diff --git a/.circleci/config.yml b/.circleci/config.yml index 91c7a947a3b..85c49ea90b9 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -2609,6 +2609,7 @@ jobs: pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" pip install "mypy==1.18.2" + pip install apscheduler - run: name: Build Docker image command: | diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml index b7f4a25d593..1d9bd201fa8 100644 --- a/.github/workflows/test-litellm.yml +++ b/.github/workflows/test-litellm.yml @@ -33,6 +33,7 @@ jobs: poetry run pip install "google-genai==1.22.0" poetry run pip install "google-cloud-aiplatform>=1.38" poetry run pip install "fastapi-offline==1.7.3" + poetry run pip install "python-multipart==0.0.18" - name: Setup litellm-enterprise as local package run: | cd enterprise @@ -40,4 +41,4 @@ jobs: cd .. - name: Run tests run: | - poetry run pytest tests/test_litellm --tb=short -vv --maxfail=10 -n 4 + poetry run pytest tests/test_litellm --tb=short -vv --maxfail=10 -n 4 --durations=50 diff --git a/README.md b/README.md index 812b20e6986..6dcebfbd3d9 100644 --- a/README.md +++ b/README.md @@ -301,53 +301,108 @@ curl 'http://0.0.0.0:4000/key/generate' \ } ``` -## Supported Providers ([Docs](https://docs.litellm.ai/docs/providers)) +## Supported Providers ([Website Supported Models](https://models.litellm.ai/) | [Docs](https://docs.litellm.ai/docs/providers)) -| Provider | [Completion](https://docs.litellm.ai/docs/#basic-usage) | [Streaming](https://docs.litellm.ai/docs/completion/stream#streaming-responses) | [Async Completion](https://docs.litellm.ai/docs/completion/stream#async-completion) | [Async Streaming](https://docs.litellm.ai/docs/completion/stream#async-streaming) | [Async Embedding](https://docs.litellm.ai/docs/embedding/supported_embedding) | [Async Image Generation](https://docs.litellm.ai/docs/image_generation) | -|-------------------------------------------------------------------------------------|---------------------------------------------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------|-------------------------------------------------------------------------| -| [openai](https://docs.litellm.ai/docs/providers/openai) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | -| [Meta - Llama API](https://docs.litellm.ai/docs/providers/meta_llama) | ✅ | ✅ | ✅ | ✅ | | | -| [azure](https://docs.litellm.ai/docs/providers/azure) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | -| [AI/ML API](https://docs.litellm.ai/docs/providers/aiml) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | -| [aws - sagemaker](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | ✅ | ✅ | | -| [aws - bedrock](https://docs.litellm.ai/docs/providers/bedrock) | ✅ | ✅ | ✅ | ✅ | ✅ | | -| [google - vertex_ai](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | -| [google - palm](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | ✅ | | | -| [google AI Studio - gemini](https://docs.litellm.ai/docs/providers/gemini) | ✅ | ✅ | ✅ | ✅ | | | -| [mistral ai api](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | ✅ | | -| [cloudflare AI Workers](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | ✅ | | | -| [CompactifAI](https://docs.litellm.ai/docs/providers/compactifai) | ✅ | ✅ | ✅ | ✅ | | | -| [cohere](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | ✅ | | -| [anthropic](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | ✅ | | | -| [empower](https://docs.litellm.ai/docs/providers/empower) | ✅ | ✅ | ✅ | ✅ | -| [huggingface](https://docs.litellm.ai/docs/providers/huggingface) | ✅ | ✅ | ✅ | ✅ | ✅ | | -| [replicate](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | ✅ | | | -| [together_ai](https://docs.litellm.ai/docs/providers/togetherai) | ✅ | ✅ | ✅ | ✅ | | | -| [openrouter](https://docs.litellm.ai/docs/providers/openrouter) | ✅ | ✅ | ✅ | ✅ | | | -| [ai21](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | ✅ | | | -| [baseten](https://docs.litellm.ai/docs/providers/baseten) | ✅ | ✅ | ✅ | ✅ | | | -| [vllm](https://docs.litellm.ai/docs/providers/vllm) | ✅ | ✅ | ✅ | ✅ | | | -| [nlp_cloud](https://docs.litellm.ai/docs/providers/nlp_cloud) | ✅ | ✅ | ✅ | ✅ | | | -| [aleph alpha](https://docs.litellm.ai/docs/providers/aleph_alpha) | ✅ | ✅ | ✅ | ✅ | | | -| [petals](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | ✅ | | | -| [ollama](https://docs.litellm.ai/docs/providers/ollama) | ✅ | ✅ | ✅ | ✅ | ✅ | | -| [deepinfra](https://docs.litellm.ai/docs/providers/deepinfra) | ✅ | ✅ | ✅ | ✅ | | | -| [perplexity-ai](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | ✅ | | | -| [Groq AI](https://docs.litellm.ai/docs/providers/groq) | ✅ | ✅ | ✅ | ✅ | | | -| [Deepseek](https://docs.litellm.ai/docs/providers/deepseek) | ✅ | ✅ | ✅ | ✅ | | | -| [anyscale](https://docs.litellm.ai/docs/providers/anyscale) | ✅ | ✅ | ✅ | ✅ | | | -| [IBM - watsonx.ai](https://docs.litellm.ai/docs/providers/watsonx) | ✅ | ✅ | ✅ | ✅ | ✅ | | -| [voyage ai](https://docs.litellm.ai/docs/providers/voyage) | | | | | ✅ | | -| [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) | | | | | ✅ | | -| [FriendliAI](https://docs.litellm.ai/docs/providers/friendliai) | ✅ | ✅ | ✅ | ✅ | | | -| [Galadriel](https://docs.litellm.ai/docs/providers/galadriel) | ✅ | ✅ | ✅ | ✅ | | | -| [GradientAI](https://docs.litellm.ai/docs/providers/gradient_ai) | ✅ | ✅ | | | | | -| [Novita AI](https://novita.ai/models/llm?utm_source=github_litellm&utm_medium=github_readme&utm_campaign=github_link) | ✅ | ✅ | ✅ | ✅ | | | -| [Featherless AI](https://docs.litellm.ai/docs/providers/featherless_ai) | ✅ | ✅ | ✅ | ✅ | | | -| [Nebius AI Studio](https://docs.litellm.ai/docs/providers/nebius) | ✅ | ✅ | ✅ | ✅ | ✅ | | -| [Heroku](https://docs.litellm.ai/docs/providers/heroku) | ✅ | ✅ | | | | | -| [OVHCloud AI Endpoints](https://docs.litellm.ai/docs/providers/ovhcloud) | ✅ | ✅ | | | | | -| [CometAPI](https://docs.litellm.ai/docs/providers/cometapi) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +| Provider | `/chat/completions` | `/messages` | `/responses` | `/embeddings` | `/image/generations` | `/audio/transcriptions` | `/audio/speech` | `/moderations` | `/batches` | `/rerank` | +|-------------------------------------------------------------------------------------|---------------------|-------------|--------------|---------------|----------------------|-------------------------|-----------------|----------------|-----------|-----------| +| [AI/ML API (`aiml`)](https://docs.litellm.ai/docs/providers/aiml) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | +| [AI21 (`ai21`)](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | | | | | | | | +| [AI21 Chat (`ai21_chat`)](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | | | | | | | | +| [Aleph Alpha](https://docs.litellm.ai/docs/providers/aleph_alpha) | ✅ | ✅ | ✅ | | | | | | | | +| [Anthropic (`anthropic`)](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | | | | | | ✅ | | +| [Anthropic Text (`anthropic_text`)](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | | | | | | ✅ | | +| [Anyscale](https://docs.litellm.ai/docs/providers/anyscale) | ✅ | ✅ | ✅ | | | | | | | | +| [AssemblyAI (`assemblyai`)](https://docs.litellm.ai/docs/pass_through/assembly_ai) | ✅ | ✅ | ✅ | | | ✅ | | | | | +| [Auto Router (`auto_router`)](https://docs.litellm.ai/docs/proxy/auto_routing) | ✅ | ✅ | ✅ | | | | | | | | +| [AWS - Bedrock (`bedrock`)](https://docs.litellm.ai/docs/providers/bedrock) | ✅ | ✅ | ✅ | ✅ | | | | | | ✅ | +| [AWS - Sagemaker (`sagemaker`)](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | ✅ | | | | | | | +| [Azure (`azure`)](https://docs.litellm.ai/docs/providers/azure) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | +| [Azure AI (`azure_ai`)](https://docs.litellm.ai/docs/providers/azure_ai) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | +| [Azure Text (`azure_text`)](https://docs.litellm.ai/docs/providers/azure) | ✅ | ✅ | ✅ | | | ✅ | ✅ | ✅ | ✅ | | +| [Baseten (`baseten`)](https://docs.litellm.ai/docs/providers/baseten) | ✅ | ✅ | ✅ | | | | | | | | +| [Bytez (`bytez`)](https://docs.litellm.ai/docs/providers/bytez) | ✅ | ✅ | ✅ | | | | | | | | +| [Cerebras (`cerebras`)](https://docs.litellm.ai/docs/providers/cerebras) | ✅ | ✅ | ✅ | | | | | | | | +| [Clarifai (`clarifai`)](https://docs.litellm.ai/docs/providers/clarifai) | ✅ | ✅ | ✅ | | | | | | | | +| [Cloudflare AI Workers (`cloudflare`)](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | | | | | | | | +| [Codestral (`codestral`)](https://docs.litellm.ai/docs/providers/codestral) | ✅ | ✅ | ✅ | | | | | | | | +| [Cohere (`cohere`)](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | | | | | | ✅ | +| [Cohere Chat (`cohere_chat`)](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | | | | | | | | +| [CometAPI (`cometapi`)](https://docs.litellm.ai/docs/providers/cometapi) | ✅ | ✅ | ✅ | ✅ | | | | | | | +| [CompactifAI (`compactifai`)](https://docs.litellm.ai/docs/providers/compactifai) | ✅ | ✅ | ✅ | | | | | | | | +| [Custom (`custom`)](https://docs.litellm.ai/docs/providers/custom_llm_server) | ✅ | ✅ | ✅ | | | | | | | | +| [Custom OpenAI (`custom_openai`)](https://docs.litellm.ai/docs/providers/openai_compatible) | ✅ | ✅ | ✅ | | | ✅ | ✅ | ✅ | ✅ | | +| [Dashscope (`dashscope`)](https://docs.litellm.ai/docs/providers/dashscope) | ✅ | ✅ | ✅ | | | | | | | | +| [Databricks (`databricks`)](https://docs.litellm.ai/docs/providers/databricks) | ✅ | ✅ | ✅ | | | | | | | | +| [DataRobot (`datarobot`)](https://docs.litellm.ai/docs/providers/datarobot) | ✅ | ✅ | ✅ | | | | | | | | +| [Deepgram (`deepgram`)](https://docs.litellm.ai/docs/providers/deepgram) | ✅ | ✅ | ✅ | | | ✅ | | | | | +| [DeepInfra (`deepinfra`)](https://docs.litellm.ai/docs/providers/deepinfra) | ✅ | ✅ | ✅ | | | | | | | | +| [Deepseek (`deepseek`)](https://docs.litellm.ai/docs/providers/deepseek) | ✅ | ✅ | ✅ | | | | | | | | +| [ElevenLabs (`elevenlabs`)](https://docs.litellm.ai/docs/providers/elevenlabs) | ✅ | ✅ | ✅ | | | | ✅ | | | | +| [Empower (`empower`)](https://docs.litellm.ai/docs/providers/empower) | ✅ | ✅ | ✅ | | | | | | | | +| [Fal AI (`fal_ai`)](https://docs.litellm.ai/docs/providers/fal_ai) | ✅ | ✅ | ✅ | | ✅ | | | | | | +| [Featherless AI (`featherless_ai`)](https://docs.litellm.ai/docs/providers/featherless_ai) | ✅ | ✅ | ✅ | | | | | | | | +| [Fireworks AI (`fireworks_ai`)](https://docs.litellm.ai/docs/providers/fireworks_ai) | ✅ | ✅ | ✅ | | | | | | | | +| [FriendliAI (`friendliai`)](https://docs.litellm.ai/docs/providers/friendliai) | ✅ | ✅ | ✅ | | | | | | | | +| [Galadriel (`galadriel`)](https://docs.litellm.ai/docs/providers/galadriel) | ✅ | ✅ | ✅ | | | | | | | | +| [GitHub Copilot (`github_copilot`)](https://docs.litellm.ai/docs/providers/github_copilot) | ✅ | ✅ | ✅ | | | | | | | | +| [GitHub Models (`github`)](https://docs.litellm.ai/docs/providers/github) | ✅ | ✅ | ✅ | | | | | | | | +| [Google - PaLM](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | | | | | | | | +| [Google - Vertex AI (`vertex_ai`)](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | +| [Google AI Studio - Gemini (`gemini`)](https://docs.litellm.ai/docs/providers/gemini) | ✅ | ✅ | ✅ | | | | | | | | +| [GradientAI (`gradient_ai`)](https://docs.litellm.ai/docs/providers/gradient_ai) | ✅ | ✅ | ✅ | | | | | | | | +| [Groq AI (`groq`)](https://docs.litellm.ai/docs/providers/groq) | ✅ | ✅ | ✅ | | | | | | | | +| [Heroku (`heroku`)](https://docs.litellm.ai/docs/providers/heroku) | ✅ | ✅ | ✅ | | | | | | | | +| [Hosted VLLM (`hosted_vllm`)](https://docs.litellm.ai/docs/providers/vllm) | ✅ | ✅ | ✅ | | | | | | | | +| [Huggingface (`huggingface`)](https://docs.litellm.ai/docs/providers/huggingface) | ✅ | ✅ | ✅ | ✅ | | | | | | ✅ | +| [Hyperbolic (`hyperbolic`)](https://docs.litellm.ai/docs/providers/hyperbolic) | ✅ | ✅ | ✅ | | | | | | | | +| [IBM - Watsonx.ai (`watsonx`)](https://docs.litellm.ai/docs/providers/watsonx) | ✅ | ✅ | ✅ | ✅ | | | | | | | +| [Infinity (`infinity`)](https://docs.litellm.ai/docs/providers/infinity) | | | | ✅ | | | | | | | +| [Jina AI (`jina_ai`)](https://docs.litellm.ai/docs/providers/jina_ai) | | | | ✅ | | | | | | | +| [Lambda AI (`lambda_ai`)](https://docs.litellm.ai/docs/providers/lambda_ai) | ✅ | ✅ | ✅ | | | | | | | | +| [Lemonade (`lemonade`)](https://docs.litellm.ai/docs/providers/lemonade) | ✅ | ✅ | ✅ | | | | | | | | +| [LiteLLM Proxy (`litellm_proxy`)](https://docs.litellm.ai/docs/providers/litellm_proxy) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | +| [Llamafile (`llamafile`)](https://docs.litellm.ai/docs/providers/llamafile) | ✅ | ✅ | ✅ | | | | | | | | +| [LM Studio (`lm_studio`)](https://docs.litellm.ai/docs/providers/lm_studio) | ✅ | ✅ | ✅ | | | | | | | | +| [Maritalk (`maritalk`)](https://docs.litellm.ai/docs/providers/maritalk) | ✅ | ✅ | ✅ | | | | | | | | +| [Meta - Llama API (`meta_llama`)](https://docs.litellm.ai/docs/providers/meta_llama) | ✅ | ✅ | ✅ | | | | | | | | +| [Mistral AI API (`mistral`)](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | | | | | | | +| [Moonshot (`moonshot`)](https://docs.litellm.ai/docs/providers/moonshot) | ✅ | ✅ | ✅ | | | | | | | | +| [Morph (`morph`)](https://docs.litellm.ai/docs/providers/morph) | ✅ | ✅ | ✅ | | | | | | | | +| [Nebius AI Studio (`nebius`)](https://docs.litellm.ai/docs/providers/nebius) | ✅ | ✅ | ✅ | ✅ | | | | | | | +| [NLP Cloud (`nlp_cloud`)](https://docs.litellm.ai/docs/providers/nlp_cloud) | ✅ | ✅ | ✅ | | | | | | | | +| [Novita AI (`novita`)](https://novita.ai/models/llm?utm_source=github_litellm&utm_medium=github_readme&utm_campaign=github_link) | ✅ | ✅ | ✅ | | | | | | | | +| [Nscale (`nscale`)](https://docs.litellm.ai/docs/providers/nscale) | ✅ | ✅ | ✅ | | | | | | | | +| [Nvidia NIM (`nvidia_nim`)](https://docs.litellm.ai/docs/providers/nvidia_nim) | ✅ | ✅ | ✅ | | | | | | | | +| [OCI (`oci`)](https://docs.litellm.ai/docs/providers/oci) | ✅ | ✅ | ✅ | | | | | | | | +| [Ollama (`ollama`)](https://docs.litellm.ai/docs/providers/ollama) | ✅ | ✅ | ✅ | ✅ | | | | | | | +| [Ollama Chat (`ollama_chat`)](https://docs.litellm.ai/docs/providers/ollama) | ✅ | ✅ | ✅ | | | | | | | | +| [Oobabooga (`oobabooga`)](https://docs.litellm.ai/docs/providers/openai_compatible) | ✅ | ✅ | ✅ | | | ✅ | ✅ | ✅ | ✅ | | +| [OpenAI (`openai`)](https://docs.litellm.ai/docs/providers/openai) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | +| [OpenAI-like (`openai_like`)](https://docs.litellm.ai/docs/providers/openai_compatible) | | | | ✅ | | | | | | | +| [OpenRouter (`openrouter`)](https://docs.litellm.ai/docs/providers/openrouter) | ✅ | ✅ | ✅ | | | | | | | | +| [OVHCloud AI Endpoints (`ovhcloud`)](https://docs.litellm.ai/docs/providers/ovhcloud) | ✅ | ✅ | ✅ | | | | | | | | +| [Perplexity AI (`perplexity`)](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | | | | | | | | +| [Petals (`petals`)](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | | | | | | | | +| [Predibase (`predibase`)](https://docs.litellm.ai/docs/providers/predibase) | ✅ | ✅ | ✅ | | | | | | | | +| [Recraft (`recraft`)](https://docs.litellm.ai/docs/providers/recraft) | | | | | ✅ | | | | | | +| [Replicate (`replicate`)](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | | | | | | | | +| [Sagemaker Chat (`sagemaker_chat`)](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | | | | | | | | +| [Sambanova (`sambanova`)](https://docs.litellm.ai/docs/providers/sambanova) | ✅ | ✅ | ✅ | | | | | | | | +| [Snowflake (`snowflake`)](https://docs.litellm.ai/docs/providers/snowflake) | ✅ | ✅ | ✅ | | | | | | | | +| [Text Completion Codestral (`text-completion-codestral`)](https://docs.litellm.ai/docs/providers/codestral) | ✅ | ✅ | ✅ | | | | | | | | +| [Text Completion OpenAI (`text-completion-openai`)](https://docs.litellm.ai/docs/providers/text_completion_openai) | ✅ | ✅ | ✅ | | | ✅ | ✅ | ✅ | ✅ | | +| [Together AI (`together_ai`)](https://docs.litellm.ai/docs/providers/togetherai) | ✅ | ✅ | ✅ | | | | | | | | +| [Topaz (`topaz`)](https://docs.litellm.ai/docs/providers/topaz) | ✅ | ✅ | ✅ | | | | | | | | +| [Triton (`triton`)](https://docs.litellm.ai/docs/providers/triton-inference-server) | ✅ | ✅ | ✅ | | | | | | | | +| [V0 (`v0`)](https://docs.litellm.ai/docs/providers/v0) | ✅ | ✅ | ✅ | | | | | | | | +| [Vercel AI Gateway (`vercel_ai_gateway`)](https://docs.litellm.ai/docs/providers/vercel_ai_gateway) | ✅ | ✅ | ✅ | | | | | | | | +| [VLLM (`vllm`)](https://docs.litellm.ai/docs/providers/vllm) | ✅ | ✅ | ✅ | | | | | | | | +| [Volcengine (`volcengine`)](https://docs.litellm.ai/docs/providers/volcano) | ✅ | ✅ | ✅ | | | | | | | | +| [Voyage AI (`voyage`)](https://docs.litellm.ai/docs/providers/voyage) | | | | ✅ | | | | | | | +| [WandB Inference (`wandb`)](https://docs.litellm.ai/docs/providers/wandb_inference) | ✅ | ✅ | ✅ | | | | | | | | +| [Watsonx Text (`watsonx_text`)](https://docs.litellm.ai/docs/providers/watsonx) | ✅ | ✅ | ✅ | | | | | | | | +| [xAI (`xai`)](https://docs.litellm.ai/docs/providers/xai) | ✅ | ✅ | ✅ | | | | | | | | +| [Xinference (`xinference`)](https://docs.litellm.ai/docs/providers/xinference) | | | | ✅ | | | | | | | [**Read the Docs**](https://docs.litellm.ai/docs/) diff --git a/batch_small.jsonl b/batch_small.jsonl new file mode 100644 index 00000000000..36792f79dec --- /dev/null +++ b/batch_small.jsonl @@ -0,0 +1,4 @@ +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Hello, how are you?"}]}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "What is the weather today?"}]}} +{"custom_id": "request-3", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Tell me a short joke"}]}} + diff --git a/docs/my-website/docs/completion/prompt_caching.md b/docs/my-website/docs/completion/prompt_caching.md index c8adf4bcccf..630c9e58d24 100644 --- a/docs/my-website/docs/completion/prompt_caching.md +++ b/docs/my-website/docs/completion/prompt_caching.md @@ -27,7 +27,7 @@ For the supported providers, LiteLLM follows the OpenAI prompt caching usage obj } ``` -- `prompt_tokens`: These are the non-cached prompt tokens (same as Anthropic, equivalent to Deepseek `prompt_cache_miss_tokens`). +- `prompt_tokens`: These are all prompt tokens including cache-miss and cache-hit input tokens. - `completion_tokens`: These are the output tokens generated by the model. - `total_tokens`: Sum of prompt_tokens + completion_tokens. - `prompt_tokens_details`: Object containing cached_tokens. diff --git a/docs/my-website/docs/files_endpoints.md b/docs/my-website/docs/files_endpoints.md index 31a02d41a3f..88493fe0bbd 100644 --- a/docs/my-website/docs/files_endpoints.md +++ b/docs/my-website/docs/files_endpoints.md @@ -57,7 +57,7 @@ client = OpenAI( client.files.create( file=wav_data, purpose="user_data", - extra_body={"custom_llm_provider": "openai"} + extra_headers={"custom-llm-provider": "openai"} ) ``` @@ -71,7 +71,7 @@ client = OpenAI( base_url="http://0.0.0.0:4000/v1" ) -files = client.files.list(extra_body={"custom_llm_provider": "openai"}) +files = client.files.list(extra_headers={"custom-llm-provider": "openai"}) print("files=", files) ``` @@ -85,7 +85,7 @@ client = OpenAI( base_url="http://0.0.0.0:4000/v1" ) -file = client.files.retrieve(file_id="file-abc123", extra_body={"custom_llm_provider": "openai"}) +file = client.files.retrieve(file_id="file-abc123", extra_headers={"custom-llm-provider": "openai"}) print("file=", file) ``` @@ -99,7 +99,7 @@ client = OpenAI( base_url="http://0.0.0.0:4000/v1" ) -response = client.files.delete(file_id="file-abc123", extra_body={"custom_llm_provider": "openai"}) +response = client.files.delete(file_id="file-abc123", extra_headers={"custom-llm-provider": "openai"}) print("delete response=", response) ``` @@ -113,7 +113,7 @@ client = OpenAI( base_url="http://0.0.0.0:4000/v1" ) -content = client.files.content(file_id="file-abc123", extra_body={"custom_llm_provider": "openai"}) +content = client.files.content(file_id="file-abc123", extra_headers={"custom-llm-provider": "openai"}) print("content=", content) ``` diff --git a/docs/my-website/docs/fine_tuning.md b/docs/my-website/docs/fine_tuning.md index f3f955cb01d..2779a478f8f 100644 --- a/docs/my-website/docs/fine_tuning.md +++ b/docs/my-website/docs/fine_tuning.md @@ -62,7 +62,7 @@ client = AsyncOpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") # base_u file_name = "openai_batch_completions.jsonl" response = await client.files.create( - extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use + extra_headers={"custom-llm-provider": "azure"}, # tell litellm proxy which provider to use file=open(file_name, "rb"), purpose="fine-tune", ) @@ -73,8 +73,8 @@ response = await client.files.create( ```shell curl http://localhost:4000/v1/files \ -H "Authorization: Bearer sk-1234" \ + -H "custom-llm-provider: azure" \ -F purpose="batch" \ - -F custom_llm_provider="azure"\ -F file="@mydata.jsonl" ``` @@ -92,7 +92,7 @@ curl http://localhost:4000/v1/files \ ft_job = await client.fine_tuning.jobs.create( model="gpt-35-turbo-1106", # Azure OpenAI model you want to fine-tune training_file="file-abc123", # file_id from create file response - extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use + extra_headers={"custom-llm-provider": "azure"}, # tell litellm proxy which provider to use ) ``` @@ -103,8 +103,8 @@ ft_job = await client.fine_tuning.jobs.create( curl http://localhost:4000/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ + -H "custom-llm-provider: azure" \ -d '{ - "custom_llm_provider": "azure", "model": "gpt-35-turbo-1106", "training_file": "file-abc123" }' @@ -215,7 +215,7 @@ curl http://localhost:4000/v1/fine_tuning/jobs \ # cancel specific fine tuning job cancel_ft_job = await client.fine_tuning.jobs.cancel( fine_tuning_job_id="123", # fine tuning job id - extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use + extra_headers={"custom-llm-provider": "azure"}, # tell litellm proxy which provider to use ) print("response from cancel ft job={}".format(cancel_ft_job)) @@ -228,7 +228,7 @@ print("response from cancel ft job={}".format(cancel_ft_job)) curl -X POST http://localhost:4000/v1/fine_tuning/jobs/ftjob-abc123/cancel \ -H "Authorization: Bearer sk-1234" \ -H "Content-Type: application/json" \ - -d '{"custom_llm_provider": "azure"}' + -H "custom-llm-provider: azure" ``` @@ -242,7 +242,7 @@ curl -X POST http://localhost:4000/v1/fine_tuning/jobs/ftjob-abc123/cancel \ ```python list_ft_jobs = await client.fine_tuning.jobs.list( - extra_query={"custom_llm_provider": "azure"} # tell litellm proxy which provider to use + extra_headers={"custom-llm-provider": "azure"} # tell litellm proxy which provider to use ) print("list of ft jobs={}".format(list_ft_jobs)) @@ -252,9 +252,10 @@ print("list of ft jobs={}".format(list_ft_jobs)) ```shell -curl -X GET 'http://localhost:4000/v1/fine_tuning/jobs?custom_llm_provider=azure' \ +curl -X GET 'http://localhost:4000/v1/fine_tuning/jobs' \ -H "Content-Type: application/json" \ - -H "Authorization: Bearer sk-1234" + -H "Authorization: Bearer sk-1234" \ + -H "custom-llm-provider: azure" ``` diff --git a/docs/my-website/docs/observability/opik_integration.md b/docs/my-website/docs/observability/opik_integration.md index 1ba1c2de210..d28c46f0b4b 100644 --- a/docs/my-website/docs/observability/opik_integration.md +++ b/docs/my-website/docs/observability/opik_integration.md @@ -220,7 +220,41 @@ curl --location --request POST 'http://0.0.0.0:4000/chat/completions' \ }' ``` +## Automatic Metadata from API Keys +In some cases, the requester may be unable or unaware of how to add Opik metadata to their requests. To ensure all Opik-related actions are properly tracked, LiteLLM Proxy can automatically associate metadata from a user-specific API key when none is provided in the request. + +### How It Works + +When you create an API key in LiteLLM Proxy, you can attach Opik-specific metadata to the key itself. This metadata will be automatically applied to all requests made with that key, unless the request explicitly provides its own Opik metadata (which takes precedence). + + +### Usage + +**Step 1: Save Opik Metadata to the corresponding Api Key** +Go to 'Virtual Keys', click on your choosen api key and edit 'Settings'. +Now save the opik metadata as user api key metdata. + + + +**Step 2: Use the key - Opik metadata is automatically applied** + +```bash +curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-key-from-step-1' \ +-d '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "What's the weather like in Boston today?" + } + ] +}' +``` + +All requests made with this key will automatically be tracked in the "TestProject" Opik project with the specified tags, without requiring the user to pass metadata in each request. ## Support & Talk to Founders diff --git a/docs/my-website/docs/providers/azure/azure.md b/docs/my-website/docs/providers/azure/azure.md index a9c08e2ef24..2f845357328 100644 --- a/docs/my-website/docs/providers/azure/azure.md +++ b/docs/my-website/docs/providers/azure/azure.md @@ -834,7 +834,7 @@ client = OpenAI( batch_input_file = client.files.create( file=open("mydata.jsonl", "rb"), purpose="batch", - extra_body={"custom_llm_provider": "azure"} + extra_headers={"custom-llm-provider": "azure"} ) file_id = batch_input_file.id ``` @@ -870,7 +870,7 @@ batch = client.batches.create( # re use client from above endpoint="/v1/chat/completions", completion_window="24h", metadata={"description": "My batch job"}, - extra_body={"custom_llm_provider": "azure"} + extra_headers={"custom-llm-provider": "azure"} ) ``` @@ -898,7 +898,7 @@ curl http://localhost:4000/v1/batches \ ```python retrieved_batch = client.batches.retrieve( batch.id, - extra_query={"custom_llm_provider": "azure"} + extra_headers={"custom-llm-provider": "azure"} ) ``` @@ -922,7 +922,7 @@ curl http://localhost:4000/v1/batches/batch_abc123 \ ```python cancelled_batch = client.batches.cancel( batch.id, - extra_body={"custom_llm_provider": "azure"} + extra_headers={"custom-llm-provider": "azure"} ) ``` @@ -945,7 +945,7 @@ curl http://localhost:4000/v1/batches/batch_abc123/cancel \ ```python -client.batches.list(extra_query={"custom_llm_provider": "azure"}) +client.batches.list(extra_headers={"custom-llm-provider": "azure"}) ``` diff --git a/docs/my-website/docs/providers/azure/videos.md b/docs/my-website/docs/providers/azure/videos.md index 22e714a8ecf..d088c63f710 100644 --- a/docs/my-website/docs/providers/azure/videos.md +++ b/docs/my-website/docs/providers/azure/videos.md @@ -31,7 +31,7 @@ os.environ["AZURE_OPENAI_API_VERSION"] = "2024-02-15-preview" ### Basic Usage ```python -from litellm import video_generation, video_status, video_retrieval +from litellm import video_generation, video_status, video_content import os import time @@ -68,7 +68,7 @@ while True: time.sleep(10) # Wait 10 seconds before checking again # Download video content when ready -video_bytes = video_retrieval( +video_bytes = video_content( video_id=response.id, model="azure/sora-2" ) @@ -146,10 +146,10 @@ client = openai.OpenAI( ) # request sent to model set on litellm proxy, `litellm --model` -response = client.videos.generations.create( +response = client.videos.create( model="azure-sora-2", prompt="A cat playing with a ball of yarn in a sunny garden", - seconds="8", + seconds=8, size="720x1280" ) @@ -210,7 +210,7 @@ general_settings: ```python # Download video content -video_bytes = video_retrieval( +video_bytes = video_content( video_id="video_1234567890", model="azure/sora-2" ) @@ -242,7 +242,7 @@ def generate_and_download_video(prompt): time.sleep(30) # Step 3: Download video - video_bytes = litellm.video_retrieval( + video_bytes = litellm.video_content( video_id=video_id, model="azure/sora-2" ) diff --git a/docs/my-website/docs/providers/fal_ai.md b/docs/my-website/docs/providers/fal_ai.md new file mode 100644 index 00000000000..d42182b57a1 --- /dev/null +++ b/docs/my-website/docs/providers/fal_ai.md @@ -0,0 +1,310 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Fal AI + +Fal AI provides fast, scalable access to state-of-the-art image generation models including FLUX, Stable Diffusion, Imagen, and more. + +## Overview + +| Property | Details | +|----------|---------| +| Description | Fal AI offers optimized infrastructure for running image generation models at scale with low latency. | +| Provider Route on LiteLLM | `fal_ai/` | +| Provider Doc | [Fal AI Documentation ↗](https://fal.ai/models) | +| Supported Operations | [`/images/generations`](#image-generation) | + +## Setup + +### API Key + +```python showLineNumbers +import os + +# Set your Fal AI API key +os.environ["FAL_AI_API_KEY"] = "your-fal-api-key" +``` + +Get your API key from [fal.ai](https://fal.ai/). + +## Supported Models + +| Model Name | Description | Documentation | +|------------|-------------|---------------| +| `fal_ai/fal-ai/flux-pro/v1.1-ultra` | FLUX Pro v1.1 Ultra - High-quality image generation | [Docs ↗](https://fal.ai/models/fal-ai/flux-pro/v1.1-ultra) | +| `fal_ai/fal-ai/imagen4/preview` | Google's Imagen 4 - Highest quality model | [Docs ↗](https://fal.ai/models/fal-ai/imagen4/preview) | +| `fal_ai/fal-ai/recraft/v3/text-to-image` | Recraft v3 - Multiple style options | [Docs ↗](https://fal.ai/models/fal-ai/recraft/v3/text-to-image) | +| `fal_ai/fal-ai/stable-diffusion-v35-medium` | Stable Diffusion v3.5 Medium | [Docs ↗](https://fal.ai/models/fal-ai/stable-diffusion-v35-medium) | +| `fal_ai/bria/text-to-image/3.2` | Bria 3.2 - Commercial-grade generation | [Docs ↗](https://fal.ai/models/bria/text-to-image/3.2) | + +## Image Generation + +### Usage - LiteLLM Python SDK + + + + +```python showLineNumbers title="Basic Image Generation" +import litellm +import os + +# Set your API key +os.environ["FAL_AI_API_KEY"] = "your-fal-api-key" + +# Generate an image +response = litellm.image_generation( + model="fal_ai/fal-ai/flux-pro/v1.1-ultra", + prompt="A serene mountain landscape at sunset with vibrant colors" +) + +print(response.data[0].url) +``` + + + + + +```python showLineNumbers title="Google Imagen 4 Generation" +import litellm +import os + +os.environ["FAL_AI_API_KEY"] = "your-fal-api-key" + +# Generate with Imagen 4 +response = litellm.image_generation( + model="fal_ai/fal-ai/imagen4/preview", + prompt="A vintage 1960s kitchen with flour package on countertop", + aspect_ratio="16:9", + num_images=1 +) + +print(response.data[0].url) +``` + + + + + +```python showLineNumbers title="Recraft v3 with Style" +import litellm +import os + +os.environ["FAL_AI_API_KEY"] = "your-fal-api-key" + +# Generate with specific style +response = litellm.image_generation( + model="fal_ai/fal-ai/recraft/v3/text-to-image", + prompt="A red panda eating bamboo", + style="realistic_image", + image_size="landscape_4_3" +) + +print(response.data[0].url) +``` + + + + + +```python showLineNumbers title="Async Image Generation" +import litellm +import asyncio +import os + +async def generate_image(): + os.environ["FAL_AI_API_KEY"] = "your-fal-api-key" + + response = await litellm.aimage_generation( + model="fal_ai/fal-ai/stable-diffusion-v35-medium", + prompt="A cyberpunk cityscape with neon lights", + guidance_scale=7.5, + num_inference_steps=50 + ) + + print(response.data[0].url) + return response + +asyncio.run(generate_image()) +``` + + + + + +```python showLineNumbers title="Advanced FLUX Pro Generation" +import litellm +import os + +os.environ["FAL_AI_API_KEY"] = "your-fal-api-key" + +# Generate with advanced parameters +response = litellm.image_generation( + model="fal_ai/fal-ai/flux-pro/v1.1-ultra", + prompt="A majestic dragon soaring over mountains", + n=2, + size="1792x1024", # Maps to aspect_ratio="16:9" + seed=42, + safety_tolerance="2", + enhance_prompt=True +) + +for image in response.data: + print(f"Generated image: {image.url}") +``` + + + + +### Usage - LiteLLM Proxy Server + +#### 1. Configure your config.yaml + +```yaml showLineNumbers title="Fal AI Image Generation Configuration" +model_list: + - model_name: flux-ultra + litellm_params: + model: fal_ai/fal-ai/flux-pro/v1.1-ultra + api_key: os.environ/FAL_AI_API_KEY + model_info: + mode: image_generation + + - model_name: imagen4 + litellm_params: + model: fal_ai/fal-ai/imagen4/preview + api_key: os.environ/FAL_AI_API_KEY + model_info: + mode: image_generation + + - model_name: stable-diffusion + litellm_params: + model: fal_ai/fal-ai/stable-diffusion-v35-medium + api_key: os.environ/FAL_AI_API_KEY + model_info: + mode: image_generation + +general_settings: + master_key: sk-1234 +``` + +#### 2. Start LiteLLM Proxy Server + +```bash showLineNumbers title="Start Proxy Server" +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +#### 3. Make requests + + + + +```python showLineNumbers title="Generate via Proxy - OpenAI SDK" +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:4000", + api_key="sk-1234" +) + +response = client.images.generate( + model="flux-ultra", + prompt="A beautiful sunset over the ocean", + n=1, + size="1024x1024" +) + +print(response.data[0].url) +``` + + + + + +```python showLineNumbers title="Generate via Proxy - LiteLLM SDK" +import litellm + +response = litellm.image_generation( + model="litellm_proxy/imagen4", + prompt="A cozy coffee shop interior", + api_base="http://localhost:4000", + api_key="sk-1234" +) + +print(response.data[0].url) +``` + + + + + +```bash showLineNumbers title="Generate via Proxy - cURL" +curl --location 'http://localhost:4000/v1/images/generations' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer sk-1234' \ +--data '{ + "model": "stable-diffusion", + "prompt": "A serene Japanese garden with cherry blossoms", + "n": 1, + "size": "1024x1024" +}' +``` + + + + + + +## Using Model-Specific Parameters + +LiteLLM forwards any additional parameters directly to the Fal AI API. You can pass model-specific parameters in your request and they will be sent to Fal AI. + +```python showLineNumbers title="Pass Model-Specific Parameters" +import litellm + +# Any parameters beyond the standard ones are forwarded to Fal AI +response = litellm.image_generation( + model="fal_ai/fal-ai/flux-pro/v1.1-ultra", + prompt="A beautiful sunset", + # Model-specific Fal AI parameters + aspect_ratio="16:9", + safety_tolerance="2", + enhance_prompt=True, + seed=42 +) +``` + +For the complete list of parameters supported by each model, see: +- [FLUX Pro v1.1-ultra Parameters ↗](https://fal.ai/models/fal-ai/flux-pro/v1.1-ultra/api) +- [Imagen 4 Parameters ↗](https://fal.ai/models/fal-ai/imagen4/preview/api) +- [Recraft v3 Parameters ↗](https://fal.ai/models/fal-ai/recraft/v3/text-to-image/api) +- [Stable Diffusion v3.5 Parameters ↗](https://fal.ai/models/fal-ai/stable-diffusion-v35-medium/api) +- [Bria 3.2 Parameters ↗](https://fal.ai/models/bria/text-to-image/3.2/api) + +## Supported Parameters + +Standard OpenAI-compatible parameters that work across all models: + +| Parameter | Type | Description | Default | +|-----------|------|-------------|---------| +| `prompt` | string | Text description of desired image | Required | +| `model` | string | Fal AI model to use | Required | +| `n` | integer | Number of images to generate (1-4) | `1` | +| `size` | string | Image dimensions (maps to model-specific format) | Model default | +| `api_key` | string | Your Fal AI API key | Environment variable | + +## Getting Started + +1. Sign up at [fal.ai](https://fal.ai/) +2. Get your API key from your account settings +3. Set `FAL_AI_API_KEY` environment variable +4. Choose a model from the [Fal AI model gallery](https://fal.ai/models) +5. Start generating images with LiteLLM + +## Additional Resources + +- [Fal AI Documentation](https://fal.ai/docs) +- [Model Gallery](https://fal.ai/models) +- [API Reference](https://fal.ai/docs/api-reference) +- [Pricing](https://fal.ai/pricing) + diff --git a/docs/my-website/docs/providers/github.md b/docs/my-website/docs/providers/github.md index b9e525ef5c1..51220166140 100644 --- a/docs/my-website/docs/providers/github.md +++ b/docs/my-website/docs/providers/github.md @@ -1,7 +1,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# 🆕 Github +# Github https://github.com/marketplace/models :::tip diff --git a/docs/my-website/docs/providers/google_ai_studio/files.md b/docs/my-website/docs/providers/google_ai_studio/files.md index 500f1d57185..ce61ce1a90b 100644 --- a/docs/my-website/docs/providers/google_ai_studio/files.md +++ b/docs/my-website/docs/providers/google_ai_studio/files.md @@ -39,7 +39,7 @@ encoded_string = base64.b64encode(wav_data).decode('utf-8') file = create_file( file=wav_data, purpose="user_data", - extra_body={"custom_llm_provider": "gemini"}, + extra_headers={"custom-llm-provider": "gemini"}, api_key=os.getenv("GEMINI_API_KEY"), ) diff --git a/docs/my-website/docs/providers/oci.md b/docs/my-website/docs/providers/oci.md index 1f52fba04f3..cea5d6824a0 100644 --- a/docs/my-website/docs/providers/oci.md +++ b/docs/my-website/docs/providers/oci.md @@ -29,17 +29,38 @@ Check the [OCI Models List](https://docs.oracle.com/en-us/iaas/Content/generativ ## Authentication -LiteLLM uses OCI signing key authentication. Follow the [official Oracle tutorial](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/apisigningkey.htm) to create a signing key and obtain the following parameters: +LiteLLM supports two authentication methods for OCI: + +### Method 1: Manual Credentials +Provide individual OCI credentials directly to LiteLLM. Follow the [official Oracle tutorial](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/apisigningkey.htm) to create a signing key and obtain the following parameters: - `user` - `fingerprint` - `tenancy` - `region` -- `key_file` +- `key_file` or `key` +- `compartment_id` + +This is the default method for LiteLLM AI Gateway (LLM Proxy) access to OCI GenAI models. + +### Method 2: OCI SDK Signer +Use an OCI SDK `Signer` object for authentication. This method: +- Leverages the official [OCI SDK for signing](https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html) +- Supports additional authentication methods (instance principals, workload identity, etc.) + +To use this method, install the OCI SDK: +```bash +pip install oci +``` + +This method is an alternative when using the LiteLLM SDK on Oracle Cloud Infrastructure (instances or Oracle Kubernetes Engine). ## Usage -Input the parameters obtained from the OCI signing key creation process into the `completion` function. + + + +Input the parameters obtained from the OCI signing key creation process into the `completion` function: ```python import os @@ -64,10 +85,119 @@ response = completion( print(response) ``` + + + +Use the OCI SDK `Signer` for authentication: + +```python +from litellm import completion +from oci.signer import Signer + +# Create an OCI Signer +signer = Signer( + tenancy="ocid1.tenancy.oc1..", + user="ocid1.user.oc1..", + fingerprint="xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx", + private_key_file_location="~/.oci/key.pem", + # Or use private_key_content="" +) + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion( + model="oci/xai.grok-4", + messages=messages, + oci_signer=signer, + oci_region="us-chicago-1", # Optional, defaults to us-ashburn-1 + oci_serving_mode="ON_DEMAND", # Optional, default is "ON_DEMAND". Other option is "DEDICATED" + oci_compartment_id="", +) +print(response) +``` + +**Alternative: Use OCI Config File** + +The OCI SDK can automatically load credentials from `~/.oci/config`: + +```python +from litellm import completion +from oci.config import from_file +from oci.signer import Signer + +# Load config from file +config = from_file("~/.oci/config", "DEFAULT") # "DEFAULT" is the profile name +signer = Signer( + tenancy=config["tenancy"], + user=config["user"], + fingerprint=config["fingerprint"], + private_key_file_location=config["key_file"], + pass_phrase=config.get("pass_phrase") # Optional if key is encrypted +) + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion( + model="oci/xai.grok-4", + messages=messages, + oci_signer=signer, + oci_region=config["region"], + oci_compartment_id="", +) +print(response) +``` + +**Instance Principal Authentication** + +For applications running on OCI compute instances: + +```python +from litellm import completion +from oci.auth.signers import InstancePrincipalsSecurityTokenSigner + +oci.auth.signers.get_oke_workload_identity_resource_principal_signer() +# Use instance principal authentication +signer = InstancePrincipalsSecurityTokenSigner() + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion( + model="oci/xai.grok-4", + messages=messages, + oci_signer=signer, + oci_region="us-chicago-1", + oci_compartment_id="", +) +print(response) +``` + +**Use workload identity authentication** + +For applications running in Oracle Kubernetes Engine (OKE): + +```python +from litellm import completion +from oci.auth.signers import get_oke_workload_identity_resource_principal_signer + +# Use instance principal authentication +signer = get_oke_workload_identity_resource_principal_signer() + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion( + model="oci/xai.grok-4", + messages=messages, + oci_signer=signer, + oci_region="us-chicago-1", + oci_compartment_id="", +) +print(response) +``` + + ## Usage - Streaming Just set `stream=True` when calling completion. + + + ```python import os from litellm import completion @@ -93,10 +223,68 @@ for chunk in response: print(chunk["choices"][0]["delta"]["content"]) # same as openai format ``` + + + +```python +from litellm import completion +from oci.signer import Signer + +signer = Signer( + tenancy="ocid1.tenancy.oc1..", + user="ocid1.user.oc1..", + fingerprint="xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx", + private_key_file_location="~/.oci/key.pem", +) + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion( + model="oci/xai.grok-4", + messages=messages, + stream=True, + oci_signer=signer, + oci_region="us-chicago-1", + oci_compartment_id="", +) +for chunk in response: + print(chunk["choices"][0]["delta"]["content"]) # same as openai format +``` + + + + ## Usage Examples by Model Type ### Using Cohere Models + + + +```python +from litellm import completion +from oci.signer import Signer + +signer = Signer( + tenancy="ocid1.tenancy.oc1..", + user="ocid1.user.oc1..", + fingerprint="xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx:xx", + private_key_file_location="~/.oci/key.pem", +) + +messages = [{"role": "user", "content": "Explain quantum computing"}] +response = completion( + model="oci/cohere.command-latest", + messages=messages, + oci_signer=signer, + oci_region="us-chicago-1", + oci_compartment_id="", +) +print(response) +``` + + + + ```python from litellm import completion @@ -112,4 +300,7 @@ response = completion( oci_compartment_id=, ) print(response) -``` \ No newline at end of file +``` + + + \ No newline at end of file diff --git a/docs/my-website/docs/providers/openai/videos.md b/docs/my-website/docs/providers/openai/videos.md index 4dc18da77bd..473279e60ef 100644 --- a/docs/my-website/docs/providers/openai/videos.md +++ b/docs/my-website/docs/providers/openai/videos.md @@ -17,7 +17,7 @@ os.environ["OPENAI_API_KEY"] = "your-api-key" ### Basic Usage ```python -from litellm import video_generation, video_retrieval +from litellm import video_generation, video_content import os os.environ["OPENAI_API_KEY"] = "your-api-key" @@ -34,7 +34,7 @@ print(f"Video ID: {response.id}") print(f"Status: {response.status}") # Download video content when ready -video_bytes = video_retrieval( +video_bytes = video_content( video_id=response.id, model="sora-2" ) @@ -63,7 +63,7 @@ with open("generated_video.mp4", "wb") as f: ```python # Download video content -video_bytes = video_retrieval( +video_bytes = video_content( video_id="video_1234567890", model="sora-2" ) @@ -95,7 +95,7 @@ def generate_and_download_video(prompt): time.sleep(30) # Step 3: Download video - video_bytes = litellm.video_retrieval( + video_bytes = litellm.video_content( video_id=video_id, model="sora-2" ) @@ -118,7 +118,7 @@ video_file = generate_and_download_video( # Video editing with reference image response = litellm.video_generation( prompt="Make the cat jump higher", - input_reference="path/to/image.jpg", # Reference image + input_reference=open("path/to/image.jpg", "rb"), # Reference image model="sora-2", seconds="8" ) diff --git a/docs/my-website/docs/providers/openrouter.md b/docs/my-website/docs/providers/openrouter.md index 58a87f68495..327634909b3 100644 --- a/docs/my-website/docs/providers/openrouter.md +++ b/docs/my-website/docs/providers/openrouter.md @@ -9,10 +9,9 @@ LiteLLM supports all the text / chat / vision models from [OpenRouter](https://o ```python import os from litellm import completion + os.environ["OPENROUTER_API_KEY"] = "" os.environ["OPENROUTER_API_BASE"] = "" # [OPTIONAL] defaults to https://openrouter.ai/api/v1 - - os.environ["OR_SITE_URL"] = "" # [OPTIONAL] os.environ["OR_APP_NAME"] = "" # [OPTIONAL] @@ -22,8 +21,32 @@ response = completion( ) ``` -## OpenRouter Completion Models +## Configuration with Environment Variables +For production environments, you can dynamically configure the base_url using environment variables: + +```python +import os +from litellm import completion + +# Configure with environment variables +OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY") +OPENROUTER_BASE_URL = os.getenv("OPENROUTER_API_BASE", "https://openrouter.ai/api/v1") + +# Set environment for LiteLLM +os.environ["OPENROUTER_API_KEY"] = OPENROUTER_API_KEY +os.environ["OPENROUTER_API_BASE"] = OPENROUTER_BASE_URL + +response = completion( + model="openrouter/google/palm-2-chat-bison", + messages=messages, + base_url=OPENROUTER_BASE_URL # Explicitly pass base_url for clarity +) +``` + +This approach provides better flexibility for managing configurations across different environments (dev, staging, production) and makes it easier to switch between self-hosted and cloud endpoints. + +## OpenRouter Completion Models 🚨 LiteLLM supports ALL OpenRouter models, send `model=openrouter/` to send it to open router. See all openrouter models [here](https://openrouter.ai/models) | Model Name | Function Call | @@ -40,12 +63,12 @@ response = completion( | openrouter/meta-llama/llama-2-70b-chat | `completion('openrouter/meta-llama/llama-2-70b-chat', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | ## Passing OpenRouter Params - transforms, models, route - Pass `transforms`, `models`, `route`as arguments to `litellm.completion()` ```python import os from litellm import completion + os.environ["OPENROUTER_API_KEY"] = "" response = completion( @@ -54,4 +77,4 @@ response = completion( transforms = [""], route= "" ) -``` \ No newline at end of file +``` diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 6f0210f270b..874b637e4db 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -2935,7 +2935,7 @@ finetune_settings: ft_job = await client.fine_tuning.jobs.create( model="gemini-1.0-pro-002", # Vertex model you want to fine-tune training_file="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl", # file_id from create file response - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm proxy which provider to use + extra_headers={"custom-llm-provider": "vertex_ai"}, # tell litellm proxy which provider to use ) ``` @@ -2946,8 +2946,8 @@ ft_job = await client.fine_tuning.jobs.create( curl http://localhost:4000/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ + -H "custom-llm-provider: vertex_ai" \ -d '{ - "custom_llm_provider": "vertex_ai", "model": "gemini-1.0-pro-002", "training_file": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl" }' @@ -2975,9 +2975,7 @@ ft_job = client.fine_tuning.jobs.create( "learning_rate_multiplier": 0.1, # learning_rate_multiplier on Vertex "adapter_size": "ADAPTER_SIZE_ONE" # type: ignore, vertex specific hyperparameter }, - extra_body={ - "custom_llm_provider": "vertex_ai", - }, + extra_headers={"custom-llm-provider": "vertex_ai"}, ) ``` @@ -2988,8 +2986,8 @@ ft_job = client.fine_tuning.jobs.create( curl http://localhost:4000/v1/fine_tuning/jobs \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ + -H "custom-llm-provider: vertex_ai" \ -d '{ - "custom_llm_provider": "vertex_ai", "model": "gemini-1.0-pro-002", "training_file": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl", "hyperparameters": { diff --git a/docs/my-website/docs/providers/vertex_batch.md b/docs/my-website/docs/providers/vertex_batch.md index 730f31be87d..01052ba32e3 100644 --- a/docs/my-website/docs/providers/vertex_batch.md +++ b/docs/my-website/docs/providers/vertex_batch.md @@ -50,7 +50,7 @@ oai_client = OpenAI( file_obj = oai_client.files.create( file=open("batch_requests.jsonl", "rb"), purpose="batch", - extra_body={"custom_llm_provider": "vertex_ai"} + extra_headers={"custom-llm-provider": "vertex_ai"} ) print(f"File uploaded with ID: {file_obj.id}") @@ -63,9 +63,9 @@ print(f"File uploaded with ID: {file_obj.id}") curl --request POST \ --url http://localhost:4000/v1/files \ --header 'Content-Type: multipart/form-data' \ + --header 'custom-llm-provider: vertex_ai' \ --form purpose=batch \ - --form file=@batch_requests.jsonl \ - --form custom_llm_provider=vertex_ai + --form file=@batch_requests.jsonl ``` @@ -100,7 +100,7 @@ create_batch_response = oai_client.batches.create( completion_window="24h", endpoint="/v1/chat/completions", input_file_id=batch_input_file_id, # e.g. "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd" - extra_body={"custom_llm_provider": "vertex_ai"} + extra_headers={"custom-llm-provider": "vertex_ai"} ) print(f"Batch created with ID: {create_batch_response.id}") @@ -113,11 +113,11 @@ print(f"Batch created with ID: {create_batch_response.id}") curl --request POST \ --url http://localhost:4000/v1/batches \ --header 'Content-Type: application/json' \ + --header 'custom-llm-provider: vertex_ai' \ --data '{ "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", "endpoint": "/v1/chat/completions", - "completion_window": "24h", - "custom_llm_provider": "vertex_ai" + "completion_window": "24h" }' ``` @@ -162,7 +162,7 @@ Check the status of your batch job. The batch will progress through states: `val ```python showLineNumbers title="retrieve_batch.py" retrieved_batch = oai_client.batches.retrieve( batch_id=create_batch_response.id, # Created batch id, e.g. 7814463557919047680 - extra_query={"custom_llm_provider": "vertex_ai"} + extra_headers={"custom-llm-provider": "vertex_ai"} ) print(f"Batch status: {retrieved_batch.status}") @@ -230,7 +230,7 @@ encoded_file_id = urllib.parse.quote_plus(output_file_id) # Get file content file_content = oai_client.files.content( file_id=encoded_file_id, - extra_body={"custom_llm_provider": "vertex_ai"} + extra_headers={"custom-llm-provider": "vertex_ai"} ) # Process the results diff --git a/docs/my-website/docs/proxy/access_control.md b/docs/my-website/docs/proxy/access_control.md index 47a17496feb..678032be9a2 100644 --- a/docs/my-website/docs/proxy/access_control.md +++ b/docs/my-website/docs/proxy/access_control.md @@ -10,8 +10,25 @@ Role-based access control (RBAC) is based on Organizations, Teams and Internal U - `Organizations` are the top-level entities that contain Teams. - `Team` - A Team is a collection of multiple `Internal Users` - `Internal Users` - users that can create keys, make LLM API calls, view usage on LiteLLM. Users can be on multiple teams. -- `Virtual Keys` - Keys are used for authentication to the LiteLLM API. Keys are tied to a `Internal User` and `Team` +- `Virtual Keys` - Keys are used for authentication to the LiteLLM API. Each key can optionally be associated with a `user_id`, a `team_id`, or both: + - **User-only key**: Has a `user_id` but no `team_id`. Tracked individually, deleted when the user is deleted. + - **Team key (Service Account)**: Has a `team_id` but no `user_id`. Shared by the team, not deleted when users are removed. [Learn more about service account keys](https://docs.litellm.ai/docs/proxy/virtual_keys#service-account-keys). + - **User + Team key**: Has both `user_id` and `team_id`. Belongs to a specific user within a team context. +### When to Use Each Key Type + +| Key Type | Use Case | Spend Tracking | Lifecycle | +|----------|----------|----------------|-----------| +| **User-only** | Personal API keys for individual developers | Tracked to the user | Deleted when user is deleted | +| **Team (Service Account)** | Production apps, CI/CD pipelines, shared services | Tracked to the team only | Persists even when team members leave | +| **User + Team** | User working within a team context | Tracked to both user and team | Deleted when user is deleted | + +**Example scenarios:** +- Use **user-only keys** for developers testing locally +- Use **team service account keys** for your production application that shouldn't break when employees leave +- Use **user + team keys** when you want individual accountability within a team budget + +--- ## User Roles diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 6c4783329bd..003e02d4d5b 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -232,7 +232,7 @@ router_settings: | max_response_size_mb | int | The maximum size for responses in MB. LLM Responses above this size will not be sent. | | proxy_budget_rescheduler_min_time | int | The minimum time (in seconds) to wait before checking db for budget resets. **Default is 597 seconds** | | proxy_budget_rescheduler_max_time | int | The maximum time (in seconds) to wait before checking db for budget resets. **Default is 605 seconds** | -| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 10 seconds** | +| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 30 seconds** | | proxy_batch_polling_interval | int | Time (in seconds) to wait before polling a batch, to check if it's completed. **Default is 6000 seconds (1 hour)** | | alerting_args | dict | Args for Slack Alerting [Doc on Slack Alerting](./alerting.md) | | custom_key_generate | str | Custom function for key generation [Doc on custom key generation](./virtual_keys.md#custom--key-generate) | @@ -361,6 +361,10 @@ router_settings: | AIOHTTP_TRUST_ENV | Flag to enable aiohttp trust environment. When this is set to True, aiohttp will respect HTTP(S)_PROXY env vars. **Default is False** | AIOHTTP_TTL_DNS_CACHE | DNS cache time-to-live for aiohttp in seconds. **Default is 300** | ALLOWED_EMAIL_DOMAINS | List of email domains allowed for access +| APSCHEDULER_COALESCE | Whether to combine multiple pending executions of a job into one. **Default is False** +| APSCHEDULER_MAX_INSTANCES | Maximum number of concurrent instances of each job. **Default is 1** +| APSCHEDULER_MISFIRE_GRACE_TIME | Grace time in seconds for misfired jobs. **Default is 1** +| APSCHEDULER_REPLACE_EXISTING | Whether to replace existing jobs with the same ID. **Default is False** | ARIZE_API_KEY | API key for Arize platform integration | ARIZE_SPACE_KEY | Space key for Arize platform | ARGILLA_BATCH_SIZE | Batch size for Argilla logging @@ -726,7 +730,7 @@ router_settings: | PROMPTLAYER_API_KEY | API key for PromptLayer integration | PROXY_ADMIN_ID | Admin identifier for proxy server | PROXY_BASE_URL | Base URL for proxy service -| PROXY_BATCH_WRITE_AT | Time in seconds to wait before batch writing spend logs to the database. Default is 10 +| PROXY_BATCH_WRITE_AT | Time in seconds to wait before batch writing spend logs to the database. Default is 30 | PROXY_BATCH_POLLING_INTERVAL | Time in seconds to wait before polling a batch, to check if it's completed. Default is 6000s (1 hour) | PROXY_BUDGET_RESCHEDULER_MAX_TIME | Maximum time in seconds to wait before checking database for budget resets. Default is 605 | PROXY_BUDGET_RESCHEDULER_MIN_TIME | Minimum time in seconds to wait before checking database for budget resets. Default is 597 diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index 7d2389383d1..45178916008 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -2,7 +2,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import Image from '@theme/IdealImage'; -# Docker, Deployment +# Docker, Helm, Terraform You can find the Dockerfile to build litellm proxy [here](https://github.com/BerriAI/litellm/blob/main/Dockerfile) @@ -195,7 +195,7 @@ docker run \ s/o [Nicholas Cecere](https://www.linkedin.com/in/nicholas-cecere-24243549/) for his LiteLLM User Management Terraform -👉 [Go here for Terraform](https://github.com/ncecere/terraform-litellm-user-mgmt) +👉 [Go here for Terraform](https://github.com/BerriAI/terraform-provider-litellm) ### Kubernetes diff --git a/docs/my-website/docs/proxy/guardrails/grayswan.md b/docs/my-website/docs/proxy/guardrails/grayswan.md index dbd9258292b..b510c870a1e 100644 --- a/docs/my-website/docs/proxy/guardrails/grayswan.md +++ b/docs/my-website/docs/proxy/guardrails/grayswan.md @@ -18,6 +18,7 @@ Cygnal returns a `violation` score between `0` and `1` (higher means more likely ```bash export GRAYSWAN_API_KEY="your-grayswan-key" +export GRAYSWAN_API_BASE="https://api.grayswan.ai" ``` ### 2. Configure `config.yaml` @@ -37,6 +38,7 @@ guardrails: guardrail: grayswan mode: [pre_call, post_call] # monitor both input and output api_key: os.environ/GRAYSWAN_API_KEY + api_base: os.environ/GRAYSWAN_API_BASE # optional optional_params: on_flagged_action: monitor # or "block" violation_threshold: 0.5 # score >= threshold is flagged diff --git a/docs/my-website/docs/proxy/guardrails/ibm_guardrails.md b/docs/my-website/docs/proxy/guardrails/ibm_guardrails.md index 6d1f2eae911..0c13d2dcea9 100644 --- a/docs/my-website/docs/proxy/guardrails/ibm_guardrails.md +++ b/docs/my-website/docs/proxy/guardrails/ibm_guardrails.md @@ -3,11 +3,18 @@ import TabItem from '@theme/TabItem'; # IBM Guardrails -LiteLLM works with IBM's FMS Guardrails for content safety. You can use it to detect jailbreaks, PII, hate speech, and more. +LiteLLM works with [IBM's FMS Guardrails](https://github.com/foundation-model-stack/fms-guardrails-orchestrator) for content safety. You can use it to detect jailbreaks, PII, hate speech, and more. ## What it does -IBM Guardrails analyzes text and tells you if it contains things you want to avoid. It gives each detection a score. Higher scores mean it's more confident. +IBM's FMS Guardrails is a framework for invoking detectors on LLM inputs and outputs. To configure these detectors, you can use e.g. [TrustyAI detectors](https://github.com/trustyai-explainability/guardrails-detectors), an open-source project maintained by the Red Hat's [TrustyAI team](https://github.com/trustyai-explainability) that allows the user to configure detectors that are: + +- regex patterns +- file type validators +- custom Python functions +- Hugging Face [AutoModelForSequenceClassification](https://huggingface.co/docs/transformers/en/model_doc/auto#transformers.AutoModelForSequenceClassification), i.e. sequence classification models + +Each detector outputs an API response based on the following [openapi schema](https://foundation-model-stack.github.io/fms-guardrails-orchestrator/docs/api/openapi_detector_api.yaml). You can run these checks: - Before sending to the LLM (on user input) @@ -73,7 +80,7 @@ curl -i http://localhost:4000/v1/chat/completions \ - `guardrail` - str - Set to `ibm_guardrails` - `auth_token` - str - Your IBM Guardrails auth token. Can use `os.environ/IBM_GUARDRAILS_AUTH_TOKEN` -- `base_url` - str - URL of your IBM Guardrails server +- `base_url` - str - URL of your IBM Detector or Guardrails server - `detector_id` - str - Which detector to use (e.g., "jailbreak-detector", "pii-detector") ### Optional params @@ -97,7 +104,7 @@ IBM Guardrails has two APIs you can use: ### Detector Server (recommended) -The simpler one. Sends all messages at once. +[This Detectors API](https://foundation-model-stack.github.io/fms-guardrails-orchestrator/?urls.primaryName=Detector+API#/Text) uses `api/v1/text/contents` endpoint to run a single detector; it can accept multiple text inputs within a request. ```yaml guardrails: @@ -113,7 +120,7 @@ guardrails: ### Orchestrator -If you're using the IBM FMS Guardrails Orchestrator, you can use this. +If you're using the IBM FMS Guardrails Orchestrator, you can use [FMS Orchestrator API](https://foundation-model-stack.github.io/fms-guardrails-orchestrator/?urls.primaryName=Orchestrator+API), specifically by leveraging the `api/v2/text/detection/content` to potentially run multiple detectors in a single request; however, this endpoint can only accept one text input per request. ```yaml guardrails: diff --git a/docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md b/docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md index 97f3e7efe54..e1d6ddf5928 100644 --- a/docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md +++ b/docs/my-website/docs/proxy/guardrails/panw_prisma_airs.md @@ -199,10 +199,44 @@ Expected successful response: | Parameter | Required | Description | Default | |-----------|----------|-------------|---------| | `api_key` | Yes | Your PANW Prisma AIRS API key from Strata Cloud Manager | - | -| `profile_name` | Yes | Security profile name configured in Strata Cloud Manager | - | +| `profile_name` | No | Security profile name configured in Strata Cloud Manager. Optional if API key has linked profile | - | +| `app_name` | No | Application identifier for tracking in Prisma AIRS analytics (will be prefixed with "LiteLLM-") | `LiteLLM` | | `api_base` | No | Custom API base URL (without /v1/scan/sync/request path) | `https://service.api.aisecurity.paloaltonetworks.com` | | `mode` | No | When to run the guardrail | `pre_call` | +## Per-Request Metadata Overrides + +You can override guardrail settings on a per-request basis using the `metadata` field: + +```json +{ + "model": "gpt-4", + "messages": [...], + "metadata": { + "profile_name": "dev-allow-all", // Override profile name + "profile_id": "uuid-here", // Override profile ID (takes precedence) + "user_ip": "192.168.1.100", // Track user IP + "app_name": "MyApp" // Custom app name (becomes "LiteLLM-MyApp") + } +} +``` + +**Supported Metadata Fields:** + +| Field | Description | Priority | +|-------|-------------|----------| +| `profile_name` | PANW AI security profile name | Per-request > config | +| `profile_id` | PANW AI security profile ID (takes precedence over profile_name) | Per-request only | +| `user_ip` | User IP address for tracking in Prisma AIRS | Per-request only | +| `app_name` | Application identifier (prefixed with "LiteLLM-") | Per-request > config > "LiteLLM" | + +:::info Profile Resolution +- If both `profile_id` and `profile_name` are provided, PANW API uses `profile_id` (it takes precedence) +- If no profile is specified in metadata, uses the config `profile_name` +- If no profile is specified at all, PANW API will use the profile linked to your API key in Strata Cloud Manager +- **Note:** If your API key is not linked to a profile, you must provide `profile_name` or `profile_id` +::: + ## Environment Variables ```bash @@ -235,6 +269,43 @@ guardrails: profile_name: "permissive-policy" # Lower security profile ``` +### Multiple API Keys (Multi-Tenant) + +For multi-tenant deployments where different customers need different PANW API keys, create separate guardrail instances: + +```yaml +guardrails: + - guardrail_name: "panw-customer-a" + litellm_params: + guardrail: panw_prisma_airs + mode: "pre_call" + api_key: os.environ/PANW_CUSTOMER_A_KEY # Linked to Customer A profile in SCM + + - guardrail_name: "panw-customer-b" + litellm_params: + guardrail: panw_prisma_airs + mode: "pre_call" + api_key: os.environ/PANW_CUSTOMER_B_KEY # Linked to Customer B profile in SCM +``` + +Then route requests to the appropriate guardrail: + +```bash +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "Hello"}], + "guardrails": ["panw-customer-a"] + }' +``` + +**Use Cases:** +- **Multi-tenant deployments**: Different customers with different security policies +- **Environment-specific policies**: Dev/staging/prod with different API keys and profiles +- **A/B testing**: Compare different security profiles side-by-side + ### Content Masking PANW Prisma AIRS can automatically mask sensitive content (PII, credit cards, SSNs, etc.) instead of blocking requests. This allows your application to continue functioning while protecting sensitive data. diff --git a/docs/my-website/docs/proxy/guardrails/pillar_security.md b/docs/my-website/docs/proxy/guardrails/pillar_security.md index a5a416839f6..5ab9f9bf8cb 100644 --- a/docs/my-website/docs/proxy/guardrails/pillar_security.md +++ b/docs/my-website/docs/proxy/guardrails/pillar_security.md @@ -29,7 +29,7 @@ Use Pillar Security for comprehensive LLM security including: Add Pillar Security to your `config.yaml`: -**🌟 Recommended Configuration (Dual Mode):** +**🌟 Recommended Configuration:** ```yaml model_list: - model_name: gpt-4.1-mini @@ -45,6 +45,8 @@ guardrails: api_key: os.environ/PILLAR_API_KEY # Your Pillar API key api_base: os.environ/PILLAR_API_BASE # Pillar API endpoint on_flagged_action: "monitor" # Log threats but allow requests + fallback_on_error: "allow" # Gracefully degrade if Pillar is down (default) + timeout: 5.0 # Timeout for Pillar API calls in seconds (default) persist_session: true # Keep conversations visible in Pillar dashboard async_mode: false # Request synchronous verdicts include_scanners: true # Return scanner category breakdown @@ -207,6 +209,8 @@ You can configure Pillar Security using environment variables: export PILLAR_API_KEY="your_api_key_here" export PILLAR_API_BASE="https://api.pillar.security" export PILLAR_ON_FLAGGED_ACTION="monitor" +export PILLAR_FALLBACK_ON_ERROR="allow" +export PILLAR_TIMEOUT="30.0" ``` ### Session Tracking @@ -245,6 +249,66 @@ Logs the violation but allows the request to proceed: on_flagged_action: "monitor" ``` +### Resilience and Error Handling + +#### Graceful Degradation (`fallback_on_error`) + +Control what happens when the Pillar API is unavailable (network errors, timeouts, service outages): + +```yaml +fallback_on_error: "allow" # Default - recommended for production resilience +``` + +**Available Options:** + +- **`allow` (Default - Recommended)**: Proceed without scanning when Pillar is unavailable + - **No service interruption** if Pillar is down + - **Best for production** where availability is critical + - Security scans are skipped during outages (logged as warnings) + + ```yaml + guardrails: + - guardrail_name: "pillar-resilient" + litellm_params: + guardrail: pillar + fallback_on_error: "allow" # Graceful degradation + ``` + +- **`block`**: Reject all requests when Pillar is unavailable + - **Fail-secure approach** - no request proceeds without scanning + - **Service interruption** during Pillar outages + - Returns 503 Service Unavailable error + + ```yaml + guardrails: + - guardrail_name: "pillar-fail-secure" + litellm_params: + guardrail: pillar + fallback_on_error: "block" # Fail secure + ``` + +#### Timeout Configuration + +Configure how long to wait for Pillar API responses: + +**Example Configurations:** + +```yaml +# Production: Default - Fast with graceful degradation +guardrails: + - guardrail_name: "pillar-production" + litellm_params: + guardrail: pillar + timeout: 5.0 # Default - fast failure detection + fallback_on_error: "allow" # Graceful degradation (required) +``` + +**Environment Variables:** +```bash +export PILLAR_FALLBACK_ON_ERROR="allow" +export PILLAR_TIMEOUT="5.0" +``` + ## Advanced Configuration **Quick takeaways** diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index 497e6e95a52..baf3d42234d 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -1415,18 +1415,26 @@ AWS_REGION_NAME = "" ```yaml model_list: - - model_name: gpt-4o + - model_name: gpt-4o litellm_params: model: gpt-4o + litellm_settings: callbacks: ["aws_sqs"] + aws_sqs_callback_params: - sqs_queue_url: https://sqs.us-west-2.amazonaws.com/123456789012/my-queue # AWS SQS Queue URL - sqs_region_name: us-west-2 # AWS Region Name for SQS - sqs_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID # use os.environ/ to pass environment variables. This is AWS Access Key ID for SQS - sqs_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY # AWS Secret Access Key for SQS - sqs_batch_size: 10 # [OPTIONAL] Number of messages to batch before sending (default: 10) - sqs_flush_interval: 30 # [OPTIONAL] Time in seconds to wait before flushing batch (default: 30) + # --- 🧱 Required Parameters --- + sqs_queue_url: https://sqs.us-west-2.amazonaws.com/123456789012/my-queue + # The AWS SQS Queue URL to which LiteLLM will send log events. + + sqs_region_name: us-west-2 + # AWS Region for your SQS queue (e.g., us-east-1, eu-central-1, etc.) + + # --- Logging Controls --- + sqs_strip_base64_files: true + # If true, LiteLLM will remove or redact base64-encoded binary data (e.g., PDFs, images, audio) + # from logged messages to avoid large payloads. SQS has a 1 MB payload size limit. + ``` **Step 3**: Start the proxy, make a test request diff --git a/docs/my-website/docs/videos.md b/docs/my-website/docs/videos.md index dd96398f8d6..344a0f852df 100644 --- a/docs/my-website/docs/videos.md +++ b/docs/my-website/docs/videos.md @@ -21,7 +21,7 @@ LiteLLM follows the [OpenAI Video Generation API specification](https://platform ### Quick Start ```python -from litellm import video_generation, video_status, video_retrieval +from litellm import video_generation, video_status, video_content import os import time @@ -56,7 +56,7 @@ while True: time.sleep(10) # Wait 10 seconds before checking again # Download video content when ready -video_bytes = video_retrieval( +video_bytes = video_content( video_id=response.id, model="openai/sora-2" ) @@ -69,7 +69,7 @@ with open("generated_video.mp4", "wb") as f: ### Async Usage ```python -from litellm import avideo_generation, avideo_status, avideo_retrieval +from litellm import avideo_generation, avideo_status, avideo_content import os, asyncio os.environ["OPENAI_API_KEY"] = "sk-.." @@ -103,7 +103,7 @@ async def test_async_video(): await asyncio.sleep(10) # Wait 10 seconds before checking again # Download video content when ready - video_bytes = await avideo_retrieval( + video_bytes = await avideo_content( video_id=response.id, model="openai/sora-2" ) @@ -241,57 +241,45 @@ litellm --config /path/to/config.yaml Test video generation request ```bash -curl http://0.0.0.0:4000/videos/generations \ - -H "Authorization: Bearer sk-1234" \ - -H "Content-Type: application/json" \ - -d '{ +curl --location 'http://localhost:4000/v1/videos' \ +--header 'Content-Type: application/json' \ +--header 'x-litellm-api-key: sk-1234' \ +--data '{ "model": "sora-2", - "prompt": "A cat playing with a ball of yarn in a sunny garden", - "seconds": "8", - "size": "720x1280" - }' + "prompt": "A beautiful sunset over the ocean" +}' ``` Test video status request ```bash -curl http://0.0.0.0:4000/videos/status \ - -H "Authorization: Bearer sk-1234" \ - -H "Content-Type: application/json" \ - -d '{ - "video_id": "video_1234567890", - "model": "sora-2" - }' +curl --location 'http://localhost:4000/v1/videos/video_id' \ +--header 'Accept: application/json' \ +--header 'x-litellm-api-key: sk-1234' + ``` Test video retrieval request ```bash -curl http://0.0.0.0:4000/videos/retrieval \ - -H "Authorization: Bearer sk-1234" \ - -H "Content-Type: application/json" \ - -d '{ - "video_id": "video_1234567890", - "model": "sora-2" - }' +curl --location 'http://localhost:4000/v1/videos/video_id/content' \ +--header 'Accept: application/json' \ +--header 'x-litellm-api-key: sk-1234' + ``` Test video remix request ```bash -curl http://0.0.0.0:4000/videos/remix \ - -H "Authorization: Bearer sk-1234" \ - -H "Content-Type: multipart/form-data" \ - -F 'model=sora-2' \ - -F 'prompt=Make the cat jump higher' \ - -F 'input_reference=@path/to/image.jpg' \ - -F 'seconds=8' +curl --location --request POST 'http://localhost:4000/v1/videos/string/remix' \ +--header 'Accept: application/json' \ +--header 'x-litellm-api-key: sk-1234' ``` Test Azure video generation request ```bash -curl http://0.0.0.0:4000/videos/generations \ +curl http://localhost:4000/v1/videos \ -H "Authorization: Bearer sk-1234" \ -H "Content-Type: application/json" \ -d '{ @@ -302,6 +290,188 @@ curl http://0.0.0.0:4000/videos/generations \ }' ``` +## **Using OpenAI Client with LiteLLM Proxy** + +You can use the standard OpenAI Python client to interact with LiteLLM's video endpoints. This provides a familiar interface while leveraging LiteLLM's provider abstraction and proxy features. + +### Setup + +First, configure your OpenAI client to point to your LiteLLM proxy: + +```python +from openai import OpenAI + +# Point the OpenAI client to your LiteLLM proxy +client = OpenAI( + api_key="sk-1234", # Your LiteLLM proxy API key + base_url="http://localhost:4000/v1" # Your LiteLLM proxy URL +) +``` + +### Video Generation + +Generate a new video using the OpenAI client interface: + +```python +# Basic video generation +response = client.videos.create( + model="sora-2", + prompt="A cat playing with a ball of yarn in a sunny garden", + seconds=8, + size="720x1280" +) + +print(f"Video ID: {response.id}") +print(f"Status: {response.status}") +``` + +### Video Generation with Reference Image + +Create a video using a reference image: + +```python +# Video generation with reference image +response = client.videos.create( + model="sora-2", + prompt="Add clouds to the video", + seconds=4, + input_reference=open("/path/to/your/image.jpg", "rb") +) + +print(f"Video ID: {response.id}") +print(f"Status: {response.status}") +``` + +### Video Status Checking + +Check the status of a video generation: + +```python +# Check video status +status_response = client.videos.retrieve( + video_id="video_6900378779308191a7359266e59b53fc01cd6bbd27a70763" +) + +print(f"Status: {status_response.status}") +print(f"Progress: {status_response.progress}%") + +# Poll until completion +import time + +while status_response.status not in ["completed", "failed"]: + time.sleep(10) # Wait 10 seconds + status_response = client.videos.retrieve( + video_id="video_6900378779308191a7359266e59b53fc01cd6bbd27a70763" + ) + print(f"Current status: {status_response.status}") +``` + +### List Videos + +Get a list of your videos: + +```python +# List all videos +videos = client.videos.list() + +for video in videos.data: + print(f"Video ID: {video.id}, Status: {video.status}") +``` + +### Download Video Content + +Download the completed video: + +```python +# Download video content +response = client.videos.download_content( + video_id="video_68fa2938848c8190bb718f977503aba6092ab18d68938fed" +) + +# Save the video to file +with open("generated_video.mp4", "wb") as f: + f.write(response.content) + +print("Video downloaded successfully!") +``` + +### Video Remix (Editing) + +Edit an existing video with new instructions: + +```python +# Remix/edit an existing video +response = client.videos.remix( + video_id="video_68fa2574bdd88190873a8af06a370ff407094ddbc4bbb91b", + prompt="Slow the cloud movement", + seconds=8 +) + +print(f"Remix Video ID: {response.id}") +print(f"Status: {response.status}") +``` + +### Complete Workflow Example + +Here's a complete example showing the full video generation workflow: + +```python +from openai import OpenAI +import time + +# Initialize client +client = OpenAI( + api_key="sk-1234", + base_url="http://localhost:4000/v1" +) + +# 1. Generate video +print("Generating video...") +response = client.videos.create( + model="sora-2", + prompt="A serene lake with mountains in the background", + seconds=8, + size="1280x720" +) + +video_id = response.id +print(f"Video generation started. ID: {video_id}") + +# 2. Poll for completion +print("Waiting for video to complete...") +while True: + status = client.videos.retrieve(video_id=video_id) + print(f"Status: {status.status}") + + if status.status == "completed": + print("Video generation completed!") + break + elif status.status == "failed": + print("Video generation failed!") + break + + time.sleep(10) + +# 3. Download video +if status.status == "completed": + print("Downloading video...") + video_content = client.videos.download_content(video_id=video_id) + + with open(f"video_{video_id}.mp4", "wb") as f: + f.write(video_content.content) + + print("Video saved successfully!") + +# 4. Optional: Remix the video +print("Creating a remix...") +remix_response = client.videos.remix( + video_id=video_id, + prompt="Add gentle ripples to the lake surface" +) + +print(f"Remix started. ID: {remix_response.id}") +``` + ## **Request/Response Format** :::info @@ -381,19 +551,21 @@ The response follows OpenAI's video generation format with the following structu ```json { - "id": "video_1234567890", - "object": "video", - "status": "queued", - "created_at": 1712697600, - "model": "sora-2", - "size": "720x1280", - "seconds": "8", - "usage": { - "prompt_tokens": 0, - "completion_tokens": 0, - "total_tokens": 0, - "duration_seconds": 8.0 - } + "id": "video_6900378779308191a7359266e59b53fc01cd6bbd27a70763", + "object": "video", + "status": "queued", + "created_at": 1761621895, + "completed_at": null, + "expires_at": null, + "error": null, + "progress": 0, + "remixed_from_video_id": null, + "seconds": "4", + "size": "720x1280", + "model": "sora-2", + "usage": { + "duration_seconds": 4.0 + } } ``` diff --git a/docs/my-website/img/opik_key_metadata.png b/docs/my-website/img/opik_key_metadata.png new file mode 100644 index 00000000000..c810f270dae Binary files /dev/null and b/docs/my-website/img/opik_key_metadata.png differ diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 4e217b939de..3b993e4620c 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -32,6 +32,7 @@ const sidebars = { items: [ "proxy/guardrails/quick_start", ...[ + "adding_provider/adding_guardrail_support", "proxy/guardrails/aim_security", "proxy/guardrails/aporia_api", "proxy/guardrails/azure_content_guardrail", @@ -431,6 +432,7 @@ const sidebars = { "providers/openai", "providers/openai/responses_api", "providers/openai/text_to_speech", + "providers/openai/videos", ] }, "providers/text_completion_openai", @@ -443,6 +445,7 @@ const sidebars = { "providers/azure/azure_responses", "providers/azure/azure_embedding", "providers/azure/azure_speech", + "providers/azure/videos", ] }, { @@ -534,6 +537,7 @@ const sidebars = { "providers/groq", "providers/deepseek", "providers/elevenlabs", + "providers/fal_ai", "providers/fireworks_ai", "providers/clarifai", "providers/compactifai", @@ -713,7 +717,7 @@ const sidebars = { items: [ "adding_provider/directory_structure", "adding_provider/new_rerank_provider", - "adding_provider/adding_guardrail_support"], + ] }, "extras/contributing", "contributing", diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index 49037678df9..d3b599edff9 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -2189,6 +2189,9 @@ class PrometheusLogger(CustomLogger): prometheus_logger.initialize_remaining_budget_metrics, "interval", minutes=PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES, + # REMOVED jitter parameter - major cause of memory leak + id="prometheus_budget_metrics_job", + replace_existing=True, ) @staticmethod diff --git a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py deleted file mode 100644 index 8b42b2549cd..00000000000 --- a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Enterprise Guardrail Routes on LiteLLM Proxy - -To see all free guardrails see litellm/proxy/guardrails/* - - -Exposed Routes: -- /mask_pii -""" -from typing import Optional - -from fastapi import APIRouter, Depends - -from litellm.integrations.custom_guardrail import CustomGuardrail -from litellm.proxy._types import UserAPIKeyAuth -from litellm.proxy.auth.user_api_key_auth import user_api_key_auth -from litellm.proxy.guardrails.guardrail_endpoints import GUARDRAIL_REGISTRY -from litellm.types.guardrails import ApplyGuardrailRequest, ApplyGuardrailResponse - -router = APIRouter(tags=["guardrails"], prefix="/guardrails") - - -@router.post("/apply_guardrail", response_model=ApplyGuardrailResponse) -async def apply_guardrail( - request: ApplyGuardrailRequest, - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), -): - """ - Mask PII from a given text, requires a guardrail to be added to litellm. - """ - active_guardrail: Optional[ - CustomGuardrail - ] = GUARDRAIL_REGISTRY.get_initialized_guardrail_callback( - guardrail_name=request.guardrail_name - ) - if active_guardrail is None: - raise Exception(f"Guardrail {request.guardrail_name} not found") - - response_text = await active_guardrail.apply_guardrail( - text=request.text, language=request.language, entities=request.entities - ) - - return ApplyGuardrailResponse(response_text=response_text) diff --git a/litellm-js/spend-logs/package-lock.json b/litellm-js/spend-logs/package-lock.json index 95f8acdec3a..b59d9f2d2a3 100644 --- a/litellm-js/spend-logs/package-lock.json +++ b/litellm-js/spend-logs/package-lock.json @@ -6,7 +6,7 @@ "": { "dependencies": { "@hono/node-server": "^1.10.1", - "hono": "^4.9.7" + "hono": "^4.10.3" }, "devDependencies": { "@types/node": "^20.11.17", @@ -463,9 +463,9 @@ } }, "node_modules/hono": { - "version": "4.9.7", - "resolved": "https://registry.npmjs.org/hono/-/hono-4.9.7.tgz", - "integrity": "sha512-t4Te6ERzIaC48W3x4hJmBwgNlLhmiEdEE5ViYb02ffw4ignHNHa5IBtPjmbKstmtKa8X6C35iWwK4HaqvrzG9w==", + "version": "4.10.3", + "resolved": "https://registry.npmjs.org/hono/-/hono-4.10.3.tgz", + "integrity": "sha512-2LOYWUbnhdxdL8MNbNg9XZig6k+cZXm5IjHn2Aviv7honhBMOHb+jxrKIeJRZJRmn+htUCKhaicxwXuUDlchRA==", "license": "MIT", "engines": { "node": ">=16.9.0" diff --git a/litellm-js/spend-logs/package.json b/litellm-js/spend-logs/package.json index 5370f7a0eca..d21a8acef23 100644 --- a/litellm-js/spend-logs/package.json +++ b/litellm-js/spend-logs/package.json @@ -4,7 +4,7 @@ }, "dependencies": { "@hono/node-server": "^1.10.1", - "hono": "^4.9.7" + "hono": "^4.10.3" }, "devDependencies": { "@types/node": "^20.11.17", diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 9cb9edc9268..ce2fc2e4753 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -580,4 +580,12 @@ model LiteLLM_SearchToolsTable { search_tool_info Json? created_at DateTime @default(now()) updated_at DateTime @updatedAt +} + +// SSO configuration table +model LiteLLM_SSOConfig { + id String @id @default("sso_config") + sso_settings Json + created_at DateTime @default(now()) + updated_at DateTime @updatedAt } \ No newline at end of file diff --git a/litellm/__init__.py b/litellm/__init__.py index 3c5aecb82c2..04463266947 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -475,6 +475,7 @@ nlp_cloud_models: Set = set() aleph_alpha_models: Set = set() bedrock_models: Set = set() bedrock_converse_models: Set = set(BEDROCK_CONVERSE_MODELS) +fal_ai_models: Set = set() fireworks_ai_models: Set = set() fireworks_ai_embedding_models: Set = set() deepinfra_models: Set = set() @@ -663,6 +664,8 @@ def add_known_models(): text_completion_codestral_models.add(key) elif value.get("litellm_provider") == "xai": xai_models.add(key) + elif value.get("litellm_provider") == "fal_ai": + fal_ai_models.add(key) elif value.get("litellm_provider") == "deepseek": deepseek_models.add(key) elif value.get("litellm_provider") == "meta_llama": @@ -813,6 +816,7 @@ model_list = list( | gemini_models | text_completion_codestral_models | xai_models + | fal_ai_models | deepseek_models | azure_ai_models | voyage_models @@ -895,6 +899,7 @@ models_by_provider: dict = { "aleph_alpha": aleph_alpha_models, "text-completion-codestral": text_completion_codestral_models, "xai": xai_models, + "fal_ai": fal_ai_models, "deepseek": deepseek_models, "mistral": mistral_chat_models, "azure_ai": azure_ai_models, diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 027c0b219c8..8289801ee30 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -1,10 +1,15 @@ import json -from typing import Any, List, Literal, Tuple, Optional +import time +from typing import Any, List, Literal, Optional, Tuple + +import httpx import litellm from litellm._logging import verbose_logger +from litellm._uuid import uuid from litellm.types.llms.openai import Batch -from litellm.types.utils import CallTypes, Usage +from litellm.types.utils import CallTypes, ModelResponse, Usage +from litellm.utils import token_counter async def calculate_batch_cost_and_usage( @@ -107,6 +112,10 @@ def calculate_vertex_ai_batch_cost_and_usage( """ Calculate both cost and usage from Vertex AI batch responses """ + from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexGeminiConfig, + ) total_cost = 0.0 total_tokens = 0 prompt_tokens = 0 @@ -115,14 +124,7 @@ def calculate_vertex_ai_batch_cost_and_usage( for response in vertex_ai_batch_responses: if response.get("status") == "JOB_STATE_SUCCEEDED": # Check if response was successful # Transform Vertex AI response to OpenAI format if needed - from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig - from litellm import ModelResponse - from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.types.utils import CallTypes - from litellm._uuid import uuid - import httpx - import time - + # Create required arguments for the transformation method model_response = ModelResponse() @@ -163,8 +165,9 @@ def calculate_vertex_ai_batch_cost_and_usage( total_cost += cost # Extract usage from the transformed response - if hasattr(openai_format_response, 'usage') and openai_format_response.usage: - usage = openai_format_response.usage + usage_obj = getattr(openai_format_response, 'usage', None) + if usage_obj: + usage = usage_obj else: # Fallback: create usage from response dict response_dict = openai_format_response.dict() if hasattr(openai_format_response, 'dict') else {} @@ -278,6 +281,33 @@ def _get_batch_job_total_usage_from_file_content( completion_tokens=completion_tokens, ) +def _get_batch_job_input_file_usage( + file_content_dictionary: List[dict], + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + model_name: Optional[str] = None, +) -> Usage: + """ + Count the number of tokens in the input file + + Used for batch rate limiting to count the number of tokens in the input file + """ + prompt_tokens: int = 0 + completion_tokens: int = 0 + + for _item in file_content_dictionary: + body = _item.get("body", {}) + model = body.get("model", model_name or "") + messages = body.get("messages", []) + + if messages: + item_tokens = token_counter(model=model, messages=messages) + prompt_tokens += item_tokens + + return Usage( + total_tokens=prompt_tokens + completion_tokens, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) def _get_batch_job_usage_from_response_body(response_body: dict) -> Usage: """ diff --git a/litellm/constants.py b/litellm/constants.py index db2794a770a..37bf68d5cde 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -1050,7 +1050,17 @@ PROXY_BATCH_POLLING_INTERVAL = int(os.getenv("PROXY_BATCH_POLLING_INTERVAL", 360 PROXY_BUDGET_RESCHEDULER_MAX_TIME = int( os.getenv("PROXY_BUDGET_RESCHEDULER_MAX_TIME", 605) ) -PROXY_BATCH_WRITE_AT = int(os.getenv("PROXY_BATCH_WRITE_AT", 10)) # in seconds +# MEMORY LEAK FIX: Increased from 10s to 30s minimum to prevent memory issues with APScheduler +# Very frequent intervals (<30s) can cause memory leaks in APScheduler's internal functions +PROXY_BATCH_WRITE_AT = int(os.getenv("PROXY_BATCH_WRITE_AT", 30)) # in seconds, increased from 10 + +# APScheduler Configuration - MEMORY LEAK FIX +# These settings prevent memory leaks in APScheduler's normalize() and _apply_jitter() functions +APSCHEDULER_COALESCE = os.getenv("APSCHEDULER_COALESCE", "True").lower() in ["true", "1"] # collapse many missed runs into one +APSCHEDULER_MISFIRE_GRACE_TIME = int(os.getenv("APSCHEDULER_MISFIRE_GRACE_TIME", 3600)) # ignore runs older than 1 hour (was 120) +APSCHEDULER_MAX_INSTANCES = int(os.getenv("APSCHEDULER_MAX_INSTANCES", 1)) # prevent concurrent job instances +APSCHEDULER_REPLACE_EXISTING = os.getenv("APSCHEDULER_REPLACE_EXISTING", "True").lower() in ["true", "1"] # always replace existing jobs + DEFAULT_HEALTH_CHECK_INTERVAL = int( os.getenv("DEFAULT_HEALTH_CHECK_INTERVAL", 300) ) # 5 minutes diff --git a/litellm/images/main.py b/litellm/images/main.py index 63603411fb1..5be5f993814 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -342,6 +342,7 @@ def image_generation( # noqa: PLR0915 litellm.LlmProviders.RECRAFT, litellm.LlmProviders.AIML, litellm.LlmProviders.GEMINI, + litellm.LlmProviders.FAL_AI, ): if image_generation_config is None: raise ValueError( diff --git a/litellm/integrations/_types/open_inference.py b/litellm/integrations/_types/open_inference.py index 65ecadcf370..af2ff2347c8 100644 --- a/litellm/integrations/_types/open_inference.py +++ b/litellm/integrations/_types/open_inference.py @@ -387,3 +387,42 @@ class OpenInferenceLLMProviderValues(Enum): GOOGLE = "google" AZURE = "azure" AWS = "aws" + + +class ErrorAttributes: + """ + Attributes for error information in spans. + + These attributes follow OpenTelemetry semantic conventions for exceptions + and are used to record error information from StandardLoggingPayloadErrorInformation. + """ + + ERROR_TYPE = "error.type" + """ + The type/class of the error (e.g., 'ValueError', 'OpenAIError', 'RateLimitError'). + Corresponds to StandardLoggingPayloadErrorInformation.error_class + """ + + ERROR_MESSAGE = "error.message" + """ + The error message describing what went wrong. + Corresponds to StandardLoggingPayloadErrorInformation.error_message + """ + + ERROR_CODE = "error.code" + """ + The error code (e.g., HTTP status code like '500', '429', or provider-specific codes). + Corresponds to StandardLoggingPayloadErrorInformation.error_code + """ + + ERROR_STACK_TRACE = "error.stack_trace" + """ + The full stack trace of the error. + Corresponds to StandardLoggingPayloadErrorInformation.traceback + """ + + ERROR_LLM_PROVIDER = "error.llm_provider" + """ + The LLM provider where the error occurred (e.g., 'openai', 'anthropic', 'azure'). + Corresponds to StandardLoggingPayloadErrorInformation.llm_provider + """ diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index d631c466865..1df16a28177 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -1,5 +1,6 @@ #### What this does #### # On success, logs events to Promptlayer +import re import traceback from typing import ( TYPE_CHECKING, @@ -15,7 +16,9 @@ from typing import ( from pydantic import BaseModel +from litellm._logging import verbose_logger from litellm.caching.caching import DualCache +from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER from litellm.types.integrations.argilla import ArgillaItem from litellm.types.llms.openai import AllMessageValues, ChatCompletionRequest from litellm.types.utils import ( @@ -53,6 +56,12 @@ else: PreRoutingHookResponse = Any +_BASE64_INLINE_PATTERN = re.compile( + r"data:(?:application|image|audio|video)/[a-zA-Z0-9.+-]+;base64,[A-Za-z0-9+/=\s]+", + re.MULTILINE, +) + + class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback#callback-class # Class variables or attributes def __init__( @@ -567,3 +576,91 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac Get the proxy server request from cold storage using the object key directly. """ pass + + + async def _strip_base64_from_messages( + self, payload: "StandardLoggingPayload", max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER + ) -> "StandardLoggingPayload": + """ + Removes or redacts base64-encoded file data (e.g., PDFs, images, audio) + from messages and responses before sending to SQS. + + Behavior: + • Drop entries with a 'file' key. + • Drop entries with type == 'file' or any non-text type. + • Keep untyped or text content. + • Recursively redact inline base64 blobs in *any* string field, at any depth. + """ + raw_messages: Any = payload.get("messages", []) + messages: list[Any] = raw_messages if isinstance(raw_messages, list) else [] + verbose_logger.debug(f"[CustomLogger] Stripping base64 from {len(messages)} messages") + + if messages: + payload["messages"] = self._process_messages(messages=messages, max_depth=max_depth) + + total_items = 0 + for m in payload.get("messages", []) or []: + if isinstance(m, dict): + content = m.get("content", []) + if isinstance(content, list): + total_items += len(content) + + verbose_logger.debug( + f"[CustomLogger] Completed base64 strip; retained {total_items} content items" + ) + return payload + + + def _redact_base64(self, value: Any, depth: int = 0, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER) -> Any: + """Recursively redact inline base64 from any nested structure with a max recursion depth limit.""" + if depth > max_depth: + verbose_logger.warning( + f"[CustomLogger] Max recursion depth {max_depth} reached while redacting base64" + ) + return "[MAX_DEPTH_REACHED]" + + if isinstance(value, str): + if _BASE64_INLINE_PATTERN.search(value): + verbose_logger.debug( + f"[CustomLogger] Redacted inline base64 string: {value[:40]}..." + ) + return _BASE64_INLINE_PATTERN.sub("[BASE64_REDACTED]", value) + return value + + if isinstance(value, list): + return [self._redact_base64(value=v, depth=depth + 1, max_depth=max_depth) for v in value] + + if isinstance(value, dict): + return {k: self._redact_base64(value=v, depth=depth + 1, max_depth=max_depth) for k, v in value.items()} + + return value + + def _should_keep_content(self, content: Any) -> bool: + """Return True if this content item should be retained.""" + if not isinstance(content, dict): + return True + if "file" in content: + return False + ctype = content.get("type") + return not (isinstance(ctype, str) and ctype != "text") + + def _process_messages(self, messages: list[Any], max_depth: int = DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER) -> List[Dict[str, Any]]: + filtered_messages: List[Dict[str, Any]] = [] + for msg in messages: + if not isinstance(msg, dict): + continue + contents: Any = msg.get("content") + if isinstance(contents, list): + cleaned: list[Any] = [] + for c in contents: + if self._should_keep_content(content=c): + cleaned.append(self._redact_base64(value=c, max_depth=max_depth)) + msg["content"] = cleaned + else: + msg["content"] = self._redact_base64(value=contents, max_depth=max_depth) + + for key, val in list(msg.items()): + if key != "content": + msg[key] = self._redact_base64(value=val, max_depth=max_depth) + filtered_messages.append(msg) + return filtered_messages diff --git a/litellm/integrations/gitlab/__init__.py b/litellm/integrations/gitlab/__init__.py index cd22afc2ba0..c73a23b6874 100644 --- a/litellm/integrations/gitlab/__init__.py +++ b/litellm/integrations/gitlab/__init__.py @@ -8,7 +8,7 @@ if TYPE_CHECKING: from litellm.types.prompts.init_prompts import SupportedPromptIntegrations from litellm.integrations.custom_prompt_management import CustomPromptManagement from litellm.types.prompts.init_prompts import PromptSpec, PromptLiteLLMParams -from .gitlab_prompt_manager import GitLabPromptManager +from .gitlab_prompt_manager import GitLabPromptManager, GitLabPromptCache # Global instances global_gitlab_config: Optional[dict] = None @@ -16,13 +16,13 @@ global_gitlab_config: Optional[dict] = None def set_global_gitlab_config(config: dict) -> None: """ - Set the global BitBucket configuration for prompt management. + Set the global gitlab configuration for prompt management. Args: - config: Dictionary containing BitBucket configuration - - workspace: BitBucket workspace name + config: Dictionary containing gitlab configuration + - workspace: gitlab workspace name - repository: Repository name - - access_token: BitBucket access token + - access_token: gitlab access token - branch: Branch to fetch prompts from (default: main) """ import litellm @@ -34,7 +34,7 @@ def prompt_initializer( litellm_params: "PromptLiteLLMParams", prompt_spec: "PromptSpec" ) -> "CustomPromptManagement": """ - Initialize a prompt from a BitBucket repository. + Initialize a prompt from a Gitlab repository. """ gitlab_config = getattr(litellm_params, "gitlab_config", None) prompt_id = getattr(litellm_params, "prompt_id", None) @@ -42,16 +42,16 @@ def prompt_initializer( if not gitlab_config: raise ValueError( - "bitbucket_config is required for BitBucket prompt integration" + "gitlab_config is required for gitlab prompt integration" ) try: - bitbucket_prompt_manager = GitLabPromptManager( + gitlab_prompt_manager = GitLabPromptManager( gitlab_config=gitlab_config, prompt_id=prompt_id, ) - return bitbucket_prompt_manager + return gitlab_prompt_manager except Exception as e: raise e @@ -90,6 +90,7 @@ prompt_initializer_registry = { # Export public API __all__ = [ "GitLabPromptManager", + "GitLabPromptCache", "set_global_gitlab_config", "global_gitlab_config", ] diff --git a/litellm/integrations/gitlab/gitlab_prompt_manager.py b/litellm/integrations/gitlab/gitlab_prompt_manager.py index b782f10ccc5..37013273cb0 100644 --- a/litellm/integrations/gitlab/gitlab_prompt_manager.py +++ b/litellm/integrations/gitlab/gitlab_prompt_manager.py @@ -12,10 +12,24 @@ from litellm.integrations.prompt_management_base import ( ) from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import StandardCallbackDynamicParams - from litellm.integrations.gitlab.gitlab_client import GitLabClient +GITLAB_PREFIX = "gitlab::" + +def encode_prompt_id(raw_id: str) -> str: + """Convert GitLab path IDs like 'invoice/extract' → 'gitlab::invoice::extract'""" + if raw_id.startswith(GITLAB_PREFIX): + return raw_id # already encoded + return f"{GITLAB_PREFIX}{raw_id.replace('/', '::')}" + +def decode_prompt_id(encoded_id: str) -> str: + """Convert 'gitlab::invoice::extract' → 'invoice/extract'""" + if not encoded_id.startswith(GITLAB_PREFIX): + return encoded_id + return encoded_id[len(GITLAB_PREFIX):].replace("::", "/") + + class GitLabPromptTemplate: def __init__( self, @@ -87,6 +101,7 @@ class GitLabTemplateManager: def _id_to_repo_path(self, prompt_id: str) -> str: """Map a prompt_id to a repo path (respects prompts_path and adds .prompt).""" + prompt_id = decode_prompt_id(prompt_id) if self.prompts_path: return f"{self.prompts_path}/{prompt_id}.prompt" return f"{prompt_id}.prompt" @@ -101,26 +116,27 @@ class GitLabTemplateManager: path = path[len(self.prompts_path.strip("/")) + 1 :] if path.endswith(".prompt"): path = path[: -len(".prompt")] - return path + return encode_prompt_id(path) # ---------- loading ---------- def _load_prompt_from_gitlab(self, prompt_id: str, *, ref: Optional[str] = None) -> None: """Load a specific .prompt file from GitLab (scoped under prompts_path if set).""" try: + # prompt_id = decode_prompt_id(prompt_id) file_path = self._id_to_repo_path(prompt_id) prompt_content = self.gitlab_client.get_file_content(file_path, ref=ref) if prompt_content: template = self._parse_prompt_file(prompt_content, prompt_id) self.prompts[prompt_id] = template except Exception as e: - raise Exception(f"Failed to load prompt '{prompt_id}' from GitLab: {e}") + raise Exception(f"Failed to load prompt '{encode_prompt_id(prompt_id)}' from GitLab: {e}") def load_all_prompts(self, *, recursive: bool = True) -> List[str]: """ Eagerly load all .prompt files from prompts_path. Returns loaded IDs. """ - files = self.list_templates(recursive=recursive) # reuse logic + files = self.list_templates(recursive=recursive) loaded: List[str] = [] for pid in files: if pid not in self.prompts: @@ -195,9 +211,6 @@ class GitLabTemplateManager: return self.prompts.get(template_id) def list_templates(self, *, recursive: bool = True) -> List[str]: - """ - List available prompt IDs discovered under prompts_path (no extension, relative to prompts_path). - """ """ List available prompt IDs under prompts_path (no extension). Compatible with both list_files signatures: @@ -248,7 +261,7 @@ class GitLabPromptManager(CustomPromptManagement): "access_token": "glpat_***", "tag": "v1.2.3", # optional; takes precedence "branch": "main", # default fallback - "prompts_path": "prompts/chat" # <--- NEW + "prompts_path": "prompts/chat" } """ @@ -438,9 +451,11 @@ class GitLabPromptManager(CustomPromptManagement): prompt_version: Optional[int] = None, ) -> PromptManagementClient: try: - if prompt_id not in self.prompt_manager.prompts: + decoded_id = decode_prompt_id(prompt_id) + if decoded_id not in self.prompt_manager.prompts: git_ref = getattr(dynamic_callback_params, "extra", {}).get("git_ref") if hasattr(dynamic_callback_params, "extra") else None - self.prompt_manager._load_prompt_from_gitlab(prompt_id, ref=git_ref) + self.prompt_manager._load_prompt_from_gitlab(decoded_id, ref=git_ref) + rendered_prompt, prompt_metadata = self.get_prompt_template( prompt_id, prompt_variables @@ -486,3 +501,148 @@ class GitLabPromptManager(CustomPromptManagement): prompt_label, prompt_version, ) + + +class GitLabPromptCache: + """ + Cache all .prompt files from a GitLab repo into memory. + + - Keys are the *repo file paths* (e.g. "prompts/chat/greet/hi.prompt") + mapped to JSON-like dicts containing content + metadata. + - Also exposes a by-ID view (ID == path relative to prompts_path without ".prompt", + e.g. "greet/hi"). + + Usage: + + cfg = { + "project": "group/subgroup/repo", + "access_token": "glpat_***", + "prompts_path": "prompts/chat", # optional, can be empty for repo root + # "branch": "main", # default is "main" + # "tag": "v1.2.3", # takes precedence over branch + # "base_url": "https://gitlab.com/api/v4" # default + } + + cache = GitLabPromptCache(cfg) + cache.load_all() # fetch + parse all .prompt files + + print(cache.list_files()) # repo file paths + print(cache.list_ids()) # template IDs relative to prompts_path + + prompt_json = cache.get_by_file("prompts/chat/greet/hi.prompt") + prompt_json2 = cache.get_by_id("greet/hi") + + # If GitLab content changes and you want to refresh: + cache.reload() # re-scan and refresh all + """ + + def __init__( + self, + gitlab_config: Dict[str, Any], + *, + ref: Optional[str] = None, + gitlab_client: Optional[GitLabClient] = None, + ) -> None: + # Build a PromptManager (which internally builds TemplateManager + Client) + self.prompt_manager = GitLabPromptManager( + gitlab_config=gitlab_config, + prompt_id=None, + ref=ref, + gitlab_client=gitlab_client, + ) + self.template_manager: GitLabTemplateManager = self.prompt_manager.prompt_manager + + # In-memory stores + self._by_file: Dict[str, Dict[str, Any]] = {} + self._by_id: Dict[str, Dict[str, Any]] = {} + + # ------------------------- + # Public API + # ------------------------- + + def load_all(self, *, recursive: bool = True) -> Dict[str, Dict[str, Any]]: + """ + Scan GitLab for all .prompt files under prompts_path, load and parse each, + and return the mapping of repo file path -> JSON-like dict. + """ + ids = self.template_manager.list_templates(recursive=recursive) # IDs relative to prompts_path + for pid in ids: + # Ensure template is loaded into TemplateManager + if pid not in self.template_manager.prompts: + self.template_manager._load_prompt_from_gitlab(pid) + + tmpl = self.template_manager.get_template(pid) + if tmpl is None: + # If something raced/failed, try once more + self.template_manager._load_prompt_from_gitlab(pid) + tmpl = self.template_manager.get_template(pid) + if tmpl is None: + continue + + file_path = self.template_manager._id_to_repo_path(pid) # "prompts/chat/..../file.prompt" + entry = self._template_to_json(pid, tmpl) + + self._by_file[file_path] = entry + # prefixed_id = pid if pid.startswith("gitlab::") else f"gitlab::{pid}" + encoded_id = encode_prompt_id(pid) + self._by_id[encoded_id] = entry + # self._by_id[pid] = entry + + return self._by_id + + def reload(self, *, recursive: bool = True) -> Dict[str, Dict[str, Any]]: + """Clear the cache and re-load from GitLab.""" + self._by_file.clear() + self._by_id.clear() + return self.load_all(recursive=recursive) + + def list_files(self) -> List[str]: + """Return the repo file paths currently cached.""" + return list(self._by_file.keys()) + + def list_ids(self) -> List[str]: + """Return the template IDs (relative to prompts_path, without extension) currently cached.""" + return list(self._by_id.keys()) + + def get_by_file(self, file_path: str) -> Optional[Dict[str, Any]]: + """Get a cached prompt JSON by repo file path.""" + return self._by_file.get(file_path) + + def get_by_id(self, prompt_id: str) -> Optional[Dict[str, Any]]: + """Get a cached prompt JSON by prompt ID (relative to prompts_path).""" + if prompt_id in self._by_id: + return self._by_id[prompt_id] + + # Try normalized forms + decoded = decode_prompt_id(prompt_id) + encoded = encode_prompt_id(decoded) + + return self._by_id.get(encoded) or self._by_id.get(decoded) + + # ------------------------- + # Internals + # ------------------------- + + def _template_to_json(self, prompt_id: str, tmpl: GitLabPromptTemplate) -> Dict[str, Any]: + """ + Normalize a GitLabPromptTemplate into a JSON-like dict that is easy to serialize. + """ + # Safer copy of metadata (avoid accidental mutation) + md = dict(tmpl.metadata or {}) + + # Pull standard fields (also present in metadata sometimes) + model = tmpl.model + temperature = tmpl.temperature + max_tokens = tmpl.max_tokens + optional_params = dict(tmpl.optional_params or {}) + + return { + "id": prompt_id, # e.g. "greet/hi" + "path": self.template_manager._id_to_repo_path(prompt_id), # e.g. "prompts/chat/greet/hi.prompt" + "content": tmpl.content, # rendered content (without frontmatter) + "metadata": md, # parsed frontmatter + "model": model, + "temperature": temperature, + "max_tokens": max_tokens, + "optional_params": optional_params, + } \ No newline at end of file diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index fbe480be95f..43d16b5e4cb 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -48,11 +48,12 @@ class LangfuseOtelLogger(OpenTelemetry): _utils.set_attributes(span, kwargs, response_obj) ######################################################### - # Set Langfuse specific attributes eg Langfuse Environment + # Set Langfuse specific attributes ######################################################### LangfuseOtelLogger._set_langfuse_specific_attributes( span=span, - kwargs=kwargs + kwargs=kwargs, + response_obj=response_obj ) return @@ -86,7 +87,7 @@ class LangfuseOtelLogger(OpenTelemetry): return metadata @staticmethod - def _set_langfuse_specific_attributes(span: Span, kwargs): + def _set_langfuse_specific_attributes(span: Span, kwargs, response_obj): """ Sets Langfuse specific metadata attributes onto the OTEL span. @@ -96,6 +97,7 @@ class LangfuseOtelLogger(OpenTelemetry): compatibility. """ from litellm.integrations.arize._utils import safe_set_attribute + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps # 1) Environment variable override langfuse_environment = os.environ.get("LANGFUSE_TRACING_ENVIRONMENT") @@ -141,6 +143,75 @@ class LangfuseOtelLogger(OpenTelemetry): value = str(value) safe_set_attribute(span, enum_attr.value, value) + # 3) Set observation input/output for better UI display + # + # These Langfuse-specific attributes provide better UI display, + # especially for tool calls and function calling. + # Set observation input (messages) + messages = kwargs.get("messages") + if messages: + safe_set_attribute( + span, + LangfuseSpanAttributes.OBSERVATION_INPUT.value, + safe_dumps(messages), + ) + + # Set observation output (response with tool_calls if present) + if response_obj and hasattr(response_obj, "get"): + choices = response_obj.get("choices", []) + if choices: + # Extract the first choice's message + first_choice = choices[0] + message = first_choice.get("message", {}) + + # Check if there are tool_calls + tool_calls = message.get("tool_calls") + if tool_calls: + # Transform tool_calls to Langfuse-expected format + transformed_tool_calls = [] + for tool_call in tool_calls: + function = tool_call.get("function", {}) + arguments_str = function.get("arguments", "{}") + + # Parse arguments from JSON string to object + try: + arguments_obj = json.loads(arguments_str) if isinstance(arguments_str, str) else arguments_str + except json.JSONDecodeError: + arguments_obj = {} + + # Create Langfuse-compatible tool call object + langfuse_tool_call = { + "id": response_obj.get("id", ""), + "name": function.get("name", ""), + "call_id": tool_call.get("id", ""), + "type": "function_call", + "arguments": arguments_obj, + } + transformed_tool_calls.append(langfuse_tool_call) + + # Set the observation output with transformed tool_calls + safe_set_attribute( + span, + LangfuseSpanAttributes.OBSERVATION_OUTPUT.value, + safe_dumps(transformed_tool_calls), + ) + else: + # No tool_calls, use regular content-based output + output_data = {} + + if message.get("role"): + output_data["role"] = message.get("role") + + if message.get("content") is not None: + output_data["content"] = message.get("content") + + if output_data: + safe_set_attribute( + span, + LangfuseSpanAttributes.OBSERVATION_OUTPUT.value, + safe_dumps(output_data), + ) + @staticmethod def _get_langfuse_otel_host() -> Optional[str]: """ diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 11f2c6b2618..9315384ad96 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -841,6 +841,10 @@ class OpenTelemetry(CustomLogger): ) span.set_status(Status(StatusCode.ERROR)) self.set_attributes(span, kwargs, response_obj) + + # Record exception information using OTEL standard method + self._record_exception_on_span(span=span, kwargs=kwargs) + span.end(end_time=self._to_ns(end_time)) # Create span for guardrail information @@ -849,6 +853,87 @@ class OpenTelemetry(CustomLogger): if parent_otel_span is not None: parent_otel_span.end(end_time=self._to_ns(datetime.now())) + def _record_exception_on_span(self, span: Span, kwargs: dict): + """ + Record exception information on the span using OTEL standard methods. + + This extracts error information from StandardLoggingPayload and: + 1. Uses span.record_exception() for the actual exception object (OTEL standard) + 2. Sets structured error attributes from StandardLoggingPayloadErrorInformation + """ + try: + from litellm.integrations._types.open_inference import ErrorAttributes + + # Get the exception object if available + exception = kwargs.get("exception") + + # Record the exception using OTEL's standard method + if exception is not None: + span.record_exception(exception) + + # Get StandardLoggingPayload for structured error information + standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object" + ) + + if standard_logging_payload is None: + return + + # Extract error_information from StandardLoggingPayload + error_information = standard_logging_payload.get("error_information") + + if error_information is None: + # Fallback to error_str if error_information is not available + error_str = standard_logging_payload.get("error_str") + if error_str: + self.safe_set_attribute( + span=span, + key=ErrorAttributes.ERROR_MESSAGE, + value=error_str, + ) + return + + # Set structured error attributes from StandardLoggingPayloadErrorInformation + if error_information.get("error_code"): + self.safe_set_attribute( + span=span, + key=ErrorAttributes.ERROR_CODE, + value=error_information["error_code"], + ) + + if error_information.get("error_class"): + self.safe_set_attribute( + span=span, + key=ErrorAttributes.ERROR_TYPE, + value=error_information["error_class"], + ) + + if error_information.get("error_message"): + self.safe_set_attribute( + span=span, + key=ErrorAttributes.ERROR_MESSAGE, + value=error_information["error_message"], + ) + + if error_information.get("llm_provider"): + self.safe_set_attribute( + span=span, + key=ErrorAttributes.ERROR_LLM_PROVIDER, + value=error_information["llm_provider"], + ) + + if error_information.get("traceback"): + self.safe_set_attribute( + span=span, + key=ErrorAttributes.ERROR_STACK_TRACE, + value=error_information["traceback"], + ) + + except Exception as e: + verbose_logger.exception( + "OpenTelemetry: Error recording exception on span: %s", str(e) + ) + def set_tools_attributes(self, span: Span, tools): import json diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py index 9fa3482f663..c28aa14a11e 100644 --- a/litellm/integrations/opik/opik.py +++ b/litellm/integrations/opik/opik.py @@ -204,6 +204,13 @@ class OpikLogger(CustomBatchLogger): # Update litellm_opik_metadata with opik metadata from requester standard_logging_metadata = standard_logging_object.get("metadata", {}) or {} requester_metadata = standard_logging_metadata.get("requester_metadata", {}) or {} + + # If requester_metadata is empty, try to get it from user_api_key_auth_metadata saved in api key + if not requester_metadata: + requester_metadata = standard_logging_metadata.get( + "user_api_key_auth_metadata", {} + ) or {} + requester_opik_metadata = requester_metadata.get("opik", {}) or {} litellm_opik_metadata.update(requester_opik_metadata) diff --git a/litellm/integrations/sqs.py b/litellm/integrations/sqs.py index ad4df97f98e..545aebbec6d 100644 --- a/litellm/integrations/sqs.py +++ b/litellm/integrations/sqs.py @@ -9,6 +9,7 @@ from __future__ import annotations import asyncio import base64 import json +import re import traceback from typing import List, Optional @@ -30,6 +31,11 @@ from litellm.types.utils import StandardLoggingPayload from .custom_batch_logger import CustomBatchLogger +_BASE64_INLINE_PATTERN = re.compile( + r"data:(?:application|image|audio|video)/[a-zA-Z0-9.+-]+;base64,[A-Za-z0-9+/=\s]+", + re.MULTILINE, +) + class SQSLogger(CustomBatchLogger, BaseAWSLLM): """Batching logger that writes logs to an AWS SQS queue, optionally encrypting the payload.""" @@ -54,6 +60,7 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM): sqs_flush_interval: Optional[int] = DEFAULT_SQS_FLUSH_INTERVAL_SECONDS, sqs_batch_size: Optional[int] = DEFAULT_SQS_BATCH_SIZE, sqs_config=None, + sqs_strip_base64_files: bool = False, # --- 🔐 Application-level encryption params --- sqs_aws_use_application_level_encryption: bool = False, sqs_app_encryption_key_b64: Optional[str] = None, @@ -84,6 +91,7 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM): sqs_aws_role_name=sqs_aws_role_name, sqs_aws_web_identity_token=sqs_aws_web_identity_token, sqs_aws_sts_endpoint=sqs_aws_sts_endpoint, + sqs_strip_base64_files=sqs_strip_base64_files, sqs_aws_use_application_level_encryption=sqs_aws_use_application_level_encryption, sqs_app_encryption_key_b64=sqs_app_encryption_key_b64, sqs_app_encryption_aad=sqs_app_encryption_aad, @@ -113,25 +121,26 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM): raise e def _init_sqs_params( - self, - sqs_queue_url: Optional[str] = None, - sqs_region_name: Optional[str] = None, - sqs_api_version: Optional[str] = None, - sqs_use_ssl: bool = True, - sqs_verify: Optional[bool] = None, - sqs_endpoint_url: Optional[str] = None, - sqs_aws_access_key_id: Optional[str] = None, - sqs_aws_secret_access_key: Optional[str] = None, - sqs_aws_session_token: Optional[str] = None, - sqs_aws_session_name: Optional[str] = None, - sqs_aws_profile_name: Optional[str] = None, - sqs_aws_role_name: Optional[str] = None, - sqs_aws_web_identity_token: Optional[str] = None, - sqs_aws_sts_endpoint: Optional[str] = None, - sqs_aws_use_application_level_encryption: bool = False, - sqs_app_encryption_key_b64: Optional[str] = None, - sqs_app_encryption_aad: Optional[str] = None, - sqs_config=None, + self, + sqs_queue_url: Optional[str] = None, + sqs_region_name: Optional[str] = None, + sqs_api_version: Optional[str] = None, + sqs_use_ssl: bool = True, + sqs_verify: Optional[bool] = None, + sqs_endpoint_url: Optional[str] = None, + sqs_aws_access_key_id: Optional[str] = None, + sqs_aws_secret_access_key: Optional[str] = None, + sqs_aws_session_token: Optional[str] = None, + sqs_aws_session_name: Optional[str] = None, + sqs_aws_profile_name: Optional[str] = None, + sqs_aws_role_name: Optional[str] = None, + sqs_aws_web_identity_token: Optional[str] = None, + sqs_aws_sts_endpoint: Optional[str] = None, + sqs_strip_base64_files: bool = False, + sqs_aws_use_application_level_encryption: bool = False, + sqs_app_encryption_key_b64: Optional[str] = None, + sqs_app_encryption_aad: Optional[str] = None, + sqs_config=None, ) -> None: litellm.aws_sqs_callback_params = litellm.aws_sqs_callback_params or {} @@ -141,55 +150,59 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM): litellm.aws_sqs_callback_params[key] = litellm.get_secret(value) self.sqs_queue_url = ( - litellm.aws_sqs_callback_params.get("sqs_queue_url") or sqs_queue_url + litellm.aws_sqs_callback_params.get("sqs_queue_url") or sqs_queue_url ) self.sqs_region_name = ( - litellm.aws_sqs_callback_params.get("sqs_region_name") or sqs_region_name + litellm.aws_sqs_callback_params.get("sqs_region_name") or sqs_region_name ) self.sqs_api_version = ( - litellm.aws_sqs_callback_params.get("sqs_api_version") or sqs_api_version + litellm.aws_sqs_callback_params.get("sqs_api_version") or sqs_api_version ) self.sqs_use_ssl = ( - litellm.aws_sqs_callback_params.get("sqs_use_ssl", True) or sqs_use_ssl + litellm.aws_sqs_callback_params.get("sqs_use_ssl", True) or sqs_use_ssl ) self.sqs_verify = litellm.aws_sqs_callback_params.get("sqs_verify") or sqs_verify self.sqs_endpoint_url = ( - litellm.aws_sqs_callback_params.get("sqs_endpoint_url") or sqs_endpoint_url + litellm.aws_sqs_callback_params.get("sqs_endpoint_url") or sqs_endpoint_url ) self.sqs_aws_access_key_id = ( - litellm.aws_sqs_callback_params.get("sqs_aws_access_key_id") - or sqs_aws_access_key_id + litellm.aws_sqs_callback_params.get("sqs_aws_access_key_id") + or sqs_aws_access_key_id ) self.sqs_aws_secret_access_key = ( - litellm.aws_sqs_callback_params.get("sqs_aws_secret_access_key") - or sqs_aws_secret_access_key + litellm.aws_sqs_callback_params.get("sqs_aws_secret_access_key") + or sqs_aws_secret_access_key ) self.sqs_aws_session_token = ( - litellm.aws_sqs_callback_params.get("sqs_aws_session_token") - or sqs_aws_session_token + litellm.aws_sqs_callback_params.get("sqs_aws_session_token") + or sqs_aws_session_token ) self.sqs_aws_session_name = ( - litellm.aws_sqs_callback_params.get("sqs_aws_session_name") or sqs_aws_session_name + litellm.aws_sqs_callback_params.get("sqs_aws_session_name") or sqs_aws_session_name ) self.sqs_aws_profile_name = ( - litellm.aws_sqs_callback_params.get("sqs_aws_profile_name") or sqs_aws_profile_name + litellm.aws_sqs_callback_params.get("sqs_aws_profile_name") or sqs_aws_profile_name ) self.sqs_aws_role_name = ( - litellm.aws_sqs_callback_params.get("sqs_aws_role_name") or sqs_aws_role_name + litellm.aws_sqs_callback_params.get("sqs_aws_role_name") or sqs_aws_role_name ) self.sqs_aws_web_identity_token = ( - litellm.aws_sqs_callback_params.get("sqs_aws_web_identity_token") - or sqs_aws_web_identity_token + litellm.aws_sqs_callback_params.get("sqs_aws_web_identity_token") + or sqs_aws_web_identity_token ) self.sqs_aws_sts_endpoint = ( - litellm.aws_sqs_callback_params.get("sqs_aws_sts_endpoint") or sqs_aws_sts_endpoint + litellm.aws_sqs_callback_params.get("sqs_aws_sts_endpoint") or sqs_aws_sts_endpoint + ) + self.sqs_strip_base64_files = ( + litellm.aws_sqs_callback_params.get("sqs_strip_base64_files", False) + or sqs_strip_base64_files ) self.sqs_aws_use_application_level_encryption = ( @@ -217,13 +230,15 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM): self.sqs_config = litellm.aws_sqs_callback_params.get("sqs_config") or sqs_config async def async_log_success_event( - self, kwargs, response_obj, start_time, end_time + self, kwargs, response_obj, start_time, end_time ) -> None: try: verbose_logger.debug( "SQS Logging - Enters logging function for model %s", kwargs ) standard_logging_payload = kwargs.get("standard_logging_object") + if self.sqs_strip_base64_files: + standard_logging_payload = await self._strip_base64_from_messages(standard_logging_payload) if standard_logging_payload is None: raise ValueError("standard_logging_payload is None") @@ -337,4 +352,3 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM): response.raise_for_status() except Exception as e: verbose_logger.exception(f"Error sending to SQS: {str(e)}") - diff --git a/litellm/litellm_core_utils/app_crypto.py b/litellm/litellm_core_utils/app_crypto.py index bb5043b2b7b..5ce6d8d77f9 100644 --- a/litellm/litellm_core_utils/app_crypto.py +++ b/litellm/litellm_core_utils/app_crypto.py @@ -1,16 +1,18 @@ import base64 import json import os +from typing import Optional from cryptography.hazmat.primitives.ciphers.aead import AESGCM + class AppCrypto: def __init__(self, master_key: bytes): if len(master_key) != 32: raise ValueError("Master key must be 32 bytes for AES-256-GCM") self.key = master_key - def encrypt_json(self, data: dict, aad: bytes | None = None) -> dict: + def encrypt_json(self, data: dict, aad: Optional[bytes] = None) -> dict: aes = AESGCM(self.key) nonce = os.urandom(12) plaintext = json.dumps(data).encode("utf-8") @@ -22,7 +24,7 @@ class AppCrypto: "tag": base64.b64encode(tag).decode(), } - def decrypt_json(self, enc: dict, aad: bytes | None = None) -> dict: + def decrypt_json(self, enc: dict, aad: Optional[bytes] = None) -> dict: aes = AESGCM(self.key) nonce = base64.b64decode(enc["nonce"]) ct = base64.b64decode(enc["ciphertext"]) diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index 7423e55b626..8b9f53cec15 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -1,6 +1,6 @@ # What is this? ## Helper utilities -from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Union +from typing import TYPE_CHECKING, Any, Iterable, List, Literal, Optional, Union import httpx @@ -138,6 +138,22 @@ def add_missing_spend_metadata_to_litellm_metadata( return litellm_metadata +def get_metadata_variable_name_from_kwargs( + kwargs: dict, +) -> Literal["metadata", "litellm_metadata"]: + """ + Helper to return what the "metadata" field should be called in the request data + + - New endpoints return `litellm_metadata` + - Old endpoints return `metadata` + + Context: + - LiteLLM used `metadata` as an internal field for storing metadata + - OpenAI then started using this field for their metadata + - LiteLLM is now moving to using `litellm_metadata` for our metadata + """ + return "litellm_metadata" if "litellm_metadata" in kwargs else "metadata" + def get_litellm_metadata_from_kwargs(kwargs: dict): """ Helper to get litellm metadata from all litellm request kwargs diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index c6d3637ffcb..61551b04236 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -67,6 +67,7 @@ class ExceptionCheckers: "string too long. expected a string with maximum length", "model's maximum context limit", "is longer than the model's context length", + "input tokens exceed the configured limit", ] for substring in known_exception_substrings: if substring in _error_str_lowercase: diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index e4e67502390..c9d766628a9 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -117,6 +117,7 @@ from litellm.types.utils import ( ) from litellm.types.videos.main import VideoObject from litellm.utils import _get_base_model_from_metadata, executor, print_verbose +from litellm.llms.base_llm.ocr.transformation import OCRResponse from ..integrations.argilla import ArgillaLogger from ..integrations.arize.arize_phoenix import ArizePhoenixLogger @@ -1619,6 +1620,7 @@ class Logging(LiteLLMLoggingBaseClass): or isinstance(logging_result, OpenAIFileObject) or isinstance(logging_result, LiteLLMRealtimeStreamLoggingObject) or isinstance(logging_result, OpenAIModerationResponse) + or isinstance(logging_result, OCRResponse) # OCR or isinstance(logging_result, dict) and logging_result.get("object") == "vector_store.search_results.page" or isinstance(logging_result, VideoObject) diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 19d5932ff28..33658d49063 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -654,6 +654,102 @@ def _get_image_mime_type_from_url(url: str) -> Optional[str]: return None +def infer_content_type_from_url_and_content( + url: str, + content: bytes, + current_content_type: Optional[str] = None, +) -> str: + """ + Infer content type from URL extension and binary content when content-type header is missing or generic. + + This helper implements a fallback strategy for determining MIME types when HTTP headers + are missing or provide generic values (like binary/octet-stream). It's commonly used + when processing images and documents from various sources (S3, URLs, etc.). + + Fallback Strategy: + 1. If current_content_type is valid (not None and not generic octet-stream), return it + 2. Try to infer from URL extension (handles query parameters) + 3. Try to detect from binary content signature (magic bytes) + 4. Raise ValueError if all methods fail + + Args: + url: The URL of the content (used to extract file extension) + content: The binary content (first ~100 bytes are sufficient for detection) + current_content_type: The current content-type from headers (may be None or generic) + + Returns: + str: The inferred MIME type (e.g., "image/png", "application/pdf") + + Raises: + ValueError: If content type cannot be determined by any method + + Example: + >>> content_type = infer_content_type_from_url_and_content( + ... url="https://s3.amazonaws.com/bucket/image.png?AWSAccessKeyId=123", + ... content=png_binary_data, + ... current_content_type="binary/octet-stream" + ... ) + >>> print(content_type) + "image/png" + """ + from litellm.litellm_core_utils.token_counter import get_image_type + + # If we have a valid content type that's not generic, use it + if current_content_type and current_content_type not in [ + "binary/octet-stream", + "application/octet-stream", + ]: + return current_content_type + + # Extension to MIME type mapping + # Supports images, documents, and other common file types + extension_to_mime = { + # Image formats + "jpg": "image/jpeg", + "jpeg": "image/jpeg", + "png": "image/png", + "gif": "image/gif", + "webp": "image/webp", + # Document formats + "pdf": "application/pdf", + "csv": "text/csv", + "doc": "application/msword", + "docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + "xls": "application/vnd.ms-excel", + "xlsx": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "html": "text/html", + "txt": "text/plain", + "md": "text/markdown", + } + + # Try to infer from URL extension + if url: + extension = url.split(".")[-1].lower().split("?")[0] # Remove query params + inferred_type = extension_to_mime.get(extension) + if inferred_type: + return inferred_type + + # Try to detect from binary content signature (magic bytes) + if content: + detected_type = get_image_type(content[:100]) + if detected_type: + type_to_mime = { + "png": "image/png", + "jpeg": "image/jpeg", + "gif": "image/gif", + "webp": "image/webp", + "heic": "image/heic", + } + if detected_type in type_to_mime: + return type_to_mime[detected_type] + + # If all fallbacks failed, raise error + raise ValueError( + f"Unable to determine content type from URL: {url}. " + f"Response content-type: {current_content_type}" + ) + + def get_tool_call_names(tools: List[ChatCompletionToolParam]) -> List[str]: """ Get tool call names from tools diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index a5a4ab6cbf2..18bd9fc1684 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -38,7 +38,11 @@ from litellm.types.llms.vertex_ai import FunctionResponse as VertexFunctionRespo from litellm.types.llms.vertex_ai import PartType as VertexPartType from litellm.types.utils import GenericImageParsingChunk -from .common_utils import convert_content_list_to_str, is_non_content_values_set +from .common_utils import ( + convert_content_list_to_str, + infer_content_type_from_url_and_content, + is_non_content_values_set, +) from .image_handling import convert_url_to_base64 @@ -2536,13 +2540,17 @@ class BedrockImageProcessor: """Handles both sync and async image processing for Bedrock conversations.""" @staticmethod - def _post_call_image_processing(response: httpx.Response) -> Tuple[str, str]: + def _post_call_image_processing(response: httpx.Response, image_url: str = "") -> Tuple[str, str]: # Check the response's content type to ensure it is an image content_type = response.headers.get("content-type") - if not content_type: - raise ValueError( - f"URL does not contain content-type (content-type: {content_type})" - ) + + # Use helper function to infer content type with fallback logic + content_type = infer_content_type_from_url_and_content( + url=image_url, + content=response.content, + current_content_type=content_type, + ) + content_type = _parse_content_type(content_type) # Convert the image content to base64 bytes @@ -2561,7 +2569,7 @@ class BedrockImageProcessor: response = await client.get(image_url, follow_redirects=True) response.raise_for_status() # Raise an exception for HTTP errors - return BedrockImageProcessor._post_call_image_processing(response) + return BedrockImageProcessor._post_call_image_processing(response, image_url) except Exception as e: raise e @@ -2574,7 +2582,7 @@ class BedrockImageProcessor: response = client.get(image_url, follow_redirects=True) response.raise_for_status() # Raise an exception for HTTP errors - return BedrockImageProcessor._post_call_image_processing(response) + return BedrockImageProcessor._post_call_image_processing(response, image_url) except Exception as e: raise e diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py index 5ac38949e2b..849cb20cdc5 100644 --- a/litellm/litellm_core_utils/redact_messages.py +++ b/litellm/litellm_core_utils/redact_messages.py @@ -14,6 +14,9 @@ import litellm from litellm.integrations.custom_logger import CustomLogger from litellm.secret_managers.main import str_to_bool from litellm.types.utils import StandardCallbackDynamicParams +from litellm.litellm_core_utils.core_helpers import ( + get_metadata_variable_name_from_kwargs, +) import asyncio if TYPE_CHECKING: @@ -37,6 +40,38 @@ def redact_message_input_output_from_custom_logger( return result +def _redact_choice_content(choice): + """Helper to redact content in a choice (message or delta).""" + if isinstance(choice, litellm.Choices): + choice.message.content = "redacted-by-litellm" + if hasattr(choice.message, "reasoning_content"): + choice.message.reasoning_content = "redacted-by-litellm" + if hasattr(choice.message, "thinking_blocks"): + choice.message.thinking_blocks = None + elif isinstance(choice, litellm.utils.StreamingChoices): + choice.delta.content = "redacted-by-litellm" + if hasattr(choice.delta, "reasoning_content"): + choice.delta.reasoning_content = "redacted-by-litellm" + if hasattr(choice.delta, "thinking_blocks"): + choice.delta.thinking_blocks = None + + +def _redact_responses_api_output(output_items): + """Helper to redact ResponsesAPIResponse output items.""" + for output_item in output_items: + if hasattr(output_item, "content") and isinstance(output_item.content, list): + for content_part in output_item.content: + if hasattr(content_part, "text"): + content_part.text = "redacted-by-litellm" + + # Redact reasoning items in output array + if hasattr(output_item, "type") and output_item.type == "reasoning": + if hasattr(output_item, "summary") and isinstance(output_item.summary, list): + for summary_item in output_item.summary: + if hasattr(summary_item, "text"): + summary_item.text = "redacted-by-litellm" + + def perform_redaction(model_call_details: dict, result): """ Performs the actual redaction on the logging object and result. @@ -56,19 +91,12 @@ def perform_redaction(model_call_details: dict, result): _streaming_response = model_call_details["complete_streaming_response"] if hasattr(_streaming_response, "choices"): for choice in _streaming_response.choices: - if isinstance(choice, litellm.Choices): - choice.message.content = "redacted-by-litellm" - elif isinstance(choice, litellm.utils.StreamingChoices): - choice.delta.content = "redacted-by-litellm" + _redact_choice_content(choice) elif hasattr(_streaming_response, "output"): - # Handle ResponsesAPIResponse format - for output_item in _streaming_response.output: - if hasattr(output_item, "content") and isinstance( - output_item.content, list - ): - for content_part in output_item.content: - if hasattr(content_part, "text"): - content_part.text = "redacted-by-litellm" + _redact_responses_api_output(_streaming_response.output) + # Redact reasoning field in ResponsesAPIResponse + if hasattr(_streaming_response, "reasoning") and _streaming_response.reasoning is not None: + _streaming_response.reasoning = None # Redact result if result is not None: @@ -84,17 +112,13 @@ def perform_redaction(model_call_details: dict, result): if isinstance(_result, litellm.ModelResponse): if hasattr(_result, "choices") and _result.choices is not None: for choice in _result.choices: - if isinstance(choice, litellm.Choices): - choice.message.content = "redacted-by-litellm" - elif isinstance(choice, litellm.utils.StreamingChoices): - choice.delta.content = "redacted-by-litellm" + _redact_choice_content(choice) elif isinstance(_result, litellm.ResponsesAPIResponse): if hasattr(_result, "output"): - for output_item in _result.output: - if hasattr(output_item, "content") and isinstance(output_item.content, list): - for content_part in output_item.content: - if hasattr(content_part, "text"): - content_part.text = "redacted-by-litellm" + _redact_responses_api_output(_result.output) + # Redact reasoning field in ResponsesAPIResponse + if hasattr(_result, "reasoning") and _result.reasoning is not None: + _result.reasoning = None elif isinstance(_result, litellm.EmbeddingResponse): if hasattr(_result, "data") and _result.data is not None: _result.data = [] @@ -107,11 +131,13 @@ def should_redact_message_logging(model_call_details: dict) -> bool: """ Determine if message logging should be redacted. """ - _request_headers = ( - model_call_details.get("litellm_params", {}).get("metadata", {}) or {} - ) - - request_headers = _request_headers.get("headers", {}) + litellm_params = model_call_details.get("litellm_params", {}) + + metadata_field = get_metadata_variable_name_from_kwargs(litellm_params) + metadata = litellm_params.get(metadata_field, {}) + + # Get headers from the metadata + request_headers = metadata.get("headers", {}) if isinstance(metadata, dict) else {} possible_request_headers = [ "litellm-enable-message-redaction", # old header. maintain backwards compatibility diff --git a/litellm/llms/azure/common_utils.py b/litellm/llms/azure/common_utils.py index dfe662cc165..d9c5bea1a3f 100644 --- a/litellm/llms/azure/common_utils.py +++ b/litellm/llms/azure/common_utils.py @@ -759,4 +759,4 @@ class BaseAzureLLM(BaseOpenAILLM): def _is_azure_v1_api_version(api_version: Optional[str]) -> bool: if api_version is None: return False - return api_version == "preview" or api_version == "latest" + return api_version in {"preview", "latest", "v1"} diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index bc0466388f9..4c854437544 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -1,6 +1,7 @@ import hashlib import json import os +import urllib.parse from datetime import datetime from typing import ( TYPE_CHECKING, @@ -331,16 +332,61 @@ class BaseAWSLLM: return provider return None + @staticmethod + def get_bedrock_model_id( + optional_params: dict, + provider: Optional[BEDROCK_INVOKE_PROVIDERS_LITERAL], + model: str, + ) -> str: + model_id = optional_params.pop("model_id", None) + if model_id is not None: + model_id = BaseAWSLLM.encode_model_id(model_id=model_id) + else: + model_id = model + + model_id = model_id.replace("invoke/", "", 1) + if provider == "llama" and "llama/" in model_id: + model_id = BaseAWSLLM._get_model_id_from_model_with_spec( + model_id, spec="llama" + ) + elif provider == "deepseek_r1" and "deepseek_r1/" in model_id: + model_id = BaseAWSLLM._get_model_id_from_model_with_spec( + model_id, spec="deepseek_r1" + ) + return model_id + + @staticmethod + def _get_model_id_from_model_with_spec( + model: str, + spec: str, + ) -> str: + """ + Remove `llama` from modelID since `llama` is simply a spec to follow for custom bedrock models + """ + model_id = model.replace(spec + "/", "") + return BaseAWSLLM.encode_model_id(model_id=model_id) + + @staticmethod + def encode_model_id(model_id: str) -> str: + """ + Double encode the model ID to ensure it matches the expected double-encoded format. + Args: + model_id (str): The model ID to encode. + Returns: + str: The double-encoded model ID. + """ + return urllib.parse.quote(model_id, safe="") + @staticmethod def get_bedrock_embedding_provider( model: str, ) -> Optional[BEDROCK_EMBEDDING_PROVIDERS_LITERAL]: """ Helper function to get the bedrock embedding provider from the model - + Handles scenarios like: 1. model=cohere.embed-english-v3:0 -> Returns `cohere` - 2. model=amazon.titan-embed-text-v1 -> Returns `amazon` + 2. model=amazon.titan-embed-text-v1 -> Returns `amazon` 3. model=us.twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` 4. model=twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` """ @@ -349,20 +395,24 @@ class BaseAWSLLM: parts = model.split(".") # Check if the second part (after potential region) is a known provider if len(parts) >= 2: - potential_provider = parts[1] # e.g., "twelvelabs" from "us.twelvelabs.marengo-embed-2-7-v1:0" + potential_provider = parts[ + 1 + ] # e.g., "twelvelabs" from "us.twelvelabs.marengo-embed-2-7-v1:0" if potential_provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, potential_provider) - + # Check if the first part is a known provider (standard format) - potential_provider = parts[0] # e.g., "cohere" from "cohere.embed-english-v3:0" + potential_provider = parts[ + 0 + ] # e.g., "cohere" from "cohere.embed-english-v3:0" if potential_provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, potential_provider) - + # Fallback: check if any provider name appears in the model string for provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): if provider in model: return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, provider) - + return None def _get_aws_region_name( @@ -984,20 +1034,23 @@ class BaseAWSLLM: raise ImportError( "Missing boto3 to call bedrock. Run 'pip install boto3'." ) - + # Filter headers for AWS signature calculation # AWS SigV4 only includes specific headers in signature calculation aws_signature_headers = self._filter_headers_for_aws_signature(headers) sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name) request = AWSRequest( - method="POST", url=endpoint_url, data=data, headers=aws_signature_headers + method="POST", + url=endpoint_url, + data=data, + headers=aws_signature_headers, ) sigv4.add_auth(request) - + # Add back all original headers (including forwarded ones) after signature calculation for header_name, header_value in headers.items(): request.headers[header_name] = header_value - + if ( extra_headers is not None and "Authorization" in extra_headers ): # prevent sigv4 from overwriting the auth header @@ -1013,16 +1066,27 @@ class BaseAWSLLM: """ aws_signature_headers = {} aws_headers = { - 'host', 'content-type', 'date', 'x-amz-date', 'x-amz-security-token', - 'x-amz-content-sha256', 'x-amz-algorithm', 'x-amz-credential', - 'x-amz-signedheaders', 'x-amz-signature' + "host", + "content-type", + "date", + "x-amz-date", + "x-amz-security-token", + "x-amz-content-sha256", + "x-amz-algorithm", + "x-amz-credential", + "x-amz-signedheaders", + "x-amz-signature", } - + for header_name, header_value in headers.items(): header_lower = header_name.lower() - if header_lower in aws_headers or header_lower.startswith('x-amz-') or header_lower.startswith('x-amzn-'): + if ( + header_lower in aws_headers + or header_lower.startswith("x-amz-") + or header_lower.startswith("x-amzn-") + ): aws_signature_headers[header_name] = header_value - + return aws_signature_headers def _sign_request( diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index 54c603e5960..fd1f6f0c893 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -1,5 +1,4 @@ import json -import urllib from typing import Any, Optional, Union import httpx @@ -84,16 +83,6 @@ class BedrockConverseLLM(BaseAWSLLM): def __init__(self) -> None: super().__init__() - def encode_model_id(self, model_id: str) -> str: - """ - Double encode the model ID to ensure it matches the expected double-encoded format. - Args: - model_id (str): The model ID to encode. - Returns: - str: The double-encoded model ID. - """ - return urllib.parse.quote(model_id, safe="") # type: ignore - async def async_streaming( self, model: str, diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 71aadffe5bb..53cbafcbe6a 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -3,20 +3,15 @@ TODO: DELETE FILE. Bedrock LLM is no longer used. Goto `litellm/llms/bedrock/cha """ import copy -import json import time import types -import urllib.parse from functools import partial from typing import ( - Any, AsyncIterator, Callable, Iterator, - List, Optional, Tuple, - Union, cast, get_args, ) @@ -672,16 +667,6 @@ class BedrockLLM(BaseAWSLLM): return model_response - def encode_model_id(self, model_id: str) -> str: - """ - Double encode the model ID to ensure it matches the expected double-encoded format. - Args: - model_id (str): The model ID to encode. - Returns: - str: The double-encoded model ID. - """ - return urllib.parse.quote(model_id, safe="") - def completion( # noqa: PLR0915 self, model: str, @@ -1176,33 +1161,6 @@ class BedrockLLM(BaseAWSLLM): return cast(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL, provider) return None - def get_bedrock_model_id( - self, - optional_params: dict, - provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL], - model: str, - ) -> str: - modelId = optional_params.pop("model_id", None) - if modelId is not None: - modelId = self.encode_model_id(model_id=modelId) - else: - modelId = model - - if provider == "llama" and "llama/" in modelId: - modelId = self._get_model_id_for_llama_like_model(modelId) - - return modelId - - def _get_model_id_for_llama_like_model( - self, - model: str, - ) -> str: - """ - Remove `llama` from modelID since `llama` is simply a spec to follow for custom bedrock models - """ - model_id = model.replace("llama/", "") - return self.encode_model_id(model_id=model_id) - def get_response_stream_shape(): global _response_stream_shape_cache diff --git a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py index 08a0690716b..e6146f1064e 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py @@ -1,7 +1,6 @@ import copy import json import time -import urllib.parse from functools import partial from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast, get_args @@ -190,14 +189,16 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): ] = True # cohere requires stream = True in inference params request_data = {"prompt": prompt, **inference_params} elif provider == "anthropic": - transformed_request = litellm.AmazonAnthropicClaudeConfig().transform_request( - model=model, - messages=messages, - optional_params=optional_params, - litellm_params=litellm_params, - headers=headers, + transformed_request = ( + litellm.AmazonAnthropicClaudeConfig().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) ) - + return transformed_request elif provider == "nova": return litellm.AmazonInvokeNovaConfig().transform_request( @@ -327,7 +328,9 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): elif provider == "meta" or provider == "llama" or provider == "deepseek_r1": outputText = completion_response["generation"] elif provider == "mistral": - outputText = litellm.AmazonMistralConfig.get_outputText(completion_response, model_response) + outputText = litellm.AmazonMistralConfig.get_outputText( + completion_response, model_response + ) else: # amazon titan outputText = completion_response.get("results")[0].get("outputText") except Exception as e: @@ -549,48 +552,6 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): return cast(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL, provider) return None - def get_bedrock_model_id( - self, - optional_params: dict, - provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL], - model: str, - ) -> str: - modelId = optional_params.pop("model_id", None) - if modelId is not None: - modelId = self.encode_model_id(model_id=modelId) - else: - modelId = model - - modelId = modelId.replace("invoke/", "", 1) - if provider == "llama" and "llama/" in modelId: - modelId = self._get_model_id_from_model_with_spec(modelId, spec="llama") - elif provider == "deepseek_r1" and "deepseek_r1/" in modelId: - modelId = self._get_model_id_from_model_with_spec( - modelId, spec="deepseek_r1" - ) - return modelId - - def _get_model_id_from_model_with_spec( - self, - model: str, - spec: str, - ) -> str: - """ - Remove `llama` from modelID since `llama` is simply a spec to follow for custom bedrock models - """ - model_id = model.replace(spec + "/", "") - return self.encode_model_id(model_id=model_id) - - def encode_model_id(self, model_id: str) -> str: - """ - Double encode the model ID to ensure it matches the expected double-encoded format. - Args: - model_id (str): The model ID to encode. - Returns: - str: The double-encoded model ID. - """ - return urllib.parse.quote(model_id, safe="") - def convert_messages_to_prompt( self, model, messages, provider, custom_prompt_dict ) -> Tuple[str, Optional[list]]: diff --git a/litellm/llms/bedrock/image/image_handler.py b/litellm/llms/bedrock/image/image_handler.py index 1c418f78296..313a1dc17bd 100644 --- a/litellm/llms/bedrock/image/image_handler.py +++ b/litellm/llms/bedrock/image/image_handler.py @@ -7,6 +7,7 @@ import httpx from pydantic import BaseModel import litellm +from litellm import BEDROCK_INVOKE_PROVIDERS_LITERAL from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LitellmLogging from litellm.llms.bedrock.image.amazon_nova_canvas_transformation import ( @@ -183,8 +184,14 @@ class BedrockImageGeneration(BaseAWSLLM): optional_params, model ) + # Use the existing ARN-aware provider detection method + bedrock_provider = self.get_bedrock_invoke_provider(model) ### SET RUNTIME ENDPOINT ### - modelId = model + modelId = self.get_bedrock_model_id( + model=model, + provider=bedrock_provider, + optional_params=optional_params, + ) _, proxy_endpoint_url = self.get_runtime_endpoint( api_base=api_base, aws_bedrock_runtime_endpoint=boto3_credentials_info.aws_bedrock_runtime_endpoint, @@ -192,7 +199,10 @@ class BedrockImageGeneration(BaseAWSLLM): ) proxy_endpoint_url = f"{proxy_endpoint_url}/model/{modelId}/invoke" data = self._get_request_body( - model=model, prompt=prompt, optional_params=optional_params + model=model, + prompt=prompt, + optional_params=optional_params, + bedrock_provider=bedrock_provider, ) # Make POST Request @@ -231,6 +241,7 @@ class BedrockImageGeneration(BaseAWSLLM): def _get_request_body( self, model: str, + bedrock_provider: Optional[BEDROCK_INVOKE_PROVIDERS_LITERAL], prompt: str, optional_params: dict, ) -> dict: @@ -242,9 +253,6 @@ class BedrockImageGeneration(BaseAWSLLM): Returns: dict: The request body to use for the Bedrock Image Generation API """ - # Use the existing ARN-aware provider detection method - bedrock_provider = self.get_bedrock_invoke_provider(model) - if bedrock_provider == "amazon" or bedrock_provider == "nova": # Handle Amazon Nova Canvas models provider = "amazon" diff --git a/litellm/llms/cohere/chat/v2_transformation.py b/litellm/llms/cohere/chat/v2_transformation.py index 50426a0883b..8f6dde1967c 100644 --- a/litellm/llms/cohere/chat/v2_transformation.py +++ b/litellm/llms/cohere/chat/v2_transformation.py @@ -6,7 +6,12 @@ import httpx import litellm from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.types.llms.cohere import CohereV2ChatResponse -from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolCallChunk +from litellm.types.llms.openai import ( + AllMessageValues, + ChatCompletionToolCallChunk, + ChatCompletionAnnotation, + ChatCompletionAnnotationURLCitation, +) from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm.types.utils import ModelResponse, Usage @@ -207,9 +212,15 @@ class CohereV2ChatConfig(OpenAIGPTConfig): ] ) - ## ADD CITATIONS - if "citations" in cohere_v2_chat_response: - setattr(model_response, "citations", cohere_v2_chat_response["citations"]) + ## ADD CITATIONS AS ANNOTATIONS + annotations: Optional[List[ChatCompletionAnnotation]] = None + citations = None + + if "message" in cohere_v2_chat_response and "citations" in cohere_v2_chat_response["message"]: + citations = cohere_v2_chat_response["message"]["citations"] + + if citations: + annotations = self._translate_citations_to_openai_annotations(citations) ## Tool calling response cohere_tools_response = cohere_v2_chat_response["message"].get("tool_calls", []) @@ -225,8 +236,13 @@ class CohereV2ChatConfig(OpenAIGPTConfig): _message = litellm.Message( tool_calls=tool_calls, content=None, + annotations=annotations, ) model_response.choices[0].message = _message # type: ignore + else: + if annotations: + current_message = model_response.choices[0].message # type: ignore + current_message.annotations = annotations ## CALCULATING USAGE - use cohere `billed_units` for returning usage token_usage = cohere_v2_chat_response["usage"].get("tokens", {}) @@ -276,3 +292,66 @@ class CohereV2ChatConfig(OpenAIGPTConfig): self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] ) -> BaseLLMException: return CohereError(status_code=status_code, message=error_message) + + def _translate_citations_to_openai_annotations(self, citations: List[dict]) -> List[ChatCompletionAnnotation]: + """ + Transform Cohere citations to OpenAI annotations format. + + Creates separate annotations for each source in a citation, allowing multiple + annotations with the same start/end index if they reference different sources. + + Args: + citations: List of Cohere citation objects with format: + { + "start": int, + "end": int, + "text": str, + "sources": [ + { + "type": "document", + "document": { + "title": str, + "snippet": str, + ... + }, + "id": str + } + ] + } + + Returns: + List of OpenAI ChatCompletionAnnotation objects (one per source) + """ + annotations: List[ChatCompletionAnnotation] = [] + + for citation in citations: + start_index = citation.get("start", 0) + end_index = citation.get("end", 0) + + # Extract source information - loop through all sources + sources = citation.get("sources", []) + if not sources: + continue + + # Create an annotation for each source + for source in sources: + if source.get("type") == "document" and "document" in source: + document = source["document"] + title = document.get("title", "") + url = source.get("url") or f"source:{source.get('id', 'unknown')}" + + url_citation: ChatCompletionAnnotationURLCitation = { + "start_index": start_index, + "end_index": end_index, + "title": title, + "url": url, + } + + annotation: ChatCompletionAnnotation = { + "type": "url_citation", + "url_citation": url_citation, + } + + annotations.append(annotation) + + return annotations \ No newline at end of file diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index accdddbc4dd..cc451e5fa95 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -3,7 +3,7 @@ import os import ssl import sys import time -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Union +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Tuple, Union import certifi import httpx @@ -47,6 +47,46 @@ headers = { _DEFAULT_TIMEOUT = httpx.Timeout(timeout=5.0, connect=5.0) +def _prepare_request_data_and_content( + data: Optional[Union[dict, str, bytes]] = None, + content: Any = None, +) -> Tuple[Optional[Union[dict, Mapping]], Any]: + """ + Helper function to route data/content parameters correctly for httpx requests + + This prevents httpx DeprecationWarnings that cause memory leaks. + + Background: + - httpx shows a DeprecationWarning when you pass bytes/str to `data=` + - It wants you to use `content=` instead for bytes/str + - The warning itself leaks memory when triggered repeatedly + + Solution: + - Move bytes/str from `data=` to `content=` before calling build_request + - Keep dicts in `data=` (that's still the correct parameter for dicts) + + Args: + data: Request data (can be dict, str, or bytes) + content: Request content (raw bytes/str) + + Returns: + Tuple of (request_data, request_content) properly routed for httpx + """ + request_data = None + request_content = content + + if data is not None: + if isinstance(data, (bytes, str)): + # Bytes/strings belong in content= (only if not already provided) + if content is None: + request_content = data + else: + # dict/Mapping stays in data= parameter + request_data = data + + return request_data, request_content + + def get_ssl_configuration( ssl_verify: Optional[VerifyTypes] = None, ) -> Union[bool, str, ssl.SSLContext]: @@ -301,17 +341,20 @@ class AsyncHTTPHandler: if timeout is None: timeout = self.timeout + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + req = self.client.build_request( "POST", url, - data=data, # type: ignore + data=request_data, json=json, params=params, headers=headers, timeout=timeout, files=files, - content=content, - ) + content=request_content, + ) response = await self.client.send(req, stream=stream) response.raise_for_status() return response @@ -364,19 +407,23 @@ class AsyncHTTPHandler: async def put( self, url: str, - data: Optional[Union[dict, str]] = None, # type: ignore + data: Optional[Union[dict, str, bytes]] = None, # type: ignore json: Optional[dict] = None, params: Optional[dict] = None, headers: Optional[dict] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, stream: bool = False, + content: Any = None, ): try: if timeout is None: timeout = self.timeout + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + req = self.client.build_request( - "PUT", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore + "PUT", url, data=request_data, json=json, params=params, headers=headers, timeout=timeout, content=request_content # type: ignore ) response = await self.client.send(req) response.raise_for_status() @@ -424,19 +471,23 @@ class AsyncHTTPHandler: async def patch( self, url: str, - data: Optional[Union[dict, str]] = None, # type: ignore + data: Optional[Union[dict, str, bytes]] = None, # type: ignore json: Optional[dict] = None, params: Optional[dict] = None, headers: Optional[dict] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, stream: bool = False, + content: Any = None, ): try: if timeout is None: timeout = self.timeout + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + req = self.client.build_request( - "PATCH", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore + "PATCH", url, data=request_data, json=json, params=params, headers=headers, timeout=timeout, content=request_content # type: ignore ) response = await self.client.send(req) response.raise_for_status() @@ -484,18 +535,23 @@ class AsyncHTTPHandler: async def delete( self, url: str, - data: Optional[Union[dict, str]] = None, # type: ignore + data: Optional[Union[dict, str, bytes]] = None, # type: ignore json: Optional[dict] = None, params: Optional[dict] = None, headers: Optional[dict] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, stream: bool = False, + content: Any = None, ): try: if timeout is None: timeout = self.timeout + + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + req = self.client.build_request( - "DELETE", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore + "DELETE", url, data=request_data, json=json, params=params, headers=headers, timeout=timeout, content=request_content # type: ignore ) response = await self.client.send(req, stream=stream) response.raise_for_status() @@ -543,8 +599,11 @@ class AsyncHTTPHandler: Used for retrying connection client errors. """ + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + req = client.build_request( - "POST", url, data=data, json=json, params=params, headers=headers, content=content # type: ignore + "POST", url, data=request_data, json=json, params=params, headers=headers, content=request_content # type: ignore ) response = await client.send(req, stream=stream) response.raise_for_status() @@ -798,21 +857,24 @@ class HTTPHandler: logging_obj: Optional[LiteLLMLoggingObject] = None, ): try: + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + if timeout is not None: req = self.client.build_request( "POST", url, - data=data, # type: ignore + data=request_data, # type: ignore json=json, params=params, headers=headers, timeout=timeout, files=files, - content=content, # type: ignore + content=request_content, # type: ignore ) else: req = self.client.build_request( - "POST", url, data=data, json=json, params=params, headers=headers, files=files, content=content # type: ignore + "POST", url, data=request_data, json=json, params=params, headers=headers, files=files, content=request_content # type: ignore ) response = self.client.send(req, stream=stream) response.raise_for_status() @@ -840,21 +902,25 @@ class HTTPHandler: def patch( self, url: str, - data: Optional[Union[dict, str]] = None, + data: Optional[Union[dict, str, bytes]] = None, json: Optional[Union[dict, str]] = None, params: Optional[dict] = None, headers: Optional[dict] = None, stream: bool = False, timeout: Optional[Union[float, httpx.Timeout]] = None, + content: Any = None, ): try: + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + if timeout is not None: req = self.client.build_request( - "PATCH", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore + "PATCH", url, data=request_data, json=json, params=params, headers=headers, timeout=timeout, content=request_content # type: ignore ) else: req = self.client.build_request( - "PATCH", url, data=data, json=json, params=params, headers=headers # type: ignore + "PATCH", url, data=request_data, json=json, params=params, headers=headers, content=request_content # type: ignore ) response = self.client.send(req, stream=stream) response.raise_for_status() @@ -883,21 +949,25 @@ class HTTPHandler: def put( self, url: str, - data: Optional[Union[dict, str]] = None, + data: Optional[Union[dict, str, bytes]] = None, json: Optional[Union[dict, str]] = None, params: Optional[dict] = None, headers: Optional[dict] = None, stream: bool = False, timeout: Optional[Union[float, httpx.Timeout]] = None, + content: Any = None, ): try: + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + if timeout is not None: req = self.client.build_request( - "PUT", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore + "PUT", url, data=request_data, json=json, params=params, headers=headers, timeout=timeout, content=request_content # type: ignore ) else: req = self.client.build_request( - "PUT", url, data=data, json=json, params=params, headers=headers # type: ignore + "PUT", url, data=request_data, json=json, params=params, headers=headers, content=request_content # type: ignore ) response = self.client.send(req, stream=stream) return response @@ -913,21 +983,25 @@ class HTTPHandler: def delete( self, url: str, - data: Optional[Union[dict, str]] = None, # type: ignore + data: Optional[Union[dict, str, bytes]] = None, # type: ignore json: Optional[dict] = None, params: Optional[dict] = None, headers: Optional[dict] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, stream: bool = False, + content: Any = None, ): try: + # Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix) + request_data, request_content = _prepare_request_data_and_content(data, content) + if timeout is not None: req = self.client.build_request( - "DELETE", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore + "DELETE", url, data=request_data, json=json, params=params, headers=headers, timeout=timeout, content=request_content # type: ignore ) else: req = self.client.build_request( - "DELETE", url, data=data, json=json, params=params, headers=headers # type: ignore + "DELETE", url, data=request_data, json=json, params=params, headers=headers, content=request_content # type: ignore ) response = self.client.send(req, stream=stream) response.raise_for_status() diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index b67e8823d0c..0a01e9cd68a 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -1774,11 +1774,16 @@ class BaseLLMHTTPHandler: provider_specific_header=provider_specific_header, custom_llm_provider=custom_llm_provider, ) + forwarded_headers = kwargs.get("headers", None) + if forwarded_headers and extra_headers: + merged_headers = {**forwarded_headers, **extra_headers} + else: + merged_headers = forwarded_headers or extra_headers ( headers, api_base, ) = anthropic_messages_provider_config.validate_anthropic_messages_environment( - headers=extra_headers or {}, + headers=merged_headers or {}, model=model, messages=messages, optional_params=anthropic_messages_optional_request_params, diff --git a/litellm/llms/deepgram/audio_transcription/transformation.py b/litellm/llms/deepgram/audio_transcription/transformation.py index 0cdfd734de7..faa68e97eae 100644 --- a/litellm/llms/deepgram/audio_transcription/transformation.py +++ b/litellm/llms/deepgram/audio_transcription/transformation.py @@ -58,7 +58,7 @@ class DeepgramAudioTranscriptionConfig(BaseAudioTranscriptionConfig): ) -> AudioTranscriptionRequestData: """ Processes the audio file input based on its type and returns AudioTranscriptionRequestData. - + For Deepgram, the binary audio data is sent directly as the request body. Args: @@ -69,12 +69,11 @@ class DeepgramAudioTranscriptionConfig(BaseAudioTranscriptionConfig): """ # Use common utility to process the audio file processed_audio = process_audio_file(audio_file) - + # Return structured data with binary content and no files # For Deepgram, we send binary data directly as request body return AudioTranscriptionRequestData( - data=processed_audio.file_content, - files=None + data=processed_audio.file_content, files=None ) def transform_audio_transcription_response( @@ -99,9 +98,11 @@ class DeepgramAudioTranscriptionConfig(BaseAudioTranscriptionConfig): # Add additional metadata matching OpenAI format response["task"] = "transcribe" - response["language"] = ( - "english" # Deepgram auto-detects but doesn't return language - ) + + # Use detected_language if available, otherwise default to "en" + detected_language = first_channel.get("detected_language") + response["language"] = detected_language if detected_language else "en" + response["duration"] = response_json["metadata"]["duration"] # Transform words to match OpenAI format @@ -150,7 +151,6 @@ class DeepgramAudioTranscriptionConfig(BaseAudioTranscriptionConfig): return url - def _format_param_value(self, value) -> str: """ Formats a parameter value for use in query string. @@ -180,7 +180,7 @@ class DeepgramAudioTranscriptionConfig(BaseAudioTranscriptionConfig): provider_specific_params = self.get_provider_specific_params( optional_params=optional_params, model=model, - openai_params=self.get_supported_openai_params(model) + openai_params=self.get_supported_openai_params(model), ) for key, value in provider_specific_params.items(): diff --git a/litellm/llms/fal_ai/__init__.py b/litellm/llms/fal_ai/__init__.py new file mode 100644 index 00000000000..492197951e9 --- /dev/null +++ b/litellm/llms/fal_ai/__init__.py @@ -0,0 +1,24 @@ +from .cost_calculator import cost_calculator +from .image_generation import ( + FalAIBaseConfig, + FalAIBriaConfig, + FalAIFluxProV11UltraConfig, + FalAIImageGenerationConfig, + FalAIImagen4Config, + FalAIRecraftV3Config, + FalAIStableDiffusionConfig, + get_fal_ai_image_generation_config, +) + +__all__ = [ + "cost_calculator", + "FalAIBaseConfig", + "FalAIImageGenerationConfig", + "FalAIImagen4Config", + "FalAIRecraftV3Config", + "FalAIBriaConfig", + "FalAIFluxProV11UltraConfig", + "FalAIStableDiffusionConfig", + "get_fal_ai_image_generation_config", +] + diff --git a/litellm/llms/fal_ai/cost_calculator.py b/litellm/llms/fal_ai/cost_calculator.py new file mode 100644 index 00000000000..b7caae3834f --- /dev/null +++ b/litellm/llms/fal_ai/cost_calculator.py @@ -0,0 +1,26 @@ +from typing import Any + +import litellm +from litellm.types.utils import ImageResponse + + +def cost_calculator( + model: str, + image_response: Any, +) -> float: + """ + fal.ai image generation cost calculator + """ + _model_info = litellm.get_model_info( + model=model, + custom_llm_provider=litellm.LlmProviders.FAL_AI.value, + ) + output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0 + num_images: int = 0 + if isinstance(image_response, ImageResponse): + if image_response.data: + num_images = len(image_response.data) + return output_cost_per_image * num_images + else: + raise ValueError(f"image_response must be of type ImageResponse got type={type(image_response)}") + diff --git a/litellm/llms/fal_ai/image_generation/__init__.py b/litellm/llms/fal_ai/image_generation/__init__.py new file mode 100644 index 00000000000..74d3b434b87 --- /dev/null +++ b/litellm/llms/fal_ai/image_generation/__init__.py @@ -0,0 +1,49 @@ +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) + +from .bria_transformation import FalAIBriaConfig +from .flux_pro_v11_ultra_transformation import FalAIFluxProV11UltraConfig +from .imagen4_transformation import FalAIImagen4Config +from .recraft_v3_transformation import FalAIRecraftV3Config +from .stable_diffusion_transformation import FalAIStableDiffusionConfig +from .transformation import FalAIBaseConfig, FalAIImageGenerationConfig + +__all__ = [ + "FalAIBaseConfig", + "FalAIImageGenerationConfig", + "FalAIImagen4Config", + "FalAIRecraftV3Config", + "FalAIBriaConfig", + "FalAIFluxProV11UltraConfig", + "FalAIStableDiffusionConfig", +] + + +def get_fal_ai_image_generation_config(model: str) -> BaseImageGenerationConfig: + """ + Get the appropriate Fal AI image generation configuration based on the model. + + Args: + model: The Fal AI model name (e.g., "fal-ai/imagen4/preview", "fal-ai/recraft/v3/text-to-image") + + Returns: + The appropriate configuration class for the specified model + """ + model_lower = model.lower() + + # Map model names to their corresponding configuration classes + if "imagen4" in model_lower or "imagen-4" in model_lower: + return FalAIImagen4Config() + elif "recraft" in model_lower: + return FalAIRecraftV3Config() + elif "bria" in model_lower: + return FalAIBriaConfig() + elif "flux-pro" in model_lower and "ultra" in model_lower: + return FalAIFluxProV11UltraConfig() + elif "stable-diffusion" in model_lower: + return FalAIStableDiffusionConfig() + + # Default to generic Fal AI configuration + return FalAIImageGenerationConfig() + diff --git a/litellm/llms/fal_ai/image_generation/bria_transformation.py b/litellm/llms/fal_ai/image_generation/bria_transformation.py new file mode 100644 index 00000000000..cb5aa6b761d --- /dev/null +++ b/litellm/llms/fal_ai/image_generation/bria_transformation.py @@ -0,0 +1,231 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams +from litellm.types.utils import ImageObject, ImageResponse + +from .transformation import FalAIBaseConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class FalAIBriaConfig(FalAIBaseConfig): + """ + Configuration for Bria Text-to-Image 3.2 model. + + Bria 3.2 is a commercial-grade text-to-image model with prompt enhancement + and multiple aspect ratio options. + + Model endpoint: bria/text-to-image/3.2 + Documentation: https://fal.ai/models/bria/text-to-image/3.2 + """ + IMAGE_GENERATION_ENDPOINT: str = "bria/text-to-image/3.2" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + Get supported OpenAI parameters for Bria 3.2. + """ + return [ + "n", + "response_format", + "size", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Bria 3.2 parameters. + + Mappings: + - size -> aspect_ratio (1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9) + - response_format -> ignored (Bria returns URLs) + - n -> ignored (Bria doesn't support multiple images in one call) + """ + supported_params = self.get_supported_openai_params(model) + + # Map OpenAI params to Bria params + param_mapping = { + "size": "aspect_ratio", + } + + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + # Use mapped parameter name if exists + mapped_key = param_mapping.get(k, k) + mapped_value = non_default_params[k] + + # Transform specific parameters + if k == "response_format": + # Bria always returns URLs, so we can ignore this + continue + elif k == "n": + # Bria doesn't support multiple images, ignore + continue + elif k == "size": + # Map OpenAI size format to Bria aspect ratio + mapped_value = self._map_aspect_ratio(mapped_value) + + optional_params[mapped_key] = mapped_value + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def _map_aspect_ratio(self, size: str) -> str: + """ + Map OpenAI size format to Bria aspect ratio format. + + OpenAI format: "1024x1024", "1792x1024", etc. + Bria format: "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9" + """ + # Map common OpenAI sizes to Bria aspect ratios + size_to_aspect_ratio = { + "1024x1024": "1:1", + "512x512": "1:1", + "1792x1024": "16:9", + "1024x1792": "9:16", + "1024x768": "4:3", + "768x1024": "3:4", + "1280x960": "4:3", + "960x1280": "3:4", + } + + if size in size_to_aspect_ratio: + return size_to_aspect_ratio[size] + + # Parse custom size format "WIDTHxHEIGHT" and calculate aspect ratio + if "x" in size: + try: + width_str, height_str = size.split("x") + width = int(width_str) + height = int(height_str) + + # Calculate aspect ratio and find closest match + ratio = width / height + + # Map to closest supported aspect ratio + if 0.95 <= ratio <= 1.05: # Close to 1:1 + return "1:1" + elif ratio >= 1.7: # Close to 16:9 + return "16:9" + elif ratio <= 0.6: # Close to 9:16 + return "9:16" + elif 1.3 <= ratio <= 1.4: # Close to 4:3 + return "4:3" + elif 0.7 <= ratio <= 0.8: # Close to 3:4 + return "3:4" + elif 1.45 <= ratio <= 1.55: # Close to 3:2 + return "3:2" + elif 0.65 <= ratio <= 0.7: # Close to 2:3 + return "2:3" + elif 1.2 <= ratio <= 1.3: # Close to 5:4 + return "5:4" + elif 0.75 <= ratio <= 0.85: # Close to 4:5 + return "4:5" + except (ValueError, AttributeError, ZeroDivisionError): + pass + + # Default to 1:1 + return "1:1" + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to Bria 3.2 request body. + + Required parameters: + - prompt: Prompt for image generation + + Optional parameters: + - aspect_ratio: "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9" (default: "1:1") + - prompt_enhancer: Improve the prompt (default: true) + - sync_mode: Return image directly in response (default: false) + - truncate_prompt: Truncate the prompt (default: true) + - guidance_scale: Guidance scale 1-10 (default: 5) + - num_inference_steps: Inference steps 20-50 (default: 30) + - seed: Random seed for reproducibility (default: 5555) + - negative_prompt: Negative prompt string + """ + bria_request_body = { + "prompt": prompt, + **optional_params, + } + + return bria_request_body + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the Bria 3.2 response to litellm ImageResponse format. + + Expected response format: + { + "image": { + "url": "https://...", + "content_type": "image/png", + "file_name": "...", + "file_size": 123456, + "width": 1024, + "height": 1024 + } + } + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + if not model_response.data: + model_response.data = [] + + # Handle Bria response format - uses "image" (singular) not "images" + image_data = response_data.get("image") + if image_data and isinstance(image_data, dict): + model_response.data.append( + ImageObject( + url=image_data.get("url", None), + b64_json=None, # Bria returns URLs only + ) + ) + + return model_response + diff --git a/litellm/llms/fal_ai/image_generation/flux_pro_v11_ultra_transformation.py b/litellm/llms/fal_ai/image_generation/flux_pro_v11_ultra_transformation.py new file mode 100644 index 00000000000..664f11d40dc --- /dev/null +++ b/litellm/llms/fal_ai/image_generation/flux_pro_v11_ultra_transformation.py @@ -0,0 +1,263 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams +from litellm.types.utils import ImageObject, ImageResponse + +from .transformation import FalAIBaseConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class FalAIFluxProV11UltraConfig(FalAIBaseConfig): + """ + Configuration for Fal AI Flux Pro v1.1-ultra model. + + FLUX Pro v1.1-ultra is a high-quality text-to-image model with enhanced detail + and support for image prompts. + + Model endpoint: fal-ai/flux-pro/v1.1-ultra + Documentation: https://fal.ai/models/fal-ai/flux-pro/v1.1-ultra + """ + IMAGE_GENERATION_ENDPOINT: str = "fal-ai/flux-pro/v1.1-ultra" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + Get supported OpenAI parameters for Flux Pro v1.1-ultra. + """ + return [ + "n", + "response_format", + "size", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Flux Pro v1.1-ultra parameters. + + Mappings: + - n -> num_images (1-4, default 1) + - response_format -> output_format (jpeg or png) + - size -> aspect_ratio (21:9, 16:9, 4:3, 3:2, 1:1, 2:3, 3:4, 9:16, 9:21) + """ + supported_params = self.get_supported_openai_params(model) + + # Map OpenAI params to Flux Pro v1.1-ultra params + param_mapping = { + "n": "num_images", + "response_format": "output_format", + "size": "aspect_ratio", + } + + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + # Use mapped parameter name if exists + mapped_key = param_mapping.get(k, k) + mapped_value = non_default_params[k] + + # Transform specific parameters + if k == "response_format": + # Map OpenAI response formats to image formats + if mapped_value in ["b64_json", "url"]: + mapped_value = "jpeg" + elif k == "size": + # Map OpenAI size format to Flux aspect ratio + mapped_value = self._map_aspect_ratio(mapped_value) + + optional_params[mapped_key] = mapped_value + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def _map_aspect_ratio(self, size: str) -> str: + """ + Map OpenAI size format to Flux Pro aspect ratio format. + + OpenAI format: "1024x1024", "1792x1024", etc. + Flux format: "21:9", "16:9", "4:3", "3:2", "1:1", "2:3", "3:4", "9:16", "9:21" + + Default: "16:9" + """ + # Map common OpenAI sizes to Flux aspect ratios + size_to_aspect_ratio = { + "1024x1024": "1:1", + "512x512": "1:1", + "1792x1024": "16:9", + "1024x1792": "9:16", + "1024x768": "4:3", + "768x1024": "3:4", + "1536x1024": "3:2", + "1024x1536": "2:3", + "2048x876": "21:9", + "876x2048": "9:21", + } + + if size in size_to_aspect_ratio: + return size_to_aspect_ratio[size] + + # Parse custom size format "WIDTHxHEIGHT" and calculate aspect ratio + if "x" in size: + try: + width_str, height_str = size.split("x") + width = int(width_str) + height = int(height_str) + + # Calculate aspect ratio and find closest match + ratio = width / height + + # Map to closest supported aspect ratio + if 0.95 <= ratio <= 1.05: # Close to 1:1 + return "1:1" + elif ratio >= 2.3: # Close to 21:9 + return "21:9" + elif 1.7 <= ratio < 2.3: # Close to 16:9 + return "16:9" + elif 1.3 <= ratio < 1.7: # Close to 4:3 + return "4:3" + elif 1.4 <= ratio < 1.6: # Close to 3:2 + return "3:2" + elif 0.6 <= ratio < 0.7: # Close to 3:4 + return "3:4" + elif 0.65 <= ratio < 0.75: # Close to 2:3 + return "2:3" + elif 0.5 <= ratio < 0.6: # Close to 9:16 + return "9:16" + elif ratio < 0.5: # Close to 9:21 + return "9:21" + except (ValueError, AttributeError, ZeroDivisionError): + pass + + # Default to 16:9 + return "16:9" + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to Flux Pro v1.1-ultra request body. + + Required parameters: + - prompt: The prompt to generate an image from + + Optional parameters: + - num_images: Number of images (1-4, default: 1) + - aspect_ratio: Aspect ratio (default: "16:9") + - raw: Generate less processed images (default: false) + - output_format: "jpeg" or "png" (default: "jpeg") + - image_url: Image URL for image-to-image generation + - sync_mode: Return data URI (default: false) + - safety_tolerance: Safety level "1"-"6" (default: "2") + - enable_safety_checker: Enable safety checker (default: true) + - seed: Random seed for reproducibility + - image_prompt_strength: Strength of image prompt 0-1 (default: 0.1) + - enhance_prompt: Enhance prompt for better results (default: false) + """ + flux_pro_request_body = { + "prompt": prompt, + **optional_params, + } + + return flux_pro_request_body + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the Flux Pro v1.1-ultra response to litellm ImageResponse format. + + Expected response format: + { + "images": [ + { + "url": "https://...", + "width": 1024, + "height": 768, + "content_type": "image/jpeg" + } + ], + "timings": {"inference": 2.5, ...}, + "seed": 42, + "has_nsfw_concepts": [false], + "prompt": "original prompt" + } + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + if not model_response.data: + model_response.data = [] + + # Handle Flux Pro v1.1-ultra response format + images = response_data.get("images", []) + if isinstance(images, list): + for image_data in images: + if isinstance(image_data, dict): + model_response.data.append( + ImageObject( + url=image_data.get("url", None), + b64_json=None, # Flux Pro returns URLs only + ) + ) + elif isinstance(image_data, str): + # If images is just a list of URLs + model_response.data.append( + ImageObject( + url=image_data, + b64_json=None, + ) + ) + + # Add additional metadata from Flux Pro response + if hasattr(model_response, "_hidden_params"): + if "seed" in response_data: + model_response._hidden_params["seed"] = response_data["seed"] + if "timings" in response_data: + model_response._hidden_params["timings"] = response_data["timings"] + if "has_nsfw_concepts" in response_data: + model_response._hidden_params["has_nsfw_concepts"] = response_data[ + "has_nsfw_concepts" + ] + + return model_response + diff --git a/litellm/llms/fal_ai/image_generation/imagen4_transformation.py b/litellm/llms/fal_ai/image_generation/imagen4_transformation.py new file mode 100644 index 00000000000..f38ced65313 --- /dev/null +++ b/litellm/llms/fal_ai/image_generation/imagen4_transformation.py @@ -0,0 +1,242 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams +from litellm.types.utils import ImageObject, ImageResponse + +from .transformation import FalAIBaseConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class FalAIImagen4Config(FalAIBaseConfig): + """ + Configuration for Fal AI Imagen4 model. + + Google's highest quality image generation model available through Fal AI. + + Model variants: + - fal-ai/imagen4/preview (Standard): $0.05 per image + - fal-ai/imagen4/preview/fast (Fast): $0.04 per image + - fal-ai/imagen4/preview/ultra (Ultra): $0.06 per image + + Documentation: https://fal.ai/models/fal-ai/imagen4/preview + """ + IMAGE_GENERATION_ENDPOINT: str = "fal-ai/imagen4/preview" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + Get supported OpenAI parameters for Imagen4. + """ + return [ + "n", + "response_format", + "size", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Imagen4 parameters. + + Mappings: + - n -> num_images (1-4, default 1) + - size -> aspect_ratio (1:1, 16:9, 9:16, 3:4, 4:3) + - response_format -> ignored (Imagen4 returns URLs) + """ + supported_params = self.get_supported_openai_params(model) + + # Map OpenAI params to Imagen4 params + param_mapping = { + "n": "num_images", + "size": "aspect_ratio", + } + + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + # Use mapped parameter name if exists + mapped_key = param_mapping.get(k, k) + mapped_value = non_default_params[k] + + # Transform specific parameters + if k == "response_format": + # Imagen4 always returns URLs, so we can ignore this + continue + elif k == "size": + # Map OpenAI size format to Imagen4 aspect ratio + mapped_value = self._map_aspect_ratio(mapped_value) + + optional_params[mapped_key] = mapped_value + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def _map_aspect_ratio(self, size: str) -> str: + """ + Map OpenAI size format to Imagen4 aspect ratio format. + + OpenAI format: "1024x1024", "1792x1024", etc. + Imagen4 format: "1:1", "16:9", "9:16", "3:4", "4:3" + + Available aspect ratios: + - 1:1 (default) + - 16:9 + - 9:16 + - 3:4 + - 4:3 + """ + # Map common OpenAI sizes to Imagen4 aspect ratios + size_to_aspect_ratio = { + "1024x1024": "1:1", + "512x512": "1:1", + "1792x1024": "16:9", + "1024x1792": "9:16", + "1024x768": "4:3", + "768x1024": "3:4", + } + + if size in size_to_aspect_ratio: + return size_to_aspect_ratio[size] + + # Parse custom size format "WIDTHxHEIGHT" and calculate aspect ratio + if "x" in size: + try: + width_str, height_str = size.split("x") + width = int(width_str) + height = int(height_str) + + # Calculate aspect ratio and find closest match + ratio = width / height + + # Map to closest supported aspect ratio + if 0.95 <= ratio <= 1.05: # Close to 1:1 + return "1:1" + elif ratio >= 1.7: # Close to 16:9 + return "16:9" + elif ratio <= 0.6: # Close to 9:16 + return "9:16" + elif ratio >= 1.2: # Close to 4:3 + return "4:3" + elif ratio <= 0.8: # Close to 3:4 + return "3:4" + except (ValueError, AttributeError, ZeroDivisionError): + pass + + # Default to 1:1 + return "1:1" + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to Imagen4 request body. + + Required parameters: + - prompt: The text prompt describing what you want to see + + Optional parameters: + - aspect_ratio: "1:1", "16:9", "9:16", "3:4", "4:3" (default: "1:1") + - num_images: Number of images (1-4, default: 1) + - resolution: "1K" or "2K" (default: "1K") + - seed: Random seed for reproducibility + - negative_prompt: Description of what to discourage (default: "") + """ + imagen4_request_body = { + "prompt": prompt, + **optional_params, + } + + return imagen4_request_body + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the Imagen4 response to litellm ImageResponse format. + + Expected response format: + { + "images": [ + { + "url": "https://...", + "content_type": "image/png", + "file_name": "z9RV14K95DvU.png", + "file_size": 4404019 + } + ], + "seed": 42 + } + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + if not model_response.data: + model_response.data = [] + + # Handle Imagen4 response format + images = response_data.get("images", []) + if isinstance(images, list): + for image_data in images: + if isinstance(image_data, dict): + model_response.data.append( + ImageObject( + url=image_data.get("url", None), + b64_json=None, # Imagen4 returns URLs only + ) + ) + elif isinstance(image_data, str): + # If images is just a list of URLs + model_response.data.append( + ImageObject( + url=image_data, + b64_json=None, + ) + ) + + # Add seed metadata from Imagen4 response + if hasattr(model_response, "_hidden_params"): + if "seed" in response_data: + model_response._hidden_params["seed"] = response_data["seed"] + + return model_response + diff --git a/litellm/llms/fal_ai/image_generation/recraft_v3_transformation.py b/litellm/llms/fal_ai/image_generation/recraft_v3_transformation.py new file mode 100644 index 00000000000..572a8a0f1c3 --- /dev/null +++ b/litellm/llms/fal_ai/image_generation/recraft_v3_transformation.py @@ -0,0 +1,226 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams +from litellm.types.utils import ImageObject, ImageResponse + +from .transformation import FalAIBaseConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class FalAIRecraftV3Config(FalAIBaseConfig): + """ + Configuration for Fal AI Recraft v3 Text-to-Image model. + + Recraft v3 is a text-to-image model with multiple style options including + realistic images, digital illustrations, and vector illustrations. + + Model endpoint: fal-ai/recraft/v3/text-to-image + Documentation: https://fal.ai/models/fal-ai/recraft/v3/text-to-image + """ + IMAGE_GENERATION_ENDPOINT: str = "fal-ai/recraft/v3/text-to-image" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + Get supported OpenAI parameters for Recraft v3. + """ + return [ + "n", + "response_format", + "size", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Recraft v3 parameters. + + Mappings: + - size -> image_size (can be preset or custom width/height) + - response_format -> ignored (Recraft returns URLs) + - n -> ignored (Recraft doesn't support multiple images) + """ + supported_params = self.get_supported_openai_params(model) + + # Map OpenAI params to Recraft v3 params + param_mapping = { + "size": "image_size", + } + + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + # Use mapped parameter name if exists + mapped_key = param_mapping.get(k, k) + mapped_value = non_default_params[k] + + # Transform specific parameters + if k == "response_format": + # Recraft always returns URLs, so we can ignore this + continue + elif k == "n": + # Recraft doesn't support multiple images, ignore + continue + elif k == "size": + # Map OpenAI size format to Recraft image_size + mapped_value = self._map_image_size(mapped_value) + + optional_params[mapped_key] = mapped_value + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def _map_image_size(self, size: str) -> Any: + """ + Map OpenAI size format to Recraft v3 image_size format. + + OpenAI format: "1024x1024", "1792x1024", etc. + Recraft format: Can be preset strings or {"width": int, "height": int} + + Available presets: + - square_hd (default) + - square + - portrait_4_3 + - portrait_16_9 + - landscape_4_3 + - landscape_16_9 + """ + # Map common OpenAI sizes to Recraft presets + size_mapping = { + "1024x1024": "square_hd", + "512x512": "square", + "768x1024": "portrait_4_3", + "576x1024": "portrait_16_9", + "1024x768": "landscape_4_3", + "1024x576": "landscape_16_9", + } + + if size in size_mapping: + return size_mapping[size] + + # Parse custom size format "WIDTHxHEIGHT" + if "x" in size: + try: + width, height = size.split("x") + return { + "width": int(width), + "height": int(height), + } + except (ValueError, AttributeError): + pass + + # Default to square_hd + return "square_hd" + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to Recraft v3 request body. + + Required parameters: + - prompt: Text prompt (max 1000 characters) + + Optional parameters: + - image_size: Preset or {"width": int, "height": int} (default: "square_hd") + - style: Style preset (default: "realistic_image") + Options: "any", "realistic_image", "digital_illustration", "vector_illustration", etc. + - colors: Array of RGB color objects [{"r": 0-255, "g": 0-255, "b": 0-255}] + - enable_safety_checker: Enable safety checker (default: false) + - style_id: UUID for custom style reference + + Note: Vector illustrations cost 2X as much. + """ + recraft_request_body = { + "prompt": prompt, + **optional_params, + } + + return recraft_request_body + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the Recraft v3 response to litellm ImageResponse format. + + Expected response format: + { + "images": [ + { + "url": "https://...", + "content_type": "image/webp", + "file_name": "...", + "file_size": 123456 + } + ] + } + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + if not model_response.data: + model_response.data = [] + + # Handle Recraft v3 response format + images = response_data.get("images", []) + if isinstance(images, list): + for image_data in images: + if isinstance(image_data, dict): + model_response.data.append( + ImageObject( + url=image_data.get("url", None), + b64_json=None, # Recraft returns URLs only + ) + ) + elif isinstance(image_data, str): + # If images is just a list of URLs + model_response.data.append( + ImageObject( + url=image_data, + b64_json=None, + ) + ) + + return model_response + diff --git a/litellm/llms/fal_ai/image_generation/stable_diffusion_transformation.py b/litellm/llms/fal_ai/image_generation/stable_diffusion_transformation.py new file mode 100644 index 00000000000..10e2c6b4161 --- /dev/null +++ b/litellm/llms/fal_ai/image_generation/stable_diffusion_transformation.py @@ -0,0 +1,281 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.types.llms.openai import OpenAIImageGenerationOptionalParams +from litellm.types.utils import ImageObject, ImageResponse + +from .transformation import FalAIBaseConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class FalAIStableDiffusionConfig(FalAIBaseConfig): + """ + Configuration for Fal AI Stable Diffusion models. + + Supports Stable Diffusion v3.5 variants and other Stable Diffusion models on Fal AI. + + Example models: + - fal-ai/stable-diffusion-v35-medium + - fal-ai/stable-diffusion-v35-large + + Documentation: https://fal.ai/models/fal-ai/stable-diffusion-v35-medium + """ + IMAGE_GENERATION_ENDPOINT: str = "" # Will be set from model name + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete url for the request. + + For Stable Diffusion models, extract the endpoint from the model name. + """ + from litellm.secret_managers.main import get_secret_str + + complete_url: str = ( + api_base + or get_secret_str("FAL_AI_API_BASE") + or self.DEFAULT_BASE_URL + ) + + complete_url = complete_url.rstrip("/") + + # Extract endpoint from model name + # e.g., "fal-ai/stable-diffusion-v35-medium" or "stable-diffusion-v35-medium" + endpoint = model + if "/" in model and not model.startswith("fal-ai/"): + # If model is like "custom/stable-diffusion-v35-medium", use full path + endpoint = model + elif not model.startswith("fal-ai/"): + # If model is just "stable-diffusion-v35-medium", prepend fal-ai + endpoint = f"fal-ai/{model}" + + complete_url = f"{complete_url}/{endpoint}" + return complete_url + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + Get supported OpenAI parameters for Stable Diffusion models. + """ + return [ + "n", + "response_format", + "size", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Stable Diffusion parameters. + + Mappings: + - n -> num_images (1-4, default 1) + - response_format -> output_format (jpeg or png) + - size -> image_size (can be preset or custom width/height) + """ + supported_params = self.get_supported_openai_params(model) + + # Map OpenAI params to Stable Diffusion params + param_mapping = { + "n": "num_images", + "response_format": "output_format", + "size": "image_size", + } + + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + # Use mapped parameter name if exists + mapped_key = param_mapping.get(k, k) + mapped_value = non_default_params[k] + + # Transform specific parameters + if k == "response_format": + # Map OpenAI response formats to image formats + if mapped_value in ["b64_json", "url"]: + mapped_value = "jpeg" + elif k == "size": + # Map OpenAI size format to Stable Diffusion image_size + mapped_value = self._map_image_size(mapped_value) + + optional_params[mapped_key] = mapped_value + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def _map_image_size(self, size: str) -> Any: + """ + Map OpenAI size format to Stable Diffusion image_size format. + + OpenAI format: "1024x1024", "1792x1024", etc. + Stable Diffusion format: Can be preset strings or {"width": int, "height": int} + + Available presets: + - square_hd + - square + - portrait_4_3 + - portrait_16_9 + - landscape_4_3 (default) + - landscape_16_9 + """ + # Map common OpenAI sizes to Stable Diffusion presets + size_mapping = { + "1024x1024": "square_hd", + "512x512": "square", + "768x1024": "portrait_4_3", + "576x1024": "portrait_16_9", + "1024x768": "landscape_4_3", + "1024x576": "landscape_16_9", + } + + if size in size_mapping: + return size_mapping[size] + + # Parse custom size format "WIDTHxHEIGHT" + if "x" in size: + try: + width, height = size.split("x") + return { + "width": int(width), + "height": int(height), + } + except (ValueError, AttributeError): + pass + + # Default to landscape_4_3 + return "landscape_4_3" + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to Stable Diffusion request body. + + Required parameters: + - prompt: The prompt to generate an image from + + Optional parameters: + - num_images: Number of images (1-4, default: 1) + - image_size: Size preset or {"width": int, "height": int} (default: landscape_4_3) + - output_format: "jpeg" or "png" (default: jpeg) + - sync_mode: Wait for image upload before returning (default: false) + - guidance_scale: CFG scale 0-20 (default: 4.5) + - num_inference_steps: Inference steps 1-50 (default: 40) + - seed: Random seed for reproducibility + - negative_prompt: Negative prompt string (default: "") + - enable_safety_checker: Enable safety checker (default: true) + """ + stable_diffusion_request_body = { + "prompt": prompt, + **optional_params, + } + + return stable_diffusion_request_body + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the Stable Diffusion response to litellm ImageResponse format. + + Expected response format: + { + "images": [ + { + "url": "https://...", + "width": 1024, + "height": 768, + "content_type": "image/jpeg" + } + ], + "timings": {"inference": 2.5, ...}, + "seed": 42, + "has_nsfw_concepts": [false], + "prompt": "original prompt" + } + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + if not model_response.data: + model_response.data = [] + + # Handle Stable Diffusion response format + images = response_data.get("images", []) + if isinstance(images, list): + for image_data in images: + if isinstance(image_data, dict): + model_response.data.append( + ImageObject( + url=image_data.get("url", None), + b64_json=None, # Stable Diffusion returns URLs only + ) + ) + elif isinstance(image_data, str): + # If images is just a list of URLs + model_response.data.append( + ImageObject( + url=image_data, + b64_json=None, + ) + ) + + # Add additional metadata from Stable Diffusion response + if hasattr(model_response, "_hidden_params"): + if "seed" in response_data: + model_response._hidden_params["seed"] = response_data["seed"] + if "timings" in response_data: + model_response._hidden_params["timings"] = response_data["timings"] + if "has_nsfw_concepts" in response_data: + model_response._hidden_params["has_nsfw_concepts"] = response_data[ + "has_nsfw_concepts" + ] + + return model_response + diff --git a/litellm/llms/fal_ai/image_generation/transformation.py b/litellm/llms/fal_ai/image_generation/transformation.py new file mode 100644 index 00000000000..04b7b167523 --- /dev/null +++ b/litellm/llms/fal_ai/image_generation/transformation.py @@ -0,0 +1,176 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIImageGenerationOptionalParams, +) +from litellm.types.utils import ImageObject, ImageResponse + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class FalAIBaseConfig(BaseImageGenerationConfig): + """ + Base configuration for Fal AI image generation models. + Handles common functionality like URL construction and authentication. + """ + DEFAULT_BASE_URL: str = "https://fal.run" + IMAGE_GENERATION_ENDPOINT: str = "" + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete url for the request + + Some providers need `model` in `api_base` + """ + complete_url: str = ( + api_base + or get_secret_str("FAL_AI_API_BASE") + or self.DEFAULT_BASE_URL + ) + + complete_url = complete_url.rstrip("/") + if self.IMAGE_GENERATION_ENDPOINT: + complete_url = f"{complete_url}/{self.IMAGE_GENERATION_ENDPOINT}" + return complete_url + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + final_api_key: Optional[str] = ( + api_key or + get_secret_str("FAL_AI_API_KEY") + ) + if not final_api_key: + raise ValueError("FAL_AI_API_KEY is not set") + + headers["Authorization"] = f"Key {final_api_key}" + return headers + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the image generation response to the litellm image response + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + if not model_response.data: + model_response.data = [] + + # Handle fal.ai response format + images = response_data.get("images", []) + if isinstance(images, list): + for image_data in images: + if isinstance(image_data, dict): + model_response.data.append(ImageObject( + url=image_data.get("url", None), + b64_json=image_data.get("b64_json", None), + )) + elif isinstance(image_data, str): + # If images is just a list of URLs + model_response.data.append(ImageObject( + url=image_data, + b64_json=None, + )) + + return model_response + + +class FalAIImageGenerationConfig(FalAIBaseConfig): + """ + Default Fal AI image generation configuration for generic models. + """ + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + Get supported OpenAI parameters for fal.ai image generation + """ + return [ + "n", + "response_format", + "size", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + supported_params = self.get_supported_openai_params(model) + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + optional_params[k] = non_default_params[k] + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to the fal.ai image generation request body + """ + fal_ai_image_generation_request_body = { + "prompt": prompt, + **optional_params, + } + return fal_ai_image_generation_request_body + diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 3ab827797c5..167ba26bacb 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -2,7 +2,8 @@ import base64 import datetime import hashlib import json -from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, List, Optional, Tuple, Union +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, List, Optional, Protocol, Tuple, Union from urllib.parse import urlparse import httpx @@ -62,6 +63,47 @@ else: LiteLLMLoggingObj = Any +class OCISignerProtocol(Protocol): + """ + Protocol for OCI request signers (e.g., oci.signer.Signer). + + This protocol defines the interface expected for OCI SDK signer objects. + Compatible with the OCI Python SDK's Signer class. + + See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html + """ + + def do_request_sign(self, request: Any, *, enforce_content_headers: bool = False) -> None: + """ + Sign an HTTP request by adding authentication headers. + + Args: + request: Request object with method, url, headers, body, and path_url attributes + enforce_content_headers: Whether to enforce content-type and content-length headers + """ + ... + + +@dataclass +class OCIRequestWrapper: + """ + Wrapper for HTTP requests compatible with OCI signer interface. + + This class wraps request data in a format compatible with OCI SDK signers, + which expect objects with method, url, headers, body, and path_url attributes. + """ + method: str + url: str + headers: dict + body: bytes + + @property + def path_url(self) -> str: + """Returns the path + query string for OCI signing.""" + parsed_url = urlparse(self.url) + return parsed_url.path + ("?" + parsed_url.query if parsed_url.query else "") + + def sha256_base64(data: bytes) -> str: digest = hashlib.sha256(data).digest() return base64.b64encode(digest).decode() @@ -228,29 +270,89 @@ class OCIChatConfig(BaseConfig): return adapted_params - def sign_request( + def _sign_with_oci_signer( self, headers: dict, optional_params: dict, request_data: dict, api_base: str, - api_key: Optional[str] = None, - model: Optional[str] = None, - stream: Optional[bool] = None, - fake_stream: Optional[bool] = None, - ) -> Tuple[dict, Optional[bytes]]: + ) -> Tuple[dict, bytes]: """ - Some providers like Bedrock require signing the request. The sign request funtion needs access to `request_data` and `complete_url` - Args: - headers: dict - optional_params: dict - request_data: dict - the request body being sent in http request - api_base: str - the complete url being sent in http request - Returns: - dict - the signed headers - """ - import json + Sign request using OCI SDK Signer object. + Args: + headers: Request headers to be signed + optional_params: Optional parameters including oci_signer + request_data: The request body dict to be sent in HTTP request + api_base: The complete URL for the HTTP request + + Returns: + Tuple of (signed_headers, encoded_body) + + Raises: + OCIError: If signing fails + ValueError: If HTTP method is unsupported + """ + oci_signer = optional_params.get("oci_signer") + body = json.dumps(request_data).encode("utf-8") + method = str(optional_params.get("method", "POST")).upper() + + if method not in ["POST", "GET", "PUT", "DELETE", "PATCH"]: + raise ValueError(f"Unsupported HTTP method: {method}") + + prepared_headers = headers.copy() + prepared_headers.setdefault("content-type", "application/json") + prepared_headers.setdefault("content-length", str(len(body))) + + request_wrapper = OCIRequestWrapper( + method=method, + url=api_base, + headers=prepared_headers, + body=body + ) + + if oci_signer is None: + raise ValueError("oci_signer cannot be None when calling _sign_with_oci_signer") + + try: + oci_signer.do_request_sign(request_wrapper, enforce_content_headers=True) + except Exception as e: + raise OCIError( + status_code=500, + message=( + f"Failed to sign request with provided oci_signer: {str(e)}. " + "The signer must implement the OCI SDK Signer interface with a " + "do_request_sign(request, enforce_content_headers=True) method. " + "See: https://docs.oracle.com/en-us/iaas/tools/python/latest/api/signing.html" + ) + ) from e + + headers.update(request_wrapper.headers) + return headers, body + + def _sign_with_manual_credentials( + self, + headers: dict, + optional_params: dict, + request_data: dict, + api_base: str, + ) -> Tuple[dict, None]: + """ + Sign request using manual OCI credentials. + + Args: + headers: Request headers to be signed + optional_params: Optional parameters including OCI credentials + request_data: The request body dict to be sent in HTTP request + api_base: The complete URL for the HTTP request + + Returns: + Tuple of (signed_headers, None) + + Raises: + Exception: If required credentials are missing + ImportError: If cryptography package is not installed + """ oci_region = optional_params.get("oci_region", "us-ashburn-1") api_base = ( api_base @@ -355,6 +457,69 @@ class OCIChatConfig(BaseConfig): return headers, None + def sign_request( + self, + headers: dict, + optional_params: dict, + request_data: dict, + api_base: str, + api_key: Optional[str] = None, + model: Optional[str] = None, + stream: Optional[bool] = None, + fake_stream: Optional[bool] = None, + ) -> Tuple[dict, Optional[bytes]]: + """ + Sign the OCI request by adding authentication headers. + + Supports two signing modes: + 1. OCI SDK Signer: Use an oci_signer object to sign the request + 2. Manual Signing: Use OCI credentials to manually sign the request + + Args: + headers: Request headers to be signed + optional_params: Optional parameters including auth credentials or oci_signer + request_data: The request body dict to be sent in HTTP request + api_base: The complete URL for the HTTP request + api_key: Optional API key (not used for OCI) + model: Optional model name + stream: Optional streaming flag + fake_stream: Optional fake streaming flag + + Returns: + Tuple of (signed_headers, encoded_body): + - If oci_signer is provided: Returns (headers, body) where body is the encoded JSON + - If manual credentials are provided: Returns (headers, None) as body is not returned + for the manual signing path + + Raises: + OCIError: If signing fails with oci_signer + Exception: If required credentials are missing + ImportError: If cryptography package is not installed (manual signing only) + + Example: + >>> from oci.signer import Signer + >>> signer = Signer( + ... tenancy="ocid1.tenancy.oc1..", + ... user="ocid1.user.oc1..", + ... fingerprint="xx:xx:xx", + ... private_key_file_location="~/.oci/key.pem" + ... ) + >>> headers, body = config.sign_request( + ... headers={}, + ... optional_params={"oci_signer": signer}, + ... request_data={"message": "Hello"}, + ... api_base="https://inference.generativeai.us-ashburn-1.oci.oraclecloud.com/..." + ... ) + """ + oci_signer = optional_params.get("oci_signer") + + # If a signer is provided, use it for request signing + if oci_signer is not None: + return self._sign_with_oci_signer(headers, optional_params, request_data, api_base) + + # Standard manual credential signing + return self._sign_with_manual_credentials(headers, optional_params, request_data, api_base) + def validate_environment( self, headers: dict, @@ -365,36 +530,67 @@ class OCIChatConfig(BaseConfig): api_key: Optional[str] = None, api_base: Optional[str] = None, ) -> dict: + """ + Validate the OCI environment and credentials. + + Supports two authentication modes: + 1. OCI SDK Signer: Pass an oci_signer object (e.g., oci.signer.Signer) + 2. Manual Credentials: Pass oci_user, oci_fingerprint, oci_tenancy, and oci_key/oci_key_file + + Args: + headers: Request headers to populate + model: Model name + messages: List of chat messages + optional_params: Optional parameters including authentication credentials + litellm_params: LiteLLM parameters + api_key: Optional API key (not used for OCI) + api_base: Optional API base URL + + Returns: + Updated headers dict + + Raises: + Exception: If required parameters are missing or invalid + """ + oci_signer = optional_params.get("oci_signer") oci_region = optional_params.get("oci_region", "us-ashburn-1") + + # Determine api_base api_base = ( api_base or litellm.api_base or f"https://inference.generativeai.{oci_region}.oci.oraclecloud.com" ) - oci_user = optional_params.get("oci_user") - oci_fingerprint = optional_params.get("oci_fingerprint") - oci_tenancy = optional_params.get("oci_tenancy") - oci_key = optional_params.get("oci_key") - oci_key_file = optional_params.get("oci_key_file") - oci_compartment_id = optional_params.get("oci_compartment_id") - - if ( - not oci_user - or not oci_fingerprint - or not oci_tenancy - or not (oci_key or oci_key_file) - or not oci_compartment_id - ): - raise Exception( - "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, oci_compartment_id " - "and at least one of oci_key or oci_key_file." - ) if not api_base: raise Exception( - "Either `api_base` must be provided or `litellm.api_base` must be set. Alternatively, you can set the `oci_region` optional parameter to use the default OCI region." + "Either `api_base` must be provided or `litellm.api_base` must be set. " + "Alternatively, you can set the `oci_region` optional parameter to use the default OCI region." ) + # Validate credentials only if signer is not provided + if oci_signer is None: + oci_user = optional_params.get("oci_user") + oci_fingerprint = optional_params.get("oci_fingerprint") + oci_tenancy = optional_params.get("oci_tenancy") + oci_key = optional_params.get("oci_key") + oci_key_file = optional_params.get("oci_key_file") + oci_compartment_id = optional_params.get("oci_compartment_id") + + if ( + not oci_user + or not oci_fingerprint + or not oci_tenancy + or not (oci_key or oci_key_file) + or not oci_compartment_id + ): + raise Exception( + "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, oci_compartment_id " + "and at least one of oci_key or oci_key_file. " + "Alternatively, provide an oci_signer object from the OCI SDK." + ) + + # Common header setup headers.update( { "content-type": "application/json", @@ -442,12 +638,12 @@ class OCIChatConfig(BaseConfig): for openai_key, oci_key in open_ai_to_oci_param_map.items(): if oci_key and openai_key in optional_params: selected_params[oci_key] = optional_params[openai_key] # type: ignore[index] - + # Also check for already-mapped OCI params (for backward compatibility) for oci_value in open_ai_to_oci_param_map.values(): if oci_value and oci_value in optional_params and oci_value not in selected_params: selected_params[oci_value] = optional_params[oci_value] # type: ignore[index] - + if "tools" in selected_params: if vendor == OCIVendors.COHERE: selected_params["tools"] = self.adapt_tool_definitions_to_cohere_standard( # type: ignore[assignment] @@ -465,7 +661,7 @@ class OCIChatConfig(BaseConfig): for msg in messages[:-1]: # All messages except the last one role = msg.get("role") content = msg.get("content") - + if isinstance(content, list): # Extract text from content array text_content = "" @@ -473,11 +669,11 @@ class OCIChatConfig(BaseConfig): if isinstance(content_item, dict) and content_item.get("type") == "text": text_content += content_item.get("text", "") content = text_content - + # Ensure content is a string if not isinstance(content, str): content = str(content) if content is not None else "" - + # Handle tool calls tool_calls: Optional[List[CohereToolCall]] = None if role == "assistant" and "tool_calls" in msg and msg.get("tool_calls"): # type: ignore[union-attr,typeddict-item] @@ -492,12 +688,12 @@ class OCIChatConfig(BaseConfig): arguments = {} else: arguments = raw_arguments - + tool_calls.append(CohereToolCall( name=str(tool_call.get("function", {}).get("name", "")), parameters=arguments )) - + if role == "user": chat_history.append(CohereMessage(role="USER", message=content)) elif role == "assistant": @@ -505,11 +701,11 @@ class OCIChatConfig(BaseConfig): elif role == "tool": # Tool messages need special handling chat_history.append(CohereMessage( - role="TOOL", + role="TOOL", message=content, toolCalls=None # Tool messages don't have tool calls )) - + return chat_history def adapt_tool_definitions_to_cohere_standard(self, tools: List[Dict[str, Any]]) -> List[CohereTool]: @@ -519,7 +715,7 @@ class OCIChatConfig(BaseConfig): function_def = tool.get("function", {}) parameters = function_def.get("parameters", {}).get("properties", {}) required = function_def.get("parameters", {}).get("required", []) - + parameter_definitions = {} for param_name, param_schema in parameters.items(): parameter_definitions[param_name] = CohereParameterDefinition( @@ -527,13 +723,13 @@ class OCIChatConfig(BaseConfig): type=param_schema.get("type", "string"), isRequired=param_name in required ) - + cohere_tools.append(CohereTool( name=function_def.get("name", ""), description=function_def.get("description", ""), parameterDefinitions=parameter_definitions )) - + return cohere_tools def _extract_text_content(self, content: Any) -> str: @@ -586,7 +782,7 @@ class OCIChatConfig(BaseConfig): user_messages = [msg for msg in messages if msg.get("role") == "user"] if not user_messages: raise Exception("No user message found for Cohere model") - + # Create Cohere-specific chat request chat_request = CohereChatRequest( @@ -595,7 +791,7 @@ class OCIChatConfig(BaseConfig): chatHistory=self.adapt_messages_to_cohere_standard(messages), **self._get_optional_params(OCIVendors.COHERE, optional_params) ) - + data = OCICompletionPayload( compartmentId=oci_compartment_id, servingMode=servingMode, @@ -616,24 +812,24 @@ class OCIChatConfig(BaseConfig): return data.model_dump(exclude_none=True) def _handle_cohere_response( - self, - json_response: dict, - model: str, + self, + json_response: dict, + model: str, model_response: ModelResponse ) -> ModelResponse: """Handle Cohere-specific response format.""" cohere_response = CohereChatResult(**json_response) # Cohere response format (uses camelCase) model_id = model - + # Set basic response info model_response.model = model_id model_response.created = int(datetime.datetime.now().timestamp()) - + # Extract the response text response_text = cohere_response.chatResponse.text oci_finish_reason = cohere_response.chatResponse.finishReason - + # Map finish reason if oci_finish_reason == "COMPLETE": finish_reason = "stop" @@ -641,7 +837,7 @@ class OCIChatConfig(BaseConfig): finish_reason = "length" else: finish_reason = "stop" - + # Handle tool calls tool_calls: Optional[List[Dict[str, Any]]] = None if cohere_response.chatResponse.toolCalls: @@ -655,7 +851,7 @@ class OCIChatConfig(BaseConfig): "arguments": json.dumps(tool_call.parameters) } }) - + # Create choice from litellm.types.utils import Choices choice = Choices( @@ -668,7 +864,7 @@ class OCIChatConfig(BaseConfig): finish_reason=finish_reason ) model_response.choices = [choice] - + # Extract usage info usage_info = cohere_response.chatResponse.usage from litellm.types.utils import Usage @@ -677,13 +873,13 @@ class OCIChatConfig(BaseConfig): completion_tokens=usage_info.completionTokens, # type: ignore[union-attr] total_tokens=usage_info.totalTokens # type: ignore[union-attr] ) - + return model_response def _handle_generic_response( - self, - json: dict, - model: str, + self, + json: dict, + model: str, model_response: ModelResponse, raw_response: httpx.Response ) -> ModelResponse: @@ -695,7 +891,7 @@ class OCIChatConfig(BaseConfig): message=f"Response cannot be casted to OCICompletionResponse: {str(e)}", status_code=raw_response.status_code, ) - + iso_str = completion_response.chatResponse.timeCreated dt = datetime.datetime.fromisoformat(iso_str.replace("Z", "+00:00")) model_response.created = int(dt.timestamp()) @@ -751,7 +947,7 @@ class OCIChatConfig(BaseConfig): ) vendor = get_vendor_from_model(model) - + # Handle response based on vendor type if vendor == OCIVendors.COHERE: model_response = self._handle_cohere_response(json, model, model_response) @@ -1080,7 +1276,7 @@ class OCIStreamWrapper(CustomStreamWrapper): if not chunk.startswith("data:"): raise ValueError(f"Chunk does not start with 'data:': {chunk}") dict_chunk = json.loads(chunk[5:]) # Remove 'data: ' prefix and parse JSON - + # Check if this is a Cohere stream chunk if "apiFormat" in dict_chunk and dict_chunk.get("apiFormat") == "COHERE": return self._handle_cohere_stream_chunk(dict_chunk) diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 324205237dc..492ed624231 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -1203,7 +1203,6 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): ) -> EmbeddingResponse: super().embedding() try: - model = model data = {"model": model, "input": input, **optional_params} max_retries = max_retries or litellm.DEFAULT_MAX_RETRIES if not isinstance(max_retries, int): @@ -1332,7 +1331,6 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): ) -> ImageResponse: data = {} try: - model = model data = {"model": model, "prompt": prompt, **optional_params} max_retries = data.pop("max_retries", 2) if not isinstance(max_retries, int): 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 257c6a599f9..112d3783787 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 @@ -220,11 +220,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): def _supports_penalty_parameters(self, model: str) -> bool: unsupported_models = ["gemini-2.5-pro-preview-06-05"] - - for pattern in unsupported_models: - if model in pattern: - return False - + if model in unsupported_models: + return False return True def get_supported_openai_params(self, model: str) -> List[str]: @@ -416,7 +413,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): # Handle tools with 'type' field (OpenAI spec compliance) Ignore this field -> https://github.com/BerriAI/litellm/issues/14644#issuecomment-3342061838 if "type" in tool: - del tool["type"] # type: ignore + tool = {k: tool[k] for k in tool if k != "type"} tool_name = list(tool.keys())[0] if len(tool.keys()) == 1 else None if tool_name and ( diff --git a/litellm/main.py b/litellm/main.py index aa52795f435..b7af4e8d39c 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -5298,11 +5298,15 @@ def moderation( or get_secret_str("OPENAI_API_KEY") ) + # Extract api_base from kwargs + api_base = kwargs.get("api_base", None) + openai_client = kwargs.get("client", None) if openai_client is None: - openai_client = openai.OpenAI( - api_key=api_key, - ) + if api_base is not None: + openai_client = openai.OpenAI(api_key=api_key, base_url=api_base) + else: + openai_client = openai.OpenAI(api_key=api_key) if model is not None: response = openai_client.moderations.create(input=input, model=model) @@ -5332,21 +5336,11 @@ async def amoderation( or litellm.openai_key or get_secret_str("OPENAI_API_KEY") ) - openai_client = kwargs.get("client", None) - if openai_client is None or not isinstance(openai_client, AsyncOpenAI): - # call helper to get OpenAI client - # _get_openai_client maintains in-memory caching logic for OpenAI clients - _openai_client: AsyncOpenAI = openai_chat_completions._get_openai_client( # type: ignore - is_async=True, - api_key=api_key, - ) - else: - _openai_client = openai_client - optional_params = GenericLiteLLMParams(**kwargs) litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get( "litellm_logging_obj", None ) + _dynamic_api_base = None try: ( model, @@ -5363,6 +5357,18 @@ async def amoderation( # `model` is optional field for moderation - get_llm_provider will throw BadRequestError if model is not set / not recognized pass + openai_client = kwargs.get("client", None) + if openai_client is None or not isinstance(openai_client, AsyncOpenAI): + # call helper to get OpenAI client + # _get_openai_client maintains in-memory caching logic for OpenAI clients + _openai_client: AsyncOpenAI = openai_chat_completions._get_openai_client( # type: ignore + is_async=True, + api_key=api_key, + api_base=optional_params.api_base or _dynamic_api_base, + ) + else: + _openai_client = openai_client + # update litellm_logging_obj with environment variables custom_llm_provider = custom_llm_provider or litellm.LlmProviders.OPENAI.value if litellm_logging_obj is not None: diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 2eced0d9095..7103ffd457a 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1106,6 +1106,7 @@ "supports_vision": true }, "azure/eu/gpt-4o-2024-08-06": { + "deprecation_date": "2026-02-27", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -1122,6 +1123,7 @@ "supports_vision": true }, "azure/eu/gpt-4o-2024-11-20": { + "deprecation_date": "2026-03-01", "cache_creation_input_token_cost": 1.38e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -1280,7 +1282,7 @@ }, "azure/global-standard/gpt-4o-2024-08-06": { "cache_read_input_token_cost": 1.25e-06, - "deprecation_date": "2025-08-20", + "deprecation_date": "2026-02-27", "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -1297,7 +1299,7 @@ }, "azure/global-standard/gpt-4o-2024-11-20": { "cache_read_input_token_cost": 1.25e-06, - "deprecation_date": "2025-12-20", + "deprecation_date": "2026-03-01", "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, @@ -1326,6 +1328,7 @@ "supports_vision": true }, "azure/global/gpt-4o-2024-08-06": { + "deprecation_date": "2026-02-27", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -1342,6 +1345,7 @@ "supports_vision": true }, "azure/global/gpt-4o-2024-11-20": { + "deprecation_date": "2026-03-01", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -1625,6 +1629,7 @@ "supports_web_search": false }, "azure/gpt-4.1-2025-04-14": { + "deprecation_date": "2026-11-04", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, @@ -1691,6 +1696,7 @@ "supports_web_search": false }, "azure/gpt-4.1-mini-2025-04-14": { + "deprecation_date": "2026-11-04", "cache_read_input_token_cost": 1e-07, "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, @@ -1756,6 +1762,7 @@ "supports_vision": true }, "azure/gpt-4.1-nano-2025-04-14": { + "deprecation_date": "2026-11-04", "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, "input_cost_per_token_batches": 5e-08, @@ -1837,6 +1844,7 @@ "supports_vision": true }, "azure/gpt-4o-2024-08-06": { + "deprecation_date": "2026-02-27", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, "litellm_provider": "azure", @@ -1853,6 +1861,7 @@ "supports_vision": true }, "azure/gpt-4o-2024-11-20": { + "deprecation_date": "2026-03-01", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -2604,6 +2613,7 @@ "supports_vision": true }, "azure/o3-2025-04-16": { + "deprecation_date": "2026-04-16", "cache_read_input_token_cost": 2.5e-06, "input_cost_per_token": 1e-05, "litellm_provider": "azure", @@ -2832,6 +2842,7 @@ "output_cost_per_token": 0.0 }, "azure/text-embedding-3-small": { + "deprecation_date": "2026-04-30", "input_cost_per_token": 2e-08, "litellm_provider": "azure", "max_input_tokens": 8191, @@ -2870,6 +2881,7 @@ "mode": "audio_speech" }, "azure/us/gpt-4o-2024-08-06": { + "deprecation_date": "2026-02-27", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -2886,6 +2898,7 @@ "supports_vision": true }, "azure/us/gpt-4o-2024-11-20": { + "deprecation_date": "2026-03-01", "cache_creation_input_token_cost": 1.38e-06, "input_cost_per_token": 2.75e-06, "litellm_provider": "azure", @@ -3385,7 +3398,7 @@ "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", "supports_reasoning": true, "supports_tool_choice": true - }, + }, "azure_ai/cohere-rerank-v3-english": { "input_cost_per_query": 0.002, "input_cost_per_token": 0.0, @@ -4911,7 +4924,7 @@ "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, - "deprecation_date": "2026-02-01", + "deprecation_date": "2026-02-19", "input_cost_per_token": 3e-06, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -4968,7 +4981,6 @@ "cache_creation_input_token_cost": 3e-07, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-08, - "deprecation_date": "2025-03-01", "input_cost_per_token": 2.5e-07, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -4988,7 +5000,7 @@ "cache_creation_input_token_cost": 1.875e-05, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 1.5e-06, - "deprecation_date": "2025-03-01", + "deprecation_date": "2026-05-01", "input_cost_per_token": 1.5e-05, "litellm_provider": "anthropic", "max_input_tokens": 200000, @@ -5172,6 +5184,7 @@ "cache_creation_input_token_cost_above_1hr": 3e-05, "cache_read_input_token_cost": 1.5e-06, "input_cost_per_token": 1.5e-05, + "deprecation_date": "2026-08-05", "litellm_provider": "anthropic", "max_input_tokens": 200000, "max_output_tokens": 32000, @@ -5199,6 +5212,7 @@ "cache_creation_input_token_cost_above_1hr": 3e-05, "cache_read_input_token_cost": 1.5e-06, "input_cost_per_token": 1.5e-05, + "deprecation_date": "2026-05-14", "litellm_provider": "anthropic", "max_input_tokens": 200000, "max_output_tokens": 32000, @@ -5222,6 +5236,7 @@ "tool_use_system_prompt_tokens": 159 }, "claude-sonnet-4-20250514": { + "deprecation_date": "2026-05-14", "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, "cache_read_input_token_cost": 3e-07, @@ -7753,7 +7768,7 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true - }, + }, "dolphin": { "input_cost_per_token": 5e-07, "litellm_provider": "nlp_cloud", @@ -7847,7 +7862,7 @@ "input_cost_per_query": 5e-03, "litellm_provider": "perplexity", "mode": "search" - }, + }, "elevenlabs/scribe_v1": { "input_cost_per_second": 6.11e-05, "litellm_provider": "elevenlabs", @@ -7993,6 +8008,7 @@ "cache_creation_input_token_cost": 1.375e-06, "cache_read_input_token_cost": 1.1e-07, "input_cost_per_token": 1.1e-06, + "deprecation_date": "2026-10-15", "litellm_provider": "bedrock", "max_input_tokens": 200000, "max_output_tokens": 8192, @@ -8243,6 +8259,46 @@ "supports_function_calling": true, "supports_tool_choice": false }, + "fal_ai/bria/text-to-image/3.2": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.0398, + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "fal_ai/fal-ai/flux-pro/v1.1-ultra": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.0398, + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "fal_ai/fal-ai/imagen4/preview": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.0398, + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "fal_ai/fal-ai/recraft/v3/text-to-image": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.0398, + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "fal_ai/fal-ai/stable-diffusion-v35-medium": { + "litellm_provider": "fal_ai", + "mode": "image_generation", + "output_cost_per_image": 0.0398, + "supported_endpoints": [ + "/v1/images/generations" + ] + }, "featherless_ai/featherless-ai/Qwerky-72B": { "litellm_provider": "featherless_ai", "max_input_tokens": 32768, @@ -10453,6 +10509,18 @@ "supports_tool_choice": true, "supports_vision": true }, + "gemini/gemini-embedding-001": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "gemini", + "max_input_tokens": 2048, + "max_tokens": 2048, + "mode": "embedding", + "output_cost_per_token": 0, + "output_vector_size": 3072, + "rpm": 10000, + "source": "https://ai.google.dev/gemini-api/docs/embeddings#model-versions", + "tpm": 10000000 + }, "gemini/gemini-1.5-flash": { "input_cost_per_token": 7.5e-08, "input_cost_per_token_above_128k_tokens": 1.5e-07, @@ -12397,6 +12465,7 @@ "supports_tool_choice": true }, "gpt-3.5-turbo-1106": { + "deprecation_date": "2026-09-28", "input_cost_per_token": 1e-06, "litellm_provider": "openai", "max_input_tokens": 16385, @@ -12466,6 +12535,7 @@ "supports_tool_choice": true }, "gpt-4-0125-preview": { + "deprecation_date": "2026-03-26", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, @@ -12506,6 +12576,7 @@ "supports_tool_choice": true }, "gpt-4-1106-preview": { + "deprecation_date": "2026-03-26", "input_cost_per_token": 1e-05, "litellm_provider": "openai", "max_input_tokens": 128000, @@ -15830,6 +15901,20 @@ "max_tokens": 8192, "mode": "embedding" }, + "mistral/codestral-embed": { + "input_cost_per_token": 0.15e-06, + "litellm_provider": "mistral", + "max_input_tokens": 8192, + "max_tokens": 8192, + "mode": "embedding" + }, + "mistral/codestral-embed-2505": { + "input_cost_per_token": 0.15e-06, + "litellm_provider": "mistral", + "max_input_tokens": 8192, + "max_tokens": 8192, + "mode": "embedding" + }, "mistral/mistral-large-2402": { "input_cost_per_token": 4e-06, "litellm_provider": "mistral", @@ -16563,6 +16648,7 @@ "supports_vision": true }, "o1-mini-2024-09-12": { + "deprecation_date": "2025-10-27", "cache_read_input_token_cost": 1.5e-06, "input_cost_per_token": 3e-06, "litellm_provider": "openai", @@ -17733,24 +17819,24 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, - "openrouter/anthropic/claude-sonnet-4.5": { - "input_cost_per_image": 0.0048, - "input_cost_per_token": 3e-06, - "input_cost_per_token_above_200k_tokens": 6e-06, - "output_cost_per_token_above_200k_tokens": 2.25e-05, + "openrouter/anthropic/claude-haiku-4.5": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, "litellm_provider": "openrouter", - "max_input_tokens": 1000000, - "max_output_tokens": 1000000, - "max_tokens": 1000000, + "max_input_tokens": 200000, + "max_output_tokens": 200000, + "max_tokens": 200000, "mode": "chat", - "output_cost_per_token": 1.5e-05, + "output_cost_per_token": 5e-06, "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 346 }, "openrouter/bytedance/ui-tars-1.5-7b": { "input_cost_per_token": 1e-07, @@ -19166,7 +19252,7 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true - }, + }, "qwen.qwen3-32b-v1:0": { "input_cost_per_token": 1.5e-07, "litellm_provider": "bedrock_converse", @@ -19178,7 +19264,7 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true - }, + }, "recraft/recraftv2": { "litellm_provider": "recraft", "mode": "image_generation", diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 59a443f7b79..583c83cca51 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -20,6 +20,55 @@ router = APIRouter( ) +def get_request_base_url(request: Request) -> str: + """ + Get the base URL for the request, considering X-Forwarded-* headers. + + When behind a proxy (like nginx), the proxy may set: + - X-Forwarded-Proto: The original protocol (http/https) + - X-Forwarded-Host: The original host (may include port) + - X-Forwarded-Port: The original port (if not in Host header) + + Args: + request: FastAPI Request object + + Returns: + The reconstructed base URL (e.g., "https://proxy.example.com") + """ + base_url = str(request.base_url).rstrip("/") + parsed = urlparse(base_url) + + # Get forwarded headers + x_forwarded_proto = request.headers.get("X-Forwarded-Proto") + x_forwarded_host = request.headers.get("X-Forwarded-Host") + x_forwarded_port = request.headers.get("X-Forwarded-Port") + + # Start with the original scheme + scheme = x_forwarded_proto if x_forwarded_proto else parsed.scheme + + # Handle host and port + if x_forwarded_host: + # X-Forwarded-Host may already include port (e.g., "example.com:8080") + if ":" in x_forwarded_host and not x_forwarded_host.startswith("["): + # Host includes port + netloc = x_forwarded_host + elif x_forwarded_port: + # Port is separate + netloc = f"{x_forwarded_host}:{x_forwarded_port}" + else: + # Just host, no explicit port + netloc = x_forwarded_host + else: + # No X-Forwarded-Host, use original netloc + netloc = parsed.netloc + if x_forwarded_port and ":" not in netloc: + # Add forwarded port if not already in netloc + netloc = f"{netloc}:{x_forwarded_port}" + + # Reconstruct the URL + return urlunparse((scheme, netloc, parsed.path, "", "", "")) + + def encode_state_with_base_url( base_url: str, original_state: str, @@ -107,7 +156,9 @@ async def authorize( # Parse it to remove any existing query parsed = urlparse(redirect_uri) base_url = urlunparse(parsed._replace(query="")) - request_base_url = str(request.base_url).rstrip("/") + + # Get the correct base URL considering X-Forwarded-* headers + request_base_url = get_request_base_url(request) # Encode the base_url, original state, PKCE params, and client redirect_uri in encrypted state encoded_state = encode_state_with_base_url( @@ -177,7 +228,8 @@ async def token_endpoint( if mcp_server.token_url is None: raise HTTPException(status_code=400, detail="MCP server token url is not set") - proxy_base_url = str(request.base_url).rstrip("/") + # Get the correct base URL considering X-Forwarded-* headers + proxy_base_url = get_request_base_url(request) # Build token request data token_data = { @@ -251,7 +303,8 @@ async def callback(code: str, state: str): async def oauth_protected_resource_mcp( request: Request, mcp_server_name: Optional[str] = None ): - request_base_url = str(request.base_url).rstrip("/") + # Get the correct base URL considering X-Forwarded-* headers + request_base_url = get_request_base_url(request) return { "authorization_servers": [ ( @@ -273,7 +326,8 @@ async def oauth_protected_resource_mcp( async def oauth_authorization_server_mcp( request: Request, mcp_server_name: Optional[str] = None ): - request_base_url = str(request.base_url).rstrip("/") + # Get the correct base URL considering X-Forwarded-* headers + request_base_url = get_request_base_url(request) authorization_endpoint = ( f"{request_base_url}/{mcp_server_name}/authorize" @@ -320,7 +374,8 @@ async def register_client(request: Request, mcp_server_name: Optional[str] = Non global_mcp_server_manager, ) - request_base_url = str(request.base_url).rstrip("/") + # Get the correct base URL considering X-Forwarded-* headers + request_base_url = get_request_base_url(request) request_data = await _read_request_body(request=request) data: dict = {**request_data} diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1162-278deed893787c5d.js b/litellm/proxy/_experimental/out/_next/static/chunks/1162-278deed893787c5d.js new file mode 100644 index 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