diff --git a/.circleci/config.yml b/.circleci/config.yml index d2c4906ef6..3019fabd6f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1475,7 +1475,7 @@ 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 + uv run --no-sync python -m pytest -v tests/otel_tests --junitxml=test-results/junit.xml --durations=5 no_output_timeout: 15m # Clean up first container - run: @@ -1935,7 +1935,7 @@ 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 @@ -2138,17 +2138,23 @@ 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/check-lazy-openapi-snapshot.yml b/.github/workflows/check-lazy-openapi-snapshot.yml deleted file mode 100644 index 2e4ed3637f..0000000000 --- a/.github/workflows/check-lazy-openapi-snapshot.yml +++ /dev/null @@ -1,75 +0,0 @@ -name: Check Lazy OpenAPI Snapshot - -on: - pull_request: - branches: - - main - - litellm_internal_staging - - "litellm_**" - -permissions: - contents: read - checks: write - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true - -jobs: - verify: - runs-on: ubuntu-latest - timeout-minutes: 10 - 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 --all-groups --all-extras - - - name: Regenerate snapshot to /tmp - id: regen - run: | - cp litellm/proxy/_lazy_openapi_snapshot.json /tmp/snapshot.committed.json - uv run --no-sync python -m litellm.proxy._lazy_openapi_snapshot - mv litellm/proxy/_lazy_openapi_snapshot.json /tmp/snapshot.fresh.json - mv /tmp/snapshot.committed.json litellm/proxy/_lazy_openapi_snapshot.json - - - name: Compare - id: diff - continue-on-error: true - run: | - diff -q /tmp/snapshot.fresh.json litellm/proxy/_lazy_openapi_snapshot.json - - - name: Mark neutral if drift - if: steps.diff.outcome == 'failure' - uses: LouisBrunner/checks-action@6b626ffbad7cc56fd58627f774b9067e6118af23 # v2.0.0 - with: - token: ${{ secrets.GITHUB_TOKEN }} - name: lazy-openapi-snapshot - conclusion: neutral - output: | - { - "title": "Lazy openapi snapshot is stale", - "summary": "Run `python -m litellm.proxy._lazy_openapi_snapshot` and commit the regenerated `litellm/proxy/_lazy_openapi_snapshot.json`. Not blocking — the snapshot will regenerate at release if not committed." - } diff --git a/.github/workflows/create-release.yml b/.github/workflows/create-release.yml index 39d078267f..a726a921a2 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. 1.84.0, 1.84.0rc1, 1.84.0.dev42, 1.84.0.post1; legacy v1.83.10-stable still accepted)" + 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: @@ -46,9 +46,11 @@ jobs: 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 isPrerelease = /(?:rc|nightly|alpha|beta|[-.]dev)/i.test(tag); const cosignSection = [ `## Verify Docker Image Signature`, diff --git a/.gitignore b/.gitignore index 38bf9554b5..59812ed6ed 100644 --- a/.gitignore +++ b/.gitignore @@ -90,7 +90,6 @@ 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 @@ -100,4 +99,5 @@ STABILIZATION_TODO.md **/test-results **/playwright-report **/*.storageState.json -**/coverage \ No newline at end of file +**/coverage +test-config \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 03779d6c88..915daff999 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@sha256:3258be472764337fd13095bcbb3182da170243b5819fd67ad4c0754590588b31 # 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 diff --git a/Makefile b/Makefile index b6b674ff3b..5dbd308a3e 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 d72fb746ed..72fd43925c 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/cookbook/litellm-ollama-docker-image/requirements.txt b/cookbook/litellm-ollama-docker-image/requirements.txt index 815a42a679..9b9181b236 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/docker/Dockerfile.alpine b/docker/Dockerfile.alpine index 2cfc5ef03f..5de588cf4e 100644 --- a/docker/Dockerfile.alpine +++ b/docker/Dockerfile.alpine @@ -3,7 +3,7 @@ ARG LITELLM_BUILD_IMAGE=python:3.11-alpine@sha256:f07e2ace46f560f09a6eeec7b4913b # 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 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index e3edcece61..671f374ca2 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 diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev index e2dc185783..ebc92a22d5 100644 --- a/docker/Dockerfile.dev +++ b/docker/Dockerfile.dev @@ -3,7 +3,7 @@ ARG LITELLM_BUILD_IMAGE=python:3.13-slim@sha256:739e7213785e88c0f702dcdc12c0973a # 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 +ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a FROM $UV_IMAGE AS uvbin diff --git a/docker/Dockerfile.health_check b/docker/Dockerfile.health_check index 07d35b5e29..a2e5cb9f71 100644 --- a/docker/Dockerfile.health_check +++ b/docker/Dockerfile.health_check @@ -1,4 +1,4 @@ -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 FROM python:3.13-slim@sha256:739e7213785e88c0f702dcdc12c0973afcbd606dbf021a589cab77d6b00b579d diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 8512ae8ad9..3fa73f4243 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 @@ -114,7 +113,6 @@ 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/docs/my-website/docs/providers/crusoe.md b/docs/my-website/docs/providers/crusoe.md new file mode 100644 index 0000000000..aa737cbdcd --- /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 4df138939a..47421c9605 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 1f26d52adf..5b2d71c5cc 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 a1db9818e5..2162370804 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 8a7a82df68..f441ce71ab 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 a368232038..e8f104c262 100644 --- a/enterprise/litellm_enterprise/proxy/auth/custom_sso_handler.py +++ b/enterprise/litellm_enterprise/proxy/auth/custom_sso_handler.py @@ -10,28 +10,21 @@ 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: """ Enterprise Custom SSO Handler for LiteLLM Proxy - + This class provides methods for handling custom SSO authentication flows where users can implement their own authentication logic by processing request headers and returning user information in OpenID format. """ - + @staticmethod async def handle_custom_ui_sso_sign_in( request: Request, @@ -40,16 +33,16 @@ class EnterpriseCustomSSOHandler: Allow a user to execute their custom code to parse incoming request headers and return a OpenID object Use this when you have an OAuth proxy in front of LiteLLM (where the OAuth proxy has already authenticated the user) - + Args: request: The FastAPI request object containing headers and other request data - + Returns: RedirectResponse: Redirect response that sends the user to the LiteLLM UI with authentication token - + Raises: ValueError: If custom_ui_sso_sign_in_handler is not configured - + Example: This method is typically called when a user has already been authenticated by an external OAuth proxy and the proxy has added custom headers containing user information. @@ -60,27 +53,44 @@ 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) - + if user_custom_ui_sso_sign_in_handler is None: - raise ValueError("custom_ui_sso_sign_in_handler is not configured. Please set it in general_settings.") - - custom_sso_login_handler = cast(CustomSSOLoginHandler, user_custom_ui_sso_sign_in_handler) - openid_response: OpenID = await custom_sso_login_handler.handle_custom_ui_sso_sign_in( + raise ValueError( + "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 + ) + openid_response: OpenID = ( + await custom_sso_login_handler.handle_custom_ui_sso_sign_in( + request=request, + ) + ) + # Import here to avoid circular imports from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler - + return await SSOAuthenticationHandler.get_redirect_response_from_openid( result=openid_response, request=request, received_response=None, generic_client_id=None, ui_access_mode=None, - ) \ No newline at end of file + ) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 60c564072a..5ed4907034 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, } @@ -125,7 +132,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): db_data["storage_backend"] = hidden_params["storage_backend"] if "storage_url" in hidden_params: db_data["storage_url"] = hidden_params["storage_url"] - + verbose_logger.debug( f"Storage metadata: storage_backend={db_data.get('storage_backend')}, " f"storage_url={db_data.get('storage_url')}" @@ -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}", @@ -285,28 +296,27 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): raise Exception( "Filtering by 'target_model_names' is not supported when using managed batches." ) - - where_clause: Dict[str, Any] = {"file_purpose": "batch"} - - # Filter by user who created the batch - if user_api_key_dict.user_id: - where_clause["created_by"] = user_api_key_dict.user_id - + + owner_filter = build_owner_filter(user_api_key_dict) + if owner_filter is None: + return build_list_page([]) + + 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( where=where_clause, take=fetch_limit, order={"created_at": "desc"}, ) - + batch_objects: List[LiteLLMBatch] = [] for batch in batches: try: @@ -314,7 +324,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if len(batch_objects) >= (limit or 20): break - batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object + batch_data = ( + json.loads(batch.file_object) + if isinstance(batch.file_object, str) + else batch.file_object + ) batch_obj = LiteLLMBatch(**batch_data) batch_obj.id = batch.unified_object_id batch_objects.append(batch_obj) @@ -324,27 +338,29 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): f"Failed to parse batch object {batch.unified_object_id}: {e}" ) 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}, } ) @@ -377,11 +393,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): """ Check if the user has access to a list of file IDs. Only checks managed (unified) file IDs. - + Args: file_ids: List of file IDs to check access for user_api_key_dict: User API key authentication details - + Raises: HTTPException: If user doesn't have access to any of the files """ @@ -419,10 +435,10 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ### HANDLE TRANSFORMATIONS ### # Check both completion and acompletion call types is_completion_call = ( - call_type == CallTypes.completion.value + call_type == CallTypes.completion.value or call_type == CallTypes.acompletion.value ) - + if is_completion_call: messages = data.get("messages") model = data.get("model", "") @@ -431,22 +447,27 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if file_ids: # Check user has access to all managed files await self.check_file_ids_access(file_ids, user_api_key_dict) - + # Check if any files are stored in storage backends and need base64 conversion # This is needed for Vertex AI/Gemini which requires base64 content - is_vertex_ai = model and ("vertex_ai" in model or "gemini" in model.lower()) + is_vertex_ai = model and ( + "vertex_ai" in model or "gemini" in model.lower() + ) if is_vertex_ai: await self._convert_storage_files_to_base64( messages=messages, file_ids=file_ids, litellm_parent_otel_span=user_api_key_dict.parent_otel_span, ) - + model_file_id_mapping = await self.get_model_file_id_mapping( file_ids, user_api_key_dict.parent_otel_span ) data["model_file_id_mapping"] = model_file_id_mapping - elif call_type == CallTypes.aresponses.value or call_type == CallTypes.responses.value: + elif ( + call_type == CallTypes.aresponses.value + or call_type == CallTypes.responses.value + ): # Handle managed files in responses API input and tools file_ids = [] @@ -611,7 +632,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if model_id is None: model_id = cast( Optional[str], - kwargs.get("litellm_metadata", {}).get("model_info", {}).get("id", None), + kwargs.get("litellm_metadata", {}) + .get("model_info", {}) + .get("id", None), ) mapped_file_id: Optional[str] = None if input_file_id and model_file_id_mapping and model_id: @@ -648,7 +671,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) -> List[str]: """ Gets file ids from responses API input. - + The input can be: - A string (no files) - A list of input items, where each item can have: @@ -656,32 +679,35 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): - content: a list that can contain items with type: "input_file" and file_id """ file_ids: List[str] = [] - + if isinstance(input, str): return file_ids - + if not isinstance(input, list): return file_ids - + for item in input: if not isinstance(item, dict): continue - + # Check for direct input_file type if item.get("type") == "input_file": file_id = item.get("file_id") if file_id: file_ids.append(file_id) - + # Check for input_file in content array content = item.get("content") if isinstance(content, list): for content_item in content: - if isinstance(content_item, dict) and content_item.get("type") == "input_file": + if ( + isinstance(content_item, dict) + and content_item.get("type") == "input_file" + ): file_id = content_item.get("file_id") if file_id: file_ids.append(file_id) - + return file_ids def get_file_ids_from_responses_tools( @@ -689,7 +715,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) -> List[str]: """ Gets file ids from responses API tools parameter. - + The tools can contain code_interpreter with container.file_ids: [ { @@ -699,14 +725,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ] """ file_ids: List[str] = [] - + if not isinstance(tools, list): return file_ids - + for tool in tools: if not isinstance(tool, dict): continue - + # Check for code_interpreter with container file_ids if tool.get("type") == "code_interpreter": container = tool.get("container") @@ -716,7 +742,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): for file_id in container_file_ids: if isinstance(file_id, str): file_ids.append(file_id) - + return file_ids def get_vector_store_ids_from_file_search_tools( @@ -916,10 +942,17 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Emit Prometheus metrics for managed file creation prom_logger = self._get_prometheus_logger() if prom_logger: - first_model = target_model_names_list[0] if target_model_names_list else None + first_model = ( + target_model_names_list[0] if target_model_names_list else None + ) first_provider = "" if responses: - first_provider = getattr(responses[0], "_hidden_params", {}).get("custom_llm_provider") or "" + first_provider = ( + getattr(responses[0], "_hidden_params", {}).get( + "custom_llm_provider" + ) + or "" + ) prom_logger.record_managed_file_created( model=first_model or "", api_provider=first_provider, @@ -1073,16 +1106,24 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): model_name=resolved_model_name, ) setattr(response, file_attr, unified_file_id) - + # Use llm_router credentials when available. Without credentials, # Azure and other auth-required providers return 500/401. file_object = None try: # Import module and use getattr for better testability with mocks import litellm.proxy.proxy_server as proxy_server_module - _llm_router = getattr(proxy_server_module, 'llm_router', None) + + _llm_router = getattr( + proxy_server_module, "llm_router", None + ) if _llm_router is not None and model_id: - _creds = _llm_router.get_deployment_credentials_with_provider(model_id) or {} + _creds = ( + _llm_router.get_deployment_credentials_with_provider( + model_id + ) + or {} + ) file_object = await litellm.afile_retrieve( file_id=original_file_id, **_creds, @@ -1099,7 +1140,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): verbose_logger.warning( f"Failed to retrieve file object for {file_attr}={original_file_id}: {str(e)}. Storing with None and will fetch on-demand." ) - + await self.store_unified_file_id( file_id=unified_file_id, file_object=file_object, @@ -1128,6 +1169,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): from litellm.litellm_core_utils.get_llm_provider_logic import ( get_llm_provider, ) + _, batch_provider, _, _ = get_llm_provider(model=model_name) except Exception: if "/" in model_name: @@ -1199,7 +1241,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): # Case 1 : This is not a managed file if not stored_file_object: raise Exception(f"LiteLLM Managed File object with id={file_id} not found") - + # Case 2: Managed file and the file object exists in the database # The stored file_object has the raw provider ID. Replace with the unified ID # so callers see a consistent ID (matching Case 3 which does response.id = file_id). @@ -1217,13 +1259,21 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) try: - model_id, model_file_id = next(iter(stored_file_object.model_mappings.items())) - credentials = llm_router.get_deployment_credentials_with_provider(model_id) or {} - response = await litellm.afile_retrieve(file_id=model_file_id, **credentials) + model_id, model_file_id = next( + iter(stored_file_object.model_mappings.items()) + ) + credentials = ( + llm_router.get_deployment_credentials_with_provider(model_id) or {} + ) + response = await litellm.afile_retrieve( + file_id=model_file_id, **credentials + ) response.id = file_id # Replace with unified ID return response except Exception as e: - raise Exception(f"Failed to retrieve file {file_id} from provider: {str(e)}") from e + raise Exception( + f"Failed to retrieve file {file_id} from provider: {str(e)}" + ) from e async def afile_list( self, @@ -1245,19 +1295,19 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): import litellm.proxy.proxy_server as proxy_server_module # Check if the scheduler has the batch cost checking job registered - scheduler = getattr(proxy_server_module, 'scheduler', None) + scheduler = getattr(proxy_server_module, "scheduler", None) if scheduler is None: return False - + # Check if the check_batch_cost_job exists in the scheduler try: - job = scheduler.get_job('check_batch_cost_job') + job = scheduler.get_job("check_batch_cost_job") if job is not None: return True except Exception: # Job not found or scheduler doesn't support get_job pass - + return False except Exception as e: verbose_logger.warning( @@ -1265,28 +1315,26 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) return False - async def _get_batches_referencing_file( - self, file_id: str - ) -> List[Dict[str, Any]]: + async def _get_batches_referencing_file(self, file_id: str) -> List[Dict[str, Any]]: """ Find batches that reference this file and still need cost tracking. Find batches that are in non-terminal state and have not yet been processed by CheckBatchCost. Args: file_id: The unified file ID to check - + Returns: List of batch objects referencing this file in non-terminal state (max 10 for error message display) """ # Prepare list of file IDs to check (both unified and provider IDs) file_ids_to_check = [file_id] - + # Get model-specific file IDs for this unified file ID if it's a managed file try: model_file_id_mapping = await self.get_model_file_id_mapping( [file_id], litellm_parent_otel_span=None ) - + if model_file_id_mapping and file_id in model_file_id_mapping: # Add all provider file IDs for this unified file provider_file_ids = list(model_file_id_mapping[file_id].values()) @@ -1296,59 +1344,67 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): f"Could not get model file ID mapping for {file_id}: {e}. " f"Will only check unified file ID." ) - MAX_MATCHES_TO_RETURN = 10 - + MAX_MATCHES_TO_RETURN = 10 + batches = await self.prisma_client.db.litellm_managedobjecttable.find_many( where={ "file_purpose": "batch", "batch_processed": False, - "status": {"not_in": ["failed", "expired", "cancelled"]} + "status": {"not_in": ["failed", "expired", "cancelled"]}, }, take=MAX_MATCHES_TO_RETURN, order={"created_at": "desc"}, ) - + referencing_batches = [] for batch in batches: try: # Parse the batch file_object to check for file references - batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object - + batch_data = ( + json.loads(batch.file_object) + if isinstance(batch.file_object, str) + else batch.file_object + ) + # Extract file IDs from batch # Batches typically reference the unified file ID in input_file_id # Output and error files are generated by the provider input_file_id = batch_data.get("input_file_id") output_file_id = batch_data.get("output_file_id") error_file_id = batch_data.get("error_file_id") - - referenced_file_ids = [fid for fid in [input_file_id, output_file_id, error_file_id] if fid] - + + referenced_file_ids = [ + fid for fid in [input_file_id, output_file_id, error_file_id] if fid + ] + # Check if any referenced file ID matches the file we're trying to delete if any(ref_id in file_ids_to_check for ref_id in referenced_file_ids): - referencing_batches.append({ - "batch_id": batch.unified_object_id, - "status": batch.status, - "created_at": batch.created_at, - }) + referencing_batches.append( + { + "batch_id": batch.unified_object_id, + "status": batch.status, + "created_at": batch.created_at, + } + ) except Exception as e: verbose_logger.warning( f"Error parsing batch object {batch.unified_object_id}: {e}" ) continue - + return referencing_batches async def _check_file_deletion_allowed(self, file_id: str) -> None: """ Check if file deletion should be blocked due to batch references. - + Blocks deletion if: 1. File is referenced by any batch in non-terminal state, AND 2. Batch polling is configured (user wants cost tracking) - + Args: file_id: The unified file ID to check - + Raises: HTTPException: If file deletion should be blocked """ @@ -1356,39 +1412,45 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if not self._is_batch_polling_enabled(): # Batch polling not configured, allow deletion return - + # Check if file is referenced by any non-terminal batches referencing_batches = await self._get_batches_referencing_file(file_id) - + if referencing_batches: # File is referenced by non-terminal batches and polling is enabled - MAX_BATCHES_IN_ERROR = 5 # Limit batches shown in error message for readability - + MAX_BATCHES_IN_ERROR = ( + 5 # Limit batches shown in error message for readability + ) + # Show up to MAX_BATCHES_IN_ERROR in the error message batches_to_show = referencing_batches[:MAX_BATCHES_IN_ERROR] - batch_statuses = [f"{b['batch_id']}: {b['status']}" for b in batches_to_show] - + batch_statuses = [ + f"{b['batch_id']}: {b['status']}" for b in batches_to_show + ] + # Determine the count message count_message = f"{len(referencing_batches)}" - if len(referencing_batches) >= 10: # MAX_MATCHES_TO_RETURN from _get_batches_referencing_file + if ( + len(referencing_batches) >= 10 + ): # MAX_MATCHES_TO_RETURN from _get_batches_referencing_file count_message = "10+" - + error_message = ( f"Cannot delete file {file_id}. " f"The file is referenced by {count_message} batch(es) in non-terminal state" ) - + # Add specific batch details if not too many if len(referencing_batches) <= MAX_BATCHES_IN_ERROR: error_message += f": {', '.join(batch_statuses)}. " else: error_message += f" (showing {MAX_BATCHES_IN_ERROR} most recent): {', '.join(batch_statuses)}. " - + error_message += ( f"To delete this file before complete cost tracking, please delete or cancel the referencing batch(es) first. " f"Alternatively, wait for all batches to complete and for cost to be computed (batch_processed=true)." ) - + # Record blocked deletion metric prom_logger = self._get_prometheus_logger() if prom_logger: @@ -1419,7 +1481,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): specific_model_file_id_mapping = model_file_id_mapping.get(file_id) if specific_model_file_id_mapping: # Remove conflicting keys from data to avoid duplicate keyword arguments - filtered_data = {k: v for k, v in data.items() if k not in ("model", "file_id")} + filtered_data = { + k: v for k, v in data.items() if k not in ("model", "file_id") + } for model_id, model_file_id in specific_model_file_id_mapping.items(): delete_response = await llm_router.afile_delete(model=model_id, file_id=model_file_id, **filtered_data) # type: ignore @@ -1480,7 +1544,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) -> None: """ Convert files stored in storage backends to base64 format for Vertex AI/Gemini. - + This method checks if any managed files are stored in storage backends, downloads them, and converts them to base64 format in the messages. """ @@ -1488,29 +1552,29 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): for file_id in file_ids: # Check if this is a base64 encoded unified file ID decoded_unified_file_id = _is_base64_encoded_unified_file_id(file_id) - + if not decoded_unified_file_id: continue - + # Check database for storage backend info # IMPORTANT: The database stores the base64 encoded unified_file_id (not the decoded version) # So we query with the original file_id (which is base64 encoded) db_file = await self.prisma_client.db.litellm_managedfiletable.find_first( where={"unified_file_id": file_id} ) - + if not db_file or not db_file.storage_backend or not db_file.storage_url: continue - + # File is stored in a storage backend, download and convert to base64 try: from litellm.llms.base_llm.files.storage_backend_factory import ( get_storage_backend, ) - + storage_backend_name = db_file.storage_backend storage_url = db_file.storage_url - + # Get storage backend (uses same env vars as callback) try: storage_backend = get_storage_backend(storage_backend_name) @@ -1519,18 +1583,22 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): f"Storage backend '{storage_backend_name}' error for file {file_id}: {str(e)}" ) continue - + file_content = await storage_backend.download_file(storage_url) - + # Determine content type from file object - content_type = self._get_content_type_from_file_object(db_file.file_object) - + content_type = self._get_content_type_from_file_object( + db_file.file_object + ) + # Convert to base64 base64_data = base64.b64encode(file_content).decode("utf-8") base64_data_uri = f"data:{content_type};base64,{base64_data}" - + # Update messages to use base64 instead of file_id - self._update_messages_with_base64_data(messages, file_id, base64_data_uri, content_type) + self._update_messages_with_base64_data( + messages, file_id, base64_data_uri, content_type + ) except Exception as e: verbose_logger.exception( f"Error converting file {file_id} from storage backend to base64: {str(e)}" @@ -1541,21 +1609,21 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): def _get_content_type_from_file_object(self, file_object: Optional[Any]) -> str: """ Determine content type from file object. - + Uses the MIME type utility for consistent detection and normalization. - + Args: file_object: The file object from the database (can be dict, JSON string, or None) - + Returns: str: MIME type (defaults to "application/octet-stream" if cannot be determined) """ # Use utility function for detection content_type = get_content_type_from_file_object(file_object) - + # Normalize for Gemini/Vertex AI (requires image/jpeg, not image/jpg) content_type = normalize_mime_type_for_provider(content_type, provider="gemini") - + return content_type def _update_messages_with_base64_data( @@ -1567,7 +1635,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) -> None: """ Update messages to replace file_id with base64 data URI. - + Args: messages: List of messages to update file_id: The file ID to replace @@ -1582,7 +1650,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if element.get("type") == "file": file_element = cast(ChatCompletionFileObject, element) file_element_file = file_element.get("file", {}) - + if file_element_file.get("file_id") == file_id: # Replace file_id with base64 data file_element_file["file_data"] = base64_data_uri @@ -1590,7 +1658,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): file_element_file["format"] = content_type # Remove file_id to ensure only file_data is used file_element_file.pop("file_id", None) - + verbose_logger.debug( f"Converted file {file_id} from storage backend to base64 with format {content_type}" ) diff --git a/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py b/enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py index f6ed7767c4..75229bacc8 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 cfbbe1f494..9698e7912d 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 = 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"dependencies": { "@hono/node-server": "1.19.13", - "hono": "4.12.12" + "hono": "4.12.16" }, "devDependencies": { "@types/node": "20.19.25", @@ -548,9 +548,9 @@ } }, "node_modules/hono": { - "version": "4.12.12", - "resolved": "https://registry.npmjs.org/hono/-/hono-4.12.12.tgz", - "integrity": "sha512-p1JfQMKaceuCbpJKAPKVqyqviZdS0eUxH9v82oWo1kb9xjQ5wA6iP3FNVAPDFlz5/p7d45lO+BpSk1tuSZMF4Q==", + "version": "4.12.16", + "resolved": "https://registry.npmjs.org/hono/-/hono-4.12.16.tgz", + "integrity": "sha512-jN0ZewiNAWSe5khM3EyCmBb250+b40wWbwNILNfEvq84VREWwOIkuUsFONk/3i3nqkz7Oe1PcpM2mwQEK2L9Kg==", "license": "MIT", "engines": { "node": ">=16.9.0" diff --git a/litellm-js/spend-logs/package.json b/litellm-js/spend-logs/package.json index d8e6a89544..5a7a95c5de 100644 --- a/litellm-js/spend-logs/package.json +++ b/litellm-js/spend-logs/package.json @@ -4,7 +4,7 @@ }, "dependencies": { "@hono/node-server": "1.19.13", - "hono": "4.12.12" + "hono": "4.12.16" }, "devDependencies": { "@types/node": "20.19.25", 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 0000000000..d6f236959b --- /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/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index a9d3911c07..84ce99557e 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -884,28 +884,32 @@ 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 { @@ -918,10 +922,12 @@ 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 { diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index b8710da343..cd569ca08e 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.70" +version = "0.4.71" 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 = "0.4.70" +version = "0.4.71" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 77fa48625d..5305edc9be 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] = ( @@ -280,6 +280,7 @@ 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 +289,7 @@ 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 +332,9 @@ 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 diff --git a/litellm/_logging.py b/litellm/_logging.py index d072cc549d..5ddafd6c6a 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/anthropic_beta_headers_config.json b/litellm/anthropic_beta_headers_config.json index 662b62cf20..d02afe3756 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 4b965d4e63..aaf083e75d 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/caching/caching.py b/litellm/caching/caching.py index ce1bc26c5e..11733ce4ce 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 7d514e648f..3cf1d911d7 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 6115a444ce..8060a65b78 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: diff --git a/litellm/caching/qdrant_semantic_cache.py b/litellm/caching/qdrant_semantic_cache.py index 5e3713e5a1..cb521efca0 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 deee4f6ea4..cb9ce475d3 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 diff --git a/litellm/caching/redis_semantic_cache.py b/litellm/caching/redis_semantic_cache.py index c76f27377d..da9e7b1e58 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/constants.py b/litellm/constants.py index 6c889a317b..6918e40cad 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -202,6 +202,12 @@ 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 @@ -399,6 +405,8 @@ 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) ) @@ -419,9 +427,6 @@ 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 diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 8a68d74be5..9b4dd80265 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/files/main.py b/litellm/files/main.py index ceccba8d80..669d50dde4 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/integrations/arize/_utils.py b/litellm/integrations/arize/_utils.py index 8dfaa8b142..a1bf65141c 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 3c83517bb5..8c3c2a5ff0 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 19af0bb955..df56d7bd39 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/bitbucket/bitbucket_client.py b/litellm/integrations/bitbucket/bitbucket_client.py index 0502422cf8..e742cc14b7 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 701f227364..844fa9f38c 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_sso_handler.py b/litellm/integrations/custom_sso_handler.py index 7f60decabc..202e488e0e 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 65296bafcf..9005798423 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 e84b37e689..1c5e30777a 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 b073948d76..a468741aea 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 e691c490c8..0efc7d6687 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 fbadf1a2fc..4a80972642 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 5f4ced3a5c..b7a565512c 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 3d4fd39ebe..81570e462c 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 b6d91d0b76..77833e5de0 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -69,6 +69,8 @@ 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 def __post_init__(self) -> None: # If endpoint is specified but exporter is still the default "console", @@ -259,16 +261,21 @@ 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 +300,12 @@ 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 _init_tracing(self, tracer_provider): from opentelemetry import trace from opentelemetry.sdk.trace import TracerProvider @@ -303,11 +316,6 @@ 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 +323,18 @@ 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 +360,9 @@ 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 +400,7 @@ 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 +417,14 @@ 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): @@ -1073,7 +1087,7 @@ 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 +1098,10 @@ 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( diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 723b142dfa..7d3856ea63 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -265,6 +265,7 @@ 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", @@ -1928,7 +1929,7 @@ 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 +2007,32 @@ 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 +2046,14 @@ 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( diff --git a/litellm/integrations/prometheus_helpers/prometheus_api.py b/litellm/integrations/prometheus_helpers/prometheus_api.py index b25da57723..0901d7b680 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/litellm_core_utils/cli_token_utils.py b/litellm/litellm_core_utils/cli_token_utils.py index e2e304931a..3776d27691 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 0000000000..daa3dc6032 --- /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/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 5a7d4e33b6..2c1d92920a 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/initialize_dynamic_callback_params.py b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py index ffb6436f38..a89dae5231 100644 --- a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py +++ b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py @@ -37,8 +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", @@ -57,6 +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, @@ -64,13 +67,15 @@ 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) validate_no_callback_env_reference( @@ -86,6 +91,8 @@ 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) validate_no_callback_env_reference( diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 829c1c9ca0..a815442c2f 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -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_request_utils.py b/litellm/litellm_core_utils/llm_request_utils.py index f5f28822ca..7be7085297 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/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 3a83162fb2..ba840bc3d8 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -4582,6 +4582,11 @@ 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( @@ -4864,6 +4869,44 @@ 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], @@ -4961,6 +5004,11 @@ 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/secret_redaction.py b/litellm/litellm_core_utils/secret_redaction.py new file mode 100644 index 0000000000..5c4e3e3dac --- /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/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index e281b17268..fa7faf3035 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 a65d0892aa..38a78ee058 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 3f03c744ef..fd67a7fbaf 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/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 61ddf801a2..345558a69f 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 +105,22 @@ else: LoggingClass = Any +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 +231,96 @@ 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 = [ @@ -794,12 +902,11 @@ 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 +925,34 @@ 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] @@ -1088,24 +1216,27 @@ 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) @@ -1527,29 +1658,31 @@ 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 diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index b095d40156..869a7c5fbc 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 829ce14d69..8ed6126d2e 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 0879788919..fe8e694efe 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( diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index 7617ad52ab..35495d5961 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 c56799f30c..56296df94a 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 aeaab4e57b..ea9bf00f50 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 a992d84d45..4ea768b02a 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 61cfd54b56..9291269d15 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 5b411095ea..2a20c55a6c 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 dffa1c9eea..0b6ecfb076 100644 --- a/litellm/llms/azure/image_edit/transformation.py +++ b/litellm/llms/azure/image_edit/transformation.py @@ -9,6 +9,19 @@ 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, @@ -83,3 +96,8 @@ 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 a9cf151464..f60e446f0c 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", ] 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 0000000000..03c425eeff --- /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/responses/transformation.py b/litellm/llms/azure/responses/transformation.py index 76a6d485bc..ca9293325f 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 c3cd06ab4d..9bae8abce8 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 e935aa1c05..e176a4d860 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 067181b946..755d44fdef 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 76c247aea8..d4144a7571 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/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py index b71ae0fdde..bec25916c4 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/image_edit/transformation.py b/litellm/llms/base_llm/image_edit/transformation.py index cea96bde74..92429573ff 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 5422af7678..c0c18aefde 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 0000000000..62027f4272 --- /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/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 61a7d4c08d..efc890d9ee 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 diff --git a/litellm/llms/bedrock/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py index 2c7135f4d8..4c667b0ce3 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 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 cff415d49e..c883ab68df 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/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py index a37af13162..c967fd334b 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 13bd87a1f0..ecf157e12e 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 3007b54808..6669363093 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 1b15ebaa76..aae2bc5e28 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, @@ -580,6 +585,17 @@ 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. diff --git a/litellm/llms/bedrock/vector_stores/transformation.py b/litellm/llms/bedrock/vector_stores/transformation.py index f028503c6a..ec20d76102 100644 --- a/litellm/llms/bedrock/vector_stores/transformation.py +++ b/litellm/llms/bedrock/vector_stores/transformation.py @@ -5,6 +5,7 @@ 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 ( @@ -209,7 +210,10 @@ class BedrockVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): 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), diff --git a/litellm/llms/black_forest_labs/image_edit/handler.py b/litellm/llms/black_forest_labs/image_edit/handler.py index dea2683a04..f5784e0836 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 5a1d885e52..8af4a236fd 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 a72f732a30..5b08670f9f 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 9e59782bf7..66e253f304 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/custom_httpx/container_handler.py b/litellm/llms/custom_httpx/container_handler.py index afdd7bc6a8..599cd705eb 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/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index dc625918b9..2ffc7acbfb 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, ) @@ -5578,6 +5579,9 @@ 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( @@ -5676,6 +5680,9 @@ 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( @@ -8948,7 +8955,10 @@ 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="", @@ -9015,7 +9025,10 @@ 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="", @@ -9214,7 +9227,10 @@ 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) @@ -9297,7 +9313,10 @@ 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) @@ -9363,7 +9382,10 @@ 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="", @@ -9428,7 +9450,10 @@ 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="", diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index c086d4ad75..09c782a475 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/elevenlabs/text_to_speech/transformation.py b/litellm/llms/elevenlabs/text_to_speech/transformation.py index 4dac2b8ba9..6a59911701 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/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py index 401d7bb9f4..63a383ebd3 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/interactions/transformation.py b/litellm/llms/gemini/interactions/transformation.py index c34da83cb8..593cbf7c2c 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/hosted_vllm/embedding/README.md b/litellm/llms/hosted_vllm/embedding/README.md index f82b3c77a6..2c58e16fc2 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/litellm_proxy/skills/handler.py b/litellm/llms/litellm_proxy/skills/handler.py index 8e5070c272..37aabd8b47 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 4622bda4e8..199f13191f 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 3381a5327e..3416616139 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 510c41304a..b3a0073a5c 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/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index 34941a545e..4e34d10b18 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -244,9 +244,11 @@ 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/containers/transformation.py b/litellm/llms/openai/containers/transformation.py index 955b9f760d..7f874ffd3b 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 c24dbf8637..66537e56a6 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/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 87c502032c..b7d5340d8d 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 cd5f10251b..52202f57fd 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 2c11d13748..bd095a0a1b 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 @@ -108,7 +109,10 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig): 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 61baa56949..2d165a7d7d 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 5dd1247001..b5e5aa4ea2 100644 --- a/litellm/llms/openai_like/providers.json +++ b/litellm/llms/openai_like/providers.json @@ -106,5 +106,13 @@ "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/pg_vector/vector_stores/transformation.py b/litellm/llms/pg_vector/vector_stores/transformation.py index 7b22edd867..fc4cfc7b08 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 @@ -82,7 +83,10 @@ class PGVectorStoreConfig(OpenAIVectorStoreConfig): 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, diff --git a/litellm/llms/ragflow/chat/transformation.py b/litellm/llms/ragflow/chat/transformation.py index d49a5fd370..990fc2b2e6 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/runwayml/videos/transformation.py b/litellm/llms/runwayml/videos/transformation.py index 8377dea952..4f84816a2b 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/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py index 7436bfef58..c627599da8 100644 --- a/litellm/llms/vertex_ai/batches/handler.py +++ b/litellm/llms/vertex_ai/batches/handler.py @@ -4,7 +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, safe_get +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, @@ -170,7 +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] @@ -413,7 +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 fae175612b..e6e3965110 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -97,7 +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: @@ -113,7 +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"}) @@ -149,8 +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 @@ -256,8 +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" @@ -653,6 +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 6636bccd6a..c31bfde69e 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 070ec50828..f30518bc7c 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 87bd484382..9afa5dec46 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -212,6 +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)) @@ -743,16 +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 474ddb402a..6278de662f 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() 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 2371bc4865..99165c37c9 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 34fc95e0af..e1b365c9f4 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/vector_stores/search_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py index 6cb7a86bea..61fb848b40 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( 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 5c3bbf61ee..4be4c2d5e7 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 0000000000..a33ad67778 --- /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 504914c479..4627d9f6df 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_model_garden/main.py b/litellm/llms/vertex_ai/vertex_model_garden/main.py index c37bb449ec..7240d9dce5 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 f6dda4dd25..99e0a958ef 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/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py index bfa55105a6..6300868a64 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 0cfcfe9841..8edfd0c27a 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/main.py b/litellm/main.py index 0079bd750c..0553cf9d42 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -4923,8 +4923,17 @@ 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 diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 13a45fd165..7946e2dcee 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -977,6 +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, @@ -1162,6 +1163,21 @@ "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, @@ -1307,6 +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, @@ -1336,6 +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, @@ -1365,6 +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, @@ -1393,6 +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, @@ -1421,6 +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, @@ -1915,6 +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 @@ -2038,6 +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, @@ -9212,6 +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, @@ -9347,6 +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, @@ -9374,6 +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, @@ -9477,7 +9503,6 @@ "us": 1.1, "fast": 6.0 }, - "supports_max_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "claude-opus-4-7-20260416": { @@ -9512,7 +9537,6 @@ "us": 1.1, "fast": 6.0 }, - "supports_max_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "claude-sonnet-4-20250514": { @@ -10790,6 +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": { @@ -15548,7 +15573,7 @@ "mode": "embedding", "output_cost_per_token": 0, "output_vector_size": 3072, - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/embeddings#multimodal", "supports_multimodal": true, "uses_embed_content": true }, @@ -17150,7 +17175,8 @@ ], "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", @@ -17663,7 +17689,8 @@ "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, @@ -19928,7 +19955,7 @@ "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, @@ -19976,7 +20003,7 @@ "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, @@ -20019,7 +20046,8 @@ "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, @@ -20062,7 +20090,8 @@ "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, @@ -21140,7 +21169,7 @@ }, "gradient_ai/alibaba-qwen3-32b": { "litellm_provider": "gradient_ai", - "max_tokens": 2048, + "max_tokens": 40960, "mode": "chat", "supported_endpoints": [ "/v1/chat/completions" @@ -21148,7 +21177,9 @@ "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, @@ -21162,7 +21193,9 @@ "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, @@ -21176,7 +21209,9 @@ "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, @@ -21190,7 +21225,9 @@ "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, @@ -21204,7 +21241,9 @@ "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, @@ -21218,7 +21257,9 @@ "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, @@ -21232,7 +21273,9 @@ "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, @@ -21246,7 +21289,9 @@ "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, @@ -21260,7 +21305,9 @@ "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", @@ -21272,7 +21319,9 @@ "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", @@ -21284,7 +21333,9 @@ "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, @@ -21298,7 +21349,9 @@ "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, @@ -21312,7 +21365,9 @@ "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, @@ -21612,11 +21667,13 @@ }, "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", @@ -21624,7 +21681,9 @@ "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", @@ -21632,7 +21691,9 @@ "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", @@ -21640,7 +21701,9 @@ "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, @@ -22061,6 +22124,98 @@ "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", @@ -22356,48 +22511,6 @@ "/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, @@ -25869,12 +25982,14 @@ "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, @@ -25993,6 +26108,7 @@ "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, @@ -26011,6 +26127,7 @@ "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, @@ -26032,6 +26149,7 @@ "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, @@ -26080,6 +26198,29 @@ "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", @@ -26445,18 +26586,22 @@ "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, @@ -26544,34 +26689,42 @@ "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, @@ -26579,7 +26732,9 @@ "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, @@ -26599,26 +26754,32 @@ "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, @@ -27126,10 +27287,12 @@ "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, @@ -27932,7 +28095,8 @@ "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", @@ -29750,14 +29914,16 @@ "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", @@ -30409,6 +30575,7 @@ "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, @@ -30437,6 +30604,7 @@ "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, @@ -30464,6 +30632,7 @@ "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, @@ -31044,6 +31213,7 @@ "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, @@ -32236,6 +32406,7 @@ "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, @@ -32262,6 +32433,7 @@ "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, @@ -32428,6 +32600,7 @@ "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, @@ -33337,6 +33510,72 @@ "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", @@ -34266,6 +34505,7 @@ "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 @@ -34281,6 +34521,7 @@ "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 @@ -34296,6 +34537,7 @@ "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 @@ -34311,6 +34553,7 @@ "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 @@ -34326,6 +34569,7 @@ "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 @@ -34342,6 +34586,7 @@ "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, @@ -34359,6 +34604,7 @@ "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, @@ -34375,6 +34621,7 @@ "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, @@ -34391,6 +34638,7 @@ "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, @@ -34407,6 +34655,7 @@ "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, @@ -34423,6 +34672,7 @@ "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, @@ -34438,38 +34688,41 @@ "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 }, @@ -34485,6 +34738,7 @@ "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 }, @@ -34500,6 +34754,7 @@ "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 }, @@ -34517,6 +34772,7 @@ "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, @@ -34537,6 +34793,7 @@ "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, @@ -34557,6 +34814,7 @@ "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, @@ -34577,6 +34835,7 @@ "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, @@ -34596,6 +34855,7 @@ "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, @@ -34612,6 +34872,7 @@ "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, @@ -34628,6 +34889,7 @@ "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, @@ -34660,6 +34922,7 @@ "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 @@ -34688,6 +34951,7 @@ "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 }, @@ -34702,6 +34966,7 @@ "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 }, @@ -34716,6 +34981,7 @@ "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 }, @@ -34747,6 +35013,20 @@ "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", @@ -39332,6 +39612,7 @@ "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, @@ -39556,6 +39837,87 @@ } ] }, + "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/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 756b2ed91d..a05af66118 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 @@ -409,9 +409,12 @@ 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 @@ -435,6 +438,10 @@ 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 @@ -459,6 +466,7 @@ 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}" ) @@ -490,6 +498,7 @@ 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 @@ -500,6 +509,30 @@ 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)}") @@ -638,6 +671,27 @@ 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: @@ -805,6 +859,120 @@ 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/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index cebd224a1a..1794cd1438 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -33,10 +33,12 @@ 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) + 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 @@ -47,34 +49,28 @@ def get_request_base_url(request: Request) -> str: base_url = str(request.base_url).rstrip("/") parsed = urlparse(base_url) - # Get forwarded headers + 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") - # 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, "", "", "")) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index f96350500d..9923c3ce4b 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -169,6 +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] = {} @@ -342,7 +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, @@ -679,7 +717,17 @@ 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), @@ -2426,7 +2474,7 @@ class MCPServerManager: ) ) - async def _call_regular_mcp_tool( + async def _call_regular_mcp_tool( # noqa: PLR0915 self, mcp_server: MCPServer, original_tool_name: str, @@ -2489,7 +2537,11 @@ class MCPServerManager: # oauth2 headers extra_headers: Optional[Dict[str, str]] = None if mcp_server.auth_type == MCPAuth.oauth2: - extra_headers = oauth2_headers + 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: @@ -2501,6 +2553,11 @@ 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 @@ -2536,6 +2593,10 @@ 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( diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index ae6055217b..54d9bbe6e2 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -153,6 +153,7 @@ 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 ( @@ -900,6 +901,20 @@ 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: @@ -1100,8 +1115,13 @@ 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: @@ -1114,11 +1134,17 @@ 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 @@ -1377,11 +1403,19 @@ 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" @@ -2104,6 +2138,47 @@ 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. diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index a3e8dd80e8..17fdb3b358 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

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

404

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