diff --git a/.circleci/config.yml b/.circleci/config.yml index 5e77729df29..4615a6a5a7e 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][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=20 \ -n 4 \ @@ -1030,7 +910,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5" no_output_timeout: 15m @@ -1074,7 +954,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 2 \ @@ -1120,7 +1000,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ --retries 3 --retry-delay 5" @@ -1211,7 +1091,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 4" @@ -1255,7 +1135,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 4" @@ -1274,40 +1154,6 @@ jobs: paths: - search_coverage.xml - search_coverage - litellm_mapped_enterprise_tests: - docker: - - *python312_image - working_directory: ~/project - resource_class: large - - steps: - - checkout - - skip_if_unrelated_changes - - setup_google_dns - - install_uv - - install_rust - - run: - name: Install Dependencies - command: | - uv sync --frozen --all-groups --all-extras --python 3.12 - - setup_litellm_enterprise_pip - - run: - name: Run enterprise tests - command: | - uv run --no-sync python -m prisma generate - mkdir -p test-results - TEST_FILES=$(circleci tests glob "tests/enterprise/**/test_*.py") - echo "$TEST_FILES" | circleci tests run \ - --verbose \ - --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ - -v -x \ - --junitxml=test-results/junit-enterprise.xml \ - --durations=10 \ - -n 4" - no_output_timeout: 15m - # Store test results - - store_test_results: - path: test-results batches_testing: docker: - *python312_image @@ -1333,7 +1179,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 2" @@ -1377,7 +1223,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 2" @@ -1422,7 +1268,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 \ -n 4" @@ -1501,7 +1347,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ -n 4 \ --junitxml=test-results/junit.xml \ --durations=5 \ @@ -1546,7 +1392,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5" no_output_timeout: 15m @@ -1599,7 +1445,7 @@ jobs: --verbose \ --command="tr ' ' '\\n' | awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ -vv -x -s \ - --cov=./litellm --cov-report=xml \ + --cov=./litellm --cov=./enterprise/litellm_enterprise --cov-report=xml \ --junitxml=test-results/junit.xml \ --durations=5 -n 2 \ --reruns 2 --reruns-delay 1" @@ -3105,12 +2951,6 @@ 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 +3007,6 @@ 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 +3029,6 @@ 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 ff8fa864d4a..f7a785b3b80 100644 --- a/.github/ci-coverage-allowlist.yml +++ b/.github/ci-coverage-allowlist.yml @@ -5,24 +5,21 @@ description: >- test_paths: - reason: >- - The caching suite in tests/local_testing, which runs nowhere. Every job that globs that - directory either deselects it (local_testing_part1 and part2 carry `-k "... and not caching - and not cache"`) or keeps only another keyword (langfuse, router, assistants), and no job - names these files the way redis_caching_unit_tests names test_dual_cache.py. Measured - 2026-08-20 by collecting the directory under each job's own selector: 118 tests across - these eight files are selected by none of them. Listed so the gap is a decision rather - than an accident, and so the --slices guard has a baseline to ratchet down from. Revisit - when tests/local_testing is ported off CircleCI, where the keyless part of this suite - belongs in a real job + What is left of the caching suite in tests/local_testing that runs nowhere. Every job that + globs that directory either deselects it (local_testing_part1 and part2 carry `-k "... and + not caching and not cache"`) or keeps only another keyword (langfuse, router, assistants), + and no job names these files the way redis_caching_unit_tests names test_dual_cache.py. + The gap was eight files and 118 tests when measured 2026-08-20; the five keyless ones now + run in the caching-local shard, leaving these three. Measured 2026-08-21 with no provider + credentials and no Redis: test_caching.py needs both (37 of 65 fail without them), + test_disk_cache_unit_tests.py needs OPENAI_API_KEY for 2 of its 4, and + test_gcs_cache_unit_tests.py needs GCS credentials for all 4. They want the keyless/live + split that porting tests/local_testing off CircleCI will force, not a job that is red by + construction paths: - - tests/local_testing/test_cache_preset_key.py - tests/local_testing/test_caching.py - - tests/local_testing/test_caching_handler.py - tests/local_testing/test_disk_cache_unit_tests.py - tests/local_testing/test_gcs_cache_unit_tests.py - - tests/local_testing/test_prompt_caching.py - - tests/local_testing/test_responses_stream_cache_keys.py - - tests/local_testing/test_unit_test_caching.py - reason: >- The end-to-end suite runs against a deployed proxy from its own in-cluster rig rather than from a pull request; it needs a live gateway and provider credentials no PR job holds @@ -48,16 +45,6 @@ test_paths: choice it informed is settled paths: - tests/code_coverage_tests/test_aio_http_image_conversion.py - - reason: >- - The last file of a second mirror that sat beside tests/test_litellm and ran nowhere. Its - other 33 files landed in the real mirror during August 2026, 30 as moves and 3 by merging - their bodies into the live file of the same name. This one cannot follow either route yet: - its live twin was rewritten from 1268 lines to 9434, and of the 19 tests here 5 have no - counterpart while 25 assertions fail against today's code, so what survives that rewrite - is a judgement about the endpoints, not a merge. Revisit by deciding which of the five - behaviours still hold - paths: - - tests/litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.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 @@ -102,17 +89,6 @@ test_paths: - tests/integration/sandbox/test_e2b_sandbox.py - tests/integration/test_oci_integration.py - tests/integration/test_oci_proxy_integration.py - - reason: >- - A unit test for the proxy-extras package that no job invokes, while the package's other - tests live under tests/proxy_migration_tests. Measured 2026-08-20: 24 of its 28 tests pass - and the 4 in TestMigrationSQLIdempotency fail, because 13 migrations from 2026-03 onward use - bare CREATE TABLE, ADD COLUMN, CREATE INDEX and ADD CONSTRAINT rather than the guarded forms - this file requires. It also matches those keywords inside SQL comments, so two further - migrations are reported that are in fact fine. Wiring it up means deciding what to do about - the 13 first, and they cannot simply be edited: Prisma checksums an applied migration, so a - changed one breaks migrate deploy for existing installs - paths: - - tests/litellm-proxy-extras/test_litellm_proxy_extras_utils.py dockerfiles: - reason: >- diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 4e428d8cebf..e85a397cbd2 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,7 +1,10 @@ + + ## TLDR - + Problem this solves: @@ -110,8 +113,20 @@ If you're seeing a delay in your PR being merged, ping the LiteLLM Team on [Slac ## Caveats (if any) - ## QA runbook @@ -134,6 +149,6 @@ Example checklists: - [ ] Sanity check: this test makes sense to add and is not hand-wavey (e.g., assert actual expected spend instead of just spend > 0) or potentially flaky --> -### Final Attestation +## Final Attestation - [ ] The tests check the right things, including the edge cases, and regressions in the respective real-world customer use-cases are not possible after this PR diff --git a/.github/scripts/assert_ci_coverage.py b/.github/scripts/assert_ci_coverage.py index c8572d9f6ef..411852acb98 100644 --- a/.github/scripts/assert_ci_coverage.py +++ b/.github/scripts/assert_ci_coverage.py @@ -312,8 +312,23 @@ def _matchable_names(relative_path: str) -> frozenset[str]: ) +def _workflow_named_tokens() -> frozenset[str]: + """Test tokens a GitHub Actions job names directly. + + A CircleCI `-k` that deselects a file no longer means the file runs nowhere once a + workflow names it, so the slice check has to credit those the same way the census does. + """ + return _invoked_test_tokens( + scalar + for path in _config_files() + if path != CIRCLECI_CONFIG + for scalar in _scalars(yaml.safe_load(path.read_text(encoding="utf-8")), path.name) + ) + + def _deselected_everywhere(allowlist: Allowlist) -> tuple[Finding, ...]: slices: Final = _slices() + named_by_workflow: Final = _workflow_named_tokens() globbed: Final = tuple( path for path in _test_files() @@ -326,6 +341,7 @@ def _deselected_everywhere(allowlist: Allowlist) -> tuple[Finding, ...]: ) for path in globbed if not allowlist.covers_test(path) + and not any(_token_covers(token, path) for token in named_by_workflow) and not any(slice_.claims(path, _matchable_names(path)) for slice_ in slices) ) diff --git a/.github/scripts/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/_test-unit-base.yml b/.github/workflows/_test-unit-base.yml index 54f50524a39..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: | ${{ runner.os }}-uv- + - name: Cache the Rust build + if: steps.changes.outputs.decision != 'skip' + timeout-minutes: 5 + uses: ./.github/actions/cache-cargo-build + - name: Install dependencies if: steps.changes.outputs.decision != 'skip' timeout-minutes: 8 @@ -144,7 +149,7 @@ jobs: --reruns "${RERUNS}" \ --reruns-delay 1 \ --durations=20 \ - --cov=./litellm \ + --cov=./litellm --cov=./enterprise/litellm_enterprise \ --cov-report=xml:coverage.xml \ --cov-config=pyproject.toml else @@ -156,7 +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/check-ui-api-types.yml b/.github/workflows/check-ui-api-types.yml index dbd663a2efa..285676a0ddd 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 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..602c26a3e98 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 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/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 e031ba46773..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: | 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-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..e46432e0e31 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 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.yml b/.github/workflows/test-unit.yml index 3d6fffe7304..c23678c51ae 100644 --- a/.github/workflows/test-unit.yml +++ b/.github/workflows/test-unit.yml @@ -55,7 +55,7 @@ jobs: workers: 2 reruns: 1 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 - shard: enterprise-routing artifact-name: enterprise-routing @@ -67,7 +67,7 @@ jobs: workers: 2 reruns: 2 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 - shard: integrations artifact-name: integrations @@ -75,7 +75,7 @@ jobs: workers: 2 reruns: 3 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 - shard: Vertex AI artifact-name: llm-vertex-ai @@ -83,7 +83,7 @@ jobs: workers: 1 reruns: 2 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 - shard: All Other Providers artifact-name: llm-other-providers @@ -91,7 +91,7 @@ jobs: workers: 2 reruns: 2 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 - shard: misc artifact-name: misc @@ -122,7 +122,7 @@ jobs: workers: 2 reruns: 2 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 - shard: proxy-auth artifact-name: proxy-auth @@ -134,7 +134,7 @@ jobs: workers: 2 reruns: 2 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 - shard: proxy-endpoints artifact-name: proxy-endpoints @@ -164,14 +164,15 @@ jobs: tests/test_litellm/proxy/public_endpoints tests/test_litellm/proxy/prompts tests/test_litellm/proxy/rag_endpoints + tests/test_litellm/proxy/rerank_endpoints tests/test_litellm/proxy/realtime_endpoints tests/test_litellm/proxy/ui_crud_endpoints tests/test_litellm/proxy/config_resolvers tests/test_litellm/proxy/utils - workers: 2 + workers: 4 reruns: 2 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 - shard: proxy-server artifact-name: proxy-server @@ -179,7 +180,7 @@ jobs: workers: 4 reruns: 2 timeout-minutes: 60 - job-timeout-minutes: 95 + job-timeout-minutes: 100 - shard: proxy-infra artifact-name: proxy-infra @@ -195,10 +196,39 @@ jobs: tests/test_litellm/proxy/types_utils tests/test_litellm/proxy/logging_endpoints tests/test_litellm/proxy/test_*.py + workers: 4 + reruns: 2 + timeout-minutes: 20 + job-timeout-minutes: 60 + + - shard: caching-local + artifact-name: caching-local + test-path: >- + tests/local_testing/test_cache_preset_key.py + tests/local_testing/test_caching_handler.py + tests/local_testing/test_prompt_caching.py + tests/local_testing/test_responses_stream_cache_keys.py + tests/local_testing/test_unit_test_caching.py workers: 2 reruns: 2 timeout-minutes: 20 - job-timeout-minutes: 55 + 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 @@ -209,7 +239,7 @@ jobs: workers: 2 reruns: 2 timeout-minutes: 20 - job-timeout-minutes: 55 + job-timeout-minutes: 60 uses: ./.github/workflows/_test-unit-base.yml with: test-path: ${{ matrix.test-path }} 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/CLAUDE.md b/CLAUDE.md index b3383b4a895..cbc6bd6848c 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 @@ -79,6 +80,8 @@ 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/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 776aecbd883..e3bac754074 100644 --- a/basedpyright-code-budget.json +++ b/basedpyright-code-budget.json @@ -1,18 +1,18 @@ { "reportAny": { - "limit": 19955 + "limit": 18505 }, "reportArgumentType": { - "limit": 2566 + "limit": 2564 }, "reportAssignmentType": { "limit": 320 }, "reportAttributeAccessIssue": { - "limit": 488 + "limit": 483 }, "reportCallIssue": { - "limit": 114 + "limit": 113 }, "reportConstantRedefinition": { "limit": 40 @@ -24,7 +24,7 @@ "limit": 19 }, "reportExplicitAny": { - "limit": 6049 + "limit": 5976 }, "reportFunctionMemberAccess": { "limit": 7 @@ -45,7 +45,7 @@ "limit": 35 }, "reportInvalidTypeForm": { - "limit": 35 + "limit": 34 }, "reportInvalidTypeVarUse": { "limit": 2 @@ -54,10 +54,10 @@ "limit": 0 }, "reportMissingParameterType": { - "limit": 5663 + "limit": 5659 }, "reportMissingTypeArgument": { - "limit": 15555 + "limit": 15504 }, "reportMissingTypeStubs": { "limit": 40 @@ -72,7 +72,7 @@ "limit": 0 }, "reportOptionalMemberAccess": { - "limit": 1061 + "limit": 1058 }, "reportOptionalOperand": { "limit": 0 @@ -84,7 +84,7 @@ "limit": 56 }, "reportPrivateUsage": { - "limit": 1823 + "limit": 1810 }, "reportRedeclaration": { "limit": 8 @@ -99,31 +99,31 @@ "limit": 0 }, "reportUnknownArgumentType": { - "limit": 44655 + "limit": 44530 }, "reportUnknownLambdaType": { "limit": 109 }, "reportUnknownMemberType": { - "limit": 39011 + "limit": 38828 }, "reportUnknownParameterType": { - "limit": 19885 + "limit": 19847 }, "reportUnknownVariableType": { - "limit": 30569 + "limit": 30386 }, "reportUnnecessaryCast": { "limit": 117 }, "reportUnnecessaryComparison": { - "limit": 699 + "limit": 697 }, "reportUnnecessaryContains": { "limit": 5 }, "reportUnnecessaryIsInstance": { - "limit": 836 + "limit": 833 }, "reportUntypedBaseClass": { "limit": 0 diff --git a/ci_cd/generate_model_prices_schema.py b/ci_cd/generate_model_prices_schema.py index 252e3675329..b2bc3ebadb4 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", } ) 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 4bb00408fc3..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 @@ -351,7 +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.""" @@ -966,6 +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/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/pyproject.toml b/enterprise/pyproject.toml index 8bbde7f3764..b7a62e52cf9 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.58" +version = "0.1.60" 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 = "0.1.58" +version = "0.1.60" 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/values.yaml b/helm/litellm/values.yaml index 998d225a317..06ba72d84b3 100644 --- a/helm/litellm/values.yaml +++ b/helm/litellm/values.yaml @@ -428,9 +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/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 26d42a33b29..98a3d8d535e 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.88" +version = "0.4.89" 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 = "0.4.88" +version = "0.4.89" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index e95b553c5d4..eebd2dad91e 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 @@ -463,6 +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 @@ -1628,6 +1634,9 @@ if TYPE_CHECKING: AmazonMantleMessagesConfig as AmazonMantleMessagesConfig, ) from .llms.together_ai.chat import TogetherAIConfig as TogetherAIConfig + from .llms.together_ai.chat.transformation import ( + TogetherAIChatConfig as TogetherAIChatConfig, + ) from .llms.nlp_cloud.chat.handler import NLPCloudConfig as NLPCloudConfig from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( VertexGeminiConfig as VertexGeminiConfig, @@ -1801,6 +1810,9 @@ 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 e55c6bc40a8..36fd51206c2 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -88,6 +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.""" @@ -97,6 +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 diff --git a/litellm/_redis.py b/litellm/_redis.py index f3f3c4424de..58f37cf569d 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -665,8 +665,16 @@ 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, ) 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..6eb13d2cba7 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 = { 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/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_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 737d212a89d..c66f6873383 100644 --- a/litellm/caching/valkey_semantic_cache.py +++ b/litellm/caching/valkey_semantic_cache.py @@ -30,6 +30,7 @@ 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 @@ -62,6 +63,7 @@ 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: Any, ): if similarity_threshold is None: @@ -80,6 +82,7 @@ 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 diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 6103b1bf484..17815976b4a 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, @@ -85,6 +89,22 @@ 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, @@ -372,8 +392,15 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): ) ) elif role == "assistant" and tool_calls and isinstance(tool_calls, list): - for r_item in _get_reasoning_items(msg): - input_items.append(_reasoning_item_to_response_input(r_item)) + input_items.extend(_reasoning_input_items(msg)) + if content: + input_items.append( + { # mutable-ok: API message payload + "type": "message", + "role": "assistant", + "content": self._convert_content_to_responses_format(content, "assistant"), + } + ) for tool_call in tool_calls: function = tool_call.get("function") custom = tool_call.get("custom") @@ -400,15 +427,16 @@ 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 @@ -1086,22 +1114,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): litellm.reasoning_auto_summary or os.getenv("LITELLM_REASONING_AUTO_SUMMARY", "false").lower() == "true" ) - # If string is passed, map with optional summary based on flag/env var - if reasoning_effort == "none": - return Reasoning(effort="none", summary="detailed") if auto_summary_enabled else Reasoning(effort="none") - elif reasoning_effort == "high": - return Reasoning(effort="high", summary="detailed") if auto_summary_enabled else Reasoning(effort="high") - elif reasoning_effort == "xhigh": - return Reasoning(effort="xhigh", summary="detailed") if auto_summary_enabled else Reasoning(effort="xhigh") - elif reasoning_effort == "medium": + if reasoning_effort in get_args(REASONING_EFFORT): return ( - Reasoning(effort="medium", summary="detailed") if auto_summary_enabled else Reasoning(effort="medium") - ) - elif reasoning_effort == "low": - return Reasoning(effort="low", summary="detailed") if auto_summary_enabled else Reasoning(effort="low") - elif reasoning_effort == "minimal": - return ( - Reasoning(effort="minimal", summary="detailed") if auto_summary_enabled else Reasoning(effort="minimal") + Reasoning(effort=reasoning_effort, summary="detailed") + if auto_summary_enabled + else Reasoning(effort=reasoning_effort) ) return None diff --git a/litellm/constants.py b/litellm/constants.py index e0dcaaf2332..23e92d26a59 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -48,6 +48,9 @@ LITELLM_MAX_STREAMING_DURATION_SECONDS: Final = ( # Data URIs exceeding this are replaced with a size placeholder. # Set to 0 to disable truncation. MAX_BASE64_LENGTH_FOR_LOGGING: Final = int(os.getenv("MAX_BASE64_LENGTH_FOR_LOGGING", 64)) +REDACTED_BY_LITELLM: Final = "redacted-by-litellm" +# in-memory stand-in handed to provider converters for redacted arguments; never stored +REDACTED_TOOL_CALL_ARGUMENTS_PLACEHOLDER: Final = "{}" MAX_STRING_LENGTH_STDOUT_LOG: Final = get_env_int("MAX_STRING_LENGTH_STDOUT_LOG", 4096) @@ -146,6 +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 @@ -436,6 +440,9 @@ 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 @@ -457,6 +464,8 @@ 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) @@ -746,6 +755,7 @@ 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", @@ -780,6 +790,7 @@ openai_compatible_endpoints: Final[list] = [ "https://pinstripes.io/v1", "https://api.meta.ai/v1", "https://api.cognition.ai/v1", + "https://api.scx.ai/v1", ] @@ -848,6 +859,7 @@ openai_compatible_providers: Final[list] = [ "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", @@ -1351,6 +1363,8 @@ X_LITELLM_DISABLE_CALLBACKS: Final = "x-litellm-disable-callbacks" LITELLM_METADATA_FIELD: Final = "litellm_metadata" OLD_LITELLM_METADATA_FIELD: Final = "metadata" RETURN_RAW_MODEL_NAME_METADATA_KEY: Final = "_complexity_router_return_raw_model_name" +AUTO_ROUTED_REQUEST_METADATA_KEY: Final = "_auto_routed_request" +ROUTER_MODEL_NAME_RESPONSE_FIELD: Final = "router_model_name" SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: Final = "_session_deployment_affinity_ttl" CONSUMED_REQUEST_TAGS_METADATA_KEY: Final = "_consumed_request_tags" INTERNAL_CALL_ORIGIN_METADATA_KEY: Final = "internal_call_origin" @@ -1531,9 +1545,12 @@ 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)) @@ -1553,6 +1570,19 @@ 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) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 8f7cd09d364..6536941a094 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -19,6 +19,7 @@ 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 ( @@ -150,6 +151,7 @@ _VIDEO_CALL_TYPES: Final = frozenset( } ) + _SPEECH_CALL_TYPES: Final = frozenset( { CallTypes.speech.value, @@ -912,6 +914,8 @@ 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 +1292,10 @@ 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 +1380,36 @@ 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 ( 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/callback_configs.json b/litellm/integrations/callback_configs.json index 590c848767a..6d2bcea8bae 100644 --- a/litellm/integrations/callback_configs.json +++ b/litellm/integrations/callback_configs.json @@ -294,12 +294,18 @@ "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 7ea60053e6f..76720682101 100644 --- a/litellm/integrations/compression_interception/handler.py +++ b/litellm/integrations/compression_interception/handler.py @@ -7,7 +7,7 @@ litellm_content_retrieve tool calls server-side via the typed agentic loop plan. import time import uuid -from typing import Any, Final, cast +from typing import Any, ClassVar, Final, cast from litellm._logging import verbose_logger from litellm.compression import compress @@ -72,6 +72,8 @@ 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_logger.py b/litellm/integrations/custom_logger.py index a0c78674ac8..c957fd6e61e 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -2,8 +2,8 @@ # On success, logs events to Promptlayer import re import traceback -from collections.abc import AsyncGenerator -from typing import TYPE_CHECKING, Any, Final, Optional +from collections.abc import AsyncGenerator, Mapping +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, Any], + 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/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index a93c45ef840..3d044c3ea15 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", ""), 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/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..244e58eddf3 100644 --- a/litellm/integrations/otel/emitter.py +++ b/litellm/integrations/otel/emitter.py @@ -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. diff --git a/litellm/integrations/otel/logger.py b/litellm/integrations/otel/logger.py index 53b9829023c..4359b222d06 100644 --- a/litellm/integrations/otel/logger.py +++ b/litellm/integrations/otel/logger.py @@ -484,10 +484,15 @@ class OpenTelemetryV2(CustomLogger): # ``pop`` is the dedup: this method runs from both the success and failure # paths, and whichever fires first removes the carrier and closes the span. carrier: Final = self._open_llm_calls.pop(call_id, None) if call_id else None - if carrier is None: + # A missing carrier does not always mean nothing happened: a team/key-scoped + # logger is a success/failure callback only, so ``pre_call`` never reaches it + # and no carrier exists. The payload plus the request-level provider-handoff + # stamp (``upstream_started``) is the affirmative signal of a real call; a + # gate rejection carries ``is_no_upstream_call`` and gets no span. + if carrier is None and (call.is_no_upstream_call or not call.upstream_started or call.payload is None): return None try: - return self._finish_carrier(carrier, call, end_time) + return self._finish_carrier(carrier, call, start_time, end_time) finally: # After the span has ended, so a release-triggered provider shutdown # force-flushes it out rather than racing its enqueue. @@ -497,8 +502,11 @@ class OpenTelemetryV2(CustomLogger): """Remember an in-flight LLM call, evicting the oldest if over budget. A call that opens but never closes (a stream that only fires stream - events) would linger otherwise; the evicted span is simply dropped - (never exported). + events) would linger otherwise. Eviction only drops the boundary carrier, + not the call: if that call later closes as a real completed call, it still + emits through the deferred branch in ``_close_llm_call`` (the same path a + team/key-scoped logger uses, since it never opens a carrier), deduplicated + by call id. Only a call that is evicted and never closes goes unexported. """ self._open_llm_calls[call_id] = carrier if len(self._open_llm_calls) > _OPEN_CALLS_MAX: @@ -512,15 +520,20 @@ class OpenTelemetryV2(CustomLogger): def _finish_carrier( self, - carrier: _LLMCallSpan, + carrier: "_LLMCallSpan | None", call: LLMCallEvent, + start_time: datetime | float | None, end_time: datetime | float | None, ) -> Span | None: payload: Final = call.payload + call_id: Final = call.call_id if payload is None: - if carrier.span is not None: + if carrier is not None and carrier.span is not None: # Opened at the boundary but the payload never materialized — end - # it (named provisionally) so it isn't leaked as an open span. + # it (named provisionally) so it isn't leaked as an open span, and + # register the dedup marker so a later payload-carrying close for + # the same call id cannot re-emit through the deferred branch. + self._emitter.mark_emitted(call_id, SpanRole.LLM_CALL) carrier.span.end(end_time=to_ns(end_time)) return None data: Final = LLMCallSpanData.from_standard_logging_payload( @@ -529,10 +542,13 @@ class OpenTelemetryV2(CustomLogger): time_to_first_chunk_seconds=call.time_to_first_chunk_seconds, ) end_time_ns: Final = to_ns(end_time) - if carrier.span is not None: + if carrier is not None and carrier.span is not None: # Born at the boundary: stamp attributes from the typed payload, set # status, and end it. Its parent (the server span) was captured at - # creation from real ambient context. + # creation from real ambient context. Register the dedup marker so a + # second close for the same call id (success then failure on one + # logging object) cannot re-emit through the deferred branch. + self._emitter.mark_emitted(call_id, SpanRole.LLM_CALL) self._emitter.finish_span(SpanRole.LLM_CALL, carrier.span, data, end_time_ns=end_time_ns) return carrier.span # Deferred: ``pre_call`` saw no recordable parent, so create the span now. @@ -549,7 +565,7 @@ class OpenTelemetryV2(CustomLogger): SpanRole.LLM_CALL, data, parent_context=(set_span_in_context(INVALID_SPAN, parent_ctx) if route.detached else parent_ctx), - start_time_ns=carrier.start_time_ns, + start_time_ns=(carrier.start_time_ns if carrier is not None else to_ns(start_time)), end_time_ns=end_time_ns, tracer=route.tracer, links=_request_trace_links(parent_ctx) if route.detached else None, diff --git a/litellm/integrations/otel/mappers/genai.py b/litellm/integrations/otel/mappers/genai.py index 79487e69ac4..5e3401cd62c 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. diff --git a/litellm/integrations/otel/model/config.py b/litellm/integrations/otel/model/config.py index 53178b48991..9e3064c2bff 100644 --- a/litellm/integrations/otel/model/config.py +++ b/litellm/integrations/otel/model/config.py @@ -39,6 +39,7 @@ class ExporterOwner(str, Enum): WEAVE_OTEL = "weave_otel" LEVO = "levo" AGENTOPS = "agentops" + NEWRELIC = "newrelic" class _OTelV2Flag(BaseSettings): @@ -97,6 +98,15 @@ class ExporterSpec(BaseModel): "auto (Simple for console/in_memory, Batch otherwise)." ), ) + requires_headers: bool = Field( + default=False, + description=( + "Skip this exporter when no headers are resolved. For destinations " + "that reject unauthenticated exports (e.g. New Relic), a spec kept " + "only as the per-request credential-stamping target would otherwise " + "export keyless traffic and produce a 4xx for every span batch." + ), + ) class OpenTelemetryV2Config(BaseSettings): diff --git a/litellm/integrations/otel/model/metadata.py b/litellm/integrations/otel/model/metadata.py index de6366e7dbd..062b2ca20b4 100644 --- a/litellm/integrations/otel/model/metadata.py +++ b/litellm/integrations/otel/model/metadata.py @@ -203,6 +203,11 @@ class LLMCallEvent: # True for synthetic proxy-gate logs (auth / rate-limit rejections): they fire # the ``pre_call`` hook but never made an upstream call, so they get no span. is_no_upstream_call: bool + # True once the request handed off to a provider (``pre_call`` stamped + # ``api_call_start_time``). The affirmative signal that an LLM call was + # actually attempted — router pre-call rejections, SDK failures before the + # provider handoff, and standalone guardrail runs all lack it. + upstream_started: bool # A best-effort ``"{operation} {model}"`` name known at ``pre_call`` time. The # span is renamed from the typed payload at close (``finish_span``); this only # needs to be reasonable for a span that never gets closed (a leak). @@ -221,6 +226,7 @@ class LLMCallEvent: dynamic_params=kwargs.get("standard_callback_dynamic_params"), auth_metadata=auth_metadata(payload, kwargs), is_no_upstream_call=bool(kwargs.get(LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL)), + upstream_started=kwargs.get("api_call_start_time") is not None, provisional_span_name=f"{operation.value} {model}".strip(), time_to_first_chunk_seconds=time_to_first_chunk_seconds(kwargs), ) diff --git a/litellm/integrations/otel/model/payloads.py b/litellm/integrations/otel/model/payloads.py index aba9cc80240..4e4ed4b7513 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 ( @@ -310,6 +312,11 @@ 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 +341,9 @@ 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 +366,8 @@ 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..1647e0a5bd1 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 +32,21 @@ 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_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 +275,11 @@ 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" @@ -352,6 +374,16 @@ _OPERATION_BY_CALL_TYPE: Final[dict[str, GenAIOperation]] = { "aembedding": GenAIOperation.EMBEDDINGS, "responses": GenAIOperation.CHAT, "aresponses": GenAIOperation.CHAT, + "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 +417,23 @@ _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 +465,11 @@ 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/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 f231df9e914..6a04dbb9bc8 100644 --- a/litellm/integrations/otel/plumbing/routing.py +++ b/litellm/integrations/otel/plumbing/routing.py @@ -28,6 +28,7 @@ from litellm.integrations.otel.plumbing.providers import ( get_tracer, ) from litellm.integrations.otel.presets import ( + dynamic_otlp_endpoint, dynamic_otlp_headers, project_routing_headers, ) @@ -129,7 +130,9 @@ class TenantTracerCache: # thread-pool workers concurrently with the event loop, so cache # updates, span counts, and retirement must be atomic. self._lock: Final = threading.Lock() - self._providers: OrderedDict[tuple[_HeaderItems, _HeaderItems], TracerProvider] = OrderedDict() + self._providers: OrderedDict[tuple[_HeaderItems, _HeaderItems, str | None], 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 @@ -182,12 +185,16 @@ class TenantTracerCache: project_headers: Final = self._project_headers(auth_metadata) if not credential_headers and not project_headers: 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, ) with self._lock: - provider: Final = self._cached_provider_locked(cache_key, credential_headers, project_headers) + provider: Final = self._cached_provider_locked(cache_key, credential_headers, project_headers, endpoint) 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: @@ -200,15 +207,16 @@ class TenantTracerCache: def _cached_provider_locked( self, - cache_key: tuple[_HeaderItems, _HeaderItems], + cache_key: tuple[_HeaderItems, _HeaderItems, str | None], credential_headers: Mapping[str, str], project_headers: Mapping[str, str], + endpoint: str | None, ) -> TracerProvider: cached: Final = self._providers.get(cache_key) if cached is not None: self._providers.move_to_end(cache_key) return cached - built: Final = build_tracer_provider(self._routed_config(credential_headers, project_headers)) + built: Final = build_tracer_provider(self._routed_config(credential_headers, project_headers, endpoint)) self._providers[cache_key] = built return built @@ -257,6 +265,7 @@ class TenantTracerCache: self, credential_headers: Mapping[str, str], project_headers: Mapping[str, str], + endpoint: str | None = None, ) -> OpenTelemetryV2Config: """Clone the config, rewriting headers on the callback's own exporter. @@ -272,7 +281,8 @@ class TenantTracerCache: ``Authorization``), which must survive routing to a project. """ exporters: Final = [ - self._routed_exporter(spec, credential_headers, project_headers) for spec in self._config.exporters + self._routed_exporter(spec, credential_headers, project_headers, endpoint) + for spec in self._config.exporters ] return self._config.model_copy(update={"exporters": exporters}) @@ -281,6 +291,7 @@ class TenantTracerCache: spec: ExporterSpec, credential_headers: Mapping[str, str], project_headers: Mapping[str, str], + endpoint: str | None = None, ) -> ExporterSpec: kind: Final = spec.kind.lower() if spec.owner != self._callback_name or kind in _NON_OTLP_KINDS: @@ -291,4 +302,10 @@ class TenantTracerCache: if project_headers and kind not in _GRPC_KINDS else base ) - return spec if routed == spec.headers else spec.model_copy(update={"headers": routed}) + update: Final = { # mutable-ok: model_copy(update=...) requires a plain dict + field: value + for field, value in (("headers", routed), ("endpoint", endpoint)) + if (field == "headers" and routed != spec.headers) + or (field == "endpoint" and endpoint is not None and endpoint != spec.endpoint) + } + return spec if not update else spec.model_copy(update=update) diff --git a/litellm/integrations/otel/presets/__init__.py b/litellm/integrations/otel/presets/__init__.py index 35b0584c697..a0cd5b3fd98 100644 --- a/litellm/integrations/otel/presets/__init__.py +++ b/litellm/integrations/otel/presets/__init__.py @@ -21,6 +21,11 @@ from litellm.integrations.otel.presets.langfuse import ( ) from litellm.integrations.otel.presets.langtrace import langtrace_preset from litellm.integrations.otel.presets.levo import levo_preset +from litellm.integrations.otel.presets.newrelic import ( + newrelic_dynamic_endpoint, + newrelic_dynamic_headers, + newrelic_preset, +) from litellm.integrations.otel.presets.phoenix import ( phoenix_preset, phoenix_project_headers, @@ -30,25 +35,45 @@ from litellm.types.utils import StandardCallbackDynamicParams #: Callback name → preset. The ``Preset`` annotation makes mypy verify every #: registered value matches the preset interface. -PRESET_BY_CALLBACK: Final[dict[str, Preset]] = { - "agentops": agentops_preset, - "arize": arize_preset, - "arize_phoenix": phoenix_preset, - "langfuse_otel": langfuse_preset, - "langtrace": langtrace_preset, - "levo": levo_preset, - "weave_otel": weave_preset, -} +PRESET_BY_CALLBACK: Final[Mapping[str, Preset]] = MappingProxyType( + { + "agentops": agentops_preset, + "arize": arize_preset, + "arize_phoenix": phoenix_preset, + "langfuse_otel": langfuse_preset, + "langtrace": langtrace_preset, + "levo": levo_preset, + "newrelic": newrelic_preset, + "weave_otel": weave_preset, + } +) #: Callback name → per-request OTLP header builder (team/key multi-tenant #: routing). Only integrations that support dynamic credentials appear here — #: Arize-Phoenix/Langtrace/Levo/AgentOps don't, so they use the logger's #: default tracer. -DYNAMIC_HEADERS_BY_CALLBACK: Final[dict[str, Callable[[StandardCallbackDynamicParams], dict[str, str]]]] = { - "arize": arize_dynamic_headers, - "langfuse_otel": langfuse_dynamic_headers, - "weave_otel": weave_dynamic_headers, -} +DYNAMIC_HEADERS_BY_CALLBACK: Final[Mapping[str, Callable[[StandardCallbackDynamicParams], dict[str, str]]]] = ( + MappingProxyType( + { + "arize": arize_dynamic_headers, + "langfuse_otel": langfuse_dynamic_headers, + "newrelic": newrelic_dynamic_headers, + "weave_otel": weave_dynamic_headers, + } + ) +) + +#: Callback name → per-request OTLP endpoint resolver. Only integrations whose +#: destination host varies per tenant (from a fixed region table, never a +#: caller-supplied URL) appear here; for everyone else the preset's endpoint is +#: authoritative. +DYNAMIC_ENDPOINT_BY_CALLBACK: Final[Mapping[str, Callable[[StandardCallbackDynamicParams], str | None]]] = ( + MappingProxyType( + { + "newrelic": newrelic_dynamic_endpoint, + } + ) +) #: Callback name → per-request *routing* header builder, sourced from the key/team @@ -98,17 +123,34 @@ def project_routing_headers( return builder(auth_metadata) +def dynamic_otlp_endpoint( + callback_name: str | None, + dynamic_params: StandardCallbackDynamicParams | None, +) -> str | None: + """Per-request OTLP endpoint for ``callback_name``, or ``None`` if N/A. + + ``None`` means "keep the preset's own endpoint". + """ + resolver: Final = DYNAMIC_ENDPOINT_BY_CALLBACK.get(callback_name or "") + if resolver is None or not dynamic_params: + return None + return resolver(dynamic_params) + + __all__ = [ + "DYNAMIC_ENDPOINT_BY_CALLBACK", "DYNAMIC_HEADERS_BY_CALLBACK", "PRESET_BY_CALLBACK", "PROJECT_HEADERS_BY_CALLBACK", "Preset", "agentops_preset", "arize_preset", + "dynamic_otlp_endpoint", "dynamic_otlp_headers", "langfuse_preset", "langtrace_preset", "levo_preset", + "newrelic_preset", "phoenix_preset", "project_routing_headers", "weave_preset", diff --git a/litellm/integrations/otel/presets/newrelic.py b/litellm/integrations/otel/presets/newrelic.py new file mode 100644 index 00000000000..4660a707355 --- /dev/null +++ b/litellm/integrations/otel/presets/newrelic.py @@ -0,0 +1,104 @@ +"""New Relic preset — OTLP/HTTP exporter to New Relic + GenAI vocabulary.""" + +from collections.abc import Mapping +from types import MappingProxyType +from typing import Final + +from pydantic import Field +from pydantic_settings import BaseSettings, SettingsConfigDict + +from litellm._logging import verbose_logger +from litellm.integrations.otel.model.config import ( + ExporterOwner, + ExporterSpec, + OpenTelemetryV2Config, +) +from litellm.integrations.otel.presets.utils import ensure_mappers +from litellm.types.utils import StandardCallbackDynamicParams + +#: Region -> OTLP base endpoint. A fixed table by design: team config picks a +#: region enum rather than a free-form endpoint, so callback vars can never +#: redirect telemetry to an arbitrary host. +NEWRELIC_OTLP_ENDPOINT_BY_REGION: Final[Mapping[str, str]] = MappingProxyType( + { + "us": "https://otlp.nr-data.net", + "eu": "https://otlp.eu01.nr-data.net", + } +) + +_DEFAULT_REGION: Final = "us" + + +class _NewRelicSettings(BaseSettings): + model_config = SettingsConfigDict(case_sensitive=False, extra="ignore") + + # The same env vars the agent-based integration documents; the key is the + # operator-level fallback for traffic without team credentials, the region + # picks that fallback's data center, and the record-content flag keeps its + # documented meaning when the OTel path replaces the agent. + license_key: str | None = Field(default=None, validation_alias="NEW_RELIC_LICENSE_KEY") + region: str | None = Field(default=None, validation_alias="NEW_RELIC_REGION") + record_content: bool | None = Field(default=None, validation_alias="NEW_RELIC_AI_MONITORING_RECORD_CONTENT_ENABLED") + + +def newrelic_preset( + *, + config_overrides: OpenTelemetryV2Config | None = None, +) -> OpenTelemetryV2Config: + settings: Final = _NewRelicSettings() + base: Final = config_overrides or OpenTelemetryV2Config() + endpoint: Final = NEWRELIC_OTLP_ENDPOINT_BY_REGION.get( + (settings.region or _DEFAULT_REGION).lower(), NEWRELIC_OTLP_ENDPOINT_BY_REGION[_DEFAULT_REGION] + ) + return base.model_copy( + update={ + "exporters": [ + *base.exporters, + ExporterSpec( + kind="otlp_http", + endpoint=endpoint, + headers=(f"api-key={settings.license_key}" if settings.license_key else None), + owner=ExporterOwner.NEWRELIC, + requires_headers=True, + ), + ], + # New Relic ingests the OTLP GenAI semantic conventions natively. + "mapper_names": ensure_mappers(base.mapper_names, "genai"), + **( + {"capture_message_content": ("span_only" if settings.record_content else "no_content")} + if settings.record_content is not None + else {} + ), + } + ) + + +def newrelic_dynamic_headers(params: StandardCallbackDynamicParams) -> dict[str, str]: + """Per-request New Relic OTLP headers from team/key dynamic params.""" + api_key: Final = params.get("newrelic_api_key") + return {header: value for header, value in (("api-key", api_key),) if value} + + +def newrelic_dynamic_endpoint(params: StandardCallbackDynamicParams) -> str: + """Per-request OTLP endpoint for the team's ``newrelic_region``. + + Always the team's own region endpoint, defaulting to US when the team left + the region unset. It never falls through to the preset's endpoint, which + follows the operator's ``NEW_RELIC_REGION`` env; a team that saved only its + ingest key must not inherit the operator's region and have its US-account + spans rejected by an EU-configured default (or vice versa). An unknown + region likewise resolves to the documented US default rather than a guess. + """ + region: Final = params.get("newrelic_region") + default_endpoint: Final = NEWRELIC_OTLP_ENDPOINT_BY_REGION[_DEFAULT_REGION] + if not region: + return default_endpoint + endpoint: Final = NEWRELIC_OTLP_ENDPOINT_BY_REGION.get(region.lower()) + if endpoint is None: + verbose_logger.warning( + "New Relic: unknown newrelic_region %r; supported regions: %s. Using the default (US) endpoint.", + region, + ", ".join(sorted(NEWRELIC_OTLP_ENDPOINT_BY_REGION)), + ) + return default_endpoint + return endpoint diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 76066f4a305..e185283d427 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: + val = _metadata.get("user_api_key_user_email") + if val is not None: + return val + val = _litellm_params_metadata.get("user_api_key_user_email") + if val is not None: + return val + 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", []), 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/websearch_interception/ARCHITECTURE.md b/litellm/integrations/websearch_interception/ARCHITECTURE.md index ce7f01c5a2a..4ea7a7ae527 100644 --- a/litellm/integrations/websearch_interception/ARCHITECTURE.md +++ b/litellm/integrations/websearch_interception/ARCHITECTURE.md @@ -207,6 +207,59 @@ response = await litellm.messages.acreate( --- +## Loop Ceiling + +One intercepted request can chain several follow-up model calls, since the model often searches again after +reading the first set of results. `max_agentic_loops` caps how many of those follow-ups run, and it defaults +to 3. LiteLLM also breaks the loop early when the model asks for the exact same tool call twice in a row. + +Set the ceiling on the feature, which the interceptor applies to `/v1/messages` requests: + +```yaml +litellm_settings: + websearch_interception_params: + enabled_providers: ["bedrock"] + max_agentic_loops: 5 +``` + +Or per deployment, which wins over the feature-level setting: + +```yaml +model_list: + - model_name: claude-sonnet-4-5 + litellm_params: + model: bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0 + max_agentic_loops: 5 +``` + +Clients cannot set it. `max_agentic_loops` is on the proxy's untrusted-field list, so a request body that +carries it is ignored and one request can never drive an unbounded number of upstream model calls. + +Both places are validated at config load, and a value that is not an integer of at least 1 stops the proxy +from starting rather than surfacing later. The per-deployment one is checked while the model list is read, +not on `LiteLLM_Params`, because the proxy builds its router with `ignore_invalid_deployments=True` and a +validator down there would drop the deployment silently instead of refusing to start. + +When the ceiling is reached on a non-streaming `/v1/messages` request, the turn ends there and the client gets +the last response back with the internal `litellm_web_search` tool call removed and `stop_reason: end_turn`. +The client never declared that tool, so leaving the block in would hand it a tool call it has no way to answer. +The answer can be less complete than it would have been with more loops, which is the tradeoff the ceiling +buys. Where the refused call was the only block left, the turn comes back with no text in it at all. + +Non-streaming is not a limitation on the client here, because a client that asked for a stream gets the same +treatment. Interception converts an intercepted `stream=True` request to non-streaming before the loop runs and +rebuilds the SSE stream from the finalized turn afterwards, so the ceiling is always reached on a response the +client has not seen yet. `AgenticStreamingIterator` is the one caller that reaches the loop with its events +already on the wire, and it keeps raising, because a finalized turn would arrive there as a second message +rather than as a replacement. + +Two other surfaces do not get that treatment yet. `/v1/responses` returns its own shape that the finalizer does +not rewrite, so it still hands back the internal call. And `/v1/chat/completions` runs its own copy of these +rails in `litellm_core_utils/chat_completion_agentic_loop.py`, which still raises rather than ending the turn. +Both are tracked separately + +--- + ## Streaming Support WebSearch interception works transparently with both streaming and non-streaming requests. diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index e59ef0449d0..13a16947fb4 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, @@ -122,6 +125,7 @@ class WebSearchInterceptionLogger(CustomLogger): self, enabled_providers: list[LlmProviders | str] | None = None, search_tool_name: str | None = None, + max_agentic_loops: int | None = None, ): """ Args: @@ -131,6 +135,9 @@ class WebSearchInterceptionLogger(CustomLogger): Default: None (all providers enabled) search_tool_name: Name of search tool configured in router's search_tools. If None, will attempt to use first available search tool. + max_agentic_loops: How many follow-up model calls one intercepted request + may chain before the loop is refused and the turn ends. + If None, LiteLLM's default of 3 applies. """ super().__init__() # Convert enum values to strings for comparison @@ -139,8 +146,16 @@ 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, @@ -398,6 +413,7 @@ class WebSearchInterceptionLogger(CustomLogger): websearch_interception_params: enabled_providers: ["bedrock"] search_tool_name: "my-perplexity-search" + max_agentic_loops: 5 Usage: config = litellm_settings.get("websearch_interception_params", {}) @@ -406,6 +422,7 @@ class WebSearchInterceptionLogger(CustomLogger): # Extract parameters from config enabled_providers_str: Final = config.get("enabled_providers", None) search_tool_name: Final = config.get("search_tool_name", None) + max_agentic_loops: Final = config.get("max_agentic_loops", None) # Convert string provider names to LlmProviders enum values enabled_providers: list[LlmProviders | str] | None = None @@ -423,6 +440,7 @@ class WebSearchInterceptionLogger(CustomLogger): return cls( enabled_providers=enabled_providers, search_tool_name=search_tool_name, + max_agentic_loops=max_agentic_loops, ) @staticmethod @@ -493,6 +511,10 @@ 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/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/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index de1092bc02f..33eef9d3ac3 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 diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index d23466938f2..dc245d42862 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 +2201,120 @@ 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 + 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, @@ -2275,6 +2429,7 @@ 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 +2470,7 @@ 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 +2629,14 @@ 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_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index e674fc37673..005e94ebe82 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -272,6 +272,14 @@ 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") @@ -707,7 +715,7 @@ 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..7a16ffe4d85 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -172,7 +172,7 @@ 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/initialize_dynamic_callback_params.py b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py index 284989ab20f..3b42ca4eaaf 100644 --- a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py +++ b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py @@ -46,7 +46,7 @@ def validate_no_callback_env_reference(param: str, value: object, *, source: str # 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", @@ -72,8 +72,10 @@ _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 +85,20 @@ _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 +137,9 @@ 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 +148,12 @@ 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/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 9b7707eabe1..626af7530a4 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -62,7 +62,7 @@ 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.llm_cost_calc.guardrail_cost import ( cost_breakdown_with_guardrail, @@ -71,6 +71,9 @@ 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 +86,10 @@ 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, @@ -2145,6 +2152,11 @@ class Logging(LiteLLMLoggingBaseClass): or isinstance(logging_result, OpenAIModerationResponse) or isinstance(logging_result, OCRResponse) # OCR or isinstance(logging_result, SearchResponse) # Search API + or ( + isinstance(logging_result, InteractionsAPIResponse) + and logging_result.usage is not None + and self._is_interactions_create_call_type() + ) or isinstance(logging_result, dict) and logging_result.get("object") == "vector_store.search_results.page" or isinstance(logging_result, dict) @@ -2157,6 +2169,87 @@ class Logging(LiteLLMLoggingBaseClass): return True return False + def _is_interactions_create_call_type(self) -> bool: + """ + Only interaction creation is billable. GET polls, deletes, and cancels + also return an ``InteractionsAPIResponse`` (with usage once completed), + so recognizing those would write spend on every poll of a background + interaction. The proxy sets ``call_type`` from its route_type + (``create_interaction``/``acreate_interaction``); the SDK sets it from + the decorated function name (``create``/``acreate``). + + Recognition additionally requires a usage block (checked at the call + site): a ``background=true`` create returns ``in_progress`` without + usage, and billing it would write a $0 spend log under the interaction + id that collides with the row the background poll task writes once the + interaction completes (see + ``litellm.interactions.background_cost_polling``). + """ + return self.call_type in ( + CallTypes.create_interaction.value, + CallTypes.acreate_interaction.value, + "create", + "acreate", + ) + + async def async_log_background_interaction_completion( + self, + result: InteractionsAPIResponse, + ) -> None: + """ + Log the terminal result of a background interaction as a fresh success + event. The create request already ran success logging for its + ``in_progress`` response (no usage, so no cost was tracked); clearing + the dedup flags lets the completed result flow through cost calculation + and spend tracking exactly once, spanning create to completion. + + The poll fetched this body through its own client call, which priced it + against a throwaway logging object holding none of this request's + deployment context: no ``model_info``, no router ``model_id``, no + deployment ``litellm_params``. Keeping that price would bill a + custom-priced deployment at the wrong rate, and it would also satisfy + the "already calculated" shortcut and skip repricing here, leaving the + cost breakdown at the zeros the usage-less create stamped and writing + those zeros to the spend log. Dropping it makes this event price the + settled body itself, against the deployment that served the create. + + The same throwaway call stamped the deployment identity that travels + with the price, so ``model_id`` and ``litellm_model_name`` go with it. + Left in place they overwrite the create's real deployment with the + poll's empty one in the payload every logging integration reads. + """ + settled_hidden_params: Final = getattr(result, "_hidden_params", None) + if isinstance(settled_hidden_params, dict): + for poll_scoped_key in ("response_cost", "model_id", "litellm_model_name"): + settled_hidden_params.pop(poll_scoped_key, None) + self._reset_success_emission_dedupe() + await self.async_success_handler(result=result) + + def _reset_success_emission_dedupe(self) -> None: + """ + Success callbacks dedupe per request, because the sync and async + handlers both fire on some paths and would otherwise report one call + twice. A settled background interaction is a genuinely second success + event on the same request, so every such marker has to be cleared or + the completion, the only event that carries usage and cost, is + discarded as a duplicate of the in-progress create. + """ + self.model_call_details.pop("has_logged_async_success", None) + litellm_params = self.model_call_details.get("litellm_params") + if not isinstance(litellm_params, dict): + return + metadata = litellm_params.get("metadata") + if not isinstance(metadata, dict): + return + otel_internal = metadata.get("_otel_internal") + if not isinstance(otel_internal, dict): + return + spans_logged = otel_internal.get("spans_logged") + if not isinstance(spans_logged, dict): + return + for scope in [key for key in spans_logged if isinstance(key, tuple) and key[-1:] == ("success",)]: + del spans_logged[scope] + def _flush_passthrough_collected_chunks_helper( self, raw_bytes: list[bytes], @@ -2282,7 +2375,9 @@ 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( @@ -2768,14 +2863,14 @@ 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: @@ -3029,6 +3124,13 @@ class Logging(LiteLLMLoggingBaseClass): if not hasattr(self, "model_call_details"): self.model_call_details = {} + if ( + self.model_call_details.get("log_event_type") == "failed_api_call" + and self.model_call_details.get("exception") is exception + and self.model_call_details.get("standard_logging_object") is not None + ): + return start_time, self.model_call_details["end_time"] + self.model_call_details["log_event_type"] = "failed_api_call" self.model_call_details["exception"] = exception self.model_call_details["traceback_exception"] = ( @@ -3558,7 +3660,7 @@ class Logging(LiteLLMLoggingBaseClass): end_time: datetime.datetime, is_async: bool, streaming_chunks: list[object], - ) -> ModelResponse | TextCompletionResponse | ResponsesAPIResponse | None: + ) -> ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse | None: if self.stream is not True: return None if isinstance(result, ModelResponse) or isinstance(result, TextCompletionResponse): @@ -3583,9 +3685,40 @@ class Logging(LiteLLMLoggingBaseClass): ), ) return result.response + elif isinstance(result, InteractionsAPIStreamingResponse): + return self._assemble_completed_interaction_response(result) else: return None + @staticmethod + def _assemble_completed_interaction_response( + result: InteractionsAPIStreamingResponse, + ) -> InteractionsAPIResponse | None: + """ + The Interactions API streaming iterator hands the terminal event to the + success handlers: the new schema (Api-Revision: 2026-05-20) emits + ``interaction.completed`` carrying the full interaction object, the + legacy schema (2026-05-07) emits a chunk with ``status="completed"`` + and usage on the chunk itself. Build the equivalent non-streaming + response so cost calculation and spend tracking see one shape. + """ + if result.event_type == "interaction.completed" and result.interaction is not None: + return InteractionsAPIResponse(**result.interaction) + if result.status == "completed": + return InteractionsAPIResponse( + **result.model_dump( + exclude={ # mutable-ok: pydantic types exclude as set[str], which a frozenset does not satisfy + "event_type", + "delta", + "index", + "step", + "interaction_id", + "interaction", + } + ) + ) + return None + def _handle_anthropic_messages_response_logging(self, result: Any) -> ModelResponse: """ Handles logging for Anthropic messages responses. @@ -4503,6 +4636,9 @@ def _init_custom_logger_compatible_class( _in_memory_loggers.append(gitlab_logger) return gitlab_logger elif logging_integration == "newrelic": + _v2 = _maybe_construct_otel_v2("newrelic", _in_memory_loggers) + if _v2 is not None: + return _v2 for callback in _in_memory_loggers: if isinstance(callback, NewRelicLogger): return callback @@ -4789,7 +4925,11 @@ 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 @@ -5085,6 +5225,8 @@ class StandardLoggingPayloadSetup: elif isinstance(usage, dict): if ResponseAPILoggingUtils._is_response_api_usage(usage): return ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(usage) + if InteractionsUsageObjectTransformation.is_interactions_usage_object(usage): + return InteractionsUsageObjectTransformation.transform_interactions_usage_object(usage) return Usage(**usage) raise ValueError(f"usage is required, got={usage} of type {type(usage)}") @@ -5111,6 +5253,8 @@ 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() @@ -5318,9 +5462,10 @@ 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) @@ -5615,6 +5760,37 @@ 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, @@ -5762,16 +5938,30 @@ 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"), @@ -5802,7 +5992,8 @@ 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), @@ -5998,6 +6189,7 @@ 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/usage_object_transformation.py b/litellm/litellm_core_utils/llm_cost_calc/usage_object_transformation.py index 210ac72cd8a..df436ef7611 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 collections.abc import Mapping, Sequence +from types import MappingProxyType from typing import Any from litellm.types.utils import ( + CompletionTokensDetailsWrapper, PromptTokensDetailsWrapper, TranscriptionUsageDurationObject, TranscriptionUsageTokensObject, @@ -34,3 +37,127 @@ class TranscriptionUsageObjectTransformation: ), ) return None + + +_INTERACTIONS_MODALITY_FIELDS: 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 = 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: + return sum( + _token_count(entry.get("count")) + for entry in tuple(usage_object.get("grounding_tool_count") or ()) + if isinstance(entry, Mapping) and entry.get("type") == "google_search" # pyright: ignore[reportUnnecessaryIsInstance] # provider JSON, not the empty tuple inferred from `or ()` + ) + + +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 = tuple(usage_object.get("input_tokens_by_modality") or ()) + tuple( + usage_object.get("tool_use_tokens_by_modality") or () + ) + cached_sums = _modality_token_sums(tuple(usage_object.get("cached_tokens_by_modality") or ())) + output_sums = _modality_token_sums(tuple(usage_object.get("output_tokens_by_modality") or ())) + + total_cached_tokens = _token_count(usage_object.get("total_cached_tokens")) + input_sums = _subtract_cached_from_input( + input_sums=_modality_token_sums(input_entries), + cached_sums=cached_sums, + total_cached_tokens=total_cached_tokens, + ) + + reasoning_tokens = _token_count(usage_object.get("total_reasoning_tokens")) or _token_count( + usage_object.get("total_thought_tokens") + ) + prompt_tokens = _token_count(usage_object.get("total_input_tokens")) + _token_count( + usage_object.get("total_tool_use_tokens") + ) + completion_tokens = _token_count(usage_object.get("total_output_tokens")) + reasoning_tokens + total_tokens = _token_count(usage_object.get("total_tokens")) or (prompt_tokens + completion_tokens) + + web_search_requests = _google_search_query_count(usage_object) + prompt_tokens_details = ( + 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 = ( + 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..0a52e1d283e 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1371,6 +1371,7 @@ 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 ( 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/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..77792671f6f 100644 --- a/litellm/litellm_core_utils/logging_worker.py +++ b/litellm/litellm_core_utils/logging_worker.py @@ -5,7 +5,7 @@ import asyncio import atexit import contextvars import logging -from collections.abc import Coroutine +from collections.abc import Coroutine, Iterator from typing import Final from typing_extensions import TypedDict @@ -61,6 +61,19 @@ class LoggingWorker: # Register cleanup handler to flush remaining events on exit atexit.register(self._flush_on_exit) + @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 +82,27 @@ 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) + if carried_over: + verbose_logger.warning( + "LoggingWorker: event loop changed; carried %d pending logging task(s) onto the new loop", + len(carried_over), + ) + 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) diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 2db5776047b..72ea85dfa33 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -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 +510,11 @@ 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): + # `litellm:;model,` encoding from the + # x-litellm-model upload path. Strip the wrapper + # so the provider sees its own 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 +542,8 @@ 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 +587,13 @@ 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): + # `litellm:;model,` encoding from the + # x-litellm-model upload path. Strip the wrapper + # so the provider sees its own 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) @@ -1549,6 +1569,44 @@ 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]: diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index b676077ab0e..86cfbf70255 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 @@ -5383,12 +5341,13 @@ 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 6923e6beb96..021210d9175 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 @@ -68,9 +68,17 @@ 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: @@ -84,6 +92,46 @@ def _named(reason: str, model_name: str | None) -> str: 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. diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py index 0d590e1ceba..064228bf8d7 100644 --- a/litellm/litellm_core_utils/redact_messages.py +++ b/litellm/litellm_core_utils/redact_messages.py @@ -13,6 +13,7 @@ import inspect 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 +85,31 @@ 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 +119,23 @@ 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 +144,17 @@ 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 +167,7 @@ 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 +191,42 @@ 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: dict) -> 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 +240,7 @@ 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 +261,13 @@ 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 +278,11 @@ 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 +293,7 @@ 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/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 485091bccd0..f6340426c1b 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -191,7 +191,7 @@ 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 @@ -2315,10 +2315,18 @@ 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, + ) + 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 +2447,35 @@ 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/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index d9bb0d7abff..5842cc2679d 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -18,6 +18,7 @@ from litellm.anthropic_beta_headers_manager import ( ) from litellm.constants import RESPONSE_FORMAT_TOOL_NAME from litellm.litellm_core_utils.core_helpers import map_finish_reason +from litellm.litellm_core_utils.json_fragment_accumulator import JSONFragmentAccumulator from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, HTTPHandler, @@ -654,7 +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 @@ -678,6 +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 @@ -1149,31 +1158,39 @@ 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. @@ -1209,13 +1226,10 @@ class ModelResponseIterator: chunk = self.response_iterator.__next__() except StopIteration: # If we have accumulated JSON when stream ends, try to parse it - if self.accumulated_json: - try: - data_json = json.loads(self.accumulated_json) - self.accumulated_json = "" - return self.chunk_parser(chunk=data_json) - except json.JSONDecodeError: - pass + if self._json_buffer: + result = self._handle_accumulated_json_chunk(data_str="", is_final=True) + if result is not None: + return result raise StopIteration except ValueError as e: raise RuntimeError(f"Error receiving chunk from stream: {e}") @@ -1258,13 +1272,10 @@ 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..26380ad0af8 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, diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 1cdbd60f943..53cc3464761 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -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+$") _DATED_RELEASE_SUFFIX_RE: Final = re.compile(r"-\d{8}$") @@ -425,6 +446,45 @@ 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: dict, # mutable-ok: in-place out-param, same contract as AnthropicConfig._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, diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index 89066e33cbc..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() @@ -616,7 +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, @@ -750,7 +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/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 34c2d837127..109017bda27 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -64,6 +64,7 @@ from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingCho from litellm.litellm_core_utils.prompt_templates.common_utils import ( parse_tool_call_arguments, + reasoning_content_from_thinking_blocks, with_prompt_cache_breakpoint, ) from litellm.litellm_core_utils.prompt_templates.factory import ( @@ -433,7 +434,7 @@ class LiteLLMAnthropicMessagesAdapter: content_items = list(content.get("content", [])) # Single-item text keeps the backward-compatible string format; a single - # image becomes a structured image_url part + # image or document becomes a structured image_url part if len(content_items) == 1: c = content_items[0] if isinstance(c, str): @@ -453,7 +454,7 @@ class LiteLLMAnthropicMessagesAdapter: ) self._add_cache_control_if_applicable(content, tool_result, model) tool_message_list.append(tool_result) - elif c.get("type") == "image": + elif c.get("type") in ("image", "document"): image_part = self._tool_result_image_part(c.get("source")) tool_result = ChatCompletionToolMessage( role="tool", @@ -481,7 +482,7 @@ class LiteLLMAnthropicMessagesAdapter: text=c.get("text", ""), ) ) - elif c.get("type") == "image": + elif c.get("type") in ("image", "document"): image_part = self._tool_result_image_part(c.get("source")) if image_part: combined_content_parts.append(image_part) @@ -592,6 +593,9 @@ 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 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..2a87afb5990 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 = getattr(user_api_key_auth, "user_model_max_budget", None) + user_id: Final = getattr(user_api_key_auth, "user_id", None) + 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 26aef666172..283c706e45e 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py @@ -12,6 +12,7 @@ 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, @@ -21,6 +22,7 @@ from litellm.llms.anthropic.common_utils import ( 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: @@ -351,13 +384,18 @@ 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: @@ -533,7 +571,7 @@ 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 +589,7 @@ 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..8387dd8310d 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 diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index 7c4986ca3fe..032bf0130ce 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -379,13 +379,19 @@ 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 @@ -568,6 +574,12 @@ 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 843cda249c5..c1ea39fd72c 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 @@ -179,7 +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): @@ -257,7 +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 25d729d8606..492e0050cfe 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -6,12 +6,14 @@ path used for OpenAI and Azure models. """ import json -from collections.abc import Iterable +from collections.abc import Iterable, Mapping +from itertools import groupby from typing import Any, Final, cast from litellm.litellm_core_utils.prompt_templates.common_utils import ( TOOL_RESULT_IMAGE_BOUNDARY, TOOL_RESULT_IMAGE_PLACEHOLDER, + responses_reasoning_item_from_thinking_blocks, with_prompt_cache_breakpoint, ) from litellm.litellm_core_utils.reasoning_effort_utils import ( @@ -36,7 +38,11 @@ from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, AnthropicUsage, ) -from litellm.types.llms.openai import ResponseAPIUsage, ResponsesAPIResponse +from litellm.types.llms.openai import ( + ChatCompletionThinkingBlock, + ResponseAPIUsage, + ResponsesAPIResponse, +) class LiteLLMAnthropicToResponsesAPIAdapter: @@ -81,6 +87,51 @@ class LiteLLMAnthropicToResponsesAPIAdapter: return source.get("url") return None + @staticmethod + def _translate_anthropic_document_block_to_file_part( + block: Mapping[str, object], + ) -> dict[str, str] | None: # mutable-ok: API message payload + """Convert an Anthropic document block to a Responses input_file part.""" + raw_source: Final = block.get("source") + if not isinstance(raw_source, Mapping): + return None + source: Final = cast(Mapping[str, object], raw_source) # cast-ok: untrusted client payload + source_type: Final = source.get("type") + if source_type == "base64": + data: Final = source.get("data") + if not isinstance(data, str) or not data: + return None + raw_media_type: Final = source.get("media_type") + media_type: Final = ( + raw_media_type if isinstance(raw_media_type, str) and raw_media_type else "application/pdf" + ) + raw_title: Final = block.get("title") + filename: Final = raw_title if isinstance(raw_title, str) and raw_title else "document.pdf" + return { # mutable-ok: API message payload + "type": "input_file", + "filename": filename, + "file_data": f"data:{media_type};base64,{data}", + } + if source_type == "url": + url: Final = source.get("url") + if not isinstance(url, str) or not url: + return None + return {"type": "input_file", "file_url": url} # mutable-ok: API message payload + return None + + @staticmethod + def _tool_result_output_value( + output_text: str, + file_parts: tuple[dict[str, str], ...], # mutable-ok: json content parts + ) -> str | list[dict[str, str]]: # mutable-ok: API message payload + """Plain string output, or a part list when document file parts are present.""" + if not file_parts: + return output_text + text_parts: Final = ( + [{"type": "input_text", "text": output_text}] if output_text else [] # mutable-ok: API message payload + ) + return [*text_parts, *file_parts] # mutable-ok: API message payload + @staticmethod def _translate_midturn_system_content_to_responses( content: str | Iterable[AnthropicSystemMessageContent], @@ -100,6 +151,58 @@ class LiteLLMAnthropicToResponsesAPIAdapter: if isinstance(block, dict) and block.get("type") == "text" and (text := block.get("text")) # pyright: ignore[reportUnnecessaryIsInstance] # untrusted client payload ] + @staticmethod + def _summary_part_text(part: object) -> str: + if isinstance(part, Mapping): + mapping: Final = cast(Mapping[str, Any], part) # cast-ok: summary parts are untyped provider json + return str(mapping.get("text") or "") + return str(getattr(part, "text", None) or "") + + @classmethod + def _thinking_blocks_from_reasoning_item( + cls, + summary: Iterable[object], + ) -> tuple[dict[str, Any], ...]: # mutable-ok: API message payload + """Anthropic thinking blocks for one Responses reasoning item. + + The signature stays empty: only Anthropic can sign a thinking block, and a stand-in + value would be replayed as a real one and rejected by every backend that verifies it. + """ + return tuple( + AnthropicResponseContentBlockThinking( + type="thinking", + thinking=text, + signature=None, + ).model_dump() + for part in summary + if (text := cls._summary_part_text(part)) + ) + + @staticmethod + def _assistant_block_group_key(indexed_block: tuple[int, Mapping[str, 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], @@ -111,8 +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]]] = [] @@ -164,9 +269,25 @@ class LiteLLMAnthropicToResponsesAPIAdapter: {"type": "input_image", "image_url": url}, block.get("prompt_cache_breakpoint") ) ) + elif btype == "document": + file_part = self._translate_anthropic_document_block_to_file_part(block) + if file_part: + user_parts.append( + with_prompt_cache_breakpoint(file_part, block.get("prompt_cache_breakpoint")) + ) elif btype == "tool_result": tool_use_id = block.get("tool_use_id", "") inner = block.get("content") + document_candidates = ( + tuple( + self._translate_anthropic_document_block_to_file_part(c) + for c in inner + if isinstance(c, dict) and c.get("type") == "document" + ) + if isinstance(inner, list) + else () + ) + tool_file_parts = tuple(part for part in document_candidates if part is not None) if inner is None: output_text = "" elif isinstance(inner, str): @@ -199,7 +320,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: { "type": "function_call_output", "call_id": tool_use_id, - "output": output_text, + "output": self._tool_result_output_value(output_text, tool_file_parts), } ) if tool_image_parts: @@ -233,27 +354,17 @@ class LiteLLMAnthropicToResponsesAPIAdapter: } ) elif isinstance(content, list): - asst_parts: list[dict[str, Any]] = [] - for block in content: - if not isinstance(block, dict): - continue - btype = block.get("type") - if btype == "text": - asst_parts.append({"type": "output_text", "text": block.get("text", "")}) - elif btype == "tool_use": - # tool_use becomes a top-level function_call item - input_items.append( - { - "type": "function_call", - "call_id": block.get("id", ""), - "name": block.get("name", ""), - "arguments": json.dumps(block.get("input", {})), - } - ) - elif btype == "thinking": - thinking_text = block.get("thinking", "") - if thinking_text: - asst_parts.append({"type": "output_text", "text": thinking_text}) + blocks = tuple(block for block in content if isinstance(block, dict)) + input_items.extend( + item + for _, group in groupby(enumerate(blocks), key=self._assistant_block_group_key) + if (item := self._assistant_group_to_input_item(tuple(block for _, block in group))) is not None + ) + asst_parts: list[dict[str, Any]] = [ # mutable-ok: API message payload + {"type": "output_text", "text": block.get("text", "")} # mutable-ok: API message payload + for block in blocks + if block.get("type") == "text" + ] if asst_parts: input_items.append( { @@ -514,16 +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: @@ -555,6 +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 c5abcf8c04c..29661572b73 100644 --- a/litellm/llms/anthropic/experimental_pass_through/utils.py +++ b/litellm/llms/anthropic/experimental_pass_through/utils.py @@ -13,6 +13,11 @@ def prompt_cache_key_from_user_id(user_id: object) -> str | 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).""" return litellm.reasoning_auto_summary or os.getenv("LITELLM_REASONING_AUTO_SUMMARY", "false").lower() == "true" 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/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/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/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/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/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index 3a093e1f939..ca5f1298360 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -35,7 +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 @@ -76,7 +76,7 @@ def make_sync_call( additional_args={"complete_input_dict": data}, ) - return completion_stream + return completion_stream, response.headers class BedrockConverseLLM(BaseAWSLLM): @@ -134,7 +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), @@ -151,6 +151,7 @@ class BedrockConverseLLM(BaseAWSLLM): model=model, custom_llm_provider="bedrock", logging_obj=logging_obj, + _response_headers=response_headers, ) return streaming_response @@ -232,7 +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, @@ -244,6 +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, @@ -541,7 +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, @@ -558,6 +561,7 @@ class BedrockConverseLLM(BaseAWSLLM): model=model, custom_llm_provider="bedrock", logging_obj=logging_obj, + _response_headers=response_headers, ) return streaming_response @@ -578,7 +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, @@ -590,3 +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..767677cbcbf 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, @@ -1571,6 +1572,12 @@ 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, @@ -1610,6 +1617,8 @@ 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 @@ -1794,6 +1803,17 @@ class AmazonConverseConfig(BaseConfig): thinking_blocks_list.append(_redacted_block) return thinking_blocks_list + @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 +1855,7 @@ 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"] @@ -1855,9 +1876,14 @@ class AmazonConverseConfig(BaseConfig): cache_creation_tokens=cache_creation_input_tokens, 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 +2291,9 @@ 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/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..a2a1acec80c 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, ) 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..3e5dd4ff87d 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, Any]") -> 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, Any]") -> "_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, Any]") -> "_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, Any]") -> "_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/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 52f30e31641..777ab576de2 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, Optional, TypeAlias, TypedDict import certifi import httpx @@ -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 9a950d7f920..e3193e8e2b0 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 +20,12 @@ 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.llm_request_utils import serialize_multipart_form_fields from litellm.litellm_core_utils.realtime_errors import realtime_error_event, websocket_close_reason from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming from litellm.litellm_core_utils.url_utils import encode_url_path_segment @@ -89,6 +95,7 @@ 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, @@ -635,6 +642,7 @@ 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): @@ -798,6 +806,7 @@ class BaseLLMHTTPHandler: model=model, custom_llm_provider=custom_llm_provider, logging_obj=logging_obj, + _response_headers=_response_headers, ) return streamwrapper @@ -1101,6 +1110,7 @@ class BaseLLMHTTPHandler: headers=headers or {}, model=model, optional_params=optional_rerank_params, + litellm_params=litellm_params, ) api_base = provider_config.get_complete_url( @@ -1193,6 +1203,7 @@ 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) @@ -1837,6 +1848,7 @@ class BaseLLMHTTPHandler: return provider_config.transform_search_response( raw_response=response, logging_obj=logging_obj, + optional_params=optional_params, ) async def async_search( @@ -1935,6 +1947,7 @@ 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( @@ -2054,7 +2067,7 @@ 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( @@ -2255,6 +2268,10 @@ 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, @@ -2265,6 +2282,8 @@ 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, @@ -5075,9 +5094,12 @@ 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: @@ -5109,6 +5131,20 @@ 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, @@ -5120,7 +5156,8 @@ 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 @@ -5128,9 +5165,9 @@ 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 @@ -5140,6 +5177,97 @@ 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, @@ -5505,14 +5633,30 @@ 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 + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.warning( + "LiteLLM.AgenticLoopRefused: ending turn [call_id=%s model=%s]: %s", + _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() @@ -6932,9 +7076,7 @@ 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, @@ -6942,9 +7084,14 @@ 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, @@ -7036,20 +7183,26 @@ 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, ) @@ -7709,6 +7862,7 @@ 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, @@ -7720,6 +7874,7 @@ 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, @@ -7773,9 +7928,10 @@ 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, @@ -7794,11 +7950,10 @@ 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( @@ -7818,6 +7973,7 @@ 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, @@ -7869,9 +8025,10 @@ 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, @@ -7890,11 +8047,10 @@ 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/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/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/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/rerank/transformation.py b/litellm/llms/fireworks_ai/rerank/transformation.py index fde4f55e75b..8ef2c9acccb 100644 --- a/litellm/llms/fireworks_ai/rerank/transformation.py +++ b/litellm/llms/fireworks_ai/rerank/transformation.py @@ -4,6 +4,7 @@ Fireworks AI Rerank API transformation Reference: https://docs.fireworks.ai/inference-api-reference/rerank """ +from collections.abc import Mapping from typing import Any, Final import httpx @@ -102,6 +103,7 @@ class FireworksAIRerankConfig(FireworksAIMixin, BaseRerankConfig): model: str, api_key: str | None = None, optional_params: dict | None = None, + litellm_params: Mapping[str, object] | None = None, ) -> dict: api_key = self._get_api_key(api_key) if api_key is None: diff --git a/litellm/llms/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 ebcbf1b5a07..2526eb3b6a4 100644 --- a/litellm/llms/infinity/rerank/transformation.py +++ b/litellm/llms/infinity/rerank/transformation.py @@ -4,6 +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 Mapping from typing import Final import httpx @@ -46,6 +47,7 @@ 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/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..3a65e4a9426 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -16,7 +16,7 @@ def _normalize_reasoning_effort_for_chat_completion( ) -> 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: 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/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/providers.json b/litellm/llms/openai_like/providers.json index 5f57aaa78d8..a458a209ea9 100644 --- a/litellm/llms/openai_like/providers.json +++ b/litellm/llms/openai_like/providers.json @@ -188,5 +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/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/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/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..d06bcb22119 --- /dev/null +++ b/litellm/llms/together_ai/chat/transformation.py @@ -0,0 +1,168 @@ +""" +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 +from typing import ( + Final, + Literal, + cast, # noqa: TID251 # rebuilding a TypedDict minus keys has no checked spelling + overload, +) + +import litellm +from litellm._logging import verbose_logger +from litellm.exceptions import UnsupportedParamsError +from litellm.types.llms.openai import AllMessageValues +from litellm.utils import supports_function_calling, 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 + + +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 _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 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") + return mapped_openai_params 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/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index d298670aa7a..d12ba24eda4 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, ) @@ -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.""" @@ -3298,8 +3307,8 @@ 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 +3316,23 @@ 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 +3356,7 @@ 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 +3380,7 @@ class ModelResponseIterator: try: chunk: Final = await self.async_response_iterator.__anext__() except StopAsyncIteration: - if self.chunk_type == "accumulated_json" and self.accumulated_json: + if self.chunk_type == "accumulated_json" and self._json_buffer: result: Final = self.handle_accumulated_json_chunk(chunk="", is_final=True) if result is not None: return result diff --git a/litellm/llms/vertex_ai/interactions/__init__.py b/litellm/llms/vertex_ai/interactions/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/vertex_ai/interactions/transformation.py b/litellm/llms/vertex_ai/interactions/transformation.py new file mode 100644 index 00000000000..0764a8bea62 --- /dev/null +++ b/litellm/llms/vertex_ai/interactions/transformation.py @@ -0,0 +1,149 @@ +from collections.abc import Callable, Mapping +from dataclasses import dataclass +from typing import Final + +from litellm.litellm_core_utils.url_utils import encode_url_path_segment +from litellm.llms.gemini.interactions.transformation import GoogleAIStudioInteractionsConfig +from litellm.llms.vertex_ai.common_utils import validate_vertex_location +from litellm.llms.vertex_ai.vertex_llm_base import VertexBase +from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES +from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders + +VERTEX_INTERACTIONS_API_VERSION: Final = "v1beta1" +VERTEX_INTERACTIONS_DEFAULT_LOCATION: Final = "global" + + +@dataclass(frozen=True, slots=True) +class VertexInteractionsTarget: + base_url: str + project_id: str + location: str + + @property + def collection_url(self) -> str: + return ( + f"{self.base_url}/{VERTEX_INTERACTIONS_API_VERSION}" + f"/projects/{self.project_id}/locations/{self.location}/interactions" + ) + + def interaction_url(self, interaction_id: str) -> str: + encoded_interaction_id: Final = encode_url_path_segment(interaction_id, field_name="interaction_id") + return f"{self.collection_url}/{encoded_interaction_id}" + + +class VertexAIInteractionsConfig(VertexBase, GoogleAIStudioInteractionsConfig): + def __init__( + self, + mint_access_token: Callable[[VERTEX_CREDENTIALS_TYPES | None, str | None], tuple[str, str]] | None = None, + ) -> None: + super().__init__() + self._mint_access_token: Final[Callable[[VERTEX_CREDENTIALS_TYPES | None, str | None], tuple[str, str]]] = ( + mint_access_token or self._mint_access_token_with_vertex_base + ) + + def _mint_access_token_with_vertex_base( + self, + credentials: VERTEX_CREDENTIALS_TYPES | None, + project_id: str | None, + ) -> tuple[str, str]: + return self._ensure_access_token( + credentials=credentials, project_id=project_id, custom_llm_provider="vertex_ai" + ) + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.VERTEX_AI + + @property + def api_version(self) -> str: + return VERTEX_INTERACTIONS_API_VERSION + + def get_default_vertex_location(self) -> str: + return VERTEX_INTERACTIONS_DEFAULT_LOCATION + + def _mint(self, litellm_params: GenericLiteLLMParams) -> tuple[str, str]: + raw_params: Final = litellm_params.model_dump() + return self._mint_access_token( + self.safe_get_vertex_ai_credentials(raw_params), + self.safe_get_vertex_ai_project(raw_params), + ) + + def _target(self, api_base: str | None, litellm_params: GenericLiteLLMParams) -> VertexInteractionsTarget: + _, project_id = self._mint(litellm_params) + if not project_id: + raise ValueError( + "Vertex AI project is required. Set vertex_project, litellm.vertex_project, or VERTEXAI_PROJECT" + ) + location: Final = validate_vertex_location( + self.explicit_vertex_ai_location(litellm_params.model_dump()) or VERTEX_INTERACTIONS_DEFAULT_LOCATION + ) + return VertexInteractionsTarget( + base_url=self.get_api_base(api_base or None, location), + project_id=project_id, + location=location, + ) + + def validate_environment( + self, + headers: Mapping[str, str], + model: str, + litellm_params: GenericLiteLLMParams | None, + ) -> dict: # mutable-ok: BaseInteractionsAPIConfig declares plain-dict headers + access_token, _ = self._mint(litellm_params or GenericLiteLLMParams()) + return { # mutable-ok: BaseInteractionsAPIConfig declares plain-dict headers + "Content-Type": "application/json", + "Authorization": f"Bearer {access_token}", + **headers, + } + + def get_complete_url( + self, + api_base: str | None, + model: str | None, + agent: str | None = None, + litellm_params: Mapping[str, object] | None = None, + stream: bool | None = None, + ) -> str: + params: Final = ( + GenericLiteLLMParams.model_validate(litellm_params) if litellm_params else GenericLiteLLMParams() + ) + collection_url: Final = self._target(api_base, params).collection_url + return f"{collection_url}?alt=sse" if stream else collection_url + + def _interaction_by_id_request( + self, + interaction_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + url_suffix: str = "", + ) -> tuple[str, dict]: # mutable-ok: BaseInteractionsAPIConfig declares a plain-dict request body + target: Final = self._target(api_base or None, litellm_params) + return f"{target.interaction_url(interaction_id)}{url_suffix}", {} # mutable-ok: same base contract + + def transform_get_interaction_request( + self, + interaction_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: Mapping[str, str], + ) -> tuple[str, dict]: # mutable-ok: BaseInteractionsAPIConfig declares a plain-dict request body + return self._interaction_by_id_request(interaction_id, api_base, litellm_params) + + def transform_delete_interaction_request( + self, + interaction_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: Mapping[str, str], + ) -> tuple[str, dict]: # mutable-ok: BaseInteractionsAPIConfig declares a plain-dict request body + return self._interaction_by_id_request(interaction_id, api_base, litellm_params) + + def transform_cancel_interaction_request( + self, + interaction_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: Mapping[str, str], + ) -> tuple[str, dict]: # mutable-ok: BaseInteractionsAPIConfig declares a plain-dict request body + return self._interaction_by_id_request(interaction_id, api_base, litellm_params, url_suffix=":cancel") diff --git a/litellm/llms/vertex_ai/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 f66767a8d42..98f92e50599 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -24,6 +24,7 @@ from concurrent import futures from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait from copy import deepcopy from functools import partial +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol, Union, cast, get_args from litellm._logging import _redact_string @@ -416,7 +417,7 @@ async def acompletion( logprobs: bool | None = None, top_logprobs: int | None = None, deployment_id=None, - reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh", "default"] | None = None, + reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh", "max", "default"] | None = None, verbosity: Literal["low", "medium", "high"] | None = None, safety_identifier: str | None = None, service_tier: str | None = None, @@ -602,7 +603,7 @@ 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 @@ -1811,6 +1812,56 @@ def _complete_fireworks_ai( return response +def _complete_together_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: + acompletion: Final = ctx.acompletion + api_base: Final = ctx.api_base + api_key: Final = ctx.api_key + client: Final = _dispatch_client_http(ctx) + custom_llm_provider: Final = ctx.custom_llm_provider + headers: Final = ctx.headers + litellm_params: Final = ctx.litellm_params + logging: Final = ctx.logging + messages: Final = ctx.messages + model: Final = ctx.model + model_response: Final = ctx.model_response + optional_params: Final = ctx.optional_params + provider_config: Final = ctx.provider_config + shared_session: Final = ctx.shared_session + stream: Final = ctx.stream + timeout: Final = ctx.timeout + + try: + response: Final = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=_get_encoding(), + stream=stream, + provider_config=provider_config, + ) + except Exception as e: + logging.post_call( + input=messages, + api_key=api_key, + original_response=str(e), + additional_args=MappingProxyType({"headers": headers}), + ) + raise + + return response + + def _complete_heroku(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: acompletion: Final = ctx.acompletion api_base: Final = ctx.api_base @@ -4920,7 +4971,7 @@ 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, @@ -5091,14 +5142,16 @@ 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 @@ -5598,6 +5651,8 @@ 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) @@ -5647,7 +5702,6 @@ 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" @@ -5697,14 +5751,6 @@ 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" @@ -5974,7 +6020,7 @@ 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, @@ -6000,7 +6046,7 @@ 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, @@ -6027,7 +6073,7 @@ 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, @@ -6826,6 +6872,8 @@ 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 @@ -6835,8 +6883,8 @@ 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 @@ -7535,6 +7583,15 @@ 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) @@ -8040,6 +8097,7 @@ 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, @@ -8118,6 +8176,7 @@ 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, @@ -8553,7 +8612,7 @@ 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 @@ -8568,7 +8627,7 @@ 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] @@ -8634,7 +8693,7 @@ def stream_chunk_builder( 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 ] @@ -8648,7 +8707,7 @@ 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 ] @@ -8661,7 +8720,7 @@ 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 ] @@ -8672,7 +8731,7 @@ 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 ] @@ -8685,7 +8744,7 @@ 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 ] @@ -8698,7 +8757,7 @@ 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 ] @@ -8715,7 +8774,7 @@ 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 ] @@ -8729,7 +8788,7 @@ 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 ] @@ -8746,7 +8805,7 @@ 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 ] diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index efbe0d2ebb7..9ca8d9e1bac 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1019,6 +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, @@ -1053,6 +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, @@ -1087,6 +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, @@ -1121,6 +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, @@ -1155,6 +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, @@ -1232,6 +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, @@ -1404,6 +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, @@ -1440,6 +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, @@ -1476,6 +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, @@ -1512,6 +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, @@ -2228,6 +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, @@ -2261,6 +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, @@ -2294,6 +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, @@ -2327,6 +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, @@ -2360,6 +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, @@ -2393,6 +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, @@ -2945,6 +2961,7 @@ "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", @@ -3021,6 +3038,7 @@ "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, @@ -3175,6 +3193,7 @@ "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, @@ -4875,6 +4894,38 @@ "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, @@ -5088,6 +5139,38 @@ "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, @@ -12419,6 +12502,7 @@ "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, @@ -12628,6 +12712,7 @@ "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, @@ -12665,6 +12750,7 @@ "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, @@ -12780,6 +12866,7 @@ "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, @@ -14480,6 +14567,8 @@ ] }, "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", @@ -14495,6 +14584,8 @@ "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", @@ -14510,10 +14601,41 @@ "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", @@ -14529,10 +14651,13 @@ "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-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", @@ -14548,10 +14673,13 @@ "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-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", @@ -14567,10 +14695,13 @@ "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-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", @@ -14586,11 +14717,14 @@ "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_output_config": true }, "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", @@ -14606,10 +14740,94 @@ "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 }, + "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", @@ -14625,10 +14843,13 @@ "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-4-1": { + "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", @@ -14644,10 +14865,13 @@ "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", @@ -14663,10 +14887,13 @@ "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-4-6": { + "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", @@ -14682,10 +14909,41 @@ "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 }, + "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", @@ -14700,9 +14958,12 @@ "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", @@ -14717,9 +14978,12 @@ "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", @@ -14734,9 +14998,12 @@ "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", @@ -14751,9 +15018,12 @@ "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", @@ -14768,9 +15038,12 @@ "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", @@ -14785,9 +15058,12 @@ "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", @@ -14803,6 +15079,8 @@ "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, "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", @@ -14815,9 +15093,12 @@ "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", @@ -14830,9 +15111,12 @@ "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", @@ -14845,9 +15129,12 @@ "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", @@ -14860,9 +15147,12 @@ "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", @@ -14875,9 +15165,12 @@ "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", @@ -14890,9 +15183,12 @@ "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", @@ -14905,9 +15201,12 @@ "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", @@ -14920,9 +15219,12 @@ "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", @@ -14935,9 +15237,12 @@ "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", @@ -14950,9 +15255,12 @@ "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", @@ -14965,9 +15273,12 @@ "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", @@ -14980,9 +15291,12 @@ "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", @@ -14998,6 +15312,8 @@ "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", @@ -15013,6 +15329,8 @@ "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", @@ -15028,6 +15346,8 @@ "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, "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", @@ -15044,6 +15364,8 @@ "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", @@ -15060,6 +15382,8 @@ "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", @@ -15076,6 +15400,8 @@ "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", @@ -15091,6 +15417,8 @@ "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", @@ -15107,6 +15435,8 @@ "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", @@ -15123,6 +15453,8 @@ "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", @@ -15139,6 +15471,8 @@ "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", @@ -15155,6 +15489,8 @@ "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", @@ -16819,6 +17155,14 @@ "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", @@ -17419,7 +17763,7 @@ "fal_ai/openai/gpt-image-2": { "litellm_provider": "fal_ai", "metadata": { - "notes": "OpenAI gpt-image-2 served through fal.ai. fal bills by token, so the flat output_cost_per_image here is fal's published per-image rate for a default request (quality=high, image_size=landscape_4_3 at 1024x768). Other canonical sizes at high quality: 1024x1024 $0.211, 1024x1536 $0.165, 1920x1080 $0.158, 2560x1440 $0.222, 3840x2160 $0.401" + "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, @@ -17429,10 +17773,190 @@ ], "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 rate, see that entry for the size and quality caveat" + "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, @@ -17442,13 +17966,373 @@ ], "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": "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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/openai/gpt-image-2/edit": { "litellm_provider": "fal_ai", "metadata": { - "notes": "Editing endpoint of gpt-image-2 on fal.ai, reached through the image generation path with fal's image_urls param since /v1/images/edits is not wired for fal_ai. Same rate as fal_ai/openai/gpt-image-2, see that entry for the size and quality caveat" + "notes": "Editing endpoint of gpt-image-2 on fal.ai, reached through the image generation path with fal's image_urls param since /v1/images/edits is not wired for fal_ai. 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": 0.145, + "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 + }, + "fal_ai/low/1024-x-1536/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.018, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/1920-x-1080/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.017, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/2560-x-1440/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.019, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/low/3840-x-2160/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.024, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/1024-x-768/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.043, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/1024-x-1024/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.061, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/1024-x-1536/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.054, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/1920-x-1080/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.053, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/2560-x-1440/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.068, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/medium/3840-x-2160/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.113, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/1024-x-768/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "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/high/1024-x-1024/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.219, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/1024-x-1536/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.178, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/1920-x-1080/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.158, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/2560-x-1440/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.234, + "source": "https://fal.ai/models/openai/gpt-image-2/edit", + "supported_endpoints": [ + "/v1/images/generations" + ], + "supports_vision": true + }, + "fal_ai/high/3840-x-2160/openai/gpt-image-2/edit": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.413, "source": "https://fal.ai/models/openai/gpt-image-2/edit", "supported_endpoints": [ "/v1/images/generations" @@ -18951,106 +19835,6 @@ }, "web_search_billing_unit": "per_query" }, - "gemini-3.1-flash-lite-image": { - "input_cost_per_image": 0.00028, - "input_cost_per_token": 2.5e-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, - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions", - "/v1/batch" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text", - "image" - ], - "supports_function_calling": false, - "supports_prompt_caching": true, - "supports_response_schema": false, - "supports_reasoning": true, - "supports_system_messages": true, - "supports_vision": true - }, - "gemini/gemini-3.1-flash-lite-image": { - "rpm": 1000, - "tpm": 4000000, - "input_cost_per_image": 0.00028, - "input_cost_per_token": 2.5e-07, - "input_cost_per_token_batches": 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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, - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions", - "/v1/batch" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text", - "image" - ], - "supports_function_calling": false, - "supports_prompt_caching": true, - "supports_response_schema": false, - "supports_reasoning": true, - "supports_system_messages": true, - "supports_vision": true - }, "gemini-3.1-flash-image": { "deprecation_date": "2027-05-28", "input_cost_per_image": 0.00056, @@ -19128,6 +19912,44 @@ }, "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, @@ -20958,6 +21780,42 @@ }, "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, @@ -22595,6 +23453,7 @@ }, "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, @@ -25494,33 +26353,33 @@ "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, - 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"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": 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": { @@ -25557,33 +26416,33 @@ "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": 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": { @@ -25825,19 +26684,19 @@ "supports_parallel_function_calling": true }, "daybreak-blue-latest": { - "cache_creation_input_token_cost": 6.25e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.25e-05, - "cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "input_cost_per_token": 5e-06, - "input_cost_per_token_above_272k_tokens": 1e-05, + 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1.5e-05, + "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", @@ -32961,6 +33837,7 @@ }, "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, @@ -33005,6 +33882,7 @@ }, "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, @@ -35079,6 +35957,7 @@ }, "perplexity/anthropic/claude-opus-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "litellm_provider": "perplexity", "mode": "responses", "supports_web_search": true, @@ -36184,6 +37063,40 @@ "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, @@ -36973,6 +37886,7 @@ "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, @@ -36989,6 +37903,7 @@ "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, @@ -37001,6 +37916,7 @@ "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, @@ -37013,6 +37929,7 @@ "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, @@ -37024,6 +37941,7 @@ "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, @@ -37036,11 +37954,15 @@ "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" + }, "mode": "chat", "output_cost_per_token": 7e-06, "supports_function_calling": true, @@ -37049,6 +37971,7 @@ "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, @@ -37066,6 +37989,9 @@ "max_input_tokens": 65536, "max_output_tokens": 8192, "max_tokens": 8192, + "metadata": { + "successor": "together_ai/deepseek-ai/DeepSeek-V4-Pro" + }, "mode": "chat", "output_cost_per_token": 1.25e-06, "supports_function_calling": true, @@ -37074,9 +38000,13 @@ "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" + }, "mode": "chat", "output_cost_per_token": 1.7e-06, "source": "https://www.together.ai/models/deepseek-v3-1", @@ -37088,6 +38018,7 @@ "max_output_tokens": 16384 }, 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"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", @@ -37166,6 +38107,7 @@ "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, @@ -37174,6 +38116,7 @@ "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, @@ -37181,6 +38124,7 @@ "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", @@ -37193,6 +38137,9 @@ "together_ai/moonshotai/Kimi-K2-Instruct": { "input_cost_per_token": 1e-06, "litellm_provider": 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"supports_response_schema": true, + "supports_tool_choice": true + }, + "together_ai/pearl-ai/gemma-4-31b-it": { + "input_cost_per_token": 2.8e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 8.6e-07, + "source": "https://docs.together.ai/docs/serverless-models" + }, + "together_ai/thinkingmachines/Inkling": { + "input_cost_per_token": 1e-06, + "litellm_provider": "together_ai", + "max_input_tokens": 524288, + "max_output_tokens": 524288, + "max_tokens": 524288, + "mode": "chat", + "output_cost_per_token": 4.05e-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/thinkingmachines/Inkling-Small": { + "input_cost_per_token": 5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 524288, + "max_output_tokens": 524288, + "max_tokens": 524288, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://docs.together.ai/docs/serverless-models" + }, + "together_ai/zai-org/GLM-5.2": { + "input_cost_per_token": 1.4e-06, + "litellm_provider": "together_ai", + "max_input_tokens": 1048575, + "max_output_tokens": 1048575, + "max_tokens": 1048575, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://docs.together.ai/docs/serverless-models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "tts-1": { "input_cost_per_character": 1.5e-05, "litellm_provider": "openai", @@ -38420,6 +39678,7 @@ }, "vercel_ai_gateway/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, @@ -39679,6 +40938,7 @@ "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, @@ -39711,6 +40971,7 @@ "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, @@ -39825,6 +41086,7 @@ "search_context_size_medium": 0.01 }, "supports_adaptive_thinking": true, + "thinking_always_on": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -39858,6 +41120,7 @@ "search_context_size_medium": 0.01 }, "supports_adaptive_thinking": true, + "thinking_always_on": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -40074,6 +41337,7 @@ "vertex_ai/claude-sonnet-4-6": { "regional_endpoint_uplift_multiplier": 1.1, "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -40466,6 +41730,44 @@ "supports_reasoning": false, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models" }, + "vertex_ai/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 + }, "vertex_ai/gemini-3.1-flash-lite-preview": { "cache_read_input_token_cost": 2.5e-08, "input_cost_per_audio_token": 5e-07, @@ -43119,10 +44421,10 @@ "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", @@ -43132,13 +44434,136 @@ "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", @@ -43148,7 +44573,23 @@ "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": { @@ -47805,6 +49246,7 @@ "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, @@ -47919,12 +49361,13 @@ "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": [ @@ -47951,12 +49394,13 @@ "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": [ @@ -47983,12 +49427,13 @@ "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": [ @@ -48005,6 +49450,156 @@ "supports_tool_choice": true, "supports_vision": true }, + "us.openai.gpt-5.6-sol": { + "input_cost_per_token": 5.5e-06, + "input_cost_per_token_above_272k_tokens": 1.1e-05, + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, + "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, + "output_cost_per_token": 3.3e-05, + "output_cost_per_token_above_272k_tokens": 4.95e-05, + "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_vision": true + }, + "global.openai.gpt-5.6-sol": { + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_272k_tokens": 1e-05, + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.25e-05, + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_272k_tokens": 1e-06, + "output_cost_per_token": 3e-05, + "output_cost_per_token_above_272k_tokens": 4.5e-05, + "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_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_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_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_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_vision": true + }, "bedrock_mantle/openai.gpt-5.5": { "input_cost_per_token": 5.5e-06, "cache_read_input_token_cost": 5.5e-07, @@ -48439,6 +50034,7 @@ }, "snowflake/claude-sonnet-4-6": { "supports_adaptive_thinking": true, + "supports_legacy_thinking": true, "max_tokens": 16384, "max_input_tokens": 200000, "max_output_tokens": 16384, @@ -48949,6 +50545,16 @@ "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, @@ -48956,7 +50562,7 @@ "mode": "chat", "supports_function_calling": true, "supports_prompt_caching": true, - "source": "https://docs.devin.ai/windsurf/plugins/cascade/models" + "source": "https://docs.devin.ai/desktop/models" }, "pinstripes/ps/glm-4.5-air": { "max_tokens": 128000, @@ -49204,6 +50810,7 @@ }, "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, @@ -49239,6 +50846,7 @@ }, "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, @@ -49417,6 +51025,22 @@ "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))?)", 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 b4d635c0fba..86c14fb4cd8 100644 --- a/litellm/provider_endpoints_support_backup.json +++ b/litellm/provider_endpoints_support_backup.json @@ -2027,6 +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..7d85f3c4908 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 @@ -39,6 +39,7 @@ 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 ( @@ -160,7 +161,7 @@ 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 @@ -812,7 +813,7 @@ 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 +836,7 @@ 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 +894,12 @@ 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 @@ -1785,11 +1786,14 @@ 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): @@ -2723,6 +2727,32 @@ 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, diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py index 28638ed9c77..4aa08020527 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 @@ -1914,7 +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 2994f98f309..aef4f5dc721 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -750,6 +750,55 @@ def _redirect_to_upstream_authorize( 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, @@ -819,6 +868,14 @@ 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=mcp_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, diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index dbe97dd5bce..7ab26db0f3e 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -2943,17 +2943,14 @@ 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 @@ -2982,26 +2979,16 @@ 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, 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/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 0dc85c0318c..3c6eb06bc71 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, @@ -3782,14 +3784,15 @@ 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/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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\ No newline at end of file diff --git a/litellm/proxy/_experimental/out/404/index.html b/litellm/proxy/_experimental/out/404/index.html index a7d19a9e907..52c40147a20 100644 --- a/litellm/proxy/_experimental/out/404/index.html +++ b/litellm/proxy/_experimental/out/404/index.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

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

404

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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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