diff --git a/.circleci/config.yml b/.circleci/config.yml index d2c4906ef6b..0966da461ec 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -350,7 +350,15 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -v tests/local_testing -x --junitxml=test-results/junit.xml --durations=5 -k "langfuse" + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -k \"langfuse\"" no_output_timeout: 15m # Store test results - store_test_results: @@ -395,7 +403,15 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -v tests/proxy_admin_ui_tests -x --junitxml=test-results/junit.xml --durations=5 -n 2 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/proxy_admin_ui_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 2" no_output_timeout: 15m # Store test results @@ -471,7 +487,15 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -v tests/router_unit_tests -x --junitxml=test-results/junit.xml --durations=5 -n 4 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/router_unit_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 4" no_output_timeout: 15m # Store test results - store_test_results: @@ -495,7 +519,15 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest tests/local_testing/ -v -k "assistants" -x --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/local_testing/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -k \"assistants\"" no_output_timeout: 15m # Store test results - store_test_results: @@ -528,14 +560,19 @@ jobs: # Add --timeout to kill hanging tests after 120s (2 min) # Add --durations=20 to show 20 slowest tests for debugging # Subdirectories with dedicated jobs (maintain this list as new jobs are added) - IGNORE_DIRS=( - "tests/llm_translation/realtime" - ) - IGNORE_ARGS="" - for dir in "${IGNORE_DIRS[@]}"; do - IGNORE_ARGS="$IGNORE_ARGS --ignore=$dir" - done - uv run --no-sync python -m pytest -v tests/llm_translation $IGNORE_ARGS --junitxml=test-results/junit.xml --durations=20 -n 4 --timeout=120 --timeout_method=thread --retries 2 --retry-delay 5 --max-worker-restart=5 + mkdir -p test-results + # Glob excludes the realtime/ subdirectory since it has its own job + TEST_FILES=$(circleci tests glob "tests/llm_translation/**/test_*.py" | grep -v "^tests/llm_translation/realtime/") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v \ + --junitxml=test-results/junit.xml \ + --durations=20 \ + -n 4 \ + --timeout=120 --timeout_method=thread \ + --retries 2 --retry-delay 5 \ + --max-worker-restart=5" no_output_timeout: 15m # Store test results @@ -560,7 +597,17 @@ jobs: command: | # Add --timeout to kill hanging tests after 120s (2 min) # Add --durations=20 to show 20 slowest tests for debugging - uv run --no-sync python -m pytest -vv tests/llm_translation/realtime --cov=litellm --cov-report=xml -v --junitxml=test-results/junit.xml --durations=20 -n 4 --timeout=120 --timeout_method=thread + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/llm_translation/realtime/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=20 \ + -n 4 \ + --timeout=120 --timeout_method=thread" no_output_timeout: 15m - run: name: Rename the coverage files @@ -593,7 +640,15 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/agent_tests --ignore=tests/agent_tests/local_only_agent_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/agent_tests/**/test_*.py" | grep -v "^tests/agent_tests/local_only_agent_tests/") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x -s \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m - run: name: Rename the coverage files @@ -626,7 +681,18 @@ jobs: - run: name: Run tests command: | - LITELLM_LOG=WARNING uv run --no-sync python -m pytest tests/guardrails_tests -vv --cov=litellm --cov-report=xml --junitxml=test-results/junit.xml --durations=5 -n 2 --timeout=120 --timeout_method=thread + mkdir -p test-results + export LITELLM_LOG=WARNING + TEST_FILES=$(circleci tests glob "tests/guardrails_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 2 \ + --timeout=120 --timeout_method=thread" no_output_timeout: 15m - run: name: Rename the coverage files @@ -660,7 +726,16 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/unified_google_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 --retries 3 --retry-delay 5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/unified_google_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x -s \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + --retries 3 --retry-delay 5" no_output_timeout: 15m - run: name: Rename the coverage files @@ -702,7 +777,15 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -v tests/llm_responses_api_testing -x --junitxml=test-results/junit.xml --durations=5 -n 8 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/llm_responses_api_testing/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 8" no_output_timeout: 15m # Store test results @@ -725,7 +808,16 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/ocr_tests --cov=litellm --cov-report=xml -x -v --junitxml=test-results/junit.xml --durations=5 -n 4 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/ocr_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 4" no_output_timeout: 15m - run: name: Rename the coverage files @@ -758,7 +850,16 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/search_tests --cov=litellm --cov-report=xml -x -v --junitxml=test-results/junit.xml --durations=5 -n 4 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/search_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 4" no_output_timeout: 15m - run: name: Rename the coverage files @@ -793,7 +894,15 @@ jobs: name: Run enterprise tests command: | uv run --no-sync python -m prisma generate - uv run --no-sync python -m pytest -v tests/enterprise -x --junitxml=test-results/junit-enterprise.xml --durations=10 -n 4 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/enterprise/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); 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: @@ -815,7 +924,16 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/batches_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 -n 2 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/batches_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x -s \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 2" no_output_timeout: 15m - run: name: Rename the coverage files @@ -848,7 +966,16 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/litellm_utils_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 -n 2 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/litellm_utils_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x -s \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 2" no_output_timeout: 15m - run: name: Rename the coverage files @@ -882,7 +1009,16 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/pass_through_unit_tests --cov=litellm --cov-report=xml -x -v --junitxml=test-results/junit.xml --durations=5 -n 4 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/pass_through_unit_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 4" no_output_timeout: 15m - run: name: Rename the coverage files @@ -916,7 +1052,15 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -v tests/image_gen_tests -n 4 -x --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/image_gen_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + -n 4" no_output_timeout: 15m # Store test results - store_test_results: @@ -939,7 +1083,18 @@ jobs: - run: name: Run tests command: | - LITELLM_LOG=WARNING uv run --no-sync python -m pytest tests/logging_callback_tests -vv --cov=litellm --cov-report=xml -n 4 --junitxml=test-results/junit.xml --durations=5 --timeout=120 --timeout_method=thread + mkdir -p test-results + export LITELLM_LOG=WARNING + TEST_FILES=$(circleci tests glob "tests/logging_callback_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv \ + --cov=litellm --cov-report=xml \ + -n 4 \ + --junitxml=test-results/junit.xml \ + --durations=5 \ + --timeout=120 --timeout_method=thread" no_output_timeout: 15m - run: name: Rename the coverage files @@ -972,7 +1127,15 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/audio_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/audio_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x -s \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m - run: name: Rename the coverage files @@ -1012,14 +1175,19 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv \ + mkdir -p test-results + TEST_FILES=$(printf "%s\n" \ tests/local_testing/test_dual_cache.py \ tests/local_testing/test_redis_batch_optimizations.py \ - tests/local_testing/test_router_utils.py \ - --cov=litellm --cov-report=xml \ - -x -s -v --junitxml=test-results/junit.xml \ - --durations=5 -n 2 \ - --reruns 2 --reruns-delay 1 + tests/local_testing/test_router_utils.py) + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x -s \ + --cov=litellm --cov-report=xml \ + --junitxml=test-results/junit.xml \ + --durations=5 -n 2 \ + --reruns 2 --reruns-delay 1" no_output_timeout: 20m - run: name: Rename the coverage files @@ -1260,8 +1428,17 @@ jobs: - run: name: Run Basic Proxy Startup Tests (Health Readiness and Chat Completion) command: | - uv run --no-sync python -m pytest -v tests/basic_proxy_startup_tests -x --junitxml=test-results/junit-2.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/basic_proxy_startup_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit-2.xml \ + --durations=5" no_output_timeout: 15m + - store_test_results: + path: test-results build_and_test: machine: @@ -1331,7 +1508,18 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -s -v tests/*.py -x --junitxml=test-results/junit.xml -n 4 --durations=5 --ignore=tests/otel_tests --ignore=tests/spend_tracking_tests --ignore=tests/pass_through_tests --ignore=tests/proxy_admin_ui_tests --ignore=tests/load_tests --ignore=tests/llm_translation --ignore=tests/llm_responses_api_testing --ignore=tests/mcp_tests --ignore=tests/guardrails_tests --ignore=tests/image_gen_tests --ignore=tests/pass_through_unit_tests + mkdir -p test-results + # Original used `tests/*.py` (top-level only); the `--ignore=...` + # flags were vestigial since shell globbing did not descend into + # subdirectories. Replicate by globbing only top-level test files. + TEST_FILES=$(circleci tests glob "tests/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -s -v -x \ + --junitxml=test-results/junit.xml \ + -n 4 \ + --durations=5" no_output_timeout: 15m # Store test results @@ -1406,7 +1594,14 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -s -vv tests/openai_endpoints_tests --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/openai_endpoints_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -s -vv \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m # Store test results @@ -1475,7 +1670,14 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -v tests/otel_tests -x --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/otel_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m # Clean up first container - run: @@ -1518,7 +1720,14 @@ jobs: - run: name: Run second round of tests command: | - uv run --no-sync python -m pytest -v tests/basic_proxy_startup_tests -x --junitxml=test-results/junit-2.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/basic_proxy_startup_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit-2.xml \ + --durations=5" no_output_timeout: 15m # Store test results @@ -1587,8 +1796,17 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/spend_tracking_tests -x --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/spend_tracking_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m + - store_test_results: + path: test-results - run: name: Stop and remove first container when: always @@ -1676,7 +1894,14 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/multi_instance_e2e_tests -x --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/multi_instance_e2e_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m # Clean up first container # Store test results @@ -1732,7 +1957,14 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/store_model_in_db_tests -x --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/store_model_in_db_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m - run: name: Stop and remove containers @@ -1805,9 +2037,18 @@ jobs: - run: name: Run tests command: | - uv run --no-sync python -m pytest -vv tests/basic_proxy_startup_tests -x --junitxml=test-results/junit-2.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/basic_proxy_startup_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x \ + --junitxml=test-results/junit-2.xml \ + --durations=5" no_output_timeout: 15m # Clean up first container + - store_test_results: + path: test-results - run: name: Stop and remove first container command: | @@ -1935,12 +2176,19 @@ jobs: name: Run Vertex AI, Google AI Studio Node.js tests command: | cd tests/pass_through_tests - npx jest . --verbose + NODE_OPTIONS=--experimental-vm-modules npx jest . --verbose no_output_timeout: 30m - run: name: Run tests command: | - uv run --no-sync python -m pytest -v tests/pass_through_tests/ -x --junitxml=test-results/junit.xml --durations=5 + mkdir -p test-results + TEST_FILES=$(circleci tests glob "tests/pass_through_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -v -x \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m # Store test results @@ -1997,9 +2245,16 @@ jobs: - run: name: Run Claude Agent SDK E2E Tests command: | + mkdir -p test-results export LITELLM_PROXY_URL="http://localhost:4000" export LITELLM_API_KEY="sk-1234" - uv run --no-sync python -m pytest -vv tests/proxy_e2e_anthropic_messages_tests/ -x -s --junitxml=test-results/junit.xml --durations=5 + TEST_FILES=$(circleci tests glob "tests/proxy_e2e_anthropic_messages_tests/**/test_*.py") + echo "$TEST_FILES" | circleci tests run \ + --verbose \ + --command="awk '/\\.py/ {print; next} {sub(/\\.[A-Z][^.]*$/, \"\"); gsub(/\\./, \"/\"); print \$0 \".py\"}' | xargs uv run --no-sync python -m pytest \ + -vv -x -s \ + --junitxml=test-results/junit.xml \ + --durations=5" no_output_timeout: 15m # Store test results @@ -2138,17 +2393,23 @@ jobs: - ~/.cache/uv - restore_cache: keys: - - ui-e2e-node-deps-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }} + - ui-e2e-node-deps-v2-{{ checksum "ui/litellm-dashboard/package-lock.json" }} - run: name: Install Node dependencies and Playwright + # The cimg/python:3.12-browsers image already ships the Chromium system + # libraries Playwright needs (libnss3, libatk-bridge2.0-0, libcups2, etc.). + # `--with-deps` triggers a redundant apt-get update + install that adds + # 5-10 minutes to the job and frequently stalls on flaky Ubuntu mirrors, + # so we install just the browser binary. command: | cd ui/litellm-dashboard npm ci - npx playwright install chromium --with-deps + npx playwright install chromium - save_cache: - key: ui-e2e-node-deps-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }} + key: ui-e2e-node-deps-v2-{{ checksum "ui/litellm-dashboard/package-lock.json" }} paths: - ui/litellm-dashboard/node_modules + - ~/.cache/ms-playwright - run: name: Build UI from source # Prior version used `cp -r out/ ../../litellm/proxy/_experimental/out/`. diff --git a/.github/workflows/create-release-branch.yml b/.github/workflows/create-release-branch.yml index 13b76c94dfa..ec2651306f2 100644 --- a/.github/workflows/create-release-branch.yml +++ b/.github/workflows/create-release-branch.yml @@ -4,7 +4,7 @@ on: workflow_dispatch: inputs: tag: - description: "Release tag (e.g. v1.83.0-stable) — branch will be named release/" + description: "Release tag (e.g. 1.84.0, 1.84.0rc1, 1.84.0.dev42, 1.84.0.post1; legacy v1.83.10-stable still accepted) — branch will be named release/" required: true type: string commit_hash: @@ -14,7 +14,7 @@ on: workflow_call: inputs: tag: - description: "Release tag" + description: "Release tag (e.g. 1.84.0, 1.84.0rc1, 1.84.0.dev42, 1.84.0.post1; legacy v1.83.10-stable still accepted)" required: true type: string commit_hash: @@ -40,8 +40,8 @@ jobs: echo "::error::commit_hash must be a full 40-character commit SHA" exit 1 fi - if ! echo "${TAG}" | grep -qE '^v[0-9]+\.[0-9]+\.[0-9]+'; then - echo "::error::tag must start with vX.Y.Z" + if ! echo "${TAG}" | grep -qE '^v?[0-9]+\.[0-9]+\.[0-9]+'; then + echo "::error::tag must start with X.Y.Z (optional leading v), e.g. 1.84.0, 1.84.0rc1, 1.84.0.dev42, or v1.83.10-stable" exit 1 fi diff --git a/.github/workflows/create-release.yml b/.github/workflows/create-release.yml index 68ab397d827..a726a921a2b 100644 --- a/.github/workflows/create-release.yml +++ b/.github/workflows/create-release.yml @@ -4,7 +4,7 @@ on: workflow_dispatch: inputs: tag: - description: "Release tag (e.g. v1.83.0-stable)" + description: "Release tag (e.g. 1.84.0, 1.84.0rc1, 1.84.0.dev42, 1.84.0-dev.2, 1.84.0.post1; legacy v1.83.10-stable still accepted)" required: true type: string commit_hash: @@ -30,8 +30,8 @@ jobs: echo "::error::commit_hash must be a full 40-character commit SHA" exit 1 fi - if ! echo "${TAG}" | grep -qE '^v[0-9]+\.[0-9]+\.[0-9]+'; then - echo "::error::tag must start with vX.Y.Z" + if ! echo "${TAG}" | grep -qE '^v?[0-9]+\.[0-9]+\.[0-9]+'; then + echo "::error::tag must start with X.Y.Z (optional leading v), e.g. 1.84.0, 1.84.0rc1, 1.84.0.dev42, or v1.83.10-stable" exit 1 fi @@ -45,6 +45,13 @@ jobs: const tag = process.env.TAG; const commitHash = process.env.COMMIT_HASH; + // Mark RC / dev / nightly / alpha / beta tags as GitHub pre-releases. + // Accept both PEP 440 (`.dev`) and SemVer (`-dev`) separators so tags + // like `1.84.0.dev2` and `1.84.0-dev.2` are both detected. + // PEP 440 post-releases (e.g. `1.84.0.post1`) and legacy `-stable[.patch.N]` + // are stable maintenance releases, not pre-releases. + const isPrerelease = /(?:rc|nightly|alpha|beta|[-.]dev)/i.test(tag); + const cosignSection = [ `## Verify Docker Image Signature`, ``, @@ -89,7 +96,7 @@ jobs: target_commitish: commitHash, name: tag, owner: context.repo.owner, - prerelease: false, + prerelease: isPrerelease, repo: context.repo.repo, tag_name: tag, }); diff --git a/.github/workflows/mutation-test.yml b/.github/workflows/mutation-test.yml new file mode 100644 index 00000000000..8094ca57467 --- /dev/null +++ b/.github/workflows/mutation-test.yml @@ -0,0 +1,131 @@ +name: "Mutation Test (manual)" + +# Manually-triggered mutation testing. Runs mutmut against the scope +# configured in [tool.mutmut] in pyproject.toml (currently the +# litellm/proxy/management_endpoints/ folder). Intended cadence is roughly +# weekly — clicked from the Actions tab when someone wants a fresh report. +# +# Uploads a structured `mutation-report.md` (Meta ACH-style: original + +# mutated function with `# MUTANT START`/`# MUTANT END` delimiters + the +# existing tests + a task instruction) as a workflow artifact. Failures +# do not block anything because nothing depends on this workflow. + +on: + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: mutation-test-${{ github.ref }} + cancel-in-progress: true + +jobs: + mutation: + name: Run mutmut + runs-on: ubuntu-latest + # Whole-folder mutation against ~15 files / ~7.5k LOC can take hours. + # 350 minutes is just under the GitHub-hosted job cap of 360 minutes. + timeout-minutes: 350 + + 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: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 + with: + version: "0.10.9" + + - name: Cache uv dependencies + uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0 + with: + path: | + ~/.cache/uv + .venv + key: ${{ runner.os }}-uv-${{ hashFiles('uv.lock') }} + restore-keys: | + ${{ runner.os }}-uv- + + - name: Install dependencies + run: | + uv sync --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router + + - name: Generate Prisma client + env: + PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache + run: | + uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma + + # mutmut 3.x runs tests inside a `mutants/` sandbox where it injects + # mutation trampolines. uv installs the project as editable by default, + # which puts the original source dir on sys.path via a .pth file and + # shadows the sandbox copy — so tests would never exercise the mutated + # code. Reinstalling non-editable removes the .pth shadow. + - name: Reinstall litellm non-editable (so mutants/ is not shadowed) + run: | + uv pip uninstall litellm + uv pip install . --no-deps + + # pytest-retry's pytest_configure hook crashes with + # `INTERNALERROR: no option named 'filtered_exceptions'` when invoked + # via mutmut's in-process pytest.main() call. The entry-point name + # doesn't normalize cleanly with `-p no:`, so just remove the + # package outright. Reruns are wrong for mutation testing anyway — + # rerunning a "failed" mutant test would mask which mutants are killed. + - name: Remove pytest plugins that conflict with mutmut + run: | + uv pip uninstall pytest-retry || true + + - name: Run mutmut + env: + # Make the mutants/ sandbox win over site-packages on sys.path so the + # trampolined files are imported instead of the installed copy. + PYTHONPATH: ${{ github.workspace }}/mutants + run: | + set -o pipefail + mkdir -p mutants + uv run --no-sync --with mutmut==3.5.0 mutmut run 2>&1 | tee mutmut-run.log + + # Generate the structured report. The script embeds the enclosing + # function source for each survivor (via Python AST) and includes the + # existing test files, so an LLM agent has enough context to write + # killing tests without further file lookups. Modeled on Meta's ACH + # prompt template (arXiv 2501.12862). + - name: Generate detailed mutation report + if: always() + run: | + set +e + uv run --no-sync --with mutmut==3.5.0 mutmut export-cicd-stats > /dev/null 2>&1 + uv run --no-sync --with mutmut==3.5.0 mutmut results > mutmut-results.txt 2>&1 + uv run --no-sync python scripts/mutation_report.py + # The full report can be very long for big test files; the run-page + # summary cuts off at 1 MB. Append the head of the report (summary + # + survivor list) and link out to the artifact for the full body. + { + head -c 900000 mutation-report.md + echo "" + echo "" + echo "_Full report (with embedded function bodies and test files) is in the workflow artifact._" + } >> "$GITHUB_STEP_SUMMARY" + + - name: Upload mutmut artifacts + if: always() + uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 + with: + name: mutmut-${{ github.run_id }}-${{ github.run_attempt }} + path: | + mutation-report.md + mutmut-results.txt + mutmut-run.log + mutants/mutmut-stats.json + mutants/mutmut-cicd-stats.json + mutants/litellm/proxy/management_endpoints/**/*.py + if-no-files-found: warn + retention-days: 14 diff --git a/.github/workflows/test-unit-proxy-db.yml b/.github/workflows/test-unit-proxy-db.yml index d5781f767f6..8ee3a1ed0cd 100644 --- a/.github/workflows/test-unit-proxy-db.yml +++ b/.github/workflows/test-unit-proxy-db.yml @@ -100,6 +100,7 @@ jobs: test-path: >- tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py + tests/proxy_unit_tests/test_deprecated_key_grace_period.py workers: 4 dist: loadscope timeout: 15 @@ -141,6 +142,8 @@ jobs: tests/proxy_unit_tests/test_server_root_path.py tests/proxy_unit_tests/test_proxy_pass_user_config.py tests/proxy_unit_tests/test_proxy_token_counter.py + tests/proxy_unit_tests/test_request_size_limit_middleware.py + tests/proxy_unit_tests/test_multipart_bypass_repro.py workers: 4 dist: loadscope timeout: 15 diff --git a/.gitignore b/.gitignore index 12d3a37692a..cc8f689676d 100644 --- a/.gitignore +++ b/.gitignore @@ -91,7 +91,6 @@ test.py litellm_config.yaml !.github/observatory/litellm_config.yaml .cursor -.vscode/launch.json litellm/proxy/to_delete_loadtest_work/* update_model_cost_map.py tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -102,3 +101,5 @@ STABILIZATION_TODO.md **/playwright-report **/*.storageState.json **/coverage +test-config +.vscode diff --git a/.npmrc b/.npmrc index 168e81a1c4e..7999681cc35 100644 --- a/.npmrc +++ b/.npmrc @@ -2,4 +2,4 @@ # Packages needing lifecycle scripts: npm rebuild ignore-scripts=true # Protects local npm install only — npm ci (used in CI) ignores this -min-release-age=3d +min-release-age=3 diff --git a/AGENTS.md b/AGENTS.md index 4bdbf26ae9d..e99bf79d783 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -241,10 +241,27 @@ When opening issues or pull requests, follow these templates: ### Running the proxy server -Start the proxy with a config file: +Create a minimal config file and start the proxy: + +```yaml +# config.yaml +model_list: + - model_name: fake-openai-endpoint + litellm_params: + model: openai/fake-model + api_key: fake-key + api_base: https://fake-api.example.com + +general_settings: + master_key: sk-1234 + +litellm_settings: + drop_params: True + telemetry: False +``` ```bash -uv run litellm --config dev_config.yaml --port 4000 +uv run litellm --config config.yaml --port 4000 ``` The proxy takes ~15-20 seconds to fully start (it runs Prisma migrations on boot). Wait for `/health` to return before sending requests. Without a PostgreSQL `DATABASE_URL`, the proxy connects to a default Neon dev database embedded in the `litellm-proxy-extras` package. diff --git a/CLAUDE.md b/CLAUDE.md index 71e5af28ee7..938801df7c7 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -146,7 +146,7 @@ LiteLLM is a unified interface for 100+ LLM providers with two main components: - **Bound large result sets.** Prisma materializes full results in memory. For results over ~10 MB, paginate with `take`/`skip` or `cursor`/`take`, always with an explicit `order`. Prefer cursor-based pagination (`skip` is O(n)). Don't paginate naturally small result sets. - **Limit fetched columns on wide tables.** Use `select` to fetch only needed fields — returns a partial object, so downstream code must not access unselected fields. - **Check index coverage.** For new or modified queries, check `schema.prisma` for a supporting index. Prefer extending an existing index (e.g. `@@index([a])` → `@@index([a, b])`) over adding a new one, unless it's a `@@unique`. Only add indexes for large/frequent queries. -- **Keep schema files in sync.** Apply schema changes to all `schema.prisma` copies (`schema.prisma`, `litellm/proxy/`, `litellm-proxy-extras/`, `litellm-js/spend-logs/` for SpendLogs) with a migration under `litellm-proxy-extras/litellm_proxy_extras/migrations/`. +- **Keep schema files in sync.** Apply schema changes to all `schema.prisma` copies (`schema.prisma`, `litellm/proxy/`, `litellm-proxy-extras/`) with a migration under `litellm-proxy-extras/litellm_proxy_extras/migrations/`. ### Setup Wizard (`litellm/setup_wizard.py`) - The wizard is implemented as a single `SetupWizard` class with `@staticmethod` methods — keep it that way. No module-level functions except `run_setup_wizard()` (the public entrypoint) and pure helpers (color, ANSI). diff --git a/Dockerfile b/Dockerfile index 03779d6c884..9ad9ab31b65 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,9 +1,9 @@ # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:f26d42a15d09d9a643b231df929fa3cf609bedc58a728eb445be89a9d8d1da9f +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:f26d42a15d09d9a643b231df929fa3cf609bedc58a728eb445be89a9d8d1da9f -ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:733b4042187702f832f7fdecb3aff14a61b288c4ca37af188bb5715c1caebaf8 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:31da6565f35af6401031c1d7aa91dc84ac76c5c48edd17fb90f0ed9e3173c7a9 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -68,8 +68,8 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root -RUN apk add --no-cache bash openssl tzdata nodejs npm python3 libsndfile supervisor && \ - npm install -g npm@11.12.1 tar@7.5.11 glob@13.0.6 @isaacs/brace-expansion@5.0.1 brace-expansion@5.0.5 minimatch@10.2.4 diff@8.0.3 picomatch@4.0.4 && \ +RUN apk add --no-cache bash openssl tzdata nodejs npm python3 libsndfile && \ + npm install -g npm@11.14.0 tar@7.5.11 glob@13.0.6 @isaacs/brace-expansion@5.0.1 brace-expansion@5.0.5 minimatch@10.2.4 diff@8.0.3 picomatch@4.0.4 && \ GLOBAL="$(npm root -g)" && \ for pkg in tar glob @isaacs/brace-expansion brace-expansion minimatch diff picomatch; do \ name="${pkg##*/}"; \ @@ -85,17 +85,17 @@ ENV PATH="/app/.venv/bin:${PATH}" COPY --from=builder /app /app # Prisma binaries live in $HOME/.cache (default prisma-python location), -# which is /root/.cache here. Copy them from the builder so they survive -# deployments that volume-mount /app/.cache (e.g. readOnlyRootFilesystem -# + emptyDir) — otherwise the mount would shadow the baked-in query engine. -COPY --from=builder /root/.cache /root/.cache +# which is /root/.cache here. Copy only the Prisma subdirs — copying the +# whole /root/.cache drags in the uv build cache (~660 MB, includes a +# setuptools wheel that surfaces as a CVE finding even though it's not +# on the runtime sys.path). +COPY --from=builder /root/.cache/prisma /root/.cache/prisma +COPY --from=builder /root/.cache/prisma-python /root/.cache/prisma-python RUN find /app/.venv -type f -path "*/tornado/test/*" -delete && \ find /app/.venv -type d -path "*/tornado/test" -delete EXPOSE 4000/tcp -COPY docker/supervisord.conf /etc/supervisord.conf - ENTRYPOINT ["docker/prod_entrypoint.sh"] CMD ["--port", "4000"] diff --git a/Makefile b/Makefile index b6b674ff3b1..5dbd308a3e2 100644 --- a/Makefile +++ b/Makefile @@ -185,3 +185,6 @@ test-llm-translation-single: install-test-deps $(UV_RUN) pytest tests/llm_translation/$(FILE) \ --junitxml=test-results/junit.xml \ -v --tb=short --maxfail=100 --timeout=300 + +test-llm-translation-flush-vcr-cache: + $(UV_RUN) python tests/_flush_vcr_cache.py diff --git a/README.md b/README.md index d72fb746ed4..72fd43925c9 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,7 @@ Managing LLM calls across providers gets complicated fast — different SDKs, au Stripe image Google ADK - Greptile + Greptile OpenHands

Netflix

OpenAI Agents SDK diff --git a/codecov.yaml b/codecov.yaml index 09fccc6b995..8609d3143d6 100644 --- a/codecov.yaml +++ b/codecov.yaml @@ -1,3 +1,8 @@ +codecov: + require_ci_to_pass: false # post coverage status even if CI has unrelated failures + notify: + wait_for_ci: false # post as soon as expected uploads arrive, don't wait on CI + component_management: individual_components: - component_id: "Router" @@ -28,7 +33,7 @@ coverage: project: default: target: auto - threshold: 1% # at maximum allow project coverage to drop by 1% + threshold: 0% # do not allow project coverage to drop patch: default: target: auto diff --git a/cookbook/litellm-ollama-docker-image/requirements.txt b/cookbook/litellm-ollama-docker-image/requirements.txt index 815a42a679e..9b9181b2360 100644 --- a/cookbook/litellm-ollama-docker-image/requirements.txt +++ b/cookbook/litellm-ollama-docker-image/requirements.txt @@ -1 +1 @@ -litellm==1.83.5 \ No newline at end of file +litellm==1.83.14 diff --git a/deploy/Dockerfile.ghcr_base b/deploy/Dockerfile.ghcr_base deleted file mode 100644 index 66e64e5b774..00000000000 --- a/deploy/Dockerfile.ghcr_base +++ /dev/null @@ -1,18 +0,0 @@ -# Use the provided base image -FROM ghcr.io/berriai/litellm:main-latest@sha256:7c311546c25e7bb6e8cafede9fcd3d0d622ac636b5c9418befaa32e85dfb0186 - -# Set the working directory to /app -WORKDIR /app - -# Copy the configuration file into the container at /app -COPY config.yaml . - -# Make sure your docker/entrypoint.sh is executable -# Convert Windows line endings to Unix -RUN sed -i 's/\r$//' docker/entrypoint.sh && chmod +x docker/entrypoint.sh - -# Expose the necessary port -EXPOSE 4000/tcp - -# Override the CMD instruction with your desired command and arguments -CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug", "--run_gunicorn"] diff --git a/deploy/charts/litellm-helm/templates/deployment.yaml b/deploy/charts/litellm-helm/templates/deployment.yaml index 97123e5df69..25f69080878 100644 --- a/deploy/charts/litellm-helm/templates/deployment.yaml +++ b/deploy/charts/litellm-helm/templates/deployment.yaml @@ -100,6 +100,16 @@ spec: - name: DATABASE_URL value: {{ .Values.db.url | quote }} {{- end }} + {{- if and .Values.db.useExisting .Values.db.secret.readReplicaUrlKey }} + - name: DATABASE_URL_READ_REPLICA + valueFrom: + secretKeyRef: + name: {{ .Values.db.secret.name }} + key: {{ .Values.db.secret.readReplicaUrlKey }} + {{- else if .Values.db.readReplicaUrl }} + - name: DATABASE_URL_READ_REPLICA + value: {{ .Values.db.readReplicaUrl | quote }} + {{- end }} - name: PROXY_MASTER_KEY valueFrom: secretKeyRef: @@ -116,21 +126,32 @@ spec: name: {{ include "redis.secretName" .Subcharts.redis }} key: {{include "redis.secretPasswordKey" .Subcharts.redis }} {{- end }} + {{- /* + Inject LITELLM_LOG only when envVars does not already define it. + */}} + {{- if and .Values.logLevel (not (hasKey (default dict .Values.envVars) "LITELLM_LOG")) }} + - name: LITELLM_LOG + value: {{ .Values.logLevel | quote }} + {{- end }} {{- if .Values.envVars }} {{- range $key, $val := .Values.envVars }} - name: {{ $key }} value: {{ $val | quote }} {{- end }} {{- end }} - {{- if .Values.separateHealthApp }} - - name: SEPARATE_HEALTH_APP - value: "1" - - name: SEPARATE_HEALTH_PORT - value: {{ .Values.separateHealthPort | default "8081" | quote }} - {{- end }} {{- with .Values.extraEnvVars }} {{- toYaml . | nindent 12 }} {{- end }} + {{- if .Values.migrationJob.enabled }} + # Schema updates are owned by the dedicated migrations Job; skip + # the proxy's startup `prisma db push` so N replicas don't race + # one DB on every rollout. Placed last (after envVars and + # extraEnvVars) so this override can't be silently shadowed by a + # user-supplied DISABLE_SCHEMA_UPDATE under last-wins duplicate-env + # semantics — same pattern the migrations Job uses. + - name: DISABLE_SCHEMA_UPDATE + value: "true" + {{- end }} envFrom: {{- range .Values.environmentSecrets }} - secretRef: @@ -158,15 +179,10 @@ spec: - name: http containerPort: {{ .Values.service.port }} protocol: TCP - {{- if .Values.separateHealthApp }} - - name: health - containerPort: {{ .Values.separateHealthPort | default 8081 }} - protocol: TCP - {{- end }} livenessProbe: httpGet: path: {{ .Values.livenessProbe.path | quote }} - port: {{ if .Values.separateHealthApp }}"health"{{ else }}"http"{{ end }} + port: "http" initialDelaySeconds: {{ .Values.livenessProbe.initialDelaySeconds }} periodSeconds: {{ .Values.livenessProbe.periodSeconds }} timeoutSeconds: {{ .Values.livenessProbe.timeoutSeconds }} @@ -175,7 +191,7 @@ spec: readinessProbe: httpGet: path: {{ .Values.readinessProbe.path | quote }} - port: {{ if .Values.separateHealthApp }}"health"{{ else }}"http"{{ end }} + port: "http" initialDelaySeconds: {{ .Values.readinessProbe.initialDelaySeconds }} periodSeconds: {{ .Values.readinessProbe.periodSeconds }} timeoutSeconds: {{ .Values.readinessProbe.timeoutSeconds }} @@ -184,7 +200,7 @@ spec: startupProbe: httpGet: path: {{ .Values.startupProbe.path | quote }} - port: {{ if .Values.separateHealthApp }}"health"{{ else }}"http"{{ end }} + port: "http" initialDelaySeconds: {{ .Values.startupProbe.initialDelaySeconds }} periodSeconds: {{ .Values.startupProbe.periodSeconds }} timeoutSeconds: {{ .Values.startupProbe.timeoutSeconds }} diff --git a/deploy/charts/litellm-helm/tests/deployment_tests.yaml b/deploy/charts/litellm-helm/tests/deployment_tests.yaml index b1cbafaf408..df6d1345644 100644 --- a/deploy/charts/litellm-helm/tests/deployment_tests.yaml +++ b/deploy/charts/litellm-helm/tests/deployment_tests.yaml @@ -257,16 +257,16 @@ tests: value: 0 - equal: path: spec.template.spec.containers[0].livenessProbe.periodSeconds - value: 10 + value: 15 - equal: path: spec.template.spec.containers[0].livenessProbe.timeoutSeconds - value: 1 + value: 5 - equal: path: spec.template.spec.containers[0].livenessProbe.successThreshold value: 1 - equal: path: spec.template.spec.containers[0].livenessProbe.failureThreshold - value: 3 + value: 5 - equal: path: spec.template.spec.containers[0].readinessProbe.httpGet.path value: /health/readiness @@ -278,7 +278,7 @@ tests: value: 10 - equal: path: spec.template.spec.containers[0].readinessProbe.timeoutSeconds - value: 1 + value: 5 - equal: path: spec.template.spec.containers[0].readinessProbe.successThreshold value: 1 @@ -296,7 +296,7 @@ tests: value: 10 - equal: path: spec.template.spec.containers[0].startupProbe.timeoutSeconds - value: 1 + value: 5 - equal: path: spec.template.spec.containers[0].startupProbe.successThreshold value: 1 diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index 690ca69e730..9c7c013341b 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -88,26 +88,20 @@ service: # optionally specify loadBalancerClass # loadBalancerClass: tailscale -# Separate health app configuration -# When enabled, health checks will use a separate port and the application -# will receive SEPARATE_HEALTH_APP=1 and SEPARATE_HEALTH_PORT from environment variables -separateHealthApp: false -separateHealthPort: 8081 - -# Probe tuning for proxy container +# Probes for LiteLLM gateway container livenessProbe: path: /health/liveliness initialDelaySeconds: 0 - periodSeconds: 10 - timeoutSeconds: 1 + periodSeconds: 15 + timeoutSeconds: 5 successThreshold: 1 - failureThreshold: 3 + failureThreshold: 5 readinessProbe: path: /health/readiness initialDelaySeconds: 0 periodSeconds: 10 - timeoutSeconds: 1 + timeoutSeconds: 5 successThreshold: 1 failureThreshold: 3 @@ -115,7 +109,7 @@ startupProbe: path: /health/readiness initialDelaySeconds: 0 periodSeconds: 10 - timeoutSeconds: 1 + timeoutSeconds: 5 successThreshold: 1 failureThreshold: 30 @@ -258,6 +252,26 @@ db: passwordKey: password # Optional: when set, DATABASE_HOST will be sourced from this secret key instead of db.endpoint endpointKey: "" + # Optional: when set, DATABASE_URL_READ_REPLICA will be sourced from this + # secret key instead of db.readReplicaUrl. Prefer this over the plain + # value: read-replica URLs typically embed credentials, and a value + # written to db.readReplicaUrl ends up visible in the rendered pod spec + # and the Helm release secret. + readReplicaUrlKey: "" + + # Optional read-replica routing. When set, the proxy sends read-only + # queries (find_*, count, group_by, query_raw/_first) to this URL while + # writes continue to go to db.url. Useful for Aurora-style clusters with + # separate reader/writer endpoints. Leave empty to keep single-DB behavior. + # When IAM_TOKEN_DB_AUTH is enabled, the reader URL is auto-refreshed + # alongside the writer (host/port/user/db are parsed from this URL once + # at startup; only the IAM token rotates). + # + # If the URL embeds credentials, prefer db.secret.readReplicaUrlKey over + # this field — the plain value is rendered into the pod spec and the + # Helm release secret. This field is intended for credential-less URLs + # only (e.g. when IAM_TOKEN_DB_AUTH supplies the token at runtime). + readReplicaUrl: "" # Use the Stackgres Helm chart to deploy an instance of a Stackgres cluster. # The Stackgres Operator must already be installed within the target @@ -329,6 +343,17 @@ migrationJob: helm: enabled: false +# Log level for the litellm proxy (sets LITELLM_LOG in the deployment env). +# Rendered as a direct `env:` entry, which in Kubernetes takes precedence over +# any `envFrom:` source. If you currently source LITELLM_LOG from an +# environmentSecret or environmentConfigMap, set `logLevel: ""` here to +# disable injection — otherwise this value silently overrides your secret / +# configmap entry. +# +# Setting LITELLM_LOG inside `envVars:` below also wins: the template skips +# this injection entirely when envVars already defines LITELLM_LOG. +logLevel: INFO + # Additional environment variables to be added to the deployment as a map of key-value pairs envVars: {} diff --git a/deploy/kubernetes/kub.yaml b/deploy/kubernetes/kub.yaml deleted file mode 100644 index d5ba500d8f0..00000000000 --- a/deploy/kubernetes/kub.yaml +++ /dev/null @@ -1,56 +0,0 @@ -apiVersion: apps/v1 -kind: Deployment -metadata: - name: litellm-deployment -spec: - replicas: 3 - selector: - matchLabels: - app: litellm - template: - metadata: - labels: - app: litellm - spec: - containers: - - name: litellm-container - image: ghcr.io/berriai/litellm:main-latest - imagePullPolicy: Always - env: - - name: AZURE_API_KEY - value: "d6f****" - - name: AZURE_API_BASE - value: "https://openai" - - name: LITELLM_MASTER_KEY - value: "sk-1234" - - name: DATABASE_URL - value: "postgresql://ishaan*********" - args: - - "--config" - - "/app/proxy_config.yaml" # Update the path to mount the config file - volumeMounts: # Define volume mount for proxy_config.yaml - - name: config-volume - mountPath: /app - readOnly: true - livenessProbe: - httpGet: - path: /health/liveliness - port: 4000 - initialDelaySeconds: 120 - periodSeconds: 15 - successThreshold: 1 - failureThreshold: 3 - timeoutSeconds: 10 - readinessProbe: - httpGet: - path: /health/readiness - port: 4000 - initialDelaySeconds: 120 - periodSeconds: 15 - successThreshold: 1 - failureThreshold: 3 - timeoutSeconds: 10 - volumes: # Define volume to mount proxy_config.yaml - - name: config-volume - configMap: - name: litellm-config diff --git a/deploy/kubernetes/service.yaml b/deploy/kubernetes/service.yaml deleted file mode 100644 index 4751c837254..00000000000 --- a/deploy/kubernetes/service.yaml +++ /dev/null @@ -1,12 +0,0 @@ -apiVersion: v1 -kind: Service -metadata: - name: litellm-service -spec: - selector: - app: litellm - ports: - - protocol: TCP - port: 4000 - targetPort: 4000 - type: LoadBalancer \ No newline at end of file diff --git a/dev_config.yaml b/dev_config.yaml deleted file mode 100644 index 64e3c14703e..00000000000 --- a/dev_config.yaml +++ /dev/null @@ -1,13 +0,0 @@ -model_list: - - model_name: fake-openai-endpoint - litellm_params: - model: openai/fake-model - api_key: fake-key - api_base: https://exampleopenaiendpoint-production.up.railway.app/ - -general_settings: - master_key: sk-1234 - -litellm_settings: - drop_params: True - telemetry: False diff --git a/docker-compose.yml b/docker-compose.yml index 988860a7877..80e1f289aad 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -16,6 +16,11 @@ services: - "4000:4000" # Map the container port to the host, change the host port if necessary environment: DATABASE_URL: "postgresql://llmproxy:dbpassword9090@db:5432/litellm" + # Optional: route read-only queries (find_*, count, group_by, query_raw/_first) + # to a separate reader endpoint, e.g. an Aurora reader. Leave unset for + # single-DB deployments. With IAM_TOKEN_DB_AUTH enabled, the reader URL + # is auto-refreshed alongside the writer. + # DATABASE_URL_READ_REPLICA: "postgresql://llmproxy:dbpassword9090@db-reader:5432/litellm" STORE_MODEL_IN_DB: "True" # allows adding models to proxy via UI env_file: - .env # Load local .env file diff --git a/docker/Dockerfile.alpine b/docker/Dockerfile.alpine deleted file mode 100644 index 2cfc5ef03ff..00000000000 --- a/docker/Dockerfile.alpine +++ /dev/null @@ -1,68 +0,0 @@ -# Base image for building -ARG LITELLM_BUILD_IMAGE=python:3.11-alpine@sha256:f07e2ace46f560f09a6eeec7b4913b80ee99546e749ef82342a419a326620856 - -# Runtime image -ARG LITELLM_RUNTIME_IMAGE=python:3.11-alpine@sha256:f07e2ace46f560f09a6eeec7b4913b80ee99546e749ef82342a419a326620856 -ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:733b4042187702f832f7fdecb3aff14a61b288c4ca37af188bb5715c1caebaf8 - -FROM $UV_IMAGE AS uvbin - -FROM $LITELLM_BUILD_IMAGE AS builder - -WORKDIR /app - -COPY --from=uvbin /uv /usr/local/bin/uv -COPY --from=uvbin /uvx /usr/local/bin/uvx - -RUN apk add --no-cache gcc python3-dev musl-dev nodejs npm libsndfile - -ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \ - UV_PROJECT_ENVIRONMENT=/app/.venv \ - UV_LINK_MODE=copy \ - XDG_CACHE_HOME=/app/.cache \ - PATH="/app/.venv/bin:${PATH}" - -# Copy dependency metadata first for layer caching -COPY pyproject.toml uv.lock ./ -COPY enterprise/pyproject.toml enterprise/ -COPY litellm-proxy-extras/pyproject.toml litellm-proxy-extras/ - -# Install third-party dependencies (cached unless pyproject.toml/uv.lock change) -RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \ - --extra proxy \ - --extra proxy-runtime \ - --extra extra_proxy \ - --extra semantic-router \ - --python python3 - -# Copy full source tree -COPY . . - -# Install project and workspace packages (fast - deps already cached) -RUN uv sync --frozen --no-default-groups --no-editable \ - --extra proxy \ - --extra proxy-runtime \ - --extra extra_proxy \ - --extra semantic-router \ - --python python3 - -RUN prisma generate --schema=./schema.prisma - -RUN sed -i 's/\r$//' docker/entrypoint.sh && chmod +x docker/entrypoint.sh && \ - sed -i 's/\r$//' docker/prod_entrypoint.sh && chmod +x docker/prod_entrypoint.sh - -FROM $LITELLM_RUNTIME_IMAGE AS runtime - -RUN apk upgrade --no-cache && apk add --no-cache libsndfile nodejs npm - -WORKDIR /app -ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \ - XDG_CACHE_HOME=/app/.cache \ - PATH="/app/.venv/bin:${PATH}" - -COPY --from=builder /app /app - -EXPOSE 4000/tcp - -ENTRYPOINT ["docker/prod_entrypoint.sh"] -CMD ["--port", "4000"] diff --git a/docker/Dockerfile.custom_ui b/docker/Dockerfile.custom_ui deleted file mode 100644 index cc44893bf92..00000000000 --- a/docker/Dockerfile.custom_ui +++ /dev/null @@ -1,86 +0,0 @@ -# Use the provided base image -# NOTE: This is a dev/branch-specific tag. Update digest when the base image is rebuilt. -FROM ghcr.io/berriai/litellm:litellm_fwd_server_root_path-dev - -# Set the working directory to /app -WORKDIR /app - -# Install Node.js and npm (adjust version as needed) -RUN apt-get update && apt-get upgrade -y \ - libxml2 \ - libexpat1 \ - openssl \ - libssl3 \ - git \ - libkrb5-3 \ - libglib2.0-0 \ - wget \ - libaom3 \ - libxslt1.1 \ - libgnutls30 \ - libc6 && \ - apt-get install -y --no-install-recommends nodejs npm && \ - npm install -g npm@11.12.1 tar@7.5.11 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.4 diff@8.0.3 && \ - GLOBAL="$(npm root -g)" && \ - find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ - done && \ - find "$GLOBAL/npm" -type d -name "glob" -path "*/node_modules/glob" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ - done && \ - find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ - done && \ - find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \ - done && \ - find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \ - done && \ - find /usr/local/lib /usr/lib -path "*/node_modules/npm/package.json" -exec \ - sed -i 's/"tar": "\^7\.5\.[0-9]*"/"tar": "^7.5.10"/g; s/"minimatch": "\^10\.[0-9.]*"/"minimatch": "^10.2.4"/g' {} + 2>/dev/null && \ - npm cache clean --force && \ - apt-get purge -y npm - -# Copy the UI source into the container -COPY ./ui/litellm-dashboard /app/ui/litellm-dashboard - -# Set an environment variable for UI_BASE_PATH -# This can be overridden at build time -# set UI_BASE_PATH to "/ui" -ENV UI_BASE_PATH="/prod/ui" - -# Build the UI with the specified UI_BASE_PATH -WORKDIR /app/ui/litellm-dashboard -RUN npm ci -RUN UI_BASE_PATH=$UI_BASE_PATH npm run build - -# Create the destination directory -RUN mkdir -p /app/litellm/proxy/_experimental/out - -# Move the built files to the appropriate location -# Assuming the build output is in ./out directory -RUN rm -rf /app/litellm/proxy/_experimental/out/* && \ - mv ./out/* /app/litellm/proxy/_experimental/out/ - -# Switch back to the main app directory -WORKDIR /app - -# Make sure your docker/entrypoint.sh is executable -# Convert Windows line endings to Unix for entrypoint scripts -RUN sed -i 's/\r$//' docker/entrypoint.sh && chmod +x docker/entrypoint.sh -RUN sed -i 's/\r$//' docker/prod_entrypoint.sh && chmod +x docker/prod_entrypoint.sh - -# Run as non-root user -RUN groupadd --gid 1000 appuser && useradd --uid 1000 --gid 1000 --no-create-home appuser \ - && chown -R appuser:appuser /app -USER appuser - -# Expose the necessary port -EXPOSE 4000/tcp - -HEALTHCHECK --interval=30s --timeout=5s --start-period=10s --retries=3 \ - CMD ["python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:4000/health')"] - -# Override the CMD instruction with your desired command and arguments -CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug"] \ No newline at end of file diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index e3edcece61c..c84003a065f 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -1,9 +1,9 @@ # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:f26d42a15d09d9a643b231df929fa3cf609bedc58a728eb445be89a9d8d1da9f +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:f26d42a15d09d9a643b231df929fa3cf609bedc58a728eb445be89a9d8d1da9f -ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:733b4042187702f832f7fdecb3aff14a61b288c4ca37af188bb5715c1caebaf8 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -66,7 +66,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root -RUN apk add --no-cache bash openssl tzdata nodejs npm python3 libsndfile supervisor && \ +RUN apk add --no-cache bash openssl tzdata nodejs npm python3 libsndfile && \ npm install -g npm@11.12.1 tar@7.5.11 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.4 diff@8.0.3 && \ GLOBAL="$(npm root -g)" && \ find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ @@ -102,7 +102,5 @@ RUN find /app/.venv -type f -path "*/tornado/test/*" -delete && \ EXPOSE 4000/tcp -COPY docker/supervisord.conf /etc/supervisord.conf - ENTRYPOINT ["docker/prod_entrypoint.sh"] CMD ["--port", "4000"] diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev deleted file mode 100644 index e2dc1857835..00000000000 --- a/docker/Dockerfile.dev +++ /dev/null @@ -1,121 +0,0 @@ -# Base image for building -ARG LITELLM_BUILD_IMAGE=python:3.13-slim@sha256:739e7213785e88c0f702dcdc12c0973afcbd606dbf021a589cab77d6b00b579d - -# Runtime image -ARG LITELLM_RUNTIME_IMAGE=python:3.13-slim@sha256:739e7213785e88c0f702dcdc12c0973afcbd606dbf021a589cab77d6b00b579d -ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:733b4042187702f832f7fdecb3aff14a61b288c4ca37af188bb5715c1caebaf8 - -FROM $UV_IMAGE AS uvbin - -FROM $LITELLM_BUILD_IMAGE AS builder - -WORKDIR /app -USER root - -COPY --from=uvbin /uv /usr/local/bin/uv -COPY --from=uvbin /uvx /usr/local/bin/uvx - -RUN apt-get update && apt-get install -y --no-install-recommends \ - gcc \ - g++ \ - python3-dev \ - libssl-dev \ - pkg-config \ - nodejs \ - npm \ - && rm -rf /var/lib/apt/lists/* - -ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \ - UV_PROJECT_ENVIRONMENT=/app/.venv \ - UV_LINK_MODE=copy \ - XDG_CACHE_HOME=/app/.cache \ - PATH="/app/.venv/bin:${PATH}" - -# Copy dependency metadata first for layer caching -COPY pyproject.toml uv.lock ./ -COPY enterprise/pyproject.toml enterprise/ -COPY litellm-proxy-extras/pyproject.toml litellm-proxy-extras/ - -# Install third-party dependencies (cached unless pyproject.toml/uv.lock change) -RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \ - --extra proxy \ - --extra proxy-runtime \ - --extra extra_proxy \ - --extra semantic-router \ - --python python - -# Copy full source tree -COPY . . - -# Build Admin UI before final sync -RUN sed -i 's/\r$//' docker/build_admin_ui.sh && chmod +x docker/build_admin_ui.sh && ./docker/build_admin_ui.sh - -# Install project and workspace packages (fast - deps already cached) -RUN uv sync --frozen --no-default-groups --no-editable \ - --extra proxy \ - --extra proxy-runtime \ - --extra extra_proxy \ - --extra semantic-router \ - --python python - -RUN prisma generate --schema=./schema.prisma - -RUN sed -i 's/\r$//' docker/entrypoint.sh && chmod +x docker/entrypoint.sh && \ - sed -i 's/\r$//' docker/prod_entrypoint.sh && chmod +x docker/prod_entrypoint.sh - -FROM $LITELLM_RUNTIME_IMAGE AS runtime - -USER root - -RUN apt-get update && apt-get upgrade -y \ - libxml2 \ - libexpat1 \ - openssl \ - libssl3 \ - git \ - libkrb5-3 \ - libglib2.0-0 \ - wget \ - libaom3 \ - libxslt1.1 \ - libgnutls30 \ - libc6 \ - && apt-get install -y --no-install-recommends \ - libssl3 \ - libatomic1 \ - nodejs \ - npm \ - && rm -rf /var/lib/apt/lists/* \ - && npm install -g npm@11.12.1 tar@7.5.11 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.4 diff@8.0.3 \ - && GLOBAL="$(npm root -g)" \ - && find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ - done \ - && find "$GLOBAL/npm" -type d -name "glob" -path "*/node_modules/glob" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ - done \ - && find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ - done \ - && find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \ - done \ - && find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \ - rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \ - done \ - && find /usr/local/lib /usr/lib -path "*/node_modules/npm/package.json" -exec \ - sed -i 's/"tar": "\^7\.5\.[0-9]*"/"tar": "^7.5.10"/g; s/"minimatch": "\^10\.[0-9.]*"/"minimatch": "^10.2.4"/g' {} + 2>/dev/null \ - && npm cache clean --force \ - && apt-get purge -y npm - -WORKDIR /app -ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \ - XDG_CACHE_HOME=/app/.cache \ - PATH="/app/.venv/bin:${PATH}" - -COPY --from=builder /app /app - -EXPOSE 4000/tcp - -ENTRYPOINT ["docker/prod_entrypoint.sh"] -CMD ["--port", "4000"] diff --git a/docker/Dockerfile.health_check b/docker/Dockerfile.health_check deleted file mode 100644 index 07d35b5e291..00000000000 --- a/docker/Dockerfile.health_check +++ /dev/null @@ -1,30 +0,0 @@ -ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:733b4042187702f832f7fdecb3aff14a61b288c4ca37af188bb5715c1caebaf8 -FROM $UV_IMAGE AS uvbin - -FROM python:3.13-slim@sha256:739e7213785e88c0f702dcdc12c0973afcbd606dbf021a589cab77d6b00b579d - -WORKDIR /app - -# Copy the uv binary and the health check script. -COPY --from=uvbin /uv /usr/local/bin/uv -COPY pyproject.toml uv.lock /app/ -COPY scripts/health_check/health_check_client.py /app/health_check_client.py - -# Resolve and install the health-check dependencies from the project lockfile -# so the runtime image stays self-contained and reproducible. -RUN uv export --frozen --no-default-groups --only-group healthcheck --no-emit-project --no-hashes --output-file /tmp/health-check-requirements.txt \ - && uv pip install --system -r /tmp/health-check-requirements.txt \ - && rm /tmp/health-check-requirements.txt \ - && rm /app/pyproject.toml /app/uv.lock \ - && chmod +x /app/health_check_client.py - -# Run as non-root user -RUN groupadd --gid 1000 appuser && useradd --uid 1000 --gid 1000 --no-create-home appuser -USER appuser - -# Health check -HEALTHCHECK --interval=30s --timeout=5s --start-period=5s --retries=3 \ - CMD ["python", "/app/health_check_client.py", "--help"] - -# Set entrypoint -ENTRYPOINT ["python", "/app/health_check_client.py"] diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 8512ae8ad92..4de4a55981d 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -1,8 +1,8 @@ # Base images -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:f26d42a15d09d9a643b231df929fa3cf609bedc58a728eb445be89a9d8d1da9f -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:f26d42a15d09d9a643b231df929fa3cf609bedc58a728eb445be89a9d8d1da9f +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 ARG PROXY_EXTRAS_SOURCE=published -ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:733b4042187702f832f7fdecb3aff14a61b288c4ca37af188bb5715c1caebaf8 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin @@ -32,7 +32,6 @@ ENV UV_PROJECT_ENVIRONMENT=/app/.venv \ PATH="/app/.venv/bin:${PATH}" \ LITELLM_NON_ROOT=true \ PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \ - PRISMA_CLI_BINARY_TARGETS="debian-openssl-3.0.x" \ XDG_CACHE_HOME=/app/.cache # Copy dependency metadata first for layer caching @@ -104,17 +103,15 @@ RUN for i in 1 2 3; do \ apk upgrade --no-cache && break || sleep 5; \ done && \ for i in 1 2 3; do \ - apk add --no-cache python3 bash openssl tzdata supervisor libsndfile nodejs && break || sleep 5; \ + apk add --no-cache python3 bash openssl tzdata libsndfile nodejs && break || sleep 5; \ done COPY --from=builder /app /app COPY --from=builder /var/lib/litellm/ui /var/lib/litellm/ui COPY --from=builder /var/lib/litellm/assets /var/lib/litellm/assets -COPY --from=builder /app/docker/supervisord.conf /etc/supervisord.conf ENV PATH="/app/.venv/bin:${PATH}" \ PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \ - PRISMA_CLI_BINARY_TARGETS="debian-openssl-3.0.x" \ HOME=/app \ LITELLM_NON_ROOT=true \ XDG_CACHE_HOME=/app/.cache \ diff --git a/docker/prod_entrypoint.sh b/docker/prod_entrypoint.sh index 28d1bdcc294..bd78bf6687b 100644 --- a/docker/prod_entrypoint.sh +++ b/docker/prod_entrypoint.sh @@ -1,14 +1,8 @@ #!/bin/sh -if [ "$SEPARATE_HEALTH_APP" = "1" ]; then - export LITELLM_ARGS="$@" - export SUPERVISORD_STOPWAITSECS="${SUPERVISORD_STOPWAITSECS:-3600}" - exec supervisord -c /etc/supervisord.conf -fi - if [ "$USE_DDTRACE" = "true" ]; then export DD_TRACE_OPENAI_ENABLED="False" exec ddtrace-run litellm "$@" else exec litellm "$@" -fi \ No newline at end of file +fi diff --git a/docker/supervisord.conf b/docker/supervisord.conf deleted file mode 100644 index ba9d99d18a5..00000000000 --- a/docker/supervisord.conf +++ /dev/null @@ -1,46 +0,0 @@ -[supervisord] -nodaemon=true -loglevel=info -logfile=/tmp/supervisord.log -pidfile=/tmp/supervisord.pid - -[group:litellm] -programs=main,health - -[program:main] -command=sh -c 'if [ "$USE_DDTRACE" = "true" ]; then export DD_TRACE_OPENAI_ENABLED="False"; exec ddtrace-run python -m litellm.proxy.proxy_cli --host 0.0.0.0 --port=4000 $LITELLM_ARGS; else exec python -m litellm.proxy.proxy_cli --host 0.0.0.0 --port=4000 $LITELLM_ARGS; fi' -autostart=true -autorestart=true -startretries=3 -priority=1 -exitcodes=0 -stopasgroup=true -killasgroup=true -stopwaitsecs=%(ENV_SUPERVISORD_STOPWAITSECS)s -stdout_logfile=/dev/stdout -stderr_logfile=/dev/stderr -stdout_logfile_maxbytes = 0 -stderr_logfile_maxbytes = 0 -environment=PYTHONUNBUFFERED=true - -[program:health] -command=sh -c '[ "$SEPARATE_HEALTH_APP" = "1" ] && exec uvicorn litellm.proxy.health_endpoints.health_app_factory:build_health_app --factory --host 0.0.0.0 --port=${SEPARATE_HEALTH_PORT:-4001} || exit 0' -autostart=true -autorestart=true -startretries=3 -priority=2 -exitcodes=0 -stopasgroup=true -killasgroup=true -stopwaitsecs=%(ENV_SUPERVISORD_STOPWAITSECS)s -stdout_logfile=/dev/stdout -stderr_logfile=/dev/stderr -stdout_logfile_maxbytes = 0 -stderr_logfile_maxbytes = 0 -environment=PYTHONUNBUFFERED=true - -[eventlistener:process_monitor] -command=python -c "from supervisor import childutils; import os, signal; [os.kill(os.getppid(), signal.SIGTERM) for h,p in iter(lambda: childutils.listener.wait(), None) if h['eventname'] in ['PROCESS_STATE_FATAL', 'PROCESS_STATE_EXITED'] and dict([x.split(':') for x in p.split(' ')])['processname'] in ['main', 'health'] or childutils.listener.ok()]" -events=PROCESS_STATE_EXITED,PROCESS_STATE_FATAL -autostart=true -autorestart=true \ No newline at end of file diff --git a/docs/images/local-testing/hosted-vllm-custom-tool-local-test.png b/docs/images/local-testing/hosted-vllm-custom-tool-local-test.png new file mode 100644 index 00000000000..9fb6665d373 Binary files /dev/null and b/docs/images/local-testing/hosted-vllm-custom-tool-local-test.png differ diff --git a/docs/my-website/docs/providers/crusoe.md b/docs/my-website/docs/providers/crusoe.md new file mode 100644 index 00000000000..aa737cbdcd8 --- /dev/null +++ b/docs/my-website/docs/providers/crusoe.md @@ -0,0 +1,196 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Crusoe + +## Overview + +| Property | Details | +|-------|-------| +| Description | Crusoe Cloud provides GPU-accelerated inference for open-source large language models, optimized for performance and cost efficiency. | +| Provider Route on LiteLLM | `crusoe/` | +| Link to Provider Doc | [Crusoe Managed Inference Documentation ↗](https://docs.crusoecloud.com/managed-inference/overview/index.html) | +| Base URL | `https://managed-inference-api-proxy.crusoecloud.com/v1` | +| Supported Operations | [`/chat/completions`](#sample-usage) | + +
+
+ +**We support ALL Crusoe models, just set `crusoe/` as a prefix when sending completion requests** + +## Available Models + +| Model | Description | Context Window | +|-------|-------------|----------------| +| `crusoe/deepseek-ai/DeepSeek-R1-0528` | DeepSeek R1 reasoning model (May 2025) | 163,840 tokens | +| `crusoe/deepseek-ai/DeepSeek-V3-0324` | DeepSeek V3 chat model (March 2025) | 163,840 tokens | +| `crusoe/google/gemma-3-12b-it` | Google Gemma 3 12B instruction-tuned | 131,072 tokens | +| `crusoe/meta-llama/Llama-3.3-70B-Instruct` | Llama 3.3 70B instruction-tuned | 131,072 tokens | +| `crusoe/moonshotai/Kimi-K2-Thinking` | Kimi K2 extended thinking model | 262,144 tokens | +| `crusoe/openai/gpt-oss-120b` | OpenAI 120B open-source model | 131,072 tokens | +| `crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507` | Qwen3 235B MoE instruction-tuned | 262,144 tokens | + +## Required Variables + +```python showLineNumbers title="Environment Variables" +os.environ["CRUSOE_API_KEY"] = "" # your Crusoe API key +``` + +## Usage - LiteLLM Python SDK + +### Non-streaming + +```python showLineNumbers title="Crusoe Non-streaming Completion" +import os +import litellm +from litellm import completion + +os.environ["CRUSOE_API_KEY"] = "" # your Crusoe API key + +messages = [{"content": "Hello, how are you?", "role": "user"}] + +# Crusoe call +response = completion( + model="crusoe/meta-llama/Llama-3.3-70B-Instruct", + messages=messages +) + +print(response) +``` + +### Streaming + +```python showLineNumbers title="Crusoe Streaming Completion" +import os +import litellm +from litellm import completion + +os.environ["CRUSOE_API_KEY"] = "" # your Crusoe API key + +messages = [{"content": "Write a short story about AI", "role": "user"}] + +# Crusoe call with streaming +response = completion( + model="crusoe/meta-llama/Llama-3.3-70B-Instruct", + messages=messages, + stream=True +) + +for chunk in response: + print(chunk) +``` + +### Function Calling + +```python showLineNumbers title="Crusoe Function Calling" +import os +import litellm +from litellm import completion + +os.environ["CRUSOE_API_KEY"] = "" # your Crusoe API key + +tools = [{ + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + } + }, + "required": ["location"] + } + } +}] + +messages = [{"role": "user", "content": "What's the weather in Boston?"}] + +response = completion( + model="crusoe/meta-llama/Llama-3.3-70B-Instruct", + messages=messages, + tools=tools, + tool_choice="auto" +) + +print(response) +``` + +## Usage - LiteLLM Proxy Server + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: llama-3.3-70b + litellm_params: + model: crusoe/meta-llama/Llama-3.3-70B-Instruct + api_key: os.environ/CRUSOE_API_KEY + - model_name: deepseek-r1 + litellm_params: + model: crusoe/deepseek-ai/DeepSeek-R1-0528 + api_key: os.environ/CRUSOE_API_KEY + - model_name: deepseek-v3 + litellm_params: + model: crusoe/deepseek-ai/DeepSeek-V3-0324 + api_key: os.environ/CRUSOE_API_KEY + - model_name: qwen3-235b + litellm_params: + model: crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507 + api_key: os.environ/CRUSOE_API_KEY + - model_name: kimi-k2 + litellm_params: + model: crusoe/moonshotai/Kimi-K2-Thinking + api_key: os.environ/CRUSOE_API_KEY +``` + +## Custom API Base + +**Option 1: Environment variable** + +```python showLineNumbers title="Custom API Base via env var" +import os +from litellm import completion + +os.environ["CRUSOE_API_BASE"] = "https://custom.crusoecloud.com/v1" +os.environ["CRUSOE_API_KEY"] = "" # your API key + +response = completion( + model="crusoe/meta-llama/Llama-3.3-70B-Instruct", + messages=[{"content": "Hello!", "role": "user"}], +) +``` + +**Option 2: Pass directly** + +```python showLineNumbers title="Custom API Base via parameter" +from litellm import completion + +response = completion( + model="crusoe/meta-llama/Llama-3.3-70B-Instruct", + messages=[{"content": "Hello!", "role": "user"}], + api_base="https://custom.crusoecloud.com/v1", + api_key="your-api-key", +) +``` + +## Supported OpenAI Parameters + +- `temperature` +- `max_tokens` +- `max_completion_tokens` +- `top_p` +- `frequency_penalty` +- `presence_penalty` +- `stop` +- `n` +- `stream` +- `tools` +- `tool_choice` +- `response_format` +- `seed` +- `user` +- `logit_bias` +- `logprobs` +- `top_logprobs` diff --git a/enterprise/enterprise_hooks/banned_keywords.py b/enterprise/enterprise_hooks/banned_keywords.py index 4df138939ad..47421c96051 100644 --- a/enterprise/enterprise_hooks/banned_keywords.py +++ b/enterprise/enterprise_hooks/banned_keywords.py @@ -11,6 +11,10 @@ from typing import Literal import litellm from litellm.caching.caching import DualCache from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.guardrails._content_utils import ( + is_text_content_call_type, + iter_message_text, +) from litellm.integrations.custom_logger import CustomLogger from litellm._logging import verbose_proxy_logger from fastapi import HTTPException @@ -73,10 +77,9 @@ class _ENTERPRISE_BannedKeywords(CustomLogger): - check if user id part of blocked list """ self.print_verbose("Inside Banned Keyword List Pre-Call Hook") - if call_type == "completion" and "messages" in data: - for m in data["messages"]: - if "content" in m and isinstance(m["content"], str): - self.test_violation(test_str=m["content"]) + if is_text_content_call_type(call_type): + for text in iter_message_text(data): + self.test_violation(test_str=text) except HTTPException as e: raise e @@ -93,11 +96,16 @@ class _ENTERPRISE_BannedKeywords(CustomLogger): user_api_key_dict: UserAPIKeyAuth, response, ): - if isinstance(response, litellm.ModelResponse) and isinstance( - response.choices[0], litellm.utils.Choices - ): - for word in self.banned_keywords_list: - self.test_violation(test_str=response.choices[0].message.content or "") + if not isinstance(response, litellm.ModelResponse): + return + + for choice in response.choices: + if not isinstance(choice, litellm.utils.Choices): + continue + message = getattr(choice, "message", None) + content = getattr(message, "content", None) + if isinstance(content, str): + self.test_violation(test_str=content) async def async_post_call_streaming_hook( self, diff --git a/enterprise/enterprise_hooks/google_text_moderation.py b/enterprise/enterprise_hooks/google_text_moderation.py index 1f26d52adf8..5b2d71c5cca 100644 --- a/enterprise/enterprise_hooks/google_text_moderation.py +++ b/enterprise/enterprise_hooks/google_text_moderation.py @@ -12,6 +12,7 @@ import litellm from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_logger import CustomLogger from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.guardrails._content_utils import iter_message_text from litellm.types.utils import CallTypesLiteral @@ -94,11 +95,9 @@ class _ENTERPRISE_GoogleTextModeration(CustomLogger): - Calls Google's Text Moderation API - Rejects request if it fails safety check """ - if "messages" in data and isinstance(data["messages"], list): - text = "" - for m in data["messages"]: # assume messages is a list - if "content" in m and isinstance(m["content"], str): - text += m["content"] + # Covers multimodal list content + Responses-API input. + text = "".join(iter_message_text(data)) + if text: document = self.language_document(content=text, type_=self.document_type) request = self.moderate_text_request( diff --git a/enterprise/enterprise_hooks/openai_moderation.py b/enterprise/enterprise_hooks/openai_moderation.py index a1db9818e5e..2162370804a 100644 --- a/enterprise/enterprise_hooks/openai_moderation.py +++ b/enterprise/enterprise_hooks/openai_moderation.py @@ -19,6 +19,7 @@ import litellm from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_logger import CustomLogger from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.guardrails._content_utils import iter_message_text from litellm.types.utils import CallTypesLiteral @@ -37,11 +38,8 @@ class _ENTERPRISE_OpenAI_Moderation(CustomLogger): user_api_key_dict: UserAPIKeyAuth, call_type: CallTypesLiteral, ): - text = "" - if "messages" in data and isinstance(data["messages"], list): - for m in data["messages"]: # assume messages is a list - if "content" in m and isinstance(m["content"], str): - text += m["content"] + # Covers multimodal list content + Responses-API input. + text = "".join(iter_message_text(data)) from litellm.proxy.proxy_server import llm_router diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/secret_detection.py b/enterprise/litellm_enterprise/enterprise_callbacks/secret_detection.py index 8a7a82df686..f441ce71ab9 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/secret_detection.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/secret_detection.py @@ -18,6 +18,7 @@ from litellm._logging import verbose_proxy_logger from litellm.caching.caching import DualCache from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.guardrails._content_utils import walk_user_text GUARDRAIL_NAME = "hide_secrets" @@ -473,23 +474,19 @@ class _ENTERPRISE_SecretDetection(CustomGuardrail): if await self.should_run_check(user_api_key_dict) is False: return - if "messages" in data and isinstance(data["messages"], list): - for message in data["messages"]: - if "content" in message and isinstance(message["content"], str): - detected_secrets = self.scan_message_for_secrets(message["content"]) + # Covers multimodal list content + Responses-API input. + def _redact_message_text(text: str) -> str: + detected_secrets = self.scan_message_for_secrets(text) + for secret in detected_secrets: + text = text.replace(secret["value"], "[REDACTED]") + if detected_secrets: + secret_types = [secret["type"] for secret in detected_secrets] + verbose_proxy_logger.warning( + f"Detected and redacted secrets in message: {secret_types}" + ) + return text - for secret in detected_secrets: - message["content"] = message["content"].replace( - secret["value"], "[REDACTED]" - ) - - if len(detected_secrets) > 0: - secret_types = [secret["type"] for secret in detected_secrets] - verbose_proxy_logger.warning( - f"Detected and redacted secrets in message: {secret_types}" - ) - else: - verbose_proxy_logger.debug("No secrets detected on input.") + walk_user_text(data, _redact_message_text) if "prompt" in data: if isinstance(data["prompt"], str): @@ -504,11 +501,15 @@ class _ENTERPRISE_SecretDetection(CustomGuardrail): f"Detected and redacted secrets in prompt: {secret_types}" ) elif isinstance(data["prompt"], list): - for item in data["prompt"]: + # Index back into the list — assigning to ``item`` would only + # rebind the loop variable and leave ``data["prompt"]`` + # carrying the unredacted secret. + for idx, item in enumerate(data["prompt"]): if isinstance(item, str): detected_secrets = self.scan_message_for_secrets(item) for secret in detected_secrets: item = item.replace(secret["value"], "[REDACTED]") + data["prompt"][idx] = item if len(detected_secrets) > 0: secret_types = [ secret["type"] for secret in detected_secrets @@ -517,31 +518,6 @@ class _ENTERPRISE_SecretDetection(CustomGuardrail): f"Detected and redacted secrets in prompt: {secret_types}" ) - if "input" in data: - if isinstance(data["input"], str): - detected_secrets = self.scan_message_for_secrets(data["input"]) - for secret in detected_secrets: - data["input"] = data["input"].replace(secret["value"], "[REDACTED]") - if len(detected_secrets) > 0: - secret_types = [secret["type"] for secret in detected_secrets] - verbose_proxy_logger.warning( - f"Detected and redacted secrets in input: {secret_types}" - ) - elif isinstance(data["input"], list): - _input_in_request = data["input"] - for idx, item in enumerate(_input_in_request): - if isinstance(item, str): - detected_secrets = self.scan_message_for_secrets(item) - for secret in detected_secrets: - _input_in_request[idx] = item.replace( - secret["value"], "[REDACTED]" - ) - if len(detected_secrets) > 0: - secret_types = [ - secret["type"] for secret in detected_secrets - ] - verbose_proxy_logger.warning( - f"Detected and redacted secrets in input: {secret_types}" - ) - verbose_proxy_logger.debug("Data after redacting input %s", data) + # ``data["input"]`` (Responses API and embeddings/moderation) is + # already covered by ``walk_user_text`` above. return diff --git a/enterprise/litellm_enterprise/proxy/auth/custom_sso_handler.py b/enterprise/litellm_enterprise/proxy/auth/custom_sso_handler.py index 1c74ca3c49c..e8f104c2625 100644 --- a/enterprise/litellm_enterprise/proxy/auth/custom_sso_handler.py +++ b/enterprise/litellm_enterprise/proxy/auth/custom_sso_handler.py @@ -10,18 +10,11 @@ has already authenticated the user) and you need to extract user information fro custom headers or other request attributes. """ -from typing import TYPE_CHECKING, Dict, Optional, Union, cast +from typing import cast from fastapi import Request from fastapi.responses import RedirectResponse -if TYPE_CHECKING: - from fastapi_sso.sso.base import OpenID -else: - from typing import Any as OpenID - -from litellm.proxy.management_endpoints.types import CustomOpenID - class EnterpriseCustomSSOHandler: """ @@ -60,9 +53,13 @@ class EnterpriseCustomSSOHandler: from litellm.integrations.custom_sso_handler import CustomSSOLoginHandler from litellm.proxy.proxy_server import ( CommonProxyErrors, + general_settings, premium_user, user_custom_ui_sso_sign_in_handler, ) + from litellm.proxy.auth.trusted_proxy_utils import ( + require_trusted_proxy_request, + ) if premium_user is not True: raise ValueError(CommonProxyErrors.not_premium_user.value) @@ -72,6 +69,12 @@ class EnterpriseCustomSSOHandler: "custom_ui_sso_sign_in_handler is not configured. Please set it in general_settings." ) + require_trusted_proxy_request( + request=request, + general_settings=general_settings, + feature_name="Custom UI SSO", + ) + custom_sso_login_handler = cast( CustomSSOLoginHandler, user_custom_ui_sso_sign_in_handler ) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 2e7112c4c76..5ed49070347 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -15,6 +15,11 @@ from litellm.caching.caching import DualCache from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.files.transformation import BaseFileEndpoints +from litellm.llms.base_llm.managed_resources.isolation import ( + build_list_page, + build_owner_filter, + can_access_resource, +) from litellm.proxy._types import ( CallTypes, LiteLLM_ManagedFileTable, @@ -99,6 +104,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): model_mappings=model_mappings, flat_model_file_ids=list(model_mappings.values()), created_by=user_api_key_dict.user_id, + team_id=user_api_key_dict.team_id, updated_by=user_api_key_dict.user_id, ) await self.internal_usage_cache.async_set_cache( @@ -114,6 +120,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "model_mappings": json.dumps(model_mappings), "flat_model_file_ids": list(model_mappings.values()), "created_by": user_api_key_dict.user_id, + "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -171,6 +178,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "model_object_id": model_object_id, "file_purpose": file_purpose, "created_by": user_api_key_dict.user_id, + "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, "status": file_object.status, }, @@ -229,15 +237,16 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): async def can_user_call_unified_file_id( self, unified_file_id: str, user_api_key_dict: UserAPIKeyAuth ) -> bool: - ## check if the user has access to the unified file id - - user_id = user_api_key_dict.user_id managed_file = await self.prisma_client.db.litellm_managedfiletable.find_first( where={"unified_file_id": unified_file_id} ) if managed_file: - return managed_file.created_by == user_id + return can_access_resource( + user_api_key_dict=user_api_key_dict, + created_by=managed_file.created_by, + resource_team_id=managed_file.team_id, + ) raise HTTPException( status_code=404, detail=f"File not found: {unified_file_id}", @@ -246,8 +255,6 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): async def can_user_call_unified_object_id( self, unified_object_id: str, user_api_key_dict: UserAPIKeyAuth ) -> bool: - ## check if the user has access to the unified object id - user_id = user_api_key_dict.user_id managed_object = ( await self.prisma_client.db.litellm_managedobjecttable.find_first( where={"unified_object_id": unified_object_id} @@ -255,7 +262,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) if managed_object: - return managed_object.created_by == user_id + return can_access_resource( + user_api_key_dict=user_api_key_dict, + created_by=managed_object.created_by, + resource_team_id=managed_object.team_id, + ) raise HTTPException( status_code=404, detail=f"Object not found: {unified_object_id}", @@ -286,19 +297,18 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "Filtering by 'target_model_names' is not supported when using managed batches." ) - where_clause: Dict[str, Any] = {"file_purpose": "batch"} + owner_filter = build_owner_filter(user_api_key_dict) + if owner_filter is None: + return build_list_page([]) - # Filter by user who created the batch - if user_api_key_dict.user_id: - where_clause["created_by"] = user_api_key_dict.user_id + where_clause: Dict[str, Any] = {"file_purpose": "batch", **owner_filter} if after: where_clause["id"] = {"gt": after} - # Fetch more than needed to allow for post-fetch filtering fetch_limit = limit or 20 if target_model_names: - # Fetch extra to account for filtering + # Oversample so post-fetch model-name filtering still has enough rows. fetch_limit = max(fetch_limit * 3, 100) batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( @@ -329,26 +339,28 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) continue - return { - "object": "list", - "data": batch_objects, - "first_id": batch_objects[0].id if batch_objects else None, - "last_id": batch_objects[-1].id if batch_objects else None, - "has_more": len(batch_objects) == (limit or 20), - } + return build_list_page( + batch_objects, has_more=len(batch_objects) == (limit or 20) + ) async def get_user_created_file_ids( self, user_api_key_dict: UserAPIKeyAuth, model_object_ids: List[str] ) -> List[OpenAIFileObject]: """ - Get all file ids created by the user for a list of model object ids + Get all file ids the caller is allowed to see for a list of model + object ids. Service-account keys (no user_id) are scoped to their + team via ``team_id``; admins see all matches. Returns: - List of OpenAIFileObject's """ + owner_filter = build_owner_filter(user_api_key_dict) + if owner_filter is None: + return [] + file_ids = await self.prisma_client.db.litellm_managedfiletable.find_many( where={ - "created_by": user_api_key_dict.user_id, + **owner_filter, "flat_model_file_ids": {"hasSome": model_object_ids}, } ) diff --git a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py index f6ed7767c46..75229bacc8f 100644 --- a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py +++ b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py @@ -588,24 +588,21 @@ async def update_project( # noqa: PLR0915 param="project_id", ) - # Validate team exists and get team object for limit + permission checks - team_id_to_check = data.team_id or existing_project.team_id - team_obj_for_checks = None - if team_id_to_check is not None: - team_obj_for_checks = await _validate_team_exists( - team_id=team_id_to_check, prisma_client=prisma_client + # Permission to *edit* the project must be evaluated against the + # project's CURRENT team. Sourcing the team from `data.team_id` + # would let an admin of any team pass the check by supplying their + # own team_id, hijacking the project (VERIA-55). + target_team_id = data.team_id or existing_project.team_id + target_team_obj = None + if target_team_id is not None: + target_team_obj = await _validate_team_exists( + team_id=target_team_id, prisma_client=prisma_client ) - # Check if user has permission to update this project has_permission = await _check_user_permission_for_project( user_api_key_dict=user_api_key_dict, team_id=existing_project.team_id, prisma_client=prisma_client, - team_object=( - LiteLLM_TeamTable(**team_obj_for_checks.model_dump()) - if team_obj_for_checks - else None - ), ) if not has_permission: @@ -614,10 +611,32 @@ async def update_project( # noqa: PLR0915 detail={"error": "Only admins or team admins can update projects"}, ) + # Reassigning to a different team also requires admin rights on the + # destination team — otherwise a team admin could shed projects into + # an unsuspecting team's namespace. + if data.team_id is not None and data.team_id != existing_project.team_id: + can_assign_to_target = await _check_user_permission_for_project( + user_api_key_dict=user_api_key_dict, + team_id=data.team_id, + prisma_client=prisma_client, + team_object=( + LiteLLM_TeamTable(**target_team_obj.model_dump()) + if target_team_obj + else None + ), + ) + if not can_assign_to_target: + raise HTTPException( + status_code=403, + detail={ + "error": "Cannot reassign project to a team you are not an admin of" + }, + ) + # Validate project limits against team limits - if team_obj_for_checks is not None: + if target_team_obj is not None: _check_team_project_limits( - team_object=LiteLLM_TeamTable(**team_obj_for_checks.model_dump()), + team_object=LiteLLM_TeamTable(**target_team_obj.model_dump()), data=data, ) @@ -857,10 +876,16 @@ async def project_info( where={"team_id": project.team_id} ) if team: - is_team_member = ( - user_api_key_dict.user_id in team.admins - or user_api_key_dict.user_id in team.members - ) + caller_user_id = user_api_key_dict.user_id + for m in team.members_with_roles or []: + m_user_id = ( + m.get("user_id") + if isinstance(m, dict) + else getattr(m, "user_id", None) + ) + if m_user_id == caller_user_id: + is_team_member = True + break if not (is_admin or is_team_member): raise HTTPException( @@ -911,20 +936,20 @@ async def list_projects( include={"litellm_budget_table": True, "object_permission": True} ) else: - # Get projects for teams the user belongs to - user_teams = await prisma_client.db.litellm_teamtable.find_many( - where={ - "OR": [ - {"members": {"has": user_api_key_dict.user_id}}, - {"admins": {"has": user_api_key_dict.user_id}}, - ] - } + # Look up the user's team memberships via the reverse-index on + # LiteLLM_UserTable.teams (maintained by team_member_add alongside + # members_with_roles). This avoids a full scan of all team rows. + user_record = await prisma_client.db.litellm_usertable.find_unique( + where={"user_id": user_api_key_dict.user_id}, + ) + user_team_ids = ( + user_record.teams + if user_record is not None and user_record.teams + else [] ) - team_ids = [team.team_id for team in user_teams] - projects = await prisma_client.db.litellm_projecttable.find_many( - where={"team_id": {"in": team_ids}}, + where={"team_id": {"in": user_team_ids}}, include={"litellm_budget_table": True, "object_permission": True}, ) diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index cfbbe1f494c..9698e7912d4 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.39" +version = "0.1.40" description = "Package for LiteLLM Enterprise features" readme = "README.md" requires-python = ">=3.9" @@ -16,7 +16,7 @@ Repository = "https://github.com/BerriAI/litellm" Documentation = "https://docs.litellm.ai" [build-system] -requires = ["uv_build==0.10.7"] +requires = ["uv_build==0.11.8"] build-backend = "uv_build" [tool.uv] @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.1.39" +version = "0.1.40" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/index.yaml b/index.yaml deleted file mode 100644 index 9b2461c36b5..00000000000 --- a/index.yaml +++ /dev/null @@ -1,108 +0,0 @@ -apiVersion: v1 -entries: - litellm-helm: - - apiVersion: v2 - appVersion: v1.43.18 - created: "2024-08-19T23:58:25.331689+08:00" - dependencies: - - condition: db.deployStandalone - name: postgresql - repository: oci://registry-1.docker.io/bitnamicharts - version: '>=13.3.0' - - condition: redis.enabled - name: redis - repository: oci://registry-1.docker.io/bitnamicharts - version: '>=18.0.0' - description: Call all LLM APIs using the OpenAI format - digest: 0411df3dc42868be8af3ad3e00cb252790e6bd7ad15f5b77f1ca5214573a8531 - name: litellm-helm - type: application - urls: - - https://berriai.github.io/litellm/litellm-helm-0.2.3.tgz - version: 0.2.3 - postgresql: - - annotations: - category: Database - images: | - - name: os-shell - image: docker.io/bitnami/os-shell:12-debian-12-r16 - - name: postgres-exporter - image: docker.io/bitnami/postgres-exporter:0.15.0-debian-12-r14 - - name: postgresql - image: docker.io/bitnami/postgresql:16.2.0-debian-12-r6 - licenses: Apache-2.0 - apiVersion: v2 - appVersion: 16.2.0 - created: "2024-08-19T23:58:25.335716+08:00" - dependencies: - - name: common - repository: oci://registry-1.docker.io/bitnamicharts - tags: - - bitnami-common - version: 2.x.x - description: PostgreSQL (Postgres) is an open source object-relational database - known for reliability and data integrity. ACID-compliant, it supports foreign - keys, joins, views, triggers and stored procedures. - digest: 3c8125526b06833df32e2f626db34aeaedb29d38f03d15349db6604027d4a167 - home: https://bitnami.com - icon: https://bitnami.com/assets/stacks/postgresql/img/postgresql-stack-220x234.png - keywords: - - postgresql - - postgres - - database - - sql - - replication - - cluster - maintainers: - - name: VMware, Inc. - url: https://github.com/bitnami/charts - name: postgresql - sources: - - https://github.com/bitnami/charts/tree/main/bitnami/postgresql - urls: - - https://berriai.github.io/litellm/charts/postgresql-14.3.1.tgz - version: 14.3.1 - redis: - - annotations: - category: Database - images: | - - name: kubectl - image: docker.io/bitnami/kubectl:1.29.2-debian-12-r3 - - name: os-shell - image: docker.io/bitnami/os-shell:12-debian-12-r16 - - name: redis - image: docker.io/bitnami/redis:7.2.4-debian-12-r9 - - name: redis-exporter - image: docker.io/bitnami/redis-exporter:1.58.0-debian-12-r4 - - name: redis-sentinel - image: docker.io/bitnami/redis-sentinel:7.2.4-debian-12-r7 - licenses: Apache-2.0 - apiVersion: v2 - appVersion: 7.2.4 - created: "2024-08-19T23:58:25.339392+08:00" - dependencies: - - name: common - repository: oci://registry-1.docker.io/bitnamicharts - tags: - - bitnami-common - version: 2.x.x - description: Redis(R) is an open source, advanced key-value store. It is often - referred to as a data structure server since keys can contain strings, hashes, - lists, sets and sorted sets. - digest: b2fa1835f673a18002ca864c54fadac3c33789b26f6c5e58e2851b0b14a8f984 - home: https://bitnami.com - icon: https://bitnami.com/assets/stacks/redis/img/redis-stack-220x234.png - keywords: - - redis - - keyvalue - - database - maintainers: - - name: VMware, Inc. - url: https://github.com/bitnami/charts - name: redis - sources: - - https://github.com/bitnami/charts/tree/main/bitnami/redis - urls: - - https://berriai.github.io/litellm/charts/redis-18.19.1.tgz - version: 18.19.1 -generated: "2024-08-19T23:58:25.322532+08:00" diff --git a/litellm-js/proxy/.npmrc b/litellm-js/proxy/.npmrc deleted file mode 100644 index 168e81a1c4e..00000000000 --- a/litellm-js/proxy/.npmrc +++ /dev/null @@ -1,5 +0,0 @@ -# Supply-chain hardening -# Packages needing lifecycle scripts: npm rebuild -ignore-scripts=true -# Protects local npm install only — npm ci (used in CI) ignores this -min-release-age=3d diff --git a/litellm-js/proxy/README.md b/litellm-js/proxy/README.md deleted file mode 100644 index cc58e962d8f..00000000000 --- a/litellm-js/proxy/README.md +++ /dev/null @@ -1,8 +0,0 @@ -``` -npm install -npm run dev -``` - -``` -npm run deploy -``` diff --git a/litellm-js/proxy/package.json b/litellm-js/proxy/package.json deleted file mode 100644 index 275fd8c20d3..00000000000 --- a/litellm-js/proxy/package.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "scripts": { - "dev": "wrangler dev src/index.ts", - "deploy": "wrangler deploy --minify src/index.ts" - }, - "dependencies": { - "hono": "4.12.12", - "openai": "4.29.2" - }, - "devDependencies": { - "@cloudflare/workers-types": "4.20240208.0", - "wrangler": "3.32.0" - } -} diff --git a/litellm-js/proxy/src/index.ts b/litellm-js/proxy/src/index.ts deleted file mode 100644 index dc5dc9c689e..00000000000 --- a/litellm-js/proxy/src/index.ts +++ /dev/null @@ -1,59 +0,0 @@ -import { Hono } from 'hono' -import { Context } from 'hono'; -import { bearerAuth } from 'hono/bearer-auth' -import OpenAI from "openai"; - -const openai = new OpenAI({ - apiKey: "sk-1234", - baseURL: "https://openai-endpoint.ishaanjaffer0324.workers.dev" -}); - -async function call_proxy() { - const completion = await openai.chat.completions.create({ - messages: [{ role: "system", content: "You are a helpful assistant." }], - model: "gpt-3.5-turbo", - }); - - return completion -} - -const app = new Hono() - -// Middleware for API Key Authentication -const apiKeyAuth = async (c: Context, next: Function) => { - const apiKey = c.req.header('Authorization'); - if (!apiKey || apiKey !== 'Bearer sk-1234') { - return c.text('Unauthorized', 401); - } - await next(); -}; - - -app.use('/*', apiKeyAuth) - - -app.get('/', (c) => { - return c.text('Hello Hono!') -}) - - - - -// Handler for chat completions -const chatCompletionHandler = async (c: Context) => { - // Assuming your logic for handling chat completion goes here - // For demonstration, just returning a simple JSON response - const response = await call_proxy() - return c.json(response); -}; - -// Register the above handler for different POST routes with the apiKeyAuth middleware -app.post('/v1/chat/completions', chatCompletionHandler); -app.post('/chat/completions', chatCompletionHandler); - -// Example showing how you might handle dynamic segments within the URL -// Here, using ':model*' to capture the rest of the path as a parameter 'model' -app.post('/openai/deployments/:model*/chat/completions', chatCompletionHandler); - - -export default app diff --git a/litellm-js/proxy/tsconfig.json b/litellm-js/proxy/tsconfig.json deleted file mode 100644 index 28fcfb58246..00000000000 --- a/litellm-js/proxy/tsconfig.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "compilerOptions": { - "target": "ESNext", - "module": "ESNext", - "moduleResolution": "Bundler", - "strict": true, - "lib": [ - "ESNext" - ], - "types": [ - "@cloudflare/workers-types" - ], - "jsx": "react-jsx", - "jsxImportSource": "hono/jsx", - "skipLibCheck": true - }, -} \ No newline at end of file diff --git a/litellm-js/proxy/wrangler.toml b/litellm-js/proxy/wrangler.toml deleted file mode 100644 index e7c323dff97..00000000000 --- a/litellm-js/proxy/wrangler.toml +++ /dev/null @@ -1,18 +0,0 @@ -name = "my-app" -compatibility_date = "2023-12-01" - -# [vars] -# MY_VAR = "my-variable" - -# [[kv_namespaces]] -# binding = "MY_KV_NAMESPACE" -# id = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" - -# [[r2_buckets]] -# binding = "MY_BUCKET" -# bucket_name = "my-bucket" - -# [[d1_databases]] -# binding = "DB" -# database_name = "my-database" -# database_id = "" diff --git a/litellm-js/spend-logs/.npmrc b/litellm-js/spend-logs/.npmrc deleted file mode 100644 index 168e81a1c4e..00000000000 --- a/litellm-js/spend-logs/.npmrc +++ /dev/null @@ -1,5 +0,0 @@ -# Supply-chain hardening -# Packages needing lifecycle scripts: npm rebuild -ignore-scripts=true -# Protects local npm install only — npm ci (used in CI) ignores this -min-release-age=3d diff --git a/litellm-js/spend-logs/Dockerfile b/litellm-js/spend-logs/Dockerfile deleted file mode 100644 index 5040dc74bf6..00000000000 --- a/litellm-js/spend-logs/Dockerfile +++ /dev/null @@ -1,26 +0,0 @@ -# Use the specific Node.js v20.11.0 image -FROM node:20.18.1-alpine3.20 - -# Set the working directory inside the container -WORKDIR /app - -# Copy package.json and package-lock.json to the working directory -COPY ./litellm-js/spend-logs/package*.json ./ - -# Install dependencies -RUN npm ci - -# Install Prisma globally -RUN npm install -g prisma - -# Copy the rest of the application code -COPY ./litellm-js/spend-logs . - -# Generate Prisma client -RUN npx prisma generate - -# Expose the port that the Node.js server will run on -EXPOSE 3000 - -# Command to run the Node.js app with npm run dev -CMD ["npm", "run", "dev"] diff --git a/litellm-js/spend-logs/README.md b/litellm-js/spend-logs/README.md deleted file mode 100644 index e12b31db70a..00000000000 --- a/litellm-js/spend-logs/README.md +++ /dev/null @@ -1,8 +0,0 @@ -``` -npm install -npm run dev -``` - -``` -open http://localhost:3000 -``` diff --git a/litellm-js/spend-logs/package-lock.json b/litellm-js/spend-logs/package-lock.json deleted file mode 100644 index ce1762f4023..00000000000 --- a/litellm-js/spend-logs/package-lock.json +++ /dev/null @@ -1,597 +0,0 @@ -{ - 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} - } -} diff --git a/litellm-js/spend-logs/package.json b/litellm-js/spend-logs/package.json deleted file mode 100644 index d8e6a895445..00000000000 --- a/litellm-js/spend-logs/package.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "scripts": { - "dev": "tsx watch src/index.ts" - }, - "dependencies": { - "@hono/node-server": "1.19.13", - "hono": "4.12.12" - }, - "devDependencies": { - "@types/node": "20.19.25", - "tsx": "4.20.6" - } -} diff --git a/litellm-js/spend-logs/schema.prisma b/litellm-js/spend-logs/schema.prisma deleted file mode 100644 index b0403f277aa..00000000000 --- a/litellm-js/spend-logs/schema.prisma +++ /dev/null @@ -1,29 +0,0 @@ -generator client { - provider = "prisma-client-js" -} - -datasource client { - provider = "postgresql" - url = env("DATABASE_URL") -} - -model LiteLLM_SpendLogs { - request_id String @id - call_type String - api_key String @default("") - spend Float @default(0.0) - total_tokens Int @default(0) - prompt_tokens Int @default(0) - completion_tokens Int @default(0) - startTime DateTime - endTime DateTime - model String @default("") - api_base String @default("") - user String @default("") - metadata Json @default("{}") - cache_hit String @default("") - cache_key String @default("") - request_tags Json @default("[]") - team_id String? - end_user String? -} \ No newline at end of file diff --git a/litellm-js/spend-logs/src/_types.ts b/litellm-js/spend-logs/src/_types.ts deleted file mode 100644 index 6a9b499171e..00000000000 --- a/litellm-js/spend-logs/src/_types.ts +++ /dev/null @@ -1,32 +0,0 @@ -export type LiteLLM_IncrementSpend = { - key_transactions: Array, // [{"key": spend},..] - user_transactions: Array, - team_transactions: Array, - spend_logs_transactions: Array -} - -export type LiteLLM_IncrementObject = { - key: string, - spend: number -} - -export type LiteLLM_SpendLogs = { - request_id: string; // @id means it's a unique identifier - call_type: string; - api_key: string; // @default("") means it defaults to an empty string if not provided - spend: number; // Float in Prisma corresponds to number in TypeScript - total_tokens: number; // Int in Prisma corresponds to number in TypeScript - prompt_tokens: number; - completion_tokens: number; - startTime: Date; // DateTime in Prisma corresponds to Date in TypeScript - endTime: Date; - model: string; // @default("") means it defaults to an empty string if not provided - api_base: string; - user: string; - metadata: any; // Json type in Prisma is represented by any in TypeScript; could also use a more specific type if the structure of JSON is known - cache_hit: string; - cache_key: string; - request_tags: any; // Similarly, this could be an array or a more specific type depending on the expected structure - team_id?: string | null; // ? indicates it's optional and can be undefined, but could also be null if not provided - end_user?: string | null; -}; \ No newline at end of file diff --git a/litellm-js/spend-logs/src/index.ts b/litellm-js/spend-logs/src/index.ts deleted file mode 100644 index 3581d95c830..00000000000 --- a/litellm-js/spend-logs/src/index.ts +++ /dev/null @@ -1,84 +0,0 @@ -import { serve } from '@hono/node-server' -import { Hono } from 'hono' -import { PrismaClient } from '@prisma/client' -import {LiteLLM_SpendLogs, LiteLLM_IncrementSpend, LiteLLM_IncrementObject} from './_types' - -const app = new Hono() -const prisma = new PrismaClient() -// In-memory storage for logs -let spend_logs: LiteLLM_SpendLogs[] = []; -const key_logs: LiteLLM_IncrementObject[] = []; -const user_logs: LiteLLM_IncrementObject[] = []; -const transaction_logs: LiteLLM_IncrementObject[] = []; - - -app.get('/', (c) => { - return c.text('Hello Hono!') -}) - -const MIN_LOGS = 1; // Minimum number of logs needed to initiate a flush -const FLUSH_INTERVAL = 5000; // Time in ms to wait before trying to flush again -const BATCH_SIZE = 100; // Preferred size of each batch to write to the database -const MAX_LOGS_PER_INTERVAL = 1000; // Maximum number of logs to flush in a single interval - -const flushLogsToDb = async () => { - if (spend_logs.length >= MIN_LOGS) { - // Limit the logs to process in this interval to MAX_LOGS_PER_INTERVAL or less - const logsToProcess = spend_logs.slice(0, MAX_LOGS_PER_INTERVAL); - - for (let i = 0; i < logsToProcess.length; i += BATCH_SIZE) { - // Create subarray for current batch, ensuring it doesn't exceed the BATCH_SIZE - const batch = logsToProcess.slice(i, i + BATCH_SIZE); - - // Convert datetime strings to Date objects - const batchWithDates = batch.map(entry => ({ - ...entry, - startTime: new Date(entry.startTime), - endTime: new Date(entry.endTime), - // Repeat for any other DateTime fields you may have - })); - - await prisma.liteLLM_SpendLogs.createMany({ - data: batchWithDates, - }); - - console.log(`Flushed ${batch.length} logs to the DB.`); - } - - // Remove the processed logs from spend_logs - spend_logs = spend_logs.slice(logsToProcess.length); - - console.log(`${logsToProcess.length} logs processed. Remaining in queue: ${spend_logs.length}`); - } else { - // This will ensure it doesn't falsely claim "No logs to flush." when it's merely below the MIN_LOGS threshold. - if(spend_logs.length > 0) { - console.log(`Accumulating logs. Currently at ${spend_logs.length}, waiting for at least ${MIN_LOGS}.`); - } else { - console.log("No logs to flush."); - } - } -}; - -// Setup interval for attempting to flush the logs -setInterval(flushLogsToDb, FLUSH_INTERVAL); - -// Route to receive log messages -app.post('/spend/update', async (c) => { - const incomingLogs = await c.req.json(); - - spend_logs.push(...incomingLogs); - - console.log(`Received and stored ${incomingLogs.length} logs. Total logs in memory: ${spend_logs.length}`); - - return c.json({ message: `Successfully stored ${incomingLogs.length} logs` }); -}); - - - -const port = 3000 -console.log(`Server is running on port ${port}`) - -serve({ - fetch: app.fetch, - port -}) diff --git a/litellm-js/spend-logs/tsconfig.json b/litellm-js/spend-logs/tsconfig.json deleted file mode 100644 index 028c03b6a81..00000000000 --- a/litellm-js/spend-logs/tsconfig.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "compilerOptions": { - "target": "ESNext", - "module": "ESNext", - "moduleResolution": "Bundler", - "strict": true, - "types": [ - "node" - ], - "jsx": "react-jsx", - "jsxImportSource": "hono/jsx", - } -} \ No newline at end of file diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260421135425_add_team_membership_total_spend/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260421135425_add_team_membership_total_spend/migration.sql index 049bd513cd8..7d7c359bf2d 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260421135425_add_team_membership_total_spend/migration.sql +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260421135425_add_team_membership_total_spend/migration.sql @@ -1,3 +1,3 @@ -- AlterTable -ALTER TABLE "LiteLLM_TeamMembership" ADD COLUMN "total_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0; +ALTER TABLE "LiteLLM_TeamMembership" ADD COLUMN IF NOT EXISTS "total_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260429120000_search_tools_on_object_permission/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260429120000_search_tools_on_object_permission/migration.sql new file mode 100644 index 00000000000..bffdaaebc57 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260429120000_search_tools_on_object_permission/migration.sql @@ -0,0 +1,2 @@ +-- Search tool allowlists live on LiteLLM_ObjectPermissionTable (with agents, MCP, vector stores). +ALTER TABLE "LiteLLM_ObjectPermissionTable" ADD COLUMN IF NOT EXISTS "search_tools" TEXT[] DEFAULT ARRAY[]::TEXT[]; diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260429161855_workflow_runs_tables/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260429161855_workflow_runs_tables/migration.sql new file mode 100644 index 00000000000..6454f267656 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260429161855_workflow_runs_tables/migration.sql @@ -0,0 +1,75 @@ +-- CreateTable +CREATE TABLE "LiteLLM_WorkflowRun" ( + "run_id" TEXT NOT NULL, + "session_id" TEXT NOT NULL, + "workflow_type" TEXT NOT NULL, + "status" TEXT NOT NULL DEFAULT 'pending', + "created_by" TEXT, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_at" TIMESTAMP(3) NOT NULL, + "input" JSONB, + "output" JSONB, + "metadata" JSONB, + + CONSTRAINT "LiteLLM_WorkflowRun_pkey" PRIMARY KEY ("run_id") +); + +-- CreateTable +CREATE TABLE "LiteLLM_WorkflowEvent" ( + "event_id" TEXT NOT NULL, + "run_id" TEXT NOT NULL, + "event_type" TEXT NOT NULL, + "step_name" TEXT NOT NULL, + "sequence_number" INTEGER NOT NULL, + "data" JSONB, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + + CONSTRAINT "LiteLLM_WorkflowEvent_pkey" PRIMARY KEY ("event_id") +); + +-- CreateTable +CREATE TABLE "LiteLLM_WorkflowMessage" ( + "message_id" TEXT NOT NULL, + "run_id" TEXT NOT NULL, + "role" TEXT NOT NULL, + "content" TEXT NOT NULL, + "sequence_number" INTEGER NOT NULL, + "session_id" TEXT, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + + CONSTRAINT "LiteLLM_WorkflowMessage_pkey" PRIMARY KEY ("message_id") +); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_WorkflowRun_session_id_key" ON "LiteLLM_WorkflowRun"("session_id"); + +-- CreateIndex +CREATE INDEX "LiteLLM_WorkflowRun_workflow_type_status_idx" ON "LiteLLM_WorkflowRun"("workflow_type", "status"); + +-- CreateIndex +CREATE INDEX "LiteLLM_WorkflowRun_session_id_idx" ON "LiteLLM_WorkflowRun"("session_id"); + +-- CreateIndex +CREATE INDEX "LiteLLM_WorkflowRun_created_at_idx" ON "LiteLLM_WorkflowRun"("created_at"); + +-- CreateIndex +CREATE INDEX "LiteLLM_WorkflowRun_created_by_idx" ON "LiteLLM_WorkflowRun"("created_by"); + +-- CreateIndex +CREATE INDEX "LiteLLM_WorkflowEvent_run_id_idx" ON "LiteLLM_WorkflowEvent"("run_id"); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_WorkflowEvent_run_id_sequence_number_key" ON "LiteLLM_WorkflowEvent"("run_id", "sequence_number"); + +-- CreateIndex +CREATE INDEX "LiteLLM_WorkflowMessage_run_id_idx" ON "LiteLLM_WorkflowMessage"("run_id"); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_WorkflowMessage_run_id_sequence_number_key" ON "LiteLLM_WorkflowMessage"("run_id", "sequence_number"); + +-- AddForeignKey +ALTER TABLE "LiteLLM_WorkflowEvent" ADD CONSTRAINT "LiteLLM_WorkflowEvent_run_id_fkey" FOREIGN KEY ("run_id") REFERENCES "LiteLLM_WorkflowRun"("run_id") ON DELETE RESTRICT ON UPDATE CASCADE; + +-- AddForeignKey +ALTER TABLE "LiteLLM_WorkflowMessage" ADD CONSTRAINT "LiteLLM_WorkflowMessage_run_id_fkey" FOREIGN KEY ("run_id") REFERENCES "LiteLLM_WorkflowRun"("run_id") ON DELETE RESTRICT ON UPDATE CASCADE; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260501195714_managed_resource_team_owner/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260501195714_managed_resource_team_owner/migration.sql new file mode 100644 index 00000000000..d6f236959be --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260501195714_managed_resource_team_owner/migration.sql @@ -0,0 +1,20 @@ +-- Adds `team_id` to managed-resource tables so service-account API +-- keys (no `user_id`) can be scoped by team instead of bypassing the +-- `created_by` filter entirely. Existing rows keep `team_id = NULL` +-- and become invisible to team-only callers — that is the intended isolation +-- outcome; backfill manually if legacy rows must remain visible. +-- +-- The composite indexes match the listing query: filter by team owner, sort by +-- created_at DESC. Tables are typically small (resources per tenant, not per +-- request); a future operator with a large table can switch to +-- CREATE INDEX CONCURRENTLY in a follow-up migration. + +ALTER TABLE "LiteLLM_ManagedFileTable" ADD COLUMN IF NOT EXISTS "team_id" TEXT; +ALTER TABLE "LiteLLM_ManagedObjectTable" ADD COLUMN IF NOT EXISTS "team_id" TEXT; +ALTER TABLE "LiteLLM_ManagedVectorStoreTable" ADD COLUMN IF NOT EXISTS "team_id" TEXT; + +-- Index names follow Prisma's auto-generated convention so `prisma migrate diff` +-- against the schema is clean. +CREATE INDEX IF NOT EXISTS "LiteLLM_ManagedFileTable_team_id_created_at_idx" ON "LiteLLM_ManagedFileTable" ("team_id", "created_at" DESC); +CREATE INDEX IF NOT EXISTS "LiteLLM_ManagedObjectTable_team_id_created_at_idx" ON "LiteLLM_ManagedObjectTable" ("team_id", "created_at" DESC); +CREATE INDEX IF NOT EXISTS "LiteLLM_ManagedVectorStoreTable_team_id_created_at_idx" ON "LiteLLM_ManagedVectorStoreTable" ("team_id", "created_at" DESC); diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260513120000_add_delegate_auth_to_upstream_to_mcp_servers/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260513120000_add_delegate_auth_to_upstream_to_mcp_servers/migration.sql new file mode 100644 index 00000000000..50a48743901 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260513120000_add_delegate_auth_to_upstream_to_mcp_servers/migration.sql @@ -0,0 +1,2 @@ +-- AlterTable +ALTER TABLE "LiteLLM_MCPServerTable" ADD COLUMN IF NOT EXISTS "delegate_auth_to_upstream" BOOLEAN NOT NULL DEFAULT false; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 8f07c5afa3f..b53507abe6a 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -277,6 +277,7 @@ model LiteLLM_ObjectPermissionTable { models String[] @default([]) blocked_tools String[] @default([]) // Tool names blocked for any key/team/user with this permission mcp_toolsets String[] @default([]) // Toolset IDs granted to this key/team/user + search_tools String[] @default([]) // search_tool_name values this key/team/user may call teams LiteLLM_TeamTable[] projects LiteLLM_ProjectTable[] verification_tokens LiteLLM_VerificationToken[] @@ -322,6 +323,7 @@ model LiteLLM_MCPServerTable { registration_url String? allow_all_keys Boolean @default(false) available_on_public_internet Boolean @default(true) + delegate_auth_to_upstream Boolean @default(false) is_byok Boolean @default(false) byok_description String[] @default([]) byok_api_key_help_url String? @@ -883,28 +885,32 @@ model LiteLLM_ManagedFileTable { storage_backend String? // Storage backend name (e.g., "azure_storage", "gcs", "default") storage_url String? // The actual storage URL where the file is stored created_at DateTime @default(now()) - created_by String? + created_by String? + team_id String? // Team that owns the resource; populated for service-account keys without a user_id so listings can isolate by team. updated_at DateTime @updatedAt updated_by String? @@index([unified_file_id]) + @@index([team_id, created_at(sort: Desc)]) } -model LiteLLM_ManagedObjectTable { // for batches or finetuning jobs which use the +model LiteLLM_ManagedObjectTable { // for batches or finetuning jobs which use the id String @id @default(uuid()) unified_object_id String @unique // The base64 encoded unified file ID - model_object_id String @unique // the id returned by the backend API provider + model_object_id String @unique // the id returned by the backend API provider file_object Json // Stores the OpenAIFileObject file_purpose String // either 'batch' or 'fine-tune' - status String? // check if batch cost has been tracked + status String? // check if batch cost has been tracked batch_processed Boolean @default(false) // set to true by CheckBatchCost after cost is computed created_at DateTime @default(now()) created_by String? + team_id String? updated_at DateTime @updatedAt - updated_by String? + updated_by String? @@index([unified_object_id]) @@index([model_object_id]) + @@index([team_id, created_at(sort: Desc)]) } model LiteLLM_ManagedVectorStoreTable { @@ -917,10 +923,12 @@ model LiteLLM_ManagedVectorStoreTable { storage_url String? // Storage URL (if applicable) created_at DateTime @default(now()) created_by String? + team_id String? updated_at DateTime @updatedAt updated_by String? @@index([unified_resource_id]) + @@index([team_id, created_at(sort: Desc)]) } model LiteLLM_ManagedVectorStoresTable { @@ -1290,3 +1298,80 @@ model LiteLLM_AdaptiveRouterSession { @@id([session_id, router_name, model_name]) @@index([last_activity_at], map: "idx_adaptive_router_session_activity") } + +// --------------------------------------------------------------------------- +// Workflow Run Tracking +// +// Generic durable state tracking for any agent or automated workflow. +// Design: three tables — run (header + materialized status), event (append-only +// source of truth for state transitions), message (conversation inbox/outbox). +// +// Usage: +// - Set `workflow_type` to identify the owning system (e.g. "shin-builder"). +// - Store domain-specific fields in `metadata` (worktree_path, pr_url, etc.). +// - `session_id` on WorkflowRun matches `x-litellm-session-id` header sent to +// the proxy — all spend logs for this run are automatically tagged. +// --------------------------------------------------------------------------- + +// One instance of work being done. `status` is a materialized cache of the +// latest event; the event log is the authoritative source of truth. +model LiteLLM_WorkflowRun { + run_id String @id @default(uuid()) + session_id String @unique @default(uuid()) + workflow_type String + status String @default("pending") + created_by String? // user_id of the key that created this run; null = created by master key + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + input Json? + output Json? + metadata Json? + + events LiteLLM_WorkflowEvent[] + messages LiteLLM_WorkflowMessage[] + + @@index([workflow_type, status]) + @@index([session_id]) + @@index([created_at]) + @@index([created_by]) +} + +// Append-only log of state transitions. Never mutate rows here. +// `step_name` and `event_type` are caller-defined strings — no hardcoded enums. +// Status auto-update rules (applied by the append endpoint): +// step.started → run.status = running +// step.failed → run.status = failed +// hook.waiting → run.status = paused +// hook.received → run.status = running +model LiteLLM_WorkflowEvent { + event_id String @id @default(uuid()) + run_id String + event_type String + step_name String + sequence_number Int + data Json? + created_at DateTime @default(now()) + + run LiteLLM_WorkflowRun @relation(fields: [run_id], references: [run_id]) + + @@unique([run_id, sequence_number]) + @@index([run_id]) +} + +// Conversation inbox/outbox — full message content, separate from the durable +// event log. Spend logs truncate messages; this table stores them in full. +// `session_id` here is the Claude --resume session ID (or similar). +model LiteLLM_WorkflowMessage { + message_id String @id @default(uuid()) + run_id String + role String + content String + sequence_number Int + session_id String? + created_at DateTime @default(now()) + + run LiteLLM_WorkflowRun @relation(fields: [run_id], references: [run_id]) + + @@unique([run_id, sequence_number]) + @@index([run_id]) +} diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 41c78296fbb..ee27cc3585e 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.69" +version = "0.4.72" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." readme = "README.md" requires-python = ">=3.9" @@ -16,7 +16,7 @@ Repository = "https://github.com/BerriAI/litellm" Documentation = "https://docs.litellm.ai" [build-system] -requires = ["uv_build==0.10.7"] +requires = ["uv_build==0.11.8"] build-backend = "uv_build" [tool.uv] @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.4.69" +version = "0.4.72" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 77fa48625d9..e1b367fb234 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -166,7 +166,7 @@ langfuse_default_tags: Optional[List[str]] = None langsmith_batch_size: Optional[int] = None prometheus_initialize_budget_metrics: Optional[bool] = False prometheus_latency_buckets: Optional[List[float]] = None -require_auth_for_metrics_endpoint: Optional[bool] = False +require_auth_for_metrics_endpoint: Optional[bool] = True argilla_batch_size: Optional[int] = None datadog_use_v1: Optional[bool] = False # if you want to use v1 datadog logged payload. gcs_pub_sub_use_v1: Optional[bool] = ( @@ -206,6 +206,7 @@ add_user_information_to_llm_headers: Optional[bool] = ( ) store_audit_logs = False # Enterprise feature, allow users to see audit logs skip_system_message_in_guardrail: bool = False +skip_tool_message_in_guardrail: bool = False ### end of callbacks ############# email: Optional[str] = ( @@ -280,6 +281,7 @@ ssl_security_level: Optional[str] = None ssl_certificate: Optional[str] = None user_url_validation: bool = True user_url_allowed_hosts: List[str] = [] +provider_url_destination_allowed_hosts: List[str] = [] ssl_ecdh_curve: Optional[str] = ( None # Set to 'X25519' to disable PQC and improve performance ) @@ -288,6 +290,7 @@ disable_token_counter: bool = False disable_add_transform_inline_image_block: bool = False disable_add_user_agent_to_request_tags: bool = False disable_anthropic_gemini_context_caching_transform: bool = False +disable_vertex_batch_output_transformation: bool = False extra_spend_tag_headers: Optional[List[str]] = None in_memory_llm_clients_cache: "LLMClientCache" safe_memory_mode: bool = False @@ -330,6 +333,9 @@ enable_model_config_credential_overrides: bool = False enable_key_alias_format_validation: bool = ( False # opt-in validation of key_alias format on /key/generate and /key/update ) +enable_gemini_default_thinking_level_low: bool = ( + False # opt-in: force thinkingLevel low/minimal for Gemini 3 thinking param mapping +) #################### logging: bool = True enable_loadbalancing_on_batch_endpoints: Optional[bool] = None @@ -383,6 +389,7 @@ anthropic_beta_headers_url: str = os.getenv( suppress_debug_info = False dynamodb_table_name: Optional[str] = None s3_callback_params: Optional[Dict] = None +s3_audit_callback_params: Optional[Dict] = None datadog_llm_observability_params: Optional[Union[DatadogLLMObsInitParams, Dict]] = None datadog_params: Optional[Union[DatadogInitParams, Dict]] = None aws_sqs_callback_params: Optional[Dict] = None @@ -409,6 +416,9 @@ custom_prometheus_metadata_labels: List[str] = [] custom_prometheus_tags: List[str] = [] prometheus_metrics_config: Optional[List] = None prometheus_emit_stream_label: bool = False +prometheus_end_user_metrics_max_series_per_metric: Optional[int] = 10000 +prometheus_end_user_metrics_ttl_seconds: Optional[float] = 3600.0 +prometheus_end_user_metrics_cleanup_interval_seconds: Optional[float] = 60.0 disable_add_prefix_to_prompt: bool = ( False # used by anthropic, to disable adding prefix to prompt ) @@ -581,6 +591,7 @@ anyscale_models: Set = set() cerebras_models: Set = set() galadriel_models: Set = set() nvidia_nim_models: Set = set() +nvidia_riva_models: Set = set() sambanova_models: Set = set() sambanova_embedding_models: Set = set() novita_models: Set = set() @@ -807,6 +818,8 @@ def add_known_models(model_cost_map: Optional[Dict] = None): galadriel_models.add(key) elif value.get("litellm_provider") == "nvidia_nim": nvidia_nim_models.add(key) + elif value.get("litellm_provider") == "nvidia_riva": + nvidia_riva_models.add(key) elif value.get("litellm_provider") == "sambanova": sambanova_models.add(key) elif value.get("litellm_provider") == "sambanova-embedding-models": @@ -966,6 +979,7 @@ model_list = list( | cerebras_models | galadriel_models | nvidia_nim_models + | nvidia_riva_models | sambanova_models | azure_text_models | novita_models @@ -1062,6 +1076,7 @@ models_by_provider: dict = { "cerebras": cerebras_models, "galadriel": galadriel_models, "nvidia_nim": nvidia_nim_models, + "nvidia_riva": nvidia_riva_models, "sambanova": sambanova_models | sambanova_embedding_models, "novita": novita_models, "nebius": nebius_models | nebius_embedding_models, @@ -1411,6 +1426,12 @@ if TYPE_CHECKING: ) from .llms.datarobot.chat.transformation import DataRobotConfig as DataRobotConfig from .llms.anthropic.chat.transformation import AnthropicConfig as AnthropicConfig + from .llms.bedrock.claude_platform.transformation import ( + BedrockClaudePlatformConfig as BedrockClaudePlatformConfig, + ) + from .llms.bedrock.claude_platform.messages_transformation import ( + BedrockClaudePlatformMessagesConfig as BedrockClaudePlatformMessagesConfig, + ) from .llms.anthropic.completion.transformation import ( AnthropicTextConfig as AnthropicTextConfig, ) @@ -1613,6 +1634,9 @@ if TYPE_CHECKING: from .llms.deepgram.audio_transcription.transformation import ( DeepgramAudioTranscriptionConfig as DeepgramAudioTranscriptionConfig, ) + from .llms.nvidia_riva.audio_transcription.transformation import ( + NvidiaRivaAudioTranscriptionConfig as NvidiaRivaAudioTranscriptionConfig, + ) from .llms.topaz.image_variations.transformation import ( TopazImageVariationConfig as TopazImageVariationConfig, ) diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 119e62a5b38..3531e8d96b9 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -131,6 +131,7 @@ LLM_CONFIG_NAMES = ( "OpenrouterConfig", "DataRobotConfig", "AnthropicConfig", + "BedrockClaudePlatformConfig", "AnthropicTextConfig", "GroqSTTConfig", "TritonConfig", @@ -170,6 +171,7 @@ LLM_CONFIG_NAMES = ( "SagemakerNovaConfig", "CohereChatConfig", "AnthropicMessagesConfig", + "BedrockClaudePlatformMessagesConfig", "AmazonAnthropicClaudeMessagesConfig", "AmazonMantleMessagesConfig", "TogetherAIConfig", @@ -610,6 +612,10 @@ _LLM_CONFIGS_IMPORT_MAP = { "OpenrouterConfig": (".llms.openrouter.chat.transformation", "OpenrouterConfig"), "DataRobotConfig": (".llms.datarobot.chat.transformation", "DataRobotConfig"), "AnthropicConfig": (".llms.anthropic.chat.transformation", "AnthropicConfig"), + "BedrockClaudePlatformConfig": ( + ".llms.bedrock.claude_platform.transformation", + "BedrockClaudePlatformConfig", + ), "AnthropicTextConfig": ( ".llms.anthropic.completion.transformation", "AnthropicTextConfig", @@ -712,6 +718,10 @@ _LLM_CONFIGS_IMPORT_MAP = { ".llms.anthropic.experimental_pass_through.messages.transformation", "AnthropicMessagesConfig", ), + "BedrockClaudePlatformMessagesConfig": ( + ".llms.bedrock.claude_platform.messages_transformation", + "BedrockClaudePlatformMessagesConfig", + ), "AmazonAnthropicClaudeMessagesConfig": ( ".llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation", "AmazonAnthropicClaudeMessagesConfig", diff --git a/litellm/_logging.py b/litellm/_logging.py index d072cc549d0..5ddafd6c6af 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -1,12 +1,12 @@ import ast import logging import os -import re import sys from datetime import datetime from logging import Formatter -from typing import Any, Dict, List, Optional +from typing import Any, Dict, Optional +from litellm.litellm_core_utils.secret_redaction import redact_string from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.litellm_core_utils.safe_json_loads import safe_json_loads @@ -21,74 +21,11 @@ _ENABLE_SECRET_REDACTION = ( os.getenv("LITELLM_DISABLE_REDACT_SECRETS", "").lower() != "true" ) -_REDACTED = "REDACTED" - - -def _build_secret_patterns() -> re.Pattern: - patterns: List[str] = [ - # ── PEM private key / certificate blocks ── - r"-----BEGIN[A-Z \-]*PRIVATE KEY-----[\s\S]*?-----END[A-Z \-]*PRIVATE KEY-----", - # ── GCP OAuth2 access tokens (ya29.*) ── - r"\bya29\.[A-Za-z0-9_.~+/-]+", - # ── Credential %s formatting (space separator, no key= prefix) ── - r"(?:client_secret|azure_password|azure_username)\s+[^\s,'\"})\]{}>]+", - # AWS access key IDs - r"(?:AKIA|ASIA)[0-9A-Z]{16}", - # AWS secrets / session tokens / access key IDs (key=value) - r"(?:aws_secret_access_key|aws_session_token|aws_access_key_id)" - r"\s*[:=]\s*[A-Za-z0-9/+=]{20,}", - # Bearer tokens (OAuth, JWT, etc.) - r"Bearer\s+[A-Za-z0-9\-._~+/]{10,}=*", - # Basic auth headers - r"Basic\s+[A-Za-z0-9+/]{10,}={0,2}", - # OpenAI / Anthropic sk- prefixed keys - r"sk-[A-Za-z0-9\-_]{20,}", - # Generic api_key / api-key / apikey (handles 'key': 'value' dict repr) - r"(?:api[_-]?key)['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]{8,}", - # x-api-key / api-key header values (handles 'key': 'value' dict repr) - r"(?:x-api-key|api-key)['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]+", - # Anthropic internal header keys - r"x-ak-[A-Za-z0-9\-_]{20,}", - # Google API keys - r"AIza[0-9A-Za-z\-_]{35}", - # Password / secret params (handles key=value and 'key': 'value') - # Word boundary prevents O(n^2) backtracking on long word-char runs. - r"(?:^|(?<=\W))\w*(?:password|passwd|client_secret|secret_key|_secret)" - r"['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]+", - # Database connection string credentials (scheme://user:pass@host) - r"(?<=://)[^\s'\"]*:[^\s'\"@]+(?=@)", - # Databricks personal access tokens - r"dapi[0-9a-f]{32}", - # ── Key-name-based redaction ── - # Catches secrets inside dicts/config dumps by matching on the KEY name - # regardless of what the value looks like. - # e.g. 'master_key': 'any-value-here', "database_url": "postgres://..." - # private_key with PEM-aware value capture - r"""private_key['\"]?\s*[:=]\s*['\"]?(?:-----BEGIN[A-Z \-]*PRIVATE KEY-----[\s\S]*?-----END[A-Z \-]*PRIVATE KEY-----|[^\s,'\"})\]{}>]+)""", - r"(?:master_key|database_url|db_url|connection_string|" - r"signing_key|encryption_key|" - r"auth_token|access_token|refresh_token|" - r"slack_webhook_url|webhook_url|" - r"database_connection_string|" - r"huggingface_token|jwt_secret)" - r"""['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]+""", - # ── Raw JWTs (without Bearer prefix) ── - r"\beyJ[A-Za-z0-9_-]{10,}\.[A-Za-z0-9_-]+\.[A-Za-z0-9_-]*", - # ── Azure SAS tokens in URLs ── - r"[?&]sig=[A-Za-z0-9%+/=]+", - # ── Full JSON service-account blobs (single-line and multi-line) ── - r'\{[^{}]*"type"\s*:\s*"service_account"[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', - ] - return re.compile("|".join(patterns), re.IGNORECASE) - - -_SECRET_RE = _build_secret_patterns() - def _redact_string(value: str) -> str: if not _ENABLE_SECRET_REDACTION: return value - return _SECRET_RE.sub(_REDACTED, value) + return redact_string(value) def redact_secrets(value: str) -> str: diff --git a/litellm/_redis.py b/litellm/_redis.py index f12afbac297..65284162663 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -19,6 +19,7 @@ import redis.asyncio as async_redis # type: ignore from litellm import get_secret, get_secret_str from litellm._redis_credential_provider import ( + AzureADCredentialProvider, GCPIAMCredentialProvider, _generate_gcp_iam_access_token, ) @@ -27,6 +28,8 @@ from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker from ._logging import verbose_logger +AZURE_REDIS_SCOPE = "https://redis.azure.com/.default" + def _get_redis_kwargs(): arg_spec = inspect.getfullargspec(redis.Redis) @@ -38,14 +41,18 @@ def _get_redis_kwargs(): "retry", } - include_args = [ + include_args = { "url", "redis_connect_func", "gcp_service_account", "gcp_ssl_ca_certs", - ] + "azure_redis_ad_token", + "azure_client_id", + "azure_tenant_id", + "azure_client_secret", + } - available_args = [x for x in arg_spec.args if x not in exclude_args] + include_args + available_args = {x for x in arg_spec.args if x not in exclude_args} | include_args return available_args @@ -77,19 +84,23 @@ def _get_redis_cluster_kwargs(client=None): # Only allow primitive arguments exclude_args = {"self", "connection_pool", "retry", "host", "port", "startup_nodes"} - available_args = [x for x in arg_spec.args if x not in exclude_args] - available_args.append("password") - available_args.append("username") - available_args.append("ssl") - available_args.append("ssl_cert_reqs") - available_args.append("ssl_check_hostname") - available_args.append("ssl_ca_certs") - available_args.append( - "redis_connect_func" - ) # Needed for sync clusters and IAM detection - available_args.append("gcp_service_account") - available_args.append("gcp_ssl_ca_certs") - available_args.append("max_connections") + available_args = {x for x in arg_spec.args if x not in exclude_args} + available_args |= { + "password", + "username", + "ssl", + "ssl_cert_reqs", + "ssl_check_hostname", + "ssl_ca_certs", + "redis_connect_func", # Needed for sync clusters and IAM detection + "gcp_service_account", + "gcp_ssl_ca_certs", + "azure_redis_ad_token", + "azure_client_id", + "azure_tenant_id", + "azure_client_secret", + "max_connections", + } return available_args @@ -155,6 +166,125 @@ def create_gcp_iam_redis_connect_func( return iam_connect +def _build_azure_credential( + azure_client_id: Optional[str] = None, + azure_tenant_id: Optional[str] = None, + azure_client_secret: Optional[str] = None, +): + """ + Build a long-lived Azure credential object. + + Azure SDK credentials cache tokens internally and handle expiry/refresh + transparently, so this should be called once and the result reused. + """ + try: + from azure.identity import ( + ClientSecretCredential, + DefaultAzureCredential, + ManagedIdentityCredential, + ) + except ImportError: + raise ImportError( + "azure-identity is required for Azure AD Redis authentication. " + "Install it with: pip install azure-identity" + ) + + _client_id = azure_client_id or os.environ.get("AZURE_CLIENT_ID") + _tenant_id = azure_tenant_id or os.environ.get("AZURE_TENANT_ID") + _client_secret = azure_client_secret or os.environ.get("AZURE_CLIENT_SECRET") + + if _client_id and _tenant_id and _client_secret: + return ClientSecretCredential( + client_id=_client_id, + tenant_id=_tenant_id, + client_secret=_client_secret, + ) + elif _client_id: + return ManagedIdentityCredential(client_id=_client_id) + else: + return DefaultAzureCredential() + + +def _generate_azure_ad_redis_token( + azure_client_id: Optional[str] = None, + azure_tenant_id: Optional[str] = None, + azure_client_secret: Optional[str] = None, +) -> str: + """ + One-shot helper that builds a credential and fetches a single Azure AD + access token for Redis. Each call rebuilds the credential and performs a + network round-trip, so it should not be used in steady-state Redis flows + — the sync (``create_azure_ad_redis_connect_func``) and async paths + (``AzureADCredentialProvider``) keep the credential alive across + connections so the Azure SDK's internal cache + silent refresh apply. + """ + credential = _build_azure_credential( + azure_client_id=azure_client_id, + azure_tenant_id=azure_tenant_id, + azure_client_secret=azure_client_secret, + ) + token = credential.get_token(AZURE_REDIS_SCOPE) + return token.token + + +def create_azure_ad_redis_connect_func( + azure_client_id: Optional[str] = None, + azure_tenant_id: Optional[str] = None, + azure_client_secret: Optional[str] = None, +) -> Callable: + """ + Creates a custom Redis connection function for Azure AD authentication. + + Used for sync Redis clients. The credential is created once (captured by the + closure) and reused across connections — the Azure SDK handles token caching + and silent renewal internally. Only ``get_token`` is called per connection. + """ + credential = _build_azure_credential( + azure_client_id=azure_client_id, + azure_tenant_id=azure_tenant_id, + azure_client_secret=azure_client_secret, + ) + + def ad_connect(self): + """Initialize the connection and authenticate using Azure AD""" + from redis.exceptions import ( + AuthenticationError, + AuthenticationWrongNumberOfArgsError, + ) + from redis.utils import str_if_bytes + + self._parser.on_connect(self) + + access_token = credential.get_token(AZURE_REDIS_SCOPE).token + + # Only include username when explicitly set — sending AUTH "" + # is invalid for most ACL-configured Azure Redis instances. + username = os.environ.get("REDIS_USERNAME", "") + if username: + auth_args = (username, access_token) + else: + auth_args = (access_token,) + + self.send_command("AUTH", *auth_args, check_health=False) + + try: + auth_response = self.read_response() + except AuthenticationWrongNumberOfArgsError: + # Fallback: try with just the token (Redis < 6 / no ACL) + self.send_command("AUTH", access_token, check_health=False) + auth_response = self.read_response() + + if str_if_bytes(auth_response) != "OK": + raise AuthenticationError("Azure AD authentication failed for Redis") + + # Attach the live credential object so async paths can wrap it in + # AzureADCredentialProvider for refresh-aware token retrieval. The raw + # client_id/tenant_id/secret are intentionally NOT exposed here — the + # credential closure already holds them. + ad_connect._azure_credential = credential # type: ignore[attr-defined] + return ad_connect + + def get_redis_url_from_environment(): if "REDIS_URL" in os.environ: return os.environ["REDIS_URL"] @@ -179,7 +309,7 @@ def get_redis_url_from_environment(): return f"{redis_protocol}://{auth_part}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" -def _get_redis_client_logic(**env_overrides): +def _get_redis_client_logic(**env_overrides): # noqa: PLR0915 """ Common functionality across sync + async redis client implementations """ @@ -253,6 +383,52 @@ def _get_redis_client_logic(**env_overrides): if _gcp_ssl_ca_certs and redis_kwargs.get("ssl", False): redis_kwargs["ssl_ca_certs"] = _gcp_ssl_ca_certs + # Handle Azure AD authentication (after GCP IAM block) + _azure_redis_ad_token = redis_kwargs.get("azure_redis_ad_token") or get_secret( + "REDIS_AZURE_AD_TOKEN" + ) + + _azure_ad_enabled = ( + _azure_redis_ad_token is not None + and str(_azure_redis_ad_token).lower() == "true" + ) + + if _azure_ad_enabled and _gcp_service_account is not None: + verbose_logger.warning( + "Both GCP IAM (gcp_service_account) and Azure AD (azure_redis_ad_token) are configured for Redis. " + "Using GCP IAM. Remove one to avoid misconfiguration." + ) + + if _azure_ad_enabled and _gcp_service_account is None: + _azure_client_id = redis_kwargs.get("azure_client_id") or get_secret_str( + "AZURE_CLIENT_ID" + ) + _azure_tenant_id = redis_kwargs.get("azure_tenant_id") or get_secret_str( + "AZURE_TENANT_ID" + ) + _azure_client_secret = redis_kwargs.get( + "azure_client_secret" + ) or get_secret_str("AZURE_CLIENT_SECRET") + + verbose_logger.debug("Setting up Azure AD authentication for Redis.") + redis_kwargs["redis_connect_func"] = create_azure_ad_redis_connect_func( + azure_client_id=_azure_client_id, + azure_tenant_id=_azure_tenant_id, + azure_client_secret=_azure_client_secret, + ) + # Marker for async paths to detect Azure AD auth. The live credential + # object is attached separately as `_azure_credential` by + # `create_azure_ad_redis_connect_func`; the raw client_id/tenant_id/secret + # are intentionally NOT exposed on the function to avoid leaking + # credentials via inspection or logging. + redis_kwargs["redis_connect_func"]._azure_redis_ad_token = True # type: ignore[attr-defined] + + # Always remove Azure-specific kwargs that shouldn't be passed to Redis client + redis_kwargs.pop("azure_redis_ad_token", None) + redis_kwargs.pop("azure_client_id", None) + redis_kwargs.pop("azure_tenant_id", None) + redis_kwargs.pop("azure_client_secret", None) + if "url" in redis_kwargs and redis_kwargs["url"] is not None: # Only strip host/port/db/password when not routing to a cluster. # When startup_nodes is also present the cluster path takes priority and @@ -303,10 +479,24 @@ def init_redis_cluster(redis_kwargs) -> redis.RedisCluster: return redis.RedisCluster(startup_nodes=new_startup_nodes, **cluster_kwargs) # type: ignore +def _get_redis_sentinel_connection_kwargs(redis_kwargs: dict) -> dict: + connection_kwargs = {} + args = _get_redis_kwargs() + for arg in redis_kwargs: + if arg in args: + connection_kwargs[arg] = redis_kwargs[arg] + + return connection_kwargs + + def _init_redis_sentinel(redis_kwargs) -> redis.Redis: sentinel_nodes = redis_kwargs.get("sentinel_nodes") sentinel_password = redis_kwargs.get("sentinel_password") service_name = redis_kwargs.get("service_name") + connection_kwargs = _get_redis_sentinel_connection_kwargs(redis_kwargs) + connection_kwargs.setdefault("socket_timeout", REDIS_SOCKET_TIMEOUT) + sentinel_kwargs = dict(connection_kwargs) + sentinel_kwargs["password"] = sentinel_password if not sentinel_nodes or not service_name: raise ValueError( @@ -318,19 +508,22 @@ def _init_redis_sentinel(redis_kwargs) -> redis.Redis: # Set up the Sentinel client sentinel = redis.Sentinel( sentinel_nodes, - socket_timeout=REDIS_SOCKET_TIMEOUT, - password=sentinel_password, + sentinel_kwargs=sentinel_kwargs, ) # Return the master instance for the given service - return sentinel.master_for(service_name) + return sentinel.master_for(service_name, **connection_kwargs) def _init_async_redis_sentinel(redis_kwargs) -> async_redis.Redis: sentinel_nodes = redis_kwargs.get("sentinel_nodes") sentinel_password = redis_kwargs.get("sentinel_password") service_name = redis_kwargs.get("service_name") + connection_kwargs = _get_redis_sentinel_connection_kwargs(redis_kwargs) + connection_kwargs.setdefault("socket_timeout", REDIS_SOCKET_TIMEOUT) + sentinel_kwargs = dict(connection_kwargs) + sentinel_kwargs["password"] = sentinel_password if not sentinel_nodes or not service_name: raise ValueError( @@ -342,13 +535,12 @@ def _init_async_redis_sentinel(redis_kwargs) -> async_redis.Redis: # Set up the Sentinel client sentinel = async_redis.Sentinel( sentinel_nodes, - socket_timeout=REDIS_SOCKET_TIMEOUT, - password=sentinel_password, + sentinel_kwargs=sentinel_kwargs, ) # Return the master instance for the given service - return sentinel.master_for(service_name) + return sentinel.master_for(service_name, **connection_kwargs) def get_redis_client(**env_overrides): @@ -373,7 +565,7 @@ def get_redis_client(**env_overrides): return redis.Redis(**redis_kwargs) -def get_redis_async_client( +def get_redis_async_client( # noqa: PLR0915 connection_pool: Optional[async_redis.BlockingConnectionPool] = None, **env_overrides, ) -> Union[async_redis.Redis, async_redis.RedisCluster]: @@ -398,6 +590,14 @@ def get_redis_async_client( cluster_kwargs["credential_provider"] = GCPIAMCredentialProvider( redis_connect_func._gcp_service_account ) + # Handle Azure AD authentication for async clusters via CredentialProvider + # so the credential's internal cache + silent refresh runs per connection + # (mirrors GCP IAM above; avoids static-token-baked-in-pool expiry). + elif redis_connect_func and hasattr(redis_connect_func, "_azure_credential"): + cluster_kwargs["credential_provider"] = AzureADCredentialProvider( + redis_connect_func._azure_credential, + username=os.environ.get("REDIS_USERNAME") or None, + ) new_startup_nodes: List[ClusterNode] = [] @@ -431,6 +631,22 @@ def get_redis_async_client( # Check for Redis Sentinel if "sentinel_nodes" in redis_kwargs and "service_name" in redis_kwargs: return _init_async_redis_sentinel(redis_kwargs) + + # Wrap GCP / Azure AD auth in a CredentialProvider for the standard async + # Redis client. The async client doesn't support redis_connect_func, but it + # does honour credential_provider — which is called per connection, so the + # underlying SDK can refresh tokens silently before they expire. + redis_connect_func = redis_kwargs.pop("redis_connect_func", None) + if redis_connect_func and hasattr(redis_connect_func, "_azure_credential"): + redis_kwargs["credential_provider"] = AzureADCredentialProvider( + redis_connect_func._azure_credential, + username=os.environ.get("REDIS_USERNAME") or None, + ) + elif redis_connect_func and hasattr(redis_connect_func, "_gcp_service_account"): + redis_kwargs["credential_provider"] = GCPIAMCredentialProvider( + redis_connect_func._gcp_service_account + ) + _pretty_print_redis_config(redis_kwargs=redis_kwargs) if connection_pool is not None: @@ -464,6 +680,21 @@ def get_redis_connection_pool( redis_kwargs["max_connections"], ) return async_redis.BlockingConnectionPool.from_url(**pool_kwargs) + + # Wrap GCP / Azure AD auth in a CredentialProvider so pool-managed + # connections re-fetch tokens via the SDK's internal cache + silent refresh + # rather than reusing a single token captured at pool creation. + redis_connect_func = redis_kwargs.pop("redis_connect_func", None) + if redis_connect_func and hasattr(redis_connect_func, "_azure_credential"): + redis_kwargs["credential_provider"] = AzureADCredentialProvider( + redis_connect_func._azure_credential, + username=os.environ.get("REDIS_USERNAME") or None, + ) + elif redis_connect_func and hasattr(redis_connect_func, "_gcp_service_account"): + redis_kwargs["credential_provider"] = GCPIAMCredentialProvider( + redis_connect_func._gcp_service_account + ) + connection_class = async_redis.Connection if "ssl" in redis_kwargs: connection_class = async_redis.SSLConnection diff --git a/litellm/_redis_credential_provider.py b/litellm/_redis_credential_provider.py index 70725fe12c4..586b1c7716c 100644 --- a/litellm/_redis_credential_provider.py +++ b/litellm/_redis_credential_provider.py @@ -1,10 +1,13 @@ import asyncio import threading import time -from typing import Dict, Tuple +from typing import Any, Dict, Optional, Tuple, Union from redis.credentials import CredentialProvider # type: ignore[attr-defined] +# Azure AD scope for Redis Cache for Azure. +AZURE_REDIS_SCOPE = "https://redis.azure.com/.default" + # GCP IAM tokens are valid for 1 hour. Cache for 55 minutes to refresh before expiry. _GCP_IAM_TOKEN_TTL_SECONDS = 3300 @@ -101,3 +104,33 @@ class GCPIAMCredentialProvider(CredentialProvider): _get_cached_gcp_iam_token, self._gcp_service_account ) return (token,) + + +class AzureADCredentialProvider(CredentialProvider): + """ + redis.credentials.CredentialProvider implementation that supplies Azure AD + tokens for Redis authentication. + + Wraps an azure-identity credential object so the Azure SDK's internal token + cache and silent refresh are honoured on every Redis connection. This avoids + the static-token-baked-in-pool issue where pool-managed connections would + fail authentication after the initial token expired (~1 hour TTL). + """ + + def __init__(self, credential: Any, username: Optional[str] = None) -> None: + self._credential = credential + self._username = username + + def get_credentials(self) -> Union[Tuple[str], Tuple[str, str]]: + token = self._credential.get_token(AZURE_REDIS_SCOPE).token + if self._username: + return (self._username, token) + return (token,) + + async def get_credentials_async(self) -> Union[Tuple[str], Tuple[str, str]]: + token_obj = await asyncio.to_thread( + self._credential.get_token, AZURE_REDIS_SCOPE + ) + if self._username: + return (self._username, token_obj.token) + return (token_obj.token,) diff --git a/litellm/anthropic_beta_headers_config.json b/litellm/anthropic_beta_headers_config.json index 662b62cf205..d02afe37569 100644 --- a/litellm/anthropic_beta_headers_config.json +++ b/litellm/anthropic_beta_headers_config.json @@ -72,7 +72,7 @@ "computer-use-2025-11-24": "computer-use-2025-11-24", "context-1m-2025-08-07": "context-1m-2025-08-07", "context-management-2025-06-27": null, - "effort-2025-11-24": null, + "effort-2025-11-24": "effort-2025-11-24", "fast-mode-2026-02-01": null, "files-api-2025-04-14": null, "fine-grained-tool-streaming-2025-05-14": null, @@ -103,7 +103,7 @@ "computer-use-2025-11-24": "computer-use-2025-11-24", "context-1m-2025-08-07": "context-1m-2025-08-07", "context-management-2025-06-27": null, - "effort-2025-11-24": null, + "effort-2025-11-24": "effort-2025-11-24", "fast-mode-2026-02-01": null, "files-api-2025-04-14": null, "fine-grained-tool-streaming-2025-05-14": null, diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 4b965d4e635..aaf083e75d6 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -387,6 +387,27 @@ def _get_batch_job_total_usage_from_file_content( ) +def _get_models_from_batch_input_file_content( + file_content_dictionary: List[dict], +) -> List[str]: + """Extract the distinct ``body.model`` values from a batch *input* file. + + Used by the proxy's batch pre-call hook to enforce that the caller is + authorized for every model named inside the JSONL — not just the one + on the outer request — so the proxy's per-key model allowlist isn't + bypassed by smuggling expensive models into the batch file. + """ + models: List[str] = [] + seen: set = set() + for _item in file_content_dictionary: + body = _item.get("body") or {} + model = body.get("model") + if model and model not in seen: + seen.add(model) + models.append(model) + return models + + def _get_batch_job_input_file_usage( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", @@ -403,11 +424,25 @@ def _get_batch_job_input_file_usage( for _item in file_content_dictionary: body = _item.get("body", {}) model = body.get("model", model_name or "") - messages = body.get("messages", []) + # Chat completion payloads. + messages = body.get("messages") if messages: - item_tokens = token_counter(model=model, messages=messages) - prompt_tokens += item_tokens + prompt_tokens += token_counter(model=model, messages=messages) + continue + + # Text completion payloads (`prompt`). + prompt = body.get("prompt") + if prompt: + prompt_tokens += _count_prompt_or_input_tokens(model=model, value=prompt) + continue + + # Embedding payloads (`input`). + input_data = body.get("input") + if input_data: + prompt_tokens += _count_prompt_or_input_tokens( + model=model, value=input_data + ) return Usage( total_tokens=prompt_tokens + completion_tokens, @@ -416,6 +451,43 @@ def _get_batch_job_input_file_usage( ) +def _count_prompt_or_input_tokens(model: str, value: Any) -> int: + """Token-count a ``prompt`` / ``input`` field that the OpenAI batch + schema allows in four shapes: + + - ``str``: a single text prompt. + - ``list[str]``: multiple text prompts. + - ``list[int]``: a pre-tokenized prompt (each int counts as 1 token). + - ``list[list[int]]``: multiple pre-tokenized prompts. + + Pre-fix only the string shapes were counted, so a caller could send + a large ``list[list[int]]`` payload and slip past TPM rate limits + with a recorded cost of zero tokens. + """ + if isinstance(value, str): + return token_counter(model=model, text=value) + if isinstance(value, list): + total = 0 + for chunk in value: + if isinstance(chunk, str): + total += token_counter(model=model, text=chunk) + elif isinstance(chunk, int): + # Single pre-tokenized prompt at the top level: each + # int counts as one token. + total += 1 + elif isinstance(chunk, list): + # Nested pre-tokenized prompt: every int contributes a + # token. Mixed string/int items still count. + total += sum(1 if isinstance(t, int) else 0 for t in chunk) + total += sum( + token_counter(model=model, text=t) + for t in chunk + if isinstance(t, str) + ) + return total + return 0 + + def _get_batch_job_usage_from_response_body(response_body: dict) -> Usage: """ Get the tokens of a batch job from the response body diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 23f444b1cea..15ee9303969 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -543,15 +543,17 @@ def _handle_retrieve_batch_providers_without_provider_config( ) else: raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( - custom_llm_provider - ), + message=( + "LiteLLM doesn't support custom_llm_provider={} for 'retrieve_batch' without a `model` kwarg. " + "Supported via this path: 'openai', 'azure', 'vertex_ai', 'anthropic'. " + "'bedrock' is supported but requires `model` to be passed so the provider config can be loaded." + ).format(custom_llm_provider), model="n/a", llm_provider=custom_llm_provider, response=httpx.Response( status_code=400, content="Unsupported provider", - request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + request=httpx.Request(method="retrieve_batch", url="https://github.com/BerriAI/litellm"), # type: ignore ), ) return response @@ -615,24 +617,35 @@ def retrieve_batch( _is_async = kwargs.pop("aretrieve_batch", False) is True client = kwargs.get("client", None) - # Check if this is an async invoke ARN (different from regular batch ARN) - # Async invoke ARNs have format: arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:[0-9]{12}:async-invoke/[a-z0-9]{12} - if ( - batch_id.startswith("arn:aws") - and ":bedrock:" in batch_id - and ":async-invoke/" in batch_id - ): - # Handle async invoke status check - # Remove aws_region_name from kwargs to avoid duplicate parameter - async_kwargs = kwargs.copy() - async_kwargs.pop("aws_region_name", None) + # Bedrock has two distinct ARN families that need different APIs: + # * async-invoke ARNs (Twelve Labs Marengo embeddings) -> bedrock-runtime data plane + # * model-invocation-job ARNs (CreateModelInvocationJob batch) -> bedrock control plane + # They live on different AWS service endpoints and can't share a handler. + # ARN shapes: + # arn:aws(-[^:]+)?:bedrock:::async-invoke/ + # arn:aws(-[^:]+)?:bedrock:::model-invocation-job/ + if batch_id.startswith("arn:aws") and ":bedrock:" in batch_id: + if ":async-invoke/" in batch_id: + # Remove aws_region_name from kwargs to avoid duplicate parameter + async_kwargs = kwargs.copy() + async_kwargs.pop("aws_region_name", None) - return BedrockBatchesHandler._handle_async_invoke_status( - batch_id=batch_id, - aws_region_name=kwargs.get("aws_region_name", "us-east-1"), - logging_obj=litellm_logging_obj, - **async_kwargs, - ) + return BedrockBatchesHandler._handle_async_invoke_status( + batch_id=batch_id, + aws_region_name=kwargs.get("aws_region_name", "us-east-1"), + logging_obj=litellm_logging_obj, + **async_kwargs, + ) + if ":model-invocation-job/" in batch_id: + mij_kwargs = kwargs.copy() + mij_kwargs.pop("aws_region_name", None) + + return BedrockBatchesHandler._handle_model_invocation_job_status( + batch_id=batch_id, + aws_region_name=kwargs.get("aws_region_name"), + logging_obj=litellm_logging_obj, + **mij_kwargs, + ) # Try to use provider config first (for providers like bedrock) model: Optional[str] = kwargs.get("model", None) diff --git a/litellm/budget_manager.py b/litellm/budget_manager.py index b25967579e0..bbebb6042cb 100644 --- a/litellm/budget_manager.py +++ b/litellm/budget_manager.py @@ -178,6 +178,18 @@ class BudgetManager: return list(self.user_dict.keys()) def reset_cost(self, user): + """ + Reset the tracked spend for a user back to zero. + + Clears both the aggregate ``current_cost`` and the per-model + ``model_cost`` breakdown stored for the given user. + + Args: + user: The user identifier whose cost should be reset. + + Returns: + dict: ``{"user": }`` reflecting the reset state. + """ self.user_dict[user]["current_cost"] = 0 self.user_dict[user]["model_cost"] = {} return {"user": self.user_dict[user]} diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index ce1bc26c5e0..11733ce4cee 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -432,9 +432,10 @@ class Cache: str: The final hashed cache key with the redis namespace. """ dynamic_cache_control: DynamicCacheControl = kwargs.get("cache", {}) + metadata = kwargs.get("metadata") or {} namespace = ( dynamic_cache_control.get("namespace") - or kwargs.get("metadata", {}).get("redis_namespace") + or metadata.get("redis_namespace") or self.namespace ) if namespace: diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py index 7d514e648fe..3cf1d911d7f 100644 --- a/litellm/caching/caching_handler.py +++ b/litellm/caching/caching_handler.py @@ -87,6 +87,18 @@ class CachingHandlerResponse(BaseModel): in_memory_cache_obj = InMemoryCache() +def _should_defer_streaming_cache_hit_callbacks(*, kwargs: Dict[str, Any]) -> bool: + """ + When stream=True, do not run success callbacks at cache-hit time. + + Cached chat/text completion replay uses CustomStreamWrapper; cached Responses + replay uses CachedResponsesAPIStreamingIterator. Both invoke logging success + handlers when the stream finishes; firing them here too would double-count + spend and callback records. + """ + return kwargs.get("stream", False) is True + + class LLMCachingHandler: def __init__( self, @@ -99,6 +111,7 @@ class LLMCachingHandler: self.async_streaming_chunks: List[ModelResponse] = [] self.sync_streaming_chunks: List[ModelResponse] = [] self.request_kwargs = request_kwargs + self.preset_cache_key: Optional[str] = None self.original_function = original_function self.start_time = start_time if litellm.cache is not None and isinstance(litellm.cache.cache, RedisCache): @@ -206,7 +219,7 @@ class LLMCachingHandler: custom_llm_provider=kwargs.get("custom_llm_provider", None), args=args, ) - if kwargs.get("stream", False) is False: + if not _should_defer_streaming_cache_hit_callbacks(kwargs=kwargs): # LOG SUCCESS self._async_log_cache_hit_on_callbacks( logging_obj=logging_obj, @@ -215,11 +228,12 @@ class LLMCachingHandler: end_time=end_time, cache_hit=cache_hit, ) - cache_key = litellm.cache.get_cache_key(**kwargs) - if ( - isinstance(cached_result, BaseModel) - or isinstance(cached_result, CustomStreamWrapper) - ) and hasattr(cached_result, "_hidden_params"): + cache_key = ( + self.preset_cache_key + or self.request_kwargs.get("cache_key") + or litellm.cache.get_cache_key(**self.request_kwargs) + ) + if hasattr(cached_result, "_hidden_params"): cached_result._hidden_params["cache_key"] = cache_key # type: ignore return CachingHandlerResponse(cached_result=cached_result) elif ( @@ -265,8 +279,6 @@ class LLMCachingHandler: kwargs: Dict[str, Any], args: Optional[Tuple[Any, ...]] = None, ) -> CachingHandlerResponse: - from litellm.utils import CustomStreamWrapper - cached_result: Optional[Any] = None # Check if caching should be performed BEFORE doing expensive kwargs copy @@ -282,6 +294,11 @@ class LLMCachingHandler: args, ) ) + if new_kwargs.get("metadata") is None: + new_kwargs.pop("metadata", None) + if new_kwargs.get("stream") is True and "cache_key" not in new_kwargs: + new_kwargs["cache_key"] = litellm.cache.get_cache_key(**new_kwargs) + self.request_kwargs = new_kwargs print_verbose("Checking Sync Cache") cached_result = litellm.cache.get_cache(**new_kwargs) if cached_result is not None: @@ -322,17 +339,19 @@ class LLMCachingHandler: is_async=False, ) - logging_obj.handle_sync_success_callbacks_for_async_calls( - result=cached_result, - start_time=start_time, - end_time=end_time, - cache_hit=cache_hit, + if not _should_defer_streaming_cache_hit_callbacks(kwargs=kwargs): + logging_obj.handle_sync_success_callbacks_for_async_calls( + result=cached_result, + start_time=start_time, + end_time=end_time, + cache_hit=cache_hit, + ) + cache_key = ( + self.preset_cache_key + or self.request_kwargs.get("cache_key") + or litellm.cache.get_cache_key(**self.request_kwargs) ) - cache_key = litellm.cache.get_cache_key(**kwargs) - if ( - isinstance(cached_result, BaseModel) - or isinstance(cached_result, CustomStreamWrapper) - ) and hasattr(cached_result, "_hidden_params"): + if hasattr(cached_result, "_hidden_params"): cached_result._hidden_params["cache_key"] = cache_key # type: ignore return CachingHandlerResponse(cached_result=cached_result) return CachingHandlerResponse(cached_result=cached_result) @@ -686,6 +705,11 @@ class LLMCachingHandler: args, ) ) + if new_kwargs.get("metadata") is None: + new_kwargs.pop("metadata", None) + if new_kwargs.get("stream") is True and "cache_key" not in new_kwargs: + new_kwargs["cache_key"] = litellm.cache.get_cache_key(**new_kwargs) + self.request_kwargs = new_kwargs cached_result: Optional[Any] = None if call_type == CallTypes.aembedding.value: if isinstance(new_kwargs["input"], str): @@ -710,14 +734,26 @@ class LLMCachingHandler: if all(result is None for result in cached_result): cached_result = None else: + request_kwargs = new_kwargs.copy() + request_cache_key = request_kwargs.pop("cache_key", None) if litellm.cache._supports_async() is True: ## check if dual cache is supported ## + self.preset_cache_key = ( + request_cache_key or litellm.cache.get_cache_key(**request_kwargs) + ) cached_result = await litellm.cache.async_get_cache( - dynamic_cache_object=self.dual_cache, **new_kwargs + dynamic_cache_object=self.dual_cache, + cache_key=self.preset_cache_key, + **request_kwargs, ) else: # fallback for caches that don't support async + self.preset_cache_key = ( + request_cache_key or litellm.cache.get_cache_key(**request_kwargs) + ) cached_result = litellm.cache.get_cache( - dynamic_cache_object=self.dual_cache, **new_kwargs + dynamic_cache_object=self.dual_cache, + cache_key=self.preset_cache_key, + **request_kwargs, ) return cached_result @@ -825,8 +861,27 @@ class LLMCachingHandler: elif (call_type == "aresponses" or call_type == "responses") and isinstance( cached_result, dict ): - # Convert cached dict back to ResponsesAPIResponse object - cached_result = ResponsesAPIResponse(**cached_result) + from litellm.responses.streaming_iterator import ( + CachedResponsesAPIStreamingIterator, + ) + + response_obj = ResponsesAPIResponse(**cached_result) + if ( + hasattr(response_obj, "_hidden_params") + and response_obj._hidden_params is not None + and isinstance(response_obj._hidden_params, dict) + ): + response_obj._hidden_params["cache_hit"] = True + + if kwargs.get("stream", False) is True: + cached_result = CachedResponsesAPIStreamingIterator( + response=response_obj, + logging_obj=logging_obj, + request_data=kwargs, + call_type=call_type, + ) + else: + cached_result = response_obj if ( hasattr(cached_result, "_hidden_params") diff --git a/litellm/caching/dual_cache.py b/litellm/caching/dual_cache.py index 34ae3638a5b..8060a65b78d 100644 --- a/litellm/caching/dual_cache.py +++ b/litellm/caching/dual_cache.py @@ -92,6 +92,25 @@ class DualCache(BaseCache): if default_redis_ttl is not None: self.default_redis_ttl = default_redis_ttl + def attach_redis_cache( + self, + redis_cache: Optional[RedisCache] = None, + *, + default_redis_ttl: Optional[float] = None, + ) -> None: + """ + Attach a Redis backend if this DualCache does not already have one. + + No-op when ``redis_cache`` is None or when Redis was already set (constructor + or a prior attach). Use this for lazy wiring after a shared Redis client exists. + Does not backfill in-memory-only keys to Redis. + """ + if redis_cache is None or self.redis_cache is not None: + return + self.redis_cache = redis_cache + if default_redis_ttl is not None: + self.default_redis_ttl = default_redis_ttl + def set_cache(self, key, value, local_only: bool = False, **kwargs): # Update both Redis and in-memory cache try: @@ -392,6 +411,7 @@ class DualCache(BaseCache): value: float, parent_otel_span: Optional[Span] = None, local_only: bool = False, + refresh_ttl: bool = False, **kwargs, ) -> Optional[float]: """ @@ -399,6 +419,9 @@ class DualCache(BaseCache): Value - float - the value you want to increment by + Refresh_ttl - bool - if True, resets the Redis TTL on every write. + Default False preserves window-style semantics. + Returns - the incremented value, or None if no cache backend is available (in_memory_cache is None and Redis failed/is absent). """ @@ -415,6 +438,7 @@ class DualCache(BaseCache): value, parent_otel_span=parent_otel_span, ttl=kwargs.get("ttl", None), + refresh_ttl=refresh_ttl, ) return result diff --git a/litellm/caching/qdrant_semantic_cache.py b/litellm/caching/qdrant_semantic_cache.py index 5e3713e5a15..cb521efca05 100644 --- a/litellm/caching/qdrant_semantic_cache.py +++ b/litellm/caching/qdrant_semantic_cache.py @@ -11,17 +11,23 @@ Has 4 methods: import ast import asyncio import json -from typing import Any, cast +import os +from typing import Any, Dict, cast import litellm from litellm._logging import print_verbose from litellm.constants import QDRANT_SCALAR_QUANTILE, QDRANT_VECTOR_SIZE +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + get_str_from_messages, +) from litellm.types.utils import EmbeddingResponse from .base_cache import BaseCache class QdrantSemanticCache(BaseCache): + CACHE_KEY_FIELD_NAME = "litellm_cache_key" + def __init__( # noqa: PLR0915 self, qdrant_api_base=None, @@ -33,8 +39,6 @@ class QdrantSemanticCache(BaseCache): host_type=None, vector_size=None, ): - import os - from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, get_async_httpx_client, @@ -115,7 +119,9 @@ class QdrantSemanticCache(BaseCache): print_verbose( f"Collection already exists.\nCollection details:{self.collection_info}" ) + self._ensure_cache_key_payload_index() else: + quantization_params: Dict[str, Any] if quantization_config is None or quantization_config == "binary": quantization_params = { "binary": { @@ -156,6 +162,7 @@ class QdrantSemanticCache(BaseCache): print_verbose( f"New collection created.\nCollection details:{self.collection_info}" ) + self._ensure_cache_key_payload_index() else: raise Exception("Error while creating new collection") @@ -170,15 +177,94 @@ class QdrantSemanticCache(BaseCache): cached_response = ast.literal_eval(cached_response) return cached_response + def _get_qdrant_cache_key_filter(self, key: str) -> dict: + return { + "must": [ + { + "key": self.CACHE_KEY_FIELD_NAME, + "match": {"value": str(key)}, + } + ] + } + + def _add_cache_key_filter_to_search_data(self, data: dict, key: str) -> None: + data["filter"] = self._get_qdrant_cache_key_filter(key) + + def _ensure_cache_key_payload_index(self) -> None: + try: + response = self.sync_client.put( + url=f"{self.qdrant_api_base}/collections/{self.collection_name}/index", + headers=self.headers, + json={ + "field_name": self.CACHE_KEY_FIELD_NAME, + "field_schema": "keyword", + }, + ) + if response.status_code not in (200, 201): + print_verbose( + "Qdrant semantic-cache could not create cache-key payload index: " + f"{response.text}" + ) + except Exception as exc: + print_verbose( + "Qdrant semantic-cache could not create cache-key payload index: " + f"{str(exc)}" + ) + + def _payload_matches_cache_key(self, payload: dict, key: str) -> bool: + # Pre-isolation points stored only prompt + response with no cache-key + # payload field. Reassigning them to a caller's key would risk + # cross-scope hits, so they're treated as misses and re-populated on + # the next set_cache. + cached_key = payload.get(self.CACHE_KEY_FIELD_NAME) + return cached_key is not None and str(cached_key) == str(key) + + async def _get_async_embedding(self, prompt: str, **kwargs) -> Any: + llm_model_list = None + llm_router = None + + try: + from litellm.proxy.proxy_server import ( + llm_model_list as proxy_llm_model_list, + llm_router as proxy_llm_router, + ) + + llm_model_list = proxy_llm_model_list + llm_router = proxy_llm_router + except ImportError: + pass + + router_model_names = ( + [m["model_name"] for m in llm_model_list] + if llm_model_list is not None + else [] + ) + if llm_router is not None and self.embedding_model in router_model_names: + user_api_key = kwargs.get("metadata", {}).get("user_api_key", "") + return await llm_router.aembedding( + model=self.embedding_model, + input=prompt, + cache={"no-store": True, "no-cache": True}, + metadata={ + "user_api_key": user_api_key, + "semantic-cache-embedding": True, + "trace_id": kwargs.get("metadata", {}).get("trace_id", None), + }, + ) + + return await litellm.aembedding( + model=self.embedding_model, + input=prompt, + cache={"no-store": True, "no-cache": True}, + ) + def set_cache(self, key, value, **kwargs): print_verbose(f"qdrant semantic-cache set_cache, kwargs: {kwargs}") from litellm._uuid import uuid # get the prompt messages = kwargs["messages"] - prompt = "" - for message in messages: - prompt += message["content"] + prompt = get_str_from_messages(messages) # create an embedding for prompt embedding_response = cast( @@ -202,6 +288,7 @@ class QdrantSemanticCache(BaseCache): "id": str(uuid.uuid4()), "vector": embedding, "payload": { + self.CACHE_KEY_FIELD_NAME: str(key), "text": prompt, "response": value, }, @@ -220,9 +307,7 @@ class QdrantSemanticCache(BaseCache): # get the messages messages = kwargs["messages"] - prompt = "" - for message in messages: - prompt += message["content"] + prompt = get_str_from_messages(messages) # convert to embedding embedding_response = cast( @@ -249,6 +334,7 @@ class QdrantSemanticCache(BaseCache): "limit": 1, "with_payload": True, } + self._add_cache_key_filter_to_search_data(data=data, key=key) search_response = self.sync_client.post( url=f"{self.qdrant_api_base}/collections/{self.collection_name}/points/search", @@ -258,21 +344,33 @@ class QdrantSemanticCache(BaseCache): results = search_response.json()["result"] if results is None: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 return None if isinstance(results, list): if len(results) == 0: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 return None similarity = results[0]["score"] - cached_prompt = results[0]["payload"]["text"] + payload = results[0]["payload"] + if not self._payload_matches_cache_key(payload=payload, key=key): + print_verbose("Qdrant semantic-cache hit did not match cache key scope") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None + + cached_prompt = payload["text"] # check similarity, if more than self.similarity_threshold, return results print_verbose( f"semantic cache: similarity threshold: {self.similarity_threshold}, similarity: {similarity}, prompt: {prompt}, closest_cached_prompt: {cached_prompt}" ) + + # update kwargs["metadata"] with similarity, don't rewrite the original metadata + kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity + if similarity >= self.similarity_threshold: # cache hit ! - cached_value = results[0]["payload"]["response"] + cached_value = payload["response"] print_verbose( f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}" ) @@ -285,40 +383,12 @@ class QdrantSemanticCache(BaseCache): async def async_set_cache(self, key, value, **kwargs): from litellm._uuid import uuid - from litellm.proxy.proxy_server import llm_model_list, llm_router - print_verbose(f"async qdrant semantic-cache set_cache, kwargs: {kwargs}") # get the prompt messages = kwargs["messages"] - prompt = "" - for message in messages: - prompt += message["content"] - # create an embedding for prompt - router_model_names = ( - [m["model_name"] for m in llm_model_list] - if llm_model_list is not None - else [] - ) - if llm_router is not None and self.embedding_model in router_model_names: - user_api_key = kwargs.get("metadata", {}).get("user_api_key", "") - embedding_response = await llm_router.aembedding( - model=self.embedding_model, - input=prompt, - cache={"no-store": True, "no-cache": True}, - metadata={ - "user_api_key": user_api_key, - "semantic-cache-embedding": True, - "trace_id": kwargs.get("metadata", {}).get("trace_id", None), - }, - ) - else: - # convert to embedding - embedding_response = await litellm.aembedding( - model=self.embedding_model, - input=prompt, - cache={"no-store": True, "no-cache": True}, - ) + prompt = get_str_from_messages(messages) + embedding_response = await self._get_async_embedding(prompt, **kwargs) # get the embedding embedding = embedding_response["data"][0]["embedding"] @@ -332,6 +402,7 @@ class QdrantSemanticCache(BaseCache): "id": str(uuid.uuid4()), "vector": embedding, "payload": { + self.CACHE_KEY_FIELD_NAME: str(key), "text": prompt, "response": value, }, @@ -348,38 +419,12 @@ class QdrantSemanticCache(BaseCache): async def async_get_cache(self, key, **kwargs): print_verbose(f"async qdrant semantic-cache get_cache, kwargs: {kwargs}") - from litellm.proxy.proxy_server import llm_model_list, llm_router # get the messages messages = kwargs["messages"] - prompt = "" - for message in messages: - prompt += message["content"] + prompt = get_str_from_messages(messages) - router_model_names = ( - [m["model_name"] for m in llm_model_list] - if llm_model_list is not None - else [] - ) - if llm_router is not None and self.embedding_model in router_model_names: - user_api_key = kwargs.get("metadata", {}).get("user_api_key", "") - embedding_response = await llm_router.aembedding( - model=self.embedding_model, - input=prompt, - cache={"no-store": True, "no-cache": True}, - metadata={ - "user_api_key": user_api_key, - "semantic-cache-embedding": True, - "trace_id": kwargs.get("metadata", {}).get("trace_id", None), - }, - ) - else: - # convert to embedding - embedding_response = await litellm.aembedding( - model=self.embedding_model, - input=prompt, - cache={"no-store": True, "no-cache": True}, - ) + embedding_response = await self._get_async_embedding(prompt, **kwargs) # get the embedding embedding = embedding_response["data"][0]["embedding"] @@ -396,6 +441,7 @@ class QdrantSemanticCache(BaseCache): "limit": 1, "with_payload": True, } + self._add_cache_key_filter_to_search_data(data=data, key=key) search_response = await self.async_client.post( url=f"{self.qdrant_api_base}/collections/{self.collection_name}/points/search", @@ -414,7 +460,13 @@ class QdrantSemanticCache(BaseCache): return None similarity = results[0]["score"] - cached_prompt = results[0]["payload"]["text"] + payload = results[0]["payload"] + if not self._payload_matches_cache_key(payload=payload, key=key): + print_verbose("Qdrant semantic-cache hit did not match cache key scope") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None + + cached_prompt = payload["text"] # check similarity, if more than self.similarity_threshold, return results print_verbose( @@ -426,7 +478,7 @@ class QdrantSemanticCache(BaseCache): if similarity >= self.similarity_threshold: # cache hit ! - cached_value = results[0]["payload"]["response"] + cached_value = payload["response"] print_verbose( f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}" ) diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index 84a2887f527..cb9ce475d30 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -551,6 +551,13 @@ class RedisCache(BaseCache): async def async_set_cache(self, key, value, **kwargs): from redis.asyncio import Redis + if key is None: + verbose_logger.debug( + "LiteLLM Redis Caching: async set() skipped — key is None, value=%r", + value, + ) + return None + start_time = time.time() try: _redis_client: Redis = self.init_async_client() # type: ignore @@ -569,8 +576,9 @@ class RedisCache(BaseCache): ) ) verbose_logger.error( - "LiteLLM Redis Caching: async set() - Got exception from REDIS %s, Writing value=%s", + "LiteLLM Redis Caching: async set() - Got exception from REDIS %s, key=%r, value=%r", str(e), + key, value, ) raise e @@ -824,6 +832,7 @@ class RedisCache(BaseCache): value: float, ttl: Optional[int] = None, parent_otel_span: Optional[Span] = None, + refresh_ttl: bool = False, ) -> float: from redis.asyncio import Redis @@ -834,11 +843,12 @@ class RedisCache(BaseCache): try: result = await _redis_client.incrbyfloat(name=key, amount=value) if _used_ttl is not None: - # check if key already has ttl, if not -> set ttl - current_ttl = await _redis_client.ttl(key) - if current_ttl == -1: - # Key has no expiration + if refresh_ttl: await _redis_client.expire(key, _used_ttl) + else: + current_ttl = await _redis_client.ttl(key) + if current_ttl == -1: + await _redis_client.expire(key, _used_ttl) ## LOGGING ## end_time = time.time() diff --git a/litellm/caching/redis_semantic_cache.py b/litellm/caching/redis_semantic_cache.py index c76f27377d8..da9e7b1e587 100644 --- a/litellm/caching/redis_semantic_cache.py +++ b/litellm/caching/redis_semantic_cache.py @@ -35,6 +35,7 @@ class RedisSemanticCache(BaseCache): """ DEFAULT_REDIS_INDEX_NAME: str = "litellm_semantic_cache_index" + CACHE_KEY_FIELD_NAME: str = "litellm_cache_key" def __init__( self, @@ -66,8 +67,8 @@ class RedisSemanticCache(BaseCache): Exception: If similarity_threshold is not provided or required Redis connection information is missing """ - from redisvl.extensions.llmcache import SemanticCache - from redisvl.utils.vectorize import CustomTextVectorizer + from redisvl.extensions.llmcache import SemanticCache # type: ignore[import-not-found, import-untyped] + from redisvl.utils.vectorize import CustomTextVectorizer # type: ignore[import-not-found, import-untyped] if index_name is None: index_name = self.DEFAULT_REDIS_INDEX_NAME @@ -109,14 +110,94 @@ class RedisSemanticCache(BaseCache): # Initialize the Redis vectorizer and cache cache_vectorizer = CustomTextVectorizer(self._get_embedding) - self.llmcache = SemanticCache( - name=index_name, + self.llmcache = self._init_semantic_cache( + semantic_cache_cls=SemanticCache, + index_name=index_name, redis_url=redis_url, - vectorizer=cache_vectorizer, - distance_threshold=self.distance_threshold, - overwrite=False, + cache_vectorizer=cache_vectorizer, ) + @classmethod + def _cache_key_filterable_field(cls) -> Dict[str, str]: + return { + "name": cls.CACHE_KEY_FIELD_NAME, + "type": "tag", + } + + def _init_semantic_cache( + self, + semantic_cache_cls: Any, + index_name: str, + redis_url: str, + cache_vectorizer: Any, + ) -> Any: + def _is_schema_mismatch(exc: ValueError) -> bool: + error_message = str(exc).lower() + return any( + phrase in error_message + for phrase in ("schema does not match", "index schema") + ) + + try: + return semantic_cache_cls( + name=index_name, + redis_url=redis_url, + vectorizer=cache_vectorizer, + distance_threshold=self.distance_threshold, + filterable_fields=[self._cache_key_filterable_field()], + overwrite=False, + ) + except ValueError as exc: + if not _is_schema_mismatch(exc): + raise + + isolated_index_name = f"{index_name}_isolated" + print_verbose( + "Redis semantic-cache existing index schema is not isolated; " + f"using isolated index - {isolated_index_name}" + ) + try: + return semantic_cache_cls( + name=isolated_index_name, + redis_url=redis_url, + vectorizer=cache_vectorizer, + distance_threshold=self.distance_threshold, + filterable_fields=[self._cache_key_filterable_field()], + overwrite=False, + ) + except ValueError as isolated_exc: + if not _is_schema_mismatch(isolated_exc): + raise + + print_verbose( + "Redis semantic-cache isolated index schema is stale; " + f"recreating isolated index - {isolated_index_name}" + ) + return semantic_cache_cls( + name=isolated_index_name, + redis_url=redis_url, + vectorizer=cache_vectorizer, + distance_threshold=self.distance_threshold, + filterable_fields=[self._cache_key_filterable_field()], + overwrite=True, + ) + + def _get_cache_filters(self, key: str) -> Dict[str, str]: + return {self.CACHE_KEY_FIELD_NAME: str(key)} + + def _get_cache_key_filter_expression(self, key: str) -> Any: + from redisvl.query.filter import Tag # type: ignore[import-not-found, import-untyped] + + return Tag(self.CACHE_KEY_FIELD_NAME) == str(key) + + def _cache_hit_matches_key(self, cache_hit: Dict[str, Any], key: str) -> bool: + # Pre-isolation entries with no ``litellm_cache_key`` field cannot be + # safely reassigned to a caller's scope and are treated as misses. + cached_key = cache_hit.get(self.CACHE_KEY_FIELD_NAME) + if isinstance(cached_key, bytes): + cached_key = cached_key.decode("utf-8") + return cached_key is not None and str(cached_key) == str(key) + def _get_ttl(self, **kwargs) -> Optional[int]: """ Get the TTL (time-to-live) value for cache entries. @@ -188,7 +269,7 @@ class RedisSemanticCache(BaseCache): Store a value in the semantic cache. Args: - key: The cache key (not directly used in semantic caching) + key: The cache key used to isolate semantic cache entries value: The response value to cache **kwargs: Additional arguments including 'messages' for the prompt and optional 'ttl' for time-to-live @@ -206,12 +287,15 @@ class RedisSemanticCache(BaseCache): prompt = get_str_from_messages(messages) value_str = str(value) + store_kwargs: Dict[str, Any] = { + "filters": self._get_cache_filters(key), + } + # Get TTL and store in Redis semantic cache ttl = self._get_ttl(**kwargs) if ttl is not None: - self.llmcache.store(prompt, value_str, ttl=int(ttl)) - else: - self.llmcache.store(prompt, value_str) + store_kwargs["ttl"] = int(ttl) + self.llmcache.store(prompt, value_str, **store_kwargs) except Exception as e: print_verbose( f"Error setting {value_str or value} in the Redis semantic cache: {str(e)}" @@ -222,7 +306,7 @@ class RedisSemanticCache(BaseCache): Retrieve a semantically similar cached response. Args: - key: The cache key (not directly used in semantic caching) + key: The cache key used to isolate semantic cache entries **kwargs: Additional arguments including 'messages' for the prompt Returns: @@ -235,18 +319,29 @@ class RedisSemanticCache(BaseCache): messages = kwargs.get("messages", []) if not messages: print_verbose("No messages provided for semantic cache lookup") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 return None prompt = get_str_from_messages(messages) - # Check the cache for semantically similar prompts - results = self.llmcache.check(prompt=prompt) + # Check the cache for semantically similar prompts in this exact + # LiteLLM cache-key scope. + check_kwargs: Dict[str, Any] = { + "prompt": prompt, + "filter_expression": self._get_cache_key_filter_expression(key), + } + results = self.llmcache.check(**check_kwargs) # Return None if no similar prompts found if not results: + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 return None # Process the best matching result cache_hit = results[0] + if not self._cache_hit_matches_key(cache_hit=cache_hit, key=key): + print_verbose("Redis semantic-cache hit did not match cache key scope") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None vector_distance = float(cache_hit["vector_distance"]) # Convert vector distance back to similarity score @@ -257,6 +352,9 @@ class RedisSemanticCache(BaseCache): cached_prompt = cache_hit["prompt"] cached_response = cache_hit["response"] + # update kwargs["metadata"] with similarity, don't rewrite the original metadata + kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity + print_verbose( f"Cache hit: similarity threshold: {self.similarity_threshold}, " f"actual similarity: {similarity}, " @@ -267,6 +365,7 @@ class RedisSemanticCache(BaseCache): return self._get_cache_logic(cached_response=cached_response) except Exception as e: print_verbose(f"Error retrieving from Redis semantic cache: {str(e)}") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 async def _get_async_embedding(self, prompt: str, **kwargs) -> List[float]: """ @@ -321,7 +420,7 @@ class RedisSemanticCache(BaseCache): Asynchronously store a value in the semantic cache. Args: - key: The cache key (not directly used in semantic caching) + key: The cache key used to isolate semantic cache entries value: The response value to cache **kwargs: Additional arguments including 'messages' for the prompt and optional 'ttl' for time-to-live @@ -341,21 +440,20 @@ class RedisSemanticCache(BaseCache): # Generate embedding for the value (response) to cache prompt_embedding = await self._get_async_embedding(prompt, **kwargs) + store_kwargs: Dict[str, Any] = { + "vector": prompt_embedding, + "filters": self._get_cache_filters(key), + } + # Get TTL and store in Redis semantic cache ttl = self._get_ttl(**kwargs) if ttl is not None: - await self.llmcache.astore( - prompt, - value_str, - vector=prompt_embedding, # Pass through custom embedding - ttl=ttl, - ) - else: - await self.llmcache.astore( - prompt, - value_str, - vector=prompt_embedding, # Pass through custom embedding - ) + store_kwargs["ttl"] = ttl + await self.llmcache.astore( + prompt, + value_str, + **store_kwargs, + ) except Exception as e: print_verbose(f"Error in async_set_cache: {str(e)}") @@ -364,7 +462,7 @@ class RedisSemanticCache(BaseCache): Asynchronously retrieve a semantically similar cached response. Args: - key: The cache key (not directly used in semantic caching) + key: The cache key used to isolate semantic cache entries **kwargs: Additional arguments including 'messages' for the prompt Returns: @@ -385,17 +483,25 @@ class RedisSemanticCache(BaseCache): # Generate embedding for the prompt prompt_embedding = await self._get_async_embedding(prompt, **kwargs) - # Check the cache for semantically similar prompts - results = await self.llmcache.acheck(prompt=prompt, vector=prompt_embedding) + # Check the cache for semantically similar prompts in this exact + # LiteLLM cache-key scope. + check_kwargs: Dict[str, Any] = { + "prompt": prompt, + "vector": prompt_embedding, + "filter_expression": self._get_cache_key_filter_expression(key), + } + results = await self.llmcache.acheck(**check_kwargs) # handle results / cache hit if not results: - kwargs.setdefault("metadata", {})[ - "semantic-similarity" - ] = 0.0 # TODO why here but not above?? + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 return None cache_hit = results[0] + if not self._cache_hit_matches_key(cache_hit=cache_hit, key=key): + print_verbose("Redis semantic-cache hit did not match cache key scope") + kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0 + return None vector_distance = float(cache_hit["vector_distance"]) # Convert vector distance back to similarity diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index da3b9184edb..32423f23314 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -119,6 +119,20 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): def __init__(self): pass + def _normalize_tool_choice_for_responses_api(self, tool_choice: Any) -> Any: + """Chat tool_choice uses function.name; Responses API expects top-level name.""" + if not isinstance(tool_choice, dict) or tool_choice.get("type") != "function": + return tool_choice + if isinstance(tool_choice.get("name"), str) and tool_choice.get("name"): + # Return only Responses shape so stray chat ``function`` key is not sent upstream. + return {"type": "function", "name": tool_choice["name"]} + fn = tool_choice.get("function") + if isinstance(fn, dict): + fn_name = fn.get("name") + if isinstance(fn_name, str) and fn_name: + return {"type": "function", "name": fn_name} + return tool_choice + def _handle_raw_dict_response_item( self, item: Dict[str, Any], index: int ) -> Tuple[Optional[Any], int]: @@ -309,6 +323,10 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): text_format = self._transform_response_format_to_text_format(value) if text_format: responses_api_request["text"] = text_format # type: ignore + elif key == "tool_choice": + responses_api_request["tool_choice"] = ( # type: ignore[assignment] + self._normalize_tool_choice_for_responses_api(value) + ) elif key in ResponsesAPIOptionalRequestParams.__annotations__.keys(): responses_api_request[key] = value # type: ignore elif key == "previous_response_id": diff --git a/litellm/constants.py b/litellm/constants.py index fc9f5730cdf..e36746326cc 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -161,6 +161,11 @@ MCP_STDIO_ALLOWED_COMMANDS: frozenset = frozenset( | (set(_MCP_STDIO_EXTRA_COMMANDS.split(",")) - {""}) ) +# MCP OAuth2 Token Exchange (OBO) Defaults +MCP_TOKEN_EXCHANGE_CACHE_MAX_SIZE = int( + os.getenv("MCP_TOKEN_EXCHANGE_CACHE_MAX_SIZE", "500") +) + LITELLM_UI_ALLOW_HEADERS = [ "x-litellm-semantic-filter", "x-litellm-semantic-filter-tools", @@ -202,6 +207,12 @@ DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET = int( DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET", 4096) ) +DEFAULT_REASONING_EFFORT_XHIGH_THINKING_BUDGET = int( + os.getenv("DEFAULT_REASONING_EFFORT_XHIGH_THINKING_BUDGET", 8192) +) +DEFAULT_REASONING_EFFORT_MAX_THINKING_BUDGET = int( + os.getenv("DEFAULT_REASONING_EFFORT_MAX_THINKING_BUDGET", 16384) +) MAX_TOKEN_TRIMMING_ATTEMPTS = int( os.getenv("MAX_TOKEN_TRIMMING_ATTEMPTS", 10) ) # Maximum number of attempts to trim the message @@ -224,6 +235,16 @@ AIOHTTP_CONNECTOR_LIMIT_PER_HOST = int( ) AIOHTTP_KEEPALIVE_TIMEOUT = int(os.getenv("AIOHTTP_KEEPALIVE_TIMEOUT", 120)) AIOHTTP_TTL_DNS_CACHE = int(os.getenv("AIOHTTP_TTL_DNS_CACHE", 300)) +# TCP keep-alive (SO_KEEPALIVE) — opt-in. Required when running behind NAT/LBs +# whose idle timeout is shorter than provider response timeouts (e.g. AWS NAT +# Gateway: 350s vs OpenAI/Azure: 600s). Without this, the kernel sends nothing +# during a long provider call and the NAT reaps the flow before the response +# arrives. Enabling SO_KEEPALIVE makes the kernel emit TCP probes that reset +# the NAT idle timer. +AIOHTTP_SO_KEEPALIVE = os.getenv("AIOHTTP_SO_KEEPALIVE", "False").lower() == "true" +AIOHTTP_TCP_KEEPIDLE = int(os.getenv("AIOHTTP_TCP_KEEPIDLE", 60)) +AIOHTTP_TCP_KEEPINTVL = int(os.getenv("AIOHTTP_TCP_KEEPINTVL", 30)) +AIOHTTP_TCP_KEEPCNT = int(os.getenv("AIOHTTP_TCP_KEEPCNT", 5)) # enable_cleanup_closed is only needed for Python versions with the SSL leak bug # Fixed in Python 3.12.7+ and 3.13.1+ (see https://github.com/python/cpython/pull/118960) # Reference: https://github.com/aio-libs/aiohttp/blob/master/aiohttp/connector.py#L74-L78 @@ -389,6 +410,8 @@ BEDROCK_MAX_POLICY_SIZE = int(os.getenv("BEDROCK_MAX_POLICY_SIZE", 75)) BEDROCK_MIN_THINKING_BUDGET_TOKENS = int( os.getenv("BEDROCK_MIN_THINKING_BUDGET_TOKENS", 1024) ) +# Anthropic's Messages API rejects thinking.budget_tokens < 1024. +ANTHROPIC_MIN_THINKING_BUDGET_TOKENS = 1024 REPLICATE_POLLING_DELAY_SECONDS = float( os.getenv("REPLICATE_POLLING_DELAY_SECONDS", 0.5) ) @@ -409,9 +432,6 @@ CACHED_STREAMING_CHUNK_DELAY = float(os.getenv("CACHED_STREAMING_CHUNK_DELAY", 0 AUDIO_SPEECH_CHUNK_SIZE = int( os.getenv("AUDIO_SPEECH_CHUNK_SIZE", 8192) ) # chunk_size for audio speech streaming. Balance between latency and memory usage -MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( - os.getenv("MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB", 512) -) DEFAULT_MAX_TOKENS_FOR_TRITON = int(os.getenv("DEFAULT_MAX_TOKENS_FOR_TRITON", 2000)) #### Networking settings #### # Sentinel used when `REQUEST_TIMEOUT` is unset: `litellm.request_timeout` keeps this @@ -1383,6 +1403,10 @@ except (ValueError, TypeError): LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME = "litellm-internal-health-check" LITTELM_CLI_SERVICE_ACCOUNT_NAME = "litellm-cli" LITELLM_INTERNAL_JOBS_SERVICE_ACCOUNT_NAME = "litellm_internal_jobs" +# Stable identifier substituted in place of the master key on UserAPIKeyAuth +# objects so the master key (or its hash) never propagates to spend logs, +# Prometheus metrics, audit trails, or any other downstream consumer. +LITELLM_PROXY_MASTER_KEY_ALIAS = "litellm_proxy_master_key" # Key Rotation Constants LITELLM_KEY_ROTATION_ENABLED = os.getenv("LITELLM_KEY_ROTATION_ENABLED", "false") @@ -1411,6 +1435,7 @@ LITELLM_PROXY_ADMIN_NAME = "default_user_id" LITELLM_CLI_SOURCE_IDENTIFIER = "litellm-cli" LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" CLI_SSO_SESSION_CACHE_KEY_PREFIX = "cli_sso_session" +CLI_SSO_SESSION_TTL_SECONDS = 600 CLI_JWT_TOKEN_NAME = "cli-jwt-token" # Support both CLI_JWT_EXPIRATION_HOURS and LITELLM_CLI_JWT_EXPIRATION_HOURS for backwards compatibility CLI_JWT_EXPIRATION_HOURS = int( @@ -1437,6 +1462,12 @@ KEY_ROTATION_JOB_NAME = "litellm_key_rotation_job" EXPIRED_UI_SESSION_KEY_CLEANUP_JOB_NAME = "litellm_expired_ui_session_key_cleanup_job" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) +SPEND_LOG_CLEANUP_MAX_CONSECUTIVE_BATCH_FAILURES = int( + os.getenv("SPEND_LOG_CLEANUP_MAX_CONSECUTIVE_BATCH_FAILURES", 3) +) +SPEND_LOG_CLEANUP_BATCH_FAILURE_BACKOFF_SECONDS = float( + os.getenv("SPEND_LOG_CLEANUP_BATCH_FAILURE_BACKOFF_SECONDS", 0.5) +) SPEND_LOG_QUEUE_SIZE_THRESHOLD = int(os.getenv("SPEND_LOG_QUEUE_SIZE_THRESHOLD", 100)) SPEND_LOG_QUEUE_POLL_INTERVAL = float(os.getenv("SPEND_LOG_QUEUE_POLL_INTERVAL", 2.0)) SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE = int( @@ -1538,6 +1569,15 @@ DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL = int( os.getenv("DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL", 60) ) DEFAULT_ACCESS_GROUP_CACHE_TTL = int(os.getenv("DEFAULT_ACCESS_GROUP_CACHE_TTL", 600)) +# Short TTL for negative MCP access-group existence lookups. Keeps unauthenticated +# callers from forcing a DB query per request for unknown names, while bounding +# staleness so a transient DB error (which surfaces as an empty list) cannot +# hide a real group for long. +DEFAULT_MCP_ACCESS_GROUP_NEGATIVE_CACHE_TTL = 10 +# Maximum number of comma-separated MCP server / access-group tokens accepted +# in a single ``/{name1,name2,...}/mcp`` URL. Bounds the per-request DB / cache +# fan-out an authenticated caller can trigger by stuffing the path with tokens. +DEFAULT_MCP_NAMESPACE_CSV_MAX_TOKENS = 16 # Sentry Scrubbing Configuration SENTRY_DENYLIST = [ diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 8a68d74be5b..9b4dd80265c 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -513,7 +513,10 @@ def cost_per_token( # noqa: PLR0915 return fireworks_ai_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "azure": return azure_openai_cost_per_token( - model=model, usage=usage_block, response_time_ms=response_time_ms + model=model, + usage=usage_block, + response_time_ms=response_time_ms, + service_tier=service_tier, ) elif custom_llm_provider == "gemini": return gemini_cost_per_token( @@ -539,6 +542,7 @@ def cost_per_token( # noqa: PLR0915 usage=usage_block, response_time_ms=response_time_ms, request_model=request_model, + service_tier=service_tier, ) else: model_info = _cached_get_model_info_helper( diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index e703a3956b9..0dc56b6a3bc 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -366,6 +366,8 @@ class MCPClient: headers["Authorization"] = f"Bearer {self._mcp_auth_value}" elif self.auth_type == MCPAuth.token: headers["Authorization"] = f"token {self._mcp_auth_value}" + elif self.auth_type == MCPAuth.oauth2_token_exchange: + headers["Authorization"] = f"Bearer {self._mcp_auth_value}" elif isinstance(self._mcp_auth_value, dict): headers.update(self._mcp_auth_value) # Note: aws_sigv4 auth is not handled here — SigV4 requires per-request diff --git a/litellm/files/main.py b/litellm/files/main.py index ceccba8d800..669d50dde41 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -10,6 +10,7 @@ import contextvars import time import uuid as uuid_module from functools import partial +from types import MappingProxyType from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast import httpx @@ -85,6 +86,16 @@ bedrock_files_instance = BedrockFilesHandler() ################################################# +def _add_trusted_model_credentials_to_litellm_params( + litellm_params_dict: Dict[str, Any], kwargs: Dict[str, Any] +) -> None: + trusted_model_credentials = kwargs.get("_litellm_internal_model_credentials") + if isinstance(trusted_model_credentials, type(MappingProxyType({}))): + litellm_params_dict["_litellm_internal_model_credentials"] = ( + trusted_model_credentials + ) + + @client async def acreate_file( file: FileTypes, @@ -373,6 +384,10 @@ def file_retrieve( ) if provider_config is not None: litellm_params_dict = get_litellm_params(**kwargs) + _add_trusted_model_credentials_to_litellm_params( + litellm_params_dict=litellm_params_dict, + kwargs=kwargs, + ) litellm_params_dict["api_key"] = optional_params.api_key litellm_params_dict["api_base"] = optional_params.api_base @@ -497,6 +512,10 @@ def file_delete( pass optional_params = GenericLiteLLMParams(**kwargs) litellm_params_dict = get_litellm_params(**kwargs) + _add_trusted_model_credentials_to_litellm_params( + litellm_params_dict=litellm_params_dict, + kwargs=kwargs, + ) ### TIMEOUT LOGIC ### timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 # set timeout for 10 minutes by default @@ -846,6 +865,10 @@ def file_content( try: optional_params = GenericLiteLLMParams(**kwargs) litellm_params_dict = get_litellm_params(**kwargs) + _add_trusted_model_credentials_to_litellm_params( + litellm_params_dict=litellm_params_dict, + kwargs=kwargs, + ) ### TIMEOUT LOGIC ### timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 client = kwargs.get("client") @@ -993,6 +1016,7 @@ def file_content( vertex_location=vertex_ai_location, timeout=timeout, max_retries=optional_params.max_retries, + litellm_params=litellm_params_dict, ) elif custom_llm_provider == "bedrock": response = bedrock_files_instance.file_content( diff --git a/litellm/google_genai/streaming_iterator.py b/litellm/google_genai/streaming_iterator.py index 8cb2ee09370..3e97b480779 100644 --- a/litellm/google_genai/streaming_iterator.py +++ b/litellm/google_genai/streaming_iterator.py @@ -1,6 +1,6 @@ import asyncio from datetime import datetime -from typing import TYPE_CHECKING, Any, List, Optional +from typing import TYPE_CHECKING, Any, Dict, List, Optional from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.proxy.pass_through_endpoints.success_handler import ( @@ -29,12 +29,14 @@ class BaseGoogleGenAIGenerateContentStreamingIterator: litellm_logging_obj: LiteLLMLoggingObj, request_body: dict, model: str, + hidden_params: Optional[Dict[str, Any]] = None, ): self.litellm_logging_obj = litellm_logging_obj self.request_body = request_body self.start_time = datetime.now() self.collected_chunks: List[bytes] = [] self.model = model + self._hidden_params: Dict[str, Any] = hidden_params or {} async def _handle_async_streaming_logging( self, @@ -76,11 +78,13 @@ class GoogleGenAIGenerateContentStreamingIterator( litellm_metadata: dict, custom_llm_provider: str, request_body: Optional[dict] = None, + hidden_params: Optional[Dict[str, Any]] = None, ): super().__init__( litellm_logging_obj=logging_obj, request_body=request_body or {}, model=model, + hidden_params=hidden_params, ) self.response = response self.model = model @@ -130,11 +134,13 @@ class AsyncGoogleGenAIGenerateContentStreamingIterator( litellm_metadata: dict, custom_llm_provider: str, request_body: Optional[dict] = None, + hidden_params: Optional[Dict[str, Any]] = None, ): super().__init__( litellm_logging_obj=logging_obj, request_body=request_body or {}, model=model, + hidden_params=hidden_params, ) self.response = response self.model = model diff --git a/litellm/integrations/arize/_utils.py b/litellm/integrations/arize/_utils.py index 8dfaa8b1425..a1bf65141c9 100644 --- a/litellm/integrations/arize/_utils.py +++ b/litellm/integrations/arize/_utils.py @@ -220,23 +220,57 @@ def _set_structured_outputs(span: "Span", response_obj, msg_attrs, span_attrs): safe_set_attribute(span, f"{prefix}.{msg_attrs.MESSAGE_ROLE}", message_role) +def _safe_get(obj, key, default=None): + """Read ``key`` from a dict-like or Pydantic-model-like object. + + The arize/langfuse_otel logger receives ``usage`` objects from many sources: + plain dicts, litellm ``Usage`` (which exposes ``.get``), and raw OpenAI + Pydantic models (e.g. ``openai.types.completion_usage.CompletionUsage`` and + nested ``CompletionTokensDetails`` / ``OutputTokensDetails``) which do NOT + expose ``.get``. Calling ``.get`` on the latter raised ``AttributeError`` — + see https://github.com/BerriAI/litellm/issues/13672. + """ + if obj is None: + return default + getter = getattr(obj, "get", None) + if callable(getter): + try: + return getter(key, default) + except TypeError: + # Some objects expose `.get` with a different signature + pass + return getattr(obj, key, default) + + def _set_usage_outputs(span: "Span", response_obj, span_attrs): usage = response_obj and response_obj.get("usage") if not usage: return safe_set_attribute( - span, span_attrs.LLM_TOKEN_COUNT_TOTAL, usage.get("total_tokens") + span, span_attrs.LLM_TOKEN_COUNT_TOTAL, _safe_get(usage, "total_tokens") + ) + completion_tokens = _safe_get(usage, "completion_tokens") or _safe_get( + usage, "output_tokens" ) - completion_tokens = usage.get("completion_tokens") or usage.get("output_tokens") if completion_tokens: safe_set_attribute( span, span_attrs.LLM_TOKEN_COUNT_COMPLETION, completion_tokens ) - prompt_tokens = usage.get("prompt_tokens") or usage.get("input_tokens") + prompt_tokens = _safe_get(usage, "prompt_tokens") or _safe_get( + usage, "input_tokens" + ) if prompt_tokens: safe_set_attribute(span, span_attrs.LLM_TOKEN_COUNT_PROMPT, prompt_tokens) - reasoning_tokens = usage.get("output_tokens_details", {}).get("reasoning_tokens") + + # Reasoning tokens live in `completion_tokens_details` for Chat Completions + # API (Usage) and in `output_tokens_details` for Responses API + # (ResponseAPIUsage). Both nested objects may be plain Pydantic models + # without `.get`. + token_details = _safe_get(usage, "completion_tokens_details") or _safe_get( + usage, "output_tokens_details" + ) + reasoning_tokens = _safe_get(token_details, "reasoning_tokens") if reasoning_tokens: safe_set_attribute( span, diff --git a/litellm/integrations/arize/arize_phoenix_client.py b/litellm/integrations/arize/arize_phoenix_client.py index 3c83517bb55..8c3c2a5ff0f 100644 --- a/litellm/integrations/arize/arize_phoenix_client.py +++ b/litellm/integrations/arize/arize_phoenix_client.py @@ -2,11 +2,23 @@ Arize Phoenix API client for fetching prompt versions from Arize Phoenix. """ +import urllib.parse from typing import Any, Dict, Optional from litellm.llms.custom_httpx.http_handler import HTTPHandler +def _sanitize_id(identifier: str) -> str: + """Reject path traversal characters and URL-encode the identifier.""" + if any(c in identifier for c in ("/", "\\", "#", "?")): + raise ValueError( + f"Invalid identifier {identifier!r}: contains disallowed characters" + ) + if ".." in identifier: + raise ValueError(f"Invalid identifier {identifier!r}: path traversal detected") + return urllib.parse.quote(identifier, safe="") + + class ArizePhoenixClient: """ Client for interacting with Arize Phoenix API to fetch prompt versions. @@ -53,7 +65,8 @@ class ArizePhoenixClient: Returns: Dictionary containing prompt version data, or None if not found """ - url = f"{self.api_base}/v1/prompt_versions/{prompt_version_id}" + safe_id = _sanitize_id(prompt_version_id) + url = f"{self.api_base}/v1/prompt_versions/{safe_id}" try: # Use the underlying httpx client directly to avoid query param extraction diff --git a/litellm/integrations/arize/arize_phoenix_prompt_manager.py b/litellm/integrations/arize/arize_phoenix_prompt_manager.py index 19af0bb9552..df56d7bd391 100644 --- a/litellm/integrations/arize/arize_phoenix_prompt_manager.py +++ b/litellm/integrations/arize/arize_phoenix_prompt_manager.py @@ -5,7 +5,8 @@ Fetches prompt versions from Arize Phoenix and provides workspace-based access c from typing import Any, Dict, List, Optional, Tuple, Union -from jinja2 import DictLoader, Environment, select_autoescape +from jinja2 import DictLoader, select_autoescape +from jinja2.sandbox import ImmutableSandboxedEnvironment from litellm.integrations.custom_prompt_management import CustomPromptManagement from litellm.integrations.prompt_management_base import ( @@ -74,7 +75,13 @@ class ArizePhoenixTemplateManager: api_key=self.api_key, api_base=self.api_base ) - self.jinja_env = Environment( + # Templates fetched from Arize Phoenix come from external workspace + # users; in a plain `Environment()` a malicious template could reach + # `__class__.__init__.__globals__` and execute arbitrary code on the + # proxy host. The sandbox blocks that attribute traversal while + # leaving normal `{{ var }}` substitution intact. Matches the + # dotprompt manager's hardening. + self.jinja_env = ImmutableSandboxedEnvironment( loader=DictLoader({}), autoescape=select_autoescape(["html", "xml"]), # Use Mustache/Handlebars-style delimiters diff --git a/litellm/integrations/azure_sentinel/azure_sentinel.py b/litellm/integrations/azure_sentinel/azure_sentinel.py index dd508e6c6c2..0cfd49cda37 100644 --- a/litellm/integrations/azure_sentinel/azure_sentinel.py +++ b/litellm/integrations/azure_sentinel/azure_sentinel.py @@ -14,16 +14,18 @@ For batching specific details see CustomBatchLogger class import asyncio import os +import time import traceback -from typing import List, Optional +from typing import List, Optional, Union from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) -from litellm.types.utils import StandardLoggingPayload +from litellm.types.utils import StandardAuditLogPayload, StandardLoggingPayload class AzureSentinelLogger(CustomBatchLogger): @@ -39,6 +41,7 @@ class AzureSentinelLogger(CustomBatchLogger): tenant_id: Optional[str] = None, client_id: Optional[str] = None, client_secret: Optional[str] = None, + audit_stream_name: Optional[str] = None, **kwargs, ): """ @@ -57,57 +60,77 @@ class AzureSentinelLogger(CustomBatchLogger): If not provided, will use AZURE_SENTINEL_CLIENT_ID or AZURE_CLIENT_ID env var. client_secret (str, optional): Azure Client Secret for OAuth2 authentication. If not provided, will use AZURE_SENTINEL_CLIENT_SECRET or AZURE_CLIENT_SECRET env var. + audit_stream_name (str, optional): Stream name from DCR for audit logs. + If not provided, audit logs use the standard stream name. """ self.async_httpx_client = get_async_httpx_client( llm_provider=httpxSpecialProvider.LoggingCallback ) - self.dcr_immutable_id = dcr_immutable_id or os.getenv( + resolved_dcr_immutable_id = dcr_immutable_id or os.getenv( "AZURE_SENTINEL_DCR_IMMUTABLE_ID" ) - self.stream_name = stream_name or os.getenv( - "AZURE_SENTINEL_STREAM_NAME", "Custom-LiteLLM" + resolved_stream_name = ( + stream_name or os.getenv("AZURE_SENTINEL_STREAM_NAME") or "Custom-LiteLLM" ) - self.endpoint = endpoint or os.getenv("AZURE_SENTINEL_ENDPOINT") - self.tenant_id = ( + resolved_audit_stream_name = audit_stream_name or resolved_stream_name + resolved_endpoint = endpoint or os.getenv("AZURE_SENTINEL_ENDPOINT") + resolved_tenant_id = ( tenant_id or os.getenv("AZURE_SENTINEL_TENANT_ID") or os.getenv("AZURE_TENANT_ID") ) - self.client_id = ( + resolved_client_id = ( client_id or os.getenv("AZURE_SENTINEL_CLIENT_ID") or os.getenv("AZURE_CLIENT_ID") ) - self.client_secret = ( + resolved_client_secret = ( client_secret or os.getenv("AZURE_SENTINEL_CLIENT_SECRET") or os.getenv("AZURE_CLIENT_SECRET") ) - if not self.dcr_immutable_id: + if not resolved_dcr_immutable_id: raise ValueError( "AZURE_SENTINEL_DCR_IMMUTABLE_ID is required. Set it as an environment variable or pass dcr_immutable_id parameter." ) - if not self.endpoint: + if not resolved_endpoint: raise ValueError( "AZURE_SENTINEL_ENDPOINT is required. Set it as an environment variable or pass endpoint parameter." ) - if not self.tenant_id: + if not resolved_tenant_id: raise ValueError( "AZURE_SENTINEL_TENANT_ID or AZURE_TENANT_ID is required. Set it as an environment variable or pass tenant_id parameter." ) - if not self.client_id: + if not resolved_client_id: raise ValueError( "AZURE_SENTINEL_CLIENT_ID or AZURE_CLIENT_ID is required. Set it as an environment variable or pass client_id parameter." ) - if not self.client_secret: + if not resolved_client_secret: raise ValueError( "AZURE_SENTINEL_CLIENT_SECRET or AZURE_CLIENT_SECRET is required. Set it as an environment variable or pass client_secret parameter." ) + self.dcr_immutable_id = resolved_dcr_immutable_id + self.stream_name = resolved_stream_name + self.audit_stream_name = resolved_audit_stream_name + self.endpoint = resolved_endpoint + self.tenant_id = resolved_tenant_id + self.client_id = resolved_client_id + self.client_secret = resolved_client_secret + # Build API endpoint: {Endpoint}/dataCollectionRules/{DCR Immutable ID}/streams/{Stream Name}?api-version=2023-01-01 - self.api_endpoint = f"{self.endpoint.rstrip('/')}/dataCollectionRules/{self.dcr_immutable_id}/streams/{self.stream_name}?api-version=2023-01-01" + self.api_endpoint = self._build_api_endpoint( + endpoint=resolved_endpoint, + dcr_immutable_id=resolved_dcr_immutable_id, + stream_name=resolved_stream_name, + ) + self.audit_api_endpoint = self._build_api_endpoint( + endpoint=resolved_endpoint, + dcr_immutable_id=resolved_dcr_immutable_id, + stream_name=resolved_audit_stream_name, + ) # OAuth2 scope for Azure Monitor self.oauth_scope = "https://monitor.azure.com/.default" @@ -118,6 +141,13 @@ class AzureSentinelLogger(CustomBatchLogger): super().__init__(**kwargs, flush_lock=self.flush_lock) asyncio.create_task(self.periodic_flush()) self.log_queue: List[StandardLoggingPayload] = [] + self.audit_log_queue: List[StandardAuditLogPayload] = [] + + @staticmethod + def _build_api_endpoint( + endpoint: str, dcr_immutable_id: str, stream_name: str + ) -> str: + return f"{endpoint.rstrip('/')}/dataCollectionRules/{dcr_immutable_id}/streams/{stream_name}?api-version=2023-01-01" async def _get_oauth_token(self) -> str: """ @@ -126,9 +156,6 @@ class AzureSentinelLogger(CustomBatchLogger): Returns: Bearer token string """ - # Check if we have a valid cached token - import time - if ( self.oauth_token and self.oauth_token_expires_at @@ -170,9 +197,6 @@ class AzureSentinelLogger(CustomBatchLogger): if not self.oauth_token: raise Exception("OAuth2 token response did not contain access_token") - # Cache token expiry time - import time - self.oauth_token_expires_at = time.time() + expires_in return self.oauth_token @@ -246,6 +270,34 @@ class AzureSentinelLogger(CustomBatchLogger): ) pass + async def async_log_audit_log_event( + self, audit_log: StandardAuditLogPayload + ) -> None: + """ + Async log LiteLLM audit log events to Azure Sentinel. + + Audit logs are queued separately from standard LLM logs so mixed callback + usage never sends schema-mismatched records in the same ingestion batch. + """ + try: + verbose_logger.debug( + "Azure Sentinel: Logging audit event id=%s action=%s table=%s", + audit_log.get("id"), + audit_log.get("action"), + audit_log.get("table_name"), + ) + + self.audit_log_queue.append(audit_log) + + if len(self.audit_log_queue) >= self.batch_size: + await self.async_send_audit_batch() + + except Exception as e: + verbose_logger.exception( + f"Azure Sentinel Audit Log Layer Error - {str(e)}\n{traceback.format_exc()}" + ) + pass + async def async_send_batch(self): """ Sends the batch of logs to Azure Monitor Logs Ingestion API @@ -253,22 +305,42 @@ class AzureSentinelLogger(CustomBatchLogger): Raises: Raises a NON Blocking verbose_logger.exception if an error occurs """ + await self._async_send_batch_to_api( + log_queue=self.log_queue, + api_endpoint=self.api_endpoint, + log_type="logs", + ) + + async def async_send_audit_batch(self): + """ + Sends the batch of audit logs to Azure Monitor Logs Ingestion API + """ + await self._async_send_batch_to_api( + log_queue=self.audit_log_queue, + api_endpoint=self.audit_api_endpoint, + log_type="audit logs", + ) + + async def _async_send_batch_to_api( + self, + log_queue: List[Union[StandardLoggingPayload, StandardAuditLogPayload]], + api_endpoint: str, + log_type: str, + ) -> None: try: - if not self.log_queue: + if not log_queue: return verbose_logger.debug( - "Azure Sentinel - about to flush %s events", len(self.log_queue) + "Azure Sentinel - about to flush %s %s", len(log_queue), log_type ) - from litellm.litellm_core_utils.safe_json_dumps import safe_dumps - # Get OAuth2 token bearer_token = await self._get_oauth_token() # Convert log queue to JSON array format expected by Logs Ingestion API # Each log entry should be a JSON object in the array - body = safe_dumps(self.log_queue) + body = safe_dumps(log_queue) # Set headers for Logs Ingestion API headers = { @@ -278,7 +350,7 @@ class AzureSentinelLogger(CustomBatchLogger): # Send the request response = await self.async_httpx_client.post( - url=self.api_endpoint, data=body.encode("utf-8"), headers=headers + url=api_endpoint, data=body.encode("utf-8"), headers=headers ) if response.status_code not in [200, 204]: @@ -301,4 +373,15 @@ class AzureSentinelLogger(CustomBatchLogger): f"Azure Sentinel Error sending batch API - {str(e)}\n{traceback.format_exc()}" ) finally: - self.log_queue.clear() + log_queue.clear() + + async def flush_queue(self): + if self.flush_lock is None: + return + + async with self.flush_lock: + if self.log_queue: + await self.async_send_batch() + if self.audit_log_queue: + await self.async_send_audit_batch() + self.last_flush_time = time.time() diff --git a/litellm/integrations/bitbucket/bitbucket_client.py b/litellm/integrations/bitbucket/bitbucket_client.py index 0502422cf8b..e742cc14b7d 100644 --- a/litellm/integrations/bitbucket/bitbucket_client.py +++ b/litellm/integrations/bitbucket/bitbucket_client.py @@ -3,11 +3,27 @@ BitBucket API client for fetching .prompt files from BitBucket repositories. """ import base64 +import urllib.parse from typing import Any, Dict, List, Optional from litellm.llms.custom_httpx.http_handler import HTTPHandler +def _sanitize_file_path(file_path: str) -> str: + """Reject path traversal and URL-encode each path segment.""" + if "#" in file_path or "?" in file_path: + raise ValueError( + f"Invalid file path {file_path!r}: contains URL special characters" + ) + parts = file_path.split("/") + for part in parts: + if part == "..": + raise ValueError( + f"Invalid file path {file_path!r}: path traversal detected" + ) + return "/".join(urllib.parse.quote(part, safe="") for part in parts) + + class BitBucketClient: """ Client for interacting with BitBucket API to fetch .prompt files. @@ -72,7 +88,8 @@ class BitBucketClient: Returns: File content as string, or None if file not found """ - url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{file_path}" + safe_path = _sanitize_file_path(file_path) + url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{safe_path}" try: response = self.http_handler.get(url, headers=self.headers) @@ -119,7 +136,8 @@ class BitBucketClient: Returns: List of file paths """ - url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{directory_path}" + safe_dir = _sanitize_file_path(directory_path) if directory_path else "" + url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{safe_dir}" try: response = self.http_handler.get(url, headers=self.headers) @@ -211,7 +229,8 @@ class BitBucketClient: Returns: Dictionary containing file metadata, or None if file not found """ - url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{file_path}" + safe_path = _sanitize_file_path(file_path) + url = f"{self.base_url}/repositories/{self.workspace}/{self.repository}/src/{self.branch}/{safe_path}" try: # Use GET with Range header to get just the headers (HEAD equivalent) diff --git a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py index 701f2273640..844fa9f38cb 100644 --- a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py +++ b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py @@ -5,7 +5,8 @@ Fetches .prompt files from BitBucket repositories and provides team-based access from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union -from jinja2 import DictLoader, Environment, select_autoescape +from jinja2 import DictLoader, select_autoescape +from jinja2.sandbox import ImmutableSandboxedEnvironment from litellm.integrations.custom_prompt_management import CustomPromptManagement @@ -74,7 +75,13 @@ class BitBucketTemplateManager: self.prompts: Dict[str, BitBucketPromptTemplate] = {} self.bitbucket_client = BitBucketClient(bitbucket_config) - self.jinja_env = Environment( + # Templates fetched from a BitBucket repo are not trustworthy: + # anyone with repo write access can ship Jinja syntax that, in a + # plain `Environment()`, would reach `__class__.__init__.__globals__` + # and pivot into RCE on the proxy host. The sandbox blocks that + # attribute traversal while leaving normal `{{ var }}` substitution + # intact. Matches the dotprompt manager's hardening. + self.jinja_env = ImmutableSandboxedEnvironment( loader=DictLoader({}), autoescape=select_autoescape(["html", "xml"]), # Use Handlebars-style delimiters to match Dotprompt spec diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index a03aef481e7..c937ad0a7bf 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -888,6 +888,15 @@ def log_guardrail_information(func): - pre_call - during_call - post_call + + Some guardrails (e.g. ``block_code_execution``) call + ``add_standard_logging_guardrail_information_to_request_data`` directly + from inside the wrapped function so they can record a richer payload + (structured detections, tracing detail) than this decorator's + "allow"/"mask"/raw-response default. To avoid double-recording in that + case (which would emit two spans, two Datadog records, two spend-log + entries, etc.), snapshot the entry count before invocation: if the + wrapped function already appended its own entry, skip the auto-record. """ import functools import inspect @@ -907,6 +916,16 @@ def log_guardrail_information(func): return GuardrailEventHooks.post_call return None + def _count_recorded_guardrail_entries(request_data: dict) -> int: + total = 0 + for container_key in ("metadata", "litellm_metadata"): + container = request_data.get(container_key) + if isinstance(container, dict): + entries = container.get("standard_logging_guardrail_information") + if isinstance(entries, list): + total += len(entries) + return total + @functools.wraps(func) async def async_wrapper(*args, **kwargs): start_time = datetime.now() # Move start_time inside the wrapper @@ -919,8 +938,11 @@ def log_guardrail_information(func): if func.__name__ == "apply_guardrail" and "inputs" in kwargs: original_inputs = kwargs.get("inputs") + entries_before = _count_recorded_guardrail_entries(request_data) try: response = await func(*args, **kwargs) + if _count_recorded_guardrail_entries(request_data) > entries_before: + return response return self._process_response( response=response, request_data=request_data, @@ -931,6 +953,8 @@ def log_guardrail_information(func): original_inputs=original_inputs, ) except Exception as e: + if _count_recorded_guardrail_entries(request_data) > entries_before: + raise return self._process_error( e=e, request_data=request_data, @@ -952,8 +976,11 @@ def log_guardrail_information(func): if func.__name__ == "apply_guardrail" and "inputs" in kwargs: original_inputs = kwargs.get("inputs") + entries_before = _count_recorded_guardrail_entries(request_data) try: response = func(*args, **kwargs) + if _count_recorded_guardrail_entries(request_data) > entries_before: + return response return self._process_response( response=response, request_data=request_data, @@ -962,6 +989,8 @@ def log_guardrail_information(func): original_inputs=original_inputs, ) except Exception as e: + if _count_recorded_guardrail_entries(request_data) > entries_before: + raise return self._process_error( e=e, request_data=request_data, diff --git a/litellm/integrations/custom_sso_handler.py b/litellm/integrations/custom_sso_handler.py index 7f60decabc3..202e488e0e4 100644 --- a/litellm/integrations/custom_sso_handler.py +++ b/litellm/integrations/custom_sso_handler.py @@ -18,6 +18,17 @@ class CustomSSOLoginHandler(CustomLogger): self, request: Request, ) -> OpenID: + from litellm.proxy.auth.trusted_proxy_utils import ( + require_trusted_proxy_request, + ) + from litellm.proxy.proxy_server import general_settings + + require_trusted_proxy_request( + request=request, + general_settings=general_settings, + feature_name="Custom UI SSO", + ) + request_headers_dict = dict(request.headers) return OpenID( id=request_headers_dict.get("x-litellm-user-id"), diff --git a/litellm/integrations/gcs_bucket/gcs_bucket.py b/litellm/integrations/gcs_bucket/gcs_bucket.py index 65296bafcf3..90057984235 100644 --- a/litellm/integrations/gcs_bucket/gcs_bucket.py +++ b/litellm/integrations/gcs_bucket/gcs_bucket.py @@ -6,12 +6,14 @@ import time from litellm._uuid import uuid from datetime import datetime, timedelta, timezone from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple -from urllib.parse import quote from litellm._logging import verbose_logger from litellm.constants import LITELLM_ASYNCIO_QUEUE_MAXSIZE from litellm.integrations.additional_logging_utils import AdditionalLoggingUtils from litellm.integrations.gcs_bucket.gcs_bucket_base import GCSBucketBase +from litellm.litellm_core_utils.cloud_storage_security import ( + sanitize_cloud_object_component, +) from litellm.proxy._types import CommonProxyErrors from litellm.types.integrations.base_health_check import IntegrationHealthCheckStatus from litellm.types.integrations.gcs_bucket import * @@ -335,7 +337,11 @@ class GCSBucketLogger(GCSBucketBase, AdditionalLoggingUtils): _litellm_params = kwargs.get("litellm_params", None) or {} _metadata = _litellm_params.get("metadata", None) or {} if "gcs_log_id" in _metadata: - object_name = _metadata["gcs_log_id"] + safe_log_id = sanitize_cloud_object_component( + _metadata.get("gcs_log_id"), fallback="" + ) + if safe_log_id: + object_name = f"{current_date}/custom-{uuid.uuid4().hex}-{safe_log_id}" return object_name @@ -367,8 +373,7 @@ class GCSBucketLogger(GCSBucketBase, AdditionalLoggingUtils): request_date_str=date_str, response_id=request_id, ) - encoded_object_name = quote(object_name, safe="") - response = await self.download_gcs_object(encoded_object_name) + response = await self.download_gcs_object(object_name) if response is not None: loaded_response = json.loads(response) diff --git a/litellm/integrations/gcs_bucket/gcs_bucket_base.py b/litellm/integrations/gcs_bucket/gcs_bucket_base.py index e84b37e689b..1c5e30777a2 100644 --- a/litellm/integrations/gcs_bucket/gcs_bucket_base.py +++ b/litellm/integrations/gcs_bucket/gcs_bucket_base.py @@ -11,6 +11,10 @@ from litellm.integrations.gcs_bucket.gcs_bucket_mock_client import ( from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.litellm_core_utils.cloud_storage_security import ( + encode_gcs_object_name_for_url, + split_configured_cloud_bucket_name, +) from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, @@ -133,8 +137,8 @@ class GCSBucketBase(CustomBatchLogger): - Returns: bucket_name="my-bucket", object_name="my-folder/dev/my-object" """ - if "/" in bucket_name: - bucket_name, prefix = bucket_name.split("/", 1) + bucket_name, prefix = split_configured_cloud_bucket_name(bucket_name) + if prefix: object_name = f"{prefix}/{object_name}" return bucket_name, object_name return bucket_name, object_name @@ -248,6 +252,7 @@ class GCSBucketBase(CustomBatchLogger): bucket_name=bucket_name, object_name=object_name, ) + object_name = encode_gcs_object_name_for_url(object_name) url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{object_name}?alt=media" @@ -288,6 +293,7 @@ class GCSBucketBase(CustomBatchLogger): bucket_name=bucket_name, object_name=object_name, ) + object_name = encode_gcs_object_name_for_url(object_name) url = f"https://storage.googleapis.com/storage/v1/b/{bucket_name}/o/{object_name}" @@ -334,10 +340,11 @@ class GCSBucketBase(CustomBatchLogger): bucket_name=bucket_name, object_name=object_name, ) + encoded_object_name = encode_gcs_object_name_for_url(object_name) response = await self.async_httpx_client.post( headers=headers, - url=f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={object_name}", + url=f"https://storage.googleapis.com/upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={encoded_object_name}", data=json_logged_payload, ) diff --git a/litellm/integrations/gitlab/gitlab_prompt_manager.py b/litellm/integrations/gitlab/gitlab_prompt_manager.py index b073948d768..a468741aead 100644 --- a/litellm/integrations/gitlab/gitlab_prompt_manager.py +++ b/litellm/integrations/gitlab/gitlab_prompt_manager.py @@ -4,7 +4,8 @@ GitLab prompt manager with configurable prompts folder. from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union -from jinja2 import DictLoader, Environment, select_autoescape +from jinja2 import DictLoader, select_autoescape +from jinja2.sandbox import ImmutableSandboxedEnvironment from litellm.integrations.custom_prompt_management import CustomPromptManagement @@ -90,7 +91,13 @@ class GitLabTemplateManager: or "" ).strip("/") - self.jinja_env = Environment( + # Templates fetched from a GitLab repo are not trustworthy: + # anyone with repo write access can ship Jinja syntax that, in a + # plain `Environment()`, would reach `__class__.__init__.__globals__` + # and pivot into RCE on the proxy host. The sandbox blocks that + # attribute traversal while leaving normal `{{ var }}` substitution + # intact. Matches the dotprompt manager's hardening. + self.jinja_env = ImmutableSandboxedEnvironment( loader=DictLoader({}), autoescape=select_autoescape(["html", "xml"]), variable_start_string="{{", diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index e691c490c85..0efc7d66876 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -90,6 +90,29 @@ def _extract_cache_read_input_tokens(usage_obj) -> int: return cache_read_input_tokens +def resolve_langfuse_credentials( + langfuse_public_key=None, + langfuse_secret=None, + langfuse_secret_key=None, + langfuse_host=None, + allow_env_credentials: bool = True, +): + if allow_env_credentials is False and langfuse_host is not None: + secret_key = langfuse_secret or langfuse_secret_key + public_key = langfuse_public_key + else: + secret_key = ( + langfuse_secret or langfuse_secret_key or os.getenv("LANGFUSE_SECRET_KEY") + ) + public_key = langfuse_public_key or os.getenv("LANGFUSE_PUBLIC_KEY") + + resolved_host = langfuse_host or os.getenv( + "LANGFUSE_HOST", "https://cloud.langfuse.com" + ) + + return public_key, secret_key, resolved_host + + class LangFuseLogger: # Class variables or attributes def __init__( @@ -98,6 +121,7 @@ class LangFuseLogger: langfuse_secret=None, langfuse_host=None, flush_interval=1, + allow_env_credentials: bool = True, ): try: import langfuse @@ -106,11 +130,13 @@ class LangFuseLogger: raise Exception( f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\n{traceback.format_exc()}\033[0m" ) - # Instance variables - self.secret_key = langfuse_secret or os.getenv("LANGFUSE_SECRET_KEY") - self.public_key = langfuse_public_key or os.getenv("LANGFUSE_PUBLIC_KEY") - self.langfuse_host = langfuse_host or os.getenv( - "LANGFUSE_HOST", "https://cloud.langfuse.com" + self.public_key, self.secret_key, self.langfuse_host = ( + resolve_langfuse_credentials( + langfuse_public_key=langfuse_public_key, + langfuse_secret=langfuse_secret, + langfuse_host=langfuse_host, + allow_env_credentials=allow_env_credentials, + ) ) if not ( self.langfuse_host.startswith("http://") @@ -160,9 +186,10 @@ class LangFuseLogger: project_id = None if os.getenv("UPSTREAM_LANGFUSE_SECRET_KEY") is not None: + upstream_langfuse_debug_env = os.getenv("UPSTREAM_LANGFUSE_DEBUG") upstream_langfuse_debug = ( - str_to_bool(self.upstream_langfuse_debug) - if self.upstream_langfuse_debug is not None + str_to_bool(upstream_langfuse_debug_env) + if upstream_langfuse_debug_env is not None else None ) self.upstream_langfuse_secret_key = os.getenv( @@ -173,7 +200,7 @@ class LangFuseLogger: ) self.upstream_langfuse_host = os.getenv("UPSTREAM_LANGFUSE_HOST") self.upstream_langfuse_release = os.getenv("UPSTREAM_LANGFUSE_RELEASE") - self.upstream_langfuse_debug = os.getenv("UPSTREAM_LANGFUSE_DEBUG") + self.upstream_langfuse_debug = upstream_langfuse_debug_env self.upstream_langfuse = Langfuse( public_key=self.upstream_langfuse_public_key, secret_key=self.upstream_langfuse_secret_key, diff --git a/litellm/integrations/langfuse/langfuse_handler.py b/litellm/integrations/langfuse/langfuse_handler.py index fbadf1a2fc7..4a809726424 100644 --- a/litellm/integrations/langfuse/langfuse_handler.py +++ b/litellm/integrations/langfuse/langfuse_handler.py @@ -115,8 +115,10 @@ class LangFuseHandler: langfuse_logger = LangFuseLogger( langfuse_public_key=credentials.get("langfuse_public_key"), - langfuse_secret=credentials.get("langfuse_secret"), + langfuse_secret=credentials.get("langfuse_secret") + or credentials.get("langfuse_secret_key"), langfuse_host=credentials.get("langfuse_host"), + allow_env_credentials=credentials.get("langfuse_host") is None, ) in_memory_dynamic_logger_cache.set_cache( credentials=credentials, diff --git a/litellm/integrations/langfuse/langfuse_prompt_management.py b/litellm/integrations/langfuse/langfuse_prompt_management.py index 5f4ced3a5cb..b7a565512c6 100644 --- a/litellm/integrations/langfuse/langfuse_prompt_management.py +++ b/litellm/integrations/langfuse/langfuse_prompt_management.py @@ -20,7 +20,7 @@ from ...litellm_core_utils.specialty_caches.dynamic_logging_cache import ( DynamicLoggingCache, ) from ..prompt_management_base import PromptManagementBase -from .langfuse import LangFuseLogger +from .langfuse import LangFuseLogger, resolve_langfuse_credentials from .langfuse_handler import LangFuseHandler if TYPE_CHECKING: @@ -46,6 +46,7 @@ def langfuse_client_init( langfuse_secret_key=None, langfuse_host=None, flush_interval=1, + allow_env_credentials: bool = True, ) -> LangfuseClass: """ Initialize Langfuse client with caching to prevent multiple initializations. @@ -70,14 +71,12 @@ def langfuse_client_init( f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\n\033[0m" ) - # Instance variables - - secret_key = ( - langfuse_secret or langfuse_secret_key or os.getenv("LANGFUSE_SECRET_KEY") - ) - public_key = langfuse_public_key or os.getenv("LANGFUSE_PUBLIC_KEY") - langfuse_host = langfuse_host or os.getenv( - "LANGFUSE_HOST", "https://cloud.langfuse.com" + public_key, secret_key, langfuse_host = resolve_langfuse_credentials( + langfuse_public_key=langfuse_public_key, + langfuse_secret=langfuse_secret, + langfuse_secret_key=langfuse_secret_key, + langfuse_host=langfuse_host, + allow_env_credentials=allow_env_credentials, ) if not ( @@ -222,6 +221,7 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge langfuse_secret=dynamic_callback_params.get("langfuse_secret"), langfuse_secret_key=dynamic_callback_params.get("langfuse_secret_key"), langfuse_host=dynamic_callback_params.get("langfuse_host"), + allow_env_credentials=dynamic_callback_params.get("langfuse_host") is None, ) langfuse_prompt_client = self._get_prompt_from_id( langfuse_prompt_id=prompt_id, @@ -246,6 +246,7 @@ class LangfusePromptManagement(LangFuseLogger, PromptManagementBase, CustomLogge langfuse_secret=dynamic_callback_params.get("langfuse_secret"), langfuse_secret_key=dynamic_callback_params.get("langfuse_secret_key"), langfuse_host=dynamic_callback_params.get("langfuse_host"), + allow_env_credentials=dynamic_callback_params.get("langfuse_host") is None, ) langfuse_prompt_client = self._get_prompt_from_id( langfuse_prompt_id=prompt_id, diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 3d4fd39ebe1..81570e462c4 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -19,6 +19,7 @@ from litellm.integrations.langsmith_mock_client import ( create_mock_langsmith_client, should_use_langsmith_mock, ) +from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, @@ -112,17 +113,28 @@ class LangsmithLogger(CustomBatchLogger): langsmith_project: Optional[str] = None, langsmith_base_url: Optional[str] = None, langsmith_tenant_id: Optional[str] = None, + allow_env_credentials: bool = True, ) -> LangsmithCredentialsObject: - _credentials_api_key = langsmith_api_key or os.getenv("LANGSMITH_API_KEY") - _credentials_project = ( - langsmith_project or os.getenv("LANGSMITH_PROJECT") or "litellm-completion" - ) - _credentials_base_url = ( - langsmith_base_url - or os.getenv("LANGSMITH_BASE_URL") - or "https://api.smith.langchain.com" - ) - _credentials_tenant_id = langsmith_tenant_id or os.getenv("LANGSMITH_TENANT_ID") + if allow_env_credentials is False and langsmith_base_url is not None: + _credentials_api_key = langsmith_api_key + _credentials_project = langsmith_project or "litellm-completion" + _credentials_base_url = langsmith_base_url + _credentials_tenant_id = langsmith_tenant_id + else: + _credentials_api_key = langsmith_api_key or os.getenv("LANGSMITH_API_KEY") + _credentials_project = ( + langsmith_project + or os.getenv("LANGSMITH_PROJECT") + or "litellm-completion" + ) + _credentials_base_url = ( + langsmith_base_url + or os.getenv("LANGSMITH_BASE_URL") + or "https://api.smith.langchain.com" + ) + _credentials_tenant_id = langsmith_tenant_id or os.getenv( + "LANGSMITH_TENANT_ID" + ) return LangsmithCredentialsObject( LANGSMITH_API_KEY=_credentials_api_key, @@ -153,6 +165,15 @@ class LangsmithLogger(CustomBatchLogger): for key in ("session_id", "thread_id", "conversation_id"): if key in requester_metadata and key not in extra_metadata: extra_metadata[key] = requester_metadata[key] + + # helper is shallow; also scrub nested requester_metadata since + # LangSmith forwards the whole dict into `extra` + extra_metadata = redact_user_api_key_info(metadata=extra_metadata) + nested = extra_metadata.get("requester_metadata") + if isinstance(nested, dict): + extra_metadata["requester_metadata"] = redact_user_api_key_info( + metadata=nested + ) return extra_metadata def _build_outputs_with_usage( @@ -540,6 +561,10 @@ class LangsmithLogger(CustomBatchLogger): langsmith_tenant_id=standard_callback_dynamic_params.get( "langsmith_tenant_id", None ), + allow_env_credentials=standard_callback_dynamic_params.get( + "langsmith_base_url", None + ) + is None, ) else: credentials = self.default_credentials diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index b6d91d0b76d..48d7a07a569 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -57,6 +57,17 @@ LITELLM_PROXY_REQUEST_SPAN_NAME = "Received Proxy Server Request" RAW_REQUEST_SPAN_NAME = "raw_gen_ai_request" LITELLM_REQUEST_SPAN_NAME = "litellm_request" +CAPTURE_MODE_NO_CONTENT = "NO_CONTENT" +CAPTURE_MODE_SPAN_ONLY = "SPAN_ONLY" +CAPTURE_MODE_EVENT_ONLY = "EVENT_ONLY" +CAPTURE_MODE_SPAN_AND_EVENT = "SPAN_AND_EVENT" +_VALID_CAPTURE_MODES = { + CAPTURE_MODE_NO_CONTENT, + CAPTURE_MODE_SPAN_ONLY, + CAPTURE_MODE_EVENT_ONLY, + CAPTURE_MODE_SPAN_AND_EVENT, +} + @dataclass class OpenTelemetryConfig: @@ -69,6 +80,11 @@ class OpenTelemetryConfig: deployment_environment: Optional[str] = None model_id: Optional[str] = None ignore_context_propagation: Optional[bool] = None + # When True, create a private TracerProvider instead of reusing or setting the global one. + skip_set_global: bool = False + # Programmatic override for OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT. + # One of NO_CONTENT, SPAN_ONLY, EVENT_ONLY, SPAN_AND_EVENT (or "true" as legacy alias). + capture_message_content: Optional[str] = None def __post_init__(self) -> None: # If endpoint is specified but exporter is still the default "console", @@ -180,6 +196,9 @@ class OpenTelemetry(CustomLogger): super().__init__(**kwargs) self._init_metrics(meter_provider) self._init_logs(logger_provider) + # Sample env-var / config / message_logging at init so subsequent + # _capture_in_span / _capture_in_event calls are deterministic. + self._capture_mode_cached = self._compute_capture_mode_from_init_state() self._init_otel_logger_on_litellm_proxy() @staticmethod @@ -218,7 +237,14 @@ class OpenTelemetry(CustomLogger): not isinstance(cb, OpenTelemetry) for cb in litellm.service_callback ): litellm.service_callback.append(self) - setattr(proxy_server, "open_telemetry_logger", self) + # avoid proxy logger ownership being overwritten by later + # handlers. Multiple integrations (default OTEL, Langfuse OTEL, + # Arize OTEL, etc.) may initialize in sequence; without this guard, + # the last one silently replaces the first and breaks expected + # routing for proxy_server.open_telemetry_logger consumers. + # Behavior: first-registered wins. + if getattr(proxy_server, "open_telemetry_logger", None) is None: + setattr(proxy_server, "open_telemetry_logger", self) def _get_or_create_provider( self, @@ -259,16 +285,21 @@ class OpenTelemetry(CustomLogger): try: existing_provider = get_existing_provider_fn() - # If a real SDK provider exists (set by another SDK like Langfuse), use it - # This uses a positive check for SDK providers instead of a negative check for proxy providers if isinstance(existing_provider, sdk_provider_class): - verbose_logger.debug( - "OpenTelemetry: Using existing %s: %s", - provider_name, - type(existing_provider).__name__, - ) - provider = existing_provider - # Don't call set_provider to preserve existing context + if skip_set_global: + verbose_logger.debug( + "OpenTelemetry: existing %s found but skip_set_global=True; creating private %s for isolation", + provider_name, + provider_name, + ) + provider = create_new_provider_fn() + else: + verbose_logger.debug( + "OpenTelemetry: Using existing %s: %s", + provider_name, + type(existing_provider).__name__, + ) + provider = existing_provider else: # Default proxy provider or unknown type, create our own verbose_logger.debug("OpenTelemetry: Creating new %s", provider_name) @@ -293,6 +324,68 @@ class OpenTelemetry(CustomLogger): return provider + def _skip_set_global(self) -> bool: + # langfuse_otel relies on the Langfuse SDK's providers; don't overwrite them. + return self.config.skip_set_global or ( + hasattr(self, "callback_name") and self.callback_name == "langfuse_otel" + ) + + def _compute_capture_mode_from_init_state(self) -> Optional[str]: + """Sample explicit settings at init. Returns the resolved mode or + None if nothing explicit is set (in which case the legacy + ``self.message_logging`` flag is consulted dynamically per request). + + ``"true"``/``"1"`` map to ``EVENT_ONLY`` per the contrib convention. + ``"false"``/``"0"`` map to ``NO_CONTENT``. + Unknown values are ignored. + """ + explicit = self.config.capture_message_content or os.getenv( + "OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT" + ) + if not explicit: + return None + normalized = explicit.upper() + if normalized in ("TRUE", "1"): + return CAPTURE_MODE_EVENT_ONLY + if normalized in ("FALSE", "0"): + return CAPTURE_MODE_NO_CONTENT + if normalized in _VALID_CAPTURE_MODES: + return normalized + return None + + def _resolve_capture_mode(self) -> str: + """Return the active capture mode for this request. + + Precedence: + 1. ``litellm.turn_off_message_logging=True`` forces ``NO_CONTENT`` + (kill-switch checked dynamically). + 2. Explicit setting sampled at init from + ``OpenTelemetryConfig.capture_message_content`` or + ``OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT``. + 3. Legacy ``self.message_logging`` (checked dynamically). + """ + if litellm.turn_off_message_logging: + return CAPTURE_MODE_NO_CONTENT + if self._capture_mode_cached is not None: + return self._capture_mode_cached + return ( + CAPTURE_MODE_SPAN_AND_EVENT + if self.message_logging + else CAPTURE_MODE_NO_CONTENT + ) + + def _capture_in_span(self) -> bool: + return self._resolve_capture_mode() in ( + CAPTURE_MODE_SPAN_ONLY, + CAPTURE_MODE_SPAN_AND_EVENT, + ) + + def _capture_in_event(self) -> bool: + return self._resolve_capture_mode() in ( + CAPTURE_MODE_EVENT_ONLY, + CAPTURE_MODE_SPAN_AND_EVENT, + ) + def _init_tracing(self, tracer_provider): from opentelemetry import trace from opentelemetry.sdk.trace import TracerProvider @@ -303,11 +396,6 @@ class OpenTelemetry(CustomLogger): provider.add_span_processor(self._get_span_processor()) return provider - # CRITICAL FIX: For Langfuse OTEL, skip setting global provider to prevent interference - skip_global = ( - hasattr(self, "callback_name") and self.callback_name == "langfuse_otel" - ) - tracer_provider = self._get_or_create_provider( provider=tracer_provider, provider_name="TracerProvider", @@ -315,16 +403,18 @@ class OpenTelemetry(CustomLogger): sdk_provider_class=TracerProvider, create_new_provider_fn=create_tracer_provider, set_provider_fn=trace.set_tracer_provider, - skip_set_global=skip_global, + skip_set_global=self._skip_set_global(), ) # Grab our tracer from the TracerProvider (not from global context) # This ensures we use the provided TracerProvider (e.g., for testing) self.tracer = tracer_provider.get_tracer(LITELLM_TRACER_NAME) + self._tracer_provider = tracer_provider self.span_kind = SpanKind def _init_metrics(self, meter_provider): if not self.config.enable_metrics: + self._meter_provider = None self._operation_duration_histogram = None self._token_usage_histogram = None self._cost_histogram = None @@ -350,7 +440,9 @@ class OpenTelemetry(CustomLogger): sdk_provider_class=MeterProvider, create_new_provider_fn=create_meter_provider, set_provider_fn=metrics.set_meter_provider, + skip_set_global=self._skip_set_global(), ) + self._meter_provider = meter_provider meter = meter_provider.get_meter(__name__) @@ -388,6 +480,7 @@ class OpenTelemetry(CustomLogger): def _init_logs(self, logger_provider): # nothing to do if events disabled if not self.config.enable_events: + self._logger_provider = None return from opentelemetry._logs import get_logger_provider, set_logger_provider @@ -404,13 +497,14 @@ class OpenTelemetry(CustomLogger): ) return provider - self._get_or_create_provider( + self._logger_provider = self._get_or_create_provider( provider=logger_provider, provider_name="LoggerProvider", get_existing_provider_fn=get_logger_provider, sdk_provider_class=OTLoggerProvider, create_new_provider_fn=create_logger_provider, set_provider_fn=set_logger_provider, + skip_set_global=self._skip_set_global(), ) def log_success_event(self, kwargs, response_obj, start_time, end_time): @@ -707,12 +801,100 @@ class OpenTelemetry(CustomLogger): # End of Team/Key Based Logging Control Flow ######################################################### + def _emit_once(self, kwargs: dict, *scope: object) -> bool: + """Return True the first time this handler is asked to emit a span + for the given (handler, scope) on this kwargs; False on repeats. + + Used to suppress duplicate span emission for two distinct patterns: + + 1. **Handler-level dual-fire**: streaming code paths trigger both + the sync and async callback for one request, so ``_handle_success`` + / ``_handle_failure`` would otherwise produce two + ``litellm_request`` spans. Scope: ``("success",)`` / ``("failure",)``. + 2. **Payload-driven multi-entrypoint emission**: a span loop that + reads entries from ``standard_logging_payload`` (currently only + guardrails) is invoked from multiple lifecycle points + (post-call hooks, success callback, failure callback). The list + can be re-read with mutated entries between calls, so dedupe + must be at entry granularity. Scope: the entry's stable identity. + + ``scope`` parts can be any hashable identity. The marker is stored + in ``kwargs["litellm_params"]["metadata"]["_otel_internal"]`` so it + is request-local (kwargs is shared across the sync/async callbacks + and lifecycle hooks for one request). + """ + litellm_params = kwargs.get("litellm_params") + if not isinstance(litellm_params, dict): + litellm_params = {} + kwargs["litellm_params"] = litellm_params + + _metadata = litellm_params.get("metadata") + if not isinstance(_metadata, dict): + _metadata = {} + litellm_params["metadata"] = _metadata + + _otel_internal = _metadata.get("_otel_internal") + if not isinstance(_otel_internal, dict): + _otel_internal = {} + _metadata["_otel_internal"] = _otel_internal + + spans_logged = _otel_internal.get("spans_logged") + if not isinstance(spans_logged, dict): + spans_logged = {} + _otel_internal["spans_logged"] = spans_logged + + dedupe_key = (self.__class__.__name__, id(self), *scope) + if spans_logged.get(dedupe_key) is True: + return False + + spans_logged[dedupe_key] = True + return True + + def _end_proxy_span_from_kwargs(self, kwargs: dict, end_time) -> None: + """Close the proxy-level parent span if it is still recording. + + This helper retrieves the proxy span directly from kwargs metadata + and closes it after all child spans have been recorded. + + Only called from the success path. The failure path deliberately + leaves the proxy span open so ``async_post_call_failure_hook`` can + append the ``"Failed Proxy Server Request"`` child span before + closing it. + + Only spans named ``LITELLM_PROXY_REQUEST_SPAN_NAME`` are closed — + externally provided spans must not be closed by LiteLLM. + """ + litellm_params = kwargs.get("litellm_params", {}) or {} + _metadata = litellm_params.get("metadata", {}) or {} + proxy_span = _metadata.get("litellm_parent_otel_span", None) + if ( + proxy_span is not None + and getattr(proxy_span, "name", None) == LITELLM_PROXY_REQUEST_SPAN_NAME + and hasattr(proxy_span, "is_recording") + and proxy_span.is_recording() + ): + proxy_span.end(end_time=self._to_ns(end_time)) + def _handle_success(self, kwargs, response_obj, start_time, end_time): + """Create the litellm_request span then close the proxy span.""" verbose_logger.debug( "OpenTelemetry Logger: Logging kwargs: %s, OTEL config settings=%s", kwargs, self.config, ) + + # sync + async success handlers can both fire for one + # request (notably in streaming code paths). Guard against duplicate + # span writes — but still close the proxy span on the skip path so + # the trace doesn't leak an open root span. + if not self._emit_once(kwargs, "success"): + verbose_logger.debug( + "OpenTelemetry: skipping duplicate success span for handler=%s", + self.__class__.__name__, + ) + self._end_proxy_span_from_kwargs(kwargs, end_time) + return + ctx, parent_span = self._get_span_context(kwargs) if self.config.ignore_context_propagation: @@ -772,7 +954,7 @@ class OpenTelemetry(CustomLogger): # 6. Do NOT end parent span - it should be managed by its creator # External spans (from Langfuse, user code, HTTP headers, global context) must not be closed by LiteLLM - # However, proxy-created spans should be closed here + # However, proxy-created spans should be closed here. if ( parent_span is not None and hasattr(parent_span, "name") @@ -780,6 +962,11 @@ class OpenTelemetry(CustomLogger): ): parent_span.end(end_time=self._to_ns(end_time)) + # close the proxy span explicitly from kwargs metadata + # after all child spans (litellm_request, guardrail, raw_request) + # have been fully recorded and exported. + self._end_proxy_span_from_kwargs(kwargs, end_time) + def _start_primary_span( self, kwargs, @@ -811,8 +998,7 @@ class OpenTelemetry(CustomLogger): from opentelemetry import trace from opentelemetry.trace import Status, StatusCode - # only log raw LLM request/response if message_logging is on and not globally turned off - if litellm.turn_off_message_logging or not self.message_logging: + if not self._capture_in_span(): return litellm_params = kwargs.get("litellm_params", {}) @@ -1073,7 +1259,7 @@ class OpenTelemetry(CustomLogger): # See: https://github.com/open-telemetry/opentelemetry-python/pull/4676 # TODO: Refactor to use the proper OTEL Logs API instead of directly creating SDK LogRecords - from opentelemetry._logs import SeverityNumber, get_logger + from opentelemetry._logs import SeverityNumber try: from opentelemetry.sdk._logs import ( # type: ignore[attr-defined] # OTEL < 1.39.0 @@ -1084,7 +1270,10 @@ class OpenTelemetry(CustomLogger): LogRecord as SdkLogRecord, # type: ignore[attr-defined] # OTEL >= 1.39.0 ) - otel_logger = get_logger(LITELLM_LOGGER_NAME) + # Resolve through the handler's own LoggerProvider (which may be a + # private one when skip_set_global=True) rather than the module-level + # get_logger() which always goes through the global provider. + otel_logger = self._logger_provider.get_logger(LITELLM_LOGGER_NAME) parent_ctx = span.get_span_context() provider = (kwargs.get("litellm_params") or {}).get( @@ -1100,9 +1289,14 @@ class OpenTelemetry(CustomLogger): } if role == "tool" and msg.get("id"): attrs["id"] = msg["id"] - if self.message_logging and msg.get("content"): + capture_event_content = self._capture_in_event() + if capture_event_content and msg.get("content"): attrs["gen_ai.prompt"] = msg["content"] + body = msg.copy() + if not capture_event_content: + body.pop("content", None) + log_record = SdkLogRecord( timestamp=self._to_ns(datetime.now()), trace_id=parent_ctx.trace_id, @@ -1110,7 +1304,7 @@ class OpenTelemetry(CustomLogger): trace_flags=parent_ctx.trace_flags, severity_number=SeverityNumber.INFO, severity_text="INFO", - body=msg.copy(), + body=body, attributes=attrs, ) otel_logger.emit(log_record) @@ -1124,14 +1318,15 @@ class OpenTelemetry(CustomLogger): "finish_reason": choice.get("finish_reason"), } body_msg = choice.get("message", {}) - if self.message_logging and body_msg.get("content"): + capture_event_content = self._capture_in_event() + if capture_event_content and body_msg.get("content"): attrs["message.content"] = body_msg["content"] body = { "index": idx, "finish_reason": choice.get("finish_reason"), "message": {"role": body_msg.get("role", "assistant")}, } - if self.message_logging and body_msg.get("content"): + if capture_event_content and body_msg.get("content"): body["message"]["content"] = body_msg["content"] log_record = SdkLogRecord( @@ -1201,6 +1396,21 @@ class OpenTelemetry(CustomLogger): for guardrail_information in guardrail_information_list: start_time_float = guardrail_information.get("start_time") end_time_float = guardrail_information.get("end_time") + + # ``_create_guardrail_span`` is called from three lifecycle + # points (``async_post_call_success_hook``, ``_handle_success``, + # ``_handle_failure``) and re-reads the (mutating) entry list + # each time. Dedupe at entry granularity so a single real + # guardrail invocation produces exactly one span per handler. + if not self._emit_once( + kwargs, + "guardrail", + guardrail_information.get("guardrail_name"), + start_time_float, + guardrail_information.get("guardrail_mode"), + ): + continue + start_time_datetime = datetime.now() if start_time_float is not None: start_time_datetime = datetime.fromtimestamp(start_time_float) @@ -1254,6 +1464,21 @@ class OpenTelemetry(CustomLogger): kwargs, self.config, ) + + # sync + async failure handlers can both fire for one + # request (notably in streaming code paths), producing two + # semantically identical ERROR spans. Unlike the success path, the + # proxy span is intentionally left open here so that + # ``async_post_call_failure_hook`` can append the + # "Failed Proxy Server Request" child span before closing it — + # there is no proxy-span side-effect to preserve on the skip path. + if not self._emit_once(kwargs, "failure"): + verbose_logger.debug( + "OpenTelemetry: skipping duplicate failure span for handler=%s", + self.__class__.__name__, + ) + return + _parent_context, parent_otel_span = self._get_span_context(kwargs) if self.config.ignore_context_propagation: @@ -1657,9 +1882,7 @@ class OpenTelemetry(CustomLogger): ########## LLM Request Medssages / tools / content Attributes ########### ######################################################################### - if litellm.turn_off_message_logging is True: - return - if self.message_logging is not True: + if not self._capture_in_span(): return if optional_params.get("tools"): @@ -1678,17 +1901,41 @@ class OpenTelemetry(CustomLogger): value=safe_dumps(transformed_messages), ) - if kwargs.get("system_instructions"): - transformed_system_instructions = ( - self._transform_messages_to_otel_semantic_conventions( - kwargs.get("system_instructions") + # Coalesce the different kwarg names that carry the system + # prompt depending on the call path: + # - "system_instructions" — Vertex AI Gemini chat-completion + # - "instructions" — OpenAI Responses API + # - "system" — Anthropic Messages API + # Use `is not None` rather than truthiness to avoid falsy + # values (e.g. []) falling through to the wrong kwarg. + system_instructions = ( + kwargs.get("system_instructions") + if kwargs.get("system_instructions") is not None + else ( + kwargs.get("instructions") + if kwargs.get("instructions") is not None + else kwargs.get("system") + ) + ) + if system_instructions: + if isinstance(system_instructions, str): + # Plain text system prompt — no transformation needed + self.safe_set_attribute( + span=span, + key=SpanAttributes.GEN_AI_SYSTEM_INSTRUCTIONS.value, + value=system_instructions, + ) + else: + transformed_system_instructions = ( + self._transform_messages_to_otel_semantic_conventions( + system_instructions + ) + ) + self.safe_set_attribute( + span=span, + key=SpanAttributes.GEN_AI_SYSTEM_INSTRUCTIONS.value, + value=safe_dumps(transformed_system_instructions), ) - ) - self.safe_set_attribute( - span=span, - key=SpanAttributes.GEN_AI_SYSTEM_INSTRUCTIONS.value, - value=safe_dumps(transformed_system_instructions), - ) self.safe_set_attribute( span=span, @@ -1747,6 +1994,57 @@ class OpenTelemetry(CustomLogger): value=value, ) + elif response_obj.get("output"): + # Responses API: ResponsesAPIResponse has an "output" + # list instead of "choices". Each item with + # type="message" contains a "content" list of + # OutputText objects (type="output_text"). + output_items = response_obj.get("output") + output_messages = self._transform_responses_api_output_to_otel( + output_items + ) + if output_messages: + self.safe_set_attribute( + span=span, + key=SpanAttributes.GEN_AI_OUTPUT_MESSAGES.value, + value=safe_dumps(output_messages), + ) + + # Emit per-tool-call span attributes (parity with + # the choices branch that calls _tool_calls_kv_pair). + # Convert Responses API function_call items to the + # ChatCompletionMessageToolCall format expected by + # _tool_calls_kv_pair. + tool_calls = [] + for out_item in output_items: + item_d = self._to_dict(out_item) + if item_d and item_d.get("type") == "function_call": + tool_calls.append( + { + "function": { + "name": item_d.get("name", ""), + "arguments": item_d.get("arguments", ""), + } + } + ) + if tool_calls: + kv_pairs = OpenTelemetry._tool_calls_kv_pair(tool_calls) # type: ignore + for key, value in kv_pairs.items(): + self.safe_set_attribute( + span=span, + key=key, + value=value, + ) + + # Extract finish reason from ResponsesAPIResponse.status + status = response_obj.get("status") + if status: + self.safe_set_attribute( + span=span, + key=SpanAttributes.GEN_AI_RESPONSE_FINISH_REASONS.value, + value=safe_dumps([status]), + ) + except Exception as e: self.handle_callback_failure( callback_name=self.callback_name or "opentelemetry" @@ -1842,6 +2140,78 @@ class OpenTelemetry(CustomLogger): transformed.append(transformed_msg) return transformed + @staticmethod + def _to_dict(obj) -> Optional[dict]: + """Normalize an object to a plain dict. + + Handles three forms that appear in practice: + + 1. Plain ``dict`` — returned as-is. + 2. LiteLLM's ``BaseLiteLLMOpenAIResponseObject`` — exposes a + ``.get()`` method that delegates to ``__dict__``. + 3. Raw Pydantic v2 models from the ``openai`` SDK (e.g. + ``ResponseOutputMessage``, ``ResponseOutputText``) — these do + **not** have ``.get()`` but do have ``.model_dump()``. + + Returns ``None`` for anything else so callers can skip it. + """ + if isinstance(obj, dict): + return obj + if hasattr(obj, "get"): + # BaseLiteLLMOpenAIResponseObject duck-type + return obj # type: ignore[return-value] + if hasattr(obj, "model_dump"): + # Raw Pydantic v2 model (e.g. openai SDK types) + return obj.model_dump() # type: ignore[union-attr] + return None + + def _transform_responses_api_output_to_otel(self, output: List) -> List[dict]: + """ + Transform Responses API output items into OTEL GenAI 1.38 format. + + The Responses API returns output as a list of items, each with a + ``type`` field. Message items (``type="message"``) contain a + ``content`` list of ``OutputText`` objects with ``type="output_text"`` + and ``text`` fields. + + Items may be plain dicts, LiteLLM wrapper objects (with ``.get()``), + or raw Pydantic v2 models from the ``openai`` SDK (with + ``.model_dump()``). We normalize each item to a dict via + ``_to_dict`` before processing. + + This method converts them to the same ``{"role": ..., "parts": [...]}`` + format used by ``_transform_choices_to_otel_semantic_conventions``. + """ + transformed = [] + for raw_item in output: + item = self._to_dict(raw_item) + if item is None: + continue + if item.get("type") == "message": + role = item.get("role", "assistant") + parts = [] + for raw_content in item.get("content", []): + content = self._to_dict(raw_content) + if content is None: + continue + if content.get("type") == "output_text": + text = content.get("text", "") + if text: + parts.append({"type": "text", "content": text}) + if parts: + transformed.append({"role": role, "parts": parts}) + elif item.get("type") == "function_call": + # Surface tool calls from Responses API output + part: dict = { + "type": "tool_call", + "name": item.get("name", ""), + "arguments": item.get("arguments", ""), + } + if item.get("call_id"): + part["id"] = item["call_id"] + transformed.append({"role": "assistant", "parts": [part]}) + return transformed + def set_raw_request_attributes(self, span: Span, kwargs, response_obj): try: # Only set provider-specific raw payload attributes on this span. @@ -1948,7 +2318,7 @@ class OpenTelemetry(CustomLogger): verbose_logger.debug( "OpenTelemetry: Using explicit parent span from metadata" ) - return trace.set_span_in_context(parent_otel_span), parent_otel_span + return trace.set_span_in_context(parent_otel_span), None # Priority 2: HTTP traceparent header if traceparent is not None: diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 723b142dfad..30af0dcb8ed 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -25,6 +25,9 @@ from typing import ( import litellm from litellm._logging import print_verbose, verbose_logger from litellm.integrations.custom_logger import CustomLogger +from litellm.integrations.prometheus_helpers.bounded_prometheus_series_tracker import ( + BoundedPrometheusSeriesTracker, +) from litellm.integrations.prometheus_helpers import ( PrometheusLabelFactoryContext, _get_cached_end_user_id_for_cost_tracking, @@ -81,6 +84,7 @@ class PrometheusLogger(CustomLogger): if _custom_buckets is not None else LATENCY_BUCKETS ) + self._bounded_prometheus_series_tracker = BoundedPrometheusSeriesTracker() # Create metric factory functions self._counter_factory = self._create_metric_factory(Counter) @@ -265,6 +269,7 @@ class PrometheusLogger(CustomLogger): ######################################## # LiteLLM Virtual API KEY metrics ######################################## + # Remaining MODEL RPM limit for API Key self.litellm_remaining_api_key_requests_for_model = self._gauge_factory( "litellm_remaining_api_key_requests_for_model", @@ -983,6 +988,40 @@ class PrometheusLogger(CustomLogger): return filtered_labels + def _track_end_user_metric_series( + self, + metric: Any, + metric_name: DEFINED_PROMETHEUS_METRICS, + labels: Dict[str, Optional[str]], + ) -> None: + """ + Cap the cardinality of metrics that include the ``end_user`` label. + + Called *after* ``metric.labels(...).inc()/observe()`` so the emission is + recorded in prometheus-client's child map before any eviction runs. + Series that get evicted before the next scrape lose updates accrued + since the last scrape — this is inherent to any cardinality cap. + """ + labelnames = self.get_labels_for_metric(metric_name) + if UserAPIKeyLabelNames.END_USER.value not in labelnames: + return + if labels.get(UserAPIKeyLabelNames.END_USER.value) is None: + return + + max_series = litellm.prometheus_end_user_metrics_max_series_per_metric + ttl_seconds = litellm.prometheus_end_user_metrics_ttl_seconds + if max_series is None and ttl_seconds is None: + return + + self._bounded_prometheus_series_tracker.track_series( + metric=metric, + metric_name=metric_name, + label_values=tuple(labels.get(label) for label in labelnames), + max_series=max_series, + ttl_seconds=ttl_seconds, + cleanup_interval_seconds=litellm.prometheus_end_user_metrics_cleanup_interval_seconds, + ) + def _inc_labeled_counter( self, counter: Any, @@ -997,6 +1036,7 @@ class PrometheusLogger(CustomLogger): label_context=label_context, ) counter.labels(**_labels).inc(amount) + self._track_end_user_metric_series(counter, metric_name, _labels) async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): # Define prometheus client @@ -1046,21 +1086,9 @@ class PrometheusLogger(CustomLogger): output_tokens = standard_logging_payload["completion_tokens"] tokens_used = standard_logging_payload["total_tokens"] response_cost = standard_logging_payload["response_cost"] - _requester_metadata: Optional[dict] = standard_logging_payload["metadata"].get( - "requester_metadata" + combined_metadata = _get_combined_custom_metadata_from_standard_logging_payload( + standard_logging_payload=standard_logging_payload ) - user_api_key_auth_metadata: Optional[dict] = standard_logging_payload[ - "metadata" - ].get("user_api_key_auth_metadata") - spend_logs_metadata: Optional[dict] = standard_logging_payload["metadata"].get( - "spend_logs_metadata" - ) - - combined_metadata: Dict[str, Any] = { - **(_requester_metadata if _requester_metadata else {}), - **(user_api_key_auth_metadata if user_api_key_auth_metadata else {}), - **(spend_logs_metadata if spend_logs_metadata else {}), - } if standard_logging_payload is not None and isinstance( standard_logging_payload, dict ): @@ -1198,6 +1226,17 @@ class PrometheusLogger(CustomLogger): label_context=label_context, ) + # Provider-agnostic fallback: providers like Bedrock and Vertex don't return + # x-ratelimit-remaining-* headers, so the gauges above only fire for OpenAI / + # Anthropic / Azure. When the proxy router has tpm/rpm configured for the + # model_group, derive remaining from configured-limit minus current usage so + # the same metric is populated for any provider. + await self._async_set_router_remaining_metrics( + standard_logging_payload=standard_logging_payload, # type: ignore + enum_values=enum_values, + label_context=label_context, + ) + # cache metrics self._increment_cache_metrics( standard_logging_payload=standard_logging_payload, # type: ignore @@ -1415,26 +1454,46 @@ class PrometheusLogger(CustomLogger): ) remaining_tokens_variable_name = f"litellm-key-remaining-tokens-{model_group}" - remaining_requests = ( - metadata.get(remaining_requests_variable_name, sys.maxsize) or sys.maxsize + remaining_requests = metadata.get(remaining_requests_variable_name) + if remaining_requests is None: + remaining_requests = sys.maxsize + remaining_tokens = metadata.get(remaining_tokens_variable_name) + if remaining_tokens is None: + remaining_tokens = sys.maxsize + + enum_values = UserAPIKeyLabelValues( + hashed_api_key=user_api_key, + api_key_alias=user_api_key_alias, + model=model_group, + model_id=model_id, + custom_metadata_labels=get_custom_labels_from_metadata( + metadata=_get_combined_custom_metadata_from_standard_logging_payload( + standard_logging_payload=kwargs.get("standard_logging_object") + ) + ), ) - remaining_tokens = ( - metadata.get(remaining_tokens_variable_name, sys.maxsize) or sys.maxsize + label_context = PrometheusLabelFactoryContext(enum_values) + requests_labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + "litellm_remaining_api_key_requests_for_model" + ), + enum_values=enum_values, + label_context=label_context, + ) + self.litellm_remaining_api_key_requests_for_model.labels(**requests_labels).set( + remaining_requests ) - self.litellm_remaining_api_key_requests_for_model.labels( - _sanitize_prometheus_label_value(user_api_key), - _sanitize_prometheus_label_value(user_api_key_alias), - _sanitize_prometheus_label_value(model_group), - _sanitize_prometheus_label_value(model_id), - ).set(remaining_requests) - - self.litellm_remaining_api_key_tokens_for_model.labels( - _sanitize_prometheus_label_value(user_api_key), - _sanitize_prometheus_label_value(user_api_key_alias), - _sanitize_prometheus_label_value(model_group), - _sanitize_prometheus_label_value(model_id), - ).set(remaining_tokens) + tokens_labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + "litellm_remaining_api_key_tokens_for_model" + ), + enum_values=enum_values, + label_context=label_context, + ) + self.litellm_remaining_api_key_tokens_for_model.labels(**tokens_labels).set( + remaining_tokens + ) def _set_latency_metrics( self, @@ -1470,6 +1529,11 @@ class PrometheusLogger(CustomLogger): self.litellm_llm_api_time_to_first_token_metric.labels( **_ttft_labels ).observe(time_to_first_token_seconds) + self._track_end_user_metric_series( + self.litellm_llm_api_time_to_first_token_metric, + "litellm_llm_api_time_to_first_token_metric", + _ttft_labels, + ) else: verbose_logger.debug( "Time to first token metric not emitted, stream option in model_parameters is not True" @@ -1490,6 +1554,11 @@ class PrometheusLogger(CustomLogger): self.litellm_llm_api_latency_metric.labels(**_labels).observe( api_call_total_time_seconds ) + self._track_end_user_metric_series( + self.litellm_llm_api_latency_metric, + "litellm_llm_api_latency_metric", + _labels, + ) # total request latency total_time_seconds = self._safe_duration_seconds( @@ -1507,6 +1576,11 @@ class PrometheusLogger(CustomLogger): self.litellm_request_total_latency_metric.labels(**_labels).observe( 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 = kwargs.get("litellm_params", {}) or {} @@ -1524,6 +1598,11 @@ class PrometheusLogger(CustomLogger): self.litellm_request_queue_time_metric.labels(**_labels).observe( queue_time_seconds ) + self._track_end_user_metric_series( + self.litellm_request_queue_time_metric, + "litellm_request_queue_time_seconds", + _labels, + ) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): verbose_logger.debug( @@ -1560,18 +1639,27 @@ class PrometheusLogger(CustomLogger): ) try: - self.litellm_llm_api_failed_requests_metric.labels( - _sanitize_prometheus_label_value(end_user_id), - _sanitize_prometheus_label_value(user_api_key), - _sanitize_prometheus_label_value(user_api_key_alias), - _sanitize_prometheus_label_value(model), - _sanitize_prometheus_label_value(user_api_team), - _sanitize_prometheus_label_value(user_api_team_alias), - _sanitize_prometheus_label_value(user_id), - _sanitize_prometheus_label_value( - standard_logging_payload.get("model_id", "") + enum_values = UserAPIKeyLabelValues( + end_user=end_user_id, + hashed_api_key=user_api_key, + api_key_alias=user_api_key_alias, + model=model, + team=user_api_team, + team_alias=user_api_team_alias, + user=user_id, + model_id=standard_logging_payload.get("model_id", ""), + custom_metadata_labels=get_custom_labels_from_metadata( + metadata=_get_combined_custom_metadata_from_standard_logging_payload( + standard_logging_payload=standard_logging_payload + ) ), - ).inc() + ) + PrometheusLogger._inc_labeled_counter( + self, + self.litellm_llm_api_failed_requests_metric, + "litellm_llm_api_failed_requests_metric", + enum_values, + ) self.set_llm_deployment_failure_metrics(kwargs) await self._set_org_budget_metrics_after_api_request( org_id=user_api_key_org_id, @@ -1928,7 +2016,7 @@ class PrometheusLogger(CustomLogger): or _litellm_params_metadata.get("user_agent"), } - def set_llm_deployment_failure_metrics(self, request_kwargs: dict): + def set_llm_deployment_failure_metrics(self, request_kwargs: dict): # noqa: PLR0915 """ Sets Failure metrics when an LLM API call fails @@ -2006,17 +2094,32 @@ class PrometheusLogger(CustomLogger): if code is not None: exception_status = str(code) - # Create enum_values for the label factory (always create for use in different metrics) + # On LiteLLM-side rejects (no deployment picked), route request_kwargs["model"] + # into requested_model and leave deployment-scoped labels empty. + deployment_selected = bool(model_id) + if deployment_selected: + label_litellm_model_name = litellm_model_name + label_model_id = model_id + label_api_base = api_base + label_api_provider = llm_provider + label_requested_model = model_group or litellm_model_name + else: + label_litellm_model_name = "" + label_model_id = "" + label_api_base = "" + label_api_provider = "" + label_requested_model = litellm_model_name or model_group or "" + enum_values = UserAPIKeyLabelValues( - litellm_model_name=litellm_model_name, - model_id=model_id, - api_base=api_base, - api_provider=llm_provider, + litellm_model_name=label_litellm_model_name, + model_id=label_model_id, + api_base=label_api_base, + api_provider=label_api_provider, exception_status=exception_status, exception_class=( self._get_exception_class_name(exception) if exception else None ), - requested_model=model_group or litellm_model_name, + requested_model=label_requested_model, hashed_api_key=hashed_api_key, api_key_alias=api_key_alias, team=team, @@ -2030,12 +2133,14 @@ class PrometheusLogger(CustomLogger): log these labels ["litellm_model_name", "model_id", "api_base", "api_provider"] """ - self.set_deployment_partial_outage( - litellm_model_name=litellm_model_name or "", - model_id=model_id, - api_base=api_base, - api_provider=llm_provider or "", - ) + # Only mark a deployment outage when one was actually picked. + if deployment_selected: + self.set_deployment_partial_outage( + litellm_model_name=litellm_model_name or "", + model_id=model_id, + api_base=api_base, + api_provider=llm_provider or "", + ) _deployment_label_ctx = PrometheusLabelFactoryContext(enum_values) if exception is not None: PrometheusLogger._inc_labeled_counter( @@ -2105,6 +2210,99 @@ class PrometheusLogger(CustomLogger): ) self.litellm_deployment_rpm_limit.labels(**_labels).set(rpm) + async def _async_set_router_remaining_metrics( + self, + standard_logging_payload: StandardLoggingPayload, + enum_values: UserAPIKeyLabelValues, + label_context: Optional[PrometheusLabelFactoryContext] = None, + ) -> None: + """ + Populate ``litellm_remaining_tokens_metric`` / + ``litellm_remaining_requests_metric`` from the router's internal usage + counters when the upstream provider did not return + ``x-ratelimit-remaining-*`` response headers. + + OpenAI / Anthropic / Azure return remaining tokens/requests in response + headers, but Bedrock and Vertex AI do not. This fallback computes + ``configured_limit - current_usage`` via + ``Router.get_remaining_model_group_usage`` so the same gauges are + emitted for every provider when tpm/rpm is configured on the + deployment. + """ + try: + additional_headers = ( + standard_logging_payload.get("hidden_params", {}) or {} + ).get("additional_headers") or {} + + already_have_tokens = ( + additional_headers.get("x_ratelimit_remaining_tokens") is not None + ) + already_have_requests = ( + additional_headers.get("x_ratelimit_remaining_requests") is not None + ) + if already_have_tokens and already_have_requests: + return + + model_group = standard_logging_payload.get("model_group") + if not model_group: + return + + try: + from litellm.proxy.proxy_server import llm_router + except ImportError: + llm_router = None + + if llm_router is None: + return + + try: + remaining_usage = await llm_router.get_remaining_model_group_usage( + model_group + ) + except Exception as e: + verbose_logger.exception( + "Prometheus: get_remaining_model_group_usage failed for " + "model_group=%s: %s", + model_group, + e, + ) + return + + if not remaining_usage: + return + + remaining_tokens = remaining_usage.get("x-ratelimit-remaining-tokens") + remaining_requests = remaining_usage.get("x-ratelimit-remaining-requests") + + if not already_have_tokens and remaining_tokens is not None: + _labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_remaining_tokens_metric" + ), + enum_values=enum_values, + label_context=label_context, + ) + self.litellm_remaining_tokens_metric.labels(**_labels).set( + remaining_tokens + ) + + if not already_have_requests and remaining_requests is not None: + _labels = prometheus_label_factory( + supported_enum_labels=self.get_labels_for_metric( + metric_name="litellm_remaining_requests_metric" + ), + enum_values=enum_values, + label_context=label_context, + ) + self.litellm_remaining_requests_metric.labels(**_labels).set( + remaining_requests + ) + except Exception as e: + verbose_logger.exception( + "Prometheus Error: _async_set_router_remaining_metrics. " + "Exception occured - {}".format(str(e)) + ) + def set_llm_deployment_success_metrics( self, request_kwargs: dict, @@ -3604,6 +3802,36 @@ def get_custom_labels_from_metadata(metadata: dict) -> Dict[str, str]: return result +def _get_combined_custom_metadata_from_standard_logging_payload( + standard_logging_payload: Optional[dict], +) -> Dict[str, Any]: + """ + Combine the metadata sources that can supply custom Prometheus labels. + """ + if not isinstance(standard_logging_payload, dict): + return {} + + standard_logging_metadata = standard_logging_payload.get("metadata") or {} + if not isinstance(standard_logging_metadata, dict): + return {} + + requester_metadata = standard_logging_metadata.get("requester_metadata") + user_api_key_auth_metadata = standard_logging_metadata.get( + "user_api_key_auth_metadata" + ) + spend_logs_metadata = standard_logging_metadata.get("spend_logs_metadata") + + return { + **(requester_metadata if isinstance(requester_metadata, dict) else {}), + **( + user_api_key_auth_metadata + if isinstance(user_api_key_auth_metadata, dict) + else {} + ), + **(spend_logs_metadata if isinstance(spend_logs_metadata, dict) else {}), + } + + def _tag_matches_wildcard_configured_pattern( tags: Sequence[str], configured_tag: str ) -> bool: diff --git a/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py b/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py new file mode 100644 index 00000000000..d834ae20142 --- /dev/null +++ b/litellm/integrations/prometheus_helpers/bounded_prometheus_series_tracker.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import time +from collections import OrderedDict +from threading import RLock +from typing import Any, Dict, Optional + + +class BoundedPrometheusSeriesTracker: + """ + Tracks Prometheus child series and removes stale/excess labelsets. + + The tracker is label-agnostic: callers decide which series should be tracked + and pass the full label tuple used by the Prometheus metric. + """ + + def __init__(self) -> None: + self._series: Dict[str, OrderedDict[tuple[Optional[str], ...], float]] = {} + self._last_ttl_cleanup: Dict[str, float] = {} + self.lock = RLock() + + def track_series( + self, + metric: Any, + metric_name: str, + label_values: tuple[Optional[str], ...], + max_series: Optional[int], + ttl_seconds: Optional[float], + cleanup_interval_seconds: Optional[float], + ) -> None: + if max_series is None and ttl_seconds is None: + return + + now = time.monotonic() + + with self.lock: + series = self._series.setdefault(metric_name, OrderedDict()) + series[label_values] = now + series.move_to_end(label_values) + + if ttl_seconds is not None and self._should_run_ttl_cleanup( + metric_name=metric_name, + now=now, + cleanup_interval_seconds=cleanup_interval_seconds, + ): + expired_label_values = [ + tracked_label_values + for tracked_label_values, last_seen in series.items() + if now - last_seen > ttl_seconds + ] + for tracked_label_values in expired_label_values: + self._remove_metric_series(metric, series, tracked_label_values) + + # max_series <= 0 is treated as "unlimited" so a misconfigured zero + # value cannot silently drop every emission for this metric. + if max_series is not None and max_series > 0: + while len(series) > max_series: + tracked_label_values = next(iter(series)) + if not self._remove_metric_child(metric, tracked_label_values): + break + del series[tracked_label_values] + + def _should_run_ttl_cleanup( + self, + metric_name: str, + now: float, + cleanup_interval_seconds: Optional[float], + ) -> bool: + if cleanup_interval_seconds is None or cleanup_interval_seconds <= 0: + self._last_ttl_cleanup[metric_name] = now + return True + + last_cleanup = self._last_ttl_cleanup.get(metric_name) + if last_cleanup is None or now - last_cleanup >= cleanup_interval_seconds: + self._last_ttl_cleanup[metric_name] = now + return True + return False + + def _remove_metric_series( + self, + metric: Any, + series: OrderedDict[tuple[Optional[str], ...], float], + label_values: tuple[Optional[str], ...], + ) -> None: + if self._remove_metric_child(metric, label_values): + series.pop(label_values, None) + + @staticmethod + def _remove_metric_child( + metric: Any, label_values: tuple[Optional[str], ...] + ) -> bool: + """ + Remove the Prometheus child for ``label_values`` and report whether the + tracker should commit the matching state change. + + Returns ``True`` when the child is no longer present in Prometheus + (either it was just removed or it was already gone), and ``False`` when + ``metric.remove()`` raised an unexpected error and the child likely + still exists. + """ + try: + metric.remove(*label_values) + return True + except KeyError: + return True + except (AttributeError, ValueError): + return False diff --git a/litellm/integrations/prometheus_helpers/prometheus_api.py b/litellm/integrations/prometheus_helpers/prometheus_api.py index b25da577237..0901d7b6801 100644 --- a/litellm/integrations/prometheus_helpers/prometheus_api.py +++ b/litellm/integrations/prometheus_helpers/prometheus_api.py @@ -2,6 +2,7 @@ Helper functions to query prometheus API """ +import json import time from datetime import datetime, timedelta from typing import Optional @@ -81,6 +82,24 @@ def is_prometheus_connected() -> bool: return False +def _quote_promql_string_literal(value: str) -> str: + """Render ``value`` as a PromQL double-quoted string literal. + + PromQL string literals follow Go's escape rules + (https://prometheus.io/docs/prometheus/latest/querying/basics/): a + backslash begins an escape sequence and a bare ``"`` ends the literal. + Without escaping, callers that accept arbitrary user-supplied values + (like the ``api_key`` filter on ``/global/spend/logs``) can inject extra + label matchers or selectors and read cross-tenant metrics. + + JSON's quoting rules are a strict subset of Go's, so ``json.dumps`` of + a Python string produces a literal Prometheus accepts: ``\\``, ``\\"``, + and the standard ``\\n`` / ``\\t`` / ``\\uNNNN`` control-character + escapes. The returned value already includes the surrounding quotes. + """ + return json.dumps(value, ensure_ascii=False) + + async def get_daily_spend_from_prometheus(api_key: Optional[str]): """ Expected Response Format: @@ -109,8 +128,11 @@ async def get_daily_spend_from_prometheus(api_key: Optional[str]): if api_key is None: query = "sum(delta(litellm_spend_metric_total[1d]))" else: + quoted_api_key = _quote_promql_string_literal(api_key) query = ( - f'sum(delta(litellm_spend_metric_total{{hashed_api_key="{api_key}"}}[1d]))' + "sum(delta(litellm_spend_metric_total{" + f"hashed_api_key={quoted_api_key}" + "}[1d]))" ) params = { diff --git a/litellm/integrations/prompt_management_base.py b/litellm/integrations/prompt_management_base.py index 71da650dc48..9c626aea849 100644 --- a/litellm/integrations/prompt_management_base.py +++ b/litellm/integrations/prompt_management_base.py @@ -87,9 +87,7 @@ class PromptManagementBase(ABC): try: messages = compiled_prompt_client["prompt_template"] + client_messages except Exception as e: - raise ValueError( - f"Error compiling prompt: {e}. Prompt id={prompt_id}, prompt_variables={prompt_variables}, client_messages={client_messages}, dynamic_callback_params={dynamic_callback_params}" - ) + raise ValueError(f"Error compiling prompt: {e}. Prompt id={prompt_id}") compiled_prompt_client["completed_messages"] = messages return compiled_prompt_client @@ -116,9 +114,7 @@ class PromptManagementBase(ABC): try: messages = compiled_prompt_client["prompt_template"] + client_messages except Exception as e: - raise ValueError( - f"Error compiling prompt: {e}. Prompt id={prompt_id}, prompt_variables={prompt_variables}, client_messages={client_messages}, dynamic_callback_params={dynamic_callback_params}" - ) + raise ValueError(f"Error compiling prompt: {e}. Prompt id={prompt_id}") compiled_prompt_client["completed_messages"] = messages return compiled_prompt_client diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py index 08ce7ed8947..332e84dd07d 100644 --- a/litellm/integrations/s3_v2.py +++ b/litellm/integrations/s3_v2.py @@ -16,6 +16,7 @@ from litellm._logging import print_verbose, verbose_logger from litellm.constants import DEFAULT_S3_BATCH_SIZE, DEFAULT_S3_FLUSH_INTERVAL_SECONDS from litellm.integrations.s3 import get_s3_object_key from litellm.litellm_core_utils.safe_json_dumps import safe_dumps +from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, @@ -53,15 +54,25 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): s3_strip_base64_files: bool = False, s3_use_key_prefix: bool = False, s3_use_virtual_hosted_style: bool = False, + s3_callback_params_override: Optional[dict] = None, **kwargs, ): try: - verbose_logger.debug( - f"in init s3 logger - s3_callback_params {litellm.s3_callback_params}" - ) + _masker = SensitiveDataMasker() + if s3_callback_params_override is not None: + verbose_logger.debug( + f"in init s3 logger (audit override) - " + f"{_masker.mask_dict(dict(s3_callback_params_override))}" + ) + else: + verbose_logger.debug( + f"in init s3 logger - s3_callback_params " + f"{_masker.mask_dict(dict(litellm.s3_callback_params or {}))}" + ) # Initialize S3 params first to get the correct s3_verify value self._init_s3_params( + params_source=s3_callback_params_override, s3_bucket_name=s3_bucket_name, s3_region_name=s3_region_name, s3_api_version=s3_api_version, @@ -139,94 +150,85 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): s3_strip_base64_files: bool = False, s3_use_key_prefix: bool = False, s3_use_virtual_hosted_style: bool = False, + params_source: Optional[dict] = None, ): """ - Initialize the s3 params for this logging callback + Initialize the s3 params for this logging callback. Reads from + `params_source` if given (e.g. `s3_audit_callback_params` for the + audit-log instance), otherwise falls back to `litellm.s3_callback_params`. + Resolves `os.environ/X` markers into a local dict; never mutates the source. """ - litellm.s3_callback_params = litellm.s3_callback_params or {} - # read in .env variables - example os.environ/AWS_BUCKET_NAME - for key, value in litellm.s3_callback_params.items(): - if isinstance(value, str) and value.startswith("os.environ/"): - litellm.s3_callback_params[key] = litellm.get_secret(value) + if params_source is None: + params_source = litellm.s3_callback_params or {} + params: dict = { + key: ( + litellm.get_secret(value) + if isinstance(value, str) and value.startswith("os.environ/") + else value + ) + for key, value in params_source.items() + } - self.s3_bucket_name = ( - litellm.s3_callback_params.get("s3_bucket_name") or s3_bucket_name - ) - self.s3_region_name = ( - litellm.s3_callback_params.get("s3_region_name") or s3_region_name - ) - self.s3_api_version = ( - litellm.s3_callback_params.get("s3_api_version") or s3_api_version - ) + self.s3_bucket_name = params.get("s3_bucket_name") or s3_bucket_name + self.s3_region_name = params.get("s3_region_name") or s3_region_name + self.s3_api_version = params.get("s3_api_version") or s3_api_version self.s3_use_ssl = ( - litellm.s3_callback_params.get("s3_use_ssl", True) - if litellm.s3_callback_params.get("s3_use_ssl") is not None + params.get("s3_use_ssl", True) + if params.get("s3_use_ssl") is not None else s3_use_ssl ) self.s3_verify = ( - litellm.s3_callback_params.get("s3_verify") - if litellm.s3_callback_params.get("s3_verify") is not None + params.get("s3_verify") + if params.get("s3_verify") is not None else s3_verify ) - self.s3_endpoint_url = ( - litellm.s3_callback_params.get("s3_endpoint_url") or s3_endpoint_url - ) + self.s3_endpoint_url = params.get("s3_endpoint_url") or s3_endpoint_url self.s3_aws_access_key_id = ( - litellm.s3_callback_params.get("s3_aws_access_key_id") - or s3_aws_access_key_id + params.get("s3_aws_access_key_id") or s3_aws_access_key_id ) self.s3_aws_secret_access_key = ( - litellm.s3_callback_params.get("s3_aws_secret_access_key") - or s3_aws_secret_access_key + params.get("s3_aws_secret_access_key") or s3_aws_secret_access_key ) self.s3_aws_session_token = ( - litellm.s3_callback_params.get("s3_aws_session_token") - or s3_aws_session_token + params.get("s3_aws_session_token") or s3_aws_session_token ) self.s3_aws_session_name = ( - litellm.s3_callback_params.get("s3_aws_session_name") or s3_aws_session_name + params.get("s3_aws_session_name") or s3_aws_session_name ) self.s3_aws_profile_name = ( - litellm.s3_callback_params.get("s3_aws_profile_name") or s3_aws_profile_name + params.get("s3_aws_profile_name") or s3_aws_profile_name ) - self.s3_aws_role_name = ( - litellm.s3_callback_params.get("s3_aws_role_name") or s3_aws_role_name - ) + self.s3_aws_role_name = params.get("s3_aws_role_name") or s3_aws_role_name self.s3_aws_web_identity_token = ( - litellm.s3_callback_params.get("s3_aws_web_identity_token") - or s3_aws_web_identity_token + params.get("s3_aws_web_identity_token") or s3_aws_web_identity_token ) self.s3_aws_sts_endpoint = ( - litellm.s3_callback_params.get("s3_aws_sts_endpoint") or s3_aws_sts_endpoint + params.get("s3_aws_sts_endpoint") or s3_aws_sts_endpoint ) - self.s3_config = litellm.s3_callback_params.get("s3_config") or s3_config - self.s3_path = litellm.s3_callback_params.get("s3_path") or s3_path - # done reading litellm.s3_callback_params + self.s3_config = params.get("s3_config") or s3_config + self.s3_path = params.get("s3_path") or s3_path self.s3_use_team_prefix = ( - bool(litellm.s3_callback_params.get("s3_use_team_prefix", False)) - or s3_use_team_prefix + bool(params.get("s3_use_team_prefix", False)) or s3_use_team_prefix ) self.s3_use_key_prefix = ( - bool(litellm.s3_callback_params.get("s3_use_key_prefix", False)) - or s3_use_key_prefix + bool(params.get("s3_use_key_prefix", False)) or s3_use_key_prefix ) self.s3_strip_base64_files = ( - bool(litellm.s3_callback_params.get("s3_strip_base64_files", False)) - or s3_strip_base64_files + bool(params.get("s3_strip_base64_files", False)) or s3_strip_base64_files ) self.s3_use_virtual_hosted_style = ( - bool(litellm.s3_callback_params.get("s3_use_virtual_hosted_style", False)) + bool(params.get("s3_use_virtual_hosted_style", False)) or s3_use_virtual_hosted_style ) diff --git a/litellm/litellm_core_utils/audio_utils/utils.py b/litellm/litellm_core_utils/audio_utils/utils.py index 2141df18738..82f5c27f836 100644 --- a/litellm/litellm_core_utils/audio_utils/utils.py +++ b/litellm/litellm_core_utils/audio_utils/utils.py @@ -53,8 +53,19 @@ def process_audio_file(audio_file: FileTypes) -> ProcessedAudioFile: # Raw bytes filename = "audio.wav" file_content = bytes(audio_file) - elif isinstance(audio_file, (str, os.PathLike)): - # File path or PathLike + elif isinstance(audio_file, str): + # Bare strings are rejected — see extract_file_data for the same + # rationale: in a proxy request handler the string is + # attacker-controlled, and opening it as a path is an arbitrary + # file read. + raise ValueError( + "process_audio_file does not accept bare str inputs. Pass bytes, " + "an open file handle, a (filename, content) tuple, or a " + "pathlib.Path." + ) + elif isinstance(audio_file, os.PathLike): + # File path or PathLike — PathLike is a Python-level type that + # HTTP form values can't fabricate. file_path = str(audio_file) with open(file_path, "rb") as f: file_content = f.read() @@ -66,8 +77,14 @@ def process_audio_file(audio_file: FileTypes) -> ProcessedAudioFile: content = audio_file[1] if isinstance(content, (bytes, bytearray)): file_content = bytes(content) - elif isinstance(content, (str, os.PathLike)): - # File path or PathLike + elif isinstance(content, str): + raise ValueError( + "process_audio_file does not accept bare str tuple " + "contents. Pass bytes, an open file handle, or a " + "pathlib.Path." + ) + elif isinstance(content, os.PathLike): + # PathLike: SDK convenience for local-file uploads. with open(str(content), "rb") as f: file_content = f.read() elif hasattr(content, "read"): @@ -149,7 +166,14 @@ def get_audio_file_content_hash(file_obj: FileTypes) -> str: try: if isinstance(file_content_obj, (bytes, bytearray)): file_content = bytes(file_content_obj) - elif isinstance(file_content_obj, (str, os.PathLike)): + elif isinstance(file_content_obj, str): + # Bare strings are not treated as file paths in this helper — + # the cache-key path is reached from request handlers where the + # value is attacker-controlled. Fall back to hashing the string + # itself rather than opening it. + fallback_filename = file_content_obj + file_content = None + elif isinstance(file_content_obj, os.PathLike): try: with open(str(file_content_obj), "rb") as f: file_content = f.read() @@ -229,8 +253,15 @@ def calculate_request_duration(file: FileTypes) -> Optional[float]: if isinstance(file, (bytes, bytearray)): # Raw bytes file_content = bytes(file) - elif isinstance(file, (str, os.PathLike)): - # File path + elif isinstance(file, str): + # Bare strings are rejected — see extract_file_data. + raise ValueError( + "calculate_request_duration does not accept bare str inputs. " + "Pass bytes, an open file handle, a (filename, content) " + "tuple, or a pathlib.Path." + ) + elif isinstance(file, os.PathLike): + # File path (PathLike): SDK convenience. with open(str(file), "rb") as f: file_content = f.read() elif isinstance(file, tuple): diff --git a/litellm/litellm_core_utils/cli_token_utils.py b/litellm/litellm_core_utils/cli_token_utils.py index e2e304931a4..3776d276912 100644 --- a/litellm/litellm_core_utils/cli_token_utils.py +++ b/litellm/litellm_core_utils/cli_token_utils.py @@ -31,15 +31,23 @@ def load_cli_token() -> Optional[dict]: return None -def get_litellm_gateway_api_key() -> Optional[str]: +def get_litellm_gateway_api_key( + expected_base_url: Optional[str] = None, +) -> Optional[str]: """ Get the stored CLI API key for use with LiteLLM SDK. This function reads the token file created by `litellm-proxy login` and returns the API key for use in Python scripts. + Args: + expected_base_url: When provided, the key is only returned if it was + originally issued for this URL. Pass the target server URL to + prevent credential leakage when the client is pointed at a + different (possibly malicious) server. + Returns: - str: The API key if found, None otherwise + str: The API key if found (and origin matches), None otherwise Example: >>> import litellm @@ -53,6 +61,10 @@ def get_litellm_gateway_api_key() -> Optional[str]: >>> ) """ token_data = load_cli_token() - if token_data and "key" in token_data: - return token_data["key"] - return None + if not token_data or "key" not in token_data: + return None + if expected_base_url is not None: + stored_url = token_data.get("base_url") + if stored_url != expected_base_url.rstrip("/"): + return None + return token_data["key"] diff --git a/litellm/litellm_core_utils/cloud_storage_security.py b/litellm/litellm_core_utils/cloud_storage_security.py new file mode 100644 index 00000000000..daa3dc60320 --- /dev/null +++ b/litellm/litellm_core_utils/cloud_storage_security.py @@ -0,0 +1,175 @@ +import posixpath +import re +from types import MappingProxyType +from typing import Any, Mapping, Optional, Sequence, Tuple, cast +from urllib.parse import quote, unquote + +from litellm._uuid import uuid + +VERTEX_AI_MANAGED_GCS_PREFIX = "litellm-vertex-files/" +BEDROCK_MANAGED_S3_BATCH_PREFIX = "litellm-bedrock-files-" +BEDROCK_MANAGED_S3_UPLOAD_PREFIX = "litellm-bedrock-files/" +BEDROCK_MANAGED_S3_OUTPUT_PREFIX = "litellm-batch-outputs/" +BEDROCK_MANAGED_S3_PREFIXES = ( + BEDROCK_MANAGED_S3_BATCH_PREFIX, + BEDROCK_MANAGED_S3_UPLOAD_PREFIX, + BEDROCK_MANAGED_S3_OUTPUT_PREFIX, +) +_MAPPING_PROXY_TYPE: type = type(MappingProxyType({})) + +_SAFE_OBJECT_COMPONENT_PATTERN = re.compile(r"[^A-Za-z0-9._-]+") + + +def sanitize_cloud_object_component( + value: Optional[str], fallback: str = "file" +) -> str: + if not isinstance(value, str): + return fallback + + component = posixpath.basename(value.replace("\\", "/")).strip() + if component in {"", ".", ".."}: + return fallback + + component = "".join( + "_" if ord(char) < 32 or ord(char) == 127 else char for char in component + ) + component = _SAFE_OBJECT_COMPONENT_PATTERN.sub("_", component) + component = component.strip("._") + if not component: + return fallback + return component[:255] + + +def sanitize_cloud_object_path(value: Optional[str], fallback: str = "file") -> str: + if not isinstance(value, str): + return fallback + + segments = [] + for segment in value.replace("\\", "/").split("/"): + sanitized_segment = sanitize_cloud_object_component(segment, fallback="") + if sanitized_segment: + segments.append(sanitized_segment) + + if not segments: + return fallback + return "/".join(segments) + + +def build_managed_cloud_object_name( + prefix: str, filename: Optional[str], fallback_filename: str = "file" +) -> str: + safe_filename = sanitize_cloud_object_component( + filename, fallback=fallback_filename + ) + return f"{prefix}{uuid.uuid4().hex}-{safe_filename}" + + +def _validate_cloud_object_path(object_name: str) -> None: + if not object_name: + raise ValueError("Cloud storage object name is required") + if object_name.startswith("/"): + raise ValueError("Cloud storage object name must be relative") + if any(ord(char) < 32 or ord(char) == 127 for char in object_name): + raise ValueError("Cloud storage object name contains control characters") + segments = object_name.split("/") + if any(segment in {".", ".."} for segment in segments): + raise ValueError("Cloud storage object name contains an invalid path segment") + if "" in segments[:-1]: + raise ValueError("Cloud storage object name contains an invalid path segment") + + +def split_configured_cloud_bucket_name(bucket_name: str) -> Tuple[str, str]: + if not isinstance(bucket_name, str) or not bucket_name.strip(): + raise ValueError("Cloud storage bucket name is required") + + bucket_name = bucket_name.strip() + if "://" in bucket_name or "?" in bucket_name or "#" in bucket_name: + raise ValueError( + "Cloud storage bucket name must not include a URI scheme or query" + ) + if any(ord(char) < 32 or ord(char) == 127 for char in bucket_name): + raise ValueError("Cloud storage bucket name contains control characters") + + bucket, _, prefix = bucket_name.partition("/") + if not bucket: + raise ValueError("Cloud storage bucket name is required") + if "\\" in bucket: + raise ValueError("Cloud storage bucket name contains an invalid separator") + + prefix = prefix.strip("/") + if prefix: + _validate_cloud_object_path(prefix) + + return bucket, prefix + + +def encode_gcs_object_name_for_url(object_name: str) -> str: + return quote(unquote(object_name), safe="") + + +def encode_s3_object_key_for_url(object_key: str) -> str: + return quote(unquote(object_key), safe="/") + + +def should_allow_legacy_cloud_file_ids( + litellm_params: Optional[Mapping[str, Any]] = None, +) -> bool: + value = None + if isinstance(litellm_params, Mapping): + trusted_model_credentials = litellm_params.get( + "_litellm_internal_model_credentials" + ) + if isinstance(trusted_model_credentials, _MAPPING_PROXY_TYPE): + value = cast(Mapping[str, Any], trusted_model_credentials).get( + "allow_legacy_cloud_file_ids" + ) + + if isinstance(value, bool): + return value + if isinstance(value, str): + return value.strip().lower() in {"1", "true", "yes", "on"} + return False + + +def validate_managed_cloud_file_id( + file_id: str, + scheme: str, + configured_bucket_name: str, + allowed_object_prefixes: Sequence[str], + allow_legacy_cloud_file_ids: bool = False, +) -> Tuple[str, str]: + decoded_file_id = unquote(file_id) + if not decoded_file_id.startswith(scheme): + raise ValueError(f"file_id must be a {scheme} URI") + + full_path = decoded_file_id[len(scheme) :] + if "/" not in full_path: + raise ValueError("file_id must include a cloud storage object name") + + bucket_name, object_name = full_path.split("/", 1) + configured_bucket, configured_prefix = split_configured_cloud_bucket_name( + configured_bucket_name + ) + if bucket_name != configured_bucket: + raise ValueError("file_id bucket does not match the configured storage bucket") + + _validate_cloud_object_path(object_name) + allowed_prefixes = tuple(allowed_object_prefixes) + if configured_prefix: + allowed_prefixes = tuple( + f"{configured_prefix.rstrip('/')}/{prefix}" for prefix in allowed_prefixes + ) + + if object_name.startswith(allowed_prefixes): + return bucket_name, object_name + + if allow_legacy_cloud_file_ids: + if configured_prefix and not object_name.startswith( + f"{configured_prefix.rstrip('/')}/" + ): + raise ValueError( + "file_id object does not match the configured storage prefix" + ) + return bucket_name, object_name + + raise ValueError("file_id must reference a LiteLLM-managed storage object") diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py index f873bfeece5..fd402b90d88 100644 --- a/litellm/litellm_core_utils/custom_logger_registry.py +++ b/litellm/litellm_core_utils/custom_logger_registry.py @@ -14,6 +14,7 @@ from litellm import _custom_logger_compatible_callbacks_literal from litellm.integrations.agentops import AgentOps from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook from litellm.integrations.argilla import ArgillaLogger +from litellm.integrations.azure_sentinel.azure_sentinel import AzureSentinelLogger from litellm.integrations.azure_storage.azure_storage import AzureBlobStorageLogger from litellm.integrations.bitbucket import BitBucketPromptManager from litellm.integrations.braintrust_logging import BraintrustLogger @@ -73,6 +74,7 @@ class CustomLoggerRegistry: "opik": OpikLogger, "argilla": ArgillaLogger, "opentelemetry": OpenTelemetry, + "azure_sentinel": AzureSentinelLogger, "azure_storage": AzureBlobStorageLogger, "humanloop": HumanloopLogger, # OTEL compatible loggers diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 5a7d4e33b6d..2c1d92920af 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -6,7 +6,8 @@ from typing import Any, Optional import httpx import litellm -from litellm._logging import _redact_string, verbose_logger +from litellm._logging import _ENABLE_SECRET_REDACTION, _redact_string, verbose_logger +from litellm.litellm_core_utils.secret_redaction import redact_string from litellm.types.utils import LlmProviders from ..exceptions import ( @@ -261,10 +262,18 @@ def exception_type( # type: ignore # noqa: PLR0915 original_exception=original_exception ) try: - error_str = str(original_exception) + error_str = ( + redact_string(str(original_exception)) + if _ENABLE_SECRET_REDACTION + else str(original_exception) + ) if model: if hasattr(original_exception, "message"): - error_str = str(original_exception.message) + error_str = ( + redact_string(str(original_exception.message)) + if _ENABLE_SECRET_REDACTION + else str(original_exception.message) + ) if isinstance(original_exception, BaseException): exception_type = type(original_exception).__name__ else: @@ -2431,7 +2440,8 @@ def exception_type( # type: ignore # noqa: PLR0915 else: raise APIConnectionError( message="{}\n{}".format( - str(original_exception), _redact_string(traceback.format_exc()) + str(original_exception), + _redact_string(traceback.format_exc()), ), llm_provider=custom_llm_provider, model=model, @@ -2461,7 +2471,8 @@ def exception_type( # type: ignore # noqa: PLR0915 raise e # it's already mapped raised_exc = APIConnectionError( message="{}\n{}".format( - original_exception, _redact_string(traceback.format_exc()) + original_exception, + _redact_string(traceback.format_exc()), ), llm_provider="", model="", diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 95bcd4d7186..ba6d438f16c 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -1,4 +1,5 @@ from typing import Optional, Tuple +from urllib.parse import urlparse import litellm from litellm.constants import REPLICATE_MODEL_NAME_WITH_ID_LENGTH @@ -8,6 +9,43 @@ from litellm.secret_managers.main import get_secret, get_secret_str from ..types.router import LiteLLM_Params +def _endpoint_matches_api_base(endpoint: str, api_base: str) -> bool: + """ + Match a registered openai-compatible endpoint against a caller-supplied + ``api_base`` using parsed-URL semantics, not unanchored substring search. + + Both inputs may be a bare hostname (``api.perplexity.ai``), host+path + (``api.deepinfra.com/v1/openai``), or a full URL + (``https://api.cerebras.ai/v1``). Hostnames must match exactly + (case-insensitive); if the registered endpoint has a non-trivial path, + the api_base path must start with it on a segment boundary. + + The naive ``endpoint in api_base`` shape lets a caller pass + ``https://attacker.com/api.groq.com/openai/v1`` to coerce the proxy + into reading the server's GROQ_API_KEY from the environment and + forwarding it to the attacker's host as a Bearer credential. + """ + + def _parse(value: str): + # Ensure urlparse sees a scheme so it populates hostname / path. + normalized = value if "://" in value else f"https://{value}" + return urlparse(normalized) + + parsed_endpoint = _parse(endpoint) + parsed_url = _parse(api_base) + + endpoint_host = (parsed_endpoint.hostname or "").lower() + url_host = (parsed_url.hostname or "").lower() + if not endpoint_host or endpoint_host != url_host: + return False + + endpoint_path = parsed_endpoint.path.rstrip("/") + if not endpoint_path: + return True + url_path = parsed_url.path.rstrip("/") + return url_path == endpoint_path or url_path.startswith(endpoint_path + "/") + + def _is_non_openai_azure_model(model: str) -> bool: try: model_name = model.split("/", 1)[1] @@ -210,7 +248,7 @@ def get_llm_provider( # noqa: PLR0915 # check if api base is a known openai compatible endpoint if api_base: for endpoint in litellm.openai_compatible_endpoints: - if endpoint in api_base: + if _endpoint_matches_api_base(endpoint, api_base): if endpoint == "api.perplexity.ai": custom_llm_provider = "perplexity" dynamic_api_key = get_secret_str("PERPLEXITYAI_API_KEY") @@ -348,6 +386,7 @@ def get_llm_provider( # noqa: PLR0915 or "ft:gpt-3.5-turbo" in model or "ft:gpt-4" in model # catches ft:gpt-4-0613, ft:gpt-4o or model in litellm.openai_image_generation_models + or model.startswith("gpt-image") or model in litellm.openai_video_generation_models ): custom_llm_provider = "openai" @@ -582,6 +621,18 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 or "https://integrate.api.nvidia.com/v1" ) # type: ignore dynamic_api_key = api_key or get_secret_str("NVIDIA_NIM_API_KEY") + elif custom_llm_provider == "nvidia_riva": + # NVIDIA Riva is gRPC-based; api_base must be a host:port like + # `grpc.nvcf.nvidia.com:443` or `localhost:50051`. There is no + # public-default endpoint, so we do not fill one in here. + api_base = api_base or get_secret_str("NVIDIA_RIVA_API_BASE") # type: ignore + # Fall back to NVIDIA_NIM_API_KEY because users running both NVCF + # services typically reuse the same nvapi-* key. + dynamic_api_key = ( + api_key + or get_secret_str("NVIDIA_RIVA_API_KEY") + or get_secret_str("NVIDIA_NIM_API_KEY") + ) elif custom_llm_provider == "cerebras": api_base = ( api_base or get_secret("CEREBRAS_API_BASE") or "https://api.cerebras.ai/v1" diff --git a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py index 563609af1e4..a89dae52316 100644 --- a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py +++ b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py @@ -23,6 +23,13 @@ def _raise_env_reference_error(param: str, *, source: str) -> None: ) +def validate_no_callback_env_reference( + param: str, value: object, *, source: str +) -> None: + if _is_env_reference(value): + _raise_env_reference_error(param, source=source) + + # Hardcoded list of supported callback params to avoid runtime inspection issues with TypedDict _supported_callback_params = [ "langfuse_public_key", @@ -30,8 +37,6 @@ _supported_callback_params = [ "langfuse_secret_key", "langfuse_host", "langfuse_prompt_version", - "gcs_bucket_name", - "gcs_path_service_account", "langsmith_api_key", "langsmith_project", "langsmith_base_url", @@ -50,6 +55,11 @@ _supported_callback_params = [ "lunary_public_key", ] +_request_blocked_callback_params = { + "gcs_bucket_name", + "gcs_path_service_account", +} + def initialize_standard_callback_dynamic_params( kwargs: Optional[Dict] = None, @@ -57,17 +67,20 @@ def initialize_standard_callback_dynamic_params( """ Initialize the standard callback dynamic params from the kwargs - checks if langfuse_secret_key, gcs_bucket_name in kwargs and sets the corresponding attributes in StandardCallbackDynamicParams + checks supported request callback params in kwargs and sets the corresponding attributes in StandardCallbackDynamicParams """ standard_callback_dynamic_params = StandardCallbackDynamicParams() if kwargs: # 1. Check top-level kwargs for param in _supported_callback_params: + if param in _request_blocked_callback_params: + continue if param in kwargs: _param_value = kwargs.get(param) - if _is_env_reference(_param_value): - _raise_env_reference_error(param, source="request body") + validate_no_callback_env_reference( + param, _param_value, source="request body" + ) standard_callback_dynamic_params[param] = _param_value # type: ignore # 2. Fallback: check "metadata" or "litellm_params" -> "metadata" @@ -78,10 +91,13 @@ def initialize_standard_callback_dynamic_params( if isinstance(metadata, dict): for param in _supported_callback_params: + if param in _request_blocked_callback_params: + continue if param not in standard_callback_dynamic_params and param in metadata: _param_value = metadata.get(param) - if _is_env_reference(_param_value): - _raise_env_reference_error(param, source="metadata") + validate_no_callback_env_reference( + param, _param_value, source="metadata" + ) standard_callback_dynamic_params[param] = _param_value # type: ignore return standard_callback_dynamic_params diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 829c1c9ca07..c73d914e6cc 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -1212,7 +1212,7 @@ class Logging(LiteLLMLoggingBaseClass): # Log the exact result from the LLM API, for streaming - log the type of response received litellm.error_logs["POST_CALL"] = locals() if isinstance(original_response, dict): - original_response = json.dumps(original_response) + original_response = json.dumps(original_response, default=str) try: self.model_call_details["input"] = input self.model_call_details["api_key"] = api_key @@ -3242,10 +3242,15 @@ class Logging(LiteLLMLoggingBaseClass): ), langfuse_secret=self.standard_callback_dynamic_params.get( "langfuse_secret" - ), + ) + or self.standard_callback_dynamic_params.get("langfuse_secret_key"), langfuse_host=self.standard_callback_dynamic_params.get( "langfuse_host" ), + allow_env_credentials=self.standard_callback_dynamic_params.get( + "langfuse_host" + ) + is None, ) return langFuseLogger @@ -4720,7 +4725,7 @@ class StandardLoggingPayloadSetup: ): for key, value in litellm_params["metadata"].items(): # Skip non-serializable objects like UserAPIKeyAuth - if key == "user_api_key_auth": + if key in {"user_api_key_auth", "user_api_key_budget_reservation"}: continue merged_metadata[key] = value diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 888999504fe..59d0465e6d4 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -982,9 +982,9 @@ class CostCalculatorUtils: image_response=completion_response, ) elif custom_llm_provider == litellm.LlmProviders.OPENAI.value: - # Check if this is a gpt-image model (token-based pricing) + # gpt-image models use token-based pricing. model_lower = model.lower() - if "gpt-image-1" in model_lower: + if "gpt-image" in model_lower: from litellm.llms.openai.image_generation.cost_calculator import ( cost_calculator as openai_gpt_image_cost_calculator, ) @@ -1004,9 +1004,9 @@ class CostCalculatorUtils: optional_params=optional_params, ) elif custom_llm_provider == litellm.LlmProviders.AZURE.value: - # Check if this is a gpt-image model (token-based pricing) + # gpt-image models use token-based pricing. model_lower = model.lower() - if "gpt-image-1" in model_lower: + if "gpt-image" in model_lower: from litellm.llms.openai.image_generation.cost_calculator import ( cost_calculator as openai_gpt_image_cost_calculator, ) diff --git a/litellm/litellm_core_utils/llm_request_utils.py b/litellm/litellm_core_utils/llm_request_utils.py index f5f28822ca1..7be70852978 100644 --- a/litellm/litellm_core_utils/llm_request_utils.py +++ b/litellm/litellm_core_utils/llm_request_utils.py @@ -77,8 +77,8 @@ def get_proxy_server_request_headers(litellm_params: Optional[dict]) -> dict: if litellm_params is None: return {} - proxy_request_headers = ( - litellm_params.get("proxy_server_request", {}).get("headers", {}) or {} - ) + proxy_request_headers = (litellm_params.get("proxy_server_request") or {}).get( + "headers" + ) or {} return proxy_request_headers diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py index 78378faa262..5fd42fe0d36 100644 --- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py +++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py @@ -824,8 +824,6 @@ def convert_to_model_response_object( # noqa: PLR0915 stream=stream, start_time=start_time, end_time=end_time, - hidden_params=hidden_params, - _response_headers=_response_headers, convert_tool_call_to_json_mode=convert_tool_call_to_json_mode, ) raise Exception( diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index b234e6c8f77..f417b4a5f61 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -436,12 +436,21 @@ def update_messages_with_model_file_ids( """ Updates messages with model file ids. + For managed files (unified file IDs), uses model_file_id_mapping if it + resolves the id, otherwise decodes the base64-encoded unified file ID + and extracts the llm_output_file_id directly. Mirrors the Responses-API + sibling `update_responses_input_with_model_file_ids`. + model_file_id_mapping: Dict[str, Dict[str, str]] = { "litellm_proxy/file_id": { "model_id": "provider_file_id" } } """ + from litellm.proxy.openai_files_endpoints.common_utils import ( + _is_base64_encoded_unified_file_id, + convert_b64_uid_to_unified_uid, + ) for message in messages: if message.get("role") == "user": @@ -450,7 +459,13 @@ def update_messages_with_model_file_ids( if isinstance(content, str): continue for c in content: - if c["type"] == "file": + if not isinstance(c, dict): + # Content list items aren't always dicts. e.g. + # text_completion forwards a token-ids list/list-of- + # lists through this path. Skip non-dict items + # instead of indexing into them. + continue + if c.get("type") == "file": file_object = cast(ChatCompletionFileObject, c) file_object_file_field = file_object.get("file") if not isinstance(file_object_file_field, dict): @@ -468,9 +483,23 @@ def update_messages_with_model_file_ids( if file_id: provider_file_id = ( model_file_id_mapping.get(file_id, {}).get(model_id) - or file_id + if model_file_id_mapping + else None + ) + if ( + not provider_file_id + and _is_base64_encoded_unified_file_id(file_id) + ): + 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] + file_object_file_field["file_id"] = ( + provider_file_id or file_id ) - file_object_file_field["file_id"] = provider_file_id if format: file_object_file_field["format"] = format return messages @@ -726,14 +755,25 @@ def extract_file_data(file_data: FileTypes) -> ExtractedFileData: else: file_content = file_data # Convert content to bytes - if isinstance(file_content, (str, PathLike)): - # If it's a path, open and read the file - # Extract filename from path if not already set + if isinstance(file_content, str): + # Bare string inputs are rejected: when this helper runs in a proxy + # request handler the string came from an attacker-controlled form + # field, and opening it as a path is an arbitrary file read on the + # proxy host. SDK callers who want to upload from a path should + # either pass a pathlib.Path (a PathLike instance — see the branch + # below) or open the file themselves and pass the handle / bytes. + raise ValueError( + "extract_file_data does not accept bare str inputs. Pass bytes, " + "an open file handle, a (filename, content) tuple, or a " + "pathlib.Path. To upload a local file from a path, call " + "open(path, 'rb') yourself." + ) + if isinstance(file_content, PathLike): + # PathLike (pathlib.Path) is a Python-level type that HTTP form + # values can't fabricate. Treat as a local file path for SDK + # convenience. if filename is None: - if isinstance(file_content, PathLike): - filename = Path(file_content).name - else: - filename = Path(str(file_content)).name + filename = Path(file_content).name with open(file_content, "rb") as f: content = f.read() elif isinstance(file_content, io.IOBase): diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index fe8387476ee..d40ca4e3597 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1661,6 +1661,20 @@ def _sanitize_anthropic_tool_use_id(tool_use_id: str) -> str: return sanitized +_ANTHROPIC_DOCUMENT_BASE64_MEDIA_TYPES = {"application/pdf", "text/plain"} + + +def _is_anthropic_document_data_uri(url: str) -> bool: + # Anthropic's base64 document source accepts only application/pdf and + # text/plain (see select_anthropic_content_block_type_for_file). Routing + # other mimes here would produce a document block the API rejects, so we + # leave them on the image code path. + match = re.match(r"data:([^;,]+)", url) + if not match: + return False + return match.group(1) in _ANTHROPIC_DOCUMENT_BASE64_MEDIA_TYPES + + def convert_to_anthropic_tool_result( message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage], force_base64: bool = False, @@ -1698,14 +1712,24 @@ def convert_to_anthropic_tool_result( """ anthropic_content: Union[ str, - List[Union[AnthropicMessagesToolResultContent, AnthropicMessagesImageParam]], + List[ + Union[ + AnthropicMessagesToolResultContent, + AnthropicMessagesImageParam, + AnthropicMessagesDocumentParam, + ] + ], ] = "" if isinstance(message["content"], str): anthropic_content = message["content"] elif isinstance(message["content"], List): content_list = message["content"] anthropic_content_list: List[ - Union[AnthropicMessagesToolResultContent, AnthropicMessagesImageParam] + Union[ + AnthropicMessagesToolResultContent, + AnthropicMessagesImageParam, + AnthropicMessagesDocumentParam, + ] ] = [] for content in content_list: if content["type"] == "text": @@ -1720,21 +1744,62 @@ def convert_to_anthropic_tool_result( text_content["cache_control"] = cache_control_value anthropic_content_list.append(text_content) elif content["type"] == "image_url": + image_url_value = content["image_url"] format = ( - content["image_url"].get("format") - if isinstance(content["image_url"], dict) + image_url_value.get("format") + if isinstance(image_url_value, dict) else None ) - _anthropic_image_param = create_anthropic_image_param( - content["image_url"], format=format, is_bedrock_invoke=force_base64 + url_str = ( + image_url_value.get("url") + if isinstance(image_url_value, dict) + else image_url_value ) - _anthropic_image_param = add_cache_control_to_content( - anthropic_content_element=_anthropic_image_param, + # Data URIs with non-image mime types (e.g. application/pdf) must + # translate to Anthropic document blocks, not image blocks — + # wrapping a PDF in `type: "image"` is rejected by the API. + if isinstance(url_str, str) and _is_anthropic_document_data_uri( + url_str + ): + synth_file_message: ChatCompletionFileObject = { + "type": "file", + "file": {"file_data": url_str}, + } + _document_block = anthropic_process_openai_file_message( + synth_file_message + ) + _document_block = add_cache_control_to_content( + anthropic_content_element=cast( + AnthropicMessagesDocumentParam, _document_block + ), + original_content_element=content, + ) + anthropic_content_list.append( + cast(AnthropicMessagesDocumentParam, _document_block) + ) + else: + _anthropic_image_param = create_anthropic_image_param( + image_url_value, + format=format, + is_bedrock_invoke=force_base64, + ) + _anthropic_image_param = add_cache_control_to_content( + anthropic_content_element=_anthropic_image_param, + original_content_element=content, + ) + anthropic_content_list.append( + cast(AnthropicMessagesImageParam, _anthropic_image_param) + ) + elif content["type"] == "file": + file_content = cast(ChatCompletionFileObject, content) + _file_block = anthropic_process_openai_file_message(file_content) + _file_block = add_cache_control_to_content( + anthropic_content_element=cast( + AnthropicMessagesDocumentParam, _file_block + ), original_content_element=content, ) - anthropic_content_list.append( - cast(AnthropicMessagesImageParam, _anthropic_image_param) - ) + anthropic_content_list.append(_file_block) anthropic_content = anthropic_content_list anthropic_tool_result: Optional[AnthropicMessagesToolResultParam] = None @@ -2059,27 +2124,62 @@ def anthropic_process_openai_file_message( ) +_EMPTY_TEXT_PLACEHOLDER = ( + "[System: Empty message content sanitised to satisfy protocol]" +) + + def _sanitize_empty_text_content( message: AllMessageValues, ) -> AllMessageValues: """ Case C: Sanitize empty text content - Replace empty or whitespace-only text content with a placeholder message. + - Handles both string content and list-of-blocks content (rewriting only + the empty text blocks in place; non-text blocks like images are left + untouched). Returns: The message with sanitized content if needed, otherwise the original message """ - if message.get("role") in ["user", "assistant"]: - content = message.get("content") - if isinstance(content, str): - if not content or not content.strip(): - message = cast(AllMessageValues, dict(message)) # Make a copy - message["content"] = ( - "[System: Empty message content sanitised to satisfy protocol]" - ) - verbose_logger.debug( - f"_sanitize_empty_text_content: Replaced empty text content in {message.get('role')} message" - ) + if message.get("role") not in ["user", "assistant"]: + return message + + content = message.get("content") + + if isinstance(content, str): + if not content or not content.strip(): + message = cast(AllMessageValues, dict(message)) # Make a copy + message["content"] = _EMPTY_TEXT_PLACEHOLDER + verbose_logger.debug( + f"_sanitize_empty_text_content: Replaced empty text content in {message.get('role')} message" + ) + return message + + if isinstance(content, list): + # Walk the blocks and rewrite any empty text blocks. We rewrite (rather + # than drop) so callers don't end up with an entirely empty content + # list, which Anthropic also rejects. + new_blocks: List[Any] = [] + rewrote_any = False + for block in content: + if isinstance(block, dict) and block.get("type") == "text": + text = block.get("text") + if not isinstance(text, str) or not text or not text.strip(): + new_block = dict(block) + new_block["text"] = _EMPTY_TEXT_PLACEHOLDER + new_blocks.append(new_block) + rewrote_any = True + continue + new_blocks.append(block) + + if rewrote_any: + message = cast(AllMessageValues, dict(message)) # Make a copy + message["content"] = new_blocks # type: ignore + verbose_logger.debug( + f"_sanitize_empty_text_content: Replaced empty text block(s) in {message.get('role')} message" + ) + return message @@ -2362,6 +2462,18 @@ def anthropic_messages_pt( # noqa: PLR0915 # Sanitize messages for tool calling issues when modify_params=True messages = sanitize_messages_for_tool_calling(messages) + # Anthropic rejects empty text content blocks with: + # "messages: text content blocks must be non-empty" + # OpenAI/other providers silently tolerate `{"role": "user", "content": ""}`, + # so callers (and upstream agent frameworks like pydantic-ai) routinely + # send empty user/assistant turns. We always rewrite these to a placeholder + # for Anthropic-shaped requests, independent of `litellm.modify_params`, + # because there is no way to "pass through" an empty text block — the + # request will always 400 otherwise. The richer tool-call sanitization + # (Cases A/B/D in `sanitize_messages_for_tool_calling`) remains gated on + # `modify_params` because it actually mutates conversation structure. + messages = [_sanitize_empty_text_content(m) for m in messages] + # add role=tool support to allow function call result/error submission user_message_types = {"user", "tool", "function"} # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them. @@ -3977,6 +4089,55 @@ def _convert_to_bedrock_tool_call_result( tool_result_content_blocks.append( BedrockToolResultContentBlock(image=_block["image"]) ) + elif "document" in _block: + tool_result_content_blocks.append( + BedrockToolResultContentBlock(document=_block["document"]) + ) + else: + verbose_logger.warning( + "Bedrock Converse: unrecognized BedrockContentBlock keys " + "%s for image_url tool-result block %s; dropping.", + list(_block.keys()), + content, + ) + elif content["type"] == "file": + # Match the user-message path (_process_file_message): accept + # either file_data (base64 data URI) or file_id (server-side + # reference / URL) and hand off to BedrockImageProcessor. Raise + # BadRequestError on both-None rather than silently dropping. + file_obj = content.get("file") or {} + file_data = file_obj.get("file_data") + file_id = file_obj.get("file_id") + if file_data is None and file_id is None: + raise litellm.BadRequestError( + message="file_data and file_id cannot both be None. Got={}".format( + content + ), + model="", + llm_provider="bedrock", + ) + file_format = file_obj.get("format") + _file_block: BedrockContentBlock = ( + BedrockImageProcessor.process_image_sync( + image_url=cast(str, file_id or file_data), + format=file_format, + ) + ) + if "document" in _file_block: + tool_result_content_blocks.append( + BedrockToolResultContentBlock(document=_file_block["document"]) + ) + elif "image" in _file_block: + tool_result_content_blocks.append( + BedrockToolResultContentBlock(image=_file_block["image"]) + ) + else: + verbose_logger.warning( + "Bedrock Converse: unrecognized BedrockContentBlock keys " + "%s for file tool-result block %s; dropping.", + list(_file_block.keys()), + content, + ) message.get("name", "") id = str(message.get("tool_call_id", str(uuid.uuid4()))) @@ -4468,6 +4629,11 @@ class BedrockConverseMessagesProcessor: message=cast(ChatCompletionFileObject, element) ) _parts.append(_part) + elif element["type"] == "document": + _part = BedrockConverseMessagesProcessor._process_document_message( + element + ) + _parts.append(_part) _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( message_block=cast( @@ -4750,6 +4916,44 @@ class BedrockConverseMessagesProcessor: image_url=cast(str, file_id or file_data), format=format ) + @staticmethod + def _process_document_message(element: dict) -> BedrockContentBlock: + """Convert a document content block to a Bedrock DocumentBlock. + + Handles the Anthropic-style document format: + {"type": "document", "source": {"type": "base64", "media_type": "application/pdf", "data": "..."}} + """ + source = element["source"] + source_type = source.get("type") + if source_type != "base64": + raise ValueError( + f"Bedrock Converse only supports base64-encoded document sources, got '{source_type}'. " + "Please convert the document to base64 before sending to Bedrock." + ) + media_type: str = source["media_type"] + data: str = source["data"] + doc_format = BedrockImageProcessor._validate_format( + mime_type=media_type, image_format=media_type.split("/")[1] + ) + + # Deterministic name using the same hashing pattern as _create_bedrock_block + HASH_SAMPLE_BYTES = 64 * 1024 + normalized = "".join(data.split()).encode("utf-8") + sample = normalized[:HASH_SAMPLE_BYTES] + hasher = hashlib.sha256() + hasher.update(sample) + hasher.update(str(len(normalized)).encode("utf-8")) + content_hash = hasher.hexdigest()[:16] + document_name = f"Document_{content_hash}_{doc_format}" + + return BedrockContentBlock( + document=BedrockDocumentBlock( + source=BedrockSourceBlock(bytes=data), + format=doc_format, + name=document_name, + ) + ) + @staticmethod def add_thinking_blocks_to_assistant_content( thinking_blocks: List[BedrockContentBlock], @@ -4773,8 +4977,9 @@ class BedrockConverseMessagesProcessor: ) if reasoning_text and not reasoning_text.get("signature"): reasoning_text_text = reasoning_text["text"] - assistants_part = BedrockContentBlock(text=reasoning_text_text) - assistant_parts.append(assistants_part) + if reasoning_text_text.strip(): + assistants_part = BedrockContentBlock(text=reasoning_text_text) + assistant_parts.append(assistants_part) else: filtered_thinking_blocks.append(block) if len(filtered_thinking_blocks) > 0: @@ -4847,6 +5052,11 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 ) ) _parts.append(_part) + elif element["type"] == "document": + _part = BedrockConverseMessagesProcessor._process_document_message( + element + ) + _parts.append(_part) _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( message_block=cast( diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 4493a58f78b..c4528ff74e3 100644 --- a/litellm/litellm_core_utils/realtime_streaming.py +++ b/litellm/litellm_core_utils/realtime_streaming.py @@ -31,6 +31,8 @@ DefaultLoggedRealTimeEventTypes = [ "session.created", "response.create", "response.done", + "conversation.item.added", # GA + "conversation.item.done", # GA ] @@ -44,6 +46,7 @@ class RealTimeStreaming: model: str = "", user_api_key_dict: Optional[Any] = None, request_data: Optional[Dict] = None, + backend_uses_beta_protocol: Optional[bool] = None, ): self.websocket = websocket self.backend_ws = backend_ws @@ -54,6 +57,14 @@ class RealTimeStreaming: self.session_tools: List[Dict] = [] self.tool_calls: List[Dict] = [] + # Detect whether the client is explicitly opting into the beta protocol. + self._client_wants_beta = self._detect_beta_header(websocket) + self._backend_uses_beta_protocol = ( + self._client_wants_beta + if backend_uses_beta_protocol is None + else backend_uses_beta_protocol + ) + _logged_real_time_event_types = litellm.logged_real_time_event_types if _logged_real_time_event_types is None: @@ -76,6 +87,27 @@ class RealTimeStreaming: # response.create can be rewritten to include the failure context. self._pending_guardrail_message: Optional[str] = None + _SESSION_EVENT_TYPES = frozenset(["session.created", "session.updated"]) + _AUDIO_FORMAT_MAP: Dict[str, Dict[str, Any]] = { + "pcm16": {"type": "audio/pcm", "rate": 24000}, + "g711_ulaw": {"type": "audio/G711-ulaw", "rate": 8000}, + "g711_alaw": {"type": "audio/G711-alaw", "rate": 8000}, + } + # GA name → beta name (when client WebSocket includes OpenAI-Beta: realtime=v1) + _GA_TO_BETA_EVENT_TYPES: Dict[str, str] = { + "conversation.item.added": "conversation.item.created", + "response.output_text.delta": "response.text.delta", + "response.output_audio.delta": "response.audio.delta", + "response.output_audio_transcript.delta": "response.audio_transcript.delta", + "response.output_text.done": "response.text.done", + "response.output_audio.done": "response.audio.done", + "response.output_audio_transcript.done": "response.audio_transcript.done", + } + _GA_TO_BETA_CONTENT_TYPES: Dict[str, str] = { + "output_text": "text", + "output_audio": "audio", + } + def _should_store_message( self, message_obj: Union[dict, OpenAIRealtimeEvents], @@ -92,24 +124,27 @@ class RealTimeStreaming: if isinstance(message, bytes): message = message.decode("utf-8") if isinstance(message, dict): - message_obj = message + # TypedDict union members do not narrow to plain dict for mypy. + message_obj: Dict[str, Any] = cast(Dict[str, Any], message) else: - message_obj = json.loads(message) + message_obj = cast(Dict[str, Any], json.loads(cast(str, message))) self._collect_tool_calls_from_response_done(cast(dict, message_obj)) try: - if ( - not isinstance(message, dict) - or message_obj.get("type") == "session.created" - or message_obj.get("type") == "session.updated" - ): - message_obj = OpenAIRealtimeStreamSessionEvents(**message_obj) # type: ignore - elif not isinstance(message, dict): - message_obj = OpenAIRealtimeStreamResponseBaseObject(**message_obj) # type: ignore + event_type = message_obj.get("type", "") + if event_type in self._SESSION_EVENT_TYPES: + typed_obj = OpenAIRealtimeStreamSessionEvents(**message_obj) # type: ignore + else: + # Use the base object as a safe catch-all for all other event types + # (both beta and GA), so unknown/new event names never raise here. + typed_obj = OpenAIRealtimeStreamResponseBaseObject(**message_obj) # type: ignore except Exception as e: verbose_logger.debug(f"Error parsing message for logging: {e}") - raise e - if self._should_store_message(message_obj): - self.messages.append(message_obj) + # Don't re-raise — a parse failure must not drop or delay the message + if self._should_store_message(message_obj): + self.messages.append(message_obj) # type: ignore[arg-type] + return + if self._should_store_message(typed_obj): + self.messages.append(typed_obj) def _collect_user_input_from_client_event(self, message: Union[str, dict]) -> None: """Extract user text content from client WebSocket events for spend logging.""" @@ -147,6 +182,8 @@ class RealTimeStreaming: tools = session.get("tools") if tools and isinstance(tools, list): self.session_tools = tools + # GA: session.type is required; log it for traceability but no action needed + verbose_logger.debug(f"Realtime session.type: {session.get('type')}") except (json.JSONDecodeError, AttributeError, TypeError): pass @@ -228,6 +265,23 @@ class RealTimeStreaming: else: await self.backend_ws.send(message) # type: ignore[union-attr, attr-defined] + def _make_disable_auto_response_message(self) -> str: + """Return a session.update that disables VAD auto-response.""" + if self._backend_uses_beta_protocol: + session: Dict[str, Any] = { + "turn_detection": {"create_response": False}, + } + else: + session = { + "type": "realtime", + "audio": { + "input": { + "turn_detection": {"create_response": False}, + } + }, + } + return json.dumps({"type": "session.update", "session": session}) + def _has_realtime_guardrails(self) -> bool: """Return True if any callback is registered for realtime guardrail event types.""" from litellm.integrations.custom_guardrail import CustomGuardrail @@ -435,14 +489,7 @@ class RealTimeStreaming: ): self.store_message(event_str) await self.websocket.send_text(event_str) - await self._send_to_backend( - json.dumps( - { - "type": "session.update", - "session": {"turn_detection": {"create_response": False}}, - } - ) - ) + await self._send_to_backend(self._make_disable_auto_response_message()) continue ## GUARDRAIL: run on transcription events in provider_config path too if ( @@ -484,14 +531,7 @@ class RealTimeStreaming: ): self.store_message(raw_response) await self.websocket.send_text(raw_response) - await self._send_to_backend( - json.dumps( - { - "type": "session.update", - "session": {"turn_detection": {"create_response": False}}, - } - ) - ) + await self._send_to_backend(self._make_disable_auto_response_message()) return True if ( @@ -542,7 +582,20 @@ class RealTimeStreaming: continue ## LOGGING self.store_message(raw_response) - await self.websocket.send_text(raw_response) + + # If the client opted into beta protocol, translate GA event + # names/shapes back to the beta equivalents before forwarding. + if self._client_wants_beta: + try: + event_dict = json.loads(raw_response) + translated = self._translate_event_to_beta(event_dict) + if translated is None: + continue # drop GA-only events (e.g. conversation.item.done) + await self.websocket.send_text(json.dumps(translated)) + except Exception: + await self.websocket.send_text(raw_response) + else: + await self.websocket.send_text(raw_response) except websockets.exceptions.ConnectionClosed as e: # type: ignore verbose_logger.exception( @@ -553,6 +606,183 @@ class RealTimeStreaming: finally: await self.log_messages() + @staticmethod + def _detect_beta_header(websocket: Any) -> bool: + """Return True if the client sent 'OpenAI-Beta: realtime=v1'. + + Checks the raw ASGI scope headers so it works for both FastAPI WebSocket + objects and any test doubles that expose a .scope dict. + """ + try: + headers = websocket.scope.get("headers", []) + for name, value in headers: + if isinstance(name, bytes): + name = name.decode("latin-1") + if isinstance(value, bytes): + value = value.decode("latin-1") + if name.lower() == "openai-beta" and "realtime=v1" in value.lower(): + return True + except Exception: + pass + return False + + @staticmethod + def _remap_beta_session_to_ga(session: dict) -> dict: + """ + Convert a beta-style session.update payload to the GA nested schema. + + Beta → GA field mappings + ───────────────────────────────────────────────────────────────────── + session.type (inject "realtime" if absent) + session.modalities → session.output_modalities + session.voice → session.audio.output.voice + session.input_audio_format → session.audio.input.format (with type/rate) + session.output_audio_format → session.audio.output.format (with type/rate) + session.turn_detection → session.audio.input.turn_detection + session.input_audio_transcription → session.audio.input.transcription + ───────────────────────────────────────────────────────────────────── + Fields not in the mapping (instructions, tools, etc.) are passed through. + GA clients that already use the nested shape are unaffected. + """ + # Work on a shallow copy so we don't mutate the caller's dict + session = dict(session) + + # 1. Ensure session.type is present + if "type" not in session: + session["type"] = "realtime" + + # 2. Rename modalities → output_modalities and normalise combinations. + # Beta allowed ["audio", "text"] together; GA only supports ["audio"] or + # ["text"] as single-element lists. When both are present we prefer + # ["audio"] because audio mode already delivers transcripts via events. + if "modalities" in session: + mods = session.pop("modalities") + if "output_modalities" not in session: + mods_set = {m.lower() for m in (mods or [])} + if "audio" in mods_set: + session["output_modalities"] = ["audio"] + elif "text" in mods_set: + session["output_modalities"] = ["text"] + + # 3-7. Lift flat audio fields into the nested audio object + audio: Dict[str, Any] = {} + inp: Dict[str, Any] = {} + out: Dict[str, Any] = {} + + # voice → audio.output.voice + if "voice" in session: + out["voice"] = session.pop("voice") + + # input_audio_format → audio.input.format + if "input_audio_format" in session: + raw = session.pop("input_audio_format") + inp["format"] = ( + RealTimeStreaming._AUDIO_FORMAT_MAP.get(raw, raw) + if isinstance(raw, str) + else raw + ) + + # output_audio_format → audio.output.format + if "output_audio_format" in session: + raw = session.pop("output_audio_format") + out["format"] = ( + RealTimeStreaming._AUDIO_FORMAT_MAP.get(raw, raw) + if isinstance(raw, str) + else raw + ) + + # turn_detection → audio.input.turn_detection + if "turn_detection" in session: + inp["turn_detection"] = session.pop("turn_detection") + + # input_audio_transcription → audio.input.transcription + if "input_audio_transcription" in session: + inp["transcription"] = session.pop("input_audio_transcription") + + if inp: + audio["input"] = inp + if out: + audio["output"] = out + + if audio: + # Merge with any existing GA-style `audio` block the client already set, + # letting the remapped values take precedence within each sub-key. + existing = session.get("audio") or {} + for sub_key, sub_val in audio.items(): + if ( + sub_key in existing + and isinstance(existing[sub_key], dict) + and isinstance(sub_val, dict) + ): + existing[sub_key] = {**existing[sub_key], **sub_val} + else: + existing[sub_key] = sub_val + session["audio"] = existing + + return session + + @staticmethod + def _translate_event_to_beta(event: dict) -> Optional[dict]: + """Translate a single GA event dict to its beta equivalent. + + Returns None if the event should be dropped entirely (e.g. the GA-only + conversation.item.done has no beta counterpart). + Returns the (possibly mutated copy of the) event otherwise. + """ + event_type = event.get("type", "") + + # conversation.item.done has no beta equivalent — the client already + # received conversation.item.created (translated from .added). + if event_type == "conversation.item.done": + return None + + # Shallow-copy so we don't mutate the stored message + translated = dict(event) + + # Rename the type field + if event_type in RealTimeStreaming._GA_TO_BETA_EVENT_TYPES: + translated["type"] = RealTimeStreaming._GA_TO_BETA_EVENT_TYPES[event_type] + + # Fix content block types inside items (response.done output list, + # conversation.item.created item content, etc.) + if "item" in translated and isinstance(translated["item"], dict): + translated["item"] = RealTimeStreaming._translate_item_content_types( + dict(translated["item"]) + ) + if "response" in translated and isinstance(translated["response"], dict): + resp = dict(translated["response"]) + if "output" in resp and isinstance(resp["output"], list): + resp["output"] = [ + ( + RealTimeStreaming._translate_item_content_types(dict(o)) + if isinstance(o, dict) + else o + ) + for o in resp["output"] + ] + translated["response"] = resp + + return translated + + @staticmethod + def _translate_item_content_types(item: dict) -> dict: + """Replace GA content type names with beta names inside a single item.""" + if "content" not in item or not isinstance(item["content"], list): + return item + new_content = [] + for block in item["content"]: + if ( + isinstance(block, dict) + and block.get("type") in RealTimeStreaming._GA_TO_BETA_CONTENT_TYPES + ): + block = dict(block) + block["type"] = RealTimeStreaming._GA_TO_BETA_CONTENT_TYPES[ + block["type"] + ] + new_content.append(block) + item["content"] = new_content + return item + async def client_ack_messages(self): try: while True: @@ -594,6 +824,19 @@ class RealTimeStreaming: self._pending_guardrail_message = None continue + # GA compatibility: remap beta-style session fields only when + # the upstream is in GA mode. Beta upstreams expect the flat + # session shape unchanged. + if ( + msg_type == "session.update" + and not self._backend_uses_beta_protocol + ): + session = msg_obj.get("session", {}) + if isinstance(session, dict): + session = self._remap_beta_session_to_ga(session) + msg_obj["session"] = session + message = json.dumps(msg_obj) + except (json.JSONDecodeError, AttributeError): pass @@ -627,3 +870,8 @@ class RealTimeStreaming: await forward_task except asyncio.CancelledError: pass + + +def client_sent_openai_beta_realtime_header(websocket: Any) -> bool: + """True when the client WebSocket includes ``OpenAI-Beta: realtime=v1``.""" + return RealTimeStreaming._detect_beta_header(websocket) diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py index f3f560b33b9..dbc9cabdc7a 100644 --- a/litellm/litellm_core_utils/redact_messages.py +++ b/litellm/litellm_core_utils/redact_messages.py @@ -60,6 +60,9 @@ 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 hasattr(output_item, "content") and isinstance(output_item.content, list): for content_part in output_item.content: if hasattr(content_part, "text"): @@ -75,6 +78,28 @@ def _redact_responses_api_output(output_items): summary_item.text = "redacted-by-litellm" +def _redact_responses_api_output_dict(output_items, redacted_str: str): + """Helper to redact ResponsesAPIResponse output items in dict form.""" + for output_item in output_items: + if not isinstance(output_item, dict): + continue + + if "text" in output_item: + 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: + 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: + summary_item["text"] = redacted_str + + def _redact_standard_logging_object(model_call_details: dict): """Redact messages and response inside standard_logging_object if present.""" standard_logging_object = model_call_details.get("standard_logging_object") @@ -93,28 +118,11 @@ def _redact_standard_logging_object(model_call_details: dict): if isinstance(response, dict) and "output" in response: # ResponsesAPIResponse format - redact content in output items if isinstance(response.get("output"), list): - for output_item in response["output"]: - if isinstance(output_item, dict) and "content" in output_item: - if isinstance(output_item["content"], list): - for content_item in output_item["content"]: - if ( - isinstance(content_item, dict) - and "text" in content_item - ): - content_item["text"] = redacted_str + _redact_responses_api_output_dict(response["output"], redacted_str) elif isinstance(response, dict) and "choices" in response: # ModelResponse dict format - redact content in choices if isinstance(response.get("choices"), list): - for choice in response["choices"]: - if isinstance(choice, dict): - if "message" in choice and isinstance(choice["message"], dict): - choice["message"]["content"] = redacted_str - if "audio" in choice["message"]: - choice["message"]["audio"] = None - elif "delta" in choice and isinstance(choice["delta"], dict): - choice["delta"]["content"] = redacted_str - if "audio" in choice["delta"]: - choice["delta"]["audio"] = None + _redact_model_response_dict_choices(response["choices"], redacted_str) elif isinstance(response, str): standard_logging_object["response"] = redacted_str else: @@ -122,6 +130,29 @@ def _redact_standard_logging_object(model_call_details: dict): standard_logging_object["response"] = {"text": redacted_str} +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"]: + 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 + elif "delta" in choice and isinstance(choice["delta"], dict): + choice["delta"]["content"] = redacted_str + if "reasoning_content" in choice["delta"]: + 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 + else: + _redact_choice_content(choice) + + def perform_redaction(model_call_details: dict, result): """ Performs the actual redaction on the logging object and result. @@ -132,6 +163,7 @@ def perform_redaction(model_call_details: dict, result): ] model_call_details["prompt"] = "" model_call_details["input"] = "" + _redact_standard_logging_object(model_call_details) # Redact streaming response if ( @@ -171,30 +203,14 @@ def perform_redaction(model_call_details: dict, result): 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: - for choice in _result["choices"]: - if isinstance(choice, dict): - if "message" in choice and isinstance(choice["message"], dict): - choice["message"]["content"] = "redacted-by-litellm" - if "reasoning_content" in choice["message"]: - choice["message"][ - "reasoning_content" - ] = "redacted-by-litellm" - if "thinking_blocks" in choice["message"]: - choice["message"]["thinking_blocks"] = None - if "audio" in choice["message"]: - choice["message"]["audio"] = None - elif "delta" in choice and isinstance(choice["delta"], dict): - choice["delta"]["content"] = "redacted-by-litellm" - if "reasoning_content" in choice["delta"]: - choice["delta"][ - "reasoning_content" - ] = "redacted-by-litellm" - if "thinking_blocks" in choice["delta"]: - choice["delta"]["thinking_blocks"] = None - if "audio" in choice["delta"]: - choice["delta"]["audio"] = None - else: - _redact_choice_content(choice) + _redact_model_response_dict_choices( + _result["choices"], "redacted-by-litellm" + ) + elif isinstance(_result, dict) and "output" in _result: + if isinstance(_result.get("output"), list): + _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) diff --git a/litellm/litellm_core_utils/secret_redaction.py b/litellm/litellm_core_utils/secret_redaction.py new file mode 100644 index 00000000000..5c4e3e3dacf --- /dev/null +++ b/litellm/litellm_core_utils/secret_redaction.py @@ -0,0 +1,81 @@ +""" +Credential/secret redaction utilities. + +This module owns the compiled regex and the public `redact_string` helper so +that any part of the codebase (logging, exception mapping, etc.) can scrub +secrets from strings without depending on the logging-configuration module. +""" + +import re +from typing import List + +_REDACTED = "REDACTED" + + +def _build_secret_patterns() -> "re.Pattern[str]": + patterns: List[str] = [ + # PEM private key / certificate blocks + r"-----BEGIN[A-Z \-]*PRIVATE KEY-----[\s\S]*?-----END[A-Z \-]*PRIVATE KEY-----", + # GCP OAuth2 access tokens (ya29.*) + r"\bya29\.[A-Za-z0-9_.~+/-]+", + # Credential %s formatting (space separator, no key= prefix) + r"(?:client_secret|azure_password|azure_username)\s+[^\s,'\"})\]{}>]+", + # AWS access key IDs + r"(?:AKIA|ASIA)[0-9A-Z]{16}", + # AWS secrets / session tokens / access key IDs (key=value) + r"(?:aws_secret_access_key|aws_session_token|aws_access_key_id)" + r"\s*[:=]\s*[A-Za-z0-9/+=]{20,}", + # Bearer tokens (OAuth, JWT, etc.) + r"Bearer\s+[A-Za-z0-9\-._~+/]{10,}=*", + # Basic auth headers + r"Basic\s+[A-Za-z0-9+/]{10,}={0,2}", + # OpenAI / Anthropic sk- prefixed keys + r"sk-[A-Za-z0-9\-_]{20,}", + # Generic api_key / api-key / apikey (handles 'key': 'value' dict repr) + r"(?:api[_-]?key)['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]{8,}", + # x-api-key / api-key header values (handles 'key': 'value' dict repr) + r"(?:x-api-key|api-key)['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]+", + # Anthropic internal header keys + r"x-ak-[A-Za-z0-9\-_]{20,}", + # Google API keys (bare key value) + r"AIza[0-9A-Za-z\-_]{35}", + # URL query-param key=VALUE (e.g. ?key=AIza... or &key=...) — catches the + # full "key=" fragment so the value is redacted regardless of format. + r"(?<=[?&])key=[^\s&'\"]{8,}", + # Password / secret params (handles key=value and 'key': 'value') + # Word boundary prevents O(n^2) backtracking on long word-char runs. + r"(?:^|(?<=\W))\w*(?:password|passwd|client_secret|secret_key|_secret)" + r"['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]+", + # Database connection string credentials (scheme://user:pass@host) + r"(?<=://)[^\s'\"]*:[^\s'\"@]+(?=@)", + # Databricks personal access tokens + r"dapi[0-9a-f]{32}", + # ── Key-name-based redaction ── + # Catches secrets inside dicts/config dumps by matching on the KEY name + # regardless of what the value looks like. + # e.g. 'master_key': 'any-value-here', "database_url": "postgres://..." + # private_key with PEM-aware value capture + r"""private_key['\"]?\s*[:=]\s*['\"]?(?:-----BEGIN[A-Z \-]*PRIVATE KEY-----[\s\S]*?-----END[A-Z \-]*PRIVATE KEY-----|[^\s,'\"})\]{}>]+)""", + r"(?:master_key|database_url|db_url|connection_string|" + r"signing_key|encryption_key|" + r"auth_token|access_token|refresh_token|" + r"slack_webhook_url|webhook_url|" + r"database_connection_string|" + r"huggingface_token|jwt_secret)" + r"""['\"]?\s*[:=]\s*['\"]?[^\s,'\"})\]{}>]+""", + # Raw JWTs (without Bearer prefix) + r"\beyJ[A-Za-z0-9_-]{10,}\.[A-Za-z0-9_-]+\.[A-Za-z0-9_-]*", + # Azure SAS tokens in URLs + r"[?&]sig=[A-Za-z0-9%+/=]+", + # Full JSON service-account blobs (single-line and multi-line) + r'\{[^{}]*"type"\s*:\s*"service_account"[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', + ] + return re.compile("|".join(patterns), re.IGNORECASE) + + +_SECRET_RE = _build_secret_patterns() + + +def redact_string(value: str) -> str: + """Scrub known secret/credential patterns from *value* and return the result.""" + return _SECRET_RE.sub(_REDACTED, value) diff --git a/litellm/litellm_core_utils/sensitive_data_masker.py b/litellm/litellm_core_utils/sensitive_data_masker.py index 8b88ef94821..d7803455b4a 100644 --- a/litellm/litellm_core_utils/sensitive_data_masker.py +++ b/litellm/litellm_core_utils/sensitive_data_masker.py @@ -21,6 +21,8 @@ class SensitiveDataMasker: "auth", "authorization", "credential", + # Plural form: Vertex uses ``vertex_credentials``; segment-exact + # matching otherwise misses it because "credential" != "credentials". "credentials", "access", "private", diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index e281b172685..fa7faf3035d 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -2244,7 +2244,7 @@ class CustomStreamWrapper: asyncio.create_task( self.logging_obj.async_failure_handler(e, traceback_exception) ) - raise e + self._handle_stream_fallback_error(e) except Exception as e: traceback_exception = traceback.format_exc() if self.logging_obj is not None: diff --git a/litellm/litellm_core_utils/url_utils.py b/litellm/litellm_core_utils/url_utils.py index a65d0892aa2..38a78ee058f 100644 --- a/litellm/litellm_core_utils/url_utils.py +++ b/litellm/litellm_core_utils/url_utils.py @@ -21,8 +21,8 @@ Admins can opt out via two ``litellm`` globals (wired from proxy config): import socket from ipaddress import ip_address, ip_network -from typing import Any, List, Set, Tuple -from urllib.parse import urlparse, urlunparse +from typing import Any, List, Optional, Set, Tuple +from urllib.parse import quote, urlparse, urlunparse import httpx @@ -46,6 +46,46 @@ class SSRFError(ValueError): pass +def encode_url_path_segment(value: Any, *, field_name: str = "path parameter") -> str: + """Percent-encode one user-controlled URL path segment. + + ``urllib.parse.quote(..., safe="")`` intentionally leaves RFC 3986 + unreserved characters such as ``.`` unescaped, so reject standalone dot + segments before they can be appended to an upstream URL and normalized by + the HTTP client. + """ + if value is None: + raise ValueError(f"{field_name} is required") + + value_str = str(value) + if value_str == "": + raise ValueError(f"{field_name} is required") + if value_str in {".", ".."}: + raise ValueError(f"{field_name} cannot be a dot path segment") + + return quote(value_str, safe="") + + +def encode_url_path_segments(value: Any, *, field_name: str = "path") -> str: + """Percent-encode a user-controlled URL path made of multiple segments. + + Empty segments are rejected, so leading, trailing, or consecutive slashes + fail closed instead of being normalized by the HTTP client. + """ + if value is None: + raise ValueError(f"{field_name} is required") + + value_str = str(value) + if value_str == "": + raise ValueError(f"{field_name} is required") + + encoded_segments = [] + for segment in value_str.split("/"): + encoded_segments.append(encode_url_path_segment(segment, field_name=field_name)) + + return "/".join(encoded_segments) + + def _is_blocked_ip(addr: str) -> bool: """Return True for any IP not safe to reach from a user-supplied URL. @@ -70,6 +110,85 @@ def _normalize_host(host: str) -> str: return host.lower().rstrip(".") +def _default_port_for_scheme(scheme: str) -> int: + return 443 if scheme == "https" else 80 + + +def _parse_url_destination_allowlist_entry( + entry: str, +) -> Optional[Tuple[str, Optional[str], Optional[int]]]: + """Parse an admin allowlist entry into host, optional scheme, optional port. + + Entries may be bare hosts (``api.example.com``), host+port + (``api.example.com:8443``), or origins (``https://api.example.com``). + URL paths are intentionally ignored so admins can paste an api_base value. + """ + entry = entry.strip() + if not entry: + return None + + has_scheme = "://" in entry + parsed = urlparse(entry if has_scheme else f"//{entry}") + if has_scheme and parsed.scheme not in _ALLOWED_SCHEMES: + return None + if parsed.username is not None or parsed.password is not None: + return None + if not parsed.hostname: + return None + + try: + port = parsed.port + except ValueError: + return None + + scheme: Optional[str] = parsed.scheme if has_scheme else None + if scheme is not None and port is None: + port = _default_port_for_scheme(scheme) + + return _normalize_host(parsed.hostname), scheme, port + + +def is_url_destination_allowed_by_host(url: str, allowed_hosts: List[str]) -> bool: + """Return True when a credential-bearing provider URL is admin-allowlisted. + + This does not fetch, resolve, or rewrite URLs. It only answers whether the + destination origin is explicitly trusted by configuration. Use ``safe_get`` + for user-controlled content fetches that require SSRF protection. + """ + parsed = urlparse(url) + if parsed.scheme not in _ALLOWED_SCHEMES: + return False + if parsed.username is not None or parsed.password is not None: + return False + if not parsed.hostname: + return False + + try: + effective_port = parsed.port or _default_port_for_scheme(parsed.scheme) + except ValueError: + return False + + normalized_host = _normalize_host(parsed.hostname) + configured_entries = ( + [allowed_hosts] if isinstance(allowed_hosts, str) else allowed_hosts + ) + for entry in configured_entries or []: + if not isinstance(entry, str): + continue + parsed_entry = _parse_url_destination_allowlist_entry(entry) + if parsed_entry is None: + continue + allowed_host, allowed_scheme, allowed_port = parsed_entry + if allowed_host != normalized_host: + continue + if allowed_scheme is not None and allowed_scheme != parsed.scheme: + continue + if allowed_port is not None and allowed_port != effective_port: + continue + return True + return False + + def _format_host_header(hostname: str, port: int, default_port: int) -> str: """Build an RFC 7230 Host header value, bracketing IPv6 literals.""" bracketed = f"[{hostname}]" if ":" in hostname else hostname @@ -145,7 +264,7 @@ def validate_url(url: str) -> Tuple[str, str]: raise SSRFError("URL has no hostname") port = parsed.port - default_port = 443 if parsed.scheme == "https" else 80 + default_port = _default_port_for_scheme(parsed.scheme) effective_port = port if port is not None else default_port host_header = _format_host_header(hostname, effective_port, default_port) @@ -199,13 +318,54 @@ def validate_url(url: str) -> Tuple[str, str]: return rewritten, host_header +def assert_same_origin(candidate_url: str, expected_url: str) -> None: + """Verify ``candidate_url`` shares scheme, host, and port with ``expected_url``. + + Use when an upstream API returns a URL meant for follow-up requests + (e.g. an async-job polling URL that will be hit with the operator's + API key in the headers). The upstream is trusted because the operator + configured ``api_base``, but the URL it hands back must actually point + back at the same origin or we'd be blindly forwarding credentials + wherever the upstream told us to. + + Hostnames are compared case-insensitively. Default ports are made + explicit (HTTP→80, HTTPS→443) so ``https://api.example.com:443/...`` + and ``https://api.example.com/...`` are treated as the same origin. + + Error messages identify *which* component mismatched but never echo + the operator's ``expected`` host or the candidate's hostname back to + the caller — in the SSRF threat model the caller is the attacker, + and reflecting host info would be a secondary leak of operator + infrastructure details. + """ + candidate = urlparse(candidate_url) + expected = urlparse(expected_url) + + if candidate.scheme not in _ALLOWED_SCHEMES: + raise SSRFError("URL scheme is not allowed") + + if candidate.scheme != expected.scheme: + raise SSRFError("Origin mismatch on scheme") + + candidate_host = _normalize_host(candidate.hostname or "") + expected_host = _normalize_host(expected.hostname or "") + if not candidate_host or candidate_host != expected_host: + raise SSRFError("Origin mismatch on host") + + default_port = 443 if candidate.scheme == "https" else 80 + candidate_port = candidate.port if candidate.port is not None else default_port + expected_port = expected.port if expected.port is not None else default_port + if candidate_port != expected_port: + raise SSRFError("Origin mismatch on port") + + _MAX_REDIRECTS = 10 def _extract_redirect_url(response: Any, request_url: str) -> str: """Extract and resolve the redirect target from a response's Location header.""" location = response.headers.get("location") - if not location: + if not isinstance(location, str) or not location: raise SSRFError("Redirect response has no Location header") # Resolve relative URLs against the request URL return str(httpx.URL(request_url).join(location)) diff --git a/litellm/llms/anthropic/batches/transformation.py b/litellm/llms/anthropic/batches/transformation.py index 3f03c744efe..fd67a7fbaf1 100644 --- a/litellm/llms/anthropic/batches/transformation.py +++ b/litellm/llms/anthropic/batches/transformation.py @@ -5,6 +5,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union, cas import httpx from httpx import Headers, Response +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.types.llms.openai import AllMessageValues, CreateBatchRequest @@ -122,7 +123,8 @@ class AnthropicBatchesConfig(BaseBatchesConfig): Complete URL for Anthropic batch retrieval: {api_base}/v1/messages/batches/{batch_id} """ api_base = api_base or self.anthropic_model_info.get_api_base(api_base) - return f"{api_base.rstrip('/')}/v1/messages/batches/{batch_id}" + encoded_batch_id = encode_url_path_segment(batch_id, field_name="batch_id") + return f"{api_base.rstrip('/')}/v1/messages/batches/{encoded_batch_id}" def transform_retrieve_batch_request( self, diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 2bb82f227bb..74dadee5ecb 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -23,7 +23,9 @@ from litellm.llms.anthropic.experimental_pass_through.adapters.transformation im from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.llms.base_llm.guardrail_translation.utils import ( effective_skip_system_message_for_guardrail, + effective_skip_tool_message_for_guardrail, openai_messages_without_system, + openai_messages_without_tool, ) from litellm.proxy.pass_through_endpoints.llm_provider_handlers.anthropic_passthrough_logging_handler import ( AnthropicPassthroughLoggingHandler, @@ -108,6 +110,7 @@ class AnthropicMessagesHandler(BaseTranslation): return data skip_system = effective_skip_system_message_for_guardrail(guardrail_to_apply) + skip_tool = effective_skip_tool_message_for_guardrail(guardrail_to_apply) chat_completion_compatible_request = self._translate_to_openai(data) @@ -117,6 +120,8 @@ class AnthropicMessagesHandler(BaseTranslation): ) if skip_system: structured_messages = openai_messages_without_system(structured_messages) + if skip_tool: + structured_messages = openai_messages_without_tool(structured_messages) texts_to_check: List[str] = [] images_to_check: List[str] = [] @@ -134,6 +139,7 @@ class AnthropicMessagesHandler(BaseTranslation): images_to_check=images_to_check, task_mappings=task_mappings, skip_system_message=skip_system, + skip_tool_message=skip_tool, ) # Step 2: Apply guardrail to all texts in batch @@ -198,13 +204,17 @@ class AnthropicMessagesHandler(BaseTranslation): images_to_check: List[str], task_mappings: List[Tuple[int, Optional[int]]], skip_system_message: bool = False, + skip_tool_message: bool = False, ) -> None: """ Extract text content and images from a message. Override this method to customize text/image extraction logic. """ - if skip_system_message and str(message.get("role") or "").lower() == "system": + role = str(message.get("role") or "").lower() + if skip_system_message and role == "system": + return + if skip_tool_message and role == "tool": return content = message.get("content", None) diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index f8e61d0166a..2fb29b32a61 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -65,7 +65,7 @@ from litellm.types.utils import ( from ...base import BaseLLM from ..common_utils import AnthropicError, process_anthropic_headers -from .transformation import AnthropicConfig +from .transformation import ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY, AnthropicConfig if TYPE_CHECKING: from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper @@ -83,6 +83,7 @@ async def make_call( timeout: Optional[Union[float, httpx.Timeout]], json_mode: bool, speed: Optional[str] = None, + tool_name_reverse_map: Optional[Dict[str, str]] = None, ) -> Tuple[Any, httpx.Headers]: if client is None: client = litellm.module_level_aclient @@ -117,6 +118,7 @@ async def make_call( sync_stream=False, json_mode=json_mode, speed=speed, + tool_name_reverse_map=tool_name_reverse_map, ) # LOGGING @@ -141,6 +143,7 @@ def make_sync_call( timeout: Optional[Union[float, httpx.Timeout]], json_mode: bool, speed: Optional[str] = None, + tool_name_reverse_map: Optional[Dict[str, str]] = None, ) -> Tuple[Any, httpx.Headers]: if client is None: client = litellm.module_level_client # re-use a module level client @@ -183,6 +186,7 @@ def make_sync_call( sync_stream=True, json_mode=json_mode, speed=speed, + tool_name_reverse_map=tool_name_reverse_map, ) # LOGGING @@ -237,6 +241,11 @@ class AnthropicChatCompletion(BaseLLM): timeout=timeout, json_mode=json_mode, speed=optional_params.get("speed") if optional_params else None, + tool_name_reverse_map=( + litellm_params.get(ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY) + if isinstance(litellm_params, dict) + else None + ), ) streamwrapper = CustomStreamWrapper( completion_stream=completion_stream, @@ -462,6 +471,11 @@ class AnthropicChatCompletion(BaseLLM): timeout=timeout, json_mode=json_mode, speed=optional_params.get("speed") if optional_params else None, + tool_name_reverse_map=( + litellm_params.get(ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY) + if isinstance(litellm_params, dict) + else None + ), ) return CustomStreamWrapper( completion_stream=completion_stream, @@ -526,6 +540,7 @@ class ModelResponseIterator: sync_stream: bool, json_mode: Optional[bool] = False, speed: Optional[str] = None, + tool_name_reverse_map: Optional[Dict[str, str]] = None, ): self.streaming_response = streaming_response self.response_iterator = self.streaming_response @@ -533,6 +548,13 @@ class ModelResponseIterator: self.tool_index = -1 self.json_mode = json_mode self.speed = speed + # rewritten-name -> caller's original. Built per-request from the + # forward map in AnthropicConfig._build_request_tool_name_maps; only + # contains entries we actually rewrote, so a tool legitimately named + # `foo_bar` is *not* reverse-mapped just because some other tool was + # rewritten to `foo_bar` in a different request. Empty/None is the + # common case (no '/' or other invalid chars in any tool name). + self.tool_name_reverse_map: Dict[str, str] = tool_name_reverse_map or {} # Generate response ID once per stream to match OpenAI-compatible behavior self.response_id = _generate_id() @@ -557,6 +579,10 @@ class ModelResponseIterator: # Accumulate compaction blocks for multi-turn reconstruction self.compaction_blocks: List[Dict[str, Any]] = [] + # Accumulate streamed thinking text so final usage can split reasoning + # tokens from regular output tokens. + self.reasoning_content_chunks: List[str] = [] + # Track server tool use inputs and results for code_interpreter_results self._server_tool_inputs: Dict[str, Any] = {} self.tool_results: List[Dict[str, Any]] = [] @@ -587,9 +613,14 @@ class ModelResponseIterator: return False def _handle_usage(self, anthropic_usage_chunk: Union[dict, UsageDelta]) -> Usage: + reasoning_content = ( + "".join(self.reasoning_content_chunks) + if self.reasoning_content_chunks + else None + ) return AnthropicConfig().calculate_usage( usage_object=cast(dict, anthropic_usage_chunk), - reasoning_content=None, + reasoning_content=reasoning_content, speed=self.speed, ) @@ -636,10 +667,13 @@ class ModelResponseIterator: "thinking" in content_block["delta"] or "signature" in content_block["delta"] ): + thinking_content = content_block["delta"].get("thinking") + if isinstance(thinking_content, str) and thinking_content: + self.reasoning_content_chunks.append(thinking_content) thinking_blocks = [ ChatCompletionThinkingBlock( type="thinking", - thinking=content_block["delta"].get("thinking") or "", + thinking=thinking_content or "", signature=str(content_block["delta"].get("signature") or ""), ) ] @@ -792,6 +826,16 @@ class ModelResponseIterator: or content_block_start["content_block"]["type"] == "server_tool_use" ): self.tool_index += 1 + # Reverse-map the (sanitized) tool name back to the + # caller's original. No-op when the map is empty. + _stream_tool_name = content_block_start["content_block"]["name"] + if ( + self.tool_name_reverse_map + and _stream_tool_name in self.tool_name_reverse_map + ): + _stream_tool_name = self.tool_name_reverse_map[ + _stream_tool_name + ] # Use empty string for arguments in content_block_start - actual arguments # come in subsequent content_block_delta chunks and get accumulated. # Using str(input) here would prepend '{}' causing invalid JSON accumulation. @@ -799,7 +843,7 @@ class ModelResponseIterator: id=content_block_start["content_block"]["id"], type="function", function=ChatCompletionToolCallFunctionChunk( - name=content_block_start["content_block"]["name"], + name=_stream_tool_name, arguments="", ), index=self.tool_index, diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 31b9bad3395..1ce80207552 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -1,18 +1,31 @@ import json import re import time -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast +from typing import ( + TYPE_CHECKING, + Any, + Dict, + List, + NoReturn, + Optional, + Tuple, + Union, + cast, +) import httpx import litellm from litellm.constants import ( + ANTHROPIC_MIN_THINKING_BUDGET_TOKENS, ANTHROPIC_WEB_SEARCH_TOOL_MAX_USES, DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS, DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET, + DEFAULT_REASONING_EFFORT_MAX_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET, + DEFAULT_REASONING_EFFORT_XHIGH_THINKING_BUDGET, RESPONSE_FORMAT_TOOL_NAME, ) from litellm.litellm_core_utils.core_helpers import map_finish_reason @@ -92,6 +105,131 @@ else: LoggingClass = Any +# Anthropic requires tool names to match ^[a-zA-Z0-9_-]{1,128}$. Any other +# character (commonly '/' or '.' from OpenAPI-derived MCP tools, e.g. +# "actions/download-job-logs-for-workflow-run") must be replaced before +# the request is sent. +# +# A naive "replace [^a-zA-Z0-9_-] with _" is unsafe because it's lossy: +# `foo/bar` and `foo_bar` both collapse to `foo_bar`. Two tools with the +# same sanitized name would either 400 at Anthropic (duplicate) or, worse, +# cause the response side to mis-translate `foo_bar` (a name the caller +# really did register) back to `foo/bar`. +# +# Instead we build a *per-request* forward map (original -> sanitized) +# whose codomain is unique within the request: when two originals collapse +# to the same candidate, or when a sanitized name collides with an already- +# valid name elsewhere in the request, we append numeric suffixes +# (`_2`, `_3`, ...) until the result is free. +# +# The reverse map (sanitized -> original) only contains entries where the +# original was actually rewritten. So a tool whose name is already valid +# round-trips identically and is *never* mistakenly re-mapped on the +# response side. +_ANTHROPIC_TOOL_NAME_INVALID_CHARS = re.compile(r"[^a-zA-Z0-9_-]") +_ANTHROPIC_TOOL_NAME_MAX_LEN = 128 +# 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_ +# params`` IS (it becomes the JSON body via ``data = {**optional_params}``). +# Keep these two channels strictly separate -- never stash internal +# coordination state in ``optional_params``. +ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY = "_anthropic_tool_name_map" + + +def _basic_sanitize_anthropic_tool_name(name: str) -> str: + """Lossy: replace [^a-zA-Z0-9_-] with '_' and truncate to 128. + + Used as a candidate generator for the per-request forward map. + Callers should NOT use this directly for translation -- always go + through the forward map so collisions are resolved. + """ + if not isinstance(name, str) or not name: + return name + return _ANTHROPIC_TOOL_NAME_INVALID_CHARS.sub("_", name)[ + :_ANTHROPIC_TOOL_NAME_MAX_LEN + ] + + +def _build_anthropic_tool_name_maps( + original_names: List[str], +) -> Tuple[Dict[str, str], Dict[str, str]]: + """Build (forward, reverse) tool-name maps for a single request. + + forward[original] = sanitized -- only present when name was rewritten + reverse[sanitized] = original -- inverse of `forward` + + Properties: + - All sanitized names satisfy ^[a-zA-Z0-9_-]{1,128}$. + - Sanitized names are unique within the request (no two originals + collide on the wire). + - A name that's already valid AND doesn't collide with another tool's + sanitized form passes through untouched and is absent from the maps. + That's the key correctness property: response-side translation only + runs on entries we actually rewrote, so a tool legitimately named + `foo_bar` is never incorrectly retyped to `foo/bar` just because + some *other* request had that pair. + - Order-dependent: when two originals would clash, the *second* one + seen gets the disambiguating suffix. Callers should preserve the + caller's tool order (we do). + """ + forward: Dict[str, str] = {} + used: set = set() + + # First pass: reserve slots for names that are already valid so they + # always have priority regardless of input order. + for original in original_names: + if not isinstance(original, str) or not original: + continue + candidate = _basic_sanitize_anthropic_tool_name(original) + if candidate == original: + used.add(candidate) + + # Second pass: sanitize/disambiguate names that need rewriting. + for original in original_names: + if not isinstance(original, str) or not original: + continue + candidate = _basic_sanitize_anthropic_tool_name(original) + if candidate == original: + continue + # Skip duplicates of the same original name. Without this guard the + # second pass would assign a fresh suffix and overwrite the forward + # map entry, causing every reference to map to the suffixed name and + # leaving the original sanitized slot orphaned in `used` with no + # reverse mapping. + if original in forward: + continue + # Disambiguate against names already chosen this request. + unique = candidate + n = 1 + while unique in used: + n += 1 + suffix = f"_{n}" + # Keep within the 128-char cap. + head = candidate[: _ANTHROPIC_TOOL_NAME_MAX_LEN - len(suffix)] + unique = f"{head}{suffix}" + forward[original] = unique + used.add(unique) + reverse = {v: k for k, v in forward.items()} + return forward, reverse + + +REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT: Dict[str, str] = { + "low": "low", + "minimal": "low", + "medium": "medium", + "high": "high", + "xhigh": "xhigh", + "max": "max", +} + +DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING = ( + "Dropping unsupported `output_config` for model=%s " + "(drop_params=True). Effort is only supported on Opus 4.5+, " + "Sonnet 4.6+, and Mythos Preview." +) + + class AnthropicConfig(AnthropicModelInfo, BaseConfig): """ Reference: https://docs.anthropic.com/claude/reference/messages_post @@ -202,17 +340,96 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _supports_effort_level(model: str, level: str) -> bool: """Check ``supports_{level}_reasoning_effort`` in the model map. - Mirrors the pattern used in ``openai/chat/gpt_5_transformation.py`` so - that adding support for a new effort level is a pure model-map change. + Strips bedrock/vertex prefixes so a provider-routed Claude still + resolves to the Anthropic model-map entry. """ + key = f"supports_{level}_reasoning_effort" try: - return _supports_factory( + if _supports_factory( model=model, custom_llm_provider="anthropic", - key=f"supports_{level}_reasoning_effort", - ) + key=key, + ): + return True except Exception: - return False + pass + candidates = [model] + for prefix in ( + "bedrock/converse/", + "bedrock/invoke/", + "bedrock/", + "vertex_ai/", + ): + if model.startswith(prefix): + candidates.append(model[len(prefix) :]) + try: + from litellm.llms.bedrock.common_utils import BedrockModelInfo + + base = BedrockModelInfo.get_base_model(model) + if base: + candidates.append(base) + candidates.append(f"bedrock/{base}") + except Exception: + pass + try: + import litellm + + for cand in candidates: + if cand in litellm.model_cost and ( + litellm.model_cost[cand].get(key) is True + ): + return True + except Exception: + pass + return False + + @staticmethod + def _validate_effort_for_model(model: str, effort: Optional[str]) -> Optional[str]: + """Return ``None`` if ``effort`` is allowed on ``model``, else an error message.""" + if effort == "max" and not ( + AnthropicConfig._is_claude_4_6_model(model) + or AnthropicConfig._is_claude_4_7_model(model) + or AnthropicConfig._supports_effort_level(model, "max") + ): + return f"effort='max' is not supported by this model. Got model: {model}" + if effort == "xhigh" and not AnthropicConfig._supports_effort_level( + model, "xhigh" + ): + return f"effort='xhigh' is not supported by this model. Got model: {model}" + return None + + @staticmethod + def _model_supports_effort_param(model: str) -> bool: + """Whether the model accepts ``output_config.effort`` at all.""" + return any( + AnthropicConfig._supports_effort_level(model, level) + for level in ("low", "minimal", "medium", "high", "xhigh", "max") + ) + + @staticmethod + def _raise_invalid_reasoning_effort( + model: str, value: Any, llm_provider: str + ) -> NoReturn: + """Raise a ``BadRequestError`` for an unrecognised ``reasoning_effort``. + + Args: + model: The model id the request was routed to (surfaced in the error). + value: The offending ``reasoning_effort`` value supplied by the caller. + llm_provider: Provider tag for the raised exception (``"anthropic"``, + ``"bedrock_converse"``, ``"databricks"``, ...). + + Raises: + litellm.exceptions.BadRequestError: Always. + """ + raise litellm.exceptions.BadRequestError( + message=( + f"Invalid reasoning_effort: {value!r}. " + f"Must be one of: 'minimal', 'low', 'medium', " + f"'high', 'xhigh', 'max', 'none'" + ), + model=model, + llm_provider=llm_provider, + ) def get_supported_openai_params(self, model: str): params = [ @@ -378,7 +595,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): } def _map_tool_choice( - self, tool_choice: Optional[str], parallel_tool_use: Optional[bool] + self, + tool_choice: Optional[str], + parallel_tool_use: Optional[bool], ) -> Optional[AnthropicMessagesToolChoice]: _tool_choice: Optional[AnthropicMessagesToolChoice] = None if tool_choice == "auto": @@ -419,7 +638,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return _tool_choice def _map_tool_helper( # noqa: PLR0915 - self, tool: ChatCompletionToolParam + self, + tool: ChatCompletionToolParam, ) -> Tuple[Optional[AllAnthropicToolsValues], Optional[AnthropicMcpServerTool]]: returned_tool: Optional[AllAnthropicToolsValues] = None mcp_server: Optional[AnthropicMcpServerTool] = None @@ -675,7 +895,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return initial_tool def _map_tools( - self, tools: List + self, + tools: List, ) -> Tuple[List[AllAnthropicToolsValues], List[AnthropicMcpServerTool]]: anthropic_tools = [] mcp_servers = [] @@ -691,6 +912,174 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): mcp_servers.append(mcp_server_tool) return anthropic_tools, mcp_servers + @staticmethod + def _rewrite_tool_names_in_messages( + messages: List[AllMessageValues], + name_forward_map: Dict[str, str], + ) -> List[AllMessageValues]: + """Return a copy of `messages` with tool_call/function_call names + rewritten using the per-request forward map. + + Only mutates messages whose tool_call/function_call name is *in* the + forward map. Names absent from the map (already valid, no collision) + round-trip untouched. We only deep-copy the entries we actually + change to keep this O(turns-with-rewritten-tools), not O(history). + """ + if not name_forward_map: + return messages + new_messages: List[AllMessageValues] = [] + for msg in messages: + if not isinstance(msg, dict): + new_messages.append(msg) + continue + tool_calls = msg.get("tool_calls") + function_call = msg.get("function_call") + if not tool_calls and not function_call: + new_messages.append(msg) + continue + new_msg = dict(msg) + if isinstance(tool_calls, list): + new_calls = [] + for tc in tool_calls: + if not isinstance(tc, dict): + new_calls.append(tc) + continue + fn = tc.get("function") + fn_name = fn.get("name") if isinstance(fn, dict) else None + if ( + isinstance(fn, dict) + and isinstance(fn_name, str) + and fn_name in name_forward_map + ): + new_fn = dict(fn) + new_fn["name"] = name_forward_map[fn_name] + new_tc = dict(tc) + new_tc["function"] = new_fn + new_calls.append(new_tc) + else: + new_calls.append(tc) + new_msg["tool_calls"] = new_calls + fc_name = ( + function_call.get("name") if isinstance(function_call, dict) else None + ) + if ( + isinstance(function_call, dict) + and isinstance(fc_name, str) + and fc_name in name_forward_map + ): + new_fc = dict(function_call) + new_fc["name"] = name_forward_map[fc_name] + new_msg["function_call"] = new_fc + new_messages.append(cast(AllMessageValues, new_msg)) + return new_messages + + @staticmethod + def _build_request_tool_name_maps( + tools: List, + ) -> Tuple[Dict[str, str], Dict[str, str]]: + """Build the (forward, reverse) tool-name maps for an OpenAI tools list. + + Operates on **OpenAI-format** tool dicts (pre-``_map_tools``). The + production sanitization path uses ``_sanitize_tool_names_in_request`` + instead, which operates on **Anthropic-format** tools (post- + ``_map_tools``, where ``type == "custom"``). This helper exists for + callers that need to compute the maps from the raw OpenAI shape -- + e.g. test setup or future pre-mapping consumers. + + See _build_anthropic_tool_name_maps for the collision rules. Pulls + the original name out of either ``{"function": {"name": ...}}`` + (legacy OpenAI shape) or ``{"name": ...}`` (rare top-level shape). + """ + original_names: List[str] = [] + for tool in tools or []: + if not isinstance(tool, dict): + continue + original = ( + tool.get("function", {}).get("name") + if isinstance(tool.get("function"), dict) + else None + ) + if original is None: + original = tool.get("name") + if isinstance(original, str) and original: + original_names.append(original) + return _build_anthropic_tool_name_maps(original_names) + + @staticmethod + def _sanitize_tool_names_in_request( + optional_params: Dict[str, Any], + ) -> Tuple[Dict[str, str], Dict[str, str]]: + """Sanitize ``optional_params['tools']`` and ``optional_params['tool_choice']`` + in place so every name matches Anthropic's ``^[a-zA-Z0-9_-]{1,128}$``. + + Returns ``(forward, reverse)`` for use by message-history rewriting + and response translation. ``forward[original] = sanitized`` is only + populated for names that were actually rewritten -- i.e. either + contained an invalid character or collided with another tool's + sanitized form. Names already valid AND unique pass through and are + absent from both maps. + + Only ``type == "custom"`` tools (the OpenAI function-tool shape) are + considered. Hosted tools (``web_search``, ``bash``, ``code_execution``, + ``computer_*``, ``mcp``, ...) own reserved names defined by Anthropic + and must not be touched. + """ + tools = optional_params.get("tools") + if not isinstance(tools, list) or not tools: + return {}, {} + + # 1. Collect originals from the Anthropic-shaped custom-tool entries. + # Order matters: the first occurrence wins the canonical slot; + # later collisions get numeric suffixes (see + # ``_build_anthropic_tool_name_maps``). + original_names: List[str] = [] + for t in tools: + if not isinstance(t, dict): + continue + if t.get("type") != "custom": + continue + name = t.get("name") + if isinstance(name, str) and name: + original_names.append(name) + + if not original_names: + return {}, {} + + forward, reverse = _build_anthropic_tool_name_maps(original_names) + if not forward: + # Every name was already valid -- nothing to do. + return forward, reverse + + # 2. Apply forward map. Build a new list with copy-on-change entries + # so a caller reusing the same tool list/dicts across requests + # doesn't see its inputs permanently rewritten (which would also + # drop the original key from `forward` on the next request). + new_tools: List[Any] = [] + for t in tools: + if ( + isinstance(t, dict) + and t.get("type") == "custom" + and isinstance(t.get("name"), str) + and t["name"] in forward + ): + new_tools.append({**t, "name": forward[t["name"]]}) + else: + new_tools.append(t) + optional_params["tools"] = new_tools + + # 3. Same for ``tool_choice`` when it targets a named tool. Copy + # rather than mutate for the same reason as above. + tool_choice = optional_params.get("tool_choice") + if isinstance(tool_choice, dict) and tool_choice.get("type") == "tool": + tc_name = tool_choice.get("name") + if isinstance(tc_name, str) and tc_name in forward: + optional_params["tool_choice"] = { + **tool_choice, + "name": forward[tc_name], + } + + return forward, reverse + def _detect_tool_search_tools(self, tools: Optional[List]) -> bool: """Check if tool search tools are present in the tools list.""" if not tools: @@ -794,12 +1183,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _map_reasoning_effort( reasoning_effort: Optional[Union[REASONING_EFFORT, str]], model: str, + llm_provider: str = "anthropic", ) -> Optional[AnthropicThinkingParam]: if reasoning_effort is None or reasoning_effort == "none": return None - if AnthropicConfig._is_claude_4_6_model( - model - ) or AnthropicConfig._is_claude_4_7_model(model): + if AnthropicConfig._is_adaptive_thinking_model(model): return AnthropicThinkingParam( type="adaptive", ) @@ -818,13 +1206,34 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): type="enabled", budget_tokens=DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET, ) + elif reasoning_effort == "xhigh": + return AnthropicThinkingParam( + type="enabled", + budget_tokens=DEFAULT_REASONING_EFFORT_XHIGH_THINKING_BUDGET, + ) + elif reasoning_effort == "max": + return AnthropicThinkingParam( + type="enabled", + budget_tokens=DEFAULT_REASONING_EFFORT_MAX_THINKING_BUDGET, + ) elif reasoning_effort == "minimal": return AnthropicThinkingParam( type="enabled", - budget_tokens=DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET, + budget_tokens=max( + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET, + ANTHROPIC_MIN_THINKING_BUDGET_TOKENS, + ), ) else: - raise ValueError(f"Unmapped reasoning effort: {reasoning_effort}") + raise litellm.exceptions.BadRequestError( + message=( + f"Unmapped reasoning effort: {reasoning_effort!r}. " + f"Must be one of: 'minimal', 'low', 'medium', 'high', " + f"'xhigh', 'max', 'none'." + ), + model=model, + llm_provider=llm_provider, + ) def _extract_json_schema_from_response_format( self, value: Optional[dict] @@ -997,6 +1406,17 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): non_default_params=non_default_params ) + # NB: ``map_openai_params`` deliberately does NOT sanitize tool names + # here. Names are the *original* OpenAI names at this stage, and must + # remain so until ``transform_request`` -- which is the single + # chokepoint where Anthropic, Bedrock-Anthropic, and Vertex-Anthropic + # all pass through. Doing it there guarantees: + # 1. one source of truth for the per-request forward/reverse maps, + # 2. the maps land on ``litellm_params`` (internal), never on + # ``optional_params`` (which is serialized into the request body + # via ``data = {**optional_params}`` and would 400 with + # ``Extra inputs are not permitted``). + for param, value in non_default_params.items(): if param == "max_tokens": optional_params["max_tokens"] = ( @@ -1007,7 +1427,6 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): value if isinstance(value, int) else max(1, int(round(value))) ) elif param == "tools": - # check if optional params already has tools anthropic_tools, mcp_servers = self._map_tools(value) optional_params = self._add_tools_to_optional_params( optional_params=optional_params, tools=anthropic_tools @@ -1088,24 +1507,27 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): elif param == "thinking": optional_params["thinking"] = value elif param == "reasoning_effort" and isinstance(value, str): - optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - reasoning_effort=value, model=model + mapped_thinking = AnthropicConfig._map_reasoning_effort( + reasoning_effort=value, + model=model, + llm_provider=self.custom_llm_provider or "anthropic", ) - # For Claude 4.6+ models, effort is controlled via output_config, - # not thinking budget_tokens. Map reasoning_effort to output_config. - if AnthropicConfig._is_claude_4_6_model( - model - ) or AnthropicConfig._is_claude_4_7_model(model): - effort_map = { - "low": "low", - "minimal": "low", - "medium": "medium", - "high": "high", - "xhigh": "xhigh", - "max": "max", - } - mapped_effort = effort_map.get(value, value) - optional_params["output_config"] = {"effort": mapped_effort} + if mapped_thinking is None: + optional_params.pop("thinking", None) + optional_params.pop("output_config", None) + else: + optional_params["thinking"] = mapped_thinking + if AnthropicConfig._is_adaptive_thinking_model(model): + mapped_effort = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get( + value + ) + if mapped_effort is None: + AnthropicConfig._raise_invalid_reasoning_effort( + model=model, + value=value, + llm_provider=self.custom_llm_provider or "anthropic", + ) + optional_params["output_config"] = {"effort": mapped_effort} elif param == "web_search_options" and isinstance(value, dict): hosted_web_search_tool = self.map_web_search_tool( cast(OpenAIWebSearchOptions, value) @@ -1387,9 +1809,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): Translate messages to anthropic format. """ ## VALIDATE REQUEST - """ - Anthropic doesn't support tool calling without `tools=` param specified. - """ + """Anthropic requires ``tools`` when messages include tool blocks; LiteLLM injects a dummy tool if omitted (no ``modify_params`` needed).""" from litellm.litellm_core_utils.prompt_templates.factory import ( anthropic_messages_pt, ) @@ -1399,16 +1819,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): and messages is not None and has_tool_call_blocks(messages) ): - if litellm.modify_params: - optional_params["tools"], _ = self._map_tools( - add_dummy_tool(custom_llm_provider="anthropic") - ) - else: - raise litellm.UnsupportedParamsError( - message="Anthropic doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.", - model="", - llm_provider="anthropic", - ) + optional_params["tools"], _ = self._map_tools( + add_dummy_tool(custom_llm_provider="anthropic") + ) # Drop thinking param if thinking is enabled but thinking_blocks are missing # This prevents the error: "Expected thinking or redacted_thinking, but found tool_use" @@ -1434,6 +1847,34 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): headers=headers, optional_params=optional_params ) + # === Tool-name sanitization (single chokepoint) === + # Anthropic enforces ^[a-zA-Z0-9_-]{1,128}$ on every tool name. We + # sanitize *here* -- not in map_openai_params -- because: + # + # - This function is the single boundary shared by AnthropicConfig, + # AmazonAnthropicConfig (Bedrock invoke), VertexAIAnthropicConfig, + # and AzureAnthropicConfig (all call ``super().transform_request`` + # or ``AnthropicConfig.transform_request(self, ...)``). Sanitizing + # once here covers every Anthropic-shaped request. + # - The forward/reverse maps are coordination state; they belong on + # ``litellm_params`` (internal-only), never on ``optional_params`` + # (which becomes the JSON body via ``{**optional_params}``). + # - It keeps ``map_openai_params`` a pure param translator with no + # side-channel state. + # + # The reverse map only contains entries for names that were actually + # rewritten -- so a tool legitimately named ``foo_bar`` is never + # incorrectly retyped to ``foo/bar`` on the response side. + # See _build_anthropic_tool_name_maps for the collision-handling + # rules and rationale. + _name_forward_map, _name_reverse_map = self._sanitize_tool_names_in_request( + optional_params=optional_params, + ) + if _name_forward_map: + messages = self._rewrite_tool_names_in_messages(messages, _name_forward_map) + if _name_reverse_map and isinstance(litellm_params, dict): + litellm_params[ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY] = _name_reverse_map + # Separate system prompt from rest of message anthropic_system_message_list = self.translate_system_message(messages=messages) # Handling anthropic API Prompt Caching @@ -1527,51 +1968,71 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): output_config = optional_params.get("output_config") if not output_config or not isinstance(output_config, dict): return + if litellm.drop_params is True and not self._model_supports_effort_param(model): + litellm.verbose_logger.warning( + DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, + model, + ) + optional_params.pop("output_config", None) + data.pop("output_config", None) + return effort = output_config.get("effort") valid_efforts = ["high", "medium", "low", "xhigh", "max"] - if effort and effort not in valid_efforts: - raise ValueError( - f"Invalid effort value: {effort}. Must be one of: " - f"'high', 'medium', 'low', 'xhigh', 'max'" + if effort is not None and effort not in valid_efforts: + raise litellm.exceptions.BadRequestError( + message=( + f"Invalid effort value: {effort!r}. Must be one of: " + f"'high', 'medium', 'low', 'xhigh', 'max'" + ), + model=model, + llm_provider=self.custom_llm_provider or "anthropic", ) - # ``max`` is for Opus 4.6+ output effort (not Sonnet 4.6, not Opus 4.5). - # Accept known Opus 4.6/4.7 id patterns and/or ``supports_max_reasoning_effort`` - # in the model map (same pattern as ``xhigh`` below). - if effort == "max" and not ( - self._is_opus_4_6_model(model) - or self._is_opus_4_7_model(model) - or self._supports_effort_level(model, "max") - ): - raise ValueError( - f"effort='max' is not supported by this model. Got model: {model}" - ) - # ``xhigh`` is data-driven via ``supports_xhigh_reasoning_effort`` so - # enabling it for a new model is a pure model-map change. - if effort == "xhigh" and not self._supports_effort_level(model, "xhigh"): - raise ValueError( - f"effort='xhigh' is not supported by this model. Got model: {model}" + gate_error = self._validate_effort_for_model(model, effort) + if gate_error is not None: + raise litellm.exceptions.BadRequestError( + message=gate_error, + model=model, + llm_provider=self.custom_llm_provider or "anthropic", ) data["output_config"] = output_config - def _transform_response_for_json_mode( + def _resolve_json_mode_non_streaming( self, json_mode: Optional[bool], tool_calls: List[ChatCompletionToolCallChunk], - ) -> Optional[LitellmMessage]: - _message: Optional[LitellmMessage] = None - if json_mode is True and len(tool_calls) == 1: - # check if tool name is the default tool name - json_mode_content_str: Optional[str] = None - if ( - "name" in tool_calls[0]["function"] - and tool_calls[0]["function"]["name"] == RESPONSE_FORMAT_TOOL_NAME - ): - json_mode_content_str = tool_calls[0]["function"].get("arguments") - if json_mode_content_str is not None: - _message = AnthropicConfig._convert_tool_response_to_message( - tool_calls=tool_calls, - ) - return _message + ) -> Tuple[ + Optional[LitellmMessage], + List[ChatCompletionToolCallChunk], + Optional[str], + ]: + """Strip internal response_format tool calls; merge payload into content when mixed with user tools.""" + if json_mode is not True or not tool_calls: + return None, tool_calls, None + + json_indices = [ + i + for i, t in enumerate(tool_calls) + if t.get("function", {}).get("name") == RESPONSE_FORMAT_TOOL_NAME + ] + if not json_indices: + return None, tool_calls, None + + if len(json_indices) == len(tool_calls): + json_tool = tool_calls[json_indices[0]] + if json_tool.get("function", {}).get("arguments") is None: + return None, tool_calls, None + _message = AnthropicConfig._convert_tool_response_to_message( + tool_calls=[json_tool] + ) + return _message, [], None + + first_json = tool_calls[json_indices[0]] + json_msg = AnthropicConfig._convert_tool_response_to_message([first_json]) + extra_content: Optional[str] = ( + json_msg.content if json_msg is not None else None + ) + filtered_tools = [t for i, t in enumerate(tool_calls) if i not in json_indices] + return None, filtered_tools, extra_content def extract_response_content(self, completion_response: dict) -> Tuple[ str, @@ -1686,8 +2147,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): speed: Optional[str] = None, ) -> Usage: # NOTE: Sometimes the usage object has None set explicitly for token counts, meaning .get() & key access returns None, and we need to account for this - prompt_tokens = usage_object.get("input_tokens", 0) or 0 - completion_tokens = usage_object.get("output_tokens", 0) or 0 + raw_prompt_tokens = usage_object.get("input_tokens", 0) or 0 + prompt_tokens: int = ( + int(raw_prompt_tokens) if isinstance(raw_prompt_tokens, (int, float)) else 0 + ) + raw_completion_tokens = usage_object.get("output_tokens", 0) or 0 + completion_tokens: int = ( + int(raw_completion_tokens) + if isinstance(raw_completion_tokens, (int, float)) + else 0 + ) _usage = usage_object cache_creation_input_tokens: int = 0 cache_read_input_tokens: int = 0 @@ -1756,11 +2225,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): text_tokens=raw_input_tokens, ) # Always populate completion_token_details, not just when there's reasoning_content - reasoning_tokens = ( + estimated_reasoning_tokens = ( token_counter(text=reasoning_content, count_response_tokens=True) if reasoning_content else 0 ) + reasoning_tokens = min(estimated_reasoning_tokens, completion_tokens) completion_token_details = CompletionTokensDetailsWrapper( reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else 0, text_tokens=( @@ -1890,6 +2360,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): json_mode: Optional[bool] = None, prefix_prompt: Optional[str] = None, speed: Optional[str] = None, + tool_name_reverse_map: Optional[Dict[str, str]] = None, ): _hidden_params: Dict = {} _hidden_params["additional_headers"] = process_anthropic_headers( @@ -1914,6 +2385,21 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): compaction_blocks, ) = self.extract_response_content(completion_response=completion_response) + # Reverse-map rewritten tool names back to caller's originals so a + # downstream OpenAI-style dispatcher can match on the registered name. + # See _build_anthropic_tool_name_maps for why this is keyed on the + # per-request reverse map (so a tool legitimately named `foo_bar` is + # never incorrectly retyped to `foo/bar`). No-op when the map is + # empty (the common case). + if tool_name_reverse_map and tool_calls: + for tc in tool_calls: + fn = tc.get("function") if isinstance(tc, dict) else None + if fn is None: + continue + _name = fn.get("name") + if isinstance(_name, str) and _name in tool_name_reverse_map: + fn["name"] = tool_name_reverse_map[_name] + if ( prefix_prompt is not None and not text_content.startswith(prefix_prompt) @@ -1931,19 +2417,27 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): tool_calls, ) + json_mode_message, tool_calls_for_message, json_extra_content = ( + self._resolve_json_mode_non_streaming( + json_mode=json_mode, + tool_calls=tool_calls, + ) + ) + merged_text = text_content or "" + if json_extra_content: + merged_text = ( + merged_text + json_extra_content if merged_text else json_extra_content + ) + _message = litellm.Message( - tool_calls=tool_calls, - content=text_content or None, + tool_calls=tool_calls_for_message, + content=merged_text or None, provider_specific_fields=provider_specific_fields, thinking_blocks=thinking_blocks, reasoning_content=reasoning_content, ) _message.provider_specific_fields = provider_specific_fields - json_mode_message = self._transform_response_for_json_mode( - json_mode=json_mode, - tool_calls=tool_calls, - ) if json_mode_message is not None: completion_response["stop_reason"] = "stop" _message = json_mode_message @@ -2032,6 +2526,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): prefix_prompt = self.get_prefix_prompt(messages=messages) speed = optional_params.get("speed") + tool_name_reverse_map: Optional[Dict[str, str]] = None + if isinstance(litellm_params, dict): + _candidate = litellm_params.get(ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY) + if isinstance(_candidate, dict): + tool_name_reverse_map = _candidate model_response = self.transform_parsed_response( completion_response=completion_response, @@ -2040,6 +2539,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): json_mode=json_mode, prefix_prompt=prefix_prompt, speed=speed, + tool_name_reverse_map=tool_name_reverse_map, ) return model_response diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index b095d40156d..869a7c5fbc4 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -273,7 +273,18 @@ class AnthropicModelInfo(BaseLLMModelInfo): @staticmethod def _is_adaptive_thinking_model(model: str) -> bool: - """Claude 4.6+ models use adaptive thinking with output_config effort.""" + """Claude 4.6+ models use adaptive thinking with ``output_config.effort``.""" + from litellm.utils import _supports_factory + + try: + if _supports_factory( + model=model, + custom_llm_provider=None, + key="supports_adaptive_thinking", + ): + return True + except Exception: + pass return AnthropicModelInfo._is_claude_4_6_model( model ) or AnthropicModelInfo._is_claude_4_7_model(model) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index 829ce14d69d..8ed6126d2eb 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py @@ -27,6 +27,16 @@ from litellm.utils import get_model_info if TYPE_CHECKING: pass + +# Anthropic-only fields that the translator above already maps into the +# OpenAI-format completion_kwargs (output_config → reasoning_effort / +# response_format, etc.). They must be filtered out of the raw +# extra_kwargs re-merge below or non-Anthropic backends reject the call +# with 400 "Extra inputs are not permitted". Add new entries here when +# extending AnthropicMessagesRequestOptionalParams with another Anthropic- +# specific key. +ANTHROPIC_ONLY_REQUEST_KEYS: frozenset[str] = frozenset({"output_config"}) + ######################################################## # init adapter ANTHROPIC_ADAPTER = AnthropicAdapter() @@ -202,8 +212,12 @@ class LiteLLMMessagesToCompletionTransformationHandler: request_data["output_format"] = output_format # Extract output_config from extra_kwargs so the translator can use it - # (e.g. output_config.effort for adaptive thinking → reasoning_effort) - extra_kwargs = extra_kwargs or {} + # (e.g. output_config.effort for adaptive thinking → reasoning_effort, + # output_config.format → response_format for structured outputs). + # Use explicit None check rather than `or {}` so an explicit empty dict + # caller-passed argument is preserved (matters for tests that drive + # the fallback inference path). + extra_kwargs = extra_kwargs if extra_kwargs is not None else {} if "output_config" in extra_kwargs: request_data["output_config"] = extra_kwargs["output_config"] @@ -225,8 +239,23 @@ class LiteLLMMessagesToCompletionTransformationHandler: "include_usage": True, } - excluded_keys = {"anthropic_messages"} - extra_kwargs = extra_kwargs or {} + # Keys that must NOT be forwarded as raw extras into the OpenAI-format + # ``completion_kwargs`` after translation. The translator above has + # already consumed the meaningful parts of these inputs (e.g. + # ``output_config.format`` → ``response_format``, ``output_config.effort`` + # → ``reasoning_effort`` for non-Claude targets). Re-adding the raw + # Anthropic-shaped key here causes 400 "Extra inputs are not permitted" + # on non-Anthropic backends (Azure OpenAI, Fireworks, Bedrock Nova, + # etc.) and is silently lossy on Anthropic-family targets, which would + # see the translated key ``response_format`` AND a duplicate, conflicting + # ``output_config``. + # + # Maintainability: when adding a new Anthropic-only request param to + # ``AnthropicMessagesRequestOptionalParams``, also extend + # ``ANTHROPIC_ONLY_REQUEST_KEYS`` here so it doesn't silently leak. + excluded_keys = ANTHROPIC_ONLY_REQUEST_KEYS | {"anthropic_messages"} + # NOTE: extra_kwargs was already coerced from None to {} at the top of + # this method (line ~220). It is guaranteed to be a dict here. for key, value in extra_kwargs.items(): if ( key == "litellm_logging_obj" diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 08797889192..0e198daf089 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -667,7 +667,7 @@ class LiteLLMAnthropicMessagesAdapter: @staticmethod def translate_anthropic_thinking_to_reasoning_effort( - thinking: Dict[str, Any] + thinking: Dict[str, Any], ) -> Optional[str]: """ Translate Anthropic's thinking parameter to OpenAI's reasoning_effort. @@ -1084,10 +1084,23 @@ class LiteLLMAnthropicMessagesAdapter: anthropic_message_request: AnthropicMessagesRequest, new_kwargs: ChatCompletionRequest, ) -> None: - """Translate output_format to response_format when applicable.""" - if "output_format" not in anthropic_message_request: - return - output_format = anthropic_message_request["output_format"] + """Translate Anthropic structured-output config to OpenAI ``response_format``. + + Accepts either the legacy top-level ``output_format`` field OR the + newer ``output_config.format`` (sub-key on ``output_config``) so that + both shapes flow through to non-Anthropic backends as + ``response_format``. Without the ``output_config.format`` branch, + callers using the new Anthropic Structured Outputs API would have + their schema silently dropped on the adapter path — only the legacy + top-level ``output_format`` was being mapped. + + ``output_format`` takes precedence when both are provided. + """ + output_format: Any = anthropic_message_request.get("output_format") + if not output_format: + output_config = anthropic_message_request.get("output_config") + if isinstance(output_config, dict): + output_format = output_config.get("format") if not output_format: return response_format = self.translate_anthropic_output_format_to_openai( @@ -1286,9 +1299,18 @@ class LiteLLMAnthropicMessagesAdapter: else truncated_name ) + # Strip Gemini thought-signature suffix from id (mirrors streaming + # path below); base64 chars (+ / =) violate Anthropic's + # `^[a-zA-Z0-9_-]+$` tool_use.id pattern when replayed. + raw_id = tool_call.id or "" + base_id = ( + raw_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] + if THOUGHT_SIGNATURE_SEPARATOR in raw_id + else raw_id + ) tool_use_block = AnthropicResponseContentBlockToolUse( type="tool_use", - id=tool_call.id, + id=base_id, name=original_name, input=parse_tool_call_arguments( tool_call.function.arguments, diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index 7617ad52ab1..35495d59610 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -47,6 +47,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): "inference_geo", "speed", "output_config", + "reasoning_effort", # TODO: Add Anthropic `metadata` support # "metadata", ] @@ -166,6 +167,62 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): return headers, api_base + @staticmethod + def _translate_reasoning_effort_to_anthropic( + model: str, optional_params: Dict + ) -> None: + """Map OpenAI-style ``reasoning_effort`` to native Anthropic params. + + Caller-supplied ``thinking`` / ``output_config`` win over the alias. + ``effort='none'`` clears both. Invalid efforts raise a 400. + """ + from litellm.exceptions import BadRequestError as _BadRequestError + from litellm.llms.anthropic.chat.transformation import ( + REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT, + AnthropicConfig, + ) + + reasoning_effort = optional_params.pop("reasoning_effort", None) + if not isinstance(reasoning_effort, str): + return + + try: + mapped_thinking = AnthropicConfig._map_reasoning_effort( + reasoning_effort=reasoning_effort, model=model + ) + except _BadRequestError as e: + raise AnthropicError(message=str(e.message), status_code=400) + + if mapped_thinking is None: + optional_params.pop("thinking", None) + optional_params.pop("output_config", None) + return + + optional_params.setdefault("thinking", mapped_thinking) + if AnthropicModelInfo._is_adaptive_thinking_model(model): + mapped_effort = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get( + reasoning_effort + ) + if mapped_effort is None: + raise AnthropicError( + message=( + f"Invalid reasoning_effort: {reasoning_effort!r}. " + f"Must be one of: 'minimal', 'low', 'medium', 'high', " + f"'xhigh', 'max', 'none'" + ), + status_code=400, + ) + gate_error = AnthropicConfig._validate_effort_for_model( + model, mapped_effort + ) + if gate_error is not None: + raise AnthropicError(message=gate_error, status_code=400) + existing_output_config = optional_params.get("output_config") + if not isinstance(existing_output_config, dict): + existing_output_config = {} + existing_output_config.setdefault("effort", mapped_effort) + optional_params["output_config"] = existing_output_config + @staticmethod def _translate_legacy_thinking_for_adaptive_model( model: str, optional_params: Dict @@ -217,6 +274,11 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): status_code=400, ) + self._translate_reasoning_effort_to_anthropic( + model=model, + optional_params=anthropic_messages_optional_request_params, + ) + self._translate_legacy_thinking_for_adaptive_model( model=model, optional_params=anthropic_messages_optional_request_params, diff --git a/litellm/llms/anthropic/files/handler.py b/litellm/llms/anthropic/files/handler.py index c56799f30cf..56296df94a1 100644 --- a/litellm/llms/anthropic/files/handler.py +++ b/litellm/llms/anthropic/files/handler.py @@ -9,6 +9,7 @@ import litellm from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.litellm_core_utils.litellm_logging import Logging +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.openai import ( FileContentRequest, @@ -89,7 +90,10 @@ class AnthropicFilesHandler: raise ValueError("Missing Anthropic API Key") # Construct the Anthropic batch results URL - results_url = f"{api_base.rstrip('/')}/v1/messages/batches/{batch_id}/results" + encoded_batch_id = encode_url_path_segment(batch_id, field_name="batch_id") + results_url = ( + f"{api_base.rstrip('/')}/v1/messages/batches/{encoded_batch_id}/results" + ) # Prepare headers headers = { diff --git a/litellm/llms/anthropic/files/transformation.py b/litellm/llms/anthropic/files/transformation.py index aeaab4e57bf..ea9bf00f505 100644 --- a/litellm/llms/anthropic/files/transformation.py +++ b/litellm/llms/anthropic/files/transformation.py @@ -19,6 +19,7 @@ from typing import Any, Dict, List, Optional, Union, cast import httpx from openai.types.file_deleted import FileDeleted +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.files.transformation import ( @@ -185,7 +186,8 @@ class AnthropicFilesConfig(BaseFilesConfig): AnthropicModelInfo.get_api_base(litellm_params.get("api_base")) or ANTHROPIC_FILES_API_BASE ) - return f"{api_base.rstrip('/')}/v1/files/{file_id}", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base.rstrip('/')}/v1/files/{encoded_file_id}", {} def transform_retrieve_file_response( self, @@ -206,7 +208,8 @@ class AnthropicFilesConfig(BaseFilesConfig): AnthropicModelInfo.get_api_base(litellm_params.get("api_base")) or ANTHROPIC_FILES_API_BASE ) - return f"{api_base.rstrip('/')}/v1/files/{file_id}", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base.rstrip('/')}/v1/files/{encoded_file_id}", {} def transform_delete_file_response( self, @@ -268,7 +271,8 @@ class AnthropicFilesConfig(BaseFilesConfig): AnthropicModelInfo.get_api_base(litellm_params.get("api_base")) or ANTHROPIC_FILES_API_BASE ) - return f"{api_base.rstrip('/')}/v1/files/{file_id}/content", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base.rstrip('/')}/v1/files/{encoded_file_id}/content", {} def transform_file_content_response( self, diff --git a/litellm/llms/anthropic/skills/transformation.py b/litellm/llms/anthropic/skills/transformation.py index a992d84d459..4ea768b02af 100644 --- a/litellm/llms/anthropic/skills/transformation.py +++ b/litellm/llms/anthropic/skills/transformation.py @@ -7,6 +7,7 @@ from typing import Any, Dict, Optional, Tuple import httpx from litellm._logging import verbose_logger +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.skills.transformation import ( BaseSkillsAPIConfig, LiteLLMLoggingObj, @@ -81,7 +82,8 @@ class AnthropicSkillsConfig(BaseSkillsAPIConfig): api_base = AnthropicModelInfo.get_api_base() if skill_id: - return f"{api_base}/v1/skills/{skill_id}" + encoded_skill_id = encode_url_path_segment(skill_id, field_name="skill_id") + return f"{api_base}/v1/skills/{encoded_skill_id}" return f"{api_base}/v1/{endpoint}" def transform_create_skill_request( diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index 61cfd54b565..9291269d153 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -16,6 +16,7 @@ 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.url_utils import SSRFError, assert_same_origin from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, HTTPHandler, @@ -43,6 +44,7 @@ from .common_utils import ( select_azure_base_url_or_endpoint, ) from .image_generation import get_azure_image_generation_config +from .image_generation.http_utils import azure_deployment_image_generation_json_body class AzureOpenAIAssistantsAPIConfig: @@ -792,6 +794,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): client=client, litellm_params=litellm_params, api_base=api_base, + api_version=api_version, ) azure_client = self.get_azure_openai_client( api_version=api_version, @@ -898,6 +901,17 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): operation_location_url = response.headers["operation-location"] else: raise AzureOpenAIError(status_code=500, message=response.text) + # Reject polling URLs that don't share an origin with ``api_base``. + # Without this an upstream-controlled or attacker-controlled + # value would receive the operator's Azure API key in the + # request headers below. VERIA-51. + try: + assert_same_origin(operation_location_url, api_base) + except SSRFError as ssrf_err: + raise AzureOpenAIError( + status_code=502, + message=f"Rejected polling URL: {ssrf_err}", + ) response = await async_handler.get( url=operation_location_url, headers=headers, @@ -908,8 +922,13 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): timeout_secs: int = AZURE_OPERATION_POLLING_TIMEOUT start_time = time.time() if "status" not in response.json(): - raise Exception( - "Expected 'status' in response. Got={}".format(response.json()) + # Don't reflect the raw response body — when the polling + # URL points at an internal JSON API (cloud metadata + # service etc.) reflecting it here turns Blind SSRF into + # Full-Read SSRF. VERIA-51. + raise AzureOpenAIError( + status_code=502, + message="Polling response missing 'status' field", ) while response.json()["status"] not in ["succeeded", "failed"]: if time.time() - start_time > timeout_secs: @@ -948,9 +967,10 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): content=json.dumps(result).encode("utf-8"), request=httpx.Request(method="POST", url="https://api.openai.com/v1"), ) + request_json = azure_deployment_image_generation_json_body(api_base, data) return await async_handler.post( url=api_base, - json=data, + json=request_json, headers=headers, ) @@ -1009,6 +1029,13 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): operation_location_url = response.headers["operation-location"] else: raise AzureOpenAIError(status_code=500, message=response.text) + try: + assert_same_origin(operation_location_url, api_base) + except SSRFError as ssrf_err: + raise AzureOpenAIError( + status_code=502, + message=f"Rejected polling URL: {ssrf_err}", + ) response = sync_handler.get( url=operation_location_url, headers=headers, @@ -1019,8 +1046,9 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): timeout_secs: int = AZURE_OPERATION_POLLING_TIMEOUT start_time = time.time() if "status" not in response.json(): - raise Exception( - "Expected 'status' in response. Got={}".format(response.json()) + raise AzureOpenAIError( + status_code=502, + message="Polling response missing 'status' field", ) while response.json()["status"] not in ["succeeded", "failed"]: if time.time() - start_time > timeout_secs: @@ -1059,9 +1087,10 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): content=json.dumps(result).encode("utf-8"), request=httpx.Request(method="POST", url="https://api.openai.com/v1"), ) + request_json = azure_deployment_image_generation_json_body(api_base, data) return sync_handler.post( url=api_base, - json=data, + json=request_json, headers=headers, ) diff --git a/litellm/llms/azure/cost_calculation.py b/litellm/llms/azure/cost_calculation.py index 5b411095ea1..2a20c55a6ce 100644 --- a/litellm/llms/azure/cost_calculation.py +++ b/litellm/llms/azure/cost_calculation.py @@ -12,7 +12,10 @@ from litellm.utils import get_model_info def cost_per_token( - model: str, usage: Usage, response_time_ms: Optional[float] = 0.0 + model: str, + usage: Usage, + response_time_ms: Optional[float] = 0.0, + service_tier: Optional[str] = None, ) -> Tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -47,4 +50,5 @@ def cost_per_token( model=model, usage=usage, custom_llm_provider="azure", + service_tier=service_tier, ) diff --git a/litellm/llms/azure/image_edit/transformation.py b/litellm/llms/azure/image_edit/transformation.py index dffa1c9eea5..a450ee0b217 100644 --- a/litellm/llms/azure/image_edit/transformation.py +++ b/litellm/llms/azure/image_edit/transformation.py @@ -3,12 +3,27 @@ from typing import Optional, cast import httpx import litellm +from litellm.llms.azure.common_utils import BaseAzureLLM from litellm.llms.openai.image_edit.transformation import OpenAIImageEditConfig from litellm.secret_managers.main import get_secret_str +from litellm.types.router import GenericLiteLLMParams from litellm.utils import _add_path_to_api_base class AzureImageEditConfig(OpenAIImageEditConfig): + @staticmethod + def azure_deployment_image_edit_form_data(data: dict, request_url: str) -> dict: + """ + Azure OpenAI ``.../openai/deployments/{deployment}/images/edits`` routes by + deployment in the URL; including ``model`` in multipart fields can break + the same way as image generations (LiteLLM #26316). + + Non-deployment edit URLs keep ``model`` when present. + """ + if "images/edits" in request_url and "/openai/deployments/" in request_url: + return {k: v for k, v in data.items() if k != "model"} + return data + def validate_environment( self, headers: dict, @@ -17,20 +32,42 @@ class AzureImageEditConfig(OpenAIImageEditConfig): litellm_params: Optional[dict] = None, api_base: Optional[str] = None, ) -> dict: - api_key = ( - api_key - or litellm.api_key - or litellm.azure_key - or get_secret_str("AZURE_OPENAI_API_KEY") - or get_secret_str("AZURE_API_KEY") - ) + """ + Validate Azure environment and set up authentication headers. - headers.update( - { - "Authorization": f"Bearer {api_key}", - } + Delegates to ``BaseAzureLLM._base_validate_azure_environment`` so the + Azure image-edit route uses the same auth resolution as every other + Azure provider (videos, vector_stores, responses, containers, ...): + + - prefers the Azure-style ``api-key`` header when an API key is available + - falls back to ``Authorization: Bearer `` only when AAD + auth is configured + + The previous implementation unconditionally set + ``Authorization: Bearer ``, which is correct for OpenAI direct + but not for Azure OpenAI / API Management gateways that expect the + ``api-key`` header. Subscription-key-based deployments (e.g., behind + Azure APIM) responded with ``401 "Access denied due to missing + subscription key"``. + + API-key precedence (matches ``AzureVideosConfig``): + + - ``litellm_params["api_key"]`` is the source of truth. + - The positional ``api_key`` kwarg only fills in when + ``litellm_params["api_key"]`` is empty. + - This is a deliberate change from the old ``or`` chain (where the + positional ``api_key`` argument won) so behavior matches every other + Azure ``validate_environment`` implementation. In production the only + caller (``llm_http_handler.image_edit``) sources both values from + the same ``litellm_params.api_key``, so the precedence only matters + for direct callers of this method. + """ + params = GenericLiteLLMParams(**(litellm_params or {})) + if api_key is not None and params.api_key is None: + params.api_key = api_key + return BaseAzureLLM._base_validate_azure_environment( + headers=headers, litellm_params=params ) - return headers def get_complete_url( self, @@ -83,3 +120,8 @@ class AzureImageEditConfig(OpenAIImageEditConfig): final_url = httpx.URL(new_url).copy_with(params=query_params) return str(final_url) + + def finalize_image_edit_request_data( + self, data: dict, resolved_request_url: str + ) -> dict: + return self.azure_deployment_image_edit_form_data(data, resolved_request_url) diff --git a/litellm/llms/azure/image_generation/__init__.py b/litellm/llms/azure/image_generation/__init__.py index fcdf49f2916..f60e446f0c4 100644 --- a/litellm/llms/azure/image_generation/__init__.py +++ b/litellm/llms/azure/image_generation/__init__.py @@ -6,11 +6,13 @@ from litellm.llms.base_llm.image_generation.transformation import ( from .dall_e_2_transformation import AzureDallE2ImageGenerationConfig from .dall_e_3_transformation import AzureDallE3ImageGenerationConfig from .gpt_transformation import AzureGPTImageGenerationConfig +from .http_utils import azure_deployment_image_generation_json_body __all__ = [ "AzureDallE2ImageGenerationConfig", "AzureDallE3ImageGenerationConfig", "AzureGPTImageGenerationConfig", + "azure_deployment_image_generation_json_body", ] @@ -24,6 +26,6 @@ def get_azure_image_generation_config(model: str) -> BaseImageGenerationConfig: return AzureDallE3ImageGenerationConfig() else: verbose_logger.debug( - f"Using AzureGPTImageGenerationConfig for model: {model}. This follows the gpt-image-1 model format." + f"Using AzureGPTImageGenerationConfig for model: {model}. This follows the gpt-image model format." ) return AzureGPTImageGenerationConfig() diff --git a/litellm/llms/azure/image_generation/gpt_transformation.py b/litellm/llms/azure/image_generation/gpt_transformation.py index 1f5f65f693a..2d46592e3fb 100644 --- a/litellm/llms/azure/image_generation/gpt_transformation.py +++ b/litellm/llms/azure/image_generation/gpt_transformation.py @@ -3,7 +3,7 @@ from litellm.llms.openai.image_generation import GPTImageGenerationConfig class AzureGPTImageGenerationConfig(GPTImageGenerationConfig): """ - Azure gpt-image-1 image generation config + Azure gpt-image image generation config """ pass diff --git a/litellm/llms/azure/image_generation/http_utils.py b/litellm/llms/azure/image_generation/http_utils.py new file mode 100644 index 00000000000..03c425eeffc --- /dev/null +++ b/litellm/llms/azure/image_generation/http_utils.py @@ -0,0 +1,17 @@ +"""HTTP helpers for Azure OpenAI image generation (REST, not SDK).""" + + +def azure_deployment_image_generation_json_body(api_base: str, data: dict) -> dict: + """ + Build the JSON body for Azure OpenAI image generation POSTs. + + For ``.../openai/deployments/{deployment}/images/generations``, routing uses the + deployment in the URL only; sending ``model`` in the body (especially the deployment + name) breaks some models (e.g. gpt-image-2). See LiteLLM #26316. + + Provider-style URLs (e.g. ``/providers/...`` for FLUX on Azure AI) keep all keys + so non–OpenAI-deployment payloads still work. + """ + if "images/generations" in api_base and "/openai/deployments/" in api_base: + return {k: v for k, v in data.items() if k != "model"} + return data diff --git a/litellm/llms/azure/realtime/handler.py b/litellm/llms/azure/realtime/handler.py index 1f3428f2ca5..1f3357fd788 100644 --- a/litellm/llms/azure/realtime/handler.py +++ b/litellm/llms/azure/realtime/handler.py @@ -89,9 +89,10 @@ class AzureOpenAIRealtime(AzureChatCompletion): if api_base is None: raise ValueError("api_base is required for Azure OpenAI calls") - if api_version is None and ( + backend_uses_beta_protocol = ( realtime_protocol is None or realtime_protocol.upper() not in ("GA", "V1") - ): + ) + if api_version is None and backend_uses_beta_protocol: raise ValueError("api_version is required for Azure OpenAI calls") url = self._construct_url( @@ -114,6 +115,7 @@ class AzureOpenAIRealtime(AzureChatCompletion): logging_obj, user_api_key_dict=user_api_key_dict, request_data={"litellm_metadata": litellm_metadata or {}}, + backend_uses_beta_protocol=backend_uses_beta_protocol, ) await realtime_streaming.bidirectional_forward() diff --git a/litellm/llms/azure/responses/transformation.py b/litellm/llms/azure/responses/transformation.py index 76a6d485bc4..ca9293325ff 100644 --- a/litellm/llms/azure/responses/transformation.py +++ b/litellm/llms/azure/responses/transformation.py @@ -5,6 +5,7 @@ import httpx from openai.types.responses import ResponseReasoningItem from litellm._logging import verbose_logger +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.azure.common_utils import BaseAzureLLM from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.types.llms.openai import * @@ -201,7 +202,10 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): # Insert the response_id at the end of the path component # Remove trailing slash if present to avoid double slashes path = parsed_url.path.rstrip("/") - new_path = f"{path}/{response_id}" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + new_path = f"{path}/{encoded_response_id}" # Reconstruct the URL with all original components but with the modified path constructed_url = urlunparse( @@ -322,7 +326,10 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): # Insert the response_id and /cancel at the end of the path component # Remove trailing slash if present to avoid double slashes path = parsed_url.path.rstrip("/") - new_path = f"{path}/{response_id}/cancel" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + new_path = f"{path}/{encoded_response_id}/cancel" # Reconstruct the URL with all original components but with the modified path cancel_url = urlunparse( diff --git a/litellm/llms/azure_ai/agents/handler.py b/litellm/llms/azure_ai/agents/handler.py index c3cd06ab4de..9bae8abce8e 100644 --- a/litellm/llms/azure_ai/agents/handler.py +++ b/litellm/llms/azure_ai/agents/handler.py @@ -36,6 +36,7 @@ from typing import ( import httpx from litellm._logging import verbose_logger +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.azure_ai.agents.transformation import ( AzureAIAgentsConfig, AzureAIAgentsError, @@ -75,20 +76,29 @@ class AzureAIAgentsHandler: def _build_messages_url( self, api_base: str, thread_id: str, api_version: str ) -> str: - return f"{api_base}/threads/{thread_id}/messages?api-version={api_version}" + encoded_thread_id = encode_url_path_segment(thread_id, field_name="thread_id") + return ( + f"{api_base}/threads/{encoded_thread_id}/messages?api-version={api_version}" + ) def _build_runs_url(self, api_base: str, thread_id: str, api_version: str) -> str: - return f"{api_base}/threads/{thread_id}/runs?api-version={api_version}" + encoded_thread_id = encode_url_path_segment(thread_id, field_name="thread_id") + return f"{api_base}/threads/{encoded_thread_id}/runs?api-version={api_version}" def _build_run_status_url( self, api_base: str, thread_id: str, run_id: str, api_version: str ) -> str: - return f"{api_base}/threads/{thread_id}/runs/{run_id}?api-version={api_version}" + encoded_thread_id = encode_url_path_segment(thread_id, field_name="thread_id") + encoded_run_id = encode_url_path_segment(run_id, field_name="run_id") + return f"{api_base}/threads/{encoded_thread_id}/runs/{encoded_run_id}?api-version={api_version}" def _build_list_messages_url( self, api_base: str, thread_id: str, api_version: str ) -> str: - return f"{api_base}/threads/{thread_id}/messages?api-version={api_version}" + encoded_thread_id = encode_url_path_segment(thread_id, field_name="thread_id") + return ( + f"{api_base}/threads/{encoded_thread_id}/messages?api-version={api_version}" + ) def _build_create_thread_and_run_url(self, api_base: str, api_version: str) -> str: """URL for the create-thread-and-run endpoint (supports streaming).""" diff --git a/litellm/llms/azure_ai/anthropic/transformation.py b/litellm/llms/azure_ai/anthropic/transformation.py index e935aa1c057..e176a4d860e 100644 --- a/litellm/llms/azure_ai/anthropic/transformation.py +++ b/litellm/llms/azure_ai/anthropic/transformation.py @@ -12,6 +12,23 @@ if TYPE_CHECKING: pass +def _promote_extra_body_to_optional_params(optional_params: dict) -> None: + """Promote anthropic-native passthrough keys out of ``extra_body``. + + ``azure_ai`` is an OpenAI-compatible provider, so non-OpenAI kwargs like + ``output_config`` get auto-routed into ``extra_body`` by + ``add_provider_specific_params_to_optional_params``. For the Azure→Anthropic + route those keys must reach the request body and be validated, so promote + them. ``setdefault`` keeps explicit top-level values authoritative. + """ + extra_body = optional_params.get("extra_body") + if not isinstance(extra_body, dict) or not extra_body: + return + for k, v in extra_body.items(): + optional_params.setdefault(k, v) + optional_params.pop("extra_body", None) + + class AzureAnthropicConfig(AnthropicConfig): """ Azure Anthropic configuration that extends AnthropicConfig. @@ -39,6 +56,8 @@ class AzureAnthropicConfig(AnthropicConfig): 1. API key via 'api-key' header 2. Azure AD token via 'Authorization: Bearer ' header """ + _promote_extra_body_to_optional_params(optional_params) + # Convert dict to GenericLiteLLMParams if needed if isinstance(litellm_params, dict): # Ensure api_key is included if provided @@ -101,7 +120,8 @@ class AzureAnthropicConfig(AnthropicConfig): Transform request using parent AnthropicConfig, then remove unsupported params. Azure Anthropic doesn't support extra_body, max_retries, or stream_options parameters. """ - # Call parent transform_request + _promote_extra_body_to_optional_params(optional_params) + data = super().transform_request( model=model, messages=messages, diff --git a/litellm/llms/azure_ai/cost_calculator.py b/litellm/llms/azure_ai/cost_calculator.py index 067181b946a..755d44fdef7 100644 --- a/litellm/llms/azure_ai/cost_calculator.py +++ b/litellm/llms/azure_ai/cost_calculator.py @@ -65,6 +65,7 @@ def cost_per_token( usage: Usage, response_time_ms: Optional[float] = 0.0, request_model: Optional[str] = None, + service_tier: Optional[str] = None, ) -> Tuple[float, float]: """ Calculate the cost per token for Azure AI models. @@ -102,6 +103,7 @@ def cost_per_token( model=model, usage=usage, custom_llm_provider="azure_ai", + service_tier=service_tier, ) except Exception as e: # For Model Router, the model name (e.g., "azure-model-router") may not be in the cost map diff --git a/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py b/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py index 76c247aea81..d4144a75718 100644 --- a/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py +++ b/litellm/llms/azure_ai/ocr/document_intelligence/transformation.py @@ -17,11 +17,13 @@ from urllib.parse import quote import httpx from litellm._logging import verbose_logger +from litellm.litellm_core_utils.url_utils import SSRFError, assert_same_origin from litellm.constants import ( AZURE_DOCUMENT_INTELLIGENCE_API_VERSION, AZURE_DOCUMENT_INTELLIGENCE_DEFAULT_DPI, AZURE_OPERATION_POLLING_TIMEOUT, ) +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.ocr.transformation import ( BaseOCRConfig, DocumentType, @@ -217,11 +219,12 @@ class AzureDocumentIntelligenceOCRConfig(BaseOCRConfig): if "/" in model: # Extract the last part after the last slash model_id = model.split("/")[-1] + encoded_model_id = encode_url_path_segment(model_id, field_name="model_id") # Azure Document Intelligence analyze endpoint # Note: API version 2024-11-30+ uses /documentintelligence/ (not /formrecognizer/) url = ( - f"{api_base}/documentintelligence/documentModels/{model_id}:analyze" + f"{api_base}/documentintelligence/documentModels/{encoded_model_id}:analyze" f"?api-version={AZURE_DOCUMENT_INTELLIGENCE_API_VERSION}" ) @@ -599,6 +602,16 @@ class AzureDocumentIntelligenceOCRConfig(BaseOCRConfig): "Azure Document Intelligence returned 202 but no Operation-Location header found" ) + # Reject cross-origin polling URLs — the auth headers + # below would otherwise leak to whatever URL the upstream + # (or an attacker-controlled upstream) returns. VERIA-51. + try: + assert_same_origin(operation_url, str(raw_response.request.url)) + except SSRFError as ssrf_err: + raise ValueError( + f"Azure Document Intelligence: rejected polling URL ({ssrf_err})" + ) + # Get headers for polling (need auth) poll_headers = { "Ocp-Apim-Subscription-Key": raw_response.request.headers.get( @@ -711,6 +724,14 @@ class AzureDocumentIntelligenceOCRConfig(BaseOCRConfig): "Azure Document Intelligence returned 202 but no Operation-Location header found" ) + # Reject cross-origin polling URLs (see sync path). VERIA-51. + try: + assert_same_origin(operation_url, str(raw_response.request.url)) + except SSRFError as ssrf_err: + raise ValueError( + f"Azure Document Intelligence: rejected polling URL ({ssrf_err})" + ) + # Get headers for polling (need auth) poll_headers = { "Ocp-Apim-Subscription-Key": raw_response.request.headers.get( diff --git a/litellm/llms/azure_ai/vector_stores/transformation.py b/litellm/llms/azure_ai/vector_stores/transformation.py index b62acb65166..d1b93c9e7a3 100644 --- a/litellm/llms/azure_ai/vector_stores/transformation.py +++ b/litellm/llms/azure_ai/vector_stores/transformation.py @@ -95,6 +95,7 @@ class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict[str, Any]]: """ Transform search request for Azure AI Search API diff --git a/litellm/llms/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py index b71ae0fddee..bec25916c4b 100644 --- a/litellm/llms/base_llm/chat/transformation.py +++ b/litellm/llms/base_llm/chat/transformation.py @@ -87,9 +87,7 @@ class BaseConfig(ABC): return { k: v for k, v in cls.__dict__.items() - if not k.startswith("__") - and not k.startswith("_abc") - and not k.startswith("_is_base_class") + if not k.startswith("_") and not isinstance( v, ( diff --git a/litellm/llms/base_llm/guardrail_translation/utils.py b/litellm/llms/base_llm/guardrail_translation/utils.py index cdd2d775371..97ece6b5eab 100644 --- a/litellm/llms/base_llm/guardrail_translation/utils.py +++ b/litellm/llms/base_llm/guardrail_translation/utils.py @@ -14,7 +14,22 @@ def effective_skip_system_message_for_guardrail(guardrail_to_apply: Any) -> bool return bool(getattr(litellm, "skip_system_message_in_guardrail", False)) +def effective_skip_tool_message_for_guardrail(guardrail_to_apply: Any) -> bool: + per = getattr(guardrail_to_apply, "skip_tool_message_in_guardrail", None) + if per is not None: + return bool(per) + import litellm + + return bool(getattr(litellm, "skip_tool_message_in_guardrail", False)) + + def openai_messages_without_system( messages: List[AllMessageValues], ) -> List[AllMessageValues]: return [m for m in messages if str((m or {}).get("role") or "").lower() != "system"] + + +def openai_messages_without_tool( + messages: List[AllMessageValues], +) -> List[AllMessageValues]: + return [m for m in messages if str((m or {}).get("role") or "").lower() != "tool"] diff --git a/litellm/llms/base_llm/image_edit/transformation.py b/litellm/llms/base_llm/image_edit/transformation.py index cea96bde74d..92429573ff8 100644 --- a/litellm/llms/base_llm/image_edit/transformation.py +++ b/litellm/llms/base_llm/image_edit/transformation.py @@ -102,6 +102,18 @@ class BaseImageEditConfig(ABC): ) -> Tuple[Dict, RequestFiles]: pass + def finalize_image_edit_request_data( + self, data: dict, resolved_request_url: str + ) -> dict: + """ + Last pass on the request dict after ``transform_image_edit_request``, using the + exact URL string used for the HTTP POST (same as ``get_complete_url`` output). + + The handler sends this dict as ``data=`` for multipart providers or ``json=`` + for JSON-only providers; default implementation returns ``data`` unchanged. + """ + return data + @abstractmethod def transform_image_edit_response( self, diff --git a/litellm/llms/base_llm/managed_resources/base_managed_resource.py b/litellm/llms/base_llm/managed_resources/base_managed_resource.py index 5422af76780..c0c18aefdeb 100644 --- a/litellm/llms/base_llm/managed_resources/base_managed_resource.py +++ b/litellm/llms/base_llm/managed_resources/base_managed_resource.py @@ -18,6 +18,11 @@ from typing import ( ) from litellm import verbose_logger +from litellm.llms.base_llm.managed_resources.isolation import ( + build_list_page, + build_owner_filter, + can_access_resource, +) from litellm.proxy._types import UserAPIKeyAuth from litellm.types.utils import SpecialEnums @@ -169,6 +174,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): "model_mappings": model_mappings, "flat_model_resource_ids": list(model_mappings.values()), "created_by": user_api_key_dict.user_id, + "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -190,6 +196,7 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): "model_mappings": json.dumps(model_mappings), "flat_model_resource_ids": list(model_mappings.values()), "created_by": user_api_key_dict.user_id, + "team_id": user_api_key_dict.team_id, "updated_by": user_api_key_dict.user_id, } @@ -316,15 +323,17 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): Returns: True if user has access, False otherwise """ - user_id = user_api_key_dict.user_id - # Use cached method instead of direct DB query resource = await self.get_unified_resource_id( unified_resource_id, litellm_parent_otel_span ) if resource: - return resource.get("created_by") == user_id + return can_access_resource( + user_api_key_dict=user_api_key_dict, + created_by=resource.get("created_by"), + resource_team_id=resource.get("team_id"), + ) return False @@ -549,11 +558,11 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): Returns: Dictionary with list of resources and pagination info """ - where_clause: Dict[str, Any] = {} + owner_filter = build_owner_filter(user_api_key_dict) + if owner_filter is None: + return build_list_page([]) - # Filter by user who created the resource - if user_api_key_dict.user_id: - where_clause["created_by"] = user_api_key_dict.user_id + where_clause: Dict[str, Any] = {**owner_filter} if after: where_clause["id"] = {"gt": after} @@ -598,10 +607,6 @@ class BaseManagedResource(ABC, Generic[ResourceObjectType]): ) continue - return { - "object": "list", - "data": resource_objects, - "first_id": resource_objects[0].id if resource_objects else None, - "last_id": resource_objects[-1].id if resource_objects else None, - "has_more": len(resource_objects) == (limit or 20), - } + return build_list_page( + resource_objects, has_more=len(resource_objects) == (limit or 20) + ) diff --git a/litellm/llms/base_llm/managed_resources/isolation.py b/litellm/llms/base_llm/managed_resources/isolation.py new file mode 100644 index 00000000000..62027f4272c --- /dev/null +++ b/litellm/llms/base_llm/managed_resources/isolation.py @@ -0,0 +1,99 @@ +""" +Tenant-isolation helpers for managed file/batch/vector-store resources. + +Returns a Prisma filter and an ownership check that scope managed resources +to the caller's identity: proxy admins see everything, user-keyed callers +see records they created, and service-account keys (no user_id) fall back +to the resource's owning team. Callers with no admin role and no +identifying ids are denied so an empty user_id can never select an +unscoped query. +""" + +from typing import Any, Dict, List, Optional + +from litellm.proxy._types import ( + UserAPIKeyAuth, + user_api_key_has_admin_view as _user_has_admin_view, +) + + +def build_list_page(items: List[Any], has_more: bool = False) -> Dict[str, Any]: + """Build the OpenAI-style paginated list response shape used by managed + file/batch/vector-store listings. ``first_id`` and ``last_id`` are + sourced from each item's ``.id`` attribute.""" + return { + "object": "list", + "data": items, + "first_id": items[0].id if items else None, + "last_id": items[-1].id if items else None, + "has_more": has_more, + } + + +def build_owner_filter( + user_api_key_dict: UserAPIKeyAuth, +) -> Optional[Dict[str, Any]]: + """Return a Prisma `where` fragment that scopes a managed-resource listing + to records the caller is allowed to see. + + - ``{}`` means no scoping (proxy admins). + - ``{"created_by": }`` for user-keyed callers. + - ``{"team_id": }`` for service-account callers + that have a team but no user_id. + - ``{"OR": [...]}`` when the caller has both — listing must include + both their own resources and team-shared ones so it stays consistent + with ``can_access_resource``. + - ``None`` means deny: callers MUST skip the query rather than fall + back to an unscoped fetch. + """ + if _user_has_admin_view(user_api_key_dict): + return {} + + user_id = user_api_key_dict.user_id + team_id = user_api_key_dict.team_id + + if user_id is not None and team_id is not None: + return { + "OR": [ + {"created_by": user_id}, + {"team_id": team_id}, + ] + } + + if user_id is not None: + return {"created_by": user_id} + + if team_id is not None: + return {"team_id": team_id} + + return None + + +def can_access_resource( + user_api_key_dict: UserAPIKeyAuth, + created_by: Optional[str], + resource_team_id: Optional[str], +) -> bool: + """Return True iff the caller may read/modify a managed resource. + + The resource's ``created_by`` and ``team_id`` fields must be non-None + to match the caller's identity — guarding against the ``None == None`` + bypass that previously let service-account keys read every keyless + resource. + """ + if _user_has_admin_view(user_api_key_dict): + return True + + user_id = user_api_key_dict.user_id + if user_id is not None and created_by is not None and created_by == user_id: + return True + + team_id = user_api_key_dict.team_id + if ( + team_id is not None + and resource_team_id is not None + and resource_team_id == team_id + ): + return True + + return False diff --git a/litellm/llms/base_llm/rerank/transformation.py b/litellm/llms/base_llm/rerank/transformation.py index 7874201f7f0..166f876ba04 100644 --- a/litellm/llms/base_llm/rerank/transformation.py +++ b/litellm/llms/base_llm/rerank/transformation.py @@ -33,6 +33,7 @@ class BaseRerankConfig(ABC): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: return {} diff --git a/litellm/llms/base_llm/vector_store/transformation.py b/litellm/llms/base_llm/vector_store/transformation.py index 5fbf0a4b19f..85a9c838264 100644 --- a/litellm/llms/base_llm/vector_store/transformation.py +++ b/litellm/llms/base_llm/vector_store/transformation.py @@ -59,6 +59,7 @@ class BaseVectorStoreConfig: api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: pass @@ -70,6 +71,7 @@ class BaseVectorStoreConfig: api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: """ Optional async version of transform_search_vector_store_request. @@ -84,6 +86,7 @@ class BaseVectorStoreConfig: api_base=api_base, litellm_logging_obj=litellm_logging_obj, litellm_params=litellm_params, + extra_body=extra_body, ) @abstractmethod diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 4e3521b119e..9dd2b055a12 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -1,11 +1,14 @@ import hashlib import json import os +import re import urllib.parse from datetime import datetime from typing import ( TYPE_CHECKING, Any, + Callable, + ClassVar, Dict, List, Literal, @@ -37,6 +40,11 @@ else: AWSPreparedRequest = Any +# Real AWS region names are lowercase letters, digits, and hyphens +# (e.g. "us-east-1", "eu-west-2", "us-gov-west-1", "cn-north-1"). +_VALID_AWS_REGION_PATTERN = re.compile(r"\A[a-z0-9-]+\Z") + + class Boto3CredentialsInfo(BaseModel): credentials: Credentials aws_region_name: str @@ -57,8 +65,16 @@ class AwsAuthError(Exception): class BaseAWSLLM: + # Process-wide IAM credential cache (shared across instances — Bedrock passthrough is per-request). + # Storage is in-process memory only: default ``DualCache()`` has no Redis backend unless attached + # elsewhere. Entry TTL: static access-key + secret + region use ``_get_default_ttl_for_boto3_credentials`` + # (~59 minutes); ambient env (``_auth_with_env_vars`` returns ``ttl=None``) uses ``InMemoryCache``'s + # ``default_ttl`` (600 seconds / 10 minutes). AssumeRole, web identity, profiles, and explicit + # session-token tuples are not cached — see ``get_credentials`` and ``_get_or_set_cached_credentials``. + _shared_iam_cache: ClassVar[DualCache] = DualCache() + def __init__(self) -> None: - self.iam_cache = DualCache() + self.iam_cache = BaseAWSLLM._shared_iam_cache super().__init__() self.aws_authentication_params = [ "aws_access_key_id", @@ -97,6 +113,79 @@ class BaseAWSLLM: credential_str = json.dumps(credential_args, sort_keys=True) return hashlib.sha256(credential_str.encode()).hexdigest() + def _get_or_set_cached_credentials( + self, + credential_args: Dict[str, Optional[str]], + credential_fetcher: Callable[[], Tuple[Any, Optional[int]]], + ) -> Any: + """ + Read-through IAM cache on the process-wide ``DualCache``. + + Only the in-memory layer is used by default (no Redis on ``_shared_iam_cache`` unless + configured globally). TTL on write: static access-key fetches pass + ``_get_default_ttl_for_boto3_credentials()`` (~59 minutes); ambient env passes ``ttl=None``, + which ``InMemoryCache.set_cache`` resolves to ``default_ttl`` (600 seconds / 10 minutes by + default). + + Used only for static access-key credentials and ambient credentials from + ``_auth_with_env_vars`` (including when skipping AssumeRole because the runtime identity + already matches ``aws_role_name``). + + AssumeRole, web identity exchange, profiles, and explicit session-token tuples are not + cached here — shared ``Credentials`` / refresh state must not span logical sessions. + """ + cache_key = self.get_cache_key(credential_args) + _cached = self.iam_cache.get_cache(cache_key) + if _cached: + return _cached + credentials, ttl = credential_fetcher() + self.iam_cache.set_cache(cache_key, credentials, ttl=ttl) + return credentials + + @staticmethod + def _is_auth_with_web_identity_token( + aws_web_identity_token: Optional[str], + aws_role_name: Optional[str], + aws_session_name: Optional[str], + ) -> bool: + return ( + aws_web_identity_token is not None + and aws_role_name is not None + and aws_session_name is not None + ) + + @staticmethod + def _is_auth_with_aws_role(aws_role_name: Optional[str]) -> bool: + return aws_role_name is not None + + @staticmethod + def _is_auth_with_aws_profile(aws_profile_name: Optional[str]) -> bool: + return aws_profile_name is not None + + @staticmethod + def _is_auth_with_aws_session_token_tuple( + aws_access_key_id: Optional[str], + aws_secret_access_key: Optional[str], + aws_session_token: Optional[str], + ) -> bool: + return ( + aws_access_key_id is not None + and aws_secret_access_key is not None + and aws_session_token is not None + ) + + @staticmethod + def _is_auth_with_access_key_and_secret_key( + aws_access_key_id: Optional[str], + aws_secret_access_key: Optional[str], + aws_region_name: Optional[str], + ) -> bool: + return ( + aws_access_key_id is not None + and aws_secret_access_key is not None + and aws_region_name is not None + ) + @tracer.wrap() def get_credentials( self, @@ -178,95 +267,97 @@ class BaseAWSLLM: aws_external_id, ) - # create cache key for non-expiring auth flows args = { k: v for k, v in locals().items() if k.startswith("aws_") or k == "ssl_verify" } - cache_key = self.get_cache_key(args) - _cached_credentials = self.iam_cache.get_cache(cache_key) - if _cached_credentials: - return _cached_credentials - ######################################################### # Handle diff boto3 auth flows # for each helper # Return: # Credentials - boto3.Credentials # cache ttl - Optional[int]. If None, the credentials are not cached. Some auth flows have no expiry time. + # + # iam_cache: static keys and ambient env only (including skip-AssumeRole path). + # Do not cache AssumeRole / web identity / profile / explicit session-token paths here. ######################################################### - if ( - aws_web_identity_token is not None - and aws_role_name is not None - and aws_session_name is not None + if self._is_auth_with_web_identity_token( + aws_web_identity_token, + aws_role_name, + aws_session_name, ): credentials, _cache_ttl = self._auth_with_web_identity_token( - aws_web_identity_token=aws_web_identity_token, - aws_role_name=aws_role_name, - aws_session_name=aws_session_name, + aws_web_identity_token=cast(str, aws_web_identity_token), + aws_role_name=cast(str, aws_role_name), + aws_session_name=cast(str, aws_session_name), aws_region_name=aws_region_name, aws_sts_endpoint=aws_sts_endpoint, aws_external_id=aws_external_id, ) - elif aws_role_name is not None: - # Check if we're already running as the target role and can skip assumption - # This handles IRSA (EKS), ECS task roles, and EC2 instance profiles - if self._is_already_running_as_role(aws_role_name, ssl_verify=ssl_verify): + return credentials + elif self._is_auth_with_aws_role(aws_role_name): + # Same role (IRSA/ECS/EC2): ambient creds via _get_or_set_cached_credentials like the + # default env branch; never pre-read cache (must run _is_already_running_as_role first). + if self._is_already_running_as_role( + cast(str, aws_role_name), ssl_verify=ssl_verify + ): verbose_logger.debug( "Already running as target role %s, using ambient credentials", aws_role_name, ) - credentials, _cache_ttl = self._auth_with_env_vars() - else: - verbose_logger.debug( - "Using role assumption: calling _auth_with_aws_role" + return self._get_or_set_cached_credentials( + args, self._auth_with_env_vars ) - # If aws_session_name is not provided, generate a default one - if aws_session_name is None: - aws_session_name = ( - f"litellm-session-{int(datetime.now().timestamp())}" - ) - credentials, _cache_ttl = self._auth_with_aws_role( - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - aws_session_token=aws_session_token, - aws_role_name=aws_role_name, - aws_session_name=aws_session_name, - aws_region_name=aws_region_name, - aws_sts_endpoint=aws_sts_endpoint, - aws_external_id=aws_external_id, - ssl_verify=ssl_verify, - ) - - elif aws_profile_name is not None: ### CHECK SESSION ### - credentials, _cache_ttl = self._auth_with_aws_profile(aws_profile_name) - elif ( - aws_access_key_id is not None - and aws_secret_access_key is not None - and aws_session_token is not None - ): - credentials, _cache_ttl = self._auth_with_aws_session_token( + verbose_logger.debug("Using role assumption: calling _auth_with_aws_role") + # If aws_session_name is not provided, generate a default one + if aws_session_name is None: + aws_session_name = f"litellm-session-{int(datetime.now().timestamp())}" + credentials, _assume_ttl = self._auth_with_aws_role( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, aws_session_token=aws_session_token, - ) - elif ( - aws_access_key_id is not None - and aws_secret_access_key is not None - and aws_region_name is not None - ): - credentials, _cache_ttl = self._auth_with_access_key_and_secret_key( - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, + aws_role_name=cast(str, aws_role_name), + aws_session_name=aws_session_name, aws_region_name=aws_region_name, + aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, + ssl_verify=ssl_verify, + ) + return credentials + + elif self._is_auth_with_aws_profile(aws_profile_name): + credentials, _cache_ttl = self._auth_with_aws_profile( + cast(str, aws_profile_name) + ) + return credentials + elif self._is_auth_with_aws_session_token_tuple( + aws_access_key_id, + aws_secret_access_key, + aws_session_token, + ): + credentials, _cache_ttl = self._auth_with_aws_session_token( + aws_access_key_id=cast(str, aws_access_key_id), + aws_secret_access_key=cast(str, aws_secret_access_key), + aws_session_token=cast(str, aws_session_token), + ) + return credentials + elif self._is_auth_with_access_key_and_secret_key( + aws_access_key_id, + aws_secret_access_key, + aws_region_name, + ): + return self._get_or_set_cached_credentials( + args, + lambda: self._auth_with_access_key_and_secret_key( + aws_access_key_id=cast(str, aws_access_key_id), + aws_secret_access_key=cast(str, aws_secret_access_key), + aws_region_name=cast(str, aws_region_name), + ), ) else: - credentials, _cache_ttl = self._auth_with_env_vars() - - self.iam_cache.set_cache(cache_key, credentials, ttl=_cache_ttl) - return credentials + return self._get_or_set_cached_credentials(args, self._auth_with_env_vars) def _get_aws_region_from_model_arn(self, model: Optional[str]) -> Optional[str]: try: @@ -284,6 +375,9 @@ class BaseAWSLLM: if not region: # Check if region is empty return None + if not _VALID_AWS_REGION_PATTERN.match(region): + return None + return region except Exception: # Catch any unexpected errors and return None @@ -481,6 +575,7 @@ class BaseAWSLLM: str: The AWS region name """ aws_region_name = optional_params.get("aws_region_name", None) + self._validate_aws_region_name(aws_region_name) ### SET REGION NAME ### if aws_region_name is None: # check model arn # @@ -519,8 +614,25 @@ class BaseAWSLLM: except Exception: aws_region_name = "us-west-2" + self._validate_aws_region_name(aws_region_name) return aws_region_name + @staticmethod + def _validate_aws_region_name(aws_region_name: Optional[str]) -> None: + """ + Validate that an AWS region name conforms to the expected format + (lowercase alphanumerics and hyphens). Raises ValueError otherwise. + """ + if aws_region_name is None: + return + if not isinstance(aws_region_name, str) or not _VALID_AWS_REGION_PATTERN.match( + aws_region_name + ): + raise ValueError( + f"Invalid AWS region format: {aws_region_name!r}. " + "Region names must contain only lowercase letters, digits, and hyphens." + ) + def get_aws_region_name_for_non_llm_api_calls( self, aws_region_name: Optional[str] = None, @@ -532,6 +644,7 @@ class BaseAWSLLM: For non-llm api calls eg. Guardrails, Vector Stores we just need to check the dynamic param or env vars. """ + self._validate_aws_region_name(aws_region_name) if aws_region_name is None: # check env # litellm_aws_region_name = get_secret("AWS_REGION_NAME", None) @@ -549,6 +662,8 @@ class BaseAWSLLM: if aws_region_name is None: aws_region_name = "us-west-2" + + self._validate_aws_region_name(aws_region_name) return aws_region_name @staticmethod @@ -1313,7 +1428,13 @@ class BaseAWSLLM: def _sign_request( self, - service_name: Literal["bedrock", "sagemaker", "bedrock-agentcore", "s3vectors"], + service_name: Literal[ + "bedrock", + "sagemaker", + "bedrock-agentcore", + "s3vectors", + "aws-external-anthropic", + ], headers: dict, optional_params: dict, request_data: dict, diff --git a/litellm/llms/bedrock/batches/handler.py b/litellm/llms/bedrock/batches/handler.py index f141bbd9ab4..c071f331337 100644 --- a/litellm/llms/bedrock/batches/handler.py +++ b/litellm/llms/bedrock/batches/handler.py @@ -1,8 +1,79 @@ +from datetime import datetime +from typing import Any, Optional, cast + from openai.types.batch import BatchRequestCounts from openai.types.batch import Metadata as OpenAIBatchMetadata from litellm.types.utils import LiteLLMBatch +# AWS Bedrock model-invocation-job statuses → OpenAI Batch statuses. +# Mirrors the mapping used by `BedrockBatchesConfig.transform_create_batch_response` +# so create / retrieve return consistent statuses. +_BEDROCK_MIJ_STATUS_TO_OPENAI = { + "Submitted": "validating", + "Validating": "validating", + "Scheduled": "validating", + "InProgress": "in_progress", + "Stopping": "cancelling", + "Stopped": "cancelled", + "Completed": "completed", + "PartiallyCompleted": "completed", + "Failed": "failed", + "Expired": "expired", +} + + +def _extract_region_from_bedrock_arn(arn: str) -> Optional[str]: + """ARN shape: ``arn:aws:bedrock:::/``""" + try: + parts = arn.split(":") + if len(parts) >= 4 and parts[2] == "bedrock": + return parts[3] or None + except Exception: + pass + return None + + +def _extract_job_id_from_arn(arn: str) -> Optional[str]: + """``arn:aws:bedrock:::model-invocation-job/`` -> ````.""" + if ":model-invocation-job/" not in arn: + return None + return arn.rsplit("/", 1)[-1] or None + + +def _predict_output_file_uri( + output_prefix: str, input_uri: str, job_id: Optional[str] +) -> Optional[str]: + """ + Compute the deterministic per-job result file URI Bedrock writes to. + + Bedrock lays results out as:: + + //.out + + We compute it client-side so OpenAI-style ``client.files.content(output_file_id)`` + works without an extra S3 ``ListObjectsV2`` round-trip. Returns ``None`` if we + don't have enough info; callers should fall back to the bare prefix. + """ + if not output_prefix or not input_uri or not job_id: + return None + if not output_prefix.endswith("/"): + output_prefix = output_prefix + "/" + input_basename = input_uri.rsplit("/", 1)[-1] + if not input_basename: + return None + return f"{output_prefix}{job_id}/{input_basename}.out" + + +def _to_epoch(value: Any) -> Optional[int]: + if value is None: + return None + if isinstance(value, (int, float)): + return int(value) + if isinstance(value, datetime): + return int(value.timestamp()) + return None + class BedrockBatchesHandler: """ @@ -97,3 +168,173 @@ class BedrockBatchesHandler: with concurrent.futures.ThreadPoolExecutor() as executor: future = executor.submit(run_in_thread) return future.result() + + @staticmethod + def _handle_model_invocation_job_status( + batch_id: str, + aws_region_name: Optional[str] = None, + logging_obj=None, + **kwargs, + ) -> "LiteLLMBatch": + """ + Handle ``GetModelInvocationJob`` status check for AWS Bedrock bulk batch + inference jobs (the ARN type returned by ``CreateModelInvocationJob``). + + ``CreateModelInvocationJob`` lives on the Bedrock **control plane** + (``bedrock..amazonaws.com``), distinct from the data-plane + ``bedrock-runtime`` endpoint that serves Twelve Labs async-invoke ARNs. + The two ARN families therefore can't share a handler — see + ``litellm/batches/main.py`` for the dispatch. + + Args: + batch_id: A ``arn:aws:bedrock:::model-invocation-job/`` + ARN (or just the trailing job id; both are accepted by + ``GetModelInvocationJob``). + aws_region_name: Region for the boto3 ``bedrock`` client. If omitted, + we fall back to parsing the region out of ``batch_id`` itself. + logging_obj: Optional litellm logging object. + **kwargs: Optional AWS credential overrides + (``aws_access_key_id``, ``aws_secret_access_key``, + ``aws_session_token``, ``aws_profile_name``, + ``aws_role_name``, ``aws_session_name``, + ``aws_web_identity_token``, ``aws_sts_endpoint``, + ``aws_external_id``). Unknown keys are ignored. + + Returns: + ``LiteLLMBatch`` shaped like an OpenAI Batch resource. Note that + ``request_counts`` is always ``(0, 0, 0)`` because + ``GetModelInvocationJob`` does not surface per-record counts; + callers that need accurate counts should parse + ``manifest.json.out`` from the output S3 prefix. + """ + try: + import boto3 + except ImportError as exc: + raise ImportError( + "Missing boto3 to call bedrock. Run 'pip install boto3'." + ) from exc + + # Resolve region: explicit > parsed-from-ARN > us-east-1 (boto3 default). + region = ( + aws_region_name or _extract_region_from_bedrock_arn(batch_id) or "us-east-1" + ) + + # Resolve credentials through the same path the rest of the bedrock + # provider uses, so model_list / env / role-assumption configs are + # honored. We instantiate BedrockBatchesConfig (which extends + # BaseAWSLLM) lazily to avoid a circular import at module load. + from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + + creds = BedrockBatchesConfig().get_credentials( + aws_access_key_id=kwargs.get("aws_access_key_id"), + aws_secret_access_key=kwargs.get("aws_secret_access_key"), + aws_session_token=kwargs.get("aws_session_token"), + aws_region_name=region, + aws_session_name=kwargs.get("aws_session_name"), + aws_profile_name=kwargs.get("aws_profile_name"), + aws_role_name=kwargs.get("aws_role_name"), + aws_web_identity_token=kwargs.get("aws_web_identity_token"), + aws_sts_endpoint=kwargs.get("aws_sts_endpoint"), + aws_external_id=kwargs.get("aws_external_id"), + ) + + client = boto3.client( + "bedrock", + region_name=region, + aws_access_key_id=creds.access_key, + aws_secret_access_key=creds.secret_key, + aws_session_token=creds.token, + ) + + if logging_obj is not None: + # Use the bare job id in the logged URL so we don't double up the + # `model-invocation-job/` segment when `batch_id` is a full ARN. + # `GetModelInvocationJob` accepts either form, but only the bare id + # produces a sensible-looking URL in logs. + url_path_id = _extract_job_id_from_arn(batch_id) or batch_id + logging_obj.pre_call( + input=batch_id, + api_key="", + additional_args={ + "complete_input_dict": {"jobIdentifier": batch_id}, + "api_base": ( + f"https://bedrock.{region}.amazonaws.com/" + f"model-invocation-job/{url_path_id}" + ), + }, + ) + + response = client.get_model_invocation_job(jobIdentifier=batch_id) + + if logging_obj is not None: + logging_obj.post_call( + input=batch_id, + api_key="", + original_response=response, + additional_args={"complete_input_dict": {"jobIdentifier": batch_id}}, + ) + + bedrock_status = str(response.get("status", "")) + openai_status = cast( + Any, + _BEDROCK_MIJ_STATUS_TO_OPENAI.get(bedrock_status, "in_progress"), + ) + + input_uri = ( + response.get("inputDataConfig", {}) + .get("s3InputDataConfig", {}) + .get("s3Uri", "") + ) + output_prefix = ( + response.get("outputDataConfig", {}) + .get("s3OutputDataConfig", {}) + .get("s3Uri", "") + ) + + # Bedrock returns the output *prefix* the user supplied at job creation. + # Actual results land at //.out — we + # surface that single-file URI as `output_file_id` so the OpenAI-style + # download flow works without an extra S3 listing call. We deliberately + # do NOT fall back to the bare prefix when prediction fails: a prefix + # is not a downloadable object, so handing it back as `output_file_id` + # would reproduce the very NoSuchKey bug this handler exists to fix. + # The bare prefix is preserved in metadata for callers that want the + # `manifest.json.out` or want to do their own listing. + job_arn = response.get("jobArn", batch_id) + job_id = _extract_job_id_from_arn(job_arn) + output_file_uri = _predict_output_file_uri(output_prefix, input_uri, job_id) + + completed_at = _to_epoch(response.get("endTime")) + + # Note: metadata uses "" (not None) for unknown URIs to satisfy the + # OpenAI Batch metadata schema, which is `dict[str, str]`. The + # `output_file_id` field on the LiteLLMBatch itself does carry None + # correctly (see below), so callers should branch on that, not on + # `metadata["output_file_uri"]`. + openai_batch_metadata: OpenAIBatchMetadata = { + "model_arn": response.get("modelId", ""), + "job_arn": job_arn, + "job_name": response.get("jobName", ""), + "failure_message": response.get("message") or "", + "input_s3_uri": input_uri, + "output_s3_uri": output_prefix, + "output_file_uri": output_file_uri or "", + } + + return LiteLLMBatch( + id=job_arn, + object="batch", + status=openai_status, + created_at=_to_epoch(response.get("submitTime")) or 0, + in_progress_at=_to_epoch(response.get("lastModifiedTime")), + completed_at=completed_at if openai_status == "completed" else None, + failed_at=completed_at if openai_status == "failed" else None, + cancelled_at=completed_at if openai_status == "cancelled" else None, + expired_at=completed_at if openai_status == "expired" else None, + request_counts=BatchRequestCounts(total=0, completed=0, failed=0), + metadata=openai_batch_metadata, + completion_window="24h", + endpoint="/v1/chat/completions", + input_file_id=input_uri, + output_file_id=output_file_uri if openai_status == "completed" else None, + ) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index a27153365d2..efc890d9ee2 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -31,7 +31,11 @@ from litellm.litellm_core_utils.prompt_templates.factory import ( _bedrock_converse_messages_pt, _bedrock_tools_pt, ) -from litellm.llms.anthropic.chat.transformation import AnthropicConfig +from litellm.llms.anthropic.chat.transformation import ( + DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, + REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT, + AnthropicConfig, +) from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.types.llms.bedrock import * from litellm.types.llms.openai import ( @@ -189,7 +193,7 @@ class AmazonConverseConfig(BaseConfig): return { k: v for k, v in cls.__dict__.items() - if not k.startswith("__") + if not k.startswith("_") and not isinstance( v, ( @@ -410,47 +414,64 @@ class AmazonConverseConfig(BaseConfig): """ Handle the reasoning_effort parameter based on the model type. - Different model families handle reasoning effort differently: - - GPT-OSS models: Keep reasoning_effort as-is (passed to additionalModelRequestFields) - - Nova 2 models: Transform to reasoningConfig structure - - Other models (Anthropic, etc.): Convert to thinking parameter - - Args: - model: The model identifier - reasoning_effort: The reasoning effort value - optional_params: Dictionary of optional parameters to update in-place - - Examples: - >>> config = AmazonConverseConfig() - >>> params = {} - >>> config._handle_reasoning_effort_parameter("gpt-oss-model", "high", params) - >>> params - {'reasoning_effort': 'high'} - - >>> params = {} - >>> config._handle_reasoning_effort_parameter("amazon.nova-2-lite-v1:0", "high", params) - >>> params - {'reasoningConfig': {'type': 'enabled', 'maxReasoningEffort': 'high'}} - - >>> params = {} - >>> config._handle_reasoning_effort_parameter("anthropic.claude-3", "high", params) - >>> params - {'thinking': {'type': 'enabled', 'budget_tokens': 10000}} + - GPT-OSS models: passed through unchanged via additionalModelRequestFields. + - Nova 2 models: transformed to reasoningConfig. + - Anthropic models: mapped to ``thinking`` (and ``output_config.effort`` on + adaptive Claude 4.6 / 4.7). """ if "gpt-oss" in model: - # GPT-OSS models: keep reasoning_effort as-is - # It will be passed through to additionalModelRequestFields optional_params["reasoning_effort"] = reasoning_effort elif self._is_nova_2_model(model): - # Nova 2 models: transform to reasoningConfig reasoning_config = self._transform_reasoning_effort_to_reasoning_config( reasoning_effort ) optional_params.update(reasoning_config) else: - # Anthropic and other models: convert to thinking parameter - optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - reasoning_effort=reasoning_effort, model=model + mapped_thinking = AnthropicConfig._map_reasoning_effort( + reasoning_effort=reasoning_effort, + model=model, + llm_provider="bedrock_converse", + ) + if mapped_thinking is None: + optional_params.pop("thinking", None) + optional_params.pop("output_config", None) + else: + optional_params["thinking"] = mapped_thinking + if AnthropicConfig._is_adaptive_thinking_model(model): + mapped_effort = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get( + reasoning_effort + ) + if mapped_effort is None: + AnthropicConfig._raise_invalid_reasoning_effort( + model=model, + value=reasoning_effort, + llm_provider="bedrock_converse", + ) + self._validate_anthropic_adaptive_effort( + model=model, effort=mapped_effort + ) + optional_params["output_config"] = {"effort": mapped_effort} + + @staticmethod + def _validate_anthropic_adaptive_effort(model: str, effort: str) -> None: + """Validate ``output_config.effort`` for adaptive-thinking Claude 4.6/4.7.""" + valid_efforts = {"high", "medium", "low", "xhigh", "max"} + if effort not in valid_efforts: + raise litellm.exceptions.BadRequestError( + message=( + f"Invalid reasoning_effort/output_config.effort value: " + f"{effort!r}. Must be one of: 'low', 'medium', 'high', " + f"'xhigh', or 'max'." + ), + model=model, + llm_provider="bedrock_converse", + ) + error = AnthropicConfig._validate_effort_for_model(model=model, effort=effort) + if error is not None: + raise litellm.exceptions.BadRequestError( + message=error, + model=model, + llm_provider="bedrock_converse", ) @staticmethod @@ -1192,9 +1213,11 @@ class AmazonConverseConfig(BaseConfig): + supported_config_params ) inference_params.pop("json_mode", None) # used for handling json_schema - # Anthropic-only key. Bedrock expects `outputConfig` (camelCase) and - # will reject `output_config` if it leaks through pass-through routes. - inference_params.pop("output_config", None) + + # Anthropic-only ``output_config`` (snake_case) — re-attached to + # ``additionalModelRequestFields`` for Anthropic models below. The + # Bedrock-native ``outputConfig`` (camelCase) is handled separately. + anthropic_output_config = inference_params.pop("output_config", None) # Extract requestMetadata before processing other parameters request_metadata = inference_params.pop("requestMetadata", None) @@ -1204,9 +1227,6 @@ class AmazonConverseConfig(BaseConfig): output_config: Optional[OutputConfigBlock] = inference_params.pop( "outputConfig", None ) - inference_params.pop( - "output_config", None - ) # Bedrock Converse doesn't support it # keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params' additional_request_params = { @@ -1249,6 +1269,27 @@ class AmazonConverseConfig(BaseConfig): additional_request_params ) + if anthropic_output_config is not None and isinstance( + anthropic_output_config, dict + ): + base_model = BedrockModelInfo.get_base_model(model) + if base_model.startswith("anthropic"): + if ( + litellm.drop_params is True + and not AnthropicConfig._model_supports_effort_param(model) + ): + litellm.verbose_logger.warning( + DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, + model, + ) + else: + effort = anthropic_output_config.get("effort") + if effort is not None: + self._validate_anthropic_adaptive_effort( + model=model, effort=effort + ) + additional_request_params["output_config"] = anthropic_output_config + return ( inference_params, additional_request_params, @@ -1372,9 +1413,25 @@ class AmazonConverseConfig(BaseConfig): # Append pre-formatted tools (systemTool etc.) after transformation bedrock_tools.extend(pre_formatted_tools) + # Opus 4.5 gates ``output_config.effort`` behind a beta header; + # Claude 4.6/4.7 accept it without one. + base_model = BedrockModelInfo.get_base_model(model) + if base_model.startswith("anthropic"): + output_config = additional_request_params.get("output_config") + if ( + isinstance(output_config, dict) + and output_config.get("effort") is not None + and not AnthropicConfig._is_adaptive_thinking_model(model) + ): + from litellm.types.llms.anthropic import ( + ANTHROPIC_EFFORT_BETA_HEADER, + ) + + if ANTHROPIC_EFFORT_BETA_HEADER not in anthropic_beta_list: + anthropic_beta_list.append(ANTHROPIC_EFFORT_BETA_HEADER) + # Set anthropic_beta in additional_request_params if we have any beta features # ONLY apply to Anthropic/Claude models - other models (e.g., Qwen, Llama) don't support this field - base_model = BedrockModelInfo.get_base_model(model) if anthropic_beta_list and base_model.startswith("anthropic"): additional_request_params["anthropic_beta"] = anthropic_beta_list @@ -1942,8 +1999,8 @@ class AmazonConverseConfig(BaseConfig): completion_response = ConverseResponseBlock(**response.json()) # type: ignore except Exception as e: raise BedrockError( - message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( - response.text, str(e) + message="Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( + str(e) ), status_code=422, ) diff --git a/litellm/llms/bedrock/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py index 2c7135f4d83..e4072c24557 100644 --- a/litellm/llms/bedrock/chat/invoke_agent/transformation.py +++ b/litellm/llms/bedrock/chat/invoke_agent/transformation.py @@ -12,6 +12,7 @@ import httpx from litellm._logging import verbose_logger from litellm._uuid import uuid +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.litellm_core_utils.prompt_templates.common_utils import ( convert_content_list_to_str, ) @@ -97,8 +98,15 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM): agent_id, agent_alias_id = self._get_agent_id_and_alias_id(model) session_id = self._get_session_id(optional_params) + encoded_agent_id = encode_url_path_segment(agent_id, field_name="agent_id") + encoded_agent_alias_id = encode_url_path_segment( + agent_alias_id, field_name="agent_alias_id" + ) + encoded_session_id = encode_url_path_segment( + session_id, field_name="session_id" + ) - endpoint_url = f"{endpoint_url}/agents/{agent_id}/agentAliases/{agent_alias_id}/sessions/{session_id}/text" + endpoint_url = f"{endpoint_url}/agents/{encoded_agent_id}/agentAliases/{encoded_agent_alias_id}/sessions/{encoded_session_id}/text" return endpoint_url @@ -291,29 +299,9 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM): ) def _get_response_stream_shape(self): - """Get the response stream shape for parsing, reusing existing logic.""" - try: - # Try to reuse the cached shape from the existing decoder - from litellm.llms.bedrock.chat.invoke_handler import ( - get_response_stream_shape, - ) + from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE - return get_response_stream_shape() - except ImportError: - # Fallback: create our own shape - try: - from botocore.loaders import Loader - from botocore.model import ServiceModel - - loader = Loader() - bedrock_service_dict = loader.load_service_model( - "bedrock-runtime", "service-2" - ) - bedrock_service_model = ServiceModel(bedrock_service_dict) - return bedrock_service_model.shape_for("ResponseStream") - except Exception as e: - verbose_logger.warning(f"Could not load response stream shape: {e}") - return None + return BEDROCK_RESPONSE_STREAM_SHAPE def _extract_response_content(self, events: InvokeAgentEventList) -> str: """Extract the final response content from parsed events.""" diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 9dfada7c418..92ca75db95b 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -67,9 +67,13 @@ from litellm.types.utils import ( from litellm.utils import CustomStreamWrapper, get_secret from ..base_aws_llm import BaseAWSLLM -from ..common_utils import BedrockError, ModelResponseIterator, get_bedrock_tool_name +from ..common_utils import ( + BEDROCK_RESPONSE_STREAM_SHAPE, + BedrockError, + ModelResponseIterator, + get_bedrock_tool_name, +) -_response_stream_shape_cache = None bedrock_tool_name_mappings: InMemoryCache = InMemoryCache( max_size_in_memory=50, default_ttl=600 ) @@ -1391,20 +1395,6 @@ class BedrockLLM(BaseAWSLLM): return None -def get_response_stream_shape(): - global _response_stream_shape_cache - if _response_stream_shape_cache is None: - from botocore.loaders import Loader - from botocore.model import ServiceModel - - loader = Loader() - bedrock_service_dict = loader.load_service_model("bedrock-runtime", "service-2") - bedrock_service_model = ServiceModel(bedrock_service_dict) - _response_stream_shape_cache = bedrock_service_model.shape_for("ResponseStream") - - return _response_stream_shape_cache - - class AWSEventStreamDecoder: def __init__(self, model: str, json_mode: Optional[bool] = False) -> None: from botocore.parsers import EventStreamJSONParser @@ -1838,8 +1828,18 @@ class AWSEventStreamDecoder: yield self._chunk_parser(chunk_data=_data) def _parse_message_from_event(self, event) -> Optional[str]: + if BEDROCK_RESPONSE_STREAM_SHAPE is None: + raise BedrockError( + status_code=500, + message=( + "Bedrock event-stream shape could not be loaded from botocore. " + "Ensure botocore is correctly installed." + ), + ) response_dict = event.to_response_dict() - parsed_response = self.parser.parse(response_dict, get_response_stream_shape()) + parsed_response = self.parser.parse( + response_dict, BEDROCK_RESPONSE_STREAM_SHAPE + ) if response_dict["status_code"] != 200: decoded_body = response_dict["body"].decode() diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index cff415d49ec..c883ab68dff 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -169,7 +169,6 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): anthropic_request.pop("model", None) anthropic_request.pop("stream", None) anthropic_request.pop("output_format", None) - anthropic_request.pop("output_config", None) if "anthropic_version" not in anthropic_request: anthropic_request["anthropic_version"] = self.anthropic_version diff --git a/litellm/llms/bedrock/claude_platform/__init__.py b/litellm/llms/bedrock/claude_platform/__init__.py new file mode 100644 index 00000000000..88d4e9783c7 --- /dev/null +++ b/litellm/llms/bedrock/claude_platform/__init__.py @@ -0,0 +1,8 @@ +from .transformation import ( + BedrockClaudePlatformConfig, +) +from .messages_transformation import ( + BedrockClaudePlatformMessagesConfig, +) + +__all__ = ["BedrockClaudePlatformConfig", "BedrockClaudePlatformMessagesConfig"] diff --git a/litellm/llms/bedrock/claude_platform/common_utils.py b/litellm/llms/bedrock/claude_platform/common_utils.py new file mode 100644 index 00000000000..121221518c8 --- /dev/null +++ b/litellm/llms/bedrock/claude_platform/common_utils.py @@ -0,0 +1,107 @@ +from typing import Literal, Optional, Tuple + +import litellm +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.secret_managers.main import get_secret_str + + +CLAUDE_PLATFORM_SERVICE_NAME: Literal["aws-external-anthropic"] = ( + "aws-external-anthropic" +) +CLAUDE_PLATFORM_BEDROCK_ROUTE = "claude_platform/" + + +def strip_claude_platform_route(model: str) -> str: + if model.startswith(CLAUDE_PLATFORM_BEDROCK_ROUTE): + return model.replace(CLAUDE_PLATFORM_BEDROCK_ROUTE, "", 1) + return model + + +class BedrockClaudePlatformMixin(BaseAWSLLM): + @staticmethod + def _get_workspace_id(optional_params: dict, litellm_params: dict) -> Optional[str]: + workspace_id = ( + optional_params.get("workspace_id") + or litellm_params.get("workspace_id") + or optional_params.get("aws_workspace_id") + or litellm_params.get("aws_workspace_id") + or optional_params.get("anthropic-workspace-id") + or litellm_params.get("anthropic-workspace-id") + ) + if workspace_id is None: + workspace_id = optional_params.get( + "anthropic_workspace_id" + ) or litellm_params.get("anthropic_workspace_id") + if workspace_id is not None: + return str(workspace_id) + return get_secret_str("ANTHROPIC_AWS_WORKSPACE_ID") or get_secret_str( + "ANTHROPIC_WORKSPACE_ID" + ) + + def _get_required_aws_region_name(self, optional_params: dict) -> str: + aws_region_name = ( + optional_params.get("aws_region_name") + or get_secret_str("AWS_REGION_NAME") + or get_secret_str("AWS_REGION") + or get_secret_str("AWS_DEFAULT_REGION") + ) + if aws_region_name is None: + raise litellm.AuthenticationError( + message=( + "Missing AWS region for Claude Platform on AWS. Pass " + "`aws_region_name` or set a standard AWS region environment value." + ), + llm_provider="bedrock", + model="", + ) + self._validate_aws_region_name(str(aws_region_name)) + return str(aws_region_name) + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + api_base = ( + api_base + or litellm.api_base + or get_secret_str("ANTHROPIC_AWS_BASE_URL") + or get_secret_str("ANTHROPIC_AWS_API_BASE") + ) + if api_base is None: + aws_region_name = self._get_required_aws_region_name(optional_params) + api_base = ( + f"https://{CLAUDE_PLATFORM_SERVICE_NAME}.{aws_region_name}.api.aws" + ) + if not api_base.endswith("/v1/messages"): + api_base = f"{api_base.rstrip('/')}/v1/messages" + return api_base + + def sign_request( + self, + headers: dict, + optional_params: dict, + request_data: dict, + api_base: str, + api_key: Optional[str] = None, + model: Optional[str] = None, + stream: Optional[bool] = None, + fake_stream: Optional[bool] = None, + ) -> Tuple[dict, Optional[bytes]]: + if api_key or get_secret_str("ANTHROPIC_AWS_API_KEY"): + return headers, None + + return self._sign_request( + service_name=CLAUDE_PLATFORM_SERVICE_NAME, + headers=headers, + optional_params=optional_params, + request_data=request_data, + api_base=api_base, + model=model, + stream=stream, + fake_stream=fake_stream, + ) diff --git a/litellm/llms/bedrock/claude_platform/messages_transformation.py b/litellm/llms/bedrock/claude_platform/messages_transformation.py new file mode 100644 index 00000000000..66158196322 --- /dev/null +++ b/litellm/llms/bedrock/claude_platform/messages_transformation.py @@ -0,0 +1,71 @@ +from typing import Any, Dict, List, Optional, Tuple + +import litellm +from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( + DEFAULT_ANTHROPIC_API_VERSION, + AnthropicMessagesConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.router import GenericLiteLLMParams + +from .common_utils import BedrockClaudePlatformMixin, strip_claude_platform_route + + +class BedrockClaudePlatformMessagesConfig( + BedrockClaudePlatformMixin, AnthropicMessagesConfig +): + def validate_anthropic_messages_environment( + self, + headers: dict, + model: str, + messages: List[Any], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> Tuple[dict, Optional[str]]: + workspace_id = self._get_workspace_id(optional_params, litellm_params) + if workspace_id is None: + raise litellm.AuthenticationError( + message=( + "Missing workspace ID for Claude Platform on AWS. Pass " + "`workspace_id` or configure the provider workspace setting." + ), + llm_provider="bedrock", + model=model, + ) + + resolved_api_key = api_key or get_secret_str("ANTHROPIC_AWS_API_KEY") + headers = { + **headers, + "anthropic-version": headers.get( + "anthropic-version", DEFAULT_ANTHROPIC_API_VERSION + ), + "content-type": headers.get("content-type", "application/json"), + "anthropic-workspace-id": workspace_id, + } + if resolved_api_key and "x-api-key" not in headers: + headers["x-api-key"] = resolved_api_key + + headers = self._update_headers_with_anthropic_beta( + headers=headers, + optional_params=optional_params, + ) + + return headers, api_base + + def transform_anthropic_messages_request( + self, + model: str, + messages: List[Dict], + anthropic_messages_optional_request_params: Dict, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Dict: + return super().transform_anthropic_messages_request( + model=strip_claude_platform_route(model), + messages=messages, + anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + litellm_params=litellm_params, + headers=headers, + ) diff --git a/litellm/llms/bedrock/claude_platform/transformation.py b/litellm/llms/bedrock/claude_platform/transformation.py new file mode 100644 index 00000000000..0167c457c96 --- /dev/null +++ b/litellm/llms/bedrock/claude_platform/transformation.py @@ -0,0 +1,94 @@ +from typing import Any, Dict, List, Optional + +import litellm +from litellm.llms.anthropic.chat.transformation import AnthropicConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues + +from .common_utils import BedrockClaudePlatformMixin + + +class BedrockClaudePlatformConfig(BedrockClaudePlatformMixin, AnthropicConfig): + """ + Bedrock Claude Platform uses Anthropic's Messages API with AWS gateway auth. + """ + + @property + def custom_llm_provider(self) -> Optional[str]: + return "bedrock" + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> Dict: + workspace_id = self._get_workspace_id(optional_params, litellm_params) + if workspace_id is None: + raise litellm.AuthenticationError( + message=( + "Missing workspace ID for Claude Platform on AWS. Pass " + "`workspace_id` or configure the provider workspace setting." + ), + llm_provider="bedrock", + model=model, + ) + + api_key = api_key or get_secret_str("ANTHROPIC_AWS_API_KEY") + anthropic_headers = self.get_anthropic_headers( + api_key=api_key, + auth_token=None, + computer_tool_used=self.is_computer_tool_used( + tools=optional_params.get("tools") + ), + prompt_caching_set=self.is_cache_control_set(messages=messages), + pdf_used=self.is_pdf_used(messages=messages), + file_id_used=self.is_file_id_used(messages=messages), + mcp_server_used=self.is_mcp_server_used( + mcp_servers=optional_params.get("mcp_servers") + ), + web_search_tool_used=self.is_web_search_tool_used( + tools=optional_params.get("tools") + ), + tool_search_used=self.is_tool_search_used( + tools=optional_params.get("tools") + ), + programmatic_tool_calling_used=self.is_programmatic_tool_calling_used( + tools=optional_params.get("tools") + ), + input_examples_used=self.is_input_examples_used( + tools=optional_params.get("tools") + ), + effort_used=self.is_effort_used( + optional_params=optional_params, model=model + ), + user_anthropic_beta_headers=self._get_user_anthropic_beta_headers( + anthropic_beta_header=headers.get("anthropic-beta") + ), + code_execution_tool_used=self.is_code_execution_tool_used( + tools=optional_params.get("tools") + ), + container_with_skills_used=self.is_container_with_skills_used( + optional_params=optional_params + ), + ) + anthropic_headers["anthropic-workspace-id"] = workspace_id + return {**headers, **anthropic_headers} + + def get_model_response_iterator( + self, + streaming_response: Any, + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + from litellm.llms.anthropic.chat.handler import ModelResponseIterator + + return ModelResponseIterator( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=bool(json_mode), + ) diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 9a97a134cc4..0256d5d4b95 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -14,6 +14,7 @@ if TYPE_CHECKING: import httpx import litellm +from litellm import verbose_logger from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, ) @@ -691,6 +692,7 @@ class BedrockModelInfo(BaseLLMModelInfo): ) -> Literal[ "converse", "invoke", + "claude_platform", "converse_like", "agent", "agentcore", @@ -705,6 +707,7 @@ class BedrockModelInfo(BaseLLMModelInfo): str, Literal[ "invoke", + "claude_platform", "converse_like", "converse", "agent", @@ -715,6 +718,7 @@ class BedrockModelInfo(BaseLLMModelInfo): ], ] = { "invoke/": "invoke", + "claude_platform/": "claude_platform", "converse_like/": "converse_like", "converse/": "converse", "agent/": "agent", @@ -752,6 +756,36 @@ class BedrockModelInfo(BaseLLMModelInfo): """ return "converse/" in model + @staticmethod + def _explicit_claude_platform_route(model: str) -> bool: + """ + Check if the model is an explicit Claude Platform on AWS route. + """ + return "claude_platform/" in model + + @staticmethod + def get_claude_platform_model(model: str) -> str: + """ + Strip the Claude Platform route prefix from a Bedrock model name. + """ + return model.replace("claude_platform/", "", 1) + + @staticmethod + def map_claude_platform_auth_params( + passed_params: dict, optional_params: dict + ) -> dict: + """ + Map Claude Platform route auth params that are not OpenAI request params. + """ + for key in ( + "workspace_id", + "aws_workspace_id", + "anthropic_workspace_id", + ): + if key in passed_params: + optional_params[key] = passed_params[key] + return optional_params + @staticmethod def _explicit_invoke_route(model: str) -> bool: """ @@ -814,6 +848,12 @@ class BedrockModelInfo(BaseLLMModelInfo): All other routes should return None since they will go through litellm.completion """ + ######################################################### + # Claude Platform route uses Anthropic Messages API via the AWS gateway. + ######################################################### + if BedrockModelInfo._explicit_claude_platform_route(model): + return litellm.BedrockClaudePlatformMessagesConfig() + ######################################################### # Converse routes should go through litellm.completion() if BedrockModelInfo._explicit_converse_route(model): @@ -859,7 +899,9 @@ def get_bedrock_chat_config(model: str): base_model = BedrockModelInfo.get_base_model(model) # Handle explicit routes first - if bedrock_route == "converse" or bedrock_route == "converse_like": + if bedrock_route == "claude_platform": + return litellm.BedrockClaudePlatformConfig() + elif bedrock_route == "converse" or bedrock_route == "converse_like": return litellm.AmazonConverseConfig() elif bedrock_route == "openai": return litellm.AmazonBedrockOpenAIConfig() @@ -917,38 +959,57 @@ def get_bedrock_chat_config(model: str): return litellm.AmazonInvokeConfig() +def _load_bedrock_response_stream_shape(): + """ + Load the ResponseStream shape from botocore's bundled bedrock-runtime schema. + + Called once at module import time; the result is stored in + ``BEDROCK_RESPONSE_STREAM_SHAPE`` and reused for the process lifetime. + Returns ``None`` if botocore is unavailable or the service model cannot be + loaded, so the module still imports cleanly. + """ + try: + from botocore.loaders import Loader + from botocore.model import ServiceModel + + loader = Loader() + service_dict = loader.load_service_model("bedrock-runtime", "service-2") + return ServiceModel(service_dict).shape_for("ResponseStream") + except Exception as e: + verbose_logger.warning( + "litellm: could not pre-load bedrock-runtime response stream shape " + "— Bedrock event-stream decoding will be unavailable. Error: %s", + e, + ) + return None + + +# Eagerly resolved once per process — avoids per-instance or per-request disk I/O. +BEDROCK_RESPONSE_STREAM_SHAPE = _load_bedrock_response_stream_shape() + + class BedrockEventStreamDecoderBase: """ Base class for event stream decoding for Bedrock """ - _response_stream_shape_cache = None - def __init__(self): from botocore.parsers import EventStreamJSONParser self.parser = EventStreamJSONParser() - def get_response_stream_shape(self): - if self._response_stream_shape_cache is None: - from botocore.loaders import Loader - from botocore.model import ServiceModel - - loader = Loader() - bedrock_service_dict = loader.load_service_model( - "bedrock-runtime", "service-2" - ) - bedrock_service_model = ServiceModel(bedrock_service_dict) - self._response_stream_shape_cache = bedrock_service_model.shape_for( - "ResponseStream" - ) - - return self._response_stream_shape_cache - def _parse_message_from_event(self, event) -> Optional[str]: + if BEDROCK_RESPONSE_STREAM_SHAPE is None: + raise BedrockError( + status_code=500, + message=( + "Bedrock event-stream shape could not be loaded from botocore. " + "Ensure botocore is correctly installed." + ), + ) response_dict = event.to_response_dict() parsed_response = self.parser.parse( - response_dict, self.get_response_stream_shape() + response_dict, BEDROCK_RESPONSE_STREAM_SHAPE ) if response_dict["status_code"] != 200: diff --git a/litellm/llms/bedrock/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py index a37af131625..c967fd334bc 100644 --- a/litellm/llms/bedrock/count_tokens/transformation.py +++ b/litellm/llms/bedrock/count_tokens/transformation.py @@ -201,13 +201,14 @@ class BedrockCountTokensConfig(BaseAWSLLM): # Remove bedrock/ prefix if present if model_id.startswith("bedrock/"): model_id = model_id[8:] # Remove "bedrock/" prefix + encoded_model_id = self.encode_model_id(model_id=model_id) base_url, _ = self.get_runtime_endpoint( api_base=api_base, aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, aws_region_name=aws_region_name, ) - endpoint = f"{base_url}/model/{model_id}/count-tokens" + endpoint = f"{base_url}/model/{encoded_model_id}/count-tokens" return endpoint diff --git a/litellm/llms/bedrock/files/handler.py b/litellm/llms/bedrock/files/handler.py index 13bd87a1f01..ecf157e12ee 100644 --- a/litellm/llms/bedrock/files/handler.py +++ b/litellm/llms/bedrock/files/handler.py @@ -1,10 +1,17 @@ import asyncio import base64 -from typing import Any, Coroutine, Optional, Tuple, Union +import os +from types import MappingProxyType +from typing import Any, Coroutine, Mapping, Optional, Tuple, Union, cast import httpx from litellm import LlmProviders +from litellm.litellm_core_utils.cloud_storage_security import ( + BEDROCK_MANAGED_S3_PREFIXES, + should_allow_legacy_cloud_file_ids, + validate_managed_cloud_file_id, +) from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.openai import ( FileContentRequest, @@ -35,7 +42,7 @@ class BedrockFilesHandler(BaseAWSLLM): The file ID can be in two formats: 1. Base64-encoded unified file ID containing: llm_output_file_id,s3://bucket/path - 2. Direct S3 URI: s3://bucket/path + 2. Direct S3 URI: s3://bucket/litellm-managed-prefix/path Args: file_id: Encoded file ID or direct S3 URI @@ -58,14 +65,19 @@ class BedrockFilesHandler(BaseAWSLLM): except Exception: pass - # If not base64 encoded or doesn't contain llm_output_file_id, assume it's already an S3 URI + # If not base64 encoded or doesn't contain llm_output_file_id, accept only + # explicit S3 URIs. Bucket and key validation happens before any S3 call. if file_id.startswith("s3://"): return file_id - # If it doesn't start with s3://, assume it's a direct S3 URI and add the prefix - return f"s3://{file_id}" + raise ValueError("file_id must be a managed LiteLLM S3 file id") - def _parse_s3_uri(self, s3_uri: str) -> Tuple[str, str]: + def _parse_s3_uri( + self, + s3_uri: str, + configured_bucket_name: str, + allow_legacy_cloud_file_ids: bool = False, + ) -> Tuple[str, str]: """ Parse S3 URI to extract bucket name and object key. @@ -75,21 +87,34 @@ class BedrockFilesHandler(BaseAWSLLM): Returns: Tuple of (bucket_name, object_key) """ - if not s3_uri.startswith("s3://"): - raise ValueError( - f"Invalid S3 URI format: {s3_uri}. Expected format: s3://bucket-name/path/to/file" + return validate_managed_cloud_file_id( + file_id=s3_uri, + scheme="s3://", + configured_bucket_name=configured_bucket_name, + allowed_object_prefixes=BEDROCK_MANAGED_S3_PREFIXES, + allow_legacy_cloud_file_ids=allow_legacy_cloud_file_ids, + ) + + def _get_configured_s3_bucket_name(self, litellm_params: dict) -> str: + trusted_model_credentials = litellm_params.get( + "_litellm_internal_model_credentials" + ) + bucket_name = None + if isinstance(trusted_model_credentials, type(MappingProxyType({}))): + trusted_model_credentials_mapping = cast( + Mapping[str, Any], trusted_model_credentials ) - - # Remove 's3://' prefix - path = s3_uri[5:] - - if "/" in path: - bucket_name, object_key = path.split("/", 1) - else: - bucket_name = path - object_key = "" - - return bucket_name, object_key + candidate_bucket_name = trusted_model_credentials_mapping.get( + "s3_bucket_name" + ) + if isinstance(candidate_bucket_name, str): + bucket_name = candidate_bucket_name + bucket_name = bucket_name or os.getenv("AWS_S3_BUCKET_NAME") + if not bucket_name: + raise ValueError( + "S3 bucket_name is required. Set 's3_bucket_name' in proxy config or AWS_S3_BUCKET_NAME for Bedrock file content retrieval." + ) + return bucket_name async def afile_content( self, @@ -119,7 +144,14 @@ class BedrockFilesHandler(BaseAWSLLM): # Extract S3 URI from file ID s3_uri = self._extract_s3_uri_from_file_id(file_id) - bucket_name, object_key = self._parse_s3_uri(s3_uri) + configured_bucket_name = self._get_configured_s3_bucket_name(optional_params) + bucket_name, object_key = self._parse_s3_uri( + s3_uri=s3_uri, + configured_bucket_name=configured_bucket_name, + allow_legacy_cloud_file_ids=should_allow_legacy_cloud_file_ids( + optional_params + ), + ) # Get AWS credentials aws_region_name = self._get_aws_region_name( diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index 3007b54808c..6669363093b 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -2,6 +2,7 @@ import json import os import time from typing import Any, Dict, List, Optional, Tuple, Union +from urllib.parse import unquote import httpx from httpx import Headers, Response @@ -10,6 +11,14 @@ from openai.types.file_deleted import FileDeleted from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.files.utils import FilesAPIUtils +from litellm.litellm_core_utils.cloud_storage_security import ( + BEDROCK_MANAGED_S3_BATCH_PREFIX, + BEDROCK_MANAGED_S3_UPLOAD_PREFIX, + build_managed_cloud_object_name, + encode_s3_object_key_for_url, + sanitize_cloud_object_component, + split_configured_cloud_bucket_name, +) from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.files.transformation import ( @@ -116,10 +125,13 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): if _model.startswith("bedrock/"): _model = _model[8:] - # Replace colons with hyphens for Bedrock S3 URI compliance - _model = _model.replace(":", "-") + safe_model = sanitize_cloud_object_component( + _model.replace(":", "-"), fallback="model" + ) - object_name = f"litellm-bedrock-files-{_model}-{uuid.uuid4()}.jsonl" + object_name = ( + f"{BEDROCK_MANAGED_S3_BATCH_PREFIX}{safe_model}-{uuid.uuid4()}.jsonl" + ) return object_name def get_object_name( @@ -146,12 +158,13 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): if len(openai_jsonl_content) > 0: return self._get_s3_object_name_from_batch_jsonl(openai_jsonl_content) - ## 2. If not jsonl, return the filename + ## 2. If not jsonl, store under a server-generated managed object name filename = extracted_file_data.get("filename") - if filename: - return filename - ## 3. If no file name, return timestamp - return str(int(time.time())) + return build_managed_cloud_object_name( + prefix=BEDROCK_MANAGED_S3_UPLOAD_PREFIX, + filename=filename, + fallback_filename="file", + ) def get_complete_file_url( self, @@ -172,6 +185,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): raise ValueError( "S3 bucket_name is required. Set 's3_bucket_name' in litellm_params or AWS_S3_BUCKET_NAME env var" ) + bucket_name, object_prefix = split_configured_cloud_bucket_name(bucket_name) s3_region_name = litellm_params.get("s3_region_name") or optional_params.get( "s3_region_name" @@ -188,14 +202,17 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): raise ValueError("purpose is required") extracted_file_data = extract_file_data(file_data) object_name = self.get_object_name(extracted_file_data, purpose) + if object_prefix: + object_name = f"{object_prefix}/{object_name}" + encoded_object_name = encode_s3_object_key_for_url(object_name) # S3 endpoint URL format s3_endpoint_url = ( optional_params.get("s3_endpoint_url") or f"https://s3.{aws_region_name}.amazonaws.com" - ) + ).rstrip("/") - return f"{s3_endpoint_url}/{bucket_name}/{object_name}" + return f"{s3_endpoint_url}/{bucket_name}/{encoded_object_name}" def get_supported_openai_params( self, model: str @@ -532,10 +549,12 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): if match1: # Pattern: https://s3.region.amazonaws.com/bucket/key region, bucket, key = match1.groups() + key = unquote(key) s3_uri = f"s3://{bucket}/{key}" elif match2: # Pattern: https://bucket.s3.region.amazonaws.com/key bucket, region, key = match2.groups() + key = unquote(key) s3_uri = f"s3://{bucket}/{key}" else: # Fallback: try to extract bucket and key from URL path @@ -545,6 +564,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): path_parts = parsed.path.lstrip("/").split("/", 1) if len(path_parts) >= 2: bucket, key = path_parts[0], path_parts[1] + key = unquote(key) s3_uri = f"s3://{bucket}/{key}" else: raise ValueError(f"Unable to parse S3 URL: {https_url}") @@ -722,7 +742,12 @@ class BedrockJsonlFilesTransformation: # Remove bedrock/ prefix if present if _model.startswith("bedrock/"): _model = _model[8:] - object_name = f"litellm-bedrock-files-{_model}-{uuid.uuid4()}.jsonl" + safe_model = sanitize_cloud_object_component( + _model.replace(":", "-"), fallback="model" + ) + object_name = ( + f"{BEDROCK_MANAGED_S3_BATCH_PREFIX}{safe_model}-{uuid.uuid4()}.jsonl" + ) return object_name def _get_content_from_openai_file(self, openai_file_content: FileTypes) -> str: diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index 1b15ebaa76f..151e0e404a0 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -12,9 +12,14 @@ from typing import ( import httpx +import litellm from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers from litellm.constants import BEDROCK_MIN_THINKING_BUDGET_TOKENS from litellm.litellm_core_utils.litellm_logging import verbose_logger +from litellm.llms.anthropic.chat.transformation import ( + DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, + AnthropicConfig, +) from litellm.llms.anthropic.common_utils import AnthropicModelInfo from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( AnthropicMessagesConfig, @@ -403,6 +408,47 @@ class AmazonAnthropicClaudeMessagesConfig( if self._supports_tool_search_on_bedrock(model): beta_set.add("tool-search-tool-2025-10-19") + @staticmethod + def _filter_context_management_for_bedrock_invoke( + anthropic_messages_request: Dict, + beta_set: set, + ) -> None: + """ + Bedrock InvokeModel accepts ``context_management`` only when it carries + ``compact_20260112`` edits paired with the ``compact-2026-01-12`` + anthropic-beta header. Other edit types (notably ``clear_thinking_20251015``, + which Claude Code sends on every request) are LiteLLM-internal and would + cause Bedrock to 400 with ``"context_management: Extra inputs are not + permitted"``. + + Filter the edits list to the supported subset, add the beta header when + compact edits remain, and drop ``context_management`` entirely when no + supported edits are left so the safety-net allowlist can pass it through. + + Ref: https://github.com/BerriAI/litellm/issues/27532 + """ + cm = anthropic_messages_request.get("context_management") + if not isinstance(cm, dict): + return + edits = cm.get("edits") + if not isinstance(edits, list): + anthropic_messages_request.pop("context_management", None) + return + + compact_edits = [ + e + for e in edits + if isinstance(e, dict) and e.get("type") == "compact_20260112" + ] + if compact_edits: + beta_set.add("compact-2026-01-12") + anthropic_messages_request["context_management"] = { + **cm, + "edits": compact_edits, + } + else: + anthropic_messages_request.pop("context_management", None) + def _convert_output_format_to_inline_schema( self, output_format: Dict, @@ -546,6 +592,11 @@ class AmazonAnthropicClaudeMessagesConfig( if injected_thinking_for_clear_thinking: beta_set.add("interleaved-thinking-2025-05-14") + self._filter_context_management_for_bedrock_invoke( + anthropic_messages_request=anthropic_messages_request, + beta_set=beta_set, + ) + self._get_tool_search_beta_header_for_bedrock( model=model, tool_search_used=tool_search_used, @@ -580,9 +631,21 @@ class AmazonAnthropicClaudeMessagesConfig( if filtered_betas: anthropic_messages_request["anthropic_beta"] = filtered_betas + if ( + litellm.drop_params is True + and "output_config" in anthropic_messages_request + and not AnthropicConfig._model_supports_effort_param(model) + ): + verbose_logger.warning( + DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, + model, + ) + anthropic_messages_request.pop("output_config", None) + # 7. Final safety net: filter top-level fields to the Bedrock Invoke allowlist. - # Catches Anthropic-only extensions (context_management, output_config, speed, - # mcp_servers, ...) and any future additions Claude Code may start sending. + # Catches Anthropic-only extensions (output_config, speed, mcp_servers, ...) + # and any future additions Claude Code may start sending. ``context_management`` + # has already been pre-filtered to its Bedrock-supported subset above. allowed = self.BEDROCK_INVOKE_ALLOWED_TOP_LEVEL_FIELDS stripped = sorted(k for k in anthropic_messages_request if k not in allowed) if stripped: diff --git a/litellm/llms/bedrock/passthrough/transformation.py b/litellm/llms/bedrock/passthrough/transformation.py index 274b0282acc..846af65c0f9 100644 --- a/litellm/llms/bedrock/passthrough/transformation.py +++ b/litellm/llms/bedrock/passthrough/transformation.py @@ -10,14 +10,12 @@ from ..base_aws_llm import BaseAWSLLM from ..common_utils import BedrockEventStreamDecoderBase, BedrockModelInfo if TYPE_CHECKING: + from httpx import URL + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.utils import CostResponseTypes -if TYPE_CHECKING: - from httpx import URL - - class BedrockPassthroughConfig( BaseAWSLLM, BedrockModelInfo, BedrockEventStreamDecoderBase, BasePassthroughConfig ): diff --git a/litellm/llms/bedrock/vector_stores/transformation.py b/litellm/llms/bedrock/vector_stores/transformation.py index 4da0a7c7791..ec20d76102b 100644 --- a/litellm/llms/bedrock/vector_stores/transformation.py +++ b/litellm/llms/bedrock/vector_stores/transformation.py @@ -1,14 +1,17 @@ -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union +from copy import deepcopy +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast from urllib.parse import urlparse import httpx +from litellm._logging import verbose_logger +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.types.integrations.rag.bedrock_knowledgebase import ( BedrockKBContent, - BedrockKBResponse, BedrockKBRetrievalConfiguration, + BedrockKBResponse, BedrockKBRetrievalQuery, ) from litellm.types.router import GenericLiteLLMParams @@ -202,35 +205,61 @@ class BedrockVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: if isinstance(query, list): query = " ".join(query) - url = f"{api_base}/{vector_store_id}/retrieve" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}/retrieve" request_body: Dict[str, Any] = { "retrievalQuery": BedrockKBRetrievalQuery(text=query), } retrieval_config: Dict[str, Any] = {} + + if isinstance(extra_body, dict): + retrieval_config = deepcopy( + extra_body.get("retrievalConfiguration") + or extra_body.get("retrieval_configuration") + or {} + ) max_results = vector_store_search_optional_params.get("max_num_results") if max_results is not None: + existing_number_of_results = retrieval_config.get( + "vectorSearchConfiguration", {} + ).get("numberOfResults") + if ( + existing_number_of_results is not None + and existing_number_of_results != max_results + ): + verbose_logger.debug( + "Overriding extra_body retrievalConfiguration.vectorSearchConfiguration.numberOfResults (%s) with max_num_results=%s", + existing_number_of_results, + max_results, + ) retrieval_config.setdefault("vectorSearchConfiguration", {})[ "numberOfResults" ] = max_results filters = vector_store_search_optional_params.get("filters") if filters is not None: + existing_filter = retrieval_config.get("vectorSearchConfiguration", {}).get( + "filter" + ) + if existing_filter is not None and existing_filter != filters: + verbose_logger.debug( + "Overriding extra_body retrievalConfiguration.vectorSearchConfiguration.filter with filters from vector_store_search_optional_params" + ) retrieval_config.setdefault("vectorSearchConfiguration", {})[ "filter" ] = filters if retrieval_config: - # Create a properly typed retrieval configuration - typed_retrieval_config: BedrockKBRetrievalConfiguration = {} - if "vectorSearchConfiguration" in retrieval_config: - typed_retrieval_config["vectorSearchConfiguration"] = retrieval_config[ - "vectorSearchConfiguration" - ] - request_body["retrievalConfiguration"] = typed_retrieval_config + request_body["retrievalConfiguration"] = cast( + BedrockKBRetrievalConfiguration, retrieval_config + ) litellm_logging_obj.model_call_details["query"] = query return url, request_body diff --git a/litellm/llms/black_forest_labs/image_edit/handler.py b/litellm/llms/black_forest_labs/image_edit/handler.py index dea2683a049..f5784e08367 100644 --- a/litellm/llms/black_forest_labs/image_edit/handler.py +++ b/litellm/llms/black_forest_labs/image_edit/handler.py @@ -15,6 +15,7 @@ import httpx import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.url_utils import SSRFError, assert_same_origin from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, HTTPHandler, @@ -331,6 +332,17 @@ class BlackForestLabsImageEdit: message="No polling_url in BFL response", ) + # Reject cross-origin polling URLs — the ``x-key`` auth header + # would otherwise leak to whatever URL the upstream returns. + # VERIA-51. + try: + assert_same_origin(polling_url, str(initial_response.request.url)) + except SSRFError as ssrf_err: + raise BlackForestLabsError( + status_code=502, + message=f"Rejected polling URL: {ssrf_err}", + ) + # Get just the auth header for polling polling_headers = {"x-key": headers.get("x-key", "")} @@ -416,6 +428,17 @@ class BlackForestLabsImageEdit: message="No polling_url in BFL response", ) + # Reject cross-origin polling URLs — the ``x-key`` auth header + # would otherwise leak to whatever URL the upstream returns. + # VERIA-51. + try: + assert_same_origin(polling_url, str(initial_response.request.url)) + except SSRFError as ssrf_err: + raise BlackForestLabsError( + status_code=502, + message=f"Rejected polling URL: {ssrf_err}", + ) + # Get just the auth header for polling polling_headers = {"x-key": headers.get("x-key", "")} diff --git a/litellm/llms/black_forest_labs/image_generation/handler.py b/litellm/llms/black_forest_labs/image_generation/handler.py index 5a1d885e527..8af4a236fd4 100644 --- a/litellm/llms/black_forest_labs/image_generation/handler.py +++ b/litellm/llms/black_forest_labs/image_generation/handler.py @@ -15,6 +15,7 @@ import httpx import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.url_utils import SSRFError, assert_same_origin from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, HTTPHandler, @@ -317,6 +318,17 @@ class BlackForestLabsImageGeneration: message="No polling_url in BFL response", ) + # Reject cross-origin polling URLs — the ``x-key`` auth header + # would otherwise leak to whatever URL the upstream returns. + # VERIA-51. + try: + assert_same_origin(polling_url, str(initial_response.request.url)) + except SSRFError as ssrf_err: + raise BlackForestLabsError( + status_code=502, + message=f"Rejected polling URL: {ssrf_err}", + ) + # Get just the auth header for polling polling_headers = {"x-key": headers.get("x-key", "")} @@ -402,6 +414,17 @@ class BlackForestLabsImageGeneration: message="No polling_url in BFL response", ) + # Reject cross-origin polling URLs — the ``x-key`` auth header + # would otherwise leak to whatever URL the upstream returns. + # VERIA-51. + try: + assert_same_origin(polling_url, str(initial_response.request.url)) + except SSRFError as ssrf_err: + raise BlackForestLabsError( + status_code=502, + message=f"Rejected polling URL: {ssrf_err}", + ) + # Get just the auth header for polling polling_headers = {"x-key": headers.get("x-key", "")} diff --git a/litellm/llms/bytez/chat/transformation.py b/litellm/llms/bytez/chat/transformation.py index a72f732a303..5b08670f9f2 100644 --- a/litellm/llms/bytez/chat/transformation.py +++ b/litellm/llms/bytez/chat/transformation.py @@ -5,6 +5,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union import httpx +from litellm.litellm_core_utils.url_utils import encode_url_path_segments from litellm.litellm_core_utils.exception_mapping_utils import exception_type from litellm.litellm_core_utils.logging_utils import track_llm_api_timing from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException @@ -149,7 +150,8 @@ class BytezChatConfig(BaseConfig): litellm_params: dict, stream: Optional[bool] = None, ) -> str: - return f"{API_BASE}/{model}" + encoded_model = encode_url_path_segments(model, field_name="model") + return f"{API_BASE}/{encoded_model}" def transform_request( self, diff --git a/litellm/llms/cloudflare/chat/transformation.py b/litellm/llms/cloudflare/chat/transformation.py index 9e59782bf73..66e253f304d 100644 --- a/litellm/llms/cloudflare/chat/transformation.py +++ b/litellm/llms/cloudflare/chat/transformation.py @@ -5,6 +5,7 @@ from typing import AsyncIterator, Iterator, List, Optional, Union import httpx import litellm +from litellm.litellm_core_utils.url_utils import encode_url_path_segments from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator from litellm.llms.base_llm.chat.transformation import ( BaseConfig, @@ -89,7 +90,8 @@ class CloudflareChatConfig(BaseConfig): api_base = ( f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/" ) - return api_base + model + encoded_model = encode_url_path_segments(model, field_name="model") + return api_base + encoded_model def get_supported_openai_params(self, model: str) -> List[str]: return [ @@ -147,9 +149,9 @@ class CloudflareChatConfig(BaseConfig): ) -> ModelResponse: completion_response = raw_response.json() - model_response.choices[0].message.content = completion_response["result"][ # type: ignore - "response" - ] + # Support both "response" and "response_text" keys (newer models like Nemotron use "response_text") + result = completion_response["result"] + model_response.choices[0].message.content = result.get("response") if result.get("response") is not None else result.get("response_text", "") # type: ignore prompt_tokens = litellm.utils.get_token_count(messages=messages, model=model) completion_tokens = len( @@ -199,8 +201,10 @@ class CloudflareChatResponseIterator(BaseModelResponseIterator): index = int(chunk.get("index", 0)) - if "response" in chunk: + if "response" in chunk and chunk["response"] is not None: text = chunk["response"] + elif "response_text" in chunk and chunk["response_text"] is not None: + text = chunk["response_text"] returned_chunk = GenericStreamingChunk( text=text, diff --git a/litellm/llms/cohere/rerank/transformation.py b/litellm/llms/cohere/rerank/transformation.py index 531b94d1805..64ae8e8ffa7 100644 --- a/litellm/llms/cohere/rerank/transformation.py +++ b/litellm/llms/cohere/rerank/transformation.py @@ -111,6 +111,7 @@ class CohereRerankConfig(BaseRerankConfig): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: if "query" not in optional_rerank_params: raise ValueError("query is required for Cohere rerank") diff --git a/litellm/llms/cohere/rerank_v2/transformation.py b/litellm/llms/cohere/rerank_v2/transformation.py index 60d22ff4be0..4c800d6455d 100644 --- a/litellm/llms/cohere/rerank_v2/transformation.py +++ b/litellm/llms/cohere/rerank_v2/transformation.py @@ -71,6 +71,7 @@ class CohereRerankV2Config(CohereRerankConfig): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: if "query" not in optional_rerank_params: raise ValueError("query is required for Cohere rerank") diff --git a/litellm/llms/custom_httpx/container_handler.py b/litellm/llms/custom_httpx/container_handler.py index afdd7bc6a8b..599cd705ebf 100644 --- a/litellm/llms/custom_httpx/container_handler.py +++ b/litellm/llms/custom_httpx/container_handler.py @@ -12,6 +12,7 @@ from typing import TYPE_CHECKING, Any, Coroutine, Dict, Optional, Type, Union import httpx import litellm +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, HTTPHandler, @@ -72,7 +73,8 @@ def _build_url( # Substitute path parameters for param, value in path_params.items(): - path_template = path_template.replace(f"{{{param}}}", value) + encoded_value = encode_url_path_segment(value, field_name=param) + path_template = path_template.replace(f"{{{param}}}", encoded_value) # Parse the api_base to extract existing query params parsed_base = httpx.URL(api_base) diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 03d2af72329..e11d8532dbf 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -1,5 +1,8 @@ import asyncio +import concurrent.futures +import inspect import os +import socket import ssl import sys import time @@ -29,6 +32,10 @@ from litellm.constants import ( AIOHTTP_CONNECTOR_LIMIT_PER_HOST, AIOHTTP_KEEPALIVE_TIMEOUT, AIOHTTP_NEEDS_CLEANUP_CLOSED, + AIOHTTP_SO_KEEPALIVE, + AIOHTTP_TCP_KEEPCNT, + AIOHTTP_TCP_KEEPIDLE, + AIOHTTP_TCP_KEEPINTVL, AIOHTTP_TTL_DNS_CACHE, COMPLETION_HTTP_FALLBACK_SECONDS, DEFAULT_SSL_CIPHERS, @@ -54,6 +61,57 @@ except Exception: version = "0.0.0" +# aiohttp 3.10+ exposes a `socket_factory` kwarg on TCPConnector. Older +# versions don't — detect once and skip the keep-alive wiring there. +# https://docs.aiohttp.org/en/stable/client_reference.html#aiohttp.TCPConnector +_AIOHTTP_SUPPORTS_SOCKET_FACTORY = ( + "socket_factory" in inspect.signature(TCPConnector.__init__).parameters +) + + +def _build_aiohttp_keepalive_socket_factory() -> ( + Optional[Callable[[Tuple[Any, ...]], socket.socket]] +): + """ + Build a socket_factory that enables SO_KEEPALIVE on aiohttp TCP sockets. + + Why: by default, aiohttp creates sockets without SO_KEEPALIVE, so the kernel + sends nothing during a long idle TCP connection. NAT/LB hops (e.g. AWS NAT + Gateway, 350s idle timeout) reap the flow well before slow provider + responses (OpenAI/Azure: up to 600s) arrive. Enabling SO_KEEPALIVE makes + the kernel emit TCP probes that reset the NAT idle timer. + + Returns None when AIOHTTP_SO_KEEPALIVE is disabled or aiohttp is too old. + """ + if not AIOHTTP_SO_KEEPALIVE or not _AIOHTTP_SUPPORTS_SOCKET_FACTORY: + return None + + def factory(addr_info: Tuple[Any, ...]) -> socket.socket: + family, type_, proto = addr_info[0], addr_info[1], addr_info[2] + sock = socket.socket(family=family, type=type_, proto=proto) + sock.setblocking(False) + sock.setsockopt(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1) + # Linux: TCP_KEEPIDLE is idle-before-first-probe. + # macOS/Darwin: TCP_KEEPALIVE is the equivalent. + if hasattr(socket, "TCP_KEEPIDLE"): + sock.setsockopt( + socket.IPPROTO_TCP, socket.TCP_KEEPIDLE, AIOHTTP_TCP_KEEPIDLE + ) + elif hasattr(socket, "TCP_KEEPALIVE"): + sock.setsockopt( + socket.IPPROTO_TCP, socket.TCP_KEEPALIVE, AIOHTTP_TCP_KEEPIDLE + ) + if hasattr(socket, "TCP_KEEPINTVL"): + sock.setsockopt( + socket.IPPROTO_TCP, socket.TCP_KEEPINTVL, AIOHTTP_TCP_KEEPINTVL + ) + if hasattr(socket, "TCP_KEEPCNT"): + sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_KEEPCNT, AIOHTTP_TCP_KEEPCNT) + return sock + + return factory + + def get_default_headers() -> dict: """ Get default headers for HTTP requests. @@ -76,6 +134,11 @@ _DEFAULT_TIMEOUT = httpx.Timeout( timeout=COMPLETION_HTTP_FALLBACK_SECONDS, connect=HTTP_HANDLER_CONNECT_TIMEOUT_SECONDS, ) +_STREAMING_ERROR_BODY_READ_TIMEOUT_SECONDS = 5.0 +_STREAMING_ERROR_BODY_READ_EXECUTOR = concurrent.futures.ThreadPoolExecutor( + max_workers=50, + thread_name_prefix="litellm-streaming-error-body-read", +) def _prepare_request_data_and_content( @@ -329,17 +392,30 @@ def _safe_get_response_text(response: httpx.Response) -> str: return "" -async def _safe_aread_response(response: httpx.Response) -> bytes: +async def _safe_aread_response( + response: httpx.Response, timeout: Optional[float] = None +) -> bytes: """Safely read async response body, falling back to empty bytes on errors.""" try: + if timeout is not None: + return await asyncio.wait_for(response.aread(), timeout=timeout) return await response.aread() except Exception: return b"" -def _safe_read_response(response: httpx.Response) -> bytes: +def _safe_read_response( + response: httpx.Response, timeout: Optional[float] = None +) -> bytes: """Safely read sync response body, falling back to empty bytes on errors.""" try: + if timeout is not None: + future = _STREAMING_ERROR_BODY_READ_EXECUTOR.submit(response.read) + try: + return future.result(timeout=timeout) + except Exception: + response.close() + return b"" return response.read() except Exception: return b"" @@ -348,8 +424,19 @@ def _safe_read_response(response: httpx.Response) -> bytes: def _raise_masked_sync_error(e: httpx.HTTPStatusError, stream: bool) -> None: """Raise a MaskedHTTPStatusError for sync HTTP handlers.""" if stream: - _body = mask_sensitive_info(_safe_read_response(e.response)) - raise MaskedHTTPStatusError(e, message=_body, text=_body) from None + try: + _body = mask_sensitive_info( + _safe_read_response( + e.response, + timeout=_STREAMING_ERROR_BODY_READ_TIMEOUT_SECONDS, + ) + ) + raise MaskedHTTPStatusError(e, message=_body, text=_body) from None + finally: + try: + e.response.close() + except Exception: + pass _text = mask_sensitive_info(_safe_get_response_text(e.response)) raise MaskedHTTPStatusError(e, message=_text, text=_text) from None @@ -357,8 +444,19 @@ def _raise_masked_sync_error(e: httpx.HTTPStatusError, stream: bool) -> None: async def _raise_masked_async_error(e: httpx.HTTPStatusError, stream: bool) -> None: """Raise a MaskedHTTPStatusError for async HTTP handlers.""" if stream: - _body = mask_sensitive_info(await _safe_aread_response(e.response)) - raise MaskedHTTPStatusError(e, message=_body, text=_body) from None + try: + _body = mask_sensitive_info( + await _safe_aread_response( + e.response, + timeout=_STREAMING_ERROR_BODY_READ_TIMEOUT_SECONDS, + ) + ) + raise MaskedHTTPStatusError(e, message=_body, text=_body) from None + finally: + try: + await e.response.aclose() + except Exception: + pass _text = mask_sensitive_info(_safe_get_response_text(e.response)) raise MaskedHTTPStatusError(e, message=_text, text=_text) from None @@ -387,11 +485,16 @@ class MaskedHTTPStatusError(httpx.HTTPStatusError): if k.lower() not in ("content-encoding", "content-length") } + try: + request_content = original_error.request.content + except httpx.RequestNotRead: + request_content = b"" + masked_request = httpx.Request( method=original_error.request.method, url=masked_url, headers=original_error.request.headers, - content=original_error.request.content, + content=request_content, ) super().__init__( @@ -935,6 +1038,11 @@ class AsyncHTTPHandler: transport_connector_kwargs["limit_per_host"] = ( AIOHTTP_CONNECTOR_LIMIT_PER_HOST ) + # Returns None when SO_KEEPALIVE is disabled or aiohttp is too old to + # accept socket_factory — version detection lives inside the builder. + socket_factory = _build_aiohttp_keepalive_socket_factory() + if socket_factory is not None: + transport_connector_kwargs["socket_factory"] = socket_factory return LiteLLMAiohttpTransport( client=lambda: ClientSession( diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index b9ada079f6b..fa1253d9005 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -26,6 +26,7 @@ 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.realtime_streaming import RealTimeStreaming +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, ) @@ -155,6 +156,30 @@ else: LiteLLMLoggingObj = Any +def _google_genai_streaming_hidden_params( + *, + api_base: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + response_headers: httpx.Headers, +) -> Dict[str, Any]: + """Pre-stream metadata for proxy response headers (mirrors CustomStreamWrapper._hidden_params).""" + from litellm.litellm_core_utils.core_helpers import process_response_headers + + _model_info: Dict[str, Any] = dict( + getattr(litellm_params, "model_info", None) or {} + ) + _raw_id = _model_info.get("id") or logging_obj.get_router_model_id() or "" + _model_id = _raw_id if isinstance(_raw_id, str) else str(_raw_id) + return { + "model_id": _model_id, + "api_base": api_base, + "cache_key": "", + "response_cost": "", + "additional_headers": process_response_headers(response_headers), + } + + class BaseLLMHTTPHandler: async def _make_common_async_call( self, @@ -983,6 +1008,7 @@ class BaseLLMHTTPHandler: api_key: Optional[str] = None, api_base: Optional[str] = None, client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + litellm_params: Optional[Dict[str, Any]] = None, ) -> RerankResponse: # get config from model, custom llm provider headers = provider_config.validate_environment( @@ -1002,6 +1028,7 @@ class BaseLLMHTTPHandler: model=model, optional_rerank_params=optional_rerank_params, headers=headers, + litellm_params=litellm_params, ) ## LOGGING @@ -2511,10 +2538,16 @@ class BaseLLMHTTPHandler: }, ) + delete_kwargs: Dict[str, Any] = { + "url": url, + "headers": headers, + "timeout": timeout, + } + if data: + delete_kwargs["json"] = data + try: - response = await async_httpx_client.delete( - url=url, headers=headers, json=data, timeout=timeout - ) + response = await async_httpx_client.delete(**delete_kwargs) except Exception as e: raise self._handle_error( @@ -2595,10 +2628,16 @@ class BaseLLMHTTPHandler: }, ) + delete_kwargs: Dict[str, Any] = { + "url": url, + "headers": headers, + "timeout": timeout, + } + if data: + delete_kwargs["json"] = data + try: - response = sync_httpx_client.delete( - url=url, headers=headers, json=data, timeout=timeout - ) + response = sync_httpx_client.delete(**delete_kwargs) except Exception as e: raise self._handle_error( @@ -5216,7 +5255,6 @@ class BaseLLMHTTPHandler: headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", - "OpenAI-Beta": "realtime=v1", } if extra_headers: @@ -5540,6 +5578,9 @@ class BaseLLMHTTPHandler: litellm_params=litellm_params, headers=headers, ) + data = image_edit_provider_config.finalize_image_edit_request_data( + data, api_base + ) ## LOGGING logging_obj.pre_call( @@ -5638,6 +5679,9 @@ class BaseLLMHTTPHandler: litellm_params=litellm_params, headers=headers, ) + data = image_edit_provider_config.finalize_image_edit_request_data( + data, api_base + ) ## LOGGING logging_obj.pre_call( @@ -8585,6 +8629,7 @@ class BaseLLMHTTPHandler: api_base=api_base, litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), + extra_body=extra_body, ) else: ( @@ -8597,6 +8642,7 @@ class BaseLLMHTTPHandler: api_base=api_base, litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), + extra_body=extra_body, ) all_optional_params: Dict[str, Any] = dict(litellm_params) all_optional_params.update(vector_store_search_optional_params or {}) @@ -8697,6 +8743,7 @@ class BaseLLMHTTPHandler: api_base=api_base, litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), + extra_body=extra_body, ) all_optional_params: Dict[str, Any] = dict(litellm_params) @@ -8907,7 +8954,10 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url = f"{api_base}/{vector_store_id}" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}" logging_obj.pre_call( input="", @@ -8974,7 +9024,10 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url = f"{api_base}/{vector_store_id}" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}" logging_obj.pre_call( input="", @@ -9173,7 +9226,10 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url = f"{api_base}/{vector_store_id}" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}" request_body: Dict[str, Any] = dict(vector_store_update_optional_params) @@ -9256,7 +9312,10 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url = f"{api_base}/{vector_store_id}" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}" request_body: Dict[str, Any] = dict(vector_store_update_optional_params) @@ -9322,7 +9381,10 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url = f"{api_base}/{vector_store_id}" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}" logging_obj.pre_call( input="", @@ -9387,7 +9449,10 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url = f"{api_base}/{vector_store_id}" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}" logging_obj.pre_call( input="", @@ -10425,6 +10490,12 @@ class BaseLLMHTTPHandler: litellm_metadata=litellm_metadata or {}, custom_llm_provider=custom_llm_provider, request_body=data, + hidden_params=_google_genai_streaming_hidden_params( + api_base=api_base, + litellm_params=litellm_params, + logging_obj=logging_obj, + response_headers=response.headers, + ), ) else: response = sync_httpx_client.post( @@ -10534,6 +10605,12 @@ class BaseLLMHTTPHandler: litellm_metadata=litellm_metadata or {}, custom_llm_provider=custom_llm_provider, request_body=data, + hidden_params=_google_genai_streaming_hidden_params( + api_base=api_base, + litellm_params=litellm_params, + logging_obj=logging_obj, + response_headers=response.headers, + ), ) else: response = await async_httpx_client.post( diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index c086d4ad755..09c782a4755 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -56,7 +56,10 @@ from litellm.types.utils import ( Usage, ) -from ...anthropic.chat.transformation import AnthropicConfig +from ...anthropic.chat.transformation import ( + REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT, + AnthropicConfig, +) from ...openai_like.chat.transformation import OpenAILikeChatConfig from ..common_utils import DatabricksBase, DatabricksException @@ -330,9 +333,30 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): ) # unsupported for claude models - if json_schema -> convert to tool call if "reasoning_effort" in non_default_params and "claude" in model: - optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - reasoning_effort=non_default_params.get("reasoning_effort"), model=model + reasoning_effort_value = non_default_params.get("reasoning_effort") + mapped_thinking = AnthropicConfig._map_reasoning_effort( + reasoning_effort=reasoning_effort_value, + model=model, + llm_provider="databricks", ) + if mapped_thinking is None: + optional_params.pop("thinking", None) + optional_params.pop("output_config", None) + else: + optional_params["thinking"] = mapped_thinking + if AnthropicConfig._is_adaptive_thinking_model(model): + mapped_effort: Optional[str] = None + if isinstance(reasoning_effort_value, str): + mapped_effort = REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get( + reasoning_effort_value + ) + if mapped_effort is None: + AnthropicConfig._raise_invalid_reasoning_effort( + model=model, + value=reasoning_effort_value, + llm_provider="databricks", + ) + optional_params["output_config"] = {"effort": mapped_effort} optional_params.pop("reasoning_effort", None) ## handle thinking tokens self.update_optional_params_with_thinking_tokens( diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py index 71e300d258c..276735f4758 100644 --- a/litellm/llms/deepinfra/rerank/transformation.py +++ b/litellm/llms/deepinfra/rerank/transformation.py @@ -132,6 +132,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: # Convert OptionalRerankParams to dict as expected by parent class if optional_rerank_params is None: diff --git a/litellm/llms/elevenlabs/text_to_speech/transformation.py b/litellm/llms/elevenlabs/text_to_speech/transformation.py index 4dac2b8ba92..6a59911701b 100644 --- a/litellm/llms/elevenlabs/text_to_speech/transformation.py +++ b/litellm/llms/elevenlabs/text_to_speech/transformation.py @@ -11,13 +11,14 @@ import httpx from httpx import Headers import litellm -from litellm.types.utils import all_litellm_params +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.text_to_speech.transformation import ( BaseTextToSpeechConfig, TextToSpeechRequestData, ) from litellm.secret_managers.main import get_secret_str +from litellm.types.utils import all_litellm_params from ..common_utils import ElevenLabsException @@ -321,7 +322,8 @@ class ElevenLabsTextToSpeechConfig(BaseTextToSpeechConfig): "ElevenLabs voice_id is required. Pass `voice` when calling `litellm.speech()`." ) - url = f"{base_url}{self.TTS_ENDPOINT_PATH}/{voice_id}" + encoded_voice_id = encode_url_path_segment(voice_id, field_name="voice_id") + url = f"{base_url}{self.TTS_ENDPOINT_PATH}/{encoded_voice_id}" query_params = litellm_params.get(self.ELEVENLABS_QUERY_PARAMS_KEY, {}) if query_params: diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index ed6d167a118..eaf01c5fe18 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -241,10 +241,13 @@ class FireworksAIConfig(OpenAIGPTConfig): disable_add_transform_inline_image_block=disable_add_transform_inline_image_block, ) filter_value_from_dict(cast(dict, message), "cache_control") - # Remove fields not permitted by FireworksAI that may cause: - # "Not permitted, field: 'messages[n].provider_specific_fields'" - if isinstance(message, dict) and "provider_specific_fields" in message: - cast(dict, message).pop("provider_specific_fields", None) + # Remove fields not permitted by FireworksAI (additionalProperties: false + # on their ChatMessage schema) that may cause: + # "Extra inputs are not permitted, field: 'messages[n].'" + if isinstance(message, dict): + m = cast(dict, message) + m.pop("provider_specific_fields", None) + m.pop("thinking_blocks", None) return messages diff --git a/litellm/llms/fireworks_ai/rerank/transformation.py b/litellm/llms/fireworks_ai/rerank/transformation.py index eb92399a058..4a7b64b9b77 100644 --- a/litellm/llms/fireworks_ai/rerank/transformation.py +++ b/litellm/llms/fireworks_ai/rerank/transformation.py @@ -127,6 +127,7 @@ class FireworksAIRerankConfig(FireworksAIMixin, BaseRerankConfig): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: """ Transform request to Fireworks AI rerank format diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py index 401d7bb9f48..63a383ebd3d 100644 --- a/litellm/llms/gemini/files/transformation.py +++ b/litellm/llms/gemini/files/transformation.py @@ -12,6 +12,7 @@ import httpx from openai.types.file_deleted import FileDeleted from litellm._logging import verbose_logger +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.files.transformation import ( BaseFilesConfig, @@ -258,10 +259,14 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): normalized_file_id = file_id normalized_file_id = normalized_file_id.strip("/") - if not normalized_file_id.startswith("files/"): - normalized_file_id = f"files/{normalized_file_id}" + if normalized_file_id.startswith("files/"): + normalized_file_id = normalized_file_id.removeprefix("files/") - return normalized_file_id + encoded_file_id = encode_url_path_segment( + normalized_file_id, field_name="file_id" + ) + + return f"files/{encoded_file_id}" def transform_retrieve_file_response( self, @@ -337,13 +342,8 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): if not api_key: raise ValueError("api_key is required") - # Extract file name from URI if full URI is provided - # file_id could be "files/abc123" or "https://generativelanguage.googleapis.com/v1beta/files/abc123" - if file_id.startswith("http"): - # Extract the file path from full URI - file_name = file_id.split("/v1beta/")[-1] - else: - file_name = file_id if file_id.startswith("files/") else f"files/{file_id}" + # Normalize and encode the file name before interpolating it into the URL. + file_name = self._normalize_gemini_file_id(file_id) # Construct the delete URL url = f"{api_base}/v1beta/{file_name}" diff --git a/litellm/llms/gemini/google_genai/transformation.py b/litellm/llms/gemini/google_genai/transformation.py index 7c4c7dba626..ee201af7e1a 100644 --- a/litellm/llms/gemini/google_genai/transformation.py +++ b/litellm/llms/gemini/google_genai/transformation.py @@ -2,6 +2,7 @@ Transformation for Calling Google models in their native format. """ +from copy import deepcopy from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast import httpx @@ -11,6 +12,10 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from litellm.llms.base_llm.google_genai.transformation import ( BaseGoogleGenAIGenerateContentConfig, ) +from litellm.llms.vertex_ai.common_utils import ( + _build_vertex_schema, + supports_response_json_schema, +) from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM from litellm.types.router import GenericLiteLLMParams @@ -302,6 +307,52 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM): litellm_params=litellm_params, ) + @staticmethod + def _normalize_response_schema( + generate_content_config_dict: Dict, model: str + ) -> None: + schema_key = next( + ( + k + for k in ("responseSchema", "response_schema") + if k in generate_content_config_dict + ), + None, + ) + json_schema_key = next( + ( + k + for k in ("responseJsonSchema", "response_json_schema") + if k in generate_content_config_dict + ), + None, + ) + + if schema_key is None: + return + + value = generate_content_config_dict[schema_key] + if not isinstance(value, dict): + return + + if supports_response_json_schema(model): + if json_schema_key is not None: + generate_content_config_dict.pop(schema_key) + return + generate_content_config_dict.pop(schema_key) + new_json_schema_key = ( + "response_json_schema" + if schema_key == "response_schema" + else "responseJsonSchema" + ) + generate_content_config_dict[new_json_schema_key] = value + else: + if json_schema_key is not None: + generate_content_config_dict.pop(json_schema_key) + generate_content_config_dict[schema_key] = _build_vertex_schema( + parameters=deepcopy(value), add_property_ordering=True + ) + def transform_generate_content_request( self, model: str, @@ -315,6 +366,8 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM): GenerateContentRequestDict, ) + self._normalize_response_schema(generate_content_config_dict, model) + typed_generate_content_request = GenerateContentRequestDict( model=model, contents=contents, diff --git a/litellm/llms/gemini/interactions/transformation.py b/litellm/llms/gemini/interactions/transformation.py index c34da83cb8f..593cbf7c2cf 100644 --- a/litellm/llms/gemini/interactions/transformation.py +++ b/litellm/llms/gemini/interactions/transformation.py @@ -15,6 +15,7 @@ import httpx from litellm._logging import verbose_logger from litellm.litellm_core_utils.core_helpers import process_response_headers +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.interactions.transformation import BaseInteractionsAPIConfig from litellm.llms.gemini.common_utils import GeminiError, GeminiModelInfo from litellm.types.interactions import ( @@ -205,8 +206,11 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): resolved_api_base = GeminiModelInfo.get_api_base(api_base) if not GeminiModelInfo.get_api_key(litellm_params.api_key): raise ValueError("Google API key is required") + encoded_interaction_id = encode_url_path_segment( + interaction_id, field_name="interaction_id" + ) return ( - f"{resolved_api_base}/{self.api_version}/interactions/{interaction_id}", + f"{resolved_api_base}/{self.api_version}/interactions/{encoded_interaction_id}", {}, ) @@ -238,8 +242,11 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): resolved_api_base = GeminiModelInfo.get_api_base(api_base) if not GeminiModelInfo.get_api_key(litellm_params.api_key): raise ValueError("Google API key is required") + encoded_interaction_id = encode_url_path_segment( + interaction_id, field_name="interaction_id" + ) return ( - f"{resolved_api_base}/{self.api_version}/interactions/{interaction_id}", + f"{resolved_api_base}/{self.api_version}/interactions/{encoded_interaction_id}", {}, ) @@ -268,8 +275,11 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): resolved_api_base = GeminiModelInfo.get_api_base(api_base) if not GeminiModelInfo.get_api_key(litellm_params.api_key): raise ValueError("Google API key is required") + encoded_interaction_id = encode_url_path_segment( + interaction_id, field_name="interaction_id" + ) return ( - f"{resolved_api_base}/{self.api_version}/interactions/{interaction_id}:cancel", + f"{resolved_api_base}/{self.api_version}/interactions/{encoded_interaction_id}:cancel", {}, ) diff --git a/litellm/llms/gemini/vector_stores/transformation.py b/litellm/llms/gemini/vector_stores/transformation.py index e6e8369643e..35d83bd2adc 100644 --- a/litellm/llms/gemini/vector_stores/transformation.py +++ b/litellm/llms/gemini/vector_stores/transformation.py @@ -118,6 +118,7 @@ class GeminiVectorStoreConfig(BaseVectorStoreConfig): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: """ Transform search request to Gemini's generateContent format. diff --git a/litellm/llms/hosted_vllm/chat/transformation.py b/litellm/llms/hosted_vllm/chat/transformation.py index b5a8b25beba..1824314865c 100644 --- a/litellm/llms/hosted_vllm/chat/transformation.py +++ b/litellm/llms/hosted_vllm/chat/transformation.py @@ -2,7 +2,18 @@ Translate from OpenAI's `/v1/chat/completions` to VLLM's `/v1/chat/completions` """ -from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, overload +from typing import ( + Any, + Coroutine, + Dict, + List, + Literal, + Optional, + Tuple, + Union, + cast, + overload, +) from litellm.litellm_core_utils.prompt_templates.common_utils import ( _get_image_mime_type_from_url, @@ -21,6 +32,61 @@ from ...openai.chat.gpt_transformation import OpenAIGPTConfig class HostedVLLMChatConfig(OpenAIGPTConfig): + def _convert_custom_tools_to_function_tools( + self, tools: List[Dict[str, Any]] + ) -> List[Dict[str, Any]]: + """ + vLLM chat completions currently accepts only OpenAI function tools. + Convert custom tools into function tools so request validation does not fail. + """ + converted_tools: List[Dict[str, Any]] = [] + for idx, tool in enumerate(tools): + if not isinstance(tool, dict): + converted_tools.append(tool) + continue + + if tool.get("type") != "custom": + converted_tools.append(tool) + continue + + custom_tool = tool.get("custom", {}) + if not isinstance(custom_tool, dict): + custom_tool = {} + + tool_name = ( + custom_tool.get("name") or tool.get("name") or f"custom_tool_{idx}" + ) + tool_description = custom_tool.get("description") or tool.get("description") + tool_parameters = custom_tool.get("input_schema") or tool.get( + "input_schema" + ) + + if not isinstance(tool_parameters, dict): + tool_parameters = { + "type": "object", + "properties": { + "input": { + "type": "string", + "description": "Raw tool input payload.", + } + }, + "required": ["input"], + } + + function_tool: Dict[str, Any] = { + "type": "function", + "function": { + "name": str(tool_name), + "parameters": tool_parameters, + }, + } + if isinstance(tool_description, str): + function_tool["function"]["description"] = tool_description + + converted_tools.append(function_tool) + + return converted_tools + def get_supported_openai_params(self, model: str) -> List[str]: params = super().get_supported_openai_params(model) params.extend(["reasoning_effort", "thinking"]) @@ -39,6 +105,8 @@ class HostedVLLMChatConfig(OpenAIGPTConfig): _tools = _remove_additional_properties(_tools) # remove 'strict' from tools _tools = _remove_strict_from_schema(_tools) + if isinstance(_tools, list): + _tools = self._convert_custom_tools_to_function_tools(_tools) if _tools is not None: non_default_params["tools"] = _tools diff --git a/litellm/llms/hosted_vllm/embedding/README.md b/litellm/llms/hosted_vllm/embedding/README.md index f82b3c77a6e..2c58e16fc23 100644 --- a/litellm/llms/hosted_vllm/embedding/README.md +++ b/litellm/llms/hosted_vllm/embedding/README.md @@ -2,4 +2,15 @@ No transformation is required for hosted_vllm embedding. VLLM is a superset of OpenAI's `embedding` endpoint. -To pass provider-specific parameters, see [this](https://docs.litellm.ai/docs/completion/provider_specific_params) \ No newline at end of file +## `encoding_format` + +For OpenAI-compatible embedding calls (including `openai/...` with a custom `api_base` pointing at vLLM), LiteLLM resolves `encoding_format` when it is not set on the request: + +1. Explicit value on the embedding call (`encoding_format=...`). +2. Model config (`litellm_params.encoding_format` on the proxy `model_list` entry). +3. Environment variable `LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT` (e.g. in `.env` or container env). +4. Default **`float`**. + +That avoids forwarding `encoding_format=None` to the provider/SDK where some servers behave poorly. + +To pass provider-specific parameters, see [provider-specific params](https://docs.litellm.ai/docs/completion/provider_specific_params). \ No newline at end of file diff --git a/litellm/llms/hosted_vllm/rerank/transformation.py b/litellm/llms/hosted_vllm/rerank/transformation.py index 8066e53afc7..60b6dc7d23d 100644 --- a/litellm/llms/hosted_vllm/rerank/transformation.py +++ b/litellm/llms/hosted_vllm/rerank/transformation.py @@ -121,6 +121,7 @@ class HostedVLLMRerankConfig(BaseRerankConfig): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: if "query" not in optional_rerank_params: raise ValueError("query is required for Hosted VLLM rerank") diff --git a/litellm/llms/huggingface/rerank/transformation.py b/litellm/llms/huggingface/rerank/transformation.py index 3f83b8e422d..2c847b617ef 100644 --- a/litellm/llms/huggingface/rerank/transformation.py +++ b/litellm/llms/huggingface/rerank/transformation.py @@ -146,6 +146,7 @@ class HuggingFaceRerankConfig(BaseRerankConfig): model: str, optional_rerank_params: Union[OptionalRerankParams, dict], headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: if "query" not in optional_rerank_params: raise ValueError("query is required for HuggingFace rerank") diff --git a/litellm/llms/jina_ai/rerank/transformation.py b/litellm/llms/jina_ai/rerank/transformation.py index 48d876f8ea2..ad4416925a6 100644 --- a/litellm/llms/jina_ai/rerank/transformation.py +++ b/litellm/llms/jina_ai/rerank/transformation.py @@ -74,7 +74,11 @@ class JinaAIRerankConfig(BaseRerankConfig): return cleaned_base def transform_rerank_request( - self, model: str, optional_rerank_params: Dict, headers: Dict + self, + model: str, + optional_rerank_params: Dict, + headers: Dict, + litellm_params: Optional[dict] = None, ) -> Dict: return {"model": model, **optional_rerank_params} diff --git a/litellm/llms/litellm_proxy/skills/handler.py b/litellm/llms/litellm_proxy/skills/handler.py index 8e5070c2724..37aabd8b477 100644 --- a/litellm/llms/litellm_proxy/skills/handler.py +++ b/litellm/llms/litellm_proxy/skills/handler.py @@ -9,42 +9,47 @@ import uuid from typing import Any, Dict, List, Optional from litellm._logging import verbose_logger -from litellm.proxy._types import LiteLLM_SkillsTable, NewSkillRequest +from litellm.caching.in_memory_cache import InMemoryCache +from litellm.proxy._types import LiteLLM_SkillsTable, NewSkillRequest, UserAPIKeyAuth +from litellm.proxy.common_utils.resource_ownership import ( + get_primary_resource_owner_scope, + get_resource_owner_scopes, + is_proxy_admin, + user_can_access_resource_owner, +) + +# Skills are looked up on every chat completion that has skills enabled +# (`SkillsInjectionHook` calls ``fetch_skill_from_db``). 60s LRU/TTL cache +# absorbs the hot read before it reaches Prisma. ``_NEGATIVE_SKILL_SENTINEL`` +# lets us cache a true "skill does not exist" so repeated misses also +# avoid the DB — ``InMemoryCache`` returns ``None`` indistinguishably for +# "miss" and "cached as None". +_NEGATIVE_SKILL_SENTINEL = "__litellm_skill_not_found__" +_SKILL_CACHE = InMemoryCache(max_size_in_memory=10000, default_ttl=60) def _prisma_skill_to_litellm(prisma_skill) -> LiteLLM_SkillsTable: - """ - Convert a Prisma skill record to LiteLLM_SkillsTable. + """Convert a Prisma skill record to LiteLLM_SkillsTable. - Handles Base64 decoding of file_content field. + Handles Base64 decoding of file_content field — model_dump() converts + Base64 fields to base64-encoded strings. """ import base64 data = prisma_skill.model_dump() - # Decode Base64 file_content back to bytes - # model_dump() converts Base64 field to base64-encoded string if data.get("file_content") is not None: if isinstance(data["file_content"], str): data["file_content"] = base64.b64decode(data["file_content"]) - elif isinstance(data["file_content"], bytes): - # Already bytes, no conversion needed - pass return LiteLLM_SkillsTable(**data) class LiteLLMSkillsHandler: - """ - Handler for LiteLLM database-backed skills operations. - - This class provides static methods for CRUD operations on skills - stored in the LiteLLM proxy database (LiteLLM_SkillsTable). - """ + """CRUD for skills stored in ``litellm_skillstable``.""" @staticmethod async def _get_prisma_client(): - """Get the prisma client from proxy server.""" from litellm.proxy.proxy_server import prisma_client if prisma_client is None: @@ -58,20 +63,21 @@ class LiteLLMSkillsHandler: async def create_skill( data: NewSkillRequest, user_id: Optional[str] = None, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, ) -> LiteLLM_SkillsTable: - """ - Create a new skill in the LiteLLM database. - - Args: - data: NewSkillRequest with skill details - user_id: Optional user ID for tracking - - Returns: - LiteLLM_SkillsTable record - """ prisma_client = await LiteLLMSkillsHandler._get_prisma_client() skill_id = f"litellm_skill_{uuid.uuid4()}" + owner = get_primary_resource_owner_scope(user_api_key_dict) or user_id + if owner is None: + # Identity-less callers (no user_id / team_id / org_id / + # api_key / token) can't be uniquely stamped on the row. + # Stamping a placeholder would let any two such callers see + # each other's skills via the shared owner. ValueError keeps + # this module FastAPI-free per the project layering rule. + raise ValueError( + "Unable to record skill ownership: caller has no identity scope." + ) skill_data: Dict[str, Any] = { "skill_id": skill_id, @@ -79,17 +85,15 @@ class LiteLLMSkillsHandler: "description": data.description, "instructions": data.instructions, "source": "custom", - "created_by": user_id, - "updated_by": user_id, + "created_by": owner, + "updated_by": owner, } - # Handle metadata if data.metadata is not None: from litellm.litellm_core_utils.safe_json_dumps import safe_dumps skill_data["metadata"] = safe_dumps(data.metadata) - # Handle file content - wrap bytes in Base64 for Prisma if data.file_content is not None: from prisma.fields import Base64 @@ -104,112 +108,103 @@ class LiteLLMSkillsHandler: ) new_skill = await prisma_client.db.litellm_skillstable.create(data=skill_data) - return _prisma_skill_to_litellm(new_skill) @staticmethod async def list_skills( limit: int = 20, offset: int = 0, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, ) -> List[LiteLLM_SkillsTable]: - """ - List skills from the LiteLLM database. - - Args: - limit: Maximum number of skills to return - offset: Number of skills to skip - - Returns: - List of LiteLLM_SkillsTable records - """ prisma_client = await LiteLLMSkillsHandler._get_prisma_client() verbose_logger.debug( f"LiteLLMSkillsHandler: Listing skills with limit={limit}, offset={offset}" ) - skills = await prisma_client.db.litellm_skillstable.find_many( - take=limit, - skip=offset, - order={"created_at": "desc"}, - ) + find_many_kwargs: Dict[str, Any] = { + "take": limit, + "skip": offset, + "order": {"created_at": "desc"}, + } + if user_api_key_dict is not None and not is_proxy_admin(user_api_key_dict): + owner_scopes = get_resource_owner_scopes(user_api_key_dict) + if not owner_scopes: + return [] + find_many_kwargs["where"] = {"created_by": {"in": owner_scopes}} + skills = await prisma_client.db.litellm_skillstable.find_many( + **find_many_kwargs + ) return [_prisma_skill_to_litellm(s) for s in skills] @staticmethod - async def get_skill(skill_id: str) -> LiteLLM_SkillsTable: + async def _load_skill(skill_id: str) -> Optional[Any]: + """Cache-first read of the Prisma skill row. Owner-scope filtering + happens on the cached row, so the cache is per-skill not per-caller. """ - Get a skill by ID from the LiteLLM database. + cached = _SKILL_CACHE.get_cache(skill_id) + if cached == _NEGATIVE_SKILL_SENTINEL: + return None + if cached is not None: + return cached - Args: - skill_id: The skill ID to retrieve - - Returns: - LiteLLM_SkillsTable record - - Raises: - ValueError: If skill not found - """ prisma_client = await LiteLLMSkillsHandler._get_prisma_client() - - verbose_logger.debug(f"LiteLLMSkillsHandler: Getting skill {skill_id}") - skill = await prisma_client.db.litellm_skillstable.find_unique( where={"skill_id": skill_id} ) + _SKILL_CACHE.set_cache( + skill_id, skill if skill is not None else _NEGATIVE_SKILL_SENTINEL + ) + return skill - if skill is None: + @staticmethod + async def get_skill( + skill_id: str, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, + ) -> LiteLLM_SkillsTable: + verbose_logger.debug(f"LiteLLMSkillsHandler: Getting skill {skill_id}") + + skill = await LiteLLMSkillsHandler._load_skill(skill_id) + # Same "not found" message for both "missing" and "cross-tenant" + # so callers can't enumerate skill IDs they don't own. + if skill is None or not user_can_access_resource_owner( + getattr(skill, "created_by", None), user_api_key_dict + ): raise ValueError(f"Skill not found: {skill_id}") return _prisma_skill_to_litellm(skill) @staticmethod - async def delete_skill(skill_id: str) -> Dict[str, str]: - """ - Delete a skill by ID from the LiteLLM database. - - Args: - skill_id: The skill ID to delete - - Returns: - Dict with id and type of deleted skill - - Raises: - ValueError: If skill not found - """ + async def delete_skill( + skill_id: str, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, + ) -> Dict[str, str]: prisma_client = await LiteLLMSkillsHandler._get_prisma_client() - verbose_logger.debug(f"LiteLLMSkillsHandler: Deleting skill {skill_id}") - # Check if skill exists - skill = await prisma_client.db.litellm_skillstable.find_unique( - where={"skill_id": skill_id} - ) - - if skill is None: + skill = await LiteLLMSkillsHandler._load_skill(skill_id) + if skill is None or not user_can_access_resource_owner( + getattr(skill, "created_by", None), user_api_key_dict + ): raise ValueError(f"Skill not found: {skill_id}") - # Delete the skill await prisma_client.db.litellm_skillstable.delete(where={"skill_id": skill_id}) + _SKILL_CACHE.set_cache(skill_id, _NEGATIVE_SKILL_SENTINEL) return {"id": skill_id, "type": "skill_deleted"} @staticmethod - async def fetch_skill_from_db(skill_id: str) -> Optional[LiteLLM_SkillsTable]: - """ - Fetch a skill from the database (used by skills injection hook). - - This is a convenience method that returns None instead of raising - an exception if the skill is not found. - - Args: - skill_id: The skill ID to fetch - - Returns: - LiteLLM_SkillsTable or None if not found - """ + async def fetch_skill_from_db( + skill_id: str, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, + ) -> Optional[LiteLLM_SkillsTable]: + """Skills-injection-hook helper: returns None instead of raising on + not-found / not-authorized so the hook can silently skip.""" try: - return await LiteLLMSkillsHandler.get_skill(skill_id) + return await LiteLLMSkillsHandler.get_skill( + skill_id, user_api_key_dict=user_api_key_dict + ) except ValueError: return None except Exception as e: diff --git a/litellm/llms/litellm_proxy/skills/transformation.py b/litellm/llms/litellm_proxy/skills/transformation.py index 4622bda4e80..199f13191fe 100644 --- a/litellm/llms/litellm_proxy/skills/transformation.py +++ b/litellm/llms/litellm_proxy/skills/transformation.py @@ -18,6 +18,7 @@ from litellm.types.utils import LlmProviders if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth class LiteLLMSkillsTransformationHandler: @@ -44,6 +45,7 @@ class LiteLLMSkillsTransformationHandler: file_type: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, user_id: Optional[str] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, _is_async: bool = False, logging_obj: Optional["LiteLLMLoggingObj"] = None, litellm_call_id: Optional[str] = None, @@ -99,6 +101,7 @@ class LiteLLMSkillsTransformationHandler: file_type=file_type, metadata=metadata, user_id=user_id, + user_api_key_dict=user_api_key_dict, ) import asyncio @@ -113,6 +116,7 @@ class LiteLLMSkillsTransformationHandler: file_type=file_type, metadata=metadata, user_id=user_id, + user_api_key_dict=user_api_key_dict, ) ) @@ -126,6 +130,7 @@ class LiteLLMSkillsTransformationHandler: file_type: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, user_id: Optional[str] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, ) -> Skill: """Async implementation of create_skill.""" # Lazy import to avoid SDK dependency on proxy @@ -145,6 +150,7 @@ class LiteLLMSkillsTransformationHandler: db_skill = await LiteLLMSkillsHandler.create_skill( data=skill_request, user_id=user_id, + user_api_key_dict=user_api_key_dict, ) return self._db_skill_to_response(db_skill) @@ -156,6 +162,7 @@ class LiteLLMSkillsTransformationHandler: _is_async: bool = False, logging_obj: Optional["LiteLLMLoggingObj"] = None, litellm_call_id: Optional[str] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, **kwargs, ) -> Union[ListSkillsResponse, Coroutine[Any, Any, ListSkillsResponse]]: """ @@ -182,18 +189,27 @@ class LiteLLMSkillsTransformationHandler: ) if _is_async: - return self._async_list_skills(limit=limit, offset=offset) + return self._async_list_skills( + limit=limit, + offset=offset, + user_api_key_dict=user_api_key_dict, + ) import asyncio return asyncio.get_event_loop().run_until_complete( - self._async_list_skills(limit=limit, offset=offset) + self._async_list_skills( + limit=limit, + offset=offset, + user_api_key_dict=user_api_key_dict, + ) ) async def _async_list_skills( self, limit: int = 20, offset: int = 0, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, ) -> ListSkillsResponse: """Async implementation of list_skills.""" # Lazy import to avoid SDK dependency on proxy @@ -202,6 +218,7 @@ class LiteLLMSkillsTransformationHandler: db_skills = await LiteLLMSkillsHandler.list_skills( limit=limit, offset=offset, + user_api_key_dict=user_api_key_dict, ) skills = [self._db_skill_to_response(s) for s in db_skills] @@ -217,6 +234,7 @@ class LiteLLMSkillsTransformationHandler: _is_async: bool = False, logging_obj: Optional["LiteLLMLoggingObj"] = None, litellm_call_id: Optional[str] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, **kwargs, ) -> Union[Skill, Coroutine[Any, Any, Skill]]: """ @@ -242,20 +260,33 @@ class LiteLLMSkillsTransformationHandler: ) if _is_async: - return self._async_get_skill(skill_id=skill_id) + return self._async_get_skill( + skill_id=skill_id, + user_api_key_dict=user_api_key_dict, + ) import asyncio return asyncio.get_event_loop().run_until_complete( - self._async_get_skill(skill_id=skill_id) + self._async_get_skill( + skill_id=skill_id, + user_api_key_dict=user_api_key_dict, + ) ) - async def _async_get_skill(self, skill_id: str) -> Skill: + async def _async_get_skill( + self, + skill_id: str, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + ) -> Skill: """Async implementation of get_skill.""" # Lazy import to avoid SDK dependency on proxy from litellm.llms.litellm_proxy.skills.handler import LiteLLMSkillsHandler - db_skill = await LiteLLMSkillsHandler.get_skill(skill_id=skill_id) + db_skill = await LiteLLMSkillsHandler.get_skill( + skill_id=skill_id, + user_api_key_dict=user_api_key_dict, + ) return self._db_skill_to_response(db_skill) def delete_skill_handler( @@ -264,6 +295,7 @@ class LiteLLMSkillsTransformationHandler: _is_async: bool = False, logging_obj: Optional["LiteLLMLoggingObj"] = None, litellm_call_id: Optional[str] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, **kwargs, ) -> Union[DeleteSkillResponse, Coroutine[Any, Any, DeleteSkillResponse]]: """ @@ -289,20 +321,33 @@ class LiteLLMSkillsTransformationHandler: ) if _is_async: - return self._async_delete_skill(skill_id=skill_id) + return self._async_delete_skill( + skill_id=skill_id, + user_api_key_dict=user_api_key_dict, + ) import asyncio return asyncio.get_event_loop().run_until_complete( - self._async_delete_skill(skill_id=skill_id) + self._async_delete_skill( + skill_id=skill_id, + user_api_key_dict=user_api_key_dict, + ) ) - async def _async_delete_skill(self, skill_id: str) -> DeleteSkillResponse: + async def _async_delete_skill( + self, + skill_id: str, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + ) -> DeleteSkillResponse: """Async implementation of delete_skill.""" # Lazy import to avoid SDK dependency on proxy from litellm.llms.litellm_proxy.skills.handler import LiteLLMSkillsHandler - result = await LiteLLMSkillsHandler.delete_skill(skill_id=skill_id) + result = await LiteLLMSkillsHandler.delete_skill( + skill_id=skill_id, + user_api_key_dict=user_api_key_dict, + ) return DeleteSkillResponse( id=result["id"], type=result.get("type", "skill_deleted"), diff --git a/litellm/llms/manus/files/transformation.py b/litellm/llms/manus/files/transformation.py index 3381a5327e8..34166161390 100644 --- a/litellm/llms/manus/files/transformation.py +++ b/litellm/llms/manus/files/transformation.py @@ -18,6 +18,7 @@ from openai.types.file_deleted import FileDeleted import litellm from litellm._logging import verbose_logger +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.files.transformation import ( @@ -306,7 +307,8 @@ class ManusFilesConfig(BaseFilesConfig): optional_params=optional_params, litellm_params=litellm_params, ) - return f"{api_base}/{file_id}", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base}/{encoded_file_id}", {} def transform_retrieve_file_response( self, @@ -336,7 +338,8 @@ class ManusFilesConfig(BaseFilesConfig): optional_params=optional_params, litellm_params=litellm_params, ) - return f"{api_base}/{file_id}", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base}/{encoded_file_id}", {} def transform_delete_file_response( self, @@ -422,7 +425,8 @@ class ManusFilesConfig(BaseFilesConfig): optional_params=optional_params, litellm_params=litellm_params, ) - return f"{api_base}/{file_id}/content", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base}/{encoded_file_id}/content", {} def transform_file_content_response( self, diff --git a/litellm/llms/manus/responses/transformation.py b/litellm/llms/manus/responses/transformation.py index 510c41304a8..b3a0073a5c2 100644 --- a/litellm/llms/manus/responses/transformation.py +++ b/litellm/llms/manus/responses/transformation.py @@ -6,6 +6,7 @@ import httpx import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.core_helpers import process_response_headers +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( _safe_convert_created_field, ) @@ -270,7 +271,10 @@ class ManusResponsesAPIConfig(OpenAIResponsesAPIConfig): Reference: https://open.manus.im/docs/openai-compatibility """ - url = f"{api_base}/{response_id}" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}" data: Dict = {} return url, data diff --git a/litellm/llms/milvus/vector_stores/transformation.py b/litellm/llms/milvus/vector_stores/transformation.py index fcf5d14db7c..867f6d4b1f5 100644 --- a/litellm/llms/milvus/vector_stores/transformation.py +++ b/litellm/llms/milvus/vector_stores/transformation.py @@ -25,7 +25,6 @@ else: LiteLLMLoggingObj = Any MILVUS_OPTIONAL_PARAMS = { - "dbName", "annsField", "limit", "filter", @@ -33,7 +32,6 @@ MILVUS_OPTIONAL_PARAMS = { "groupingField", "outputFields", "searchParams", - "partitionNames", "consistencyLevel", } @@ -130,6 +128,7 @@ class MilvusVectorStoreConfig(BaseVectorStoreConfig): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict[str, Any]]: """ Transform search request for Azure AI Search API @@ -172,13 +171,21 @@ class MilvusVectorStoreConfig(BaseVectorStoreConfig): url = f"{api_base}/v2/vectordb/entities/search" # Build the request body for Azure AI Search with vector search - request_body = { + request_body: Dict[str, Any] = { "collectionName": index_name, "data": [query_vector], "annsField": "book_intro_vector", **vector_store_search_optional_params, } + db_name = litellm_params.get("milvus_db_name") + if db_name: + request_body["dbName"] = db_name + + partition_names = litellm_params.get("milvus_partition_names") + if partition_names: + request_body["partitionNames"] = partition_names + ######################################################### # Update logging object with details of the request ######################################################### diff --git a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py index 757d874bf31..b9a46b8ac2b 100644 --- a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py +++ b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py @@ -66,6 +66,7 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: """ Transform request, using clean model name without 'ranking/' prefix. @@ -75,4 +76,5 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): model=clean_model, optional_rerank_params=optional_rerank_params, headers=headers, + litellm_params=litellm_params, ) diff --git a/litellm/llms/nvidia_nim/rerank/transformation.py b/litellm/llms/nvidia_nim/rerank/transformation.py index bd5abac60c8..fc317293acc 100644 --- a/litellm/llms/nvidia_nim/rerank/transformation.py +++ b/litellm/llms/nvidia_nim/rerank/transformation.py @@ -177,6 +177,7 @@ class NvidiaNimRerankConfig(BaseRerankConfig): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: """ Transform request to Nvidia NIM format. diff --git a/litellm/llms/nvidia_riva/__init__.py b/litellm/llms/nvidia_riva/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/nvidia_riva/audio_transcription/__init__.py b/litellm/llms/nvidia_riva/audio_transcription/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py b/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py new file mode 100644 index 00000000000..253d6d2f73f --- /dev/null +++ b/litellm/llms/nvidia_riva/audio_transcription/audio_utils.py @@ -0,0 +1,232 @@ +""" +Audio resampling utilities for the NVIDIA Riva STT provider. + +We intentionally avoid a hard dependency on ``ffmpeg`` so this works in +slim Python environments. Format coverage: + +- ``soundfile`` handles wav / flac / ogg out of the box (libsndfile). +- ``audioread`` is tried for everything ``soundfile`` cannot decode (mp3, + m4a, mp4, webm, ...). This is a soft optional dependency. + +If neither library can decode the input we raise a clear error instructing +the caller to convert the audio upstream. +""" + +import io +import os +import tempfile +from dataclasses import dataclass +from typing import Any, Tuple, cast + +from litellm.llms.nvidia_riva.audio_transcription.transformation import ( + RIVA_TARGET_NUM_CHANNELS, + RIVA_TARGET_SAMPLE_RATE_HZ, +) +from litellm.llms.nvidia_riva.common_utils import NvidiaRivaException + +# Keep this as Any: the module intentionally avoids importing numpy at module +# import time (optional dependency), and project-wide mypy config evaluates this +# file in contexts where conditional type aliases can degrade to "FloatArray?". +FloatArray = Any + + +_INSTALL_HINT = ( + "Install Riva STT extras to enable automatic audio resampling: " + "`pip install 'litellm[stt-nvidia-riva]'`" +) + + +@dataclass +class ResampledAudio: + pcm_bytes: bytes + duration_seconds: float + sample_rate_hz: int + num_channels: int + + +def resample_to_riva_pcm(file_bytes: bytes) -> ResampledAudio: + """ + Decode ``file_bytes`` and produce 16 kHz mono LINEAR_PCM (int16 little + endian) suitable for streaming to Riva, plus the audio duration in + seconds (used for cost calculation when Riva does not return usage). + """ + try: + import numpy as np # type: ignore + except ImportError as e: + raise NvidiaRivaException( + status_code=500, + message=f"numpy is required for Riva audio resampling. {_INSTALL_HINT}", + ) from e + + samples_float, source_rate = _decode_to_float32(file_bytes) + + # Downmix to mono by averaging channels. + if samples_float.ndim == 2 and samples_float.shape[1] > 1: + samples_float = samples_float.mean(axis=1) + elif samples_float.ndim == 2: + samples_float = samples_float[:, 0] + + samples_float = np.asarray(samples_float, dtype=np.float32).ravel() + + if source_rate != RIVA_TARGET_SAMPLE_RATE_HZ: + samples_float = _resample( + samples_float, source_rate, RIVA_TARGET_SAMPLE_RATE_HZ + ) + + # Clip + convert float [-1, 1] to int16 little-endian PCM. + np.clip(samples_float, -1.0, 1.0, out=samples_float) + pcm_int16 = (samples_float * 32767.0).astype(" Tuple["FloatArray", int]: + """ + Decode arbitrary audio bytes into a float32 array shaped either + ``(n_samples,)`` (mono) or ``(n_samples, n_channels)`` plus the source + sample rate. + + Tries ``soundfile`` first (wav/flac/ogg), then falls back to + ``audioread`` for compressed formats. Raises a clear error if neither + works. + """ + import numpy as np # type: ignore + + sf_error: Exception | None = None + try: + import soundfile as sf # type: ignore + + with io.BytesIO(file_bytes) as buf: + data, source_rate = sf.read(buf, dtype="float32", always_2d=False) + return cast("FloatArray", data), int(source_rate) + except ImportError as e: + sf_error = e + except Exception as e: + # soundfile raises RuntimeError / LibsndfileError for formats it + # cannot decode (mp3 on older libsndfile, m4a, webm, ...). + sf_error = e + + try: + import audioread # type: ignore + except ImportError as e: + raise NvidiaRivaException( + status_code=400, + message=( + "Could not decode audio for Riva STT. Install audio extras " + f"(`pip install 'litellm[stt-nvidia-riva]'`) or convert your " + f"audio to wav/flac/ogg before calling the API. " + f"Underlying error: {sf_error}" + ), + ) from e + + # audioread backends (FFmpeg subprocess, GStreamer, Core Audio) require a + # filesystem path, so spill the bytes to a temp file. mkstemp is portable + # to Windows where re-opening a NamedTemporaryFile is not allowed. + fd, tmp_path = tempfile.mkstemp(suffix=".audio") + try: + with os.fdopen(fd, "wb") as tmp_file: + tmp_file.write(file_bytes) + try: + with audioread.audio_open(tmp_path) as src: + source_rate = int(src.samplerate) + channels = int(src.channels) + chunks = [] + for buf in src: + chunks.append(np.frombuffer(buf, dtype=np.int16)) + if not chunks: + raise NvidiaRivaException( + status_code=400, + message="Audio decode produced no samples.", + ) + interleaved = np.concatenate(chunks).astype(np.float32) / 32768.0 + if channels > 1: + interleaved = interleaved.reshape(-1, channels) + return cast("FloatArray", interleaved), source_rate + except NvidiaRivaException: + raise + except Exception as e: + raise NvidiaRivaException( + status_code=400, + message=( + "Could not decode audio for Riva STT. Convert your audio to " + f"wav/flac/ogg before calling the API. Underlying error: {e}" + ), + ) from e + finally: + try: + os.unlink(tmp_path) + except OSError: + pass + + +def _resample( + samples: "FloatArray", source_rate: int, target_rate: int +) -> "FloatArray": + """ + Resample mono float32 ``samples`` from ``source_rate`` to ``target_rate``. + + Prefers high-quality polyphase resampling when ``soxr`` or ``scipy`` is + available (anti-aliased, important for downsampling 44.1/48 kHz -> 16 kHz + where naive interpolation folds high frequencies back into the speech + band). Falls back to linear interpolation if neither is installed — + acceptable for speech-only mono input but lossy for wideband content. + """ + import numpy as np # type: ignore + + if source_rate == target_rate or samples.size == 0: + return samples + + try: + import soxr # type: ignore + + return cast( + "FloatArray", + np.asarray( + soxr.resample(samples, source_rate, target_rate), dtype=np.float32 + ), + ) + except ImportError: + pass + + try: + from math import gcd + + from scipy.signal import resample_poly # type: ignore + + g = gcd(int(source_rate), int(target_rate)) + up = int(target_rate) // g + down = int(source_rate) // g + return cast( + "FloatArray", np.asarray(resample_poly(samples, up, down), dtype=np.float32) + ) + except ImportError: + pass + + return _linear_resample(samples, source_rate, target_rate) + + +def _linear_resample( + samples: "FloatArray", source_rate: int, target_rate: int +) -> "FloatArray": + """Linear-interpolation fallback. See :func:`_resample` for caveats.""" + import numpy as np # type: ignore + + duration = samples.size / float(source_rate) + target_length = int(round(duration * target_rate)) + if target_length <= 1: + return samples.astype(np.float32) + + src_indices = np.linspace(0, samples.size - 1, num=target_length, dtype=np.float64) + left = np.floor(src_indices).astype(np.int64) + right = np.minimum(left + 1, samples.size - 1) + frac = (src_indices - left).astype(np.float32) + + return ((1.0 - frac) * samples[left] + frac * samples[right]).astype(np.float32) diff --git a/litellm/llms/nvidia_riva/audio_transcription/handler.py b/litellm/llms/nvidia_riva/audio_transcription/handler.py new file mode 100644 index 00000000000..9740162ba1c --- /dev/null +++ b/litellm/llms/nvidia_riva/audio_transcription/handler.py @@ -0,0 +1,444 @@ +""" +NVIDIA Riva STT handler. + +This module bridges litellm's transcription dispatch to NVIDIA Riva's gRPC +streaming ASR API. We do *not* go through ``base_llm_http_handler`` because +Riva is gRPC-only: HTTP-shaped abstractions (``httpx.Response``, +``api_base/v1/...`` URLs, multipart bodies) do not apply. + +The handler is intentionally a thin orchestration layer: + +1. Resample the inbound audio to 16 kHz mono LINEAR_PCM (Riva's required + wire format). +2. Build ``RecognitionConfig`` / ``StreamingRecognitionConfig`` protobufs + from the structured dict produced by + :class:`NvidiaRivaAudioTranscriptionConfig`. +3. Construct ``riva.client.Auth`` honoring NVCF (function-id metadata + TLS) + vs self-hosted (any host:port, optional TLS) modes. +4. Stream the audio through Riva's ``streaming_response_generator`` and + aggregate ``is_final`` results into a single transcript. +5. Return a normalized ``TranscriptionResponse`` with ``duration`` exposed + on ``_hidden_params`` so cost calculation works. + +``riva-client`` is imported lazily so ``litellm`` core remains usable +without the optional STT extras installed. +""" + +import asyncio +import inspect +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple + +from litellm.litellm_core_utils.audio_utils.utils import ( + get_audio_file_name, + process_audio_file, +) +from litellm.llms.nvidia_riva.audio_transcription.audio_utils import ( + resample_to_riva_pcm, +) +from litellm.llms.nvidia_riva.audio_transcription.transformation import ( + NvidiaRivaAudioTranscriptionConfig, + RIVA_TARGET_NUM_CHANNELS, + RIVA_TARGET_SAMPLE_RATE_HZ, +) +from litellm.llms.nvidia_riva.common_utils import ( + NvidiaRivaException, + grpc_error_to_litellm_exception, +) +from litellm.types.utils import FileTypes, TranscriptionResponse +from litellm.utils import convert_to_model_response_object + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import ( + Logging as LiteLLMLoggingObj, + ) + +# Stream audio to Riva in ~50 ms slices (1600 samples at 16 kHz). Matches +# NVIDIA's recommended chunk size for streaming ASR — small enough for +# responsive endpointing, large enough to keep per-RPC overhead low. +_DEFAULT_CHUNK_SAMPLES = 1600 +_DEFAULT_CHUNK_BYTES = _DEFAULT_CHUNK_SAMPLES * 2 # int16 = 2 bytes/sample + + +_RIVA_INSTALL_HINT = ( + "NVIDIA Riva client is not installed. " + "Install with `pip install 'litellm[stt-nvidia-riva]'`." +) + + +class NvidiaRivaAudioTranscription: + """Sync + async entry point for Riva ASR.""" + + def audio_transcriptions( + self, + model: str, + audio_file: FileTypes, + optional_params: dict, + litellm_params: dict, + model_response: TranscriptionResponse, + timeout: float, + logging_obj: "LiteLLMLoggingObj", + api_key: Optional[str], + api_base: Optional[str], + atranscription: bool = False, + provider_config: Optional[NvidiaRivaAudioTranscriptionConfig] = None, + ): + if provider_config is None: + provider_config = NvidiaRivaAudioTranscriptionConfig() + + if atranscription: + return self.async_audio_transcriptions( + model=model, + audio_file=audio_file, + optional_params=optional_params, + litellm_params=litellm_params, + model_response=model_response, + timeout=timeout, + logging_obj=logging_obj, + api_key=api_key, + api_base=api_base, + provider_config=provider_config, + ) + + return self._run_sync( + model=model, + audio_file=audio_file, + optional_params=optional_params, + litellm_params=litellm_params, + model_response=model_response, + timeout=timeout, + logging_obj=logging_obj, + api_key=api_key, + api_base=api_base, + provider_config=provider_config, + atranscription=atranscription, + ) + + async def async_audio_transcriptions( + self, + model: str, + audio_file: FileTypes, + optional_params: dict, + litellm_params: dict, + model_response: TranscriptionResponse, + timeout: float, + logging_obj: "LiteLLMLoggingObj", + api_key: Optional[str], + api_base: Optional[str], + provider_config: Optional[NvidiaRivaAudioTranscriptionConfig] = None, + ) -> TranscriptionResponse: + # ``riva-client`` exposes a sync streaming generator, so we offload + # the blocking call to a worker thread to keep the event loop free. + return await asyncio.to_thread( + self._run_sync, + model=model, + audio_file=audio_file, + optional_params=optional_params, + litellm_params=litellm_params, + model_response=model_response, + timeout=timeout, + logging_obj=logging_obj, + api_key=api_key, + api_base=api_base, + provider_config=provider_config or NvidiaRivaAudioTranscriptionConfig(), + atranscription=True, + ) + + def _run_sync( + self, + model: str, + audio_file: FileTypes, + optional_params: dict, + litellm_params: dict, + model_response: TranscriptionResponse, + timeout: float, + logging_obj: "LiteLLMLoggingObj", + api_key: Optional[str], + api_base: Optional[str], + provider_config: NvidiaRivaAudioTranscriptionConfig, + atranscription: bool = False, + ) -> TranscriptionResponse: + if not api_base: + raise NvidiaRivaException( + status_code=400, + message=( + "NVIDIA Riva requires `api_base` (host:port for the gRPC " + "endpoint, e.g. `grpc.nvcf.nvidia.com:443` or " + "`localhost:50051`). Set it in litellm_params or via " + "NVIDIA_RIVA_API_BASE." + ), + ) + + processed = process_audio_file(audio_file) + resampled = resample_to_riva_pcm(processed.file_content) + + request_payload = provider_config.transform_audio_transcription_request( + model=model, + audio_file=audio_file, + optional_params=optional_params, + litellm_params={ + **litellm_params, + "api_base": api_base, + "api_key": api_key, + }, + ).data + if not isinstance(request_payload, dict): + raise NvidiaRivaException( + status_code=500, + message="NvidiaRivaAudioTranscriptionConfig produced an unexpected request payload type.", + ) + + recognition_config_dict: Dict[str, Any] = request_payload["recognition_config"] + # The wire format is fixed by our resampler; override anything stale + # the caller passed in so the gRPC config matches the bytes we send. + recognition_config_dict["sample_rate_hertz"] = RIVA_TARGET_SAMPLE_RATE_HZ + recognition_config_dict["audio_channel_count"] = RIVA_TARGET_NUM_CHANNELS + recognition_config_dict["encoding"] = "LINEAR_PCM" + + response_format = request_payload.get("response_format") or "json" + timestamp_granularities = request_payload.get("timestamp_granularities") + + riva_module, riva_asr_module = _import_riva() + auth_obj = self._construct_auth( + riva_module=riva_module, + api_base=api_base, + api_key=api_key, + optional_params=optional_params, + ) + + recognition_config = self._build_recognition_config_proto( + riva_asr_module=riva_asr_module, + recognition_config_dict=recognition_config_dict, + ) + streaming_config = riva_asr_module.StreamingRecognitionConfig( + config=recognition_config, interim_results=False + ) + + logging_obj.pre_call( + input=None, + api_key=api_key, + additional_args={ + "api_base": api_base, + "atranscription": atranscription, + "complete_input_dict": { + "recognition_config": recognition_config_dict, + "nvcf_function_id_set": bool( + optional_params.get("nvcf_function_id") + ), + "use_ssl": optional_params.get("use_ssl"), + }, + }, + ) + + try: + asr_service = riva_module.ASRService(auth_obj) + audio_chunks = self._iter_audio_chunks(resampled.pcm_bytes) + stream_kwargs: Dict[str, Any] = { + "audio_chunks": audio_chunks, + "streaming_config": streaming_config, + } + # Forward the deadline so the stream cannot block forever if the + # server stalls. Older riva-client versions do not accept a + # ``timeout`` kwarg, so pass it only when supported. + if timeout is not None and self._supports_timeout_kwarg( + asr_service.streaming_response_generator + ): + stream_kwargs["timeout"] = float(timeout) + stream = asr_service.streaming_response_generator(**stream_kwargs) + final_results = self._collect_final_results(stream) + except NvidiaRivaException: + raise + except Exception as e: + raise grpc_error_to_litellm_exception(e) from e + + transcription = NvidiaRivaAudioTranscriptionConfig.build_transcription_response( + final_results=final_results, + response_format=response_format, + duration_seconds=resampled.duration_seconds, + timestamp_granularities=timestamp_granularities, + ) + + stringified_response = dict(transcription) + + logging_obj.post_call( + input=get_audio_file_name(audio_file), + api_key=api_key, + additional_args={"complete_input_dict": recognition_config_dict}, + original_response=stringified_response, + ) + + hidden_params = { + "model": model, + "custom_llm_provider": "nvidia_riva", + "audio_transcription_duration": resampled.duration_seconds, + } + + final_response: TranscriptionResponse = convert_to_model_response_object( # type: ignore + response_object=stringified_response, + model_response_object=model_response, + hidden_params=hidden_params, + response_type="audio_transcription", + ) + + return final_response + + def _construct_auth( + self, + riva_module: Any, + api_base: str, + api_key: Optional[str], + optional_params: dict, + ) -> Any: + """ + Build a ``riva.client.Auth`` object. + + - When ``nvcf_function_id`` is provided we attach the NVCF + ``function-id`` and bearer ``authorization`` metadata, and default + ``use_ssl`` to True (NVCF endpoints are TLS-only). + - Otherwise (self-hosted) we default ``use_ssl`` to False but still + honor an explicit override — self-hosted Riva behind an ingress + with TLS termination is a real deployment topology. + """ + nvcf_function_id = optional_params.get("nvcf_function_id") + use_ssl_override = optional_params.get("use_ssl") + use_ssl = ( + bool(use_ssl_override) + if use_ssl_override is not None + else bool(nvcf_function_id) + ) + + metadata: List[Tuple[str, str]] = [] + if nvcf_function_id: + metadata.append(("function-id", str(nvcf_function_id))) + if api_key: + metadata.append(("authorization", f"Bearer {api_key}")) + + try: + return riva_module.Auth( + uri=api_base, use_ssl=use_ssl, metadata_args=metadata + ) + except TypeError: + # Older riva-client signatures used positional-only args. + return riva_module.Auth(None, use_ssl, api_base, metadata) + + def _build_recognition_config_proto( + self, riva_asr_module: Any, recognition_config_dict: Dict[str, Any] + ): + encoding_name = ( + recognition_config_dict.get("encoding") or "LINEAR_PCM" + ).upper() + encoding_enum = getattr( + riva_asr_module.AudioEncoding, + encoding_name, + riva_asr_module.AudioEncoding.LINEAR_PCM, + ) + + config = riva_asr_module.RecognitionConfig( + encoding=encoding_enum, + sample_rate_hertz=int(recognition_config_dict["sample_rate_hertz"]), + language_code=recognition_config_dict["language_code"], + audio_channel_count=int(recognition_config_dict["audio_channel_count"]), + enable_automatic_punctuation=bool( + recognition_config_dict.get("enable_automatic_punctuation", True) + ), + enable_word_time_offsets=bool( + recognition_config_dict.get("enable_word_time_offsets", False) + ), + max_alternatives=int(recognition_config_dict.get("max_alternatives", 1)), + model=recognition_config_dict.get("model", "") or "", + verbatim_transcripts=bool( + recognition_config_dict.get("verbatim_transcripts", False) + ), + profanity_filter=bool( + recognition_config_dict.get("profanity_filter", False) + ), + ) + + endpointing = recognition_config_dict.get("endpointing_config") + if isinstance(endpointing, dict) and endpointing: + try: + ep = riva_asr_module.EndpointingConfig(**endpointing) + config.endpointing_config.CopyFrom(ep) + except Exception: + # If the user supplied an unknown EndpointingConfig field + # (older Riva server), fall back to Riva's defaults rather + # than failing the whole request. + pass + + return config + + @staticmethod + def _supports_timeout_kwarg(callable_obj: Any) -> bool: + try: + sig = inspect.signature(callable_obj) + except (TypeError, ValueError): + return False + params = sig.parameters + if "timeout" in params: + return True + return any(p.kind == inspect.Parameter.VAR_KEYWORD for p in params.values()) + + @staticmethod + def _iter_audio_chunks(pcm_bytes: bytes): + for offset in range(0, len(pcm_bytes), _DEFAULT_CHUNK_BYTES): + chunk = pcm_bytes[offset : offset + _DEFAULT_CHUNK_BYTES] + if not chunk: + continue + yield chunk + + @staticmethod + def _collect_final_results(stream) -> List[Dict[str, Any]]: + """ + Walk the gRPC stream, ignore empty / non-final chunks, and return a + list of normalized final-result dicts. Matching the user's note: the + ``id`` blocks with no ``results`` are streaming heartbeats and must + be skipped. + """ + final_results: List[Dict[str, Any]] = [] + for response in stream: + results = getattr(response, "results", None) or [] + for result in results: + if not getattr(result, "is_final", False): + continue + alternatives = getattr(result, "alternatives", None) or [] + if not alternatives: + continue + top = alternatives[0] + transcript = getattr(top, "transcript", "") or "" + words_proto = getattr(top, "words", None) or [] + words = [] + for word in words_proto: + words.append( + { + "word": getattr(word, "word", ""), + "start_time_ms": int(getattr(word, "start_time", 0) or 0), + "end_time_ms": int(getattr(word, "end_time", 0) or 0), + } + ) + final_results.append({"transcript": transcript, "words": words}) + return final_results + + +def _import_riva(): + """ + Lazy import of ``riva.client`` and ``riva.client.proto.riva_asr_pb2``. + + We try the SDK first (preferred) and fall back to importing the proto + module separately when the SDK packaging changes between versions. + """ + try: + import riva.client as riva_client # type: ignore + except ImportError as e: + raise NvidiaRivaException(status_code=500, message=_RIVA_INSTALL_HINT) from e + + riva_asr_module = riva_client + if not hasattr(riva_asr_module, "RecognitionConfig"): + try: + import riva.client.proto.riva_asr_pb2 as riva_asr_pb2 # type: ignore + + riva_asr_module = riva_asr_pb2 + except ImportError as e: + raise NvidiaRivaException( + status_code=500, message=_RIVA_INSTALL_HINT + ) from e + + return riva_client, riva_asr_module diff --git a/litellm/llms/nvidia_riva/audio_transcription/transformation.py b/litellm/llms/nvidia_riva/audio_transcription/transformation.py new file mode 100644 index 00000000000..c2dfc25d945 --- /dev/null +++ b/litellm/llms/nvidia_riva/audio_transcription/transformation.py @@ -0,0 +1,284 @@ +""" +Translates from OpenAI's `/v1/audio/transcriptions` to NVIDIA Riva's gRPC +streaming recognize API. + +Riva is gRPC-only, so unlike most providers in this directory the request +"transformation" produced here is a structured dict consumed directly by the +gRPC handler (rather than HTTP form-data). The handler builds Riva +``RecognitionConfig`` / ``StreamingRecognitionConfig`` protobufs from this +dict at call time. + +Reference: https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-overview.html +""" + +from typing import Any, Dict, List, Optional, Union + +from httpx import Headers, Response + +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIAudioTranscriptionOptionalParams, +) +from litellm.types.utils import FileTypes, TranscriptionResponse + +from ...base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, + BaseAudioTranscriptionConfig, +) +from ..common_utils import NvidiaRivaException + +# Riva expects a fixed wire format for the audio chunks we stream in. +RIVA_TARGET_SAMPLE_RATE_HZ = 16000 +RIVA_TARGET_NUM_CHANNELS = 1 +RIVA_TARGET_ENCODING = "LINEAR_PCM" + + +class NvidiaRivaAudioTranscriptionConfig(BaseAudioTranscriptionConfig): + """ + Config for NVIDIA Riva ASR (gRPC). + + Supports both NVCF-hosted (``api_base=grpc.nvcf.nvidia.com:443`` + + ``nvcf_function_id``) and self-hosted deployments (any ``host:port``, + optional TLS via ``use_ssl``). + """ + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIAudioTranscriptionOptionalParams]: + # Riva natively understands language + word timestamps. + # `response_format` is honored at response-shaping time in the handler. + return ["language", "response_format", "timestamp_granularities"] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + for key, value in non_default_params.items(): + if value is None: + continue + + if key == "language": + optional_params["language_code"] = self._normalize_language_code(value) + elif key == "timestamp_granularities": + # OpenAI accepts ["word"], ["segment"], or both. Riva only + # natively exposes word timing, so we toggle it on whenever + # "word" is requested. Segment timing is reconstructed in the + # response transformer. + if isinstance(value, list) and "word" in value: + optional_params["enable_word_time_offsets"] = True + optional_params["timestamp_granularities"] = value + elif key == "response_format": + # Stored verbatim; consumed by transform_audio_transcription_response. + optional_params["response_format"] = value + else: + optional_params[key] = value + + return optional_params + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, Headers] + ) -> BaseLLMException: + return NvidiaRivaException( + message=error_message, status_code=status_code, headers=headers + ) + + def transform_audio_transcription_request( + self, + model: str, + audio_file: FileTypes, + optional_params: dict, + litellm_params: dict, + ) -> AudioTranscriptionRequestData: + """ + Build a structured dict that the gRPC handler consumes. We do *not* + construct protobufs here, so this module remains importable without + ``nvidia-riva-client`` being installed (matching how other providers + defer SDK imports to handler-call time). + """ + recognition_config = self._build_recognition_config_dict( + model=model, + optional_params=optional_params, + ) + + endpointing_config = self._build_endpointing_config_dict(optional_params) + if endpointing_config is not None: + recognition_config["endpointing_config"] = endpointing_config + + request_payload: Dict[str, Any] = { + "recognition_config": recognition_config, + "response_format": optional_params.get("response_format") or "json", + "timestamp_granularities": optional_params.get("timestamp_granularities"), + } + + return AudioTranscriptionRequestData(data=request_payload, files=None) + + def transform_audio_transcription_response( + self, + raw_response: Response, + ) -> TranscriptionResponse: + # Not used: Riva responses come from a gRPC stream, not an httpx + # response. The handler calls _build_transcription_response directly. + raise NotImplementedError( + "NvidiaRivaAudioTranscriptionConfig.transform_audio_transcription_response " + "is not used. The handler builds the TranscriptionResponse directly " + "from Riva's gRPC streaming results." + ) + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + # gRPC auth is constructed in the handler, not via HTTP headers. + return headers + + def _build_recognition_config_dict( + self, model: str, optional_params: dict + ) -> Dict[str, Any]: + """ + Build the Riva ``RecognitionConfig`` shape as a plain dict. + + ``model`` is intentionally left empty when the user has not supplied + ``riva_model_name``. Riva auto-selects the right deployment from + ``language_code`` + ``sample_rate_hertz``. NVIDIA's internal + deployment names (e.g. ``parakeet-1.1b-en-US-asr-streaming-...``) + change across NIM versions, regions, and self-hosted builds, so + hardcoding any name here would break unpredictably. + """ + return { + "language_code": optional_params.get("language_code", "en-US"), + "sample_rate_hertz": optional_params.get( + "sample_rate_hertz", RIVA_TARGET_SAMPLE_RATE_HZ + ), + "encoding": optional_params.get("encoding", RIVA_TARGET_ENCODING), + "audio_channel_count": optional_params.get( + "audio_channel_count", RIVA_TARGET_NUM_CHANNELS + ), + "enable_automatic_punctuation": optional_params.get( + "enable_automatic_punctuation", True + ), + "enable_word_time_offsets": bool( + optional_params.get("enable_word_time_offsets", False) + ), + "max_alternatives": optional_params.get("max_alternatives", 1), + "model": optional_params.get("riva_model_name", ""), + "verbatim_transcripts": optional_params.get("verbatim_transcripts", False), + "profanity_filter": optional_params.get("profanity_filter", False), + } + + def _build_endpointing_config_dict( + self, optional_params: dict + ) -> Optional[Dict[str, Any]]: + """ + Translate an OpenAI-style ``chunking_strategy`` into Riva's + ``EndpointingConfig`` shape, or pass through an explicit + ``endpointing_config`` dict. + + Returns ``None`` when neither is provided so Riva uses its built-in + VAD defaults. + """ + explicit = optional_params.get("endpointing_config") + if isinstance(explicit, dict): + return dict(explicit) + + chunking = optional_params.get("chunking_strategy") + if chunking in (None, "auto"): + return None + + if isinstance(chunking, dict) and chunking.get("type") == "server_vad": + config: Dict[str, Any] = {} + if "threshold" in chunking: + threshold = float(chunking["threshold"]) + config["start_threshold"] = threshold + config["stop_threshold"] = threshold + if "silence_duration_ms" in chunking: + config["stop_history"] = int(chunking["silence_duration_ms"]) + if "prefix_padding_ms" in chunking: + config["stop_history_eou"] = int(chunking["prefix_padding_ms"]) + return config or None + + return None + + @staticmethod + def _normalize_language_code(language: str) -> str: + """ + OpenAI accepts bare ISO-639 codes like ``en``; Riva requires BCP-47 + like ``en-US``. Normalize the most common bare codes; pass through + anything that already looks like BCP-47. + """ + if not isinstance(language, str) or not language: + return "en-US" + if "-" in language: + return language + bare_to_bcp47 = { + "en": "en-US", + "es": "es-ES", + "de": "de-DE", + "fr": "fr-FR", + "it": "it-IT", + "pt": "pt-BR", + "ja": "ja-JP", + "ko": "ko-KR", + "zh": "zh-CN", + "ru": "ru-RU", + "hi": "hi-IN", + "ar": "ar-SA", + } + return bare_to_bcp47.get(language.lower(), language) + + @staticmethod + def build_transcription_response( + final_results: List[Dict[str, Any]], + response_format: str, + duration_seconds: Optional[float], + timestamp_granularities: Optional[List[str]], + ) -> TranscriptionResponse: + """ + Aggregate a list of normalized "final result" dicts into a + ``TranscriptionResponse`` shaped for the requested ``response_format``. + + Each entry in ``final_results`` is expected to look like:: + + { + "transcript": str, + "words": [{"word": str, "start_time_ms": int, "end_time_ms": int}, ...], + } + + which the handler produces by walking the gRPC stream and keeping + only ``result.is_final`` entries (empty/non-final chunks are + ignored). + """ + full_transcript = "".join( + (item.get("transcript") or "") for item in final_results + ).strip() + + response = TranscriptionResponse(text=full_transcript) + response["task"] = "transcribe" + + if response_format == "verbose_json": + words: List[Dict[str, Any]] = [] + if timestamp_granularities and "word" in timestamp_granularities: + for item in final_results: + for word in item.get("words", []) or []: + words.append( + { + "word": word.get("word", ""), + "start": (float(word.get("start_time_ms", 0)) / 1000.0), + "end": float(word.get("end_time_ms", 0)) / 1000.0, + } + ) + if words: + response["words"] = words + if duration_seconds is not None: + response["duration"] = duration_seconds + + return response diff --git a/litellm/llms/nvidia_riva/common_utils.py b/litellm/llms/nvidia_riva/common_utils.py new file mode 100644 index 00000000000..a3071cf7060 --- /dev/null +++ b/litellm/llms/nvidia_riva/common_utils.py @@ -0,0 +1,92 @@ +""" +Common utilities and exceptions for the NVIDIA Riva STT provider +""" + +from typing import Any, Optional + +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class NvidiaRivaException(BaseLLMException): + """ + Exception raised for NVIDIA Riva (gRPC) errors. + + ``status_code`` is an HTTP-equivalent code derived from the underlying + gRPC ``StatusCode`` (when available) so that litellm's existing error + classifiers (RateLimitError, AuthenticationError, etc.) keep working. + """ + + pass + + +# Mapping from grpc.StatusCode.name -> equivalent HTTP status code. +# Kept as a plain dict (rather than importing grpc enums) so this module is +# importable without grpc installed. +_GRPC_STATUS_CODE_TO_HTTP: dict = { + "OK": 200, + "CANCELLED": 499, + "UNKNOWN": 500, + "INVALID_ARGUMENT": 400, + "DEADLINE_EXCEEDED": 504, + "NOT_FOUND": 404, + "ALREADY_EXISTS": 409, + "PERMISSION_DENIED": 403, + "RESOURCE_EXHAUSTED": 429, + "FAILED_PRECONDITION": 400, + "ABORTED": 409, + "OUT_OF_RANGE": 400, + "UNIMPLEMENTED": 501, + "INTERNAL": 500, + "UNAVAILABLE": 503, + "DATA_LOSS": 500, + "UNAUTHENTICATED": 401, +} + + +def _extract_grpc_status_name(error: Any) -> Optional[str]: + """ + Best-effort extraction of a gRPC StatusCode name from an arbitrary error. + + Works for ``grpc.RpcError`` instances (which expose ``.code()``) as well + as plain exceptions whose string representation contains a status name. + """ + code_fn = getattr(error, "code", None) + if callable(code_fn): + try: + code = code_fn() + except Exception: + code = None + name = getattr(code, "name", None) + if isinstance(name, str): + return name + return None + + +def _extract_grpc_details(error: Any) -> Optional[str]: + """Best-effort extraction of a human-readable detail string from a gRPC error.""" + details_fn = getattr(error, "details", None) + if callable(details_fn): + try: + details = details_fn() + except Exception: + details = None + if isinstance(details, str) and details: + return details + return None + + +def grpc_error_to_litellm_exception(error: Exception) -> NvidiaRivaException: + """ + Convert a gRPC error (or any exception raised from the Riva client) into + a ``NvidiaRivaException`` with an appropriate HTTP-equivalent status code. + """ + status_name = _extract_grpc_status_name(error) + http_status = _GRPC_STATUS_CODE_TO_HTTP.get(status_name or "", 500) + + detail = _extract_grpc_details(error) or str(error) + message = ( + f"NVIDIA Riva gRPC error ({status_name}): {detail}" + if status_name + else f"NVIDIA Riva error: {detail}" + ) + return NvidiaRivaException(status_code=http_status, message=message) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index 48534799c97..e36150a4954 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -507,10 +507,10 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator): # PROCESS REASONING CONTENT reasoning_content: Optional[str] = None content: Optional[str] = None - if chunk["message"].get("thinking") is not None: + if chunk["message"].get("thinking"): reasoning_content = chunk["message"].get("thinking") self.started_reasoning_content = True - elif chunk["message"].get("content") is not None: + if chunk["message"].get("content"): if ( self.started_reasoning_content and not self.finished_reasoning_content diff --git a/litellm/llms/ollama/common_utils.py b/litellm/llms/ollama/common_utils.py index 8aedd9b3500..8ca8b7d383a 100644 --- a/litellm/llms/ollama/common_utils.py +++ b/litellm/llms/ollama/common_utils.py @@ -108,7 +108,7 @@ class OllamaModelInfo(BaseLLMModelInfo): continue nm = entry.get("name") or entry.get("model") if isinstance(nm, str): - names.add(nm) + names.add(nm if nm.startswith("ollama/") else f"ollama/{nm}") except Exception as e: verbose_logger.warning(f"Error retrieving ollama tag endpoint: {e}") # If tags endpoint fails, fall back to static list diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index 34941a545eb..9ccb2e1c267 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -3,7 +3,10 @@ from typing import Optional, Union import litellm -from litellm.utils import _is_explicitly_disabled_factory, _supports_factory +from litellm.utils import ( + _is_explicitly_disabled_factory, + _supports_factory, +) from .gpt_transformation import OpenAIGPTConfig @@ -244,9 +247,11 @@ class OpenAIGPT5Config(OpenAIGPTConfig): ), status_code=400, ) - elif effective_effort == "minimal": - # minimal is opt-out: unknown models pass through; only block when - # the model map explicitly sets supports_minimal_reasoning_effort=false. + elif effective_effort in ("minimal", "low"): + # minimal/low are opt-out: unknown models pass through; only block when + # the model map explicitly sets supports_{level}_reasoning_effort=false. + # Example: gpt-5.5-pro only accepts {medium, high, xhigh}, so it sets + # supports_low_reasoning_effort=false (and supports_minimal=false). if self._is_reasoning_effort_level_explicitly_disabled( model, effective_effort ): diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index 86ca6625629..d413a244539 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -21,7 +21,9 @@ from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.llms.base_llm.guardrail_translation.utils import ( effective_skip_system_message_for_guardrail, + effective_skip_tool_message_for_guardrail, openai_messages_without_system, + openai_messages_without_tool, ) from litellm.main import stream_chunk_builder from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam @@ -73,6 +75,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): return data skip_system = effective_skip_system_message_for_guardrail(guardrail_to_apply) + skip_tool = effective_skip_tool_message_for_guardrail(guardrail_to_apply) texts_to_check: List[str] = [] images_to_check: List[str] = [] @@ -91,6 +94,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): text_task_mappings=text_task_mappings, tool_call_task_mappings=tool_call_task_mappings, skip_system_message=skip_system, + skip_tool_message=skip_tool, ) # Step 2: Apply guardrail to all texts and tool calls in batch @@ -102,11 +106,15 @@ class OpenAIChatCompletionsHandler(BaseTranslation): inputs["tool_calls"] = tool_calls_to_check # type: ignore structured_messages = self.get_structured_messages(data) if structured_messages: - inputs["structured_messages"] = ( - openai_messages_without_system(structured_messages) - if skip_system - else structured_messages - ) + if skip_system: + structured_messages = openai_messages_without_system( + structured_messages + ) + if skip_tool: + structured_messages = openai_messages_without_tool( + structured_messages + ) + inputs["structured_messages"] = structured_messages # Pass tools (function definitions) to the guardrail tools = data.get("tools") if tools: @@ -176,13 +184,17 @@ class OpenAIChatCompletionsHandler(BaseTranslation): text_task_mappings: List[Tuple[int, Optional[int]]], tool_call_task_mappings: List[Tuple[int, int]], skip_system_message: bool = False, + skip_tool_message: bool = False, ) -> None: """ Extract text content, images, and tool calls from a message. Override this method to customize text/image/tool call extraction logic. """ - if skip_system_message and str(message.get("role") or "").lower() == "system": + role = str(message.get("role") or "").lower() + if skip_system_message and role == "system": + return + if skip_tool_message and role == "tool": return content = message.get("content", None) diff --git a/litellm/llms/openai/containers/transformation.py b/litellm/llms/openai/containers/transformation.py index 955b9f760d1..7f874ffd3b1 100644 --- a/litellm/llms/openai/containers/transformation.py +++ b/litellm/llms/openai/containers/transformation.py @@ -6,6 +6,7 @@ import litellm from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( StandardBuiltInToolCostTracking, ) +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.secret_managers.main import get_secret_str from litellm.types.containers.main import ( ContainerCreateOptionalRequestParams, @@ -198,7 +199,10 @@ class OpenAIContainerConfig(BaseContainerConfig): ) -> Tuple[str, Dict]: """Transform the OpenAI container retrieve request.""" # For container retrieve, we just need to construct the URL - url = join_container_api_base_path(api_base, f"/{container_id}") + encoded_container_id = encode_url_path_segment( + container_id, field_name="container_id" + ) + url = join_container_api_base_path(api_base, f"/{encoded_container_id}") # No additional data needed for GET request data: Dict[str, Any] = {} @@ -230,7 +234,10 @@ class OpenAIContainerConfig(BaseContainerConfig): - DELETE /v1/containers/{container_id} """ # Construct the URL for container delete - url = join_container_api_base_path(api_base, f"/{container_id}") + encoded_container_id = encode_url_path_segment( + container_id, field_name="container_id" + ) + url = join_container_api_base_path(api_base, f"/{encoded_container_id}") # No data needed for DELETE request data: Dict[str, Any] = {} @@ -267,7 +274,10 @@ class OpenAIContainerConfig(BaseContainerConfig): - GET /v1/containers/{container_id}/files """ # Construct the URL for container files - url = join_container_api_base_path(api_base, f"/{container_id}/files") + encoded_container_id = encode_url_path_segment( + container_id, field_name="container_id" + ) + url = join_container_api_base_path(api_base, f"/{encoded_container_id}/files") # Prepare query parameters params: Dict[str, Any] = {} @@ -311,8 +321,12 @@ class OpenAIContainerConfig(BaseContainerConfig): - GET /v1/containers/{container_id}/files/{file_id}/content """ # Construct the URL for container file content + encoded_container_id = encode_url_path_segment( + container_id, field_name="container_id" + ) + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") url = join_container_api_base_path( - api_base, f"/{container_id}/files/{file_id}/content" + api_base, f"/{encoded_container_id}/files/{encoded_file_id}/content" ) # No query parameters needed diff --git a/litellm/llms/openai/evals/transformation.py b/litellm/llms/openai/evals/transformation.py index c24dbf8637a..66537e56a6f 100644 --- a/litellm/llms/openai/evals/transformation.py +++ b/litellm/llms/openai/evals/transformation.py @@ -7,6 +7,7 @@ from typing import Any, Dict, Optional, Tuple import httpx from litellm._logging import verbose_logger +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.evals.transformation import ( BaseEvalsAPIConfig, LiteLLMLoggingObj, @@ -76,7 +77,8 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig): api_base = "https://api.openai.com" if eval_id: - return f"{api_base}/v1/evals/{eval_id}" + encoded_eval_id = encode_url_path_segment(eval_id, field_name="eval_id") + return f"{api_base}/v1/evals/{encoded_eval_id}" return f"{api_base}/v1/{endpoint}" def transform_create_eval_request( @@ -276,7 +278,8 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig): if litellm_params and litellm_params.api_base: api_base = litellm_params.api_base - url = f"{api_base}/v1/evals/{eval_id}/runs" + encoded_eval_id = encode_url_path_segment(eval_id, field_name="eval_id") + url = f"{api_base}/v1/evals/{encoded_eval_id}/runs" # Build request body request_body = {k: v for k, v in create_request.items() if v is not None} @@ -310,7 +313,8 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig): if litellm_params and litellm_params.api_base: api_base = litellm_params.api_base - url = f"{api_base}/v1/evals/{eval_id}/runs" + encoded_eval_id = encode_url_path_segment(eval_id, field_name="eval_id") + url = f"{api_base}/v1/evals/{encoded_eval_id}/runs" # Build query parameters query_params: Dict[str, Any] = {} @@ -350,7 +354,9 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig): headers: dict, ) -> Tuple[str, Dict]: """Transform get run request for OpenAI""" - url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}" + encoded_eval_id = encode_url_path_segment(eval_id, field_name="eval_id") + encoded_run_id = encode_url_path_segment(run_id, field_name="run_id") + url = f"{api_base}/v1/evals/{encoded_eval_id}/runs/{encoded_run_id}" verbose_logger.debug("Get run request - URL: %s", url) @@ -376,7 +382,9 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig): headers: dict, ) -> Tuple[str, Dict, Dict]: """Transform cancel run request for OpenAI""" - url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}/cancel" + encoded_eval_id = encode_url_path_segment(eval_id, field_name="eval_id") + encoded_run_id = encode_url_path_segment(run_id, field_name="run_id") + url = f"{api_base}/v1/evals/{encoded_eval_id}/runs/{encoded_run_id}/cancel" # Empty body for cancel request request_body: Dict[str, Any] = {} @@ -405,7 +413,9 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig): headers: dict, ) -> Tuple[str, Dict, Dict]: """Transform delete run request for OpenAI""" - url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}" + encoded_eval_id = encode_url_path_segment(eval_id, field_name="eval_id") + encoded_run_id = encode_url_path_segment(run_id, field_name="run_id") + url = f"{api_base}/v1/evals/{encoded_eval_id}/runs/{encoded_run_id}" # Empty body for delete request request_body: Dict[str, Any] = {} diff --git a/litellm/llms/openai/image_generation/cost_calculator.py b/litellm/llms/openai/image_generation/cost_calculator.py index 8bca75172fa..d009a085fab 100644 --- a/litellm/llms/openai/image_generation/cost_calculator.py +++ b/litellm/llms/openai/image_generation/cost_calculator.py @@ -1,5 +1,5 @@ """ -Cost calculator for OpenAI image generation models (gpt-image-1, gpt-image-1-mini) +Cost calculator for OpenAI image generation models (gpt-image family) These models use token-based pricing instead of pixel-based pricing like DALL-E. """ @@ -17,13 +17,13 @@ def cost_calculator( custom_llm_provider: Optional[str] = None, ) -> float: """ - Calculate cost for OpenAI gpt-image-1 and gpt-image-1-mini models. + Calculate cost for OpenAI gpt-image models. Uses the same usage format as Responses API, so we reuse the helper to transform to chat completion format and use generic_cost_per_token. Args: - model: The model name (e.g., "gpt-image-1", "gpt-image-1-mini") + model: The model name (e.g., "gpt-image-1", "gpt-image-2") image_response: The ImageResponse containing usage data custom_llm_provider: Optional provider name diff --git a/litellm/llms/openai/image_generation/gpt_transformation.py b/litellm/llms/openai/image_generation/gpt_transformation.py index c106d7f17b6..68f799e5747 100644 --- a/litellm/llms/openai/image_generation/gpt_transformation.py +++ b/litellm/llms/openai/image_generation/gpt_transformation.py @@ -15,7 +15,7 @@ if TYPE_CHECKING: class GPTImageGenerationConfig(BaseImageGenerationConfig): """ - OpenAI gpt-image-1 image generation config + OpenAI gpt-image image generation config """ def get_supported_openai_params( diff --git a/litellm/llms/openai/realtime/handler.py b/litellm/llms/openai/realtime/handler.py index 5ca0a3186f7..f34dae2df09 100644 --- a/litellm/llms/openai/realtime/handler.py +++ b/litellm/llms/openai/realtime/handler.py @@ -6,12 +6,15 @@ This requires websockets, and is currently only supported on LiteLLM Proxy. from typing import Any, Optional, cast -from litellm._logging import _redact_string +from litellm._logging import _redact_string, verbose_logger from litellm.constants import REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES from litellm.types.realtime import RealtimeQueryParams from ....litellm_core_utils.litellm_logging import Logging as LiteLLMLogging -from ....litellm_core_utils.realtime_streaming import RealTimeStreaming +from ....litellm_core_utils.realtime_streaming import ( + RealTimeStreaming, + client_sent_openai_beta_realtime_header, +) from ....llms.custom_httpx.http_handler import get_shared_realtime_ssl_context from ..openai import OpenAIChatCompletion @@ -33,21 +36,24 @@ class OpenAIRealtime(OpenAIChatCompletion): """ return "https://api.openai.com/" - def _get_additional_headers(self, api_key: str) -> dict: + def _get_additional_headers( + self, + api_key: str, + *, + openai_beta_realtime: bool = False, + ) -> dict: """ - Get additional headers beyond Authorization. - Override this in subclasses to customize headers (e.g., remove OpenAI-Beta). + Headers for the upstream OpenAI Realtime WebSocket. - Args: - api_key: API key for authentication - - Returns: - Dictionary of additional headers + When the client sent ``OpenAI-Beta: realtime=v1`` on the proxy WebSocket, + ``openai_beta_realtime`` is True and the same header is forwarded upstream + so the legacy beta API is used. GA clients omit that header on the client + connection and must send GA-shaped ``session.update`` payloads. """ - return { - "Authorization": f"Bearer {api_key}", - "OpenAI-Beta": "realtime=v1", - } + headers: dict = {"Authorization": f"Bearer {api_key}"} + if openai_beta_realtime: + headers["OpenAI-Beta"] = "realtime=v1" + return headers def _get_ssl_config(self, url: str) -> Any: """ @@ -120,8 +126,16 @@ class OpenAIRealtime(OpenAIChatCompletion): # Get provider-specific SSL configuration ssl_config = self._get_ssl_config(url) - # Get provider-specific headers - headers = self._get_additional_headers(api_key) + openai_beta_realtime = client_sent_openai_beta_realtime_header(websocket) + if not openai_beta_realtime: + verbose_logger.debug( + "OpenAI Realtime: connecting with GA protocol (no OpenAI-Beta header). " + "If your client expects beta event names, add 'OpenAI-Beta: realtime=v1' " + "to the WebSocket headers sent to the LiteLLM proxy." + ) + headers = self._get_additional_headers( + api_key, openai_beta_realtime=openai_beta_realtime + ) # Log a masked request preview consistent with other endpoints. logging_obj.pre_call( diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 87c502032cc..b7d5340d8d4 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -7,6 +7,7 @@ from pydantic import BaseModel, ValidationError import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.core_helpers import process_response_headers +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( _safe_convert_created_field, ) @@ -421,7 +422,10 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): OpenAI API expects the following request - DELETE /v1/responses/{response_id} """ - url = f"{api_base}/{response_id}" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}" data: Dict = {} return url, data @@ -457,7 +461,10 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): OpenAI API expects the following request - GET /v1/responses/{response_id} """ - url = f"{api_base}/{response_id}" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}" data: Dict = {} return url, data @@ -498,7 +505,10 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): limit: int = 20, order: Literal["asc", "desc"] = "desc", ) -> Tuple[str, Dict]: - url = f"{api_base}/{response_id}/input_items" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}/input_items" params: Dict[str, Any] = {} if after is not None: params["after"] = after @@ -540,7 +550,10 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): OpenAI API expects the following request - POST /v1/responses/{response_id}/cancel """ - url = f"{api_base}/{response_id}/cancel" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}/cancel" data: Dict = {} return url, data diff --git a/litellm/llms/openai/vector_store_files/transformation.py b/litellm/llms/openai/vector_store_files/transformation.py index cd5f10251bb..52202f57fd3 100644 --- a/litellm/llms/openai/vector_store_files/transformation.py +++ b/litellm/llms/openai/vector_store_files/transformation.py @@ -3,6 +3,7 @@ from typing import Any, Dict, Optional, Tuple, cast import httpx import litellm +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.vector_store_files.transformation import ( BaseVectorStoreFilesConfig, ) @@ -98,7 +99,10 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig): or "https://api.openai.com/v1" ) base_url = base_url.rstrip("/") - return f"{base_url}/vector_stores/{vector_store_id}/files" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + return f"{base_url}/vector_stores/{encoded_vector_store_id}/files" def transform_create_vector_store_file_request( self, @@ -163,7 +167,8 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig): file_id: str, api_base: str, ) -> Tuple[str, Dict[str, Any]]: - return f"{api_base}/{file_id}", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base}/{encoded_file_id}", {} def transform_retrieve_vector_store_file_response( self, @@ -186,7 +191,8 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig): file_id: str, api_base: str, ) -> Tuple[str, Dict[str, Any]]: - return f"{api_base}/{file_id}/content", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base}/{encoded_file_id}/content", {} def transform_retrieve_vector_store_file_content_response( self, @@ -218,7 +224,8 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig): payload["attributes"] = filtered_attributes else: payload.pop("attributes", None) - return f"{api_base}/{file_id}", payload + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base}/{encoded_file_id}", payload def transform_update_vector_store_file_response( self, @@ -241,7 +248,8 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig): file_id: str, api_base: str, ) -> Tuple[str, Dict[str, Any]]: - return f"{api_base}/{file_id}", {} + encoded_file_id = encode_url_path_segment(file_id, field_name="file_id") + return f"{api_base}/{encoded_file_id}", {} def transform_delete_vector_store_file_response( self, diff --git a/litellm/llms/openai/vector_stores/transformation.py b/litellm/llms/openai/vector_stores/transformation.py index c763ed1c8da..bd095a0a1b7 100644 --- a/litellm/llms/openai/vector_stores/transformation.py +++ b/litellm/llms/openai/vector_stores/transformation.py @@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast import httpx import litellm +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig from litellm.secret_managers.main import get_secret_str from litellm.types.router import GenericLiteLLMParams @@ -106,8 +107,12 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: - url = f"{api_base}/{vector_store_id}/search" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}/search" typed_request_body = VectorStoreSearchRequest( query=query, filters=vector_store_search_optional_params.get("filters", None), diff --git a/litellm/llms/openai/videos/transformation.py b/litellm/llms/openai/videos/transformation.py index 61baa56949c..2d165a7d7df 100644 --- a/litellm/llms/openai/videos/transformation.py +++ b/litellm/llms/openai/videos/transformation.py @@ -1,11 +1,13 @@ import mimetypes from io import BufferedReader, BytesIO from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast +from urllib.parse import quote import httpx from httpx._types import RequestFiles import litellm +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.videos.transformation import BaseVideoConfig from litellm.llms.openai.image_edit.transformation import ImageEditRequestUtils from litellm.secret_managers.main import get_secret_str @@ -220,11 +222,18 @@ class OpenAIVideoConfig(BaseVideoConfig): - GET /v1/videos/{video_id}/content?variant=thumbnail """ original_video_id = extract_original_video_id(video_id) + encoded_video_id = encode_url_path_segment( + original_video_id, field_name="video_id" + ) # Construct the URL for video content download - url = f"{api_base.rstrip('/')}/{original_video_id}/content" + url = f"{api_base.rstrip('/')}/{encoded_video_id}/content" if variant is not None: - url = f"{url}?variant={variant}" + # Encode the user-controlled ``variant`` so a value like + # ``thumbnail&extra=1`` cannot inject additional query params + # into the upstream request — same hardening rationale as the + # path-segment encoding above. + url = f"{url}?variant={quote(variant, safe='')}" # No additional data needed for GET content request data: Dict[str, Any] = {} @@ -247,9 +256,12 @@ class OpenAIVideoConfig(BaseVideoConfig): - POST /v1/videos/{video_id}/remix """ original_video_id = extract_original_video_id(video_id) + encoded_video_id = encode_url_path_segment( + original_video_id, field_name="video_id" + ) # Construct the URL for video remix - url = f"{api_base.rstrip('/')}/{original_video_id}/remix" + url = f"{api_base.rstrip('/')}/{encoded_video_id}/remix" # Prepare the request data data = {"prompt": prompt} @@ -391,9 +403,12 @@ class OpenAIVideoConfig(BaseVideoConfig): - DELETE /v1/videos/{video_id} """ original_video_id = extract_original_video_id(video_id) + encoded_video_id = encode_url_path_segment( + original_video_id, field_name="video_id" + ) # Construct the URL for video delete - url = f"{api_base.rstrip('/')}/{original_video_id}" + url = f"{api_base.rstrip('/')}/{encoded_video_id}" # No data needed for DELETE request data: Dict[str, Any] = {} @@ -427,9 +442,12 @@ class OpenAIVideoConfig(BaseVideoConfig): """ # Extract the original video_id (remove provider encoding if present) original_video_id = extract_original_video_id(video_id) + encoded_video_id = encode_url_path_segment( + original_video_id, field_name="video_id" + ) # For video retrieve, we just need to construct the URL - url = f"{api_base.rstrip('/')}/{original_video_id}" + url = f"{api_base.rstrip('/')}/{encoded_video_id}" # No additional data needed for GET request data: Dict[str, Any] = {} @@ -494,7 +512,11 @@ class OpenAIVideoConfig(BaseVideoConfig): litellm_params: GenericLiteLLMParams, headers: dict, ) -> Tuple[str, Dict]: - url = f"{api_base.rstrip('/')}/characters/{character_id}" + original_character_id = extract_original_character_id(character_id) + encoded_character_id = encode_url_path_segment( + original_character_id, field_name="character_id" + ) + url = f"{api_base.rstrip('/')}/characters/{encoded_character_id}" return url, {} def transform_video_get_character_response( diff --git a/litellm/llms/openai_like/providers.json b/litellm/llms/openai_like/providers.json index 275c352b39e..b5e5aa4ea28 100644 --- a/litellm/llms/openai_like/providers.json +++ b/litellm/llms/openai_like/providers.json @@ -101,5 +101,18 @@ "param_mappings": { "max_completion_tokens": "max_tokens" } + }, + "aihubmix": { + "base_url": "https://aihubmix.com/v1", + "api_key_env": "AIHUBMIX_API_KEY", + "api_base_env": "AIHUBMIX_API_BASE" + }, + "crusoe": { + "base_url": "https://managed-inference-api-proxy.crusoecloud.com/v1", + "api_key_env": "CRUSOE_API_KEY", + "api_base_env": "CRUSOE_API_BASE", + "param_mappings": { + "max_completion_tokens": "max_tokens" + } } } diff --git a/litellm/llms/ovhcloud/audio_transcription/transformation.py b/litellm/llms/ovhcloud/audio_transcription/transformation.py index 7ff6dc986be..f49f31d7ecd 100644 --- a/litellm/llms/ovhcloud/audio_transcription/transformation.py +++ b/litellm/llms/ovhcloud/audio_transcription/transformation.py @@ -156,5 +156,17 @@ class OVHCloudAudioTranscriptionConfig(BaseAudioTranscriptionConfig): text = response_json.get("text") or response_json.get("transcript") or "" response = TranscriptionResponse(text=text) + # OVHCloud field migration (deadline: 2026-05-11): + # `duration` is replaced by `seconds` in STT responses. + # Prefer `seconds`, fall back to `duration`, normalize to `duration` + # so downstream consumers see a consistent key. + duration = ( + response_json["seconds"] + if "seconds" in response_json and response_json["seconds"] is not None + else response_json.get("duration") + ) + if duration is not None: + response_json["duration"] = duration + response._hidden_params = response_json return response diff --git a/litellm/llms/ovhcloud/chat/transformation.py b/litellm/llms/ovhcloud/chat/transformation.py index ae9271ddb16..62f51f1e9da 100644 --- a/litellm/llms/ovhcloud/chat/transformation.py +++ b/litellm/llms/ovhcloud/chat/transformation.py @@ -13,6 +13,7 @@ from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm.llms.ovhcloud.utils import OVHCloudException from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator from litellm.llms.base_llm.chat.transformation import BaseLLMException + from litellm.types.llms.openai import AllMessageValues @@ -98,10 +99,16 @@ class OVHCloudChatCompletionStreamingHandler(BaseModelResponseIterator): new_choices = [] for choice in chunk["choices"]: - if "delta" in choice and "reasoning" in choice["delta"]: - choice["delta"]["reasoning_content"] = choice["delta"].get( - "reasoning" - ) + if "delta" in choice: + delta = choice["delta"] + # OVHCloud field migration (deadline: 2026-05-11): + # `reasoning_content` is replaced by `reasoning`. + # Normalise to `reasoning_content` so downstream consumers + # see a consistent key during the transition window. + reasoning_new = delta.get("reasoning") + reasoning_legacy = delta.get("reasoning_content") + if reasoning_new is not None and reasoning_legacy is None: + delta["reasoning_content"] = reasoning_new new_choices.append(choice) return ModelResponseStream( diff --git a/litellm/llms/pg_vector/vector_stores/transformation.py b/litellm/llms/pg_vector/vector_stores/transformation.py index ba87a8f2b01..fc4cfc7b083 100644 --- a/litellm/llms/pg_vector/vector_stores/transformation.py +++ b/litellm/llms/pg_vector/vector_stores/transformation.py @@ -1,5 +1,6 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.openai.vector_stores.transformation import OpenAIVectorStoreConfig from litellm.secret_managers.main import get_secret_str from litellm.types.router import GenericLiteLLMParams @@ -80,8 +81,12 @@ class PGVectorStoreConfig(OpenAIVectorStoreConfig): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: - url = f"{api_base}/{vector_store_id}/search" + encoded_vector_store_id = encode_url_path_segment( + vector_store_id, field_name="vector_store_id" + ) + url = f"{api_base}/{encoded_vector_store_id}/search" _, request_body = super().transform_search_vector_store_request( vector_store_id=vector_store_id, query=query, @@ -89,5 +94,6 @@ class PGVectorStoreConfig(OpenAIVectorStoreConfig): api_base=api_base, litellm_logging_obj=litellm_logging_obj, litellm_params=litellm_params, + extra_body=extra_body, ) return url, request_body diff --git a/litellm/llms/ragflow/chat/transformation.py b/litellm/llms/ragflow/chat/transformation.py index d49a5fd370f..990fc2b2e61 100644 --- a/litellm/llms/ragflow/chat/transformation.py +++ b/litellm/llms/ragflow/chat/transformation.py @@ -13,6 +13,7 @@ Model name format: from typing import List, Optional, Tuple import litellm +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.openai.openai import OpenAIConfig from litellm.secret_managers.main import get_secret, get_secret_str from litellm.types.llms.openai import AllMessageValues @@ -126,10 +127,11 @@ class RAGFlowConfig(OpenAIConfig): api_base = api_base[:-3] # Remove /v1 # Construct the RAGFlow-specific path + encoded_entity_id = encode_url_path_segment(entity_id, field_name="entity_id") if endpoint_type == "chat": - path = f"/api/v1/chats_openai/{entity_id}/chat/completions" + path = f"/api/v1/chats_openai/{encoded_entity_id}/chat/completions" else: # agent - path = f"/api/v1/agents_openai/{entity_id}/chat/completions" + path = f"/api/v1/agents_openai/{encoded_entity_id}/chat/completions" # Ensure path starts with / if not path.startswith("/"): diff --git a/litellm/llms/ragflow/vector_stores/transformation.py b/litellm/llms/ragflow/vector_stores/transformation.py index ed5397eef0c..3238d3e9c14 100644 --- a/litellm/llms/ragflow/vector_stores/transformation.py +++ b/litellm/llms/ragflow/vector_stores/transformation.py @@ -102,6 +102,7 @@ class RAGFlowVectorStoreConfig(BaseVectorStoreConfig): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: """RAGFlow vector stores are management-only, search is not supported.""" raise NotImplementedError( diff --git a/litellm/llms/runwayml/videos/transformation.py b/litellm/llms/runwayml/videos/transformation.py index 8377dea952e..4f84816a2bc 100644 --- a/litellm/llms/runwayml/videos/transformation.py +++ b/litellm/llms/runwayml/videos/transformation.py @@ -6,6 +6,7 @@ from httpx._types import RequestFiles import litellm from litellm.constants import RUNWAYML_DEFAULT_API_VERSION +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.videos.transformation import BaseVideoConfig from litellm.llms.custom_httpx.http_handler import ( @@ -334,9 +335,12 @@ class RunwayMLVideoConfig(BaseVideoConfig): We'll retrieve the task and extract the video URL. """ original_video_id = extract_original_video_id(video_id) + encoded_video_id = encode_url_path_segment( + original_video_id, field_name="video_id" + ) # Get task status to retrieve video URL - url = f"{api_base}/tasks/{original_video_id}" + url = f"{api_base}/tasks/{encoded_video_id}" params: Dict[str, Any] = {} @@ -495,9 +499,12 @@ class RunwayMLVideoConfig(BaseVideoConfig): RunwayML uses task cancellation. """ original_video_id = extract_original_video_id(video_id) + encoded_video_id = encode_url_path_segment( + original_video_id, field_name="video_id" + ) # Construct the URL for task cancellation - url = f"{api_base}/tasks/{original_video_id}/cancel" + url = f"{api_base}/tasks/{encoded_video_id}/cancel" data: Dict[str, Any] = {} @@ -533,9 +540,12 @@ class RunwayMLVideoConfig(BaseVideoConfig): RunwayML uses GET /v1/tasks/{task_id} to retrieve task status. """ original_video_id = extract_original_video_id(video_id) + encoded_video_id = encode_url_path_segment( + original_video_id, field_name="video_id" + ) # Construct the full URL for task status retrieval - url = f"{api_base}/tasks/{original_video_id}" + url = f"{api_base}/tasks/{encoded_video_id}" # Empty dict for GET request (no body) data: Dict[str, Any] = {} diff --git a/litellm/llms/s3_vectors/vector_stores/transformation.py b/litellm/llms/s3_vectors/vector_stores/transformation.py index 19b59769863..8270e99d456 100644 --- a/litellm/llms/s3_vectors/vector_stores/transformation.py +++ b/litellm/llms/s3_vectors/vector_stores/transformation.py @@ -79,6 +79,7 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: """Sync version - generates embedding synchronously.""" # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name @@ -140,6 +141,7 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict]: """Async version - generates embedding asynchronously.""" # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name diff --git a/litellm/llms/sagemaker/common_utils.py b/litellm/llms/sagemaker/common_utils.py index ad6b24d85a3..50c8ee4220e 100644 --- a/litellm/llms/sagemaker/common_utils.py +++ b/litellm/llms/sagemaker/common_utils.py @@ -9,7 +9,27 @@ from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.types.utils import GenericStreamingChunk as GChunk from litellm.types.utils import StreamingChatCompletionChunk -_response_stream_shape_cache = None + +def _load_sagemaker_response_stream_shape(): + try: + from botocore.loaders import Loader + from botocore.model import ServiceModel + + loader = Loader() + service_dict = loader.load_service_model("sagemaker-runtime", "service-2") + return ServiceModel(service_dict).shape_for( + "InvokeEndpointWithResponseStreamOutput" + ) + except Exception as e: + verbose_logger.warning( + "litellm: could not pre-load sagemaker-runtime response stream shape " + "— SageMaker event-stream decoding will be unavailable. Error: %s", + e, + ) + return None + + +SAGEMAKER_RESPONSE_STREAM_SHAPE = _load_sagemaker_response_stream_shape() class SagemakerError(BaseLLMException): @@ -187,8 +207,18 @@ class AWSEventStreamDecoder: verbose_logger.error(f"Final error parsing accumulated JSON: {e}") def _parse_message_from_event(self, event) -> Optional[str]: + if SAGEMAKER_RESPONSE_STREAM_SHAPE is None: + raise SagemakerError( + status_code=500, + message=( + "SageMaker event-stream shape could not be loaded from botocore. " + "Ensure botocore is correctly installed." + ), + ) response_dict = event.to_response_dict() - parsed_response = self.parser.parse(response_dict, get_response_stream_shape()) + parsed_response = self.parser.parse( + response_dict, SAGEMAKER_RESPONSE_STREAM_SHAPE + ) if response_dict["status_code"] != 200: raise ValueError(f"Bad response code, expected 200: {response_dict}") @@ -204,20 +234,3 @@ class AWSEventStreamDecoder: return None return chunk.decode() # type: ignore[no-any-return] - - -def get_response_stream_shape(): - global _response_stream_shape_cache - if _response_stream_shape_cache is None: - from botocore.loaders import Loader - from botocore.model import ServiceModel - - loader = Loader() - sagemaker_service_dict = loader.load_service_model( - "sagemaker-runtime", "service-2" - ) - sagemaker_service_model = ServiceModel(sagemaker_service_dict) - _response_stream_shape_cache = sagemaker_service_model.shape_for( - "InvokeEndpointWithResponseStreamOutput" - ) - return _response_stream_shape_cache diff --git a/litellm/llms/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py index 028e02eb0ca..c627599da8d 100644 --- a/litellm/llms/vertex_ai/batches/handler.py +++ b/litellm/llms/vertex_ai/batches/handler.py @@ -4,6 +4,11 @@ from typing import Any, Coroutine, Dict, Optional, Union import httpx import litellm +from litellm.litellm_core_utils.url_utils import ( + async_safe_get, + encode_url_path_segment, + safe_get, +) from litellm.llms.custom_httpx.http_handler import ( _get_httpx_client, get_async_httpx_client, @@ -169,7 +174,8 @@ class VertexAIBatchPrediction(VertexLLM): ) # Append batch_id to the URL - default_api_base = f"{default_api_base}/{batch_id}" + encoded_batch_id = encode_url_path_segment(batch_id, field_name="batch_id") + default_api_base = f"{default_api_base}/{encoded_batch_id}" if len(default_api_base.split(":")) > 1: endpoint = default_api_base.split(":")[-1] @@ -224,8 +230,14 @@ class VertexAIBatchPrediction(VertexLLM): }, ) - response = sync_handler.get( - url=api_base, + # ``api_base`` here can come from caller-supplied request kwargs + # (clientside override). Wrap the fetch in ``safe_get`` so DNS + # rebind / private / cloud-metadata targets are rejected; the + # proxy auth gate already blocks malicious clientside ``api_base`` + # at the boundary — this is defense-in-depth for SDK callers. + response = safe_get( + sync_handler, + api_base, headers=headers, ) @@ -270,8 +282,13 @@ class VertexAIBatchPrediction(VertexLLM): }, ) - response = await client.get( - url=api_base, + # Mirror the sync path: ``api_base`` may come from caller-supplied + # request kwargs, so wrap the fetch in ``async_safe_get`` to reject + # DNS-rebind / private / cloud-metadata targets. Defense-in-depth + # behind the proxy auth gate's clientside ``api_base`` check. + response = await async_safe_get( + client, + api_base, headers=headers, ) if response.status_code != 200: @@ -401,7 +418,8 @@ class VertexAIBatchPrediction(VertexLLM): vertex_project=vertex_project or project_id, ) - retrieve_api_base_default = f"{default_api_base}/{batch_id}" + encoded_batch_id = encode_url_path_segment(batch_id, field_name="batch_id") + retrieve_api_base_default = f"{default_api_base}/{encoded_batch_id}" cancel_api_base_default = f"{retrieve_api_base_default}:cancel" _, api_base = self._check_custom_proxy( diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index b4bfde5f541..e6e39651109 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -27,6 +27,53 @@ class VertexAIError(BaseLLMException): super().__init__(message=message, status_code=status_code, headers=headers) +def vertex_request_labels_from_litellm_params( + litellm_params: Optional[dict], +) -> Optional[Dict[str, str]]: + """ + Build Vertex/GCP billing labels from LiteLLM user metadata on ``litellm_params``: + ``metadata`` (``completion(..., metadata=...)``) or ``litellm_metadata``, + using ``requester_metadata`` string key-value pairs (same convention as Gemini). + ``metadata`` is tried first when both are present. + """ + if not litellm_params: + return None + for key in ("metadata", "litellm_metadata"): + if key not in litellm_params: + continue + metadata = litellm_params[key] + if metadata is None or not isinstance(metadata, dict): + continue + if "requester_metadata" not in metadata: + continue + rm = metadata["requester_metadata"] + if not isinstance(rm, dict): + continue + labels = {k: v for k, v in rm.items() if isinstance(v, str)} + if labels: + return labels + return None + + +def pop_vertex_request_labels( + optional_params: Optional[dict], + litellm_params: Optional[dict], +) -> Optional[Dict[str, str]]: + """ + Resolve labels from optional ``labels`` (Gemini-style) and/or + ``litellm_params["metadata"]`` / ``litellm_params["litellm_metadata"]`` + (``requester_metadata``). Pops ``labels`` from optional_params when present. + """ + labels: Optional[Dict[str, str]] = None + if optional_params is not None and "labels" in optional_params: + raw = optional_params.pop("labels") + if isinstance(raw, dict): + labels = {k: v for k, v in raw.items() if isinstance(v, str)} + if not labels: + labels = vertex_request_labels_from_litellm_params(litellm_params) + return labels if labels else None + + class VertexAIModelRoute(str, Enum): """Enum for Vertex AI model routing""" @@ -50,7 +97,7 @@ def get_vertex_ai_model_route( Determine which handler to use for a Vertex AI model based on the model name. Args: - model: The model name (e.g., "llama3-405b", "gemini-pro", "gemma/gemma-3-12b-it", "openai/gpt-oss-120b") + model: The model name (e.g., "llama3-405b", "gemini-pro", "gemma/gemma-3-12b-it", "xai/grok-4.1-fast-non-reasoning") litellm_params: Optional litellm parameters dict that may contain base_model for routing Returns: @@ -66,7 +113,7 @@ def get_vertex_ai_model_route( >>> get_vertex_ai_model_route("gemma/gemma-3-12b-it") VertexAIModelRoute.GEMMA - >>> get_vertex_ai_model_route("openai/gpt-oss-120b") + >>> get_vertex_ai_model_route("xai/grok-4.1-fast-non-reasoning") VertexAIModelRoute.MODEL_GARDEN >>> get_vertex_ai_model_route("1234567890", {"api_base": "http://10.96.32.8"}) @@ -102,8 +149,11 @@ def get_vertex_ai_model_route( if "gemma/" in model: return VertexAIModelRoute.GEMMA - # Check for model garden openai models - if "openai" in model: + # Check for model garden OpenAI-compatible publisher models. + # Examples: + # - openai/gpt-oss-120b-maas + # - xai/grok-4.1-fast-non-reasoning + if "openai" in model or model.startswith("xai/"): return VertexAIModelRoute.MODEL_GARDEN # Check for gemini models @@ -209,8 +259,8 @@ def get_vertex_base_model_name(model: str) -> str: >>> get_vertex_base_model_name("gemma/gemma-3-12b-it") "gemma-3-12b-it" - >>> get_vertex_base_model_name("openai/gpt-oss-120b") - "gpt-oss-120b" + >>> get_vertex_base_model_name("xai/grok-4.1-fast-non-reasoning") + "grok-4.1-fast-non-reasoning" >>> get_vertex_base_model_name("1234567890") "1234567890" @@ -606,6 +656,8 @@ def process_items(schema, depth=0): and ("items" not in schema or schema.get("items") == {}) ): schema["items"] = {"type": "object"} + elif schema.get("type") == "array" and "items" not in schema: + schema["items"] = {"type": "object"} for key, value in schema.items(): if isinstance(value, dict): process_items(value, depth + 1) diff --git a/litellm/llms/vertex_ai/files/handler.py b/litellm/llms/vertex_ai/files/handler.py index 6636bccd6a3..c31bfde69e7 100644 --- a/litellm/llms/vertex_ai/files/handler.py +++ b/litellm/llms/vertex_ai/files/handler.py @@ -1,5 +1,6 @@ import asyncio -import urllib.parse +import time +from urllib.parse import unquote from typing import Any, Coroutine, Optional, Tuple, Union import httpx @@ -9,6 +10,11 @@ from litellm.integrations.gcs_bucket.gcs_bucket_base import ( GCSBucketBase, GCSLoggingConfig, ) +from litellm.litellm_core_utils.cloud_storage_security import ( + VERTEX_AI_MANAGED_GCS_PREFIX, + should_allow_legacy_cloud_file_ids, + validate_managed_cloud_file_id, +) from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.openai import ( CreateFileRequest, @@ -16,9 +22,10 @@ from litellm.types.llms.openai import ( HttpxBinaryResponseContent, OpenAIFileObject, ) +from litellm.litellm_core_utils.litellm_logging import Logging from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES -from .transformation import VertexAIJsonlFilesTransformation +from .transformation import VertexAIFilesConfig, VertexAIJsonlFilesTransformation vertex_ai_files_transformation = VertexAIJsonlFilesTransformation() @@ -112,34 +119,31 @@ class VertexAIFilesHandler(GCSBucketBase): ) ) - def _extract_bucket_and_object_from_file_id(self, file_id: str) -> Tuple[str, str]: + def _extract_bucket_and_object_from_file_id( + self, + file_id: str, + configured_bucket_name: str, + litellm_params: Optional[dict] = None, + ) -> Tuple[str, str]: """ - Extract bucket name and object path from URL-encoded file_id. + Validate and extract bucket name and object path from file_id. - Expected format: gs%3A%2F%2Fbucket-name%2Fpath%2Fto%2Ffile - Which decodes to: gs://bucket-name/path/to/file + Expected format: gs://bucket-name/litellm-vertex-files/path/to/file Returns: - tuple: (bucket_name, url_encoded_object_path) + tuple: (bucket_name, object_path) - bucket_name: "bucket-name" - - url_encoded_object_path: "path%2Fto%2Ffile" + - object_path: "litellm-vertex-files/path/to/file" """ - decoded_path = urllib.parse.unquote(file_id) - - if decoded_path.startswith("gs://"): - full_path = decoded_path[5:] # Remove 'gs://' prefix - else: - full_path = decoded_path - - if "/" in full_path: - bucket_name, object_path = full_path.split("/", 1) - else: - bucket_name = full_path - object_path = "" - - encoded_object_path = urllib.parse.quote(object_path, safe="") - - return bucket_name, encoded_object_path + return validate_managed_cloud_file_id( + file_id=file_id, + scheme="gs://", + configured_bucket_name=configured_bucket_name, + allowed_object_prefixes=(VERTEX_AI_MANAGED_GCS_PREFIX,), + allow_legacy_cloud_file_ids=should_allow_legacy_cloud_file_ids( + litellm_params + ), + ) async def afile_content( self, @@ -149,6 +153,7 @@ class VertexAIFilesHandler(GCSBucketBase): vertex_location: Optional[str], timeout: Union[float, httpx.Timeout], max_retries: Optional[int], + litellm_params: Optional[dict] = None, ) -> HttpxBinaryResponseContent: """ Download file content from GCS bucket for VertexAI files. @@ -168,31 +173,57 @@ class VertexAIFilesHandler(GCSBucketBase): if not file_id: raise ValueError("file_id is required in file_content_request") - bucket_name, encoded_object_path = self._extract_bucket_and_object_from_file_id( - file_id + gcs_logging_config: GCSLoggingConfig = await self.get_gcs_logging_config( + kwargs={} + ) + bucket_name, object_path = self._extract_bucket_and_object_from_file_id( + file_id=file_id, + configured_bucket_name=gcs_logging_config["bucket_name"], + litellm_params=litellm_params, ) download_kwargs = { - "standard_callback_dynamic_params": {"gcs_bucket_name": bucket_name} + "standard_callback_dynamic_params": { + "gcs_bucket_name": bucket_name, + "gcs_path_service_account": gcs_logging_config["path_service_account"], + } } file_content = await self.download_gcs_object( - object_name=encoded_object_path, **download_kwargs + object_name=object_path, **download_kwargs ) + decoded_file_id = unquote(file_id) if file_content is None: - decoded_path = urllib.parse.unquote(file_id) - raise ValueError(f"Failed to download file from GCS: {decoded_path}") + raise ValueError(f"Failed to download file from GCS: {decoded_file_id}") - decoded_path = urllib.parse.unquote(file_id) mock_response = httpx.Response( status_code=200, content=file_content, - headers={"content-type": "application/octet-stream"}, - request=httpx.Request(method="GET", url=decoded_path), + headers={ + "content-type": "application/octet-stream", + "content-length": str(len(file_content)), + }, + request=httpx.Request(method="GET", url=decoded_file_id), ) - return HttpxBinaryResponseContent(response=mock_response) + # Apply transformation to convert Vertex AI batch outputs to OpenAI format + config = VertexAIFilesConfig() + + # Create a logging object for transformation + logging_obj = Logging( + model="", + messages=[], + stream=False, + call_type="afile_content", + start_time=time.time(), + litellm_call_id="", + function_id="", + ) + + return config.transform_file_content_response( + raw_response=mock_response, logging_obj=logging_obj, litellm_params={} + ) def file_content( self, @@ -204,6 +235,7 @@ class VertexAIFilesHandler(GCSBucketBase): vertex_location: Optional[str], timeout: Union[float, httpx.Timeout], max_retries: Optional[int], + litellm_params: Optional[dict] = None, ) -> Union[ HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent] ]: @@ -232,6 +264,7 @@ class VertexAIFilesHandler(GCSBucketBase): vertex_location=vertex_location, timeout=timeout, max_retries=max_retries, + litellm_params=litellm_params, ) else: return asyncio.run( @@ -242,5 +275,6 @@ class VertexAIFilesHandler(GCSBucketBase): vertex_location=vertex_location, timeout=timeout, max_retries=max_retries, + litellm_params=litellm_params, ) ) diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index 070ec508283..f30518bc7ca 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -1,13 +1,27 @@ +import base64 import json import os +import re import time -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +import httpx from httpx import Headers, Response from openai.types.file_deleted import FileDeleted +import litellm from litellm._uuid import uuid from litellm.files.utils import FilesAPIUtils +from litellm.litellm_core_utils.cloud_storage_security import ( + VERTEX_AI_MANAGED_GCS_PREFIX, + build_managed_cloud_object_name, + encode_gcs_object_name_for_url, + sanitize_cloud_object_path, + should_allow_legacy_cloud_file_ids, + split_configured_cloud_bucket_name, + validate_managed_cloud_file_id, +) +from litellm.litellm_core_utils.litellm_logging import Logging from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.files.transformation import ( @@ -31,11 +45,135 @@ from litellm.types.llms.openai import ( PathLike, ) from litellm.types.llms.vertex_ai import GcsBucketResponse -from litellm.types.utils import ExtractedFileData, LlmProviders +from litellm.types.utils import ExtractedFileData, LlmProviders, ModelResponse from ..common_utils import VertexAIError from ..vertex_llm_base import VertexBase +_GCP_LABEL_VALUE_MAX_LEN = 63 +_CUSTOM_ID_RAW_LABEL_PREFIX = "b32_" + + +def _sanitize_gcp_label_value(value: str) -> str: + """ + Sanitize a string to meet GCP label value constraints. + + GCP label values must: + - Be lowercase + - Contain only letters, numbers, underscores, and hyphens + - Be max 63 characters + + Args: + value: The string to sanitize + + Returns: + A sanitized string that meets GCP label constraints + """ + sanitized = re.sub(r"[^a-z0-9_-]", "_", value.lower()) + return sanitized[:_GCP_LABEL_VALUE_MAX_LEN] + + +def _encode_gcp_label_value_chunks(value: str) -> List[str]: + """Encode arbitrary text across one or more GCP-label-safe values.""" + max_encoded_len = _GCP_LABEL_VALUE_MAX_LEN - len(_CUSTOM_ID_RAW_LABEL_PREFIX) + encoded = ( + base64.b32encode(value.encode("utf-8")).decode("ascii").rstrip("=").lower() + ) + return [ + f"{_CUSTOM_ID_RAW_LABEL_PREFIX}{encoded[i : i + max_encoded_len]}" + for i in range(0, len(encoded), max_encoded_len) + ] or [_CUSTOM_ID_RAW_LABEL_PREFIX] + + +def _decode_gcp_label_value_chunks(values: List[str]) -> Optional[str]: + """Decode values produced by _encode_gcp_label_value_chunks.""" + encoded_parts = [] + for value in values: + if not value.startswith(_CUSTOM_ID_RAW_LABEL_PREFIX): + return None + encoded_parts.append(value[len(_CUSTOM_ID_RAW_LABEL_PREFIX) :]) + encoded = "".join(encoded_parts).upper() + padding = "=" * (-len(encoded) % 8) + try: + return base64.b32decode(encoded + padding).decode("utf-8") + except Exception: + return None + + +def _set_litellm_batch_custom_id_labels(labels: Dict[str, str], custom_id: Any) -> None: + """ + Store OpenAI batch custom_id for Vertex batch correlation. + + ``litellm_custom_id`` is GCP-label-safe (may alter casing and characters). + ``litellm_custom_id_raw`` encodes the original string for + round-trip correlation in batch output transforms. + """ + custom_id_str = str(custom_id) + labels["litellm_custom_id"] = _sanitize_gcp_label_value(custom_id_str) + raw_label_chunks = _encode_gcp_label_value_chunks(custom_id_str) + labels["litellm_custom_id_raw"] = raw_label_chunks[0] + for index, raw_label_chunk in enumerate(raw_label_chunks[1:], start=1): + labels[f"litellm_custom_id_raw_{index}"] = raw_label_chunk + + +def _get_litellm_batch_custom_id_from_labels(labels: Dict[str, Any]) -> str: + """Prefer encoded custom_id when present (see _set_litellm_batch_custom_id_labels).""" + raw = labels.get("litellm_custom_id_raw") + if raw: + raw_chunks = [str(raw)] + chunk_prefix = "litellm_custom_id_raw_" + indexed_chunks = [] + for key, value in labels.items(): + if key.startswith(chunk_prefix) and key[len(chunk_prefix) :].isdigit(): + indexed_chunks.append((int(key[len(chunk_prefix) :]), str(value))) + raw_chunks.extend( + raw_label_chunk + for _, raw_label_chunk in sorted(indexed_chunks, key=lambda item: item[0]) + ) + decoded = _decode_gcp_label_value_chunks(raw_chunks) + if decoded is not None: + return decoded + return str(raw) + return str(labels.get("litellm_custom_id", "unknown")) + + +def _openai_batch_jsonl_entries_to_vertex_wrapped_requests( + openai_jsonl_content: List[Dict[str, Any]], + map_openai_to_vertex_params: Callable[[Dict[str, Any]], Dict[str, Any]], +) -> List[Dict[str, Any]]: + """ + Transforms OpenAI JSONL batch entries to Vertex AI JSONL lines. + + jsonl body for vertex is {"request": } + Example Vertex jsonl + {"request":{"contents": [{"role": "user", "parts": [{"text": "What is the relation between the following video and image samples?"}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/video/animals.mp4", "mimeType": "video/mp4"}}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/image/cricket.jpeg", "mimeType": "image/jpeg"}}]}]}} + {"request":{"contents": [{"role": "user", "parts": [{"text": "Describe what is happening in this video."}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/video/another_video.mov", "mimeType": "video/mov"}}]}]}} + """ + + vertex_jsonl_content = [] + for _openai_jsonl_content in openai_jsonl_content: + openai_request_body = _openai_jsonl_content.get("body") or {} + vertex_request_body = _transform_request_body( + messages=openai_request_body.get("messages", []), + model=openai_request_body.get("model", ""), + optional_params=map_openai_to_vertex_params(openai_request_body), + custom_llm_provider="vertex_ai", + litellm_params={}, + cached_content=None, + ) + + # Add custom_id as a label for correlation in batch outputs + custom_id = _openai_jsonl_content.get("custom_id") + if custom_id is not None: + if "labels" not in vertex_request_body: + vertex_request_body["labels"] = {} + _set_litellm_batch_custom_id_labels( + vertex_request_body["labels"], custom_id + ) + + vertex_jsonl_content.append({"request": vertex_request_body}) + return vertex_jsonl_content + class VertexAIFilesConfig(VertexBase, BaseFilesConfig): """ @@ -119,7 +257,8 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): _model = openai_jsonl_content[0].get("body", {}).get("model", "") if "publishers/google/models" not in _model: _model = f"publishers/google/models/{_model}" - object_name = f"litellm-vertex-files/{_model}/{uuid.uuid4()}" + safe_model_path = sanitize_cloud_object_path(_model, fallback="model") + object_name = f"{VERTEX_AI_MANAGED_GCS_PREFIX}{safe_model_path}/{uuid.uuid4()}" return object_name def get_object_name( @@ -146,12 +285,19 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): if len(openai_jsonl_content) > 0: return self._get_gcs_object_name_from_batch_jsonl(openai_jsonl_content) - ## 2. If not jsonl, return the filename + ## 2. If not jsonl, store under a server-generated managed object name filename = extracted_file_data.get("filename") - if filename: - return filename - ## 3. If no file name, return timestamp - return str(int(time.time())) + return build_managed_cloud_object_name( + prefix=f"{VERTEX_AI_MANAGED_GCS_PREFIX}uploads/", + filename=filename, + fallback_filename="file", + ) + + def _get_configured_bucket_name(self, litellm_params: Dict) -> str: + bucket_name = litellm_params.get("bucket_name") or os.getenv("GCS_BUCKET_NAME") + if not bucket_name: + raise ValueError("GCS bucket_name is required") + return bucket_name def get_complete_file_url( self, @@ -165,13 +311,8 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): """ Get the complete url for the request """ - bucket_name = ( - litellm_params.get("bucket_name") - or litellm_params.get("litellm_metadata", {}).pop("gcs_bucket_name", None) - or os.getenv("GCS_BUCKET_NAME") - ) - if not bucket_name: - raise ValueError("GCS bucket_name is required") + bucket_name = self._get_configured_bucket_name(litellm_params) + bucket_name, object_prefix = split_configured_cloud_bucket_name(bucket_name) file_data = data.get("file") purpose = data.get("purpose") if file_data is None: @@ -180,9 +321,10 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): raise ValueError("purpose is required") extracted_file_data = extract_file_data(file_data) object_name = self.get_object_name(extracted_file_data, purpose) - endpoint = ( - f"upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={object_name}" - ) + if object_prefix: + object_name = f"{object_prefix}/{object_name}" + encoded_object_name = encode_gcs_object_name_for_url(object_name) + endpoint = f"upload/storage/v1/b/{bucket_name}/o?uploadType=media&name={encoded_object_name}" api_base = api_base or "https://storage.googleapis.com" if not api_base: raise ValueError("api_base is required") @@ -227,28 +369,10 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): def _transform_openai_jsonl_content_to_vertex_ai_jsonl_content( self, openai_jsonl_content: List[Dict[str, Any]] ) -> List[Dict[str, Any]]: - """ - Transforms OpenAI JSONL content to VertexAI JSONL content - - jsonl body for vertex is {"request": } - Example Vertex jsonl - {"request":{"contents": [{"role": "user", "parts": [{"text": "What is the relation between the following video and image samples?"}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/video/animals.mp4", "mimeType": "video/mp4"}}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/image/cricket.jpeg", "mimeType": "image/jpeg"}}]}]}} - {"request":{"contents": [{"role": "user", "parts": [{"text": "Describe what is happening in this video."}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/video/another_video.mov", "mimeType": "video/mov"}}]}]}} - """ - - vertex_jsonl_content = [] - for _openai_jsonl_content in openai_jsonl_content: - openai_request_body = _openai_jsonl_content.get("body") or {} - vertex_request_body = _transform_request_body( - messages=openai_request_body.get("messages", []), - model=openai_request_body.get("model", ""), - optional_params=self._map_openai_to_vertex_params(openai_request_body), - custom_llm_provider="vertex_ai", - litellm_params={}, - cached_content=None, - ) - vertex_jsonl_content.append({"request": vertex_request_body}) - return vertex_jsonl_content + return _openai_batch_jsonl_entries_to_vertex_wrapped_requests( + openai_jsonl_content=openai_jsonl_content, + map_openai_to_vertex_params=self._map_openai_to_vertex_params, + ) def transform_create_file_request( self, @@ -339,27 +463,23 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): status_code=status_code, message=error_message, headers=headers ) - def _parse_gcs_uri(self, file_id: str) -> Tuple[str, str]: + def _parse_gcs_uri( + self, file_id: str, litellm_params: Optional[Dict] = None + ) -> Tuple[str, str]: """ - Parse a GCS URI (gs://bucket/path/to/object) into (bucket, url-encoded-object-path). - Handles both raw and URL-encoded input. + Validate a managed GCS file_id and return (bucket, url-encoded-object-path). """ - import urllib.parse - - decoded = urllib.parse.unquote(file_id) - if decoded.startswith("gs://"): - full_path = decoded[5:] - else: - full_path = decoded - - if "/" in full_path: - bucket_name, object_path = full_path.split("/", 1) - else: - bucket_name = full_path - object_path = "" - - encoded_object = urllib.parse.quote(object_path, safe="") - return bucket_name, encoded_object + configured_bucket_name = self._get_configured_bucket_name(litellm_params or {}) + bucket_name, object_path = validate_managed_cloud_file_id( + file_id=file_id, + scheme="gs://", + configured_bucket_name=configured_bucket_name, + allowed_object_prefixes=(VERTEX_AI_MANAGED_GCS_PREFIX,), + allow_legacy_cloud_file_ids=should_allow_legacy_cloud_file_ids( + litellm_params + ), + ) + return bucket_name, encode_gcs_object_name_for_url(object_path) def transform_retrieve_file_request( self, @@ -367,7 +487,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): optional_params: dict, litellm_params: dict, ) -> tuple[str, dict]: - bucket, encoded_object = self._parse_gcs_uri(file_id) + bucket, encoded_object = self._parse_gcs_uri(file_id, litellm_params) url = f"https://storage.googleapis.com/storage/v1/b/{bucket}/o/{encoded_object}" return url, {} @@ -399,7 +519,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): optional_params: dict, litellm_params: dict, ) -> tuple[str, dict]: - bucket, encoded_object = self._parse_gcs_uri(file_id) + bucket, encoded_object = self._parse_gcs_uri(file_id, litellm_params) url = f"https://storage.googleapis.com/storage/v1/b/{bucket}/o/{encoded_object}" return url, {} @@ -443,7 +563,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): litellm_params: dict, ) -> tuple[str, dict]: file_id = file_content_request.get("file_id", "") - bucket, encoded_object = self._parse_gcs_uri(file_id) + bucket, encoded_object = self._parse_gcs_uri(file_id, litellm_params) url = f"https://storage.googleapis.com/storage/v1/b/{bucket}/o/{encoded_object}?alt=media" return url, {} @@ -453,8 +573,229 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): logging_obj: LiteLLMLoggingObj, litellm_params: dict, ) -> HttpxBinaryResponseContent: + """ + Transform file content response, converting Vertex AI batch output to OpenAI format if applicable. + + This method automatically detects and transforms Vertex AI batch prediction outputs + (predictions.jsonl files) into OpenAI-compatible batch response format. + + If the file is not a batch output or transformation fails, the original content + is returned as-is to maintain backward compatibility. + """ + try: + # Allow users to opt out of automatic Vertex batch output -> OpenAI + # transformation, e.g. if they consume raw `predictions.jsonl` directly. + if getattr(litellm, "disable_vertex_batch_output_transformation", False): + return HttpxBinaryResponseContent(response=raw_response) + + # Try to transform batch output if it's a JSONL file + content = raw_response.content + if content: + transformed_content = self._try_transform_vertex_batch_output_to_openai( + content=content, + logging_obj=logging_obj, + ) + if transformed_content != content: + # Create a new response with transformed content and updated Content-Length + # Update headers with correct Content-Length + new_headers = dict(raw_response.headers) + new_headers["content-length"] = str(len(transformed_content)) + + mock_response = httpx.Response( + status_code=raw_response.status_code, + content=transformed_content, + headers=new_headers, + request=raw_response.request, + ) + return HttpxBinaryResponseContent(response=mock_response) + except Exception: + # If transformation fails, return as-is + pass + return HttpxBinaryResponseContent(response=raw_response) + def _try_transform_vertex_batch_output_to_openai( + self, content: bytes, logging_obj: Optional[LiteLLMLoggingObj] = None + ) -> bytes: + """ + Try to transform Vertex AI batch output to OpenAI format. + If conversion fails at any point, return the original content as-is. + + Vertex AI batch output format (predictions.jsonl): + { + "request": {"contents": [...], "labels": {"litellm_custom_id": "request-1", "litellm_custom_id_raw": "..."}}, + "status": "", + "response": {"candidates": [...], "modelVersion": "gemini-2.5-flash", ...}, + "processed_time": "2026-04-13T10:18:18.102004+00:00" + } + + OpenAI batch output format: + { + "id": "batch_req_...", + "custom_id": "request-1", + "response": { + "status_code": 200, + "request_id": "chatcmpl-...", + "body": {} + }, + "error": null + } + """ + try: + # Decode content + content_str = content.decode("utf-8") + + # Check if it's JSONL (multiple lines) + lines = content_str.strip().split("\n") + if not lines: + return content + + # Try to parse the first line to see if it's Vertex AI batch output + first_line = json.loads(lines[0]) + + # Check if it has Vertex AI batch output structure with discriminating fields + # Must have request, response, and processed_time + # Plus either candidates (success) or status (error) + has_base_structure = ( + "response" in first_line + and "request" in first_line + and "processed_time" in first_line + ) + has_success_or_error = ( + "candidates" in first_line.get("response", {}) + or "promptFeedback" in first_line.get("response", {}) + or bool(first_line.get("status")) + ) + + if not (has_base_structure and has_success_or_error): + # Not a Vertex AI batch output, return as-is + return content + + vertex_gemini_config = VertexGeminiConfig() + # Always use a fresh local Logging object for the per-line transformation + # so we never mutate the caller's logging_obj (which already went through + # pre_call and has its own model/start_time/optional_params set). + batch_transform_logging_obj = Logging( + model="", + messages=[], + stream=False, + call_type="batch_transform", + start_time=time.time(), + litellm_call_id="", + function_id="", + ) + batch_transform_logging_obj.optional_params = {} + mock_httpx_response = httpx.Response( + status_code=200, + headers={"content-type": "application/json"}, + request=httpx.Request(method="POST", url="https://example.com"), + ) + + # Transform all lines + transformed_lines = [] + for line in lines: + if not line.strip(): + continue + + try: + vertex_output = json.loads(line) + openai_output = ( + self._transform_single_vertex_batch_output_to_openai( + vertex_output=vertex_output, + vertex_gemini_config=vertex_gemini_config, + logging_obj=batch_transform_logging_obj, + mock_httpx_response=mock_httpx_response, + ) + ) + transformed_lines.append(json.dumps(openai_output)) + except Exception: + # If any line fails, return original content + return content + + # Return transformed content + return "\n".join(transformed_lines).encode("utf-8") + + except Exception: + # If anything fails, return original content + return content + + def _transform_single_vertex_batch_output_to_openai( + self, + vertex_output: Dict[str, Any], + vertex_gemini_config: VertexGeminiConfig, + logging_obj: Logging, + mock_httpx_response: httpx.Response, + ) -> Dict[str, Any]: + """ + Transform a single Vertex AI batch output line to OpenAI format. + Uses the existing VertexGeminiConfig transformation for the response. + """ + # Extract custom_id from request labels (prefer raw for OpenAI round-trip) + request_data = vertex_output.get("request", {}) + labels = request_data.get("labels", {}) or {} + custom_id = _get_litellm_batch_custom_id_from_labels(labels) + + # Check if there's an error + status = vertex_output.get("status", "") + has_error = bool(status) + + if has_error: + return { + "id": f"batch_req_{uuid.uuid4()}", + "custom_id": custom_id, + "response": None, + "error": { + "code": "vertex_ai_error", + "message": status, + }, + } + + # Transform successful response using existing transformation + vertex_response = vertex_output.get("response", {}) + + # Extract model from response + model = vertex_response.get("modelVersion", "gemini-1.5-flash-001") + if "@" in model: + model = model.split("@")[0] + + try: + # Use existing VertexGeminiConfig transformation + model_response = ModelResponse() + + transformed_response = vertex_gemini_config._transform_google_generate_content_to_openai_model_response( + completion_response=vertex_response, + model_response=model_response, + model=model, + logging_obj=logging_obj, + raw_response=mock_httpx_response, + ) + + # Convert ModelResponse to dict + response_dict = transformed_response.model_dump() + + # Return in OpenAI batch format + return { + "id": f"batch_req_{uuid.uuid4()}", + "custom_id": custom_id, + "response": { + "status_code": 200, + "request_id": response_dict.get("id", ""), + "body": response_dict, + }, + "error": None, + } + + except Exception as e: + return { + "id": f"batch_req_{uuid.uuid4()}", + "custom_id": custom_id, + "response": None, + "error": { + "code": "transformation_error", + "message": f"Failed to transform response: {str(e)}", + }, + } + class VertexAIJsonlFilesTransformation(VertexGeminiConfig): """ @@ -492,29 +833,11 @@ class VertexAIJsonlFilesTransformation(VertexGeminiConfig): def _transform_openai_jsonl_content_to_vertex_ai_jsonl_content( self, openai_jsonl_content: List[Dict[str, Any]] - ): - """ - Transforms OpenAI JSONL content to VertexAI JSONL content - - jsonl body for vertex is {"request": } - Example Vertex jsonl - {"request":{"contents": [{"role": "user", "parts": [{"text": "What is the relation between the following video and image samples?"}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/video/animals.mp4", "mimeType": "video/mp4"}}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/image/cricket.jpeg", "mimeType": "image/jpeg"}}]}]}} - {"request":{"contents": [{"role": "user", "parts": [{"text": "Describe what is happening in this video."}, {"fileData": {"fileUri": "gs://cloud-samples-data/generative-ai/video/another_video.mov", "mimeType": "video/mov"}}]}]}} - """ - - vertex_jsonl_content = [] - for _openai_jsonl_content in openai_jsonl_content: - openai_request_body = _openai_jsonl_content.get("body") or {} - vertex_request_body = _transform_request_body( - messages=openai_request_body.get("messages", []), - model=openai_request_body.get("model", ""), - optional_params=self._map_openai_to_vertex_params(openai_request_body), - custom_llm_provider="vertex_ai", - litellm_params={}, - cached_content=None, - ) - vertex_jsonl_content.append({"request": vertex_request_body}) - return vertex_jsonl_content + ) -> List[Dict[str, Any]]: + return _openai_batch_jsonl_entries_to_vertex_wrapped_requests( + openai_jsonl_content=openai_jsonl_content, + map_openai_to_vertex_params=self._map_openai_to_vertex_params, + ) def _get_gcs_object_name( self, @@ -528,7 +851,8 @@ class VertexAIJsonlFilesTransformation(VertexGeminiConfig): _model = openai_jsonl_content[0].get("body", {}).get("model", "") if "publishers/google/models" not in _model: _model = f"publishers/google/models/{_model}" - object_name = f"litellm-vertex-files/{_model}/{uuid.uuid4()}" + safe_model_path = sanitize_cloud_object_path(_model, fallback="model") + object_name = f"{VERTEX_AI_MANAGED_GCS_PREFIX}{safe_model_path}/{uuid.uuid4()}" return object_name def _map_openai_to_vertex_params( diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 533bd06d2d8..9afa5dec465 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -24,6 +24,7 @@ from litellm.litellm_core_utils.prompt_templates.factory import ( response_schema_prompt, ) from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.llms.vertex_ai.common_utils import pop_vertex_request_labels from litellm.types.files import ( get_file_mime_type_for_file_type, get_file_type_from_extension, @@ -211,6 +212,22 @@ def _process_gemini_media( return _apply_gemini_metadata( part, model, media_resolution_enum, video_metadata ) + elif image_url.startswith( + "https://generativelanguage.googleapis.com/v1beta/files/" + ): + # Gemini Files API URIs — the file is already uploaded to Google's + # servers; pass the URI through as file_data without fetching it. + # These URLs return 403 when accessed directly, so we must not try + # to resolve their MIME type via HTTP. + if format: + file_data = FileDataType(mime_type=format, file_uri=image_url) + else: + # Gemini Files API references can be passed through as URI-only. + file_data = cast(FileDataType, {"file_uri": image_url}) + part = {"file_data": file_data} + return _apply_gemini_metadata( + part, model, media_resolution_enum, video_metadata + ) elif ( "https://" in image_url and (image_type := format or _get_image_mime_type_from_url(image_url)) @@ -714,16 +731,8 @@ def _transform_request_body( # noqa: PLR0915 optional_params.pop("output_config", None) config_fields = GenerationConfig.__annotations__.keys() - # If the LiteLLM client sends Gemini-supported parameter "labels", add it - # as "labels" field to the request sent to the Gemini backend. - labels: Optional[dict[str, str]] = optional_params.pop("labels", None) - # If the LiteLLM client sends OpenAI-supported parameter "metadata", add it - # as "labels" field to the request sent to the Gemini backend. - if labels is None and "metadata" in litellm_params: - metadata = litellm_params["metadata"] - if metadata is not None and "requester_metadata" in metadata: - rm = metadata["requester_metadata"] - labels = {k: v for k, v in rm.items() if isinstance(v, str)} + # labels: optional explicit param and/or metadata.requester_metadata (OpenAI metadata) + labels = pop_vertex_request_labels(optional_params, litellm_params) filtered_params = { k: v @@ -750,16 +759,22 @@ def _transform_request_body( # noqa: PLR0915 ] data = RequestBody(contents=content) - if system_instructions is not None: - data["system_instruction"] = system_instructions - if tools is not None: - data["tools"] = tools - if tool_choice is not None: - data["toolConfig"] = tool_choice - if include_server_side_tool_invocations: - if "toolConfig" not in data: - data["toolConfig"] = {} - data["toolConfig"]["includeServerSideToolInvocations"] = True + # Vertex rejects system_instruction/tools/toolConfig alongside cachedContent. + # Treat dropping these fields as a request mutation guarded by modify_params. + can_send_cache_incompatible_fields = ( + cached_content is None or litellm.modify_params is False + ) + if can_send_cache_incompatible_fields: + if system_instructions is not None: + data["system_instruction"] = system_instructions + if tools is not None: + data["tools"] = tools + if tool_choice is not None: + data["toolConfig"] = tool_choice + if include_server_side_tool_invocations: + if "toolConfig" not in data: + data["toolConfig"] = {} + data["toolConfig"]["includeServerSideToolInvocations"] = True if safety_settings is not None: data["safetySettings"] = safety_settings if generation_config is not None and len(generation_config) > 0: 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 a90e05919fb..6278de662f8 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 @@ -979,15 +979,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): params["includeThoughts"] = False else: params["includeThoughts"] = True - if thinking_budget >= 10000: - is_gemini3flash = ( - "gemini-3-flash-preview" in model.lower() - or "gemini-3-flash" in model.lower() - ) - params["thinkingLevel"] = ( - "minimal" if is_gemini3flash else "low" - ) - else: + # Follow provider defaults unless explicitly opted into legacy behavior. + if litellm.enable_gemini_default_thinking_level_low is True: is_gemini3flash = ( "gemini-3-flash-preview" in model.lower() or "gemini-3-flash" in model.lower() @@ -2395,8 +2388,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): completion_response = GenerateContentResponseBody(**raw_response.json()) # type: ignore except Exception as e: raise VertexAIError( - message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( - raw_response.text, str(e) + message="Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( + str(e) ), status_code=422, headers=raw_response.headers, @@ -2530,8 +2523,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): except Exception as e: raise VertexAIError( - message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( - completion_response, str(e) + message="Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( + str(e) ), status_code=422, headers=raw_response.headers, diff --git a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py index 2371bc4865a..99165c37c93 100644 --- a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py +++ b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py @@ -3,7 +3,7 @@ Google AI Studio /batchEmbedContents Embeddings Endpoint """ import json -from typing import Any, Dict, Literal, Optional, Union +from typing import Any, Dict, List, Literal, Optional, Tuple, Union import httpx @@ -13,8 +13,8 @@ from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, get_async_httpx_client, ) -from litellm.types.llms.openai import EmbeddingInput from litellm.types.llms.vertex_ai import ( + GeminiEmbeddingInput, VertexAIBatchEmbeddingsRequestBody, VertexAIBatchEmbeddingsResponseObject, ) @@ -23,7 +23,6 @@ from litellm.types.utils import EmbeddingResponse from ..gemini.vertex_and_google_ai_studio_gemini import VertexLLM from .batch_embed_content_transformation import ( _is_file_reference, - _is_multimodal_input, process_embed_content_response, process_response, transform_openai_input_gemini_content, @@ -32,9 +31,24 @@ from .batch_embed_content_transformation import ( class GoogleBatchEmbeddings(VertexLLM): + @staticmethod + def _flatten_and_detect_file_refs( + input: GeminiEmbeddingInput, + ) -> Tuple[List[str], bool]: + """Flatten nested input lists and detect file references.""" + input_list = [input] if isinstance(input, str) else input + flat_elements = [ + e + for item in input_list + for e in (item if isinstance(item, list) else [item]) + if isinstance(e, str) + ] + has_file_refs = any(_is_file_reference(e) for e in flat_elements) + return flat_elements, has_file_refs + def _resolve_file_references( self, - input: EmbeddingInput, + input: GeminiEmbeddingInput, api_key: str, sync_handler: HTTPHandler, ) -> Dict[str, Dict[str, str]]: @@ -42,7 +56,7 @@ class GoogleBatchEmbeddings(VertexLLM): Resolve Gemini file references (files/...) to get mime_type and uri. Args: - input: EmbeddingInput that may contain file references + input: GeminiEmbeddingInput that may contain file references api_key: Gemini API key sync_handler: HTTP client @@ -73,7 +87,7 @@ class GoogleBatchEmbeddings(VertexLLM): async def _async_resolve_file_references( self, - input: EmbeddingInput, + input: GeminiEmbeddingInput, api_key: str, async_handler: AsyncHTTPHandler, ) -> Dict[str, Dict[str, str]]: @@ -81,7 +95,7 @@ class GoogleBatchEmbeddings(VertexLLM): Async version of _resolve_file_references. Args: - input: EmbeddingInput that may contain file references + input: GeminiEmbeddingInput that may contain file references api_key: Gemini API key async_handler: Async HTTP client @@ -110,10 +124,10 @@ class GoogleBatchEmbeddings(VertexLLM): return resolved_files - def batch_embeddings( + def batch_embeddings( # noqa: PLR0915 self, model: str, - input: EmbeddingInput, + input: GeminiEmbeddingInput, print_verbose, model_response: EmbeddingResponse, custom_llm_provider: Literal["gemini", "vertex_ai"], @@ -151,8 +165,7 @@ class GoogleBatchEmbeddings(VertexLLM): optional_params = optional_params or {} - is_multimodal = _is_multimodal_input(input) - use_embed_content = is_multimodal or (custom_llm_provider == "vertex_ai") + use_embed_content = custom_llm_provider == "vertex_ai" mode: Literal["embedding", "batch_embedding"] if use_embed_content: mode = "embedding" @@ -215,8 +228,22 @@ class GoogleBatchEmbeddings(VertexLLM): resolved_files=resolved_files, ) else: + flat_elements, has_file_refs = self._flatten_and_detect_file_refs(input) + if has_file_refs and not api_key: + raise ValueError( + "An API key is required to resolve Gemini file references (files/...). " + "Pass api_key= or set GEMINI_API_KEY." + ) + resolved_files = {} + if api_key and has_file_refs: + resolved_files = self._resolve_file_references( + input=flat_elements, api_key=api_key, sync_handler=sync_handler + ) request_data = transform_openai_input_gemini_content( - input=input, model=model, optional_params=optional_params + input=input, + model=model, + optional_params=optional_params, + resolved_files=resolved_files, ) ## LOGGING @@ -264,7 +291,7 @@ class GoogleBatchEmbeddings(VertexLLM): url: str, data: Optional[Union[VertexAIBatchEmbeddingsRequestBody, dict]], model_response: EmbeddingResponse, - input: EmbeddingInput, + input: GeminiEmbeddingInput, timeout: Optional[Union[float, httpx.Timeout]], headers={}, client: Optional[AsyncHTTPHandler] = None, @@ -303,8 +330,22 @@ class GoogleBatchEmbeddings(VertexLLM): resolved_files=resolved_files, ) else: + flat_elements, has_file_refs = self._flatten_and_detect_file_refs(input) + if has_file_refs and not api_key: + raise ValueError( + "An API key is required to resolve Gemini file references (files/...). " + "Pass api_key= or set GEMINI_API_KEY." + ) + resolved_files = {} + if api_key and has_file_refs: + resolved_files = await self._async_resolve_file_references( + input=flat_elements, api_key=api_key, async_handler=async_handler + ) data = transform_openai_input_gemini_content( - input=input, model=model, optional_params=optional_params or {} + input=input, + model=model, + optional_params=optional_params or {}, + resolved_files=resolved_files, ) ## LOGGING diff --git a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py index 34fc95e0af7..e1b365c9f42 100644 --- a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py +++ b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py @@ -6,12 +6,12 @@ Why separate file? Make it easy to see how transformation works from typing import Dict, List, Optional, Tuple -from litellm.types.llms.openai import EmbeddingInput from litellm.types.llms.vertex_ai import ( BlobType, ContentType, EmbedContentRequest, FileDataType, + GeminiEmbeddingInput, PartType, VertexAIBatchEmbeddingsRequestBody, VertexAIBatchEmbeddingsResponseObject, @@ -114,33 +114,77 @@ def _parse_data_url(data_url: str) -> Tuple[str, str]: return media_type, base64_data -def _is_multimodal_input(input: EmbeddingInput) -> bool: +def _is_multimodal_input(input: GeminiEmbeddingInput) -> bool: """ - Check if the input contains multimodal data (data URIs, file references, or GCS URLs). + Check if the input contains multimodal data (data URIs, file references, + GCS URLs, or nested lists for combined embeddings). Args: - input: EmbeddingInput (str or List[str]) + input: GeminiEmbeddingInput — str, List[str], or List[List[str]] for combined embeddings Returns: - bool: True if any element is a data URI, file reference, or GCS URL + bool: True if any element is multimodal or a nested list """ if isinstance(input, str): - input_list = [input] - else: - input_list = input + return _is_multimodal_element(input) - for element in input_list: - if isinstance(element, str): - if element.startswith("data:") and ";base64," in element: - return True - if _is_file_reference(element): - return True - if _is_gcs_url(element): + for element in input: + if isinstance(element, list): + if any( + _is_multimodal_element(sub) for sub in element if isinstance(sub, str) + ): return True + elif isinstance(element, str) and _is_multimodal_element(element): + return True return False +def _is_multimodal_element(element: str) -> bool: + """Check if a single string element is multimodal.""" + if element.startswith("data:") and ";base64," in element: + return True + if _is_file_reference(element): + return True + if _is_gcs_url(element): + return True + return False + + +def _build_part_for_input( + element: str, + resolved_files: Optional[Dict[str, Dict[str, str]]] = None, +) -> PartType: + """ + Build a single PartType for an input element, handling text, data URIs, + file references, and GCS URLs. + """ + resolved_files = resolved_files or {} + + if element.startswith("data:") and ";base64," in element: + mime_type, base64_data = _parse_data_url(element) + blob: BlobType = {"mime_type": mime_type, "data": base64_data} + return PartType(inline_data=blob) + elif _is_gcs_url(element): + mime_type = _infer_mime_type_from_gcs_url(element) + file_data: FileDataType = { + "mime_type": mime_type, + "file_uri": element, + } + return PartType(file_data=file_data) + elif _is_file_reference(element): + if element not in resolved_files: + raise ValueError(f"File reference {element} not resolved") + file_info = resolved_files[element] + file_data_ref: FileDataType = { + "mime_type": file_info["mime_type"], + "file_uri": file_info["uri"], + } + return PartType(file_data=file_data_ref) + else: + return PartType(text=element) + + _SUPPORTED_EMBED_PARAMS = {"outputDimensionality", "taskType", "title"} @@ -155,37 +199,60 @@ def _filter_embed_params(optional_params: dict) -> dict: def transform_openai_input_gemini_content( - input: EmbeddingInput, model: str, optional_params: dict + input: GeminiEmbeddingInput, + model: str, + optional_params: dict, + resolved_files: Optional[Dict[str, Dict[str, str]]] = None, ) -> VertexAIBatchEmbeddingsRequestBody: """ - The content to embed. Only the parts.text fields will be counted. + Transform OpenAI embedding input to Gemini batchEmbedContents format. + + Each input element becomes a separate EmbedContentRequest, supporting + text, data URIs, file references, and GCS URLs. + + If an element is a list (nested input), all sub-elements are combined + into a single content with multiple parts, producing one combined + embedding for the group. + + Examples: + input=["text", "image"] → 2 separate embeddings + input=[["text", "image"]] → 1 combined embedding + input=[["text", "image"], "x"] → 2 embeddings (1 combined + 1 separate) """ gemini_model_name = "models/{}".format(model) gemini_params = _filter_embed_params(optional_params) + input_list = [input] if isinstance(input, str) else input requests: List[EmbedContentRequest] = [] - if isinstance(input, str): + + for element in input_list: + if isinstance(element, list): + if not element: + raise ValueError("Nested input list must not be empty") + for sub in element: + if not isinstance(sub, str): + raise ValueError( + f"Elements inside a nested input list must be strings, got {type(sub)}" + ) + parts = [ + _build_part_for_input(sub, resolved_files=resolved_files) + for sub in element + ] + else: + parts = [_build_part_for_input(element, resolved_files=resolved_files)] request = EmbedContentRequest( model=gemini_model_name, - content=ContentType(parts=[PartType(text=input)]), + content=ContentType(parts=parts), **gemini_params, ) requests.append(request) - else: - for i in input: - request = EmbedContentRequest( - model=gemini_model_name, - content=ContentType(parts=[PartType(text=i)]), - **gemini_params, - ) - requests.append(request) return VertexAIBatchEmbeddingsRequestBody(requests=requests) def transform_openai_input_gemini_embed_content( - input: EmbeddingInput, + input: GeminiEmbeddingInput, model: str, optional_params: dict, resolved_files: Optional[Dict[str, Dict[str, str]]] = None, @@ -194,7 +261,7 @@ def transform_openai_input_gemini_embed_content( Transform OpenAI embedding input to Gemini embedContent format (multimodal). Args: - input: EmbeddingInput (str or List[str]) with text, data URIs, or file references + input: GeminiEmbeddingInput with text, data URIs, or file references model: Model name optional_params: Additional parameters (taskType, outputDimensionality, etc.) resolved_files: Dict mapping file names (files/abc) to {mime_type, uri} @@ -210,31 +277,14 @@ def transform_openai_input_gemini_embed_content( parts: List[PartType] = [] for element in input_list: + if isinstance(element, list): + raise ValueError( + "Nested (combined) embeddings are not supported on the embedContent path. " + "Use the batchEmbedContents path or pass a flat list instead." + ) if not isinstance(element, str): raise ValueError(f"Unsupported input type: {type(element)}") - - if element.startswith("data:") and ";base64," in element: - mime_type, base64_data = _parse_data_url(element) - blob: BlobType = {"mime_type": mime_type, "data": base64_data} - parts.append(PartType(inline_data=blob)) - elif _is_gcs_url(element): - mime_type = _infer_mime_type_from_gcs_url(element) - file_data: FileDataType = { - "mime_type": mime_type, - "file_uri": element, - } - parts.append(PartType(file_data=file_data)) - elif _is_file_reference(element): - if element not in resolved_files: - raise ValueError(f"File reference {element} not resolved") - file_info = resolved_files[element] - file_data_ref: FileDataType = { - "mime_type": file_info["mime_type"], - "file_uri": file_info["uri"], - } - parts.append(PartType(file_data=file_data_ref)) - else: - parts.append(PartType(text=element)) + parts.append(_build_part_for_input(element, resolved_files=resolved_files)) request_body: dict = { "content": ContentType(parts=parts), @@ -245,7 +295,7 @@ def transform_openai_input_gemini_embed_content( def process_embed_content_response( - input: EmbeddingInput, + input: GeminiEmbeddingInput, model_response: EmbeddingResponse, model: str, response_json: dict, @@ -291,7 +341,7 @@ def process_embed_content_response( def process_response( - input: EmbeddingInput, + input: GeminiEmbeddingInput, model_response: EmbeddingResponse, model: str, _predictions: VertexAIBatchEmbeddingsResponseObject, @@ -308,8 +358,29 @@ def process_response( model_response.data = openai_embeddings model_response.model = model - input_text = get_formatted_prompt(data={"input": input}, call_type="embedding") - prompt_tokens = token_counter(model=model, text=input_text) + has_nested = isinstance(input, list) and any(isinstance(e, list) for e in input) + if _is_multimodal_input(input) or has_nested: + input_list = input if isinstance(input, list) else [input] + text_elements: List[str] = [] + for e in input_list: + if isinstance(e, list): + text_elements.extend( + sub + for sub in e + if isinstance(sub, str) and not _is_multimodal_element(sub) + ) + elif isinstance(e, str) and not _is_multimodal_element(e): + text_elements.append(e) + if text_elements: + input_text = get_formatted_prompt( + data={"input": text_elements}, call_type="embedding" + ) + prompt_tokens = token_counter(model=model, text=input_text) + else: + prompt_tokens = 0 + else: + input_text = get_formatted_prompt(data={"input": input}, call_type="embedding") + prompt_tokens = token_counter(model=model, text=input_text) model_response.usage = Usage( prompt_tokens=prompt_tokens, total_tokens=prompt_tokens ) diff --git a/litellm/llms/vertex_ai/google_genai/transformation.py b/litellm/llms/vertex_ai/google_genai/transformation.py index d7a4ceeb3e7..c1120d9ab8b 100644 --- a/litellm/llms/vertex_ai/google_genai/transformation.py +++ b/litellm/llms/vertex_ai/google_genai/transformation.py @@ -79,6 +79,9 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig): Transform the generate content request for Vertex AI. Since Vertex AI natively supports Google GenAI format, we can pass most fields directly. """ + if generate_content_config_dict: + self._normalize_response_schema(generate_content_config_dict, model) + # Build the request in Google GenAI format that Vertex AI expects result = { "model": model, diff --git a/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py b/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py index 1c7696d55a2..05ebd685d91 100644 --- a/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py +++ b/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py @@ -7,7 +7,10 @@ import litellm from litellm.llms.base_llm.image_generation.transformation import ( BaseImageGenerationConfig, ) -from litellm.llms.vertex_ai.common_utils import get_vertex_base_url +from litellm.llms.vertex_ai.common_utils import ( + get_vertex_base_url, + pop_vertex_request_labels, +) from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ( @@ -203,13 +206,16 @@ class VertexAIImagenImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): "sampleCount": 1, } - # Merge with optional params + labels = pop_vertex_request_labels(optional_params, litellm_params) + # Merge with optional params (after popping labels so they are not sent as Imagen parameters) parameters = {**default_params, **optional_params} - request_body = { + request_body: dict = { "instances": [{"prompt": prompt}], "parameters": parameters, } + if labels: + request_body["labels"] = labels return request_body diff --git a/litellm/llms/vertex_ai/rerank/transformation.py b/litellm/llms/vertex_ai/rerank/transformation.py index 53651839671..3b84972e946 100644 --- a/litellm/llms/vertex_ai/rerank/transformation.py +++ b/litellm/llms/vertex_ai/rerank/transformation.py @@ -11,12 +11,15 @@ import httpx import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig +from litellm.llms.vertex_ai.common_utils import ( + vertex_request_labels_from_litellm_params, +) from litellm.llms.vertex_ai.vertex_llm_base import VertexBase from litellm.secret_managers.main import get_secret_str from litellm.types.rerank import ( + RerankBilledUnits, RerankResponse, RerankResponseMeta, - RerankBilledUnits, RerankResponseResult, ) @@ -109,6 +112,7 @@ class VertexAIRerankConfig(BaseRerankConfig, VertexBase): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: """ Transform the request from Cohere format to Vertex AI Discovery Engine format @@ -145,6 +149,10 @@ class VertexAIRerankConfig(BaseRerankConfig, VertexBase): # When return_documents is False, we want to ignore record details (return only IDs) request_data["ignoreRecordDetailsInResponse"] = not return_documents + user_labels = vertex_request_labels_from_litellm_params(litellm_params) + if user_labels: + request_data["userLabels"] = user_labels + return request_data def transform_rerank_response( diff --git a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py index 4baa5774c48..d31e1f6c8f2 100644 --- a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py @@ -100,6 +100,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict[str, Any]]: """ Transform search request for Vertex AI RAG API diff --git a/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py index 179bd7aeff1..61fb848b40a 100644 --- a/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py @@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import httpx from litellm import get_model_info +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig from litellm.llms.vertex_ai.vertex_llm_base import VertexBase from litellm.types.router import GenericLiteLLMParams @@ -91,12 +92,18 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): raise ValueError("vector_store_id is required") if api_base: return api_base.rstrip("/") + encoded_collection_id = encode_url_path_segment( + collection_id, field_name="vertex_collection_id" + ) + encoded_datastore_id = encode_url_path_segment( + datastore_id, field_name="vector_store_id" + ) # Vertex AI Search API endpoint for search return ( f"https://discoveryengine.googleapis.com/v1/" f"projects/{vertex_project}/locations/{vertex_location}/" - f"collections/{collection_id}/dataStores/{datastore_id}/servingConfigs/default_config" + f"collections/{encoded_collection_id}/dataStores/{encoded_datastore_id}/servingConfigs/default_config" ) def transform_search_vector_store_request( @@ -107,6 +114,7 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): api_base: str, litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, + extra_body: Optional[Dict[str, Any]] = None, ) -> Tuple[str, Dict[str, Any]]: """ Transform search request for Vertex AI RAG API diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py index 5c3bbf61ee2..4be4c2d5e78 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py @@ -13,6 +13,7 @@ from litellm.types.llms.vertex_ai import VertexPartnerProvider from litellm.types.router import GenericLiteLLMParams from ....vertex_llm_base import VertexBase +from ..output_params_utils import sanitize_vertex_anthropic_output_params class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, VertexBase): @@ -158,12 +159,6 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert "model", None ) # do not pass model in request body to vertex ai - anthropic_messages_request.pop( - "output_format", None - ) # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet - - anthropic_messages_request.pop( - "output_config", None - ) # do not pass output_config in request body to vertex ai - vertex ai does not support output_config + sanitize_vertex_anthropic_output_params(anthropic_messages_request) return anthropic_messages_request diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/output_params_utils.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/output_params_utils.py new file mode 100644 index 00000000000..a33ad677789 --- /dev/null +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/output_params_utils.py @@ -0,0 +1,48 @@ +""" +Shared sanitization for ``output_config`` / ``output_format`` on Vertex AI +Claude. Lives in its own module so both the chat-completion transformation +(``transformation.py``) and the Messages pass-through transformation +(``experimental_pass_through/transformation.py``) can import it without +forming a cycle through the parent module's heavier imports. + +CodeQL flagged the ``..transformation`` import path as a potential cyclic +import; extracting the helper into a leaf module resolves the warning and +keeps the parent module's import surface narrow. +""" + +# Keys inside ``output_config`` that Vertex AI Claude does not accept. +# Add an entry only when a 400 "Extra inputs are not permitted" is +# reproducible against the live Vertex endpoint. +VERTEX_UNSUPPORTED_OUTPUT_CONFIG_KEYS: frozenset = frozenset() + + +def sanitize_vertex_anthropic_output_params(data: dict) -> None: + """ + Strip Vertex-unsupported keys from ``output_config`` / + ``output_format`` in-place; forward whatever remains. + + Behavior: + * ``output_config`` containing only unsupported keys (e.g. ``effort`` + alone) is removed entirely so the request body has no empty dict. + * ``output_config`` containing a mix of supported + unsupported keys + has the unsupported subset filtered out and the rest forwarded. + * ``output_config`` that is supported in full passes through unchanged. + * ``output_format`` is forwarded as-is (Vertex AI Claude accepts it). + * Non-dict values for ``output_config`` are dropped to avoid sending + malformed payloads downstream. + """ + output_config = data.get("output_config") + if output_config is None: + return + if not isinstance(output_config, dict): + data.pop("output_config", None) + return + sanitized = { + k: v + for k, v in output_config.items() + if k not in VERTEX_UNSUPPORTED_OUTPUT_CONFIG_KEYS + } + if sanitized: + data["output_config"] = sanitized + else: + data.pop("output_config", None) diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py index 504914c4796..4627d9f6df3 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py @@ -10,6 +10,7 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse from ....anthropic.chat.transformation import AnthropicConfig +from .output_params_utils import sanitize_vertex_anthropic_output_params class VertexAIError(Exception): @@ -105,11 +106,7 @@ class VertexAIAnthropicConfig(AnthropicConfig): data.pop("model", None) # vertex anthropic doesn't accept 'model' parameter - # VertexAI doesn't support output_format parameter, remove it if present - data.pop("output_format", None) - - # VertexAI doesn't support output_config parameter, remove it if present - data.pop("output_config", None) + sanitize_vertex_anthropic_output_params(data) tools = optional_params.get("tools") tool_search_used = self.is_tool_search_used(tools) diff --git a/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py b/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py index 18c5ec3d839..696341598e5 100644 --- a/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py +++ b/litellm/llms/vertex_ai/vertex_embeddings/embedding_handler.py @@ -1,4 +1,4 @@ -from typing import Literal, Optional, Union +from typing import Dict, Literal, Optional, Union import httpx @@ -44,6 +44,7 @@ class VertexEmbedding(VertexBase): vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = None, gemini_api_key: Optional[str] = None, extra_headers: Optional[dict] = None, + litellm_params: Optional[Dict] = None, ) -> EmbeddingResponse: if aembedding is True: return self.async_embedding( # type: ignore @@ -61,6 +62,7 @@ class VertexEmbedding(VertexBase): vertex_credentials=vertex_credentials, gemini_api_key=gemini_api_key, extra_headers=extra_headers, + litellm_params=litellm_params, ) should_use_v1beta1_features = self.is_using_v1beta1_features( @@ -92,7 +94,10 @@ class VertexEmbedding(VertexBase): headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers) vertex_request: VertexEmbeddingRequest = ( litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request( - input=input, optional_params=optional_params, model=model + input=input, + optional_params=optional_params, + model=model, + litellm_params=litellm_params, ) ) @@ -156,6 +161,7 @@ class VertexEmbedding(VertexBase): gemini_api_key: Optional[str] = None, extra_headers: Optional[dict] = None, encoding=None, + litellm_params: Optional[Dict] = None, ) -> EmbeddingResponse: """ Async embedding implementation @@ -188,7 +194,10 @@ class VertexEmbedding(VertexBase): headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers) vertex_request: VertexEmbeddingRequest = ( litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request( - input=input, optional_params=optional_params, model=model + input=input, + optional_params=optional_params, + model=model, + litellm_params=litellm_params, ) ) diff --git a/litellm/llms/vertex_ai/vertex_embeddings/transformation.py b/litellm/llms/vertex_ai/vertex_embeddings/transformation.py index 132f29987af..24396628dbd 100644 --- a/litellm/llms/vertex_ai/vertex_embeddings/transformation.py +++ b/litellm/llms/vertex_ai/vertex_embeddings/transformation.py @@ -3,6 +3,7 @@ from typing import List, Literal, Optional, Union from pydantic import BaseModel +from litellm.llms.vertex_ai.common_utils import pop_vertex_request_labels from litellm.types.utils import EmbeddingResponse, Usage from .types import * @@ -100,7 +101,11 @@ class VertexAITextEmbeddingConfig(BaseModel): return optional_params def transform_openai_request_to_vertex_embedding_request( - self, input: Union[list, str], optional_params: dict, model: str + self, + input: Union[list, str], + optional_params: dict, + model: str, + litellm_params: Optional[dict] = None, ) -> VertexEmbeddingRequest: """ Transforms an openai request to a vertex embedding request. @@ -108,16 +113,26 @@ class VertexAITextEmbeddingConfig(BaseModel): # Import here to avoid circular import issues with litellm.__init__ from litellm.llms.vertex_ai.vertex_embeddings.bge import VertexBGEConfig + labels = pop_vertex_request_labels(optional_params, litellm_params) + if model.isdigit(): - return self._transform_openai_request_to_fine_tuned_embedding_request( - input, optional_params, model + vertex_request = ( + self._transform_openai_request_to_fine_tuned_embedding_request( + input, optional_params, model + ) ) + if labels: + vertex_request["labels"] = labels + return vertex_request if VertexBGEConfig.is_bge_model(model): - return VertexBGEConfig.transform_request( + vertex_request = VertexBGEConfig.transform_request( input=input, optional_params=optional_params, model=model ) + if labels: + vertex_request["labels"] = labels + return vertex_request - vertex_request: VertexEmbeddingRequest = VertexEmbeddingRequest() + vertex_request = VertexEmbeddingRequest() vertex_text_embedding_input_list: List[TextEmbeddingInput] = [] task_type: Optional[TaskType] = optional_params.get("task_type") title = optional_params.get("title") @@ -133,6 +148,8 @@ class VertexAITextEmbeddingConfig(BaseModel): vertex_request["instances"] = vertex_text_embedding_input_list vertex_request["parameters"] = EmbeddingParameters(**optional_params) + if labels: + vertex_request["labels"] = labels return vertex_request diff --git a/litellm/llms/vertex_ai/vertex_embeddings/types.py b/litellm/llms/vertex_ai/vertex_embeddings/types.py index 317b9c4fb81..bf73f4d193a 100644 --- a/litellm/llms/vertex_ai/vertex_embeddings/types.py +++ b/litellm/llms/vertex_ai/vertex_embeddings/types.py @@ -3,7 +3,7 @@ Types for Vertex Embeddings Requests """ from enum import Enum -from typing import List, Optional, Union +from typing import Dict, List, Optional, Union from typing_extensions import TypedDict @@ -56,6 +56,7 @@ class VertexEmbeddingRequest(TypedDict, total=False): List[TextEmbeddingFineTunedInput], ] parameters: Optional[Union[EmbeddingParameters, TextEmbeddingFineTunedParameters]] + labels: Optional[Dict[str, str]] # Example usage: diff --git a/litellm/llms/vertex_ai/vertex_model_garden/main.py b/litellm/llms/vertex_ai/vertex_model_garden/main.py index c37bb449ecf..7240d9dce57 100644 --- a/litellm/llms/vertex_ai/vertex_model_garden/main.py +++ b/litellm/llms/vertex_ai/vertex_model_garden/main.py @@ -27,6 +27,17 @@ from ..common_utils import VertexAIError, get_vertex_base_model_name from ..vertex_llm_base import VertexBase +def _vertex_model_garden_model_id_in_json_body(model: str) -> bool: + """ + Vertex catalog / publisher models are addressed as publisher/model (e.g. + xai/grok-4.1-fast-reasoning) on the shared OpenAPI URL, with the id in the JSON body. + + Deployed Model Garden endpoints are typically a single segment (often numeric) + and use .../endpoints/{ENDPOINT_ID}/chat/completions with an empty model field. + """ + return "/" in model + + def create_vertex_url( vertex_location: str, vertex_project: str, @@ -34,8 +45,13 @@ def create_vertex_url( model: str, api_base: Optional[str] = None, ) -> str: - """Return the base url for the vertex garden models""" + """Return the api base for vertex model garden (without /chat/completions).""" base_url = get_vertex_base_url(vertex_location) + if _vertex_model_garden_model_id_in_json_body(model): + return ( + f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}" + "/endpoints/openapi" + ) return f"{base_url}/v1beta1/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}" @@ -129,7 +145,10 @@ class VertexAIModelGardenModels(VertexBase): vertex_location=vertex_location or "us-central1", vertex_api_version="v1beta1", ) - model = "" + # Publisher/catalog models: model id must be sent in the JSON body (OpenAPI route). + # Single-segment endpoint ids: model is encoded in the URL path; body model stays empty. + if not _vertex_model_garden_model_id_in_json_body(model): + model = "" return openai_like_chat_completions.completion( model=model, messages=messages, diff --git a/litellm/llms/volcengine/responses/transformation.py b/litellm/llms/volcengine/responses/transformation.py index f6dda4dd25b..99e0a958ef1 100644 --- a/litellm/llms/volcengine/responses/transformation.py +++ b/litellm/llms/volcengine/responses/transformation.py @@ -17,6 +17,7 @@ from pydantic import fields as pyd_fields import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.core_helpers import process_response_headers +from litellm.litellm_core_utils.url_utils import encode_url_path_segment from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( _safe_convert_created_field, ) @@ -300,7 +301,10 @@ class VolcEngineResponsesAPIConfig(OpenAIResponsesAPIConfig): litellm_params: GenericLiteLLMParams, headers: dict, ) -> Tuple[str, Dict]: - url = f"{api_base}/{response_id}" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}" data: Dict = {} return url, data @@ -333,7 +337,10 @@ class VolcEngineResponsesAPIConfig(OpenAIResponsesAPIConfig): litellm_params: GenericLiteLLMParams, headers: dict, ) -> Tuple[str, Dict]: - url = f"{api_base}/{response_id}" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}" data: Dict = {} return url, data @@ -372,7 +379,10 @@ class VolcEngineResponsesAPIConfig(OpenAIResponsesAPIConfig): limit: int = 20, order: Literal["asc", "desc"] = "desc", ) -> Tuple[str, Dict]: - url = f"{api_base}/{response_id}/input_items" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}/input_items" params: Dict[str, Any] = {} if after is not None: params["after"] = after @@ -408,7 +418,10 @@ class VolcEngineResponsesAPIConfig(OpenAIResponsesAPIConfig): litellm_params: GenericLiteLLMParams, headers: dict, ) -> Tuple[str, Dict]: - url = f"{api_base}/{response_id}/cancel" + encoded_response_id = encode_url_path_segment( + response_id, field_name="response_id" + ) + url = f"{api_base}/{encoded_response_id}/cancel" data: Dict = {} return url, data diff --git a/litellm/llms/voyage/rerank/transformation.py b/litellm/llms/voyage/rerank/transformation.py index 521dae980d5..d64450a1211 100644 --- a/litellm/llms/voyage/rerank/transformation.py +++ b/litellm/llms/voyage/rerank/transformation.py @@ -67,7 +67,11 @@ class VoyageRerankConfig(BaseRerankConfig): return api_base def transform_rerank_request( - self, model: str, optional_rerank_params: Dict, headers: Dict + self, + model: str, + optional_rerank_params: Dict, + headers: Dict, + litellm_params: Optional[dict] = None, ) -> Dict: return {"model": model, **optional_rerank_params} diff --git a/litellm/llms/watsonx/rerank/transformation.py b/litellm/llms/watsonx/rerank/transformation.py index 4f8e196f254..202760f68a6 100644 --- a/litellm/llms/watsonx/rerank/transformation.py +++ b/litellm/llms/watsonx/rerank/transformation.py @@ -143,6 +143,7 @@ class IBMWatsonXRerankConfig(IBMWatsonXMixin, BaseRerankConfig): model: str, optional_rerank_params: Dict, headers: dict, + litellm_params: Optional[dict] = None, ) -> dict: """ Transform request to IBM watsonx.ai rerank format diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py index bfa55105a6c..6300868a641 100644 --- a/litellm/llms/xai/chat/transformation.py +++ b/litellm/llms/xai/chat/transformation.py @@ -43,6 +43,7 @@ class XAIChatConfig(OpenAIGPTConfig): "logprobs", "max_tokens", "n", + "parallel_tool_calls", "presence_penalty", "response_format", "seed", @@ -222,8 +223,43 @@ class XAIChatConfig(OpenAIGPTConfig): self._enhance_usage_with_xai_web_search_fields(response, raw_response_json) except Exception as e: verbose_logger.debug(f"Error extracting X.AI web search usage: {e}") + + self._fold_reasoning_tokens_into_completion(response) return response + @staticmethod + def _fold_reasoning_tokens_into_completion(model_response: ModelResponse) -> None: + """Reconcile xAI Usage to the OpenAI invariant. + + xAI accounts ``reasoning_tokens`` separately from + ``completion_tokens`` while still summing them into ``total_tokens``. + OpenAI's contract (o1/o3) folds reasoning into ``completion_tokens``, + so fold here to keep ``total = prompt + completion``. Idempotent. + """ + usage = getattr(model_response, "usage", None) + if usage is None: + return + + details = getattr(usage, "completion_tokens_details", None) + reasoning_tokens = ( + int(getattr(details, "reasoning_tokens", 0) or 0) if details else 0 + ) + if reasoning_tokens <= 0: + return + + prompt_tokens = int(getattr(usage, "prompt_tokens", 0) or 0) + completion_tokens = int(getattr(usage, "completion_tokens", 0) or 0) + total_tokens = int(getattr(usage, "total_tokens", 0) or 0) + + if total_tokens == prompt_tokens + completion_tokens: + return + + # Guard against double-counting if xAI changes accounting. + if total_tokens != prompt_tokens + completion_tokens + reasoning_tokens: + return + + usage.completion_tokens = completion_tokens + reasoning_tokens + def _enhance_usage_with_xai_web_search_fields( self, model_response: ModelResponse, raw_response_json: dict ) -> None: diff --git a/litellm/llms/xai/cost_calculator.py b/litellm/llms/xai/cost_calculator.py index 0cfcfe98415..8edfd0c27ad 100644 --- a/litellm/llms/xai/cost_calculator.py +++ b/litellm/llms/xai/cost_calculator.py @@ -25,16 +25,25 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ - # XAI-specific completion cost calculation - # For XAI models, completion is billed as (visible completion tokens + reasoning tokens) + # XAI-specific completion cost: completion is billed as visible + reasoning + # tokens. Detect when the transformation layer already folded them so we + # don't double-count; fall back to raw xAI shape for callers that bypass + # the transformation (e.g. proxy logs replayed into cost calc). + prompt_tokens = int(getattr(usage, "prompt_tokens", 0) or 0) completion_tokens = int(getattr(usage, "completion_tokens", 0) or 0) + total_tokens = int(getattr(usage, "total_tokens", 0) or 0) reasoning_tokens = 0 if hasattr(usage, "completion_tokens_details") and usage.completion_tokens_details: reasoning_tokens = int( getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0 ) - total_completion_tokens = completion_tokens + reasoning_tokens + already_normalised = total_tokens == prompt_tokens + completion_tokens + total_completion_tokens = ( + completion_tokens + if already_normalised + else completion_tokens + reasoning_tokens + ) modified_usage = Usage( prompt_tokens=usage.prompt_tokens, diff --git a/litellm/llms/xai/realtime/handler.py b/litellm/llms/xai/realtime/handler.py index 805cce5a264..eab19f4a6c8 100644 --- a/litellm/llms/xai/realtime/handler.py +++ b/litellm/llms/xai/realtime/handler.py @@ -28,7 +28,12 @@ class XAIRealtime(OpenAIRealtime): """xAI uses a different API base URL.""" return XAI_API_BASE - def _get_additional_headers(self, api_key: str) -> dict: + def _get_additional_headers( + self, + api_key: str, + *, + openai_beta_realtime: bool = False, + ) -> dict: """ xAI does NOT require the OpenAI-Beta header. Only send Authorization header. diff --git a/litellm/main.py b/litellm/main.py index daa0fb063aa..c3d1c2e05b0 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -1,3 +1,5 @@ +# LiteLLM main module: public completion, embedding, streaming, and moderation entrypoints. +# # +-----------------------------------------------+ # | | # | Give Feedback / Get Help | @@ -59,7 +61,13 @@ import litellm from litellm import client # Other utils are imported directly to avoid circular imports -from litellm.utils import exception_type, get_litellm_params, get_optional_params +from litellm.utils import ( + exception_type, + get_litellm_params, + get_optional_params, + peek_reasoning_summary_aliases, + strip_reasoning_summary_aliases_from_optional_params, +) # Logging is imported lazily when needed to avoid loading litellm_logging at import time if TYPE_CHECKING: @@ -211,6 +219,12 @@ from .llms.oobabooga.chat import oobabooga from .llms.openai.completion.handler import OpenAITextCompletion from .llms.openai.image_variations.handler import OpenAIImageVariationsHandler from .llms.openai.openai import OpenAIChatCompletion +from .llms.nvidia_riva.audio_transcription.handler import ( + NvidiaRivaAudioTranscription, +) +from .llms.nvidia_riva.audio_transcription.transformation import ( + NvidiaRivaAudioTranscriptionConfig, +) from .llms.openai.transcriptions.handler import OpenAIAudioTranscription from .llms.openai_like.chat.handler import OpenAILikeChatHandler from .llms.openai_like.embedding.handler import OpenAILikeEmbeddingHandler @@ -266,6 +280,7 @@ from .types.utils import ( openai_chat_completions = OpenAIChatCompletion() openai_text_completions = OpenAITextCompletion() openai_audio_transcriptions = OpenAIAudioTranscription() +nvidia_riva_audio_transcriptions = NvidiaRivaAudioTranscription() openai_image_variations = OpenAIImageVariationsHandler() groq_chat_completions = GroqChatCompletion() sap_gen_ai_hub_chat_completions = GenAIHubOrchestration() @@ -939,6 +954,7 @@ def responses_api_bridge_check( web_search_options: Optional[OpenAIWebSearchOptions] = None, tools: Optional[List[Any]] = None, reasoning_effort: Optional[Any] = None, + reasoning_summary: Optional[Any] = None, ) -> Tuple[dict, str]: model_info: Dict[str, Any] = {} @@ -975,14 +991,23 @@ def responses_api_bridge_check( mode = "responses" model_info["mode"] = mode - # OpenAI/Azure gpt-5.4+ chat-completions calls with both tools + reasoning_effort - # must be bridged to Responses API. + # OpenAI/Azure GPT-5 chat-completions that need Responses-only fields (e.g. + # ``reasoningSummary`` in ``extra_body``) must be bridged; Chat Completions rejects + # those keys. + # + # - gpt-5.4+: tools + reasoning_effort (original) or any reasoning-summary alias. + # - Older GPT-5 names (e.g. ``gpt-5``, ``gpt-5.1``): bridge only when a reasoning + # summary alias is present with ``reasoning_effort`` (tools alone stay on chat). if ( custom_llm_provider in ("openai", "azure") - and OpenAIGPT5Config.is_model_gpt_5_4_plus_model(model) - and tools - and reasoning_effort is not None and model_info.get("mode") != "responses" + and OpenAIGPT5Config.is_model_gpt_5_model(model) + and not OpenAIGPT5Config.is_model_gpt_5_search_model(model) + and reasoning_effort is not None + and ( + reasoning_summary is not None + or (OpenAIGPT5Config.is_model_gpt_5_4_plus_model(model) and tools) + ) ): model_info["mode"] = "responses" model = model.replace("responses/", "") @@ -1452,14 +1477,14 @@ def completion( # type: ignore # noqa: PLR0915 if eos_token: custom_prompt_dict[model]["eos_token"] = eos_token - if kwargs.get("model_file_id_mapping"): - messages = update_messages_with_model_file_ids( - messages=messages, - model_id=kwargs.get("model_info", {}).get("id", None), - model_file_id_mapping=cast( - Dict[str, Dict[str, str]], kwargs.get("model_file_id_mapping") - ), - ) + messages = update_messages_with_model_file_ids( + messages=messages, + model_id=kwargs.get("model_info", {}).get("id", None), + model_file_id_mapping=cast( + Dict[str, Dict[str, str]], + kwargs.get("model_file_id_mapping") or {}, + ), + ) provider_config: Optional[BaseConfig] = None if custom_llm_provider is not None and custom_llm_provider in [ @@ -1503,7 +1528,11 @@ def completion( # type: ignore # noqa: PLR0915 "logit_bias": logit_bias, "user": user, # params to identify the model - "model": model, + "model": ( + model_info.get("base_model") + if isinstance(model_info, dict) and model_info.get("base_model") + else model + ), "custom_llm_provider": custom_llm_provider, "response_format": response_format, "seed": seed, @@ -1627,8 +1656,10 @@ def completion( # type: ignore # noqa: PLR0915 ## RESPONSES API BRIDGE LOGIC ## - check if model has 'mode: responses' in litellm.model_cost map # Only run the second bridge check if the first one didn't already # detect responses mode (e.g. via the "responses/" prefix). The second - # check handles cases like gpt-5.4+ with tools+reasoning_effort that - # the first (early) check doesn't cover. + # check handles cases like gpt-5.4+ with tools+reasoning_effort or + # reasoningSummary/reasoning_summary without tools (AI SDK) that the first + # (early) check doesn't cover. + _reasoning_summary_for_bridge = peek_reasoning_summary_aliases(optional_params) if responses_api_model_info.get("mode") != "responses": responses_api_model_info, model = responses_api_bridge_check( model=model, @@ -1636,14 +1667,29 @@ def completion( # type: ignore # noqa: PLR0915 web_search_options=web_search_options, tools=tools, reasoning_effort=reasoning_effort, + reasoning_summary=_reasoning_summary_for_bridge, ) if responses_api_model_info.get("mode") == "responses": from litellm.completion_extras import responses_api_bridge + optional_params, rs_val = ( + strip_reasoning_summary_aliases_from_optional_params(optional_params) + ) + if isinstance(reasoning_effort, dict) and "summary" in reasoning_effort: - optional_params = dict(optional_params) optional_params["reasoning_effort"] = reasoning_effort + elif rs_val is not None: + eff = optional_params.get("reasoning_effort", reasoning_effort) + if isinstance(eff, dict): + optional_params["reasoning_effort"] = {**eff, "summary": rs_val} + elif eff is not None: + optional_params["reasoning_effort"] = { + "effort": eff, + "summary": rs_val, + } + else: + optional_params["reasoning_effort"] = {"summary": rs_val} return responses_api_bridge.completion( model=model, @@ -1662,6 +1708,16 @@ def completion( # type: ignore # noqa: PLR0915 encoding=_get_encoding(), stream=stream, ) + elif ( + custom_llm_provider == "openai" + and OpenAIGPT5Config.is_model_gpt_5_model(model) + ) or ( + custom_llm_provider == "azure" + and litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model) + ): + optional_params, _ = strip_reasoning_summary_aliases_from_optional_params( + optional_params + ) if custom_llm_provider == "azure": # azure configs @@ -3806,7 +3862,33 @@ def completion( # type: ignore # noqa: PLR0915 ) bedrock_route = BedrockModelInfo.get_bedrock_route(model) - if bedrock_route == "converse": + if bedrock_route == "claude_platform": + provider_config = ProviderConfigManager.get_provider_chat_config( + model=model, + provider=LlmProviders.BEDROCK, + ) + model = BedrockModelInfo.get_claude_platform_model(model) + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider="bedrock", + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + provider_config=provider_config, + ) + return response + elif bedrock_route == "converse": model = model.replace("converse/", "") response = bedrock_converse_chat_completion.completion( model=model, @@ -4923,8 +5005,17 @@ def embedding( # noqa: PLR0915 if encoding_format is not None: optional_params["encoding_format"] = encoding_format else: - # Omiting causes openai sdk to add default value of "float" - optional_params["encoding_format"] = None + env_fmt = get_secret_str("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT") + if env_fmt is not None and env_fmt.strip().lower() == "none": + optional_params.pop("encoding_format", None) + else: + _default_fmt = ( + optional_params.get("encoding_format") or env_fmt or "float" + ) + if _default_fmt.strip().lower() == "none": + optional_params.pop("encoding_format", None) + else: + optional_params["encoding_format"] = _default_fmt api_version = None @@ -5311,6 +5402,7 @@ def embedding( # noqa: PLR0915 api_key=api_key, api_base=api_base, client=client, + litellm_params=litellm_params_dict, ) elif custom_llm_provider == "oobabooga": response = oobabooga.embedding( @@ -6595,6 +6687,26 @@ def transcription( litellm_params=litellm_params_dict, shared_session=shared_session, ) + elif custom_llm_provider == "nvidia_riva": + # NVIDIA Riva is gRPC-based, not HTTP. It has its own dedicated handler + # rather than going through base_llm_http_handler. + response = nvidia_riva_audio_transcriptions.audio_transcriptions( + model=model, + audio_file=file, + optional_params=optional_params, + litellm_params=litellm_params_dict, + model_response=model_response, + atranscription=atranscription, + timeout=timeout, + logging_obj=litellm_logging_obj, + api_base=api_base, + api_key=api_key, + provider_config=( + provider_config + if isinstance(provider_config, NvidiaRivaAudioTranscriptionConfig) + else None + ), + ) elif provider_config is not None: response = base_llm_http_handler.audio_transcriptions( model=model, diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 8511d785fb7..543172b8381 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -712,6 +712,7 @@ }, "anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.25e-06, + "cache_creation_input_token_cost_above_1hr": 2e-06, "cache_read_input_token_cost": 1e-07, "input_cost_per_token": 1e-06, "litellm_provider": "bedrock_converse", @@ -735,6 +736,7 @@ }, "anthropic.claude-haiku-4-5@20251001": { "cache_creation_input_token_cost": 1.25e-06, + "cache_creation_input_token_cost_above_1hr": 2e-06, "cache_read_input_token_cost": 1e-07, "input_cost_per_token": 1e-06, "litellm_provider": "bedrock_converse", @@ -955,6 +957,7 @@ }, "anthropic.claude-opus-4-5-20251101-v1:0": { "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 5e-06, "litellm_provider": "bedrock_converse", @@ -974,6 +977,7 @@ "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, + "supports_minimal_reasoning_effort": true, "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, @@ -982,6 +986,7 @@ }, "anthropic.claude-opus-4-6-v1": { "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 5e-06, "litellm_provider": "bedrock_converse", @@ -1011,6 +1016,7 @@ }, "global.anthropic.claude-opus-4-6-v1": { "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 5e-06, "litellm_provider": "bedrock_converse", @@ -1040,6 +1046,7 @@ }, "us.anthropic.claude-opus-4-6-v1": { "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, "input_cost_per_token": 5.5e-06, "litellm_provider": "bedrock_converse", @@ -1127,6 +1134,7 @@ }, "anthropic.claude-opus-4-7": { "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 5e-06, "litellm_provider": "bedrock_converse", @@ -1155,8 +1163,24 @@ "supports_max_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, + "anthropic.claude-mythos-preview": { + "input_cost_per_token": 0, + "output_cost_per_token": 0, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_vision": true, + "supports_prompt_caching": false, + "supports_reasoning": true, + "supports_minimal_reasoning_effort": true, + "supports_tool_choice": true + }, "global.anthropic.claude-opus-4-7": { "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 5e-06, "litellm_provider": "bedrock_converse", @@ -1187,6 +1211,7 @@ }, "us.anthropic.claude-opus-4-7": { "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, "input_cost_per_token": 5.5e-06, "litellm_provider": "bedrock_converse", @@ -1277,6 +1302,7 @@ }, "anthropic.claude-sonnet-4-6": { "cache_creation_input_token_cost": 3.75e-06, + "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "litellm_provider": "bedrock_converse", @@ -1297,6 +1323,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -1305,6 +1332,7 @@ }, "global.anthropic.claude-sonnet-4-6": { "cache_creation_input_token_cost": 3.75e-06, + "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "litellm_provider": "bedrock_converse", @@ -1325,6 +1353,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -1333,6 +1362,7 @@ }, "us.anthropic.claude-sonnet-4-6": { "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, "input_cost_per_token": 3.3e-06, "litellm_provider": "bedrock_converse", @@ -1353,6 +1383,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -1381,6 +1412,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -1409,6 +1441,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -1447,11 +1480,13 @@ }, "anthropic.claude-sonnet-4-5-20250929-v1:0": { "cache_creation_input_token_cost": 3.75e-06, + "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.2e-05, "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, @@ -1901,6 +1936,7 @@ "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, + "supports_minimal_reasoning_effort": true, "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true @@ -2024,6 +2060,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -3484,7 +3521,7 @@ "supports_tool_choice": true }, "azure/gpt-4o-mini-transcribe": { - "input_cost_per_audio_token": 3e-06, + "input_cost_per_audio_token": 1.25e-06, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 16000, @@ -3559,7 +3596,7 @@ "supports_tool_choice": true }, "azure/gpt-4o-transcribe": { - "input_cost_per_audio_token": 6e-06, + "input_cost_per_audio_token": 2.5e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 16000, @@ -3571,7 +3608,7 @@ ] }, "azure/gpt-4o-transcribe-diarize": { - "input_cost_per_audio_token": 6e-06, + "input_cost_per_audio_token": 2.5e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 16000, @@ -5103,6 +5140,38 @@ "/v1/images/edits" ] }, + "azure/gpt-image-2": { + "cache_read_input_image_token_cost": 2e-06, + "cache_read_input_token_cost": 1.25e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_image_token": 8e-06, + "litellm_provider": "azure", + "mode": "image_generation", + "output_cost_per_token": 1e-05, + "output_cost_per_image_token": 3e-05, + "supported_endpoints": [ + "/v1/images/generations", + "/v1/images/edits" + ], + "supports_vision": true, + "supports_pdf_input": true + }, + "azure/gpt-image-2-2026-04-21": { + "cache_read_input_image_token_cost": 2e-06, + "cache_read_input_token_cost": 1.25e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_image_token": 8e-06, + "litellm_provider": "azure", + "mode": "image_generation", + "output_cost_per_token": 1e-05, + "output_cost_per_image_token": 3e-05, + "supported_endpoints": [ + "/v1/images/generations", + "/v1/images/edits" + ], + "supports_vision": true, + "supports_pdf_input": true + }, "azure/low/1024-x-1024/gpt-image-1-mini": { "input_cost_per_pixel": 2.0751953125e-09, "litellm_provider": "azure", @@ -8905,7 +8974,7 @@ "supports_vision": true }, "gpt-4o-transcribe-diarize": { - "input_cost_per_audio_token": 6e-06, + "input_cost_per_audio_token": 2.5e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", "max_input_tokens": 16000, @@ -9166,6 +9235,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -9301,6 +9371,7 @@ "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, + "supports_minimal_reasoning_effort": true, "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, @@ -9328,6 +9399,7 @@ "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, + "supports_minimal_reasoning_effort": true, "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, @@ -9431,7 +9503,6 @@ "us": 1.1, "fast": 6.0 }, - "supports_max_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "claude-opus-4-7-20260416": { @@ -9466,7 +9537,6 @@ "us": 1.1, "fast": 6.0 }, - "supports_max_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "claude-sonnet-4-20250514": { @@ -10744,6 +10814,7 @@ "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, + "supports_minimal_reasoning_effort": true, "supports_tool_choice": true }, "databricks/databricks-claude-sonnet-4": { @@ -15480,14 +15551,17 @@ "uses_embed_content": true }, "vertex_ai/gemini-embedding-2-preview": { - "input_cost_per_token": 1.5e-07, + "input_cost_per_audio_per_second": 0.00016, + "input_cost_per_image": 0.00012, + "input_cost_per_token": 2e-07, + "input_cost_per_video_per_second": 0.00079, "litellm_provider": "vertex_ai", "max_input_tokens": 8192, "max_tokens": 8192, "mode": "embedding", "output_cost_per_token": 0, "output_vector_size": 3072, - "source": "https://ai.google.dev/gemini-api/docs/embeddings#multimodal", + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "supports_multimodal": true, "uses_embed_content": true }, @@ -17104,7 +17178,8 @@ ], "supports_function_calling": true, "supports_parallel_function_calling": true, - "supports_vision": true + "supports_vision": true, + "supports_minimal_reasoning_effort": true }, "github_copilot/claude-opus-4.6-fast": { "litellm_provider": "github_copilot", @@ -17617,7 +17692,8 @@ "mode": "chat", "output_cost_per_token": 2.5e-05, "supports_function_calling": true, - "supports_vision": true + "supports_vision": true, + "supports_minimal_reasoning_effort": true }, "gmi/anthropic/claude-sonnet-4.5": { "input_cost_per_token": 3e-06, @@ -17889,11 +17965,13 @@ }, "global.anthropic.claude-sonnet-4-5-20250929-v1:0": { "cache_creation_input_token_cost": 3.75e-06, + "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.2e-05, "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, @@ -17950,6 +18028,7 @@ }, "global.anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.25e-06, + "cache_creation_input_token_cost_above_1hr": 2e-06, "cache_read_input_token_cost": 1e-07, "input_cost_per_token": 1e-06, "litellm_provider": "bedrock_converse", @@ -18912,7 +18991,7 @@ "supports_vision": true }, "gpt-4o-mini-transcribe": { - "input_cost_per_audio_token": 3e-06, + "input_cost_per_audio_token": 1.25e-06, "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 16000, @@ -19042,7 +19121,7 @@ "supports_vision": true }, "gpt-4o-transcribe": { - "input_cost_per_audio_token": 6e-06, + "input_cost_per_audio_token": 2.5e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "openai", "max_input_tokens": 16000, @@ -19083,6 +19162,38 @@ "supports_vision": true, "supports_pdf_input": true }, + "gpt-image-2": { + "cache_read_input_image_token_cost": 2e-06, + "cache_read_input_token_cost": 1.25e-06, + "input_cost_per_token": 5e-06, + "litellm_provider": "openai", + "mode": "image_generation", + "output_cost_per_token": 1e-05, + "input_cost_per_image_token": 8e-06, + "output_cost_per_image_token": 3e-05, + "supported_endpoints": [ + "/v1/images/generations", + "/v1/images/edits" + ], + "supports_vision": true, + "supports_pdf_input": true + }, + "gpt-image-2-2026-04-21": { + "cache_read_input_image_token_cost": 2e-06, + "cache_read_input_token_cost": 1.25e-06, + "input_cost_per_token": 5e-06, + "litellm_provider": "openai", + "mode": "image_generation", + "output_cost_per_token": 1e-05, + "input_cost_per_image_token": 8e-06, + "output_cost_per_image_token": 3e-05, + "supported_endpoints": [ + "/v1/images/generations", + "/v1/images/edits" + ], + "supports_vision": true, + "supports_pdf_input": true + }, "low/1024-x-1024/gpt-image-1.5": { "input_cost_per_image": 0.009, "litellm_provider": "openai", @@ -19847,7 +19958,7 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": false }, "gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5e-07, @@ -19895,7 +20006,7 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": false }, "gpt-5.5-pro": { "cache_read_input_token_cost": 3e-06, @@ -19938,7 +20049,8 @@ "supports_web_search": true, "supports_none_reasoning_effort": false, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": false, + "supports_low_reasoning_effort": false }, "gpt-5.5-pro-2026-04-23": { "cache_read_input_token_cost": 3e-06, @@ -19981,7 +20093,8 @@ "supports_web_search": true, "supports_none_reasoning_effort": false, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": false, + "supports_low_reasoning_effort": false }, "gpt-5.4": { "cache_read_input_token_cost": 2.5e-07, @@ -20994,6 +21107,38 @@ "supports_system_messages": true, "supports_tool_choice": true }, + "gpt-realtime-2": { + "cache_creation_input_audio_token_cost": 4e-07, + "cache_read_input_token_cost": 4e-07, + "input_cost_per_audio_token": 3.2e-05, + "input_cost_per_image": 5e-06, + "input_cost_per_token": 4e-06, + "litellm_provider": "openai", + "max_input_tokens": 32000, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_audio_token": 6.4e-05, + "output_cost_per_token": 1.6e-05, + "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 + }, "gpt-realtime-mini": { "cache_creation_input_audio_token_cost": 3e-07, "cache_read_input_audio_token_cost": 3e-07, @@ -21059,7 +21204,7 @@ }, "gradient_ai/alibaba-qwen3-32b": { "litellm_provider": "gradient_ai", - "max_tokens": 2048, + "max_tokens": 40960, "mode": "chat", "supported_endpoints": [ "/v1/chat/completions" @@ -21067,7 +21212,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 131072, + "max_output_tokens": 40960 }, "gradient_ai/anthropic-claude-3-opus": { "input_cost_per_token": 1.5e-05, @@ -21081,7 +21228,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 200000, + "max_output_tokens": 1024 }, "gradient_ai/anthropic-claude-3.5-haiku": { "input_cost_per_token": 8e-07, @@ -21095,7 +21244,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 200000, + "max_output_tokens": 1024 }, "gradient_ai/anthropic-claude-3.5-sonnet": { "input_cost_per_token": 3e-06, @@ -21109,7 +21260,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 200000, + "max_output_tokens": 1024 }, "gradient_ai/anthropic-claude-3.7-sonnet": { "input_cost_per_token": 3e-06, @@ -21123,7 +21276,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 200000, + "max_output_tokens": 1024 }, "gradient_ai/deepseek-r1-distill-llama-70b": { "input_cost_per_token": 9.9e-07, @@ -21137,7 +21292,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 32768, + "max_output_tokens": 8000 }, "gradient_ai/llama3-8b-instruct": { "input_cost_per_token": 2e-07, @@ -21151,7 +21308,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 8192, + "max_output_tokens": 512 }, "gradient_ai/llama3.3-70b-instruct": { "input_cost_per_token": 6.5e-07, @@ -21165,7 +21324,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 128000, + "max_output_tokens": 2048 }, "gradient_ai/mistral-nemo-instruct-2407": { "input_cost_per_token": 3e-07, @@ -21179,7 +21340,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 128000, + "max_output_tokens": 512 }, "gradient_ai/openai-gpt-4o": { "litellm_provider": "gradient_ai", @@ -21191,7 +21354,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 128000, + "max_output_tokens": 16384 }, "gradient_ai/openai-gpt-4o-mini": { "litellm_provider": "gradient_ai", @@ -21203,7 +21368,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 128000, + "max_output_tokens": 16384 }, "gradient_ai/openai-o3": { "input_cost_per_token": 2e-06, @@ -21217,7 +21384,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 200000, + "max_output_tokens": 100000 }, "gradient_ai/openai-o3-mini": { "input_cost_per_token": 1.1e-06, @@ -21231,7 +21400,9 @@ "supported_modalities": [ "text" ], - "supports_tool_choice": false + "supports_tool_choice": false, + "max_input_tokens": 200000, + "max_output_tokens": 100000 }, "lemonade/Qwen3-Coder-30B-A3B-Instruct-GGUF": { "input_cost_per_token": 0, @@ -21531,11 +21702,13 @@ }, "heroku/claude-3-5-haiku": { "litellm_provider": "heroku", - "max_tokens": 4096, + "max_tokens": 8192, "mode": "chat", "supports_function_calling": true, "supports_system_messages": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 200000, + "max_output_tokens": 8192 }, "heroku/claude-3-5-sonnet-latest": { "litellm_provider": "heroku", @@ -21543,7 +21716,9 @@ "mode": "chat", "supports_function_calling": true, "supports_system_messages": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 200000, + "max_output_tokens": 8192 }, "heroku/claude-3-7-sonnet": { "litellm_provider": "heroku", @@ -21551,7 +21726,9 @@ "mode": "chat", "supports_function_calling": true, "supports_system_messages": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 200000, + "max_output_tokens": 8192 }, "heroku/claude-4-sonnet": { "litellm_provider": "heroku", @@ -21559,7 +21736,9 @@ "mode": "chat", "supports_function_calling": true, "supports_system_messages": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 200000, + "max_output_tokens": 8192 }, "high/1024-x-1024/gpt-image-1": { "input_cost_per_image": 0.167, @@ -21980,6 +22159,98 @@ "tool_use_system_prompt_tokens": 346, "supports_native_structured_output": true }, + "crusoe/deepseek-ai/DeepSeek-R1-0528": { + "input_cost_per_token": 3e-06, + "litellm_provider": "crusoe", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 7e-06, + "supports_function_calling": false, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": false + }, + "crusoe/deepseek-ai/DeepSeek-V3-0324": { + "input_cost_per_token": 1.5e-06, + "litellm_provider": "crusoe", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "crusoe/google/gemma-3-12b-it": { + "input_cost_per_token": 1e-07, + "litellm_provider": "crusoe", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "crusoe/meta-llama/Llama-3.3-70B-Instruct": { + "input_cost_per_token": 2e-07, + "litellm_provider": "crusoe", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 2e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "crusoe/moonshotai/Kimi-K2-Thinking": { + "input_cost_per_token": 2.5e-06, + "litellm_provider": "crusoe", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "supports_function_calling": false, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": false + }, + "crusoe/openai/gpt-oss-120b": { + "input_cost_per_token": 8e-07, + "litellm_provider": "crusoe", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 8e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507": { + "input_cost_per_token": 3e-06, + "litellm_provider": "crusoe", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, "lambda_ai/deepseek-llama3.3-70b": { "input_cost_per_token": 2e-07, "litellm_provider": "lambda_ai", @@ -22275,48 +22546,6 @@ "/v1/images/generations" ] }, - "luminous-base": { - "input_cost_per_token": 3e-05, - "litellm_provider": "aleph_alpha", - "max_tokens": 2048, - "mode": "completion", - "output_cost_per_token": 3.3e-05 - }, - "luminous-base-control": { - "input_cost_per_token": 3.75e-05, - "litellm_provider": "aleph_alpha", - "max_tokens": 2048, - "mode": "chat", - "output_cost_per_token": 4.125e-05 - }, - "luminous-extended": { - "input_cost_per_token": 4.5e-05, - "litellm_provider": "aleph_alpha", - "max_tokens": 2048, - "mode": "completion", - "output_cost_per_token": 4.95e-05 - }, - "luminous-extended-control": { - "input_cost_per_token": 5.625e-05, - "litellm_provider": "aleph_alpha", - "max_tokens": 2048, - "mode": "chat", - "output_cost_per_token": 6.1875e-05 - }, - "luminous-supreme": { - "input_cost_per_token": 0.000175, - "litellm_provider": "aleph_alpha", - "max_tokens": 2048, - "mode": "completion", - "output_cost_per_token": 0.0001925 - }, - "luminous-supreme-control": { - "input_cost_per_token": 0.00021875, - "litellm_provider": "aleph_alpha", - "max_tokens": 2048, - "mode": "chat", - "output_cost_per_token": 0.000240625 - }, "max-x-max/50-steps/stability.stable-diffusion-xl-v0": { "litellm_provider": "bedrock", "max_input_tokens": 77, @@ -25788,12 +26017,14 @@ "input_cost_per_image": 0.0004, "input_cost_per_token": 2.5e-07, "litellm_provider": "openrouter", - "max_tokens": 200000, + "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.25e-06, "supports_function_calling": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "max_input_tokens": 200000, + "max_output_tokens": 4096 }, "openrouter/anthropic/claude-3.5-sonnet": { "input_cost_per_token": 3e-06, @@ -25912,6 +26143,7 @@ "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 159, @@ -25930,6 +26162,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_minimal_reasoning_effort": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -25951,6 +26184,7 @@ "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -25999,6 +26233,29 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 346 }, + "openrouter/anthropic/claude-opus-4.7": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 5e-06, + "litellm_provider": "openrouter", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_max_reasoning_effort": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true, + "tool_use_system_prompt_tokens": 346 + }, "openrouter/bytedance/ui-tars-1.5-7b": { "input_cost_per_token": 1e-07, "litellm_provider": "openrouter", @@ -26364,18 +26621,22 @@ "openrouter/mancer/weaver": { "input_cost_per_token": 5.625e-06, "litellm_provider": "openrouter", - "max_tokens": 8000, + "max_tokens": 2000, "mode": "chat", "output_cost_per_token": 5.625e-06, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 8000, + "max_output_tokens": 2000 }, "openrouter/meta-llama/llama-3-70b-instruct": { "input_cost_per_token": 5.9e-07, "litellm_provider": "openrouter", - "max_tokens": 8192, + "max_tokens": 8000, "mode": "chat", "output_cost_per_token": 7.9e-07, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 8192, + "max_output_tokens": 8000 }, "openrouter/minimax/minimax-m2": { "input_cost_per_token": 2.55e-07, @@ -26463,34 +26724,42 @@ "openrouter/mistralai/mistral-7b-instruct": { "input_cost_per_token": 1.3e-07, "litellm_provider": "openrouter", - "max_tokens": 8192, + "max_tokens": 8191, "mode": "chat", "output_cost_per_token": 1.3e-07, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 32768, + "max_output_tokens": 8191 }, "openrouter/mistralai/mistral-large": { "input_cost_per_token": 8e-06, "litellm_provider": "openrouter", - "max_tokens": 32000, + "max_tokens": 8191, "mode": "chat", "output_cost_per_token": 2.4e-05, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 128000, + "max_output_tokens": 8191 }, "openrouter/mistralai/mistral-small-3.1-24b-instruct": { "input_cost_per_token": 1e-07, "litellm_provider": "openrouter", - "max_tokens": 32000, + "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 3e-07, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 131072, + "max_output_tokens": 131072 }, "openrouter/mistralai/mistral-small-3.2-24b-instruct": { "input_cost_per_token": 1e-07, "litellm_provider": "openrouter", - "max_tokens": 32000, + "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 3e-07, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 128000, + "max_output_tokens": 128000 }, "openrouter/mistralai/mixtral-8x22b-instruct": { "input_cost_per_token": 6.5e-07, @@ -26498,7 +26767,9 @@ "max_tokens": 65536, "mode": "chat", "output_cost_per_token": 6.5e-07, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 65536, + "max_output_tokens": 65536 }, "openrouter/moonshotai/kimi-k2.5": { "cache_read_input_token_cost": 1e-07, @@ -26518,26 +26789,32 @@ "openrouter/openai/gpt-3.5-turbo": { "input_cost_per_token": 1.5e-06, "litellm_provider": "openrouter", - "max_tokens": 4095, + "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 2e-06, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 16385, + "max_output_tokens": 4096 }, "openrouter/openai/gpt-3.5-turbo-16k": { "input_cost_per_token": 3e-06, "litellm_provider": "openrouter", - "max_tokens": 16383, + "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 4e-06, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 16385, + "max_output_tokens": 4096 }, "openrouter/openai/gpt-4": { "input_cost_per_token": 3e-05, "litellm_provider": "openrouter", - "max_tokens": 8192, + "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-05, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 8191, + "max_output_tokens": 4096 }, "openrouter/openai/gpt-4.1": { "cache_read_input_token_cost": 5e-07, @@ -26945,6 +27222,20 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "openrouter/qwen/qwen3.6-plus": { + "input_cost_per_token": 3.25e-07, + "litellm_provider": "openrouter", + "max_input_tokens": 1000000, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 1.95e-06, + "source": "https://openrouter.ai/qwen/qwen3.6-plus", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, "openrouter/qwen/qwen3.5-35b-a3b": { "input_cost_per_token": 2.5e-07, "litellm_provider": "openrouter", @@ -27045,10 +27336,12 @@ "openrouter/undi95/remm-slerp-l2-13b": { "input_cost_per_token": 1.875e-06, "litellm_provider": "openrouter", - "max_tokens": 6144, + "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.875e-06, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 6144, + "max_output_tokens": 4096 }, "openrouter/x-ai/grok-4": { "input_cost_per_token": 3e-06, @@ -27851,7 +28144,8 @@ "mode": "responses", "supports_web_search": true, "supports_reasoning": false, - "supports_function_calling": true + "supports_function_calling": true, + "supports_minimal_reasoning_effort": true }, "perplexity/anthropic/claude-sonnet-4-5": { "litellm_provider": "perplexity", @@ -28629,6 +28923,19 @@ "mode": "chat", "output_cost_per_token": 0.0 }, + "sambanova/MiniMax-M2.7": { + "input_cost_per_token": 3e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 204800, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "sambanova/DeepSeek-R1": { "input_cost_per_token": 5e-06, "litellm_provider": "sambanova", @@ -29669,14 +29976,16 @@ "together_ai/deepseek-ai/DeepSeek-V3.1": { "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", - "max_tokens": 128000, + "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.7e-06, "source": "https://www.together.ai/models/deepseek-v3-1", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "max_input_tokens": 128000, + "max_output_tokens": 16384 }, "together_ai/meta-llama/Llama-3.2-3B-Instruct-Turbo": { "litellm_provider": "together_ai", @@ -30052,6 +30361,7 @@ }, "us.anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.375e-06, + "cache_creation_input_token_cost_above_1hr": 2.2e-06, "cache_read_input_token_cost": 1.1e-07, "input_cost_per_token": 1.1e-06, "litellm_provider": "bedrock_converse", @@ -30203,11 +30513,13 @@ }, "us.anthropic.claude-sonnet-4-5-20250929-v1:0": { "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, "input_cost_per_token": 3.3e-06, "input_cost_per_token_above_200k_tokens": 6.6e-06, "output_cost_per_token_above_200k_tokens": 2.475e-05, "cache_creation_input_token_cost_above_200k_tokens": 8.25e-06, + "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.32e-05, "cache_read_input_token_cost_above_200k_tokens": 6.6e-07, "litellm_provider": "bedrock_converse", "max_input_tokens": 200000, @@ -30308,6 +30620,7 @@ }, "us.anthropic.claude-opus-4-5-20251101-v1:0": { "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, "input_cost_per_token": 5.5e-06, "litellm_provider": "bedrock_converse", @@ -30324,6 +30637,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_minimal_reasoning_effort": true, "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -30335,6 +30649,7 @@ }, "global.anthropic.claude-opus-4-5-20251101-v1:0": { "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 5e-06, "litellm_provider": "bedrock_converse", @@ -30351,6 +30666,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_minimal_reasoning_effort": true, "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -30378,6 +30694,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_minimal_reasoning_effort": true, "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -30958,6 +31275,7 @@ "output_cost_per_token": 2.5e-05, "supports_assistant_prefill": true, "supports_computer_use": true, + "supports_minimal_reasoning_effort": true, "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -32150,6 +32468,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_minimal_reasoning_effort": true, "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -32176,6 +32495,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_minimal_reasoning_effort": true, "supports_pdf_input": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -32342,6 +32662,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -33251,6 +33572,72 @@ "source": "https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/gpt-oss-120b-maas", "supports_reasoning": true }, + "vertex_ai/xai/grok-4.1-fast-non-reasoning": { + "cache_read_input_token_cost": 5e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "vertex_ai", + "max_input_tokens": 2000000, + "max_output_tokens": 2000000, + "max_tokens": 2000000, + "mode": "chat", + "output_cost_per_token": 5e-07, + "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "vertex_ai/xai/grok-4.1-fast-reasoning": { + "cache_read_input_token_cost": 5e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "vertex_ai", + "max_input_tokens": 2000000, + "max_output_tokens": 2000000, + "max_tokens": 2000000, + "mode": "chat", + "output_cost_per_token": 5e-07, + "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "vertex_ai/xai/grok-4.20-non-reasoning": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 2e-06, + "litellm_provider": "vertex_ai", + "max_input_tokens": 2000000, + "max_output_tokens": 2000000, + "max_tokens": 2000000, + "mode": "chat", + "output_cost_per_token": 6e-06, + "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "vertex_ai/xai/grok-4.20-reasoning": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 2e-06, + "litellm_provider": "vertex_ai", + "max_input_tokens": 2000000, + "max_output_tokens": 2000000, + "max_tokens": 2000000, + "mode": "chat", + "output_cost_per_token": 6e-06, + "source": "https://docs.x.ai/docs/models (Vertex AI Model Garden)", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, "vertex_ai/qwen/qwen3-235b-a22b-instruct-2507-maas": { "input_cost_per_token": 2.5e-07, "litellm_provider": "vertex_ai-qwen_models", @@ -34180,6 +34567,7 @@ "output_cost_per_token": 1.5e-05, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, "supports_web_search": true @@ -34195,6 +34583,7 @@ "output_cost_per_token": 1.5e-05, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, "supports_web_search": true @@ -34210,6 +34599,7 @@ "output_cost_per_token": 2.5e-05, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, "supports_web_search": true @@ -34225,6 +34615,7 @@ "output_cost_per_token": 2.5e-05, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, "supports_web_search": true @@ -34240,6 +34631,7 @@ "output_cost_per_token": 1.5e-05, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_response_schema": false, "supports_tool_choice": true, "supports_web_search": true @@ -34256,6 +34648,7 @@ "output_cost_per_token": 5e-07, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -34273,6 +34666,7 @@ "output_cost_per_token": 5e-07, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -34289,6 +34683,7 @@ "output_cost_per_token": 4e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -34305,6 +34700,7 @@ "output_cost_per_token": 4e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -34321,6 +34717,7 @@ "output_cost_per_token": 4e-06, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -34337,6 +34734,7 @@ "output_cost_per_token": 5e-07, "source": "https://x.ai/api#pricing", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -34352,38 +34750,41 @@ "output_cost_per_token": 1.5e-05, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true, "supports_web_search": true }, "xai/grok-4-fast-reasoning": { + "cache_read_input_token_cost": 5e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_128k_tokens": 4e-07, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, "max_tokens": 2000000.0, "mode": "chat", - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, "output_cost_per_token": 5e-07, "output_cost_per_token_above_128k_tokens": 1e-06, - "cache_read_input_token_cost": 5e-08, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true, "supports_web_search": true }, "xai/grok-4-fast-non-reasoning": { + "cache_read_input_token_cost": 5e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_128k_tokens": 4e-07, "litellm_provider": "xai", "max_input_tokens": 2000000.0, "max_output_tokens": 2000000.0, - "cache_read_input_token_cost": 5e-08, "max_tokens": 2000000.0, "mode": "chat", - "input_cost_per_token": 2e-07, - "input_cost_per_token_above_128k_tokens": 4e-07, "output_cost_per_token": 5e-07, "output_cost_per_token_above_128k_tokens": 1e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true, "supports_web_search": true }, @@ -34399,6 +34800,7 @@ "output_cost_per_token_above_128k_tokens": 3e-05, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true, "supports_web_search": true }, @@ -34414,6 +34816,7 @@ "output_cost_per_token_above_128k_tokens": 3e-05, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_tool_choice": true, "supports_web_search": true }, @@ -34431,6 +34834,7 @@ "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -34451,6 +34855,7 @@ "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -34471,6 +34876,7 @@ "source": "https://docs.x.ai/docs/models/grok-4-1-fast-reasoning", "supports_audio_input": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -34491,6 +34897,7 @@ "source": "https://docs.x.ai/docs/models/grok-4-1-fast-non-reasoning", "supports_audio_input": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, @@ -34510,6 +34917,7 @@ "source": "https://docs.x.ai/docs/models/grok-4-1-fast-non-reasoning", "supports_audio_input": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, @@ -34526,6 +34934,7 @@ "output_cost_per_token": 6e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, @@ -34542,6 +34951,7 @@ "output_cost_per_token": 6e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, @@ -34574,6 +34984,49 @@ "output_cost_per_token": 6e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "xai/grok-4.3": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "input_cost_per_token": 1.25e-06, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "litellm_provider": "xai", + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, + "xai/grok-4.3-latest": { + "cache_read_input_token_cost": 2e-07, + "cache_read_input_token_cost_above_200k_tokens": 4e-07, + "input_cost_per_token": 1.25e-06, + "input_cost_per_token_above_200k_tokens": 2.5e-06, + "litellm_provider": "xai", + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "output_cost_per_token_above_200k_tokens": 5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true @@ -34602,6 +35055,7 @@ "output_cost_per_token": 1.5e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -34616,6 +35070,7 @@ "output_cost_per_token": 1.5e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -34630,6 +35085,7 @@ "output_cost_per_token": 1.5e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, @@ -34661,6 +35117,20 @@ "supports_tool_choice": true, "source": "https://aws.amazon.com/bedrock/pricing/" }, + "zai.glm-5": { + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3.2e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "zai.glm-4.7-flash": { "input_cost_per_token": 7e-08, "litellm_provider": "bedrock_converse", @@ -38463,7 +38933,7 @@ ] }, "gpt-4o-mini-transcribe-2025-03-20": { - "input_cost_per_audio_token": 3e-06, + "input_cost_per_audio_token": 1.25e-06, "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 16000, @@ -38475,7 +38945,7 @@ ] }, "gpt-4o-mini-transcribe-2025-12-15": { - "input_cost_per_audio_token": 3e-06, + "input_cost_per_audio_token": 1.25e-06, "input_cost_per_token": 1.25e-06, "litellm_provider": "openai", "max_input_tokens": 16000, @@ -39246,6 +39716,7 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, + "supports_max_reasoning_effort": true, "supports_tool_choice": true, "supports_vision": true, "tool_use_system_prompt_tokens": 346, @@ -39470,6 +39941,87 @@ } ] }, + "zai.glm-5": { + "input_cost_per_token": 1e-06, + "output_cost_per_token": 3.2e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-east-1/zai.glm-5": { + "input_cost_per_token": 1e-06, + "output_cost_per_token": 3.2e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-west-2/zai.glm-5": { + "input_cost_per_token": 1e-06, + "output_cost_per_token": 3.2e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "minimax.minimax-m2.5": { + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-east-1/minimax.minimax-m2.5": { + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, + "bedrock/us-west-2/minimax.minimax-m2.5": { + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "bedrock/us-gov-east-1/anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.5e-06, "cache_read_input_token_cost": 1.2e-07, diff --git a/litellm/ocr/main.py b/litellm/ocr/main.py index 5d73ddc8972..b27082c361a 100644 --- a/litellm/ocr/main.py +++ b/litellm/ocr/main.py @@ -10,7 +10,6 @@ import os import re from functools import partial from io import IOBase -from pathlib import Path from typing import Any, Coroutine, Dict, Optional, Union import httpx @@ -376,11 +375,13 @@ def convert_file_document_to_url_document(document: Dict[str, Any]) -> Dict[str, with an inline base64 data URI. Accepts document dicts like: - {"type": "file", "file": "/path/to/document.pdf"} # file path string {"type": "file", "file": Path("/path/to/doc.pdf")} # pathlib.Path {"type": "file", "file": } # file-like object (BinaryIO) {"type": "file", "file": b"raw bytes"} # raw bytes + Bare ``str`` paths are not accepted — pass a ``pathlib.Path`` or + ``open(path, "rb")`` instead. See the str check below for the rationale. + Returns: {"type": "document_url", "document_url": "data:;base64,"} or {"type": "image_url", "image_url": "data:;base64,"} @@ -389,14 +390,28 @@ def convert_file_document_to_url_document(document: Dict[str, Any]) -> Dict[str, if file_input is None: raise ValueError( "document with type='file' must include a 'file' field containing " - "a file path (str), pathlib.Path, file-like object, or bytes" + "a pathlib.Path, file-like object, or bytes" ) file_bytes: bytes mime_type: str = "application/octet-stream" file_name: Optional[str] = None - if isinstance(file_input, (str, Path)): + if isinstance(file_input, str): + # Bare strings are rejected here. The OCR ``document`` accepts a + # ``{"type": "file", "file": }`` shape, and when this helper + # runs in a proxy request handler ```` is attacker-controlled. + # Opening it as a path is an arbitrary local file read on the proxy + # host, which is then base64-encoded and forwarded to the OCR + # provider — an exfiltration primitive. + raise ValueError( + "OCR file input does not accept bare str values. Pass bytes, " + "a pathlib.Path, or a file-like object. To OCR a local file " + "from a path, call open(path, 'rb') yourself." + ) + if isinstance(file_input, os.PathLike): + # os.PathLike (pathlib.Path and custom __fspath__ classes) is a + # Python-level type that HTTP form values can't fabricate. file_path = str(file_input) if not os.path.isfile(file_path): raise FileNotFoundError(f"File not found: {file_path}") @@ -417,7 +432,7 @@ def convert_file_document_to_url_document(document: Dict[str, Any]) -> Dict[str, else: raise ValueError( f"Unsupported file input type: {type(file_input)}. " - "Expected str (file path), pathlib.Path, bytes, or a file-like object." + "Expected pathlib.Path, bytes, or a file-like object." ) if not file_bytes: diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index edee50bdfc4..c4c9aea6f64 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -21,6 +21,7 @@ import httpx from httpx._types import CookieTypes, QueryParamTypes, RequestFiles import litellm +from litellm._logging import verbose_logger from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler @@ -390,19 +391,28 @@ def _sync_streaming( ): from litellm.utils import executor + raw_bytes: List[bytes] = [] + flush_scheduled = False try: - raw_bytes: List[bytes] = [] for chunk in response.iter_bytes(): # type: ignore raw_bytes.append(chunk) yield chunk - - executor.submit( - litellm_logging_obj.flush_passthrough_collected_chunks, - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - except Exception as e: - raise e + finally: + if not flush_scheduled and raw_bytes: + flush_scheduled = True + try: + executor.submit( + litellm_logging_obj.flush_passthrough_collected_chunks, + raw_bytes=raw_bytes, + provider_config=provider_config, + ) + except Exception as e: + verbose_logger.exception( + "Failed to schedule passthrough spend-tracking flush " + "in _sync_streaming; %d buffered chunks dropped: %s", + len(raw_bytes), + e, + ) async def _async_streaming( @@ -411,23 +421,45 @@ async def _async_streaming( provider_config: "BasePassthroughConfig", ): iter_response = await response + try: iter_response.raise_for_status() - raw_bytes: List[bytes] = [] - - async for chunk in iter_response.aiter_bytes(): # type: ignore - raw_bytes.append(chunk) - yield chunk - - asyncio.create_task( - litellm_logging_obj.async_flush_passthrough_collected_chunks( - raw_bytes=raw_bytes, - provider_config=provider_config, - ) - ) except Exception: try: await iter_response.aclose() except Exception: pass raise + + raw_bytes: List[bytes] = [] + flush_scheduled = False + try: + async for chunk in iter_response.aiter_bytes(): # type: ignore + raw_bytes.append(chunk) + yield chunk + except Exception: + try: + await iter_response.aclose() + except Exception: + pass + raise + finally: + # GeneratorExit (raised on client disconnect) is not caught by + # `except Exception`; the finally block ensures partial usage + # still gets flushed for spend tracking. See LIT-2642. + if not flush_scheduled and raw_bytes: + flush_scheduled = True + try: + asyncio.create_task( + litellm_logging_obj.async_flush_passthrough_collected_chunks( + raw_bytes=raw_bytes, + provider_config=provider_config, + ) + ) + except Exception as e: + verbose_logger.exception( + "Failed to schedule passthrough spend-tracking flush " + "in _async_streaming; %d buffered chunks dropped: %s", + len(raw_bytes), + e, + ) diff --git a/litellm/proxy/_experimental/mcp_server/auth/token_exchange.py b/litellm/proxy/_experimental/mcp_server/auth/token_exchange.py new file mode 100644 index 00000000000..97a16ad3e15 --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/auth/token_exchange.py @@ -0,0 +1,196 @@ +""" +OAuth 2.0 Token Exchange (RFC 8693) handler for MCP servers. + +Exchanges a user's incoming JWT (subject_token) for a scoped access token +at an IDP's token exchange endpoint. The exchanged token is then used to +authenticate requests to the upstream MCP server. + +See: https://datatracker.ietf.org/doc/html/rfc8693 +""" + +import asyncio +import hashlib +import weakref +from typing import TYPE_CHECKING, Dict, Tuple + +import httpx + +from litellm._logging import verbose_logger +from litellm.caching.in_memory_cache import InMemoryCache +from litellm.constants import ( + MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL, + MCP_OAUTH2_TOKEN_CACHE_MIN_TTL, + MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS, + MCP_TOKEN_EXCHANGE_CACHE_MAX_SIZE, +) +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client +from litellm.types.llms.custom_http import httpxSpecialProvider + +if TYPE_CHECKING: + from litellm.types.mcp_server.mcp_server_manager import MCPServer + +# RFC 8693 grant type constant +TOKEN_EXCHANGE_GRANT_TYPE = "urn:ietf:params:oauth:grant-type:token-exchange" + +DEFAULT_SUBJECT_TOKEN_TYPE = "urn:ietf:params:oauth:token-type:access_token" + + +class TokenExchangeHandler: + """Handles OAuth 2.0 Token Exchange (RFC 8693) for MCP servers. + + Caches exchanged tokens keyed by ``hash(subject_token + server_id)`` so + repeated calls with the same user token skip the IDP round-trip. + """ + + def __init__(self) -> None: + self._cache = InMemoryCache( + max_size_in_memory=MCP_TOKEN_EXCHANGE_CACHE_MAX_SIZE, + default_ttl=MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL, + ) + # WeakValueDictionary so locks are GC'd once no coroutine holds a reference, + # preventing unbounded growth with many rotating user tokens. + self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = ( + weakref.WeakValueDictionary() + ) + + def _get_lock(self, cache_key: str) -> asyncio.Lock: + lock = self._locks.get(cache_key) + if lock is None: + lock = asyncio.Lock() + self._locks[cache_key] = lock + return lock + + @staticmethod + def _cache_key(subject_token: str, server_id: str) -> str: + raw = f"{subject_token}:{server_id}" + return hashlib.sha256(raw.encode()).hexdigest() + + async def exchange_token( + self, + subject_token: str, + server: "MCPServer", + ) -> str: + """Exchange *subject_token* for a scoped access token. + + Returns the exchanged ``access_token`` string (suitable for a + ``Bearer`` header). + + Raises ``ValueError`` on configuration or IDP errors. + """ + cache_key = self._cache_key(subject_token, server.server_id) + + # Fast path + cached = self._cache.get_cache(cache_key) + if cached is not None: + return cached + + # Slow path — one exchange at a time per (user, server) pair + async with self._get_lock(cache_key): + cached = self._cache.get_cache(cache_key) + if cached is not None: + return cached + + token, ttl = await self._do_exchange(subject_token, server) + self._cache.set_cache(cache_key, token, ttl=ttl) + return token + + async def _do_exchange( + self, + subject_token: str, + server: "MCPServer", + ) -> Tuple[str, int]: + """POST to the token exchange endpoint with RFC 8693 parameters. + + Returns ``(access_token, ttl_seconds)``. + """ + endpoint = server.token_exchange_endpoint or server.token_url + if not endpoint: + raise ValueError( + f"MCP server '{server.server_id}' has auth_type=oauth2_token_exchange " + f"but no token_exchange_endpoint or token_url configured" + ) + if not server.client_id or not server.client_secret: + raise ValueError( + f"MCP server '{server.server_id}' has auth_type=oauth2_token_exchange " + f"but missing client_id or client_secret" + ) + + data: Dict[str, str] = { + "grant_type": TOKEN_EXCHANGE_GRANT_TYPE, + "subject_token": subject_token, + "subject_token_type": server.subject_token_type + or DEFAULT_SUBJECT_TOKEN_TYPE, + "client_id": server.client_id, + "client_secret": server.client_secret, + } + if server.audience: + data["audience"] = server.audience + if server.scopes: + data["scope"] = " ".join(server.scopes) + + verbose_logger.debug( + "Exchanging token for MCP server %s at %s (audience=%s)", + server.server_id, + endpoint, + server.audience, + ) + + client = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) + try: + response = await client.post(endpoint, data=data) + response.raise_for_status() + except httpx.HTTPStatusError as exc: + verbose_logger.debug( + "Token exchange IDP error for MCP server %s (status %d)", + server.server_id, + exc.response.status_code, + ) + raise ValueError( + f"Token exchange for MCP server '{server.server_id}' " + f"failed with status {exc.response.status_code}" + ) from exc + + body = response.json() + if not isinstance(body, dict): + raise ValueError( + f"Token exchange response for MCP server '{server.server_id}' " + f"returned non-object JSON (got {type(body).__name__})" + ) + + access_token = body.get("access_token") + if not access_token: + raise ValueError( + f"Token exchange response for MCP server '{server.server_id}' " + f"missing 'access_token'" + ) + + raw_expires_in = body.get("expires_in") + try: + expires_in = ( + int(raw_expires_in) + if raw_expires_in is not None + else MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL + ) + except (TypeError, ValueError): + expires_in = MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL + + ttl = max( + expires_in - MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS, + MCP_OAUTH2_TOKEN_CACHE_MIN_TTL, + ) + + verbose_logger.info( + "Token exchange succeeded for MCP server %s (expires in %ds)", + server.server_id, + expires_in, + ) + return access_token, ttl + + def invalidate(self, subject_token: str, server_id: str) -> None: + """Remove a cached exchanged token (e.g. after a 401).""" + cache_key = self._cache_key(subject_token, server_id) + self._cache.delete_cache(cache_key) + + +# Module-level singleton +mcp_token_exchange_handler = TokenExchangeHandler() 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 d4799e9f208..c87e8c414cd 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py +++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py @@ -1,3 +1,4 @@ +import re from typing import Dict, List, Optional, Set, Tuple, cast from fastapi import HTTPException @@ -117,7 +118,28 @@ class MCPRequestHandler: return b"{}" request.body = mock_body # type: ignore - if ".well-known" in str(request.url): # public routes + # Only OAuth metadata routes registered under /.well-known/ are public. + # Match on request.url.path (path-only, exact prefix) so the substring + # cannot be smuggled via query string, hostname, or a deeper URL segment. + if request.url.path.startswith("/.well-known/"): + validated_user_api_key_auth = UserAPIKeyAuth() + elif ( + not litellm_api_key + and MCPRequestHandler._target_servers_delegate_auth_to_upstream( # noqa: E501 + path=request.url.path, mcp_servers=mcp_servers + ) + ): + # Operator opted this oauth2 server into upstream-delegated auth + # (PKCE passthrough): skip LiteLLM API-key/SSO entirely so the + # client authenticates directly with the upstream MCP server. + # Fires ONLY when neither x-litellm-api-key nor Authorization is + # present. If any LiteLLM key is supplied (primary or secondary + # header), we fall through so user_id is resolved, spend/rate + # limiting apply, and any stored OAuth token can be retrieved + # and forwarded upstream. Gated by + # _target_servers_delegate_auth_to_upstream, which only returns + # True when EVERY target is auth_type=oauth2 AND has the + # delegate_auth_to_upstream flag set — fails closed otherwise. validated_user_api_key_auth = UserAPIKeyAuth() elif has_explicit_litellm_key: # Explicit x-litellm-api-key provided - always validate normally @@ -126,27 +148,37 @@ class MCPRequestHandler: ) elif oauth2_headers: # No x-litellm-api-key, but Authorization header present. - # Could be a LiteLLM key (backward compat) OR an OAuth2 token - # from an upstream MCP provider (e.g. Atlassian). - # Try LiteLLM auth first; on auth failure, treat as OAuth2 passthrough. + # Could be a LiteLLM key (backward compat) OR an opaque OAuth2 token + # the operator wants forwarded to an upstream OAuth2-mode MCP server. + # Try LiteLLM auth first; on auth failure, only fall back to anonymous + # passthrough when the request actually targets a server whose operator + # configured ``auth_type=oauth2``. For any other server (api_key, + # bearer_token, basic, etc.), a failed LiteLLM auth is a real failure + # and must propagate — otherwise an attacker can exchange any garbage + # bearer for an anonymous session. try: validated_user_api_key_auth = await user_api_key_auth( api_key=litellm_api_key, request=request ) - except HTTPException as e: - if e.status_code in (401, 403): + except (HTTPException, ProxyException) as e: + # HTTPException.status_code is int; ProxyException.code is + # normalized to str in its __init__ but can be ``"None"`` or any + # non-numeric string when the caller didn't supply a numeric + # code, so we compare against both int and str forms rather + # than coercing (``int("None")`` would raise ValueError and + # rewrite the auth error as a 500). + status = e.status_code if isinstance(e, HTTPException) else e.code + if status in ( + 401, + 403, + "401", + "403", + ) and MCPRequestHandler._target_servers_use_oauth2( + path=request.url.path, mcp_servers=mcp_servers + ): verbose_logger.debug( - "MCP OAuth2: Authorization header is not a valid LiteLLM key, " - "treating as OAuth2 token passthrough" - ) - validated_user_api_key_auth = UserAPIKeyAuth() - else: - raise - except ProxyException as e: - if str(e.code) in ("401", "403"): - verbose_logger.debug( - "MCP OAuth2: Authorization header is not a valid LiteLLM key, " - "treating as OAuth2 token passthrough" + "MCP OAuth2: target server is OAuth2-mode, treating " + "Authorization as upstream OAuth2 token passthrough" ) validated_user_api_key_auth = UserAPIKeyAuth() else: @@ -165,6 +197,173 @@ class MCPRequestHandler: dict(headers), ) + @staticmethod + def _extract_target_server_names_from_path(path: str) -> List[str]: + """ + Extract the target MCP server name(s) from the standard MCP transport + URL patterns: ``/mcp/{server_name_or_csv}[/...]`` and + ``/{server_name}/mcp[/...]``. Returns ``[]`` for any other path so + callers fail closed when the target cannot be resolved. + + Mirrors the regex-based parser in ``server.py::_get_mcp_servers_in_path`` + so the names used for auth gating match the names used for downstream + filtering. Without this alignment, an attacker could craft + ``/mcp//`` so that auth treats the request + as targeting the delegate server (bypassing LiteLLM auth) while + downstream filtering sees a different (non-existent) target and falls + back to the caller's full allowed-server set. + + REST/admin endpoints, OAuth2 server endpoints + (``/{server_name}/authorize``, ``/token`` etc.), and ``.well-known`` + discovery routes intentionally fall through — those flows do not need + OAuth2 token passthrough. Clients aggregating multiple servers should + use ``x-mcp-servers`` on a path that does not encode a target. + """ + # ``/{server_name}/mcp[/...]`` form — single server. The literal + # ``mcp`` must be the second segment (not the first, which would be + # the ``/mcp/...`` form handled below). This branch must stay in sync + # with ``server.py::_get_mcp_servers_in_path``, which also accepts the + # un-rewritten form (some entry points may skip the + # ``dynamic_mcp_route`` rewrite). + segments = [s for s in path.split("/") if s] + if len(segments) >= 2 and segments[1] == "mcp" and segments[0] != "mcp": + return [segments[0]] + + # ``/mcp/...`` form — server name(s) may contain a slash (e.g. + # ``custom_solutions/user_123``) and may be a comma-separated list. + # Use the same parsing logic as ``_get_mcp_servers_in_path`` so the + # parsed names match downstream routing. + mcp_path_match = re.match(r"^/mcp/([^?#]+)(?:\?.*)?(?:#.*)?$", path) + if not mcp_path_match: + return [] + servers_and_path = mcp_path_match.group(1) + if not servers_and_path: + return [] + + if "," in servers_and_path: + # Comma-separated servers, possibly followed by a trailing path. + path_match = re.search(r"/([^/,]+(?:/[^/,]+)*)$", servers_and_path) + if path_match: + servers_part = servers_and_path[: -(len(path_match.group(1)) + 1)] + else: + servers_part = servers_and_path + return [s.strip() for s in servers_part.split(",") if s.strip()] + + # Single-server case — server name may contain at most one slash. + single_server_match = re.match( + r"^([^/]+(?:/[^/]+)?)(?:/.*)?$", servers_and_path + ) + if single_server_match: + return [single_server_match.group(1)] + return [servers_and_path] + + @staticmethod + def _target_servers_use_oauth2(path: str, mcp_servers: Optional[List[str]]) -> bool: + """ + True only when EVERY MCP server the request targets is configured for + ``auth_type == oauth2``. If any target is non-OAuth2 — or if the target + cannot be resolved at all — return False so the caller fails closed. + + Used to gate the "treat Authorization as opaque OAuth2 token" fallback + in :meth:`process_mcp_request` so a failed LiteLLM-auth cannot be + exchanged for an anonymous session against a non-OAuth2 server. + """ + # Inline imports avoid a circular dependency: mcp_server_manager imports + # from this module. + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + from litellm.types.mcp import MCPAuth + + # Resolve the same target list downstream routing will use. For + # ``/mcp/...`` routes, ``extract_mcp_auth_context`` overrides the + # ``x-mcp-servers`` header with path-derived names, so we must mirror + # that here — otherwise a caller could set the header to a permissive + # server while the path targets a stricter one (header/path TOCTOU). + target_names = MCPRequestHandler._resolve_target_server_names( + path=path, mcp_servers_header=mcp_servers + ) + if not target_names: + return False + + for name in target_names: + server = global_mcp_server_manager.get_mcp_server_by_name(name) + if server is None or server.auth_type != MCPAuth.oauth2: + return False + return True + + @staticmethod + def _target_servers_delegate_auth_to_upstream( + path: str, mcp_servers: Optional[List[str]] + ) -> bool: + """ + True only when EVERY MCP server the request targets is configured for + ``auth_type == oauth2`` AND has ``delegate_auth_to_upstream=True``. + Fails closed when any target does not opt in or cannot be resolved. + + Used by :meth:`process_mcp_request` to skip LiteLLM API-key/SSO auth + entirely (PKCE passthrough) so the client authenticates directly with + the upstream MCP server. Mixed-target requests (e.g. one delegated + + one non-delegated server) fall back to normal LiteLLM auth. + """ + # Inline imports avoid a circular dependency: mcp_server_manager imports + # from this module. + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + from litellm.types.mcp import MCPAuth + + # See _target_servers_use_oauth2: must mirror the downstream + # header-vs-path override or an attacker could set + # ``x-mcp-servers`` to a delegate-enabled server while the URL path + # targets a non-delegate server, skipping LiteLLM auth for it. + target_names = MCPRequestHandler._resolve_target_server_names( + path=path, mcp_servers_header=mcp_servers + ) + if not target_names: + return False + + for name in target_names: + server = global_mcp_server_manager.get_mcp_server_by_name(name) + if server is None or server.auth_type != MCPAuth.oauth2: + return False + # `is True` is intentional: opt-in must be an explicit boolean + # True. A MagicMock attribute (in tests) or any other truthy + # non-bool must not silently enable the bypass. + if getattr(server, "delegate_auth_to_upstream", False) is not True: + return False + if not getattr(server, "available_on_public_internet", True): + return False + # Never delegate for M2M (client_credentials) servers: LiteLLM + # fetches the upstream token automatically using stored credentials, + # so allowing anonymous bypass would let any external caller invoke + # tools authenticated as LiteLLM's service account. + if server.has_client_credentials: + return False + return True + + @staticmethod + def _resolve_target_server_names( + path: str, mcp_servers_header: Optional[List[str]] + ) -> List[str]: + """ + Resolve the target MCP server names exactly as downstream routing + does (``server.py::extract_mcp_auth_context``). + + For ``/mcp/...`` paths, downstream routing **overrides** any + ``x-mcp-servers`` header value with the path-derived names. Mirror + that here so an attacker cannot use a permissive header value to + flip an auth gate while the path targets a stricter server + (header/path TOCTOU). For non-``/mcp/...`` paths (where the path + does not encode targets), fall back to the header. + """ + path_targets = MCPRequestHandler._extract_target_server_names_from_path(path) + if path_targets: + return path_targets + # Path did not resolve to /mcp/... targets — trust the header + # (including an explicitly empty list, which means "no targets"). + return mcp_servers_header if mcp_servers_header is not None else [] + @staticmethod def _get_mcp_auth_header_from_headers(headers: Headers) -> Optional[str]: """ @@ -340,9 +539,12 @@ class MCPRequestHandler: Permission hierarchy (all rules are intersections): 1. Get allowed servers from key permissions - 2. Get allowed servers from team permissions - 3. Get allowed servers from end_user permissions - 4. Final result = intersection of key/team AND end_user (if end_user has permissions set) + 2. Get allowed servers from team permissions (key inherits from team, or intersection) + 3. Get allowed servers from end_user permissions (intersected if set) + 4. Get allowed servers from agent permissions (intersected if set) + 5. Get allowed servers from org permissions — org acts as a ceiling: if the org + has an explicit MCP server list, the combined key/team/end_user/agent result is + capped to that list. If the org has no list, no extra restriction is applied. Returns: List[str]: List of allowed MCP servers by server id @@ -366,6 +568,10 @@ class MCPRequestHandler: # Calculate key/team allowed servers using inheritance and intersection logic ######################################################### allowed_mcp_servers: List[str] = [] + has_lower_level_mcp_restrictions = ( + len(allowed_mcp_servers_for_key) > 0 + or len(allowed_mcp_servers_for_team) > 0 + ) if len(allowed_mcp_servers_for_team) > 0: if len(allowed_mcp_servers_for_key) > 0: # Key has its own MCP permissions - use intersection with team permissions @@ -390,6 +596,7 @@ class MCPRequestHandler: # If end_user has explicit MCP server permissions, apply intersection if len(allowed_mcp_servers_for_end_user) > 0: + has_lower_level_mcp_restrictions = True verbose_logger.debug( f"End user {user_api_key_auth.end_user_id} has explicit MCP permissions: {allowed_mcp_servers_for_end_user}" ) @@ -421,6 +628,7 @@ class MCPRequestHandler: ) ) if len(allowed_mcp_servers_for_agent) > 0: + has_lower_level_mcp_restrictions = True # Intersect: agent can only use servers allowed by BOTH key/team AND agent config allowed_mcp_servers = [ s @@ -431,6 +639,30 @@ class MCPRequestHandler: f"Applied agent intersection filter. Final allowed servers: {allowed_mcp_servers}" ) + ######################################################### + # Apply org-level ceiling if org_id is set + ######################################################### + if user_api_key_auth and user_api_key_auth.org_id: + allowed_mcp_servers_for_org = ( + await MCPRequestHandler._get_allowed_mcp_servers_for_org( + user_api_key_auth + ) + ) + if len(allowed_mcp_servers_for_org) > 0: + if has_lower_level_mcp_restrictions: + # Lower-level restrictions exist, so org can only cap them. + allowed_mcp_servers = [ + s + for s in allowed_mcp_servers + if s in allowed_mcp_servers_for_org + ] + else: + # No lower-level restrictions → org list becomes the ceiling + allowed_mcp_servers = allowed_mcp_servers_for_org + verbose_logger.debug( + f"Applied org ceiling filter. Final allowed servers: {allowed_mcp_servers}" + ) + return list(set(allowed_mcp_servers)) except Exception as e: verbose_logger.warning(f"Failed to get allowed MCP servers: {str(e)}") @@ -569,6 +801,27 @@ class MCPRequestHandler: allowed_tools = list(set(allowed_tools) & set(agent_tools)) else: allowed_tools = agent_tools + + # Apply org-level tool ceiling if org_id is set + if user_api_key_auth.org_id: + # _get_org_object_permission uses user_api_key_cache, so this is not a + # fresh DB round-trip when get_allowed_mcp_servers was already called. + org_obj_perm = await MCPRequestHandler._get_org_object_permission( + user_api_key_auth + ) + org_tools = ( + global_mcp_server_manager.expand_tool_permissions( + org_obj_perm.mcp_tool_permissions + ).get(server_id) + if org_obj_perm and org_obj_perm.mcp_tool_permissions + else None + ) + if org_tools is not None: + if allowed_tools is not None: + allowed_tools = list(set(allowed_tools) & set(org_tools)) + else: + allowed_tools = list(org_tools) + return allowed_tools except Exception as e: @@ -736,6 +989,120 @@ class MCPRequestHandler: ) return [] + # Sentinel stored in cache when an org has no object_permission, so we + # don't re-query the DB on every MCP request for that org. + _ORG_NO_PERMISSION_SENTINEL = "__org_no_mcp_permission__" + + @staticmethod + async def _get_org_object_permission( + user_api_key_auth: Optional[UserAPIKeyAuth] = None, + ): + """ + Get org object_permission, using user_api_key_cache to avoid DB hits on every request. + + Caches both positive results and the absence of an object_permission so that orgs + with no MCP permissions configured (the common default) do not trigger a DB query + on every request. + """ + from litellm.proxy.proxy_server import prisma_client, user_api_key_cache + + if not user_api_key_auth or not user_api_key_auth.org_id: + return None + + if prisma_client is None: + verbose_logger.debug("prisma_client is None") + return None + + org_id = user_api_key_auth.org_id + cache_key = f"org_object_permission:{org_id}" + + from litellm.proxy._types import LiteLLM_ObjectPermissionTable + + try: + cached = await user_api_key_cache.async_get_cache(key=cache_key) + if cached is not None: + # Sentinel means the DB confirmed no object_permission for this org + if cached == MCPRequestHandler._ORG_NO_PERMISSION_SENTINEL: + return None + # Redis deserialises to a plain dict; reconstruct the Pydantic model + # so callers can access .mcp_servers / .mcp_tool_permissions as attrs. + if isinstance(cached, dict): + return LiteLLM_ObjectPermissionTable(**cached) + return cached + + org_row = await prisma_client.db.litellm_organizationtable.find_unique( + where={"organization_id": org_id}, + include={"object_permission": True}, + ) + + if org_row is None or org_row.object_permission is None: + # Cache the negative result so subsequent calls skip the DB + await user_api_key_cache.async_set_cache( + key=cache_key, + value=MCPRequestHandler._ORG_NO_PERMISSION_SENTINEL, + ) + return None + + # Convert raw Prisma model → Pydantic before caching. Caching the + # Pydantic .dict() ensures the value survives a Redis JSON round-trip + # as a plain dict that we can reconstruct above (same pattern used by + # get_end_user_object / get_team_object in auth_checks.py). + obj_perm = LiteLLM_ObjectPermissionTable(**org_row.object_permission.dict()) + await user_api_key_cache.async_set_cache( + key=cache_key, value=obj_perm.dict() + ) + return obj_perm + except Exception as e: + verbose_logger.warning(f"Failed to get org object permission: {str(e)}") + return None + + @staticmethod + async def _get_allowed_mcp_servers_for_org( + user_api_key_auth: Optional[UserAPIKeyAuth] = None, + ) -> List[str]: + """ + Get allowed MCP servers for an organization. + + Returns the MCP servers from the org's object_permission. + An empty result means the org places no restriction (allow-all from this level). + """ + try: + object_permissions = await MCPRequestHandler._get_org_object_permission( + user_api_key_auth + ) + + if object_permissions is None: + return [] + + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + + # Expand names/aliases to canonical server IDs (consistent with key/team/end-user path) + direct_mcp_servers = global_mcp_server_manager.expand_permission_list( + object_permissions.mcp_servers or [] + ) + + access_group_servers = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + object_permissions.mcp_access_groups or [] + ) + ) + + tool_perm_servers = list( + global_mcp_server_manager.expand_tool_permissions( + object_permissions.mcp_tool_permissions + ).keys() + ) + + all_servers = direct_mcp_servers + access_group_servers + tool_perm_servers + return list(set(all_servers)) + except Exception as e: + verbose_logger.warning( + f"Failed to get allowed MCP servers for org: {str(e)}" + ) + return [] + @staticmethod async def _get_allowed_mcp_servers_for_end_user( user_api_key_auth: Optional[UserAPIKeyAuth] = None, diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py index 21abaa3f981..a6f0d145e9b 100644 --- a/litellm/proxy/_experimental/mcp_server/db.py +++ b/litellm/proxy/_experimental/mcp_server/db.py @@ -1,4 +1,5 @@ import base64 +import binascii import json from datetime import datetime, timedelta, timezone from typing import Any, Dict, Iterable, List, Optional, Set, Union, cast @@ -498,6 +499,82 @@ async def rotate_mcp_server_credentials_master_key( ) +def _decode_user_credential(stored: str) -> Optional[str]: + """Read back a value persisted in ``LiteLLM_MCPUserCredentials.credential_b64``. + + Tries nacl decryption first (current write format). Falls back to a + plain ``urlsafe_b64decode`` for rows persisted by older code that wrote + the credential without encryption. Returns ``None`` when neither path + yields a valid string. + """ + decrypted = decrypt_value_helper( + value=stored, + key="mcp_user_credential", + exception_type="debug", + return_original_value=False, + ) + if decrypted is not None: + return decrypted + try: + return base64.urlsafe_b64decode(stored).decode() + except (binascii.Error, UnicodeDecodeError, ValueError, TypeError): + return None + + +def _decode_oauth_payload(stored: str) -> Optional[Dict[str, Any]]: + """Return the OAuth2 payload dict if ``stored`` holds one, else ``None``. + + A row is considered an OAuth2 credential iff its decoded value parses as + a JSON object with ``"type": "oauth2"``. Plain BYOK credentials (which + share the same column) decode to a non-JSON string and return ``None``. + """ + decoded = _decode_user_credential(stored) + if decoded is None: + return None + try: + parsed = json.loads(decoded) + except (ValueError, TypeError): + return None + if isinstance(parsed, dict) and parsed.get("type") == "oauth2": + return parsed + return None + + +async def rotate_mcp_user_credentials_master_key( + prisma_client: PrismaClient, new_master_key: str +): + """Re-encrypt every ``LiteLLM_MCPUserCredentials`` row with ``new_master_key``. + + Reads each ``credential_b64`` with the current salt key (falling back to + legacy plain base64 for unmigrated rows) and writes it back encrypted + under the new master key. Rows that are unreadable under both paths + are logged and skipped so one corrupt row does not abort the rotation. + """ + rows = await prisma_client.db.litellm_mcpusercredentials.find_many() + for row in rows: + plaintext = _decode_user_credential(row.credential_b64) + if plaintext is None: + verbose_proxy_logger.warning( + "rotate_mcp_user_credentials_master_key: could not decode " + "credential for user_id=%s server_id=%s, skipping", + row.user_id, + row.server_id, + ) + continue + re_encrypted = encrypt_value_helper( + plaintext, new_encryption_key=new_master_key + ) + await prisma_client.db.litellm_mcpusercredentials.update( + where={ + "user_id_server_id": { + "user_id": row.user_id, + "server_id": row.server_id, + } + }, + data={"credential_b64": re_encrypted}, + ) + + async def store_user_credential( prisma_client: PrismaClient, user_id: str, @@ -506,7 +583,7 @@ async def store_user_credential( ) -> None: """Store a user credential for a BYOK MCP server.""" - encoded = base64.urlsafe_b64encode(credential.encode()).decode() + encoded = encrypt_value_helper(credential) await prisma_client.db.litellm_mcpusercredentials.upsert( where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}}, data={ @@ -532,16 +609,7 @@ async def get_user_credential( ) if row is None: return None - try: - return base64.urlsafe_b64decode(row.credential_b64).decode() - except Exception: - # Fall back to nacl decryption for credentials stored by older code - return decrypt_value_helper( - value=row.credential_b64, - key="byok_credential", - exception_type="debug", - return_original_value=False, - ) + return _decode_user_credential(row.credential_b64) async def has_user_credential( @@ -582,7 +650,7 @@ async def store_user_oauth_credential( ) -> None: """Persist an OAuth2 access token for a user+server pair. - The payload is JSON-serialised and stored base64-encoded in the same + The payload is JSON-serialised and stored encrypted in the same ``credential_b64`` column used by BYOK. A ``"type": "oauth2"`` key differentiates it from plain BYOK API keys. """ @@ -606,29 +674,27 @@ async def store_user_oauth_credential( payload["scopes"] = scopes # Guard against silently overwriting a BYOK credential with an OAuth token. - # BYOK credentials lack a "type" field (or use a non-"oauth2" type). # Skip the guard when the caller knows the row is already an OAuth2 credential # (e.g. during token refresh), saving an extra DB round-trip. if not skip_byok_guard: existing = await prisma_client.db.litellm_mcpusercredentials.find_unique( where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}} ) - if existing is not None: - _byok_error = ValueError( - f"A non-OAuth2 credential already exists for user {user_id} " - f"and server {server_id}. Refusing to overwrite." + if ( + existing is not None + and _decode_oauth_payload(existing.credential_b64) is None + ): + # Existing row is either a BYOK secret or an OAuth2 row that no + # longer decrypts (e.g. after a salt-key rotation). In either + # case, refuse to overwrite — the caller would clobber data + # that may still be recoverable. + raise ValueError( + f"Existing credential for user {user_id} and server " + f"{server_id} could not be verified as an OAuth2 token. " + f"Refusing to overwrite." ) - try: - raw = json.loads( - base64.urlsafe_b64decode(existing.credential_b64).decode() - ) - except Exception: - # Credential is not base64+JSON — it's a plain-text BYOK key. - raise _byok_error - if raw.get("type") != "oauth2": - raise _byok_error - encoded = base64.urlsafe_b64encode(json.dumps(payload).encode()).decode() + encoded = encrypt_value_helper(json.dumps(payload)) await prisma_client.db.litellm_mcpusercredentials.upsert( where={"user_id_server_id": {"user_id": user_id, "server_id": server_id}}, data={ @@ -672,15 +738,7 @@ async def get_user_oauth_credential( ) if row is None: return None - try: - decoded = base64.urlsafe_b64decode(row.credential_b64).decode() - parsed = json.loads(decoded) - if isinstance(parsed, dict) and parsed.get("type") == "oauth2": - return parsed - # Row exists but is a BYOK (plain string), not an OAuth token - return None - except Exception: - return None + return _decode_oauth_payload(row.credential_b64) async def list_user_oauth_credentials( @@ -694,14 +752,11 @@ async def list_user_oauth_credentials( ) results: List[Dict[str, Any]] = [] for row in rows: - try: - decoded = base64.urlsafe_b64decode(row.credential_b64).decode() - parsed = json.loads(decoded) - if isinstance(parsed, dict) and parsed.get("type") == "oauth2": - parsed["server_id"] = row.server_id - results.append(parsed) - except Exception: - pass # Skip non-OAuth rows (BYOK plain strings) + payload = _decode_oauth_payload(row.credential_b64) + if payload is None: + continue + payload["server_id"] = row.server_id + results.append(payload) return results diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 13b19aa2d65..62691641234 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -12,7 +12,8 @@ from litellm.llms.custom_httpx.http_handler import ( ) from litellm.proxy._experimental.mcp_server.oauth_utils import ( TOKEN_NO_CACHE_HEADERS, - validate_loopback_redirect_uri, + get_request_base_url, + validate_trusted_redirect_uri, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.common_utils.encrypt_decrypt_utils import ( @@ -29,55 +30,6 @@ router = APIRouter( ) -def get_request_base_url(request: Request) -> str: - """ - Get the base URL for the request, considering X-Forwarded-* headers. - - When behind a proxy (like nginx), the proxy may set: - - X-Forwarded-Proto: The original protocol (http/https) - - X-Forwarded-Host: The original host (may include port) - - X-Forwarded-Port: The original port (if not in Host header) - - Args: - request: FastAPI Request object - - Returns: - The reconstructed base URL (e.g., "https://proxy.example.com") - """ - base_url = str(request.base_url).rstrip("/") - parsed = urlparse(base_url) - - # Get forwarded headers - x_forwarded_proto = request.headers.get("X-Forwarded-Proto") - x_forwarded_host = request.headers.get("X-Forwarded-Host") - x_forwarded_port = request.headers.get("X-Forwarded-Port") - - # Start with the original scheme - scheme = x_forwarded_proto if x_forwarded_proto else parsed.scheme - - # Handle host and port - if x_forwarded_host: - # X-Forwarded-Host may already include port (e.g., "example.com:8080") - if ":" in x_forwarded_host and not x_forwarded_host.startswith("["): - # Host includes port - netloc = x_forwarded_host - elif x_forwarded_port: - # Port is separate - netloc = f"{x_forwarded_host}:{x_forwarded_port}" - else: - # Just host, no explicit port - netloc = x_forwarded_host - else: - # No X-Forwarded-Host, use original netloc - netloc = parsed.netloc - if x_forwarded_port and ":" not in netloc: - # Add forwarded port if not already in netloc - netloc = f"{netloc}:{x_forwarded_port}" - - # Reconstruct the URL - return urlunparse((scheme, netloc, parsed.path, "", "", "")) - - def encode_state_with_base_url( base_url: str, original_state: str, @@ -131,6 +83,24 @@ def decode_state_hash(encrypted_state: str) -> dict: return state_data +def _get_validated_client_redirect_uri( + request: Request, state_data: Dict[str, Any] +) -> str: + """Return a trusted (same-origin or loopback) client redirect URI from OAuth state.""" + redirect_uri = state_data.get("client_redirect_uri") or state_data.get("base_url") + if not redirect_uri or not isinstance(redirect_uri, str): + raise HTTPException(status_code=400, detail="Invalid redirect URI") + validate_trusted_redirect_uri(request, redirect_uri) + return redirect_uri + + +def _append_query_params(url: str, params: Dict[str, str]) -> str: + parsed = urlparse(url) + query_params = parse_qsl(parsed.query, keep_blank_values=True) + query_params.extend(params.items()) + return urlunparse(parsed._replace(query=urlencode(query_params))) + + def _resolve_oauth2_server_for_root_endpoints( client_ip: Optional[str] = None, ) -> Optional[MCPServer]: @@ -326,12 +296,12 @@ async def authorize_with_server( status_code=400, detail="MCP server authorization url is not set" ) - # Loopback-only redirect_uri. The URI is encrypted into the OAuth - # state and decoded on /callback to redirect the user back; a non- - # loopback URI would be an open-redirect + code-theft primitive - # (VERIA-57 root cause B). MCP clients are native apps — loopback is - # the spec-compliant callback pattern. - validate_loopback_redirect_uri(redirect_uri) + # Loopback OR same-origin redirect_uri. The URI is encrypted into the + # OAuth state and decoded on /callback to redirect the user back; + # restricting to trusted origins blocks the open-redirect + + # code-theft primitive (VERIA-57 root cause B). Loopback supports + # native MCP clients; same-origin supports the proxy's own UI callback. + validate_trusted_redirect_uri(request, redirect_uri) parsed = urlparse(redirect_uri) base_url = urlunparse(parsed._replace(query="")) request_base_url = get_request_base_url(request) @@ -568,7 +538,7 @@ async def authorize( else None ) if mcp_server is None and mcp_server_name is None: - mcp_server = _resolve_oauth2_server_for_root_endpoints() + mcp_server = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) if mcp_server is None: raise HTTPException(status_code=404, detail="MCP server not found") # Use server's stored client_id when caller doesn't supply one. @@ -630,7 +600,7 @@ async def token_endpoint( lookup_name, client_ip=client_ip ) if mcp_server is None and mcp_server_name is None: - mcp_server = _resolve_oauth2_server_for_root_endpoints() + mcp_server = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) if mcp_server is None: raise HTTPException(status_code=404, detail="MCP server not found") return await exchange_token_with_server( @@ -648,21 +618,21 @@ async def token_endpoint( @router.get("/callback") -async def callback(code: str, state: str): +async def callback(request: Request, code: str, state: str): try: state_data = decode_state_hash(state) - base_url = state_data["base_url"] original_state = state_data["original_state"] - # Re-validate loopback at the sink. /authorize rejects non-loopback + # Re-validate at the sink. /authorize rejects untrusted # redirect_uri before encoding into state, but encrypted states # minted before that check was added have no expiry and remain - # valid indefinitely. Validating here blocks the open-redirect + - # code-theft primitive even for pre-fix states. - validate_loopback_redirect_uri(base_url) + # valid indefinitely. Validating here (same-origin OR loopback) + # blocks the open-redirect + code-theft primitive even for pre-fix + # states while allowing the UI's same-origin callback to work. + redirect_uri = _get_validated_client_redirect_uri(request, state_data) params = {"code": code, "state": original_state} - complete_returned_url = f"{base_url}?{urlencode(params)}" + complete_returned_url = _append_query_params(redirect_uri, params) return RedirectResponse(url=complete_returned_url, status_code=302) except HTTPException: @@ -719,16 +689,16 @@ def _build_oauth_protected_resource_response( ) request_base_url = get_request_base_url(request) + client_ip = IPAddressUtils.get_mcp_client_ip(request) # When no server name provided, try to resolve the single OAuth2 server if mcp_server_name is None: - resolved = _resolve_oauth2_server_for_root_endpoints() + resolved = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) if resolved: mcp_server_name = resolved.server_name or resolved.name mcp_server: Optional[MCPServer] = None if mcp_server_name: - client_ip = IPAddressUtils.get_mcp_client_ip(request) mcp_server = global_mcp_server_manager.get_mcp_server_by_name( mcp_server_name, client_ip=client_ip ) @@ -835,10 +805,11 @@ def _build_oauth_authorization_server_response( ) request_base_url = get_request_base_url(request) + client_ip = IPAddressUtils.get_mcp_client_ip(request) # When no server name provided, try to resolve the single OAuth2 server if mcp_server_name is None: - resolved = _resolve_oauth2_server_for_root_endpoints() + resolved = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) if resolved: mcp_server_name = resolved.server_name or resolved.name @@ -855,7 +826,6 @@ def _build_oauth_authorization_server_response( mcp_server: Optional[MCPServer] = None if mcp_server_name: - client_ip = IPAddressUtils.get_mcp_client_ip(request) mcp_server = global_mcp_server_manager.get_mcp_server_by_name( mcp_server_name, client_ip=client_ip ) @@ -1007,8 +977,9 @@ async def register_client(request: Request, mcp_server_name: Optional[str] = Non "client_secret": "dummy", "redirect_uris": [f"{request_base_url}/callback"], } + client_ip = IPAddressUtils.get_mcp_client_ip(request) if not mcp_server_name: - resolved = _resolve_oauth2_server_for_root_endpoints() + resolved = _resolve_oauth2_server_for_root_endpoints(client_ip=client_ip) if resolved: return await register_client_with_server( request=request, @@ -1021,7 +992,6 @@ async def register_client(request: Request, mcp_server_name: Optional[str] = Non ) return dummy_return - client_ip = IPAddressUtils.get_mcp_client_ip(request) mcp_server = global_mcp_server_manager.get_mcp_server_by_name( mcp_server_name, client_ip=client_ip ) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 251f271903b..31ed0918f3c 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -41,6 +41,7 @@ from litellm.constants import ( MCP_TOOL_LISTING_TIMEOUT, ) from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException +from litellm.litellm_core_utils.url_utils import SSRFError, async_safe_get from litellm.experimental_mcp_client.client import MCPClient, MCPSigV4Auth from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( @@ -50,8 +51,11 @@ from litellm.proxy._experimental.mcp_server.oauth2_token_cache import resolve_mc from litellm.proxy._experimental.mcp_server.utils import ( MCP_TOOL_PREFIX_SEPARATOR, add_server_prefix_to_name, + compute_short_server_prefix, get_server_prefix, + is_short_mcp_tool_prefix_enabled, is_tool_name_prefixed, + iter_known_server_prefixes, merge_mcp_headers, normalize_server_name, split_server_prefix_from_name, @@ -106,6 +110,12 @@ if not _separator_probe.is_valid: SEP_986_URL, ) +_AZURE_ENTRA_HOSTS = { + "login.microsoftonline.com", # Global + "login.microsoftonline.us", # US Government + "login.chinacloudapi.cn", # China +} + def _warn_on_server_name_fields( *, @@ -159,6 +169,37 @@ def _deserialize_json_dict(data: Any) -> Optional[Dict[str, str]]: class MCPServerManager: _STDIO_ENV_TEMPLATE_PATTERN = re.compile(r"^\$\{(X-[^}]+)\}$") + @staticmethod + def _resolve_oauth2_flow( + *, + auth_type: Optional[MCPAuthType], + oauth2_flow: Optional[str], + token_url: Optional[str], + authorization_url: Optional[str], + client_id: Optional[str], + client_secret: Optional[str], + ) -> Optional[Literal["client_credentials", "authorization_code"]]: + """Infer oauth2_flow for legacy records that omit the field. + + DB rows created before oauth2_flow support may have OAuth2 client + credentials + token_url but a null oauth2_flow. Treat these as M2M, + unless authorization_url is present (interactive OAuth). + """ + if oauth2_flow in ("client_credentials", "authorization_code"): + return cast( + Literal["client_credentials", "authorization_code"], oauth2_flow + ) + if oauth2_flow: + # Ignore unknown/untyped values and continue legacy inference. + return None + if auth_type != MCPAuth.oauth2: + return None + if authorization_url: + return None + if token_url and client_id and client_secret: + return "client_credentials" + return None + def __init__(self): self.registry: Dict[str, MCPServer] = {} self.config_mcp_servers: Dict[str, MCPServer] = {} @@ -332,7 +373,14 @@ class MCPServerManager: # oauth specific fields client_id=server_config.get("client_id", None), client_secret=server_config.get("client_secret", None), - oauth2_flow=server_config.get("oauth2_flow", None), + oauth2_flow=self._resolve_oauth2_flow( + auth_type=auth_type, + oauth2_flow=server_config.get("oauth2_flow", None), + token_url=resolved_token_url, + authorization_url=resolved_authorization_url, + client_id=server_config.get("client_id", None), + client_secret=server_config.get("client_secret", None), + ), scopes=resolved_scopes, authorization_url=resolved_authorization_url, token_url=resolved_token_url, @@ -354,6 +402,9 @@ class MCPServerManager: available_on_public_internet=bool( server_config.get("available_on_public_internet", True) ), + delegate_auth_to_upstream=bool( + server_config.get("delegate_auth_to_upstream", False) + ), # AWS SigV4 fields aws_access_key_id=server_config.get("aws_access_key_id", None), aws_secret_access_key=server_config.get("aws_secret_access_key", None), @@ -363,7 +414,17 @@ class MCPServerManager: aws_role_name=server_config.get("aws_role_name", None), aws_session_name=server_config.get("aws_session_name", None), instructions=server_config.get("instructions", None), + # Token Exchange (OBO) fields + token_exchange_endpoint=server_config.get( + "token_exchange_endpoint", None + ), + audience=server_config.get("audience", None), + subject_token_type=server_config.get( + "subject_token_type", + "urn:ietf:params:oauth:token-type:access_token", + ), ) + self._assign_unique_short_prefix(new_server) self.config_mcp_servers[server_id] = new_server # Check if this is an OpenAPI-based server @@ -448,7 +509,8 @@ class MCPServerManager: # Add any static headers from server config. # # Note: `extra_headers` on MCPServer is a List[str] of header names to forward - # from the client request (not available in this OpenAPI tool generation step). + # from each client MCP request; values are applied at call time via + # `_request_extra_headers` in server.py (not baked in here). # `static_headers` is a dict of concrete headers to always send. headers = ( merge_mcp_headers( @@ -540,16 +602,57 @@ class MCPServerManager: ) raise e + def _cleanup_server_tool_routing_artifacts(self, server: MCPServer) -> None: + """Drop OpenAPI global tools and name-mapping rows owned by ``server``. + + When a server leaves ``self.registry`` (eviction, ``remove_server``, etc.), + OpenAPI tools remain in ``global_mcp_tool_registry`` and + ``tool_name_to_mcp_server_name_mapping`` unless removed here. Stale + mappings make ``_get_mcp_server_from_tool_name`` resolve to a prefix that + no longer exists in the live registry. + """ + from litellm.proxy._experimental.mcp_server.tool_registry import ( + global_mcp_tool_registry, + ) + + prefix_root = normalize_server_name(get_server_prefix(server)) + if server.spec_path and prefix_root: + openapi_key_prefix = prefix_root + MCP_TOOL_PREFIX_SEPARATOR + global_mcp_tool_registry.unregister_tools_with_prefix(openapi_key_prefix) + + owned_raw: Set[str] = set() + for p in iter_known_server_prefixes(server): + if p: + owned_raw.add(p) + if server.name: + owned_raw.add(server.name) + + owned_normalized = {normalize_server_name(x) for x in owned_raw} + + stale_mapping_keys: List[str] = [] + for tool_name, mapped_server in list( + self.tool_name_to_mcp_server_name_mapping.items() + ): + if mapped_server in owned_raw: + stale_mapping_keys.append(tool_name) + elif normalize_server_name(str(mapped_server)) in owned_normalized: + stale_mapping_keys.append(tool_name) + + for key in stale_mapping_keys: + del self.tool_name_to_mcp_server_name_mapping[key] + def remove_server(self, mcp_server: LiteLLM_MCPServerTable): """ Remove a server from the registry """ - if mcp_server.server_name in self.get_registry(): - del self.registry[mcp_server.server_name] - verbose_logger.debug(f"Removed MCP Server: {mcp_server.server_name}") - elif mcp_server.server_id in self.get_registry(): - del self.registry[mcp_server.server_id] - verbose_logger.debug(f"Removed MCP Server: {mcp_server.server_id}") + evicted: Optional[MCPServer] = self.registry.pop(mcp_server.server_id, None) + if evicted is None and mcp_server.server_name: + evicted = self.registry.pop(mcp_server.server_name, None) + if evicted is not None: + verbose_logger.debug( + "Removed MCP Server: %s", mcp_server.server_id or mcp_server.server_name + ) + self._cleanup_server_tool_routing_artifacts(evicted) else: verbose_logger.warning( f"Server ID {mcp_server.server_id} not found in registry" @@ -668,7 +771,17 @@ class MCPServerManager: client_id=client_id_value or getattr(mcp_server, "client_id", None), client_secret=client_secret_value or getattr(mcp_server, "client_secret", None), - oauth2_flow=getattr(mcp_server, "oauth2_flow", None), + oauth2_flow=self._resolve_oauth2_flow( + auth_type=auth_type, + oauth2_flow=getattr(mcp_server, "oauth2_flow", None), + token_url=mcp_server.token_url + or getattr(mcp_oauth_metadata, "token_url", None), + authorization_url=mcp_server.authorization_url + or getattr(mcp_oauth_metadata, "authorization_url", None), + client_id=client_id_value or getattr(mcp_server, "client_id", None), + client_secret=client_secret_value + or getattr(mcp_server, "client_secret", None), + ), scopes=resolved_scopes, authorization_url=mcp_server.authorization_url or getattr(mcp_oauth_metadata, "authorization_url", None), @@ -686,6 +799,9 @@ class MCPServerManager: available_on_public_internet=bool( getattr(mcp_server, "available_on_public_internet", True) ), + delegate_auth_to_upstream=bool( + getattr(mcp_server, "delegate_auth_to_upstream", False) + ), created_at=getattr(mcp_server, "created_at", None), updated_at=getattr(mcp_server, "updated_at", None), tool_name_to_display_name=_deserialize_json_dict( @@ -706,10 +822,23 @@ class MCPServerManager: aws_role_name=aws_creds.get("aws_role_name"), aws_session_name=aws_creds.get("aws_session_name"), instructions=mcp_server.instructions, + # Token Exchange (OBO) fields — read from credentials JSON blob + token_exchange_endpoint=( + credentials_dict.get("token_exchange_endpoint") + if credentials_dict + else None + ), + audience=(credentials_dict.get("audience") if credentials_dict else None), + subject_token_type=( + credentials_dict.get("subject_token_type") if credentials_dict else None + ) + or "urn:ietf:params:oauth:token-type:access_token", ) return new_server - async def _maybe_register_openapi_tools(self, server: MCPServer): + async def _maybe_register_openapi_tools( + self, server: MCPServer, *, initialize_mapping: bool = True + ): """Register OpenAPI tools if the server has a spec_path configured.""" if server.spec_path: verbose_logger.info( @@ -720,12 +849,21 @@ class MCPServerManager: server=server, base_url=server.url or "", ) - self.initialize_tool_name_to_mcp_server_name_mapping() + if initialize_mapping: + self.initialize_tool_name_to_mcp_server_name_mapping() async def add_server(self, mcp_server: LiteLLM_MCPServerTable): + # The runtime registry is the allowlist for tool calls and health + # probes (which spawn the underlying transport, including stdio + # subprocesses). Match the eligibility set used by the bulk DB + # filter in reload_servers_from_database() — NULL is legacy and + # "approved" is a legacy alias for "active". + if mcp_server.approval_status not in (None, "active", "approved"): + return try: if mcp_server.server_id not in self.registry: new_server = await self.build_mcp_server_from_table(mcp_server) + self._assign_unique_short_prefix(new_server) self.registry[mcp_server.server_id] = new_server await self._maybe_register_openapi_tools(new_server) verbose_logger.debug(f"Added MCP Server: {new_server.name}") @@ -735,9 +873,25 @@ class MCPServerManager: raise e async def update_server(self, mcp_server: LiteLLM_MCPServerTable): + # If a previously-active server has been moved out of the active + # state, evict any stale registry entry so subsequent tool calls and + # health probes can't reach it. + if mcp_server.approval_status not in (None, "active", "approved"): + evicted = self.registry.pop(mcp_server.server_id, None) + if evicted is None and mcp_server.server_name: + evicted = self.registry.pop(mcp_server.server_name, None) + if evicted is not None: + self._cleanup_server_tool_routing_artifacts(evicted) + return try: if mcp_server.server_id in self.registry: new_server = await self.build_mcp_server_from_table(mcp_server) + # Carry the previously-resolved short prefix across so the + # tool names stay stable for clients holding cached lists. + existing_prefix = self.registry[mcp_server.server_id].short_prefix + if existing_prefix and not new_server.short_prefix: + new_server.short_prefix = existing_prefix + self._assign_unique_short_prefix(new_server) self.registry[mcp_server.server_id] = new_server await self._maybe_register_openapi_tools(new_server) verbose_logger.debug(f"Updated MCP Server: {new_server.name}") @@ -819,6 +973,34 @@ class MCPServerManager: if not in_toolset_scope: combined_servers.update(allow_all_server_ids) + # For anonymous callers (no user_id, no role), also surface any + # servers the operator has opted into upstream-delegated auth. + # These servers handle their own auth at the upstream level, so + # LiteLLM granting access here does not bypass any security gate. + is_anonymous = not ( + user_api_key_auth + and ( + getattr(user_api_key_auth, "user_id", None) + or getattr(user_api_key_auth, "user_role", None) + or getattr(user_api_key_auth, "api_key", None) + ) + ) + if is_anonymous: + delegate_server_ids = [ + server.server_id + for server in self.get_registry().values() + if getattr(server, "auth_type", None) == MCPAuth.oauth2 + and getattr(server, "delegate_auth_to_upstream", False) is True + # M2M servers must not be exposed anonymously: an + # unauthenticated caller would get LiteLLM to proxy tool + # calls using its stored client_credentials. + and not server.has_client_credentials + # Internal-only servers must not be reachable from public + # internet callers who happen to carry an upstream token. + and getattr(server, "available_on_public_internet", True) + ] + combined_servers.update(delegate_server_ids) + if len(combined_servers) == 0: verbose_logger.debug( "No allowed MCP Servers found for user api key auth." @@ -1070,6 +1252,29 @@ class MCPServerManager: ######################################################### # Methods that call the upstream MCP servers ######################################################### + @staticmethod + def _extract_bearer_token( + oauth2_headers: Optional[Dict[str, str]], + raw_headers: Optional[Dict[str, str]], + ) -> Optional[str]: + """Extract the bare Bearer token from oauth2_headers or raw_headers. + + Returns the token string without the ``Bearer `` prefix, or ``None`` + if no Authorization header is found. + """ + auth_value: Optional[str] = None + if oauth2_headers and "Authorization" in oauth2_headers: + auth_value = oauth2_headers["Authorization"] + elif raw_headers: + # raw_headers may have lowercase keys depending on the ASGI server + normalized = {k.lower(): v for k, v in raw_headers.items()} + auth_value = normalized.get("authorization") + if auth_value: + if auth_value.startswith("Bearer "): + return auth_value[len("Bearer ") :] + return auth_value + return None + def _build_stdio_env( self, server: MCPServer, @@ -1103,25 +1308,30 @@ class MCPServerManager: mcp_auth_header: Optional[Union[str, Dict[str, str]]] = None, extra_headers: Optional[Dict[str, str]] = None, stdio_env: Optional[Dict[str, str]] = None, + subject_token: Optional[str] = None, ) -> MCPClient: """ Create an MCPClient instance for the given server. Auth resolution (single place for all auth logic): 1. ``mcp_auth_header`` — per-request/per-user override - 2. OAuth2 client_credentials token — auto-fetched and cached - 3. ``server.authentication_token`` — static token from config/DB + 2. OAuth2 Token Exchange (OBO) — exchange user token for scoped token + 3. OAuth2 client_credentials token — auto-fetched and cached + 4. ``server.authentication_token`` — static token from config/DB Args: server: The server configuration. mcp_auth_header: Optional per-request auth override. extra_headers: Additional headers to forward. stdio_env: Environment variables for stdio transport. + subject_token: Optional user JWT for token exchange (OBO) flow. Returns: Configured MCP client instance. """ - auth_value = await resolve_mcp_auth(server, mcp_auth_header) + auth_value = await resolve_mcp_auth( + server, mcp_auth_header, subject_token=subject_token + ) transport = server.transport or MCPTransport.sse @@ -1236,7 +1446,11 @@ class MCPServerManager: ## HANDLE OPENAPI TOOLS if server.spec_path: - _tools = global_mcp_tool_registry.list_tools(tool_prefix=server.name) + # OpenAPI tools were stored in the registry under the prefix + # active at registration time — fetch by that same prefix. + _tools = global_mcp_tool_registry.list_tools( + tool_prefix=get_server_prefix(server) + ) tools = global_mcp_tool_registry.convert_tools_to_mcp_sdk_tool_type( _tools ) @@ -1478,6 +1692,47 @@ class MCPServerManager: ) return await client.get_prompt(get_prompt_request_params) + @staticmethod + def _is_same_authority_metadata_url(url: str, server_url: str) -> bool: + """ + Whether ``url`` shares scheme, host, and port with ``server_url``. + + Same-authority metadata URLs are produced by our well-known discovery + construction and by resource servers that publish protected-resource + metadata on the resource origin. These must keep working for + administrator-configured internal MCP servers, so they are fetched + directly. Cross-origin URLs are fetched through ``async_safe_get``. + """ + try: + target = urlparse(url) + base = urlparse(server_url) + except Exception: + return False + + if target.scheme not in ("http", "https") or not target.hostname: + return False + + target_port = target.port or (443 if target.scheme == "https" else 80) + base_port = base.port or (443 if base.scheme == "https" else 80) + return ( + base.scheme == target.scheme + and (base.hostname or "").lower() == target.hostname.lower() + and base_port == target_port + ) + + async def _fetch_oauth_discovery_url(self, url: str, server_url: str) -> Any: + client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.MCP, + params={"timeout": MCP_METADATA_TIMEOUT}, + ) + if self._is_same_authority_metadata_url(url, server_url): + # Same-authority URLs may point at administrator-configured + # internal MCP servers. Do not run them through user URL + # validation, but also do not follow redirects because the + # redirect target would not inherit the same-authority guarantee. + return await client.get(url, follow_redirects=False) + return await async_safe_get(client, url) + async def _descovery_metadata( self, server_url: str, @@ -1488,11 +1743,28 @@ class MCPServerManager: client = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) response = await client.get(server_url) response.raise_for_status() - verbose_logger.warning( - "MCP OAuth discovery unexpectedly succeeded for %s; server did not challenge", - server_url, + ( + authorization_servers, + resource_scopes, + ) = await self._attempt_well_known_discovery(server_url) + metadata = await self._fetch_authorization_server_metadata( + authorization_servers, server_url ) - raise RuntimeError("OAuth discovery must not succeed without a challenge") + if ( + metadata is None + and not resource_scopes + and authorization_servers + and response.status_code == 200 + ): + verbose_logger.warning( + "MCP OAuth discovery for %s received 200 OK without RFC 9728 challenge and no discoverable authorization metadata.", + server_url, + ) + if metadata is None and resource_scopes: + return MCPOAuthMetadata(scopes=resource_scopes) + if metadata is not None and resource_scopes: + metadata.scopes = resource_scopes + return metadata except HTTPStatusError as exc: verbose_logger.debug( "MCP OAuth discovery for %s received status error: %s", @@ -1510,14 +1782,14 @@ class MCPServerManager: header_value ) - authorization_servers: List[str] = [] - resource_scopes: Optional[List[str]] = None + authorization_servers = [] + resource_scopes = None if resource_metadata_url: ( authorization_servers, resource_scopes, ) = await self._fetch_oauth_metadata_from_resource( - resource_metadata_url + resource_metadata_url, server_url ) else: ( @@ -1538,7 +1810,7 @@ class MCPServerManager: if authorization_servers: metadata = await self._fetch_authorization_server_metadata( - authorization_servers + authorization_servers, server_url ) preferred_scopes = scopes or resource_scopes @@ -1578,19 +1850,26 @@ class MCPServerManager: return resource_metadata_url, scopes async def _fetch_oauth_metadata_from_resource( - self, resource_metadata_url: str + self, resource_metadata_url: str, server_url: str ) -> Tuple[List[str], Optional[List[str]]]: if not resource_metadata_url: return [], None try: - client = get_async_httpx_client( - llm_provider=httpxSpecialProvider.MCP, - params={"timeout": MCP_METADATA_TIMEOUT}, + response = await self._fetch_oauth_discovery_url( + resource_metadata_url, server_url ) - response = await client.get(resource_metadata_url) response.raise_for_status() data = response.json() + except SSRFError as exc: + verbose_logger.warning( + "MCP OAuth discovery: refusing to fetch resource metadata from %s " + "(rejected by SSRF guard for server %s): %s", + resource_metadata_url, + server_url, + exc, + ) + return [], None except Exception as exc: # pragma: no cover - network issues verbose_logger.debug( "Failed to fetch MCP OAuth metadata from %s: %s", @@ -1639,23 +1918,25 @@ class MCPServerManager: ( authorization_servers, scopes, - ) = await self._fetch_oauth_metadata_from_resource(url) + ) = await self._fetch_oauth_metadata_from_resource(url, server_url) if authorization_servers: return authorization_servers, scopes return [], None async def _fetch_authorization_server_metadata( - self, authorization_servers: List[str] + self, authorization_servers: List[str], server_url: str ) -> Optional[MCPOAuthMetadata]: for issuer in authorization_servers: - metadata = await self._fetch_single_authorization_server_metadata(issuer) + metadata = await self._fetch_single_authorization_server_metadata( + issuer, server_url + ) if metadata is not None: return metadata return None async def _fetch_single_authorization_server_metadata( - self, issuer_url: str + self, issuer_url: str, server_url: str ) -> Optional[MCPOAuthMetadata]: try: parsed = urlparse(issuer_url) @@ -1674,19 +1955,27 @@ class MCPServerManager: f"{base}/.well-known/oauth-authorization-server/{path}" ) candidate_urls.append(f"{base}/.well-known/openid-configuration/{path}") + candidate_urls.append( + f"{issuer_url.rstrip('/')}/.well-known/openid-configuration" + ) candidate_urls.append(f"{base}/.well-known/oauth-authorization-server") candidate_urls.append(f"{base}/.well-known/openid-configuration") candidate_urls.append(issuer_url.rstrip("/")) for url in candidate_urls: try: - client = get_async_httpx_client( - llm_provider=httpxSpecialProvider.MCP, - params={"timeout": MCP_METADATA_TIMEOUT}, - ) - response = await client.get(url) + response = await self._fetch_oauth_discovery_url(url, server_url) response.raise_for_status() data = response.json() + except SSRFError as exc: + verbose_logger.warning( + "MCP OAuth discovery: refusing to fetch authorization-server " + "metadata from %s (rejected by SSRF guard for server %s): %s", + url, + server_url, + exc, + ) + continue except Exception as exc: # pragma: no cover - network issues verbose_logger.debug( "Failed to fetch authorization metadata from %s: %s", @@ -1713,7 +2002,28 @@ class MCPServerManager: ): return metadata - return None + return self._build_azure_authorization_server_metadata(parsed) + + @staticmethod + def _build_azure_authorization_server_metadata( + parsed_issuer_url: Any, + ) -> Optional[MCPOAuthMetadata]: + path_parts = [ + part for part in (parsed_issuer_url.path or "").split("/") if part + ] + if ( + parsed_issuer_url.netloc not in _AZURE_ENTRA_HOSTS + or len(path_parts) != 2 + or path_parts[1] != "v2.0" + ): + return None + + tenant = path_parts[0] + base = f"{parsed_issuer_url.scheme}://{parsed_issuer_url.netloc}/{tenant}" + return MCPOAuthMetadata( + authorization_url=f"{base}/oauth2/v2.0/authorize", + token_url=f"{base}/oauth2/v2.0/token", + ) @staticmethod def _decrypt_credential_field( @@ -1810,6 +2120,68 @@ class MCPServerManager: verbose_logger.warning(f"Error listing tools from {server_name}: {str(e)}") return [] + _SHORT_PREFIX_MAX_REHASH_ATTEMPTS = 1024 + + def _assign_unique_short_prefix( + self, + server: MCPServer, + registry: Optional[Dict[str, MCPServer]] = None, + ) -> None: + """Resolve and cache a collision-free short tool prefix on ``server``. + + Called at registration time for every MCP server entering the + registry. Mutates ``server.short_prefix`` in place. No-ops when + ``LITELLM_USE_SHORT_MCP_TOOL_PREFIX`` is disabled, when the server + has no ``server_id`` (synthetic temp-server objects), or when a + prefix is already cached. + + Collision strategy: take the natural hash; if it's already used by + a *different* server in the combined registry, rehash with an + incrementing attempt counter until we find an unused slot. The + attempt counter is folded into the hash so the resulting prefix is + still deterministic for a given (server_id, set-of-other-server-ids) + pair within one process. + """ + if not is_short_mcp_tool_prefix_enabled(): + return + if server.short_prefix: + return + if not server.server_id: + return + + used: Dict[str, str] = {} + registry_for_collision_check = registry or self.get_registry() + for other in registry_for_collision_check.values(): + if other.server_id == server.server_id: + continue + if other.short_prefix: + used[other.short_prefix] = other.server_id + + for attempt in range(self._SHORT_PREFIX_MAX_REHASH_ATTEMPTS): + candidate = compute_short_server_prefix(server.server_id, attempt=attempt) + if candidate not in used: + server.short_prefix = candidate + if attempt > 0: + verbose_logger.info( + "MCP short-prefix collision resolved for server %s: " + "natural hash collided with %s, using rehashed prefix " + "%s (attempt=%d).", + server.server_id, + used.get( + compute_short_server_prefix(server.server_id, attempt=0), + "", + ), + candidate, + attempt, + ) + return + + raise RuntimeError( + f"Unable to assign a unique short MCP tool prefix for server " + f"{server.server_id} after {self._SHORT_PREFIX_MAX_REHASH_ATTEMPTS} " + "attempts; the 3-character prefix space is too crowded." + ) + def _create_prefixed_tools( self, tools: List[MCPTool], server: MCPServer, add_prefix: bool = True ) -> List[MCPTool]: @@ -1838,9 +2210,13 @@ class MCPServerManager: tool_copy.name = name_to_use prefixed_tools.append(tool_copy) - # Update tool to server mapping for resolution (support both forms) + # Register every known prefix form (alias, server_name, server_id, + # short ID) so call_tool can resolve regardless of which form a + # caller / cached client is using. self.tool_name_to_mcp_server_name_mapping[original_name] = prefix - self.tool_name_to_mcp_server_name_mapping[prefixed_name] = prefix + for known_prefix in iter_known_server_prefixes(server): + qualified = add_server_prefix_to_name(original_name, known_prefix) + self.tool_name_to_mcp_server_name_mapping[qualified] = prefix verbose_logger.info( f"Successfully fetched {len(prefixed_tools)} tools from server {server.name}" @@ -2247,7 +2623,7 @@ class MCPServerManager: ) ) - async def _call_regular_mcp_tool( + async def _call_regular_mcp_tool( # noqa: PLR0915 self, mcp_server: MCPServer, original_tool_name: str, @@ -2307,10 +2683,17 @@ class MCPServerManager: if server_auth_header is None: server_auth_header = mcp_auth_header - # oauth2 headers + # Extract subject token for OAuth2 Token Exchange (OBO) flow + subject_token: Optional[str] = None extra_headers: Optional[Dict[str, str]] = None - if mcp_server.auth_type == MCPAuth.oauth2: - extra_headers = oauth2_headers + if mcp_server.auth_type == MCPAuth.oauth2_token_exchange: + subject_token = self._extract_bearer_token(oauth2_headers, raw_headers) + elif mcp_server.auth_type == MCPAuth.oauth2: + if mcp_server.has_client_credentials: + # For M2M OAuth servers, Authorization must come from token fetch. + extra_headers = None + else: + extra_headers = oauth2_headers if mcp_server.extra_headers and raw_headers: if extra_headers is None: @@ -2322,6 +2705,11 @@ class MCPServerManager: for header in mcp_server.extra_headers: if not isinstance(header, str): continue + if ( + mcp_server.has_client_credentials + and header.lower() == "authorization" + ): + continue header_value = normalized_raw_headers.get(header.lower()) if header_value is None: continue @@ -2357,6 +2745,10 @@ class MCPServerManager: ) extra_headers.update(hook_extra_headers) + # Reset to None if no headers were actually added + if extra_headers is not None and len(extra_headers) == 0: + extra_headers = None + stdio_env = self._build_stdio_env(mcp_server, raw_headers) client = await self._create_mcp_client( @@ -2364,6 +2756,7 @@ class MCPServerManager: mcp_auth_header=server_auth_header, extra_headers=extra_headers, stdio_env=stdio_env, + subject_token=subject_token, ) call_tool_params = MCPCallToolRequestParams( @@ -2601,37 +2994,43 @@ class MCPServerManager: Returns: MCPServer if found, None otherwise """ + registry_servers = list(self.get_registry().values()) + + # Build prefix → server lookup covering every known form a tool name + # may take (alias / server_name / server_id / short ID). This is what + # makes the short-prefix mode work without breaking historical names. + prefix_to_server: Dict[str, MCPServer] = {} + for server in registry_servers: + for known_prefix in iter_known_server_prefixes(server): + normalised = normalize_server_name(known_prefix) + prefix_to_server.setdefault(normalised, server) + # First try with the original tool name if tool_name in self.tool_name_to_mcp_server_name_mapping: server_name = self.tool_name_to_mcp_server_name_mapping[tool_name] - for server in self.get_registry().values(): - if normalize_server_name(server.name) == normalize_server_name( - server_name - ): + normalised_lookup = normalize_server_name(server_name) + if normalised_lookup in prefix_to_server: + return prefix_to_server[normalised_lookup] + for server in registry_servers: + if normalize_server_name(server.name) == normalised_lookup: return server - # If not found and tool name is prefixed, try extracting server name from prefix - known_prefixes = { - normalize_server_name(get_server_prefix(s)) - for s in self.get_registry().values() - if get_server_prefix(s) - } - if is_tool_name_prefixed(tool_name, known_server_prefixes=known_prefixes): + # If not found and tool name is prefixed, extract the prefix and + # match against any known form. + if is_tool_name_prefixed( + tool_name, known_server_prefixes=set(prefix_to_server.keys()) + ): ( original_tool_name, server_name_from_prefix, ) = split_server_prefix_from_name(tool_name) - if original_tool_name in self.tool_name_to_mcp_server_name_mapping: - for server in self.get_registry().values(): - if server.server_name is None: - if normalize_server_name(server.name) == normalize_server_name( - server_name_from_prefix - ): - return server - elif normalize_server_name( - server.server_name - ) == normalize_server_name(server_name_from_prefix): - return server + normalised_prefix = normalize_server_name(server_name_from_prefix) + matched_server = prefix_to_server.get(normalised_prefix) + if matched_server is not None and ( + original_tool_name in self.tool_name_to_mcp_server_name_mapping + or tool_name in self.tool_name_to_mcp_server_name_mapping + ): + return matched_server return None @@ -2666,36 +3065,76 @@ class MCPServerManager: previous_registry = self.registry new_registry: Dict[str, MCPServer] = {} + # Stage one: build every server. Stage two assigns short prefixes + # against the *full* set so dedup is deterministic regardless of + # iteration order. for server in db_mcp_servers: - existing_server = previous_registry.get(server.server_id) + try: + existing_server = previous_registry.get(server.server_id) - if ( - existing_server is not None - and existing_server.updated_at is not None - and server.updated_at is not None - and existing_server.updated_at == server.updated_at - ): - # Re-use existing server instance to avoid re-running build_mcp_server_from_table() - # which can perform network discovery for OAuth2 servers. - new_registry[server.server_id] = existing_server - continue + if ( + existing_server is not None + and existing_server.updated_at is not None + and server.updated_at is not None + and existing_server.updated_at == server.updated_at + ): + # Re-use existing server instance to avoid re-running build_mcp_server_from_table() + # which can perform network discovery for OAuth2 servers. + new_registry[server.server_id] = existing_server + continue - _warn_on_server_name_fields( - server_id=server.server_id, - alias=getattr(server, "alias", None), - server_name=getattr(server, "server_name", None), - ) - verbose_logger.debug( - f"Building server from DB: {server.server_id} ({server.server_name})" - ) - new_server = await self.build_mcp_server_from_table(server) - new_registry[server.server_id] = new_server - await self._maybe_register_openapi_tools(new_server) + _warn_on_server_name_fields( + server_id=server.server_id, + alias=getattr(server, "alias", None), + server_name=getattr(server, "server_name", None), + ) + verbose_logger.debug( + f"Building server from DB: {server.server_id} ({server.server_name})" + ) + new_server = await self.build_mcp_server_from_table(server) + # Carry the cached short_prefix from the previous registry entry + # (if any) so the prefix is stable across reloads. + if existing_server is not None and existing_server.short_prefix: + new_server.short_prefix = existing_server.short_prefix + new_registry[server.server_id] = new_server + except Exception as e: + verbose_logger.exception( + "Skipping MCP server %s (%s) during DB reload: %s", + server.server_id, + getattr(server, "alias", None), + e, + ) - self.registry = new_registry + # Assign short prefixes against the full candidate set without + # publishing the staged registry to concurrent callers. + registered_registry: Dict[str, MCPServer] = {} + registered_openapi_tools = False + for server_id, new_server in new_registry.items(): + try: + self._assign_unique_short_prefix(new_server, registry=new_registry) + # Register OpenAPI tools *after* the final short prefix is assigned + # so the tools are stored in the global registry under the same + # prefix that lookups will use. + await self._maybe_register_openapi_tools( + new_server, initialize_mapping=False + ) + registered_registry[server_id] = new_server + if new_server.spec_path: + registered_openapi_tools = True + except Exception as e: + verbose_logger.exception( + "Skipping MCP server %s (%s) during DB reload: %s", + new_server.server_id, + getattr(new_server, "alias", None), + e, + ) + + self.registry = registered_registry + if registered_openapi_tools: + self.initialize_tool_name_to_mcp_server_name_mapping() verbose_logger.debug( - "MCP registry refreshed (%s servers in registry)", len(new_registry) + "MCP registry refreshed (%s servers in registry)", len(registered_registry) ) def get_mcp_servers_from_ids(self, server_ids: List[str]) -> List[MCPServer]: diff --git a/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py b/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py index 476e215666e..92ef57d8cd5 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py +++ b/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py @@ -26,6 +26,7 @@ from litellm.proxy.common_utils.encrypt_decrypt_utils import ( decrypt_value_helper, encrypt_value_helper, ) +from litellm.proxy._experimental.mcp_server.auth import token_exchange from litellm.types.llms.custom_http import httpxSpecialProvider if TYPE_CHECKING: @@ -50,12 +51,23 @@ class MCPOAuth2TokenCache(InMemoryCache): def _get_lock(self, server_id: str) -> asyncio.Lock: return self._locks.setdefault(server_id, asyncio.Lock()) - async def async_get_token(self, server: "MCPServer") -> Optional[str]: + @staticmethod + def _has_client_credentials_config(server: "MCPServer") -> bool: + return bool(server.client_id and server.client_secret and server.token_url) + + async def async_get_token( + self, + server: "MCPServer", + *, + require_client_credentials_flow: bool = True, + ) -> Optional[str]: """Return a valid access token, fetching or refreshing as needed. Returns ``None`` when the server lacks client credentials config. """ - if not server.has_client_credentials: + if require_client_credentials_flow and not server.has_client_credentials: + return None + if not self._has_client_credentials_config(server): return None server_id = server.server_id @@ -263,16 +275,38 @@ mcp_per_user_token_cache = MCPPerUserTokenCache() async def resolve_mcp_auth( server: "MCPServer", mcp_auth_header: Optional[Union[str, Dict[str, str]]] = None, + subject_token: Optional[str] = None, ) -> Optional[Union[str, Dict[str, str]]]: """Resolve the auth value for an MCP server. Priority: 1. ``mcp_auth_header`` — per-request/per-user override - 2. OAuth2 client_credentials token — auto-fetched and cached - 3. ``server.authentication_token`` — static token from config/DB + 2. OAuth2 Token Exchange (OBO / RFC 8693) — exchange user token for scoped token + 3. OAuth2 client_credentials token — auto-fetched and cached + 4. ``server.authentication_token`` — static token from config/DB """ if mcp_auth_header: return mcp_auth_header + if server.has_token_exchange_config: + if subject_token: + return await token_exchange.mcp_token_exchange_handler.exchange_token( + subject_token, server + ) + # No subject_token — fall back to client_credentials using the same client + # credentials and token_url so M2M scenarios still work. + if server.client_id and server.client_secret and server.token_url: + return await mcp_oauth2_token_cache.async_get_token( + server, + require_client_credentials_flow=False, + ) + # OBO configured but no subject_token and missing client credentials — warn + # rather than silently proceeding unauthenticated. + verbose_logger.warning( + "MCP server '%s' is configured for token exchange (OBO) but no subject_token " + "was provided and client credentials (client_id/client_secret/token_url) are " + "incomplete. The request will proceed without authentication.", + server.server_id, + ) if server.has_client_credentials: return await mcp_oauth2_token_cache.async_get_token(server) return server.authentication_token diff --git a/litellm/proxy/_experimental/mcp_server/oauth_utils.py b/litellm/proxy/_experimental/mcp_server/oauth_utils.py index b13cf83058c..343d1bee613 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth_utils.py +++ b/litellm/proxy/_experimental/mcp_server/oauth_utils.py @@ -2,15 +2,63 @@ (BYOK + discoverable / pass-through OAuth proxy).""" from ipaddress import ip_address -from urllib.parse import urlparse +from urllib.parse import urlparse, urlunparse -from fastapi import HTTPException +from fastapi import HTTPException, Request + +from litellm._logging import verbose_logger +from litellm.proxy.auth.ip_address_utils import IPAddressUtils # RFC 6749 §5.1 / OAuth 2.1 draft-15 §4.1.3: token-endpoint responses # must not be cached — both success and error bodies may reveal secrets. TOKEN_NO_CACHE_HEADERS = {"Cache-Control": "no-store", "Pragma": "no-cache"} +def get_request_base_url(request: Request) -> str: + """ + Get the base URL for the request, considering X-Forwarded-* headers. + + X-Forwarded-Proto / X-Forwarded-Host / X-Forwarded-Port are only honoured + when the request comes from a configured trusted proxy + (``use_x_forwarded_for`` enabled AND caller in ``mcp_trusted_proxy_ranges``). + Otherwise the request's literal ``base_url`` is returned, so an + untrusted caller cannot poison OAuth-discovery / redirect_uri values + by injecting headers. + + Args: + request: FastAPI Request object + + Returns: + The reconstructed base URL (e.g., "https://proxy.example.com") + """ + base_url = str(request.base_url).rstrip("/") + parsed = urlparse(base_url) + + if not IPAddressUtils.is_request_from_trusted_proxy(request): + return base_url + + x_forwarded_proto = request.headers.get("X-Forwarded-Proto") + x_forwarded_host = request.headers.get("X-Forwarded-Host") + x_forwarded_port = request.headers.get("X-Forwarded-Port") + + scheme = x_forwarded_proto if x_forwarded_proto else parsed.scheme + + if x_forwarded_host: + # X-Forwarded-Host may already include port (e.g., "example.com:8080") + if ":" in x_forwarded_host and not x_forwarded_host.startswith("["): + netloc = x_forwarded_host + elif x_forwarded_port: + netloc = f"{x_forwarded_host}:{x_forwarded_port}" + else: + netloc = x_forwarded_host + else: + netloc = parsed.netloc + if x_forwarded_port and ":" not in netloc: + netloc = f"{netloc}:{x_forwarded_port}" + + return urlunparse((scheme, netloc, parsed.path, "", "", "")) + + def validate_loopback_redirect_uri(redirect_uri: str) -> None: """Require a loopback ``redirect_uri`` (OAuth 2.1 §4.1.2.1 + RFC 8252 §7.3 native-app pattern). MCP clients are native apps that listen on @@ -46,3 +94,60 @@ def validate_loopback_redirect_uri(redirect_uri: str) -> None: # don't let it bubble up as a 500. pass raise HTTPException(status_code=400, detail="invalid_request") + + +def validate_trusted_redirect_uri(request: Request, redirect_uri: str) -> None: + """Accept same-origin (proxy's own origin) OR loopback ``redirect_uri``. + + Same-origin is required for the LiteLLM UI's OAuth flow: the UI + redirects to ``/ui/mcp/oauth/callback`` which is not loopback + but is on the proxy's own trusted HTTPS origin. An attacker cannot + host content on the proxy's own origin without already owning the + proxy, so the open-redirect / code-theft primitive that motivated + :func:`validate_loopback_redirect_uri` does not apply here. + + Loopback continues to be accepted for native MCP clients (per + OAuth 2.1 §4.1.2.1 + RFC 8252 §7.3). + + Use this in the discoverable OAuth proxy endpoints that serve both + native clients and the proxy's own UI. BYOK endpoints that only + support native clients should keep + :func:`validate_loopback_redirect_uri`. + """ + try: + parsed = urlparse(redirect_uri) + except ValueError: + raise HTTPException(status_code=400, detail="invalid_request") + if parsed.scheme not in ("http", "https"): + raise HTTPException(status_code=400, detail="invalid_request") + if parsed.fragment: + raise HTTPException(status_code=400, detail="invalid_request") + + # Same-origin: scheme + netloc (host[:port]) must match the proxy's + # own base URL at this request (honouring trusted X-Forwarded-*). + try: + proxy_base = urlparse(get_request_base_url(request)) + if ( + parsed.netloc + and parsed.scheme == proxy_base.scheme + and parsed.netloc.lower() == proxy_base.netloc.lower() + ): + return + except Exception as exc: + # If we can't determine the proxy's origin, fall through to + # loopback. Log so the failure is diagnosable in production. + verbose_logger.warning( + "validate_trusted_redirect_uri: could not determine proxy origin, " + "falling back to loopback-only check. error=%s", + exc, + ) + + host = (parsed.hostname or "").lower() + if host == "localhost": + return + try: + if ip_address(host).is_loopback: + return + except ValueError: + pass + raise HTTPException(status_code=400, detail="invalid_request") diff --git a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py index 3b2fa097b70..271517bb1e6 100644 --- a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py +++ b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py @@ -6,10 +6,34 @@ import asyncio import contextvars import json import os +import re from pathlib import PurePosixPath from typing import Any, Dict, List, Optional from urllib.parse import quote +# Tool names emitted from OpenAPI specs must work across all major LLM providers. +# OpenAI/Anthropic/Bedrock all enforce a character class roughly equivalent to +# ^[a-zA-Z0-9_-]+$ on tool names. Many specs (notably GitHub's REST API) use +# tag-namespaced operationIds like "actions/download-job-logs-for-workflow-run" +# which include '/'. Sanitize here so the same regex passes everywhere downstream. +_OPENAPI_TOOL_NAME_INVALID_CHARS = re.compile(r"[^a-zA-Z0-9_-]") +_OPENAPI_TOOL_NAME_MAX_LEN = 128 + + +def sanitize_openapi_tool_name(raw_name: str) -> str: + """Map an OpenAPI operationId / fallback to a provider-safe tool name. + + Replaces any character outside ``[a-zA-Z0-9_-]`` with ``_`` and caps the + result at 128 chars (the most restrictive of the major providers). + Lowercased to match the existing convention in + ``register_tools_from_openapi``. + """ + if not raw_name: + return raw_name + sanitized = _OPENAPI_TOOL_NAME_INVALID_CHARS.sub("_", raw_name).lower() + return sanitized[:_OPENAPI_TOOL_NAME_MAX_LEN] + + from litellm._logging import verbose_logger from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, @@ -31,6 +55,13 @@ _request_auth_header: contextvars.ContextVar[Optional[str]] = contextvars.Contex "_request_auth_header", default=None ) +# Per-request extra headers forwarded from the client request. +# Populated from MCPServer.extra_headers names matched against raw request +# headers in server.py before dispatching to a local/OpenAPI tool handler. +_request_extra_headers: contextvars.ContextVar[Optional[Dict[str, str]]] = ( + contextvars.ContextVar("_request_extra_headers", default=None) +) + def _sanitize_path_parameter_value(param_value: Any, param_name: str) -> str: """Ensure path params cannot introduce directory traversal.""" @@ -273,6 +304,46 @@ def build_input_schema(operation: Dict[str, Any]) -> Dict[str, Any]: } +def _merge_openapi_tool_request_headers( + static_headers: Dict[str, str] +) -> Dict[str, str]: + """Merge static closure headers with per-request ContextVar overrides. + + Precedence (highest to lowest): + 1. ``_request_auth_header`` — BYOK override of ``Authorization`` + 2. ``static_headers`` — operator-configured headers baked into the + tool closure at registration time + 3. ``_request_extra_headers`` — per-request headers forwarded from + the MCP caller (allowlisted by ``MCPServer.extra_headers``) + + This matches the existing MCP invariant in + :func:`litellm.proxy._experimental.mcp_server.utils.merge_mcp_headers` + and the managed MCP path, where ``static_headers`` always wins over + caller-forwarded headers. Keeping the same precedence here prevents an + authenticated caller from overriding an operator-configured value + (e.g. a tenant id or upstream API key) by sending the same header name. + + Header names are compared case-insensitively so different casing cannot + bypass the precedence rules. + """ + request_extra = _request_extra_headers.get() or {} + static = static_headers or {} + + static_lower_names = {k.lower() for k in static} + effective_headers: Dict[str, str] = { + k: v for k, v in request_extra.items() if k.lower() not in static_lower_names + } + effective_headers.update(static) + + override_auth = _request_auth_header.get() + if override_auth: + for existing in [k for k in effective_headers if k.lower() == "authorization"]: + del effective_headers[existing] + effective_headers["Authorization"] = override_auth + + return effective_headers + + def create_tool_function( path: str, method: str, @@ -310,14 +381,7 @@ def create_tool_function( The function safely handles parameter names that aren't valid Python identifiers by using **kwargs instead of named parameters. """ - # Allow per-request auth override (e.g. BYOK credential set via ContextVar). - # The ContextVar holds the full Authorization header value, including the - # correct prefix (Bearer / ApiKey / Basic) formatted by the caller in - # server.py based on the server's configured auth_type. - effective_headers = dict(headers) - override_auth = _request_auth_header.get() - if override_auth: - effective_headers["Authorization"] = override_auth + effective_headers = _merge_openapi_tool_request_headers(headers) # Build URL from base_url and path url = base_url + path @@ -399,17 +463,36 @@ def create_tool_function( def register_tools_from_openapi(spec: Dict[str, Any], base_url: str): """Register MCP tools from OpenAPI specification.""" paths = spec.get("paths", {}) + used_names: set = set() for path, path_item in paths.items(): for method in ["get", "post", "put", "delete", "patch"]: if method in path_item: operation = path_item[method] - # Generate tool name - operation_id = operation.get( - "operationId", f"{method}_{path.replace('/', '_')}" - ) - tool_name = operation_id.replace(" ", "_").lower() + # Generate tool name. Sanitize to ^[a-zA-Z0-9_-]+$ (lowercase) + # so the resulting name is valid across OpenAI/Anthropic/Bedrock. + # Many specs (e.g. GitHub REST) use tag-namespaced operationIds + # like "actions/download-job-logs-for-workflow-run" which + # contain '/' and would 400 at the LLM provider boundary. + operation_id = operation.get("operationId", f"{method}_{path}") + tool_name = sanitize_openapi_tool_name(operation_id) + + # Disambiguate collisions: two operationIds that differ only + # by sanitized characters (e.g. "foo/list" and "foo.list") + # would both become "foo_list". Append _2, _3, … to keep + # every tool reachable, mirroring the Anthropic-side logic + # in _build_anthropic_tool_name_maps. + unique = tool_name + n = 1 + while unique in used_names: + n += 1 + suffix = f"_{n}" + unique = ( + tool_name[: _OPENAPI_TOOL_NAME_MAX_LEN - len(suffix)] + suffix + ) + tool_name = unique + used_names.add(tool_name) # Get description description = operation.get( diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index 3047fb73325..829863d2dbb 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -857,6 +857,7 @@ if MCP_AVAILABLE: ######################################################## from litellm.proxy.management_endpoints.mcp_management_endpoints import ( NewMCPServerRequest, + _inherit_credentials_from_existing_server, ) def _extract_credentials( @@ -975,9 +976,11 @@ if MCP_AVAILABLE: async def _preview_openapi_tools(spec_path: str) -> dict: """Generate tool previews from an OpenAPI spec without creating a server.""" from litellm.proxy._experimental.mcp_server.openapi_to_mcp_generator import ( + _OPENAPI_TOOL_NAME_MAX_LEN, build_input_schema, load_openapi_spec_async, resolve_operation_params, + sanitize_openapi_tool_name, ) try: @@ -985,8 +988,9 @@ if MCP_AVAILABLE: paths = spec.get("paths", {}) components = spec.get("components", {}) tools: List[dict] = [] + used_names: set = set() for path, path_item in paths.items(): - for method in ("get", "post", "put", "patch", "delete"): + for method in ("get", "post", "put", "delete", "patch"): operation = path_item.get(method) if operation is None: continue @@ -995,7 +999,23 @@ if MCP_AVAILABLE: operation, path_item, components ) - op_id = operation.get("operationId", f"{method}_{path}") + raw_op_id = operation.get("operationId", f"{method}_{path}") + # Match what register_tools_from_openapi does so the preview + # the user sees in the dashboard equals the names that get + # registered (and shipped to LLM providers, which enforce + # ^[a-zA-Z0-9_-]+$). See sanitize_openapi_tool_name docstring. + op_id = sanitize_openapi_tool_name(raw_op_id) + + unique = op_id + n = 1 + while unique in used_names: + n += 1 + suffix = f"_{n}" + unique = ( + op_id[: _OPENAPI_TOOL_NAME_MAX_LEN - len(suffix)] + suffix + ) + op_id = unique + used_names.add(op_id) summary = operation.get("summary", "") description = operation.get("description", summary) input_schema = build_input_schema(resolved_op) @@ -1068,6 +1088,10 @@ if MCP_AVAILABLE: }, ) + new_mcp_server_request = _inherit_credentials_from_existing_server( + new_mcp_server_request + ) + # For OpenAPI spec servers, generate tools from the spec directly if new_mcp_server_request.spec_path: return await _preview_openapi_tools(new_mcp_server_request.spec_path) diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index c2e998f01e5..0a74a92f9ce 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -23,6 +23,7 @@ from typing import ( cast, ) +import httpx from fastapi import FastAPI, HTTPException from pydantic import AnyUrl, ConfigDict from starlette.requests import Request as StarletteRequest @@ -49,6 +50,11 @@ from litellm.proxy._experimental.mcp_server.utils import ( LITELLM_MCP_SERVER_VERSION, add_server_prefix_to_name, get_server_prefix, + iter_known_server_prefixes, +) +from litellm.llms.custom_httpx.http_handler import ( + get_async_httpx_client, + httpxSpecialProvider, ) from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.ip_address_utils import IPAddressUtils @@ -56,7 +62,7 @@ from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, get_chain_id_from_headers, ) -from litellm.types.mcp import MCPAuth +from litellm.types.mcp import MCPAuth, MCPSpecVersion from litellm.types.mcp_server.mcp_server_manager import MCPInfo, MCPServer from litellm.types.utils import CallTypes, StandardLoggingMCPToolCall from litellm.utils import Rules, client, function_setup @@ -152,10 +158,12 @@ if MCP_AVAILABLE: MCPAuthenticatedUser, ) from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + MCPServerManager, global_mcp_server_manager, ) from litellm.proxy._experimental.mcp_server.openapi_to_mcp_generator import ( _request_auth_header, + _request_extra_headers, ) from litellm.proxy._experimental.mcp_server.sse_transport import SseServerTransport from litellm.proxy._experimental.mcp_server.tool_registry import ( @@ -711,13 +719,7 @@ if MCP_AVAILABLE: for server in allowed_mcp_servers: if server: match_list = [ - s.lower() - for s in [ - server.alias, - server.server_name, - server.server_id, - ] - if s is not None + s.lower() for s in iter_known_server_prefixes(server) if s ] if server_or_group.lower() in match_list: @@ -905,6 +907,20 @@ if MCP_AVAILABLE: allowed_mcp_server_id ) if mcp_server is not None: + # Apply oauth2_flow resolution for legacy DB rows where it may be NULL + resolved_flow = MCPServerManager._resolve_oauth2_flow( + auth_type=mcp_server.auth_type, + oauth2_flow=mcp_server.oauth2_flow, + token_url=mcp_server.token_url, + authorization_url=mcp_server.authorization_url, + client_id=mcp_server.client_id, + client_secret=mcp_server.client_secret, + ) + if resolved_flow and resolved_flow != mcp_server.oauth2_flow: + # Create a new instance with the resolved flow for this request + mcp_server = mcp_server.model_copy( + update={"oauth2_flow": resolved_flow} + ) allowed_mcp_servers.append(mcp_server) if mcp_servers is not None: @@ -1105,8 +1121,13 @@ if MCP_AVAILABLE: extra_headers: Optional[Dict[str, str]] = None if server.auth_type == MCPAuth.oauth2: - # Copy to avoid mutating the original dict (important for parallel fetching) - extra_headers = oauth2_headers.copy() if oauth2_headers else None + # For OAuth2 M2M servers, upstream Authorization must come from + # client_credentials token fetch, never from caller headers. + if server.has_client_credentials: + extra_headers = None + else: + # Copy to avoid mutating the original dict (important for parallel fetching) + extra_headers = oauth2_headers.copy() if oauth2_headers else None if server.extra_headers and raw_headers: if extra_headers is None: @@ -1119,11 +1140,17 @@ if MCP_AVAILABLE: for header in server.extra_headers: if not isinstance(header, str): continue + if server.has_client_credentials and header.lower() == "authorization": + continue header_value = normalized_raw_headers.get(header.lower()) if header_value is None: continue extra_headers[header] = header_value + # Reset to None if no headers were actually added + if extra_headers is not None and len(extra_headers) == 0: + extra_headers = None + if server_auth_header is None: server_auth_header = mcp_auth_header @@ -1310,8 +1337,24 @@ if MCP_AVAILABLE: raw_headers=raw_headers, ) - # If no OAuth2 token came from request headers, fall back to pre-fetched creds - if extra_headers is None and server.auth_type == MCPAuth.oauth2: + # Prefer server-stored per-user OAuth when configured, so a stale + # Authorization header from the MCP client cannot override Redis/DB + # (same issue as call_tool in mcp_server_manager: VS Code caches tokens). + if ( + server.auth_type == MCPAuth.oauth2 + and getattr(server, "needs_user_oauth_token", False) + and user_api_key_auth is not None + ): + db_headers = await _get_user_oauth_extra_headers_from_db( + server, + user_api_key_auth, + prefetched_creds=_prefetched_oauth_creds, + ) + if db_headers: + extra_headers = db_headers + + # If still no OAuth2 token, fall back to pre-fetched creds (non-stale-client path) + elif extra_headers is None and server.auth_type == MCPAuth.oauth2: extra_headers = await _get_user_oauth_extra_headers_from_db( server, user_api_key_auth, @@ -1382,11 +1425,19 @@ if MCP_AVAILABLE: spend_meta["per_server_tool_counts"] = per_server_tool_counts end_time = datetime.now() - await litellm_logging_obj.async_success_handler( - result=all_tools, - start_time=list_tools_start_time, - end_time=end_time, - ) + try: + await litellm_logging_obj.async_success_handler( + result=all_tools, + start_time=list_tools_start_time, + end_time=end_time, + ) + except Exception as log_exc: + # list_tools responses must not be dropped due to non-blocking + # observability/serialization failures. + verbose_logger.warning( + "MCP list_tools success logging failed (continuing): %s", + log_exc, + ) verbose_logger.info( f"Successfully fetched {len(all_tools)} tools total from all MCP servers" @@ -2031,11 +2082,13 @@ if MCP_AVAILABLE: # Remove prefix from tool name for logging and processing original_tool_name, server_name = split_server_prefix_from_name(name) - # If tool name is unprefixed, resolve its server so we can enforce permissions - if not server_name: - mcp_server = global_mcp_server_manager._get_mcp_server_from_tool_name(name) - if mcp_server: - server_name = mcp_server.name + # Resolve the actual MCP server up-front so the permission check uses + # the canonical server.name even when the tool name is prefixed with a + # short ID (LITELLM_USE_SHORT_MCP_TOOL_PREFIX) that doesn't match the + # server's display name directly. + mcp_server = global_mcp_server_manager._get_mcp_server_from_tool_name(name) + if mcp_server is not None: + server_name = mcp_server.name # Only enforce server-level permissions when we can resolve a server if server_name: @@ -2107,6 +2160,47 @@ if MCP_AVAILABLE: ######################################################### local_tool = global_mcp_tool_registry.get_tool(name) if local_tool: + # OpenAPI-backed tools used to bypass `pre_call_tool_check` — + # only the managed path ran allowed/banned-tool checks, key/team + # tool permissions, and parameter validation. Run the same checks + # before dispatching to the local registry. Refuse the call if + # we cannot resolve a server: tools registered via + # openapi_to_mcp_generator are always tied to a server, so a + # missing mcp_server here means the tool->server mapping has + # not finished initializing or the registry entry is orphaned. + # Skipping the check would re-open the same authorization gap. + if mcp_server is None: + raise HTTPException( + status_code=503, + detail=( + f"MCP server for tool '{name}' is not available; " + "refusing to dispatch without authorization checks. " + "Retry once the server is registered." + ), + ) + + # `pre_call_tool_check` calls into `proxy_logging_obj` for the + # pre-call guardrail hooks, so source it from the canonical + # `proxy_server` module the same way `_handle_managed_mcp_tool` + # does. `kwargs.get("proxy_logging_obj")` is None on the MCP + # entry path and would crash with AttributeError after the + # security checks pass. + from litellm.proxy.proxy_server import proxy_logging_obj + + hook_result = await global_mcp_server_manager.pre_call_tool_check( + name=original_tool_name, + arguments=arguments or {}, + server_name=server_name or mcp_server.name, + user_api_key_auth=user_api_key_auth, + proxy_logging_obj=proxy_logging_obj, + server=mcp_server, + raw_headers=raw_headers, + ) + # `pre_call_tool_check` may return guardrail-modified + # arguments; honor them on the local path too. + if isinstance(hook_result, dict) and "arguments" in hook_result: + arguments = hook_result["arguments"] + verbose_logger.debug(f"Executing local registry tool: {name}") # For BYOK servers the credential must be injected via a ContextVar # because the tool function has headers baked into its closure. @@ -2123,11 +2217,40 @@ if MCP_AVAILABLE: auth_header_value = f"Basic {mcp_auth_header}" else: auth_header_value = f"Bearer {mcp_auth_header}" + + # Forward named client headers to OpenAPI tool upstream requests. + # MCPServer.extra_headers lists header names to copy from raw_headers. + # OAuth2 M2M: never take Authorization from the caller (matches + # _prepare_mcp_server_headers for managed MCP). + forwarded_headers: Optional[Dict[str, str]] = None + if mcp_server and mcp_server.extra_headers and raw_headers: + normalized_raw = { + str(k).lower(): v + for k, v in raw_headers.items() + if isinstance(k, str) + } + skip_caller_authorization = bool(mcp_server.has_client_credentials) + for header_name in mcp_server.extra_headers: + if not isinstance(header_name, str): + continue + if ( + skip_caller_authorization + and header_name.lower() == "authorization" + ): + continue + value = normalized_raw.get(header_name.lower()) + if value is not None: + if forwarded_headers is None: + forwarded_headers = {} + forwarded_headers[header_name] = value + _auth_token = _request_auth_header.set(auth_header_value) + _extra_token = _request_extra_headers.set(forwarded_headers) try: local_content = await _handle_local_mcp_tool(name, arguments) finally: _request_auth_header.reset(_auth_token) + _request_extra_headers.reset(_extra_token) response = CallToolResult(content=cast(Any, local_content), isError=False) # Try managed MCP server tool (pass the full prefixed name) @@ -2434,6 +2557,10 @@ if MCP_AVAILABLE: import re mcp_servers_from_path: Optional[List[str]] = None + segments = [s for s in path.split("/") if s] + if len(segments) >= 2 and segments[1] == "mcp" and segments[0] != "mcp": + return [segments[0]] + # Match /mcp/ # Where servers can be comma-separated list of server names # Server names can contain slashes (e.g., "custom_solutions/user_123") @@ -2652,6 +2779,157 @@ if MCP_AVAILABLE: ) return user_api_key_auth.model_copy(update={"object_permission": updated_op}) + def _get_forwarded_auth_from_scope(scope: Scope) -> Optional[str]: + """Return the upstream-bound ``Authorization`` header value, or None. + + Only returns the ``Authorization`` header when ``x-litellm-api-key`` is + also present. In that case ``Authorization`` is unambiguously the + upstream token the caller wants forwarded to the MCP server. When + ``x-litellm-api-key`` is absent the ``Authorization`` header may itself + be the LiteLLM proxy API key (backward-compat path in + ``MCPRequestHandler.process_mcp_request``), and forwarding it upstream + would leak the proxy key to a third-party MCP server. + """ + authorization = None + has_litellm_key_header = False + for key, value in scope.get("headers", []): + key_lower = key.lower() + if key_lower == b"authorization": + authorization = value.decode("latin-1") + elif key_lower == b"x-litellm-api-key": + has_litellm_key_header = True + if not has_litellm_key_header: + return None + return authorization + + async def _probe_upstream_auth( + url: str, + auth_header: str, + timeout: float = 5.0, + ) -> tuple: + """JSON-RPC initialize-probe the upstream URL to check whether the token is accepted. + + Uses POST so StreamableHTTP MCP servers run the same auth path as a + real client request. Returns (status_code, www_authenticate). + Fails-open with (200, None) on network errors so a transient hiccup + does not block valid requests. + + Uses the public ``AsyncHTTPHandler.post()`` interface and catches + ``httpx.HTTPStatusError`` separately so the 401/403 we want to surface + is not swallowed by the broad fail-open ``except Exception`` below. + """ + client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.MCP, + params={"timeout": timeout}, + ) + probe_payload = { + "jsonrpc": "2.0", + "id": "litellm-mcp-auth-probe", + "method": "initialize", + "params": { + "protocolVersion": MCPSpecVersion.jun_2025.value, + "capabilities": {}, + "clientInfo": { + "name": "litellm-mcp-auth-probe", + "version": "1.0.0", + }, + }, + } + probe_headers = { + "Authorization": auth_header, + "Accept": "application/json, text/event-stream", + } + try: + resp = await client.post( + url=url, + headers=probe_headers, + json=probe_payload, + timeout=timeout, + ) + return resp.status_code, resp.headers.get("www-authenticate") + except httpx.HTTPStatusError as exc: + # AsyncHTTPHandler.post() calls raise_for_status(); a 401/403 from + # upstream lands here. Return its status so the caller can map it + # to the appropriate response. + return exc.response.status_code, exc.response.headers.get( + "www-authenticate" + ) + except Exception as exc: + verbose_logger.debug( + f"_probe_upstream_auth: probe to {url} failed ({exc}), allowing request through" + ) + return 200, None + + async def _check_passthrough_upstream_auth( + scope: Scope, + user_api_key_auth: Optional[UserAPIKeyAuth], + mcp_servers: Optional[List[str]], + client_ip: Optional[str], + ) -> None: + """Probe pass-through upstream servers in parallel before the MCP session starts. + + Only servers the caller's key is already authorized to reach are probed — + the list is derived from _get_allowed_mcp_servers so that a user cannot + trigger an upstream probe against a server their key is not permitted for. + + The MCP SDK commits HTTP 200 headers before invoking handlers, so a 401 + can only be returned before that point. This function raises HTTPException(401) + with a WWW-Authenticate header if any upstream rejects the client token. + Fails-open: network errors are logged and the request is allowed through. + """ + forwarded_auth = _get_forwarded_auth_from_scope(scope) + if not forwarded_auth: + return + + # Use the authorized server set, not the raw user-supplied names, so that + # a caller cannot force a probe to a server their key is not allowed to use. + allowed_servers = await _get_allowed_mcp_servers( + user_api_key_auth=user_api_key_auth, + mcp_servers=mcp_servers, + client_ip=client_ip, + ) + passthrough_servers = [ + srv + for srv in allowed_servers + if srv.extra_headers + and any(h.lower() == "authorization" for h in srv.extra_headers) + # Exclude M2M servers: _prepare_mcp_server_headers skips caller + # Authorization when has_client_credentials is set, so probing + # those with the caller's token would send the wrong credential. + and not srv.has_client_credentials + ] + if not passthrough_servers: + return + + probe_results = await asyncio.gather( + *[ + _probe_upstream_auth(srv.url or "", forwarded_auth) + for srv in passthrough_servers + ] + ) + request = StarletteRequest(scope) + base_url = get_request_base_url(request) + for srv, (probe_status, _) in zip(passthrough_servers, probe_results): + if probe_status == 401: + # Token is missing or expired — direct the client to re-authorize. + authorization_uri = ( + f"Bearer authorization_uri=" + f"{base_url}/.well-known/oauth-authorization-server/{srv.name}" + ) + raise HTTPException( + status_code=401, + detail="Unauthorized", + headers={"WWW-Authenticate": authorization_uri}, + ) + if probe_status == 403: + # Token is valid but the caller lacks permission — do not hint + # at re-authorization (RFC 9110: a fresh token with the same + # scopes would just hit 403 again and loop indefinitely). + raise HTTPException( + status_code=403, + detail="Forbidden", + ) + async def handle_streamable_http_mcp( scope: Scope, receive: Receive, send: Send ) -> None: @@ -2725,6 +3003,13 @@ if MCP_AVAILABLE: user_api_key_auth, active_toolset_id ) + # Pre-flight auth check for pass-through servers. Must run after + # toolset scoping so the probe list is derived from the fully-authorized + # server set, not the raw user-supplied names. + await _check_passthrough_upstream_auth( + scope, user_api_key_auth, mcp_servers, _client_ip + ) + # Inject masked debug headers when client sends x-litellm-mcp-debug: true _debug_headers = MCPDebug.maybe_build_debug_headers( raw_headers=raw_headers, diff --git a/litellm/proxy/_experimental/mcp_server/tool_registry.py b/litellm/proxy/_experimental/mcp_server/tool_registry.py index 58570aafadf..bb30ff55c5c 100644 --- a/litellm/proxy/_experimental/mcp_server/tool_registry.py +++ b/litellm/proxy/_experimental/mcp_server/tool_registry.py @@ -59,6 +59,22 @@ class MCPToolRegistry: ] return list(self.tools.values()) + def unregister_tools_with_prefix(self, prefix: str) -> int: + """Remove tools whose registered name starts with ``prefix``. + + Used when an OpenAPI-backed MCP server leaves the runtime registry so + stale tool handlers cannot be invoked after eviction. + """ + if not prefix: + return 0 + removed = 0 + for name in list(self.tools.keys()): + if name.startswith(prefix): + del self.tools[name] + removed += 1 + verbose_logger.debug("Unregistered MCP tool %s", name) + return removed + def convert_tools_to_mcp_sdk_tool_type( self, tools: List[MCPTool] ) -> List["MCPToolSDKTool"]: @@ -76,13 +92,20 @@ class MCPToolRegistry: ] def load_tools_from_config( - self, mcp_tools_config: Optional[Dict[str, Any]] = None + self, + mcp_tools_config: Optional[Dict[str, Any]] = None, + config_file_path: Optional[str] = None, ) -> None: """ Load and register tools from the proxy config Args: mcp_tools_config: The mcp_tools config from the proxy config + config_file_path: Path to the operator's config.yaml. Threaded + through to ``get_instance_fn`` so an ``s3://``/``gcs://`` + ``handler`` declared in the YAML resolves; callers from a + non-YAML path must leave this ``None`` so the runtime gate + fires. """ if mcp_tools_config is None: raise ValueError( @@ -105,7 +128,7 @@ class MCPToolRegistry: # First check if it's a module path (e.g., "module.submodule.function") if handler_name is None: raise ValueError(f"handler is required for tool {name}") - handler = get_instance_fn(handler_name) + handler = get_instance_fn(handler_name, config_file_path) if handler is None: verbose_logger.warning( diff --git a/litellm/proxy/_experimental/mcp_server/utils.py b/litellm/proxy/_experimental/mcp_server/utils.py index 146ba10bb76..df5705c3425 100644 --- a/litellm/proxy/_experimental/mcp_server/utils.py +++ b/litellm/proxy/_experimental/mcp_server/utils.py @@ -2,10 +2,11 @@ MCP Server Utilities """ -from typing import Any, Dict, Mapping, Optional, Tuple +from typing import Any, Dict, Iterator, Mapping, Optional, Tuple -import os +import hashlib import importlib +import os # Constants LITELLM_MCP_SERVER_NAME = "litellm-mcp-server" @@ -14,6 +15,89 @@ LITELLM_MCP_SERVER_DESCRIPTION = "MCP Server for LiteLLM" MCP_TOOL_PREFIX_SEPARATOR = os.environ.get("MCP_TOOL_PREFIX_SEPARATOR", "-") MCP_TOOL_PREFIX_FORMAT = "{server_name}{separator}{tool_name}" +# --------------------------------------------------------------------------- +# Short-ID tool prefix (opt-in) +# --------------------------------------------------------------------------- +# When LITELLM_USE_SHORT_MCP_TOOL_PREFIX is truthy the prefix attached to MCP +# tool / prompt / resource / resource-template names switches from the +# (potentially long) human-readable server name to a deterministic three +# character ID derived from the server's ``server_id``. +# +# Why three characters? +# * The first character is restricted to 52 alphabetic characters +# ([A-Za-z]) and the remaining two characters use the full base62 +# alphabet ([0-9A-Za-z]). That guarantees the prefix never starts +# with a digit so it remains a valid identifier for every model API +# (some providers historically required a leading alphabetic char). +# * 52 * 62 * 62 = 199_888 distinct IDs. The chance of a real local +# tool name happening to begin with the exact prefix LiteLLM assigned +# to a given MCP server is negligible in practice. +# * The IDs are short enough that prefixed tool names stay well under +# the 60-character upper bound enforced by some model APIs (Anthropic +# etc.) even for long upstream tool names. +# * The mapping is deterministic (SHA-256 of ``server_id`` → three +# characters drawn from the alphabets above), so the prefix is stable +# across processes, workers and restarts without any persistence +# layer. Two servers with different ``server_id`` values can in +# principle hash to the same three chars; that natural-hash collision +# IS a routing-correctness issue (the second registrant would otherwise +# have its tools misrouted to the first), so registration goes through +# ``MCPServerManager._assign_unique_short_prefix`` which rehashes with +# a deterministic attempt counter until it finds an unused prefix and +# caches the result on ``MCPServer.short_prefix``. A collision is +# logged at INFO when it happens. +# +# This flag is intentionally opt-in for the first release so customers can +# migrate. It will become the default in a future release. +SHORT_MCP_TOOL_PREFIX_LENGTH = 3 +_BASE62_ALPHABET = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +# Subset of _BASE62_ALPHABET used for the *first* character only, to +# guarantee the prefix never starts with a digit. +_BASE52_ALPHA_ALPHABET = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" + + +def is_short_mcp_tool_prefix_enabled() -> bool: + """Return True when the short-ID tool prefix mode is enabled. + + Read at call time (not import time) so tests and runtime config changes + take effect without reimporting the module. + """ + raw = os.environ.get("LITELLM_USE_SHORT_MCP_TOOL_PREFIX", "") + return raw.strip().lower() in ("1", "true", "yes", "on") + + +def compute_short_server_prefix(server_id: str, attempt: int = 0) -> str: + """Derive the deterministic three-character prefix for a server. + + Uses SHA-256 of ``f"{server_id}#{attempt}"`` and folds the first eight + bytes into a fixed-length string whose first character is drawn from + ``_BASE52_ALPHA_ALPHABET`` (so the prefix never starts with a digit) + and whose remaining characters are drawn from the full base62 + alphabet. Pass ``attempt > 0`` to rehash to a different prefix when + the natural hash collides with a prefix already assigned to another + server (see ``MCPServerManager._assign_unique_short_prefix``). An + empty ``server_id`` raises ``ValueError`` — short prefixes require a + stable identifier to be deterministic. + """ + if not server_id: + raise ValueError("compute_short_server_prefix requires a non-empty server_id") + + seed = server_id if attempt == 0 else f"{server_id}#{attempt}" + digest = hashlib.sha256(seed.encode("utf-8")).digest() + value = int.from_bytes(digest[:8], "big") + + # Build chars from least-significant to most-significant; we reverse + # at the end so the first emitted char comes from the high-order + # bits of the digest (which is the position we constrain to be + # alphabetic). + chars = [] + for position in range(SHORT_MCP_TOOL_PREFIX_LENGTH): + is_first_char = position == SHORT_MCP_TOOL_PREFIX_LENGTH - 1 + alphabet = _BASE52_ALPHA_ALPHABET if is_first_char else _BASE62_ALPHABET + value, idx = divmod(value, len(alphabet)) + chars.append(alphabet[idx]) + return "".join(reversed(chars)) + def is_mcp_available() -> bool: """ @@ -82,7 +166,25 @@ def add_server_prefix_to_name(name: str, server_name: str) -> str: def get_server_prefix(server: Any) -> str: - """Return the prefix for a server: alias if present, else server_name, else server_id""" + """Return the prefix for a server. + + When the short-prefix mode is enabled (``LITELLM_USE_SHORT_MCP_TOOL_PREFIX``) + a three-character base62 ID is returned. We prefer the cached + ``server.short_prefix`` value when set — that field is populated at + registration time by ``MCPServerManager._assign_unique_short_prefix`` + and resolves natural-hash collisions deterministically — and only fall + back to the natural hash for ad-hoc / temp-server objects without a + cached value. In default mode the historical behaviour is preserved: + alias if present, else server_name, else server_id. + """ + if is_short_mcp_tool_prefix_enabled(): + cached = getattr(server, "short_prefix", None) + if cached: + return cached + server_id = getattr(server, "server_id", None) + if server_id: + return compute_short_server_prefix(server_id) + if hasattr(server, "alias") and server.alias: return server.alias if hasattr(server, "server_name") and server.server_name: @@ -92,6 +194,36 @@ def get_server_prefix(server: Any) -> str: return "" +def iter_known_server_prefixes(server: Any) -> Iterator[str]: + """Yield every prefix form that may appear in tool names for ``server``. + + Always includes the *current* prefix returned by ``get_server_prefix``. + Additionally yields the historical (alias / server_name / server_id) and + short-ID forms so the routing layer can resolve tool names regardless of + which prefix mode was active when the client first observed them. + """ + seen = set() + + def _emit(value: Optional[str]) -> Iterator[str]: + if value and value not in seen: + seen.add(value) + yield value + + yield from _emit(get_server_prefix(server)) + yield from _emit(getattr(server, "short_prefix", None)) + + server_id = getattr(server, "server_id", None) + if server_id: + try: + yield from _emit(compute_short_server_prefix(server_id)) + except ValueError: + pass + + yield from _emit(getattr(server, "alias", None)) + yield from _emit(getattr(server, "server_name", None)) + yield from _emit(server_id) + + def split_server_prefix_from_name(prefixed_name: str) -> Tuple[str, str]: """Return the unprefixed name plus the server name used as prefix.""" if MCP_TOOL_PREFIX_SEPARATOR in prefixed_name: diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index 375e9eefe94..3e20cf89e47 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

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