages, + logging_obj=logging_obj, + fake_stream=True if "ai21" in api_base else False, + bedrock_invoke_provider=self.get_bedrock_invoke_provider(model), + json_mode=json_mode, + ) streaming_response: Final = CustomStreamWrapper( - completion_stream=None, - make_call=partial( - make_call, - client=client, - api_base=api_base, - headers=headers, - data=json.dumps(data), - model=model, - messages=messages, - logging_obj=logging_obj, - fake_stream=True if "ai21" in api_base else False, - bedrock_invoke_provider=self.get_bedrock_invoke_provider(model), - json_mode=json_mode, - ), + completion_stream=completion_stream, model=model, custom_llm_provider="bedrock", logging_obj=logging_obj, + _response_headers=response_headers, ) return streaming_response @@ -481,27 +480,28 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): json_mode: bool | None = None, signed_json_body: bytes | None = None, ) -> CustomStreamWrapper: - if client is None or isinstance(client, AsyncHTTPHandler): - client = _get_httpx_client(params={}) + sync_client: Final = ( + _get_httpx_client(params={}) if client is None or isinstance(client, AsyncHTTPHandler) else client + ) + completion_stream, response_headers = make_sync_call( + client=sync_client, + api_base=api_base, + headers=headers, + data=json.dumps(data), + signed_json_body=signed_json_body, + model=model, + messages=messages, + logging_obj=logging_obj, + fake_stream=True if "ai21" in api_base else False, + bedrock_invoke_provider=self.get_bedrock_invoke_provider(model), + json_mode=json_mode, + ) streaming_response: Final = CustomStreamWrapper( - completion_stream=None, - make_call=partial( - make_sync_call, - client=client, - api_base=api_base, - headers=headers, - data=json.dumps(data), - signed_json_body=signed_json_body, - model=model, - messages=messages, - logging_obj=logging_obj, - fake_stream=True if "ai21" in api_base else False, - bedrock_invoke_provider=self.get_bedrock_invoke_provider(model), - json_mode=json_mode, - ), + completion_stream=completion_stream, model=model, custom_llm_provider="bedrock", logging_obj=logging_obj, + _response_headers=response_headers, ) return streaming_response diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index 082bf7ee2d9..ddc93228b0a 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -58,6 +58,7 @@ 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 +85,7 @@ 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 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/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_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/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/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/custom_httpx/aiohttp_handler.py b/litellm/llms/custom_httpx/aiohttp_handler.py index 9f579fd6f55..e9140e63cb3 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 @@ -18,6 +19,7 @@ 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 @@ -56,7 +58,11 @@ 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 +85,19 @@ 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: diff --git a/litellm/llms/custom_httpx/container_handler.py b/litellm/llms/custom_httpx/container_handler.py index 343cf043187..dd20a8c2ed4 100644 --- a/litellm/llms/custom_httpx/container_handler.py +++ b/litellm/llms/custom_httpx/container_handler.py @@ -11,7 +11,6 @@ from pathlib import Path from typing import TYPE_CHECKING, Any, Final import httpx -from pydantic import BaseModel from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm @@ -66,13 +65,17 @@ class ContainerResponseBody(TypedDict, total=False): error: ReadOnly[ContainerErrorDetail] +_ContainerResponseModel = ContainerFileListResponse | ContainerFileObject | DeleteContainerFileResponse + # Response type mapping -RESPONSE_TYPES: Final[Mapping[str, type[BaseModel]]] = { +RESPONSE_TYPES: Final[Mapping[str, type[_ContainerResponseModel]]] = { "ContainerFileListResponse": ContainerFileListResponse, "ContainerFileObject": ContainerFileObject, "DeleteContainerFileResponse": DeleteContainerFileResponse, } +ContainerEndpointResponse = _ContainerResponseModel | bytes | ContainerResponseBody + def _load_endpoints_config() -> EndpointsConfig: """Load the endpoints configuration from JSON file.""" @@ -90,7 +93,7 @@ def _get_endpoint_config(endpoint_name: str) -> EndpointConfig | None: return None -def _response_model(response_type_name: str) -> type[BaseModel] | 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) @@ -136,6 +139,51 @@ def _build_query_params( 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: + try: + body: Final = response.json() + except ValueError: + return response.text + + if isinstance(body, dict) and isinstance(body.get("error"), dict): + message: Final = body["error"].get("message") + if isinstance(message, str): + return message + + return response.text + + +def _transform_response( + response: httpx.Response, + returns_binary: bool, + response_type_name: str, +) -> ContainerEndpointResponse: + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + if httpx.codes.is_error(response.status_code): + raise BaseLLMException( + status_code=response.status_code, + message=_error_message_from_response(response), + headers=dict(response.headers), + ) + + if returns_binary: + return response.content + + response_json: Final[ContainerResponseBody] = response.json() + if "error" in response_json: + raise BaseLLMException( + status_code=response.status_code, + message=response_json["error"].get("message", str(response_json)), + headers=dict(response.headers), + ) + + response_type: Final = _response_model(response_type_name) + if response_type: + return response_type.model_validate(response_json) + return response_json + + def _prepare_multipart_file_upload( file: Any, headers: dict[str, object], @@ -342,27 +390,11 @@ class GenericContainerHandler: else: raise ValueError(f"Unsupported HTTP method: {method}") - # For binary responses, return raw content - if returns_binary: - return response.content - - # Check for error response - response_json: Final[ContainerResponseBody] = response.json() - if "error" in response_json: - from litellm.llms.base_llm.chat.transformation import BaseLLMException - - error_msg: Final = response_json["error"].get("message", str(response_json)) - raise BaseLLMException( - status_code=response.status_code, - message=error_msg, - headers=dict(response.headers), - ) - - # Parse response - response_type: Final = _response_model(endpoint_config["response_type"]) - if response_type: - return response_type(**response_json) - return response_json + return _transform_response( + response=response, + returns_binary=returns_binary, + response_type_name=endpoint_config["response_type"], + ) except Exception as e: raise e @@ -438,27 +470,11 @@ class GenericContainerHandler: else: raise ValueError(f"Unsupported HTTP method: {method}") - # For binary responses, return raw content - if returns_binary: - return response.content - - # Check for error response - response_json: Final[ContainerResponseBody] = response.json() - if "error" in response_json: - from litellm.llms.base_llm.chat.transformation import BaseLLMException - - error_msg: Final = response_json["error"].get("message", str(response_json)) - raise BaseLLMException( - status_code=response.status_code, - message=error_msg, - headers=dict(response.headers), - ) - - # Parse response - response_type: Final = _response_model(endpoint_config["response_type"]) - if response_type: - return response_type(**response_json) - return response_json + return _transform_response( + response=response, + returns_binary=returns_binary, + response_type_name=endpoint_config["response_type"], + ) except Exception as e: raise e 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 cc522aed1ee..417ecd80be2 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 +20,17 @@ 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 from litellm.llms.base_llm.anthropic_messages.transformation import ( @@ -88,6 +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, @@ -634,6 +646,7 @@ class BaseLLMHTTPHandler: model=model, custom_llm_provider=custom_llm_provider, logging_obj=logging_obj, + _response_headers=headers, ) if client is None or not isinstance(client, HTTPHandler): @@ -797,6 +810,7 @@ class BaseLLMHTTPHandler: model=model, custom_llm_provider=custom_llm_provider, logging_obj=logging_obj, + _response_headers=_response_headers, ) return streamwrapper @@ -1100,6 +1114,7 @@ class BaseLLMHTTPHandler: headers=headers or {}, model=model, optional_params=optional_rerank_params, + litellm_params=litellm_params, ) api_base = provider_config.get_complete_url( @@ -1192,6 +1207,7 @@ 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) @@ -1284,9 +1300,23 @@ 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, @@ -1836,6 +1866,7 @@ class BaseLLMHTTPHandler: return provider_config.transform_search_response( raw_response=response, logging_obj=logging_obj, + optional_params=optional_params, ) async def async_search( @@ -1934,6 +1965,7 @@ 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( @@ -2053,7 +2085,7 @@ class BaseLLMHTTPHandler: # Prepare headers kwargs = kwargs or {} provider_specific_header: Final = cast( - litellm.types.utils.ProviderSpecificHeader | None, + litellm.types.utils.ProviderSpecificHeader | Sequence[litellm.types.utils.ProviderSpecificHeader] | None, kwargs.get("provider_specific_header", None), ) provider_specific_headers: Final = ProviderSpecificHeaderUtils.get_provider_specific_headers( @@ -2254,6 +2286,10 @@ 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, @@ -2264,6 +2300,8 @@ 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, @@ -5074,9 +5112,12 @@ 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: @@ -5108,6 +5149,20 @@ 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, @@ -5119,7 +5174,8 @@ 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 @@ -5127,9 +5183,9 @@ 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 @@ -5139,6 +5195,97 @@ 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, @@ -5491,10 +5638,9 @@ 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), ) @@ -5504,14 +5650,29 @@ 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() @@ -5786,11 +5947,13 @@ 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) @@ -5810,13 +5973,17 @@ 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: @@ -5976,8 +6143,19 @@ class BaseLLMHTTPHandler: await websocket.close(code=e.status_code, reason=_redact_string(str(e))) except Exception as e: verbose_logger.exception("Error connecting to backend: %s", e) + redacted_error: Final = _redact_string(str(e)) try: - await websocket.close(code=1011, reason=_redact_string(f"Internal server error: {e}")) + await websocket.send_text(realtime_error_event(redacted_error, error_type="server_error")) + except Exception: # noqa: BLE001 # best-effort notice: a dead client socket must not skip the close below + verbose_logger.debug("Could not send realtime error event to client; closing anyway") + try: + await websocket.close( + code=1011, + reason=websocket_close_reason( + _redact_string(f"Internal server error: {e}"), + fallback="Internal server error", + ), + ) except RuntimeError as close_error: if "already completed" in str(close_error) or "websocket.close" in str(close_error): # The WebSocket is already closed or the response is completed, so we can ignore this error @@ -6920,9 +7098,7 @@ 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, @@ -6930,9 +7106,14 @@ 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, @@ -7024,20 +7205,26 @@ 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, ) @@ -7697,6 +7884,7 @@ 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, @@ -7708,6 +7896,7 @@ 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, @@ -7761,9 +7950,10 @@ 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, @@ -7782,11 +7972,10 @@ 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( @@ -7806,6 +7995,7 @@ 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, @@ -7857,9 +8047,10 @@ 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, @@ -7878,11 +8069,10 @@ 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( diff --git a/litellm/llms/dashscope/image_generation/transformation.py b/litellm/llms/dashscope/image_generation/transformation.py index 9652a5738c8..e655e2ea87d 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} } """ @@ -46,6 +46,8 @@ 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 +61,8 @@ 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]: @@ -82,8 +85,8 @@ 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 get_complete_url( @@ -95,7 +98,10 @@ 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 image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE def validate_environment( self, diff --git a/litellm/llms/dashscope/rerank/transformation.py b/litellm/llms/dashscope/rerank/transformation.py index 3c7801d4d3c..98be4e4f2e7 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 @@ -85,6 +86,7 @@ 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") 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..e52c56af82b 100644 --- a/litellm/llms/deepinfra/rerank/transformation.py +++ b/litellm/llms/deepinfra/rerank/transformation.py @@ -2,6 +2,7 @@ Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format. """ +from collections.abc import Mapping from typing import Any, Final import httpx @@ -67,6 +68,7 @@ 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") 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/cost_calculator.py b/litellm/llms/fal_ai/cost_calculator.py index 8c5ad5a8c64..74848784c5b 100644 --- a/litellm/llms/fal_ai/cost_calculator.py +++ b/litellm/llms/fal_ai/cost_calculator.py @@ -1,25 +1,75 @@ -from typing import Any, Final +from collections.abc import Mapping +from types import MappingProxyType +from typing import Final import litellm from litellm.types.utils import ImageResponse +FAL_KEYED_PRICING_DEFAULT_QUALITY: Final[str] = "high" +FAL_TEXT_TO_IMAGE_DEFAULT_SIZE: Final[str] = "1024-x-768" +FAL_NAMED_IMAGE_SIZES: Final[Mapping[str, str]] = MappingProxyType( + { + "square_hd": "1024-x-1024", + "square": "512-x-512", + "portrait_4_3": "768-x-1024", + "portrait_16_9": "576-x-1024", + "landscape_4_3": "1024-x-768", + "landscape_16_9": "1024-x-576", + } +) + + +def _keyed_size(model: str, optional_params: Mapping[str, object]) -> str | None: + image_size: Final = optional_params.get("image_size") + if image_size is None: + return None if model.endswith("/edit") else FAL_TEXT_TO_IMAGE_DEFAULT_SIZE + if isinstance(image_size, Mapping): + width: Final = image_size.get("width") + height: Final = image_size.get("height") + if isinstance(width, int) and isinstance(height, int): + return f"{width}-x-{height}" + return None + if isinstance(image_size, str): + return FAL_NAMED_IMAGE_SIZES.get(image_size) + return None + + +def _keyed_cost_per_image(model: str, optional_params: Mapping[str, object] | None) -> float | None: + if optional_params is None: + return None + size: Final = _keyed_size(model=model, optional_params=optional_params) + if size is None: + return None + raw_quality: Final = optional_params.get("quality") + quality: Final = ( + raw_quality if isinstance(raw_quality, str) and raw_quality != "auto" else FAL_KEYED_PRICING_DEFAULT_QUALITY + ) + keyed_entry: Final = litellm.model_cost.get(f"fal_ai/{quality}/{size}/{model}") + if keyed_entry is None: + return None + keyed_cost: Final = keyed_entry.get("output_cost_per_image") + return float(keyed_cost) if isinstance(keyed_cost, (int, float)) else None + def cost_calculator( model: str, - image_response: Any, + image_response: object, + optional_params: Mapping[str, object] | None = None, ) -> float: """ fal.ai image generation cost calculator """ + if not isinstance(image_response, ImageResponse): + raise ValueError(f"image_response must be of type ImageResponse got type={type(image_response)}") + # the proxy cost path passes the provider-prefixed model name + model = model.removeprefix(f"{litellm.LlmProviders.FAL_AI.value}/") + num_images: Final[int] = len(image_response.data) if image_response.data else 0 + keyed_cost_per_image: Final = _keyed_cost_per_image(model=model, optional_params=optional_params) + if keyed_cost_per_image is not None: + return keyed_cost_per_image * num_images _model_info: Final = litellm.get_model_info( model=model, custom_llm_provider=litellm.LlmProviders.FAL_AI.value, ) output_cost_per_image: Final[float] = _model_info.get("output_cost_per_image") or 0.0 - num_images: int = 0 - if isinstance(image_response, ImageResponse): - if image_response.data: - num_images = len(image_response.data) - return output_cost_per_image * num_images - else: - raise ValueError(f"image_response must be of type ImageResponse got type={type(image_response)}") + return output_cost_per_image * num_images diff --git a/litellm/llms/fal_ai/image_generation/__init__.py b/litellm/llms/fal_ai/image_generation/__init__.py index fb38855b35e..2b305c8f234 100644 --- a/litellm/llms/fal_ai/image_generation/__init__.py +++ b/litellm/llms/fal_ai/image_generation/__init__.py @@ -12,6 +12,7 @@ from .bytedance_transformation import ( from .flux_pro_v11_transformation import FalAIFluxProV11Config from .flux_pro_v11_ultra_transformation import FalAIFluxProV11UltraConfig from .flux_schnell_transformation import FalAIFluxSchnellConfig +from .gpt_image_2_transformation import FalAIGPTImage2Config from .ideogram_v3_transformation import FalAIIdeogramV3Config from .imagen4_transformation import FalAIImagen4Config from .nano_banana_transformation import FalAINanoBananaConfig @@ -27,6 +28,7 @@ __all__ = [ "FalAIFluxProV11Config", "FalAIFluxProV11UltraConfig", "FalAIFluxSchnellConfig", + "FalAIGPTImage2Config", "FalAIIdeogramV3Config", "FalAIImageGenerationConfig", "FalAIImagen4Config", @@ -49,7 +51,9 @@ def get_fal_ai_image_generation_config(model: str) -> BaseImageGenerationConfig: model_lower: Final = model.lower() # Map model names to their corresponding configuration classes - if "nano-banana" in model_lower or "gemini-25-flash-image" in model_lower: + if "gpt-image-2" in model_lower: + return FalAIGPTImage2Config() + elif "nano-banana" in model_lower or "gemini-25-flash-image" in model_lower: return FalAINanoBananaConfig() elif "imagen4" in model_lower or "imagen-4" in model_lower: return FalAIImagen4Config() diff --git a/litellm/llms/fal_ai/image_generation/gpt_image_2_transformation.py b/litellm/llms/fal_ai/image_generation/gpt_image_2_transformation.py new file mode 100644 index 00000000000..b91ae8ce2b0 --- /dev/null +++ b/litellm/llms/fal_ai/image_generation/gpt_image_2_transformation.py @@ -0,0 +1,124 @@ +from collections.abc import Mapping +from types import MappingProxyType +from typing import Final + +from typing_extensions import ReadOnly, TypedDict + +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams + +from .transformation import FalAIBaseConfig + + +class FalAIImageSize(TypedDict): + width: ReadOnly[int] + height: ReadOnly[int] + + +SUPPORTED_OPENAI_PARAMS: Final[tuple[OpenAIImageGenerationOptionalParams, ...]] = ( + "n", + "output_format", + "quality", + "response_format", + "size", +) + + +class FalAIGPTImage2Config(FalAIBaseConfig): + """ + Configuration for OpenAI's GPT Image 2 served through Fal AI. + + Model endpoints: + - openai/gpt-image-2 (text-to-image) + - openai/gpt-image-2/edit (editing, with optional mask) + + Documentation: https://fal.ai/models/openai/gpt-image-2/api + """ + + MODEL_PREFIX: Final[str] = "openai/" + SUPPORTED_QUALITIES: Final[frozenset[str]] = frozenset({"auto", "low", "medium", "high"}) + OPENAI_QUALITY_ALIASES: Final[Mapping[str, str]] = MappingProxyType({"hd": "high", "standard": "medium"}) + PARAM_TRANSLATION: Final[Mapping[str, str]] = MappingProxyType( + { + "n": "num_images", + "size": "image_size", + "quality": "quality", + "output_format": "output_format", + } + ) + + 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: + base_url: Final[str] = (api_base or get_secret_str("FAL_AI_API_BASE") or self.DEFAULT_BASE_URL).rstrip("/") + endpoint: Final[str] = model if model.startswith(self.MODEL_PREFIX) else f"{self.MODEL_PREFIX}{model}" + return f"{base_url}/{endpoint}" + + def get_supported_openai_params( # mutable-ok: base class contract returns a list + self, model: str + ) -> list[OpenAIImageGenerationOptionalParams]: + return list(SUPPORTED_OPENAI_PARAMS) # mutable-ok: base class contract returns a list + + def map_openai_params( # mutable-ok: base class contract returns a dict + self, + non_default_params: Mapping[str, object], + optional_params: Mapping[str, object], + model: str, + drop_params: bool, + ) -> dict: + unsupported_params: Final = tuple( + key for key in non_default_params if key not in SUPPORTED_OPENAI_PARAMS and key not in optional_params + ) + if unsupported_params and not drop_params: + raise ValueError( + f"Parameters {unsupported_params} are not supported for model {model}. " + f"Supported parameters are {SUPPORTED_OPENAI_PARAMS}. " + "Set drop_params=True to drop unsupported parameters." + ) + translated_params: Final[Mapping[str, object]] = MappingProxyType( + { + self.PARAM_TRANSLATION[key]: self._translate_value(key, value) + for key, value in non_default_params.items() + if key in self.PARAM_TRANSLATION and self.PARAM_TRANSLATION[key] not in optional_params + } + ) + return {**optional_params, **translated_params} # mutable-ok: base class contract returns a dict + + def _translate_value(self, key: str, value: object) -> object: + if key == "size": + return self._map_image_size(value) + if key == "quality": + return self._map_quality(value) + return value + + def _map_image_size(self, size: object) -> object: + if not isinstance(size, str) or size == "auto": + return size + try: + width, height = (int(part) for part in size.lower().split("x")) + except ValueError: + return size + image_size: Final[FalAIImageSize] = {"width": width, "height": height} + return image_size + + def _map_quality(self, quality: object) -> object: + if not isinstance(quality, str): + return quality + normalized: Final[str] = self.OPENAI_QUALITY_ALIASES.get(quality, quality) + return normalized if normalized in self.SUPPORTED_QUALITIES else "auto" + + def transform_image_generation_request( # mutable-ok: base class contract returns a dict + self, + model: str, + prompt: str, + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + headers: Mapping[str, str], + ) -> dict: + return {"prompt": prompt, **optional_params} # mutable-ok: base class contract returns a dict diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index e64237da978..4e9731ef485 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -504,6 +504,7 @@ 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 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/tests/litellm/llms/azure/__init__.py b/litellm/llms/gemini/audio_transcription/__init__.py similarity index 100% rename from tests/litellm/llms/azure/__init__.py rename to litellm/llms/gemini/audio_transcription/__init__.py 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/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/realtime/transformation.py b/litellm/llms/gemini/realtime/transformation.py index ea576750cf3..367619db37d 100644 --- a/litellm/llms/gemini/realtime/transformation.py +++ b/litellm/llms/gemini/realtime/transformation.py @@ -4,6 +4,7 @@ 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 import litellm @@ -52,6 +53,7 @@ from litellm.types.llms.vertex_ai import ( ) from litellm.types.realtime import ( ALL_DELTA_TYPES, + RealtimeInputAudioTranscriptionUsage, RealtimeModalityResponseTransformOutput, RealtimeResponseTransformInput, RealtimeResponseTypedDict, @@ -72,6 +74,40 @@ 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}) + + +# 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 +117,7 @@ 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 @@ -282,12 +319,7 @@ 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 +398,28 @@ 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[Any]) -> 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") @@ -392,8 +427,6 @@ 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( @@ -425,7 +458,7 @@ 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") @@ -547,9 +580,10 @@ 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, @@ -1140,6 +1174,26 @@ 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 +1233,7 @@ 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 +1243,7 @@ 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 +1280,12 @@ 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 = { @@ -1572,7 +1634,9 @@ 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/videos/transformation.py b/litellm/llms/gemini/videos/transformation.py index db75be3b6a2..6a1fc144c42 100644 --- a/litellm/llms/gemini/videos/transformation.py +++ b/litellm/llms/gemini/videos/transformation.py @@ -566,6 +566,7 @@ class GeminiVideoConfig(BaseVideoConfig): api_base, litellm_params, headers, + video_file=None, extra_body=None, prefetched_source_data=None, ): 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/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/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 df4adf0c9a2..e089b3fecbe 100644 --- a/litellm/llms/infinity/rerank/transformation.py +++ b/litellm/llms/infinity/rerank/transformation.py @@ -4,7 +4,7 @@ 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 Sequence +from collections.abc import Mapping, Sequence from typing import Final import httpx @@ -73,6 +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 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/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..0c95fd4df07 100644 --- a/litellm/llms/mistral/chat/transformation.py +++ b/litellm/llms/mistral/chat/transformation.py @@ -292,7 +292,7 @@ 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 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/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/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..9b7c5a3f857 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -18,6 +18,7 @@ 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, ) @@ -336,7 +337,8 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): self, messages: list[AllMessageValues], model: str, is_async: bool = False ) -> list[AllMessageValues] | Coroutine[Any, Any, 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: 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/openai.py b/litellm/llms/openai/openai.py index 4fc6655ca54..ee0efb88a38 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -22,7 +22,7 @@ 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 @@ -1365,9 +1365,21 @@ 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( @@ -1450,9 +1462,10 @@ 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 +1514,7 @@ 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 +1531,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): project=project, max_retries=max_retries, timeout=timeout, + logging_obj=logging_obj, client=client, shared_session=shared_session, ) @@ -1531,7 +1546,17 @@ 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 +1576,7 @@ 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 +1593,15 @@ 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/transformation.py b/litellm/llms/openai/responses/transformation.py index b2a69564908..2fa44cfc2e3 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -61,6 +61,20 @@ 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 +130,17 @@ 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, 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_like/json_loader.py b/litellm/llms/openai_like/json_loader.py index 38f3866cfc3..5cdaff90d24 100644 --- a/litellm/llms/openai_like/json_loader.py +++ b/litellm/llms/openai_like/json_loader.py @@ -65,6 +65,11 @@ class JSONProviderRegistry: """Check if a provider is defined via JSON""" return slug in cls._providers + @classmethod + def get_by_base_url(cls, base_url: str) -> SimpleProviderConfig | None: + """Get a provider configuration by its default base url""" + return next((provider for provider in cls._providers.values() if provider.base_url == base_url), None) + @classmethod def supports_responses_api(cls, slug: str) -> bool: """Check if a JSON provider supports the Responses API""" diff --git a/litellm/llms/openai_like/providers.json b/litellm/llms/openai_like/providers.json index 164100d4194..a458a209ea9 100644 --- a/litellm/llms/openai_like/providers.json +++ b/litellm/llms/openai_like/providers.json @@ -175,6 +175,11 @@ "base_class": "openai_gpt", "supported_endpoints": ["/v1/chat/completions", "/v1/responses", "/v1/messages"] }, + "cognition": { + "base_url": "https://api.cognition.ai/v1", + "api_key_env": "COGNITION_API_KEY", + "api_base_env": "COGNITION_API_BASE" + }, "pinstripes": { "base_url": "https://pinstripes.io/v1", "api_key_env": "PINSTRIPES_API_KEY", @@ -183,5 +188,17 @@ "max_completion_tokens": "max_tokens" }, "supported_endpoints": ["/v1/chat/completions", "/v1/responses", "/v1/embeddings"] + }, + "scx-ai": { + "base_url": "https://api.scx.ai/v1", + "api_key_env": "SCX_API_KEY", + "api_base_env": "SCX_API_BASE", + "param_mappings": { + "max_completion_tokens": "max_tokens" + }, + "constraints": { + "temperature_max": 1.99 + }, + "supported_endpoints": ["/v1/chat/completions"] } } 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/perplexity/cost_calculator.py b/litellm/llms/perplexity/cost_calculator.py index 337fa8e630d..27835ecbfe8 100644 --- a/litellm/llms/perplexity/cost_calculator.py +++ b/litellm/llms/perplexity/cost_calculator.py @@ -21,14 +21,19 @@ def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ ## USE PRE-CALCULATED COST FROM PERPLEXITY IF AVAILABLE - ## Perplexity returns accurate cost in usage.cost.total_cost including request fees + ## Perplexity returns accurate cost in usage.cost.total_cost including request fees. + ## By the time it reaches here, ResponseAPIUsage.parse_cost has already flattened + ## that dict down to a float, so both shapes must be accepted. cost_info: Final = getattr(usage, "cost", None) - if cost_info is not None and isinstance(cost_info, dict): - total_cost: Final = cost_info.get("total_cost") - if total_cost is not None: - # Return total cost as completion_cost (prompt_cost=0) since Perplexity - # doesn't break down by input/output in their cost object - return (0.0, float(total_cost)) + total_cost: float | None = None + if isinstance(cost_info, dict): + total_cost = cost_info.get("total_cost") + elif isinstance(cost_info, (int, float)) and not isinstance(cost_info, bool): + total_cost = float(cost_info) + if total_cost is not None: + # Return total cost as completion_cost (prompt_cost=0) since Perplexity + # doesn't break down by input/output in their cost object + return (0.0, float(total_cost)) ## FALLBACK: Calculate cost manually if Perplexity doesn't provide it ## GET MODEL INFO diff --git a/litellm/llms/runwayml/videos/transformation.py b/litellm/llms/runwayml/videos/transformation.py index b8e57fa7cc0..8dd77db08c0 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 """ @@ -195,31 +243,36 @@ class RunwayMLVideoConfig(BaseVideoConfig): """ 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 +338,15 @@ 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 @@ -581,8 +636,9 @@ 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 +672,7 @@ class RunwayMLVideoConfig(BaseVideoConfig): api_base, litellm_params, headers, + video_file=None, extra_body=None, prefetched_source_data=None, ): @@ -646,9 +703,7 @@ 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/sagemaker/chat/handler.py b/litellm/llms/sagemaker/chat/handler.py index b3e9ed671fc..79837170624 100644 --- a/litellm/llms/sagemaker/chat/handler.py +++ b/litellm/llms/sagemaker/chat/handler.py @@ -34,6 +34,7 @@ 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 +61,7 @@ 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 diff --git a/litellm/llms/sagemaker/chat/transformation.py b/litellm/llms/sagemaker/chat/transformation.py index 99543e7add1..37ddd813d6f 100644 --- a/litellm/llms/sagemaker/chat/transformation.py +++ b/litellm/llms/sagemaker/chat/transformation.py @@ -54,7 +54,30 @@ class SagemakerChatConfig(OpenAIGPTConfig, BaseAWSLLM): api_key: str | None = None, api_base: str | None = None, ) -> dict: - return headers + inference_component_name: Final = optional_params.get("model_id") + if not isinstance(inference_component_name, str): + return headers + return {**headers, "X-Amzn-SageMaker-Inference-Component": inference_component_name} + + def transform_request( + self, + model: str, + messages: list[AllMessageValues], # mutable-ok: matches the base chat transform signature + optional_params: dict, # mutable-ok: matches the base chat transform signature + litellm_params: dict, # mutable-ok: matches the base chat transform signature + headers: dict, # mutable-ok: matches the base chat transform signature + ) -> dict: # mutable-ok: the handler sends this body straight to httpx + request: Final = super().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) + served_model_name: Final = litellm_params.get("hf_model_name") + if not isinstance(served_model_name, str): + return request + return {**request, "model": served_model_name} def get_complete_url( self, diff --git a/litellm/llms/sagemaker/completion/handler.py b/litellm/llms/sagemaker/completion/handler.py index 84cad56f0d4..91941075c4b 100644 --- a/litellm/llms/sagemaker/completion/handler.py +++ b/litellm/llms/sagemaker/completion/handler.py @@ -57,6 +57,7 @@ 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 +84,7 @@ 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 diff --git a/litellm/llms/soniox/common_utils.py b/litellm/llms/soniox/common_utils.py index 90be94b8133..e2b18736e0e 100644 --- a/litellm/llms/soniox/common_utils.py +++ b/litellm/llms/soniox/common_utils.py @@ -4,6 +4,11 @@ Shared utilities for the Soniox provider (https://soniox.com). from typing import Any, Final +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 # Soniox API base URL. @@ -109,121 +114,13 @@ def render_soniox_tokens(tokens: list[dict[str, Any]]) -> str: 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 _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 _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]]: - """ - 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. - """ - 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 +def _soniox_token_to_subtitle_token(token: dict[str, Any]) -> SubtitleToken: + return SubtitleToken( + text=token.get("text", ""), + start_ms=token.get("start_ms"), + end_ms=token.get("end_ms"), + speaker=token.get("speaker"), + ) def render_soniox_tokens_as_srt(tokens: list[dict[str, Any]]) -> str: @@ -232,20 +129,7 @@ def render_soniox_tokens_as_srt(tokens: list[dict[str, Any]]) -> str: 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(tuple(_soniox_token_to_subtitle_token(token) for token in tokens)) def render_soniox_tokens_as_vtt(tokens: list[dict[str, Any]]) -> str: @@ -254,14 +138,4 @@ def render_soniox_tokens_as_vtt(tokens: list[dict[str, Any]]) -> str: 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(tuple(_soniox_token_to_subtitle_token(token) for token in tokens)) 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/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index 26f797cf5b2..1de2337d8eb 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -1164,21 +1164,31 @@ class VertexAITokenCounter(BaseTokenCounter): original_response=result, ) else: - # Use standard Vertex AI (Gemini) token counter from litellm.llms.vertex_ai.count_tokens.handler import VertexAITokenCounter + from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, # pyright: ignore[reportPrivateUsage] # shared helper already used by gemini/chat, context_caching, and vertex_and_google_ai_studio_gemini + ) + + resolved_contents: Final = ( + contents + if contents is not None + else _gemini_convert_messages_with_history( + messages=messages or [] # mutable-ok: fallback for None messages; helper signature requires list + ) + ) count_tokens_params: Final = { "model": model_to_use, - "contents": contents, + "contents": resolved_contents, } count_tokens_params_request.update(count_tokens_params) result = await VertexAITokenCounter().acount_tokens( **count_tokens_params_request, ) - if result is not None: + if result is not None and "totalTokens" in result: return TokenCountResponse( - total_tokens=result.get("totalTokens", 0), + total_tokens=result["totalTokens"], request_model=request_model, model_used=model_to_use, tokenizer_type=result.get("tokenizer_used", ""), 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/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 f2d318a9ffd..11c026010ee 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -645,10 +645,9 @@ def _collect_tool_call_thought_signatures( the text part as well would send two copies and double-bill the previous turn's reasoning tokens on gemini-3 and newer models. - Detection deliberately calls _get_thought_signature_from_tool without the - model argument: with a gemini-3 model that helper synthesizes a dummy - signature for unsigned tool calls, which must not suppress a real - text-part signature (e.g. replaying gemini-2.5 history to a newer model). + Only real signatures count here; a synthesized placeholder must not + suppress a genuine text-part signature (e.g. replaying gemini-2.5 history + to a newer model). """ signatures: tuple[str, ...] = () 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..d8b1e7ba17c 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, @@ -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/tests/litellm/llms/deepseek/__init__.py b/litellm/llms/vertex_ai/interactions/__init__.py similarity index 100% rename from tests/litellm/llms/deepseek/__init__.py rename to litellm/llms/vertex_ai/interactions/__init__.py 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/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/videos/transformation.py b/litellm/llms/vertex_ai/videos/transformation.py index 16e72e3062d..25db3a673a1 100644 --- a/litellm/llms/vertex_ai/videos/transformation.py +++ b/litellm/llms/vertex_ai/videos/transformation.py @@ -7,11 +7,11 @@ Based on: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-refer import base64 import time -from collections.abc import Sequence +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final, TypedDict, cast import httpx -from httpx._types import RequestFiles +from httpx._types import FileContent, RequestFiles from typing_extensions import ReadOnly from litellm.constants import DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS @@ -677,9 +677,10 @@ 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 +728,7 @@ 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/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/main.py b/litellm/main.py index f0b20eba9b6..cafa1e4718f 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 @@ -75,7 +76,7 @@ from litellm.litellm_core_utils.chat_completion_agentic_loop import ( from litellm.litellm_core_utils.completion_timeout import CompletionTimeout from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.get_litellm_params import ( - AWS_CREDENTIAL_KWARGS_KEYS, + FORWARDED_KWARGS_KEYS, OPTIONAL_KWARGS_KEYS, ) from litellm.litellm_core_utils.get_provider_specific_headers import ( @@ -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, @@ -507,6 +508,7 @@ async def acompletion( custom_llm_provider=cast(str | None, custom_llm_provider), # cast-ok: read from untyped kwargs tools=tools, enable_prompt_caching=cast(bool | None, kwargs.get("enable_prompt_caching")), # cast-ok: untyped kwargs + api_base=kwargs.get("api_base") or base_url, ) if isinstance(litellm_logging_obj, LiteLLMLoggingObj) and ( @@ -529,6 +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, @@ -601,7 +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 @@ -1217,6 +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, ) @@ -1810,6 +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 @@ -4919,7 +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, @@ -5090,14 +5144,16 @@ def completion( model_info: Final = kwargs.get("model_info", None) proxy_server_request: Final = kwargs.get("proxy_server_request", None) fallbacks = kwargs.get("fallbacks", None) - provider_specific_header: Final = cast(ProviderSpecificHeader | None, kwargs.get("provider_specific_header", None)) + provider_specific_header: Final = cast( + ProviderSpecificHeader | Sequence[ProviderSpecificHeader] | None, + kwargs.get("provider_specific_header", None), + ) headers = kwargs.get("headers", None) or extra_headers ensure_alternating_roles: Final[bool | None] = kwargs.get("ensure_alternating_roles", None) user_continue_message: Final[ChatCompletionUserMessage | None] = kwargs.get("user_continue_message", None) assistant_continue_message: ChatCompletionAssistantMessage | None = kwargs.get("assistant_continue_message", None) - if headers is None: - headers = {} + headers = {} if headers is None else dict(headers) if extra_headers is not None: headers.update(extra_headers) # Inject proxy auth headers if configured @@ -5171,6 +5227,7 @@ def completion( custom_llm_provider=cast(str | None, kwargs.get("custom_llm_provider")), # cast-ok: untyped kwargs tools=tools, enable_prompt_caching=cast(bool | None, kwargs.get("enable_prompt_caching")), # cast-ok: untyped kwargs + api_base=kwargs.get("api_base") or base_url, ) if isinstance(litellm_logging_obj, LiteLLMLoggingObj) and ( @@ -5190,6 +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 ### @@ -5449,7 +5507,7 @@ def completion( tpm=kwargs.get("tpm"), rpm=kwargs.get("rpm"), use_xai_oauth=kwargs.get("use_xai_oauth", False), - **{key: kwargs[key] for key in AWS_CREDENTIAL_KWARGS_KEYS if key in kwargs}, + **{key: kwargs[key] for key in FORWARDED_KWARGS_KEYS if key in kwargs}, ) cast(LiteLLMLoggingObj, logging).update_environment_variables( model=model, @@ -5596,6 +5654,8 @@ 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) @@ -5645,7 +5705,6 @@ 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" @@ -5695,14 +5754,6 @@ 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" @@ -5972,7 +6023,7 @@ def embedding( # Optional params dimensions: int | None = None, encoding_format: str | None = None, - timeout=600, # default to 10 minutes + timeout: float = 600, # default to 10 minutes # set api_base, api_version, api_key api_base: str | None = None, api_version: str | None = None, @@ -5998,7 +6049,7 @@ def embedding( # Optional params dimensions: int | None = None, encoding_format: str | None = None, - timeout=600, # default to 10 minutes + timeout: float = 600, # default to 10 minutes # set api_base, api_version, api_key api_base: str | None = None, api_version: str | None = None, @@ -6025,7 +6076,7 @@ def embedding( # Optional params dimensions: int | None = None, encoding_format: str | None = None, - timeout=600, # default to 10 minutes + timeout: float = 600, # default to 10 minutes # set api_base, api_version, api_key api_base: str | None = None, api_version: str | None = None, @@ -6824,6 +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 @@ -6833,8 +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 @@ -7533,6 +7586,15 @@ 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) @@ -7954,7 +8016,7 @@ 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( @@ -8038,6 +8100,7 @@ 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, @@ -8116,6 +8179,7 @@ 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, @@ -8526,6 +8590,47 @@ 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 = getattr(response, "model", None) + usage: Final = getattr(response, "usage", None) + if not isinstance(model_name, str) or 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 stream_chunk_builder( chunks: list, messages: list | None = None, @@ -8551,7 +8656,7 @@ 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 @@ -8566,7 +8671,7 @@ 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] @@ -8626,13 +8731,15 @@ def stream_chunk_builder( "cost", logging_obj._response_cost_calculator(result=response), ) + _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 ] @@ -8646,7 +8753,7 @@ 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 ] @@ -8659,7 +8766,7 @@ 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 ] @@ -8670,7 +8777,7 @@ 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 ] @@ -8683,7 +8790,7 @@ 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 ] @@ -8696,7 +8803,7 @@ 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 ] @@ -8713,7 +8820,7 @@ 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 ] @@ -8727,7 +8834,7 @@ 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 ] @@ -8744,24 +8851,32 @@ 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]) @@ -8798,6 +8913,8 @@ def stream_chunk_builder( if litellm.include_cost_in_streaming_usage and logging_obj is not None: setattr(usage, "cost", logging_obj._response_cost_calculator(result=response)) + _set_stream_builder_response_cost(response, logging_obj) + processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj) return response except Exception as e: diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index d0eca17272d..bebbcc32181 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -759,7 +759,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 4096 + "prompt_cache_min_tokens": 4096, + "input_cost_per_token_batches": 5e-07, + "output_cost_per_token_batches": 2.5e-06 }, "anthropic.claude-haiku-4-5@20251001": { "cache_creation_input_token_cost": 1.25e-06, @@ -1017,6 +1019,7 @@ }, "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, @@ -1051,6 +1054,7 @@ }, "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, @@ -1085,6 +1089,7 @@ }, "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, @@ -1119,6 +1124,7 @@ }, "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, @@ -1153,6 +1159,7 @@ }, "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, @@ -1230,6 +1237,7 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", + "thinking_always_on": true, "supports_function_calling": true, "supports_vision": true, "supports_prompt_caching": false, @@ -1402,6 +1410,7 @@ "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, @@ -1419,7 +1428,7 @@ "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": { "cache_creation_input_token_cost": 1.25e-05, @@ -1438,6 +1447,7 @@ "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, @@ -1455,7 +1465,7 @@ "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": { "cache_creation_input_token_cost": 1.375e-05, @@ -1474,6 +1484,7 @@ "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, @@ -1491,7 +1502,7 @@ "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": { "cache_creation_input_token_cost": 1.375e-05, @@ -1510,6 +1521,7 @@ "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, @@ -1527,7 +1539,7 @@ "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-opus-5": { "bedrock_converse_supports_strict_tools": false, @@ -2226,6 +2238,7 @@ }, "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, @@ -2259,6 +2272,7 @@ }, "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, @@ -2292,6 +2306,7 @@ }, "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, @@ -2325,6 +2340,7 @@ }, "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, @@ -2358,6 +2374,7 @@ }, "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, @@ -2391,6 +2408,7 @@ }, "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, @@ -2487,7 +2505,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "input_cost_per_token_batches": 1.5e-06, + "output_cost_per_token_batches": 7.5e-06 }, "anthropic.claude-v1": { "input_cost_per_token": 8e-06, @@ -2743,7 +2763,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 4096 + "prompt_cache_min_tokens": 4096, + "input_cost_per_token_batches": 5.5e-07, + "output_cost_per_token_batches": 2.75e-06 }, "apac.anthropic.claude-3-sonnet-20240229-v1:0": { "deprecation_date": "2026-07-30", @@ -2839,7 +2861,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "input_cost_per_token_batches": 1.65e-06, + "output_cost_per_token_batches": 8.25e-06 }, "azure/ada": { "input_cost_per_token": 1e-07, @@ -2909,7 +2933,8 @@ "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", @@ -2932,11 +2957,13 @@ "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", @@ -2962,7 +2989,8 @@ "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", @@ -2993,7 +3021,8 @@ "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": 2048 }, "azure_ai/claude-fable-5": { "supports_mid_conversation_system": true, @@ -3013,6 +3042,7 @@ "cache_creation_input_token_cost_above_1hr": 2e-05, "cache_read_input_token_cost": 1e-06, "supports_adaptive_thinking": true, + "thinking_always_on": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -3024,7 +3054,8 @@ "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": 512 }, "azure_ai/claude-opus-5": { "supports_mid_conversation_system": true, @@ -3087,7 +3118,8 @@ "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", @@ -3109,7 +3141,8 @@ "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", @@ -3131,7 +3164,8 @@ "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-5": { "supports_mid_conversation_system": true, @@ -3162,11 +3196,13 @@ "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, @@ -3187,7 +3223,8 @@ "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, @@ -3372,6 +3409,7 @@ "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 }, @@ -3419,6 +3457,7 @@ "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 }, @@ -3552,6 +3591,7 @@ "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 }, @@ -3593,6 +3633,7 @@ "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 }, @@ -3634,6 +3675,7 @@ "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 }, @@ -3675,6 +3717,7 @@ "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 }, @@ -3900,7 +3943,8 @@ "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, @@ -3935,7 +3979,8 @@ "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", @@ -4210,7 +4255,8 @@ "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, @@ -4245,7 +4291,8 @@ "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", @@ -4654,7 +4701,7 @@ "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, @@ -4687,7 +4734,7 @@ "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, @@ -4867,6 +4914,38 @@ "supports_tool_choice": true, "supports_vision": false }, + "azure/gpt-audio-mini": { + "deprecation_date": "2027-04-06", + "input_cost_per_audio_token": 1e-05, + "input_cost_per_token": 6e-07, + "litellm_provider": "azure", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_audio_token": 2e-05, + "output_cost_per_token": 2.4e-06, + "supported_endpoints": [ + "/v1/chat/completions" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "text", + "audio" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": false, + "supports_reasoning": false, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": false + }, "azure/gpt-audio-mini-2025-10-06": { "deprecation_date": "2027-04-06", "input_cost_per_audio_token": 1e-05, @@ -5080,6 +5159,38 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "azure/gpt-realtime-mini": { + "cache_creation_input_audio_token_cost": 3e-07, + "cache_read_input_token_cost": 6e-08, + "input_cost_per_audio_token": 1e-05, + "input_cost_per_image": 8e-07, + "input_cost_per_token": 6e-07, + "litellm_provider": "azure", + "max_input_tokens": 32000, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "realtime", + "output_cost_per_audio_token": 2e-05, + "output_cost_per_token": 2.4e-06, + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "text", + "image", + "audio" + ], + "supported_output_modalities": [ + "text", + "audio" + ], + "supports_audio_input": true, + "supports_audio_output": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, "azure/gpt-realtime-mini-2025-10-06": { "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_token_cost": 6e-08, @@ -5266,6 +5377,7 @@ "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": { @@ -5303,7 +5415,8 @@ "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, @@ -5732,7 +5845,8 @@ "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, @@ -5767,7 +5881,8 @@ "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", @@ -6214,6 +6329,7 @@ "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 }, @@ -6253,6 +6369,7 @@ "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 }, @@ -6292,6 +6409,7 @@ "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 }, @@ -6337,6 +6455,7 @@ "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 }, @@ -6376,6 +6495,7 @@ "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 +6535,7 @@ "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 }, @@ -6501,6 +6622,10 @@ "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, @@ -6510,7 +6635,7 @@ "input_cost_per_token_priority": 1e-05, "input_cost_per_token_above_272k_tokens_priority": 2e-05, "litellm_provider": "azure", - "max_input_tokens": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", @@ -6551,6 +6676,10 @@ "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, @@ -6561,7 +6690,7 @@ "input_cost_per_token_priority": 1e-05, "input_cost_per_token_above_272k_tokens_priority": 2e-05, "litellm_provider": "azure", - "max_input_tokens": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", @@ -6602,6 +6731,10 @@ "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, @@ -6612,7 +6745,7 @@ "input_cost_per_token_priority": 4e-06, "input_cost_per_token_above_272k_tokens_priority": 8e-06, "litellm_provider": "azure", - "max_input_tokens": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", @@ -6653,6 +6786,10 @@ "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, @@ -6663,7 +6800,7 @@ "input_cost_per_token_priority": 4e-07, "input_cost_per_token_above_272k_tokens_priority": 8e-07, "litellm_provider": "azure", - "max_input_tokens": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", @@ -6704,20 +6841,27 @@ "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": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "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, @@ -6751,21 +6895,28 @@ "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": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "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, @@ -6799,21 +6950,28 @@ "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": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "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, @@ -6847,21 +7005,28 @@ "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": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "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, @@ -6895,20 +7060,27 @@ "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": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "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, @@ -6942,21 +7114,28 @@ "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": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "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, @@ -6990,21 +7169,28 @@ "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": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "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, @@ -7038,21 +7224,28 @@ "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": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "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, @@ -7490,6 +7683,7 @@ "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": { @@ -7531,6 +7725,7 @@ "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": { @@ -7572,6 +7767,7 @@ "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": { @@ -7613,6 +7809,7 @@ "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": { @@ -8683,7 +8880,8 @@ "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, @@ -8718,7 +8916,8 @@ "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", @@ -9142,6 +9341,11 @@ "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", @@ -12191,7 +12395,7 @@ }, "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, @@ -12210,7 +12414,7 @@ }, "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, @@ -12373,6 +12577,7 @@ "search_context_size_medium": 0.01 }, "supports_adaptive_thinking": true, + "supports_mid_conversation_system": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -12400,8 +12605,8 @@ "input_cost_per_token": 3e-06, "litellm_provider": "anthropic", "max_input_tokens": 1000000, - "max_output_tokens": 64000, - "max_tokens": 64000, + "max_output_tokens": 128000, + "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.5e-05, "search_context_cost_per_query": { @@ -12410,6 +12615,7 @@ "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, @@ -12422,7 +12628,10 @@ "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, @@ -12451,7 +12660,9 @@ "supports_tool_choice": true, "supports_vision": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "input_cost_per_token_batches": 1.5e-06, + "output_cost_per_token_batches": 7.5e-06 }, "claude-opus-4-1": { "cache_creation_input_token_cost": 1.875e-05, @@ -12617,6 +12828,7 @@ "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, @@ -12628,8 +12840,7 @@ "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, @@ -12654,6 +12865,7 @@ "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, @@ -12665,8 +12877,7 @@ "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, @@ -12705,8 +12916,7 @@ "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, @@ -12744,8 +12954,7 @@ "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, @@ -12769,6 +12978,8 @@ "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_function_calling": true, @@ -12785,7 +12996,8 @@ "us": 1.1 }, "supports_output_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "supports_native_structured_output": true }, "claude-opus-5": { "deprecation_date": "2027-07-24", @@ -12805,6 +13017,7 @@ "search_context_size_medium": 0.01 }, "supports_adaptive_thinking": true, + "supports_mid_conversation_system": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -12844,6 +13057,7 @@ "search_context_size_medium": 0.01 }, "supports_adaptive_thinking": true, + "supports_mid_conversation_system": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -13346,7 +13560,8 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "completion", - "output_cost_per_token": 2e-06 + "output_cost_per_token": 2e-06, + "deprecation_date": "2025-09-15" }, "command-a-03-2025": { "input_cost_per_token": 2.5e-06, @@ -13367,7 +13582,8 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-07, - "supports_tool_choice": true + "supports_tool_choice": true, + "deprecation_date": "2025-09-15" }, "command-nightly": { "input_cost_per_token": 1e-06, @@ -13387,7 +13603,8 @@ "mode": "chat", "output_cost_per_token": 6e-07, "supports_function_calling": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "deprecation_date": "2025-09-15" }, "command-r-08-2024": { "input_cost_per_token": 1.5e-07, @@ -13409,7 +13626,8 @@ "mode": "chat", "output_cost_per_token": 1e-05, "supports_function_calling": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "deprecation_date": "2025-09-15" }, "command-r-plus-08-2024": { "input_cost_per_token": 2.5e-06, @@ -14460,7 +14678,25 @@ "/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" + ] + }, "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", @@ -14476,6 +14712,8 @@ "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", @@ -14491,10 +14729,41 @@ "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", @@ -14510,10 +14779,14 @@ "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", @@ -14529,10 +14802,14 @@ "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", @@ -14548,10 +14825,14 @@ "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", @@ -14567,11 +14848,15 @@ "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", @@ -14587,10 +14872,95 @@ "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", @@ -14606,10 +14976,14 @@ "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", @@ -14625,10 +14999,13 @@ "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", @@ -14644,10 +15021,14 @@ "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", @@ -14663,10 +15044,42 @@ "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-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", @@ -14681,9 +15094,12 @@ "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", @@ -14698,9 +15114,12 @@ "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", @@ -14715,9 +15134,12 @@ "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", @@ -14732,9 +15154,12 @@ "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", @@ -14749,9 +15174,12 @@ "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", @@ -14766,9 +15194,12 @@ "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", @@ -14783,7 +15214,60 @@ "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", @@ -14796,9 +15280,12 @@ "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", @@ -14811,9 +15298,12 @@ "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", @@ -14826,9 +15316,12 @@ "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", @@ -14841,9 +15334,12 @@ "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", @@ -14856,9 +15352,12 @@ "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", @@ -14871,9 +15370,12 @@ "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", @@ -14886,9 +15388,12 @@ "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", @@ -14901,9 +15406,12 @@ "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", @@ -14916,9 +15424,12 @@ "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", @@ -14931,9 +15442,12 @@ "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", @@ -14946,9 +15460,12 @@ "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", @@ -14961,9 +15478,12 @@ "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", @@ -14979,6 +15499,8 @@ "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", @@ -14994,6 +15516,8 @@ "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", @@ -15008,7 +15532,38 @@ "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", @@ -15025,6 +15580,8 @@ "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", @@ -15041,6 +15598,8 @@ "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", @@ -15057,6 +15616,8 @@ "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", @@ -15072,6 +15633,8 @@ "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", @@ -15088,6 +15651,8 @@ "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", @@ -15104,6 +15669,8 @@ "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", @@ -15120,6 +15687,8 @@ "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", @@ -15136,6 +15705,8 @@ "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", @@ -15668,12 +16239,13 @@ "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, @@ -15690,11 +16262,12 @@ "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, @@ -15711,12 +16284,13 @@ "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, @@ -15744,12 +16318,13 @@ "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, @@ -15767,11 +16342,12 @@ "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, @@ -15788,23 +16364,25 @@ "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, @@ -15821,23 +16399,27 @@ "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, @@ -15864,11 +16446,12 @@ "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, @@ -15994,36 +16577,41 @@ "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, @@ -16077,33 +16665,36 @@ "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, @@ -16141,34 +16732,37 @@ "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, @@ -16216,12 +16810,13 @@ "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, @@ -16239,11 +16834,12 @@ "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, @@ -16270,12 +16866,13 @@ "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, @@ -16357,14 +16954,16 @@ }, "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, @@ -16381,23 +16980,25 @@ "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, @@ -16800,6 +17401,14 @@ "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", @@ -17023,7 +17632,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 4096 + "prompt_cache_min_tokens": 4096, + "input_cost_per_token_batches": 5.5e-07, + "output_cost_per_token_batches": 2.75e-06 }, "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, @@ -17246,7 +17857,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "input_cost_per_token_batches": 1.65e-06, + "output_cost_per_token_batches": 8.25e-06 }, "eu.meta.llama3-2-1b-instruct-v1:0": { "input_cost_per_token": 1.3e-07, @@ -17393,6 +18006,585 @@ "/v1/images/generations" ] }, + "fal_ai/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "metadata": { + "notes": "OpenAI gpt-image-2 served through fal.ai. fal bills by token but publishes deterministic per-image prices per size and quality, mirrored here as keyed entries fal_ai/{quality}/{width}-x-{height}/openai/gpt-image-2 that litellm's fal_ai cost calculator picks from the request params. This flat entry is the fallback when no keyed entry matches and carries the default request rate (quality=high, image_size=landscape_4_3 at 1024x768). quality=auto is priced as high" + }, + "mode": "image_generation", + "output_cost_per_image": 0.145, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1024-x-768/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.005, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1024-x-1024/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.006, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1024-x-1536/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.005, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1920-x-1080/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.005, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/2560-x-1440/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.007, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/3840-x-2160/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.012, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/1024-x-768/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.037, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/1024-x-1024/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.053, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/1024-x-1536/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.042, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/1920-x-1080/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.04, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/2560-x-1440/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.056, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/3840-x-2160/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.101, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/1024-x-768/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.145, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/1024-x-1024/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.211, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/1024-x-1536/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.165, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/1920-x-1080/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.158, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/2560-x-1440/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.222, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/3840-x-2160/openai/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.401, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/gpt-image-2": { + "litellm_provider": "fal_ai", + "metadata": { + "notes": "Alias of fal_ai/openai/gpt-image-2, which litellm also accepts without the openai/ prefix. Same rates, including the keyed fal_ai/{quality}/{width}-x-{height}/gpt-image-2 entries; see that entry for details" + }, + "mode": "image_generation", + "output_cost_per_image": 0.145, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1024-x-768/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.005, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1024-x-1024/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.006, + "source": "https://fal.ai/models/openai/gpt-image-2", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1024-x-1536/gpt-image-2": { + "litellm_provider": "fal_ai", + "mode": 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Prices include one input image and live in keyed entries fal_ai/{quality}/{width}-x-{height}/openai/gpt-image-2/edit. This flat entry is the fallback for the default edit request (quality=high, image_size=auto, inferred from the input image, priced as 1024x768 high)" + }, + "mode": "image_generation", + "output_cost_per_image": 0.151, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1024-x-768/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.011, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1024-x-1024/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.015, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + 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"max_input_tokens": 32768, @@ -17601,6 +18793,22 @@ "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", @@ -18761,6 +19969,7 @@ "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": { @@ -18966,6 +20175,44 @@ }, "web_search_billing_unit": "per_query" }, + "gemini-3.1-flash-lite-image": { + "cache_read_input_token_cost": 2.5e-08, + "input_cost_per_image": 0.00028, + "input_cost_per_token": 2.5e-07, + "input_cost_per_token_batches": 1.25e-07, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 65536, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "image_generation", + "output_cost_per_image": 0.0336, + "output_cost_per_image_token": 3e-05, + "output_cost_per_token": 1.5e-06, + "output_cost_per_token_batches": 7.5e-07, + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "video" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": false, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": false, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_video_input": true, + "supports_vision": true + }, "gemini-3.1-flash-lite-preview": { "cache_read_input_token_cost": 2.5e-08, "input_cost_per_audio_token": 5e-07, @@ -19012,7 +20259,8 @@ "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", @@ -19069,12 +20317,13 @@ "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, @@ -19125,7 +20374,8 @@ "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, @@ -19205,6 +20455,7 @@ "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": { @@ -19250,10 +20501,11 @@ "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", @@ -19263,7 +20515,7 @@ "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", @@ -19295,12 +20547,56 @@ "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": { + "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" + ], + "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, @@ -19344,7 +20640,7 @@ "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, @@ -19389,7 +20685,7 @@ }, "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", @@ -19399,7 +20695,7 @@ "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", @@ -19431,6 +20727,7 @@ "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": { @@ -19476,7 +20773,8 @@ "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", @@ -19590,7 +20888,8 @@ "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, @@ -19642,7 +20941,8 @@ "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, @@ -19745,14 +21045,15 @@ "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, "deprecation_date": "2027-05-19", "cache_read_input_token_cost": 1.5e-07, "input_cost_per_token": 1.5e-06, - "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token": 1.5e-06, "litellm_provider": "vertex_ai", "max_input_tokens": 1048576, "max_output_tokens": 65535, @@ -19791,7 +21092,7 @@ "supports_web_search": true, "supports_native_streaming": true, "input_cost_per_token_priority": 2.7e-06, - "input_cost_per_audio_token_priority": 1.8e-06, + "input_cost_per_audio_token_priority": 2.7e-06, "output_cost_per_token_priority": 1.62e-05, "cache_read_input_token_cost_priority": 2.7e-07, "search_context_cost_per_query": { @@ -19799,7 +21100,13 @@ "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, + "input_cost_per_token_batches": 7.5e-07, + "output_cost_per_token_batches": 4.5e-06, + "input_cost_per_token_flex": 7.5e-07, + "output_cost_per_token_flex": 4.5e-06, + "cache_read_input_token_cost_flex": 7.5e-08 }, "vertex_ai/gemini-3.6-flash": { "prompt_cache_min_tokens": 4096, @@ -19855,7 +21162,8 @@ "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, @@ -19911,7 +21219,8 @@ "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": { "prompt_cache_min_tokens": 4096, @@ -19969,7 +21278,8 @@ "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, @@ -20027,22 +21337,20 @@ "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" ], @@ -20566,6 +21874,7 @@ "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": { @@ -20791,6 +22100,42 @@ }, "web_search_billing_unit": "per_query" }, + "gemini/gemini-3.1-flash-lite-image": { + "input_cost_per_image": 0.00028, + "input_cost_per_token": 2.5e-07, + "input_cost_per_token_batches": 1.25e-07, + "litellm_provider": "gemini", + "max_input_tokens": 65536, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "image_generation", + "output_cost_per_image": 0.0336, + "output_cost_per_image_token": 3e-05, + "output_cost_per_token": 1.5e-06, + "output_cost_per_token_batches": 7.5e-07, + "rpm": 1000, + "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite-image", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": true, + "supports_prompt_caching": false, + "supports_reasoning": false, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_vision": true, + "tpm": 4000000 + }, "gemini/deep-research-pro-preview-12-2025": { "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, @@ -20877,6 +22222,7 @@ "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": { @@ -20925,10 +22271,11 @@ "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, @@ -20940,7 +22287,7 @@ "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", @@ -20973,10 +22320,11 @@ "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", @@ -20987,100 +22335,7 @@ "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", @@ -21113,14 +22368,110 @@ "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" ], @@ -21174,7 +22525,8 @@ "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, @@ -21311,7 +22663,8 @@ "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, @@ -21370,7 +22723,8 @@ "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, @@ -21427,7 +22781,8 @@ "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, @@ -21479,12 +22834,13 @@ "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/gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, "cache_read_input_token_cost": 1.5e-07, - "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token": 1.5e-06, "input_cost_per_token": 1.5e-06, "litellm_provider": "gemini", "max_input_tokens": 1048576, @@ -21526,7 +22882,7 @@ "supports_native_streaming": true, "tpm": 800000, "input_cost_per_token_priority": 2.7e-06, - "input_cost_per_audio_token_priority": 1.8e-06, + "input_cost_per_audio_token_priority": 2.7e-06, "output_cost_per_token_priority": 1.62e-05, "cache_read_input_token_cost_priority": 2.7e-07, "search_context_cost_per_query": { @@ -21534,7 +22890,13 @@ "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, + "input_cost_per_token_batches": 7.5e-07, + "output_cost_per_token_batches": 4.5e-06, + "input_cost_per_token_flex": 7.5e-07, + "output_cost_per_token_flex": 4.5e-06, + "cache_read_input_token_cost_flex": 8e-08 }, "gemini/gemini-3.6-flash": { "prompt_cache_min_tokens": 4096, @@ -21592,7 +22954,8 @@ "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, @@ -21650,15 +23013,16 @@ "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-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, @@ -21666,7 +23030,7 @@ "rpm": 2000, "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ - "/v1/chat/completions" + "/v1beta/interactions" ], "supported_modalities": [ "text", @@ -21741,7 +23105,8 @@ "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, @@ -21799,7 +23164,8 @@ "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, @@ -21849,7 +23215,8 @@ "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, @@ -21886,7 +23253,7 @@ "prompt_cache_min_tokens": 4096, "deprecation_date": "2027-05-19", "cache_read_input_token_cost": 1.5e-07, - "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token": 1.5e-06, "input_cost_per_token": 1.5e-06, "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 1048576, @@ -21926,7 +23293,7 @@ "supports_web_search": true, "supports_native_streaming": true, "input_cost_per_token_priority": 2.7e-06, - "input_cost_per_audio_token_priority": 1.8e-06, + "input_cost_per_audio_token_priority": 2.7e-06, "output_cost_per_token_priority": 1.62e-05, "cache_read_input_token_cost_priority": 2.7e-07, "search_context_cost_per_query": { @@ -21934,7 +23301,13 @@ "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, + "input_cost_per_token_batches": 7.5e-07, + "output_cost_per_token_batches": 4.5e-06, + "input_cost_per_token_flex": 7.5e-07, + "output_cost_per_token_flex": 4.5e-06, + "cache_read_input_token_cost_flex": 7.5e-08 }, "gemini-3.6-flash": { "prompt_cache_min_tokens": 4096, @@ -21990,7 +23363,8 @@ "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, @@ -22046,23 +23420,21 @@ "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-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" ], @@ -22418,6 +23790,7 @@ }, "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, @@ -23264,7 +24637,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "input_cost_per_token_batches": 1.5e-06, + "output_cost_per_token_batches": 7.5e-06 }, "global.anthropic.claude-sonnet-4-20250514-v1:0": { "cache_creation_input_token_cost": 3.75e-06, @@ -23322,7 +24697,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 4096 + "prompt_cache_min_tokens": 4096, + "input_cost_per_token_batches": 5e-07, + "output_cost_per_token_batches": 2.5e-06 }, "global.amazon.nova-2-lite-v1:0": { "cache_read_input_token_cost": 7.5e-08, @@ -24138,7 +25515,8 @@ "supports_response_schema": false, "supports_system_messages": true, "supports_tool_choice": true, - "supports_vision": false + "supports_vision": false, + "deprecation_date": "2027-01-20" }, "gpt-4o-mini": { "cache_read_input_token_cost": 7.5e-08, @@ -24963,6 +26341,7 @@ "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 }, @@ -25007,6 +26386,7 @@ "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 }, @@ -25052,6 +26432,7 @@ "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 }, @@ -25097,6 +26478,7 @@ "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 }, @@ -25142,6 +26524,7 @@ "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 }, @@ -25312,33 +26695,33 @@ "supports_minimal_reasoning_effort": true }, "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_above_272k_tokens_flex": 6.25e-06, - "cache_creation_input_token_cost_flex": 3.125e-06, - "cache_creation_input_token_cost_priority": 1.25e-05, - "cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07, - "cache_read_input_token_cost_flex": 2.5e-07, - "cache_read_input_token_cost_priority": 1e-06, - "input_cost_per_token": 5e-06, - "input_cost_per_token_above_272k_tokens": 1e-05, - "input_cost_per_token_above_272k_tokens_flex": 5e-06, - "input_cost_per_token_batches": 2.5e-06, - "input_cost_per_token_flex": 2.5e-06, - "input_cost_per_token_priority": 1e-05, + "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_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_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_batches": 2e-06, + "input_cost_per_token_flex": 2e-06, + "input_cost_per_token_priority": 8e-06, "litellm_provider": "openai", - "max_input_tokens": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3e-05, - "output_cost_per_token_above_272k_tokens": 4.5e-05, - "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, - "output_cost_per_token_batches": 1.5e-05, - "output_cost_per_token_flex": 1.5e-05, - "output_cost_per_token_priority": 6e-05, + "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_batches": 1e-05, + "output_cost_per_token_flex": 1e-05, + "output_cost_per_token_priority": 4e-05, "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, "search_context_cost_per_query": { @@ -25364,6 +26747,7 @@ "supports_none_reasoning_effort": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, + "supports_prompt_cache_breakpoint": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, @@ -25374,33 +26758,33 @@ "supports_xhigh_reasoning_effort": true }, "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_above_272k_tokens_flex": 6.25e-06, - "cache_creation_input_token_cost_flex": 3.125e-06, - "cache_creation_input_token_cost_priority": 1.25e-05, - "cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07, - "cache_read_input_token_cost_flex": 2.5e-07, - "cache_read_input_token_cost_priority": 1e-06, - "input_cost_per_token": 5e-06, - "input_cost_per_token_above_272k_tokens": 1e-05, - "input_cost_per_token_above_272k_tokens_flex": 5e-06, - "input_cost_per_token_batches": 2.5e-06, - "input_cost_per_token_flex": 2.5e-06, - "input_cost_per_token_priority": 1e-05, + "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_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_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_batches": 2e-06, + "input_cost_per_token_flex": 2e-06, + "input_cost_per_token_priority": 8e-06, "litellm_provider": "openai", - "max_input_tokens": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3e-05, - "output_cost_per_token_above_272k_tokens": 4.5e-05, - "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, - "output_cost_per_token_batches": 1.5e-05, - "output_cost_per_token_flex": 1.5e-05, - "output_cost_per_token_priority": 6e-05, + "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_batches": 1e-05, + "output_cost_per_token_flex": 1e-05, + "output_cost_per_token_priority": 4e-05, "regional_processing_uplift_multiplier_eu": 1.1, "regional_processing_uplift_multiplier_us": 1.1, "search_context_cost_per_query": { @@ -25420,12 +26804,14 @@ "supported_output_modalities": [ "text" ], + "supports_computer_use": true, "supports_function_calling": true, "supports_minimal_reasoning_effort": false, "supports_native_streaming": true, "supports_none_reasoning_effort": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, + "supports_prompt_cache_breakpoint": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, @@ -25453,7 +26839,7 @@ "input_cost_per_token_flex": 1e-06, "input_cost_per_token_priority": 4e-06, "litellm_provider": "openai", - "max_input_tokens": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", @@ -25488,6 +26874,7 @@ "supports_none_reasoning_effort": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, + "supports_prompt_cache_breakpoint": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, @@ -25515,7 +26902,7 @@ "input_cost_per_token_flex": 1e-07, "input_cost_per_token_priority": 4e-07, "litellm_provider": "openai", - "max_input_tokens": 1050000, + "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", @@ -25550,6 +26937,7 @@ "supports_none_reasoning_effort": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, + "supports_prompt_cache_breakpoint": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, @@ -25559,6 +26947,155 @@ "supports_web_search": true, "supports_xhigh_reasoning_effort": true }, + "gpt-5.6-cyber": { + "cache_creation_input_token_cost": 1.5625e-05, + "cache_creation_input_token_cost_above_272k_tokens": 3.125e-05, + "cache_read_input_token_cost": 1.25e-06, + "cache_read_input_token_cost_above_272k_tokens": 2.5e-06, + "input_cost_per_token": 1.25e-05, + "input_cost_per_token_above_272k_tokens": 2.5e-05, + "litellm_provider": "openai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 7.5e-05, + "output_cost_per_token_above_272k_tokens": 0.0001125, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": 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, + "supports_web_search": true, + "source": "https://platform.openai.com/docs/models/gpt-5.6-cyber", + "supports_computer_use": true, + "supports_parallel_function_calling": true + }, + "daybreak-red-latest": { + "cache_creation_input_token_cost": 1.5625e-05, + "cache_creation_input_token_cost_above_272k_tokens": 3.125e-05, + "cache_read_input_token_cost": 1.25e-06, + "cache_read_input_token_cost_above_272k_tokens": 2.5e-06, + "input_cost_per_token": 1.25e-05, + "input_cost_per_token_above_272k_tokens": 2.5e-05, + "litellm_provider": "openai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 7.5e-05, + "output_cost_per_token_above_272k_tokens": 0.0001125, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": 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, + "supports_web_search": true, + "source": "https://platform.openai.com/docs/models/daybreak-red-latest", + "supports_computer_use": true, + "supports_parallel_function_calling": true + }, + "daybreak-blue-latest": { + "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, + "input_cost_per_token": 4e-06, + "input_cost_per_token_above_272k_tokens": 8e-06, + "litellm_provider": "openai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2e-05, + "output_cost_per_token_above_272k_tokens": 3e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_computer_use": true, + "supports_function_calling": true, + "supports_native_streaming": 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, + "supports_web_search": true, + "source": "https://platform.openai.com/docs/models/daybreak-blue-latest", + "supports_parallel_function_calling": true + }, + "chat-latest": { + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 5e-06, + "litellm_provider": "openai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "source": "https://platform.openai.com/docs/models/chat-latest", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": 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_vision": true, + "supports_web_search": true + }, "gpt-5.5": { "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, @@ -25812,6 +27349,7 @@ "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 }, @@ -25860,6 +27398,7 @@ "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 }, @@ -26009,6 +27548,7 @@ "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 }, @@ -26060,6 +27600,7 @@ "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 }, @@ -26108,6 +27649,7 @@ "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 }, @@ -26156,6 +27698,7 @@ "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 }, @@ -28073,7 +29616,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "input_cost_per_token_batches": 1.65e-06, + "output_cost_per_token_batches": 8.25e-06 }, "jp.anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.375e-06, @@ -28099,7 +29644,9 @@ "supports_vision": true, "supports_native_structured_output": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 4096 + "prompt_cache_min_tokens": 4096, + "input_cost_per_token_batches": 5.5e-07, + "output_cost_per_token_batches": 2.75e-06 }, "crusoe/deepseek-ai/DeepSeek-R1-0528": { "input_cost_per_token": 3e-06, @@ -29320,28 +30867,32 @@ "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": 256000, - "max_output_tokens": 256000, - "max_tokens": 256000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 9e-07, - "source": "https://mistral.ai/news/codestral-25-08", + "source": "https://docs.mistral.ai/models/model-cards/codestral-25-08", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true }, "mistral/codestral-latest": { - "input_cost_per_token": 1e-06, + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 3e-07, "litellm_provider": "mistral", - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "max_tokens": 8191, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3e-06, + "output_cost_per_token": 9e-07, + "source": "https://docs.mistral.ai/models/model-cards/codestral-25-08", "supports_assistant_prefill": true, + "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true }, @@ -29474,6 +31025,186 @@ "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, + "litellm_provider": "mistral", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.mistral.ai/models/zai-glm-5-2", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "mistral/glm-5-2": { + "cache_read_input_token_cost": 1.4e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "mistral", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.mistral.ai/models/zai-glm-5-2", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "mistral/magistral-medium-2506": { "deprecation_date": "2025-11-30", "input_cost_per_token": 2e-06, @@ -29542,6 +31273,16 @@ ], "source": "https://mistral.ai/pricing#api-pricing" }, + "mistral/mistral-ocr-4-1": { + "annotation_cost_per_page": 0.005, + "litellm_provider": "mistral", + "mode": "ocr", + "ocr_cost_per_page": 0.004, + "source": "https://docs.mistral.ai/models/model-cards/ocr-4-1", + "supported_endpoints": [ + "/v1/ocr" + ] + }, "mistral/mistral-ocr-2505-completion": { "deprecation_date": "2026-05-31", "litellm_provider": "mistral", @@ -29633,6 +31374,7 @@ "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, @@ -29640,6 +31382,7 @@ "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, @@ -29689,6 +31432,7 @@ "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, @@ -29704,6 +31448,7 @@ "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, @@ -29719,6 +31464,7 @@ "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, @@ -29789,6 +31535,7 @@ "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, @@ -29805,6 +31552,7 @@ "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, @@ -29837,6 +31585,7 @@ "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, @@ -29866,16 +31615,18 @@ "supports_tool_choice": true }, "mistral/mistral-small-latest": { - "input_cost_per_token": 6e-08, + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", - "max_input_tokens": 131072, - "max_output_tokens": 131072, - "max_tokens": 131072, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 1.8e-07, - "source": "https://mistral.ai/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_vision": true @@ -29897,6 +31648,7 @@ "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, @@ -29912,6 +31664,7 @@ "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, @@ -29927,6 +31680,7 @@ "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, @@ -29942,6 +31696,7 @@ "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, @@ -29957,6 +31712,7 @@ "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, @@ -30164,6 +31920,24 @@ "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", @@ -30213,6 +31987,28 @@ "supports_video_input": true, "supports_vision": true }, + "moonshot/kimi-k3": { + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 3e-06, + "litellm_provider": "moonshot", + "max_input_tokens": 1048576, + "max_output_tokens": 1048576, + "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, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true + }, "moonshot/kimi-latest": { "cache_read_input_token_cost": 1.5e-07, "deprecation_date": "2026-01-28", @@ -32527,7 +34323,8 @@ "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, @@ -32547,7 +34344,8 @@ "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, @@ -32570,10 +34368,12 @@ "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, @@ -32595,7 +34395,8 @@ "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, @@ -32614,10 +34415,12 @@ "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, @@ -32634,7 +34437,8 @@ "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, @@ -32657,7 +34461,8 @@ "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, @@ -32675,7 +34480,8 @@ "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, @@ -32698,6 +34504,32 @@ "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "prompt_cache_min_tokens": 2048 + }, + "openrouter/anthropic/claude-opus-5": { + "prompt_cache_min_tokens": 512, + "supports_adaptive_thinking": true, + "cache_creation_input_token_cost": 6.25e-06, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 5e-06, + "litellm_provider": "openrouter", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "source": "https://openrouter.ai/anthropic/claude-opus-5", + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_max_reasoning_effort": true, + "supports_tool_choice": true, + "supports_vision": true, "supports_xhigh_reasoning_effort": true }, "openrouter/bytedance/ui-tars-1.5-7b": { @@ -32808,6 +34640,38 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "openrouter/deepseek/deepseek-v4-pro": { + "input_cost_per_token": 1.32e-06, + "input_cost_per_token_cache_hit": 4.4e-08, + "litellm_provider": "openrouter", + "max_input_tokens": 1048576, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 3.96e-06, + "source": "https://openrouter.ai/deepseek/deepseek-v4-pro", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "openrouter/deepseek/deepseek-v4-pro-0813": { + "input_cost_per_token": 1.32e-06, + "input_cost_per_token_cache_hit": 4.4e-08, + "litellm_provider": "openrouter", + "max_input_tokens": 1048576, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 3.96e-06, + "source": "https://openrouter.ai/deepseek/deepseek-v4-pro-0813", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "openrouter/google/gemini-2.0-flash-001": { "deprecation_date": "2026-06-01", "input_cost_per_audio_token": 7e-07, @@ -34635,6 +36499,7 @@ }, "perplexity/anthropic/claude-opus-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "litellm_provider": "perplexity", "mode": "responses", "supports_web_search": true, @@ -34716,6 +36581,58 @@ "supports_reasoning": false, "supports_function_calling": true }, + "perplexity/perplexity/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 2.8e-08, + "input_cost_per_token": 1.3e-07, + "litellm_provider": "perplexity", + "mode": "responses", + "output_cost_per_token": 2.6e-07, + "source": "https://docs.perplexity.ai/docs/agent-api/models", + "supports_web_search": true, + "supports_reasoning": true, + "supports_function_calling": true + }, + "perplexity/perplexity/glm-5.2": { + "cache_read_input_token_cost": 1.4e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "perplexity", + "mode": "responses", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.perplexity.ai/docs/agent-api/models", + "supports_web_search": true, + "supports_reasoning": true, + "supports_function_calling": true + }, + "perplexity/perplexity/kimi-k3": { + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 3e-06, + "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, + "supports_function_calling": true + }, + "perplexity/perplexity/kimi-k2.7-code": { + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "perplexity", + "mode": "responses", + "output_cost_per_token": 4e-06, + "source": "https://docs.perplexity.ai/docs/agent-api/models", + "supports_web_search": true, + "supports_reasoning": false, + "supports_function_calling": true + }, "perplexity/pplx-embed-v1-0.6b": { "input_cost_per_token": 4e-09, "litellm_provider": "perplexity", @@ -34798,7 +36715,9 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_native_structured_output": true + "supports_native_structured_output": true, + "input_cost_per_token_batches": 1.1e-07, + "output_cost_per_token_batches": 4.4e-07 }, "qwen.qwen3-coder-30b-a3b-v1:0": { "input_cost_per_token": 1.5e-07, @@ -35061,7 +36980,8 @@ "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, @@ -35143,7 +37063,8 @@ "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, @@ -35218,7 +37139,8 @@ "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, @@ -35333,7 +37255,8 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "rerank", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "deprecation_date": "2025-04-30" }, "rerank-english-v3.0": { "input_cost_per_query": 0.002, @@ -35353,7 +37276,8 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "rerank", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "deprecation_date": "2025-04-30" }, "rerank-multilingual-v3.0": { "input_cost_per_query": 0.002, @@ -35692,6 +37616,40 @@ "supports_vision": true, "source": "https://cloud.sambanova.ai/plans/pricing" }, + "scx-ai/GLM-5.2": { + "cache_read_input_token_cost": 2.2e-07, + "input_cost_per_token": 6.1e-07, + "litellm_provider": "scx-ai", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.98e-06, + "source": "https://scx.ai/pricing", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "scx-ai/Qwen3.8-Max": { + "cache_read_input_token_cost": 2.1e-07, + "input_cost_per_token": 1.65e-06, + "litellm_provider": "scx-ai", + "max_input_tokens": 1000000, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.99e-06, + "source": "https://scx.ai/pricing", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "snowflake/claude-3-5-sonnet": { "litellm_provider": "snowflake", "max_input_tokens": 200000, @@ -36481,6 +38439,7 @@ "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, @@ -36497,6 +38456,7 @@ "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, @@ -36509,6 +38469,7 @@ "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, @@ -36521,6 +38482,7 @@ "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, @@ -36532,6 +38494,7 @@ "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, @@ -36544,11 +38507,15 @@ "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, @@ -36557,6 +38524,7 @@ "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, @@ -36574,6 +38542,9 @@ "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, @@ -36582,9 +38553,13 @@ "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", @@ -36596,6 +38571,7 @@ "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, @@ -36604,16 +38580,21 @@ "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_output_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", @@ -36624,6 +38605,7 @@ "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", @@ -36634,6 +38616,7 @@ "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", @@ -36644,6 +38627,7 @@ "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", @@ -36654,6 +38638,7 @@ "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", @@ -36664,6 +38649,7 @@ "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", @@ -36674,6 +38660,7 @@ "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, @@ -36682,6 +38669,7 @@ "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, @@ -36689,6 +38677,7 @@ "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", @@ -36701,6 +38690,9 @@ "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", @@ -36727,7 +38719,7 @@ "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", @@ -36744,6 +38736,7 @@ "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, @@ -36761,6 +38754,9 @@ "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", @@ -36770,11 +38766,15 @@ "supports_tool_choice": true }, "together_ai/zai-org/GLM-4.7": { + "deprecation_date": 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"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", @@ -36809,9 +38817,13 @@ "supports_tool_choice": true }, "together_ai/Qwen/Qwen3-Next-80B-A3B-Instruct": { + "deprecation_date": "2026-04-02", "input_cost_per_token": 1.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, + "metadata": { + "successor": "together_ai/Qwen/Qwen3.7-Plus" + }, "mode": "chat", "output_cost_per_token": 1.5e-06, "source": "https://www.together.ai/models/qwen3-next-80b-a3b-instruct", @@ -36821,9 +38833,13 @@ "supports_tool_choice": true }, "together_ai/Qwen/Qwen3-Next-80B-A3B-Thinking": { + "deprecation_date": "2026-02-25", "input_cost_per_token": 1.5e-07, "litellm_provider": "together_ai", "max_input_tokens": 262144, + "metadata": { + "successor": "together_ai/Qwen/Qwen3.6-Plus" + }, "mode": "chat", "output_cost_per_token": 1.5e-06, "source": 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+ "supports_vision": true, + "prompt_cache_min_tokens": 1024 }, "vercel_ai_gateway/anthropic/claude-sonnet-4.5": { "cache_creation_input_token_cost": 3.75e-06, @@ -37976,7 +40349,8 @@ "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, @@ -39181,6 +41555,7 @@ "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, @@ -39213,6 +41588,7 @@ "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, 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"mode": "chat" + "mode": "chat", + "source": "https://wandb.ai/site/pricing/tokens/" }, "wandb/deepseek-ai/DeepSeek-R1-0528": { "max_tokens": 161000, @@ -41183,10 +43610,11 @@ "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, @@ -41572,85 +44000,6 @@ "/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, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_web_search": true - }, - "xai/grok-2-1212": { - "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, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_web_search": true - }, - "xai/grok-2-latest": { - "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, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_web_search": true - }, - "xai/grok-2-vision": { - "input_cost_per_image": 2e-06, - "input_cost_per_token": 2e-06, - "litellm_provider": "xai", - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 1e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "supports_web_search": true - }, - "xai/grok-2-vision-1212": { - "deprecation_date": "2026-02-28", - "input_cost_per_image": 2e-06, - "input_cost_per_token": 2e-06, - "litellm_provider": "xai", - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 1e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "supports_web_search": true - }, - "xai/grok-2-vision-latest": { - "input_cost_per_image": 2e-06, - "input_cost_per_token": 2e-06, - "litellm_provider": "xai", - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 1e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "supports_web_search": true - }, "xai/grok-3": { "cache_read_input_token_cost": 7.5e-07, "input_cost_per_token": 3e-06, @@ -41730,7 +44079,8 @@ "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15" }, "xai/grok-3-mini": { "cache_read_input_token_cost": 7.5e-08, @@ -41848,7 +44198,8 @@ "supports_function_calling": true, "supports_prompt_caching": true, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15" }, "xai/grok-4-fast-reasoning": { "cache_read_input_token_cost": 5e-08, @@ -41917,7 +44268,8 @@ "supports_function_calling": true, "supports_prompt_caching": true, "supports_tool_choice": true, - "supports_web_search": true + "supports_web_search": true, + "deprecation_date": "2026-05-15" }, "xai/grok-4-1-fast": { "cache_read_input_token_cost": 5e-08, @@ -42033,7 +44385,7 @@ "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, @@ -42045,7 +44397,10 @@ "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-4.20-beta-0309-reasoning": { "cache_read_input_token_cost": 2e-07, @@ -42214,19 +44569,6 @@ "supports_vision": true, "supports_web_search": true }, - "xai/grok-beta": { - "input_cost_per_token": 5e-06, - "litellm_provider": "xai", - "max_input_tokens": 131072, - "max_output_tokens": 131072, - "max_tokens": 131072, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "supports_web_search": true - }, "xai/grok-code-fast": { "cache_read_input_token_cost": 2e-07, "input_cost_per_token": 1e-06, @@ -42245,7 +44587,8 @@ "output_cost_per_token_above_200k_tokens": 4e-06, "cache_read_input_token_cost_above_200k_tokens": 4e-07, "supports_response_schema": true, - "supports_vision": true + "supports_vision": true, + "deprecation_date": "2026-05-15" }, "xai/grok-code-fast-1": { "cache_read_input_token_cost": 2e-07, @@ -42265,7 +44608,8 @@ "output_cost_per_token_above_200k_tokens": 4e-06, "cache_read_input_token_cost_above_200k_tokens": 4e-07, "supports_response_schema": true, - "supports_vision": true + "supports_vision": true, + "deprecation_date": "2026-05-15" }, "xai/grok-code-fast-1-0825": { "cache_read_input_token_cost": 2e-07, @@ -42285,21 +44629,8 @@ "output_cost_per_token_above_200k_tokens": 4e-06, "cache_read_input_token_cost_above_200k_tokens": 4e-07, "supports_response_schema": true, - "supports_vision": true - }, - "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 + "deprecation_date": "2026-05-15" }, "zai.glm-4.7": { "input_cost_per_token": 6e-07, @@ -42359,6 +44690,37 @@ "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, @@ -42564,6 +44926,7 @@ ] }, "azure/sora-2": { + "deprecation_date": "2026-10-15", "litellm_provider": "azure", "mode": "video_generation", "output_cost_per_video_per_second": 0.1, @@ -42615,10 +44978,10 @@ "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", @@ -42628,13 +44991,136 @@ "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", @@ -42644,7 +45130,23 @@ "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": { @@ -45008,8 +47510,8 @@ "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, @@ -45018,8 +47520,8 @@ "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": { @@ -45039,14 +47541,16 @@ "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", @@ -45062,7 +47566,8 @@ "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", @@ -45100,7 +47605,9 @@ "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", @@ -45165,7 +47672,8 @@ "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", @@ -45277,8 +47785,8 @@ "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, @@ -45288,8 +47796,8 @@ "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, @@ -45335,8 +47843,8 @@ "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, @@ -45349,8 +47857,8 @@ "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, @@ -45397,7 +47905,8 @@ "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", @@ -45476,13 +47985,14 @@ "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", @@ -45522,7 +48032,8 @@ "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", @@ -45562,7 +48073,8 @@ "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", @@ -45572,7 +48084,8 @@ "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", @@ -45619,7 +48132,8 @@ "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", @@ -45630,7 +48144,8 @@ "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", @@ -45766,7 +48281,9 @@ "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", @@ -45804,7 +48321,8 @@ "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", @@ -45872,7 +48390,9 @@ "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", @@ -45993,10 +48513,12 @@ "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", @@ -46004,8 +48526,8 @@ "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, @@ -46635,7 +49157,8 @@ "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_system_messages": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "deprecation_date": "2027-01-20" }, "gpt-realtime-whisper": { "input_cost_per_second": 0.0002833333333333333, @@ -46787,15 +49310,16 @@ } }, "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" ], @@ -46812,15 +49336,16 @@ "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" ], @@ -46837,15 +49362,16 @@ "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" ], @@ -46895,15 +49421,16 @@ "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" ], @@ -46922,15 +49449,16 @@ "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" ], @@ -46949,15 +49477,16 @@ "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" ], @@ -47026,11 +49555,11 @@ "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" ] @@ -47079,7 +49608,8 @@ "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, @@ -47125,7 +49655,8 @@ "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, @@ -47170,7 +49701,8 @@ "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, @@ -47215,7 +49747,8 @@ "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, @@ -47300,6 +49833,7 @@ "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, @@ -47414,12 +49948,13 @@ "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": [ @@ -47446,7 +49981,36 @@ "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", @@ -47478,12 +50042,13 @@ "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": [ @@ -47500,12 +50065,171 @@ "supports_tool_choice": true, "supports_vision": true }, + "us.openai.gpt-5.6-sol": { + "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, + "max_tokens": 128000, + "mode": "chat", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": true + }, + "global.openai.gpt-5.6-sol": { + "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, + "max_tokens": 128000, + "mode": "chat", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": true + }, + "us.openai.gpt-5.6-terra": { + "input_cost_per_token": 2.2e-06, + "input_cost_per_token_above_272k_tokens": 4.4e-06, + "cache_creation_input_token_cost": 2.75e-06, + "cache_creation_input_token_cost_above_272k_tokens": 5.5e-06, + "cache_read_input_token_cost": 2.2e-07, + "cache_read_input_token_cost_above_272k_tokens": 4.4e-07, + "output_cost_per_token": 1.32e-05, + "output_cost_per_token_above_272k_tokens": 1.98e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": true + }, + "global.openai.gpt-5.6-terra": { + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_272k_tokens": 4e-06, + "cache_creation_input_token_cost": 2.5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 5e-06, + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_272k_tokens": 4e-07, + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_above_272k_tokens": 1.8e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": true + }, + "us.openai.gpt-5.6-luna": { + "input_cost_per_token": 2.2e-07, + "input_cost_per_token_above_272k_tokens": 4.4e-07, + "cache_creation_input_token_cost": 2.75e-07, + "cache_creation_input_token_cost_above_272k_tokens": 5.5e-07, + "cache_read_input_token_cost": 2.2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4.4e-08, + "output_cost_per_token": 1.32e-06, + "output_cost_per_token_above_272k_tokens": 1.98e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_vision": true + }, + "global.openai.gpt-5.6-luna": { + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "cache_creation_input_token_cost": 2.5e-07, + "cache_creation_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "output_cost_per_token": 1.2e-06, + "output_cost_per_token_above_272k_tokens": 1.8e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "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", @@ -47529,10 +50253,13 @@ }, "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", @@ -47632,6 +50359,57 @@ "supports_vision": true, "source": "https://aws.amazon.com/bedrock/pricing/" }, + "bedrock_mantle/xai.grok-4.6": { + "use_openai_responses_path": true, + "input_cost_per_token": 2.2e-06, + "output_cost_per_token": 6.6e-06, + "cache_read_input_token_cost": 5.5e-07, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 500000, + "max_output_tokens": 500000, + "max_tokens": 500000, + "mode": "chat", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "us.xai.grok-4.6": { + "input_cost_per_token": 2.2e-06, + "output_cost_per_token": 6.6e-06, + "cache_read_input_token_cost": 5.5e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 500000, + "max_output_tokens": 500000, + "max_tokens": 500000, + "mode": "chat", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "global.xai.grok-4.6": { + "input_cost_per_token": 2e-06, + "output_cost_per_token": 6e-06, + "cache_read_input_token_cost": 5e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 500000, + "max_output_tokens": 500000, + "max_tokens": 500000, + "mode": "chat", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, "volcengine/doubao-seed-2-0-pro-260215": { "litellm_provider": "volcengine", "max_input_tokens": 256000, @@ -47879,10 +50657,12 @@ "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, @@ -47895,7 +50675,8 @@ "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, @@ -47910,7 +50691,8 @@ "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, @@ -47926,7 +50708,8 @@ "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, @@ -47941,7 +50724,8 @@ "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, @@ -48252,6 +51036,32 @@ "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, @@ -48304,6 +51114,32 @@ "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, @@ -48382,6 +51218,56 @@ "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, + "cache_read_input_token_cost": 2e-07, + "litellm_provider": "cognition", + "mode": "chat", + "supports_function_calling": true, + "supports_prompt_caching": true, + "source": "https://docs.devin.ai/windsurf/plugins/cascade/models" + }, + "cognition/swe-1.7": { + "input_cost_per_token": 5e-07, + "output_cost_per_token": 2.5e-06, + "cache_read_input_token_cost": 2e-07, + "litellm_provider": "cognition", + "mode": "chat", + "supports_function_calling": true, + "supports_prompt_caching": true, + "source": "https://docs.devin.ai/desktop/models" + }, + "cognition/swe-1.7-lightning": { + "input_cost_per_token": 2.5e-06, + "output_cost_per_token": 1.25e-05, + "cache_read_input_token_cost": 1e-06, + "litellm_provider": "cognition", + "mode": "chat", + "supports_function_calling": true, + "supports_prompt_caching": true, + "source": "https://docs.devin.ai/desktop/models" + }, "pinstripes/ps/glm-4.5-air": { "max_tokens": 128000, "max_input_tokens": 128000, @@ -48521,19 +51407,22 @@ "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, @@ -48628,6 +51517,8 @@ }, "source": "https://docs.claude.com/en/docs/about-claude/models/overview", "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_mid_conversation_system": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -48640,7 +51531,11 @@ "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": true, + "supports_native_structured_output": true, + "provider_specific_entry": { + "us": 1.1 + } }, "claude-mythos-preview": { "cache_creation_input_token_cost": 1.25e-05, @@ -48661,6 +51556,7 @@ }, "source": "https://docs.claude.com/en/docs/about-claude/models/overview", "supports_adaptive_thinking": true, + "thinking_always_on": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -48673,7 +51569,11 @@ "supports_sampling_params": false, "supports_tool_choice": true, "supports_vision": true, - "supports_xhigh_reasoning_effort": true + "supports_xhigh_reasoning_effort": 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, @@ -48707,6 +51607,7 @@ "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, @@ -48719,7 +51620,8 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/labs-leanstral-1-5": { "input_cost_per_token": 0.0, @@ -48837,6 +51739,22 @@ "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)-", + "description": "Any Claude Fable or Mythos id, under any provider namespace and any version. These families always think and reject thinking.type=disabled with a 400; the Anthropic transformations omit the param instead, so the model falls back to its default adaptive thinking.", + "model_info": { + "thinking_always_on": true + } + }, { "name": "claude-mid-conversation-system", "pattern": "claude-[a-z]+-(?:4[-._](?:[89]|[1-9]\\d)(?!\\d)|(?:[5-9]|[1-9]\\d)(?!\\d)(?:[-._]\\d{1,2}(?!\\d))?)", @@ -48846,5 +51764,2837 @@ } } ] + }, + "gemini/gemini-3.5-live-translate-preview": { + "input_cost_per_audio_token": 3.5e-06, + "input_cost_per_token": 3.5e-06, + "litellm_provider": "gemini", + "mode": "chat", + "output_cost_per_audio_token": 2.1e-05, + "output_cost_per_token": 2.1e-05, + "rpm": 10, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/realtime" + ], + "supported_modalities": [ + "audio" + ], + "supported_output_modalities": [ + "audio" + ], + "supports_audio_input": true, + "supports_audio_output": true, + "tpm": 250000 + }, + 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"output_cost_per_token_above_200k_tokens": 5e-06, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "supports_response_schema": true + }, + "xai/grok-4.20-multi-agent-latest": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 1.25e-06, + "litellm_provider": "xai", + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, + "mode": "responses", + "output_cost_per_token": 2.5e-06, + "source": "https://docs.x.ai/docs/models", + "supported_endpoints": [ + "/v1/responses" + ], + "supports_function_calling": false, + "supports_prompt_caching": true, + "supports_reasoning": 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 } } 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/passthrough/main.py b/litellm/passthrough/main.py index 8a2ee2a3af8..4b30afb2f98 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -199,7 +199,7 @@ 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 ( diff --git a/litellm/provider_endpoints_support_backup.json b/litellm/provider_endpoints_support_backup.json index dd7712aabca..86c14fb4cd8 100644 --- a/litellm/provider_endpoints_support_backup.json +++ b/litellm/provider_endpoints_support_backup.json @@ -528,6 +528,23 @@ "interactions": true } }, + "cognition": { + "display_name": "Cognition (`cognition`)", + "url": "https://docs.litellm.ai/docs/providers/cognition", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": false, + "image_generations": false, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": false, + "a2a": false + } + }, "cohere": { "display_name": "Cohere (`cohere`)", "url": "https://docs.litellm.ai/docs/providers/cohere", @@ -2010,6 +2027,23 @@ "interactions": true } }, + "scx-ai": { + "display_name": "SCX.ai (`scx-ai`)", + "url": "https://docs.litellm.ai/docs/providers/scx_ai", + "endpoints": { + "chat_completions": true, + "messages": false, + "responses": false, + "embeddings": false, + "image_generations": false, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": false, + "a2a": false + } + }, "snowflake": { "display_name": "Snowflake (`snowflake`)", "url": "https://docs.litellm.ai/docs/providers/snowflake", 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 d13b39661ad..d5461f01ed8 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, ) @@ -39,9 +42,11 @@ from litellm.proxy._types import ( SpecialMCPServerName, SpecialMCPServerNames, UserAPIKeyAuth, + user_api_key_has_admin_view, ) 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, ) @@ -62,6 +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".""" @@ -160,7 +168,7 @@ def _is_mcp_admitted_user_subject(user_api_key_auth: UserAPIKeyAuth | None) -> b """True when this auth is a keyless subject admitted by the gateway session / bridge user path, as opposed to a JWT or other keyless auth that merely lacks a ``team_id``. - Reads the server-only ``mcp_admitted_user_subject`` field, set only by ``_reload_admitted_user``. It + Reads the server-only ``mcp_admitted_user_subject`` field, set only by ``reload_admitted_user``. It is deliberately NOT a ``metadata`` key, which is caller-controlled at key creation and so forgeable on a personal key to gain the team grant union or dodge the egress scrub; this field cannot be.""" return user_api_key_auth is not None and user_api_key_auth.mcp_admitted_user_subject is True @@ -297,6 +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: @@ -415,7 +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, @@ -436,27 +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, @@ -722,10 +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 @@ -739,17 +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, @@ -797,10 +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, @@ -812,7 +895,7 @@ class MCPRequestHandler: Identity-only sibling of :meth:`_admit_dcr_bridge_delegate`: the session token seals no upstream credential (those are vaulted per user, resolved at egress), so authorization is - resolved fresh via :meth:`_reload_admitted_user` + the centralized policy gate rather than a + resolved fresh via :meth:`reload_admitted_user` + the centralized policy gate rather than a mint-time snapshot. Pre-DB gates (size, IP, route allowlist) run first, mirroring the standard pipeline. Fails closed with the requested scope's ``invalid_token`` challenge on an expired, tampered, foreign, or refresh token, or a missing/deactivated/policy-rejected user.""" @@ -835,7 +918,7 @@ class MCPRequestHandler: match result: case SessionBearerAdmitted(): try: - admitted: Final = await MCPRequestHandler._reload_admitted_user(result.principal.user_id) + admitted: Final = await MCPRequestHandler.reload_admitted_user(result.principal.user_id) admitted.mcp_session_resource_server_id = result.principal.resource_server_id await MCPRequestHandler._enforce_admitted_live_policy( admitted=admitted, request=request, route=route @@ -893,12 +976,12 @@ class MCPRequestHandler: case "key_hash": return await MCPRequestHandler._reload_admitted_key(identity.subject) case "user_id": - return await MCPRequestHandler._reload_admitted_user(identity.subject) + return await MCPRequestHandler.reload_admitted_user(identity.subject) case _: assert_never(identity.subject_type) @staticmethod - async def _reload_admitted_user(user_id: str) -> UserAPIKeyAuth: + async def reload_admitted_user(user_id: str) -> UserAPIKeyAuth: """Reload the live user an interactively-minted envelope references and admit them as themselves. The user's own object permission and ``org_id`` ride on the returned ``UserAPIKeyAuth``, and the @@ -1417,7 +1500,11 @@ 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: @@ -1785,11 +1872,14 @@ class MCPRequestHandler: global_mcp_server_manager, ) - # An OPEN channel (allow_all_keys, the user's own BYOM) makes the server REACHABLE through the - # user, though no grant source names it — without this the union returns [], listable but - # uninvokable. Reachability is ALL it confers, NOT a ceiling waiver: the user's own - # mcp_tool_permissions and org tool ceiling still bind, exactly as a key's do on an allow_all server. - reachable_via_open_channel: Final = server_id in await global_mcp_server_manager.operator_open_server_ids(auth) + # An OPEN channel (allow_all_keys, the user's own BYOM, an unscoped admin-view role) makes the + # server REACHABLE through the user, though no grant source names it — without this the union + # returns [], listable but uninvokable. Reachability is ALL it confers, NOT a ceiling waiver: + # the user's own mcp_tool_permissions and org tool ceiling still bind, exactly as a key's do + # on an allow_all server or an admin key's do on any server. + reachable_via_open_channel: Final = server_id in await global_mcp_server_manager.operator_open_server_ids( + auth + ) or await MCPRequestHandler.admin_view_unscoped(auth) allowed: Final[set[str]] = set() for source, granted in await MCPRequestHandler.admitted_source_grants(auth): @@ -1858,6 +1948,105 @@ 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, @@ -1921,12 +2110,17 @@ 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: @@ -2011,11 +2205,13 @@ 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) @@ -2257,7 +2453,8 @@ 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, ) @@ -2274,6 +2471,7 @@ 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) ) @@ -2332,6 +2530,8 @@ 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 [] @@ -2463,7 +2663,13 @@ 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 @@ -2657,8 +2863,8 @@ 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, @@ -2676,7 +2882,8 @@ 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 @@ -2723,6 +2930,32 @@ class MCPRequestHandler: entitled_servers: Final = await MCPRequestHandler._get_allowed_mcp_servers_for_user(user_api_key_auth) return entitled_servers is None or len(entitled_servers) > 0 + @staticmethod + async def admin_view_unscoped(user_api_key_auth: UserAPIKeyAuth | None = None) -> bool: + """Whether this principal's admin-view role grants the unscoped MCP resolution, whatever + credential carries it (admin key, dashboard session, or OAuth-admitted session subject). + + Two bounds disqualify, one per ownership of the row. A CREDENTIAL's explicit + ``object_permission.mcp_servers`` scope wins even for admins, including the empty list. An + admitted subject's object_permission is the user's own row, whose ``mcp_servers`` column is + [] by DB default, so for that shape the row binds through the entitlement ceiling instead + (any non-empty entitlement, or an unresolved one, disqualifies), exactly as + ``operator_open_server_ids`` reads the same row. The one owner of this predicate: the + server-axis registry resolution in ``get_allowed_mcp_servers`` and the tools-axis open + channel in ``_resolve_admitted_subject_tools`` both consult it, so the two axes cannot + disagree.""" + if user_api_key_auth is None or not user_api_key_has_admin_view(user_api_key_auth): + return False + object_permission: Final = user_api_key_auth.object_permission + credential_scoped: Final = ( + not _is_mcp_admitted_user_subject(user_api_key_auth) + and object_permission is not None + and object_permission.mcp_servers is not None + ) + if credential_scoped: + return False + return not await MCPRequestHandler._user_places_mcp_ceiling(user_api_key_auth) + @staticmethod async def _apply_user_tool_ceiling( allowed_tools: Sequence[str] | None, @@ -2751,12 +2984,14 @@ 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 1d8d545023d..09a3703e904 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -15,6 +15,7 @@ from litellm.proxy._experimental.mcp_server.oauth_utils import TOKEN_NO_CACHE_HE from litellm.types.mcp_server.mcp_server_manager import MCPServer if TYPE_CHECKING: + from litellm.models.user import LiteLLM_UserTable from litellm.proxy._experimental.mcp_server.discoverable_endpoints import _BridgeAuthorizationCode from litellm.proxy._experimental.mcp_server.outbound_credentials.envelope import ( EnvelopeIdentity, @@ -181,7 +182,13 @@ async def _reload_active_key_by_hash(key_hash: str) -> "_ResolvedKey | _KeyResol async def _reload_active_user_by_id(user_id: str) -> "_KeyResolutionFailure | None": - """Re-validate a live litellm user by id, returning ``None`` when the user is active or a precise + """``None`` when the user is live, else the precise failure ``load_active_user_by_id`` found.""" + loaded: Final = await load_active_user_by_id(user_id) + return loaded if isinstance(loaded, str) else None + + +async def load_active_user_by_id(user_id: str) -> "LiteLLM_UserTable | _KeyResolutionFailure": + """Load a live litellm user by id, returning the record when the user is active or a precise failure otherwise. The interactive DCR client authenticates via SSO, so its refresh envelope seals a user subject; renewing it must re-check the user is still live (present and not SCIM-deactivated) so a deactivated user cannot keep refreshing, mirroring how admission re-validates the same user subject on @@ -226,7 +233,7 @@ async def _reload_active_user_by_id(user_id: str) -> "_KeyResolutionFailure | No return "no_active_key" if isinstance(user_object.metadata, dict) and user_object.metadata.get("scim_active") is False: return "no_active_key" - return None + return user_object async def _key_owner_scim_deactivated(key: "UserAPIKeyAuth") -> bool: @@ -299,15 +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`` / @@ -328,7 +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 @@ -350,8 +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, @@ -380,6 +387,7 @@ _BridgeMintError = Literal[ "not_configured", "no_upstream_token", "upstream_token_expired", + "upstream_lifetime_unrepresentable", "too_large", ] @@ -449,6 +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, @@ -612,6 +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, ) @@ -620,6 +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 2f07a8b716c..cf74cbd187e 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) @@ -658,7 +635,7 @@ 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 +722,13 @@ 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 +743,19 @@ 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 +837,7 @@ 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 +857,7 @@ 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 +959,7 @@ 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 +1070,9 @@ 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 +1158,7 @@ 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 @@ -1224,14 +1201,28 @@ def _decode_user_credential(stored: str) -> str | None: return None -def _decode_oauth_payload(stored: str) -> OAuthCredentialPayload | None: - """Return the OAuth2 payload dict if ``stored`` holds one, else ``None``. +def _warn_undecryptable_credential(user_id: str, server_id: str) -> None: + """Log the one credential state that otherwise reads as "user never authorized".""" + verbose_proxy_logger.warning( + "MCP user credential for user=%s server=%s could not be decrypted (likely written under a " + "previous LITELLM_SALT_KEY); the user is treated as not connected and must re-authorize.", + user_id, + server_id, + ) + + +def _parse_oauth_payload(decoded: str | None) -> OAuthCredentialPayload | None: + """Return the OAuth2 payload dict if ``decoded`` holds one, else ``None``. A row is considered an OAuth2 credential iff its decoded value parses as a JSON object with ``"type": "oauth2"``. Plain BYOK credentials (which share the same column) decode to a non-JSON string and return ``None``. + + Callers that need to tell an unreadable row from a readable non-OAuth2 one + pass the result of :func:`_decode_user_credential` so a single decode + answers both questions: ``None`` there means the value can be neither + decrypted nor base64-decoded, so no caller can ever recover it. """ - decoded: Final = _decode_user_credential(stored) if decoded is None: return None parsed: OAuthCredentialPayload | None @@ -1244,6 +1235,11 @@ def _decode_oauth_payload(stored: str) -> OAuthCredentialPayload | None: return None +def _decode_oauth_payload(stored: str) -> OAuthCredentialPayload | None: + """Return the OAuth2 payload dict held in ``stored``, else ``None``.""" + return _parse_oauth_payload(_decode_user_credential(stored)) + + async def rotate_mcp_user_credentials_master_key(prisma_client: PrismaClient, new_master_key: str): """Re-encrypt every ``LiteLLM_MCPUserCredentials`` row with ``new_master_key``. @@ -1415,15 +1411,25 @@ async def store_user_oauth_credential( # (e.g. during token refresh), saving an extra DB round-trip. if not skip_byok_guard: existing: Final = await _db_find_user_credential_row(prisma_client, user_id, server_id) - if existing is not None and _decode_oauth_payload(existing.credential_b64) is None: - # Existing row is either a BYOK secret or an OAuth2 row that no - # longer decrypts (e.g. after a salt-key rotation). In either - # case, refuse to overwrite — the caller would clobber data - # that may still be recoverable. - raise ValueError( - f"Existing credential for user {user_id} and server " - f"{server_id} could not be verified as an OAuth2 token. " - f"Refusing to overwrite." + decoded: Final = _decode_user_credential(existing.credential_b64) if existing is not None else None + if existing is not None and _parse_oauth_payload(decoded) is None: + # Refuse only while the row still holds readable content, which is a live BYOK + # secret that overwriting would destroy. A row that does not decode was written + # under a different LITELLM_SALT_KEY, and one that decodes to nothing holds no + # secret at all; refusing either preserves nothing and instead wedges the user + # out of the OAuth flow for good, since re-authorizing is their only recovery. + if decoded: + raise ValueError( + f"Existing credential for user {user_id} and server " + f"{server_id} could not be verified as an OAuth2 token. " + f"Refusing to overwrite." + ) + verbose_proxy_logger.warning( + "store_user_oauth_credential: existing credential for user=%s server=%s could not be " + "decrypted (likely written under a previous LITELLM_SALT_KEY); replacing it with the " + "newly authorized OAuth2 token.", + user_id, + server_id, ) encoded: Final = encrypt_value_helper(json.dumps(payload)) @@ -1461,7 +1467,10 @@ async def get_user_oauth_credential( row: Final = await _db_find_user_credential_row(prisma_client, user_id, server_id) if row is None: return None - return _decode_oauth_payload(row.credential_b64) + decoded: Final = _decode_user_credential(row.credential_b64) + if decoded is None: + _warn_undecryptable_credential(user_id, server_id) + return _parse_oauth_payload(decoded) async def list_user_oauth_credentials( @@ -1473,7 +1482,10 @@ async def list_user_oauth_credentials( rows: Final = await _db_find_user_credential_rows(prisma_client, {"user_id": user_id}) results: Final[list[OAuthCredentialPayload]] = [] for row in rows: - payload = _decode_oauth_payload(row.credential_b64) + decoded = _decode_user_credential(row.credential_b64) + if decoded is None: + _warn_undecryptable_credential(user_id, row.server_id) + payload = _parse_oauth_payload(decoded) if payload is None: continue payload["server_id"] = row.server_id @@ -1588,7 +1600,7 @@ 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. @@ -1597,7 +1609,7 @@ 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) @@ -1879,7 +1891,7 @@ 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 1ca4c657706..55a21a14d64 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -3,7 +3,7 @@ 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 @@ -47,8 +47,12 @@ from litellm.proxy._experimental.mcp_server.gateway_dcr_flow import ( aggregate_token, complete_connect_flow, is_gateway_dcr_client_id, + is_proxy_api_resource, + native_client_auth_contract, + native_client_authorize, register_aggregate_client, relative_request_url, + revoke_refresh_token, ) from litellm.proxy._experimental.mcp_server.oauth_utils import ( TOKEN_NO_CACHE_HEADERS, @@ -58,6 +62,10 @@ from litellm.proxy._experimental.mcp_server.oauth_utils import ( validate_trusted_redirect_uri, well_known_root_suffix, ) +from litellm.proxy._experimental.mcp_server.proxy_api_credentials import ( + lookup_consent_teams, + mint_proxy_credential, +) from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.common_utils.encrypt_decrypt_utils import ( decrypt_value_helper, @@ -655,6 +663,26 @@ 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, @@ -689,7 +717,7 @@ 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( @@ -737,11 +765,60 @@ 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)))) +def _bridge_access_denied_redirect(redirect_uri: str, state: str, mcp_server: MCPServer) -> RedirectResponse: + """RFC 6749 section 4.1.2.1 denial for the interactive bridge authorize, delivered to the + already-validated client redirect_uri so a DCR client surfaces the failure at connect time.""" + server_label: Final = mcp_server.alias or mcp_server.server_name or mcp_server.server_id + params: Final = { + "error": "access_denied", + "error_description": ( + f"the signed-in user has no access to MCP server '{server_label}' on this gateway; " + "grant it through a team or user object permission, or mark the server allow_all_keys" + ), + **({"state": state} if state else {}), + } + return RedirectResponse(_append_query_params(redirect_uri, params), status_code=302) + + +async def _bridge_authorize_access_denial( + litellm_user_id: str, + mcp_server: MCPServer, + redirect_uri: str, + state: str, +) -> RedirectResponse | None: + """The denial redirect for a signed-in user who cannot reach the target server, or None to proceed. + + Admits the user exactly as MCP egress will (the same ``reload_admitted_user`` constructor and the + same ``get_allowed_mcp_servers`` resolver), so an envelope is minted only when the resulting + session can actually list and call the server's tools. Without this gate the flow completes, the + client shows connected, and every tool request fail-closes to an empty list with nothing telling + the operator why. An availability fault (5xx, e.g. a DB outage's 503) propagates; an unknown or + deactivated user denies like a missing grant, fail closed. + """ + from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( + MCPRequestHandler, + ) + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + + try: + admitted: Final = await MCPRequestHandler.reload_admitted_user(litellm_user_id) + except HTTPException as exc: + if exc.status_code >= 500: + raise + return _bridge_access_denied_redirect(redirect_uri, state, mcp_server) + allowed_server_ids: Final = await global_mcp_server_manager.get_allowed_mcp_servers(admitted) + if mcp_server.server_id in allowed_server_ids: + return None + return _bridge_access_denied_redirect(redirect_uri, state, mcp_server) + + async def authorize_with_server( request: Request, mcp_server: MCPServer, @@ -755,18 +832,19 @@ 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 @@ -775,9 +853,9 @@ 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, @@ -803,7 +881,7 @@ 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, ) @@ -811,6 +889,14 @@ async def authorize_with_server( litellm_user_id = _user_id_from_session_cookie(request) if litellm_user_id is None: return _redirect_to_litellm_login(request) + denial: Final = await _bridge_authorize_access_denial( + litellm_user_id=litellm_user_id, + mcp_server=resolved_server, + redirect_uri=redirect_uri, + state=state, + ) + if denial is not None: + return denial encoded_state: Final = encode_state_with_base_url( base_url=base_url, @@ -819,7 +905,7 @@ 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 @@ -829,26 +915,26 @@ 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))) @@ -881,11 +967,13 @@ 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)", @@ -900,16 +988,16 @@ 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, @@ -922,14 +1010,14 @@ 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 @@ -966,13 +1054,13 @@ 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, @@ -994,7 +1082,7 @@ 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 @@ -1002,17 +1090,16 @@ 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 @@ -1025,35 +1112,30 @@ 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, ) @@ -1061,7 +1143,7 @@ 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: @@ -1071,7 +1153,7 @@ 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 @@ -1082,7 +1164,9 @@ 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 @@ -1444,16 +1528,10 @@ 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 @@ -1486,7 +1564,8 @@ 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}" @@ -1506,7 +1585,7 @@ 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, ) @@ -1552,7 +1631,7 @@ 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", @@ -1562,6 +1641,29 @@ 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, @@ -1596,21 +1698,23 @@ 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, @@ -1625,15 +1729,17 @@ 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 @@ -1663,6 +1769,18 @@ async def authorize( ) if mcp_server_name is None and client_id and is_gateway_dcr_client_id(client_id): + if is_proxy_api_resource(request, resource): + return await native_client_authorize( + request=request, + client_id=client_id, + redirect_uri=redirect_uri, + state=state, + code_challenge=code_challenge, + code_challenge_method=code_challenge_method, + response_type=response_type, + session_user_id=_session_cookie_user_id(request), + lookup_consent_teams=lookup_consent_teams, + ) return aggregate_authorize( request=request, client_id=client_id, @@ -1678,17 +1796,10 @@ 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) @@ -1764,18 +1875,14 @@ async def token_endpoint( reload_user=_reload_active_user_by_id, cache=user_api_key_cache, resource=resource, + mint_proxy_credential=mint_proxy_credential, ) 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( @@ -1793,12 +1900,19 @@ async def token_endpoint( @router.post("/authorize/complete") -async def authorize_complete(request: Request, flow: str = Form(...), delivery: str | None = Form(None)): +async def authorize_complete( + request: Request, + flow: str = Form(...), + delivery: str | None = Form(None), + team_id: str | None = Form(None), + decision: str | None = Form(None), +) -> Response: """Finish an aggregate connect flow: mint the gateway authorization code for the signed-in user and hand it back to the DCR client, by 303 redirect (default) or, for a loopback client on a different machine, as a copyable callback URL (``delivery=manual``). POST plus the per-flow HttpOnly cookie set at /authorize; an - anonymous or bad-flow request just 400s.""" + anonymous or bad-flow request just 400s. The native-client consent page adds + ``decision`` (approve or deny) and the ``team_id`` the credential is attributed to.""" from litellm.proxy.proxy_server import user_api_key_cache # noqa: PLC0415 # circular import at module load return await complete_connect_flow( @@ -1807,9 +1921,31 @@ async def authorize_complete(request: Request, flow: str = Form(...), delivery: session_user_id=_session_cookie_user_id(request), cache=user_api_key_cache, delivery=delivery, + team_id=team_id, + decision=decision, ) +@router.post("/revoke") +async def revoke_endpoint(request: Request, token: str = Form(...), client_id: str = Form(...)) -> Response: + """RFC 7009 revocation for the gateway's refresh tokens (``lite logout``): 200 for a known + client whatever the token's state, 503 when the shared single-use record cannot be written; + access tokens expire on their own.""" + from litellm.proxy.proxy_server import ( # noqa: PLC0415 # circular import at module load + master_key, + user_api_key_cache, + ) + + return await revoke_refresh_token(token=token, client_id=client_id, master_key=master_key, 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 + language) reads to sign a user in through the browser and obtain a proxy credential.""" + return JSONResponse(native_client_auth_contract(request), headers=TOKEN_NO_CACHE_HEADERS) + + # Per RFC 6749 §4.1.2.1, an IdP that rejects an OAuth authorization request # redirects back to the configured redirect URI with ``error`` / # ``error_description`` / ``error_uri`` query params and no ``code``. The MCP @@ -2577,10 +2713,9 @@ 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", []), @@ -2590,10 +2725,7 @@ 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 85885fc75f5..314c80adbc4 100644 --- a/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py +++ b/litellm/proxy/_experimental/mcp_server/gateway_dcr_flow.py @@ -42,15 +42,16 @@ import hmac import html import secrets from base64 import urlsafe_b64encode -from collections.abc import Awaitable, Callable, Mapping +from collections.abc import Awaitable, Callable, Iterable, Mapping from datetime import datetime, timezone -from typing import Final, Literal, TypeVar +from types import MappingProxyType +from typing import Final, Literal, Protocol, TypeVar from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse from fastapi import HTTPException, Request from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse, Response from pydantic import BaseModel, ConfigDict, Field, ValidationError -from typing_extensions import assert_never +from typing_extensions import ReadOnly, TypedDict, assert_never from litellm._logging import verbose_logger from litellm.caching.caching import DualCache @@ -70,6 +71,7 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.session_credent from litellm.proxy._experimental.mcp_server.outbound_credentials.session_token import ( SESSION_REFRESH_TTL_SECONDS, MintedSessionToken, + SessionAudience, SessionKeys, SessionPrincipal, mint_session_refresh_token, @@ -79,6 +81,9 @@ from litellm.proxy.common_utils.encrypt_decrypt_utils import ( decrypt_value_helper, encrypt_value_helper, ) +from litellm.proxy.common_utils.html_forms.native_client_consent import ( + render_native_client_consent_page, +) from litellm.types.mcp_server.mcp_server_manager import MCPServer GATEWAY_DCR_CLIENT_ID_PREFIX: Final = "llm_dcrc_" @@ -144,6 +149,47 @@ ReloadUser = Callable[[str], Awaitable[ReloadUserFailure | None]] ``None`` means the user is active; ``unavailable`` is a retryable DB outage; anything else fails the grant closed.""" +PROXY_API_AUDIENCE: Final[SessionAudience] = "proxy_api" +"""The audience a native client (``lite login --pkce``, a Go CLI) asks for by sending the +proxy base URL itself as the RFC 8707 ``resource``: the grant then mints the proxy-API CLI +credential that LLM routes accept, instead of the MCP-only session pair.""" + +ProxyCredentialMintFailure = Literal[ReloadUserFailure, "not_a_member", "team_required"] + + +class MintedProxyCredential(BaseModel): + model_config = ConfigDict(frozen=True) + key: str = Field(min_length=1) + expires_in: int = Field(gt=0) + user_id: str = Field(min_length=1) + team_id: str | None = None + + +class MintProxyCredential(Protocol): + """Injected proxy-API credential minter ``(user_id, team_id)``: reloads the user live, + checks team membership, refuses a teamless grant for a user who has teams to pick from, + and mints the same credential ``lite login`` mints.""" + + def __call__( + self, user_id: str, team_id: str | None, / + ) -> Awaitable[MintedProxyCredential | ProxyCredentialMintFailure]: ... + + +class ConsentTeam(BaseModel): + model_config = ConfigDict(frozen=True) + team_id: str = Field(min_length=1) + team_alias: str | None = None + + +class LookupConsentTeams(Protocol): + """Injected lookup of the teams a signed-in user may bind a proxy-API credential to.""" + + def __call__(self, user_id: str, /) -> Awaitable[tuple[ConsentTeam, ...] | ReloadUserFailure]: ... + + +async def _refuse_proxy_credential(user_id: str, team_id: str | None) -> ProxyCredentialMintFailure: + return "unresolvable" + class GatewayDcrClient(BaseModel): """The registration record sealed into a gateway DCR ``client_id``. @@ -173,6 +219,7 @@ class _ConnectFlow(BaseModel): jti: str = Field(min_length=1) exp: int resource_server_id: str | None = None + audience: SessionAudience | None = None class _GatewayAuthCode(BaseModel): @@ -190,6 +237,8 @@ class _GatewayAuthCode(BaseModel): iat: int exp: int resource_server_id: str | None = None + audience: SessionAudience | None = None + team_id: str | None = None def is_gateway_dcr_client_id(client_id: str | None) -> bool: @@ -318,9 +367,9 @@ def _cookie_path_and_secure(request: Request) -> tuple[str, bool]: return parsed.path or "/", parsed.scheme == "https" -def _append_query_params(url: str, params: dict[str, str]) -> str: +def _append_query_params(url: str, params: Iterable[tuple[str, str]]) -> str: parsed: Final = urlparse(url) - query: Final = parse_qsl(parsed.query, keep_blank_values=True) + list(params.items()) + query: Final = (*parse_qsl(parsed.query, keep_blank_values=True), *params) return urlunparse(parsed._replace(query=urlencode(query))) @@ -392,6 +441,155 @@ def aggregate_authorize( section 4.1.2.1 an unvalidated redirect URI must not receive an error redirect, and once the client is at fault there is no trusted place to send the browser. """ + rejected: Final = _rejected_authorize_request( + client_id, redirect_uri, state, code_challenge, code_challenge_method, response_type + ) + if rejected is not None: + return rejected + base_url: Final = get_request_base_url(request) + if session_user_id is None: + return _login_redirect(base_url, request) + scoped_server: Final = resolve_scoped_resource_server(request, resource) + handle: Final = secrets.token_urlsafe(24) + flow: Final = _new_connect_flow( + session_user_id=session_user_id, + client_id=client_id, + redirect_uri=redirect_uri, + state=state, + code_challenge=code_challenge or "", + resource_server_id=scoped_server.server_id if scoped_server is not None else None, + audience=None, + ) + connect_url: Final = _append_query_params( + f"{base_url}/ui/connect", + (("connect_flow", handle), ("connect_client", _origin_only(redirect_uri))), + ) + response: Final = RedirectResponse(connect_url, status_code=303) + _set_flow_cookie(response, request, handle, flow) + return response + + +async def native_client_authorize( + request: Request, + client_id: str, + redirect_uri: str, + state: str, + code_challenge: str | None, + code_challenge_method: str | None, + response_type: str | None, + session_user_id: str | None, + lookup_consent_teams: LookupConsentTeams, +) -> Response: + """The authorize verb for a native client that named the proxy API itself as its + RFC 8707 ``resource``: the same client, redirect, PKCE, and sign-in checks as the + aggregate verb plus a loopback-only redirect (the credential this grant mints is the + user's personal proxy key, which belongs on their own machine and never behind a hosted + callback), then the consent page rendered right here (no connect-page interlude, since + there is no per-server vaulting to do) with the flow sealed into the per-flow cookie + and its handle carried only in the form, never in a URL.""" + rejected: Final = _rejected_authorize_request( + client_id, redirect_uri, state, code_challenge, code_challenge_method, response_type + ) + if rejected is not None: + return rejected + if not is_loopback_redirect_host(urlparse(redirect_uri)): + return _oauth_error(400, "invalid_request", "a proxy-API grant may only redirect to a loopback address") + base_url: Final = get_request_base_url(request) + if session_user_id is None: + return _login_redirect(base_url, request) + teams: Final = await lookup_consent_teams(session_user_id) + if not isinstance(teams, tuple): + return _consent_lookup_failure_response(teams) + handle: Final = secrets.token_urlsafe(24) + flow: Final = _new_connect_flow( + session_user_id=session_user_id, + client_id=client_id, + redirect_uri=redirect_uri, + state=state, + code_challenge=code_challenge or "", + resource_server_id=None, + audience=PROXY_API_AUDIENCE, + ) + page: Final = render_native_client_consent_page( + client_origin=_origin_only(redirect_uri), + user_id=session_user_id, + teams=tuple((team.team_id, team.team_alias or team.team_id) for team in teams), + flow_handle=handle, + complete_url=f"{base_url}/authorize/complete", + ) + response: Final = HTMLResponse(page, headers=_CONSENT_PAGE_HEADERS) + _set_flow_cookie(response, request, handle, flow) + return response + + +_CONSENT_PAGE_HEADERS: Final = MappingProxyType( + { + **TOKEN_NO_CACHE_HEADERS, + "X-Frame-Options": "DENY", + "Content-Security-Policy": "frame-ancestors 'none'", + } +) + +NATIVE_CLIENT_AUTH_CONTRACT_VERSION: Final = 1 +"""The version a native client checks before trusting the rest of the discovery document. +Bump it only when an existing field changes meaning or goes away; adding fields is free.""" + + +class NativeClientAuthContract(TypedDict): + contract_version: ReadOnly[int] + issuer: ReadOnly[str] + authorization_endpoint: ReadOnly[str] + token_endpoint: ReadOnly[str] + registration_endpoint: ReadOnly[str] + revocation_endpoint: ReadOnly[str] + resource: ReadOnly[str] + response_types_supported: ReadOnly[tuple[str, ...]] + grant_types_supported: ReadOnly[tuple[str, ...]] + code_challenge_methods_supported: ReadOnly[tuple[str, ...]] + token_endpoint_auth_methods_supported: ReadOnly[tuple[str, ...]] + revocation_endpoint_auth_methods_supported: ReadOnly[tuple[str, ...]] + + +def native_client_auth_contract(request: Request) -> NativeClientAuthContract: + """The versioned discovery document at ``/.well-known/litellm-cli-auth``: everything a + native client (in any language) needs to run the sign-in without reading LiteLLM + source. ``resource`` is the exact value to send as the RFC 8707 ``resource`` parameter + on authorize and token requests so the grant is issued for the proxy API.""" + base_url: Final = get_request_base_url(request) + contract: Final[NativeClientAuthContract] = { + "contract_version": NATIVE_CLIENT_AUTH_CONTRACT_VERSION, + "issuer": base_url, + "authorization_endpoint": f"{base_url}/authorize", + "token_endpoint": f"{base_url}/token", + "registration_endpoint": f"{base_url}/register", + "revocation_endpoint": f"{base_url}/revoke", + "resource": base_url, + "response_types_supported": ("code",), + "grant_types_supported": ("authorization_code", "refresh_token"), + "code_challenge_methods_supported": ("S256",), + "token_endpoint_auth_methods_supported": ("none",), + "revocation_endpoint_auth_methods_supported": ("none",), + } + return contract + + +def is_proxy_api_resource(request: Request, resource: str | None) -> bool: + """True when the RFC 8707 ``resource`` names the proxy itself (its base URL), which is + how a native client asks for the proxy-API audience rather than an MCP session.""" + if resource is None: + return False + canonical: Final = canonical_resource_uri(resource) + return canonical is not None and canonical == canonicalize_url_identity(get_request_base_url(request)) + + +def _rejected_authorize_request( + client_id: str, + redirect_uri: str, + state: str, + code_challenge: str | None, + code_challenge_method: str | None, + response_type: str | None, +) -> Response | None: client: Final = open_gateway_dcr_client(client_id) if client is None: return _oauth_error(400, "invalid_client", "unknown or malformed client_id") @@ -407,14 +605,25 @@ def aggregate_authorize( ) if len(state) > MAX_STATE_LENGTH: return _oauth_error(400, "invalid_request", f"state must be at most {MAX_STATE_LENGTH} characters") - base_url: Final = get_request_base_url(request) - if session_user_id is None: - login_url: Final = f"{base_url}/sso/key/generate?{urlencode({'return_to': relative_request_url(request)})}" - return RedirectResponse(login_url, status_code=303) + return None + + +def _login_redirect(base_url: str, request: Request) -> Response: + return_to: Final = urlencode((("return_to", relative_request_url(request)),)) + return RedirectResponse(f"{base_url}/sso/key/generate?{return_to}", status_code=303) + + +def _new_connect_flow( + session_user_id: str, + client_id: str, + redirect_uri: str, + state: str, + code_challenge: str, + resource_server_id: str | None, + audience: SessionAudience | None, +) -> _ConnectFlow: now: Final = datetime.now(timezone.utc) - scoped_server: Final = resolve_scoped_resource_server(request, resource) - handle: Final = secrets.token_urlsafe(24) - flow: Final = _ConnectFlow( + return _ConnectFlow( user_id=session_user_id, client_id=client_id, redirect_uri=redirect_uri, @@ -422,13 +631,12 @@ def aggregate_authorize( code_challenge=code_challenge, jti=secrets.token_urlsafe(24), exp=int(now.timestamp()) + CONNECT_FLOW_TTL_SECONDS, - resource_server_id=scoped_server.server_id if scoped_server is not None else None, + resource_server_id=resource_server_id, + audience=audience, ) - connect_url: Final = _append_query_params( - f"{base_url}/ui/connect", - {"connect_flow": handle, "connect_client": _origin_only(redirect_uri)}, - ) - response: Final = RedirectResponse(connect_url, status_code=303) + + +def _set_flow_cookie(response: Response, request: Request, handle: str, flow: _ConnectFlow) -> None: path, secure = _cookie_path_and_secure(request) response.set_cookie( key=_flow_cookie_name(handle), @@ -439,7 +647,18 @@ def aggregate_authorize( httponly=True, samesite="lax", ) - return response + + +def _consent_lookup_failure_response(failure: ReloadUserFailure) -> Response: + match failure: + case "unavailable": + return _oauth_error(503, "temporarily_unavailable", "the gateway database is unavailable; retry") + case "unresolvable": + return _oauth_error(500, "server_error", "the gateway is not configured to resolve users") + case "no_active_key": + return _oauth_error(403, "access_denied", "the signed-in user is not active") + case _: + assert_never(failure) def _origin_only(url: str) -> str: @@ -455,6 +674,8 @@ async def complete_connect_flow( session_user_id: str | None, cache: DualCache, delivery: str | None = None, + team_id: str | None = None, + decision: str | None = None, ) -> Response: """The deliberate finish step of the connect flow: mint the gateway authorization code and send the browser back to the client. @@ -479,9 +700,16 @@ async def complete_connect_flow( party. Unknown ``delivery`` values are rejected rather than defaulted: a client that asked for manual delivery and got a dead redirect instead would silently lose its code. + + ``decision`` and ``team_id`` come from the native-client consent page. ``"deny"`` + burns the flow and sends the client ``error=access_denied`` so it stops waiting; + ``team_id`` is sealed into the code only for proxy-API flows, where it picks which of + the user's teams the minted credential is attributed to. """ if delivery not in (None, "redirect", "manual"): return _oauth_error(400, "invalid_request", "delivery must be 'redirect' or 'manual'") + if decision not in (None, "approve", "deny"): + return _oauth_error(400, "invalid_request", "decision must be 'approve' or 'deny'") sealed_flow: Final = request.cookies.get(_flow_cookie_name(flow_handle)) if sealed_flow is None: return _oauth_error(400, "invalid_request", "unknown or expired connect flow") @@ -495,10 +723,34 @@ async def complete_connect_flow( return _oauth_error(401, "login_required", "sign in to LiteLLM to finish connecting") if session_user_id != flow.user_id: return _oauth_error(403, "access_denied", "the signed-in user does not match this connect flow") - if not await _SingleUseGuard(cache).claim( - f"{_USED_FLOW_CACHE_PREFIX}{flow.jti}", CONNECT_FLOW_TTL_SECONDS + _CLAIM_TTL_BUFFER_SECONDS - ): - return _oauth_error(400, "invalid_request", "this connect flow was already completed; restart the connection") + flow_refusal: Final = _claim_refusal( + await _SingleUseGuard(cache).claim( + f"{_USED_FLOW_CACHE_PREFIX}{flow.jti}", CONNECT_FLOW_TTL_SECONDS + _CLAIM_TTL_BUFFER_SECONDS + ), + replayed=_oauth_error( + 400, "invalid_request", "this connect flow was already completed; restart the connection" + ), + ) + if flow_refusal is not None: + return flow_refusal + response: Final = ( + _denied_flow_response(flow) if decision == "deny" else _approved_flow_response(flow, delivery, team_id, now) + ) + path, secure = _cookie_path_and_secure(request) + response.delete_cookie(key=_flow_cookie_name(flow_handle), path=path, secure=secure, httponly=True, samesite="lax") + return response + + +def _state_param(flow: _ConnectFlow) -> tuple[tuple[str, str], ...]: + return (("state", flow.state),) if flow.state else () + + +def _denied_flow_response(flow: _ConnectFlow) -> Response: + params: Final = (("error", "access_denied"), *_state_param(flow)) + return RedirectResponse(_append_query_params(flow.redirect_uri, params), status_code=303) + + +def _approved_flow_response(flow: _ConnectFlow, delivery: str | None, team_id: str | None, now: datetime) -> Response: manual_delivery: Final = delivery == "manual" and is_loopback_redirect_host(urlparse(flow.redirect_uri)) code_ttl: Final = MANUAL_DELIVERY_AUTH_CODE_TTL_SECONDS if manual_delivery else GATEWAY_AUTH_CODE_TTL_SECONDS code: Final = _seal( @@ -512,16 +764,14 @@ async def complete_connect_flow( iat=int(now.timestamp()), exp=int(now.timestamp()) + code_ttl, resource_server_id=flow.resource_server_id, + audience=flow.audience, + team_id=(team_id or None) if flow.audience == PROXY_API_AUDIENCE else None, ), ) - params: Final = {"code": code, **({"state": flow.state} if flow.state else {})} - callback_url: Final = _append_query_params(flow.redirect_uri, params) - response: Final[Response] = ( - _manual_delivery_response(callback_url) if manual_delivery else RedirectResponse(callback_url, status_code=303) - ) - path, secure = _cookie_path_and_secure(request) - response.delete_cookie(key=_flow_cookie_name(flow_handle), path=path, secure=secure, httponly=True, samesite="lax") - return response + callback_url: Final = _append_query_params(flow.redirect_uri, (("code", code), *_state_param(flow))) + if manual_delivery: + return _manual_delivery_response(callback_url) + return RedirectResponse(callback_url, status_code=303) def _manual_delivery_response(callback_url: str) -> Response: @@ -562,6 +812,27 @@ def _pkce_verifier_matches(code_verifier: str, code_challenge: str) -> bool: return hmac.compare_digest(computed, code_challenge.encode("utf-8")) +ClaimOutcome = Literal["first", "replayed", "unavailable"] + +_CLAIM_UNAVAILABLE_DESCRIPTION: Final = "the single-use record is unavailable right now; try again shortly" + + +def _claim_refusal(outcome: ClaimOutcome, replayed: Response) -> Response | None: + """A claim that is not the first caller's is refused, but the two reasons must stay apart on the + wire: a replay is the grant's own 4xx, while a shared backend that could not record the claim is + a 503 (RFC 7009 section 2.2.1, RFC 6749 section 5.2 ``temporarily_unavailable``), so the client + keeps the still-valid token and retries instead of being told it was already used.""" + match outcome: + case "first": + return None + case "replayed": + return replayed + case "unavailable": + return _oauth_error(503, "temporarily_unavailable", _CLAIM_UNAVAILABLE_DESCRIPTION) + case _: + assert_never(outcome) + + class _SingleUseGuard: """Atomic single-use claim for a one-time id (an auth-code, connect-flow ``jti``, or refresh-token ``jti``) over the injected proxy cache. @@ -585,9 +856,10 @@ class _SingleUseGuard: def __init__(self, cache: DualCache) -> None: self._cache = cache - async def claim(self, key: str, ttl_seconds: int) -> bool: - """Atomically claim ``key``. ``True`` iff this caller is the first (increment to 1); ``False`` - on a replay (>1) or when the claim could not be recorded in the shared backend (fail closed).""" + async def claim(self, key: str, ttl_seconds: int) -> ClaimOutcome: + """Atomically claim ``key``. ``"first"`` iff this caller is the first (increment to 1), + ``"replayed"`` on a replay (>1), and ``"unavailable"`` when the claim could not be recorded in + the shared backend, which every caller treats as a refusal (fail closed).""" from litellm.proxy.proxy_server import redis_usage_cache # noqa: PLC0415 # circular import at module load # Resolve the shared authority HERE rather than trusting the injected cache: callers pass @@ -606,11 +878,11 @@ class _SingleUseGuard: verbose_logger.warning( "mcp gateway single-use claim: shared cache backend unavailable, failing closed: %s", e ) - return False - return count == 1 + return "unavailable" + return "first" if count == 1 else "replayed" # No shared backend configured (single-replica): the in-memory increment is authoritative. count = await self._cache.async_increment_cache(key, 1, ttl=ttl_seconds, local_only=True) - return count == 1 + return "first" if count == 1 else "replayed" def _session_token_pair(principal: SessionPrincipal, keys: SessionKeys, now: datetime) -> Response: @@ -630,6 +902,37 @@ def _session_token_pair(principal: SessionPrincipal, keys: SessionKeys, now: dat ) +class _ProxyCredentialTokenResponse(TypedDict): + access_token: ReadOnly[str] + token_type: ReadOnly[Literal["Bearer"]] + expires_in: ReadOnly[int] + refresh_token: ReadOnly[str] + user_id: ReadOnly[str] + team_id: ReadOnly[str | None] + + +def _proxy_credential_response( + minted: MintedProxyCredential, principal: SessionPrincipal, keys: SessionKeys, 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 + the refresh token is a gateway-sealed rotating token bound to the team the credential + was minted for, so a renewal keeps the team the user consented to.""" + bound_principal: Final = principal.model_copy(update=MappingProxyType({"team_id": minted.team_id})) + refresh: Final = mint_session_refresh_token(bound_principal, keys, now) + if not isinstance(refresh, MintedSessionToken): + return _oauth_error(500, "server_error", "failed to mint the session credential") + body: Final[_ProxyCredentialTokenResponse] = { + "access_token": minted.key, + "token_type": "Bearer", + "expires_in": minted.expires_in, + "refresh_token": refresh.token.get_secret_value(), + "user_id": minted.user_id, + "team_id": minted.team_id, + } + return JSONResponse(status_code=200, content=body, headers=TOKEN_NO_CACHE_HEADERS) + + def _reload_failure_response(failure: ReloadUserFailure) -> Response: """Map the live-user revalidation failure onto its OAuth error, exhaustively, so a new ``ReloadUserFailure`` member is a type error here rather than silently 400ing.""" @@ -644,6 +947,22 @@ def _reload_failure_response(failure: ReloadUserFailure) -> Response: assert_never(failure) +def _mint_failure_response(failure: ProxyCredentialMintFailure) -> Response: + match failure: + case "not_a_member": + return _oauth_error( + 400, "invalid_grant", "the user is no longer a member of the team this grant was issued for" + ) + case "team_required": + return _oauth_error( + 400, "invalid_grant", "this user belongs to a team; sign in again and pick the team for this credential" + ) + case "unavailable" | "unresolvable" | "no_active_key": + return _reload_failure_response(failure) + case _: + assert_never(failure) + + def _resource_conflicts_with_scope( request: Request, resource: str | None, sealed_resource_server_id: str | None ) -> bool: @@ -670,15 +989,26 @@ async def aggregate_token( reload_user: ReloadUser, cache: DualCache, resource: str | None = None, + mint_proxy_credential: MintProxyCredential = _refuse_proxy_credential, ) -> Response: """The aggregate token verb: authorization_code and refresh_token grants for the - identity-only session pair. Every path re-validates the litellm user live before - minting, so a deactivated user cannot obtain or renew a session.""" + identity-only session pair, or for the proxy-API credential when the grant was issued + with that audience. Every path re-validates the litellm user live before minting, so a + deactivated user cannot obtain or renew a session.""" 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) now: Final = datetime.now(timezone.utc) + issue: Final = _GrantIssuer( + request=request, + resource=resource, + keys=keys, + now=now, + reload_user=reload_user, + mint_proxy_credential=mint_proxy_credential, + guard=_SingleUseGuard(cache), + ) if grant_type == "authorization_code": return await _authorization_code_grant( request=request, @@ -687,10 +1017,8 @@ async def aggregate_token( client_id=client_id, code_verifier=code_verifier, resource=resource, - keys=keys, now=now, - reload_user=reload_user, - guard=_SingleUseGuard(cache), + issue=issue, ) if grant_type == "refresh_token": return await _refresh_token_grant( @@ -700,12 +1028,78 @@ async def aggregate_token( resource=resource, keys=keys, now=now, - reload_user=reload_user, - guard=_SingleUseGuard(cache), + issue=issue, ) return _oauth_error(400, "unsupported_grant_type", "grant_type must be authorization_code or refresh_token") +class _GrantIssuer: + """The tail every grant shares once its own proof (code + PKCE, or a refresh token) + has checked out: revalidate the user live, claim the single-use marker, mint. The + claim comes AFTER revalidation and minting so a transient DB 503 never burns a + still-valid code or refresh token, and fails closed when it cannot be recorded.""" + + def __init__( + self, + request: Request, + resource: str | None, + keys: SessionKeys, + now: datetime, + reload_user: ReloadUser, + mint_proxy_credential: MintProxyCredential, + guard: _SingleUseGuard, + ) -> None: + self._request: Final = request + self._resource: Final = resource + self._keys: Final = keys + self._now: Final = now + self._reload_user: Final = reload_user + self._mint_proxy_credential: Final = mint_proxy_credential + self._guard: Final = guard + + async def __call__( + self, principal: SessionPrincipal, claim_key: str, claim_ttl_seconds: int, replayed: str + ) -> Response: + match principal.audience: + case None: + return await self._issue_session_pair(principal, claim_key, claim_ttl_seconds, replayed) + case "proxy_api": + return await self._issue_proxy_credential(principal, claim_key, claim_ttl_seconds, replayed) + case _: + assert_never(principal.audience) + + async def _issue_session_pair( + self, principal: SessionPrincipal, claim_key: str, claim_ttl_seconds: int, replayed: str + ) -> Response: + failure: Final = await self._reload_user(principal.user_id) + if failure is not None: + return _reload_failure_response(failure) + refusal: Final = await self._claim_refusal(claim_key, claim_ttl_seconds, replayed) + if refusal is not None: + return refusal + return _session_token_pair(principal, self._keys, self._now) + + async def _issue_proxy_credential( + self, principal: SessionPrincipal, claim_key: str, claim_ttl_seconds: int, replayed: str + ) -> Response: + if self._resource is not None and not is_proxy_api_resource(self._request, self._resource): + return _oauth_error( + 400, "invalid_target", "resource does not match the proxy API this grant was issued for" + ) + minted: Final = await self._mint_proxy_credential(principal.user_id, principal.team_id) + if not isinstance(minted, MintedProxyCredential): + return _mint_failure_response(minted) + refusal: Final = await self._claim_refusal(claim_key, claim_ttl_seconds, replayed) + if refusal is not None: + return refusal + return _proxy_credential_response(minted, principal, self._keys, self._now) + + async def _claim_refusal(self, claim_key: str, claim_ttl_seconds: int, replayed: str) -> Response | None: + return _claim_refusal( + await self._guard.claim(claim_key, claim_ttl_seconds), replayed=_oauth_error(400, "invalid_grant", replayed) + ) + + async def _authorization_code_grant( request: Request, code: str | None, @@ -713,10 +1107,8 @@ async def _authorization_code_grant( client_id: str, code_verifier: str | None, resource: str | None, - keys: SessionKeys, now: datetime, - reload_user: ReloadUser, - guard: _SingleUseGuard, + issue: _GrantIssuer, ) -> Response: if not code or not redirect_uri or not code_verifier: return _oauth_error(400, "invalid_request", "code, redirect_uri, and code_verifier are required") @@ -733,23 +1125,19 @@ async def _authorization_code_grant( return _oauth_error(400, "invalid_target", "resource does not match the scope this code was issued for") if not _pkce_verifier_matches(code_verifier, parsed.code_challenge): return _oauth_error(400, "invalid_grant", "PKCE verification failed") - # Revalidate the user BEFORE claiming the code, so a transient DB outage (a retryable - # 503) does not consume a still-valid code and force the client to restart sign-in. - failure: Final = await reload_user(parsed.user_id) - if failure is not None: - return _reload_failure_response(failure) - # Atomic single-use claim is the gate: on a concurrent double-redeem exactly one caller - # wins, and a claim that cannot be recorded fails closed. The marker's TTL derives from - # the code's own remaining lifetime so it outlives whichever lifetime the code was minted with. - if not await guard.claim( - f"{_USED_CODE_CACHE_PREFIX}{parsed.jti}", - parsed.exp - int(now.timestamp()) + _CLAIM_TTL_BUFFER_SECONDS, - ): - return _oauth_error(400, "invalid_grant", "the authorization code was already used") - return _session_token_pair( - SessionPrincipal(user_id=parsed.user_id, client_id=client_id, resource_server_id=parsed.resource_server_id), - keys, - now, + # The marker's TTL derives from the code's own remaining lifetime so it outlives + # whichever lifetime the code was minted with. + return await issue( + SessionPrincipal( + user_id=parsed.user_id, + client_id=client_id, + resource_server_id=parsed.resource_server_id, + audience=parsed.audience, + team_id=parsed.team_id, + ), + claim_key=f"{_USED_CODE_CACHE_PREFIX}{parsed.jti}", + claim_ttl_seconds=parsed.exp - int(now.timestamp()) + _CLAIM_TTL_BUFFER_SECONDS, + replayed="the authorization code was already used", ) @@ -760,8 +1148,7 @@ async def _refresh_token_grant( resource: str | None, keys: SessionKeys, now: datetime, - reload_user: ReloadUser, - guard: _SingleUseGuard, + issue: _GrantIssuer, ) -> Response: if not refresh_token: return _oauth_error(400, "invalid_request", "refresh_token is required") @@ -770,16 +1157,38 @@ async def _refresh_token_grant( return _oauth_error(400, "invalid_grant", "the refresh token is invalid for this client") if _resource_conflicts_with_scope(request, resource, opened.principal.resource_server_id): return _oauth_error(400, "invalid_target", "resource does not match the scope this token was issued for") - failure: Final = await reload_user(opened.principal.user_id) - if failure is not None: - return _reload_failure_response(failure) # Refresh-token rotation (OAuth 2.0 Security BCP section 4.13): the presented refresh token is - # single-use. Claim its jti before issuing the replacement pair, so a captured or replayed - # refresh token cannot mint a second pair after the legitimate holder rotated. Claimed AFTER - # user revalidation so a transient DB 503 does not burn a still-valid token; a claim that - # cannot be recorded fails closed, exactly like the authorization-code path. - if not await guard.claim( - f"{_USED_REFRESH_CACHE_PREFIX}{opened.jti}", SESSION_REFRESH_TTL_SECONDS + _CLAIM_TTL_BUFFER_SECONDS - ): - return _oauth_error(400, "invalid_grant", "the refresh token was already used") - return _session_token_pair(opened.principal, keys, now) + # single-use, so a captured or replayed refresh token cannot mint a second pair after the + # legitimate holder rotated. + return await issue( + opened.principal, + claim_key=f"{_USED_REFRESH_CACHE_PREFIX}{opened.jti}", + claim_ttl_seconds=SESSION_REFRESH_TTL_SECONDS + _CLAIM_TTL_BUFFER_SECONDS, + replayed="the refresh token was already used", + ) + + +async def revoke_refresh_token(token: str, client_id: str, master_key: str | None, cache: DualCache) -> Response: + """RFC 7009 revocation for the gateway's refresh tokens: burn the presented token's + ``jti`` so neither the holder nor a thief can rotate it again. Access tokens are + stateless and expire on their own (the proxy-API credential within + ``CLI_JWT_EXPIRATION_HOURS``), so per RFC 7009 section 2.2 an unrecognized or already + dead token still answers 200; only an unknown client is refused. A live token whose + burn could not be recorded in the shared backend answers 503 (section 2.2.1), so the + client knows the token still stands and retries instead of reporting a logout that + never happened.""" + if not is_gateway_dcr_client_id(client_id) or open_gateway_dcr_client(client_id) is None: + return _oauth_error(401, "invalid_client", "unknown or malformed client_id") + 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) + now: Final = datetime.now(timezone.utc) + opened: Final = open_session_refresh_bearer(token, keys, now, expected_client_id=client_id) + if isinstance(opened, SessionRefreshOpened): + burned: Final = await _SingleUseGuard(cache).claim( + f"{_USED_REFRESH_CACHE_PREFIX}{opened.jti}", SESSION_REFRESH_TTL_SECONDS + _CLAIM_TTL_BUFFER_SECONDS + ) + if burned == "unavailable": + return _oauth_error(503, "temporarily_unavailable", _CLAIM_UNAVAILABLE_DESCRIPTION) + return Response(content="{}", media_type="application/json", headers=TOKEN_NO_CACHE_HEADERS) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 26a6f8d1251..2330120adad 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -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 @@ -46,7 +47,7 @@ from litellm.constants import ( MCP_TOOL_LISTING_TIMEOUT, ) from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException -from litellm.experimental_mcp_client.client import MCPClient, MCPSigV4Auth +from litellm.experimental_mcp_client.client import MCPClient, MCPSigV4Auth, strip_auth_scheme from litellm.integrations.custom_guardrail import ( _sync_guardrail_info_to_logging_obj, # pyright: ignore[reportPrivateUsage] - the same bridge @log_guardrail_information uses; reimplementing it here would fork the metadata-key logic ) @@ -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, ) @@ -828,25 +834,18 @@ 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.""" + """Format a raw BYOK credential for OpenAPI tool ``Authorization`` injection. + + A non-BYOK server short-circuits ``_resolve_byok_mcp_auth_header``, so the value here can also + be the deprecated global ``x-mcp-auth``, which is a complete header value and would otherwise + be given a second scheme. + """ if mcp_server.auth_type == MCPAuth.api_key: - return f"ApiKey {mcp_auth_header}" + return f"ApiKey {strip_auth_scheme(mcp_auth_header, 'ApiKey')}" if mcp_server.auth_type == MCPAuth.basic: - return f"Basic {mcp_auth_header}" - return f"Bearer {mcp_auth_header}" + return f"Basic {strip_auth_scheme(mcp_auth_header, 'Basic')}" + return f"Bearer {strip_auth_scheme(mcp_auth_header, 'Bearer')}" def _openapi_forwarded_extra_headers( @@ -909,7 +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 @@ -976,7 +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 @@ -989,7 +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( @@ -2161,6 +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), @@ -2693,6 +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 @@ -2938,17 +2941,14 @@ class MCPServerManager: 2. If admin and no object_permission, return all servers 3. Otherwise, use standard permission checks """ - from litellm.proxy.management_endpoints.common_utils import _user_has_admin_view - allow_all_server_ids: Final = self.get_allow_all_keys_server_ids() # A keyless admitted subject is resolved per grant source, and channel decisions that are # absolute for a scoped KEY credential are not absolute for it: its own opt-out silences its - # own source (handled per source in the resolver), never its teams' grants, and its admin - # role does not swallow the grant model — a session bearer is a third-party client - # credential, not the dashboard, so an admin signing in through the connect flow gets their - # grants like anyone else rather than handing the client the full registry ahead of every - # per-team org ceiling. + # own source (handled per source in the resolver), never its teams' grants. Its admin role + # rides the HUMAN, not the credential: an admin's session resolves the same registry their + # dashboard shows (connect-page parity), bounded like an admin key by explicit + # object_permission scope, the entitlement ceiling, and the session resource scope below. is_admitted_subject: Final = _is_mcp_admitted_user_subject(user_api_key_auth) # The key explicitly opted out of every MCP server. Return zero before @@ -2977,26 +2977,16 @@ class MCPServerManager: ) try: - # If admin but NO explicit object permission, get all servers (never for an admitted - # subject — see is_admitted_subject above) - if ( - user_api_key_auth - and not is_admitted_subject - and _user_has_admin_view(user_api_key_auth) - and not has_explicit_object_permission - # An entitlement attached to the HUMAN binds them whatever their role: it is the - # person's scope, not the credential's, so an admin role is not a waiver of it. An - # UNRESOLVED entitlement also skips the shortcut, so the resolver denies rather than - # handing over the whole registry on a transient fault. - and not await MCPRequestHandler._user_places_mcp_ceiling(user_api_key_auth) - ): - verbose_logger.debug("Admin user without explicit object_permission - returning all servers") - return list(self.get_registry().keys()) - - # Get allowed servers from object permissions (respects object_permission even for admins) - allowed_mcp_servers: Final = await MCPRequestHandler.get_allowed_mcp_servers(user_api_key_auth) - verbose_logger.debug("Allowed MCP Servers for user api key auth: %s", allowed_mcp_servers) - combined_servers: Final = set(allowed_mcp_servers) + # Admin view with no explicit object permission and no entitlement ceiling resolves the + # whole registry, for keys AND admitted session subjects alike (one predicate owns the + # question). Seeded into the union rather than returned early so the session resource + # scope below still bounds a per-server envelope held by an admin. + combined_servers: Final = ( + set(self.get_registry().keys()) + if await MCPRequestHandler.admin_view_unscoped(user_api_key_auth) + else set(await MCPRequestHandler.get_allowed_mcp_servers(user_api_key_auth)) + ) + verbose_logger.debug("Allowed MCP Servers for user api key auth: %s", combined_servers) combined_servers.update( await self.operator_open_server_ids( user_api_key_auth, @@ -3533,10 +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, @@ -3548,9 +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 @@ -3658,6 +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 = ( @@ -3766,6 +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, @@ -5264,7 +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 @@ -5312,7 +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, @@ -5355,27 +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: @@ -6213,14 +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_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/oauth_utils.py b/litellm/proxy/_experimental/mcp_server/oauth_utils.py index a30b5ee9e49..1ca2ffc703d 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth_utils.py +++ b/litellm/proxy/_experimental/mcp_server/oauth_utils.py @@ -180,6 +180,22 @@ def well_known_root_suffix() -> str: return "" if root == "/" else root +def get_route_relative_request_path(scope: Scope) -> str: + """The request path the MCP route shapes are written against: the raw ASGI path with the + deployment's ``root_path`` removed. + + ``scope["path"]`` and ``_original_path`` are both raw request-line paths, so on a sub-path + deployment they still carry the ``SERVER_ROOT_PATH`` prefix (``/litellm/{server}/mcp``) while + every route shape compared against them is root-relative. Mirrors the segment-boundary strip in + :func:`litellm.proxy.auth.auth_utils.get_request_route`, which the rest of the MCP auth path + already routes through, so ``/litellmfoo`` is not truncated under ``root_path=/litellm``.""" + raw_path = str(scope.get("_original_path") or scope.get("path", "") or "") + root_path = str(scope.get("app_root_path") or scope.get("root_path") or "").rstrip("/") + if root_path and (raw_path == root_path or raw_path.startswith(f"{root_path}/")): + return raw_path[len(root_path) :] + return raw_path + + def get_passthrough_resource_metadata_url(scope: Scope, server_name: str) -> str: """The per-server protected-resource metadata URL matching the spelling the request arrived on, so a strict RFC 9728 client resolves the same route the proxy registered. @@ -188,7 +204,7 @@ def get_passthrough_resource_metadata_url(scope: Scope, server_name: str) -> str the route decorators insert it (see :func:`well_known_root_suffix`).""" request: Final = Request(scope) base_url: Final = get_request_base_url(request) - _path: Final = scope.get("_original_path") or scope.get("path", "") or "" + _path: Final = get_route_relative_request_path(scope) if _path.startswith(f"/{server_name}/mcp"): return f"{base_url}/.well-known/oauth-protected-resource{well_known_root_suffix()}/{server_name}/mcp" 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..da00abfe604 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/client_credentials.py @@ -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, ) @@ -328,14 +329,21 @@ 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 +351,8 @@ 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_token.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py index 15f5f82c4b6..2c7b970ca0e 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/session_token.py @@ -54,9 +54,9 @@ 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 @@ -76,6 +76,13 @@ SessionTokenKind = Literal["session", "session_refresh"] on open, so a signature-valid token of one kind cannot be replayed as the other even if its wire prefix is swapped (the prefix is not part of the signed payload; this claim is).""" +SessionAudience = Literal["proxy_api"] +"""The non-MCP audience a session REFRESH token can be minted for. ``None`` (the default and +the only value ever on an MCP wire) means the aggregate MCP gateway; ``"proxy_api"`` means the +refresh grant re-mints the proxy-API CLI credential instead of an MCP session pair. The audience +is read only from the signed claims, never from the request, so a token of one audience can +never be redeemed as the other.""" + class SessionPrincipal(BaseModel): """The litellm user a session token identifies and the DCR client it was issued to. @@ -97,6 +104,8 @@ class SessionPrincipal(BaseModel): user_id: str = Field(min_length=1) client_id: str = Field(min_length=1) resource_server_id: str | None = None + audience: SessionAudience | None = None + team_id: str | None = None class SessionKeys(BaseModel): @@ -194,6 +203,8 @@ class _SessionClaims(BaseModel): user_id: str = Field(min_length=1) client_id: str = Field(min_length=1) resource_server_id: str | None = None + audience: SessionAudience | None = None + team_id: str | None = None def is_session_token(candidate: str) -> bool: @@ -295,6 +306,8 @@ def _mint( user_id=principal.user_id, client_id=principal.client_id, resource_server_id=principal.resource_server_id, + 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 @@ -333,7 +346,11 @@ def _open( return SessionExpired() return OpenedSessionToken( principal=SessionPrincipal( - user_id=claims.user_id, client_id=claims.client_id, resource_server_id=claims.resource_server_id + user_id=claims.user_id, + client_id=claims.client_id, + resource_server_id=claims.resource_server_id, + audience=claims.audience, + team_id=claims.team_id, ), jti=claims.jti, ) 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/proxy_api_credentials.py b/litellm/proxy/_experimental/mcp_server/proxy_api_credentials.py new file mode 100644 index 00000000000..27d0ebbd5e6 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/proxy_api_credentials.py @@ -0,0 +1,90 @@ +"""The proxy-API side of the native-client sign-in: turning a consented OAuth grant into +the same per-user credential ``lite login`` stores, so the bearer a CLI obtains through +the browser flow is accepted on every proxy route with user and team attribution.""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Final + +from litellm.constants import CLI_JWT_EXPIRATION_HOURS +from litellm.proxy._experimental.mcp_server.bridge_token_flow import load_active_user_by_id +from litellm.proxy._experimental.mcp_server.gateway_dcr_flow import ( + ConsentTeam, + MintedProxyCredential, + ProxyCredentialMintFailure, + ReloadUserFailure, +) +from litellm.proxy._types import LiteLLM_UserTable +from litellm.proxy.auth.auth_checks import ExperimentalUIJWTToken +from litellm.proxy.management_endpoints.ui_sso import ( + CliSsoTeamDetail, + fetch_cli_sso_team_details, + selected_cli_sso_team_detail, +) + + +async def lookup_consent_teams(user_id: str) -> tuple[ConsentTeam, ...] | ReloadUserFailure: + user: Final = await load_active_user_by_id(user_id) + if isinstance(user, str): + return user + details: Final = await _team_details(user.teams) + if details is None: + return "unavailable" + return tuple( + ConsentTeam(team_id=detail.team_id, team_alias=detail.team_alias) + for detail in details + if detail.team_id is not None + ) + + +async def mint_proxy_credential( + user_id: str, team_id: str | None +) -> MintedProxyCredential | ProxyCredentialMintFailure: + """Mint the ``lite login`` credential for a consented grant. Membership is checked + live, so a team the user left between consent and redemption (or between refreshes) + refuses the grant instead of minting a credential attributed to a team they are no + longer on. The team is exactly the one the consent page sealed into the grant; nothing + is picked on the user's behalf here, so a refresh can never move the credential, and a + grant that names no team is refused for a user with a live team to pick from (the same + rule ``lite login`` applies), so a user cannot step outside their teams' attribution by + posting the consent form without one. Memberships whose team rows are gone count as no + team at all, the way ``lite login`` treats them, so they can never lock a user out. The + user row handed to the minter carries no team list, exactly like ``lite login``'s, so + the minter's own first-team fallback stays inert.""" + user: Final = await load_active_user_by_id(user_id) + if isinstance(user, str): + return user + if user.user_role is None: + return "no_active_key" + if team_id is not None and team_id not in user.teams: + return "not_a_member" + details: Final = await _team_details(user.teams) if user.teams else () + if details is None: + return "unavailable" + if team_id is None and any(detail.team_id is not None for detail in details): + return "team_required" + selected: Final = selected_cli_sso_team_detail(details, team_id) + if selected is None: + return "not_a_member" + key: Final = ExperimentalUIJWTToken.get_cli_jwt_auth_token( + user_info=LiteLLM_UserTable(user_id=user.user_id, user_role=user.user_role, models=user.models), + team_id=team_id, + team_alias=selected.team_alias, + team_models=selected.team_models, + team_model_aliases=selected.team_model_aliases, + ) + return MintedProxyCredential( + key=key, + expires_in=CLI_JWT_EXPIRATION_HOURS * 3600, + user_id=user.user_id, + team_id=team_id, + ) + + +async def _team_details(teams: Sequence[str]) -> tuple[CliSsoTeamDetail, ...] | None: + from litellm.proxy.proxy_server import prisma_client # noqa: PLC0415 # rebound after startup, so read it per call + + if prisma_client is None: + return None + return await fetch_cli_sso_team_details(prisma_client, teams) diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index 3a8fd6de5e5..3efb6429326 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -168,8 +168,11 @@ 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 +185,14 @@ 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, @@ -939,12 +950,9 @@ 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 diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 0dc85c0318c..989b08b929a 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -51,6 +51,8 @@ from litellm.proxy._experimental.mcp_server.mcp_debug import MCPDebug from litellm.proxy._experimental.mcp_server.oauth_utils import ( _redact_mcp_resource_url, get_passthrough_www_authenticate, + get_route_relative_request_path, + well_known_root_suffix, ) from litellm.proxy._experimental.mcp_server.utils import ( LITELLM_MCP_SERVER_DESCRIPTION, @@ -244,11 +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.*``, @@ -430,7 +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 ( @@ -449,6 +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 # @@ -909,14 +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( @@ -949,6 +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, @@ -1730,7 +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( @@ -3782,14 +3794,15 @@ if MCP_AVAILABLE: request = StarletteRequest(scope) base_url = get_request_base_url(request) - _path = scope.get("_original_path") or scope.get("path", "") or "" + _path = get_route_relative_request_path(scope) # Pick the well-known AS-metadata form that matches the inbound route # so strict RFC 9728 §3.2 clients can resolve it correctly. + as_metadata_root = f"{base_url}/.well-known/oauth-authorization-server{well_known_root_suffix()}" if _path.startswith(f"/mcp/{server_name}"): - _as_url = f"{base_url}/.well-known/oauth-authorization-server/mcp/{server_name}" + _as_url = f"{as_metadata_root}/mcp/{server_name}" else: - _as_url = f"{base_url}/.well-known/oauth-authorization-server/{server_name}" + _as_url = f"{as_metadata_root}/{server_name}" authorization_uri = f'Bearer authorization_uri="{_as_url}"' raise HTTPException( 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/ui_session_utils.py b/litellm/proxy/_experimental/mcp_server/ui_session_utils.py index 5ee118fb693..188bfce1484 100644 --- a/litellm/proxy/_experimental/mcp_server/ui_session_utils.py +++ b/litellm/proxy/_experimental/mcp_server/ui_session_utils.py @@ -91,7 +91,7 @@ async def admitted_user_context(user_api_key_auth: UserAPIKeyAuth) -> UserAPIKey ) try: - admitted: Final = await MCPRequestHandler._reload_admitted_user(user_id) + admitted: Final = await MCPRequestHandler.reload_admitted_user(user_id) except HTTPException as e: verbose_logger.warning("MCP dashboard session: admitted-subject reload failed for %s: %s", user_id, e.detail) return None diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index a7d19a9e907..52c40147a20 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

404

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\ No newline at end of file +404: This page could not be found.LiteLLM Dashboard

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404

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\ No newline at end of file +404: This page could not be found.LiteLLM Dashboard

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H=e=>null==e?"-":0===e?"$0.00":e<.01?`$${e.toFixed(6)}`:e<1?`$${e.toFixed(4)}`:`$${(0,B.formatNumberWithCommas)(e,2)}`,G=e=>null==e?"-":(0,B.formatNumberWithCommas)(e,0),U=({multiResult:e})=>e.entries.some(e=>null!==e.result)?(0,t.jsxs)(O.DropdownMenu,{children:[(0,t.jsxs)(O.DropdownMenuTrigger,{className:(0,n.buttonVariants)({variant:"secondary",size:"xs"}),children:[(0,t.jsx)(z.Download,{}),"Export"]}),(0,t.jsxs)(O.DropdownMenuContent,{align:"end",className:"w-44",children:[(0,t.jsxs)(O.DropdownMenuItem,{onClick:()=>(e=>{let t=window.open("","_blank");if(!t)return void alert("Please allow popups to export PDF");let s=e.entries.filter(e=>null!==e.result),r=s.length,a=` - - - - Multi-Model Cost Estimate Report - - - -

LLM Cost Estimate Report

-

${r} model${1!==r?"s":""} configured

- -
-

Combined Totals

-
-
-
Total Per Request
-
${H(e.totals.cost_per_request)}
-
-
-
Total Daily
-
${H(e.totals.daily_cost)}
-
-
-
Total Monthly
-
${H(e.totals.monthly_cost)}
-
-
- ${e.totals.margin_per_request>0?` -
-
-
Margin/Request
-
${H(e.totals.margin_per_request)}
-
-
-
Daily Margin
-
${H(e.totals.daily_margin)}
-
-
-
Monthly Margin
-
${H(e.totals.monthly_margin)}
-
-
- `:""} -
- -

Model Breakdown

- ${s.map(e=>{let t;return t=e.result,` -
-

${t.model} ${t.provider?`(${t.provider})`:""}

- -
-

Input Tokens per Request: ${G(t.input_tokens)}

-

Output Tokens per Request: ${G(t.output_tokens)}

- ${t.num_requests_per_day?`

Requests per Day: ${G(t.num_requests_per_day)}

`:""} - ${t.num_requests_per_month?`

Requests per Month: ${G(t.num_requests_per_month)}

`:""} -
- - - - - - ${null!==t.daily_cost?"":""} - ${null!==t.monthly_cost?"":""} - - - - - ${null!==t.daily_cost?``:""} - ${null!==t.monthly_cost?``:""} - - - - - ${null!==t.daily_cost?``:""} - ${null!==t.monthly_cost?``:""} - - - - - ${null!==t.daily_cost?``:""} - ${null!==t.monthly_cost?``:""} - - - - - ${null!==t.daily_cost?``:""} - ${null!==t.monthly_cost?``:""} - -
Cost TypePer RequestDailyMonthly
Input Cost${H(t.input_cost_per_request)}${H(t.daily_input_cost)}${H(t.monthly_input_cost)}
Output Cost${H(t.output_cost_per_request)}${H(t.daily_output_cost)}${H(t.monthly_output_cost)}
Margin/Fee${H(t.margin_cost_per_request)}${H(t.daily_margin_cost)}${H(t.monthly_margin_cost)}
Total${H(t.cost_per_request)}${H(t.daily_cost)}${H(t.monthly_cost)}
-
- `}).join("")} - -
-

Generated by LiteLLM Pricing Calculator on ${new Date().toLocaleString()}

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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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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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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,$.set(this,!1),O.set(this,!1),L.set(this,void 0),U.set(this,void 0),D.set(this,void 0),B.set(this,void 0),z.set(this,void 0),q.set(this,0),tK(this,L,{params:{...t,messages:structuredClone(t.messages)}},"f");const r=["BetaToolRunner",...s3(t.tools,t.messages)].join(", ");tK(this,U,{...s,headers:sQ([{"x-stainless-helper":r},s?.headers])},"f"),tK(this,z,rb(),"f"),t.compactionControl?.enabled&&console.warn('Anthropic: The `compactionControl` parameter is deprecated and will be removed in a future version. 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this._client.getAPIList("/v1/sessions?beta=true",sU,{query:r,...t,headers:sQ([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(s1`/v1/sessions/${e}?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t={},s){let{betas:r}=t??{};return this._client.post(s1`/v1/sessions/${e}/archive?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}rT.Events=rk,rT.Resources=rC;class rE extends sX{create(e,t={},s){let{betas:r,...a}=t??{};return this._client.post(s1`/v1/skills/${e}/versions?beta=true`,sF({body:a,...s,headers:sQ([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])},this._client))}retrieve(e,t,s){let{skill_id:r,betas:a}=t;return this._client.get(s1`/v1/skills/${r}/versions/${e}?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...a??[],"skills-2025-10-02"].toString()},s?.headers])})}list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s1`/v1/skills/${e}/versions?beta=true`,sU,{query:a,...s,headers:sQ([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}delete(e,t,s){let{skill_id:r,betas:a}=t;return this._client.delete(s1`/v1/skills/${r}/versions/${e}?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...a??[],"skills-2025-10-02"].toString()},s?.headers])})}}class rA extends sX{constructor(){super(...arguments),this.versions=new rE(this._client)}create(e={},t){let{betas:s,...r}=e??{};return this._client.post("/v1/skills?beta=true",sF({body:r,...t,headers:sQ([{"anthropic-beta":[...s??[],"skills-2025-10-02"].toString()},t?.headers])},this._client,!1))}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s1`/v1/skills/${e}?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/skills?beta=true",sU,{query:r,...t,headers:sQ([{"anthropic-beta":[...s??[],"skills-2025-10-02"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(s1`/v1/skills/${e}?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}}rA.Versions=rE;class rP extends sX{create(e,t,s){let{betas:r,...a}=t;return this._client.post(s1`/v1/vaults/${e}/credentials?beta=true`,{body:a,...s,headers:sQ([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}retrieve(e,t,s){let{vault_id:r,betas:a}=t;return this._client.get(s1`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{vault_id:r,betas:a,...n}=t;return this._client.post(s1`/v1/vaults/${r}/credentials/${e}?beta=true`,{body:n,...s,headers:sQ([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s1`/v1/vaults/${e}/credentials?beta=true`,sU,{query:a,...s,headers:sQ([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}delete(e,t,s){let{vault_id:r,betas:a}=t;return this._client.delete(s1`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t,s){let{vault_id:r,betas:a}=t;return this._client.post(s1`/v1/vaults/${r}/credentials/${e}/archive?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}class rI extends sX{constructor(){super(...arguments),this.credentials=new rP(this._client)}create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/vaults?beta=true",{body:r,...t,headers:sQ([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s1`/v1/vaults/${e}?beta=true`,{...s,headers:sQ([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{betas:r,...a}=t;return this._client.post(s1`/v1/vaults/${e}?beta=true`,{body:a,...s,headers:sQ([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return 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t0("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},ec=function(){if(0===this.receivedMessages.length)throw new t0("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new t0("stream ended without producing a content block with type=text");return e.join(" ")},eu=function(){this.ended||tK(this,H,void 0,"f")},em=function(e){if(this.ended)return;let t=tX(this,V,"m",ep).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let 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0,"f"),r$(e,tX(this,G,"f"),{logger:tX(this,el,"f")})},ep=function(e){let t=tX(this,H,"f");if("message_start"===e.type){if(t)throw new t0(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new t0(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push({...e.content_block}),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(t.content[e.index]={...s,text:(s.text||"")+e.delta.text});break;case"citations_delta":s?.type==="text"&&(t.content[e.index]={...s,citations:[...s.citations??[],e.delta.citation]});break;case"input_json_delta":if(s&&rU(s)){let r=s[rL]||"";r+=e.delta.partial_json;let a={...s};Object.defineProperty(a,rL,{value:r,enumerable:!1,writable:!0}),r&&(a.input=rm(r)),t.content[e.index]=a}break;case"thinking_delta":s?.type==="thinking"&&(t.content[e.index]={...s,thinking:s.thinking+e.delta.thinking});break;case"signature_delta":s?.type==="thinking"&&(t.content[e.index]={...s,signature:e.delta.signature});break;default:rB(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new sT(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function rB(e){}class rz extends sX{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(s1`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",sL,{query:e,...t})}delete(e,t){return this._client.delete(s1`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(s1`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new t0(`No batch \`results_url\`; Has it finished processing? 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Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`),rW.includes(e.model)&&e.thinking&&"enabled"===e.thinking.type&&console.warn(`Using Claude with ${e.model} and 'thinking.type=enabled' is deprecated. Use 'thinking.type=adaptive' instead which results in better model performance in our testing: https://platform.claude.com/docs/en/build-with-claude/adaptive-thinking`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=rl[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}let r=s6(e.tools,e.messages);return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,headers:sQ([r,t?.headers]),stream:e.stream??!1})}parse(e,t){return this.create(e,t).then(t=>rO(t,e,{logger:this._client.logger??console}))}stream(e,t){return rD.createMessage(this,e,t,{logger:this._client.logger??console})}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let rF={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-3-opus-20240229":"January 5th, 2026","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025","claude-3-7-sonnet-latest":"February 19th, 2026","claude-3-7-sonnet-20250219":"February 19th, 2026","claude-3-5-haiku-latest":"February 19th, 2026","claude-3-5-haiku-20241022":"February 19th, 2026","claude-opus-4-0":"June 15th, 2026","claude-opus-4-20250514":"June 15th, 2026","claude-sonnet-4-0":"June 15th, 2026","claude-sonnet-4-20250514":"June 15th, 2026"},rW=["claude-mythos-preview","claude-opus-4-6"];rq.Batches=rz;class rV extends sX{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s1`/v1/models/${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. 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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. 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Please try again. Error: ${e}`),e}}async function r1(e,t,s,r,a,n,i,l){console.log=function(){};let o=l||(0,eD.getProxyBaseUrl)(),c=new eH.default.OpenAI({apiKey:a,baseURL:o,dangerouslyAllowBrowser:!0,defaultHeaders:n&&n.length>0?{"x-litellm-tags":n.join(",")}:void 0});try{let a=Array.isArray(e)?e:[e],n=[];for(let e=0;e1&&eU.default.success(`Successfully processed ${n.length} images`)}catch(e){if(console.error("Error making image edit request:",e),i?.aborted);else{let t="Failed to edit image(s)";e?.error?.message?t=e.error.message:e?.message&&(t=e.message),eU.default.fromBackend(`Image edit failed: ${t}`)}throw e}}async function r2(e,t,s,r,a,n,i){console.log=function(){};let l=i||(0,eD.getProxyBaseUrl)(),o=new eH.default.OpenAI({apiKey:r,baseURL:l,dangerouslyAllowBrowser:!0,defaultHeaders:a&&a.length>0?{"x-litellm-tags":a.join(",")}:void 0});try{let r=await o.images.generate({model:s,prompt:e},{signal:n});if(r.data&&r.data[0])if(r.data[0].url)t(r.data[0].url,s);else if(r.data[0].b64_json){let e=r.data[0].b64_json;t(`data:image/png;base64,${e}`,s)}else throw Error("No image data found in response");else throw Error("Invalid response format")}catch(e){throw n?.aborted||eU.default.fromBackend(`Error occurred while generating image. 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E.164",jwt:"JWT",template_literal:"مدخل"},i=>{switch(i.code){case"invalid_type":return`مدخلات غير مقبولة: يفترض إدخال ${i.expected}، ولكن تم إدخال ${(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`مدخلات غير مقبولة: يفترض إدخال ${V.stringifyPrimitive(i.values[0])}`;return`اختيار غير مقبول: يتوقع انتقاء أحد هذه الخيارات: ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return` أكبر من اللازم: يفترض أن تكون ${i.origin??"القيمة"} ${t} ${i.maximum.toString()} ${r.unit??"عنصر"}`;return`أكبر من اللازم: يفترض أن تكون ${i.origin??"القيمة"} ${t} ${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`أصغر من اللازم: يفترض لـ ${i.origin} أن 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e,t;return{localeError:(e={string:{unit:"simvol",verb:"olmalıdır"},file:{unit:"bayt",verb:"olmalıdır"},array:{unit:"element",verb:"olmalıdır"},set:{unit:"element",verb:"olmalıdır"}},t={regex:"input",email:"email address",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 datetime",date:"ISO date",time:"ISO time",duration:"ISO duration",ipv4:"IPv4 address",ipv6:"IPv6 address",cidrv4:"IPv4 range",cidrv6:"IPv6 range",base64:"base64-encoded string",base64url:"base64url-encoded string",json_string:"JSON string",e164:"E.164 number",jwt:"JWT",template_literal:"input"},i=>{switch(i.code){case"invalid_type":return`Yanlış dəyər: g\xf6zlənilən ${i.expected}, daxil olan ${(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`Yanlış dəyər: g\xf6zlənilən ${V.stringifyPrimitive(i.values[0])}`;return`Yanlış se\xe7im: aşağıdakılardan biri olmalıdır: ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`\xc7ox b\xf6y\xfck: g\xf6zlənilən ${i.origin??"dəyər"} ${t}${i.maximum.toString()} ${r.unit??"element"}`;return`\xc7ox b\xf6y\xfck: g\xf6zlənilən ${i.origin??"dəyər"} ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`\xc7ox ki\xe7ik: g\xf6zlənilən ${i.origin} ${t}${i.minimum.toString()} ${r.unit}`;return`\xc7ox ki\xe7ik: g\xf6zlənilən ${i.origin} ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Yanlış mətn: "${i.prefix}" ilə başlamalıdır`;if("ends_with"===i.format)return`Yanlış mətn: "${i.suffix}" ilə bitməlidir`;if("includes"===i.format)return`Yanlış mətn: "${i.includes}" daxil olmalıdır`;if("regex"===i.format)return`Yanlış mətn: ${i.pattern} şablonuna uyğun olmalıdır`;return`Yanlış ${t[i.format]??i.format}`;case"not_multiple_of":return`Yanlış ədəd: ${i.divisor} ilə b\xf6l\xfcnə bilən olmalıdır`;case"unrecognized_keys":return`Tanınmayan a\xe7ar${i.keys.length>1?"lar":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`${i.origin} daxilində yanlış a\xe7ar`;case"invalid_union":default:return"Yanlış dəyər";case"invalid_element":return`${i.origin} daxilində yanlış dəyər`}})}},"be",0,function(){let 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 Number.isNaN(e)?"NaN":"лік";case"object":if(Array.isArray(e))return"масіў";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`Няправільны ўвод: чакалася ${V.stringifyPrimitive(i.values[0])}`;return`Няправільны варыянт: чакаўся адзін з ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r){let e=tE(Number(i.maximum),r.unit.one,r.unit.few,r.unit.many);return`Занадта вялікі: чакалася, што ${i.origin??"значэнне"} павінна ${r.verb} ${t}${i.maximum.toString()} ${e}`}return`Занадта вялікі: чакалася, што ${i.origin??"значэнне"} павінна быць ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r){let e=tE(Number(i.minimum),r.unit.one,r.unit.few,r.unit.many);return`Занадта малы: чакалася, што ${i.origin} павінна ${r.verb} ${t}${i.minimum.toString()} ${e}`}return`Занадта малы: чакалася, што ${i.origin} павінна быць ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Няправільны радок: павінен пачынацца з "${i.prefix}"`;if("ends_with"===i.format)return`Няправільны радок: павінен заканчвацца на "${i.suffix}"`;if("includes"===i.format)return`Няправільны радок: павінен змяшчаць "${i.includes}"`;if("regex"===i.format)return`Няправільны радок: павінен адпавядаць шаблону ${i.pattern}`;return`Няправільны ${t[i.format]??i.format}`;case"not_multiple_of":return`Няправільны лік: павінен быць кратным ${i.divisor}`;case"unrecognized_keys":return`Нераспазнаны ${i.keys.length>1?"ключы":"ключ"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Няправільны ключ у ${i.origin}`;case"invalid_union":default:return"Няправільны ўвод";case"invalid_element":return`Няправільнае значэнне ў ${i.origin}`}})}},"ca",0,function(){let 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 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`Valor inv\xe0lid: s'esperava ${V.stringifyPrimitive(i.values[0])}`;return`Opci\xf3 inv\xe0lida: s'esperava una de ${V.joinValues(i.values," o ")}`;case"too_big":{let t=i.inclusive?"com a màxim":"menys de",r=e[i.origin]??null;if(r)return`Massa gran: s'esperava que ${i.origin??"el valor"} contingu\xe9s ${t} ${i.maximum.toString()} ${r.unit??"elements"}`;return`Massa gran: s'esperava que ${i.origin??"el valor"} fos ${t} ${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?"com a mínim":"més de",r=e[i.origin]??null;if(r)return`Massa petit: s'esperava que ${i.origin} contingu\xe9s ${t} ${i.minimum.toString()} ${r.unit}`;return`Massa petit: s'esperava que ${i.origin} fos ${t} ${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Format inv\xe0lid: ha de comen\xe7ar amb "${i.prefix}"`;if("ends_with"===i.format)return`Format inv\xe0lid: ha d'acabar amb "${i.suffix}"`;if("includes"===i.format)return`Format inv\xe0lid: ha d'incloure "${i.includes}"`;if("regex"===i.format)return`Format inv\xe0lid: ha de coincidir amb el patr\xf3 ${i.pattern}`;return`Format inv\xe0lid per a ${t[i.format]??i.format}`;case"not_multiple_of":return`N\xfamero inv\xe0lid: ha de ser m\xfaltiple de ${i.divisor}`;case"unrecognized_keys":return`Clau${i.keys.length>1?"s":""} no reconeguda${i.keys.length>1?"s":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Clau inv\xe0lida a ${i.origin}`;case"invalid_union":default:return"Entrada invàlida";case"invalid_element":return`Element inv\xe0lid a ${i.origin}`}})}},"cs",0,function(){let 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 mal\xe1: ${i.origin??"hodnota"} mus\xed m\xedt ${t}${i.minimum.toString()} ${r.unit??"prvků"}`;return`Hodnota je př\xedliš mal\xe1: ${i.origin??"hodnota"} mus\xed b\xfdt ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Neplatn\xfd řetězec: mus\xed zač\xednat na "${i.prefix}"`;if("ends_with"===i.format)return`Neplatn\xfd řetězec: mus\xed končit na "${i.suffix}"`;if("includes"===i.format)return`Neplatn\xfd řetězec: mus\xed obsahovat "${i.includes}"`;if("regex"===i.format)return`Neplatn\xfd řetězec: mus\xed odpov\xeddat vzoru ${i.pattern}`;return`Neplatn\xfd form\xe1t ${t[i.format]??i.format}`;case"not_multiple_of":return`Neplatn\xe9 č\xedslo: mus\xed b\xfdt n\xe1sobkem ${i.divisor}`;case"unrecognized_keys":return`Nezn\xe1m\xe9 kl\xedče: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Neplatn\xfd kl\xedč v ${i.origin}`;case"invalid_union":default:return"Neplatný vstup";case"invalid_element":return`Neplatn\xe1 hodnota v ${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 Number.isNaN(e)?"NaN":"Zahl";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`Ung\xfcltige Eingabe: erwartet ${V.stringifyPrimitive(i.values[0])}`;return`Ung\xfcltige Option: erwartet eine von ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Zu gro\xdf: erwartet, dass ${i.origin??"Wert"} ${t}${i.maximum.toString()} ${r.unit??"Elemente"} hat`;return`Zu gro\xdf: erwartet, dass ${i.origin??"Wert"} ${t}${i.maximum.toString()} ist`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Zu klein: erwartet, dass ${i.origin} ${t}${i.minimum.toString()} ${r.unit} hat`;return`Zu klein: erwartet, dass ${i.origin} ${t}${i.minimum.toString()} ist`}case"invalid_format":if("starts_with"===i.format)return`Ung\xfcltiger String: muss mit "${i.prefix}" beginnen`;if("ends_with"===i.format)return`Ung\xfcltiger String: muss mit "${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 Number.isNaN(e)?"NaN":"nombro";case"object":if(Array.isArray(e))return"tabelo";if(null===e)return"senvalora";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`Nevalida enigo: atendiĝis ${V.stringifyPrimitive(i.values[0])}`;return`Nevalida opcio: atendiĝis unu el ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Tro granda: atendiĝis ke ${i.origin??"valoro"} havu ${t}${i.maximum.toString()} ${r.unit??"elementojn"}`;return`Tro granda: atendiĝis ke ${i.origin??"valoro"} havu ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Tro malgranda: atendiĝis ke ${i.origin} havu ${t}${i.minimum.toString()} ${r.unit}`;return`Tro malgranda: atendiĝis ke ${i.origin} estu ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Nevalida karaktraro: devas komenciĝi per "${i.prefix}"`;if("ends_with"===i.format)return`Nevalida karaktraro: devas finiĝi per "${i.suffix}"`;if("includes"===i.format)return`Nevalida karaktraro: devas inkluzivi "${i.includes}"`;if("regex"===i.format)return`Nevalida karaktraro: devas kongrui kun la modelo ${i.pattern}`;return`Nevalida ${t[i.format]??i.format}`;case"not_multiple_of":return`Nevalida nombro: devas esti oblo de ${i.divisor}`;case"unrecognized_keys":return`Nekonata${i.keys.length>1?"j":""} ŝlosilo${i.keys.length>1?"j":""}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return`Nevalida ŝlosilo en ${i.origin}`;case"invalid_union":default:return"Nevalida enigo";case"invalid_element":return`Nevalida valoro en ${i.origin}`}})}},"es",0,function(){let e,t;return{localeError:(e={string:{unit:"caracteres",verb:"tener"},file:{unit:"bytes",verb:"tener"},array:{unit:"elementos",verb:"tener"},set:{unit:"elementos",verb:"tener"}},t={regex:"entrada",email:"dirección de correo 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}, oli ${(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`Virheellinen sy\xf6te: t\xe4ytyy olla ${V.stringifyPrimitive(i.values[0])}`;return`Virheellinen valinta: t\xe4ytyy olla yksi seuraavista: ${V.joinValues(i.values,"|")}`;case"too_big":{let t=i.inclusive?"<=":"<",r=e[i.origin]??null;if(r)return`Liian suuri: ${r.subject} t\xe4ytyy olla ${t}${i.maximum.toString()} ${r.unit}`.trim();return`Liian suuri: arvon t\xe4ytyy olla ${t}${i.maximum.toString()}`}case"too_small":{let t=i.inclusive?">=":">",r=e[i.origin]??null;if(r)return`Liian pieni: ${r.subject} t\xe4ytyy olla ${t}${i.minimum.toString()} ${r.unit}`.trim();return`Liian pieni: arvon t\xe4ytyy olla ${t}${i.minimum.toString()}`}case"invalid_format":if("starts_with"===i.format)return`Virheellinen sy\xf6te: t\xe4ytyy alkaa "${i.prefix}"`;if("ends_with"===i.format)return`Virheellinen sy\xf6te: t\xe4ytyy loppua "${i.suffix}"`;if("includes"===i.format)return`Virheellinen sy\xf6te: t\xe4ytyy sis\xe4lt\xe4\xe4 "${i.includes}"`;if("regex"===i.format)return`Virheellinen sy\xf6te: t\xe4ytyy vastata s\xe4\xe4nn\xf6llist\xe4 lauseketta ${i.pattern}`;return`Virheellinen ${t[i.format]??i.format}`;case"not_multiple_of":return`Virheellinen luku: t\xe4ytyy olla luvun ${i.divisor} monikerta`;case"unrecognized_keys":return`${i.keys.length>1?"Tuntemattomat avaimet":"Tuntematon avain"}: ${V.joinValues(i.keys,", ")}`;case"invalid_key":return"Virheellinen avain tietueessa";case"invalid_union":return"Virheellinen unioni";case"invalid_element":return"Virheellinen arvo joukossa";default:return"Virheellinen syöte"}})}},"fr",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 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 ${i.origin}`}})}},"frCA",0,function(){let e,t;return{localeError:(e={string:{unit:"caractères",verb:"avoir"},file:{unit:"octets",verb:"avoir"},array:{unit:"éléments",verb:"avoir"},set:{unit:"éléments",verb:"avoir"}},t={regex:"entrée",email:"adresse 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${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 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