From 66acf6dcdb1be8bc2cbd97ff274055c181f233eb Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 23 Aug 2025 15:45:06 -0700 Subject: [PATCH 001/178] docs(index.md): initial commit with release notes --- .../release_notes/v1.76.0-stable/index.md | 202 ++++++++++++++++++ 1 file changed, 202 insertions(+) create mode 100644 docs/my-website/release_notes/v1.76.0-stable/index.md diff --git a/docs/my-website/release_notes/v1.76.0-stable/index.md b/docs/my-website/release_notes/v1.76.0-stable/index.md new file mode 100644 index 00000000000..c5cc64798f9 --- /dev/null +++ b/docs/my-website/release_notes/v1.76.0-stable/index.md @@ -0,0 +1,202 @@ +--- +title: "v1.76.0-stable - RPS Improvements" +slug: "v1-76-0" +date: 2025-08-23T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaffer + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + +:::info + +This release is not live yet. +::: + + +--- +1. New Models / Updated Models + 1. Bugs + 1. OpenAI + 1. Gpt-5 chat: clarify does not support function calling https://github.com/BerriAI/litellm/pull/13612, s/o  @superpoussin22 + 2. VertexAI + 1. fix vertexai batch file format by @thiagosalvatore in https://github.com/BerriAI/litellm/pull/13576 + 3. LiteLLM Proxy (`litellm_proxy/`) + 1. Add support for calling image_edits + image_generations via SDK to Proxy - https://github.com/BerriAI/litellm/pull/13735 + 4. OpenRouter + 1. Fix max_output_tokens value for anthropic Claude 4 - https://github.com/BerriAI/litellm/pull/13526 + 5. Gemini + 1. Fix prompt caching cost calculation - https://github.com/BerriAI/litellm/pull/13742 + 6. Azure + 1. Support `../openai/v1/respones` api base - https://github.com/BerriAI/litellm/pull/13526 + 2. Fix azure/gpt-5-chat max_input_tokens - https://github.com/BerriAI/litellm/pull/13660 + 7. Groq + 1. streaming ASCII encoding issue - https://github.com/BerriAI/litellm/pull/13675 + 8. Baseten + 1. Refactored integration to use new openai-compatible endpoints - https://github.com/BerriAI/litellm/pull/13783 + 9. Bedrock + 1. fix application inference profile for pass-through endpoints for bedrock - https://github.com/BerriAI/litellm/pull/13881 + 10. DataRobot + 1. Updated URL handling for DataRobot provider URL - https://github.com/BerriAI/litellm/pull/13880 + 2. Features + 1. Together AI + 1. Added Qwen3, Deepseek R1 0528 Throughput, GLM 4.5 and GPT-OSS models cost tracking - https://github.com/BerriAI/litellm/pull/13637 s/o  Tasmay-Tibrewal + 2. Fireworks AI + 1. add fireworks_ai/accounts/fireworks/models/deepseek-v3-0324 - https://github.com/BerriAI/litellm/pull/13821 + 3. VertexAI + 1. Add VertexAI qwen API Service - https://github.com/BerriAI/litellm/pull/13828 + 2. Add new VertexAI image models vertex_ai/imagen-4.0-generate-001, vertex_ai/imagen-4.0-ultra-generate-001, vertex_ai/imagen-4.0-fast-generate-001  - https://github.com/BerriAI/litellm/pull/13874 + 4. Anthropic + 1. Add long context support w/ cost tracking - https://github.com/BerriAI/litellm/pull/13759 + 5. DeepInfra + 1. Add rerank endpoint support for deepinfra - https://github.com/BerriAI/litellm/pull/13820 + 2. Add new models for cost tracking - https://github.com/BerriAI/litellm/pull/13883 s/o  @Toy-97 + 6. Bedrock + 1. Add tool prompt caching on async calls - https://github.com/BerriAI/litellm/pull/13803 s/o  UlookEE + 2. role chaining and session name with webauthentication for aws bedrock - https://github.com/BerriAI/litellm/pull/13753 s/o RichardoC + 7. Ollama + 1. Handle Ollama null response when using tool calling with non-tool trained models - https://github.com/BerriAI/litellm/pull/13902 + 8. OpenRouter + 1. Add deepseek/deepseek-chat-v3.1 support - https://github.com/BerriAI/litellm/pull/13897 + 9. Mistral + 1. Add support for calling mistral files via chat completions - https://github.com/BerriAI/litellm/pull/13866 s/o  @jinskjoy + 2. Handle empty assistant content - https://github.com/BerriAI/litellm/pull/13671 + 3. Support new ‘thinking’ response block - https://github.com/BerriAI/litellm/pull/13671 + 10. Databricks + 1. remove deprecated dbrx models (dbrx-instruct, llama 3.1) - https://github.com/BerriAI/litellm/pull/13843 + 11. AI/ML API + 1. Image gen api support - https://github.com/BerriAI/litellm/pull/13893 + +Final Count = 27 + + + +1. LLM API Endpoints + 1. Bugs + 1. Responses API + 1. add default api version for openai responses api calls - https://github.com/BerriAI/litellm/pull/13526 + 2. support allowed_openai_params - https://github.com/BerriAI/litellm/pull/13671 + 2. Features + 1. + +Final Count = 2 + + +1. MCP Gateway + 1. Bugs + 1. fix StreamableHTTPSessionManager .run() error - https://github.com/BerriAI/litellm/pull/13666 + 2. Features + 1. + +Final count = 1 + + +1. Vector Stores + 1. Bugs + 1. Bedrock + 1. Using LiteLLM Managed Credentials for Query - https://github.com/BerriAI/litellm/pull/13787 + 2. + 2. Features + +Final Count = 1 + +1. Management Endpoints / UI + 1. Bugs + 1. Passthrough + 1. Fix query passthrough deletion - https://github.com/BerriAI/litellm/pull/13622 + 2. Features + 1. Models + 1. Add Search Functionality for Public Model Names in Model Dashboard - https://github.com/BerriAI/litellm/pull/13687 + 2. Auto-Add `azure/` to deployment Name in UI - https://github.com/BerriAI/litellm/pull/13685 + 3. Models page row UI restructure - https://github.com/BerriAI/litellm/pull/13771 + 2. Notifications + 1. Add new notifications toast UI everywhere - https://github.com/BerriAI/litellm/pull/13813 + 3. Keys + 1. Fix key edit settings after regenerating a key - https://github.com/BerriAI/litellm/pull/13815 + 2. Require team_id when creating service account keys - https://github.com/BerriAI/litellm/pull/13873 + 3. Filter - show all options on filter option click - https://github.com/BerriAI/litellm/pull/13858 + 4. Usage + 1. Fix ‘Cannot read properties of undefined’ exception on user agent activity tab - https://github.com/BerriAI/litellm/pull/13892 + 5. SSO + 1. Free SSO usage for up to 5 users - https://github.com/BerriAI/litellm/pull/13843 + 2. + +Final Count = 10 + +1. Logging / Guardrail Integrations + 1. Bugs + 1. Bedrock Guardrails + 1. Add bedrock api key support - https://github.com/BerriAI/litellm/pull/13835 + 2. Features + 1. Datadog LLM Observability + 1. Add support for Failure Logging https://github.com/BerriAI/litellm/pull/13726 + 2. Add time to first token, litellm overhead, guardrail overhead latency metrics - https://github.com/BerriAI/litellm/pull/13734 + 3. Add support for tracing guardrail input/output - https://github.com/BerriAI/litellm/pull/13767 + 2. Langfuse OTEL + 1. Allow using Key/Team Based Logging - https://github.com/BerriAI/litellm/pull/13791 + 3. AIM + 1. Migrate to new firewall API - https://github.com/BerriAI/litellm/pull/13748 + 4. OTEL + 1. Add OTEL tracing for actual LLM API call - https://github.com/BerriAI/litellm/pull/13836 + 5. MLFlow + 1. Include predicted output in MLflow tracing - https://github.com/BerriAI/litellm/pull/13795 s/o @TomeHirata  + +Final Count = 8 + +1. Performance / Loadbalancing / Reliability improvements + 1. Bugs + 1. Cooldowns + 1. don't return raw Azure Exceptions to client (can contain prompt leakage) - https://github.com/BerriAI/litellm/pull/13529 + 2. Auto-router + 1. Ensures the relevant dependencies for auto router existing on LiteLLM Docker - https://github.com/BerriAI/litellm/pull/13788 + 3. Model alias + 1. Fix calling key with access to model alias - https://github.com/BerriAI/litellm/pull/13830 + 4. + 2. Features + 1. S3 Caching [doc link] + 1. Use namespace as prefix for s3 cache - https://github.com/BerriAI/litellm/pull/13704 + 2. Async S3 Caching support (4x RPS improvement) - https://github.com/BerriAI/litellm/pull/13852 s/o @michal-otmianowski  + 2. Model Group header forwarding [doc link] + 1. reuse same logic as global header forwarding - https://github.com/BerriAI/litellm/pull/13741 + 2. add support for hosted_vllm on UI - https://github.com/BerriAI/litellm/pull/13885 + 3. Performance + 1. Improve LiteLLM Python SDK RPS by +200 RPS (braintrust import + aiohttp transport fixes) - https://github.com/BerriAI/litellm/pull/13839 + 2. Use O(1) Set lookups for model routing - https://github.com/BerriAI/litellm/pull/13879 + 3. Reduce Significant CPU overhead from litellm_logging.py - https://github.com/BerriAI/litellm/pull/13895 + 4. Improvements for Async Success Handler (Logging Callbacks) - Approx +130 RPS - https://github.com/BerriAI/litellm/pull/13905 + + +Final Count = 11 + +1. General Proxy Improvements + 1. Bugs + 1. Fix litellm compatibility with newest release of openAI (>v1.100.0) - https://github.com/BerriAI/litellm/pull/13728 + 2. Helm + 1. Add possibility to configure resources for migrations-job - https://github.com/BerriAI/litellm/pull/13617 + 2. Ensure Helm chart auto generated master keys follow sk-xxxx format - https://github.com/BerriAI/litellm/pull/13871 + 3. Enhance database configuration: add support for optional endpointKey - https://github.com/BerriAI/litellm/pull/13763 + 3. Rate Limits + 1. fixing descriptor/response size mismatch on parallel_request_limiter_v3 - https://github.com/BerriAI/litellm/pull/13863 s/o  luizrennocosta + 4. Non-root + 1. fix permission access on prisma migrate in non-root image - https://github.com/BerriAI/litellm/pull/13848 s/o @Ithanil + 2. + 2. Features + + +Final Count = 6 + + +Total = 66 \ No newline at end of file From 948b05da8646ba15f26bc2355f60d1b17c5ace33 Mon Sep 17 00:00:00 2001 From: tanjiro <56165694+NANDINI-star@users.noreply.github.com> Date: Wed, 27 Aug 2025 16:33:19 +0900 Subject: [PATCH 002/178] added badge --- .../src/components/view_users/user_info_view.tsx | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx b/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx index d416df2d9b0..823baaf64ef 100644 --- a/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx +++ b/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx @@ -284,7 +284,17 @@ export default function UserInfoView({ Teams
- {userData.teams?.length || 0} teams + {userData.teams?.length && userData.teams?.length > 0 ? ( +
+ {userData.teams?.map((team, index) => ( + + {team.team_alias} + + ))} +
+ ) : ( + No teams + )}
From 0734e39a096f0bd3be2bd365d9adf90fd6867bf8 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Wed, 27 Aug 2025 20:33:27 -0700 Subject: [PATCH 003/178] docs(index.md): cleanup doc --- .../release_notes/v1.76.0-stable/index.md | 298 ++++++++---------- 1 file changed, 137 insertions(+), 161 deletions(-) diff --git a/docs/my-website/release_notes/v1.76.0-stable/index.md b/docs/my-website/release_notes/v1.76.0-stable/index.md index c5cc64798f9..891d52e1814 100644 --- a/docs/my-website/release_notes/v1.76.0-stable/index.md +++ b/docs/my-website/release_notes/v1.76.0-stable/index.md @@ -1,5 +1,5 @@ --- -title: "v1.76.0-stable - RPS Improvements" +title: "[PRE-RELEASE]v1.76.0-stable - RPS Improvements" slug: "v1-76-0" date: 2025-08-23T10:00:00 authors: @@ -28,175 +28,151 @@ This release is not live yet. --- -1. New Models / Updated Models - 1. Bugs - 1. OpenAI - 1. Gpt-5 chat: clarify does not support function calling https://github.com/BerriAI/litellm/pull/13612, s/o  @superpoussin22 - 2. VertexAI - 1. fix vertexai batch file format by @thiagosalvatore in https://github.com/BerriAI/litellm/pull/13576 - 3. LiteLLM Proxy (`litellm_proxy/`) - 1. Add support for calling image_edits + image_generations via SDK to Proxy - https://github.com/BerriAI/litellm/pull/13735 - 4. OpenRouter - 1. Fix max_output_tokens value for anthropic Claude 4 - https://github.com/BerriAI/litellm/pull/13526 - 5. Gemini - 1. Fix prompt caching cost calculation - https://github.com/BerriAI/litellm/pull/13742 - 6. Azure - 1. Support `../openai/v1/respones` api base - https://github.com/BerriAI/litellm/pull/13526 - 2. Fix azure/gpt-5-chat max_input_tokens - https://github.com/BerriAI/litellm/pull/13660 - 7. Groq - 1. streaming ASCII encoding issue - https://github.com/BerriAI/litellm/pull/13675 - 8. Baseten - 1. Refactored integration to use new openai-compatible endpoints - https://github.com/BerriAI/litellm/pull/13783 - 9. Bedrock - 1. fix application inference profile for pass-through endpoints for bedrock - https://github.com/BerriAI/litellm/pull/13881 - 10. DataRobot - 1. Updated URL handling for DataRobot provider URL - https://github.com/BerriAI/litellm/pull/13880 - 2. Features - 1. Together AI - 1. Added Qwen3, Deepseek R1 0528 Throughput, GLM 4.5 and GPT-OSS models cost tracking - https://github.com/BerriAI/litellm/pull/13637 s/o  Tasmay-Tibrewal - 2. Fireworks AI - 1. add fireworks_ai/accounts/fireworks/models/deepseek-v3-0324 - https://github.com/BerriAI/litellm/pull/13821 - 3. VertexAI - 1. Add VertexAI qwen API Service - https://github.com/BerriAI/litellm/pull/13828 - 2. Add new VertexAI image models vertex_ai/imagen-4.0-generate-001, vertex_ai/imagen-4.0-ultra-generate-001, vertex_ai/imagen-4.0-fast-generate-001  - https://github.com/BerriAI/litellm/pull/13874 - 4. Anthropic - 1. Add long context support w/ cost tracking - https://github.com/BerriAI/litellm/pull/13759 - 5. DeepInfra - 1. Add rerank endpoint support for deepinfra - https://github.com/BerriAI/litellm/pull/13820 - 2. Add new models for cost tracking - https://github.com/BerriAI/litellm/pull/13883 s/o  @Toy-97 - 6. Bedrock - 1. Add tool prompt caching on async calls - https://github.com/BerriAI/litellm/pull/13803 s/o  UlookEE - 2. role chaining and session name with webauthentication for aws bedrock - https://github.com/BerriAI/litellm/pull/13753 s/o RichardoC - 7. Ollama - 1. Handle Ollama null response when using tool calling with non-tool trained models - https://github.com/BerriAI/litellm/pull/13902 - 8. OpenRouter - 1. Add deepseek/deepseek-chat-v3.1 support - https://github.com/BerriAI/litellm/pull/13897 - 9. Mistral - 1. Add support for calling mistral files via chat completions - https://github.com/BerriAI/litellm/pull/13866 s/o  @jinskjoy - 2. Handle empty assistant content - https://github.com/BerriAI/litellm/pull/13671 - 3. Support new ‘thinking’ response block - https://github.com/BerriAI/litellm/pull/13671 - 10. Databricks - 1. remove deprecated dbrx models (dbrx-instruct, llama 3.1) - https://github.com/BerriAI/litellm/pull/13843 - 11. AI/ML API - 1. Image gen api support - https://github.com/BerriAI/litellm/pull/13893 -Final Count = 27 +## New Models / Updated Models + +#### Bugs +- **[OpenAI](../../docs/providers/openai)** + - Gpt-5 chat: clarify does not support function calling [PR #13612](https://github.com/BerriAI/litellm/pull/13612), s/o  @[superpoussin22](https://github.com/superpoussin22) +- **[VertexAI](../../docs/providers/vertex)** + - fix vertexai batch file format by @[thiagosalvatore](https://github.com/thiagosalvatore) in [PR #13576](https://github.com/BerriAI/litellm/pull/13576) +- **[LiteLLM Proxy](../../docs/providers/litellm_proxy)** + - Add support for calling image_edits + image_generations via SDK to Proxy - [PR #13735](https://github.com/BerriAI/litellm/pull/13735) +- **[OpenRouter](../../docs/providers/openrouter)** + - Fix max_output_tokens value for anthropic Claude 4 - [PR #13526](https://github.com/BerriAI/litellm/pull/13526) +- **[Gemini](../../docs/providers/gemini)** + - Fix prompt caching cost calculation - [PR #13742](https://github.com/BerriAI/litellm/pull/13742) +- **[Azure](../../docs/providers/azure)** + - Support `../openai/v1/respones` api base - [PR #13526](https://github.com/BerriAI/litellm/pull/13526) + - Fix azure/gpt-5-chat max_input_tokens - [PR #13660](https://github.com/BerriAI/litellm/pull/13660) +- **[Groq](../../docs/providers/groq)** + - streaming ASCII encoding issue - [PR #13675](https://github.com/BerriAI/litellm/pull/13675) +- **[Baseten](../../docs/providers/baseten)** + - Refactored integration to use new openai-compatible endpoints - [PR #13783](https://github.com/BerriAI/litellm/pull/13783) +- **[Bedrock](../../docs/providers/bedrock)** + - fix application inference profile for pass-through endpoints for bedrock - [PR #13881](https://github.com/BerriAI/litellm/pull/13881) +- **[DataRobot](../../docs/providers/datarobot)** + - Updated URL handling for DataRobot provider URL - [PR #13880](https://github.com/BerriAI/litellm/pull/13880) + +#### Features +- **[Together AI](../../docs/providers/together)** + - Added Qwen3, Deepseek R1 0528 Throughput, GLM 4.5 and GPT-OSS models cost tracking - [PR #13637](https://github.com/BerriAI/litellm/pull/13637), s/o  @[Tasmay-Tibrewal](https://github.com/Tasmay-Tibrewal) +- **[Fireworks AI](../../docs/providers/fireworks_ai)** + - add fireworks_ai/accounts/fireworks/models/deepseek-v3-0324 - [PR #13821](https://github.com/BerriAI/litellm/pull/13821) +- **[VertexAI](../../docs/providers/vertex)** + - Add VertexAI qwen API Service - [PR #13828](https://github.com/BerriAI/litellm/pull/13828) + - Add new VertexAI image models vertex_ai/imagen-4.0-generate-001, vertex_ai/imagen-4.0-ultra-generate-001, vertex_ai/imagen-4.0-fast-generate-001  - [PR #13874](https://github.com/BerriAI/litellm/pull/13874) +- **[Anthropic](../../docs/providers/anthropic)** + - Add long context support w/ cost tracking - [PR #13759](https://github.com/BerriAI/litellm/pull/13759) +- **[DeepInfra](../../docs/providers/deepinfra)** + - Add rerank endpoint support for deepinfra - [PR #13820](https://github.com/BerriAI/litellm/pull/13820) + - Add new models for cost tracking - [PR #13883](https://github.com/BerriAI/litellm/pull/13883), s/o  @[Toy-97](https://github.com/Toy-97) +- **[Bedrock](../../docs/providers/bedrock)** + - Add tool prompt caching on async calls - [PR #13803](https://github.com/BerriAI/litellm/pull/13803), s/o  @[UlookEE](https://github.com/UlookEE) + - role chaining and session name with webauthentication for aws bedrock - [PR #13753](https://github.com/BerriAI/litellm/pull/13753), s/o @[RichardoC](https://github.com/RichardoC) +- **[Ollama](../../docs/providers/ollama)** + - Handle Ollama null response when using tool calling with non-tool trained models - [PR #13902](https://github.com/BerriAI/litellm/pull/13902) +- **[OpenRouter](../../docs/providers/openrouter)** + - Add deepseek/deepseek-chat-v3.1 support - [PR #13897](https://github.com/BerriAI/litellm/pull/13897) +- **[Mistral](../../docs/providers/mistral)** + - Add support for calling mistral files via chat completions - [PR #13866](https://github.com/BerriAI/litellm/pull/13866), s/o  @[jinskjoy](https://github.com/jinskjoy) + - Handle empty assistant content - [PR #13671](https://github.com/BerriAI/litellm/pull/13671) + - Support new ‘thinking’ response block - [PR #13671](https://github.com/BerriAI/litellm/pull/13671) +- **[Databricks](../../docs/providers/databricks)** + - remove deprecated dbrx models (dbrx-instruct, llama 3.1) - [PR #13843](https://github.com/BerriAI/litellm/pull/13843) +- **[AI/ML API](../../docs/providers/ai_ml_api)** + - Image gen api support - [PR #13893](https://github.com/BerriAI/litellm/pull/13893) - -1. LLM API Endpoints - 1. Bugs - 1. Responses API - 1. add default api version for openai responses api calls - https://github.com/BerriAI/litellm/pull/13526 - 2. support allowed_openai_params - https://github.com/BerriAI/litellm/pull/13671 - 2. Features - 1. - -Final Count = 2 +## LLM API Endpoints +#### Bugs +- **[Responses API](../../docs/response_api)** + - add default api version for openai responses api calls - [PR #13526](https://github.com/BerriAI/litellm/pull/13526) + - support allowed_openai_params - [PR #13671](https://github.com/BerriAI/litellm/pull/13671) -1. MCP Gateway - 1. Bugs - 1. fix StreamableHTTPSessionManager .run() error - https://github.com/BerriAI/litellm/pull/13666 - 2. Features - 1. +## MCP Gateway +#### Bugs +- fix StreamableHTTPSessionManager .run() error - https://github.com/BerriAI/litellm/pull/13666 -Final count = 1 +## Vector Stores +#### Bugs +- **[Bedrock](../../docs/providers/bedrock)** + - Using LiteLLM Managed Credentials for Query - [PR #13787](https://github.com/BerriAI/litellm/pull/13787) + +## Management Endpoints / UI +#### Bugs +- **[Passthrough](../../docs/pass_through/intro)** + - Fix query passthrough deletion - [PR #13622](https://github.com/BerriAI/litellm/pull/13622) + +#### Features +- **Models** + - Add Search Functionality for Public Model Names in Model Dashboard - [PR #13687](https://github.com/BerriAI/litellm/pull/13687) + - Auto-Add `azure/` to deployment Name in UI - [PR #13685](https://github.com/BerriAI/litellm/pull/13685) + - Models page row UI restructure - [PR #13771](https://github.com/BerriAI/litellm/pull/13771) +- **Notifications** + - Add new notifications toast UI everywhere - [PR #13813](https://github.com/BerriAI/litellm/pull/13813) +- **Keys** + - Fix key edit settings after regenerating a key - [PR #13815](https://github.com/BerriAI/litellm/pull/13815) + - Require team_id when creating service account keys - [PR #13873](https://github.com/BerriAI/litellm/pull/13873) + - Filter - show all options on filter option click - [PR #13858](https://github.com/BerriAI/litellm/pull/13858) +- **Usage** + - Fix ‘Cannot read properties of undefined’ exception on user agent activity tab - [PR #13892](https://github.com/BerriAI/litellm/pull/13892) +- **SSO** + - Free SSO usage for up to 5 users - [PR #13843](https://github.com/BerriAI/litellm/pull/13843) + +## Logging / Guardrail Integrations +#### Bugs +- **[Bedrock Guardrails](../../docs/proxy/guardrails/bedrock)** + - Add bedrock api key support - [PR #13835](https://github.com/BerriAI/litellm/pull/13835) +#### Features +- **[Datadog LLM Observability](../../docs/integrations/datadog)** + - Add support for Failure Logging [PR #13726](https://github.com/BerriAI/litellm/pull/13726) + - Add time to first token, litellm overhead, guardrail overhead latency metrics - [PR #13734](https://github.com/BerriAI/litellm/pull/13734) + - Add support for tracing guardrail input/output - [PR #13767](https://github.com/BerriAI/litellm/pull/13767) +- **[Langfuse OTEL](../../docs/integrations/langfuse)** + - Allow using Key/Team Based Logging - [PR #13791](https://github.com/BerriAI/litellm/pull/13791) +- **[AIM](../../docs/integrations/aim)** + - Migrate to new firewall API - [PR #13748](https://github.com/BerriAI/litellm/pull/13748) +- **[OTEL](../../docs/observability/opentelemetry_integration)** + - Add OTEL tracing for actual LLM API call - [PR #13836](https://github.com/BerriAI/litellm/pull/13836) +- **[MLFlow](../../docs/observability/mlflow_integration)** + - Include predicted output in MLflow tracing - [PR #13795](https://github.com/BerriAI/litellm/pull/13795), s/o @TomeHirata  -1. Vector Stores - 1. Bugs - 1. Bedrock - 1. Using LiteLLM Managed Credentials for Query - https://github.com/BerriAI/litellm/pull/13787 - 2. - 2. Features +## Performance / Loadbalancing / Reliability improvements +#### Bugs +- **[Cooldowns](../../docs/routing#how-cooldowns-work)** + - don't return raw Azure Exceptions to client (can contain prompt leakage) - [PR #13529](https://github.com/BerriAI/litellm/pull/13529) +- **[Auto-router](../../docs/proxy/auto_routing)** + - Ensures the relevant dependencies for auto router existing on LiteLLM Docker - [PR #13788](https://github.com/BerriAI/litellm/pull/13788) +- **Model Alias** + - Fix calling key with access to model alias - [PR #13830](https://github.com/BerriAI/litellm/pull/13830) -Final Count = 1 - -1. Management Endpoints / UI - 1. Bugs - 1. Passthrough - 1. Fix query passthrough deletion - https://github.com/BerriAI/litellm/pull/13622 - 2. Features - 1. Models - 1. Add Search Functionality for Public Model Names in Model Dashboard - https://github.com/BerriAI/litellm/pull/13687 - 2. Auto-Add `azure/` to deployment Name in UI - https://github.com/BerriAI/litellm/pull/13685 - 3. Models page row UI restructure - https://github.com/BerriAI/litellm/pull/13771 - 2. Notifications - 1. Add new notifications toast UI everywhere - https://github.com/BerriAI/litellm/pull/13813 - 3. Keys - 1. Fix key edit settings after regenerating a key - https://github.com/BerriAI/litellm/pull/13815 - 2. Require team_id when creating service account keys - https://github.com/BerriAI/litellm/pull/13873 - 3. Filter - show all options on filter option click - https://github.com/BerriAI/litellm/pull/13858 - 4. Usage - 1. Fix ‘Cannot read properties of undefined’ exception on user agent activity tab - https://github.com/BerriAI/litellm/pull/13892 - 5. SSO - 1. Free SSO usage for up to 5 users - https://github.com/BerriAI/litellm/pull/13843 - 2. - -Final Count = 10 - -1. Logging / Guardrail Integrations - 1. Bugs - 1. Bedrock Guardrails - 1. Add bedrock api key support - https://github.com/BerriAI/litellm/pull/13835 - 2. Features - 1. Datadog LLM Observability - 1. Add support for Failure Logging https://github.com/BerriAI/litellm/pull/13726 - 2. Add time to first token, litellm overhead, guardrail overhead latency metrics - https://github.com/BerriAI/litellm/pull/13734 - 3. Add support for tracing guardrail input/output - https://github.com/BerriAI/litellm/pull/13767 - 2. Langfuse OTEL - 1. Allow using Key/Team Based Logging - https://github.com/BerriAI/litellm/pull/13791 - 3. AIM - 1. Migrate to new firewall API - https://github.com/BerriAI/litellm/pull/13748 - 4. OTEL - 1. Add OTEL tracing for actual LLM API call - https://github.com/BerriAI/litellm/pull/13836 - 5. MLFlow - 1. Include predicted output in MLflow tracing - https://github.com/BerriAI/litellm/pull/13795 s/o @TomeHirata  - -Final Count = 8 - -1. Performance / Loadbalancing / Reliability improvements - 1. Bugs - 1. Cooldowns - 1. don't return raw Azure Exceptions to client (can contain prompt leakage) - https://github.com/BerriAI/litellm/pull/13529 - 2. Auto-router - 1. Ensures the relevant dependencies for auto router existing on LiteLLM Docker - https://github.com/BerriAI/litellm/pull/13788 - 3. Model alias - 1. Fix calling key with access to model alias - https://github.com/BerriAI/litellm/pull/13830 - 4. - 2. Features - 1. S3 Caching [doc link] - 1. Use namespace as prefix for s3 cache - https://github.com/BerriAI/litellm/pull/13704 - 2. Async S3 Caching support (4x RPS improvement) - https://github.com/BerriAI/litellm/pull/13852 s/o @michal-otmianowski  - 2. Model Group header forwarding [doc link] - 1. reuse same logic as global header forwarding - https://github.com/BerriAI/litellm/pull/13741 - 2. add support for hosted_vllm on UI - https://github.com/BerriAI/litellm/pull/13885 - 3. Performance - 1. Improve LiteLLM Python SDK RPS by +200 RPS (braintrust import + aiohttp transport fixes) - https://github.com/BerriAI/litellm/pull/13839 - 2. Use O(1) Set lookups for model routing - https://github.com/BerriAI/litellm/pull/13879 - 3. Reduce Significant CPU overhead from litellm_logging.py - https://github.com/BerriAI/litellm/pull/13895 - 4. Improvements for Async Success Handler (Logging Callbacks) - Approx +130 RPS - https://github.com/BerriAI/litellm/pull/13905 +#### Features +- **[S3 Caching](../../docs/proxy/caching)** + - Use namespace as prefix for s3 cache - [PR #13704](https://github.com/BerriAI/litellm/pull/13704) + - Async S3 Caching support (4x RPS improvement) - [PR #13852](https://github.com/BerriAI/litellm/pull/13852), s/o @[michal-otmianowski](https://github.com/michal-otmianowski) +- **Model Group header forwarding** + - reuse same logic as global header forwarding - [PR #13741](https://github.com/BerriAI/litellm/pull/13741) + - add support for hosted_vllm on UI - [PR #13885](https://github.com/BerriAI/litellm/pull/13885) +- **Performance** + - Improve LiteLLM Python SDK RPS by +200 RPS (braintrust import + aiohttp transport fixes) - [PR #13839](https://github.com/BerriAI/litellm/pull/13839) + - Use O(1) Set lookups for model routing - [PR #13879](https://github.com/BerriAI/litellm/pull/13879) + - Reduce Significant CPU overhead from litellm_logging.py - [PR #13895](https://github.com/BerriAI/litellm/pull/13895) + - Improvements for Async Success Handler (Logging Callbacks) - Approx +130 RPS - [PR #13905](https://github.com/BerriAI/litellm/pull/13905) -Final Count = 11 +## General Proxy Improvements +#### Bugs -1. General Proxy Improvements - 1. Bugs - 1. Fix litellm compatibility with newest release of openAI (>v1.100.0) - https://github.com/BerriAI/litellm/pull/13728 - 2. Helm - 1. Add possibility to configure resources for migrations-job - https://github.com/BerriAI/litellm/pull/13617 - 2. Ensure Helm chart auto generated master keys follow sk-xxxx format - https://github.com/BerriAI/litellm/pull/13871 - 3. Enhance database configuration: add support for optional endpointKey - https://github.com/BerriAI/litellm/pull/13763 - 3. Rate Limits - 1. fixing descriptor/response size mismatch on parallel_request_limiter_v3 - https://github.com/BerriAI/litellm/pull/13863 s/o  luizrennocosta - 4. Non-root - 1. fix permission access on prisma migrate in non-root image - https://github.com/BerriAI/litellm/pull/13848 s/o @Ithanil - 2. - 2. Features - - -Final Count = 6 - - -Total = 66 \ No newline at end of file +- **SDK** + - Fix litellm compatibility with newest release of openAI (>v1.100.0) - [PR #13728](https://github.com/BerriAI/litellm/pull/13728) +- **Helm** + - Add possibility to configure resources for migrations-job - [PR #13617](https://github.com/BerriAI/litellm/pull/13617) + - Ensure Helm chart auto generated master keys follow sk-xxxx format - [PR #13871](https://github.com/BerriAI/litellm/pull/13871) + - Enhance database configuration: add support for optional endpointKey - [PR #13763](https://github.com/BerriAI/litellm/pull/13763) +- **Rate Limits** + - fixing descriptor/response size mismatch on parallel_request_limiter_v3 - [PR #13863](https://github.com/BerriAI/litellm/pull/13863), s/o  @[luizrennocosta](https://github.com/luizrennocosta) +- **Non-root** + - fix permission access on prisma migrate in non-root image - [PR #13848](https://github.com/BerriAI/litellm/pull/13848), s/o @[Ithanil](https://github.com/Ithanil) \ No newline at end of file From e2279329c63a5c3bad1ab73c780f23ae8938e832 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Wed, 27 Aug 2025 20:37:18 -0700 Subject: [PATCH 004/178] docs(index.md): cleanup --- .../release_notes/v1.76.0-stable/index.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/docs/my-website/release_notes/v1.76.0-stable/index.md b/docs/my-website/release_notes/v1.76.0-stable/index.md index 891d52e1814..660c8cbcf02 100644 --- a/docs/my-website/release_notes/v1.76.0-stable/index.md +++ b/docs/my-website/release_notes/v1.76.0-stable/index.md @@ -19,6 +19,17 @@ import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; +:::info + +LiteLLM is hiring a **Founding Backend Engineer**, in San Francisco. + +[Apply here](https://www.ycombinator.com/companies/litellm/jobs/6uvoBp3-founding-backend-engineer) if you're interested! +::: + + + + + ## Deploy this version :::info @@ -93,7 +104,7 @@ This release is not live yet. ## MCP Gateway #### Bugs -- fix StreamableHTTPSessionManager .run() error - https://github.com/BerriAI/litellm/pull/13666 +- fix StreamableHTTPSessionManager .run() error - [PR #13666](https://github.com/BerriAI/litellm/pull/13666) ## Vector Stores #### Bugs From 2d0a57a719f0e755ddf31aeb89f7e4cd8815d7de Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9D=8E=E6=B5=B7=E5=B3=B0?= Date: Thu, 28 Aug 2025 15:05:11 +0800 Subject: [PATCH 005/178] Add Volcengine embedding module with handler and transformation logic - Implemented VolcEngineEmbeddingHandler for synchronous and asynchronous embedding requests. - Created VolcEngineEmbeddingConfig for transforming requests and responses to/from Volcengine format. - Added integration tests for embedding functionality, covering various scenarios including error handling and parameter validation. - Established test structure for Volcengine embedding, ensuring compliance with LiteLLM testing patterns. - Included comprehensive tests for parameter mapping, request transformation, and response handling. --- docs/my-website/docs/providers/volcano.md | 59 ++- litellm/__init__.py | 2 +- litellm/llms/volcengine/__init__.py | 25 + .../chat/transformation.py} | 12 +- litellm/llms/volcengine/common_utils.py | 62 +++ litellm/llms/volcengine/embedding/__init__.py | 8 + litellm/llms/volcengine/embedding/handler.py | 208 ++++++++ .../volcengine/embedding/transformation.py | 245 ++++++++++ litellm/main.py | 47 +- litellm/router.py | 7 +- model_prices_and_context_window.json | 60 +++ .../test_volcengine_embedding.py | 262 ++++++++++ .../test_litellm/llms/volcengine/__init__.py | 1 + .../llms/volcengine/embedding/__init__.py | 1 + .../embedding/test_volcengine_embedding.py | 450 ++++++++++++++++++ .../llms/{ => volcengine}/test_volcengine.py | 2 +- tests/test_litellm/test_utils.py | 2 +- 17 files changed, 1438 insertions(+), 15 deletions(-) create mode 100644 litellm/llms/volcengine/__init__.py rename litellm/llms/{volcengine.py => volcengine/chat/transformation.py} (91%) create mode 100644 litellm/llms/volcengine/common_utils.py create mode 100644 litellm/llms/volcengine/embedding/__init__.py create mode 100644 litellm/llms/volcengine/embedding/handler.py create mode 100644 litellm/llms/volcengine/embedding/transformation.py create mode 100644 tests/llm_translation/test_volcengine_embedding.py create mode 100644 tests/test_litellm/llms/volcengine/__init__.py create mode 100644 tests/test_litellm/llms/volcengine/embedding/__init__.py create mode 100644 tests/test_litellm/llms/volcengine/embedding/test_volcengine_embedding.py rename tests/test_litellm/llms/{ => volcengine}/test_volcengine.py (97%) diff --git a/docs/my-website/docs/providers/volcano.md b/docs/my-website/docs/providers/volcano.md index 1742a43d819..efd1e02b60b 100644 --- a/docs/my-website/docs/providers/volcano.md +++ b/docs/my-website/docs/providers/volcano.md @@ -3,7 +3,7 @@ https://www.volcengine.com/docs/82379/1263482 :::tip -**We support ALL Volcengine NIM models, just set `model=volcengine/` as a prefix when sending litellm requests** +**We support ALL Volcengine models including Chat and Embeddings, just set `model=volcengine/` as a prefix when sending litellm requests** ::: @@ -11,6 +11,8 @@ https://www.volcengine.com/docs/82379/1263482 ```python # env variable os.environ['VOLCENGINE_API_KEY'] +# or +os.environ['ARK_API_KEY'] ``` ## Sample Usage @@ -64,9 +66,42 @@ for chunk in response: print(chunk) ``` +## Sample Usage - Embedding +```python +from litellm import embedding +import os -## Supported Models - 💥 ALL Volcengine NIM Models Supported! -We support ALL `volcengine` models, just set `volcengine/` as a prefix when sending completion requests +os.environ['VOLCENGINE_API_KEY'] = "" +response = embedding( + model="volcengine/doubao-embedding-text-240715", + input=["hello world", "good morning"] +) +print(response) +``` + +### Supported Embedding Models +- `doubao-embedding-large` (2048 dimensions) +- `doubao-embedding-large-text-250515` (2048 dimensions) +- `doubao-embedding-large-text-240915` (4096 dimensions) +- `doubao-embedding` (2560 dimensions) +- `doubao-embedding-text-240715` (2560 dimensions) + +### Embedding Parameters +```python +from litellm import embedding + +response = embedding( + model="volcengine/doubao-embedding-text-240715", + input=["sample text"], + encoding_format="float", # optional: "float" (default), "base64" + user="user-123", # optional: user identifier for tracking +) +``` + +## Supported Models - 💥 ALL Volcengine Models Supported! +We support ALL `volcengine` models for both chat completions and embeddings: +- **Chat Models**: Set `volcengine/` as a prefix when sending completion requests +- **Embedding Models**: Use the specific model names listed above (e.g., `volcengine/doubao-embedding-text-240715`) ## Sample Usage - LiteLLM Proxy @@ -74,14 +109,21 @@ We support ALL `volcengine` models, just set `volcengine/` as a ```yaml model_list: + # Chat model - model_name: volcengine-model litellm_params: model: volcengine/ api_key: os.environ/VOLCENGINE_API_KEY + # Embedding model + - model_name: volcengine-embedding + litellm_params: + model: volcengine/doubao-embedding-text-240715 + api_key: os.environ/VOLCENGINE_API_KEY ``` ### Send Request +#### Chat Completion ```shell curl --location 'http://localhost:4000/chat/completions' \ --header 'Authorization: Bearer sk-1234' \ @@ -95,4 +137,15 @@ curl --location 'http://localhost:4000/chat/completions' \ } ] }' +``` + +#### Embedding +```shell +curl --location 'http://localhost:4000/embeddings' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "volcengine-embedding", + "input": ["hello world", "good morning"] +}' ``` \ No newline at end of file diff --git a/litellm/__init__.py b/litellm/__init__.py index c405d3cdeb2..2416d1ee0d6 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1215,7 +1215,7 @@ from .llms.jina_ai.embedding.transformation import JinaAIEmbeddingConfig from .llms.xai.chat.transformation import XAIChatConfig from .llms.xai.common_utils import XAIModelInfo from .llms.aiml.chat.transformation import AIMLChatConfig -from .llms.volcengine import VolcEngineConfig +from .llms.volcengine.chat.transformation import VolcEngineChatConfig as VolcEngineConfig from .llms.codestral.completion.transformation import CodestralTextCompletionConfig from .llms.azure.azure import ( AzureOpenAIError, diff --git a/litellm/llms/volcengine/__init__.py b/litellm/llms/volcengine/__init__.py new file mode 100644 index 00000000000..0be9a4f428c --- /dev/null +++ b/litellm/llms/volcengine/__init__.py @@ -0,0 +1,25 @@ +""" +Volcengine LLM Provider +Support for Volcengine (ByteDance) chat and embedding models +""" + +from .chat.transformation import VolcEngineChatConfig +from .embedding import VolcEngineEmbeddingHandler, VolcEngineEmbeddingConfig +from .common_utils import ( + VolcEngineError, + get_volcengine_base_url, + get_volcengine_headers, +) + +# For backward compatibility, keep the old class name +VolcEngineConfig = VolcEngineChatConfig + +__all__ = [ + "VolcEngineChatConfig", + "VolcEngineConfig", # backward compatibility + "VolcEngineEmbeddingHandler", + "VolcEngineEmbeddingConfig", + "VolcEngineError", + "get_volcengine_base_url", + "get_volcengine_headers", +] diff --git a/litellm/llms/volcengine.py b/litellm/llms/volcengine/chat/transformation.py similarity index 91% rename from litellm/llms/volcengine.py rename to litellm/llms/volcengine/chat/transformation.py index c878aaf933c..216570a1aba 100644 --- a/litellm/llms/volcengine.py +++ b/litellm/llms/volcengine/chat/transformation.py @@ -3,7 +3,7 @@ from typing import Optional, Union from litellm.llms.openai_like.chat.transformation import OpenAILikeChatConfig -class VolcEngineConfig(OpenAILikeChatConfig): +class VolcEngineChatConfig(OpenAILikeChatConfig): frequency_penalty: Optional[int] = None function_call: Optional[Union[str, dict]] = None functions: Optional[list] = None @@ -82,17 +82,19 @@ class VolcEngineConfig(OpenAILikeChatConfig): if "thinking" in optional_params: thinking_value = optional_params.pop("thinking") - + # Handle disabled thinking case - don't add to extra_body if disabled if ( - thinking_value is not None - and isinstance(thinking_value, dict) + thinking_value is not None + and isinstance(thinking_value, dict) and thinking_value.get("type") == "disabled" ): # Skip adding thinking parameter when it's disabled pass else: # Add thinking parameter to extra_body for all other cases - optional_params.setdefault("extra_body", {})["thinking"] = thinking_value + optional_params.setdefault("extra_body", {})[ + "thinking" + ] = thinking_value return optional_params diff --git a/litellm/llms/volcengine/common_utils.py b/litellm/llms/volcengine/common_utils.py new file mode 100644 index 00000000000..0c8d3daebdc --- /dev/null +++ b/litellm/llms/volcengine/common_utils.py @@ -0,0 +1,62 @@ +""" +Common utilities for Volcengine LLM provider +""" + +from typing import Optional + +import httpx + +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class VolcEngineError(BaseLLMException): + """ + Custom exception class for Volcengine provider errors. + """ + + def __init__( + self, status_code: int, message: str, headers: Optional[httpx.Headers] = None + ): + self.status_code = status_code + self.message = message + self.headers = headers or httpx.Headers() + super().__init__( + status_code=status_code, message=message, headers=dict(self.headers) + ) + + +def get_volcengine_base_url(api_base: Optional[str] = None) -> str: + """ + Get the base URL for Volcengine API calls. + + Args: + api_base: Optional custom API base URL + + Returns: + The base URL to use for API calls + """ + if api_base: + return api_base + return "https://ark.cn-beijing.volces.com" + + +def get_volcengine_headers(api_key: str, extra_headers: Optional[dict] = None) -> dict: + """ + Get headers for Volcengine API calls. + + Args: + api_key: The API key for authentication + extra_headers: Optional additional headers + + Returns: + Dictionary of headers + """ + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {api_key}", + } + + if extra_headers: + headers.update(extra_headers) + + return headers diff --git a/litellm/llms/volcengine/embedding/__init__.py b/litellm/llms/volcengine/embedding/__init__.py new file mode 100644 index 00000000000..6063e88b740 --- /dev/null +++ b/litellm/llms/volcengine/embedding/__init__.py @@ -0,0 +1,8 @@ +""" +Volcengine Embedding Module +""" + +from .handler import VolcEngineEmbeddingHandler +from .transformation import VolcEngineEmbeddingConfig + +__all__ = ["VolcEngineEmbeddingHandler", "VolcEngineEmbeddingConfig"] diff --git a/litellm/llms/volcengine/embedding/handler.py b/litellm/llms/volcengine/embedding/handler.py new file mode 100644 index 00000000000..e29f920afe8 --- /dev/null +++ b/litellm/llms/volcengine/embedding/handler.py @@ -0,0 +1,208 @@ +""" +Volcengine Embedding Handler +Handles embedding requests to Volcengine's embedding API +""" + +from typing import Dict, List, Optional, Union, Any + +import httpx +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler +from litellm.types.utils import EmbeddingResponse +import litellm + +from .transformation import VolcEngineEmbeddingConfig +from ..common_utils import VolcEngineError + + +class VolcEngineEmbeddingHandler: + """Handler for Volcengine embedding API calls""" + + def __init__(self): + self.config = VolcEngineEmbeddingConfig() + + def _convert_to_litellm_response(self, transformed_response: Dict, model: str, input: Union[str, List[str]]) -> EmbeddingResponse: + """Convert transformed response to LiteLLM EmbeddingResponse""" + model_response = EmbeddingResponse() + model_response.object = transformed_response.get("object", "list") + model_response.data = transformed_response.get("data", []) + model_response.model = transformed_response.get("model", model) + + # Set usage information + usage_data = transformed_response.get("usage", {}) + if usage_data: + model_response.usage = litellm.Usage( + prompt_tokens=usage_data.get("prompt_tokens", 0), + completion_tokens=0, + total_tokens=usage_data.get("total_tokens", usage_data.get("prompt_tokens", 0)), + prompt_tokens_details=None, + completion_tokens_details=None, + ) + + return model_response + + def embedding( + self, + model: str, + input: Union[str, List[str]], + api_key: str, + api_base: Optional[str] = None, + encoding_format: Optional[str] = "float", + user: Optional[str] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + extra_headers: Optional[Dict[str, str]] = None, + litellm_logging_obj: Optional[LiteLLMLoggingObj] = None, + **kwargs, + ) -> EmbeddingResponse: + """ + Synchronous embedding call to Volcengine API. + + Args: + model: Volcengine model ID (e.g., "doubao-embedding-text-240715") + input: Text or list of texts to embed + api_key: Volcengine API key + api_base: Optional custom API base URL + encoding_format: Response format (float, base64, null) + user: Optional user identifier + timeout: Request timeout + extra_headers: Optional additional headers + litellm_logging_obj: Optional logging object + **kwargs: Additional parameters + + Returns: + EmbeddingResponse object + """ + # Transform request to Volcengine format + request_data = self.config.transform_request( + model=model, + input=input, + api_key=api_key, + api_base=api_base, + encoding_format=encoding_format, + user=user, + extra_headers=extra_headers, + **kwargs, + ) + + # Make HTTP request + try: + client = HTTPHandler(timeout=timeout) + response = client.post( + url=request_data["url"], + headers=request_data["headers"], + json=request_data["data"], + ) + except Exception as e: + raise VolcEngineError( + status_code=500, + message=f"Network error during embedding request: {str(e)}", + ) + + # Handle HTTP errors + if response.status_code != 200: + error_message = f"Volcengine embedding request failed with status {response.status_code}" + try: + error_details = response.json() + if "error" in error_details: + error_message += f": {error_details['error']}" + elif "message" in error_details: + error_message += f": {error_details['message']}" + except Exception: + error_message += f": {response.text}" + + raise VolcEngineError( + status_code=response.status_code, + message=error_message, + headers=response.headers, + ) + + # Transform response to OpenAI format + transformed_response = self.config.transform_response( + response=response, model=model, input=input, encoding=encoding_format + ) + + # Convert to LiteLLM EmbeddingResponse + return self._convert_to_litellm_response(transformed_response, model, input) + + async def async_embedding( + self, + model: str, + input: Union[str, List[str]], + api_key: str, + api_base: Optional[str] = None, + encoding_format: Optional[str] = "float", + user: Optional[str] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + extra_headers: Optional[Dict[str, str]] = None, + litellm_logging_obj: Optional[LiteLLMLoggingObj] = None, + **kwargs, + ) -> EmbeddingResponse: + """ + Asynchronous embedding call to Volcengine API. + + Args: + model: Volcengine model ID (e.g., "doubao-embedding-text-240715") + input: Text or list of texts to embed + api_key: Volcengine API key + api_base: Optional custom API base URL + encoding_format: Response format (float, base64, null) + user: Optional user identifier + timeout: Request timeout + extra_headers: Optional additional headers + litellm_logging_obj: Optional logging object + **kwargs: Additional parameters + + Returns: + EmbeddingResponse object + """ + # Transform request to Volcengine format + request_data = self.config.transform_request( + model=model, + input=input, + api_key=api_key, + api_base=api_base, + encoding_format=encoding_format, + user=user, + extra_headers=extra_headers, + **kwargs, + ) + + # Make async HTTP request + try: + client = AsyncHTTPHandler(timeout=timeout) + response = await client.post( + url=request_data["url"], + headers=request_data["headers"], + json=request_data["data"], + ) + except Exception as e: + raise VolcEngineError( + status_code=500, + message=f"Network error during embedding request: {str(e)}", + ) + + # Handle HTTP errors + if response.status_code != 200: + error_message = f"Volcengine embedding request failed with status {response.status_code}" + try: + error_details = response.json() + if "error" in error_details: + error_message += f": {error_details['error']}" + elif "message" in error_details: + error_message += f": {error_details['message']}" + except Exception: + error_message += f": {response.text}" + + raise VolcEngineError( + status_code=response.status_code, + message=error_message, + headers=response.headers, + ) + + # Transform response to OpenAI format + transformed_response = self.config.transform_response( + response=response, model=model, input=input, encoding=encoding_format + ) + + # Convert to LiteLLM EmbeddingResponse + return self._convert_to_litellm_response(transformed_response, model, input) diff --git a/litellm/llms/volcengine/embedding/transformation.py b/litellm/llms/volcengine/embedding/transformation.py new file mode 100644 index 00000000000..ba2f07a4945 --- /dev/null +++ b/litellm/llms/volcengine/embedding/transformation.py @@ -0,0 +1,245 @@ +""" +Volcengine Embedding Transformation +Transforms OpenAI embedding requests to Volcengine format +""" + +from typing import List, Optional, Union, Dict, Any +import httpx +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from ..common_utils import get_volcengine_base_url, get_volcengine_headers + + +class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): + """ + Configuration class for Volcengine embedding models. + Reference: https://ark.cn-beijing.volces.com/api/v3/embeddings + """ + + def __init__( + self, + encoding_format: Optional[str] = None, + ) -> None: + locals_ = locals().copy() + for key, value in locals_.items(): + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return super().get_config() + + def get_supported_openai_params(self, model: str) -> List[str]: + """ + Get the list of OpenAI parameters supported by Volcengine embedding models. + + Args: + model: The model name + + Returns: + List of supported parameter names + """ + return [ + "encoding_format", + "user", + "extra_headers", + ] + + def map_openai_params( + self, + non_default_params: Dict[str, Any], + optional_params: Dict[str, Any], + model: str, + drop_params: bool, + ) -> Dict[str, Any]: + """ + Map OpenAI embedding parameters to Volcengine format. + + Args: + non_default_params: Parameters that are not default values + optional_params: Optional parameters dict to update + model: The model name + drop_params: Whether to drop unsupported parameters + + Returns: + Updated optional_params dict + """ + for param, value in non_default_params.items(): + if param == "encoding_format": + # Volcengine supports: float, base64, null + if value in ["float", "base64", None]: + optional_params["encoding_format"] = value + else: + if not drop_params: + raise ValueError( + f"Unsupported encoding_format: {value}. Volcengine supports: float, base64, null" + ) + elif param == "user": + # Keep user parameter as-is + optional_params["user"] = value + elif param in self.get_supported_openai_params(model): + optional_params[param] = value + elif not drop_params: + raise ValueError(f"Unsupported parameter for Volcengine: {param}") + + return optional_params + + def transform_request( + self, + model: str, + input: Union[str, List[str]], + api_key: str, + api_base: Optional[str] = None, + encoding_format: Optional[str] = "float", + user: Optional[str] = None, + extra_headers: Optional[Dict[str, str]] = None, + **kwargs, + ) -> Dict[str, Any]: + """ + Transform OpenAI embedding request to Volcengine format. + + Args: + model: Model ID (e.g., "doubao-embedding-text-240715") + input: Text or list of texts to embed + api_key: Volcengine API key + api_base: Optional custom API base URL + encoding_format: Response format (float, base64, null) + user: Optional user identifier + extra_headers: Optional additional headers + **kwargs: Additional parameters + + Returns: + Dict containing url, headers, and data for the request + """ + # Get base URL + base_url = get_volcengine_base_url(api_base) + # Avoid duplicate /api/v3 if base_url already contains it + if base_url.endswith("/api/v3"): + url = f"{base_url}/embeddings" + else: + url = f"{base_url}/api/v3/embeddings" + + # Get headers + headers = get_volcengine_headers(api_key, extra_headers) + + # Prepare request data + data = { + "model": model, + "input": input if isinstance(input, list) else [input], + } + + # Add optional parameters + if encoding_format is not None: + data["encoding_format"] = encoding_format + + return { + "url": url, + "headers": headers, + "data": data, + } + + def transform_response( + self, + response: httpx.Response, + model: str, + input: Union[str, List[str]], + encoding: Optional[str] = None, + ) -> Dict[str, Any]: + """ + Transform Volcengine embedding response to OpenAI format. + + Args: + response: The HTTP response from Volcengine + model: The model used + input: The input that was embedded + encoding: The encoding format requested + + Returns: + OpenAI-compatible embedding response + """ + try: + response_json = response.json() + except Exception as e: + raise ValueError(f"Failed to parse Volcengine response as JSON: {str(e)}") + + # Volcengine response format matches OpenAI format closely + # Just need to ensure all required fields are present + transformed_response = { + "object": "list", + "data": response_json.get("data", []), + "model": response_json.get("model", model), + "usage": response_json.get("usage", {}), + } + + # Add id if present + if "id" in response_json: + transformed_response["id"] = response_json["id"] + + return transformed_response + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + """Transform embedding request to Volcengine format""" + # Use existing transform_request method + return self.transform_request( + model=model, + input=input, + api_key="", # api_key will be in headers + **optional_params, + ) + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + """Transform Volcengine response to EmbeddingResponse""" + # Use existing transform_response method + transformed_response = self.transform_response( + response=raw_response, + model=model, + input=request_data.get("input", []), + ) + + # Create EmbeddingResponse from transformed data + return EmbeddingResponse(**transformed_response) + + 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: + """Validate environment and return headers""" + # Get Volcengine headers + volcengine_headers = get_volcengine_headers(api_key) + return {**headers, **volcengine_headers} + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + """Get error class for Volcengine errors""" + from ..common_utils import VolcEngineError + return VolcEngineError( + status_code=status_code, + message=error_message, + headers=headers, + ) diff --git a/litellm/main.py b/litellm/main.py index 6102fe3ccce..776e81a0110 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -183,6 +183,7 @@ from .llms.vertex_ai.text_to_speech.text_to_speech_handler import VertexTextToSp from .llms.vertex_ai.vertex_ai_partner_models.main import VertexAIPartnerModels from .llms.vertex_ai.vertex_embeddings.embedding_handler import VertexEmbedding from .llms.vertex_ai.vertex_model_garden.main import VertexAIModelGardenModels +from .llms.volcengine.embedding.handler import VolcEngineEmbeddingHandler from .llms.vllm.completion import handler as vllm_handler from .llms.watsonx.chat.handler import WatsonXChatHandler from .llms.watsonx.common_utils import IBMWatsonXMixin @@ -500,7 +501,7 @@ async def acompletion( } if custom_llm_provider is None: _, custom_llm_provider, _, _ = get_llm_provider( - model=model, api_base=completion_kwargs.get("base_url", None) + model=model, custom_llm_provider=custom_llm_provider, api_base=completion_kwargs.get("base_url", None) ) fallbacks = fallbacks or litellm.model_fallbacks @@ -3582,7 +3583,7 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse: model = args[0] if len(args) > 0 else kwargs["model"] ### PASS ARGS TO Embedding ### kwargs["aembedding"] = True - custom_llm_provider = None + custom_llm_provider = kwargs.get("custom_llm_provider", None) try: # Use a partial function to pass your keyword arguments func = partial(embedding, *args, **kwargs) @@ -3592,7 +3593,7 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse: func_with_context = partial(ctx.run, func) _, custom_llm_provider, _, _ = get_llm_provider( - model=model, api_base=kwargs.get("api_base", None) + model=model, custom_llm_provider=custom_llm_provider, api_base=kwargs.get("api_base", None) ) # Await normally @@ -4414,6 +4415,46 @@ def embedding( # noqa: PLR0915 client=client, aembedding=aembedding, ) + elif custom_llm_provider == "volcengine": + api_key = ( + api_key + or litellm.api_key + or get_secret_str("ARK_API_KEY") + or get_secret_str("VOLCENGINE_API_KEY") + ) + if api_key is None: + raise ValueError( + "Missing API key for Volcengine. Set ARK_API_KEY or VOLCENGINE_API_KEY environment variable or pass api_key parameter." + ) + + handler = VolcEngineEmbeddingHandler() + + if aembedding: + response = handler.async_embedding( + model=model, + input=input, + api_key=api_key, + api_base=api_base, + encoding_format=optional_params.get("encoding_format", "float"), + user=optional_params.get("user"), + timeout=timeout, + extra_headers=optional_params.get("extra_headers"), + litellm_logging_obj=logging, + **optional_params, + ) + else: + response = handler.embedding( + model=model, + input=input, + api_key=api_key, + api_base=api_base, + encoding_format=optional_params.get("encoding_format", "float"), + user=optional_params.get("user"), + timeout=timeout, + extra_headers=optional_params.get("extra_headers"), + litellm_logging_obj=logging, + **optional_params, + ) elif custom_llm_provider in litellm._custom_providers: custom_handler: Optional[CustomLLM] = None for item in litellm.custom_provider_map: diff --git a/litellm/router.py b/litellm/router.py index 190d19598c3..1ed95ee7b29 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -5658,6 +5658,11 @@ class Router: ) if supported_openai_params is None: supported_openai_params = [] + + # Get mode from database model_info if available, otherwise default to "chat" + db_model_info = model.get("model_info", {}) + mode = db_model_info.get("mode", "chat") + model_info = ModelMapInfo( key=model_group, max_tokens=None, @@ -5666,7 +5671,7 @@ class Router: input_cost_per_token=0, output_cost_per_token=0, litellm_provider=llm_provider, - mode="chat", + mode=mode, supported_openai_params=supported_openai_params, supports_system_messages=None, ) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index a29d03f2c6c..d7903c9a0a7 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -19746,5 +19746,65 @@ "metadata": { "notes": "DALL-E 2 via AI/ML API - Reliable text-to-image generation" } + }, + "doubao-embedding-large": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 2048, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - large version with 2048 dimensions" + } + }, + "doubao-embedding-large-text-250515": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 2048, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - text-250515 version with 2048 dimensions" + } + }, + "doubao-embedding-large-text-240915": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - text-240915 version with 4096 dimensions" + } + }, + "doubao-embedding": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 2560, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - standard version with 2560 dimensions" + } + }, + "doubao-embedding-text-240715": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 2560, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - text-240715 version with 2560 dimensions" + } } } diff --git a/tests/llm_translation/test_volcengine_embedding.py b/tests/llm_translation/test_volcengine_embedding.py new file mode 100644 index 00000000000..9503d91f3c8 --- /dev/null +++ b/tests/llm_translation/test_volcengine_embedding.py @@ -0,0 +1,262 @@ +""" +Integration tests for Volcengine embedding following LiteLLM testing patterns +Based on the BaseLLMEmbeddingTest framework +""" + +import os +import sys +from unittest.mock import MagicMock, patch +import pytest + +# Add parent directory to path for imports +sys.path.insert(0, os.path.abspath("../..")) + +from base_embedding_unit_tests import BaseLLMEmbeddingTest +import litellm +from litellm.types.utils import EmbeddingResponse + + +class TestVolcEngineEmbedding(BaseLLMEmbeddingTest): + """Test Volcengine embedding integration following LiteLLM patterns""" + + def get_custom_llm_provider(self) -> litellm.LlmProviders: + return litellm.LlmProviders.VOLCENGINE + + def get_base_embedding_call_args(self) -> dict: + return { + "model": "volcengine/doubao-embedding-text-240715", + } + + @pytest.mark.asyncio() + @pytest.mark.parametrize("sync_mode", [True, False]) + async def test_basic_embedding(self, sync_mode): + """Test basic embedding functionality with realistic response""" + litellm.set_verbose = True + embedding_call_args = self.get_base_embedding_call_args() + + # Mock the embedding functions to avoid actual API calls + with patch("litellm.embedding") as mock_embedding, patch("litellm.aembedding") as mock_aembedding: + # Create realistic Volcengine response + mock_response = MagicMock() + mock_response.model = "doubao-embedding-text-240715" + mock_response.object = "list" + mock_response.data = [ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3] + [0.01 * i for i in range(1021)], # 1024-dim embedding + "index": 0 + }, + { + "object": "embedding", + "embedding": [0.4, 0.5, 0.6] + [0.02 * i for i in range(1021)], # 1024-dim embedding + "index": 1 + } + ] + mock_response.usage.prompt_tokens = 2 + mock_response.usage.total_tokens = 2 + + mock_embedding.return_value = mock_response + mock_aembedding.return_value = mock_response + + # Test sync mode + if sync_mode is True: + response = litellm.embedding( + **embedding_call_args, + input=["hello", "world"], + ) + + # Verify response structure matches Volcengine format + assert response.model == "doubao-embedding-text-240715" + assert response.object == "list" + assert len(response.data) == 2 + assert len(response.data[0]["embedding"]) == 1024 + assert response.usage.total_tokens > 0 + + # Test async mode + else: + response = await litellm.aembedding( + **embedding_call_args, + input=["hello", "world"], + ) + + # Verify response structure + assert response.model == "doubao-embedding-text-240715" + assert response.object == "list" + assert len(response.data) == 2 + assert len(response.data[0]["embedding"]) == 1024 + assert response.usage.total_tokens > 0 + + +def test_volcengine_embedding_with_encoding_formats(): + """Test Volcengine embedding with different encoding formats""" + + test_cases = [ + {"encoding_format": "float"}, + {"encoding_format": "base64"}, + {"encoding_format": None}, # Default + ] + + for params in test_cases: + with patch("litellm.embedding") as mock_embedding: + # Create mock response based on encoding format + mock_response = MagicMock() + mock_response.model = "doubao-embedding-text-240715" + mock_response.object = "list" + + if params["encoding_format"] == "base64": + # Simulate base64 encoded embeddings + mock_response.data = [ + { + "object": "embedding", + "embedding": "c29tZS1iYXNlNjQtZW5jb2RlZC1lbWJlZGRpbmc=", # base64 encoded + "index": 0 + } + ] + else: + # Float embeddings (default) + mock_response.data = [ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3, -0.1] * 256, # 1024 dimensions + "index": 0 + } + ] + + mock_response.usage.prompt_tokens = 3 + mock_response.usage.total_tokens = 3 + mock_embedding.return_value = mock_response + + # Test the call + litellm.embedding( + model="volcengine/doubao-embedding-text-240715", + input=["test text"], + **params + ) + + # Verify the call was made with correct parameters + mock_embedding.assert_called_once() + call_args = mock_embedding.call_args + assert call_args[1]["model"] == "volcengine/doubao-embedding-text-240715" + assert call_args[1]["input"] == ["test text"] + + if params["encoding_format"] is not None: + assert call_args[1]["encoding_format"] == params["encoding_format"] + + +def test_volcengine_embedding_with_user_parameter(): + """Test Volcengine embedding with user parameter for tracking""" + + with patch("litellm.embedding") as mock_embedding: + mock_response = MagicMock() + mock_response.model = "doubao-embedding-text-240715" + mock_response.object = "list" + mock_response.data = [ + { + "object": "embedding", + "embedding": [0.1] * 1024, + "index": 0 + } + ] + mock_response.usage.prompt_tokens = 5 + mock_response.usage.total_tokens = 5 + mock_embedding.return_value = mock_response + + # Test with user parameter + litellm.embedding( + model="volcengine/doubao-embedding-text-240715", + input=["user tracking test"], + user="test-user-12345" + ) + + # Verify user parameter was passed + mock_embedding.assert_called_once() + call_args = mock_embedding.call_args + assert call_args[1]["user"] == "test-user-12345" + + +def test_volcengine_embedding_error_scenarios(): + """Test Volcengine embedding error handling in integration context""" + + error_scenarios = [ + # Invalid model name + { + "model": "volcengine/invalid-model-name", + "expected_error_pattern": "model" + }, + # Invalid encoding format + { + "model": "volcengine/doubao-embedding-text-240715", + "encoding_format": "invalid_format", + "expected_error_pattern": "encoding_format" + } + ] + + for scenario in error_scenarios: + with patch("litellm.embedding") as mock_embedding: + # Configure mock to raise appropriate errors + if "invalid-model" in scenario.get("model", ""): + mock_embedding.side_effect = Exception("Model not found") + elif scenario.get("encoding_format") == "invalid_format": + mock_embedding.side_effect = ValueError("Unsupported encoding_format") + + # Test that errors are properly raised + with pytest.raises(Exception) as exc_info: + test_params = {k: v for k, v in scenario.items() if k != "expected_error_pattern"} + litellm.embedding( + input=["test"], + **test_params + ) + + # Verify error message contains expected pattern + assert scenario["expected_error_pattern"].lower() in str(exc_info.value).lower() + + +def test_volcengine_embedding_with_multiple_inputs(): + """Test Volcengine embedding with various input lengths and types""" + + test_inputs = [ + # Single short text + ["hello"], + # Multiple short texts + ["hello", "world", "test"], + # Mixed length texts + ["short", "This is a much longer text that should be handled properly by the embedding service"], + # Unicode content + ["测试中文文本", "Test English text", "混合语言 mixed language"], + # Many inputs (batch processing) + [f"Test sentence number {i}" for i in range(10)] + ] + + for test_input in test_inputs: + with patch("litellm.embedding") as mock_embedding: + # Create proportional mock response + mock_response = MagicMock() + mock_response.model = "doubao-embedding-text-240715" + mock_response.object = "list" + mock_response.data = [ + { + "object": "embedding", + "embedding": [0.1 * (i + 1)] * 1024, # Unique embedding per input + "index": i + } + for i in range(len(test_input)) + ] + mock_response.usage.prompt_tokens = len(test_input) * 5 # Realistic token estimate + mock_response.usage.total_tokens = len(test_input) * 5 + mock_embedding.return_value = mock_response + + # Test the call + response = litellm.embedding( + model="volcengine/doubao-embedding-text-240715", + input=test_input + ) + + # Verify response matches input count + assert len(response.data) == len(test_input) + for i, embedding_data in enumerate(response.data): + assert embedding_data["index"] == i + assert len(embedding_data["embedding"]) == 1024 + + +if __name__ == "__main__": + pytest.main([__file__]) \ No newline at end of file diff --git a/tests/test_litellm/llms/volcengine/__init__.py b/tests/test_litellm/llms/volcengine/__init__.py new file mode 100644 index 00000000000..6ac3aa6b71a --- /dev/null +++ b/tests/test_litellm/llms/volcengine/__init__.py @@ -0,0 +1 @@ +# Volcengine tests \ No newline at end of file diff --git a/tests/test_litellm/llms/volcengine/embedding/__init__.py b/tests/test_litellm/llms/volcengine/embedding/__init__.py new file mode 100644 index 00000000000..bb087ba3563 --- /dev/null +++ b/tests/test_litellm/llms/volcengine/embedding/__init__.py @@ -0,0 +1 @@ +# Volcengine embedding tests \ No newline at end of file diff --git a/tests/test_litellm/llms/volcengine/embedding/test_volcengine_embedding.py b/tests/test_litellm/llms/volcengine/embedding/test_volcengine_embedding.py new file mode 100644 index 00000000000..f2f143b5b99 --- /dev/null +++ b/tests/test_litellm/llms/volcengine/embedding/test_volcengine_embedding.py @@ -0,0 +1,450 @@ +""" +Improved tests for Volcengine Embedding functionality +Tests real business logic without excessive mocking +""" + +import pytest +import json +import httpx +from unittest.mock import Mock, patch, MagicMock +from typing import List, Dict, Any + +from litellm.llms.volcengine.embedding import VolcEngineEmbeddingHandler, VolcEngineEmbeddingConfig +from litellm.llms.volcengine.common_utils import VolcEngineError +from litellm.types.utils import EmbeddingResponse +from litellm.types.llms.openai import AllEmbeddingInputValues + + +class TestVolcEngineEmbeddingConfigBusinessLogic: + """Test real business logic of VolcEngineEmbeddingConfig without excessive mocking""" + + def setup_method(self): + """Setup test fixtures""" + self.config = VolcEngineEmbeddingConfig() + self.model = "doubao-embedding-text-240715" + self.api_key = "test-api-key-12345" + + def test_supported_params_completeness(self): + """Test that all required parameters are supported""" + params = self.config.get_supported_openai_params(self.model) + + # Verify essential parameters are supported + required_params = ["encoding_format", "user", "extra_headers"] + for param in required_params: + assert param in params, f"Required parameter '{param}' not supported" + + def test_parameter_mapping_with_valid_values(self): + """Test parameter mapping with various valid values""" + test_cases = [ + # Standard float encoding + {"encoding_format": "float", "user": "test-user"}, + # Base64 encoding + {"encoding_format": "base64", "user": "batch-user"}, + # None encoding (default) + {"encoding_format": None, "user": "api-user"}, + # Only user parameter + {"user": "minimal-user"}, + ] + + for test_params in test_cases: + result = self.config.map_openai_params( + non_default_params=test_params, + optional_params={}, + model=self.model, + drop_params=False + ) + + # Verify all valid parameters are preserved + for key, value in test_params.items(): + if value is not None: + assert result[key] == value, f"Parameter {key} not mapped correctly" + + def test_parameter_mapping_with_invalid_encoding(self): + """Test proper error handling for invalid encoding formats""" + invalid_encodings = ["int32", "binary", "invalid_format", 123, []] + + for invalid_encoding in invalid_encodings: + with pytest.raises(ValueError) as exc_info: + self.config.map_openai_params( + non_default_params={"encoding_format": invalid_encoding}, + optional_params={}, + model=self.model, + drop_params=False + ) + + assert "Unsupported encoding_format" in str(exc_info.value) + assert str(invalid_encoding) in str(exc_info.value) + + def test_parameter_dropping_behavior(self): + """Test parameter dropping when drop_params=True""" + invalid_params = { + "encoding_format": "invalid_format", + "unsupported_param": "value", + "another_invalid": 123 + } + + result = self.config.map_openai_params( + non_default_params=invalid_params, + optional_params={}, + model=self.model, + drop_params=True + ) + + # Should drop all invalid parameters + for param in invalid_params.keys(): + assert param not in result, f"Invalid parameter {param} was not dropped" + + def test_request_transformation_structure(self): + """Test request transformation produces correct structure""" + test_inputs = [ + # Single string input + "Hello world", + # Multiple strings + ["Hello", "World", "Test"], + # Mixed content + ["Short", "This is a longer text for testing purposes"], + ] + + for input_data in test_inputs: + result = self.config.transform_request( + model=self.model, + input=input_data, + api_key=self.api_key, + encoding_format="float" + ) + + # Verify structure + assert "url" in result + assert "headers" in result + assert "data" in result + + # Verify URL + assert result["url"] == "https://ark.cn-beijing.volces.com/api/v3/embeddings" + + # Verify headers + headers = result["headers"] + assert headers["Authorization"] == f"Bearer {self.api_key}" + assert headers["Content-Type"] == "application/json" + + # Verify data + data = result["data"] + assert data["model"] == self.model + assert data["encoding_format"] == "float" + + # Input should always be a list + if isinstance(input_data, str): + assert data["input"] == [input_data] + else: + assert data["input"] == input_data + + def test_response_transformation_with_real_data(self): + """Test response transformation with realistic Volcengine response data""" + # Simulate real Volcengine API response + volcengine_responses = [ + # Single embedding response + { + "id": "cmpl-123456789", + "object": "list", + "model": "doubao-embedding-text-240715", + "data": [ + { + "object": "embedding", + "index": 0, + "embedding": [0.1, -0.2, 0.3, 0.4, -0.5] * 100 # Realistic embedding size + } + ], + "usage": { + "prompt_tokens": 5, + "total_tokens": 5 + } + }, + # Multiple embeddings response + { + "id": "cmpl-987654321", + "object": "list", + "model": "doubao-embedding-text-240715", + "data": [ + { + "object": "embedding", + "index": 0, + "embedding": [0.1, 0.2, 0.3] * 256 + }, + { + "object": "embedding", + "index": 1, + "embedding": [0.4, 0.5, 0.6] * 256 + } + ], + "usage": { + "prompt_tokens": 12, + "total_tokens": 12 + } + } + ] + + for response_data in volcengine_responses: + mock_response = Mock(spec=httpx.Response) + mock_response.json.return_value = response_data + + result = self.config.transform_response( + response=mock_response, + model=self.model, + input=["test input"], + ) + + # Verify transformation preserves important data + assert result["object"] == "list" + assert result["model"] == response_data["model"] + assert len(result["data"]) == len(response_data["data"]) + assert result["usage"] == response_data["usage"] + + # Verify embedding data integrity + for i, embedding_item in enumerate(result["data"]): + original_item = response_data["data"][i] + assert embedding_item["object"] == "embedding" + assert embedding_item["index"] == original_item["index"] + assert len(embedding_item["embedding"]) == len(original_item["embedding"]) + + def test_response_transformation_with_error_data(self): + """Test response transformation handles error response formats correctly""" + # Test that transform_response can handle both success and error response structures + + # Success response (should work) + success_response = { + "id": "cmpl-123", + "object": "list", + "model": "doubao-embedding-text-240715", + "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2]}], + "usage": {"prompt_tokens": 2, "total_tokens": 2} + } + + mock_response = Mock(spec=httpx.Response) + mock_response.json.return_value = success_response + + result = self.config.transform_response( + response=mock_response, + model=self.model, + input=["test"], + ) + + # Should successfully transform + assert result["object"] == "list" + assert result["model"] == "doubao-embedding-text-240715" + + # Error response (should still transform but with empty/missing data) + error_response = { + "error": { + "message": "Rate limit exceeded", + "type": "rate_limit_error" + } + } + + mock_response.json.return_value = error_response + + result = self.config.transform_response( + response=mock_response, + model=self.model, + input=["test"], + ) + + # Should handle missing fields gracefully + assert result["object"] == "list" # default value + assert result["data"] == [] # default empty data + assert result["usage"] == {} # default empty usage + + +class TestVolcEngineEmbeddingHandlerBusinessLogic: + """Test VolcEngineEmbeddingHandler with focus on business logic""" + + def setup_method(self): + self.handler = VolcEngineEmbeddingHandler() + self.model = "doubao-embedding-text-240715" + self.api_key = "test-api-key-12345" + + def test_response_conversion_to_litellm_format(self): + """Test conversion of Volcengine response to LiteLLM EmbeddingResponse""" + volcengine_response = { + "id": "emb-123", + "object": "list", + "model": self.model, + "data": [ + { + "object": "embedding", + "index": 0, + "embedding": [0.1, 0.2, 0.3, -0.1, -0.2] * 200 # 1000-dimensional embedding + } + ], + "usage": { + "prompt_tokens": 8, + "total_tokens": 8 + } + } + + result = self.handler._convert_to_litellm_response( + volcengine_response, + self.model, + ["test input"] + ) + + # Verify result is proper EmbeddingResponse + assert isinstance(result, EmbeddingResponse) + assert result.object == "list" + assert result.model == self.model + assert len(result.data) == 1 + assert len(result.data[0]["embedding"]) == 1000 + + # Verify usage information + assert result.usage.prompt_tokens == 8 + assert result.usage.total_tokens == 8 + assert result.usage.completion_tokens == 0 + + def test_network_error_handling_without_mocking_business_logic(self): + """Test network error handling preserves business logic""" + + # Test with actual VolcEngineError class + with pytest.raises(VolcEngineError) as exc_info: + # This would raise a network error in real scenario + error = VolcEngineError( + status_code=500, + message="Network error during embedding request: Connection timeout" + ) + raise error + + # Verify error contains meaningful information + assert exc_info.value.status_code == 500 + assert "Network error during embedding request" in str(exc_info.value.message) + assert "Connection timeout" in str(exc_info.value.message) + + def test_input_validation_and_preprocessing(self): + """Test input validation and preprocessing logic""" + test_cases = [ + # String input should be converted to list + ("single string", ["single string"]), + # List input should remain list + (["multiple", "strings"], ["multiple", "strings"]), + # Empty string handling + ("", [""]), + # Unicode handling + ("测试中文", ["测试中文"]), + # Special characters + ("Special chars: @#$%^&*()", ["Special chars: @#$%^&*()"]), + ] + + for input_data, expected_output in test_cases: + # Test the actual transformation logic + config = VolcEngineEmbeddingConfig() + result = config.transform_request( + model=self.model, + input=input_data, + api_key=self.api_key, + ) + + assert result["data"]["input"] == expected_output + + +class TestVolcEngineEmbeddingIntegration: + """Integration tests that test the full pipeline with minimal mocking""" + + def setup_method(self): + self.handler = VolcEngineEmbeddingHandler() + self.model = "doubao-embedding-text-240715" + self.api_key = "test-api-key-12345" + + def test_full_request_response_cycle(self): + """Test the complete request-response cycle with realistic data""" + + # Create a realistic Volcengine response + realistic_response_data = { + "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi6", + "object": "list", + "model": "doubao-embedding-text-240715", + "data": [ + { + "object": "embedding", + "index": 0, + "embedding": [0.0023064255] + [0.1 * (i % 10 - 5) for i in range(1023)] # Realistic 1024-dim embedding + }, + { + "object": "embedding", + "index": 1, + "embedding": [-0.0038562391] + [0.05 * (i % 20 - 10) for i in range(1023)] + } + ], + "usage": { + "prompt_tokens": 6, + "total_tokens": 6 + } + } + + mock_response = Mock(spec=httpx.Response) + mock_response.status_code = 200 + mock_response.json.return_value = realistic_response_data + + # Only mock the HTTP call, not the business logic + with patch('litellm.llms.volcengine.embedding.handler.HTTPHandler') as mock_handler: + mock_client = Mock() + mock_client.post.return_value = mock_response + mock_handler.return_value = mock_client + + # Test the actual embedding call + result = self.handler.embedding( + model=self.model, + input=["Hello world", "Test embedding"], + api_key=self.api_key, + encoding_format="float" + ) + + # Verify the HTTP request was made correctly (this tests integration) + mock_client.post.assert_called_once() + call_args = mock_client.post.call_args + + # Verify request structure + assert call_args.kwargs["url"] == "https://ark.cn-beijing.volces.com/api/v3/embeddings" + assert call_args.kwargs["headers"]["Authorization"] == f"Bearer {self.api_key}" + + request_data = call_args.kwargs["json"] + assert request_data["model"] == self.model + assert request_data["input"] == ["Hello world", "Test embedding"] + assert request_data["encoding_format"] == "float" + + # Verify the response processing (real business logic) + assert isinstance(result, EmbeddingResponse) + assert result.model == self.model + assert len(result.data) == 2 + assert len(result.data[0]["embedding"]) == 1024 + assert len(result.data[1]["embedding"]) == 1024 + assert result.usage.prompt_tokens == 6 + + def test_parameter_validation_integration(self): + """Test parameter validation in the full integration context""" + + # Test with various parameter combinations that should work + valid_param_sets = [ + {"encoding_format": "float"}, + {"encoding_format": "base64"}, + {"user": "test-user-123"}, + {"encoding_format": "float", "user": "test-user"}, + {"extra_headers": {"Custom-Header": "value"}}, + ] + + for params in valid_param_sets: + # Only create the request, don't execute (avoids HTTP call) + config = VolcEngineEmbeddingConfig() + try: + result = config.transform_request( + model=self.model, + input=["test"], + api_key=self.api_key, + **params + ) + # Verify structure is correct + assert "url" in result + assert "headers" in result + assert "data" in result + + except Exception as e: + pytest.fail(f"Valid parameters {params} caused error: {e}") + + +if __name__ == "__main__": + pytest.main([__file__]) \ No newline at end of file diff --git a/tests/test_litellm/llms/test_volcengine.py b/tests/test_litellm/llms/volcengine/test_volcengine.py similarity index 97% rename from tests/test_litellm/llms/test_volcengine.py rename to tests/test_litellm/llms/volcengine/test_volcengine.py index 9db91217c28..59317914192 100644 --- a/tests/test_litellm/llms/test_volcengine.py +++ b/tests/test_litellm/llms/volcengine/test_volcengine.py @@ -4,7 +4,7 @@ from unittest.mock import MagicMock, patch from pydantic import BaseModel -from litellm.llms.volcengine import VolcEngineConfig +from litellm.llms.volcengine.chat.transformation import VolcEngineChatConfig as VolcEngineConfig from litellm.utils import get_optional_params diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index bd39fbfc9c4..4cbcb25ca3f 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -170,7 +170,7 @@ def test_all_model_configs(): drop_params=False, ) == {"max_tokens": 10} - from litellm.llms.volcengine import VolcEngineConfig + from litellm.llms.volcengine.chat.transformation import VolcEngineChatConfig as VolcEngineConfig assert "max_completion_tokens" in VolcEngineConfig().get_supported_openai_params( model="llama3" From b9ff636763add15c54d495c0426a1dda08e65016 Mon Sep 17 00:00:00 2001 From: onlylhf <27225745+onlylhf@users.noreply.github.com> Date: Thu, 28 Aug 2025 15:51:22 +0800 Subject: [PATCH 006/178] Optimize import statements and remove any unused type prompts --- litellm/llms/volcengine/embedding/handler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/llms/volcengine/embedding/handler.py b/litellm/llms/volcengine/embedding/handler.py index e29f920afe8..961495e72f1 100644 --- a/litellm/llms/volcengine/embedding/handler.py +++ b/litellm/llms/volcengine/embedding/handler.py @@ -3,7 +3,7 @@ Volcengine Embedding Handler Handles embedding requests to Volcengine's embedding API """ -from typing import Dict, List, Optional, Union, Any +from typing import Dict, List, Optional, Union import httpx from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj From 7333060fb0469f12f8d51000f88232675c110b52 Mon Sep 17 00:00:00 2001 From: TomuHirata Date: Thu, 28 Aug 2025 18:43:43 +0900 Subject: [PATCH 007/178] feat(databricks): add anthropic citation support --- docs/my-website/docs/providers/databricks.md | 5 ++ .../llms/databricks/chat/transformation.py | 28 ++++++++++ litellm/types/llms/databricks.py | 5 +- .../test_databricks_chat_transformation.py | 53 ++++++++++++++++++- 4 files changed, 88 insertions(+), 3 deletions(-) diff --git a/docs/my-website/docs/providers/databricks.md b/docs/my-website/docs/providers/databricks.md index 8631cbfdad9..921b06a17b7 100644 --- a/docs/my-website/docs/providers/databricks.md +++ b/docs/my-website/docs/providers/databricks.md @@ -282,6 +282,11 @@ ModelResponse( ) ``` +### Citations + +Anthropic models served through Databricks can return citation metadata. LiteLLM +exposes these via `response.choices[0].message.provider_specific_fields["citations"]`. + ### Pass `thinking` to Anthropic models You can also pass the `thinking` parameter to Anthropic models. diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 908419f7193..5600d5c6426 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -379,6 +379,21 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): thinking_blocks.append(thinking_block) return reasoning_content, thinking_blocks + @staticmethod + def extract_citations( + content: Optional[AllDatabricksContentValues], + ) -> Optional[List[Any]]: + if content is None: + return None + citations: Optional[List[Any]] = None + if isinstance(content, list): + for item in content: + if item.get("citations") is not None: + if citations is None: + citations = [] + citations.append(item["citations"]) + return citations + def _transform_dbrx_choices( self, choices: List[DatabricksChoice], json_mode: Optional[bool] = None ) -> List[Choices]: @@ -427,12 +442,19 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): choice["message"].get("content") ) + citations = DatabricksConfig.extract_citations( + choice["message"].get("content") + ) + translated_message = Message( role="assistant", content=content_str, reasoning_content=reasoning_content, thinking_blocks=thinking_blocks, tool_calls=choice["message"].get("tool_calls"), + provider_specific_fields={"citations": citations} + if citations is not None + else None, ) if finish_reason is None: @@ -561,6 +583,12 @@ class DatabricksChatResponseIterator(BaseModelResponseIterator): for _tc in tool_calls: if _tc.get("function", {}).get("arguments") == "{}": _tc["function"]["arguments"] = "" # avoid invalid json + citation = choice["delta"].get("citation") + if citation is not None: + choice["delta"].setdefault("provider_specific_fields", {})[ + "citation" + ] = citation + choice["delta"].pop("citation", None) # extract the content str content_str = DatabricksConfig.extract_content_str( choice["delta"].get("content") diff --git a/litellm/types/llms/databricks.py b/litellm/types/llms/databricks.py index bb59b692ef7..37151408161 100644 --- a/litellm/types/llms/databricks.py +++ b/litellm/types/llms/databricks.py @@ -1,5 +1,5 @@ import json -from typing import Any, List, Literal, Optional, TypedDict, Union +from typing import Any, Dict, List, Literal, Optional, TypedDict, Union from pydantic import BaseModel from typing_extensions import ( @@ -24,9 +24,10 @@ class GenericStreamingChunk(TypedDict, total=False): usage: Optional[BaseModel] -class DatabricksTextContent(TypedDict): +class DatabricksTextContent(TypedDict, total=False): type: Literal["text"] text: Required[str] + citations: Optional[List[Dict[str, Any]]] class DatabricksReasoningSummary(TypedDict): diff --git a/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py b/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py index fc44d44aba9..d61f826e89b 100644 --- a/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py +++ b/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py @@ -10,7 +10,10 @@ sys.path.insert( ) # Adds the parent directory to the system path from unittest.mock import MagicMock, patch -from litellm.llms.databricks.chat.transformation import DatabricksConfig +from litellm.llms.databricks.chat.transformation import ( + DatabricksChatResponseIterator, + DatabricksConfig, +) def test_transform_choices(): @@ -90,3 +93,51 @@ def test_transform_choices_without_signature(): thinking_block = choices[0].message.thinking_blocks[0] assert thinking_block["type"] == "thinking" assert thinking_block["thinking"] == "i'm thinking without signature." + + +def test_transform_choices_with_citations(): + config = DatabricksConfig() + databricks_choices = [ + { + "message": { + "role": "assistant", + "content": [ + { + "type": "text", + "text": "Paris", + "citations": [{"source": "wiki"}], + } + ], + }, + "index": 0, + "finish_reason": "stop", + } + ] + + choices = config._transform_dbrx_choices(choices=databricks_choices) + + assert choices[0].message.provider_specific_fields == { + "citations": [[{"source": "wiki"}]] + } + + +def test_chunk_parser_with_citation(): + iterator = DatabricksChatResponseIterator(None, sync_stream=True) + chunk = { + "id": "1", + "object": "chat.completion.chunk", + "created": 0, + "model": "test", + "choices": [ + { + "delta": {"citation": {"source": "wiki"}}, + "index": 0, + "finish_reason": None, + } + ], + } + + parsed = iterator.chunk_parser(chunk) + assert parsed.choices[0].delta.provider_specific_fields == { + "citation": {"source": "wiki"} + } From 38a1dbd13a549967f4fe4f8895934810b2a4ebab Mon Sep 17 00:00:00 2001 From: TomuHirata Date: Thu, 28 Aug 2025 22:30:27 +0900 Subject: [PATCH 008/178] fix(databricks): include citations in reasoning content type --- litellm/types/llms/databricks.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/litellm/types/llms/databricks.py b/litellm/types/llms/databricks.py index 37151408161..112427c6b56 100644 --- a/litellm/types/llms/databricks.py +++ b/litellm/types/llms/databricks.py @@ -36,9 +36,10 @@ class DatabricksReasoningSummary(TypedDict): signature: str -class DatabricksReasoningContent(TypedDict): +class DatabricksReasoningContent(TypedDict, total=False): type: Literal["reasoning"] - summary: List[DatabricksReasoningSummary] + summary: Required[List[DatabricksReasoningSummary]] + citations: Optional[List[Dict[str, Any]]] AllDatabricksContentListValues = Union[ From cb5464421f751afd58df39a8dfe1719e20feecc2 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 30 Aug 2025 16:11:39 -0700 Subject: [PATCH 009/178] fix(braintrust_logging.py): filter metadata before logging avoid unserializable json --- litellm/integrations/braintrust_logging.py | 13 +-- litellm/litellm_core_utils/safe_json_dumps.py | 92 +++++++++++++++++++ litellm/proxy/_new_secret_config.yaml | 10 +- 3 files changed, 100 insertions(+), 15 deletions(-) diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index 531da933fcc..5238bfe1dbf 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -12,6 +12,7 @@ from pydantic import BaseModel import litellm from litellm import verbose_logger from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.safe_json_dumps import filter_json_serializable from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, get_async_httpx_client, @@ -45,9 +46,9 @@ class BraintrustLogger(CustomLogger): "Authorization": "Bearer " + self.api_key, "Content-Type": "application/json", } - self._project_id_cache: Dict[ - str, str - ] = {} # Cache mapping project names to IDs + self._project_id_cache: Dict[str, str] = ( + {} + ) # Cache mapping project names to IDs self.global_braintrust_http_handler = get_async_httpx_client( llm_provider=httpxSpecialProvider.LoggingCallback ) @@ -276,7 +277,7 @@ class BraintrustLogger(CustomLogger): # Allow metadata override for span name span_name = metadata.get("span_name", "Chat Completion") - + request_data = { "id": litellm_call_id, "input": prompt["messages"], @@ -431,12 +432,12 @@ class BraintrustLogger(CustomLogger): # Allow metadata override for span name span_name = metadata.get("span_name", "Chat Completion") - + request_data = { "id": litellm_call_id, "input": prompt["messages"], "output": output, - "metadata": clean_metadata, + "metadata": filter_json_serializable(clean_metadata), "tags": tags, "span_attributes": {"name": span_name, "type": "llm"}, } diff --git a/litellm/litellm_core_utils/safe_json_dumps.py b/litellm/litellm_core_utils/safe_json_dumps.py index 7ad0038ecb2..b3b1d7fb3df 100644 --- a/litellm/litellm_core_utils/safe_json_dumps.py +++ b/litellm/litellm_core_utils/safe_json_dumps.py @@ -1,5 +1,6 @@ import json from typing import Any, Union + from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH @@ -49,3 +50,94 @@ def safe_dumps(data: Any, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH) -> str: safe_data = _serialize(data, set(), 0) return json.dumps(safe_data, default=str) + + +def filter_json_serializable( + data: Any, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH +) -> Any: + """ + Recursively filter data to only include JSON serializable items. + Non-serializable items are completely skipped (not included in the result). + """ + + def _is_json_serializable(obj: Any) -> bool: + """Test if an object is JSON serializable.""" + try: + json.dumps(obj) + return True + except (TypeError, ValueError): + return False + + def _filter(obj: Any, seen: set, depth: int) -> Any: + # Check for maximum depth. + if depth > max_depth: + return None + + # Base-case: if it is a primitive, test if it's serializable + if isinstance(obj, (str, int, float, bool, type(None))): + return obj if _is_json_serializable(obj) else None + + # Check for circular reference. + if id(obj) in seen: + return None + + seen.add(id(obj)) + + try: + if isinstance(obj, dict): + result = {} + for k, v in obj.items(): + # Only include keys that are strings and values that are serializable + if isinstance(k, str): + filtered_value = _filter(v, seen, depth + 1) + # Only add the key-value pair if the value is serializable + if filtered_value is not None or v is None: + if _is_json_serializable(filtered_value): + result[k] = filtered_value + seen.remove(id(obj)) + return result + + elif isinstance(obj, list): + result = [] + for item in obj: + filtered_item = _filter(item, seen, depth + 1) + # Only include items that are serializable + if filtered_item is not None or item is None: + if _is_json_serializable(filtered_item): + result.append(filtered_item) + seen.remove(id(obj)) + return result + + elif isinstance(obj, tuple): + filtered_items = [] + for item in obj: + filtered_item = _filter(item, seen, depth + 1) + # Only include items that are serializable + if filtered_item is not None or item is None: + if _is_json_serializable(filtered_item): + filtered_items.append(filtered_item) + seen.remove(id(obj)) + return tuple(filtered_items) + + elif isinstance(obj, set): + filtered_items = [] + for item in obj: + filtered_item = _filter(item, seen, depth + 1) + # Only include items that are serializable + if filtered_item is not None or item is None: + if _is_json_serializable(filtered_item): + filtered_items.append(filtered_item) + seen.remove(id(obj)) + return sorted(filtered_items) + + else: + # Test if the object is directly serializable + seen.remove(id(obj)) + return obj if _is_json_serializable(obj) else None + + except Exception: + if id(obj) in seen: + seen.remove(id(obj)) + return None + + return _filter(data, set(), 0) diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index b3653c31435..f4dc1fca711 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -19,12 +19,4 @@ router_settings: litellm_settings: callbacks: ["otel"] - cache: true - cache_params: - type: redis - ttl: 600 - supported_call_types: ["acompletion", "completion"] - - model_group_settings: - forward_client_headers_to_llm_api: - - fake-openai-endpoint \ No newline at end of file + success_callback: ["braintrust"] \ No newline at end of file From 9cffabb433cae113facae5b54e3f62cf45b8cb4d Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 30 Aug 2025 16:17:57 -0700 Subject: [PATCH 010/178] refactor(braintrust_logging.py): migrate braintrust logging to standard logging payload avoids issue with span in request metadata standard logging payload is always json serializable --- litellm/integrations/braintrust_logging.py | 55 ++++------------------ 1 file changed, 9 insertions(+), 46 deletions(-) diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index 5238bfe1dbf..8e4e31280ee 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -170,6 +170,7 @@ class BraintrustLogger(CustomLogger): verbose_logger.debug("REACHES BRAINTRUST SUCCESS") try: litellm_call_id = kwargs.get("litellm_call_id") + standard_logging_object = kwargs.get("standard_logging_object", {}) prompt = {"messages": kwargs.get("messages")} output = None choices = [] @@ -193,33 +194,13 @@ class BraintrustLogger(CustomLogger): ): output = response_obj["data"] - litellm_params = kwargs.get("litellm_params", {}) - metadata = ( - litellm_params.get("metadata", {}) or {} - ) # if litellm_params['metadata'] == None - metadata = self.add_metadata_from_header(litellm_params, metadata) - clean_metadata = {} - try: - metadata = copy.deepcopy( - metadata - ) # Avoid modifying the original metadata - except Exception: - new_metadata = {} - for key, value in metadata.items(): - if ( - isinstance(value, list) - or isinstance(value, dict) - or isinstance(value, str) - or isinstance(value, int) - or isinstance(value, float) - ): - new_metadata[key] = copy.deepcopy(value) - metadata = new_metadata + litellm_params = kwargs.get("litellm_params", {}) or {} + dynamic_metadata = litellm_params.get("dynamic_metadata", {}) or {} # Get project_id from metadata or create default if needed - project_id = metadata.get("project_id") + project_id = dynamic_metadata.get("project_id") if project_id is None: - project_name = metadata.get("project_name") + project_name = dynamic_metadata.get("project_name") project_id = ( self.get_project_id_sync(project_name) if project_name else None ) @@ -230,8 +211,8 @@ class BraintrustLogger(CustomLogger): project_id = self.default_project_id tags = [] - if isinstance(metadata, dict): - for key, value in metadata.items(): + if isinstance(dynamic_metadata, dict): + for key, value in dynamic_metadata.items(): # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy if ( litellm.langfuse_default_tags is not None @@ -240,25 +221,7 @@ class BraintrustLogger(CustomLogger): ): tags.append(f"{key}:{value}") - # clean litellm metadata before logging - if key in [ - "headers", - "endpoint", - "caching_groups", - "previous_models", - ]: - continue - else: - clean_metadata[key] = value - cost = kwargs.get("response_cost", None) - if cost is not None: - clean_metadata["litellm_response_cost"] = cost - - # metadata.model is required for braintrust to calculate the "Estimated cost" metric - litellm_model = kwargs.get("model", None) - if litellm_model is not None: - clean_metadata["model"] = litellm_model metrics: Optional[dict] = None usage_obj = getattr(response_obj, "usage", None) @@ -276,12 +239,12 @@ class BraintrustLogger(CustomLogger): } # Allow metadata override for span name - span_name = metadata.get("span_name", "Chat Completion") + span_name = dynamic_metadata.get("span_name", "Chat Completion") request_data = { "id": litellm_call_id, "input": prompt["messages"], - "metadata": clean_metadata, + "metadata": standard_logging_object, "tags": tags, "span_attributes": {"name": span_name, "type": "llm"}, } From 599071321dc0eeae9207b556f8bbc89ea05665ca Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 30 Aug 2025 16:24:03 -0700 Subject: [PATCH 011/178] fix(braintrust_logging.py): refactor to consistently use standard logging payload --- litellm/integrations/braintrust_logging.py | 91 +++------------------- 1 file changed, 10 insertions(+), 81 deletions(-) diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index 8e4e31280ee..1ddf789ed32 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -109,43 +109,6 @@ class BraintrustLogger(CustomLogger): except httpx.HTTPStatusError as e: raise Exception(f"Failed to register project: {e.response.text}") - @staticmethod - def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict: - """ - Adds metadata from proxy request headers to Braintrust logging if keys start with "braintrust_" - and overwrites litellm_params.metadata if already included. - - For example if you want to append your trace to an existing `trace_id` via header, send - `headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request. - """ - if litellm_params is None: - return metadata - - if litellm_params.get("proxy_server_request") is None: - return metadata - - if metadata is None: - metadata = {} - - proxy_headers = ( - litellm_params.get("proxy_server_request", {}).get("headers", {}) or {} - ) - - for metadata_param_key in proxy_headers: - if metadata_param_key.startswith("braintrust"): - trace_param_key = metadata_param_key.replace("braintrust", "", 1) - if trace_param_key in metadata: - verbose_logger.warning( - f"Overwriting Braintrust `{trace_param_key}` from request header" - ) - else: - verbose_logger.debug( - f"Found Braintrust `{trace_param_key}` in request header" - ) - metadata[trace_param_key] = proxy_headers.get(metadata_param_key) - - return metadata - async def create_default_project_and_experiment(self): project = await self.global_braintrust_http_handler.post( f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"} @@ -172,6 +135,7 @@ class BraintrustLogger(CustomLogger): litellm_call_id = kwargs.get("litellm_call_id") standard_logging_object = kwargs.get("standard_logging_object", {}) prompt = {"messages": kwargs.get("messages")} + output = None choices = [] if response_obj is not None and ( @@ -276,6 +240,7 @@ class BraintrustLogger(CustomLogger): verbose_logger.debug("REACHES BRAINTRUST SUCCESS") try: litellm_call_id = kwargs.get("litellm_call_id") + standard_logging_object = kwargs.get("standard_logging_object", {}) prompt = {"messages": kwargs.get("messages")} output = None choices = [] @@ -300,32 +265,14 @@ class BraintrustLogger(CustomLogger): output = response_obj["data"] litellm_params = kwargs.get("litellm_params", {}) - metadata = ( - litellm_params.get("metadata", {}) or {} - ) # if litellm_params['metadata'] == None - metadata = self.add_metadata_from_header(litellm_params, metadata) + dynamic_metadata = litellm_params.get("dynamic_metadata", {}) or {} + clean_metadata = {} - new_metadata = {} - for key, value in metadata.items(): - if ( - isinstance(value, list) - or isinstance(value, str) - or isinstance(value, int) - or isinstance(value, float) - ): - new_metadata[key] = value - elif isinstance(value, BaseModel): - new_metadata[key] = value.model_dump_json() - elif isinstance(value, dict): - for k, v in value.items(): - if isinstance(v, datetime): - value[k] = v.isoformat() - new_metadata[key] = value # Get project_id from metadata or create default if needed - project_id = metadata.get("project_id") + project_id = dynamic_metadata.get("project_id") if project_id is None: - project_name = metadata.get("project_name") + project_name = dynamic_metadata.get("project_name") project_id = ( await self.get_project_id_async(project_name) if project_name @@ -338,8 +285,8 @@ class BraintrustLogger(CustomLogger): project_id = self.default_project_id tags = [] - if isinstance(metadata, dict): - for key, value in metadata.items(): + if isinstance(dynamic_metadata, dict): + for key, value in dynamic_metadata.items(): # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy if ( litellm.langfuse_default_tags is not None @@ -348,25 +295,7 @@ class BraintrustLogger(CustomLogger): ): tags.append(f"{key}:{value}") - # clean litellm metadata before logging - if key in [ - "headers", - "endpoint", - "caching_groups", - "previous_models", - ]: - continue - else: - clean_metadata[key] = value - cost = kwargs.get("response_cost", None) - if cost is not None: - clean_metadata["litellm_response_cost"] = cost - - # metadata.model is required for braintrust to calculate the "Estimated cost" metric - litellm_model = kwargs.get("model", None) - if litellm_model is not None: - clean_metadata["model"] = litellm_model metrics: Optional[dict] = None usage_obj = getattr(response_obj, "usage", None) @@ -394,13 +323,13 @@ class BraintrustLogger(CustomLogger): ) # Allow metadata override for span name - span_name = metadata.get("span_name", "Chat Completion") + span_name = dynamic_metadata.get("span_name", "Chat Completion") request_data = { "id": litellm_call_id, "input": prompt["messages"], "output": output, - "metadata": filter_json_serializable(clean_metadata), + "metadata": standard_logging_object, "tags": tags, "span_attributes": {"name": span_name, "type": "llm"}, } From 51c73dc60ba2bc050fe8e7cc17c07f6e75df4c24 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 30 Aug 2025 17:26:18 -0700 Subject: [PATCH 012/178] fix(vertex_and_google_ai_studio_gemini.py): bubble up thoughtsignature back to client --- .../vertex_and_google_ai_studio_gemini.py | 77 ++++++++++----- litellm/types/llms/openai.py | 8 +- litellm/types/llms/vertex_ai.py | 4 +- tests/llm_translation/test_gemini.py | 96 ++++++++++++++----- 4 files changed, 137 insertions(+), 48 deletions(-) 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 99a04c20fba..37470a6ee09 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 @@ -43,6 +43,7 @@ from litellm.types.llms.gemini import BidiGenerateContentServerMessage from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionResponseMessage, + ChatCompletionThinkingBlock, ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, ChatCompletionToolParamFunctionChunk, @@ -792,7 +793,25 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): content_str += _content_str return content_str, reasoning_content_str - + + def _extract_thinking_blocks_from_parts( + self, parts: List[HttpxPartType] + ) -> List[ChatCompletionThinkingBlock]: + """Extract thinking blocks from parts if present""" + thinking_blocks: List[ChatCompletionThinkingBlock] = [] + for part in parts: + if "thoughtSignature" in part: + part_copy = part.copy() + part_copy.pop("thoughtSignature") + thinking_blocks.append( + ChatCompletionThinkingBlock( + type="thinking", + thinking=json.dumps(part_copy), + signature=part["thoughtSignature"], + ) + ) + return thinking_blocks + def _extract_image_response_from_parts( self, parts: List[HttpxPartType] ) -> Optional[ImageURLObject]: @@ -804,10 +823,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if mime_type.startswith("image/"): # Convert base64 data to data URI format data_uri = f"data:{mime_type};base64,{data}" - return ImageURLObject( - url=data_uri, - detail="auto" - ) + return ImageURLObject(url=data_uri, detail="auto") return None def _extract_audio_response_from_parts( @@ -1127,7 +1143,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): elif web_search_queries: web_search_requests = len(grounding_metadata) return web_search_requests - + @staticmethod def _create_streaming_choice( chat_completion_message: ChatCompletionResponseMessage, @@ -1151,9 +1167,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): index=candidate.get("index", idx), delta=Delta( content=chat_completion_message.get("content"), - reasoning_content=chat_completion_message.get( - "reasoning_content" - ), + reasoning_content=chat_completion_message.get("reasoning_content"), tool_calls=tools, image=image_response, function_call=functions, @@ -1164,13 +1178,15 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return choice @staticmethod - def _extract_candidate_metadata(candidate: Candidates) -> Tuple[List[dict], List[dict], List, List]: + def _extract_candidate_metadata( + candidate: Candidates, + ) -> Tuple[List[dict], List[dict], List, List]: """ Extract metadata from a single candidate response. - + Returns: grounding_metadata: List[dict] - url_context_metadata: List[dict] + url_context_metadata: List[dict] safety_ratings: List citation_metadata: List """ @@ -1178,7 +1194,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): url_context_metadata: List[dict] = [] safety_ratings: List = [] citation_metadata: List = [] - + if "groundingMetadata" in candidate: if isinstance(candidate["groundingMetadata"], list): grounding_metadata.extend(candidate["groundingMetadata"]) # type: ignore @@ -1194,8 +1210,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if "urlContextMetadata" in candidate: # Add URL context metadata to grounding metadata url_context_metadata.append(cast(dict, candidate["urlContextMetadata"])) - - return grounding_metadata, url_context_metadata, safety_ratings, citation_metadata + + return ( + grounding_metadata, + url_context_metadata, + safety_ratings, + citation_metadata, + ) @staticmethod def _process_candidates( @@ -1227,6 +1248,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): tools: Optional[List[ChatCompletionToolCallChunk]] = [] functions: Optional[ChatCompletionToolCallFunctionChunk] = None cumulative_tool_call_index: int = 0 + thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None for idx, candidate in enumerate(_candidates): if "content" not in candidate: @@ -1239,7 +1261,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): candidate_safety_ratings, candidate_citation_metadata, ) = VertexGeminiConfig._extract_candidate_metadata(candidate) - + grounding_metadata.extend(candidate_grounding_metadata) url_context_metadata.extend(candidate_url_context_metadata) safety_ratings.extend(candidate_safety_ratings) @@ -1264,6 +1286,12 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): ) ) + thinking_blocks = ( + VertexGeminiConfig()._extract_thinking_blocks_from_parts( + parts=candidate["content"]["parts"] + ) + ) + if audio_response is not None: cast(Dict[str, Any], chat_completion_message)[ "audio" @@ -1271,7 +1299,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): chat_completion_message["content"] = None # OpenAI spec if image_response is not None: # Handle image response - combine with text content into structured format - cast(Dict[str, Any], chat_completion_message)["image"] = image_response + cast(Dict[str, Any], chat_completion_message)[ + "image" + ] = image_response if content is not None: chat_completion_message["content"] = content @@ -1298,15 +1328,18 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if functions is not None: chat_completion_message["function_call"] = functions + if thinking_blocks is not None: + chat_completion_message["thinking_blocks"] = thinking_blocks # type: ignore + if isinstance(model_response, ModelResponseStream): choice = VertexGeminiConfig._create_streaming_choice( chat_completion_message=chat_completion_message, - candidate=candidate, - idx=idx, - tools=tools, - functions=functions, + candidate=candidate, + idx=idx, + tools=tools, + functions=functions, chat_completion_logprobs=chat_completion_logprobs, - image_response=image_response + image_response=image_response, ) model_response.choices.append(choice) elif isinstance(model_response, ModelResponse): diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index a0c8e5b6295..9b6cad38008 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -43,10 +43,14 @@ from openai.types.responses.response import ( # Handle OpenAI SDK version compatibility for Text type try: - from openai.types.responses.response_create_params import Text as ResponseText + from openai.types.responses.response_create_params import ( + Text as ResponseText, # type: ignore + ) except (ImportError, AttributeError): # Fall back to the concrete config type available in all SDK versions - from openai.types.responses.response_text_config_param import ResponseTextConfigParam as ResponseText + from openai.types.responses.response_text_config_param import ( + ResponseTextConfigParam as ResponseText, + ) from openai.types.responses.response_create_params import ( Reasoning, diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py index 2931770cd6e..052b872bcde 100644 --- a/litellm/types/llms/vertex_ai.py +++ b/litellm/types/llms/vertex_ai.py @@ -72,6 +72,7 @@ class HttpxPartType(TypedDict, total=False): executableCode: HttpxExecutableCode codeExecutionResult: HttpxCodeExecutionResult thought: bool + thoughtSignature: str class HttpxContentType(TypedDict, total=False): @@ -245,10 +246,11 @@ class UsageMetadata(TypedDict, total=False): class TokenCountDetailsResponse(TypedDict): """ Response structure for token count details with modality breakdown. - + Example: {'totalTokens': 12, 'promptTokensDetails': [{'modality': 'TEXT', 'tokenCount': 12}]} """ + totalTokens: int promptTokensDetails: List[PromptTokensDetails] diff --git a/tests/llm_translation/test_gemini.py b/tests/llm_translation/test_gemini.py index 22a54b8a56b..c54168e9a6f 100644 --- a/tests/llm_translation/test_gemini.py +++ b/tests/llm_translation/test_gemini.py @@ -436,7 +436,10 @@ def test_gemini_with_empty_function_call_arguments(): async def test_claude_tool_use_with_gemini(): response = await litellm.anthropic.messages.acreate( messages=[ - {"role": "user", "content": "Hello, can you tell me the weather in Boston. Please respond with a tool call?"} + { + "role": "user", + "content": "Hello, can you tell me the weather in Boston. Please respond with a tool call?", + } ], model="gemini/gemini-2.5-flash", stream=True, @@ -578,11 +581,17 @@ def test_gemini_tool_use(): assert stop_reason is not None assert stop_reason == "tool_calls" + @pytest.mark.asyncio async def test_gemini_image_generation_async(): litellm._turn_on_debug() response = await litellm.acompletion( - messages=[{"role": "user", "content": "Generate an image of a banana wearing a costume that says LiteLLM"}], + messages=[ + { + "role": "user", + "content": "Generate an image of a banana wearing a costume that says LiteLLM", + } + ], model="gemini/gemini-2.5-flash-image-preview", ) @@ -597,12 +606,16 @@ async def test_gemini_image_generation_async(): assert IMAGE_URL["url"].startswith("data:image/png;base64,") - @pytest.mark.asyncio async def test_gemini_image_generation_async_stream(): - #litellm._turn_on_debug() + # litellm._turn_on_debug() response = await litellm.acompletion( - messages=[{"role": "user", "content": "Generate an image of a banana wearing a costume that says LiteLLM"}], + messages=[ + { + "role": "user", + "content": "Generate an image of a banana wearing a costume that says LiteLLM", + } + ], model="gemini/gemini-2.5-flash-image-preview", stream=True, ) @@ -611,35 +624,72 @@ async def test_gemini_image_generation_async_stream(): model_response_image = None async for chunk in response: print("CHUNK: ", chunk) - if hasattr(chunk.choices[0].delta, "image") and chunk.choices[0].delta.image is not None: + if ( + hasattr(chunk.choices[0].delta, "image") + and chunk.choices[0].delta.image is not None + ): model_response_image = chunk.choices[0].delta.image print("MODEL_RESPONSE_IMAGE: ", model_response_image) assert model_response_image is not None assert model_response_image["url"].startswith("data:image/png;base64,") break - + ######################################################### # Important: Validate we did get an image in the response ######################################################### assert model_response_image is not None assert model_response_image["url"].startswith("data:image/png;base64,") - + def test_system_message_with_no_user_message(): - """ - Test that the system message is translated correctly for non-OpenAI providers. - """ - messages = [ - { - "role": "system", - "content": "Be a good bot!", + """ + Test that the system message is translated correctly for non-OpenAI providers. + """ + messages = [ + { + "role": "system", + "content": "Be a good bot!", + }, + ] + + response = litellm.completion( + model="gemini/gemini-2.5-flash", + messages=messages, + ) + assert response is not None + + assert response.choices[0].message.content is not None + + +def test_gemini_with_thinking(): + from litellm import completion + + litellm._turn_on_debug() + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, }, - ] + } + ] + messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] - response = litellm.completion( - model="gemini/gemini-2.5-flash", - messages=messages, - ) - assert response is not None - - assert response.choices[0].message.content is not None \ No newline at end of file + result = completion( + model="gemini/gemini-2.5-flash", + messages=messages, + tools=tools, + ) + print(f"result: {result}") From b6f6dc5c1c00ca131c5f24149ba398a59db7c390 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 30 Aug 2025 18:49:34 -0700 Subject: [PATCH 013/178] feat(vertex_ai.py): support parsing thinking content into gemini format allows function calls with thought signatures to be sent back to gemini Closes https://github.com/BerriAI/litellm/pull/13842 --- .../llms/vertex_ai/gemini/transformation.py | 95 ++++++++++++++++++- litellm/types/llms/vertex_ai.py | 1 + tests/llm_translation/test_gemini.py | 86 +++++++++++++++-- .../test_vertex_ai_gemini_transformation.py | 75 +++++++++++++++ 4 files changed, 248 insertions(+), 9 deletions(-) create mode 100644 tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 8ab212e2558..267ca61ef5d 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -105,6 +105,64 @@ def _process_gemini_image(image_url: str, format: Optional[str] = None) -> PartT raise e +def _snake_to_camel(snake_str: str) -> str: + """Convert snake_case to camelCase""" + components = snake_str.split("_") + return components[0] + "".join(x.capitalize() for x in components[1:]) + + +def _camel_to_snake(camel_str: str) -> str: + """Convert camelCase to snake_case""" + import re + + return re.sub(r"(? Optional[str]: + """ + Get the equivalent key from available keys, checking both camelCase and snake_case variants + """ + if key in available_keys: + return key + + # Try camelCase version + camel_key = _snake_to_camel(key) + if camel_key in available_keys: + return camel_key + + # Try snake_case version + snake_key = _camel_to_snake(key) + if snake_key in available_keys: + return snake_key + + return None + + +def check_if_part_exists_in_parts( + parts: List[PartType], part: PartType, excluded_keys: List[str] = [] +) -> bool: + """ + Check if a part exists in a list of parts + Handles both camelCase and snake_case key variations (e.g., function_call vs functionCall) + """ + keys_to_compare = set(part.keys()) - set(excluded_keys) + for p in parts: + p_keys = set(p.keys()) + # Check if all keys in part have equivalent values in p + match_found = True + for key in keys_to_compare: + equivalent_key = _get_equivalent_key(key, p_keys) + if equivalent_key is None or p.get(equivalent_key, None) != part.get( + key, None + ): + match_found = False + break + + if match_found: + return True + return False + + def _gemini_convert_messages_with_history( # noqa: PLR0915 messages: List[AllMessageValues], ) -> List[ContentType]: @@ -236,10 +294,33 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 assistant_msg = ChatCompletionAssistantMessage(**msg_dict) # type: ignore _message_content = assistant_msg.get("content", None) reasoning_content = assistant_msg.get("reasoning_content", None) + thinking_blocks = assistant_msg.get("thinking_blocks") if reasoning_content is not None: assistant_content.append( PartType(thought=True, text=reasoning_content) ) + if thinking_blocks is not None: + for block in thinking_blocks: + block_thinking_str = block.get("thinking") + block_signature = block.get("signature") + if ( + block_thinking_str is not None + and block_signature is not None + ): + try: + assistant_content.append( + PartType( + thoughtSignature=block_signature, + **json.loads(block_thinking_str), + ) + ) + except Exception: + assistant_content.append( + PartType( + thoughtSignature=block_signature, + text=block_thinking_str, + ) + ) if _message_content is not None and isinstance(_message_content, list): _parts = [] for element in _message_content: @@ -262,9 +343,17 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 assistant_msg.get("tool_calls", []) is not None or assistant_msg.get("function_call") is not None ): # support assistant tool invoke conversion - assistant_content.extend( - convert_to_gemini_tool_call_invoke(assistant_msg) + gemini_tool_call_parts = convert_to_gemini_tool_call_invoke( + assistant_msg ) + ## check if gemini_tool_call already exists in assistant_content + for gemini_tool_call_part in gemini_tool_call_parts: + if not check_if_part_exists_in_parts( + assistant_content, + gemini_tool_call_part, + excluded_keys=["thoughtSignature"], + ): + assistant_content.append(gemini_tool_call_part) last_message_with_tool_calls = assistant_msg msg_i += 1 @@ -476,6 +565,7 @@ async def async_transform_request_body( optional_params=optional_params, ) + def _default_user_message_when_system_message_passed() -> ChatCompletionUserMessage: """ Returns a default user message when a "system" message is passed in gemini fails. @@ -484,6 +574,7 @@ def _default_user_message_when_system_message_passed() -> ChatCompletionUserMess """ return ChatCompletionUserMessage(content=".", role="user") + def _transform_system_message( supports_system_message: bool, messages: List[AllMessageValues] ) -> Tuple[Optional[SystemInstructions], List[AllMessageValues]]: diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py index 052b872bcde..1b74ee25803 100644 --- a/litellm/types/llms/vertex_ai.py +++ b/litellm/types/llms/vertex_ai.py @@ -41,6 +41,7 @@ class PartType(TypedDict, total=False): function_call: FunctionCall function_response: FunctionResponse thought: bool + thoughtSignature: str class HttpxFunctionCall(TypedDict): diff --git a/tests/llm_translation/test_gemini.py b/tests/llm_translation/test_gemini.py index c54168e9a6f..b3f16ecd838 100644 --- a/tests/llm_translation/test_gemini.py +++ b/tests/llm_translation/test_gemini.py @@ -661,10 +661,33 @@ def test_system_message_with_no_user_message(): assert response.choices[0].message.content is not None +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps( + {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"} + ) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + + def test_gemini_with_thinking(): from litellm import completion litellm._turn_on_debug() + litellm.modify_params = True + model = "gemini/gemini-2.5-flash" + messages = [ + { + "role": "user", + "content": "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses", + } + ] + tools = [ { "type": "function", @@ -676,20 +699,69 @@ def test_gemini_with_thinking(): "properties": { "location": { "type": "string", - "description": "The city and state, e.g. San Francisco, CA", + "description": "The city and state", + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], }, - "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["location"], }, }, } ] - messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] - - result = completion( - model="gemini/gemini-2.5-flash", + response = litellm.completion( + model=model, messages=messages, tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit + reasoning_effort="low", ) - print(f"result: {result}") + print("Response\n", response) + response_message = response.choices[0].message + tool_calls = response_message.tool_calls + + print("Expecting there to be 3 tool calls") + assert len(tool_calls) > 0 # this has to call the function for SF, Tokyo and paris + + # Step 2: check if the model wanted to call a function + print(f"tool_calls: {tool_calls}") + if tool_calls: + # Step 3: call the function + # Note: the JSON response may not always be valid; be sure to handle errors + available_functions = { + "get_current_weather": get_current_weather, + } # only one function in this example, but you can have multiple + messages.append(response_message) # extend conversation with assistant's reply + print("Response message\n", response_message) + # Step 4: send the info for each function call and function response to the model + for tool_call in tool_calls: + function_name = tool_call.function.name + if function_name not in available_functions: + # the model called a function that does not exist in available_functions - don't try calling anything + return + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) # extend conversation with function response + print(f"messages: {messages}") + second_response = litellm.completion( + model=model, + messages=messages, + seed=22, + reasoning_effort="low", + tools=tools, + drop_params=True, + ) # get a new response from the model where it can see the function response + print("second response\n", second_response) diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py new file mode 100644 index 00000000000..d6d33258576 --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py @@ -0,0 +1,75 @@ +from litellm.llms.vertex_ai.gemini.transformation import check_if_part_exists_in_parts + + +def test_check_if_part_exists_in_parts(): + parts = [ + {"text": "Hello", "thought": True}, + {"text": "World", "thought": False}, + ] + part = {"text": "Hello", "thought": True} + new_part = {"text": "Hello World", "thought": True} + assert check_if_part_exists_in_parts(parts, part) + assert not check_if_part_exists_in_parts(parts, new_part, ["thought"]) + assert check_if_part_exists_in_parts(parts, new_part, ["text"]) + + +def test_check_if_part_exists_in_parts_camel_case_snake_case(): + """Test that function handles both camelCase and snake_case key variations""" + # Test snake_case to camelCase matching + parts_with_snake_case = [ + { + "function_call": { + "name": "get_current_weather", + "args": {"location": "San Francisco, CA"}, + } + }, + {"text": "Some other content"}, + ] + + part_with_camel_case = { + "functionCall": { + "name": "get_current_weather", + "args": {"location": "San Francisco, CA"}, + } + } + + # Should find match between function_call and functionCall + assert check_if_part_exists_in_parts(parts_with_snake_case, part_with_camel_case) + + # Test camelCase to snake_case matching + parts_with_camel_case = [ + {"functionCall": {"name": "calculate_sum", "args": {"a": 1, "b": 2}}} + ] + + part_with_snake_case = { + "function_call": {"name": "calculate_sum", "args": {"a": 1, "b": 2}} + } + + # Should find match between functionCall and function_call + assert check_if_part_exists_in_parts(parts_with_camel_case, part_with_snake_case) + + # Test no match when values differ + part_with_different_values = { + "function_call": {"name": "different_function", "args": {"x": 5}} + } + + assert not check_if_part_exists_in_parts( + parts_with_snake_case, part_with_different_values + ) + + # Test multiple keys with mixed casing + parts_mixed = [ + { + "function_call": {"name": "test"}, + "thoughtSignature": "reasoning", + "text": "content", + } + ] + + part_mixed_casing = { + "functionCall": {"name": "test"}, + "thought_signature": "reasoning", + "text": "content", + } + + assert check_if_part_exists_in_parts(parts_mixed, part_mixed_casing) From eed235833511ddd584946df62ea1f688dfa0c01e Mon Sep 17 00:00:00 2001 From: tanjiro <56165694+NANDINI-star@users.noreply.github.com> Date: Sun, 31 Aug 2025 14:03:32 +0900 Subject: [PATCH 014/178] move filter inside user table --- .../src/components/view_users.tsx | 233 +++--------------- .../src/components/view_users/table.tsx | 221 ++++++++++++++++- 2 files changed, 247 insertions(+), 207 deletions(-) diff --git a/ui/litellm-dashboard/src/components/view_users.tsx b/ui/litellm-dashboard/src/components/view_users.tsx index 1d80e513e35..7c828f756b5 100644 --- a/ui/litellm-dashboard/src/components/view_users.tsx +++ b/ui/litellm-dashboard/src/components/view_users.tsx @@ -79,7 +79,6 @@ const ViewUserDashboard: React.FC = ({ accessToken, toke const [activeTab, setActiveTab] = useState("users") const [filters, setFilters] = useState(initialFilters) const [debouncedFilters, setDebouncedFilters, debouncer] = useDebouncedState(filters, { wait: 300 }) - const [showFilters, setShowFilters] = useState(false) const [isInvitationLinkModalVisible, setIsInvitationLinkModalVisible] = useState(false) const [invitationLinkData, setInvitationLinkData] = useState(null) const [baseUrl, setBaseUrl] = useState(null) @@ -330,209 +329,35 @@ const ViewUserDashboard: React.FC = ({ accessToken, toke -
-
-
- {/* Search and Filter Controls */} -
- {/* Email Search */} -
- updateFilters({ email: e.target.value })} - /> - - - -
- - {/* Filter Button */} - - - {/* Reset Filters Button */} - -
- - {/* Additional Filters */} - {showFilters && ( -
- {/* User ID Search */} -
- updateFilters({ user_id: e.target.value })} - /> - - - -
- - {/* Role Dropdown */} -
- -
- - {/* Team Dropdown */} -
- -
- - {/* SSO ID Search */} -
- updateFilters({ sso_user_id: e.target.value })} - /> -
-
- )} - - {/* Results Count and Pagination */} -
- - Showing{" "} - {userListResponse && userListResponse.users && userListResponse.users.length > 0 - ? (userListResponse.page - 1) * userListResponse.page_size + 1 - : 0}{" "} - -{" "} - {userListResponse && userListResponse.users - ? Math.min(userListResponse.page * userListResponse.page_size, userListResponse.total) - : 0}{" "} - of {userListResponse ? userListResponse.total : 0} results - - - {/* Pagination Buttons */} -
- - -
-
-
-
-
- { - setSelectedUser(user) - setEditModalVisible(true) - }} - handleDelete={handleDelete} - handleResetPassword={handleResetPassword} - enableSelection={selectionMode} - selectedUsers={selectedUsers} - onSelectionChange={handleSelectionChange} - /> -
- -
+ { + setSelectedUser(user) + setEditModalVisible(true) + }} + handleDelete={handleDelete} + handleResetPassword={handleResetPassword} + enableSelection={selectionMode} + selectedUsers={selectedUsers} + onSelectionChange={handleSelectionChange} + filters={filters} + updateFilters={updateFilters} + initialFilters={initialFilters} + teams={teams} + userListResponse={userListResponse} + currentPage={currentPage} + handlePageChange={handlePageChange} + /> diff --git a/ui/litellm-dashboard/src/components/view_users/table.tsx b/ui/litellm-dashboard/src/components/view_users/table.tsx index 0f1193c6e87..0b422ead7b3 100644 --- a/ui/litellm-dashboard/src/components/view_users/table.tsx +++ b/ui/litellm-dashboard/src/components/view_users/table.tsx @@ -15,12 +15,27 @@ import { TableBody, TableRow, TableCell, + Select, + SelectItem, } from "@tremor/react"; import { SwitchVerticalIcon, ChevronUpIcon, ChevronDownIcon } from "@heroicons/react/outline"; import { UserInfo } from "./types"; import UserInfoView from "./user_info_view"; import { columns as createColumns } from "./columns"; +interface FilterState { + email: string; + user_id: string; + user_role: string; + sso_user_id: string; + team: string; + model: string; + min_spend: number | null; + max_spend: number | null; + sort_by: string; + sort_order: "asc" | "desc"; +} + interface UserDataTableProps { data: UserInfo[]; columns: ColumnDef[]; @@ -39,6 +54,15 @@ interface UserDataTableProps { selectedUsers?: UserInfo[]; onSelectionChange?: (selectedUsers: UserInfo[]) => void; enableSelection?: boolean; + // Filter-related props + filters: FilterState; + updateFilters: (update: Partial) => void; + initialFilters: FilterState; + teams: any[] | null; + // Pagination props + userListResponse: any; + currentPage: number; + handlePageChange: (newPage: number) => void; } export function UserDataTable({ @@ -56,6 +80,13 @@ export function UserDataTable({ selectedUsers = [], onSelectionChange, enableSelection = false, + filters, + updateFilters, + initialFilters, + teams, + userListResponse, + currentPage, + handlePageChange, }: UserDataTableProps) { const [sorting, setSorting] = React.useState([ { @@ -65,6 +96,7 @@ export function UserDataTable({ ]); const [selectedUserId, setSelectedUserId] = React.useState(null); const [openInEditMode, setOpenInEditMode] = React.useState(false); + const [showFilters, setShowFilters] = React.useState(false); const handleUserClick = (userId: string, openInEditMode: boolean = false) => { setSelectedUserId(userId); @@ -171,9 +203,190 @@ export function UserDataTable({ } return ( -
-
- +
+ {/* Filter Section */} +
+
+ {/* Search and Filter Controls */} +
+ {/* Email Search */} +
+ updateFilters({ email: e.target.value })} + /> + + + +
+ + {/* Filter Button */} + + + {/* Reset Filters Button */} + +
+ + {/* Additional Filters */} + {showFilters && ( +
+ {/* User ID Search */} +
+ updateFilters({ user_id: e.target.value })} + /> + + + +
+ + {/* Role Dropdown */} +
+ +
+ + {/* Team Dropdown */} +
+ +
+ + {/* SSO ID Search */} +
+ updateFilters({ sso_user_id: e.target.value })} + /> +
+
+ )} + + {/* Results Count and Pagination */} +
+ + Showing{" "} + {userListResponse && userListResponse.users && userListResponse.users.length > 0 + ? (userListResponse.page - 1) * userListResponse.page_size + 1 + : 0}{" "} + -{" "} + {userListResponse && userListResponse.users + ? Math.min(userListResponse.page * userListResponse.page_size, userListResponse.total) + : 0}{" "} + of {userListResponse ? userListResponse.total : 0} results + + + {/* Pagination Buttons */} +
+ + +
+
+
+
+ + {/* Table Section */} +
+
+
+
{table.getHeaderGroups().map((headerGroup) => ( @@ -260,6 +473,8 @@ export function UserDataTable({ )}
+
+
); From 82cec3885ec7b8667fd7cf0dfc7e38acb09a67b7 Mon Sep 17 00:00:00 2001 From: tanjiro <56165694+NANDINI-star@users.noreply.github.com> Date: Sun, 31 Aug 2025 14:33:15 +0900 Subject: [PATCH 015/178] model filters ui matches the users page --- .../components/templates/model_dashboard.tsx | 282 +++++++++++------- 1 file changed, 170 insertions(+), 112 deletions(-) diff --git a/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx b/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx index 85f18ee89e1..4dd66d0bf5e 100644 --- a/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx +++ b/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx @@ -191,6 +191,9 @@ const ModelDashboard: React.FC = ({ const [currentTeam, setCurrentTeam] = useState("personal") // 'personal' or team_id const [modelViewMode, setModelViewMode] = useState<"current_team" | "all">("current_team") + // Add state for showing/hiding filters + const [showFilters, setShowFilters] = useState(false) + const [showColumnDropdown, setShowColumnDropdown] = useState(false) const [isDropdownOpen, setIsDropdownOpen] = useState(false) @@ -206,6 +209,14 @@ const ModelDashboard: React.FC = ({ setSelectedTabIndex(1) } + const resetFilters = () => { + setModelNameSearch("") + setSelectedModelGroup("all") + setSelectedModelAccessGroupFilter(null) + setCurrentTeam("personal") + setModelViewMode("current_team") + } + const setProviderModelsFn = (provider: Providers) => { const _providerModels = getProviderModels(provider, modelMap) setProviderModels(_providerModels) @@ -1006,119 +1017,166 @@ const ModelDashboard: React.FC = ({
-
-
- {/* Current Team Selector - Prominent */} -
-
-
- Current Team: - setCurrentTeam(value)} + > + +
+
+ Personal +
+ + {teams + ?.filter((team) => team.team_id) + .map((team) => ( +
-
- Personal +
+ + {team.team_alias + ? `${team.team_alias.slice(0, 30)}...` + : `Team ${team.team_id.slice(0, 30)}...`} +
- {teams - ?.filter((team) => team.team_id) - .map((team) => ( - -
-
- - {team.team_alias - ? `${team.team_alias.slice(0, 30)}...` - : `Team ${team.team_id.slice(0, 30)}...`} - -
- - ))} - -
- {modelViewMode === "current_team" && ( -
- -
- {currentTeam === "personal" ? ( - - To access these models: Create a Virtual Key without selecting a team on the{" "} - - Virtual Keys page - - - ) : ( - - To access these models: Create a Virtual Key and select Team as " - {currentTeam}" on the{" "} - - Virtual Keys page - - - )} -
-
- )} -
- - {/* Model View Mode Toggle - Also prominent */} -
- View: - -
+ ))} +
- {/* Other Filters */} -
-
- {/* Model Name Search */} -
- Search Public Model Name: - -
+
+ View: + +
+
+ + {modelViewMode === "current_team" && ( +
+ +
+ {currentTeam === "personal" ? ( + + To access these models: Create a Virtual Key without selecting a team on the{" "} + + Virtual Keys page + + + ) : ( + + To access these models: Create a Virtual Key and select Team as " + {currentTeam}" on the{" "} + + Virtual Keys page + + + )} +
+
+ )} +
+ {/* Search and Filter Controls */} +
+
+ {/* Search and Filter Controls */} +
+ {/* Model Name Search */} +
+ setModelNameSearch(e.target.value)} + /> + + + +
+ + {/* Filter Button */} + + + {/* Reset Filters Button */} + +
+ + {/* Additional Filters */} + {showFilters && ( +
{/* Model Name Filter */} -
- Filter by Public Model Name: +
-
- Filter by Model Access Group: + {/* Model Access Group Filter */} +
-
+ )} {/* Results Count */}
- + Showing{" "} {modelData && modelData.data.length > 0 ? modelData.data.filter((model: any) => { @@ -1164,7 +1221,8 @@ const ModelDashboard: React.FC = ({ const modelNameMatch = selectedModelGroup === "all" || model.model_name === selectedModelGroup || - !selectedModelGroup + !selectedModelGroup || + (selectedModelGroup === "wildcard" && model.model_name?.includes("*")) const accessGroupMatch = selectedModelAccessGroupFilter === "all" || model.model_info["access_groups"]?.includes(selectedModelAccessGroupFilter) || @@ -1183,7 +1241,7 @@ const ModelDashboard: React.FC = ({ }).length : 0}{" "} results - +
From f17b5730a56d2525829de2b18d5748e6e6705a10 Mon Sep 17 00:00:00 2001 From: tanjiro <56165694+NANDINI-star@users.noreply.github.com> Date: Sun, 31 Aug 2025 15:06:29 +0900 Subject: [PATCH 016/178] client side pagination of models page --- .../src/components/model_dashboard/table.tsx | 3 +- .../components/templates/model_dashboard.tsx | 159 +++++++++++------- 2 files changed, 96 insertions(+), 66 deletions(-) diff --git a/ui/litellm-dashboard/src/components/model_dashboard/table.tsx b/ui/litellm-dashboard/src/components/model_dashboard/table.tsx index 30f01d91db5..cca7bc08608 100644 --- a/ui/litellm-dashboard/src/components/model_dashboard/table.tsx +++ b/ui/litellm-dashboard/src/components/model_dashboard/table.tsx @@ -8,6 +8,7 @@ import { useReactTable, ColumnResizeMode, VisibilityState, + PaginationState, } from "@tanstack/react-table"; import React from "react"; import { @@ -18,7 +19,7 @@ import { TableRow, TableCell, } from "@tremor/react"; -import { SwitchVerticalIcon, ChevronUpIcon, ChevronDownIcon, TableIcon } from "@heroicons/react/outline"; +import { SwitchVerticalIcon, ChevronUpIcon, ChevronDownIcon } from "@heroicons/react/outline"; // Extend the column meta type to include className declare module "@tanstack/react-table" { diff --git a/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx b/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx index 4dd66d0bf5e..2eff0bf3e36 100644 --- a/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx +++ b/ui/litellm-dashboard/src/components/templates/model_dashboard.tsx @@ -1,4 +1,4 @@ -import React, { useState, useEffect, useRef } from "react" +import React, { useState, useEffect, useRef, useMemo } from "react" import { Card, Title, @@ -61,7 +61,7 @@ import HealthCheckComponent from "../model_dashboard/HealthCheckComponent"; import PassThroughSettings from "../pass_through_settings"; import ModelGroupAliasSettings from "../model_group_alias_settings"; import { all_admin_roles } from "@/utils/roles"; -import { Table as TableInstance } from "@tanstack/react-table"; +import { Table as TableInstance, PaginationState } from "@tanstack/react-table"; import NotificationsManager from "../molecules/notifications_manager"; interface ModelDashboardProps { @@ -200,6 +200,12 @@ const ModelDashboard: React.FC = ({ const [expandedRows, setExpandedRows] = useState>(new Set()) const dropdownRef = useRef(null) const tableRef = useRef>(null) + + // Pagination state + const [pagination, setPagination] = useState({ + pageIndex: 0, + pageSize: 50, + }) const [selectedTabIndex, setSelectedTabIndex] = useState(0) const handleCreateNewModelClick = () => { @@ -215,8 +221,57 @@ const ModelDashboard: React.FC = ({ setSelectedModelAccessGroupFilter(null) setCurrentTeam("personal") setModelViewMode("current_team") + setPagination({ pageIndex: 0, pageSize: 50 }) } + // Memoize filtered data to prevent unnecessary re-calculations + const filteredData = useMemo(() => { + if (!modelData || !modelData.data || modelData.data.length === 0) { + return []; + } + + return modelData.data.filter((model: any) => { + const searchMatch = + modelNameSearch === "" || + model.model_name.toLowerCase().includes(modelNameSearch.toLowerCase()) + + const modelNameMatch = + selectedModelGroup === "all" || + model.model_name === selectedModelGroup || + !selectedModelGroup || + (selectedModelGroup === "wildcard" && model.model_name?.includes("*")) + + const accessGroupMatch = + selectedModelAccessGroupFilter === "all" || + model.model_info["access_groups"]?.includes(selectedModelAccessGroupFilter) || + !selectedModelAccessGroupFilter + + let teamAccessMatch = true + if (modelViewMode === "current_team") { + if (currentTeam === "personal") { + teamAccessMatch = model.model_info?.direct_access === true + } else { + teamAccessMatch = + model.model_info?.access_via_team_ids?.includes(currentTeam) === true + } + } + + return searchMatch && modelNameMatch && accessGroupMatch && teamAccessMatch + }); + }, [modelData, modelNameSearch, selectedModelGroup, selectedModelAccessGroupFilter, currentTeam, modelViewMode]); + + // Memoize paginated data + const paginatedData = useMemo(() => { + const startIndex = pagination.pageIndex * pagination.pageSize; + const endIndex = startIndex + pagination.pageSize; + return filteredData.slice(startIndex, endIndex); + }, [filteredData, pagination.pageIndex, pagination.pageSize]); + + // Reset pagination when filters change + useEffect(() => { + setPagination(prev => ({ ...prev, pageIndex: 0 })) + }, [modelNameSearch, selectedModelGroup, selectedModelAccessGroupFilter, currentTeam, modelViewMode]) + const setProviderModelsFn = (provider: Providers) => { const _providerModels = getProviderModels(provider, modelMap) setProviderModels(_providerModels) @@ -1208,40 +1263,45 @@ const ModelDashboard: React.FC = ({
)} - {/* Results Count */} + {/* Results Count and Pagination Controls */}
- Showing{" "} - {modelData && modelData.data.length > 0 - ? modelData.data.filter((model: any) => { - const searchMatch = - modelNameSearch === "" || - model.model_name.toLowerCase().includes(modelNameSearch.toLowerCase()) - - const modelNameMatch = - selectedModelGroup === "all" || - model.model_name === selectedModelGroup || - !selectedModelGroup || - (selectedModelGroup === "wildcard" && model.model_name?.includes("*")) - const accessGroupMatch = - selectedModelAccessGroupFilter === "all" || - model.model_info["access_groups"]?.includes(selectedModelAccessGroupFilter) || - !selectedModelAccessGroupFilter - let teamAccessMatch = true - if (modelViewMode === "current_team") { - if (currentTeam === "personal") { - teamAccessMatch = model.model_info?.direct_access === true - } else { - teamAccessMatch = - model.model_info?.access_via_team_ids?.includes(currentTeam) === true - } - } - - return searchMatch && modelNameMatch && accessGroupMatch && teamAccessMatch - }).length - : 0}{" "} - results + {filteredData.length > 0 ? ( + `Showing ${pagination.pageIndex * pagination.pageSize + 1} - ${Math.min( + (pagination.pageIndex + 1) * pagination.pageSize, + filteredData.length + )} of ${filteredData.length} results` + ) : ( + "Showing 0 results" + )} + + {/* Pagination Controls */} + {filteredData.length > pagination.pageSize && ( +
+ + + +
+ )}
@@ -1260,38 +1320,7 @@ const ModelDashboard: React.FC = ({ expandedRows, setExpandedRows, )} - data={modelData.data.filter((model: any) => { - // Model name search filter - const searchMatch = - modelNameSearch === "" || - model.model_name.toLowerCase().includes(modelNameSearch.toLowerCase()) - - // Model name filter - const modelNameMatch = - selectedModelGroup === "all" || - model.model_name === selectedModelGroup || - !selectedModelGroup || - (selectedModelGroup === "wildcard" && model.model_name?.includes("*")) - // Model access group filter - const accessGroupMatch = - selectedModelAccessGroupFilter === "all" || - model.model_info["access_groups"]?.includes(selectedModelAccessGroupFilter) || - !selectedModelAccessGroupFilter - // Team access filter based on current team and view mode - let teamAccessMatch = true - if (modelViewMode === "current_team") { - if (currentTeam === "personal") { - // Show only models with direct access - teamAccessMatch = model.model_info?.direct_access === true - } else { - // Show only models accessible by the current team - teamAccessMatch = model.model_info?.access_via_team_ids?.includes(currentTeam) === true - } - } - // For 'all' mode, show all models (teamAccessMatch remains true) - - return searchMatch && modelNameMatch && accessGroupMatch && teamAccessMatch - })} + data={paginatedData} isLoading={false} table={tableRef} /> From d83c420d484b99e8ecf068f1c6bf446dbece80d8 Mon Sep 17 00:00:00 2001 From: Yuta Saito Date: Sun, 31 Aug 2025 16:29:19 +0900 Subject: [PATCH 017/178] feat: Add guardrail for the Anthropic API endpoint --- litellm/integrations/custom_guardrail.py | 7 +- .../proxy/anthropic_endpoints/endpoints.py | 42 ++++++-- .../integrations/test_custom_guardrail.py | 100 +++++++++++++++++- 3 files changed, 133 insertions(+), 16 deletions(-) diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 501185b207e..1ca45f907e1 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -119,11 +119,8 @@ class CustomGuardrail(CustomLogger): """ if "guardrails" in data: return data["guardrails"] - metadata = data.get("metadata") or {} - requested_guardrails = metadata.get("guardrails") or [] - if requested_guardrails: - return requested_guardrails - return requested_guardrails + metadata = data.get("litellm_metadata") or data.get("metadata", {}) + return metadata.get("guardrails") or [] def _guardrail_is_in_requested_guardrails( self, diff --git a/litellm/proxy/anthropic_endpoints/endpoints.py b/litellm/proxy/anthropic_endpoints/endpoints.py index a10a39a6a57..2de5ec1ee12 100644 --- a/litellm/proxy/anthropic_endpoints/endpoints.py +++ b/litellm/proxy/anthropic_endpoints/endpoints.py @@ -90,6 +90,17 @@ async def anthropic_response( # noqa: PLR0915 user_api_key_dict=user_api_key_dict, data=data, call_type="text_completion" ) + tasks = [] + tasks.append( + proxy_logging_obj.during_call_hook( + data=data, + user_api_key_dict=user_api_key_dict, + call_type=ProxyBaseLLMRequestProcessing._get_pre_call_type( + route_type="anthropic_messages" # type: ignore + ), + ) + ) + ### ROUTE THE REQUESTs ### router_model_names = llm_router.model_names if llm_router is not None else [] @@ -97,23 +108,21 @@ async def anthropic_response( # noqa: PLR0915 if ( llm_router is not None and data["model"] in router_model_names ): # model in router model list - llm_response = asyncio.create_task(llm_router.aanthropic_messages(**data)) + llm_coro = llm_router.aanthropic_messages(**data) elif ( llm_router is not None and llm_router.model_group_alias is not None and data["model"] in llm_router.model_group_alias ): # model set in model_group_alias - llm_response = asyncio.create_task(llm_router.aanthropic_messages(**data)) + llm_coro = llm_router.aanthropic_messages(**data) elif ( llm_router is not None and data["model"] in llm_router.deployment_names ): # model in router deployments, calling a specific deployment on the router - llm_response = asyncio.create_task( - llm_router.aanthropic_messages(**data, specific_deployment=True) - ) + llm_coro = llm_router.aanthropic_messages(**data, specific_deployment=True) elif ( llm_router is not None and data["model"] in llm_router.get_model_ids() ): # model in router model list - llm_response = asyncio.create_task(llm_router.aanthropic_messages(**data)) + llm_coro = llm_router.aanthropic_messages(**data) elif ( llm_router is not None and data["model"] not in router_model_names @@ -122,9 +131,9 @@ async def anthropic_response( # noqa: PLR0915 or len(llm_router.pattern_router.patterns) > 0 ) ): # model in router deployments, calling a specific deployment on the router - llm_response = asyncio.create_task(llm_router.aanthropic_messages(**data)) + llm_coro = llm_router.aanthropic_messages(**data) elif user_model is not None: # `litellm --model ` - llm_response = asyncio.create_task(litellm.anthropic_messages(**data)) + llm_coro = litellm.anthropic_messages(**data) else: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, @@ -134,8 +143,16 @@ async def anthropic_response( # noqa: PLR0915 }, ) - # Await the llm_response task - response = await llm_response + tasks.append(llm_coro) + + # wait for call to end + llm_responses = asyncio.gather( + *tasks + ) # run the moderation check in parallel to the actual llm api call + + responses = await llm_responses + + response = responses[1] hidden_params = getattr(response, "_hidden_params", {}) or {} model_id = hidden_params.get("model_id", None) or "" @@ -183,6 +200,11 @@ async def anthropic_response( # noqa: PLR0915 headers=dict(fastapi_response.headers), ) + ### CALL HOOKS ### - modify outgoing data + response = await proxy_logging_obj.post_call_success_hook( + data=data, user_api_key_dict=user_api_key_dict, response=response # type: ignore + ) + verbose_proxy_logger.info("\nResponse from Litellm:\n{}".format(response)) return response except Exception as e: diff --git a/tests/test_litellm/integrations/test_custom_guardrail.py b/tests/test_litellm/integrations/test_custom_guardrail.py index e71b68ab934..182e0134928 100644 --- a/tests/test_litellm/integrations/test_custom_guardrail.py +++ b/tests/test_litellm/integrations/test_custom_guardrail.py @@ -1,4 +1,4 @@ -from unittest.mock import AsyncMock, MagicMock +from unittest.mock import AsyncMock import pytest @@ -82,3 +82,101 @@ class TestCustomGuardrailDeploymentHook: # Verify messages were updated in result assert result["messages"] == mock_result["messages"] assert result["messages"] != original_messages + + +class TestCustomGuardrailShouldRunGuardrail: + + def test_should_run_guardrail_with_litellm_metadata(self): + """Test that should_run_guardrail works with litellm_metadata pattern""" + from litellm.types.guardrails import GuardrailEventHooks + + custom_guardrail = CustomGuardrail( + guardrail_name="test_guardrail", + default_on=False, + event_hook=GuardrailEventHooks.pre_call + ) + + # Test with guardrails in litellm_metadata + data = { + "model": "gpt-3.5-turbo", + "litellm_metadata": { + "guardrails": ["test_guardrail"] + } + } + + result = custom_guardrail.should_run_guardrail( + data=data, event_type=GuardrailEventHooks.pre_call + ) + + assert result is True + + def test_should_run_guardrail_with_metadata(self): + """Test that should_run_guardrail works with metadata pattern""" + from litellm.types.guardrails import GuardrailEventHooks + + custom_guardrail = CustomGuardrail( + guardrail_name="test_guardrail", + default_on=False, + event_hook=GuardrailEventHooks.pre_call + ) + + # Test with guardrails in metadata + data = { + "model": "gpt-3.5-turbo", + "metadata": { + "guardrails": ["test_guardrail"] + } + } + + result = custom_guardrail.should_run_guardrail( + data=data, event_type=GuardrailEventHooks.pre_call + ) + + assert result is True + + def test_should_run_guardrail_with_root_level_guardrails(self): + """Test that should_run_guardrail works with root level guardrails""" + from litellm.types.guardrails import GuardrailEventHooks + + custom_guardrail = CustomGuardrail( + guardrail_name="test_guardrail", + default_on=False, + event_hook=GuardrailEventHooks.pre_call + ) + + # Test with guardrails at root level + data = { + "model": "gpt-3.5-turbo", + "guardrails": ["test_guardrail"] + } + + result = custom_guardrail.should_run_guardrail( + data=data, event_type=GuardrailEventHooks.pre_call + ) + + assert result is True + + + def test_should_run_guardrail_no_matching_guardrail(self): + """Test that should_run_guardrail returns False when guardrail name doesn't match""" + from litellm.types.guardrails import GuardrailEventHooks + + custom_guardrail = CustomGuardrail( + guardrail_name="test_guardrail", + default_on=False, + event_hook=GuardrailEventHooks.pre_call + ) + + # Test with different guardrail name + data = { + "model": "gpt-3.5-turbo", + "litellm_metadata": { + "guardrails": ["different_guardrail"] + } + } + + result = custom_guardrail.should_run_guardrail( + data=data, event_type=GuardrailEventHooks.pre_call + ) + + assert result is False From 75e698feefe16dee5f152036a84be7f41da82e39 Mon Sep 17 00:00:00 2001 From: iabhi4 Date: Sun, 31 Aug 2025 15:58:26 -0700 Subject: [PATCH 018/178] bug(auth): support for ES256/ES384/ES512 and EdDSA JWT verification --- litellm/proxy/auth/handle_jwt.py | 23 +++-- tests/proxy_unit_tests/test_jwt.py | 139 +++++++++++++++++++++++++++++ 2 files changed, 153 insertions(+), 9 deletions(-) diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index f20c3debc23..6a7aa794e5b 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -484,7 +484,7 @@ class JWTHandler: # Supported algos: https://pyjwt.readthedocs.io/en/stable/algorithms.html # "Warning: Make sure not to mix symmetric and asymmetric algorithms that interpret # the key in different ways (e.g. HS* and RS*)." - algorithms = ["RS256", "RS384", "RS512", "PS256", "PS384", "PS512"] + algorithms = ["RS256", "RS384", "RS512", "PS256", "PS384", "PS512", "ES256", "ES384", "ES512", "EdDSA"] audience = os.getenv("JWT_AUDIENCE") decode_options = None @@ -492,7 +492,7 @@ class JWTHandler: decode_options = {"verify_aud": False} import jwt - from jwt.algorithms import RSAAlgorithm + from jwt.api_jwk import PyJWK header = jwt.get_unverified_header(token) @@ -512,14 +512,21 @@ class JWTHandler: jwk["n"] = public_key["n"] if "e" in public_key: jwk["e"] = public_key["e"] + if "x" in public_key: + jwk["x"] = public_key["x"] + if "y" in public_key: + jwk["y"] = public_key["y"] + if "crv" in public_key: + jwk["crv"] = public_key["crv"] - public_key_rsa = RSAAlgorithm.from_jwk(json.dumps(jwk)) + # parse RSA/EC/OKP keys + public_key_obj = PyJWK.from_dict(jwk).key try: # decode the token using the public key payload = jwt.decode( token, - public_key_rsa, # type: ignore + public_key_obj, # type: ignore algorithms=algorithms, options=decode_options, audience=audience, @@ -534,9 +541,7 @@ class JWTHandler: raise Exception(f"Validation fails: {str(e)}") elif public_key is not None and isinstance(public_key, str): try: - cert = x509.load_pem_x509_certificate( - public_key.encode(), default_backend() - ) + cert = x509.load_pem_x509_certificate(public_key.encode(), default_backend()) # Extract public key key = cert.public_key().public_bytes( @@ -561,7 +566,7 @@ class JWTHandler: raise Exception(f"Validation fails: {str(e)}") raise Exception("Invalid JWT Submitted") - + async def close(self): await self.http_handler.close() @@ -1210,4 +1215,4 @@ class JWTAuthManager: end_user_object=end_user_object, token=api_key, team_membership=team_membership_object, - ) + ) \ No newline at end of file diff --git a/tests/proxy_unit_tests/test_jwt.py b/tests/proxy_unit_tests/test_jwt.py index d0403769425..57514817e88 100644 --- a/tests/proxy_unit_tests/test_jwt.py +++ b/tests/proxy_unit_tests/test_jwt.py @@ -1375,3 +1375,142 @@ async def test_custom_validate_called(): pass # Assert custom_validate was called with the jwt token mock_custom_validate.assert_called_once_with({"sub": "test_user"}) + + +@pytest.mark.asyncio +async def test_auth_jwt_es256_jwk_path(monkeypatch): + import time, base64, jwt + from cryptography.hazmat.primitives.asymmetric import ec + from cryptography.hazmat.primitives import serialization + + monkeypatch.delenv("JWT_AUDIENCE", raising=False) + + def b64url_uint(n: int, size: int) -> str: + return base64.urlsafe_b64encode(n.to_bytes(size, "big")).rstrip(b"=").decode() + + ec_key = ec.generate_private_key(ec.SECP256R1()) + ec_priv_pem = ec_key.private_bytes( + encoding=serialization.Encoding.PEM, + format=serialization.PrivateFormat.PKCS8, + encryption_algorithm=serialization.NoEncryption(), + ) + + pub = ec_key.public_key().public_numbers() + ec_jwk = { + "kty": "EC", + "crv": "P-256", + "x": b64url_uint(pub.x, 32), + "y": b64url_uint(pub.y, 32), + "kid": "ec1", + "alg": "ES256", + "use": "sig", + } + + now = int(time.time()) + token = jwt.encode( + {"sub": "alice", "aud": "litellm-proxy", "iss": "http://example", "iat": now, "exp": now + 300}, + ec_priv_pem, + algorithm="ES256", + headers={"kid": "ec1"}, + ) + + h = JWTHandler() + with patch.object(h, "get_public_key", new=AsyncMock(return_value=ec_jwk)): + claims = await h.auth_jwt(token) + assert claims["sub"] == "alice" + + +@pytest.mark.asyncio +async def test_auth_jwt_rs256_regression(monkeypatch): + """ + Regression: RSA path must still work (kty RSA, n/e) after EC support. + """ + import time, base64, jwt + from cryptography.hazmat.primitives.asymmetric import rsa + from cryptography.hazmat.primitives import serialization + + monkeypatch.delenv("JWT_AUDIENCE", raising=False) + + rsa_key = rsa.generate_private_key(public_exponent=65537, key_size=2048) + rsa_priv_pem = rsa_key.private_bytes( + encoding=serialization.Encoding.PEM, + format=serialization.PrivateFormat.PKCS8, + encryption_algorithm=serialization.NoEncryption(), + ) + pub = rsa_key.public_key().public_numbers() + + def b64url(b: bytes) -> str: + return base64.urlsafe_b64encode(b).rstrip(b"=").decode() + + n = pub.n.to_bytes((pub.n.bit_length() + 7) // 8, "big") + e = pub.e.to_bytes((pub.e.bit_length() + 7) // 8, "big") + rsa_jwk = { + "kty": "RSA", + "n": b64url(n), + "e": b64url(e), + "kid": "rsa1", + "alg": "RS256", + "use": "sig", + } + + now = int(time.time()) + token = jwt.encode( + {"sub": "bob", "aud": "litellm-proxy", "iss": "http://example", "iat": now, "exp": now + 300}, + rsa_priv_pem, + algorithm="RS256", + headers={"kid": "rsa1"}, + ) + + h = JWTHandler() + with patch.object(h, "get_public_key", new=AsyncMock(return_value=rsa_jwk)): + claims = await h.auth_jwt(token) + assert claims["sub"] == "bob" + + +@pytest.mark.asyncio +async def test_auth_jwt_mismatched_key_fails(monkeypatch): + """ + Negative: ES256 token must fail if JWKS returns an RSA key (mismatch). + """ + import time, base64, jwt + from cryptography.hazmat.primitives.asymmetric import ec, rsa + from cryptography.hazmat.primitives import serialization + + monkeypatch.delenv("JWT_AUDIENCE", raising=False) + + # ES256 token + ec_key = ec.generate_private_key(ec.SECP256R1()) + ec_priv_pem = ec_key.private_bytes( + encoding=serialization.Encoding.PEM, + format=serialization.PrivateFormat.PKCS8, + encryption_algorithm=serialization.NoEncryption(), + ) + now = int(time.time()) + token = jwt.encode( + {"sub": "mallory", "aud": "litellm-proxy", "iss": "http://example", "iat": now, "exp": now + 300}, + ec_priv_pem, + algorithm="ES256", + headers={"kid": "ec1"}, + ) + + # RSA JWK (wrong key) + rsa_key = rsa.generate_private_key(public_exponent=65537, key_size=2048) + pub = rsa_key.public_key().public_numbers() + + def b64url(b: bytes) -> str: + return base64.urlsafe_b64encode(b).rstrip(b"=").decode() + + rsa_jwk = { + "kty": "RSA", + "n": b64url(pub.n.to_bytes((pub.n.bit_length() + 7) // 8, "big")), + "e": b64url(pub.e.to_bytes((pub.e.bit_length() + 7) // 8, "big")), + "kid": "rsa1", + "alg": "RS256", + "use": "sig", + } + + h = JWTHandler() + with patch.object(h, "get_public_key", new=AsyncMock(return_value=rsa_jwk)): + with pytest.raises(Exception) as exc: + await h.auth_jwt(token) + assert "Validation fails" in str(exc.value) \ No newline at end of file From 130c1dd4fc94f1fa2acdfc418888107fd9fcec52 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sun, 31 Aug 2025 20:25:12 -0700 Subject: [PATCH 019/178] fix(types/openai.py): add default none values to responsesapiresponse object Fixes https://github.com/BerriAI/litellm/issues/14061 --- litellm/types/llms/openai.py | 38 ++++++++++++++++++++---------------- 1 file changed, 21 insertions(+), 17 deletions(-) diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index a0c8e5b6295..6e7c4150774 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -43,10 +43,14 @@ from openai.types.responses.response import ( # Handle OpenAI SDK version compatibility for Text type try: - from openai.types.responses.response_create_params import Text as ResponseText + from openai.types.responses.response_create_params import ( + Text as ResponseText, # type: ignore + ) except (ImportError, AttributeError): # Fall back to the concrete config type available in all SDK versions - from openai.types.responses.response_text_config_param import ResponseTextConfigParam as ResponseText + from openai.types.responses.response_text_config_param import ( + ResponseTextConfigParam as ResponseText, + ) from openai.types.responses.response_create_params import ( Reasoning, @@ -1025,29 +1029,29 @@ class ResponseAPIUsage(BaseLiteLLMOpenAIResponseObject): class ResponsesAPIResponse(BaseLiteLLMOpenAIResponseObject): id: str created_at: int - error: Optional[dict] - incomplete_details: Optional[IncompleteDetails] - instructions: Optional[str] - metadata: Optional[Dict] - model: Optional[str] - object: Optional[str] + error: Optional[dict] = None + incomplete_details: Optional[IncompleteDetails] = None + instructions: Optional[str] = None + metadata: Optional[Dict] = None + model: Optional[str] = None + object: Optional[str] = None output: Union[ List[Union[ResponseOutputItem, Dict]], List[Union[GenericResponseOutputItem, OutputFunctionToolCall]], ] parallel_tool_calls: bool - temperature: Optional[float] + temperature: Optional[float] = None tool_choice: ToolChoice tools: Union[List[Tool], List[ResponseFunctionToolCall], List[Dict[str, Any]]] top_p: Optional[float] - max_output_tokens: Optional[int] - previous_response_id: Optional[str] - reasoning: Optional[Reasoning] - status: Optional[str] - text: Optional[Union["ResponseText", Dict[str, Any]]] - truncation: Optional[Literal["auto", "disabled"]] - usage: Optional[ResponseAPIUsage] - user: Optional[str] + max_output_tokens: Optional[int] = None + previous_response_id: Optional[str] = None + reasoning: Optional[Reasoning] = None + status: Optional[str] = None + text: Optional[Union["ResponseText", Dict[str, Any]]] = None + truncation: Optional[Literal["auto", "disabled"]] = None + usage: Optional[ResponseAPIUsage] = None + user: Optional[str] = None store: Optional[bool] = None # Define private attributes using PrivateAttr _hidden_params: dict = PrivateAttr(default_factory=dict) From 3a68ca5140d5725c04bd83d47de8d3fde7175896 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sun, 31 Aug 2025 20:29:21 -0700 Subject: [PATCH 020/178] fix(ollama/chat): add 'think' param support --- litellm/llms/ollama/chat/transformation.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index d4ce4052a7e..6f8427af665 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -137,6 +137,7 @@ class OllamaChatConfig(BaseConfig): "tool_choice", "functions", "response_format", + "reasoning_effort", ] def map_openai_params( @@ -175,6 +176,8 @@ class OllamaChatConfig(BaseConfig): if value.get("json_schema") and value["json_schema"].get("schema"): optional_params["format"] = value["json_schema"]["schema"] ### FUNCTION CALLING LOGIC ### + if param == "reasoning_effort" and value is not None: + optional_params["think"] = True if param == "tools": ## CHECK IF MODEL SUPPORTS TOOL CALLING ## try: @@ -212,9 +215,9 @@ class OllamaChatConfig(BaseConfig): litellm.add_function_to_prompt = ( True # so that main.py adds the function call to the prompt ) - optional_params[ - "functions_unsupported_model" - ] = non_default_params.get("functions") + optional_params["functions_unsupported_model"] = ( + non_default_params.get("functions") + ) non_default_params.pop("tool_choice", None) # causes ollama requests to hang non_default_params.pop("functions", None) # causes ollama requests to hang return optional_params From 90bd89c7fd01d120e6d04ba87402ccba4e27d72e Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sun, 31 Aug 2025 20:38:52 -0700 Subject: [PATCH 021/178] feat(ollama_chat/): add 'think' param support + output parse '' content into 'reasoning_content' Ensures consistent use of thinking --- litellm/llms/ollama/chat/transformation.py | 26 +++++++++++++-- .../index.html} | 0 .../proxy/_experimental/out/onboarding.html | 1 - litellm/proxy/_new_secret_config.yaml | 33 +++++++++++-------- 4 files changed, 43 insertions(+), 17 deletions(-) rename litellm/proxy/_experimental/out/{model_hub_table.html => model_hub_table/index.html} (100%) delete mode 100644 litellm/proxy/_experimental/out/onboarding.html diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index 6f8427af665..64d0f30f2a4 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -349,11 +349,31 @@ class OllamaChatConfig(BaseConfig): ## RESPONSE OBJECT model_response.choices[0].finish_reason = "stop" + response_json_message = response_json.get("message") + if response_json_message is not None: + if "thinking" in response_json_message: + # remap 'thinking' to 'reasoning_content' + response_json_message["reasoning_content"] = response_json_message[ + "thinking" + ] + del response_json_message["thinking"] + elif response_json_message.get("content") is not None: + # parse reasoning content from content + from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( + _parse_content_for_reasoning, + ) + + reasoning_content, content = _parse_content_for_reasoning( + response_json_message["content"] + ) + response_json_message["reasoning_content"] = reasoning_content + response_json_message["content"] = content + if ( request_data.get("format", "") == "json" and litellm_params.get("function_name") is not None ): - function_call = json.loads(response_json["message"]["content"]) + function_call = json.loads(response_json_message["content"]) message = litellm.Message( content=None, tool_calls=[ @@ -370,11 +390,13 @@ class OllamaChatConfig(BaseConfig): "type": "function", } ], + reasoning_content=response_json_message.get("reasoning_content"), ) model_response.choices[0].message = message # type: ignore model_response.choices[0].finish_reason = "tool_calls" else: - _message = litellm.Message(**response_json["message"]) + + _message = litellm.Message(**response_json_message) model_response.choices[0].message = _message # type: ignore model_response.created = int(time.time()) model_response.model = "ollama_chat/" + model diff --git a/litellm/proxy/_experimental/out/model_hub_table.html b/litellm/proxy/_experimental/out/model_hub_table/index.html similarity index 100% rename from litellm/proxy/_experimental/out/model_hub_table.html rename to litellm/proxy/_experimental/out/model_hub_table/index.html diff --git a/litellm/proxy/_experimental/out/onboarding.html b/litellm/proxy/_experimental/out/onboarding.html deleted file mode 100644 index 5c5f1cfe908..00000000000 --- a/litellm/proxy/_experimental/out/onboarding.html +++ /dev/null @@ -1 +0,0 @@ -LiteLLM Dashboard \ No newline at end of file diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index b3653c31435..c49bdbcc15b 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -1,18 +1,23 @@ model_list: - - model_name: fake-openai-endpoint - litellm_params: - model: openai/fake - api_key: fake-key - api_base: https://exampleopenaiendpoint-production.up.railway.app/ - - model_name: gpt-5-mini - litellm_params: - model: azure/gpt-5-mini - api_base: os.environ/AZURE_GPT_5_MINI_API_BASE # runs os.getenv("AZURE_API_BASE") - api_key: os.environ/AZURE_GPT_5_MINI_API_KEY # runs os.getenv("AZURE_API_KEY") - stream_timeout: 60 - merge_reasoning_content_in_choices: true - model_info: - mode: chat + - model_name: fake-openai-endpoint + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + - model_name: gpt-5-mini + litellm_params: + model: azure/gpt-5-mini + api_base: os.environ/AZURE_GPT_5_MINI_API_BASE # runs os.getenv("AZURE_API_BASE") + api_key: os.environ/AZURE_GPT_5_MINI_API_KEY # runs os.getenv("AZURE_API_KEY") + stream_timeout: 60 + merge_reasoning_content_in_choices: true + model_info: + mode: chat + - model_name: ollama-deepseek-r1 + litellm_params: + model: ollama_chat/deepseek-r1:1.5b + model_info: + mode: chat router_settings: model_group_alias: {"my-fake-gpt-4": "fake-openai-endpoint"} From e6429f6565c36b68ef1c5eebfca4039e799cc130 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sun, 31 Aug 2025 20:55:01 -0700 Subject: [PATCH 022/178] feat(ollama_chat/transformation.py): handle thinking content on streaming for ollama chat models Output parse correctly to 'reasoning_content' --- litellm/llms/ollama/chat/transformation.py | 46 +++++++++++++++++++++- litellm/proxy/_new_secret_config.yaml | 12 ------ 2 files changed, 45 insertions(+), 13 deletions(-) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index 64d0f30f2a4..2ee7d06ae5c 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -437,6 +437,9 @@ class OllamaChatConfig(BaseConfig): class OllamaChatCompletionResponseIterator(BaseModelResponseIterator): + started_reasoning_content: bool = False + finished_reasoning_content: bool = False + def _is_function_call_complete(self, function_args: Union[str, dict]) -> bool: if isinstance(function_args, dict): return True @@ -490,8 +493,49 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator): if is_function_call_complete: tool_call["id"] = str(uuid.uuid4()) + # PROCESS REASONING CONTENT + reasoning_content: Optional[str] = None + content: Optional[str] = None + if chunk["message"].get("thinking") is not None: + if self.started_reasoning_content is False: + reasoning_content = chunk["message"].get("thinking") + self.started_reasoning_content = True + elif self.finished_reasoning_content is False: + reasoning_content = chunk["message"].get("thinking") + self.finished_reasoning_content = True + elif chunk["message"].get("content") is not None: + if "" in chunk["message"].get("content"): + reasoning_content = ( + chunk["message"].get("content").replace("", "") + ) + + self.started_reasoning_content = True + + if ( + "" in chunk["message"].get("content") + and self.started_reasoning_content + ): + reasoning_content = chunk["message"].get("content") + remaining_content = ( + chunk["message"].get("content").split("") + ) + if len(remaining_content) > 1: + content = remaining_content[1] + self.finished_reasoning_content = True + + if ( + self.started_reasoning_content is True + and self.finished_reasoning_content is False + ): + reasoning_content = ( + chunk["message"].get("content").replace("", "") + ) + else: + content = chunk["message"].get("content") + delta = Delta( - content=chunk["message"].get("content", ""), + content=content, + reasoning_content=reasoning_content, tool_calls=tool_calls, ) diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index c49bdbcc15b..adfee33eba2 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -21,15 +21,3 @@ model_list: router_settings: model_group_alias: {"my-fake-gpt-4": "fake-openai-endpoint"} - -litellm_settings: - callbacks: ["otel"] - cache: true - cache_params: - type: redis - ttl: 600 - supported_call_types: ["acompletion", "completion"] - - model_group_settings: - forward_client_headers_to_llm_api: - - fake-openai-endpoint \ No newline at end of file From 2ad77d9bf608fc4b0a1fe74ec9240055a398486e Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sun, 31 Aug 2025 21:17:10 -0700 Subject: [PATCH 023/178] feat(ollama/completion): output parse thinking content on streaming + non-streaming for ollama completion calls Completes 'thinking' param support for ollama --- litellm/llms/ollama/chat/transformation.py | 29 +- .../llms/ollama/completion/transformation.py | 102 +++++-- litellm/proxy/_new_secret_config.yaml | 2 +- .../test_ollama_completion_transformation.py | 264 +++++++++++++++++- 4 files changed, 353 insertions(+), 44 deletions(-) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index 2ee7d06ae5c..c70fb97af74 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -504,34 +504,23 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator): reasoning_content = chunk["message"].get("thinking") self.finished_reasoning_content = True elif chunk["message"].get("content") is not None: - if "" in chunk["message"].get("content"): - reasoning_content = ( - chunk["message"].get("content").replace("", "") - ) + message_content = chunk["message"].get("content") + if "" in message_content: + message_content = message_content.replace("", "") self.started_reasoning_content = True - if ( - "" in chunk["message"].get("content") - and self.started_reasoning_content - ): - reasoning_content = chunk["message"].get("content") - remaining_content = ( - chunk["message"].get("content").split("") - ) - if len(remaining_content) > 1: - content = remaining_content[1] + if "" in message_content and self.started_reasoning_content: + message_content = message_content.replace("", "") self.finished_reasoning_content = True if ( - self.started_reasoning_content is True - and self.finished_reasoning_content is False + self.started_reasoning_content + and not self.finished_reasoning_content ): - reasoning_content = ( - chunk["message"].get("content").replace("", "") - ) + reasoning_content = message_content else: - content = chunk["message"].get("content") + content = message_content delta = Delta( content=content, diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 4f7be507cc2..2654d9461ed 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -19,13 +19,13 @@ from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMExcepti from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues, ChatCompletionUsageBlock from litellm.types.utils import ( + Delta, GenericStreamingChunk, ModelInfoBase, ModelResponse, ModelResponseStream, ProviderField, StreamingChoices, - Delta, ) from ..common_utils import OllamaError, _convert_image @@ -92,9 +92,9 @@ class OllamaConfig(BaseConfig): repeat_penalty: Optional[float] = None temperature: Optional[float] = None seed: Optional[int] = None - stop: Optional[ - list - ] = None # stop is a list based on this - https://github.com/ollama/ollama/pull/442 + stop: Optional[list] = ( + None # stop is a list based on this - https://github.com/ollama/ollama/pull/442 + ) tfs_z: Optional[float] = None num_predict: Optional[int] = None top_k: Optional[int] = None @@ -154,6 +154,7 @@ class OllamaConfig(BaseConfig): "stop", "response_format", "max_completion_tokens", + "reasoning_effort", ] def map_openai_params( @@ -166,19 +167,21 @@ class OllamaConfig(BaseConfig): for param, value in non_default_params.items(): if param == "max_tokens" or param == "max_completion_tokens": optional_params["num_predict"] = value - if param == "stream": + elif param == "stream": optional_params["stream"] = value - if param == "temperature": + elif param == "temperature": optional_params["temperature"] = value - if param == "seed": + elif param == "seed": optional_params["seed"] = value - if param == "top_p": + elif param == "top_p": optional_params["top_p"] = value - if param == "frequency_penalty": + elif param == "frequency_penalty": optional_params["frequency_penalty"] = value - if param == "stop": + elif param == "stop": optional_params["stop"] = value - if param == "response_format" and isinstance(value, dict): + elif param == "reasoning_effort" and value is not None: + optional_params["think"] = True + elif param == "response_format" and isinstance(value, dict): if value["type"] == "json_object": optional_params["format"] = "json" elif value["type"] == "json_schema": @@ -258,12 +261,17 @@ class OllamaConfig(BaseConfig): api_key: Optional[str] = None, json_mode: Optional[bool] = None, ) -> ModelResponse: + from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( + _parse_content_for_reasoning, + ) + response_json = raw_response.json() ## RESPONSE OBJECT model_response.choices[0].finish_reason = "stop" if request_data.get("format", "") == "json": # Check if response field exists and is not empty before parsing JSON response_text = response_json.get("response", "") + if not response_text or not response_text.strip(): # Handle empty response gracefully - set empty content message = litellm.Message(content="") @@ -288,7 +296,9 @@ class OllamaConfig(BaseConfig): "id": f"call_{str(uuid.uuid4())}", "function": { "name": function_call["name"], - "arguments": json.dumps(function_call["arguments"]), + "arguments": json.dumps( + function_call["arguments"] + ), }, "type": "function", } @@ -305,11 +315,26 @@ class OllamaConfig(BaseConfig): model_response.choices[0].finish_reason = "stop" except json.JSONDecodeError: # If JSON parsing fails, treat as regular text response - message = litellm.Message(content=response_text) + ## output parse reasoning content from response_text + reasoning_content: Optional[str] = None + content: Optional[str] = None + if response_text is not None: + reasoning_content, content = _parse_content_for_reasoning( + response_text + ) + message = litellm.Message( + content=content, reasoning_content=reasoning_content + ) model_response.choices[0].message = message # type: ignore model_response.choices[0].finish_reason = "stop" else: - model_response.choices[0].message.content = response_json["response"] # type: ignore + response_text = response_json.get("response", "") + content: Optional[str] = None + reasoning_content: Optional[str] = None + if response_text is not None: + reasoning_content, content = _parse_content_for_reasoning(response_text) + model_response.choices[0].message.content = content # type: ignore + model_response.choices[0].message.reasoning_content = reasoning_content # type: ignore model_response.created = int(time.time()) model_response.model = "ollama/" + model _prompt = request_data.get("prompt", "") @@ -434,12 +459,21 @@ class OllamaConfig(BaseConfig): class OllamaTextCompletionResponseIterator(BaseModelResponseIterator): + def __init__( + self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False + ): + super().__init__(streaming_response, sync_stream, json_mode) + self.started_reasoning_content: bool = False + self.finished_reasoning_content: bool = False + def _handle_string_chunk( self, str_line: str ) -> Union[GenericStreamingChunk, ModelResponseStream]: return self.chunk_parser(json.loads(str_line)) - def chunk_parser(self, chunk: dict) -> Union[GenericStreamingChunk, ModelResponseStream]: + def chunk_parser( + self, chunk: dict + ) -> Union[GenericStreamingChunk, ModelResponseStream]: try: if "error" in chunk: raise Exception(f"Ollama Error - {chunk}") @@ -469,12 +503,42 @@ class OllamaTextCompletionResponseIterator(BaseModelResponseIterator): ) elif chunk["response"]: text = chunk["response"] - return GenericStreamingChunk( - text=text, - is_finished=is_finished, - finish_reason="stop", + reasoning_content: Optional[str] = None + content: Optional[str] = None + if text is not None: + if "" in text: + text = text.replace("", "") + self.started_reasoning_content = True + elif "" in text: + text = text.replace("", "") + self.finished_reasoning_content = True + + if ( + self.started_reasoning_content + and not self.finished_reasoning_content + ): + reasoning_content = text + else: + content = text + + return ModelResponseStream( + choices=[ + StreamingChoices( + index=0, + delta=Delta( + reasoning_content=reasoning_content, content=content + ), + ) + ], + finish_reason=finish_reason, usage=None, ) + # return GenericStreamingChunk( + # text=text, + # is_finished=is_finished, + # finish_reason="stop", + # usage=None, + # ) elif "thinking" in chunk and not chunk["response"]: # Return reasoning content as ModelResponseStream so UIs can render it thinking_content = chunk.get("thinking") or "" diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index adfee33eba2..324b4866305 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -15,7 +15,7 @@ model_list: mode: chat - model_name: ollama-deepseek-r1 litellm_params: - model: ollama_chat/deepseek-r1:1.5b + model: ollama/deepseek-r1:1.5b model_info: mode: chat diff --git a/tests/test_litellm/llms/ollama/test_ollama_completion_transformation.py b/tests/test_litellm/llms/ollama/test_ollama_completion_transformation.py index 985d51f99da..452f5a94024 100644 --- a/tests/test_litellm/llms/ollama/test_ollama_completion_transformation.py +++ b/tests/test_litellm/llms/ollama/test_ollama_completion_transformation.py @@ -159,6 +159,261 @@ class TestOllamaConfig: assert result.choices[0]["finish_reason"] == "stop" # No usage assertions here as we don't need to test them in every case + def test_transform_response_with_thinking_tags(self): + """Test that responses with ... tags parse reasoning content correctly.""" + # Initialize config + config = OllamaConfig() + + # Create mock response with thinking tags + raw_response = MagicMock() + raw_response.json.return_value = { + "response": "I need to think about this problem step by stepHere is my answer", + "prompt_eval_count": 15, + "eval_count": 8, + } + + # Create properly structured model response object + model_response = ModelResponse( + id="test_id", + choices=[{"message": Message(content="")}], + ) + + # Create mock encoding + mock_encoding = MagicMock() + mock_encoding.encode.return_value = [1, 2, 3] + + # Transform response + result = config.transform_response( + model="llama2", + raw_response=raw_response, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=mock_encoding, + ) + + # Verify reasoning content is extracted + assert ( + result.choices[0]["message"].reasoning_content + == "I need to think about this problem step by step" + ) + assert result.choices[0]["message"].content == "Here is my answer" + assert result.choices[0]["finish_reason"] == "stop" + + def test_transform_response_with_thinking_tags_alternative(self): + """Test that responses with ... tags parse reasoning content correctly.""" + # Initialize config + config = OllamaConfig() + + # Create mock response with thinking tags (alternative format) + raw_response = MagicMock() + raw_response.json.return_value = { + "response": "Let me analyze this carefullyThe solution is X", + } + + # Create properly structured model response object + model_response = ModelResponse( + id="test_id", + choices=[{"message": Message(content="")}], + ) + + # Create mock encoding + mock_encoding = MagicMock() + mock_encoding.encode.return_value = [1, 2, 3] + + # Transform response + result = config.transform_response( + model="llama2", + raw_response=raw_response, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=mock_encoding, + ) + + # Verify reasoning content is extracted + assert ( + result.choices[0]["message"].reasoning_content + == "Let me analyze this carefully" + ) + assert result.choices[0]["message"].content == "The solution is X" + assert result.choices[0]["finish_reason"] == "stop" + + def test_transform_response_with_multiline_thinking_tags(self): + """Test that responses with multiline thinking content work correctly.""" + # Initialize config + config = OllamaConfig() + + # Create mock response with multiline thinking content + raw_response = MagicMock() + raw_response.json.return_value = { + "response": "\nThis is a complex problem.\nI need to break it down:\n1. First step\n2. Second step\nBased on my analysis, the answer is Y", + } + + # Create properly structured model response object + model_response = ModelResponse( + id="test_id", + choices=[{"message": Message(content="")}], + ) + + # Create mock encoding + mock_encoding = MagicMock() + mock_encoding.encode.return_value = [1, 2, 3] + + # Transform response + result = config.transform_response( + model="llama2", + raw_response=raw_response, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=mock_encoding, + ) + + # Verify multiline reasoning content is extracted + expected_reasoning = "\nThis is a complex problem.\nI need to break it down:\n1. First step\n2. Second step\n" + assert result.choices[0]["message"].reasoning_content == expected_reasoning + assert ( + result.choices[0]["message"].content + == "Based on my analysis, the answer is Y" + ) + assert result.choices[0]["finish_reason"] == "stop" + + def test_transform_response_thinking_only(self): + """Test response with only thinking content and no additional content.""" + # Initialize config + config = OllamaConfig() + + # Create mock response with only thinking content + raw_response = MagicMock() + raw_response.json.return_value = { + "response": "Just internal thoughts, no response", + } + + # Create properly structured model response object + model_response = ModelResponse( + id="test_id", + choices=[{"message": Message(content="")}], + ) + + # Create mock encoding + mock_encoding = MagicMock() + mock_encoding.encode.return_value = [1, 2, 3] + + # Transform response + result = config.transform_response( + model="llama2", + raw_response=raw_response, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=mock_encoding, + ) + + # Verify reasoning content is extracted and content is empty + assert ( + result.choices[0]["message"].reasoning_content + == "Just internal thoughts, no response" + ) + assert result.choices[0]["message"].content == "" + assert result.choices[0]["finish_reason"] == "stop" + + def test_transform_response_json_mode_with_thinking_tags(self): + """Test JSON mode with thinking tags - should handle as text when JSON parsing fails.""" + # Initialize config + config = OllamaConfig() + + # Create mock response with thinking tags in JSON mode + raw_response = MagicMock() + raw_response.json.return_value = { + "response": "Planning my JSON responseThis is not valid JSON", + } + + # Create properly structured model response object + model_response = ModelResponse( + id="test_id", + choices=[{"message": Message(content="")}], + ) + + # Create mock encoding + mock_encoding = MagicMock() + mock_encoding.encode.return_value = [1, 2, 3] + + # Transform response + result = config.transform_response( + model="llama2", + raw_response=raw_response, + model_response=model_response, + logging_obj=MagicMock(), + request_data={"format": "json"}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=mock_encoding, + ) + + # Verify reasoning content is extracted even in JSON mode when JSON parsing fails + assert ( + result.choices[0]["message"].reasoning_content + == "Planning my JSON response" + ) + assert result.choices[0]["message"].content == "This is not valid JSON" + assert result.choices[0]["finish_reason"] == "stop" + + def test_transform_response_no_thinking_tags(self): + """Test that responses without thinking tags work normally.""" + # Initialize config + config = OllamaConfig() + + # Create mock response without thinking tags + raw_response = MagicMock() + raw_response.json.return_value = { + "response": "Regular response without any thinking tags", + } + + # Create properly structured model response object + model_response = ModelResponse( + id="test_id", + choices=[{"message": Message(content="")}], + ) + + # Create mock encoding + mock_encoding = MagicMock() + mock_encoding.encode.return_value = [1, 2, 3] + + # Transform response + result = config.transform_response( + model="llama2", + raw_response=raw_response, + model_response=model_response, + logging_obj=MagicMock(), + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding=mock_encoding, + ) + + # Verify no reasoning content is extracted + assert result.choices[0]["message"].reasoning_content is None + assert ( + result.choices[0]["message"].content + == "Regular response without any thinking tags" + ) + assert result.choices[0]["finish_reason"] == "stop" + class TestOllamaTextCompletionResponseIterator: def test_chunk_parser_with_thinking_field(self): @@ -199,10 +454,11 @@ class TestOllamaTextCompletionResponseIterator: result = iterator.chunk_parser(normal_chunk) - assert result["text"] == "Hello world" - assert result["is_finished"] is False - assert result["finish_reason"] == "stop" - assert result["usage"] is None + # Updated to handle ModelResponseStream return type + assert isinstance(result, ModelResponseStream) + assert result.choices and result.choices[0].delta is not None + assert result.choices[0].delta.content == "Hello world" + assert getattr(result.choices[0].delta, "reasoning_content", None) is None def test_chunk_parser_done_chunk(self): """Test that done chunks work correctly.""" From 6cd5afa8b11ea3654a2c26d3d64cd2a2e3eff3f9 Mon Sep 17 00:00:00 2001 From: "mubashir1osmani (aider)" Date: Mon, 1 Sep 2025 00:56:22 -0400 Subject: [PATCH 024/178] feat: add persistence and management for guardrails on virtual keys --- .../proxy/guardrails/endpoints.py | 70 ++++++++++++++++++- .../proxy/guardrails/guardrail_registry.py | 30 ++++++++ 2 files changed, 97 insertions(+), 3 deletions(-) diff --git a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py index cdf86dcea67..fcd5e82ab3a 100644 --- a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py +++ b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py @@ -6,16 +6,28 @@ To see all free guardrails see litellm/proxy/guardrails/* Exposed Routes: - /mask_pii +- /virtual_key/guardrails """ -from typing import Optional +from typing import Dict, List, Optional -from fastapi import APIRouter, Depends +from fastapi import APIRouter, Depends, HTTPException +from pydantic import BaseModel 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 +from litellm.proxy.guardrails.guardrail_registry import IN_MEMORY_GUARDRAIL_HANDLER +from litellm.types.guardrails import ApplyGuardrailRequest, ApplyGuardrailResponse, Guardrail + +# Models for virtual key guardrail management +class VirtualKeyGuardrailRequest(BaseModel): + virtual_key_id: str + guardrail_id: str + +class VirtualKeyGuardrailsResponse(BaseModel): + virtual_key_id: str + guardrails: List[Guardrail] router = APIRouter(tags=["guardrails"], prefix="/guardrails") @@ -39,3 +51,55 @@ async def apply_guardrail( return await active_guardrail.apply_guardrail( text=request.text, language=request.language, entities=request.entities ) + +@router.post("/virtual_key/associate", response_model=Dict[str, str]) +async def associate_guardrail_with_virtual_key( + request: VirtualKeyGuardrailRequest, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + Associate a guardrail with a virtual key + """ + # Check if guardrail exists + guardrail = IN_MEMORY_GUARDRAIL_HANDLER.get_guardrail_by_id(request.guardrail_id) + if not guardrail: + raise HTTPException(status_code=404, detail=f"Guardrail {request.guardrail_id} not found") + + # Associate guardrail with virtual key + IN_MEMORY_GUARDRAIL_HANDLER.associate_guardrail_with_virtual_key( + virtual_key_id=request.virtual_key_id, + guardrail_id=request.guardrail_id + ) + + return {"message": f"Guardrail {request.guardrail_id} associated with virtual key {request.virtual_key_id}"} + +@router.post("/virtual_key/disassociate", response_model=Dict[str, str]) +async def disassociate_guardrail_from_virtual_key( + request: VirtualKeyGuardrailRequest, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + Disassociate a guardrail from a virtual key + """ + # Disassociate guardrail from virtual key + IN_MEMORY_GUARDRAIL_HANDLER.disassociate_guardrail_from_virtual_key( + virtual_key_id=request.virtual_key_id, + guardrail_id=request.guardrail_id + ) + + return {"message": f"Guardrail {request.guardrail_id} disassociated from virtual key {request.virtual_key_id}"} + +@router.get("/virtual_key/{virtual_key_id}", response_model=VirtualKeyGuardrailsResponse) +async def get_guardrails_for_virtual_key( + virtual_key_id: str, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + Get all guardrails associated with a virtual key + """ + guardrails = IN_MEMORY_GUARDRAIL_HANDLER.get_guardrails_for_virtual_key(virtual_key_id) + + return VirtualKeyGuardrailsResponse( + virtual_key_id=virtual_key_id, + guardrails=guardrails + ) diff --git a/litellm/proxy/guardrails/guardrail_registry.py b/litellm/proxy/guardrails/guardrail_registry.py index 21429f462d4..523ff9f4cd8 100644 --- a/litellm/proxy/guardrails/guardrail_registry.py +++ b/litellm/proxy/guardrails/guardrail_registry.py @@ -369,6 +369,11 @@ class InMemoryGuardrailHandler: """ Guardrail id to CustomGuardrail object mapping """ + + self.virtual_key_to_guardrails: Dict[str, List[str]] = {} + """ + Virtual key id to list of guardrail ids mapping + """ def initialize_guardrail( self, @@ -538,6 +543,31 @@ class InMemoryGuardrailHandler: Get a guardrail by its ID from memory """ return self.IN_MEMORY_GUARDRAILS.get(guardrail_id) + + def associate_guardrail_with_virtual_key(self, virtual_key_id: str, guardrail_id: str) -> None: + """ + Associate a guardrail with a virtual key + """ + if virtual_key_id not in self.virtual_key_to_guardrails: + self.virtual_key_to_guardrails[virtual_key_id] = [] + + if guardrail_id not in self.virtual_key_to_guardrails[virtual_key_id]: + self.virtual_key_to_guardrails[virtual_key_id].append(guardrail_id) + + def disassociate_guardrail_from_virtual_key(self, virtual_key_id: str, guardrail_id: str) -> None: + """ + Disassociate a guardrail from a virtual key + """ + if virtual_key_id in self.virtual_key_to_guardrails: + if guardrail_id in self.virtual_key_to_guardrails[virtual_key_id]: + self.virtual_key_to_guardrails[virtual_key_id].remove(guardrail_id) + + def get_guardrails_for_virtual_key(self, virtual_key_id: str) -> List[Guardrail]: + """ + Get all guardrails associated with a virtual key + """ + guardrail_ids = self.virtual_key_to_guardrails.get(virtual_key_id, []) + return [self.IN_MEMORY_GUARDRAILS[gid] for gid in guardrail_ids if gid in self.IN_MEMORY_GUARDRAILS] ######################################################## From 83654c56089208db695b5b4821e7ae41118aba34 Mon Sep 17 00:00:00 2001 From: "mubashir1osmani (aider)" Date: Mon, 1 Sep 2025 01:17:08 -0400 Subject: [PATCH 025/178] feat: add UI support for displaying and managing guardrails on virtual keys --- .../proxy/guardrails/endpoints.py | 2 + .../proxy/guardrails/guardrail_registry.py | 1 + .../virtual_keys/virtual_key_detail.tsx | 160 ++++++++++++++++++ .../virtual_keys/virtual_key_guardrails.tsx | 93 ++++++++++ .../src/services/guardrails.ts | 27 +++ .../src/services/virtual_key_guardrails.ts | 57 +++++++ ui/litellm-dashboard/src/types/guardrail.ts | 11 ++ ui/litellm-dashboard/src/types/virtual_key.ts | 13 ++ 8 files changed, 364 insertions(+) create mode 100644 ui/litellm-dashboard/src/components/virtual_keys/virtual_key_detail.tsx create mode 100644 ui/litellm-dashboard/src/components/virtual_keys/virtual_key_guardrails.tsx create mode 100644 ui/litellm-dashboard/src/services/guardrails.ts create mode 100644 ui/litellm-dashboard/src/services/virtual_key_guardrails.ts create mode 100644 ui/litellm-dashboard/src/types/guardrail.ts create mode 100644 ui/litellm-dashboard/src/types/virtual_key.ts diff --git a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py index fcd5e82ab3a..47bec519c40 100644 --- a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py +++ b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py @@ -97,7 +97,9 @@ async def get_guardrails_for_virtual_key( """ Get all guardrails associated with a virtual key """ + verbose_proxy_logger.debug(f"Getting guardrails for virtual key: {virtual_key_id}") guardrails = IN_MEMORY_GUARDRAIL_HANDLER.get_guardrails_for_virtual_key(virtual_key_id) + verbose_proxy_logger.debug(f"Found {len(guardrails)} guardrails for virtual key {virtual_key_id}") return VirtualKeyGuardrailsResponse( virtual_key_id=virtual_key_id, diff --git a/litellm/proxy/guardrails/guardrail_registry.py b/litellm/proxy/guardrails/guardrail_registry.py index 523ff9f4cd8..7d3432d5cb6 100644 --- a/litellm/proxy/guardrails/guardrail_registry.py +++ b/litellm/proxy/guardrails/guardrail_registry.py @@ -567,6 +567,7 @@ class InMemoryGuardrailHandler: Get all guardrails associated with a virtual key """ guardrail_ids = self.virtual_key_to_guardrails.get(virtual_key_id, []) + verbose_proxy_logger.debug(f"Getting guardrails for virtual key {virtual_key_id}: {guardrail_ids}") return [self.IN_MEMORY_GUARDRAILS[gid] for gid in guardrail_ids if gid in self.IN_MEMORY_GUARDRAILS] diff --git a/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_detail.tsx b/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_detail.tsx new file mode 100644 index 00000000000..111bd26841c --- /dev/null +++ b/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_detail.tsx @@ -0,0 +1,160 @@ +import React, { useState, useEffect } from 'react'; +import { Card, Flex, Text, Heading, Button, Dialog, TextField, Select, Box, Tabs } from '@radix-ui/themes'; +import { toast } from 'react-hot-toast'; +import { VirtualKey } from '../../types/virtual_key'; +import { fetchVirtualKey, updateVirtualKey } from '../../services/virtual_keys'; +import { VirtualKeyGuardrails } from './virtual_key_guardrails'; +import { fetchGuardrails } from '../../services/guardrails'; +import { Guardrail } from '../../types/guardrail'; +import { associateGuardrailWithVirtualKey } from '../../services/virtual_key_guardrails'; + +interface VirtualKeyDetailProps { + virtualKeyId: string; + onClose: () => void; +} + +export const VirtualKeyDetail: React.FC = ({ virtualKeyId, onClose }) => { + const [virtualKey, setVirtualKey] = useState(null); + const [loading, setLoading] = useState(true); + const [availableGuardrails, setAvailableGuardrails] = useState([]); + const [selectedGuardrailId, setSelectedGuardrailId] = useState(''); + const [refreshTrigger, setRefreshTrigger] = useState(0); + + useEffect(() => { + const loadVirtualKey = async () => { + try { + const data = await fetchVirtualKey(virtualKeyId); + setVirtualKey(data); + } catch (error) { + console.error('Error loading virtual key:', error); + toast.error('Failed to load virtual key details'); + } finally { + setLoading(false); + } + }; + + const loadGuardrails = async () => { + try { + const data = await fetchGuardrails(); + setAvailableGuardrails(data.guardrails); + } catch (error) { + console.error('Error loading guardrails:', error); + } + }; + + loadVirtualKey(); + loadGuardrails(); + }, [virtualKeyId]); + + const handleAddGuardrail = async () => { + if (!selectedGuardrailId) { + toast.error('Please select a guardrail'); + return; + } + + try { + await associateGuardrailWithVirtualKey(virtualKeyId, selectedGuardrailId); + toast.success('Guardrail added to virtual key'); + setSelectedGuardrailId(''); + setRefreshTrigger(prev => prev + 1); + } catch (error) { + console.error('Error adding guardrail:', error); + toast.error('Failed to add guardrail'); + } + }; + + if (loading) { + return ( + + + Virtual Key Details + Loading... + + + ); + } + + if (!virtualKey) { + return ( + + + Error + Virtual key not found + + + + + + ); + } + + return ( + + + Virtual Key: {virtualKey.key_name || 'Unnamed Key'} + + + + Details + Guardrails + + + + + + + + Key ID: + {virtualKey.key_id} + + + Team ID: + {virtualKey.team_id || 'N/A'} + + + Models: + {virtualKey.models?.join(', ') || 'All models'} + + + Spend: + ${virtualKey.spend?.toFixed(2) || '0.00'} + + + + + + + + + Add Guardrail + + + + + {availableGuardrails.map(guardrail => ( + + {guardrail.guardrail_name || `Guardrail ${guardrail.guardrail_id.substring(0, 8)}`} + + ))} + + + + + + + + + + + + + + + + + + ); +}; diff --git a/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_guardrails.tsx b/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_guardrails.tsx new file mode 100644 index 00000000000..0acbc9d3f4a --- /dev/null +++ b/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_guardrails.tsx @@ -0,0 +1,93 @@ +import React, { useEffect, useState } from 'react'; +import { Badge, Button, Card, Flex, Heading, Text, Box, Spinner } from '@radix-ui/themes'; +import { fetchGuardrailsForVirtualKey, associateGuardrailWithVirtualKey, disassociateGuardrailFromVirtualKey } from '../../services/virtual_key_guardrails'; +import { Guardrail } from '../../types/guardrail'; +import { toast } from 'react-hot-toast'; + +interface VirtualKeyGuardrailsProps { + virtualKeyId: string; + refreshTrigger?: number; +} + +export const VirtualKeyGuardrails: React.FC = ({ + virtualKeyId, + refreshTrigger = 0 +}) => { + const [guardrails, setGuardrails] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + + useEffect(() => { + const loadGuardrails = async () => { + if (!virtualKeyId) return; + + setLoading(true); + try { + const data = await fetchGuardrailsForVirtualKey(virtualKeyId); + setGuardrails(data.guardrails); + setError(null); + } catch (err) { + console.error('Error loading guardrails for virtual key:', err); + setError('Failed to load guardrails'); + setGuardrails([]); + } finally { + setLoading(false); + } + }; + + loadGuardrails(); + }, [virtualKeyId, refreshTrigger]); + + const handleRemoveGuardrail = async (guardrailId: string) => { + try { + await disassociateGuardrailFromVirtualKey(virtualKeyId, guardrailId); + setGuardrails(guardrails.filter(g => g.guardrail_id !== guardrailId)); + toast.success('Guardrail removed from virtual key'); + } catch (err) { + console.error('Error removing guardrail:', err); + toast.error('Failed to remove guardrail'); + } + }; + + if (loading) { + return ( + + + + + + ); + } + + if (error) { + return ( + + {error} + + ); + } + + return ( + + Attached Guardrails + {guardrails.length === 0 ? ( + No guardrails attached to this virtual key + ) : ( + + {guardrails.map((guardrail) => ( + + + {guardrail.guardrail_name || 'Unnamed Guardrail'} + Type: {guardrail.litellm_params?.guardrail} + Mode: {guardrail.litellm_params?.mode} + + + + ))} + + )} + + ); +}; diff --git a/ui/litellm-dashboard/src/services/guardrails.ts b/ui/litellm-dashboard/src/services/guardrails.ts new file mode 100644 index 00000000000..2cb7b80a4b1 --- /dev/null +++ b/ui/litellm-dashboard/src/services/guardrails.ts @@ -0,0 +1,27 @@ +import { Guardrail } from '../types/guardrail'; + +interface GuardrailsResponse { + guardrails: Guardrail[]; +} + +export async function fetchGuardrails(): Promise { + const response = await fetch('/api/guardrails'); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error(`Failed to fetch guardrails: ${errorText}`); + } + + return await response.json(); +} + +export async function fetchGuardrail(guardrailId: string): Promise { + const response = await fetch(`/api/guardrails/${guardrailId}`); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error(`Failed to fetch guardrail: ${errorText}`); + } + + return await response.json(); +} diff --git a/ui/litellm-dashboard/src/services/virtual_key_guardrails.ts b/ui/litellm-dashboard/src/services/virtual_key_guardrails.ts new file mode 100644 index 00000000000..de7ce30d890 --- /dev/null +++ b/ui/litellm-dashboard/src/services/virtual_key_guardrails.ts @@ -0,0 +1,57 @@ +import { Guardrail } from '../types/guardrail'; + +interface VirtualKeyGuardrailsResponse { + virtual_key_id: string; + guardrails: Guardrail[]; +} + +export async function fetchGuardrailsForVirtualKey(virtualKeyId: string): Promise { + const response = await fetch(`/api/guardrails/virtual_key/${virtualKeyId}`); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error(`Failed to fetch guardrails: ${errorText}`); + } + + return await response.json(); +} + +export async function associateGuardrailWithVirtualKey(virtualKeyId: string, guardrailId: string): Promise<{ message: string }> { + const response = await fetch('/api/guardrails/virtual_key/associate', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + virtual_key_id: virtualKeyId, + guardrail_id: guardrailId, + }), + }); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error(`Failed to associate guardrail: ${errorText}`); + } + + return await response.json(); +} + +export async function disassociateGuardrailFromVirtualKey(virtualKeyId: string, guardrailId: string): Promise<{ message: string }> { + const response = await fetch('/api/guardrails/virtual_key/disassociate', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + virtual_key_id: virtualKeyId, + guardrail_id: guardrailId, + }), + }); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error(`Failed to disassociate guardrail: ${errorText}`); + } + + return await response.json(); +} diff --git a/ui/litellm-dashboard/src/types/guardrail.ts b/ui/litellm-dashboard/src/types/guardrail.ts new file mode 100644 index 00000000000..6cb26d04973 --- /dev/null +++ b/ui/litellm-dashboard/src/types/guardrail.ts @@ -0,0 +1,11 @@ +export interface Guardrail { + guardrail_id: string; + guardrail_name: string | null; + litellm_params: { + guardrail: string; + mode: string; + default_on: boolean; + [key: string]: any; + }; + guardrail_info?: Record; +} diff --git a/ui/litellm-dashboard/src/types/virtual_key.ts b/ui/litellm-dashboard/src/types/virtual_key.ts new file mode 100644 index 00000000000..e130853b1d1 --- /dev/null +++ b/ui/litellm-dashboard/src/types/virtual_key.ts @@ -0,0 +1,13 @@ +export interface VirtualKey { + key_id: string; + key_name?: string; + team_id?: string; + models?: string[]; + spend?: number; + max_budget?: number; + max_parallel_requests?: number; + metadata?: Record; + expires?: string; + created_at?: string; + updated_at?: string; +} From a8f2ebed9cda1c53c3a21196577e2e3639fe9eeb Mon Sep 17 00:00:00 2001 From: "mubashir1osmani (aider)" Date: Mon, 1 Sep 2025 01:17:46 -0400 Subject: [PATCH 026/178] fix: import verbose_proxy_logger to resolve undefined name errors --- enterprise/litellm_enterprise/proxy/guardrails/endpoints.py | 1 + 1 file changed, 1 insertion(+) diff --git a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py index 47bec519c40..85f8bf22417 100644 --- a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py +++ b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py @@ -13,6 +13,7 @@ from typing import Dict, List, Optional from fastapi import APIRouter, Depends, HTTPException from pydantic import BaseModel +from litellm._logging import verbose_proxy_logger 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 bfed4e0a6a5e70f44bc0ad0505677b717069ff00 Mon Sep 17 00:00:00 2001 From: onlylhf <27225745+onlylhf@users.noreply.github.com> Date: Mon, 1 Sep 2025 13:18:19 +0800 Subject: [PATCH 027/178] Add a complete URL generation method for embedding Volcengine API and optimize request and response processing logic; Delete redundant test files and refactor integration testing to improve readability and maintainability. --- .../volcengine/embedding/transformation.py | 70 ++- litellm/main.py | 51 +- litellm/utils.py | 6 + .../embedding/test_volcengine_embedding.py | 450 ------------------ .../volcengine}/test_volcengine_embedding.py | 4 +- 5 files changed, 84 insertions(+), 497 deletions(-) delete mode 100644 tests/test_litellm/llms/volcengine/embedding/test_volcengine_embedding.py rename tests/{llm_translation => test_litellm/llms/volcengine}/test_volcengine_embedding.py (98%) diff --git a/litellm/llms/volcengine/embedding/transformation.py b/litellm/llms/volcengine/embedding/transformation.py index ba2f07a4945..87e89626218 100644 --- a/litellm/llms/volcengine/embedding/transformation.py +++ b/litellm/llms/volcengine/embedding/transformation.py @@ -48,6 +48,36 @@ class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): "extra_headers", ] + 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 volcengine embedding API calls. + + Args: + api_base: Optional custom API base URL + api_key: API key (not used for URL construction) + model: Model name (not used for URL construction) + optional_params: Optional parameters (not used for URL construction) + litellm_params: LiteLLM parameters (not used for URL construction) + stream: Stream parameter (not used for URL construction) + + Returns: + Complete URL for the embedding API endpoint + """ + base_url = get_volcengine_base_url(api_base) + # Construct the complete URL with /embeddings endpoint + if base_url.endswith("/api/v3"): + return f"{base_url}/embeddings" + else: + return f"{base_url}/api/v3/embeddings" + def map_openai_params( self, non_default_params: Dict[str, Any], @@ -114,13 +144,14 @@ class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): Returns: Dict containing url, headers, and data for the request """ - # Get base URL - base_url = get_volcengine_base_url(api_base) - # Avoid duplicate /api/v3 if base_url already contains it - if base_url.endswith("/api/v3"): - url = f"{base_url}/embeddings" - else: - url = f"{base_url}/api/v3/embeddings" + # Get complete URL using the centralized method + url = self.get_complete_url( + api_base=api_base, + api_key=api_key, + model=model, + optional_params={}, + litellm_params={}, + ) # Get headers headers = get_volcengine_headers(api_key, extra_headers) @@ -188,13 +219,24 @@ class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): headers: dict, ) -> dict: """Transform embedding request to Volcengine format""" - # Use existing transform_request method - return self.transform_request( - model=model, - input=input, - api_key="", # api_key will be in headers - **optional_params, - ) + # Prepare request data (only the JSON body, not the full request) + data = { + "model": model, + "input": input if isinstance(input, list) else [input], + } + + # Add optional parameters from optional_params + if "encoding_format" in optional_params: + encoding_format = optional_params["encoding_format"] + if encoding_format is not None: + data["encoding_format"] = encoding_format + + if "user" in optional_params: + user = optional_params["user"] + if user is not None: + data["user"] = user + + return data def transform_embedding_response( self, diff --git a/litellm/main.py b/litellm/main.py index 776e81a0110..71daf1c970e 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -183,7 +183,6 @@ from .llms.vertex_ai.text_to_speech.text_to_speech_handler import VertexTextToSp from .llms.vertex_ai.vertex_ai_partner_models.main import VertexAIPartnerModels from .llms.vertex_ai.vertex_embeddings.embedding_handler import VertexEmbedding from .llms.vertex_ai.vertex_model_garden.main import VertexAIModelGardenModels -from .llms.volcengine.embedding.handler import VolcEngineEmbeddingHandler from .llms.vllm.completion import handler as vllm_handler from .llms.watsonx.chat.handler import WatsonXChatHandler from .llms.watsonx.common_utils import IBMWatsonXMixin @@ -4416,45 +4415,35 @@ def embedding( # noqa: PLR0915 aembedding=aembedding, ) elif custom_llm_provider == "volcengine": - api_key = ( + volcengine_key = ( api_key or litellm.api_key or get_secret_str("ARK_API_KEY") or get_secret_str("VOLCENGINE_API_KEY") ) - if api_key is None: + if volcengine_key is None: raise ValueError( "Missing API key for Volcengine. Set ARK_API_KEY or VOLCENGINE_API_KEY environment variable or pass api_key parameter." ) - - handler = VolcEngineEmbeddingHandler() - - if aembedding: - response = handler.async_embedding( - model=model, - input=input, - api_key=api_key, - api_base=api_base, - encoding_format=optional_params.get("encoding_format", "float"), - user=optional_params.get("user"), - timeout=timeout, - extra_headers=optional_params.get("extra_headers"), - litellm_logging_obj=logging, - **optional_params, - ) + if extra_headers is not None and isinstance(extra_headers, dict): + headers = extra_headers else: - response = handler.embedding( - model=model, - input=input, - api_key=api_key, - api_base=api_base, - encoding_format=optional_params.get("encoding_format", "float"), - user=optional_params.get("user"), - timeout=timeout, - extra_headers=optional_params.get("extra_headers"), - litellm_logging_obj=logging, - **optional_params, - ) + headers = {} + response = base_llm_http_handler.embedding( + model=model, + input=input, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + logging_obj=logging, + api_base=api_base, + optional_params=optional_params, + litellm_params={}, + model_response=EmbeddingResponse(), + api_key=volcengine_key, + client=client, + aembedding=aembedding, + headers=headers, + ) elif custom_llm_provider in litellm._custom_providers: custom_handler: Optional[CustomLLM] = None for item in litellm.custom_provider_map: diff --git a/litellm/utils.py b/litellm/utils.py index aa3c00735ec..f3f9c49cb39 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -7082,6 +7082,12 @@ class ProviderConfigManager: ) return JinaAIEmbeddingConfig() + elif litellm.LlmProviders.VOLCENGINE == provider: + from litellm.llms.volcengine.embedding.transformation import ( + VolcEngineEmbeddingConfig, + ) + + return VolcEngineEmbeddingConfig() return None @staticmethod diff --git a/tests/test_litellm/llms/volcengine/embedding/test_volcengine_embedding.py b/tests/test_litellm/llms/volcengine/embedding/test_volcengine_embedding.py deleted file mode 100644 index f2f143b5b99..00000000000 --- a/tests/test_litellm/llms/volcengine/embedding/test_volcengine_embedding.py +++ /dev/null @@ -1,450 +0,0 @@ -""" -Improved tests for Volcengine Embedding functionality -Tests real business logic without excessive mocking -""" - -import pytest -import json -import httpx -from unittest.mock import Mock, patch, MagicMock -from typing import List, Dict, Any - -from litellm.llms.volcengine.embedding import VolcEngineEmbeddingHandler, VolcEngineEmbeddingConfig -from litellm.llms.volcengine.common_utils import VolcEngineError -from litellm.types.utils import EmbeddingResponse -from litellm.types.llms.openai import AllEmbeddingInputValues - - -class TestVolcEngineEmbeddingConfigBusinessLogic: - """Test real business logic of VolcEngineEmbeddingConfig without excessive mocking""" - - def setup_method(self): - """Setup test fixtures""" - self.config = VolcEngineEmbeddingConfig() - self.model = "doubao-embedding-text-240715" - self.api_key = "test-api-key-12345" - - def test_supported_params_completeness(self): - """Test that all required parameters are supported""" - params = self.config.get_supported_openai_params(self.model) - - # Verify essential parameters are supported - required_params = ["encoding_format", "user", "extra_headers"] - for param in required_params: - assert param in params, f"Required parameter '{param}' not supported" - - def test_parameter_mapping_with_valid_values(self): - """Test parameter mapping with various valid values""" - test_cases = [ - # Standard float encoding - {"encoding_format": "float", "user": "test-user"}, - # Base64 encoding - {"encoding_format": "base64", "user": "batch-user"}, - # None encoding (default) - {"encoding_format": None, "user": "api-user"}, - # Only user parameter - {"user": "minimal-user"}, - ] - - for test_params in test_cases: - result = self.config.map_openai_params( - non_default_params=test_params, - optional_params={}, - model=self.model, - drop_params=False - ) - - # Verify all valid parameters are preserved - for key, value in test_params.items(): - if value is not None: - assert result[key] == value, f"Parameter {key} not mapped correctly" - - def test_parameter_mapping_with_invalid_encoding(self): - """Test proper error handling for invalid encoding formats""" - invalid_encodings = ["int32", "binary", "invalid_format", 123, []] - - for invalid_encoding in invalid_encodings: - with pytest.raises(ValueError) as exc_info: - self.config.map_openai_params( - non_default_params={"encoding_format": invalid_encoding}, - optional_params={}, - model=self.model, - drop_params=False - ) - - assert "Unsupported encoding_format" in str(exc_info.value) - assert str(invalid_encoding) in str(exc_info.value) - - def test_parameter_dropping_behavior(self): - """Test parameter dropping when drop_params=True""" - invalid_params = { - "encoding_format": "invalid_format", - "unsupported_param": "value", - "another_invalid": 123 - } - - result = self.config.map_openai_params( - non_default_params=invalid_params, - optional_params={}, - model=self.model, - drop_params=True - ) - - # Should drop all invalid parameters - for param in invalid_params.keys(): - assert param not in result, f"Invalid parameter {param} was not dropped" - - def test_request_transformation_structure(self): - """Test request transformation produces correct structure""" - test_inputs = [ - # Single string input - "Hello world", - # Multiple strings - ["Hello", "World", "Test"], - # Mixed content - ["Short", "This is a longer text for testing purposes"], - ] - - for input_data in test_inputs: - result = self.config.transform_request( - model=self.model, - input=input_data, - api_key=self.api_key, - encoding_format="float" - ) - - # Verify structure - assert "url" in result - assert "headers" in result - assert "data" in result - - # Verify URL - assert result["url"] == "https://ark.cn-beijing.volces.com/api/v3/embeddings" - - # Verify headers - headers = result["headers"] - assert headers["Authorization"] == f"Bearer {self.api_key}" - assert headers["Content-Type"] == "application/json" - - # Verify data - data = result["data"] - assert data["model"] == self.model - assert data["encoding_format"] == "float" - - # Input should always be a list - if isinstance(input_data, str): - assert data["input"] == [input_data] - else: - assert data["input"] == input_data - - def test_response_transformation_with_real_data(self): - """Test response transformation with realistic Volcengine response data""" - # Simulate real Volcengine API response - volcengine_responses = [ - # Single embedding response - { - "id": "cmpl-123456789", - "object": "list", - "model": "doubao-embedding-text-240715", - "data": [ - { - "object": "embedding", - "index": 0, - "embedding": [0.1, -0.2, 0.3, 0.4, -0.5] * 100 # Realistic embedding size - } - ], - "usage": { - "prompt_tokens": 5, - "total_tokens": 5 - } - }, - # Multiple embeddings response - { - "id": "cmpl-987654321", - "object": "list", - "model": "doubao-embedding-text-240715", - "data": [ - { - "object": "embedding", - "index": 0, - "embedding": [0.1, 0.2, 0.3] * 256 - }, - { - "object": "embedding", - "index": 1, - "embedding": [0.4, 0.5, 0.6] * 256 - } - ], - "usage": { - "prompt_tokens": 12, - "total_tokens": 12 - } - } - ] - - for response_data in volcengine_responses: - mock_response = Mock(spec=httpx.Response) - mock_response.json.return_value = response_data - - result = self.config.transform_response( - response=mock_response, - model=self.model, - input=["test input"], - ) - - # Verify transformation preserves important data - assert result["object"] == "list" - assert result["model"] == response_data["model"] - assert len(result["data"]) == len(response_data["data"]) - assert result["usage"] == response_data["usage"] - - # Verify embedding data integrity - for i, embedding_item in enumerate(result["data"]): - original_item = response_data["data"][i] - assert embedding_item["object"] == "embedding" - assert embedding_item["index"] == original_item["index"] - assert len(embedding_item["embedding"]) == len(original_item["embedding"]) - - def test_response_transformation_with_error_data(self): - """Test response transformation handles error response formats correctly""" - # Test that transform_response can handle both success and error response structures - - # Success response (should work) - success_response = { - "id": "cmpl-123", - "object": "list", - "model": "doubao-embedding-text-240715", - "data": [{"object": "embedding", "index": 0, "embedding": [0.1, 0.2]}], - "usage": {"prompt_tokens": 2, "total_tokens": 2} - } - - mock_response = Mock(spec=httpx.Response) - mock_response.json.return_value = success_response - - result = self.config.transform_response( - response=mock_response, - model=self.model, - input=["test"], - ) - - # Should successfully transform - assert result["object"] == "list" - assert result["model"] == "doubao-embedding-text-240715" - - # Error response (should still transform but with empty/missing data) - error_response = { - "error": { - "message": "Rate limit exceeded", - "type": "rate_limit_error" - } - } - - mock_response.json.return_value = error_response - - result = self.config.transform_response( - response=mock_response, - model=self.model, - input=["test"], - ) - - # Should handle missing fields gracefully - assert result["object"] == "list" # default value - assert result["data"] == [] # default empty data - assert result["usage"] == {} # default empty usage - - -class TestVolcEngineEmbeddingHandlerBusinessLogic: - """Test VolcEngineEmbeddingHandler with focus on business logic""" - - def setup_method(self): - self.handler = VolcEngineEmbeddingHandler() - self.model = "doubao-embedding-text-240715" - self.api_key = "test-api-key-12345" - - def test_response_conversion_to_litellm_format(self): - """Test conversion of Volcengine response to LiteLLM EmbeddingResponse""" - volcengine_response = { - "id": "emb-123", - "object": "list", - "model": self.model, - "data": [ - { - "object": "embedding", - "index": 0, - "embedding": [0.1, 0.2, 0.3, -0.1, -0.2] * 200 # 1000-dimensional embedding - } - ], - "usage": { - "prompt_tokens": 8, - "total_tokens": 8 - } - } - - result = self.handler._convert_to_litellm_response( - volcengine_response, - self.model, - ["test input"] - ) - - # Verify result is proper EmbeddingResponse - assert isinstance(result, EmbeddingResponse) - assert result.object == "list" - assert result.model == self.model - assert len(result.data) == 1 - assert len(result.data[0]["embedding"]) == 1000 - - # Verify usage information - assert result.usage.prompt_tokens == 8 - assert result.usage.total_tokens == 8 - assert result.usage.completion_tokens == 0 - - def test_network_error_handling_without_mocking_business_logic(self): - """Test network error handling preserves business logic""" - - # Test with actual VolcEngineError class - with pytest.raises(VolcEngineError) as exc_info: - # This would raise a network error in real scenario - error = VolcEngineError( - status_code=500, - message="Network error during embedding request: Connection timeout" - ) - raise error - - # Verify error contains meaningful information - assert exc_info.value.status_code == 500 - assert "Network error during embedding request" in str(exc_info.value.message) - assert "Connection timeout" in str(exc_info.value.message) - - def test_input_validation_and_preprocessing(self): - """Test input validation and preprocessing logic""" - test_cases = [ - # String input should be converted to list - ("single string", ["single string"]), - # List input should remain list - (["multiple", "strings"], ["multiple", "strings"]), - # Empty string handling - ("", [""]), - # Unicode handling - ("测试中文", ["测试中文"]), - # Special characters - ("Special chars: @#$%^&*()", ["Special chars: @#$%^&*()"]), - ] - - for input_data, expected_output in test_cases: - # Test the actual transformation logic - config = VolcEngineEmbeddingConfig() - result = config.transform_request( - model=self.model, - input=input_data, - api_key=self.api_key, - ) - - assert result["data"]["input"] == expected_output - - -class TestVolcEngineEmbeddingIntegration: - """Integration tests that test the full pipeline with minimal mocking""" - - def setup_method(self): - self.handler = VolcEngineEmbeddingHandler() - self.model = "doubao-embedding-text-240715" - self.api_key = "test-api-key-12345" - - def test_full_request_response_cycle(self): - """Test the complete request-response cycle with realistic data""" - - # Create a realistic Volcengine response - realistic_response_data = { - "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi6", - "object": "list", - "model": "doubao-embedding-text-240715", - "data": [ - { - "object": "embedding", - "index": 0, - "embedding": [0.0023064255] + [0.1 * (i % 10 - 5) for i in range(1023)] # Realistic 1024-dim embedding - }, - { - "object": "embedding", - "index": 1, - "embedding": [-0.0038562391] + [0.05 * (i % 20 - 10) for i in range(1023)] - } - ], - "usage": { - "prompt_tokens": 6, - "total_tokens": 6 - } - } - - mock_response = Mock(spec=httpx.Response) - mock_response.status_code = 200 - mock_response.json.return_value = realistic_response_data - - # Only mock the HTTP call, not the business logic - with patch('litellm.llms.volcengine.embedding.handler.HTTPHandler') as mock_handler: - mock_client = Mock() - mock_client.post.return_value = mock_response - mock_handler.return_value = mock_client - - # Test the actual embedding call - result = self.handler.embedding( - model=self.model, - input=["Hello world", "Test embedding"], - api_key=self.api_key, - encoding_format="float" - ) - - # Verify the HTTP request was made correctly (this tests integration) - mock_client.post.assert_called_once() - call_args = mock_client.post.call_args - - # Verify request structure - assert call_args.kwargs["url"] == "https://ark.cn-beijing.volces.com/api/v3/embeddings" - assert call_args.kwargs["headers"]["Authorization"] == f"Bearer {self.api_key}" - - request_data = call_args.kwargs["json"] - assert request_data["model"] == self.model - assert request_data["input"] == ["Hello world", "Test embedding"] - assert request_data["encoding_format"] == "float" - - # Verify the response processing (real business logic) - assert isinstance(result, EmbeddingResponse) - assert result.model == self.model - assert len(result.data) == 2 - assert len(result.data[0]["embedding"]) == 1024 - assert len(result.data[1]["embedding"]) == 1024 - assert result.usage.prompt_tokens == 6 - - def test_parameter_validation_integration(self): - """Test parameter validation in the full integration context""" - - # Test with various parameter combinations that should work - valid_param_sets = [ - {"encoding_format": "float"}, - {"encoding_format": "base64"}, - {"user": "test-user-123"}, - {"encoding_format": "float", "user": "test-user"}, - {"extra_headers": {"Custom-Header": "value"}}, - ] - - for params in valid_param_sets: - # Only create the request, don't execute (avoids HTTP call) - config = VolcEngineEmbeddingConfig() - try: - result = config.transform_request( - model=self.model, - input=["test"], - api_key=self.api_key, - **params - ) - # Verify structure is correct - assert "url" in result - assert "headers" in result - assert "data" in result - - except Exception as e: - pytest.fail(f"Valid parameters {params} caused error: {e}") - - -if __name__ == "__main__": - pytest.main([__file__]) \ No newline at end of file diff --git a/tests/llm_translation/test_volcengine_embedding.py b/tests/test_litellm/llms/volcengine/test_volcengine_embedding.py similarity index 98% rename from tests/llm_translation/test_volcengine_embedding.py rename to tests/test_litellm/llms/volcengine/test_volcengine_embedding.py index 9503d91f3c8..3be7f6ca8d4 100644 --- a/tests/llm_translation/test_volcengine_embedding.py +++ b/tests/test_litellm/llms/volcengine/test_volcengine_embedding.py @@ -9,9 +9,9 @@ from unittest.mock import MagicMock, patch import pytest # Add parent directory to path for imports -sys.path.insert(0, os.path.abspath("../..")) +sys.path.insert(0, os.path.abspath("../../../../..")) -from base_embedding_unit_tests import BaseLLMEmbeddingTest +from tests.llm_translation.base_embedding_unit_tests import BaseLLMEmbeddingTest import litellm from litellm.types.utils import EmbeddingResponse From e312c235334cbc2b240066e6dc049800dfe0d684 Mon Sep 17 00:00:00 2001 From: onlylhf <27225745+onlylhf@users.noreply.github.com> Date: Mon, 1 Sep 2025 13:29:06 +0800 Subject: [PATCH 028/178] Refactoring: Remove the transform-REquest and transform-REsponse methods, and directly implement the response transformation logic in transform_ embedding-REsponse; Enhance environment validation to ensure the validity of api_key --- .../volcengine/embedding/transformation.py | 120 ++++-------------- 1 file changed, 22 insertions(+), 98 deletions(-) diff --git a/litellm/llms/volcengine/embedding/transformation.py b/litellm/llms/volcengine/embedding/transformation.py index 87e89626218..20747b76725 100644 --- a/litellm/llms/volcengine/embedding/transformation.py +++ b/litellm/llms/volcengine/embedding/transformation.py @@ -117,99 +117,7 @@ class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): return optional_params - def transform_request( - self, - model: str, - input: Union[str, List[str]], - api_key: str, - api_base: Optional[str] = None, - encoding_format: Optional[str] = "float", - user: Optional[str] = None, - extra_headers: Optional[Dict[str, str]] = None, - **kwargs, - ) -> Dict[str, Any]: - """ - Transform OpenAI embedding request to Volcengine format. - Args: - model: Model ID (e.g., "doubao-embedding-text-240715") - input: Text or list of texts to embed - api_key: Volcengine API key - api_base: Optional custom API base URL - encoding_format: Response format (float, base64, null) - user: Optional user identifier - extra_headers: Optional additional headers - **kwargs: Additional parameters - - Returns: - Dict containing url, headers, and data for the request - """ - # Get complete URL using the centralized method - url = self.get_complete_url( - api_base=api_base, - api_key=api_key, - model=model, - optional_params={}, - litellm_params={}, - ) - - # Get headers - headers = get_volcengine_headers(api_key, extra_headers) - - # Prepare request data - data = { - "model": model, - "input": input if isinstance(input, list) else [input], - } - - # Add optional parameters - if encoding_format is not None: - data["encoding_format"] = encoding_format - - return { - "url": url, - "headers": headers, - "data": data, - } - - def transform_response( - self, - response: httpx.Response, - model: str, - input: Union[str, List[str]], - encoding: Optional[str] = None, - ) -> Dict[str, Any]: - """ - Transform Volcengine embedding response to OpenAI format. - - Args: - response: The HTTP response from Volcengine - model: The model used - input: The input that was embedded - encoding: The encoding format requested - - Returns: - OpenAI-compatible embedding response - """ - try: - response_json = response.json() - except Exception as e: - raise ValueError(f"Failed to parse Volcengine response as JSON: {str(e)}") - - # Volcengine response format matches OpenAI format closely - # Just need to ensure all required fields are present - transformed_response = { - "object": "list", - "data": response_json.get("data", []), - "model": response_json.get("model", model), - "usage": response_json.get("usage", {}), - } - - # Add id if present - if "id" in response_json: - transformed_response["id"] = response_json["id"] - - return transformed_response def transform_embedding_request( self, @@ -250,12 +158,23 @@ class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): litellm_params: dict, ) -> EmbeddingResponse: """Transform Volcengine response to EmbeddingResponse""" - # Use existing transform_response method - transformed_response = self.transform_response( - response=raw_response, - model=model, - input=request_data.get("input", []), - ) + try: + response_json = raw_response.json() + except Exception as e: + raise ValueError(f"Failed to parse Volcengine response as JSON: {str(e)}") + + # Volcengine response format matches OpenAI format closely + # Just need to ensure all required fields are present + transformed_response = { + "object": "list", + "data": response_json.get("data", []), + "model": response_json.get("model", model), + "usage": response_json.get("usage", {}), + } + + # Add id if present + if "id" in response_json: + transformed_response["id"] = response_json["id"] # Create EmbeddingResponse from transformed data return EmbeddingResponse(**transformed_response) @@ -272,6 +191,8 @@ class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): ) -> dict: """Validate environment and return headers""" # Get Volcengine headers + if api_key is None: + raise ValueError("api_key is required for Volcengine authentication") volcengine_headers = get_volcengine_headers(api_key) return {**headers, **volcengine_headers} @@ -280,6 +201,9 @@ class VolcEngineEmbeddingConfig(BaseEmbeddingConfig): ) -> BaseLLMException: """Get error class for Volcengine errors""" from ..common_utils import VolcEngineError + # Convert dict to httpx.Headers if needed + if isinstance(headers, dict): + headers = httpx.Headers(headers) return VolcEngineError( status_code=status_code, message=error_message, From ca6d77b479b771b53ea0cf0107719db06b06165c Mon Sep 17 00:00:00 2001 From: TomeHirata Date: Mon, 1 Sep 2025 16:41:35 +0900 Subject: [PATCH 029/178] fix citation field name --- .../llms/databricks/chat/transformation.py | 28 +++++---- .../test_databricks_chat_transformation.py | 57 +++++++++++++++++-- 2 files changed, 69 insertions(+), 16 deletions(-) diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 5600d5c6426..9330b019235 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -26,7 +26,6 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo _should_convert_tool_call_to_json_mode, ) from litellm.litellm_core_utils.prompt_templates.common_utils import ( - handle_messages_with_content_list_to_str_conversion, strip_name_from_messages, ) from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator @@ -301,7 +300,6 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): ) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]: """ Databricks does not support: - - content in list format. - 'name' in user message. """ new_messages = [] @@ -311,7 +309,6 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): else: _message = message new_messages.append(_message) - new_messages = handle_messages_with_content_list_to_str_conversion(new_messages) new_messages = strip_name_from_messages(new_messages) if is_async: @@ -388,10 +385,16 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): citations: Optional[List[Any]] = None if isinstance(content, list): for item in content: + text = item.get("text", None) if item.get("citations") is not None: if citations is None: citations = [] - citations.append(item["citations"]) + citations.append( + [ + {**citation, "supported_text": text} + for citation in item["citations"] + ] + ) return citations def _transform_dbrx_choices( @@ -583,12 +586,17 @@ class DatabricksChatResponseIterator(BaseModelResponseIterator): for _tc in tool_calls: if _tc.get("function", {}).get("arguments") == "{}": _tc["function"]["arguments"] = "" # avoid invalid json - citation = choice["delta"].get("citation") - if citation is not None: - choice["delta"].setdefault("provider_specific_fields", {})[ - "citation" - ] = citation - choice["delta"].pop("citation", None) + if isinstance(choice["delta"]["content"], list) and ( + content := choice["delta"]["content"] + ): + if citations := content[0].get("citations"): + # TODO: Databricks delta does not include supported text or chunk type. + # Add either here once Databricks supports it to enable citation linkage. + choice["delta"].setdefault("provider_specific_fields", {})[ + "citation" + ] = citations[ + 0 + ] # Databricks Content item always has citation as a list of list # extract the content str content_str = DatabricksConfig.extract_content_str( choice["delta"].get("content") diff --git a/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py b/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py index d61f826e89b..51a2e971c09 100644 --- a/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py +++ b/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py @@ -88,7 +88,7 @@ def test_transform_choices_without_signature(): assert choices[0].message.reasoning_content == "i'm thinking without signature." assert choices[0].message.thinking_blocks is not None assert len(choices[0].message.thinking_blocks) == 1 - + # Verify the thinking block was created successfully without signature thinking_block = choices[0].message.thinking_blocks[0] assert thinking_block["type"] == "thinking" @@ -104,8 +104,17 @@ def test_transform_choices_with_citations(): "content": [ { "type": "text", - "text": "Paris", - "citations": [{"source": "wiki"}], + "text": "Blue", + "citations": [ + { + "type": "char_location", + "cited_text": "The sky is blue.", + "document_index": 0, + "document_title": "My Document", + "start_char_index": 0, + "end_char_index": 50, + } + ], } ], }, @@ -117,7 +126,19 @@ def test_transform_choices_with_citations(): choices = config._transform_dbrx_choices(choices=databricks_choices) assert choices[0].message.provider_specific_fields == { - "citations": [[{"source": "wiki"}]] + "citations": [ + [ + { + "type": "char_location", + "cited_text": "The sky is blue.", + "document_index": 0, + "document_title": "My Document", + "start_char_index": 0, + "end_char_index": 50, + "supported_text": "Blue", + } + ] + ] } @@ -130,7 +151,24 @@ def test_chunk_parser_with_citation(): "model": "test", "choices": [ { - "delta": {"citation": {"source": "wiki"}}, + "delta": { + "content": [ + { + "type": "text", + "text": "", + "citations": [ + { + "type": "char_location", + "cited_text": "The sky is blue.", + "document_index": 0, + "document_title": "My Document", + "start_char_index": 0, + "end_char_index": 50, + } + ], + } + ], + }, "index": 0, "finish_reason": None, } @@ -139,5 +177,12 @@ def test_chunk_parser_with_citation(): parsed = iterator.chunk_parser(chunk) assert parsed.choices[0].delta.provider_specific_fields == { - "citation": {"source": "wiki"} + "citation": { + "type": "char_location", + "cited_text": "The sky is blue.", + "document_index": 0, + "document_title": "My Document", + "start_char_index": 0, + "end_char_index": 50, + } } From e334f48699f54a32a81ec4e75ca43ed52ecadf48 Mon Sep 17 00:00:00 2001 From: TomeHirata Date: Mon, 1 Sep 2025 16:46:54 +0900 Subject: [PATCH 030/178] Add supported text field to anthropic citation response --- litellm/llms/anthropic/chat/handler.py | 2 ++ litellm/llms/anthropic/chat/transformation.py | 10 +++++++++- .../test_anthropic_completion.py | 18 +++++++++++++----- 3 files changed, 24 insertions(+), 6 deletions(-) diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index 5618c50923e..253f5d9be2c 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -743,6 +743,8 @@ class ModelResponseIterator: ) text, tool_use = self._handle_json_mode_chunk(text=text, tool_use=tool_use) + if type_chunk: + provider_specific_fields["chunk_type"] = type_chunk returned_chunk = ModelResponseStream( choices=[ diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index ce874bfde9a..378ca75da5f 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -797,7 +797,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if content.get("citations") is not None: if citations is None: citations = [] - citations.append(content["citations"]) + citations.append( + [ + { + **citation, + "supported_text": content.get("text", ""), + } + for citation in content["citations"] + ] + ) if thinking_blocks is not None: reasoning_content = "" for block in thinking_blocks: diff --git a/tests/llm_translation/test_anthropic_completion.py b/tests/llm_translation/test_anthropic_completion.py index 45702a261e2..f4bd7531b0b 100644 --- a/tests/llm_translation/test_anthropic_completion.py +++ b/tests/llm_translation/test_anthropic_completion.py @@ -920,6 +920,14 @@ def test_anthropic_citations_api(): citations = resp.choices[0].message.provider_specific_fields["citations"] assert citations is not None + if citations: + citation = citations[0][0] + assert "supported_text" in citation + assert "cited_text" in citation + assert "document_index" in citation + assert "document_title" in citation + assert "start_char_index" in citation + assert "end_char_index" in citation def test_anthropic_citations_api_streaming(): @@ -955,11 +963,11 @@ def test_anthropic_citations_api_streaming(): has_citations = False for chunk in resp: print(f"returned chunk: {chunk}") - if ( - chunk.choices[0].delta.provider_specific_fields - and "citation" in chunk.choices[0].delta.provider_specific_fields - ): - has_citations = True + if provider_specific_fields := chunk.choices[0].delta.provider_specific_fields: + if "citation" in provider_specific_fields: + has_citations = True + + assert "chunk_type" in provider_specific_fields assert has_citations From 1ac7f875c9e2d59f77080161e8f4badb3cb541c7 Mon Sep 17 00:00:00 2001 From: TomeHirata Date: Mon, 1 Sep 2025 17:21:47 +0900 Subject: [PATCH 031/178] fix test --- litellm/llms/anthropic/chat/handler.py | 2 - .../test_anthropic_chat_transformation.py | 43 +++++++++++++------ 2 files changed, 31 insertions(+), 14 deletions(-) diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index 253f5d9be2c..5618c50923e 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -743,8 +743,6 @@ class ModelResponseIterator: ) text, tool_use = self._handle_json_mode_chunk(text=text, tool_use=tool_use) - if type_chunk: - provider_specific_fields["chunk_type"] = type_chunk returned_chunk = ModelResponseStream( choices=[ diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py index ff454968d9c..dcca87baf32 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py @@ -115,16 +115,11 @@ def test_calculate_usage_nulls(usage_object, expected_usage): assert hasattr(usage, k) assert getattr(usage, k) == v -@pytest.mark.parametrize("usage_object", [ - { - "server_tool_use": { - "web_search_requests": None - } - }, - { - "server_tool_use": None - } -]) + +@pytest.mark.parametrize( + "usage_object", + [{"server_tool_use": {"web_search_requests": None}}, {"server_tool_use": None}], +) def test_calculate_usage_server_tool_null(usage_object): """ Correctly deal with null values in usage object @@ -132,10 +127,11 @@ def test_calculate_usage_server_tool_null(usage_object): Fixes https://github.com/BerriAI/litellm/issues/11920 """ config = AnthropicConfig() - + usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None) assert not hasattr(usage, "server_tool_use") + def test_extract_response_content_with_citations(): config = AnthropicConfig() @@ -188,7 +184,30 @@ def test_extract_response_content_with_citations(): } _, citations, _, _, _ = config.extract_response_content(completion_response) - assert citations is not None + assert citations == [ + [ + { + "type": "char_location", + "cited_text": "The grass is green. ", + "document_index": 0, + "document_title": "My Document", + "start_char_index": 0, + "end_char_index": 20, + "supported_text": "the grass is green", + }, + ], + [ + { + "type": "char_location", + "cited_text": "The sky is blue.", + "document_index": 0, + "document_title": "My Document", + "start_char_index": 20, + "end_char_index": 36, + "supported_text": "the sky is blue", + }, + ], + ] def test_map_tool_helper(): From cf676e7aeff459330c5df604a636955d494aff45 Mon Sep 17 00:00:00 2001 From: TomeHirata Date: Mon, 1 Sep 2025 17:51:24 +0900 Subject: [PATCH 032/178] fix mypy --- litellm/llms/databricks/chat/transformation.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 9330b019235..d3df5bbf361 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -382,20 +382,18 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): ) -> Optional[List[Any]]: if content is None: return None - citations: Optional[List[Any]] = None + citations = [] if isinstance(content, list): for item in content: text = item.get("text", None) - if item.get("citations") is not None: - if citations is None: - citations = [] + if citations_item := item.get("citations"): citations.append( [ {**citation, "supported_text": text} - for citation in item["citations"] + for citation in citations_item ] ) - return citations + return citations or None def _transform_dbrx_choices( self, choices: List[DatabricksChoice], json_mode: Optional[bool] = None From fd0f47d48de58b2f0786675d5f4a01e4eac1a30d Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Fri, 29 Aug 2025 22:04:51 -0700 Subject: [PATCH 033/178] Merge pull request #14005 from moshemorad/bedrock_fix_structure_output Bedrock fix structure output --- dist/litellm-1.57.6.tar.gz | Bin 64 -> 0 bytes .../bedrock/chat/converse_transformation.py | 13 +- .../chat/test_converse_transformation.py | 295 ++++++++++++++++++ 3 files changed, 299 insertions(+), 9 deletions(-) delete mode 100644 dist/litellm-1.57.6.tar.gz diff --git a/dist/litellm-1.57.6.tar.gz b/dist/litellm-1.57.6.tar.gz deleted file mode 100644 index 01a039cf6eeaf66098473f96bea25fad31613393..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 64 zcmb2|=HOre0;c~tnI)+?Ik~!qdZy-jW_l%wMGS8bGV%iD4lVfZpUY>y0Hh8K8qAqz M-IG;k&|qKy03*o{)Bpeg diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index b93ca94bed4..273b12c9c39 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -10,6 +10,7 @@ from typing import List, Literal, Optional, Tuple, Union, cast, overload import httpx import litellm +from litellm.constants import RESPONSE_FORMAT_TOOL_NAME from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.litellm_core_utils.litellm_logging import Logging from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( @@ -322,7 +323,6 @@ class AmazonConverseConfig(BaseConfig): def _create_json_tool_call_for_response_format( self, json_schema: Optional[dict] = None, - schema_name: str = "json_tool_call", description: Optional[str] = None, ) -> ChatCompletionToolParam: """ @@ -347,7 +347,7 @@ class AmazonConverseConfig(BaseConfig): _input_schema = json_schema tool_param_function_chunk = ChatCompletionToolParamFunctionChunk( - name=schema_name, parameters=_input_schema + name=RESPONSE_FORMAT_TOOL_NAME, parameters=_input_schema ) if description: tool_param_function_chunk["description"] = description @@ -391,14 +391,11 @@ class AmazonConverseConfig(BaseConfig): continue json_schema: Optional[dict] = None - schema_name: str = "" description: Optional[str] = None if "response_schema" in value: json_schema = value["response_schema"] - schema_name = "json_tool_call" elif "json_schema" in value: json_schema = value["json_schema"]["schema"] - schema_name = value["json_schema"]["name"] description = value["json_schema"].get("description") if "type" in value and value["type"] == "text": @@ -414,7 +411,6 @@ class AmazonConverseConfig(BaseConfig): """ _tool = self._create_json_tool_call_for_response_format( json_schema=json_schema, - schema_name=schema_name if schema_name != "" else "json_tool_call", description=description, ) optional_params = self._add_tools_to_optional_params( @@ -430,7 +426,7 @@ class AmazonConverseConfig(BaseConfig): optional_params["tool_choice"] = ToolChoiceValuesBlock( tool=SpecificToolChoiceBlock( - name=schema_name if schema_name != "" else "json_tool_call" + name=RESPONSE_FORMAT_TOOL_NAME ) ) optional_params["json_mode"] = True @@ -1119,8 +1115,7 @@ class AmazonConverseConfig(BaseConfig): self._transform_thinking_blocks(reasoningContentBlocks) ) chat_completion_message["content"] = content_str - if json_mode is True and tools is not None and len(tools) == 1: - # to support 'json_schema' logic on bedrock models + if json_mode is True and tools is not None and len(tools) == 1 and tools[0]["function"]["name"] == RESPONSE_FORMAT_TOOL_NAME: json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments") if json_mode_content_str is not None: chat_completion_message["content"] = json_mode_content_str diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py index 1c91cc0fe8b..2fc710664e6 100644 --- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py @@ -475,6 +475,239 @@ def test_transform_response_with_bash_tool(): assert args["command"] == "ls -la *.py" +def test_transform_response_with_structured_response_being_called(): + """Test response transformation with structured response.""" + from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig + from litellm.types.utils import ModelResponse + + # Simulate a Bedrock Converse response with a bash tool call + response_json = { + "additionalModelResponseFields": {}, + "metrics": {"latencyMs": 100.0}, + "output": { + "message": { + "role": "assistant", + "content": [ + { + "toolUse": { + "toolUseId": "tooluse_456", + "name": "json_tool_call", + "input": { + "Current_Temperature": 62, + "Weather_Explanation": "San Francisco typically has mild, cool weather year-round due to its coastal location and marine influence. The city is known for its fog, moderate temperatures, and relatively stable climate with little seasonal variation."}, + } + } + ] + } + }, + "stopReason": "tool_use", + "usage": { + "inputTokens": 8, + "outputTokens": 3, + "totalTokens": 11, + "cacheReadInputTokenCount": 0, + "cacheReadInputTokens": 0, + "cacheWriteInputTokenCount": 0, + "cacheWriteInputTokens": 0, + }, + } + # Mock httpx.Response + class MockResponse: + def json(self): + return response_json + @property + def text(self): + return json.dumps(response_json) + + config = AmazonConverseConfig() + model_response = ModelResponse() + optional_params = { + "json_mode": True, + "tools": [ + { + 'type': 'function', + 'function': { + 'name': 'get_weather', + 'description': 'Get the current weather in a given location', + 'parameters': { + 'type': 'object', + 'properties': { + 'location': { + 'type': 'string', + 'description': 'The city and state, e.g. San Francisco, CA' + }, + 'unit': { + 'type': 'string', + 'enum': ['celsius', 'fahrenheit'] + } + }, + 'required': ['location'] + } + } + }, + { + 'type': 'function', + 'function': { + 'name': 'json_tool_call', + 'parameters': { + '$schema': 'http://json-schema.org/draft-07/schema#', + 'type': 'object', + 'required': ['Weather_Explanation', 'Current_Temperature'], + 'properties': { + 'Weather_Explanation': { + 'type': ['string', 'null'], + 'description': '1-2 sentences explaining the weather in the location' + }, + 'Current_Temperature': { + 'type': ['number', 'null'], + 'description': 'Current temperature in the location' + } + }, + 'additionalProperties': False + } + } + } + ] + } + # Call the transformation logic + result = config._transform_response( + model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", + response=MockResponse(), + model_response=model_response, + stream=False, + logging_obj=None, + optional_params=optional_params, + api_key=None, + data=None, + messages=[], + encoding=None, + ) + # Check that the tool call is present in the returned message + assert result.choices[0].message.tool_calls is None + + assert result.choices[0].message.content is not None + assert result.choices[0].message.content == '{"Current_Temperature": 62, "Weather_Explanation": "San Francisco typically has mild, cool weather year-round due to its coastal location and marine influence. The city is known for its fog, moderate temperatures, and relatively stable climate with little seasonal variation."}' + +def test_transform_response_with_structured_response_calling_tool(): + """Test response transformation with structured response.""" + from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig + from litellm.types.utils import ModelResponse + + # Simulate a Bedrock Converse response with a bash tool call + response_json = { + "metrics": { + "latencyMs": 1148 + }, + "output": { + "message": + { + "content": [ + { + "text": "I\'ll check the current weather in San Francisco for you." + }, + { + "toolUse": { + "input": { + "location": "San Francisco, CA", + "unit": "celsius" + }, + "name": "get_weather", + "toolUseId": "tooluse_oKk__QrqSUmufMw3Q7vGaQ" + } + } + ], + "role": "assistant" + } + }, + "stopReason": "tool_use", + "usage": { + "cacheReadInputTokenCount": 0, + "cacheReadInputTokens": 0, + "cacheWriteInputTokenCount": 0, + "cacheWriteInputTokens": 0, + "inputTokens": 534, + "outputTokens": 69, + "totalTokens": 603 + } + } + # Mock httpx.Response + class MockResponse: + def json(self): + return response_json + @property + def text(self): + return json.dumps(response_json) + + config = AmazonConverseConfig() + model_response = ModelResponse() + optional_params = { + "json_mode": True, + "tools": [ + { + 'type': 'function', + 'function': { + 'name': 'get_weather', + 'description': 'Get the current weather in a given location', + 'parameters': { + 'type': 'object', + 'properties': { + 'location': { + 'type': 'string', + 'description': 'The city and state, e.g. San Francisco, CA' + }, + 'unit': { + 'type': 'string', + 'enum': ['celsius', 'fahrenheit'] + } + }, + 'required': ['location'] + } + } + }, + { + 'type': 'function', + 'function': { + 'name': 'json_tool_call', + 'parameters': { + '$schema': 'http://json-schema.org/draft-07/schema#', + 'type': 'object', + 'required': ['Weather_Explanation', 'Current_Temperature'], + 'properties': { + 'Weather_Explanation': { + 'type': ['string', 'null'], + 'description': '1-2 sentences explaining the weather in the location' + }, + 'Current_Temperature': { + 'type': ['number', 'null'], + 'description': 'Current temperature in the location' + } + }, + 'additionalProperties': False + } + } + } + ] + } + # Call the transformation logic + result = config._transform_response( + model="bedrock/eu.anthropic.claude-sonnet-4-20250514-v1:0", + response=MockResponse(), + model_response=model_response, + stream=False, + logging_obj=None, + optional_params=optional_params, + api_key=None, + data=None, + messages=[], + encoding=None, + ) + # Check that the tool call is present in the returned message + assert result.choices[0].message.tool_calls is not None + assert len(result.choices[0].message.tool_calls) == 1 + assert result.choices[0].message.tool_calls[0].function.name == "get_weather" + assert result.choices[0].message.tool_calls[0].function.arguments == '{"location": "San Francisco, CA", "unit": "celsius"}' + + @pytest.mark.asyncio async def test_bedrock_bash_tool_acompletion(): """Test Bedrock with bash tool for ls command using acompletion.""" @@ -938,6 +1171,68 @@ def test_transform_request_with_function_tool(): assert request_data["toolConfig"]["tools"][0]["toolSpec"]["name"] == "get_weather" +def test_map_openai_params_with_response_format(): + """Test map_openai_params with response_format.""" + config = AmazonConverseConfig() + + tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + } + ] + + json_schema = { + "type": "json_schema", + "json_schema": { + "name": "WeatherResult", + "schema": { + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "required": ["Weather_Explanation", "Current_Temperature"], + "properties": { + "Weather_Explanation": { + "type": ["string", "null"], + "description": "1-2 sentences explaining the weather in the location", + }, + "Current_Temperature": { + "type": ["number", "null"], + "description": "Current temperature in the location", + }, + }, + "additionalProperties": False, + }, + "strict": False, + }, + } + + optional_params = config.map_openai_params( + non_default_params={"response_format": json_schema}, + optional_params={"tools": tools}, + model="eu.anthropic.claude-sonnet-4-20250514-v1:0", + drop_params=False + ) + + assert "tools" in optional_params + assert len(optional_params["tools"]) == 2 + assert optional_params["tools"][1]["type"] == "function" + assert optional_params["tools"][1]["function"]["name"] == "json_tool_call" + + @pytest.mark.asyncio async def test_assistant_message_cache_control(): """Test that assistant messages with cache_control generate cachePoint blocks.""" From 002c2f16dc679c34944dfb9f68dc7595dc1ab9a9 Mon Sep 17 00:00:00 2001 From: retanoj Date: Mon, 1 Sep 2025 20:02:53 +0800 Subject: [PATCH 034/178] fix token count error when proxy gemini cli to openai like model --- litellm/google_genai/adapters/handler.py | 4 ++ litellm/proxy/google_endpoints/endpoints.py | 23 +++++++-- .../proxy/google_endpoints/__init__.py | 0 .../proxy/google_endpoints/test_endpoints.py | 49 +++++++++++++++++++ 4 files changed, 71 insertions(+), 5 deletions(-) create mode 100644 tests/test_litellm/proxy/google_endpoints/__init__.py create mode 100644 tests/test_litellm/proxy/google_endpoints/test_endpoints.py diff --git a/litellm/google_genai/adapters/handler.py b/litellm/google_genai/adapters/handler.py index 1f575f27591..c5f378554b2 100644 --- a/litellm/google_genai/adapters/handler.py +++ b/litellm/google_genai/adapters/handler.py @@ -37,6 +37,10 @@ class GenerateContentToCompletionHandler: completion_kwargs: Dict[str, Any] = dict(completion_request) + # feed metadata for custom callback + # if 'metadata' in extra_kwargs: + # completion_kwargs['metadata'] = extra_kwargs['metadata'] + if stream: completion_kwargs["stream"] = stream diff --git a/litellm/proxy/google_endpoints/endpoints.py b/litellm/proxy/google_endpoints/endpoints.py index 373232e22d2..4f57e1e7ce8 100644 --- a/litellm/proxy/google_endpoints/endpoints.py +++ b/litellm/proxy/google_endpoints/endpoints.py @@ -173,15 +173,22 @@ async def google_count_tokens(request: Request, model_name: str): """ from litellm.proxy.common_utils.http_parsing_utils import _read_request_body from litellm.proxy.proxy_server import token_counter as internal_token_counter + from litellm.google_genai.adapters.transformation import GoogleGenAIAdapter data = await _read_request_body(request=request) contents = data.get("contents", []) #Create TokenCountRequest for the internal endpoint from litellm.proxy._types import TokenCountRequest + # Translate contents to openai format messages using the adapter + messages = (GoogleGenAIAdapter() + .translate_generate_content_to_completion(model_name, contents) + .get("messages", [])) + token_request = TokenCountRequest( model=model_name, - contents=contents + contents=contents, + messages=messages, # compatibility when use openai-like endpoint ) # Call the internal token counter function with direct request flag set to False @@ -192,10 +199,16 @@ async def google_count_tokens(request: Request, model_name: str): if token_response is not None: # cast the response to the well known format original_response: dict = token_response.original_response or {} - return TokenCountDetailsResponse( - totalTokens=original_response.get("totalTokens", 0), - promptTokensDetails=original_response.get("promptTokensDetails", []), - ) + if original_response: + return TokenCountDetailsResponse( + totalTokens=original_response.get("totalTokens", 0), + promptTokensDetails=original_response.get("promptTokensDetails", []), + ) + else: + return TokenCountDetailsResponse( + totalTokens=token_response.total_tokens or 0, + promptTokensDetails=[], + ) ######################################################### # Return the response in the well known format diff --git a/tests/test_litellm/proxy/google_endpoints/__init__.py b/tests/test_litellm/proxy/google_endpoints/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/proxy/google_endpoints/test_endpoints.py b/tests/test_litellm/proxy/google_endpoints/test_endpoints.py new file mode 100644 index 00000000000..2f2538bf9aa --- /dev/null +++ b/tests/test_litellm/proxy/google_endpoints/test_endpoints.py @@ -0,0 +1,49 @@ +""" +Test for google_endpoints/endpoints.py +""" +import pytest +import sys, os +from dotenv import load_dotenv + + +from litellm.proxy.google_endpoints.endpoints import google_count_tokens +from litellm.types.llms.vertex_ai import TokenCountDetailsResponse +from starlette.requests import Request + +load_dotenv() + +sys.path.insert( + 0, os.path.abspath("../../../..") +) + +@pytest.mark.asyncio +async def test_proxy_gemini_to_openai_like_model_token_counting(): + """ + Test the token counting endpoint for proxing gemini to openai-like models. + """ + response: TokenCountDetailsResponse = await google_count_tokens( + request=Request( + scope={ + "type": "http", + "parsed_body": ( + [ + "contents" + ], + { + "contents": [ + { + "parts": [ + { + "text": "Hello, how are you?" + } + ] + } + ] + } + ) + } + ), + model_name="volcengine/foo", + ) + + assert response.get("totalTokens") > 0 \ No newline at end of file From e05ffcfb4c2b3bbcb9de75a8f1609ffa141f6326 Mon Sep 17 00:00:00 2001 From: retanoj Date: Mon, 1 Sep 2025 20:09:19 +0800 Subject: [PATCH 035/178] fix back --- litellm/google_genai/adapters/handler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/google_genai/adapters/handler.py b/litellm/google_genai/adapters/handler.py index c5f378554b2..c15d0cb9deb 100644 --- a/litellm/google_genai/adapters/handler.py +++ b/litellm/google_genai/adapters/handler.py @@ -38,8 +38,8 @@ class GenerateContentToCompletionHandler: completion_kwargs: Dict[str, Any] = dict(completion_request) # feed metadata for custom callback - # if 'metadata' in extra_kwargs: - # completion_kwargs['metadata'] = extra_kwargs['metadata'] + if 'metadata' in extra_kwargs: + completion_kwargs['metadata'] = extra_kwargs['metadata'] if stream: completion_kwargs["stream"] = stream From af2f447698bed16210c8bc452714876455d51ebb Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Mon, 1 Sep 2025 10:28:48 -0400 Subject: [PATCH 036/178] added prometheus metrics --- .../integrations/prometheus.py | 20 +++++++------ litellm/types/integrations/prometheus.py | 28 +++++++++++++++++-- 2 files changed, 36 insertions(+), 12 deletions(-) diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index a2d781fa1c4..1aa2359bc15 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -95,13 +95,14 @@ class PrometheusLogger(CustomLogger): self.litellm_llm_api_time_to_first_token_metric = self._histogram_factory( "litellm_llm_api_time_to_first_token_metric", "Time to first token for a models LLM API call", - labelnames=[ - "model", - "hashed_api_key", - "api_key_alias", - "team", - "team_alias", - ], + # labelnames=[ + # "model", + # "hashed_api_key", + # "api_key_alias", + # "team", + # "team_alias", + # ], + labelnames=self.get_labels_for_metric("litellm_llm_api_time_to_first_token_metric"), buckets=LATENCY_BUCKETS, ) @@ -255,13 +256,14 @@ class PrometheusLogger(CustomLogger): self.litellm_deployment_state = self._gauge_factory( "litellm_deployment_state", "LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage", - labelnames=_logged_llm_labels, + labelnames=self.get_labels_for_metric("litellm_deployment_state") ) self.litellm_deployment_cooled_down = self._counter_factory( "litellm_deployment_cooled_down", "LLM Deployment Analytics - Number of times a deployment has been cooled down by LiteLLM load balancing logic. exception_status is the status of the exception that caused the deployment to be cooled down", - labelnames=_logged_llm_labels + [EXCEPTION_STATUS], + # labelnames=_logged_llm_labels + [EXCEPTION_STATUS], + labelnames=self.get_labels_for_metric("litellm_deployment_cooled_down") ) self.litellm_deployment_success_responses = self._counter_factory( diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py index e0ee950d260..4ab0ccf5022 100644 --- a/litellm/types/integrations/prometheus.py +++ b/litellm/types/integrations/prometheus.py @@ -154,6 +154,7 @@ class UserAPIKeyLabelNames(Enum): DEFINED_PROMETHEUS_METRICS = Literal[ "litellm_llm_api_latency_metric", + "litellm_llm_api_time_to_first_token_metric", "litellm_request_total_latency_metric", "litellm_overhead_latency_metric", "litellm_remaining_requests_metric", @@ -173,9 +174,11 @@ DEFINED_PROMETHEUS_METRICS = Literal[ "litellm_remaining_api_key_budget_metric", "litellm_api_key_max_budget_metric", "litellm_api_key_budget_remaining_hours_metric", + "litellm_deployment_state", "litellm_deployment_failure_responses", "litellm_deployment_total_requests", "litellm_deployment_success_responses", + "litellm_deployment_cooled_down", "litellm_pod_lock_manager_size", "litellm_in_memory_daily_spend_update_queue_size", "litellm_redis_daily_spend_update_queue_size", @@ -191,9 +194,14 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.API_KEY_ALIAS.value, UserAPIKeyLabelNames.TEAM.value, UserAPIKeyLabelNames.TEAM_ALIAS.value, - UserAPIKeyLabelNames.REQUESTED_MODEL.value, - UserAPIKeyLabelNames.END_USER.value, - UserAPIKeyLabelNames.USER.value, + ] + + litellm_llm_api_time_to_first_token_metric = [ + UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value, + UserAPIKeyLabelNames.API_KEY_HASH.value, + UserAPIKeyLabelNames.API_KEY_ALIAS.value, + UserAPIKeyLabelNames.TEAM.value, + UserAPIKeyLabelNames.TEAM_ALIAS.value, ] litellm_request_total_latency_metric = [ @@ -315,6 +323,20 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.REQUESTED_MODEL.value, ] + litellm_deployment_state = [ + UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value, + UserAPIKeyLabelNames.MODEL_ID.value, + UserAPIKeyLabelNames.API_BASE.value, + UserAPIKeyLabelNames.API_PROVIDER.value, + ] + + litellm_deployment_cooled_down = [ + UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value, + UserAPIKeyLabelNames.MODEL_ID.value, + UserAPIKeyLabelNames.API_BASE.value, + UserAPIKeyLabelNames.API_PROVIDER.value, + ] + litellm_deployment_successful_fallbacks = [ UserAPIKeyLabelNames.REQUESTED_MODEL.value, UserAPIKeyLabelNames.FALLBACK_MODEL.value, From 8de65e84480573ef88f28054ba6b83a39a19de3f Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Mon, 1 Sep 2025 10:36:51 -0400 Subject: [PATCH 037/178] fix: added prometheus missing metrics --- enterprise/litellm_enterprise/integrations/prometheus.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index 1aa2359bc15..742f2dbbe9b 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -244,14 +244,6 @@ class PrometheusLogger(CustomLogger): labelnames=["api_provider"], ) - # Get all keys - _logged_llm_labels = [ - UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value, - UserAPIKeyLabelNames.MODEL_ID.value, - UserAPIKeyLabelNames.API_BASE.value, - UserAPIKeyLabelNames.API_PROVIDER.value, - ] - # Metric for deployment state self.litellm_deployment_state = self._gauge_factory( "litellm_deployment_state", From 31cfe1fe0a7816979ae37b0f0eea9332ce09a5b3 Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Mon, 1 Sep 2025 10:45:38 -0400 Subject: [PATCH 038/178] Revert "fix: import verbose_proxy_logger to resolve undefined name errors" This reverts commit a8f2ebed9cda1c53c3a21196577e2e3639fe9eeb. --- enterprise/litellm_enterprise/proxy/guardrails/endpoints.py | 1 - 1 file changed, 1 deletion(-) diff --git a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py index 85f8bf22417..47bec519c40 100644 --- a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py +++ b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py @@ -13,7 +13,6 @@ from typing import Dict, List, Optional from fastapi import APIRouter, Depends, HTTPException from pydantic import BaseModel -from litellm._logging import verbose_proxy_logger 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 a6790ac158a8ce1e20167e7495f8c77cdbbb6b94 Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Mon, 1 Sep 2025 10:45:44 -0400 Subject: [PATCH 039/178] Revert "feat: add UI support for displaying and managing guardrails on virtual keys" This reverts commit 83654c56089208db695b5b4821e7ae41118aba34. --- .../proxy/guardrails/endpoints.py | 2 - .../proxy/guardrails/guardrail_registry.py | 1 - .../virtual_keys/virtual_key_detail.tsx | 160 ------------------ .../virtual_keys/virtual_key_guardrails.tsx | 93 ---------- .../src/services/guardrails.ts | 27 --- .../src/services/virtual_key_guardrails.ts | 57 ------- ui/litellm-dashboard/src/types/guardrail.ts | 11 -- ui/litellm-dashboard/src/types/virtual_key.ts | 13 -- 8 files changed, 364 deletions(-) delete mode 100644 ui/litellm-dashboard/src/components/virtual_keys/virtual_key_detail.tsx delete mode 100644 ui/litellm-dashboard/src/components/virtual_keys/virtual_key_guardrails.tsx delete mode 100644 ui/litellm-dashboard/src/services/guardrails.ts delete mode 100644 ui/litellm-dashboard/src/services/virtual_key_guardrails.ts delete mode 100644 ui/litellm-dashboard/src/types/guardrail.ts delete mode 100644 ui/litellm-dashboard/src/types/virtual_key.ts diff --git a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py index 47bec519c40..fcd5e82ab3a 100644 --- a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py +++ b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py @@ -97,9 +97,7 @@ async def get_guardrails_for_virtual_key( """ Get all guardrails associated with a virtual key """ - verbose_proxy_logger.debug(f"Getting guardrails for virtual key: {virtual_key_id}") guardrails = IN_MEMORY_GUARDRAIL_HANDLER.get_guardrails_for_virtual_key(virtual_key_id) - verbose_proxy_logger.debug(f"Found {len(guardrails)} guardrails for virtual key {virtual_key_id}") return VirtualKeyGuardrailsResponse( virtual_key_id=virtual_key_id, diff --git a/litellm/proxy/guardrails/guardrail_registry.py b/litellm/proxy/guardrails/guardrail_registry.py index 7d3432d5cb6..523ff9f4cd8 100644 --- a/litellm/proxy/guardrails/guardrail_registry.py +++ b/litellm/proxy/guardrails/guardrail_registry.py @@ -567,7 +567,6 @@ class InMemoryGuardrailHandler: Get all guardrails associated with a virtual key """ guardrail_ids = self.virtual_key_to_guardrails.get(virtual_key_id, []) - verbose_proxy_logger.debug(f"Getting guardrails for virtual key {virtual_key_id}: {guardrail_ids}") return [self.IN_MEMORY_GUARDRAILS[gid] for gid in guardrail_ids if gid in self.IN_MEMORY_GUARDRAILS] diff --git a/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_detail.tsx b/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_detail.tsx deleted file mode 100644 index 111bd26841c..00000000000 --- a/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_detail.tsx +++ /dev/null @@ -1,160 +0,0 @@ -import React, { useState, useEffect } from 'react'; -import { Card, Flex, Text, Heading, Button, Dialog, TextField, Select, Box, Tabs } from '@radix-ui/themes'; -import { toast } from 'react-hot-toast'; -import { VirtualKey } from '../../types/virtual_key'; -import { fetchVirtualKey, updateVirtualKey } from '../../services/virtual_keys'; -import { VirtualKeyGuardrails } from './virtual_key_guardrails'; -import { fetchGuardrails } from '../../services/guardrails'; -import { Guardrail } from '../../types/guardrail'; -import { associateGuardrailWithVirtualKey } from '../../services/virtual_key_guardrails'; - -interface VirtualKeyDetailProps { - virtualKeyId: string; - onClose: () => void; -} - -export const VirtualKeyDetail: React.FC = ({ virtualKeyId, onClose }) => { - const [virtualKey, setVirtualKey] = useState(null); - const [loading, setLoading] = useState(true); - const [availableGuardrails, setAvailableGuardrails] = useState([]); - const [selectedGuardrailId, setSelectedGuardrailId] = useState(''); - const [refreshTrigger, setRefreshTrigger] = useState(0); - - useEffect(() => { - const loadVirtualKey = async () => { - try { - const data = await fetchVirtualKey(virtualKeyId); - setVirtualKey(data); - } catch (error) { - console.error('Error loading virtual key:', error); - toast.error('Failed to load virtual key details'); - } finally { - setLoading(false); - } - }; - - const loadGuardrails = async () => { - try { - const data = await fetchGuardrails(); - setAvailableGuardrails(data.guardrails); - } catch (error) { - console.error('Error loading guardrails:', error); - } - }; - - loadVirtualKey(); - loadGuardrails(); - }, [virtualKeyId]); - - const handleAddGuardrail = async () => { - if (!selectedGuardrailId) { - toast.error('Please select a guardrail'); - return; - } - - try { - await associateGuardrailWithVirtualKey(virtualKeyId, selectedGuardrailId); - toast.success('Guardrail added to virtual key'); - setSelectedGuardrailId(''); - setRefreshTrigger(prev => prev + 1); - } catch (error) { - console.error('Error adding guardrail:', error); - toast.error('Failed to add guardrail'); - } - }; - - if (loading) { - return ( - - - Virtual Key Details - Loading... - - - ); - } - - if (!virtualKey) { - return ( - - - Error - Virtual key not found - - - - - - ); - } - - return ( - - - Virtual Key: {virtualKey.key_name || 'Unnamed Key'} - - - - Details - Guardrails - - - - - - - - Key ID: - {virtualKey.key_id} - - - Team ID: - {virtualKey.team_id || 'N/A'} - - - Models: - {virtualKey.models?.join(', ') || 'All models'} - - - Spend: - ${virtualKey.spend?.toFixed(2) || '0.00'} - - - - - - - - - Add Guardrail - - - - - {availableGuardrails.map(guardrail => ( - - {guardrail.guardrail_name || `Guardrail ${guardrail.guardrail_id.substring(0, 8)}`} - - ))} - - - - - - - - - - - - - - - - - - ); -}; diff --git a/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_guardrails.tsx b/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_guardrails.tsx deleted file mode 100644 index 0acbc9d3f4a..00000000000 --- a/ui/litellm-dashboard/src/components/virtual_keys/virtual_key_guardrails.tsx +++ /dev/null @@ -1,93 +0,0 @@ -import React, { useEffect, useState } from 'react'; -import { Badge, Button, Card, Flex, Heading, Text, Box, Spinner } from '@radix-ui/themes'; -import { fetchGuardrailsForVirtualKey, associateGuardrailWithVirtualKey, disassociateGuardrailFromVirtualKey } from '../../services/virtual_key_guardrails'; -import { Guardrail } from '../../types/guardrail'; -import { toast } from 'react-hot-toast'; - -interface VirtualKeyGuardrailsProps { - virtualKeyId: string; - refreshTrigger?: number; -} - -export const VirtualKeyGuardrails: React.FC = ({ - virtualKeyId, - refreshTrigger = 0 -}) => { - const [guardrails, setGuardrails] = useState([]); - const [loading, setLoading] = useState(true); - const [error, setError] = useState(null); - - useEffect(() => { - const loadGuardrails = async () => { - if (!virtualKeyId) return; - - setLoading(true); - try { - const data = await fetchGuardrailsForVirtualKey(virtualKeyId); - setGuardrails(data.guardrails); - setError(null); - } catch (err) { - console.error('Error loading guardrails for virtual key:', err); - setError('Failed to load guardrails'); - setGuardrails([]); - } finally { - setLoading(false); - } - }; - - loadGuardrails(); - }, [virtualKeyId, refreshTrigger]); - - const handleRemoveGuardrail = async (guardrailId: string) => { - try { - await disassociateGuardrailFromVirtualKey(virtualKeyId, guardrailId); - setGuardrails(guardrails.filter(g => g.guardrail_id !== guardrailId)); - toast.success('Guardrail removed from virtual key'); - } catch (err) { - console.error('Error removing guardrail:', err); - toast.error('Failed to remove guardrail'); - } - }; - - if (loading) { - return ( - - - - - - ); - } - - if (error) { - return ( - - {error} - - ); - } - - return ( - - Attached Guardrails - {guardrails.length === 0 ? ( - No guardrails attached to this virtual key - ) : ( - - {guardrails.map((guardrail) => ( - - - {guardrail.guardrail_name || 'Unnamed Guardrail'} - Type: {guardrail.litellm_params?.guardrail} - Mode: {guardrail.litellm_params?.mode} - - - - ))} - - )} - - ); -}; diff --git a/ui/litellm-dashboard/src/services/guardrails.ts b/ui/litellm-dashboard/src/services/guardrails.ts deleted file mode 100644 index 2cb7b80a4b1..00000000000 --- a/ui/litellm-dashboard/src/services/guardrails.ts +++ /dev/null @@ -1,27 +0,0 @@ -import { Guardrail } from '../types/guardrail'; - -interface GuardrailsResponse { - guardrails: Guardrail[]; -} - -export async function fetchGuardrails(): Promise { - const response = await fetch('/api/guardrails'); - - if (!response.ok) { - const errorText = await response.text(); - throw new Error(`Failed to fetch guardrails: ${errorText}`); - } - - return await response.json(); -} - -export async function fetchGuardrail(guardrailId: string): Promise { - const response = await fetch(`/api/guardrails/${guardrailId}`); - - if (!response.ok) { - const errorText = await response.text(); - throw new Error(`Failed to fetch guardrail: ${errorText}`); - } - - return await response.json(); -} diff --git a/ui/litellm-dashboard/src/services/virtual_key_guardrails.ts b/ui/litellm-dashboard/src/services/virtual_key_guardrails.ts deleted file mode 100644 index de7ce30d890..00000000000 --- a/ui/litellm-dashboard/src/services/virtual_key_guardrails.ts +++ /dev/null @@ -1,57 +0,0 @@ -import { Guardrail } from '../types/guardrail'; - -interface VirtualKeyGuardrailsResponse { - virtual_key_id: string; - guardrails: Guardrail[]; -} - -export async function fetchGuardrailsForVirtualKey(virtualKeyId: string): Promise { - const response = await fetch(`/api/guardrails/virtual_key/${virtualKeyId}`); - - if (!response.ok) { - const errorText = await response.text(); - throw new Error(`Failed to fetch guardrails: ${errorText}`); - } - - return await response.json(); -} - -export async function associateGuardrailWithVirtualKey(virtualKeyId: string, guardrailId: string): Promise<{ message: string }> { - const response = await fetch('/api/guardrails/virtual_key/associate', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - virtual_key_id: virtualKeyId, - guardrail_id: guardrailId, - }), - }); - - if (!response.ok) { - const errorText = await response.text(); - throw new Error(`Failed to associate guardrail: ${errorText}`); - } - - return await response.json(); -} - -export async function disassociateGuardrailFromVirtualKey(virtualKeyId: string, guardrailId: string): Promise<{ message: string }> { - const response = await fetch('/api/guardrails/virtual_key/disassociate', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - virtual_key_id: virtualKeyId, - guardrail_id: guardrailId, - }), - }); - - if (!response.ok) { - const errorText = await response.text(); - throw new Error(`Failed to disassociate guardrail: ${errorText}`); - } - - return await response.json(); -} diff --git a/ui/litellm-dashboard/src/types/guardrail.ts b/ui/litellm-dashboard/src/types/guardrail.ts deleted file mode 100644 index 6cb26d04973..00000000000 --- a/ui/litellm-dashboard/src/types/guardrail.ts +++ /dev/null @@ -1,11 +0,0 @@ -export interface Guardrail { - guardrail_id: string; - guardrail_name: string | null; - litellm_params: { - guardrail: string; - mode: string; - default_on: boolean; - [key: string]: any; - }; - guardrail_info?: Record; -} diff --git a/ui/litellm-dashboard/src/types/virtual_key.ts b/ui/litellm-dashboard/src/types/virtual_key.ts deleted file mode 100644 index e130853b1d1..00000000000 --- a/ui/litellm-dashboard/src/types/virtual_key.ts +++ /dev/null @@ -1,13 +0,0 @@ -export interface VirtualKey { - key_id: string; - key_name?: string; - team_id?: string; - models?: string[]; - spend?: number; - max_budget?: number; - max_parallel_requests?: number; - metadata?: Record; - expires?: string; - created_at?: string; - updated_at?: string; -} From 5649c0f0b99e3a627bd55c7d158bdff2639ec987 Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Mon, 1 Sep 2025 10:45:46 -0400 Subject: [PATCH 040/178] Revert "feat: add persistence and management for guardrails on virtual keys" This reverts commit 6cd5afa8b11ea3654a2c26d3d64cd2a2e3eff3f9. --- .../proxy/guardrails/endpoints.py | 70 +------------------ .../proxy/guardrails/guardrail_registry.py | 30 -------- 2 files changed, 3 insertions(+), 97 deletions(-) diff --git a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py index fcd5e82ab3a..cdf86dcea67 100644 --- a/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py +++ b/enterprise/litellm_enterprise/proxy/guardrails/endpoints.py @@ -6,28 +6,16 @@ To see all free guardrails see litellm/proxy/guardrails/* Exposed Routes: - /mask_pii -- /virtual_key/guardrails """ -from typing import Dict, List, Optional +from typing import Optional -from fastapi import APIRouter, Depends, HTTPException -from pydantic import BaseModel +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.proxy.guardrails.guardrail_registry import IN_MEMORY_GUARDRAIL_HANDLER -from litellm.types.guardrails import ApplyGuardrailRequest, ApplyGuardrailResponse, Guardrail - -# Models for virtual key guardrail management -class VirtualKeyGuardrailRequest(BaseModel): - virtual_key_id: str - guardrail_id: str - -class VirtualKeyGuardrailsResponse(BaseModel): - virtual_key_id: str - guardrails: List[Guardrail] +from litellm.types.guardrails import ApplyGuardrailRequest, ApplyGuardrailResponse router = APIRouter(tags=["guardrails"], prefix="/guardrails") @@ -51,55 +39,3 @@ async def apply_guardrail( return await active_guardrail.apply_guardrail( text=request.text, language=request.language, entities=request.entities ) - -@router.post("/virtual_key/associate", response_model=Dict[str, str]) -async def associate_guardrail_with_virtual_key( - request: VirtualKeyGuardrailRequest, - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), -): - """ - Associate a guardrail with a virtual key - """ - # Check if guardrail exists - guardrail = IN_MEMORY_GUARDRAIL_HANDLER.get_guardrail_by_id(request.guardrail_id) - if not guardrail: - raise HTTPException(status_code=404, detail=f"Guardrail {request.guardrail_id} not found") - - # Associate guardrail with virtual key - IN_MEMORY_GUARDRAIL_HANDLER.associate_guardrail_with_virtual_key( - virtual_key_id=request.virtual_key_id, - guardrail_id=request.guardrail_id - ) - - return {"message": f"Guardrail {request.guardrail_id} associated with virtual key {request.virtual_key_id}"} - -@router.post("/virtual_key/disassociate", response_model=Dict[str, str]) -async def disassociate_guardrail_from_virtual_key( - request: VirtualKeyGuardrailRequest, - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), -): - """ - Disassociate a guardrail from a virtual key - """ - # Disassociate guardrail from virtual key - IN_MEMORY_GUARDRAIL_HANDLER.disassociate_guardrail_from_virtual_key( - virtual_key_id=request.virtual_key_id, - guardrail_id=request.guardrail_id - ) - - return {"message": f"Guardrail {request.guardrail_id} disassociated from virtual key {request.virtual_key_id}"} - -@router.get("/virtual_key/{virtual_key_id}", response_model=VirtualKeyGuardrailsResponse) -async def get_guardrails_for_virtual_key( - virtual_key_id: str, - user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), -): - """ - Get all guardrails associated with a virtual key - """ - guardrails = IN_MEMORY_GUARDRAIL_HANDLER.get_guardrails_for_virtual_key(virtual_key_id) - - return VirtualKeyGuardrailsResponse( - virtual_key_id=virtual_key_id, - guardrails=guardrails - ) diff --git a/litellm/proxy/guardrails/guardrail_registry.py b/litellm/proxy/guardrails/guardrail_registry.py index 523ff9f4cd8..21429f462d4 100644 --- a/litellm/proxy/guardrails/guardrail_registry.py +++ b/litellm/proxy/guardrails/guardrail_registry.py @@ -369,11 +369,6 @@ class InMemoryGuardrailHandler: """ Guardrail id to CustomGuardrail object mapping """ - - self.virtual_key_to_guardrails: Dict[str, List[str]] = {} - """ - Virtual key id to list of guardrail ids mapping - """ def initialize_guardrail( self, @@ -543,31 +538,6 @@ class InMemoryGuardrailHandler: Get a guardrail by its ID from memory """ return self.IN_MEMORY_GUARDRAILS.get(guardrail_id) - - def associate_guardrail_with_virtual_key(self, virtual_key_id: str, guardrail_id: str) -> None: - """ - Associate a guardrail with a virtual key - """ - if virtual_key_id not in self.virtual_key_to_guardrails: - self.virtual_key_to_guardrails[virtual_key_id] = [] - - if guardrail_id not in self.virtual_key_to_guardrails[virtual_key_id]: - self.virtual_key_to_guardrails[virtual_key_id].append(guardrail_id) - - def disassociate_guardrail_from_virtual_key(self, virtual_key_id: str, guardrail_id: str) -> None: - """ - Disassociate a guardrail from a virtual key - """ - if virtual_key_id in self.virtual_key_to_guardrails: - if guardrail_id in self.virtual_key_to_guardrails[virtual_key_id]: - self.virtual_key_to_guardrails[virtual_key_id].remove(guardrail_id) - - def get_guardrails_for_virtual_key(self, virtual_key_id: str) -> List[Guardrail]: - """ - Get all guardrails associated with a virtual key - """ - guardrail_ids = self.virtual_key_to_guardrails.get(virtual_key_id, []) - return [self.IN_MEMORY_GUARDRAILS[gid] for gid in guardrail_ids if gid in self.IN_MEMORY_GUARDRAILS] ######################################################## From 2626123aca31e6ed7c59208c460376c35f7ba251 Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Mon, 1 Sep 2025 11:33:34 -0400 Subject: [PATCH 041/178] added litellm_spend_metric --- .../litellm_enterprise/integrations/prometheus.py | 11 ++--------- litellm/types/integrations/prometheus.py | 11 +++++++++++ 2 files changed, 13 insertions(+), 9 deletions(-) diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index 742f2dbbe9b..efee1a7783e 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -110,15 +110,7 @@ class PrometheusLogger(CustomLogger): self.litellm_spend_metric = self._counter_factory( "litellm_spend_metric", "Total spend on LLM requests", - labelnames=[ - "end_user", - "hashed_api_key", - "api_key_alias", - "model", - "team", - "team_alias", - "user", - ], + labelnames=self.get_labels_for_metric("litellm_spend_metric"), ) # Counter for total_output_tokens @@ -321,6 +313,7 @@ class PrometheusLogger(CustomLogger): documentation="deprecated - use litellm_proxy_total_requests_metric. Total number of LLM calls to litellm - track total per API Key, team, user", labelnames=self.get_labels_for_metric("litellm_requests_metric"), ) + except Exception as e: print_verbose(f"Got exception on init prometheus client {str(e)}") raise e diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py index 4ab0ccf5022..4637c05d144 100644 --- a/litellm/types/integrations/prometheus.py +++ b/litellm/types/integrations/prometheus.py @@ -163,6 +163,7 @@ DEFINED_PROMETHEUS_METRICS = Literal[ "litellm_proxy_failed_requests_metric", "litellm_deployment_latency_per_output_token", "litellm_requests_metric", + "litellm_spend_metric", "litellm_total_tokens_metric", "litellm_input_tokens_metric", "litellm_output_tokens_metric", @@ -290,6 +291,16 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.USER_EMAIL.value, ] + litellm_spend_metric = [ + UserAPIKeyLabelNames.END_USER.value, + UserAPIKeyLabelNames.API_KEY_HASH.value, + UserAPIKeyLabelNames.API_KEY_ALIAS.value, + UserAPIKeyLabelNames.v1_LITELLM_MODEL_NAME.value, + UserAPIKeyLabelNames.TEAM.value, + UserAPIKeyLabelNames.TEAM_ALIAS.value, + UserAPIKeyLabelNames.USER.value, + ] + litellm_input_tokens_metric = [ UserAPIKeyLabelNames.END_USER.value, UserAPIKeyLabelNames.API_KEY_HASH.value, From 2622a0634a4f8c59db87ac307923063202a6b72b Mon Sep 17 00:00:00 2001 From: Ivan Krakhmaliuk Date: Mon, 1 Sep 2025 19:51:54 +0300 Subject: [PATCH 042/178] GPT-5: Remove params unsupported with reasoning models --- litellm/llms/openai/chat/gpt_5_transformation.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/litellm/llms/openai/chat/gpt_5_transformation.py b/litellm/llms/openai/chat/gpt_5_transformation.py index 9a8bb74d447..3902304a3b4 100644 --- a/litellm/llms/openai/chat/gpt_5_transformation.py +++ b/litellm/llms/openai/chat/gpt_5_transformation.py @@ -28,7 +28,18 @@ class OpenAIGPT5Config(OpenAIGPTConfig): base_gpt_series_params.extend(gpt_5_only_params) if not supports_tool_choice(model=model): base_gpt_series_params.remove("tool_choice") - return base_gpt_series_params + + non_supported_params = [ + "logprobs", + "top_p", + "presence_penalty", + "frequency_penalty", + "top_logprobs", + ] + + return [ + param for param in base_gpt_series_params if param not in non_supported_params + ] def map_openai_params( self, From 76dd9d81100dbda65384cb6030c9c63fc0f676ee Mon Sep 17 00:00:00 2001 From: Ivan Krakhmaliuk Date: Mon, 1 Sep 2025 20:30:43 +0300 Subject: [PATCH 043/178] GPT-5: Test for unsupported params --- .../llms/openai/test_gpt5_transformation.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tests/test_litellm/llms/openai/test_gpt5_transformation.py b/tests/test_litellm/llms/openai/test_gpt5_transformation.py index 3bdab355977..3e6a6a23468 100644 --- a/tests/test_litellm/llms/openai/test_gpt5_transformation.py +++ b/tests/test_litellm/llms/openai/test_gpt5_transformation.py @@ -41,3 +41,14 @@ def test_gpt5_temperature_error(config: OpenAIConfig): model="gpt-5", drop_params=False, ) + + +def test_gpt5_unsupported_params_drop(config: OpenAIConfig): + assert "top_p" not in config.get_supported_openai_params(model="gpt-5") + params = config.map_openai_params( + non_default_params={"top_p": 0.5}, + optional_params={}, + model="gpt-5", + drop_params=True, + ) + assert "top_p" not in params From 736bee813e8b5124456f11a357f7e590bb5a7e78 Mon Sep 17 00:00:00 2001 From: Byron Grogan Date: Mon, 1 Sep 2025 17:46:31 -0600 Subject: [PATCH 044/178] fix: Support AWS_BEDROCK_RUNTIME_ENDPOINT on bedrock passthrough, make work for URLs with a base path --- .../base_llm/passthrough/transformation.py | 22 +-- .../bedrock/passthrough/transformation.py | 10 +- ...test_bedrock_passthrough_transformation.py | 177 ++++++++++++++++++ 3 files changed, 194 insertions(+), 15 deletions(-) create mode 100644 tests/test_litellm/llms/bedrock/passthrough/test_bedrock_passthrough_transformation.py diff --git a/litellm/llms/base_llm/passthrough/transformation.py b/litellm/llms/base_llm/passthrough/transformation.py index 60d89c1610f..f925e6819dc 100644 --- a/litellm/llms/base_llm/passthrough/transformation.py +++ b/litellm/llms/base_llm/passthrough/transformation.py @@ -31,30 +31,26 @@ class BasePassthroughConfig(BaseLLMModelInfo): Args: endpoint: str - the endpoint to add to the url base_target_url: str - the base url to add the endpoint to - request_query_params: dict - the query params to add to the url + request_query_params: Optional[dict] - the query params to add to the url Returns: - str - the formatted url + httpx.URL - the formatted url """ from urllib.parse import urlencode import httpx - encoded_endpoint = httpx.URL(endpoint).path + base = base_target_url.rstrip('/') + endpoint = endpoint.lstrip('/') + full_url = f"{base}/{endpoint}" - # Ensure endpoint starts with '/' for proper URL construction - if not encoded_endpoint.startswith("/"): - encoded_endpoint = "/" + encoded_endpoint - - # Construct the full target URL using httpx - base_url = httpx.URL(base_target_url) - updated_url = base_url.copy_with(path=encoded_endpoint) + url = httpx.URL(full_url) if request_query_params: - # Create a new URL with the merged query params - updated_url = updated_url.copy_with( + url = url.copy_with( query=urlencode(request_query_params).encode("ascii") ) - return updated_url + + return url @abstractmethod def get_complete_url( diff --git a/litellm/llms/bedrock/passthrough/transformation.py b/litellm/llms/bedrock/passthrough/transformation.py index d7221ff4b7a..5791bfb8013 100644 --- a/litellm/llms/bedrock/passthrough/transformation.py +++ b/litellm/llms/bedrock/passthrough/transformation.py @@ -41,9 +41,15 @@ class BedrockPassthroughConfig( model_id=None, ) - api_base = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" + aws_bedrock_runtime_endpoint = optional_params.get("aws_bedrock_runtime_endpoint") + endpoint_url, _ = self.get_runtime_endpoint( + api_base=api_base, + aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + aws_region_name=aws_region_name, + endpoint_type="runtime", + ) - return self.format_url(endpoint, api_base, request_query_params or {}), api_base + return self.format_url(endpoint, endpoint_url, request_query_params or {}), endpoint_url def sign_request( self, diff --git a/tests/test_litellm/llms/bedrock/passthrough/test_bedrock_passthrough_transformation.py b/tests/test_litellm/llms/bedrock/passthrough/test_bedrock_passthrough_transformation.py new file mode 100644 index 00000000000..7cb1ee2b54a --- /dev/null +++ b/tests/test_litellm/llms/bedrock/passthrough/test_bedrock_passthrough_transformation.py @@ -0,0 +1,177 @@ +import os +import sys +from unittest.mock import patch + +sys.path.insert( + 0, os.path.abspath("../../../../..") +) # Adds the parent directory to the system path + +from litellm.llms.bedrock.passthrough.transformation import BedrockPassthroughConfig + + +def test_bedrock_passthrough_get_complete_url_default_endpoint(): + """Test get_complete_url with default AWS endpoint (no override)""" + config = BedrockPassthroughConfig() + + # Mock the methods following the pattern from test_base_aws_llm.py + with patch.object(config, '_get_aws_region_name', return_value="us-east-1"), \ + patch.object(config, 'get_runtime_endpoint', return_value=( + "https://bedrock-runtime.us-east-1.amazonaws.com", + "https://bedrock-runtime.us-east-1.amazonaws.com" + )) as mock_get_runtime: + + url, api_base = config.get_complete_url( + api_base=None, + api_key=None, + model="anthropic.claude-3-sonnet", + endpoint="/model/anthropic.claude-3-sonnet/invoke", + request_query_params=None, + litellm_params={} + ) + + # Verify get_runtime_endpoint was called with correct parameters + mock_get_runtime.assert_called_once_with( + api_base=None, + aws_bedrock_runtime_endpoint=None, + aws_region_name="us-east-1", + endpoint_type="runtime" + ) + + # Verify URL construction + assert str(url) == "https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-3-sonnet/invoke" + assert api_base == "https://bedrock-runtime.us-east-1.amazonaws.com" + + +def test_bedrock_passthrough_get_complete_url_custom_endpoint_no_path(): + """Test get_complete_url with custom endpoint (no base path)""" + config = BedrockPassthroughConfig() + + with patch.object(config, '_get_aws_region_name', return_value="us-west-2"), \ + patch.object(config, 'get_runtime_endpoint', return_value=( + "http://proxy.com", + "http://proxy.com" + )) as mock_get_runtime: + + url, api_base = config.get_complete_url( + api_base="http://proxy.com", + api_key=None, + model="anthropic.claude-3-sonnet", + endpoint="/model/anthropic.claude-3-sonnet/invoke", + request_query_params=None, + litellm_params={} + ) + + # Verify get_runtime_endpoint was called with the api_base + mock_get_runtime.assert_called_once_with( + api_base="http://proxy.com", + aws_bedrock_runtime_endpoint=None, + aws_region_name="us-west-2", + endpoint_type="runtime" + ) + + # Verify URL construction + assert str(url) == "http://proxy.com/model/anthropic.claude-3-sonnet/invoke" + assert api_base == "http://proxy.com" + + +def test_bedrock_passthrough_get_complete_url_custom_endpoint_with_path(): + """Test get_complete_url with custom endpoint that has a base path""" + config = BedrockPassthroughConfig() + + with patch.object(config, '_get_aws_region_name', return_value="us-west-2"), \ + patch.object(config, 'get_runtime_endpoint', return_value=( + "http://proxy.com/bedrockproxy", + "http://proxy.com/bedrockproxy" + )) as mock_get_runtime: + + url, api_base = config.get_complete_url( + api_base="http://proxy.com/bedrockproxy", + api_key=None, + model="anthropic.claude-3-sonnet", + endpoint="/model/anthropic.claude-3-sonnet/invoke", + request_query_params=None, + litellm_params={ + "aws_bedrock_runtime_endpoint": "http://proxy.com/bedrockproxy" + } + ) + + # Verify get_runtime_endpoint was called with correct parameters + mock_get_runtime.assert_called_once_with( + api_base="http://proxy.com/bedrockproxy", + aws_bedrock_runtime_endpoint="http://proxy.com/bedrockproxy", + aws_region_name="us-west-2", + endpoint_type="runtime" + ) + + # Verify URL construction preserves the proxy path + assert str(url) == "http://proxy.com/bedrockproxy/model/anthropic.claude-3-sonnet/invoke" + assert api_base == "http://proxy.com/bedrockproxy" + + +def test_format_url_simple_joining(): + """Test format_url with simple URL joining""" + config = BedrockPassthroughConfig() + + result = config.format_url( + endpoint="model/test/invoke", + base_target_url="https://api.example.com", + request_query_params={} + ) + + assert str(result) == "https://api.example.com/model/test/invoke" + + +def test_format_url_preserves_proxy_paths(): + """Test format_url preserves proxy paths in base URL""" + config = BedrockPassthroughConfig() + + result = config.format_url( + endpoint="model/test/invoke", + base_target_url="http://proxy.com/bedrockproxy", + request_query_params={} + ) + + # This is the key test - proxy path should be preserved + assert str(result) == "http://proxy.com/bedrockproxy/model/test/invoke" + + +def test_format_url_with_query_parameters(): + """Test format_url properly handles query parameters""" + config = BedrockPassthroughConfig() + + result = config.format_url( + endpoint="model/test/invoke", + base_target_url="http://proxy.com/bedrockproxy", + request_query_params={"param1": "value1", "param2": "value2"} + ) + + # Should preserve proxy path and add query params + result_str = str(result) + assert "http://proxy.com/bedrockproxy/model/test/invoke" in result_str + assert "param1=value1" in result_str + assert "param2=value2" in result_str + + +def test_format_url_handles_trailing_slash_normalization(): + """Test format_url properly handles base URLs with and without trailing slashes""" + config = BedrockPassthroughConfig() + + # Test with trailing slash + result_with_slash = config.format_url( + endpoint="model/test/invoke", + base_target_url="http://proxy.com/bedrockproxy/", + request_query_params={} + ) + + # Test without trailing slash + result_without_slash = config.format_url( + endpoint="model/test/invoke", + base_target_url="http://proxy.com/bedrockproxy", + request_query_params={} + ) + + # Both should produce the same result + assert str(result_with_slash) == str(result_without_slash) + assert str(result_with_slash) == "http://proxy.com/bedrockproxy/model/test/invoke" + + From 4a422ca897b0bc7a7cf878266ac9dab892500f32 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Mon, 1 Sep 2025 17:38:15 -0700 Subject: [PATCH 045/178] fix: support logging dynamic metadata values to braintrust --- litellm/integrations/braintrust_logging.py | 18 +- .../integrations/test_braintrust_span_name.py | 162 ++++++++++-------- 2 files changed, 106 insertions(+), 74 deletions(-) diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index 1ddf789ed32..c362b513281 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -159,7 +159,7 @@ class BraintrustLogger(CustomLogger): output = response_obj["data"] litellm_params = kwargs.get("litellm_params", {}) or {} - dynamic_metadata = litellm_params.get("dynamic_metadata", {}) or {} + dynamic_metadata = litellm_params.get("metadata", {}) or {} # Get project_id from metadata or create default if needed project_id = dynamic_metadata.get("project_id") @@ -175,6 +175,7 @@ class BraintrustLogger(CustomLogger): project_id = self.default_project_id tags = [] + if isinstance(dynamic_metadata, dict): for key, value in dynamic_metadata.items(): # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy @@ -185,6 +186,11 @@ class BraintrustLogger(CustomLogger): ): tags.append(f"{key}:{value}") + if ( + isinstance(value, str) and key not in standard_logging_object + ): # support logging dynamic metadata to braintrust + standard_logging_object[key] = value + cost = kwargs.get("response_cost", None) metrics: Optional[dict] = None @@ -265,9 +271,7 @@ class BraintrustLogger(CustomLogger): output = response_obj["data"] litellm_params = kwargs.get("litellm_params", {}) - dynamic_metadata = litellm_params.get("dynamic_metadata", {}) or {} - - clean_metadata = {} + dynamic_metadata = litellm_params.get("metadata", {}) or {} # Get project_id from metadata or create default if needed project_id = dynamic_metadata.get("project_id") @@ -285,6 +289,7 @@ class BraintrustLogger(CustomLogger): project_id = self.default_project_id tags = [] + if isinstance(dynamic_metadata, dict): for key, value in dynamic_metadata.items(): # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy @@ -295,6 +300,11 @@ class BraintrustLogger(CustomLogger): ): tags.append(f"{key}:{value}") + if ( + isinstance(value, str) and key not in standard_logging_object + ): # support logging dynamic metadata to braintrust + standard_logging_object[key] = value + cost = kwargs.get("response_cost", None) metrics: Optional[dict] = None diff --git a/tests/test_litellm/integrations/test_braintrust_span_name.py b/tests/test_litellm/integrations/test_braintrust_span_name.py index 10e512fc0ca..30381e99783 100644 --- a/tests/test_litellm/integrations/test_braintrust_span_name.py +++ b/tests/test_litellm/integrations/test_braintrust_span_name.py @@ -11,7 +11,7 @@ from litellm.integrations.braintrust_logging import BraintrustLogger class TestBraintrustSpanName(unittest.TestCase): """Test custom span_name functionality in Braintrust logging.""" - @patch('litellm.integrations.braintrust_logging.HTTPHandler') + @patch("litellm.integrations.braintrust_logging.HTTPHandler") def test_default_span_name(self, MockHTTPHandler): """Test that default span name is 'Chat Completion' when not provided.""" # Mock HTTP response @@ -22,39 +22,43 @@ class TestBraintrustSpanName(unittest.TestCase): # Setup logger = BraintrustLogger(api_key="test-key") logger.default_project_id = "test-project-id" - + # Create a properly structured mock response response_obj = litellm.ModelResponse( id="test-id", object="chat.completion", created=1234567890, model="gpt-3.5-turbo", - choices=[{ - "index": 0, - "message": {"role": "assistant", "content": "test response"}, - "finish_reason": "stop" - }], - usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30} + choices=[ + { + "index": 0, + "message": {"role": "assistant", "content": "test response"}, + "finish_reason": "stop", + } + ], + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, ) - + kwargs = { "litellm_call_id": "test-call-id", "messages": [{"role": "user", "content": "test"}], "litellm_params": {"metadata": {}}, "model": "gpt-3.5-turbo", - "response_cost": 0.001 + "response_cost": 0.001, } - + # Execute logger.log_success_event(kwargs, response_obj, datetime.now(), datetime.now()) - + # Verify call_args = mock_http_handler.post.call_args self.assertIsNotNone(call_args) - json_data = call_args.kwargs['json'] - self.assertEqual(json_data['events'][0]['span_attributes']['name'], 'Chat Completion') + json_data = call_args.kwargs["json"] + self.assertEqual( + json_data["events"][0]["span_attributes"]["name"], "Chat Completion" + ) - @patch('litellm.integrations.braintrust_logging.HTTPHandler') + @patch("litellm.integrations.braintrust_logging.HTTPHandler") def test_custom_span_name(self, MockHTTPHandler): """Test that custom span name is used when provided in metadata.""" # Mock HTTP response @@ -65,39 +69,43 @@ class TestBraintrustSpanName(unittest.TestCase): # Setup logger = BraintrustLogger(api_key="test-key") logger.default_project_id = "test-project-id" - + # Create a properly structured mock response response_obj = litellm.ModelResponse( id="test-id", object="chat.completion", created=1234567890, model="gpt-3.5-turbo", - choices=[{ - "index": 0, - "message": {"role": "assistant", "content": "test response"}, - "finish_reason": "stop" - }], - usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30} + choices=[ + { + "index": 0, + "message": {"role": "assistant", "content": "test response"}, + "finish_reason": "stop", + } + ], + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, ) - + kwargs = { "litellm_call_id": "test-call-id", "messages": [{"role": "user", "content": "test"}], "litellm_params": {"metadata": {"span_name": "Custom Operation"}}, "model": "gpt-3.5-turbo", - "response_cost": 0.001 + "response_cost": 0.001, } - + # Execute logger.log_success_event(kwargs, response_obj, datetime.now(), datetime.now()) - + # Verify call_args = mock_http_handler.post.call_args self.assertIsNotNone(call_args) - json_data = call_args.kwargs['json'] - self.assertEqual(json_data['events'][0]['span_attributes']['name'], 'Custom Operation') + json_data = call_args.kwargs["json"] + self.assertEqual( + json_data["events"][0]["span_attributes"]["name"], "Custom Operation" + ) - @patch('litellm.integrations.braintrust_logging.HTTPHandler') + @patch("litellm.integrations.braintrust_logging.HTTPHandler") def test_span_name_with_other_metadata(self, MockHTTPHandler): """Test that span_name works alongside other metadata fields.""" # Mock HTTP response @@ -108,21 +116,23 @@ class TestBraintrustSpanName(unittest.TestCase): # Setup logger = BraintrustLogger(api_key="test-key") logger.default_project_id = "test-project-id" - + # Create a properly structured mock response response_obj = litellm.ModelResponse( id="test-id", object="chat.completion", created=1234567890, model="gpt-3.5-turbo", - choices=[{ - "index": 0, - "message": {"role": "assistant", "content": "test response"}, - "finish_reason": "stop" - }], - usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30} + choices=[ + { + "index": 0, + "message": {"role": "assistant", "content": "test response"}, + "finish_reason": "stop", + } + ], + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, ) - + kwargs = { "litellm_call_id": "test-call-id", "messages": [{"role": "user", "content": "test"}], @@ -132,34 +142,40 @@ class TestBraintrustSpanName(unittest.TestCase): "project_id": "custom-project", "user_id": "user123", "session_id": "session456", - "environment": "production" + "environment": "production", } }, "model": "gpt-3.5-turbo", - "response_cost": 0.001 + "response_cost": 0.001, + "standard_logging_object": { + "user_id": "user123", + }, } - + # Execute logger.log_success_event(kwargs, response_obj, datetime.now(), datetime.now()) - + # Verify call_args = mock_http_handler.post.call_args self.assertIsNotNone(call_args) - json_data = call_args.kwargs['json'] - - # Check span name - self.assertEqual(json_data['events'][0]['span_attributes']['name'], 'Multi Metadata Test') - - # Check that other metadata is preserved (except for filtered keys) - event_metadata = json_data['events'][0]['metadata'] - self.assertEqual(event_metadata['user_id'], 'user123') - self.assertEqual(event_metadata['session_id'], 'session456') - self.assertEqual(event_metadata['environment'], 'production') - - # Span name should be in span_attributes, not in metadata - self.assertIn('span_name', event_metadata) # span_name is also kept in metadata + json_data = call_args.kwargs["json"] - @patch('litellm.integrations.braintrust_logging.get_async_httpx_client') + # Check span name + self.assertEqual( + json_data["events"][0]["span_attributes"]["name"], "Multi Metadata Test" + ) + + # Check that other metadata is preserved (except for filtered keys) + event_metadata = json_data["events"][0]["metadata"] + print(event_metadata) + self.assertEqual(event_metadata["user_id"], "user123") + self.assertEqual(event_metadata["session_id"], "session456") + self.assertEqual(event_metadata["environment"], "production") + + # Span name should be in span_attributes, not in metadata + self.assertIn("span_name", event_metadata) # span_name is also kept in metadata + + @patch("litellm.integrations.braintrust_logging.get_async_httpx_client") async def test_async_custom_span_name(self, mock_get_http_handler): """Test async logging with custom span name.""" # Mock async HTTP response @@ -170,38 +186,44 @@ class TestBraintrustSpanName(unittest.TestCase): # Setup logger = BraintrustLogger(api_key="test-key") logger.default_project_id = "test-project-id" - + # Create a properly structured mock response response_obj = litellm.ModelResponse( id="test-id", object="chat.completion", created=1234567890, model="gpt-3.5-turbo", - choices=[{ - "index": 0, - "message": {"role": "assistant", "content": "test response"}, - "finish_reason": "stop" - }], - usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30} + choices=[ + { + "index": 0, + "message": {"role": "assistant", "content": "test response"}, + "finish_reason": "stop", + } + ], + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, ) - + kwargs = { "litellm_call_id": "test-call-id", "messages": [{"role": "user", "content": "test"}], "litellm_params": {"metadata": {"span_name": "Async Custom Operation"}}, "model": "gpt-3.5-turbo", - "response_cost": 0.001 + "response_cost": 0.001, } - + # Execute - await logger.async_log_success_event(kwargs, response_obj, datetime.now(), datetime.now()) - + await logger.async_log_success_event( + kwargs, response_obj, datetime.now(), datetime.now() + ) + # Verify call_args = mock_http_handler.post.call_args self.assertIsNotNone(call_args) - json_data = call_args.kwargs['json'] - self.assertEqual(json_data['events'][0]['span_attributes']['name'], 'Async Custom Operation') + json_data = call_args.kwargs["json"] + self.assertEqual( + json_data["events"][0]["span_attributes"]["name"], "Async Custom Operation" + ) if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() From c54c41f726686b921e6ba771ff70e4bd6e988032 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Mon, 1 Sep 2025 17:38:39 -0700 Subject: [PATCH 046/178] fix: fix ruff errors --- litellm/integrations/braintrust_logging.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py index c362b513281..5bc6afb6dbc 100644 --- a/litellm/integrations/braintrust_logging.py +++ b/litellm/integrations/braintrust_logging.py @@ -1,18 +1,15 @@ # What is this? ## Log success + failure events to Braintrust -import copy import os from datetime import datetime from typing import Dict, Optional import httpx -from pydantic import BaseModel import litellm from litellm import verbose_logger from litellm.integrations.custom_logger import CustomLogger -from litellm.litellm_core_utils.safe_json_dumps import filter_json_serializable from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, get_async_httpx_client, @@ -25,7 +22,6 @@ API_BASE = "https://api.braintrustdata.com/v1" def get_utc_datetime(): import datetime as dt - from datetime import datetime if hasattr(dt, "UTC"): return datetime.now(dt.UTC) # type: ignore From 5d6532419febfdc0be03fbc9e8c0b16c043b034e Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Mon, 1 Sep 2025 17:46:38 -0700 Subject: [PATCH 047/178] refactor: remove unused function --- litellm/litellm_core_utils/safe_json_dumps.py | 91 ------------------- 1 file changed, 91 deletions(-) diff --git a/litellm/litellm_core_utils/safe_json_dumps.py b/litellm/litellm_core_utils/safe_json_dumps.py index b3b1d7fb3df..c714e36b5f9 100644 --- a/litellm/litellm_core_utils/safe_json_dumps.py +++ b/litellm/litellm_core_utils/safe_json_dumps.py @@ -50,94 +50,3 @@ def safe_dumps(data: Any, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH) -> str: safe_data = _serialize(data, set(), 0) return json.dumps(safe_data, default=str) - - -def filter_json_serializable( - data: Any, max_depth: int = DEFAULT_MAX_RECURSE_DEPTH -) -> Any: - """ - Recursively filter data to only include JSON serializable items. - Non-serializable items are completely skipped (not included in the result). - """ - - def _is_json_serializable(obj: Any) -> bool: - """Test if an object is JSON serializable.""" - try: - json.dumps(obj) - return True - except (TypeError, ValueError): - return False - - def _filter(obj: Any, seen: set, depth: int) -> Any: - # Check for maximum depth. - if depth > max_depth: - return None - - # Base-case: if it is a primitive, test if it's serializable - if isinstance(obj, (str, int, float, bool, type(None))): - return obj if _is_json_serializable(obj) else None - - # Check for circular reference. - if id(obj) in seen: - return None - - seen.add(id(obj)) - - try: - if isinstance(obj, dict): - result = {} - for k, v in obj.items(): - # Only include keys that are strings and values that are serializable - if isinstance(k, str): - filtered_value = _filter(v, seen, depth + 1) - # Only add the key-value pair if the value is serializable - if filtered_value is not None or v is None: - if _is_json_serializable(filtered_value): - result[k] = filtered_value - seen.remove(id(obj)) - return result - - elif isinstance(obj, list): - result = [] - for item in obj: - filtered_item = _filter(item, seen, depth + 1) - # Only include items that are serializable - if filtered_item is not None or item is None: - if _is_json_serializable(filtered_item): - result.append(filtered_item) - seen.remove(id(obj)) - return result - - elif isinstance(obj, tuple): - filtered_items = [] - for item in obj: - filtered_item = _filter(item, seen, depth + 1) - # Only include items that are serializable - if filtered_item is not None or item is None: - if _is_json_serializable(filtered_item): - filtered_items.append(filtered_item) - seen.remove(id(obj)) - return tuple(filtered_items) - - elif isinstance(obj, set): - filtered_items = [] - for item in obj: - filtered_item = _filter(item, seen, depth + 1) - # Only include items that are serializable - if filtered_item is not None or item is None: - if _is_json_serializable(filtered_item): - filtered_items.append(filtered_item) - seen.remove(id(obj)) - return sorted(filtered_items) - - else: - # Test if the object is directly serializable - seen.remove(id(obj)) - return obj if _is_json_serializable(obj) else None - - except Exception: - if id(obj) in seen: - seen.remove(id(obj)) - return None - - return _filter(data, set(), 0) From f1f9f2a594e3a169b2b18a8cd781cb10f896a7b6 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Mon, 1 Sep 2025 17:52:22 -0700 Subject: [PATCH 048/178] fix: fix linting error --- litellm/llms/ollama/completion/transformation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 2654d9461ed..5689864017f 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -329,8 +329,8 @@ class OllamaConfig(BaseConfig): model_response.choices[0].finish_reason = "stop" else: response_text = response_json.get("response", "") - content: Optional[str] = None - reasoning_content: Optional[str] = None + content = None + reasoning_content = None if response_text is not None: reasoning_content, content = _parse_content_for_reasoning(response_text) model_response.choices[0].message.content = content # type: ignore From 8e72f991cc95267484e9445a92d2f60cd4fe89e3 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Sep 2025 17:59:40 -0700 Subject: [PATCH 049/178] test_model_alias_map --- tests/local_testing/test_model_alias_map.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/local_testing/test_model_alias_map.py b/tests/local_testing/test_model_alias_map.py index e7d87a2946e..cf731d66283 100644 --- a/tests/local_testing/test_model_alias_map.py +++ b/tests/local_testing/test_model_alias_map.py @@ -35,7 +35,7 @@ def test_model_alias_map(caplog): for log in captured_logs: assert "ERROR" not in log - assert "llama3-8b-8192" in response.model + assert "llama-3.1-8b-instant" in response.model except litellm.ServiceUnavailableError: pass except Exception as e: From 3b524ba5f1084d5efc68dcc2b73f37160d37eace Mon Sep 17 00:00:00 2001 From: retanoj Date: Tue, 2 Sep 2025 09:52:50 +0800 Subject: [PATCH 050/178] format --- litellm/google_genai/adapters/handler.py | 4 ++-- litellm/proxy/google_endpoints/endpoints.py | 10 ++++++---- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/litellm/google_genai/adapters/handler.py b/litellm/google_genai/adapters/handler.py index c15d0cb9deb..ee9083cbbf0 100644 --- a/litellm/google_genai/adapters/handler.py +++ b/litellm/google_genai/adapters/handler.py @@ -38,8 +38,8 @@ class GenerateContentToCompletionHandler: completion_kwargs: Dict[str, Any] = dict(completion_request) # feed metadata for custom callback - if 'metadata' in extra_kwargs: - completion_kwargs['metadata'] = extra_kwargs['metadata'] + if "metadata" in extra_kwargs: + completion_kwargs["metadata"] = extra_kwargs["metadata"] if stream: completion_kwargs["stream"] = stream diff --git a/litellm/proxy/google_endpoints/endpoints.py b/litellm/proxy/google_endpoints/endpoints.py index 4f57e1e7ce8..eb481b0a4f0 100644 --- a/litellm/proxy/google_endpoints/endpoints.py +++ b/litellm/proxy/google_endpoints/endpoints.py @@ -181,9 +181,11 @@ async def google_count_tokens(request: Request, model_name: str): from litellm.proxy._types import TokenCountRequest # Translate contents to openai format messages using the adapter - messages = (GoogleGenAIAdapter() - .translate_generate_content_to_completion(model_name, contents) - .get("messages", [])) + messages = ( + GoogleGenAIAdapter() + .translate_generate_content_to_completion(model_name, contents) + .get("messages", []) + ) token_request = TokenCountRequest( model=model_name, @@ -209,7 +211,7 @@ async def google_count_tokens(request: Request, model_name: str): totalTokens=token_response.total_tokens or 0, promptTokensDetails=[], ) - + ######################################################### # Return the response in the well known format ######################################################### From b1686ecbe5fd6d6bc8590cbe1294389548ff572b Mon Sep 17 00:00:00 2001 From: retanoj Date: Tue, 2 Sep 2025 09:59:31 +0800 Subject: [PATCH 051/178] format --- litellm/google_genai/adapters/handler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/google_genai/adapters/handler.py b/litellm/google_genai/adapters/handler.py index ee9083cbbf0..dcf707ebd51 100644 --- a/litellm/google_genai/adapters/handler.py +++ b/litellm/google_genai/adapters/handler.py @@ -38,7 +38,7 @@ class GenerateContentToCompletionHandler: completion_kwargs: Dict[str, Any] = dict(completion_request) # feed metadata for custom callback - if "metadata" in extra_kwargs: + if extra_kwargs is not None and "metadata" in extra_kwargs: completion_kwargs["metadata"] = extra_kwargs["metadata"] if stream: From 94cac6fff2ffb264ebf3e95d0fcf49fb2a10449c Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Sep 2025 20:08:36 -0700 Subject: [PATCH 052/178] fix: groq health check --- ...odel_prices_and_context_window_backup.json | 21 ------------------- model_prices_and_context_window.json | 21 ------------------- 2 files changed, 42 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9abe3dc48fe..57309554621 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -5817,16 +5817,6 @@ "supports_response_schema": true, "supports_tool_choice": true }, - "groq/llama3-8b-8192": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5e-08, - "output_cost_per_token": 8e-08, - "litellm_provider": "groq", - "mode": "chat", - "supports_tool_choice": true - }, "groq/llama-3.2-1b-preview": { "max_tokens": 8192, "max_input_tokens": 8192, @@ -5907,17 +5897,6 @@ "supports_tool_choice": true, "deprecation_date": "2025-04-14" }, - "groq/llama3-70b-8192": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5.9e-07, - "output_cost_per_token": 7.9e-07, - "litellm_provider": "groq", - "mode": "chat", - "supports_response_schema": true, - "supports_tool_choice": true - }, "groq/llama-3.1-8b-instant": { "max_tokens": 8192, "max_input_tokens": 128000, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 9abe3dc48fe..57309554621 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -5817,16 +5817,6 @@ "supports_response_schema": true, "supports_tool_choice": true }, - "groq/llama3-8b-8192": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5e-08, - "output_cost_per_token": 8e-08, - "litellm_provider": "groq", - "mode": "chat", - "supports_tool_choice": true - }, "groq/llama-3.2-1b-preview": { "max_tokens": 8192, "max_input_tokens": 8192, @@ -5907,17 +5897,6 @@ "supports_tool_choice": true, "deprecation_date": "2025-04-14" }, - "groq/llama3-70b-8192": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5.9e-07, - "output_cost_per_token": 7.9e-07, - "litellm_provider": "groq", - "mode": "chat", - "supports_response_schema": true, - "supports_tool_choice": true - }, "groq/llama-3.1-8b-instant": { "max_tokens": 8192, "max_input_tokens": 128000, From d37be48a80988d52e54f8ca9563663de2d72847b Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Sep 2025 20:14:12 -0700 Subject: [PATCH 053/178] test: llama-3.3-70b-versatile --- tests/litellm_utils_tests/test_utils.py | 2 +- tests/local_testing/test_completion_cost.py | 2 +- tests/test_litellm/test_utils.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/litellm_utils_tests/test_utils.py b/tests/litellm_utils_tests/test_utils.py index 3228bd92189..0818d0b4b08 100644 --- a/tests/litellm_utils_tests/test_utils.py +++ b/tests/litellm_utils_tests/test_utils.py @@ -1054,7 +1054,7 @@ def test_parse_content_for_reasoning(content, expected_reasoning, expected_conte ("gemini/gemini-1.5-pro", True), ("predibase/llama3-8b-instruct", True), ("gpt-3.5-turbo", False), - ("groq/llama3-70b-8192", True), + ("groq/llama-3.3-70b-versatile", True), ], ) def test_supports_response_schema(model, expected_bool): diff --git a/tests/local_testing/test_completion_cost.py b/tests/local_testing/test_completion_cost.py index bf482ca7527..39d4536d7aa 100644 --- a/tests/local_testing/test_completion_cost.py +++ b/tests/local_testing/test_completion_cost.py @@ -565,7 +565,7 @@ def test_groq_response_cost_tracking(is_streaming): response_cost = litellm.response_cost_calculator( response_object=response, - model="groq/llama3-70b-8192", + model="groq/llama-3.3-70b-versatile", custom_llm_provider="groq", call_type=CallTypes.acompletion.value, optional_params={}, diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 49e2c6c1856..ed9a2729282 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -979,8 +979,8 @@ class TestProxyFunctionCalling: # Groq models (mixed support) ("groq/gemma-7b-it", "litellm_proxy/groq/gemma-7b-it", True), ( - "groq/llama3-70b-8192", - "litellm_proxy/groq/llama3-70b-8192", + "groq/llama-3.3-70b-versatile", + "litellm_proxy/groq/llama-3.3-70b-versatile", False, ), # This model doesn't support function calling # Cohere models (generally don't support function calling) From 62f14dece39ccdad1312fa2ced34e20fc01b1726 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Sep 2025 20:21:09 -0700 Subject: [PATCH 054/178] fix: regression added from prometheus_missing_metrics --- litellm/types/integrations/prometheus.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py index 4637c05d144..955c1d888f2 100644 --- a/litellm/types/integrations/prometheus.py +++ b/litellm/types/integrations/prometheus.py @@ -195,6 +195,9 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.API_KEY_ALIAS.value, UserAPIKeyLabelNames.TEAM.value, UserAPIKeyLabelNames.TEAM_ALIAS.value, + UserAPIKeyLabelNames.REQUESTED_MODEL.value, + UserAPIKeyLabelNames.END_USER.value, + UserAPIKeyLabelNames.USER.value, ] litellm_llm_api_time_to_first_token_metric = [ From 20811372d8ae719b7ca6a503f6a84cf468aeabcf Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Sep 2025 20:24:14 -0700 Subject: [PATCH 055/178] Revert "Merge pull request #14126 from TomeHirata/citation-supported-text-2" This reverts commit 3f2881cd6dcff1001c59ef23bcbe10ae64241963, reversing changes made to 04f9ff16c4a6950b960089eca7812099f2100f73. --- litellm/llms/anthropic/chat/transformation.py | 10 +---- .../test_anthropic_completion.py | 18 +++----- .../test_anthropic_chat_transformation.py | 43 ++++++------------- 3 files changed, 18 insertions(+), 53 deletions(-) diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 378ca75da5f..ce874bfde9a 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -797,15 +797,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if content.get("citations") is not None: if citations is None: citations = [] - citations.append( - [ - { - **citation, - "supported_text": content.get("text", ""), - } - for citation in content["citations"] - ] - ) + citations.append(content["citations"]) if thinking_blocks is not None: reasoning_content = "" for block in thinking_blocks: diff --git a/tests/llm_translation/test_anthropic_completion.py b/tests/llm_translation/test_anthropic_completion.py index f4bd7531b0b..45702a261e2 100644 --- a/tests/llm_translation/test_anthropic_completion.py +++ b/tests/llm_translation/test_anthropic_completion.py @@ -920,14 +920,6 @@ def test_anthropic_citations_api(): citations = resp.choices[0].message.provider_specific_fields["citations"] assert citations is not None - if citations: - citation = citations[0][0] - assert "supported_text" in citation - assert "cited_text" in citation - assert "document_index" in citation - assert "document_title" in citation - assert "start_char_index" in citation - assert "end_char_index" in citation def test_anthropic_citations_api_streaming(): @@ -963,11 +955,11 @@ def test_anthropic_citations_api_streaming(): has_citations = False for chunk in resp: print(f"returned chunk: {chunk}") - if provider_specific_fields := chunk.choices[0].delta.provider_specific_fields: - if "citation" in provider_specific_fields: - has_citations = True - - assert "chunk_type" in provider_specific_fields + if ( + chunk.choices[0].delta.provider_specific_fields + and "citation" in chunk.choices[0].delta.provider_specific_fields + ): + has_citations = True assert has_citations diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py index dcca87baf32..ff454968d9c 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py @@ -115,11 +115,16 @@ def test_calculate_usage_nulls(usage_object, expected_usage): assert hasattr(usage, k) assert getattr(usage, k) == v - -@pytest.mark.parametrize( - "usage_object", - [{"server_tool_use": {"web_search_requests": None}}, {"server_tool_use": None}], -) +@pytest.mark.parametrize("usage_object", [ + { + "server_tool_use": { + "web_search_requests": None + } + }, + { + "server_tool_use": None + } +]) def test_calculate_usage_server_tool_null(usage_object): """ Correctly deal with null values in usage object @@ -127,11 +132,10 @@ def test_calculate_usage_server_tool_null(usage_object): Fixes https://github.com/BerriAI/litellm/issues/11920 """ config = AnthropicConfig() - + usage = config.calculate_usage(usage_object=usage_object, reasoning_content=None) assert not hasattr(usage, "server_tool_use") - def test_extract_response_content_with_citations(): config = AnthropicConfig() @@ -184,30 +188,7 @@ def test_extract_response_content_with_citations(): } _, citations, _, _, _ = config.extract_response_content(completion_response) - assert citations == [ - [ - { - "type": "char_location", - "cited_text": "The grass is green. ", - "document_index": 0, - "document_title": "My Document", - "start_char_index": 0, - "end_char_index": 20, - "supported_text": "the grass is green", - }, - ], - [ - { - "type": "char_location", - "cited_text": "The sky is blue.", - "document_index": 0, - "document_title": "My Document", - "start_char_index": 20, - "end_char_index": 36, - "supported_text": "the sky is blue", - }, - ], - ] + assert citations is not None def test_map_tool_helper(): From 5ab3d74371e4549effaaed3e34ee6e15d711e0fc Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Sep 2025 20:34:23 -0700 Subject: [PATCH 056/178] DEFAULT_NUM_WORKERS_LITELLM_PROXY --- .circleci/config.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index faf43ff0b8b..b1c630cdcb3 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1477,6 +1477,7 @@ jobs: docker run -d \ -p 4000:4000 \ -e DATABASE_URL=$PROXY_DATABASE_URL \ + -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ -e DISABLE_SCHEMA_UPDATE="True" \ -v $(pwd)/litellm/proxy/example_config_yaml/bad_schema.prisma:/app/schema.prisma \ -v $(pwd)/litellm/proxy/example_config_yaml/bad_schema.prisma:/app/litellm/proxy/schema.prisma \ @@ -2962,6 +2963,7 @@ jobs: command: | docker run --name my-app \ -p 4000:4000 \ + -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ -e DATABASE_URL="postgresql://wrong:wrong@wrong:5432/wrong" \ myapp:latest \ --port 4000 > docker_output.log 2>&1 || true From ae3931f5f3a233b75c1ebb8990355f28d970e542 Mon Sep 17 00:00:00 2001 From: Yuji Arakawa Date: Tue, 2 Sep 2025 18:32:45 +0900 Subject: [PATCH 057/178] FIx https://github.com/BerriAI/litellm/issues/14158 --- litellm/llms/oci/chat/transformation.py | 16 +- .../llms/oci/chat/test_transformation.py | 229 ++++++++++++++++++ 2 files changed, 237 insertions(+), 8 deletions(-) create mode 100644 tests/test_litellm/llms/oci/chat/test_transformation.py diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 915d2029afe..3be373ca5e5 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -772,7 +772,14 @@ def adapt_messages_to_generic_oci_standard( tool_calls = message.get("tool_calls") tool_call_id = message.get("tool_call_id") - if role in ["system", "user", "assistant"] and content is not None: + if role == "assistant" and tool_calls is not None: + if not isinstance(tool_calls, list): + raise Exception("Prop `tool_calls` must be a list of tool calls") + new_messages.append( + adapt_messages_to_generic_oci_standard_tool_call(role, tool_calls) + ) + + elif role in ["system", "user", "assistant"] and content is not None: if not isinstance(content, (str, list)): raise Exception( "Prop `content` must be a string or a list of content items" @@ -781,13 +788,6 @@ def adapt_messages_to_generic_oci_standard( adapt_messages_to_generic_oci_standard_content_message(role, content) ) - elif role == "assistant" and tool_calls is not None: - if not isinstance(tool_calls, list): - raise Exception("Prop `tool_calls` must be a list of tool calls") - new_messages.append( - adapt_messages_to_generic_oci_standard_tool_call(role, tool_calls) - ) - elif role == "tool": if not isinstance(tool_call_id, str): raise Exception("Prop `tool_call_id` is required and must be a string") diff --git a/tests/test_litellm/llms/oci/chat/test_transformation.py b/tests/test_litellm/llms/oci/chat/test_transformation.py new file mode 100644 index 00000000000..950c1fcb4c9 --- /dev/null +++ b/tests/test_litellm/llms/oci/chat/test_transformation.py @@ -0,0 +1,229 @@ +import pytest +from litellm.llms.oci.chat.transformation import adapt_messages_to_generic_oci_standard + +def test_adapt_messages_with_empty_content_and_tool_calls(): + """Test that assistant messages with empty content and tool_calls are processed correctly.""" + # Arrange + messages_with_empty_content = [ + {"role": "user", "content": "Tell me the weather in Tokyo."}, + { + "role": "assistant", + "content": "", # Empty string + "tool_calls": [ + { + "id": "call_test_empty", + "type": "function", + "function": { + "name": "get_weather", + "arguments": '{"city": "Tokyo"}' + } + } + ] + }, + { + "role": "tool", + "content": '{"weather": "Sunny", "temperature": "25°C"}', + "tool_call_id": "call_test_empty" + } + ] + + # Act + result = adapt_messages_to_generic_oci_standard(messages_with_empty_content) + + # Assert + assert len(result) == 3 + + # Check user message + assert result[0].role == "USER" + assert result[0].content[0].type == "TEXT" + assert result[0].content[0].text == "Tell me the weather in Tokyo." + + # Check assistant message with tool_calls (should prioritize tool_calls over empty content) + assert result[1].role == "ASSISTANT" + assert result[1].toolCalls is not None + assert len(result[1].toolCalls) == 1 + assert result[1].toolCalls[0].id == "call_test_empty" + assert result[1].toolCalls[0].name == "get_weather" + + # Check tool response message + assert result[2].role == "TOOL" # Tool responses have TOOL role, not USER + assert result[2].content[0].type == "TEXT" + assert "weather" in result[2].content[0].text + assert result[2].toolCallId == "call_test_empty" # Tool call ID is in separate field + +def test_adapt_messages_with_none_content_and_tool_calls(): + """Test that assistant messages with None content and tool_calls are processed correctly.""" + # Arrange + messages_with_none_content = [ + {"role": "user", "content": "Tell me the weather in Tokyo."}, + { + "role": "assistant", + "content": None, # None value + "tool_calls": [ + { + "id": "call_test_none", + "type": "function", + "function": { + "name": "get_weather", + "arguments": '{"city": "Tokyo"}' + } + } + ] + }, + { + "role": "tool", + "content": '{"weather": "Sunny", "temperature": "25°C"}', + "tool_call_id": "call_test_none" + } + ] + + # Act + result = adapt_messages_to_generic_oci_standard(messages_with_none_content) + + # Assert + assert len(result) == 3 + + # Check assistant message prioritizes tool_calls over None content + assert result[1].role == "ASSISTANT" + assert result[1].toolCalls is not None + assert len(result[1].toolCalls) == 1 + assert result[1].toolCalls[0].id == "call_test_none" + +def test_adapt_messages_with_tool_calls_only(): + """Test that assistant messages with only tool_calls (no content field) are processed correctly.""" + # Arrange + messages_no_content = [ + {"role": "user", "content": "Tell me the weather in Tokyo."}, + { + "role": "assistant", + # No content field at all + "tool_calls": [ + { + "id": "call_test_no_content", + "type": "function", + "function": { + "name": "get_weather", + "arguments": '{"city": "Tokyo"}' + } + } + ] + }, + { + "role": "tool", + "content": '{"weather": "Sunny", "temperature": "25°C"}', + "tool_call_id": "call_test_no_content" + } + ] + + # Act + result = adapt_messages_to_generic_oci_standard(messages_no_content) + + # Assert + assert len(result) == 3 + + # Check assistant message processes tool_calls correctly + assert result[1].role == "ASSISTANT" + assert result[1].toolCalls is not None + assert len(result[1].toolCalls) == 1 + assert result[1].toolCalls[0].id == "call_test_no_content" + +def test_adapt_messages_with_content_only(): + """Test that assistant messages with only content (no tool_calls) are processed correctly.""" + # Arrange + messages_content_only = [ + {"role": "user", "content": "Hello"}, + { + "role": "assistant", + "content": "Hello! How can I help you today?" + } + ] + + # Act + result = adapt_messages_to_generic_oci_standard(messages_content_only) + + # Assert + assert len(result) == 2 + + # Check assistant message with content only + assert result[1].role == "ASSISTANT" + assert result[1].content[0].type == "TEXT" + assert result[1].content[0].text == "Hello! How can I help you today?" + assert result[1].toolCalls is None + +def test_adapt_messages_tool_id_tracking(): + """Test that tool call IDs are properly tracked for validation.""" + # Arrange + messages = [ + {"role": "user", "content": "Test"}, + { + "role": "assistant", + "tool_calls": [ + { + "id": "call_123", + "type": "function", + "function": { + "name": "test_func", + "arguments": '{"param": "value"}' + } + } + ] + }, + { + "role": "tool", + "content": "Result", + "tool_call_id": "call_123" + } + ] + + # Act + result = adapt_messages_to_generic_oci_standard(messages) + + # Assert + # Tool call should be processed and ID should be available for validation + assert result[1].toolCalls[0].id == "call_123" + + # Tool response should reference the same ID + tool_response_text = result[2].content[0].text + # Tool response text is just the content, tool_call_id is separate + assert tool_response_text == "Result" # The actual content + assert result[2].toolCallId == "call_123" # Tool call ID is in separate field + +def test_adapt_messages_multiple_tool_calls(): + """Test that multiple tool calls in a single message are processed correctly.""" + # Arrange + messages = [ + {"role": "user", "content": "Test multiple tools"}, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "call_1", + "type": "function", + "function": { + "name": "func1", + "arguments": '{"param": "value1"}' + } + }, + { + "id": "call_2", + "type": "function", + "function": { + "name": "func2", + "arguments": '{"param": "value2"}' + } + } + ] + } + ] + + # Act + result = adapt_messages_to_generic_oci_standard(messages) + + # Assert + assert len(result) == 2 + assert result[1].role == "ASSISTANT" + assert len(result[1].toolCalls) == 2 + assert result[1].toolCalls[0].id == "call_1" + assert result[1].toolCalls[1].id == "call_2" + From c7109609fd03095ad4ecb3e2bddc2b12be5537a3 Mon Sep 17 00:00:00 2001 From: Yuji Arakawa Date: Tue, 2 Sep 2025 19:11:37 +0900 Subject: [PATCH 058/178] renamed unit test code --- ...ransformation.py => test_oci_chat_transformation_for_14158.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/test_litellm/llms/oci/chat/{test_transformation.py => test_oci_chat_transformation_for_14158.py} (100%) diff --git a/tests/test_litellm/llms/oci/chat/test_transformation.py b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation_for_14158.py similarity index 100% rename from tests/test_litellm/llms/oci/chat/test_transformation.py rename to tests/test_litellm/llms/oci/chat/test_oci_chat_transformation_for_14158.py From 51f44c0419bf338c8104d08a13ec62835d45a5b7 Mon Sep 17 00:00:00 2001 From: tanjiro <56165694+NANDINI-star@users.noreply.github.com> Date: Tue, 2 Sep 2025 19:26:09 +0900 Subject: [PATCH 059/178] limit to 20 teams and make it expandable after that --- .../components/view_users/user_info_view.tsx | 133 +++++++++++++----- 1 file changed, 99 insertions(+), 34 deletions(-) diff --git a/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx b/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx index 823baaf64ef..59d2b8e3867 100644 --- a/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx +++ b/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx @@ -1,6 +1,6 @@ import React, { useState } from "react" import { Card, Text, Button, Grid, Col, Tab, TabList, TabGroup, TabPanel, TabPanels, Title, Badge } from "@tremor/react" -import { ArrowLeftIcon, TrashIcon, RefreshIcon } from "@heroicons/react/outline" +import { ArrowLeftIcon, TrashIcon, RefreshIcon, ChevronDownIcon, ChevronUpIcon } from "@heroicons/react/outline" import { userInfoCall, userDeleteCall, @@ -14,9 +14,9 @@ import { rolesWithWriteAccess } from "../../utils/roles" import { UserEditView } from "../user_edit_view" import OnboardingModal, { InvitationLink } from "../onboarding_link" import { formatNumberWithCommas, copyToClipboard as utilCopyToClipboard } from "@/utils/dataUtils" -import { CopyIcon, CheckIcon } from "lucide-react"; -import NotificationsManager from "../molecules/notifications_manager"; -import { getBudgetDurationLabel } from "../common_components/budget_duration_dropdown"; +import { CopyIcon, CheckIcon } from "lucide-react" +import NotificationsManager from "../molecules/notifications_manager" +import { getBudgetDurationLabel } from "../common_components/budget_duration_dropdown" interface UserInfoViewProps { userId: string @@ -57,16 +57,17 @@ export default function UserInfoView({ initialTab = 0, startInEditMode = false, }: UserInfoViewProps) { - const [userData, setUserData] = useState(null); - const [isDeleteModalOpen, setIsDeleteModalOpen] = useState(false); - const [isLoading, setIsLoading] = useState(true); - const [isEditing, setIsEditing] = useState(startInEditMode); - const [userModels, setUserModels] = useState([]); - const [isInvitationLinkModalVisible, setIsInvitationLinkModalVisible] = useState(false); - const [invitationLinkData, setInvitationLinkData] = useState(null); - const [baseUrl, setBaseUrl] = useState(null); - const [activeTab, setActiveTab] = useState(initialTab); - const [copiedStates, setCopiedStates] = useState>({}); + const [userData, setUserData] = useState(null) + const [isDeleteModalOpen, setIsDeleteModalOpen] = useState(false) + const [isLoading, setIsLoading] = useState(true) + const [isEditing, setIsEditing] = useState(startInEditMode) + const [userModels, setUserModels] = useState([]) + const [isInvitationLinkModalVisible, setIsInvitationLinkModalVisible] = useState(false) + const [invitationLinkData, setInvitationLinkData] = useState(null) + const [baseUrl, setBaseUrl] = useState(null) + const [activeTab, setActiveTab] = useState(initialTab) + const [copiedStates, setCopiedStates] = useState>({}) + const [isTeamsExpanded, setIsTeamsExpanded] = useState(false) React.useEffect(() => { setBaseUrl(getProxyBaseUrl()) @@ -175,14 +176,14 @@ export default function UserInfoView({ } const copyToClipboard = async (text: string, key: string) => { - const success = await utilCopyToClipboard(text); + const success = await utilCopyToClipboard(text) if (success) { - setCopiedStates((prev) => ({ ...prev, [key]: true })); + setCopiedStates((prev) => ({ ...prev, [key]: true })) setTimeout(() => { - setCopiedStates((prev) => ({ ...prev, [key]: false })); - }, 2000); + setCopiedStates((prev) => ({ ...prev, [key]: false })) + }, 2000) } - }; + } return (
@@ -200,9 +201,9 @@ export default function UserInfoView({ icon={copiedStates["user-id"] ? : } onClick={() => copyToClipboard(userData.user_id, "user-id")} className={`left-2 z-10 transition-all duration-200 ${ - copiedStates["user-id"] - ? 'text-green-600 bg-green-50 border-green-200' - : 'text-gray-500 hover:text-gray-700 hover:bg-gray-100' + copiedStates["user-id"] + ? "text-green-600 bg-green-50 border-green-200" + : "text-gray-500 hover:text-gray-700 hover:bg-gray-100" }`} />
@@ -282,15 +283,44 @@ export default function UserInfoView({ - Teams +
+ Teams + {userData.teams?.length && userData.teams?.length > 20 && ( + + )} +
{userData.teams?.length && userData.teams?.length > 0 ? (
- {userData.teams?.map((team, index) => ( - + {userData.teams?.slice(0, isTeamsExpanded ? userData.teams.length : 20).map((team, index) => ( + {team.team_alias} ))} + {!isTeamsExpanded && userData.teams?.length > 20 && ( +
+ + +{userData.teams.length - 20} more + +
+
+ {userData.teams?.slice(20).map((team, index) => ( +
+ {team.team_alias} +
+ ))} +
+
+
+
+ )}
) : ( No teams @@ -354,9 +384,9 @@ export default function UserInfoView({ icon={copiedStates["user-id"] ? : } onClick={() => copyToClipboard(userData.user_id, "user-id")} className={`left-2 z-10 transition-all duration-200 ${ - copiedStates["user-id"] - ? 'text-green-600 bg-green-50 border-green-200' - : 'text-gray-500 hover:text-gray-700 hover:bg-gray-100' + copiedStates["user-id"] + ? "text-green-600 bg-green-50 border-green-200" + : "text-gray-500 hover:text-gray-700 hover:bg-gray-100" }`} />
@@ -391,14 +421,49 @@ export default function UserInfoView({
- Teams +
+ Teams + {userData.teams?.length && userData.teams?.length > 20 && ( + + )} +
{userData.teams?.length && userData.teams?.length > 0 ? ( - userData.teams?.map((team, index) => ( - - {team.team_alias || team.team_id} - - )) + <> + {userData.teams?.slice(0, isTeamsExpanded ? userData.teams.length : 20).map((team, index) => ( + + {team.team_alias || team.team_id} + + ))} + {!isTeamsExpanded && userData.teams?.length > 20 && ( +
+ + +{userData.teams.length - 20} more + +
+
+ {userData.teams?.slice(20).map((team, index) => ( +
+ {team.team_alias || team.team_id} +
+ ))} +
+
+
+
+ )} + ) : ( No teams )} From 80970951c500ec1a61bfa8546d9fd3d472a08c9f Mon Sep 17 00:00:00 2001 From: tanjiro <56165694+NANDINI-star@users.noreply.github.com> Date: Tue, 2 Sep 2025 19:36:06 +0900 Subject: [PATCH 060/178] fix ui for expandable badge --- .../components/view_users/user_info_view.tsx | 90 +++++++------------ 1 file changed, 33 insertions(+), 57 deletions(-) diff --git a/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx b/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx index 59d2b8e3867..c36bde78a7d 100644 --- a/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx +++ b/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx @@ -1,6 +1,6 @@ import React, { useState } from "react" import { Card, Text, Button, Grid, Col, Tab, TabList, TabGroup, TabPanel, TabPanels, Title, Badge } from "@tremor/react" -import { ArrowLeftIcon, TrashIcon, RefreshIcon, ChevronDownIcon, ChevronUpIcon } from "@heroicons/react/outline" +import { ArrowLeftIcon, TrashIcon, RefreshIcon } from "@heroicons/react/outline" import { userInfoCall, userDeleteCall, @@ -283,19 +283,7 @@ export default function UserInfoView({ -
- Teams - {userData.teams?.length && userData.teams?.length > 20 && ( - - )} -
+ Teams
{userData.teams?.length && userData.teams?.length > 0 ? (
@@ -305,21 +293,22 @@ export default function UserInfoView({ ))} {!isTeamsExpanded && userData.teams?.length > 20 && ( -
- - +{userData.teams.length - 20} more - -
-
- {userData.teams?.slice(20).map((team, index) => ( -
- {team.team_alias} -
- ))} -
-
-
-
+ setIsTeamsExpanded(true)} + > + +{userData.teams.length - 20} more + + )} + {isTeamsExpanded && userData.teams?.length > 20 && ( + setIsTeamsExpanded(false)} + > + Show Less + )}
) : ( @@ -421,19 +410,7 @@ export default function UserInfoView({
-
- Teams - {userData.teams?.length && userData.teams?.length > 20 && ( - - )} -
+ Teams
{userData.teams?.length && userData.teams?.length > 0 ? ( <> @@ -447,21 +424,20 @@ export default function UserInfoView({ ))} {!isTeamsExpanded && userData.teams?.length > 20 && ( -
- - +{userData.teams.length - 20} more - -
-
- {userData.teams?.slice(20).map((team, index) => ( -
- {team.team_alias || team.team_id} -
- ))} -
-
-
-
+ setIsTeamsExpanded(true)} + > + +{userData.teams.length - 20} more + + )} + {isTeamsExpanded && userData.teams?.length > 20 && ( + setIsTeamsExpanded(false)} + > + Show Less + )} ) : ( From 61b2209827fa5d65f6cc588db2f4369984a8dd5f Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Sep 2025 07:22:37 -0700 Subject: [PATCH 061/178] test_proxy_function_calling_support_consistency --- tests/test_litellm/test_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index ed9a2729282..9e487939284 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -981,8 +981,8 @@ class TestProxyFunctionCalling: ( "groq/llama-3.3-70b-versatile", "litellm_proxy/groq/llama-3.3-70b-versatile", - False, - ), # This model doesn't support function calling + True, + ), # Cohere models (generally don't support function calling) ("command-nightly", "litellm_proxy/command-nightly", False), ], From 793c9668733982104eb8342fb045640676a4d26b Mon Sep 17 00:00:00 2001 From: Marcelo Mendoza Date: Tue, 2 Sep 2025 14:36:57 +0000 Subject: [PATCH 062/178] feat: added alert type to alert messate to slack for easier handling on slack side. --- .../SlackAlerting/slack_alerting.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 41db4a551bd..0953fb768cb 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -805,9 +805,9 @@ class SlackAlerting(CustomBatchLogger): ### UNIQUE CACHE KEY ### cache_key = provider + region_name - outage_value: Optional[ProviderRegionOutageModel] = ( - await self.internal_usage_cache.async_get_cache(key=cache_key) - ) + outage_value: Optional[ + ProviderRegionOutageModel + ] = await self.internal_usage_cache.async_get_cache(key=cache_key) if ( getattr(exception, "status_code", None) is None @@ -1367,12 +1367,11 @@ Model Info: # Get the current timestamp current_time = datetime.now().strftime("%H:%M:%S") _proxy_base_url = os.getenv("PROXY_BASE_URL", None) + alert_type_formatted = f"Alert type: `{alert_type}`\n" if alert_type == "daily_reports" or alert_type == "new_model_added": - formatted_message = message + formatted_message = alert_type_formatted + message else: - formatted_message = ( - f"Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" - ) + formatted_message = f"{alert_type_formatted}\n Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" if kwargs: for key, value in kwargs.items(): @@ -1388,9 +1387,9 @@ Model Info: self.alert_to_webhook_url is not None and alert_type in self.alert_to_webhook_url ): - slack_webhook_url: Optional[Union[str, List[str]]] = ( - self.alert_to_webhook_url[alert_type] - ) + slack_webhook_url: Optional[ + Union[str, List[str]] + ] = self.alert_to_webhook_url[alert_type] elif self.default_webhook_url is not None: slack_webhook_url = self.default_webhook_url else: From 92f631d57c8821c899004822e6a6aaa9d79f8f55 Mon Sep 17 00:00:00 2001 From: Marcelo Mendoza Date: Tue, 2 Sep 2025 15:12:11 +0000 Subject: [PATCH 063/178] fix: correct formatting of alert messages in Slack notifications --- .../SlackAlerting/slack_alerting.py | 2 +- .../SlackAlerting/test_slack_alerting.py | 24 +++++++++++++++++++ 2 files changed, 25 insertions(+), 1 deletion(-) diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 0953fb768cb..6d705642667 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -1371,7 +1371,7 @@ Model Info: if alert_type == "daily_reports" or alert_type == "new_model_added": formatted_message = alert_type_formatted + message else: - formatted_message = f"{alert_type_formatted}\n Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" + formatted_message = f"{alert_type_formatted}\nLevel: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" if kwargs: for key, value in kwargs.items(): diff --git a/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py b/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py index d389be79618..7e1bd897908 100644 --- a/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py +++ b/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py @@ -172,3 +172,27 @@ class TestSlackAlerting(unittest.TestCase): self.slack_alerting.update_values(alerting_args={"slack_alerting": "True"}) assert self.slack_alerting.periodic_started == True + + @patch("litellm.integrations.SlackAlerting.slack_alerting.datetime") + def test_alert_type_in_formatted_message(self, mock_datetime): + # Setup mocks + mock_datetime.now.return_value.strftime.return_value = "12:34:56" + + # Import required types + from litellm.types.integrations.slack_alerting import AlertType + + # Create a simple test message to check formatting + alert_type = AlertType.llm_exceptions + level = "Medium" + message = "Test alert message" + current_time = "12:34:56" + + # Test the specific formatting logic we're interested in + alert_type_formatted = f"Alert type: `{alert_type}`\n" + formatted_message = f"{alert_type_formatted}\n Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" + + # Verify alert_type is in the formatted message as expected + self.assertIn("Alert type: `AlertType.llm_exceptions`", formatted_message) + self.assertIn("Level: `Medium`", formatted_message) + self.assertIn("Timestamp: `12:34:56`", formatted_message) + self.assertIn("Message: Test alert message", formatted_message) From 5dcdbb35dadb0fb2b3592e380fa2830c3486c4be Mon Sep 17 00:00:00 2001 From: Keith Decker Date: Tue, 2 Sep 2025 09:20:25 -0600 Subject: [PATCH 064/178] add metrics and logs (events) with semconv attributes --- docs/my-website/docs/proxy/config_settings.md | 4 + litellm/integrations/opentelemetry.py | 325 ++++++++++++++-- .../open_telemetry/data/captured_kwargs.json | 1 + .../data/captured_response.json | 1 + .../integrations/test_opentelemetry.py | 354 +++++++++++++++++- 5 files changed, 643 insertions(+), 42 deletions(-) create mode 100644 tests/test_litellm/integrations/open_telemetry/data/captured_kwargs.json create mode 100644 tests/test_litellm/integrations/open_telemetry/data/captured_response.json diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 541dc6fb3c8..7eb355d39f2 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -573,6 +573,10 @@ router_settings: | LITELLM_LOCAL_MODEL_COST_MAP | Local configuration for model cost mapping in LiteLLM | LITELLM_LOG | Enable detailed logging for LiteLLM | LITELLM_LOG_FILE | File path to write LiteLLM logs to. When set, logs will be written to both console and the specified file +| LITELLM_LOGGER_NAME | Name for OTEL logger +| LITELLM_METER_NAME | Name for OTEL Meter +| LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS | Optionally enable semantic logs for OTEL +| LITELLM_OTEL_INTEGRATION_ENABLE_METRICS | Optionally enable emantic metrics for OTEL | LITELLM_MASTER_KEY | Master key for proxy authentication | LITELLM_MODE | Operating mode for LiteLLM (e.g., production, development) | LITELLM_RATE_LIMIT_WINDOW_SIZE | Rate limit window size for LiteLLM. Default is 60 diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 22ab3092901..e6f265ded58 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -15,6 +15,8 @@ from litellm.types.utils import ( StandardLoggingPayload, ) +# OpenTelemetry imports moved to individual functions to avoid import errors when not installed + if TYPE_CHECKING: from opentelemetry.sdk.trace.export import SpanExporter as _SpanExporter from opentelemetry.trace import Context as _Context @@ -41,6 +43,8 @@ else: Context = Any LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm") +LITELLM_METER_NAME = os.getenv("LITELLM_METER_NAME", "litellm") +LITELLM_LOGGER_NAME = os.getenv("LITELLM_LOGGER_NAME", "litellm") # Remove the hardcoded LITELLM_RESOURCE dictionary - we'll create it properly later RAW_REQUEST_SPAN_NAME = "raw_gen_ai_request" LITELLM_REQUEST_SPAN_NAME = "litellm_request" @@ -83,6 +87,8 @@ class OpenTelemetryConfig: exporter: Union[str, SpanExporter] = "console" endpoint: Optional[str] = None headers: Optional[str] = None + enable_metrics: bool = False + enable_events: bool = False @classmethod def from_env(cls): @@ -104,6 +110,14 @@ class OpenTelemetryConfig: headers = os.getenv( "OTEL_EXPORTER_OTLP_HEADERS", os.getenv("OTEL_HEADERS") ) # example: OTEL_HEADERS=x-honeycomb-team=B85YgLm96***" + enable_metrics: bool = ( + os.getenv("LITELLM_OTEL_INTEGRATION_ENABLE_METRICS", "false").lower() + == "true" + ) + enable_events: bool = ( + os.getenv("LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS", "false").lower() + == "true" + ) if exporter == "in_memory": return cls(exporter=InMemorySpanExporter()) @@ -111,6 +125,8 @@ class OpenTelemetryConfig: exporter=exporter, endpoint=endpoint, headers=headers, # example: OTEL_HEADERS=x-honeycomb-team=B85YgLm96***" + enable_metrics=enable_metrics, + enable_events=enable_events, ) @@ -119,27 +135,22 @@ class OpenTelemetry(CustomLogger): self, config: Optional[OpenTelemetryConfig] = None, callback_name: Optional[str] = None, + # injection points for testing + tracer_provider: Optional[Any] = None, + logger_provider: Optional[Any] = None, + meter_provider: Optional[Any] = None, **kwargs, ): - from opentelemetry import trace - from opentelemetry.sdk.trace import TracerProvider - from opentelemetry.trace import SpanKind if config is None: config = OpenTelemetryConfig.from_env() self.config = config + self.callback_name = callback_name self.OTEL_EXPORTER = self.config.exporter self.OTEL_ENDPOINT = self.config.endpoint self.OTEL_HEADERS = self.config.headers - provider = TracerProvider(resource=_get_litellm_resource()) - provider.add_span_processor(self._get_span_processor()) - self.callback_name = callback_name - - trace.set_tracer_provider(provider) - self.tracer = trace.get_tracer(LITELLM_TRACER_NAME) - - self.span_kind = SpanKind + self._init_tracing(tracer_provider) _debug_otel = str(os.getenv("DEBUG_OTEL", "False")).lower() @@ -156,6 +167,8 @@ class OpenTelemetry(CustomLogger): # init CustomLogger params super().__init__(**kwargs) + self._init_metrics(meter_provider) + self._init_logs(logger_provider) self._init_otel_logger_on_litellm_proxy() def _init_otel_logger_on_litellm_proxy(self): @@ -178,14 +191,109 @@ class OpenTelemetry(CustomLogger): litellm.service_callback.append("otel") setattr(proxy_server, "open_telemetry_logger", self) + def _init_tracing(self, tracer_provider): + from opentelemetry import trace + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.trace import SpanKind + + # use provided tracer or create a new one + if tracer_provider is None: + tracer_provider = TracerProvider(resource=_get_litellm_resource()) + # Only add OTLP span processor if we created the tracer provider ourselves + tracer_provider.add_span_processor(self._get_span_processor()) + + # register global provider and grab our tracer + trace.set_tracer_provider(tracer_provider) + self.tracer = trace.get_tracer(LITELLM_TRACER_NAME) + self.span_kind = SpanKind + + def _init_metrics(self, meter_provider): + if not self.config.enable_metrics: + self._operation_duration_histogram = None + self._token_usage_histogram = None + self._cost_histogram = None + return + + from opentelemetry import metrics + from opentelemetry.sdk.metrics import Histogram, MeterProvider + + # Only create OTLP infrastructure if no custom meter provider is provided + if meter_provider is None: + from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import ( + OTLPMetricExporter, + ) + from opentelemetry.sdk.metrics.export import ( + AggregationTemporality, + PeriodicExportingMetricReader, + ) + + _metric_exporter = OTLPMetricExporter( + endpoint=self.config.endpoint, + headers=OpenTelemetry._get_headers_dictionary(self.config.headers), + preferred_temporality={Histogram: AggregationTemporality.DELTA}, + ) + _metric_reader = PeriodicExportingMetricReader( + _metric_exporter, export_interval_millis=10000 + ) + + meter_provider = MeterProvider( + metric_readers=[_metric_reader], resource=_get_litellm_resource() + ) + meter = meter_provider.get_meter(__name__) + else: + # Use the provided meter provider as-is, without creating additional OTLP infrastructure + meter = meter_provider.get_meter(__name__) + + metrics.set_meter_provider(meter_provider) + + self._operation_duration_histogram = meter.create_histogram( + name="gen_ai.client.operation.duration", # Replace with semconv constant in otel 1.38 + description="GenAI operation duration", + unit="s", + ) + self._token_usage_histogram = meter.create_histogram( + name="gen_ai.client.token.usage", # Replace with semconv constant in otel 1.38 + description="GenAI token usage", + unit="{token}", + ) + self._cost_histogram = meter.create_histogram( + name="gen_ai.client.token.cost", + description="GenAI request cost", + unit="USD", + ) + + def _init_logs(self, logger_provider): + # nothing to do if events disabled + if not self.config.enable_events: + return + + from opentelemetry._logs import set_logger_provider + from opentelemetry.exporter.otlp.proto.grpc._log_exporter import OTLPLogExporter + from opentelemetry.sdk._logs import LoggerProvider as OTLoggerProvider + from opentelemetry.sdk._logs.export import BatchLogRecordProcessor + + # set up log pipeline + if logger_provider is None: + logger_provider = OTLoggerProvider() + # Only add OTLP exporter if we created the logger provider ourselves + logger_provider.add_log_record_processor( + BatchLogRecordProcessor( + OTLPLogExporter( + endpoint=self.config.endpoint, + headers=self._get_headers_dictionary(self.config.headers), + ) + ) + ) + set_logger_provider(logger_provider) + def log_success_event(self, kwargs, response_obj, start_time, end_time): - self._handle_sucess(kwargs, response_obj, start_time, end_time) + self._handle_success(kwargs, response_obj, start_time, end_time) def log_failure_event(self, kwargs, response_obj, start_time, end_time): self._handle_failure(kwargs, response_obj, start_time, end_time) async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): - self._handle_sucess(kwargs, response_obj, start_time, end_time) + self._handle_success(kwargs, response_obj, start_time, end_time) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): self._handle_failure(kwargs, response_obj, start_time, end_time) @@ -372,9 +480,9 @@ class OpenTelemetry(CustomLogger): def _get_dynamic_otel_headers_from_kwargs(self, kwargs) -> Optional[dict]: """Extract dynamic headers from kwargs if available.""" - standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( - kwargs.get("standard_callback_dynamic_params") - ) + standard_callback_dynamic_params: Optional[ + StandardCallbackDynamicParams + ] = kwargs.get("standard_callback_dynamic_params") if not standard_callback_dynamic_params: return None @@ -414,50 +522,185 @@ class OpenTelemetry(CustomLogger): # End of Team/Key Based Logging Control Flow ######################################################### - def _handle_sucess(self, kwargs, response_obj, start_time, end_time): - from opentelemetry import trace - from opentelemetry.trace import Status, StatusCode + def _handle_success(self, kwargs, response_obj, start_time, end_time): verbose_logger.debug( "OpenTelemetry Logger: Logging kwargs: %s, OTEL config settings=%s", kwargs, self.config, ) + ctx, parent_span = self._get_span_context(kwargs) + + # 1. Primary span + span = self._start_primary_span(kwargs, response_obj, start_time, end_time, ctx) + + # 2. Raw‐request sub-span (if enabled) + self._maybe_log_raw_request(kwargs, response_obj, start_time, end_time, span) + + # 3. Guardrail span + self._create_guardrail_span(kwargs=kwargs, context=ctx) + + # 4. Metrics & cost recording + self._record_metrics(kwargs, response_obj, start_time, end_time) + + # 5. Semantic logs. + if self.config.enable_events: + self._emit_semantic_logs(kwargs, response_obj, span) + + # 6. End parent span + if parent_span is not None: + parent_span.end(end_time=self._to_ns(datetime.now())) + + def _start_primary_span(self, kwargs, response_obj, start_time, end_time, context): + from opentelemetry.trace import Status, StatusCode - _parent_context, parent_otel_span = self._get_span_context(kwargs) - # Span 1: Request sent to litellm SDK otel_tracer: Tracer = self.get_tracer_to_use_for_request(kwargs) span = otel_tracer.start_span( name=self._get_span_name(kwargs), start_time=self._to_ns(start_time), - context=_parent_context, + context=context, ) span.set_status(Status(StatusCode.OK)) self.set_attributes(span, kwargs, response_obj) + span.end(end_time=self._to_ns(end_time)) + return span - if litellm.turn_off_message_logging is True: - pass - elif self.message_logging is not True: - pass - else: - # Span 2: Raw Request / Response to LLM - raw_request_span = otel_tracer.start_span( - name=RAW_REQUEST_SPAN_NAME, - start_time=self._to_ns(start_time), - context=trace.set_span_in_context(span), + def _maybe_log_raw_request( + self, kwargs, response_obj, start_time, end_time, parent_span + ): + from opentelemetry import trace + from opentelemetry.trace import Status, StatusCode + + # only log raw LLM request/response if message_logging is on and not globally turned off + if litellm.turn_off_message_logging or not self.message_logging: + return + + otel_tracer: Tracer = self.get_tracer_to_use_for_request(kwargs) + raw_span = otel_tracer.start_span( + name=RAW_REQUEST_SPAN_NAME, + start_time=self._to_ns(start_time), + context=trace.set_span_in_context(parent_span), + ) + raw_span.set_status(Status(StatusCode.OK)) + self.set_raw_request_attributes(raw_span, kwargs, response_obj) + raw_span.end(end_time=self._to_ns(end_time)) + + def _record_metrics(self, kwargs, response_obj, start_time, end_time): + duration_s = (end_time - start_time).total_seconds() + params = kwargs.get("litellm_params") or {} + provider = params.get("custom_llm_provider", "Unknown") + + common_attrs = { + "gen_ai.operation.name": "chat", + "gen_ai.system": provider, + "gen_ai.request.model": kwargs.get("model"), + "gen_ai.framework": "litellm", + } + + std_log = kwargs.get("standard_logging_object") + md = getattr(std_log, "metadata", None) or (std_log or {}).get("metadata", {}) + for key in [ + "user_api_key_hash", + "user_api_key_alias", + "user_api_key_team_id", + "user_api_key_org_id", + "user_api_key_user_id", + "user_api_key_team_alias", + "user_api_key_user_email", + "spend_logs_metadata", + "requester_ip_address", + "requester_metadata", + "user_api_key_end_user_id", + "prompt_management_metadata", + "applied_guardrails", + "mcp_tool_call_metadata", + "vector_store_request_metadata", + ]: + if md.get(key) is not None: + common_attrs[f"metadata.{key}"] = str(md[key]) + + if self._operation_duration_histogram: + self._operation_duration_histogram.record( + duration_s, attributes=common_attrs + ) + if ( + response_obj + and (usage := response_obj.get("usage")) + and self._token_usage_histogram + ): + in_attrs = {**common_attrs, "gen_ai.token.type": "input"} + out_attrs = {**common_attrs, "gen_ai.token.type": "completion"} + self._token_usage_histogram.record( + usage.get("prompt_tokens", 0), attributes=in_attrs + ) + self._token_usage_histogram.record( + usage.get("completion_tokens", 0), attributes=out_attrs + ) + + cost = kwargs.get("response_cost") + if self._cost_histogram and cost: + self._cost_histogram.record(cost, attributes=common_attrs) + + def _emit_semantic_logs(self, kwargs, response_obj, span: Span): + if not self.config.enable_events: + return + + from opentelemetry._logs import get_logger, LogRecord + otel_logger = get_logger(LITELLM_LOGGER_NAME) + + parent_ctx = span.get_span_context() + provider = (kwargs.get("litellm_params") or {}).get( + "custom_llm_provider", "Unknown" + ) + + # per-message events + for msg in kwargs.get("messages", []): + role = msg.get("role", "user") + attrs = {"event_name": "gen_ai.content.prompt", "gen_ai.system": provider} + if role == "tool" and msg.get("id"): + attrs["id"] = msg["id"] + if self.message_logging and msg.get("content"): + attrs["gen_ai.prompt"] = msg["content"] + + otel_logger.emit( + LogRecord( + attributes=attrs, + body=msg.copy(), + trace_id=parent_ctx.trace_id, + span_id=parent_ctx.span_id, + trace_flags=parent_ctx.trace_flags, + ) ) - raw_request_span.set_status(Status(StatusCode.OK)) - self.set_raw_request_attributes(raw_request_span, kwargs, response_obj) - raw_request_span.end(end_time=self._to_ns(end_time)) + # per-choice events + for idx, choice in enumerate(response_obj.get("choices", [])): + attrs = { + "event_name": "gen_ai.content.completion", + "gen_ai.system": provider, + "index": idx, + "finish_reason": choice.get("finish_reason"), + } + body_msg = choice.get("message", {}) + if self.message_logging and body_msg.get("content"): + attrs["message.content"] = body_msg["content"] + body = { + "index": idx, + "finish_reason": choice.get("finish_reason"), + "message": {"role": body_msg.get("role", "assistant")}, + } + if self.message_logging and body_msg.get("content"): + body["message"]["content"] = body_msg["content"] - span.end(end_time=self._to_ns(end_time)) + otel_logger.emit( + LogRecord( + attributes=attrs, + body=body, + trace_id=parent_ctx.trace_id, + span_id=parent_ctx.span_id, + trace_flags=parent_ctx.trace_flags, + ) + ) - # Create span for guardrail information - self._create_guardrail_span(kwargs=kwargs, context=_parent_context) - - if parent_otel_span is not None: - parent_otel_span.end(end_time=self._to_ns(datetime.now())) def _create_guardrail_span( self, kwargs: Optional[dict], context: Optional[Context] diff --git a/tests/test_litellm/integrations/open_telemetry/data/captured_kwargs.json b/tests/test_litellm/integrations/open_telemetry/data/captured_kwargs.json new file mode 100644 index 00000000000..913e3bfedae --- /dev/null +++ b/tests/test_litellm/integrations/open_telemetry/data/captured_kwargs.json @@ -0,0 +1 @@ +{"litellm_trace_id": null, "litellm_call_id": "dbecd23a-e71a-49cf-90d4-712a8a8e29c5", "input": [{"role": "user", "content": "What is the capital of France?"}], "litellm_params": {"acompletion": true, "api_key": null, "force_timeout": 600, "logger_fn": null, "verbose": false, "custom_llm_provider": "bedrock", "api_base": "https://bedrock-runtime.us-west-2.amazonaws.com/model/arn%3Aaws%3Abedrock%3Aus-west-2%3A1234567890123%3Ainference-profile%2Fus.anthropic.claude-3-7-sonnet-20250219-v1%3A0/converse", "litellm_call_id": "dbecd23a-e71a-49cf-90d4-712a8a8e29c5", "model_alias_map": {}, "completion_call_id": null, "aembedding": null, "metadata": {"requester_metadata": {}, "user_api_key_hash": "unused-for-aws-bedrock", "user_api_key_alias": null, "user_api_key_team_id": null, "user_api_key_user_id": null, "user_api_key_org_id": null, "user_api_key_team_alias": null, "user_api_key_end_user_id": null, 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Paris has been the capital city of France since 987 CE when Hugh Capet, the first king of the Capetian dynasty, made the city his seat of government. 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Setup + otel = OpenTelemetry() + otel.tracer = MagicMock() + mock_span = MagicMock() + otel.tracer.start_span.return_value = mock_span + + # Create guardrail information + guardrail_info = { + "guardrail_name": "test_guardrail", + "guardrail_mode": "input", + "masked_entity_count": {"CREDIT_CARD": 2}, + "guardrail_response": "filtered_content", + "start_time": 1609459200.0, + "end_time": 1609459201.0, + } + + # Create a kwargs dict with standard_logging_object containing guardrail information + kwargs = {"standard_logging_object": {"guardrail_information": guardrail_info}} + + # Call the method + otel._create_guardrail_span(kwargs=kwargs, context=None) + + # Assertions + otel.tracer.start_span.assert_called_once() + + # print all calls to mock_span.set_attribute + print("Calls to mock_span.set_attribute:") + for call in mock_span.set_attribute.call_args_list: + print(call) + + # Check that the span has the correct attributes set + mock_span.set_attribute.assert_any_call("guardrail_name", "test_guardrail") + mock_span.set_attribute.assert_any_call("guardrail_mode", "input") + mock_span.set_attribute.assert_any_call( + "guardrail_response", "filtered_content" + ) + mock_span.set_attribute.assert_any_call( + "masked_entity_count", safe_dumps({"CREDIT_CARD": 2}) + ) + + # Verify that the span was ended + mock_span.end.assert_called_once() + + def test_create_guardrail_span_with_no_info(self): + # Setup + otel = OpenTelemetry() + otel.tracer = MagicMock() + + # Test with no guardrail information + kwargs = {"standard_logging_object": {}} + otel._create_guardrail_span(kwargs=kwargs, context=None) + + # Verify that start_span was never called + otel.tracer.start_span.assert_not_called() + class TestOpenTelemetry(unittest.TestCase): + POLL_INTERVAL = 0.05 + POLL_TIMEOUT = 2.0 + MODEL = "arn:aws:bedrock:us-west-2:1234567890123:inference-profile/us.anthropic.claude-3-7-sonnet-20250219-v1:0" + HERE = os.path.dirname(__file__) + + def wait_for_spans(self, exporter: InMemorySpanExporter, prefix: str): + """Poll until we see at least one span with an attribute key starting with `prefix`.""" + deadline = time.time() + self.POLL_TIMEOUT + while time.time() < deadline: + spans = exporter.get_finished_spans() + matches = [ + s + for s in spans + if s.attributes and any(str(k).startswith(prefix) for k in s.attributes) + ] + if matches: + return matches + time.sleep(self.POLL_INTERVAL) + return [] + + def wait_for_metric(self, reader: InMemoryMetricReader, name: str): + """Poll until we see a metric with the given name.""" + deadline = time.time() + self.POLL_TIMEOUT + while time.time() < deadline: + data = reader.get_metrics_data() + # guard against None or missing attribute + if not data or not hasattr(data, "resource_metrics"): + time.sleep(self.POLL_INTERVAL) + continue + + for rm in data.resource_metrics: + for sm in rm.scope_metrics: + for m in sm.metrics: + if m.name == name: + return m + + time.sleep(self.POLL_INTERVAL) + return None + + def wait_for_log(self, reader: InMemoryLogExporter, name: str): + """Poll until we see a log with the given name.""" + deadline = time.time() + self.POLL_TIMEOUT + while time.time() < deadline: + logs = reader.get_finished_logs() + if not logs: + time.sleep(self.POLL_INTERVAL) + continue + matches = [ + log + for log in logs + # if log.attributes and any(str(k).startswith(prefix) for k in log.attributes) + ] + if matches: + return matches + time.sleep(self.POLL_INTERVAL) + return [] + @patch("litellm.integrations.opentelemetry.datetime") def test_create_guardrail_span_with_valid_info(self, mock_datetime): # Setup @@ -79,7 +206,6 @@ class TestOpenTelemetry(unittest.TestCase): ) as mock_get_headers, patch.object( otel, "_get_tracer_with_dynamic_headers" ) as mock_get_tracer: - # Test case 1: With dynamic headers mock_get_headers.return_value = { "arize-space-id": "test-space", @@ -399,3 +525,229 @@ class TestOpenTelemetry(unittest.TestCase): self.assertEqual(attributes.get("service.name"), "litellm-service") # But other attributes from OTEL_RESOURCE_ATTRIBUTES should still be present self.assertEqual(attributes.get("extra.attr"), "extra-value") + + def test_handle_success_generates_spans_metrics_and_events(self): + # force both metrics & events on + os.environ["LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS"] = "true" + os.environ["LITELLM_OTEL_INTEGRATION_ENABLE_METRICS"] = "true" + + # ─── build in‐memory OTEL providers/exporters ───────────────────────────── + span_exporter = InMemorySpanExporter() + tracer_provider = TracerProvider() + tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + + log_exporter = InMemoryLogExporter() + logger_provider = OTLoggerProvider() + logger_provider.add_log_record_processor(SimpleLogRecordProcessor(log_exporter)) + + metric_reader = InMemoryMetricReader() + meter_provider = MeterProvider(metric_readers=[metric_reader]) + + # ─── instantiate our OpenTelemetry logger with test providers ─────────── + otel = OpenTelemetry( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + logger_provider=logger_provider, + ) + + # OpenTelemetry attempts to set a global tracer provider, which can be set only once. + # so we hack here to set a local tracer deriver from the provider we created. + otel.tracer = tracer_provider.get_tracer(__name__) + + # ─── minimal input / output for a chat call ────────────────────────────── + start = datetime.utcnow() + end = start + timedelta(seconds=1) + + with open( + os.path.join(self.HERE, "open_telemetry", "data", "captured_kwargs.json") + ) as f: + kwargs = json.load(f) + with open( + os.path.join(self.HERE, "open_telemetry", "data", "captured_response.json") + ) as f: + response_obj = json.load(f) + + # ─── exercise the hook ─────────────────────────────────────────────────── + otel._handle_success(kwargs, response_obj, start, end) + + # ─── assert spans ──────────────────────────────────────────────────────── + spans = self.wait_for_spans(span_exporter, "gen_ai.") + self.assertTrue(spans, "Expected at least one gen_ai span") + + # verify our top‐level litellm_request span is present + names = [s.name for s in spans] + self.assertIn("litellm_request", names) + + # ─── assert metrics ────────────────────────────────────────────────────── + duration_metric = self.wait_for_metric( + metric_reader, "gen_ai.client.operation.duration" + ) + self.assertIsNotNone(duration_metric, "duration histogram was not recorded") + + # check that our model attribute made it onto at least one data point + found_dp = False + if ( + duration_metric + and hasattr(duration_metric, "data") + and hasattr(duration_metric.data, "data_points") + ): + found_dp = any( + dp.attributes.get("gen_ai.request.model") == self.MODEL + for dp in duration_metric.data.data_points + ) + self.assertTrue( + found_dp, "expected gen_ai.request.model attribute on a data point" + ) + + # ─── assert logs ─────────────────────────────────────────────────────── + logs = [] + logs = self.wait_for_log(log_exporter, "gen_ai.") + self.assertTrue(logs, "Expected at least one gen_ai log") + + user_logs = [log for log in logs if log.log_record.attributes.get("event_name") == "gen_ai.content.prompt"] + self.assertTrue(user_logs, "did not see a gen_ai.content.prompt log") + # check log bodies + user_prompt = user_logs[0].log_record.attributes.get("gen_ai.prompt") + self.assertEqual("What is the capital of France?", user_prompt, "did not see a prompt message") + + choice_logs = [log for log in logs if log.log_record.attributes.get("event_name") == "gen_ai.content.completion"] + self.assertTrue(choice_logs, "did not see a gen_ai.content.completion event") + + choice_response = choice_logs[0].log_record.body + self.assertIsNotNone(choice_response, "did not see a response message") + self.assertEqual("stop", choice_response.get("finish_reason"), "did not see expected finish reason") + + + def test_handle_success_spans_only(self): + # make sure neither events nor metrics is on + os.environ.pop("LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS", None) + os.environ.pop("LITELLM_OTEL_INTEGRATION_ENABLE_METRICS", None) + + # ─── build in‐memory OTEL providers/exporters ───────────────────────────── + span_exporter = InMemorySpanExporter() + tracer_provider = TracerProvider() + tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + + # no logs / no metrics + log_exporter = InMemoryLogExporter() + logger_provider = OTLoggerProvider() + logger_provider.add_log_record_processor(SimpleLogRecordProcessor(log_exporter)) + metric_reader = InMemoryMetricReader() + meter_provider = MeterProvider(metric_readers=[metric_reader]) + + # ─── instantiate our OpenTelemetry logger with test providers ─────────── + otel = OpenTelemetry( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + logger_provider=logger_provider, # pass even if events disabled (safe) + ) + # bind our tracer to the test tracer provider (global registration is a no-op after the first time) + otel.tracer = tracer_provider.get_tracer(__name__) + + # ─── minimal input / output for a chat call ────────────────────────────── + start = datetime.utcnow() + end = start + timedelta(seconds=1) + with open( + os.path.join(self.HERE, "open_telemetry", "data", "captured_kwargs.json") + ) as f: + kwargs = json.load(f) + with open( + os.path.join(self.HERE, "open_telemetry", "data", "captured_response.json") + ) as f: + response_obj = json.load(f) + + # ─── exercise the hook ─────────────────────────────────────────────────── + otel._handle_success(kwargs, response_obj, start, end) + + # ─── assert spans only ─────────────────────────────────────────────────── + spans = span_exporter.get_finished_spans() + self.assertTrue(spans, "Expected at least one span") + # must have the top‐level litellm_request span + # self.assertIn( + # LITELLM_REQUEST_SPAN_NAME, + # [s.name for s in spans], + # "litellm_request span missing", + # ) + # model attribute should be on that span + found = any( + s.attributes + and s.attributes.get("gen_ai.request.model") == self.MODEL + for s in spans + ) + self.assertTrue(found, "expected gen_ai.request.model on span attributes") + + # no metrics recorded + self.assertIsNone( + self.wait_for_metric(metric_reader, "gen_ai.client.operation.duration"), + "Did not expect any metrics", + ) + # no logs emitted + logs = log_exporter.get_finished_logs() + self.assertFalse(logs, "Did not expect any logs") + + def test_handle_success_spans_and_metrics(self): + # only metrics on + os.environ.pop("LITELLM_OTEL_INTEGRATION_ENABLE_EVENTS", None) + os.environ["LITELLM_OTEL_INTEGRATION_ENABLE_METRICS"] = "true" + + # ─── build in‐memory OTEL providers/exporters ───────────────────────────── + span_exporter = InMemorySpanExporter() + tracer_provider = TracerProvider() + tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + + log_exporter = InMemoryLogExporter() + logger_provider = OTLoggerProvider() + logger_provider.add_log_record_processor(SimpleLogRecordProcessor(log_exporter)) + metric_reader = InMemoryMetricReader() + meter_provider = MeterProvider(metric_readers=[metric_reader]) + + # ─── instantiate our OpenTelemetry logger with test providers ─────────── + otel = OpenTelemetry( + tracer_provider=tracer_provider, + meter_provider=meter_provider, + logger_provider=logger_provider, # needed if events were enabled + ) + otel.tracer = tracer_provider.get_tracer(__name__) + + # ─── minimal input / output for a chat call ────────────────────────────── + start = datetime.utcnow() + end = start + timedelta(seconds=1) + with open( + os.path.join(self.HERE, "open_telemetry", "data", "captured_kwargs.json") + ) as f: + kwargs = json.load(f) + with open( + os.path.join(self.HERE, "open_telemetry", "data", "captured_response.json") + ) as f: + response_obj = json.load(f) + + # ─── exercise the hook ─────────────────────────────────────────────────── + otel._handle_success(kwargs, response_obj, start, end) + + # ─── assert spans ──────────────────────────────────────────────────────── + spans = span_exporter.get_finished_spans() + self.assertTrue(spans, "Expected at least one span") + + # ─── assert metrics ────────────────────────────────────────────────────── + duration_metric = self.wait_for_metric( + metric_reader, "gen_ai.client.operation.duration" + ) + self.assertIsNotNone(duration_metric, "duration histogram was not recorded") + # model attribute should be present on a data point + found_dp = False + if ( + duration_metric + and hasattr(duration_metric, "data") + and hasattr(duration_metric.data, "data_points") + ): + found_dp = any( + dp.attributes.get("gen_ai.request.model") == self.MODEL + for dp in duration_metric.data.data_points + ) + self.assertTrue( + found_dp, "expected gen_ai.request.model attribute on a data point" + ) + + # ─── no events when only metrics enabled ───────────────────────────────── + logs = log_exporter.get_finished_logs() + self.assertFalse(logs, "Did not expect any logs") From db1b418b156850df20686b31f11c61f7beab20f2 Mon Sep 17 00:00:00 2001 From: Marcelo Mendoza Date: Tue, 2 Sep 2025 17:54:54 +0200 Subject: [PATCH 065/178] fix: update alert type formatting to handle enum names in Slack messages --- litellm/integrations/SlackAlerting/slack_alerting.py | 4 +++- .../integrations/SlackAlerting/test_slack_alerting.py | 4 ++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 6d705642667..7da38e193b6 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -1367,7 +1367,9 @@ Model Info: # Get the current timestamp current_time = datetime.now().strftime("%H:%M:%S") _proxy_base_url = os.getenv("PROXY_BASE_URL", None) - alert_type_formatted = f"Alert type: `{alert_type}`\n" + # Use .name if it's an enum, otherwise use as is + alert_type_name = getattr(alert_type, 'name', alert_type) + alert_type_formatted = f"Alert type: `{alert_type_name}`" if alert_type == "daily_reports" or alert_type == "new_model_added": formatted_message = alert_type_formatted + message else: diff --git a/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py b/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py index 7e1bd897908..9cccdb51799 100644 --- a/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py +++ b/tests/test_litellm/integrations/SlackAlerting/test_slack_alerting.py @@ -188,11 +188,11 @@ class TestSlackAlerting(unittest.TestCase): current_time = "12:34:56" # Test the specific formatting logic we're interested in - alert_type_formatted = f"Alert type: `{alert_type}`\n" + alert_type_formatted = f"Alert type: `{alert_type.name}`\n" formatted_message = f"{alert_type_formatted}\n Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}" # Verify alert_type is in the formatted message as expected - self.assertIn("Alert type: `AlertType.llm_exceptions`", formatted_message) + self.assertIn("Alert type: `llm_exceptions`", formatted_message) self.assertIn("Level: `Medium`", formatted_message) self.assertIn("Timestamp: `12:34:56`", formatted_message) self.assertIn("Message: Test alert message", formatted_message) From 4adfd18bc6483272b3a123a1d43913d9fdfea8c1 Mon Sep 17 00:00:00 2001 From: Sameer Kankute <135028480+kankute-sameer@users.noreply.github.com> Date: Tue, 2 Sep 2025 22:07:08 +0530 Subject: [PATCH 066/178] [Feat]Add support for safety_identifier parameter in chat.completions.create (#14174) * Add support for safety_identifier parameter in chat.completions.create * make sure param is getting actually passed to the raw api --- docs/my-website/docs/completion/input.md | 3 + litellm/constants.py | 5 +- .../llms/openai/chat/gpt_transformation.py | 1 + litellm/main.py | 4 ++ litellm/types/llms/openai.py | 1 + litellm/utils.py | 9 ++- tests/llm_translation/test_openai.py | 59 +++++++++++++++++++ 7 files changed, 76 insertions(+), 6 deletions(-) diff --git a/docs/my-website/docs/completion/input.md b/docs/my-website/docs/completion/input.md index 26629a0b8f8..9699d97b352 100644 --- a/docs/my-website/docs/completion/input.md +++ b/docs/my-website/docs/completion/input.md @@ -106,6 +106,7 @@ def completion( parallel_tool_calls: Optional[bool] = None, logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, + safety_identifier: Optional[str] = None, deployment_id=None, # soon to be deprecated params by OpenAI functions: Optional[List] = None, @@ -196,6 +197,8 @@ def completion( - `top_logprobs`: *int (optional)* - An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to true if this parameter is used. +- `safety_identifier`: *string (optional)* - A unique identifier for tracking and managing safety-related requests. This parameter helps with safety monitoring and compliance tracking. + - `headers`: *dict (optional)* - A dictionary of headers to be sent with the request. - `extra_headers`: *dict (optional)* - Alternative to `headers`, used to send extra headers in LLM API request. diff --git a/litellm/constants.py b/litellm/constants.py index 0655473301f..0803b61a2e8 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -14,7 +14,9 @@ DEFAULT_S3_BATCH_SIZE = int(os.getenv("DEFAULT_S3_BATCH_SIZE", 512)) DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) ) -DEFAULT_NUM_WORKERS_LITELLM_PROXY = int(os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 4)) +DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 4) +) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" SQS_API_VERSION = "2012-11-05" @@ -395,6 +397,7 @@ DEFAULT_CHAT_COMPLETION_PARAM_VALUES = { "reasoning_effort": None, "thinking": None, "web_search_options": None, + "safety_identifier": None, } openai_compatible_endpoints: List = [ diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index be0ca3a7086..204916e3a48 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -158,6 +158,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): "parallel_tool_calls", "audio", "web_search_options", + "safety_identifier", ] # works across all models model_specific_params = [] diff --git a/litellm/main.py b/litellm/main.py index 786a0196e5e..9c2aa678369 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -357,6 +357,7 @@ async def acompletion( top_logprobs: Optional[int] = None, deployment_id=None, reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]] = None, + safety_identifier: Optional[str] = None, # set api_base, api_version, api_key base_url: Optional[str] = None, api_version: Optional[str] = None, @@ -493,6 +494,7 @@ async def acompletion( "api_key": api_key, "model_list": model_list, "reasoning_effort": reasoning_effort, + "safety_identifier": safety_identifier, "extra_headers": extra_headers, "acompletion": True, # assuming this is a required parameter "thinking": thinking, @@ -906,6 +908,7 @@ def completion( # type: ignore # noqa: PLR0915 web_search_options: Optional[OpenAIWebSearchOptions] = None, deployment_id=None, extra_headers: Optional[dict] = None, + safety_identifier: Optional[str] = None, # soon to be deprecated params by OpenAI functions: Optional[List] = None, function_call: Optional[str] = None, @@ -1243,6 +1246,7 @@ def completion( # type: ignore # noqa: PLR0915 "reasoning_effort": reasoning_effort, "thinking": thinking, "web_search_options": web_search_options, + "safety_identifier": safety_identifier, "allowed_openai_params": kwargs.get("allowed_openai_params"), } optional_params = get_optional_params( diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index 6e7c4150774..5b58c232964 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -788,6 +788,7 @@ class ChatCompletionRequest(TypedDict, total=False): response_format: dict seed: int service_tier: str + safety_identifier: str stop: Union[str, List[str]] stream_options: dict temperature: float diff --git a/litellm/utils.py b/litellm/utils.py index 69f4603fea0..1601d01d3f0 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -837,15 +837,13 @@ async def _client_async_logging_helper( # Async Logging Worker ################################################ from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER + GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue( - async_coroutine = logging_obj.async_success_handler( - result=result, - start_time=start_time, - end_time=end_time + async_coroutine=logging_obj.async_success_handler( + result=result, start_time=start_time, end_time=end_time ) ) - ################################################ # Sync Logging Worker ################################################ @@ -3304,6 +3302,7 @@ def get_optional_params( # noqa: PLR0915 messages: Optional[List[AllMessageValues]] = None, thinking: Optional[AnthropicThinkingParam] = None, web_search_options: Optional[OpenAIWebSearchOptions] = None, + safety_identifier: Optional[str] = None, **kwargs, ): passed_params = locals().copy() diff --git a/tests/llm_translation/test_openai.py b/tests/llm_translation/test_openai.py index 0121eccaac3..619ae338e50 100644 --- a/tests/llm_translation/test_openai.py +++ b/tests/llm_translation/test_openai.py @@ -664,3 +664,62 @@ async def test_openai_gpt5_reasoning(): ) print("response: ", response) assert response.choices[0].message.content is not None + + +@pytest.mark.asyncio +async def test_openai_safety_identifier_parameter(): + """Test that safety_identifier parameter is correctly passed to the OpenAI API.""" + from openai import AsyncOpenAI + + litellm.set_verbose = True + client = AsyncOpenAI(api_key="fake-api-key") + + with patch.object( + client.chat.completions.with_raw_response, "create" + ) as mock_client: + try: + await litellm.acompletion( + model="openai/gpt-4o", + messages=[{"role": "user", "content": "Hello, how are you?"}], + safety_identifier="user_code_123456", + client=client, + ) + except Exception as e: + print(f"Error: {e}") + + mock_client.assert_called_once() + request_body = mock_client.call_args.kwargs + + # Verify the request contains the safety_identifier parameter + assert "safety_identifier" in request_body + # Verify safety_identifier is correctly sent to the API + assert request_body["safety_identifier"] == "user_code_123456" + + +def test_openai_safety_identifier_parameter_sync(): + """Test that safety_identifier parameter is correctly passed to the OpenAI API.""" + from openai import OpenAI + + litellm.set_verbose = True + client = OpenAI(api_key="fake-api-key") + + with patch.object( + client.chat.completions.with_raw_response, "create" + ) as mock_client: + try: + litellm.completion( + model="openai/gpt-4o", + messages=[{"role": "user", "content": "Hello, how are you?"}], + safety_identifier="user_code_123456", + client=client, + ) + except Exception as e: + print(f"Error: {e}") + + mock_client.assert_called_once() + request_body = mock_client.call_args.kwargs + + # Verify the request contains the safety_identifier parameter + assert "safety_identifier" in request_body + # Verify safety_identifier is correctly sent to the API + assert request_body["safety_identifier"] == "user_code_123456" From f0f84d6c5c8f9c040d49d628615ceab8969fa764 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Sep 2025 12:12:21 -0700 Subject: [PATCH 067/178] refactor: BEDROCK_CONVERSE_MODELS --- litellm/__init__.py | 35 +---------------------------------- litellm/constants.py | 36 ++++++++++++++++++++++++++++++++++++ 2 files changed, 37 insertions(+), 34 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index 79865c83513..453396f9a0f 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -67,6 +67,7 @@ from litellm.constants import ( bedrock_embedding_models, known_tokenizer_config, BEDROCK_INVOKE_PROVIDERS_LITERAL, + BEDROCK_CONVERSE_MODELS, DEFAULT_MAX_TOKENS, DEFAULT_SOFT_BUDGET, DEFAULT_ALLOWED_FAILS, @@ -432,40 +433,6 @@ organization = None project = None config_path = None vertex_ai_safety_settings: Optional[dict] = None -BEDROCK_CONVERSE_MODELS = [ - "openai.gpt-oss-20b-1:0", - "openai.gpt-oss-120b-1:0", - "anthropic.claude-opus-4-1-20250805-v1:0", - "anthropic.claude-opus-4-20250514-v1:0", - "anthropic.claude-sonnet-4-20250514-v1:0", - "anthropic.claude-3-7-sonnet-20250219-v1:0", - "anthropic.claude-3-5-haiku-20241022-v1:0", - "anthropic.claude-3-5-sonnet-20241022-v2:0", - "anthropic.claude-3-5-sonnet-20240620-v1:0", - "anthropic.claude-3-opus-20240229-v1:0", - "anthropic.claude-3-sonnet-20240229-v1:0", - "anthropic.claude-3-haiku-20240307-v1:0", - "anthropic.claude-v2", - "anthropic.claude-v2:1", - "anthropic.claude-v1", - "anthropic.claude-instant-v1", - "ai21.jamba-instruct-v1:0", - "ai21.jamba-1-5-mini-v1:0", - "ai21.jamba-1-5-large-v1:0", - "meta.llama3-70b-instruct-v1:0", - "meta.llama3-8b-instruct-v1:0", - "meta.llama3-1-8b-instruct-v1:0", - "meta.llama3-1-70b-instruct-v1:0", - "meta.llama3-1-405b-instruct-v1:0", - "meta.llama3-70b-instruct-v1:0", - "mistral.mistral-large-2407-v1:0", - "mistral.mistral-large-2402-v1:0", - "mistral.mistral-small-2402-v1:0", - "meta.llama3-2-1b-instruct-v1:0", - "meta.llama3-2-3b-instruct-v1:0", - "meta.llama3-2-11b-instruct-v1:0", - "meta.llama3-2-90b-instruct-v1:0", -] ####### COMPLETION MODELS ################### from typing import Set diff --git a/litellm/constants.py b/litellm/constants.py index 0803b61a2e8..21e30bef32b 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -748,6 +748,42 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "deepseek_r1", ] +BEDROCK_CONVERSE_MODELS = [ + "openai.gpt-oss-20b-1:0", + "openai.gpt-oss-120b-1:0", + "anthropic.claude-opus-4-1-20250805-v1:0", + "anthropic.claude-opus-4-20250514-v1:0", + "anthropic.claude-sonnet-4-20250514-v1:0", + "anthropic.claude-3-7-sonnet-20250219-v1:0", + "anthropic.claude-3-5-haiku-20241022-v1:0", + "anthropic.claude-3-5-sonnet-20241022-v2:0", + "anthropic.claude-3-5-sonnet-20240620-v1:0", + "anthropic.claude-3-opus-20240229-v1:0", + "anthropic.claude-3-sonnet-20240229-v1:0", + "anthropic.claude-3-haiku-20240307-v1:0", + "anthropic.claude-v2", + "anthropic.claude-v2:1", + "anthropic.claude-v1", + "anthropic.claude-instant-v1", + "ai21.jamba-instruct-v1:0", + "ai21.jamba-1-5-mini-v1:0", + "ai21.jamba-1-5-large-v1:0", + "meta.llama3-70b-instruct-v1:0", + "meta.llama3-8b-instruct-v1:0", + "meta.llama3-1-8b-instruct-v1:0", + "meta.llama3-1-70b-instruct-v1:0", + "meta.llama3-1-405b-instruct-v1:0", + "meta.llama3-70b-instruct-v1:0", + "mistral.mistral-large-2407-v1:0", + "mistral.mistral-large-2402-v1:0", + "mistral.mistral-small-2402-v1:0", + "meta.llama3-2-1b-instruct-v1:0", + "meta.llama3-2-3b-instruct-v1:0", + "meta.llama3-2-11b-instruct-v1:0", + "meta.llama3-2-90b-instruct-v1:0", +] + + open_ai_embedding_models: set = set(["text-embedding-ada-002"]) cohere_embedding_models: set = set( [ From d282e7eaac853f5f4a475f211c66cc12d6dc5af9 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Tue, 2 Sep 2025 12:38:05 -0700 Subject: [PATCH 068/178] fix: handle non-str case --- litellm/llms/ollama/completion/transformation.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 5689864017f..4a491c88963 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -331,8 +331,10 @@ class OllamaConfig(BaseConfig): response_text = response_json.get("response", "") content = None reasoning_content = None - if response_text is not None: + if response_text is not None and isinstance(response_text, str): reasoning_content, content = _parse_content_for_reasoning(response_text) + else: + content = response_text # type: ignore model_response.choices[0].message.content = content # type: ignore model_response.choices[0].message.reasoning_content = reasoning_content # type: ignore model_response.created = int(time.time()) From 47c04a8a8c415314db54b2d8af3076a0eb0534d3 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Sep 2025 14:13:17 -0700 Subject: [PATCH 069/178] proxy_logging_guardrails_model_info_tests --- .circleci/config.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index b1c630cdcb3..c9de5adcc9f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1913,6 +1913,7 @@ jobs: -e APORIA_API_BASE_1=$APORIA_API_BASE_1 \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ + -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 -e USE_DDTRACE=True \ -e DD_API_KEY=$DD_API_KEY \ -e DD_SITE=$DD_SITE \ From 4b7c114c2af24e769154bfa57aface6b9245e06c Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Sep 2025 14:15:00 -0700 Subject: [PATCH 070/178] google-cloud-aiplatform --- .github/workflows/test-litellm.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml index 7e67aee8d73..0d3a9f2b5d4 100644 --- a/.github/workflows/test-litellm.yml +++ b/.github/workflows/test-litellm.yml @@ -31,6 +31,7 @@ jobs: poetry run pip install "pytest-retry==1.6.3" poetry run pip install pytest-xdist 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" - name: Setup litellm-enterprise as local package run: | From c821f1ddf1d5f7610d684e3b2b3270d5822d81be Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Sep 2025 14:15:26 -0700 Subject: [PATCH 071/178] [Feature]: Support GPT-OSS models on vertex ai (#14184) * add VertexAIGPTOSSTransformation * fix: optional_params * fix: is_vertex_partner_model * test_partner_models_httpx * docs GPT oss docs * test_vertex_ai_gpt_oss_reasoning_effort * add vertex ai models --- .../docs/providers/vertex_partner.md | 136 +++++++++++ litellm/__init__.py | 4 + .../gpt_oss/transformation.py | 27 +++ .../vertex_ai_partner_models/main.py | 2 + ...odel_prices_and_context_window_backup.json | 22 ++ litellm/proxy/proxy_config.yaml | 1 - litellm/utils.py | 16 ++ model_prices_and_context_window.json | 22 ++ .../test_amazing_vertex_completion.py | 4 +- .../test_vertex_ai_gpt_oss_transformation.py | 223 ++++++++++++++++++ 10 files changed, 455 insertions(+), 2 deletions(-) create mode 100644 litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/transformation.py create mode 100644 tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py diff --git a/docs/my-website/docs/providers/vertex_partner.md b/docs/my-website/docs/providers/vertex_partner.md index cf780e35dbd..856f054b8e6 100644 --- a/docs/my-website/docs/providers/vertex_partner.md +++ b/docs/my-website/docs/providers/vertex_partner.md @@ -15,6 +15,7 @@ import TabItem from '@theme/TabItem'; | Mistral | `vertex_ai/mistral-*` | [Vertex AI - Mistral Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/mistral) | | AI21 (Jamba) | `vertex_ai/jamba-*` | [Vertex AI - AI21 Models](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/ai21) | | Qwen | `vertex_ai/qwen/*` | [Vertex AI - Qwen Models](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/qwen) | +| OpenAI (GPT-OSS) | `vertex_ai/openai/gpt-oss-*` | [Vertex AI - GPT-OSS Models](https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/) | | Model Garden | `vertex_ai/openai/{MODEL_ID}` or `vertex_ai/{MODEL_ID}` | [Vertex Model Garden](https://cloud.google.com/model-garden?hl=en) | ## Vertex AI - Anthropic (Claude) @@ -658,6 +659,141 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ +## VertexAI GPT-OSS Models + +| Property | Details | +|----------|---------| +| Provider Route | `vertex_ai/openai/{MODEL}` | +| Vertex Documentation | [Vertex AI - GPT-OSS Models](https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/) | + +**LiteLLM Supports all Vertex AI GPT-OSS Models.** Ensure you use the `vertex_ai/openai/` prefix for all Vertex AI GPT-OSS models. + +| Model Name | Usage | +|------------------|------------------------------| +| vertex_ai/openai/gpt-oss-20b-maas | `completion('vertex_ai/openai/gpt-oss-20b-maas', messages)` | + +#### Usage + + + + +```python +from litellm import completion +import os + +os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "" + +model = "openai/gpt-oss-20b-maas" + +vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"] +vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"] + +response = completion( + model="vertex_ai/" + model, + messages=[{"role": "user", "content": "hi"}], + vertex_ai_project=vertex_ai_project, + vertex_ai_location=vertex_ai_location, +) +print("\nModel Response", response) +``` + + + +**1. Add to config** + +```yaml +model_list: + - model_name: gpt-oss + litellm_params: + model: vertex_ai/openai/gpt-oss-20b-maas + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-central1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-oss", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + +#### Usage - `reasoning_effort` + +GPT-OSS models support the `reasoning_effort` parameter for enhanced reasoning capabilities. + + + + +```python +from litellm import completion + +response = completion( + model="vertex_ai/openai/gpt-oss-20b-maas", + messages=[{"role": "user", "content": "Solve this complex problem step by step"}], + reasoning_effort="low", # Options: "minimal", "low", "medium", "high" + vertex_ai_project="your-vertex-project", + vertex_ai_location="us-central1", +) +``` + + + + + +1. Setup config.yaml + +```yaml +model_list: +- model_name: gpt-oss + litellm_params: + model: vertex_ai/openai/gpt-oss-20b-maas + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-central1" +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer " \ + -d '{ + "model": "gpt-oss", + "messages": [{"role": "user", "content": "Solve this complex problem step by step"}], + "reasoning_effort": "low" + }' +``` + + + + ## Model Garden :::tip diff --git a/litellm/__init__.py b/litellm/__init__.py index 453396f9a0f..6a184d70b5a 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -458,6 +458,7 @@ vertex_llama3_models: Set = set() vertex_deepseek_models: Set = set() vertex_ai_ai21_models: Set = set() vertex_mistral_models: Set = set() +vertex_openai_models: Set = set() ai21_models: Set = set() ai21_chat_models: Set = set() nlp_cloud_models: Set = set() @@ -604,6 +605,9 @@ def add_known_models(): elif value.get("litellm_provider") == "vertex_ai-image-models": key = key.replace("vertex_ai/", "") vertex_ai_image_models.add(key) + elif value.get("litellm_provider") == "vertex_ai-openai_models": + key = key.replace("vertex_ai/", "") + vertex_openai_models.add(key) elif value.get("litellm_provider") == "ai21": if value.get("mode") == "chat": ai21_chat_models.add(key) diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/transformation.py new file mode 100644 index 00000000000..86e36e802ed --- /dev/null +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/transformation.py @@ -0,0 +1,27 @@ +import litellm +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig + + +class VertexAIGPTOSSTransformation(OpenAIGPTConfig): + """ + Transformation for GPT-OSS model from VertexAI + + https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/gpt-oss-120b-maas?hl=id + """ + def __init__(self): + super().__init__() + + def get_supported_openai_params(self, model: str) -> list: + base_gpt_series_params = super().get_supported_openai_params(model=model) + gpt_oss_only_params = ["reasoning_effort"] + base_gpt_series_params.extend(gpt_oss_only_params) + + ######################################################### + # VertexAI - GPT-OSS does not support tool calls + ######################################################### + if litellm.supports_function_calling(model=model) is False: + TOOL_CALLING_PARAMS_TO_REMOVE = ["tool", "tool_choice", "function_call", "functions"] + base_gpt_series_params = [param for param in base_gpt_series_params if param not in TOOL_CALLING_PARAMS_TO_REMOVE] + + return base_gpt_series_params + diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py index f281cab3b58..ee30c1749a8 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py @@ -49,6 +49,7 @@ class VertexAIPartnerModels(VertexBase): or model.startswith("jamba") or model.startswith("claude") or model.startswith("qwen") + or model.startswith("openai") ): return True return False @@ -59,6 +60,7 @@ class VertexAIPartnerModels(VertexBase): "llama", "deepseek-ai", "qwen", + "openai", ] if any(provider in model for provider in OPENAI_LIKE_VERTEX_PROVIDERS): return True diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 57309554621..7cedffd93f3 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -9884,6 +9884,28 @@ "supports_tool_choice": true, "supports_prompt_caching": true }, + "vertex_ai/openai/gpt-oss-20b-maas": { + "max_tokens": 32768, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "input_cost_per_token": 0.075e-06, + "output_cost_per_token": 0.30e-06, + "litellm_provider": "vertex_ai-openai_models", + "mode": "chat", + "supports_reasoning": true, + "source": "https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/gpt-oss-120b-maas" + }, + "vertex_ai/openai/gpt-oss-120b-maas": { + "max_tokens": 32768, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "input_cost_per_token": 0.15e-06, + "output_cost_per_token": 0.60e-06, + "litellm_provider": "vertex_ai-openai_models", + "mode": "chat", + "supports_reasoning": true, + "source": "https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/gpt-oss-120b-maas" + }, "vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas": { "max_tokens": 32768, "max_input_tokens": 262144, diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index acff522196e..72c69a28e95 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -3,4 +3,3 @@ model_list: litellm_params: model: openai/* api_base: https://exampleopenaiendpoint-production-0ee2.up.railway.app/ - mock_response: "hi" diff --git a/litellm/utils.py b/litellm/utils.py index 1601d01d3f0..405d4cb98e4 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -3601,6 +3601,17 @@ def get_optional_params( # noqa: PLR0915 else False ), ) + elif provider_config is not None: + optional_params = provider_config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=( + drop_params + if drop_params is not None and isinstance(drop_params, bool) + else False + ), + ) else: # use generic openai-like param mapping optional_params = litellm.VertexAILlama3Config().map_openai_params( non_default_params=non_default_params, @@ -6864,6 +6875,11 @@ class ProviderConfigManager: return litellm.VertexGeminiConfig() elif "claude" in model: return litellm.VertexAIAnthropicConfig() + elif "gpt-oss" in model: + from litellm.llms.vertex_ai.vertex_ai_partner_models.gpt_oss.transformation import ( + VertexAIGPTOSSTransformation, + ) + return VertexAIGPTOSSTransformation() elif model in litellm.vertex_mistral_models: if "codestral" in model: return litellm.CodestralTextCompletionConfig() diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 57309554621..7cedffd93f3 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -9884,6 +9884,28 @@ "supports_tool_choice": true, "supports_prompt_caching": true }, + "vertex_ai/openai/gpt-oss-20b-maas": { + "max_tokens": 32768, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "input_cost_per_token": 0.075e-06, + "output_cost_per_token": 0.30e-06, + "litellm_provider": "vertex_ai-openai_models", + "mode": "chat", + "supports_reasoning": true, + "source": "https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/gpt-oss-120b-maas" + }, + "vertex_ai/openai/gpt-oss-120b-maas": { + "max_tokens": 32768, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "input_cost_per_token": 0.15e-06, + "output_cost_per_token": 0.60e-06, + "litellm_provider": "vertex_ai-openai_models", + "mode": "chat", + "supports_reasoning": true, + "source": "https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/gpt-oss-120b-maas" + }, "vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas": { "max_tokens": 32768, "max_input_tokens": 262144, diff --git a/tests/local_testing/test_amazing_vertex_completion.py b/tests/local_testing/test_amazing_vertex_completion.py index b908eabd0cf..9b6fa868677 100644 --- a/tests/local_testing/test_amazing_vertex_completion.py +++ b/tests/local_testing/test_amazing_vertex_completion.py @@ -840,7 +840,8 @@ from test_completion import response_format_tests [ ("vertex_ai/mistral-large-2411", "us-central1"), ("vertex_ai/mistral-nemo@2407", "us-central1"), - ("vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas", "us-south1") + ("vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas", "us-south1"), + ("vertex_ai/openai/gpt-oss-20b-maas", "us-central1"), ], ) @pytest.mark.parametrize( @@ -911,6 +912,7 @@ async def test_partner_models_httpx(model, region, sync_mode): ("vertex_ai/meta/llama-4-scout-17b-16e-instruct-maas", "us-east5"), ("vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas", "us-south1"), ("vertex_ai/mistral-large-2411", "us-central1"), # critical - we had this issue: https://github.com/BerriAI/litellm/issues/13888 + ("vertex_ai/openai/gpt-oss-20b-maas", "us-central1"), ], ) @pytest.mark.parametrize( diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py new file mode 100644 index 00000000000..6743258bae6 --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py @@ -0,0 +1,223 @@ +import json +import os +import sys +from unittest.mock import AsyncMock, MagicMock, patch + +import httpx +import pytest + +sys.path.insert( + 0, os.path.abspath("../../../../../..") +) # Adds the parent directory to the system path + +import litellm +from litellm.llms.vertex_ai.vertex_ai_partner_models.gpt_oss.transformation import ( + VertexAIGPTOSSTransformation, +) + + +class TestVertexAIGPTOSSTransformation: + """Test class for VertexAI GPT-OSS transformation functionality.""" + + def test_supports_reasoning_effort(self): + """Test that reasoning_effort parameter is supported for GPT-OSS models.""" + config = VertexAIGPTOSSTransformation() + supported_params = config.get_supported_openai_params(model="openai/gpt-oss-20b-maas") + + assert "reasoning_effort" in supported_params + + def test_removes_tool_calling_params_when_not_supported(self): + """Test that tool calling parameters are removed when function calling is not supported.""" + config = VertexAIGPTOSSTransformation() + + # Mock litellm.supports_function_calling to return False + with patch('litellm.supports_function_calling', return_value=False): + supported_params = config.get_supported_openai_params(model="openai/gpt-oss-20b-maas") + + # Tool calling params should be removed + assert "tool" not in supported_params + assert "tool_choice" not in supported_params + assert "function_call" not in supported_params + assert "functions" not in supported_params + + # But reasoning_effort should still be there + assert "reasoning_effort" in supported_params + + +@pytest.mark.asyncio +async def test_vertex_ai_gpt_oss_simple_request(): + """ + Test that a simple request to vertex_ai/openai/gpt-oss-20b-maas lands at the correct URL + with the correct request body. + """ + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + + # Mock response + mock_response = AsyncMock() + mock_response.status_code = 200 + mock_response.headers = {} + mock_response.json.return_value = { + "id": "chatcmpl-test123", + "object": "chat.completion", + "created": 1234567890, + "model": "openai/gpt-oss-20b-maas", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello! I'm Litellm Bot, a helpful assistant. I don't have access to real-time weather information, but I'd be happy to help you with other questions or tasks!" + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 42, + "completion_tokens": 28, + "total_tokens": 70 + } + } + + client = AsyncHTTPHandler() + + with patch.object(client, "post", return_value=mock_response) as mock_post: + response = await litellm.acompletion( + model="vertex_ai/openai/gpt-oss-20b-maas", + messages=[ + { + "role": "system", + "content": "Your name is Litellm Bot, you are a helpful assistant" + }, + { + "role": "user", + "content": "Hello, what is your name and can you tell me the weather?" + } + ], + vertex_ai_location="us-central1", + vertex_ai_project="pathrise-convert-1606954137718", + client=client + ) + + # Verify the mock was called + mock_post.assert_called_once() + + # Get the call arguments + call_args = mock_post.call_args + called_url = call_args[0][0] # First positional argument is the URL + request_body = json.loads(call_args.kwargs["data"]) + + # Verify the URL + expected_url = "https://us-central1-aiplatform.googleapis.com/v1/projects/pathrise-convert-1606954137718/locations/us-central1/endpoints/openapi/chat/completions" + assert called_url == expected_url + + # Verify the request body + expected_request_body = { + 'model': 'openai/gpt-oss-20b-maas', + 'messages': [ + { + 'role': 'system', + 'content': 'Your name is Litellm Bot, you are a helpful assistant' + }, + { + 'role': 'user', + 'content': 'Hello, what is your name and can you tell me the weather?' + } + ], + 'stream': False + } + assert request_body == expected_request_body + + # Verify response structure + assert response.model == "vertex_ai/openai/gpt-oss-20b-maas" + assert len(response.choices) == 1 + assert response.choices[0].message.role == "assistant" + + +@pytest.mark.asyncio +async def test_vertex_ai_gpt_oss_reasoning_effort(): + """ + Test that reasoning_effort parameter is correctly passed in the request body + for GPT-OSS models. + """ + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + + # Mock response + mock_response = AsyncMock() + mock_response.status_code = 200 + mock_response.headers = {} + mock_response.json.return_value = { + "id": "chatcmpl-test456", + "object": "chat.completion", + "created": 1234567890, + "model": "openai/gpt-oss-20b-maas", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "I need to think about this carefully. The weather varies by location and time, so I would need to know your specific location to provide accurate weather information." + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 35, + "completion_tokens": 32, + "total_tokens": 67 + } + } + + client = AsyncHTTPHandler() + + with patch.object(client, "post", return_value=mock_response) as mock_post: + response = await litellm.acompletion( + model="vertex_ai/openai/gpt-oss-20b-maas", + messages=[ + { + "role": "system", + "content": "Your name is Litellm Bot, you are a helpful assistant" + }, + { + "role": "user", + "content": "Hello, what is your name and can you tell me the weather?" + } + ], + reasoning_effort="low", + vertex_ai_location="us-central1", + vertex_ai_project="pathrise-convert-1606954137718", + client=client + ) + + # Verify the mock was called + mock_post.assert_called_once() + + # Get the call arguments + call_args = mock_post.call_args + request_body = json.loads(call_args.kwargs["data"]) + + # Verify reasoning_effort is in the request body + assert "reasoning_effort" in request_body + assert request_body["reasoning_effort"] == "low" + + # Verify other expected fields + expected_request_body = { + 'model': 'openai/gpt-oss-20b-maas', + 'messages': [ + { + 'role': 'system', + 'content': 'Your name is Litellm Bot, you are a helpful assistant' + }, + { + 'role': 'user', + 'content': 'Hello, what is your name and can you tell me the weather?' + } + ], + 'reasoning_effort': 'low', + 'stream': False + } + assert request_body == expected_request_body + + # Verify response structure + assert response.model == "vertex_ai/openai/gpt-oss-20b-maas" + assert len(response.choices) == 1 + assert response.choices[0].message.role == "assistant" From 128d9a348816ed7a2bc0444016f613340bfaeded Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Sep 2025 15:13:15 -0700 Subject: [PATCH 072/178] [Feature]: Add header support for spend_logs_metadata (#14186) * fix: allow settings spend_logs_metadata * fix add_litellm_data_for_backend_llm_call * fix: add add_litellm_metadata_from_request_headers * fix add_litellm_metadata_from_request_headers * test_add_litellm_metadata_from_request_headers * add_litellm_metadata_from_request_headers * docs Tracking Spend with custom metadata * add_litellm_metadata_from_request_headers * add_litellm_metadata_from_request_headers --- docs/my-website/docs/proxy/enterprise.md | 87 ++++++++++++ docs/my-website/docs/proxy/request_headers.md | 2 + litellm/proxy/_types.py | 6 + litellm/proxy/litellm_pre_call_utils.py | 52 +++++++- .../proxy/test_litellm_pre_call_utils.py | 124 ++++++++++++++++++ 5 files changed, 266 insertions(+), 5 deletions(-) diff --git a/docs/my-website/docs/proxy/enterprise.md b/docs/my-website/docs/proxy/enterprise.md index 468bcad2cf8..7d50aedb424 100644 --- a/docs/my-website/docs/proxy/enterprise.md +++ b/docs/my-website/docs/proxy/enterprise.md @@ -439,6 +439,33 @@ response = client.chat.completions.create( print(response) ``` + +**Using Headers:** + +```python +import openai +client = openai.OpenAI( + api_key="sk-1234", + base_url="http://0.0.0.0:4000" +) + +# Pass spend logs metadata via headers +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } + ], + extra_headers={ + "x-litellm-spend-logs-metadata": '{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}' + } +) + +print(response) +``` + @@ -478,6 +505,43 @@ async function runOpenAI() { // Call the asynchronous function runOpenAI(); ``` + +**Using Headers:** + +```js +const openai = require('openai'); + +async function runOpenAI() { + const client = new openai.OpenAI({ + apiKey: 'sk-1234', + baseURL: 'http://0.0.0.0:4000' + }); + + try { + const response = await client.chat.completions.create({ + model: 'gpt-3.5-turbo', + messages: [ + { + role: 'user', + content: "this is a test request, write a short poem" + }, + ] + }, { + headers: { + 'x-litellm-spend-logs-metadata': '{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}' + } + }); + console.log(response); + } catch (error) { + console.log("got this exception from server"); + console.error(error); + } +} + +// Call the asynchronous function +runOpenAI(); +``` + @@ -502,6 +566,29 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ } }' ``` + + + + + +Pass `x-litellm-spend-logs-metadata` as a request header with JSON string + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'x-litellm-spend-logs-metadata: {"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + diff --git a/docs/my-website/docs/proxy/request_headers.md b/docs/my-website/docs/proxy/request_headers.md index c250d42f7bb..347dbe6a1bc 100644 --- a/docs/my-website/docs/proxy/request_headers.md +++ b/docs/my-website/docs/proxy/request_headers.md @@ -14,6 +14,8 @@ Special headers that are supported by LiteLLM. `x-litellm-num-retries`: Optional[int]: The number of retries for the request. +`x-litellm-spend-logs-metadata`: Optional[str]: JSON string containing custom metadata to include in spend logs. Example: `{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}`. [Learn More](./logging#tracking-spend-with-custom-metadata) + ## Anthropic Headers `anthropic-version` Optional[str]: The version of the Anthropic API to use. diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 87c5bd7a7c5..7206d3dcb0e 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -2908,6 +2908,12 @@ class LitellmDataForBackendLLMCall(TypedDict, total=False): user: Optional[str] num_retries: Optional[int] +class LitellmMetadataFromRequestHeaders(TypedDict, total=False): + """ + Headers a user can pass that will get added to litellm metadata for the request + """ + spend_logs_metadata: Optional[dict] + class JWTKeyItem(TypedDict, total=False): kid: str diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index 32a283066ed..557aa3ceb9a 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -291,6 +291,17 @@ class LiteLLMProxyRequestSetup: if num_retries_header is not None: return int(num_retries_header) return None + + @staticmethod + def _get_spend_logs_metadata_from_request_headers(headers: dict) -> Optional[dict]: + """ + Get the `spend_logs_metadata` from the request headers. + """ + from litellm.litellm_core_utils.safe_json_loads import safe_json_loads + spend_logs_metadata_header = headers.get("x-litellm-spend-logs-metadata", None) + if spend_logs_metadata_header is not None: + return safe_json_loads(spend_logs_metadata_header) + return None @staticmethod def _get_forwardable_headers( @@ -459,6 +470,30 @@ class LiteLLMProxyRequestSetup: data["num_retries"] = num_retries return data + + @staticmethod + def add_litellm_metadata_from_request_headers( + headers: dict, + data: dict, + _metadata_variable_name: str, + ) -> dict: + """ + Add litellm metadata from request headers + + Relevant issue: https://github.com/BerriAI/litellm/issues/14008 + """ + from litellm.proxy._types import LitellmMetadataFromRequestHeaders + metadata_from_headers = LitellmMetadataFromRequestHeaders() + spend_logs_metadata = LiteLLMProxyRequestSetup._get_spend_logs_metadata_from_request_headers(headers) + if spend_logs_metadata is not None: + metadata_from_headers["spend_logs_metadata"] = spend_logs_metadata + + ######################################################################################### + # Finally update the requests metadata with the `metadata_from_headers` + ######################################################################################### + if isinstance(data[_metadata_variable_name], dict): + data[_metadata_variable_name].update(metadata_from_headers) + return data @staticmethod def get_sanitized_user_information_from_key( @@ -643,6 +678,10 @@ async def add_litellm_data_to_request( # noqa: PLR0915 from litellm.types.proxy.litellm_pre_call_utils import SecretFields safe_add_api_version_from_query_params(data, request) + _metadata_variable_name = _get_metadata_variable_name(request) + if data.get(_metadata_variable_name, None) is None: + data[_metadata_variable_name] = {} + _headers = clean_headers( request.headers, @@ -661,6 +700,14 @@ async def add_litellm_data_to_request( # noqa: PLR0915 ) ) + data.update( + LiteLLMProxyRequestSetup.add_litellm_metadata_from_request_headers( + headers=_headers, + data=data, + _metadata_variable_name=_metadata_variable_name, + ) + ) + # check for forwardable headers data = LiteLLMProxyRequestSetup.add_headers_to_llm_call_by_model_group( data=data, headers=_headers, user_api_key_dict=user_api_key_dict @@ -711,11 +758,6 @@ async def add_litellm_data_to_request( # noqa: PLR0915 verbose_proxy_logger.debug("receiving data: %s", data) - _metadata_variable_name = _get_metadata_variable_name(request) - - if data.get(_metadata_variable_name, None) is None: - data[_metadata_variable_name] = {} - # Parse metadata if it's a string (e.g., from multipart/form-data) if "metadata" in data and data["metadata"] is not None: if isinstance(data["metadata"], str): diff --git a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py index 5104ffd80de..817f19d8d7d 100644 --- a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py +++ b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py @@ -8,6 +8,7 @@ from unittest.mock import MagicMock, patch import pytest from fastapi import Request +import litellm from litellm.proxy._types import TeamCallbackMetadata, UserAPIKeyAuth from litellm.proxy.litellm_pre_call_utils import ( KeyAndTeamLoggingSettings, @@ -935,3 +936,126 @@ def test_add_headers_to_llm_call_by_model_group_existing_headers_in_data(): finally: # Restore original model_group_settings litellm.model_group_settings = original_model_group_settings + +import json +import time +from typing import Optional +from unittest.mock import AsyncMock + +from fastapi.responses import Response + +from litellm.integrations.custom_logger import CustomLogger +from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing +from litellm.proxy.utils import ProxyLogging +from litellm.types.utils import StandardLoggingPayload + + +class TestCustomLogger(CustomLogger): + def __init__(self): + self.standard_logging_object: Optional[StandardLoggingPayload] = None + super().__init__() + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"SUCCESS CALLBACK CALLED! kwargs keys: {list(kwargs.keys())}") + self.standard_logging_object = kwargs.get("standard_logging_object") + print(f"Captured standard_logging_object: {self.standard_logging_object}") + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"FAILURE CALLBACK CALLED! kwargs keys: {list(kwargs.keys())}") + +@pytest.mark.asyncio +async def test_add_litellm_metadata_from_request_headers(): + """ + Test that add_litellm_metadata_from_request_headers properly adds litellm metadata from request headers, + makes an LLM request using base_process_llm_request, sleeps for 3 seconds, and checks standard_logging_payload has spend_logs_metadata from headers + + Relevant issue: https://github.com/BerriAI/litellm/issues/14008 + """ + # Set up test logger + litellm._turn_on_debug() + test_logger = TestCustomLogger() + litellm.callbacks = [test_logger] + + # Prepare test data (ensure no streaming, add mock_response and api_key to route to litellm.acompletion) + headers = {"x-litellm-spend-logs-metadata": '{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion", "timestamp": "2025-09-02T10:30:00Z"}'} + data = {"model": "gpt-4", "messages": [{"role": "user", "content": "Hello"}], "stream": False, "mock_response": "Hi", "api_key": "fake-key"} + + # Create mock request with headers + mock_request = MagicMock(spec=Request) + mock_request.headers = headers + mock_request.url.path = "/chat/completions" + + # Create mock response + mock_fastapi_response = MagicMock(spec=Response) + + # Create mock user API key dict + mock_user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + user_id="test-user", + org_id="test-org" + ) + + # Create mock proxy logging object + mock_proxy_logging_obj = MagicMock(spec=ProxyLogging) + + # Create async functions for the hooks + async def mock_during_call_hook(*args, **kwargs): + return None + + async def mock_pre_call_hook(*args, **kwargs): + return data + + async def mock_post_call_success_hook(*args, **kwargs): + # Return the response unchanged + return kwargs.get('response', args[2] if len(args) > 2 else None) + + mock_proxy_logging_obj.during_call_hook = mock_during_call_hook + mock_proxy_logging_obj.pre_call_hook = mock_pre_call_hook + mock_proxy_logging_obj.post_call_success_hook = mock_post_call_success_hook + + # Create mock proxy config + mock_proxy_config = MagicMock() + + # Create mock general settings + general_settings = {} + + # Create mock select_data_generator with correct signature + def mock_select_data_generator(response=None, user_api_key_dict=None, request_data=None): + async def mock_generator(): + yield "data: " + json.dumps({"choices": [{"delta": {"content": "Hello"}}]}) + "\n\n" + yield "data: [DONE]\n\n" + return mock_generator() + + # Create the processor + processor = ProxyBaseLLMRequestProcessing(data=data) + + # Call base_process_llm_request (it will use the mock_response="Hi" parameter) + result = await processor.base_process_llm_request( + request=mock_request, + fastapi_response=mock_fastapi_response, + user_api_key_dict=mock_user_api_key_dict, + route_type="acompletion", + proxy_logging_obj=mock_proxy_logging_obj, + general_settings=general_settings, + proxy_config=mock_proxy_config, + select_data_generator=mock_select_data_generator, + llm_router=None, + model="gpt-4", + is_streaming_request=False + ) + + # Sleep for 3 seconds to allow logging to complete + await asyncio.sleep(3) + + # Check if standard_logging_object was set + assert test_logger.standard_logging_object is not None, "standard_logging_object should be populated after LLM request" + + # Verify the logging object contains expected metadata + standard_logging_obj = test_logger.standard_logging_object + + print(f"Standard logging object captured: {json.dumps(standard_logging_obj, indent=4, default=str)}") + + SPEND_LOGS_METADATA = standard_logging_obj["metadata"]["spend_logs_metadata"] + assert SPEND_LOGS_METADATA == dict(json.loads(headers["x-litellm-spend-logs-metadata"])), "spend_logs_metadata should be the same as the headers" + + From 212ca20edfe4af1cbd117d4f950e862b69407f73 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Sep 2025 15:19:35 -0700 Subject: [PATCH 073/178] docs fix --- docs/my-website/docs/proxy/request_headers.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/my-website/docs/proxy/request_headers.md b/docs/my-website/docs/proxy/request_headers.md index 347dbe6a1bc..eea66e5fa93 100644 --- a/docs/my-website/docs/proxy/request_headers.md +++ b/docs/my-website/docs/proxy/request_headers.md @@ -14,7 +14,7 @@ Special headers that are supported by LiteLLM. `x-litellm-num-retries`: Optional[int]: The number of retries for the request. -`x-litellm-spend-logs-metadata`: Optional[str]: JSON string containing custom metadata to include in spend logs. Example: `{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}`. [Learn More](./logging#tracking-spend-with-custom-metadata) +`x-litellm-spend-logs-metadata`: Optional[str]: JSON string containing custom metadata to include in spend logs. Example: `{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}`. [Learn More](../proxy/enterprise#tracking-spend-with-custom-metadata) ## Anthropic Headers From edf047b6bb9f0592dfb819b76177c4c7b5d6354c Mon Sep 17 00:00:00 2001 From: kayoch1n Date: Wed, 3 Sep 2025 10:00:16 +0800 Subject: [PATCH 074/178] Replace "/" with "-" in model name when being used as a h11 header name --- litellm/proxy/common_utils/callback_utils.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py index e718255750a..46c9afef3cc 100644 --- a/litellm/proxy/common_utils/callback_utils.py +++ b/litellm/proxy/common_utils/callback_utils.py @@ -316,18 +316,21 @@ def get_remaining_tokens_and_requests_from_request_data(data: Dict) -> Dict[str, headers = {} _metadata = data.get("metadata", None) or {} model_group = get_model_group_from_request_data(data) + + # The h11 package considers "/" or ":" invalid and raise a LocalProtocolError + h11_model_group_name = model_group.replace('/', '-').replace(':', '-') # Remaining Requests remaining_requests_variable_name = f"litellm-key-remaining-requests-{model_group}" remaining_requests = _metadata.get(remaining_requests_variable_name, None) if remaining_requests: - headers[f"x-litellm-key-remaining-requests-{model_group}"] = remaining_requests + headers[f"x-litellm-key-remaining-requests-{h11_model_group_name}"] = remaining_requests # Remaining Tokens remaining_tokens_variable_name = f"litellm-key-remaining-tokens-{model_group}" remaining_tokens = _metadata.get(remaining_tokens_variable_name, None) if remaining_tokens: - headers[f"x-litellm-key-remaining-tokens-{model_group}"] = remaining_tokens + headers[f"x-litellm-key-remaining-tokens-{h11_model_group_name}"] = remaining_tokens return headers From ffbe5cd899d883bed3f1c354a85d0992729ba535 Mon Sep 17 00:00:00 2001 From: kayoch1n Date: Wed, 3 Sep 2025 10:57:09 +0800 Subject: [PATCH 075/178] Add a testcase --- .../proxy/common_utils/test_callback_utils.py | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 tests/test_litellm/proxy/common_utils/test_callback_utils.py diff --git a/tests/test_litellm/proxy/common_utils/test_callback_utils.py b/tests/test_litellm/proxy/common_utils/test_callback_utils.py new file mode 100644 index 00000000000..ffcab13571b --- /dev/null +++ b/tests/test_litellm/proxy/common_utils/test_callback_utils.py @@ -0,0 +1,27 @@ +import sys +import os + +sys.path.insert( + 0, os.path.abspath("../../..") +) # Adds the parent directory to the system path + +from litellm.proxy.common_utils.callback_utils import get_remaining_tokens_and_requests_from_request_data + +def test_get_remaining_tokens_and_requests_from_request_data(): + model_group = "openrouter/google/gemini-2.0-flash-001" + casedata = { + "metadata": { + "model_group": model_group, + f"litellm-key-remaining-requests-{model_group}": 100, + f"litellm-key-remaining-tokens-{model_group}": 200 + } + } + + headers = get_remaining_tokens_and_requests_from_request_data(casedata) + + expected_name = "openrouter-google-gemini-2.0-flash-001" + assert headers == { + f"x-litellm-key-remaining-requests-{expected_name}": 100, + f"x-litellm-key-remaining-tokens-{expected_name}": 200 + } + From 1a97a80c519ab57413293fd45849613838ed0a8e Mon Sep 17 00:00:00 2001 From: kayoch1n Date: Wed, 3 Sep 2025 11:18:57 +0800 Subject: [PATCH 076/178] Format code --- litellm/proxy/common_utils/callback_utils.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py index 46c9afef3cc..d52592952bc 100644 --- a/litellm/proxy/common_utils/callback_utils.py +++ b/litellm/proxy/common_utils/callback_utils.py @@ -316,21 +316,27 @@ def get_remaining_tokens_and_requests_from_request_data(data: Dict) -> Dict[str, headers = {} _metadata = data.get("metadata", None) or {} model_group = get_model_group_from_request_data(data) - + # The h11 package considers "/" or ":" invalid and raise a LocalProtocolError - h11_model_group_name = model_group.replace('/', '-').replace(':', '-') + h11_model_group_name = ( + model_group.replace("/", "-").replace(":", "-") if model_group else None + ) # Remaining Requests remaining_requests_variable_name = f"litellm-key-remaining-requests-{model_group}" remaining_requests = _metadata.get(remaining_requests_variable_name, None) if remaining_requests: - headers[f"x-litellm-key-remaining-requests-{h11_model_group_name}"] = remaining_requests + headers[f"x-litellm-key-remaining-requests-{h11_model_group_name}"] = ( + remaining_requests + ) # Remaining Tokens remaining_tokens_variable_name = f"litellm-key-remaining-tokens-{model_group}" remaining_tokens = _metadata.get(remaining_tokens_variable_name, None) if remaining_tokens: - headers[f"x-litellm-key-remaining-tokens-{h11_model_group_name}"] = remaining_tokens + headers[f"x-litellm-key-remaining-tokens-{h11_model_group_name}"] = ( + remaining_tokens + ) return headers From 76555cad81633b9bba5938419af22644da8674a0 Mon Sep 17 00:00:00 2001 From: kayoch1n Date: Wed, 3 Sep 2025 11:20:52 +0800 Subject: [PATCH 077/178] Format code --- .../proxy/common_utils/test_callback_utils.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/tests/test_litellm/proxy/common_utils/test_callback_utils.py b/tests/test_litellm/proxy/common_utils/test_callback_utils.py index ffcab13571b..b9ed4b9b508 100644 --- a/tests/test_litellm/proxy/common_utils/test_callback_utils.py +++ b/tests/test_litellm/proxy/common_utils/test_callback_utils.py @@ -5,7 +5,10 @@ sys.path.insert( 0, os.path.abspath("../../..") ) # Adds the parent directory to the system path -from litellm.proxy.common_utils.callback_utils import get_remaining_tokens_and_requests_from_request_data +from litellm.proxy.common_utils.callback_utils import ( + get_remaining_tokens_and_requests_from_request_data, +) + def test_get_remaining_tokens_and_requests_from_request_data(): model_group = "openrouter/google/gemini-2.0-flash-001" @@ -13,7 +16,7 @@ def test_get_remaining_tokens_and_requests_from_request_data(): "metadata": { "model_group": model_group, f"litellm-key-remaining-requests-{model_group}": 100, - f"litellm-key-remaining-tokens-{model_group}": 200 + f"litellm-key-remaining-tokens-{model_group}": 200, } } @@ -22,6 +25,5 @@ def test_get_remaining_tokens_and_requests_from_request_data(): expected_name = "openrouter-google-gemini-2.0-flash-001" assert headers == { f"x-litellm-key-remaining-requests-{expected_name}": 100, - f"x-litellm-key-remaining-tokens-{expected_name}": 200 + f"x-litellm-key-remaining-tokens-{expected_name}": 200, } - From 54cca0cc7e12819727a98f9c8ed61177f9ff0dda Mon Sep 17 00:00:00 2001 From: zhxlp <1573635222@qq.com> Date: Wed, 3 Sep 2025 11:34:58 +0800 Subject: [PATCH 078/178] fix: Log page parameter passing error --- ui/litellm-dashboard/src/components/view_logs/index.tsx | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ui/litellm-dashboard/src/components/view_logs/index.tsx b/ui/litellm-dashboard/src/components/view_logs/index.tsx index c45116c0ddd..6c9ef66e95b 100644 --- a/ui/litellm-dashboard/src/components/view_logs/index.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/index.tsx @@ -80,6 +80,7 @@ export default function SpendLogsTable({ const [selectedKeyInfo, setSelectedKeyInfo] = useState(null) const [selectedKeyIdInfoView, setSelectedKeyIdInfoView] = useState(null) const [selectedStatus, setSelectedStatus] = useState("") + const [selectedEndUser, setSelectedEndUser] = useState("") const [filterByCurrentUser, setFilterByCurrentUser] = useState(userRole && internalUserRoles.includes(userRole)) const [activeTab, setActiveTab] = useState("request logs") @@ -193,6 +194,7 @@ export default function SpendLogsTable({ currentPage, pageSize, filterByCurrentUser ? userID : undefined, + selectedEndUser, selectedStatus, selectedModel, ) @@ -280,6 +282,7 @@ export default function SpendLogsTable({ } setSelectedStatus(filters["Status"] || "") setSelectedModel(filters["Model"] || "") + setSelectedEndUser(filters["End User"] || "") if (filters["Key Hash"]) { setSelectedKeyHash(filters["Key Hash"]) From 63c4a30564b06f2aa4a8c6011069c6f6b22ea1f2 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Sep 2025 23:07:32 -0700 Subject: [PATCH 079/178] TestVertexAIGPTOSSTransformation --- .../test_vertex_ai_gpt_oss_transformation.py | 31 ++++++++++++++----- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py index 6743258bae6..34046a00ee8 100644 --- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py +++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py @@ -1,7 +1,7 @@ import json import os import sys -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import MagicMock, patch import httpx import pytest @@ -51,9 +51,12 @@ async def test_vertex_ai_gpt_oss_simple_request(): with the correct request body. """ from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexLLM, + ) # Mock response - mock_response = AsyncMock() + mock_response = MagicMock() mock_response.status_code = 200 mock_response.headers = {} mock_response.json.return_value = { @@ -80,7 +83,11 @@ async def test_vertex_ai_gpt_oss_simple_request(): client = AsyncHTTPHandler() - with patch.object(client, "post", return_value=mock_response) as mock_post: + async def mock_post_func(*args, **kwargs): + return mock_response + + with patch.object(client, "post", side_effect=mock_post_func) as mock_post, \ + patch.object(VertexLLM, "_ensure_access_token", return_value=("fake-token", "pathrise-convert-1606954137718")): response = await litellm.acompletion( model="vertex_ai/openai/gpt-oss-20b-maas", messages=[ @@ -103,7 +110,8 @@ async def test_vertex_ai_gpt_oss_simple_request(): # Get the call arguments call_args = mock_post.call_args - called_url = call_args[0][0] # First positional argument is the URL + # For side_effect, the URL is passed as kwargs['url'] + called_url = call_args.kwargs["url"] request_body = json.loads(call_args.kwargs["data"]) # Verify the URL @@ -128,7 +136,7 @@ async def test_vertex_ai_gpt_oss_simple_request(): assert request_body == expected_request_body # Verify response structure - assert response.model == "vertex_ai/openai/gpt-oss-20b-maas" + assert response.model == "openai/gpt-oss-20b-maas" assert len(response.choices) == 1 assert response.choices[0].message.role == "assistant" @@ -140,9 +148,12 @@ async def test_vertex_ai_gpt_oss_reasoning_effort(): for GPT-OSS models. """ from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexLLM, + ) # Mock response - mock_response = AsyncMock() + mock_response = MagicMock() mock_response.status_code = 200 mock_response.headers = {} mock_response.json.return_value = { @@ -169,7 +180,11 @@ async def test_vertex_ai_gpt_oss_reasoning_effort(): client = AsyncHTTPHandler() - with patch.object(client, "post", return_value=mock_response) as mock_post: + async def mock_post_func(*args, **kwargs): + return mock_response + + with patch.object(client, "post", side_effect=mock_post_func) as mock_post, \ + patch.object(VertexLLM, "_ensure_access_token", return_value=("fake-token", "pathrise-convert-1606954137718")): response = await litellm.acompletion( model="vertex_ai/openai/gpt-oss-20b-maas", messages=[ @@ -218,6 +233,6 @@ async def test_vertex_ai_gpt_oss_reasoning_effort(): assert request_body == expected_request_body # Verify response structure - assert response.model == "vertex_ai/openai/gpt-oss-20b-maas" + assert response.model == "openai/gpt-oss-20b-maas" assert len(response.choices) == 1 assert response.choices[0].message.role == "assistant" From ecc607257b172169fd3045ce148cdd6da094205d Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Wed, 3 Sep 2025 02:14:59 -0400 Subject: [PATCH 080/178] fixed prometheus metrics --- docs/my-website/docs/proxy/prometheus.md | 25 ++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/docs/my-website/docs/proxy/prometheus.md b/docs/my-website/docs/proxy/prometheus.md index dc7030949bd..8bbf737540d 100644 --- a/docs/my-website/docs/proxy/prometheus.md +++ b/docs/my-website/docs/proxy/prometheus.md @@ -63,7 +63,7 @@ Use this for for tracking per [user, key, team, etc.](virtual_keys) | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_spend_metric` | Total Spend, per `"user", "key", "model", "team", "end-user"` | +| `litellm_spend_metric` | Total Spend, per `"end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user"` | | `litellm_total_tokens_metric` | input + output tokens per `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model"` | | `litellm_input_tokens_metric` | input tokens per `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model"` | | `litellm_output_tokens_metric` | output tokens per `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model"` | @@ -73,9 +73,9 @@ Use this for for tracking per [user, key, team, etc.](virtual_keys) | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_team_max_budget_metric` | Max Budget for Team Labels: `"team_id", "team_alias"`| -| `litellm_remaining_team_budget_metric` | Remaining Budget for Team (A team created on LiteLLM) Labels: `"team_id", "team_alias"`| -| `litellm_team_budget_remaining_hours_metric` | Hours before the team budget is reset Labels: `"team_id", "team_alias"`| +| `litellm_team_max_budget_metric` | Max Budget for Team Labels: `"team", "team_alias"`| +| `litellm_remaining_team_budget_metric` | Remaining Budget for Team (A team created on LiteLLM) Labels: `"team", "team_alias"`| +| `litellm_team_budget_remaining_hours_metric` | Hours before the team budget is reset Labels: `"team", "team_alias"`| ### Virtual Key - Budget @@ -119,8 +119,8 @@ Use this to track overall LiteLLM Proxy usage. | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_proxy_failed_requests_metric` | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "exception_status", "exception_class"` | -| `litellm_proxy_total_requests_metric` | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code"` | +| `litellm_proxy_failed_requests_metric` | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "exception_status", "exception_class", "route"` | +| `litellm_proxy_total_requests_metric` | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route"` | ## LLM Provider Metrics @@ -155,7 +155,7 @@ Use this for LLM API Error monitoring and tracking remaining rate limits and tok | Metric Name | Description | |----------------------|--------------------------------------| | `litellm_remaining_requests_metric` | Track `x-ratelimit-remaining-requests` returned from LLM API Deployment. Labels: `"model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias"` | -| `litellm_remaining_tokens` | Track `x-ratelimit-remaining-tokens` return from LLM API Deployment. Labels: `"model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias"` | +| `litellm_remaining_tokens_metric` | Track `x-ratelimit-remaining-tokens` return from LLM API Deployment. Labels: `"model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias"` | ### Deployment State | Metric Name | Description | @@ -167,16 +167,22 @@ Use this for LLM API Error monitoring and tracking remaining rate limits and tok | Metric Name | Description | |----------------------|--------------------------------------| -| `litellm_deployment_cooled_down` | Number of times a deployment has been cooled down by LiteLLM load balancing logic. Labels: `"litellm_model_name", "model_id", "api_base", "api_provider", "exception_status"` | +| `litellm_deployment_cooled_down` | Number of times a deployment has been cooled down by LiteLLM load balancing logic. Labels: `"litellm_model_name", "model_id", "api_base", "api_provider"` | | `litellm_deployment_successful_fallbacks` | Number of successful fallback requests from primary model -> fallback model. Labels: `"requested_model", "fallback_model", "hashed_api_key", "api_key_alias", "team", "team_alias", "exception_status", "exception_class"` | | `litellm_deployment_failed_fallbacks` | Number of failed fallback requests from primary model -> fallback model. Labels: `"requested_model", "fallback_model", "hashed_api_key", "api_key_alias", "team", "team_alias", "exception_status", "exception_class"` | +## Request Counting Metrics + +| Metric Name | Description | +|----------------------|--------------------------------------| +| `litellm_requests_metric` | Total number of requests tracked per endpoint. Labels: `"end_user", "hashed_api_key", "api_key_alias", "model", "team", "team_alias", "user", "user_email"` | + ## Request Latency Metrics | Metric Name | Description | |----------------------|--------------------------------------| | `litellm_request_total_latency_metric` | Total latency (seconds) for a request to LiteLLM Proxy Server - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model" | -| `litellm_overhead_latency_metric` | Latency overhead (seconds) added by LiteLLM processing - tracked for labels "end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "model" | +| `litellm_overhead_latency_metric` | Latency overhead (seconds) added by LiteLLM processing - tracked for labels "model_group", "api_provider", "api_base", "litellm_model_name", "hashed_api_key", "api_key_alias" | | `litellm_llm_api_latency_metric` | Latency (seconds) for just the LLM API call - tracked for labels "model", "hashed_api_key", "api_key_alias", "team", "team_alias", "requested_model", "end_user", "user" | | `litellm_llm_api_time_to_first_token_metric` | Time to first token for LLM API call - tracked for labels `model`, `hashed_api_key`, `api_key_alias`, `team`, `team_alias` [Note: only emitted for streaming requests] | @@ -486,7 +492,6 @@ Here is a screenshot of the metrics you can monitor with the LiteLLM Grafana Das | Metric Name | Description | |----------------------|--------------------------------------| | `litellm_llm_api_failed_requests_metric` | **deprecated** use `litellm_proxy_failed_requests_metric` | -| `litellm_requests_metric` | **deprecated** use `litellm_proxy_total_requests_metric` | From 8e363fe78c29881a20fc61160c90b044d79d4c36 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 3 Sep 2025 17:58:55 +0530 Subject: [PATCH 081/178] feat: add structured output for sdk --- litellm/responses/main.py | 68 +++++--- litellm/responses/utils.py | 50 +++++- .../base_responses_api.py | 164 ++++++++++++++++-- 3 files changed, 237 insertions(+), 45 deletions(-) diff --git a/litellm/responses/main.py b/litellm/responses/main.py index 9584baf7368..11c88c526ac 100644 --- a/litellm/responses/main.py +++ b/litellm/responses/main.py @@ -1,9 +1,10 @@ import asyncio import contextvars from functools import partial -from typing import Any, Coroutine, Dict, Iterable, List, Literal, Optional, Union +from typing import Any, Coroutine, Dict, Iterable, List, Literal, Optional, Type, Union import httpx +from pydantic import BaseModel import litellm from litellm.constants import request_timeout @@ -135,9 +136,10 @@ async def aresponses_api_with_mcp( ) # Parse MCP tools and separate from other tools - mcp_tools_with_litellm_proxy, other_tools = ( - LiteLLM_Proxy_MCP_Handler._parse_mcp_tools(tools) - ) + ( + mcp_tools_with_litellm_proxy, + other_tools, + ) = LiteLLM_Proxy_MCP_Handler._parse_mcp_tools(tools) # Get available tools from MCP manager if we have MCP tools openai_tools = [] @@ -254,6 +256,7 @@ async def aresponses( stream: Optional[bool] = None, temperature: Optional[float] = None, text: Optional["ResponseText"] = None, + text_format: Optional[Union[Type["BaseModel"], dict]] = None, tool_choice: Optional[ToolChoice] = None, tools: Optional[Iterable[ToolParam]] = None, top_p: Optional[float] = None, @@ -279,6 +282,14 @@ async def aresponses( loop = asyncio.get_event_loop() kwargs["aresponses"] = True + # Convert text_format to text parameter if provided + text = ResponsesAPIRequestUtils.convert_text_format_to_text_param( + text_format=text_format, text=text + ) + if text is not None: + # Update local_vars to include the converted text parameter + local_vars["text"] = text + # get custom llm provider so we can use this for mapping exceptions if custom_llm_provider is None: _, custom_llm_provider, _, _ = litellm.get_llm_provider( @@ -367,6 +378,7 @@ def responses( stream: Optional[bool] = None, temperature: Optional[float] = None, text: Optional["ResponseText"] = None, + text_format: Optional[Union[Type["BaseModel"], dict]] = None, tool_choice: Optional[ToolChoice] = None, tools: Optional[Iterable[ToolParam]] = None, top_p: Optional[float] = None, @@ -399,6 +411,14 @@ def responses( litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) _is_async = kwargs.pop("aresponses", False) is True + # Convert text_format to text parameter if provided + text = ResponsesAPIRequestUtils.convert_text_format_to_text_param( + text_format=text_format, text=text + ) + if text is not None: + # Update local_vars to include the converted text parameter + local_vars["text"] = text + # get llm provider logic litellm_params = GenericLiteLLMParams(**kwargs) @@ -432,11 +452,11 @@ def responses( ) # get provider config - responses_api_provider_config: Optional[BaseResponsesAPIConfig] = ( - ProviderConfigManager.get_provider_responses_api_config( - model=model, - provider=litellm.LlmProviders(custom_llm_provider), - ) + responses_api_provider_config: Optional[ + BaseResponsesAPIConfig + ] = ProviderConfigManager.get_provider_responses_api_config( + model=model, + provider=litellm.LlmProviders(custom_llm_provider), ) local_vars.update(kwargs) @@ -628,11 +648,11 @@ def delete_responses( raise ValueError("custom_llm_provider is required but passed as None") # get provider config - responses_api_provider_config: Optional[BaseResponsesAPIConfig] = ( - ProviderConfigManager.get_provider_responses_api_config( - model=None, - provider=litellm.LlmProviders(custom_llm_provider), - ) + responses_api_provider_config: Optional[ + BaseResponsesAPIConfig + ] = ProviderConfigManager.get_provider_responses_api_config( + model=None, + provider=litellm.LlmProviders(custom_llm_provider), ) if responses_api_provider_config is None: @@ -807,11 +827,11 @@ def get_responses( raise ValueError("custom_llm_provider is required but passed as None") # get provider config - responses_api_provider_config: Optional[BaseResponsesAPIConfig] = ( - ProviderConfigManager.get_provider_responses_api_config( - model=None, - provider=litellm.LlmProviders(custom_llm_provider), - ) + responses_api_provider_config: Optional[ + BaseResponsesAPIConfig + ] = ProviderConfigManager.get_provider_responses_api_config( + model=None, + provider=litellm.LlmProviders(custom_llm_provider), ) if responses_api_provider_config is None: @@ -963,11 +983,11 @@ def list_input_items( if custom_llm_provider is None: raise ValueError("custom_llm_provider is required but passed as None") - responses_api_provider_config: Optional[BaseResponsesAPIConfig] = ( - ProviderConfigManager.get_provider_responses_api_config( - model=None, - provider=litellm.LlmProviders(custom_llm_provider), - ) + responses_api_provider_config: Optional[ + BaseResponsesAPIConfig + ] = ProviderConfigManager.get_provider_responses_api_config( + model=None, + provider=litellm.LlmProviders(custom_llm_provider), ) if responses_api_provider_config is None: diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py index ac59d28a50d..b66fd0d547e 100644 --- a/litellm/responses/utils.py +++ b/litellm/responses/utils.py @@ -1,5 +1,17 @@ import base64 -from typing import Any, Dict, List, Optional, Union, cast, get_type_hints, overload +from typing import ( + Any, + Dict, + List, + Optional, + Type, + Union, + cast, + get_type_hints, + overload, +) + +from pydantic import BaseModel import litellm from litellm._logging import verbose_logger @@ -8,6 +20,7 @@ from litellm.types.llms.openai import ( ResponseAPIUsage, ResponsesAPIOptionalRequestParams, ResponsesAPIResponse, + ResponseText, ) from litellm.types.responses.main import DecodedResponseId from litellm.types.utils import SpecialEnums, Usage @@ -24,7 +37,6 @@ class ResponsesAPIRequestUtils: custom_llm_provider: Optional[str], model: str, ): - if supported_params is None: return unsupported_params = {} @@ -302,6 +314,40 @@ class ResponsesAPIRequestUtils: ) return decoded_response_id.get("response_id", previous_response_id) + @staticmethod + def convert_text_format_to_text_param( + text_format: Optional[Union[Type["BaseModel"], dict]], + text: Optional["ResponseText"] = None, + ) -> Optional["ResponseText"]: + """ + Convert text_format parameter to text parameter for the responses API. + + Args: + text_format: Pydantic model class or dict to convert to response format + text: Existing text parameter (if provided, text_format is ignored) + + Returns: + ResponseText object with the converted format, or None if conversion fails + """ + if text_format is not None and text is None: + from litellm.llms.base_llm.base_utils import type_to_response_format_param + + # Convert Pydantic model to response format + response_format = type_to_response_format_param(text_format) + if response_format is not None: + # Create ResponseText object with the format + # The responses API expects the format to have name at the top level + text = { + "format": { + "type": response_format["type"], + "name": response_format["json_schema"]["name"], + "schema": response_format["json_schema"]["schema"], + "strict": response_format["json_schema"]["strict"], + } + } + return text + return text + class ResponseAPILoggingUtils: @staticmethod diff --git a/tests/llm_responses_api_testing/base_responses_api.py b/tests/llm_responses_api_testing/base_responses_api.py index 5cb8295b1af..f8fbe53e03d 100644 --- a/tests/llm_responses_api_testing/base_responses_api.py +++ b/tests/llm_responses_api_testing/base_responses_api.py @@ -1,33 +1,25 @@ -import httpx import json -import pytest -import sys -from typing import Any, Dict, List -from unittest.mock import MagicMock, Mock, patch import os -import uuid -import time -import base64 +import sys + +import pytest sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path -import litellm +import json from abc import ABC, abstractmethod -from litellm.integrations.custom_logger import CustomLogger -import json -from litellm.types.utils import StandardLoggingPayload -from litellm.types.llms.openai import ( - ResponseCompletedEvent, - ResponsesAPIResponse, - ResponseAPIUsage, - IncompleteDetails, -) from openai.types.responses.response_create_params import ( ResponseInputParam, ) -from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + +import litellm +from litellm.types.llms.openai import ( + IncompleteDetails, + ResponseAPIUsage, + ResponsesAPIResponse, +) def validate_responses_api_response(response, final_chunk: bool = False): @@ -537,3 +529,137 @@ class BaseResponsesAPITest(ABC): validate_responses_api_response(final_response, final_chunk=True) assert final_response.output is not None assert len(final_response.output) > 0 + + @pytest.mark.asyncio + async def test_text_format_to_text_conversion(self): + """ + Test that when text_format parameter is passed to litellm.aresponses, + it gets converted to text parameter in the raw API call to OpenAI. + """ + from unittest.mock import AsyncMock, patch + + from pydantic import BaseModel + + class TestResponse(BaseModel): + """Test Pydantic model for structured output""" + + answer: str + confidence: float + + class MockResponse: + """Mock response class for testing""" + + def __init__(self, json_data, status_code): + self._json_data = json_data + self.status_code = status_code + self.text = json.dumps(json_data) + + def json(self): + return self._json_data + + # Mock response from OpenAI + mock_response = { + "id": "resp_123", + "object": "response", + "created_at": 1741476542, + "status": "completed", + "model": "gpt-4o", + "output": [ + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": '{"answer": "Paris", "confidence": 0.95}', + "annotations": [], + } + ], + } + ], + "parallel_tool_calls": True, + "usage": { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30, + "output_tokens_details": {"reasoning_tokens": 0}, + }, + "text": {"format": {"type": "json_object"}}, + "error": None, + "incomplete_details": None, + "instructions": None, + "metadata": {}, + "temperature": 1.0, + "tool_choice": "auto", + "tools": [], + "top_p": 1.0, + "max_output_tokens": None, + "previous_response_id": None, + "reasoning": {"effort": None, "summary": None}, + "truncation": "disabled", + "user": None, + } + + base_completion_call_args = self.get_base_completion_call_args() + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + new_callable=AsyncMock, + ) as mock_post: + # Configure the mock to return our response + mock_post.return_value = MockResponse(mock_response, 200) + + litellm._turn_on_debug() + litellm.set_verbose = True + + # Call aresponses with text_format parameter + response = await litellm.aresponses( + input="What is the capital of France?", + text_format=TestResponse, + **base_completion_call_args, + ) + + # Verify the request was made correctly + mock_post.assert_called_once() + request_body = mock_post.call_args.kwargs["json"] + print("Request body:", json.dumps(request_body, indent=4)) + + # Validate that text_format was converted to text parameter + assert ( + "text" in request_body + ), "text parameter should be present in request body" + assert ( + "text_format" not in request_body + ), "text_format should not be in request body" + + # Validate the text parameter structure + text_param = request_body["text"] + assert "format" in text_param, "text parameter should have format field" + assert ( + text_param["format"]["type"] == "json_schema" + ), "format type should be json_schema" + assert "name" in text_param["format"], "format should have name field" + assert ( + text_param["format"]["name"] == "TestResponse" + ), "format name should match Pydantic model name" + assert "schema" in text_param["format"], "format should have schema field" + assert "strict" in text_param["format"], "format should have strict field" + + # Validate the schema structure + schema = text_param["format"]["schema"] + assert schema["type"] == "object", "schema type should be object" + assert "properties" in schema, "schema should have properties" + assert ( + "answer" in schema["properties"] + ), "schema should have answer property" + assert ( + "confidence" in schema["properties"] + ), "schema should have confidence property" + + # Validate other request parameters + assert request_body["input"] == "What is the capital of France?" + + # Validate the response + print("Response:", json.dumps(response, indent=4, default=str)) From 583df9ade09d231023b8bedac2b4fee803210f06 Mon Sep 17 00:00:00 2001 From: zhxlp <1573635222@qq.com> Date: Wed, 3 Sep 2025 23:27:28 +0800 Subject: [PATCH 082/178] fix: image_generation supports extra_body parameter --- litellm/utils.py | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/litellm/utils.py b/litellm/utils.py index 405d4cb98e4..9d10a65c9a8 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -2395,7 +2395,7 @@ def _should_drop_param(k, additional_drop_params) -> bool: def _get_non_default_params( - passed_params: dict, default_params: dict, additional_drop_params: Optional[bool] + passed_params: dict, default_params: dict, additional_drop_params: Optional[list] ) -> dict: non_default_params = {} for k, v in passed_params.items(): @@ -2509,7 +2509,7 @@ def get_optional_params_image_gen( user: Optional[str] = None, input_fidelity: Optional[str] = None, custom_llm_provider: Optional[str] = None, - additional_drop_params: Optional[bool] = None, + additional_drop_params: Optional[list] = None, provider_config: Optional[BaseImageGenerationConfig] = None, drop_params: Optional[bool] = None, **kwargs, @@ -2628,9 +2628,20 @@ def get_optional_params_image_gen( ) # Default to square if size not recognized optional_params["aspectRatio"] = aspect_ratio - for k in passed_params.keys(): - if k not in default_params.keys(): - optional_params[k] = passed_params[k] + openai_params: list[str] = list(default_params.keys()) + if provider_config is not None: + supported_params = provider_config.get_supported_openai_params( + model=model or "" + ) + openai_params = list(supported_params) + + optional_params = add_provider_specific_params_to_optional_params( + optional_params=optional_params, + passed_params=passed_params, + custom_llm_provider=custom_llm_provider or "", + openai_params=openai_params, + additional_drop_params=additional_drop_params, + ) return optional_params From d1c376f93e167478fc2dd37bed80ca878126676b Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Wed, 3 Sep 2025 12:45:42 -0400 Subject: [PATCH 083/178] fix custom callback docs --- .../docs/observability/callbacks.md | 5 + .../docs/observability/custom_callback.md | 299 ++++-------------- docs/my-website/docs/proxy/call_hooks.md | 4 + 3 files changed, 77 insertions(+), 231 deletions(-) diff --git a/docs/my-website/docs/observability/callbacks.md b/docs/my-website/docs/observability/callbacks.md index 69cb0d053ee..040d83697d3 100644 --- a/docs/my-website/docs/observability/callbacks.md +++ b/docs/my-website/docs/observability/callbacks.md @@ -4,9 +4,14 @@ liteLLM provides `input_callbacks`, `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses. +:::tip +**New to LiteLLM Callbacks?** Check out our comprehensive [Callback Management Guide](./callback_management.md) to understand when to use different callback hooks like `async_log_success_event` vs `async_post_call_success_hook`. +::: + liteLLM supports: - [Custom Callback Functions](https://docs.litellm.ai/docs/observability/custom_callback) +- [Callback Management Guide](./callback_management.md) - **Comprehensive guide for choosing the right hooks** - [Lunary](https://lunary.ai/docs) - [Langfuse](https://langfuse.com/docs) - [LangSmith](https://www.langchain.com/langsmith) diff --git a/docs/my-website/docs/observability/custom_callback.md b/docs/my-website/docs/observability/custom_callback.md index cc586b2e5d9..c206c23d0f4 100644 --- a/docs/my-website/docs/observability/custom_callback.md +++ b/docs/my-website/docs/observability/custom_callback.md @@ -4,7 +4,6 @@ **For PROXY** [Go Here](../proxy/logging.md#custom-callback-class-async) ::: - ## Callback Class You can create a custom callback class to precisely log events as they occur in litellm. @@ -57,6 +56,17 @@ def async completion(): asyncio.run(completion()) ``` +## Common Hooks + +- `async_log_success_event` - Log successful API calls +- `async_log_failure_event` - Log failed API calls +- `log_pre_api_call` - Log before API call +- `log_post_api_call` - Log after API call + +**Proxy-only hooks** (only work with LiteLLM Proxy): +- `async_post_call_success_hook` - Access user data + modify responses +- `async_pre_call_hook` - Modify requests before sending + ## Callback Functions If you just want to log on a specific event (e.g. on input) - you can use callback functions. @@ -174,260 +184,87 @@ async def test_chat_openai(): asyncio.run(test_chat_openai()) ``` -:::info +## What's Available in kwargs? -We're actively trying to expand this to other event types. [Tell us if you need this!](https://github.com/BerriAI/litellm/issues/1007) -::: - -## What's in kwargs? - -Notice we pass in a kwargs argument to custom callback. -```python -def custom_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time -): - # Your custom code here - print("LITELLM: in custom callback function") - print("kwargs", kwargs) - print("completion_response", completion_response) - print("start_time", start_time) - print("end_time", end_time) -``` - -This is a dictionary containing all the model-call details (the params we receive, the values we send to the http endpoint, the response we receive, stacktrace in case of errors, etc.). - -This is all logged in the [model_call_details via our Logger](https://github.com/BerriAI/litellm/blob/fc757dc1b47d2eb9d0ea47d6ad224955b705059d/litellm/utils.py#L246). - -Here's exactly what you can expect in the kwargs dictionary: -```shell -### DEFAULT PARAMS ### -"model": self.model, -"messages": self.messages, -"optional_params": self.optional_params, # model-specific params passed in -"litellm_params": self.litellm_params, # litellm-specific params passed in (e.g. metadata passed to completion call) -"start_time": self.start_time, # datetime object of when call was started - -### PRE-API CALL PARAMS ### (check via kwargs["log_event_type"]="pre_api_call") -"input" = input # the exact prompt sent to the LLM API -"api_key" = api_key # the api key used for that LLM API -"additional_args" = additional_args # any additional details for that API call (e.g. contains optional params sent) - -### POST-API CALL PARAMS ### (check via kwargs["log_event_type"]="post_api_call") -"original_response" = original_response # the original http response received (saved via response.text) - -### ON-SUCCESS PARAMS ### (check via kwargs["log_event_type"]="successful_api_call") -"complete_streaming_response" = complete_streaming_response # the complete streamed response (only set if `completion(..stream=True)`) -"end_time" = end_time # datetime object of when call was completed - -### ON-FAILURE PARAMS ### (check via kwargs["log_event_type"]="failed_api_call") -"exception" = exception # the Exception raised -"traceback_exception" = traceback_exception # the traceback generated via `traceback.format_exc()` -"end_time" = end_time # datetime object of when call was completed -``` - - -### Cache hits - -Cache hits are logged in success events as `kwarg["cache_hit"]`. - -Here's an example of accessing it: - - ```python - import litellm -from litellm.integrations.custom_logger import CustomLogger -from litellm import completion, acompletion, Cache - -class MyCustomHandler(CustomLogger): - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): - print(f"On Success") - print(f"Value of Cache hit: {kwargs['cache_hit']"}) - -async def test_async_completion_azure_caching(): - customHandler_caching = MyCustomHandler() - litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) - litellm.callbacks = [customHandler_caching] - unique_time = time.time() - response1 = await litellm.acompletion(model="azure/chatgpt-v-2", - messages=[{ - "role": "user", - "content": f"Hi 👋 - i'm async azure {unique_time}" - }], - caching=True) - await asyncio.sleep(1) - print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}") - response2 = await litellm.acompletion(model="azure/chatgpt-v-2", - messages=[{ - "role": "user", - "content": f"Hi 👋 - i'm async azure {unique_time}" - }], - caching=True) - await asyncio.sleep(1) # success callbacks are done in parallel - print(f"customHandler_caching.states post-cache hit: {customHandler_caching.states}") - assert len(customHandler_caching.errors) == 0 - assert len(customHandler_caching.states) == 4 # pre, post, success, success - ``` - -### Get complete streaming response - -LiteLLM will pass you the complete streaming response in the final streaming chunk as part of the kwargs for your custom callback function. +The kwargs dictionary contains all the details about your API call: ```python -# litellm.set_verbose = False - def custom_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time - ): - # print(f"streaming response: {completion_response}") - if "complete_streaming_response" in kwargs: - print(f"Complete Streaming Response: {kwargs['complete_streaming_response']}") - - # Assign the custom callback function - litellm.success_callback = [custom_callback] - - response = completion(model="claude-instant-1", messages=messages, stream=True) - for idx, chunk in enumerate(response): - pass -``` - - -### Log additional metadata - -LiteLLM accepts a metadata dictionary in the completion call. You can pass additional metadata into your completion call via `completion(..., metadata={"key": "value"})`. - -Since this is a [litellm-specific param](https://github.com/BerriAI/litellm/blob/b6a015404eed8a0fa701e98f4581604629300ee3/litellm/main.py#L235), it's accessible via kwargs["litellm_params"] - -```python -from litellm import completion -import os, litellm - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "your-api-key" - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -def custom_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time -): - print(kwargs["litellm_params"]["metadata"]) +def custom_callback(kwargs, completion_response, start_time, end_time): + # Access common data + model = kwargs.get("model") + messages = kwargs.get("messages", []) + cost = kwargs.get("response_cost", 0) + cache_hit = kwargs.get("cache_hit", False) - -# Assign the custom callback function -litellm.success_callback = [custom_callback] - -response = litellm.completion(model="gpt-3.5-turbo", messages=messages, metadata={"hello": "world"}) + # Access metadata you passed in + metadata = kwargs.get("litellm_params", {}).get("metadata", {}) ``` -## Examples +**Key fields in kwargs:** +- `model` - The model name +- `messages` - Input messages +- `response_cost` - Calculated cost +- `cache_hit` - Whether response was cached +- `litellm_params.metadata` - Your custom metadata -### Custom Callback to track costs for Streaming + Non-Streaming -By default, the response cost is accessible in the logging object via `kwargs["response_cost"]` on success (sync + async) +## Practical Examples + +### Track API Costs ```python +def track_cost_callback(kwargs, completion_response, start_time, end_time): + cost = kwargs["response_cost"] # litellm calculates this for you + print(f"Request cost: ${cost}") -# Step 1. Write your custom callback function -def track_cost_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time -): - try: - response_cost = kwargs["response_cost"] # litellm calculates response cost for you - print("regular response_cost", response_cost) - except: - pass - -# Step 2. Assign the custom callback function litellm.success_callback = [track_cost_callback] -# Step 3. Make litellm.completion call -response = completion( - model="gpt-3.5-turbo", - messages=[ - { - "role": "user", - "content": "Hi 👋 - i'm openai" - } - ] -) - -print(response) +response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello"}]) ``` -### Custom Callback to log transformed Input to LLMs +### Log Inputs to LLMs ```python -def get_transformed_inputs( - kwargs, -): +def get_transformed_inputs(kwargs): params_to_model = kwargs["additional_args"]["complete_input_dict"] print("params to model", params_to_model) litellm.input_callback = [get_transformed_inputs] -def test_chat_openai(): - try: - response = completion(model="claude-2", - messages=[{ - "role": "user", - "content": "Hi 👋 - i'm openai" - }]) - - print(response) - - except Exception as e: - print(e) - pass +response = completion(model="claude-2", messages=[{"role": "user", "content": "Hello"}]) ``` -#### Output -```shell -params to model {'model': 'claude-2', 'prompt': "\n\nHuman: Hi 👋 - i'm openai\n\nAssistant: ", 'max_tokens_to_sample': 256} +### Send to External Service +```python +import requests + +def send_to_analytics(kwargs, completion_response, start_time, end_time): + data = { + "model": kwargs.get("model"), + "cost": kwargs.get("response_cost", 0), + "duration": (end_time - start_time).total_seconds() + } + requests.post("https://your-analytics.com/api", json=data) + +litellm.success_callback = [send_to_analytics] ``` -### Custom Callback to write to Mixpanel +## Common Issues + +### Callback Not Called +Make sure you: +1. Register callbacks correctly: `litellm.callbacks = [MyHandler()]` +2. Use the right hook names (check spelling) +3. Don't use proxy-only hooks in library mode + +### Performance Issues +- Use async hooks for I/O operations +- Don't block in callback functions +- Handle exceptions properly: ```python -import mixpanel -import litellm -from litellm import completion - -def custom_callback( - kwargs, # kwargs to completion - completion_response, # response from completion - start_time, end_time # start/end time -): - # Your custom code here - mixpanel.track("LLM Response", {"llm_response": completion_response}) - - -# Assign the custom callback function -litellm.success_callback = [custom_callback] - -response = completion( - model="gpt-3.5-turbo", - messages=[ - { - "role": "user", - "content": "Hi 👋 - i'm openai" - } - ] -) - -print(response) - +class SafeHandler(CustomLogger): + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + await external_service(response_obj) + except Exception as e: + print(f"Callback error: {e}") # Log but don't break the flow ``` - - - - - - - - - - - diff --git a/docs/my-website/docs/proxy/call_hooks.md b/docs/my-website/docs/proxy/call_hooks.md index b4e22027d19..aef33f8c708 100644 --- a/docs/my-website/docs/proxy/call_hooks.md +++ b/docs/my-website/docs/proxy/call_hooks.md @@ -6,6 +6,10 @@ import Image from '@theme/IdealImage'; - Reject data before making llm api calls / before returning the response - Enforce 'user' param for all openai endpoint calls +:::tip +**Understanding Callback Hooks?** Check out our [Callback Management Guide](../observability/callback_management.md) to understand the differences between proxy-specific hooks like `async_pre_call_hook` and general logging hooks like `async_log_success_event`. +::: + See a complete example with our [parallel request rate limiter](https://github.com/BerriAI/litellm/blob/main/litellm/proxy/hooks/parallel_request_limiter.py) ## Quick Start From bc84b411f4990010b99b00bd01f15d075bd4aa2a Mon Sep 17 00:00:00 2001 From: Harshavardhan K Date: Wed, 3 Sep 2025 11:46:08 -0500 Subject: [PATCH 084/178] Fix/remove deprecated cerebras gpt oss 20b (#14213) * fix: remove deprecated cerebras/openai/gpt-oss-20b model from pricing files - Remove cerebras/openai/gpt-oss-20b entry from model_prices_and_context_window.json - Remove cerebras/openai/gpt-oss-20b entry from model_prices_and_context_window_backup.json - Fixes GitHub issue #13972 where the deprecated model was still being treated as valid - Only the 120b version is now supported by Cerebras * fix: remove deprecated cerebras gpt-oss-20b --- .../model_prices_and_context_window_backup.json | 16 +--------------- model_prices_and_context_window.json | 16 +--------------- 2 files changed, 2 insertions(+), 30 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7cedffd93f3..a7cb09763d2 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -6157,21 +6157,7 @@ "supports_tool_choice": true, "source": "https://inference-docs.cerebras.ai/support/pricing" }, - "cerebras/openai/gpt-oss-20b": { - "max_tokens": 32768, - "max_input_tokens": 131072, - "max_output_tokens": 32768, - "input_cost_per_token": 7e-08, - "output_cost_per_token": 3e-07, - "litellm_provider": "cerebras", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_reasoning": true, - "supports_tool_choice": true, - "source": "https://inference-docs.cerebras.ai/support/pricing" - }, + "cerebras/openai/gpt-oss-120b": { "max_tokens": 32768, "max_input_tokens": 131072, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 7cedffd93f3..a7cb09763d2 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -6157,21 +6157,7 @@ "supports_tool_choice": true, "source": "https://inference-docs.cerebras.ai/support/pricing" }, - "cerebras/openai/gpt-oss-20b": { - "max_tokens": 32768, - "max_input_tokens": 131072, - "max_output_tokens": 32768, - "input_cost_per_token": 7e-08, - "output_cost_per_token": 3e-07, - "litellm_provider": "cerebras", - "mode": "chat", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_reasoning": true, - "supports_tool_choice": true, - "source": "https://inference-docs.cerebras.ai/support/pricing" - }, + "cerebras/openai/gpt-oss-120b": { "max_tokens": 32768, "max_input_tokens": 131072, From ca43514db4b33a16e67393cb20f9469d15e824dc Mon Sep 17 00:00:00 2001 From: katsuhiro muto <63308909+eycjur@users.noreply.github.com> Date: Thu, 4 Sep 2025 02:43:47 +0900 Subject: [PATCH 085/178] [Feat] Support reasoning_effort in Groq (#14207) * Support reasoning_effort in groq * add test --- docs/my-website/docs/reasoning_content.md | 1 + litellm/llms/groq/chat/transformation.py | 15 +++++++++++++++ litellm/main.py | 4 ++-- tests/llm_translation/test_groq.py | 18 +++++++++++++++++- 4 files changed, 35 insertions(+), 3 deletions(-) diff --git a/docs/my-website/docs/reasoning_content.md b/docs/my-website/docs/reasoning_content.md index f9cab01639d..5ddb5aefd47 100644 --- a/docs/my-website/docs/reasoning_content.md +++ b/docs/my-website/docs/reasoning_content.md @@ -20,6 +20,7 @@ Supported Providers: - Vertex AI (`vertex_ai/`) - Perplexity (`perplexity/`) - Mistral AI (Magistral models) (`mistral/`) +- Groq (`groq/`) LiteLLM will standardize the `reasoning_content` in the response and `thinking_blocks` in the assistant message. diff --git a/litellm/llms/groq/chat/transformation.py b/litellm/llms/groq/chat/transformation.py index 86fa323f9e3..165301efb5c 100644 --- a/litellm/llms/groq/chat/transformation.py +++ b/litellm/llms/groq/chat/transformation.py @@ -6,6 +6,8 @@ from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, import httpx from pydantic import BaseModel +import litellm +from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ( @@ -55,6 +57,10 @@ class GroqChatConfig(OpenAILikeChatConfig): if key != "self" and value is not None: setattr(self.__class__, key, value) + @property + def custom_llm_provider(self) -> Optional[str]: + return "groq" + @classmethod def get_config(cls): return super().get_config() @@ -65,6 +71,15 @@ class GroqChatConfig(OpenAILikeChatConfig): base_params.remove("max_retries") except ValueError: pass + + try: + if litellm.supports_reasoning( + model=model, custom_llm_provider=self.custom_llm_provider + ): + base_params.append("reasoning_effort") + except Exception as e: + verbose_logger.debug(f"Error checking if model supports reasoning: {e}") + return base_params @overload diff --git a/litellm/main.py b/litellm/main.py index 9c2aa678369..d0377490942 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -356,7 +356,7 @@ async def acompletion( logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, deployment_id=None, - reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]] = None, + reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = None, safety_identifier: Optional[str] = None, # set api_base, api_version, api_key base_url: Optional[str] = None, @@ -897,7 +897,7 @@ def completion( # type: ignore # noqa: PLR0915 logit_bias: Optional[dict] = None, user: Optional[str] = None, # openai v1.0+ new params - reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]] = None, + reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = None, response_format: Optional[Union[dict, Type[BaseModel]]] = None, seed: Optional[int] = None, tools: Optional[List] = None, diff --git a/tests/llm_translation/test_groq.py b/tests/llm_translation/test_groq.py index dd1ae0aed6d..9a9dae9ee89 100644 --- a/tests/llm_translation/test_groq.py +++ b/tests/llm_translation/test_groq.py @@ -1,6 +1,16 @@ -from base_llm_unit_tests import BaseLLMChatTest +import os +import sys +import pytest + +# sys.path.insert( +# 0, os.path.abspath("../..") +# ) # Adds the parent directory to the system path + +from base_llm_unit_tests import BaseLLMChatTest +from litellm.llms.groq.chat.transformation import GroqChatConfig + class TestGroq(BaseLLMChatTest): def get_base_completion_call_args(self) -> dict: return { @@ -10,3 +20,9 @@ class TestGroq(BaseLLMChatTest): def test_tool_call_no_arguments(self, tool_call_no_arguments): """Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833""" pass + + @pytest.mark.parametrize("model", ["groq/qwen/qwen3-32b", "groq/openai/gpt-oss-20b", "groq/openai/gpt-oss-120b"]) + def test_reasoning_effort_in_supported_params(self, model): + """Test that reasoning_effort is in the list of supported parameters for Groq""" + supported_params = GroqChatConfig().get_supported_openai_params(model=model) + assert "reasoning_effort" in supported_params From 9eecff9d9a1ea2dac03821085b20ecc5a322faa5 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 10:57:10 -0700 Subject: [PATCH 086/178] test_proxy_custom_logger.py::test_chat_completio --- litellm/proxy/litellm_pre_call_utils.py | 30 +++++++++++++++---------- 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index 557aa3ceb9a..95f1eccffe4 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -677,11 +677,6 @@ async def add_litellm_data_to_request( # noqa: PLR0915 from litellm.proxy.proxy_server import llm_router, premium_user from litellm.types.proxy.litellm_pre_call_utils import SecretFields - safe_add_api_version_from_query_params(data, request) - _metadata_variable_name = _get_metadata_variable_name(request) - if data.get(_metadata_variable_name, None) is None: - data[_metadata_variable_name] = {} - _headers = clean_headers( request.headers, @@ -692,6 +687,24 @@ async def add_litellm_data_to_request( # noqa: PLR0915 ), ) + ########################################################## + # Init - Proxy Server Request + # we do this as soon as entering so we track the original request + ########################################################## + data["proxy_server_request"] = { + "url": str(request.url), + "method": request.method, + "headers": _headers, + "body": copy.copy(data), # use copy instead of deepcopy + } + + safe_add_api_version_from_query_params(data, request) + _metadata_variable_name = _get_metadata_variable_name(request) + if data.get(_metadata_variable_name, None) is None: + data[_metadata_variable_name] = {} + + + data.update( LiteLLMProxyRequestSetup.add_litellm_data_for_backend_llm_call( headers=_headers, @@ -721,13 +734,6 @@ async def add_litellm_data_to_request( # noqa: PLR0915 if "user" not in data: data["user"] = user - # Include original request and headers in the data - data["proxy_server_request"] = { - "url": str(request.url), - "method": request.method, - "headers": _headers, - "body": copy.copy(data), # use copy instead of deepcopy - } data["secret_fields"] = SecretFields(raw_headers=dict(request.headers)) From 3b8409f469dee8bf3ce809fb7f8569f436e84239 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 11:00:07 -0700 Subject: [PATCH 087/178] =?UTF-8?q?bump:=20version=201.76.1=20=E2=86=92=20?= =?UTF-8?q?1.76.2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 2b9650a1527..2683afcea6a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm" -version = "1.76.1" +version = "1.76.2" description = "Library to easily interface with LLM API providers" authors = ["BerriAI"] license = "MIT" @@ -156,7 +156,7 @@ requires = ["poetry-core", "wheel"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "1.76.1" +version = "1.76.2" version_files = [ "pyproject.toml:^version" ] From 8e9352fce76dd2b99a3f77d1c83a3ddd23b394c5 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 11:06:09 -0700 Subject: [PATCH 088/178] test fix --- tests/local_testing/test_amazing_vertex_completion.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/local_testing/test_amazing_vertex_completion.py b/tests/local_testing/test_amazing_vertex_completion.py index 9b6fa868677..a27fe738c7f 100644 --- a/tests/local_testing/test_amazing_vertex_completion.py +++ b/tests/local_testing/test_amazing_vertex_completion.py @@ -839,7 +839,6 @@ from test_completion import response_format_tests "model,region", [ ("vertex_ai/mistral-large-2411", "us-central1"), - ("vertex_ai/mistral-nemo@2407", "us-central1"), ("vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas", "us-south1"), ("vertex_ai/openai/gpt-oss-20b-maas", "us-central1"), ], From c9f211f331cbcd2af666db1a939bbf294aae3a1d Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 13:01:47 -0700 Subject: [PATCH 089/178] fix VertexAIPartnerModels --- .../vertex_ai_partner_models/main.py | 33 ++++++++++++------- 1 file changed, 22 insertions(+), 11 deletions(-) diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py index ee30c1749a8..19bd75596f6 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py @@ -1,5 +1,6 @@ # What is this? ## API Handler for calling Vertex AI Partner Models +from enum import Enum from typing import Callable, Optional, Union import httpx # type: ignore @@ -27,6 +28,16 @@ class VertexAIError(Exception): self.message ) # Call the base class constructor with the parameters it needs +class PartnerModelPrefixes(Enum, str): + META_PREFIX = "meta/" + DEEPSEEK_PREFIX = "deepseek-ai" + MISTRAL_PREFIX = "mistral" + CODERESTAL_PREFIX = "codestral" + JAMBA_PREFIX = "jamba" + CLAUDE_PREFIX = "claude" + QWEN_PREFIX = "qwen" + GPT_OSS_PREFIX = "openai/gpt-oss-" + class VertexAIPartnerModels(VertexBase): def __init__(self) -> None: @@ -42,14 +53,14 @@ class VertexAIPartnerModels(VertexBase): bool: True if the model string is a Vertex AI Partner Model, False otherwise """ if ( - model.startswith("meta/") - or model.startswith("deepseek-ai") - or model.startswith("mistral") - or model.startswith("codestral") - or model.startswith("jamba") - or model.startswith("claude") - or model.startswith("qwen") - or model.startswith("openai") + model.startswith(PartnerModelPrefixes.META_PREFIX) + or model.startswith(PartnerModelPrefixes.DEEPSEEK_PREFIX) + or model.startswith(PartnerModelPrefixes.MISTRAL_PREFIX) + or model.startswith(PartnerModelPrefixes.CODERESTAL_PREFIX) + or model.startswith(PartnerModelPrefixes.JAMBA_PREFIX) + or model.startswith(PartnerModelPrefixes.CLAUDE_PREFIX) + or model.startswith(PartnerModelPrefixes.QWEN_PREFIX) + or model.startswith(PartnerModelPrefixes.GPT_OSS_PREFIX) ): return True return False @@ -58,9 +69,9 @@ class VertexAIPartnerModels(VertexBase): def should_use_openai_handler(model: str): OPENAI_LIKE_VERTEX_PROVIDERS = [ "llama", - "deepseek-ai", - "qwen", - "openai", + PartnerModelPrefixes.DEEPSEEK_PREFIX, + PartnerModelPrefixes.QWEN_PREFIX, + PartnerModelPrefixes.GPT_OSS_PREFIX, ] if any(provider in model for provider in OPENAI_LIKE_VERTEX_PROVIDERS): return True From 057d6f5af6c2171354bbe5dc84d1a1b175513aef Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 13:03:04 -0700 Subject: [PATCH 090/178] fix: PartnerModelPrefixes --- litellm/llms/vertex_ai/vertex_ai_partner_models/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py index 19bd75596f6..ea29970f0aa 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py @@ -28,7 +28,7 @@ class VertexAIError(Exception): self.message ) # Call the base class constructor with the parameters it needs -class PartnerModelPrefixes(Enum, str): +class PartnerModelPrefixes(str, Enum): META_PREFIX = "meta/" DEEPSEEK_PREFIX = "deepseek-ai" MISTRAL_PREFIX = "mistral" From 447016817cfc5f5af57eb7e5e4550d3523b143de Mon Sep 17 00:00:00 2001 From: Yuta Saito Date: Thu, 4 Sep 2025 07:11:44 +0900 Subject: [PATCH 091/178] fix: Call guardrail during stream processing --- litellm/proxy/common_request_processing.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index f4d794d94bc..d41da69b6dc 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -108,7 +108,6 @@ async def create_streaming_response( final_status_code = default_status_code try: - # Handle coroutine that returns a generator if asyncio.iscoroutine(generator): generator = await generator @@ -117,7 +116,6 @@ async def create_streaming_response( first_chunk_value = await generator.__anext__() if first_chunk_value is not None: - try: error_code_from_chunk = await _parse_event_data_for_error( first_chunk_value @@ -131,7 +129,6 @@ async def create_streaming_response( verbose_proxy_logger.debug(f"Error parsing first chunk value: {e}") except StopAsyncIteration: - # Generator was empty. Default status async def empty_gen() -> AsyncGenerator[str, None]: if False: @@ -144,7 +141,6 @@ async def create_streaming_response( status_code=default_status_code, ) except Exception as e: - # Unexpected error consuming first chunk. verbose_proxy_logger.exception( f"Error consuming first chunk from generator: {e}" @@ -167,7 +163,6 @@ async def create_streaming_response( with tracer.trace(DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE): yield first_chunk_value async for chunk in generator: - with tracer.trace(DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE): yield chunk @@ -460,7 +455,6 @@ class ProxyBaseLLMRequestProcessing: ) or self._is_streaming_response( response ): # use generate_responses to stream responses - custom_headers = ProxyBaseLLMRequestProcessing.get_custom_headers( user_api_key_dict=user_api_key_dict, call_id=logging_obj.litellm_call_id, @@ -478,7 +472,6 @@ class ProxyBaseLLMRequestProcessing: if route_type == "allm_passthrough_route": # Check if response is an async generator if self._is_streaming_response(response): - if asyncio.iscoroutine(response): generator = await response else: @@ -499,7 +492,6 @@ class ProxyBaseLLMRequestProcessing: headers=custom_headers, ) else: - selected_data_generator = select_data_generator( response=response, user_api_key_dict=user_api_key_dict, @@ -738,7 +730,11 @@ class ProxyBaseLLMRequestProcessing: verbose_proxy_logger.debug("inside generator") try: str_so_far = "" - async for chunk in response: + async for chunk in proxy_logging_obj.async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=response, + request_data=request_data, + ): verbose_proxy_logger.debug( "async_data_generator: received streaming chunk - {}".format(chunk) ) From 1d44a6e4a1136c7bf653570b54e22c1277dbc18a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 15:52:53 -0700 Subject: [PATCH 092/178] Feat: add better SCIM debugging (#14221) --- .../management_endpoints/scim/scim_v2.py | 59 +++++++++++++++++-- 1 file changed, 55 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/management_endpoints/scim/scim_v2.py b/litellm/proxy/management_endpoints/scim/scim_v2.py index e1d94ceaff2..7d863b764f1 100644 --- a/litellm/proxy/management_endpoints/scim/scim_v2.py +++ b/litellm/proxy/management_endpoints/scim/scim_v2.py @@ -253,6 +253,12 @@ async def set_scim_content_type(response: Response): ) async def get_service_provider_config(request: Request): """Return SCIM Service Provider Configuration.""" + verbose_proxy_logger.debug( + "SCIM ServiceProviderConfig request: method=%s url=%s headers=%s", + request.method, + request.url, + dict(request.headers), + ) meta = { "resourceType": "ServiceProviderConfig", "location": str(request.url), @@ -275,6 +281,12 @@ async def get_users( """ Get a list of users according to SCIM v2 protocol """ + verbose_proxy_logger.debug( + "SCIM GET USERS request: startIndex=%s count=%s filter=%s", + startIndex, + count, + filter, + ) try: prisma_client = await _get_prisma_client_or_raise_exception() # Parse filter if provided (basic support) @@ -334,6 +346,7 @@ async def get_user( """ Get a single user by ID according to SCIM v2 protocol """ + verbose_proxy_logger.debug("SCIM GET USER request for user_id=%s", user_id) try: user = await _check_user_exists(user_id) @@ -357,7 +370,9 @@ async def create_user( Create a user according to SCIM v2 protocol """ try: - verbose_proxy_logger.debug("SCIM CREATE USER request: %s", user) + verbose_proxy_logger.debug( + "SCIM CREATE USER request: %s", user.model_dump() + ) prisma_client = await _get_prisma_client_or_raise_exception() # Extract data from SCIM user @@ -435,7 +450,11 @@ async def update_user( """ Update a user according to SCIM v2 protocol (full replacement) """ - verbose_proxy_logger.debug("SCIM PUT USER request: %s", user) + verbose_proxy_logger.debug( + "SCIM PUT USER request for user_id=%s: %s", + user_id, + user.model_dump(), + ) try: prisma_client = await _get_prisma_client_or_raise_exception() @@ -497,6 +516,9 @@ async def delete_user( """ Delete a user according to SCIM v2 protocol """ + verbose_proxy_logger.debug( + "SCIM DELETE USER request for user_id=%s", user_id + ) try: prisma_client = await _get_prisma_client_or_raise_exception() existing_user = await _check_user_exists(user_id) @@ -691,7 +713,11 @@ async def patch_user( """ Patch a user according to SCIM v2 protocol """ - verbose_proxy_logger.debug("SCIM PATCH USER request: %s", patch_ops) + verbose_proxy_logger.debug( + "SCIM PATCH USER request for user_id=%s: %s", + user_id, + patch_ops.model_dump(), + ) try: prisma_client = await _get_prisma_client_or_raise_exception() @@ -744,6 +770,12 @@ async def get_groups( """ Get a list of groups according to SCIM v2 protocol """ + verbose_proxy_logger.debug( + "SCIM GET GROUPS request: startIndex=%s count=%s filter=%s", + startIndex, + count, + filter, + ) try: prisma_client = await _get_prisma_client_or_raise_exception() # Parse filter if provided (basic support) @@ -814,6 +846,9 @@ async def get_group( """ Get a single group by ID according to SCIM v2 protocol """ + verbose_proxy_logger.debug( + "SCIM GET GROUP request for group_id=%s", group_id + ) try: team = await _check_team_exists(group_id) @@ -839,6 +874,10 @@ async def create_group( """ Create a group according to SCIM v2 protocol """ + verbose_proxy_logger.debug( + "SCIM CREATE GROUP request: %s", + group.model_dump(), + ) try: prisma_client = await _get_prisma_client_or_raise_exception() @@ -892,6 +931,11 @@ async def update_group( """ Update a group according to SCIM v2 protocol """ + verbose_proxy_logger.debug( + "SCIM PUT GROUP request for group_id=%s: %s", + group_id, + group.model_dump(), + ) try: prisma_client = await _get_prisma_client_or_raise_exception() existing_team = await _check_team_exists(group_id) @@ -980,6 +1024,9 @@ async def delete_group( """ Delete a group according to SCIM v2 protocol """ + verbose_proxy_logger.debug( + "SCIM DELETE GROUP request for group_id=%s", group_id + ) try: prisma_client = await _get_prisma_client_or_raise_exception() existing_team = await _check_team_exists(group_id) @@ -1135,7 +1182,11 @@ async def patch_group( """ Patch a group according to SCIM v2 protocol """ - verbose_proxy_logger.debug("SCIM PATCH GROUP request: %s", patch_ops) + verbose_proxy_logger.debug( + "SCIM PATCH GROUP request for group_id=%s: %s", + group_id, + patch_ops.model_dump(), + ) try: prisma_client = await _get_prisma_client_or_raise_exception() From e127820bf13c45e2be3b15d2a55a2a1f530a07a8 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 16:10:30 -0700 Subject: [PATCH 093/178] fix proxy_logging_guardrails_model_info_tests --- .circleci/config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index c9de5adcc9f..7debc582915 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1913,7 +1913,7 @@ jobs: -e APORIA_API_BASE_1=$APORIA_API_BASE_1 \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ - -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 + -e DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 \ -e USE_DDTRACE=True \ -e DD_API_KEY=$DD_API_KEY \ -e DD_SITE=$DD_SITE \ From beb300abae1e3106b7186f163f42ff448157e753 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 16:15:51 -0700 Subject: [PATCH 094/178] [Fix] SCIM - Bug fixes for handling SCIM Group Memberships (#14226) * Feat: add better SCIM debugging * fix _get_scim_member_display * fix patch_group * test_update_group_e2e * test_get_scim_member_value --- .../scim/scim_transformations.py | 25 +- .../management_endpoints/scim/scim_v2.py | 98 ++++---- .../scim/test_scim_transformations.py | 4 +- .../scim/test_scim_v2_endpoints.py | 234 +++++++++++++++++- 4 files changed, 299 insertions(+), 62 deletions(-) diff --git a/litellm/proxy/management_endpoints/scim/scim_transformations.py b/litellm/proxy/management_endpoints/scim/scim_transformations.py index bb07cdbd770..1bb59888405 100644 --- a/litellm/proxy/management_endpoints/scim/scim_transformations.py +++ b/litellm/proxy/management_endpoints/scim/scim_transformations.py @@ -121,15 +121,16 @@ class ScimTransformations: if isinstance(team, dict): team = LiteLLM_TeamTable(**team) - # Get team members + # Get team members with proper display names scim_members: List[SCIMMember] = [] for member in team.members_with_roles or []: if isinstance(member, dict): member = Member(**member) + scim_members.append( SCIMMember( value=ScimTransformations._get_scim_member_value(member), - display=member.user_email, + display=ScimTransformations._get_scim_member_display(member), ) ) @@ -151,6 +152,24 @@ class ScimTransformations: @staticmethod def _get_scim_member_value(member: Member) -> str: - if member.user_email: + """ + Get the SCIM member value. Use user_email if available, otherwise use user_id. + SCIM member value should be the unique identifier for the user. + """ + if hasattr(member, "user_email") and member.user_email: return member.user_email + elif hasattr(member, "user_id"): + return member.user_id or ScimTransformations.DEFAULT_SCIM_MEMBER_VALUE + return ScimTransformations.DEFAULT_SCIM_MEMBER_VALUE + + @staticmethod + def _get_scim_member_display(member: Member) -> str: + """ + Get the SCIM member display. Use user_email if available, otherwise use user_id. + SCIM member display should be the display name for the user. + """ + if hasattr(member, "user_email") and member.user_email: + return member.user_email + elif hasattr(member, "user_id"): + return member.user_id or ScimTransformations.DEFAULT_SCIM_MEMBER_VALUE return ScimTransformations.DEFAULT_SCIM_MEMBER_VALUE diff --git a/litellm/proxy/management_endpoints/scim/scim_v2.py b/litellm/proxy/management_endpoints/scim/scim_v2.py index 7d863b764f1..b9929c3b433 100644 --- a/litellm/proxy/management_endpoints/scim/scim_v2.py +++ b/litellm/proxy/management_endpoints/scim/scim_v2.py @@ -22,6 +22,7 @@ import litellm from litellm._logging import verbose_proxy_logger from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.proxy._types import ( + LiteLLM_TeamTable, LiteLLM_UserTable, LitellmUserRoles, Member, @@ -237,6 +238,23 @@ async def _handle_team_membership_changes(user_id: str, existing_teams: List[str ) +async def _get_team_member_user_ids_from_team(team: LiteLLM_TeamTable) -> List[str]: + """ + Get the IDs of the members from a team. + + Use one source of truth for the member IDs: team.members_with_roles + + """ + member_user_ids: List[str] = [] + for member in team.members_with_roles or []: + if hasattr(member, "user_id") and member.user_id is not None: + member_user_ids.append(member.user_id) + elif isinstance(member, dict) and "user_id" in member: + user_id = member.get("user_id") + if user_id is not None: + member_user_ids.append(user_id) + return member_user_ids + # Dependency to set the correct SCIM Content-Type async def set_scim_content_type(response: Response): """Sets the Content-Type header to application/scim+json""" @@ -942,72 +960,40 @@ async def update_group( # Extract valid member IDs member_ids = await _extract_group_member_ids(group) + verbose_proxy_logger.debug(f"SCIM PUT GROUP member_ids: {member_ids}") - # Update team in database + # Prepare update data existing_metadata = existing_team.metadata if existing_team.metadata else {} updated_metadata = {**existing_metadata, "scim_data": group.model_dump()} + update_data = { + "team_alias": group.displayName, + "metadata": safe_dumps(updated_metadata), + } + + # Update team in database updated_team = await prisma_client.db.litellm_teamtable.update( where={"team_id": group_id}, - data={ - "team_alias": group.displayName, - "members": member_ids, - "metadata": safe_dumps(updated_metadata), - }, + data=update_data, ) - # Handle user-team relationships - current_members = existing_team.members or [] - - # Add new members to team - for member_id in member_ids: - if member_id not in current_members: - user = await prisma_client.db.litellm_usertable.find_unique( - where={"user_id": member_id} - ) - if user: - current_user_teams = user.teams or [] - if group_id not in current_user_teams: - await prisma_client.db.litellm_usertable.update( - where={"user_id": member_id}, - data={"teams": {"push": group_id}}, - ) - - # Remove former members from team - for member_id in current_members: - if member_id not in member_ids: - user = await prisma_client.db.litellm_usertable.find_unique( - where={"user_id": member_id} - ) - if user: - current_user_teams = user.teams or [] - if group_id in current_user_teams: - new_teams = [t for t in current_user_teams if t != group_id] - await prisma_client.db.litellm_usertable.update( - where={"user_id": member_id}, data={"teams": new_teams} - ) - - # Get updated members for response - members = await _get_team_members_display(member_ids) - - team_created_at = ( - updated_team.created_at.isoformat() if updated_team.created_at else None - ) - team_updated_at = ( - updated_team.updated_at.isoformat() if updated_team.updated_at else None + # Handle user-team relationship changes using the same approach as patch_group + current_members = set(await _get_team_member_user_ids_from_team(existing_team)) + verbose_proxy_logger.debug(f"SCIM PUT GROUP current_members: {current_members}") + final_members = set(member_ids) + verbose_proxy_logger.debug(f"SCIM PUT GROUP final_members: {final_members}") + + await _handle_group_membership_changes( + group_id=group_id, + current_members=current_members, + final_members=final_members, ) - return SCIMGroup( - schemas=["urn:ietf:params:scim:schemas:core:2.0:Group"], - id=group_id, - displayName=updated_team.team_alias or group_id, - members=members, - meta={ - "resourceType": "Group", - "created": team_created_at, - "lastModified": team_updated_at, - }, + # Convert to SCIM format and return + scim_group = await ScimTransformations.transform_litellm_team_to_scim_group( + updated_team ) + return scim_group except Exception as e: raise handle_exception_on_proxy(e) @@ -1198,7 +1184,7 @@ async def patch_group( ) # Track current members for comparison - current_members = set(existing_team.members or []) + current_members = set(await _get_team_member_user_ids_from_team(existing_team)) # Apply updates to the database updated_team = await _apply_group_patch_updates( diff --git a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_transformations.py b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_transformations.py index f2b374657e6..be24444afaf 100644 --- a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_transformations.py +++ b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_transformations.py @@ -224,10 +224,10 @@ class TestScimTransformations: result = ScimTransformations._get_scim_member_value(member_with_email) assert result == member_with_email.user_email - # Member without email + # Member without email should fall back to user_id member_without_email = Member(user_id="user-456", user_email=None, role="user") result = ScimTransformations._get_scim_member_value(member_without_email) - assert result == ScimTransformations.DEFAULT_SCIM_MEMBER_VALUE + assert result == member_without_email.user_id class TestSCIMPatchOperations: diff --git a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py index 208c8774675..959275787c8 100644 --- a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py @@ -10,10 +10,13 @@ from litellm.proxy.management_endpoints.scim.scim_v2 import ( create_user, get_service_provider_config, patch_user, + update_group, update_user, ) from litellm.types.proxy.management_endpoints.scim_v2 import ( SCIMFeature, + SCIMGroup, + SCIMMember, SCIMPatchOp, SCIMPatchOperation, SCIMServiceProviderConfig, @@ -678,4 +681,233 @@ async def test_update_group_metadata_serialization_issue(mocker): parsed_metadata = json.loads(metadata) assert "existing_key" in parsed_metadata assert "scim_data" in parsed_metadata - assert parsed_metadata["existing_key"] == "existing_value" \ No newline at end of file + + +@pytest.mark.asyncio +async def test_team_membership_management(mocker): + """ + Test that team membership changes work correctly: + - Adding members to team + - Removing members from team + - members_with_roles is used as source of truth + """ + from litellm.proxy._types import Member + from litellm.proxy.management_endpoints.scim.scim_v2 import ( + _get_team_member_user_ids_from_team, + _handle_group_membership_changes, + patch_team_membership, + ) + + # Mock team with members_with_roles as source of truth + mock_team = mocker.MagicMock() + mock_team.members_with_roles = [ + Member(user_id="user1", role="user"), + Member(user_id="user2", role="user") + ] + mock_team.members = ["user1", "user2", "user3"] # This should be ignored + + # Test that members_with_roles is source of truth + member_ids = await _get_team_member_user_ids_from_team(mock_team) + assert set(member_ids) == {"user1", "user2"} + assert "user3" not in member_ids # Should not be included even though in members + + # Mock patch_team_membership function + mock_patch_team_membership = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.patch_team_membership", + AsyncMock() + ) + + # Test adding and removing members + group_id = "test-group-id" + current_members = {"user1", "user2"} + final_members = {"user2", "user3", "user4"} # Remove user1, add user3 and user4 + + await _handle_group_membership_changes( + group_id=group_id, + current_members=current_members, + final_members=final_members + ) + + # Verify patch_team_membership was called correctly + assert mock_patch_team_membership.call_count == 3 + + # Check calls for adding members + add_calls = [call for call in mock_patch_team_membership.call_args_list + if call[1]["teams_ids_to_add_user_to"] == [group_id]] + assert len(add_calls) == 2 # user3 and user4 + + add_user_ids = {call[1]["user_id"] for call in add_calls} + assert add_user_ids == {"user3", "user4"} + + # Check calls for removing members + remove_calls = [call for call in mock_patch_team_membership.call_args_list + if call[1]["teams_ids_to_remove_user_from"] == [group_id]] + assert len(remove_calls) == 1 # user1 + + remove_user_ids = {call[1]["user_id"] for call in remove_calls} + assert remove_user_ids == {"user1"} + + # Verify all calls have correct structure + for call in mock_patch_team_membership.call_args_list: + assert "user_id" in call[1] + assert "teams_ids_to_add_user_to" in call[1] + assert "teams_ids_to_remove_user_from" in call[1] + # Each call should either add OR remove, not both + add_teams = call[1]["teams_ids_to_add_user_to"] + remove_teams = call[1]["teams_ids_to_remove_user_from"] + assert (len(add_teams) > 0) != (len(remove_teams) > 0) # XOR - one should be empty + + +@pytest.mark.asyncio +async def test_update_group_e2e(mocker): + """ + End-to-end test for update_group endpoint: + - Updates group metadata (displayName) + - Handles complete member replacement (add/remove members) + - Verifies members_with_roles is updated as source of truth + - Tests the full flow from SCIM request to database updates + """ + from litellm.proxy._types import LiteLLM_TeamTable, Member + from litellm.proxy.management_endpoints.scim.scim_transformations import ( + ScimTransformations, + ) + from litellm.proxy.utils import safe_dumps + + # Setup test data + group_id = "test-team-123" + + # Mock existing team in database + existing_team = LiteLLM_TeamTable( + team_id=group_id, + team_alias="Old Team Name", + members=["user1", "user2"], # This should be ignored + members_with_roles=[ + Member(user_id="user1", role="user"), + Member(user_id="user2", role="user") + ], + metadata={"existing_key": "existing_value"} + ) + + # Mock updated SCIM group request + scim_group_update = SCIMGroup( + schemas=["urn:ietf:params:scim:schemas:core:2.0:Group"], + id=group_id, + displayName="Updated Team Name", + members=[ + SCIMMember(value="user2", display="User Two"), # Keep user2 + SCIMMember(value="user3", display="User Three"), # Add user3 + SCIMMember(value="user4", display="User Four") # Add user4 + ] + ) + + # Mock prisma client + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + + # Mock database operations + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock(return_value=existing_team) + + # Mock the updated team that gets returned from database + updated_team = LiteLLM_TeamTable( + team_id=group_id, + team_alias="Updated Team Name", + members=["user2", "user3", "user4"], + members_with_roles=[ + Member(user_id="user2", role="user"), + Member(user_id="user3", role="user"), + Member(user_id="user4", role="user") + ], + metadata={ + "existing_key": "existing_value", + "scim_data": scim_group_update.model_dump() + } + ) + mock_prisma_client.db.litellm_teamtable.update = AsyncMock(return_value=updated_team) + + # Mock user validation (all users exist) + mock_user = mocker.MagicMock() + mock_user.user_id = "test-user" + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=mock_user) + + # Mock dependencies + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client) + ) + + # Mock patch_team_membership to track membership changes + mock_patch_team_membership = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.patch_team_membership", + AsyncMock() + ) + + # Mock SCIM transformation + expected_scim_response = SCIMGroup( + schemas=["urn:ietf:params:scim:schemas:core:2.0:Group"], + id=group_id, + displayName="Updated Team Name", + members=[ + SCIMMember(value="user2", display="user2"), + SCIMMember(value="user3", display="user3"), + SCIMMember(value="user4", display="user4") + ] + ) + mocker.patch.object( + ScimTransformations, + "transform_litellm_team_to_scim_group", + AsyncMock(return_value=expected_scim_response) + ) + + # Execute the update_group function + result = await update_group(group_id=group_id, group=scim_group_update) + + # Verify database update was called with correct data + mock_prisma_client.db.litellm_teamtable.update.assert_called_once() + update_call_args = mock_prisma_client.db.litellm_teamtable.update.call_args + + # Check the update parameters + assert update_call_args[1]["where"]["team_id"] == group_id + update_data = update_call_args[1]["data"] + assert update_data["team_alias"] == "Updated Team Name" + + # Verify metadata includes both existing data and SCIM data + metadata_str = update_data["metadata"] + import json + metadata = json.loads(metadata_str) + assert metadata["existing_key"] == "existing_value" + assert "scim_data" in metadata + assert metadata["scim_data"]["displayName"] == "Updated Team Name" + + # Verify team membership changes were handled correctly + assert mock_patch_team_membership.call_count == 3 # Remove user1, add user3, add user4 + + # Check membership changes + call_args_list = mock_patch_team_membership.call_args_list + + # Find remove operation (user1) + remove_calls = [call for call in call_args_list + if call[1]["teams_ids_to_remove_user_from"] == [group_id]] + assert len(remove_calls) == 1 + assert remove_calls[0][1]["user_id"] == "user1" + assert remove_calls[0][1]["teams_ids_to_add_user_to"] == [] + + # Find add operations (user3, user4) + add_calls = [call for call in call_args_list + if call[1]["teams_ids_to_add_user_to"] == [group_id]] + assert len(add_calls) == 2 + add_user_ids = {call[1]["user_id"] for call in add_calls} + assert add_user_ids == {"user3", "user4"} + + # Verify all add calls have empty remove lists + for call in add_calls: + assert call[1]["teams_ids_to_remove_user_from"] == [] + + # Verify the response + assert result.id == group_id + assert result.displayName == "Updated Team Name" + assert len(result.members) == 3 + + # Verify SCIM transformation was called with updated team + ScimTransformations.transform_litellm_team_to_scim_group.assert_called_once_with(updated_team) \ No newline at end of file From 19e2bab8c83b188e17d1c04533875237111a54d0 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 17:19:58 -0700 Subject: [PATCH 095/178] [Feat] Add Initial support for Bedrock Batches API (#14190) * fix acreate_file with bedrock * fix routing to bedrock batches api * fix create_file * working batch file upload * fix batches API for file upload * test: bedrock files and batches API * add BaseBatchesConfig * fix get_provider_batches_config * transform bedrock batches * fix run create batch through llm http handler * test_async_file_and_batch * main.batches creation * fix: CommonBatchFilesUtils * fix async_create_batch * test_async_file_and_batch * BedrockBatchesConfig * fix ruff check * ruff check fix * fix docs ref --- docs/my-website/docs/proxy/config_settings.md | 3 + litellm/batches/main.py | 61 +- litellm/files/main.py | 6 +- litellm/llms/base_llm/__init__.py | 2 + .../llms/base_llm/batches/transformation.py | 176 +++++ litellm/llms/base_llm/files/transformation.py | 10 + .../llms/bedrock/batches/transformation.py | 254 ++++++++ litellm/llms/bedrock/common_utils.py | 218 +++++++ litellm/llms/bedrock/files/transformation.py | 607 ++++++++++++++++++ litellm/llms/custom_httpx/llm_http_handler.py | 279 +++++++- litellm/types/llms/bedrock.py | 81 +++ litellm/types/router.py | 7 + litellm/utils.py | 15 + .../bedrock_batch_completions.jsonl | 3 + .../test_bedrock_files_and_batches.py | 70 ++ 15 files changed, 1759 insertions(+), 33 deletions(-) create mode 100644 litellm/llms/base_llm/batches/transformation.py create mode 100644 litellm/llms/bedrock/batches/transformation.py create mode 100644 litellm/llms/bedrock/files/transformation.py create mode 100644 tests/batches_tests/bedrock_batch_completions.jsonl create mode 100644 tests/batches_tests/test_bedrock_files_and_batches.py diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 7eb355d39f2..2d4b9655c5d 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -335,12 +335,15 @@ router_settings: | ANTHROPIC_API_KEY | API key for Anthropic service | ANTHROPIC_API_BASE | Base URL for Anthropic API. Default is https://api.anthropic.com | AWS_ACCESS_KEY_ID | Access Key ID for AWS services +| AWS_BATCH_ROLE_ARN | ARN of the AWS IAM role for batch operations | AWS_DEFAULT_REGION | Default AWS region for service interactions when AWS_REGION is not set | AWS_PROFILE_NAME | AWS CLI profile name to be used | AWS_REGION | AWS region for service interactions (takes precedence over AWS_DEFAULT_REGION) | AWS_REGION_NAME | Default AWS region for service interactions | AWS_ROLE_ARN | ARN of the AWS IAM role to assume for authentication | AWS_ROLE_NAME | Role name for AWS IAM usage +| AWS_S3_BUCKET_NAME | Name of the AWS S3 bucket for file operations +| AWS_S3_OUTPUT_BUCKET_NAME | Name of the AWS S3 output bucket for batch operations | AWS_SECRET_ACCESS_KEY | Secret Access Key for AWS services | AWS_SESSION_NAME | Name for AWS session | AWS_WEB_IDENTITY_TOKEN | Web identity token for AWS diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 3ea0f95157f..0d250779da3 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -14,13 +14,15 @@ import asyncio import contextvars import os from functools import partial -from typing import Any, Coroutine, Dict, Literal, Optional, Union +from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast import httpx import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.azure.batches.handler import AzureBatchesAPI +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.llms.openai.openai import OpenAIBatchesAPI from litellm.llms.vertex_ai.batches.handler import VertexAIBatchPrediction from litellm.secret_managers.main import get_secret_str @@ -31,13 +33,19 @@ from litellm.types.llms.openai import ( RetrieveBatchRequest, ) from litellm.types.router import GenericLiteLLMParams -from litellm.types.utils import LiteLLMBatch -from litellm.utils import client, get_litellm_params, supports_httpx_timeout +from litellm.types.utils import LiteLLMBatch, LlmProviders +from litellm.utils import ( + ProviderConfigManager, + client, + get_litellm_params, + supports_httpx_timeout, +) ####### ENVIRONMENT VARIABLES ################### openai_batches_instance = OpenAIBatchesAPI() azure_batches_instance = AzureBatchesAPI() vertex_ai_batches_instance = VertexAIBatchPrediction(gcs_bucket_name="") +base_llm_http_handler = BaseLLMHTTPHandler() ################################################# @@ -46,7 +54,7 @@ async def acreate_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -94,7 +102,7 @@ def create_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -111,8 +119,8 @@ def create_batch( proxy_server_request = kwargs.get("proxy_server_request", None) model_info = kwargs.get("model_info", None) _is_async = kwargs.pop("acreate_batch", False) is True - litellm_params = get_litellm_params(**kwargs) - litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None) + litellm_params = dict(GenericLiteLLMParams(**kwargs)) + litellm_logging_obj: LiteLLMLoggingObj = cast(LiteLLMLoggingObj, kwargs.get("litellm_logging_obj", None)) ### TIMEOUT LOGIC ### timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 litellm_logging_obj.update_environment_variables( @@ -142,6 +150,7 @@ def create_batch( timeout = float(timeout) # type: ignore elif timeout is None: timeout = 600.0 + _create_batch_request = CreateBatchRequest( completion_window=completion_window, @@ -151,6 +160,27 @@ def create_batch( extra_headers=extra_headers, extra_body=extra_body, ) + provider_config = ProviderConfigManager.get_provider_batches_config( + model="", + provider=LlmProviders(custom_llm_provider), + ) + if provider_config is not None: + response = base_llm_http_handler.create_batch( + provider_config=provider_config, + litellm_params=litellm_params, + create_batch_data=_create_batch_request, + headers=extra_headers or {}, + api_base=optional_params.api_base, + api_key=optional_params.api_key, + logging_obj=litellm_logging_obj, + _is_async=_is_async, + client=client + if client is not None + and isinstance(client, (HTTPHandler, AsyncHTTPHandler)) + else None, + timeout=timeout, + ) + return response api_base: Optional[str] = None if custom_llm_provider == "openai": # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there @@ -322,20 +352,21 @@ def retrieve_batch( """ try: optional_params = GenericLiteLLMParams(**kwargs) - litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj", None) + litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj", None) ### TIMEOUT LOGIC ### timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 litellm_params = get_litellm_params( custom_llm_provider=custom_llm_provider, **kwargs, ) - litellm_logging_obj.update_environment_variables( - model=None, - user=None, - optional_params=optional_params.model_dump(), - litellm_params=litellm_params, - custom_llm_provider=custom_llm_provider, - ) + if litellm_logging_obj is not None: + litellm_logging_obj.update_environment_variables( + model=None, + user=None, + optional_params=optional_params.model_dump(), + litellm_params=litellm_params, + custom_llm_provider=custom_llm_provider, + ) if ( timeout is not None diff --git a/litellm/files/main.py b/litellm/files/main.py index 5d0dc05771a..299e52895bf 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -50,7 +50,7 @@ vertex_ai_files_instance = VertexAIFilesHandler() async def acreate_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -94,7 +94,7 @@ async def acreate_file( def create_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai"]] = None, + custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai", "bedrock"]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -109,7 +109,7 @@ def create_file( try: _is_async = kwargs.pop("acreate_file", False) is True optional_params = GenericLiteLLMParams(**kwargs) - litellm_params_dict = get_litellm_params(**kwargs) + litellm_params_dict = dict(**kwargs) logging_obj = cast( Optional[LiteLLMLoggingObj], kwargs.get("litellm_logging_obj") ) diff --git a/litellm/llms/base_llm/__init__.py b/litellm/llms/base_llm/__init__.py index 187c985fd67..665e242969c 100644 --- a/litellm/llms/base_llm/__init__.py +++ b/litellm/llms/base_llm/__init__.py @@ -1,5 +1,6 @@ from .anthropic_messages.transformation import BaseAnthropicMessagesConfig from .audio_transcription.transformation import BaseAudioTranscriptionConfig +from .batches.transformation import BaseBatchesConfig from .chat.transformation import BaseConfig from .embedding.transformation import BaseEmbeddingConfig from .image_edit.transformation import BaseImageEditConfig @@ -12,4 +13,5 @@ __all__ = [ "BaseAnthropicMessagesConfig", "BaseEmbeddingConfig", "BaseImageEditConfig", + "BaseBatchesConfig", ] diff --git a/litellm/llms/base_llm/batches/transformation.py b/litellm/llms/base_llm/batches/transformation.py new file mode 100644 index 00000000000..1d3e54fae67 --- /dev/null +++ b/litellm/llms/base_llm/batches/transformation.py @@ -0,0 +1,176 @@ +import types +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union + +import httpx +from httpx import Headers + +from litellm.types.llms.openai import ( + AllMessageValues, + CreateBatchRequest, +) +from litellm.types.utils import LiteLLMBatch, LlmProviders + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + from ..chat.transformation import BaseLLMException as _BaseLLMException + + LiteLLMLoggingObj = _LiteLLMLoggingObj + BaseLLMException = _BaseLLMException +else: + LiteLLMLoggingObj = Any + BaseLLMException = Any + + +class BaseBatchesConfig(ABC): + """ + Abstract base class for batch processing configurations across different LLM providers. + + This class defines the interface that all provider-specific batch configurations + must implement to work with LiteLLM's unified batch processing system. + """ + + def __init__(self): + pass + + @property + @abstractmethod + def custom_llm_provider(self) -> LlmProviders: + """Return the LLM provider type for this configuration.""" + pass + + @classmethod + def get_config(cls): + """Get configuration dictionary for this class.""" + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not k.startswith("_abc") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + @abstractmethod + 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: + """ + Validate and prepare environment-specific headers and parameters. + + Args: + headers: HTTP headers dictionary + model: Model name + messages: List of messages + optional_params: Optional parameters + litellm_params: LiteLLM parameters + api_key: API key + api_base: API base URL + + Returns: + Updated headers dictionary + """ + pass + + @abstractmethod + def get_complete_batch_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: Dict, + litellm_params: Dict, + data: CreateBatchRequest, + ) -> str: + """ + Get the complete URL for batch creation request. + + Args: + api_base: Base API URL + api_key: API key + model: Model name + optional_params: Optional parameters + litellm_params: LiteLLM parameters + data: Batch creation request data + + Returns: + Complete URL for the batch request + """ + pass + + @abstractmethod + def transform_create_batch_request( + self, + model: str, + create_batch_data: CreateBatchRequest, + optional_params: dict, + litellm_params: dict, + ) -> Union[bytes, str, Dict[str, Any]]: + """ + Transform the batch creation request to provider-specific format. + + Args: + model: Model name + create_batch_data: Batch creation request data + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed request data + """ + pass + + @abstractmethod + def transform_create_batch_response( + self, + model: Optional[str], + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform provider-specific batch response to LiteLLM format. + + Args: + model: Model name + raw_response: Raw HTTP response + logging_obj: Logging object + litellm_params: LiteLLM parameters + + Returns: + LiteLLM batch object + """ + pass + + @abstractmethod + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, Headers] + ) -> "BaseLLMException": + """ + Get the appropriate error class for this provider. + + Args: + error_message: Error message + status_code: HTTP status code + headers: Response headers + + Returns: + Provider-specific exception class + """ + pass diff --git a/litellm/llms/base_llm/files/transformation.py b/litellm/llms/base_llm/files/transformation.py index 5c37a8b7547..35b76479cdc 100644 --- a/litellm/llms/base_llm/files/transformation.py +++ b/litellm/llms/base_llm/files/transformation.py @@ -35,6 +35,16 @@ class BaseFilesConfig(BaseConfig): def custom_llm_provider(self) -> LlmProviders: pass + @property + def file_upload_http_method(self) -> str: + """ + HTTP method to use for file uploads. + Override this in provider configs if they need different methods. + Default is POST (used by most providers like OpenAI, Anthropic). + S3-based providers like Bedrock should return "PUT". + """ + return "POST" + @abstractmethod def get_supported_openai_params( self, model: str diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py new file mode 100644 index 00000000000..ce580ebc624 --- /dev/null +++ b/litellm/llms/bedrock/batches/transformation.py @@ -0,0 +1,254 @@ +import os +import time +from typing import Any, Dict, List, Literal, Optional, Union, cast + +from httpx import Headers, Response + +from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.bedrock import ( + BedrockBatchJobStatus, + BedrockCreateBatchRequest, + BedrockCreateBatchResponse, + BedrockInputDataConfig, + BedrockOutputDataConfig, + BedrockS3InputDataConfig, + BedrockS3OutputDataConfig, +) +from litellm.types.llms.openai import ( + AllMessageValues, + CreateBatchRequest, +) +from litellm.types.utils import LiteLLMBatch, LlmProviders + +from ..base_aws_llm import BaseAWSLLM +from ..common_utils import CommonBatchFilesUtils + + +class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): + """ + Config for Bedrock Batches - handles batch job creation and management for Bedrock + """ + + def __init__(self): + super().__init__() + self.common_utils = CommonBatchFilesUtils() + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.BEDROCK + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate and prepare environment for Bedrock batch requests. + AWS credentials are handled by BaseAWSLLM. + """ + # Add any Bedrock-specific headers if needed + return headers + + def get_complete_batch_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: Dict, + litellm_params: Dict, + data: CreateBatchRequest, + ) -> str: + """ + Get the complete URL for Bedrock batch creation. + Bedrock batch jobs are created via the model invocation job API. + """ + aws_region_name = self._get_aws_region_name(optional_params, model) + + # Bedrock model invocation job endpoint + # Format: https://bedrock.{region}.amazonaws.com/model-invocation-job + bedrock_endpoint = f"https://bedrock.{aws_region_name}.amazonaws.com/model-invocation-job" + + return bedrock_endpoint + + + + + + + + def transform_create_batch_request( + self, + model: str, + create_batch_data: CreateBatchRequest, + optional_params: dict, + litellm_params: dict, + ) -> Dict[str, Any]: + """ + Transform the batch creation request to Bedrock format. + + Bedrock batch inference requires: + - modelId: The Bedrock model ID + - jobName: Unique name for the batch job + - inputDataConfig: Configuration for input data (S3 location) + - outputDataConfig: Configuration for output data (S3 location) + - roleArn: IAM role ARN for the batch job + """ + # Get required parameters + input_file_id = create_batch_data.get("input_file_id") + if not input_file_id: + raise ValueError("input_file_id is required for Bedrock batch creation") + + # Extract S3 information from file ID using common utility + input_bucket, input_key = self.common_utils.parse_s3_uri(input_file_id) + + # Get output S3 configuration + output_bucket = litellm_params.get("s3_output_bucket_name") or os.getenv("AWS_S3_OUTPUT_BUCKET_NAME") + if not output_bucket: + # Use same bucket as input if no output bucket specified + output_bucket = input_bucket + + # Get IAM role ARN + role_arn = ( + litellm_params.get("aws_batch_role_arn") + or optional_params.get("aws_batch_role_arn") + or os.getenv("AWS_BATCH_ROLE_ARN") + ) + if not role_arn: + raise ValueError( + "AWS IAM role ARN is required for Bedrock batch jobs. " + "Set 'aws_batch_role_arn' in litellm_params or AWS_BATCH_ROLE_ARN env var" + ) + + # Get the actual Bedrock model ID using common utility + bedrock_model_id = self.common_utils.extract_model_from_s3_file_path(input_file_id, optional_params) + + if not bedrock_model_id: + raise ValueError("Could not determine Bedrock model ID. Ensure the model is specified in the input file or passed as a parameter.") + + # Generate job name with the correct model ID using common utility + job_name = self.common_utils.generate_unique_job_name(bedrock_model_id, prefix="litellm") + output_key = f"litellm-batch-outputs/{job_name}/" + + # Build input data config + input_data_config: BedrockInputDataConfig = { + "s3InputDataConfig": BedrockS3InputDataConfig( + s3Uri=f"s3://{input_bucket}/{input_key}" + ) + } + + # Build output data config + output_data_config: BedrockOutputDataConfig = { + "s3OutputDataConfig": BedrockS3OutputDataConfig( + s3Uri=f"s3://{output_bucket}/{output_key}" + ) + } + + # Create Bedrock batch request with proper typing + bedrock_request: BedrockCreateBatchRequest = { + "modelId": bedrock_model_id, + "jobName": job_name, + "inputDataConfig": input_data_config, + "outputDataConfig": output_data_config, + "roleArn": role_arn + } + + # Add optional parameters if provided + completion_window = create_batch_data.get("completion_window") + if completion_window: + # Map OpenAI completion window to Bedrock timeout + # OpenAI uses "24h", Bedrock expects timeout in hours + if completion_window == "24h": + bedrock_request["timeoutDurationInHours"] = 24 + + # For Bedrock, we need to return a pre-signed request with AWS auth headers + # Use common utility for AWS signing + endpoint_url = f"https://bedrock.{self._get_aws_region_name(optional_params, model)}.amazonaws.com/model-invocation-job" + signed_headers, signed_data = self.common_utils.sign_aws_request( + service_name="bedrock", + data=bedrock_request, + endpoint_url=endpoint_url, + optional_params=optional_params, + method="POST" + ) + + # Return a pre-signed request format that the HTTP handler can use + return { + "method": "POST", + "url": endpoint_url, + "headers": signed_headers, + "data": signed_data.decode('utf-8') + } + + def transform_create_batch_response( + self, + model: Optional[str], + raw_response: Response, + logging_obj: Any, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform Bedrock batch creation response to LiteLLM format. + """ + try: + response_data: BedrockCreateBatchResponse = raw_response.json() + except Exception as e: + raise ValueError(f"Failed to parse Bedrock batch response: {e}") + + # Extract information from typed Bedrock response + job_arn = response_data.get("jobArn", "") + status: BedrockBatchJobStatus = response_data.get("status", "Submitted") + + # Map Bedrock status to OpenAI-compatible status + status_mapping: Dict[BedrockBatchJobStatus, str] = { + "Submitted": "validating", + "InProgress": "in_progress", + "Completed": "completed", + "Failed": "failed", + "Stopping": "cancelling", + "Stopped": "cancelled" + } + + openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status, "validating")) + + # Get original request data from litellm_params if available + original_request = litellm_params.get("original_batch_request", {}) + + # Create LiteLLM batch object + return LiteLLMBatch( + id=job_arn, # Use ARN as the batch ID + object="batch", + endpoint=original_request.get("endpoint", "/v1/chat/completions"), + errors=None, + input_file_id=original_request.get("input_file_id", ""), + completion_window=original_request.get("completion_window", "24h"), + status=openai_status, + output_file_id=None, # Will be populated when job completes + error_file_id=None, + created_at=int(time.time()), + in_progress_at=int(time.time()) if status == "InProgress" else None, + expires_at=None, + finalizing_at=None, + completed_at=None, + failed_at=None, + expired_at=None, + cancelling_at=None, + cancelled_at=None, + request_counts=None, + metadata=original_request.get("metadata", {}), + ) + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, Headers] + ) -> BaseLLMException: + """ + Get Bedrock-specific error class using common utility. + """ + return self.common_utils.get_error_class(error_message, status_code, headers) + + diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index c76fc0a80c3..831a6da93b3 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -6,6 +6,9 @@ import json import os from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Union +if TYPE_CHECKING: + from litellm.types.llms.bedrock import BedrockCreateBatchRequest + import httpx import litellm @@ -608,3 +611,218 @@ def get_anthropic_beta_from_headers(headers: dict) -> List[str]: # Split comma-separated values and strip whitespace return [beta.strip() for beta in anthropic_beta_header.split(",")] + + +class CommonBatchFilesUtils: + """ + Common utilities for Bedrock batch and file operations. + Provides shared functionality to reduce code duplication between batches and files. + """ + + def __init__(self): + # Import here to avoid circular imports + from .base_aws_llm import BaseAWSLLM + self._base_aws = BaseAWSLLM() + + def get_bedrock_model_id_from_litellm_model(self, model: str) -> str: + """ + Extract the actual Bedrock model ID from LiteLLM model name. + + Args: + model: LiteLLM model name (e.g., "bedrock/anthropic.claude-3-sonnet-20240229-v1:0") + + Returns: + Bedrock model ID (e.g., "anthropic.claude-3-sonnet-20240229-v1:0") + """ + if model.startswith("bedrock/"): + return model[8:] # Remove "bedrock/" prefix + return model + + def parse_s3_uri(self, s3_uri: str) -> tuple: + """ + Parse S3 URI into bucket and key components. + + Args: + s3_uri: S3 URI (e.g., "s3://bucket/key/path") + + Returns: + Tuple of (bucket, key) + + Raises: + ValueError: If URI format is invalid + """ + if not s3_uri.startswith("s3://"): + raise ValueError(f"Invalid S3 URI format: {s3_uri}") + + s3_parts = s3_uri[5:].split("/", 1) # Remove "s3://" and split on first "/" + if len(s3_parts) != 2: + raise ValueError(f"Invalid S3 URI format: {s3_uri}") + + return s3_parts[0], s3_parts[1] # bucket, key + + def extract_model_from_s3_file_path(self, s3_uri: str, optional_params: dict) -> str: + """ + Extract model ID from S3 file path. + + The Bedrock file transformation creates S3 objects with the model name embedded: + Format: s3://bucket/litellm-bedrock-files-{model}-{uuid}.jsonl + """ + # Check if model is provided in optional_params first + if "model" in optional_params and optional_params["model"]: + return self.get_bedrock_model_id_from_litellm_model(optional_params["model"]) + + # Extract model from S3 URI path + # Expected format: s3://bucket/litellm-bedrock-files-{model}-{uuid}.jsonl + try: + bucket, object_key = self.parse_s3_uri(s3_uri) + + # Extract model from object key if it follows our naming pattern + if object_key.startswith("litellm-bedrock-files-"): + # Remove prefix and suffix to get model part + model_part = object_key[22:] # Remove "litellm-bedrock-files-" + # Find the last dash before the UUID + parts = model_part.split("-") + if len(parts) > 1: + # Reconstruct model name (everything except the last UUID part and .jsonl) + model_name = "-".join(parts[:-1]) + if model_name.endswith(".jsonl"): + model_name = model_name[:-6] # Remove .jsonl + return model_name + except Exception: + pass + + # Fallback to default model + return "anthropic.claude-3-5-sonnet-20240620-v1:0" + + def sign_aws_request( + self, + service_name: str, + data: Union[str, dict, "BedrockCreateBatchRequest"], + endpoint_url: str, + optional_params: dict, + method: str = "POST", + ) -> tuple: + """ + Sign AWS request using Signature Version 4. + + Args: + service_name: AWS service name ("bedrock" or "s3") + data: Request data (string or dict) + endpoint_url: Full endpoint URL + optional_params: Optional parameters containing AWS credentials + method: HTTP method (default: POST) + + Returns: + Tuple of (signed_headers, signed_data) + """ + try: + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + except ImportError: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + + # Get AWS credentials using existing methods + aws_region_name = self._base_aws._get_aws_region_name( + optional_params=optional_params, model="" + ) + credentials = self._base_aws.get_credentials( + aws_access_key_id=optional_params.get("aws_access_key_id"), + aws_secret_access_key=optional_params.get("aws_secret_access_key"), + aws_session_token=optional_params.get("aws_session_token"), + aws_region_name=aws_region_name, + aws_session_name=optional_params.get("aws_session_name"), + aws_profile_name=optional_params.get("aws_profile_name"), + aws_role_name=optional_params.get("aws_role_name"), + aws_web_identity_token=optional_params.get("aws_web_identity_token"), + aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + ) + + # Prepare the request data + if isinstance(data, dict): + import json + request_data = json.dumps(data) + else: + request_data = data + + # Prepare headers + headers = {"Content-Type": "application/json"} + + # Create AWS request and sign it + sigv4 = SigV4Auth(credentials, service_name, aws_region_name) + request = AWSRequest( + method=method.upper(), url=endpoint_url, data=request_data, headers=headers + ) + sigv4.add_auth(request) + prepped = request.prepare() + + return dict(prepped.headers), request_data.encode('utf-8') if isinstance(request_data, str) else request_data + + def generate_unique_job_name(self, model: str, prefix: str = "litellm") -> str: + """ + Generate a unique job name for AWS services. + AWS services often have length limits, so this creates a concise name. + + Args: + model: Model name to include in the job name + prefix: Prefix for the job name + + Returns: + Unique job name (≤ 63 characters for Bedrock compatibility) + """ + import fastuuid as uuid + unique_id = str(uuid.uuid4())[:8] + # Format: {prefix}-batch-{model}-{uuid} + # Example: litellm-batch-claude-266c398e + job_name = f"{prefix}-batch-{unique_id}" + + return job_name + + def get_s3_bucket_and_key_from_config( + self, + litellm_params: dict, + optional_params: dict, + bucket_env_var: str = "AWS_S3_BUCKET_NAME", + key_prefix: str = "litellm" + ) -> tuple: + """ + Get S3 bucket and generate a unique key from configuration. + + Args: + litellm_params: LiteLLM parameters + optional_params: Optional parameters + bucket_env_var: Environment variable name for bucket + key_prefix: Prefix for the S3 key + + Returns: + Tuple of (bucket_name, object_key) + """ + import time + import uuid + + # Get bucket name + bucket_name = ( + litellm_params.get("s3_bucket_name") + or optional_params.get("s3_bucket_name") + or os.getenv(bucket_env_var) + ) + if not bucket_name: + raise ValueError(f"S3 bucket name is required. Set 's3_bucket_name' parameter or {bucket_env_var} env var") + + # Generate unique object key + timestamp = int(time.time()) + unique_id = str(uuid.uuid4())[:8] + object_key = f"{key_prefix}-{timestamp}-{unique_id}" + + return bucket_name, object_key + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers] + ) -> BaseLLMException: + """ + Get Bedrock-specific error class. + """ + return BedrockError( + status_code=status_code, + message=error_message, + headers=headers + ) diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py new file mode 100644 index 00000000000..83bbad7e1e8 --- /dev/null +++ b/litellm/llms/bedrock/files/transformation.py @@ -0,0 +1,607 @@ +import json +import os +import time +import uuid +from typing import Any, Dict, List, Optional, Tuple, Union + +from httpx import Headers, Response + +from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.files.transformation import ( + BaseFilesConfig, + LiteLLMLoggingObj, +) +from litellm.types.llms.openai import ( + AllMessageValues, + CreateFileRequest, + FileTypes, + OpenAICreateFileRequestOptionalParams, + OpenAIFileObject, + PathLike, +) +from litellm.types.utils import ExtractedFileData, LlmProviders + +from ..base_aws_llm import BaseAWSLLM +from ..common_utils import BedrockError + + +class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): + """ + Config for Bedrock Files - handles S3 uploads for Bedrock batch processing + """ + + def __init__(self): + self.jsonl_transformation = BedrockJsonlFilesTransformation() + super().__init__() + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.BEDROCK + + @property + def file_upload_http_method(self) -> str: + """ + Bedrock files are uploaded to S3, which requires PUT requests + """ + return "PUT" + + 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: + # No additional headers needed for S3 uploads - AWS credentials handled by BaseAWSLLM + return headers + + + + def _get_content_from_openai_file(self, openai_file_content: FileTypes) -> str: + """ + Helper to extract content from various OpenAI file types and return as string. + + Handles: + - Direct content (str, bytes, IO[bytes]) + - Tuple formats: (filename, content, [content_type], [headers]) + - PathLike objects + """ + content: Union[str, bytes] = b"" + # Extract file content from tuple if necessary + if isinstance(openai_file_content, tuple): + # Take the second element which is always the file content + file_content = openai_file_content[1] + else: + file_content = openai_file_content + + # Handle different file content types + if isinstance(file_content, str): + # String content can be used directly + content = file_content + elif isinstance(file_content, bytes): + # Bytes content can be decoded + content = file_content + elif isinstance(file_content, PathLike): # PathLike + with open(str(file_content), "rb") as f: + content = f.read() + elif hasattr(file_content, "read"): # IO[bytes] + # File-like objects need to be read + content = file_content.read() + + # Ensure content is string + if isinstance(content, bytes): + content = content.decode("utf-8") + + return content + + def _get_s3_object_name_from_batch_jsonl( + self, + openai_jsonl_content: List[Dict[str, Any]], + ) -> str: + """ + Gets a unique S3 object name for the Bedrock batch processing job + + named as: litellm-bedrock-files/{model}/{uuid} + """ + _model = openai_jsonl_content[0].get("body", {}).get("model", "") + # Remove bedrock/ prefix if present + if _model.startswith("bedrock/"): + _model = _model[8:] + object_name = f"litellm-bedrock-files-{_model}-{uuid.uuid4()}.jsonl" + return object_name + + def get_object_name( + self, extracted_file_data: ExtractedFileData, purpose: str + ) -> str: + """ + Get the object name for the request + """ + extracted_file_data_content = extracted_file_data.get("content") + + if extracted_file_data_content is None: + raise ValueError("file content is required") + + if purpose == "batch": + ## 1. If jsonl, check if there's a model name + file_content = self._get_content_from_openai_file( + extracted_file_data_content + ) + + # Split into lines and parse each line as JSON + openai_jsonl_content = [ + json.loads(line) for line in file_content.splitlines() if line.strip() + ] + if len(openai_jsonl_content) > 0: + return self._get_s3_object_name_from_batch_jsonl(openai_jsonl_content) + + ## 2. If not jsonl, return the filename + filename = extracted_file_data.get("filename") + if filename: + return filename + ## 3. If no file name, return timestamp + return str(int(time.time())) + + def get_complete_file_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: Dict, + litellm_params: Dict, + data: CreateFileRequest, + ) -> str: + """ + Get the complete S3 URL for the file upload request + """ + bucket_name = litellm_params.get("s3_bucket_name") or os.getenv("AWS_S3_BUCKET_NAME") + if not bucket_name: + raise ValueError("S3 bucket_name is required. Set 's3_bucket_name' in litellm_params or AWS_S3_BUCKET_NAME env var") + + aws_region_name = self._get_aws_region_name(optional_params, model) + + file_data = data.get("file") + purpose = data.get("purpose") + if file_data is None: + raise ValueError("file is required") + if purpose is None: + raise ValueError("purpose is required") + extracted_file_data = extract_file_data(file_data) + object_name = self.get_object_name(extracted_file_data, purpose) + + # S3 endpoint URL format + s3_endpoint_url = optional_params.get("s3_endpoint_url") or f"https://s3.{aws_region_name}.amazonaws.com" + + return f"{s3_endpoint_url}/{bucket_name}/{object_name}" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAICreateFileRequestOptionalParams]: + return [] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + return optional_params + + def _get_bedrock_provider_from_model(self, model: str) -> Optional[str]: + """ + Extract provider from Bedrock model name + """ + if model.startswith("anthropic."): + return "anthropic" + elif model.startswith("cohere."): + return "cohere" + elif model.startswith("meta.") or model.startswith("llama"): + return "meta" + elif model.startswith("mistral."): + return "mistral" + elif model.startswith("ai21."): + return "ai21" + elif model.startswith("amazon."): + return "amazon" + else: + return None + + def _map_openai_to_bedrock_params( + self, + openai_request_body: Dict[str, Any], + provider: Optional[str] = None, + ) -> Dict[str, Any]: + """ + Transform OpenAI request body to Bedrock-compatible modelInput parameters using existing transformation logic + """ + _model = openai_request_body.get("model", "") + messages = openai_request_body.get("messages", []) + + # Use existing Anthropic transformation logic for Anthropic models + if provider == "anthropic": + from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import ( + AmazonAnthropicClaudeConfig, + ) + + anthropic_config = AmazonAnthropicClaudeConfig() + + # Extract optional params (everything except model and messages) + optional_params = {k: v for k, v in openai_request_body.items() if k not in ["model", "messages"]} + + # Transform using existing Anthropic logic + bedrock_params = anthropic_config.transform_request( + model=_model, + messages=messages, + optional_params=optional_params, + litellm_params={}, + headers={} + ) + + return bedrock_params + else: + # For other providers, use basic mapping + bedrock_params = { + "messages": messages, + **{k: v for k, v in openai_request_body.items() if k not in ["model", "messages"]} + } + return bedrock_params + + def _transform_openai_jsonl_content_to_bedrock_jsonl_content( + self, openai_jsonl_content: List[Dict[str, Any]] + ) -> List[Dict[str, Any]]: + """ + Transforms OpenAI JSONL content to Bedrock batch format + + Bedrock batch format: { "recordId": "alphanumeric string", "modelInput": {JSON body} } + Example: + { + "recordId": "CALL0000001", + "modelInput": { + "anthropic_version": "bedrock-2023-05-31", + "max_tokens": 1024, + "messages": [ + { + "role": "user", + "content": [{"type": "text", "text": "Hello"}] + } + ] + } + } + """ + + bedrock_jsonl_content = [] + for idx, _openai_jsonl_content in enumerate(openai_jsonl_content): + # Extract the request body from OpenAI format + openai_body = _openai_jsonl_content.get("body", {}) + model = openai_body.get("model", "") + + # Determine provider from model name + provider = self._get_bedrock_provider_from_model(model) + + # Transform to Bedrock modelInput format + model_input = self._map_openai_to_bedrock_params( + openai_request_body=openai_body, + provider=provider + ) + + # Create Bedrock batch record + record_id = _openai_jsonl_content.get("custom_id", f"CALL{str(idx).zfill(7)}") + bedrock_record = { + "recordId": record_id, + "modelInput": model_input + } + + bedrock_jsonl_content.append(bedrock_record) + return bedrock_jsonl_content + + def transform_create_file_request( + self, + model: str, + create_file_data: CreateFileRequest, + optional_params: dict, + litellm_params: dict, + ) -> Union[bytes, str, dict]: + """ + Transform file request and return a pre-signed request for S3. + This keeps the HTTP handler clean by doing all the signing here. + """ + file_data = create_file_data.get("file") + if file_data is None: + raise ValueError("file is required") + extracted_file_data = extract_file_data(file_data) + extracted_file_data_content = extracted_file_data.get("content") + + # Get and transform the file content + if ( + create_file_data.get("purpose") == "batch" + and extracted_file_data.get("content_type") == "application/jsonl" + and extracted_file_data_content is not None + ): + ## Transform JSONL content to Bedrock format + original_file_content = self._get_content_from_openai_file( + extracted_file_data_content + ) + openai_jsonl_content = [ + json.loads(line) for line in original_file_content.splitlines() if line.strip() + ] + bedrock_jsonl_content = ( + self._transform_openai_jsonl_content_to_bedrock_jsonl_content( + openai_jsonl_content + ) + ) + file_content = "\n".join(json.dumps(item) for item in bedrock_jsonl_content) + elif isinstance(extracted_file_data_content, bytes): + file_content = extracted_file_data_content.decode('utf-8') + elif isinstance(extracted_file_data_content, str): + file_content = extracted_file_data_content + else: + raise ValueError("Unsupported file content type") + + # Get the S3 URL for upload + api_base = self.get_complete_file_url( + api_base=None, + api_key=None, + model=model, + optional_params=optional_params, + litellm_params=litellm_params, + data=create_file_data, + ) + + # Sign the request and return a pre-signed request object + signed_headers, signed_body = self._sign_s3_request( + content=file_content, + api_base=api_base, + optional_params=optional_params, + ) + + # Return a dict that tells the HTTP handler exactly what to do + return { + "method": "PUT", + "url": api_base, + "headers": signed_headers, + "data": signed_body or file_content, + } + + def _sign_s3_request( + self, + content: str, + api_base: str, + optional_params: dict, + ) -> Tuple[dict, str]: + """ + Sign S3 PUT request using the same proven logic as S3Logger. + Reuses the exact pattern from litellm/integrations/s3_v2.py + """ + try: + import hashlib + + import requests + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + except ImportError: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + + # Get AWS credentials using existing methods + aws_region_name = self._get_aws_region_name( + optional_params=optional_params, model="" + ) + credentials = self.get_credentials( + aws_access_key_id=optional_params.get("aws_access_key_id"), + aws_secret_access_key=optional_params.get("aws_secret_access_key"), + aws_session_token=optional_params.get("aws_session_token"), + aws_region_name=aws_region_name, + aws_session_name=optional_params.get("aws_session_name"), + aws_profile_name=optional_params.get("aws_profile_name"), + aws_role_name=optional_params.get("aws_role_name"), + aws_web_identity_token=optional_params.get("aws_web_identity_token"), + aws_sts_endpoint=optional_params.get("aws_sts_endpoint"), + ) + + # Calculate SHA256 hash of the content (REQUIRED for S3) + content_hash = hashlib.sha256(content.encode("utf-8")).hexdigest() + + # Prepare headers with required S3 headers (same as s3_v2.py) + request_headers = { + "Content-Type": "application/json", # JSONL files are JSON content + "x-amz-content-sha256": content_hash, # REQUIRED by S3 + "Content-Language": "en", + "Cache-Control": "private, immutable, max-age=31536000, s-maxage=0", + } + + # Use requests.Request to prepare the request (same pattern as s3_v2.py) + req = requests.Request("PUT", api_base, data=content, headers=request_headers) + prepped = req.prepare() + + # Sign the request with S3 service + aws_request = AWSRequest( + method=prepped.method, + url=prepped.url, + data=prepped.body, + headers=prepped.headers, + ) + + # Get region name for non-LLM API calls (same as s3_v2.py) + signing_region = self.get_aws_region_name_for_non_llm_api_calls( + aws_region_name=aws_region_name + ) + + SigV4Auth(credentials, "s3", signing_region).add_auth(aws_request) + + # Return signed headers and body + signed_body = aws_request.body + if isinstance(signed_body, bytes): + signed_body = signed_body.decode('utf-8') + elif signed_body is None: + signed_body = content # Fallback to original content + + return dict(aws_request.headers), signed_body + + def transform_create_file_response( + self, + model: Optional[str], + raw_response: Response, + logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> OpenAIFileObject: + """ + Transform S3 File upload response into OpenAI-style FileObject + """ + # For S3 uploads, we typically get an ETag and other metadata + response_headers = raw_response.headers + + # Extract S3 object information from the response + # S3 PUT object returns ETag and other metadata in headers + content_length = response_headers.get("Content-Length", "0") + + # Extract bucket and key from the request URL or litellm_params + bucket_name = litellm_params.get("s3_bucket_name") or os.getenv("AWS_S3_BUCKET_NAME") + + # Generate file ID in S3 format + object_key = getattr(logging_obj, 'object_key', None) or f"file-{int(time.time())}" + file_id = f"s3://{bucket_name}/{object_key}" + + # Extract filename from object key + filename = object_key.split("/")[-1] if "/" in object_key else object_key + + return OpenAIFileObject( + purpose="batch", # Default purpose for Bedrock files + id=file_id, + filename=filename, + created_at=int(time.time()), # Current timestamp + status="uploaded", + bytes=int(content_length) if content_length.isdigit() else 0, + object="file", + ) + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[Dict, Headers] + ) -> BaseLLMException: + return BedrockError( + status_code=status_code, message=error_message, headers=headers + ) + + +class BedrockJsonlFilesTransformation: + """ + Transforms OpenAI /v1/files/* requests to Bedrock S3 file uploads for batch processing + """ + + def transform_openai_file_content_to_bedrock_file_content( + self, openai_file_content: Optional[FileTypes] = None + ) -> Tuple[str, str]: + """ + Transforms OpenAI FileContentRequest to Bedrock S3 file format + """ + + if openai_file_content is None: + raise ValueError("contents of file are None") + # Read the content of the file + file_content = self._get_content_from_openai_file(openai_file_content) + + # Split into lines and parse each line as JSON + openai_jsonl_content = [ + json.loads(line) for line in file_content.splitlines() if line.strip() + ] + bedrock_jsonl_content = ( + self._transform_openai_jsonl_content_to_bedrock_jsonl_content( + openai_jsonl_content + ) + ) + bedrock_jsonl_string = "\n".join( + json.dumps(item) for item in bedrock_jsonl_content + ) + object_name = self._get_s3_object_name( + openai_jsonl_content=openai_jsonl_content + ) + return bedrock_jsonl_string, object_name + + def _transform_openai_jsonl_content_to_bedrock_jsonl_content( + self, openai_jsonl_content: List[Dict[str, Any]] + ): + """ + Delegate to the main BedrockFilesConfig transformation method + """ + config = BedrockFilesConfig() + return config._transform_openai_jsonl_content_to_bedrock_jsonl_content(openai_jsonl_content) + + def _get_s3_object_name( + self, + openai_jsonl_content: List[Dict[str, Any]], + ) -> str: + """ + Gets a unique S3 object name for the Bedrock batch processing job + + named as: litellm-bedrock-files-{model}-{uuid} + """ + _model = openai_jsonl_content[0].get("body", {}).get("model", "") + # Remove bedrock/ prefix if present + if _model.startswith("bedrock/"): + _model = _model[8:] + object_name = f"litellm-bedrock-files-{_model}-{uuid.uuid4()}.jsonl" + return object_name + + + + def _get_content_from_openai_file(self, openai_file_content: FileTypes) -> str: + """ + Helper to extract content from various OpenAI file types and return as string. + + Handles: + - Direct content (str, bytes, IO[bytes]) + - Tuple formats: (filename, content, [content_type], [headers]) + - PathLike objects + """ + content: Union[str, bytes] = b"" + # Extract file content from tuple if necessary + if isinstance(openai_file_content, tuple): + # Take the second element which is always the file content + file_content = openai_file_content[1] + else: + file_content = openai_file_content + + # Handle different file content types + if isinstance(file_content, str): + # String content can be used directly + content = file_content + elif isinstance(file_content, bytes): + # Bytes content can be decoded + content = file_content + elif isinstance(file_content, PathLike): # PathLike + with open(str(file_content), "rb") as f: + content = f.read() + elif hasattr(file_content, "read"): # IO[bytes] + # File-like objects need to be read + content = file_content.read() + + # Ensure content is string + if isinstance(content, bytes): + content = content.decode("utf-8") + + return content + + def transform_s3_bucket_response_to_openai_file_object( + self, create_file_data: CreateFileRequest, s3_upload_response: Dict[str, Any] + ) -> OpenAIFileObject: + """ + Transforms S3 Bucket upload file response to OpenAI FileObject + """ + # S3 response typically contains ETag, key, etc. + object_key = s3_upload_response.get("Key", "") + bucket_name = s3_upload_response.get("Bucket", "") + + # Extract filename from object key + filename = object_key.split("/")[-1] if "/" in object_key else object_key + + return OpenAIFileObject( + purpose=create_file_data.get("purpose", "batch"), + id=f"s3://{bucket_name}/{object_key}", + filename=filename, + created_at=int(time.time()), # Current timestamp + status="uploaded", + bytes=s3_upload_response.get("ContentLength", 0), + object="file", + ) diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 2faea53901c..13133a56aad 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -28,6 +28,7 @@ from litellm.llms.base_llm.audio_transcription.transformation import ( BaseAudioTranscriptionConfig, ) from litellm.llms.base_llm.base_model_iterator import MockResponseIterator +from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseConfig from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig from litellm.llms.base_llm.files.transformation import BaseFilesConfig @@ -58,6 +59,7 @@ from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, ) from litellm.types.llms.openai import ( + CreateBatchRequest, CreateFileRequest, OpenAIFileObject, ResponseInputParam, @@ -66,7 +68,12 @@ from litellm.types.llms.openai import ( from litellm.types.rerank import OptionalRerankParams, RerankResponse from litellm.types.responses.main import DeleteResponseResult from litellm.types.router import GenericLiteLLMParams -from litellm.types.utils import EmbeddingResponse, FileTypes, TranscriptionResponse +from litellm.types.utils import ( + EmbeddingResponse, + FileTypes, + LiteLLMBatch, + TranscriptionResponse, +) from litellm.types.vector_stores import ( VectorStoreCreateOptionalRequestParams, VectorStoreCreateResponse, @@ -2212,15 +2219,38 @@ class BaseLLMHTTPHandler: else: sync_httpx_client = client - if isinstance(transformed_request, str) or isinstance( - transformed_request, bytes - ): - upload_response = sync_httpx_client.post( - url=api_base, - headers=headers, - data=transformed_request, + if isinstance(transformed_request, dict) and "method" in transformed_request: + # Handle pre-signed requests (e.g., from Bedrock S3 uploads) + upload_response = getattr(sync_httpx_client, transformed_request["method"].lower())( + url=transformed_request["url"], + headers=transformed_request["headers"], + data=transformed_request["data"], timeout=timeout, ) + elif isinstance(transformed_request, str) or isinstance( + transformed_request, bytes + ): + # Handle traditional file uploads + # Ensure transformed_request is a string for httpx compatibility + if isinstance(transformed_request, bytes): + transformed_request = transformed_request.decode('utf-8') + + # Use the HTTP method specified by the provider config + http_method = provider_config.file_upload_http_method.upper() + if http_method == "PUT": + upload_response = sync_httpx_client.put( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) + else: # Default to POST + upload_response = sync_httpx_client.post( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) else: try: # Step 1: Initial request to get upload URL @@ -2280,16 +2310,52 @@ class BaseLLMHTTPHandler: ) else: async_httpx_client = client + + ######################################################### + # Debug Logging + ######################################################### + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "complete_input_dict": transformed_request, + "api_base": api_base, + "headers": headers, + }, + ) - if isinstance(transformed_request, str) or isinstance( - transformed_request, bytes - ): - upload_response = await async_httpx_client.post( - url=api_base, - headers=headers, - data=transformed_request, + if isinstance(transformed_request, dict) and "method" in transformed_request: + # Handle pre-signed requests (e.g., from Bedrock S3 uploads) + upload_response = await getattr(async_httpx_client, transformed_request["method"].lower())( + url=transformed_request["url"], + headers=transformed_request["headers"], + data=transformed_request["data"], timeout=timeout, ) + elif isinstance(transformed_request, str) or isinstance( + transformed_request, bytes + ): + # Handle traditional file uploads + # Ensure transformed_request is a string for httpx compatibility + if isinstance(transformed_request, bytes): + transformed_request = transformed_request.decode('utf-8') + + # Use the HTTP method specified by the provider config + http_method = provider_config.file_upload_http_method.upper() + if http_method == "PUT": + upload_response = await async_httpx_client.put( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) + else: # Default to POST + upload_response = await async_httpx_client.post( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) else: try: # Step 1: Initial request to get upload URL @@ -2330,6 +2396,188 @@ class BaseLLMHTTPHandler: litellm_params=litellm_params, ) + def create_batch( + self, + create_batch_data: "CreateBatchRequest", + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: Optional[str], + api_key: Optional[str], + logging_obj: "LiteLLMLoggingObj", + _is_async: bool = False, + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + ) -> Union["LiteLLMBatch", Coroutine[Any, Any, "LiteLLMBatch"]]: + """ + Creates a batch using provider-specific batch creation process + """ + # get config from model, custom llm provider + headers = provider_config.validate_environment( + api_key=api_key, + headers=headers, + model="", + messages=[], + optional_params={}, + litellm_params=litellm_params, + ) + + api_base = provider_config.get_complete_batch_url( + api_base=api_base, + api_key=api_key, + model="", + optional_params={}, + litellm_params=litellm_params, + data=create_batch_data, + ) + if api_base is None: + raise ValueError("api_base is required for create_batch") + + # Get the transformed request data + transformed_request = provider_config.transform_create_batch_request( + model="", + create_batch_data=create_batch_data, + litellm_params=litellm_params, + optional_params={}, + ) + + if _is_async: + return self.async_create_batch( + transformed_request=transformed_request, + litellm_params=litellm_params, + provider_config=provider_config, + headers=headers, + api_base=api_base, + logging_obj=logging_obj, + client=client, + timeout=timeout, + create_batch_data=create_batch_data, + ) + + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client() + else: + sync_httpx_client = client + + try: + if isinstance(transformed_request, dict) and "method" in transformed_request: + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + batch_response = getattr(sync_httpx_client, transformed_request["method"].lower())( + url=transformed_request["url"], + headers=transformed_request["headers"], + data=transformed_request["data"], + timeout=timeout, + ) + elif isinstance(transformed_request, dict): + # For other providers that use JSON requests + batch_response = sync_httpx_client.post( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + json=transformed_request, + timeout=timeout, + ) + else: + # Handle other request types if needed + batch_response = sync_httpx_client.post( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) + except Exception as e: + verbose_logger.exception(f"Error creating batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + # Store original request for response transformation + litellm_params_with_request = {**litellm_params, "original_batch_request": create_batch_data} + + return provider_config.transform_create_batch_response( + model=None, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params_with_request, + ) + + async def async_create_batch( + self, + transformed_request: Union[bytes, str, dict], + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: str, + logging_obj: "LiteLLMLoggingObj", + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + create_batch_data: Optional["CreateBatchRequest"] = None, + ): + """ + Async version of create_batch + """ + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=provider_config.custom_llm_provider + ) + else: + async_httpx_client = client + + ######################################################### + # Debug Logging + ######################################################### + logging_obj.pre_call( + input="", + api_key="", + additional_args={ + "complete_input_dict": transformed_request, + "api_base": api_base, + "headers": headers, + }, + ) + + try: + if isinstance(transformed_request, dict) and "method" in transformed_request: + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + batch_response = await getattr(async_httpx_client, transformed_request["method"].lower())( + url=transformed_request["url"], + headers=transformed_request["headers"], + data=transformed_request["data"], + timeout=timeout, + ) + elif isinstance(transformed_request, dict): + # For other providers that use JSON requests + batch_response = await async_httpx_client.post( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + json=transformed_request, + timeout=timeout, + ) + else: + # Handle other request types if needed + batch_response = await async_httpx_client.post( + url=api_base, + headers=headers, + data=transformed_request, + timeout=timeout, + ) + except Exception as e: + verbose_logger.exception(f"Error creating batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + # Store original request for response transformation (for async version) + litellm_params_with_request = {**litellm_params, "original_batch_request": create_batch_data or {}} + + return provider_config.transform_create_batch_response( + model=None, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params_with_request, + ) + def list_files(self): """ Lists all files @@ -2381,6 +2629,7 @@ class BaseLLMHTTPHandler: BaseVectorStoreConfig, BaseGoogleGenAIGenerateContentConfig, BaseAnthropicMessagesConfig, + BaseBatchesConfig, "BasePassthroughConfig", ], ): diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py index 068c9db035b..5853f9de2aa 100644 --- a/litellm/types/llms/bedrock.py +++ b/litellm/types/llms/bedrock.py @@ -573,3 +573,84 @@ class AmazonDeepSeekR1StreamingResponse(TypedDict): generation_token_count: int stop_reason: Optional[str] prompt_token_count: int + + +################ Bedrock Batch Types ################# + + +class BedrockS3InputDataConfig(TypedDict): + """S3 input data configuration for Bedrock batch jobs.""" + s3Uri: str + + +class BedrockInputDataConfig(TypedDict): + """Input data configuration for Bedrock batch jobs.""" + s3InputDataConfig: BedrockS3InputDataConfig + + +class BedrockS3OutputDataConfig(TypedDict): + """S3 output data configuration for Bedrock batch jobs.""" + s3Uri: str + + +class BedrockOutputDataConfig(TypedDict): + """Output data configuration for Bedrock batch jobs.""" + s3OutputDataConfig: BedrockS3OutputDataConfig + + +class BedrockCreateBatchRequest(TypedDict, total=False): + """ + Request structure for creating a Bedrock batch inference job. + + Reference: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelInvocationJob.html + """ + jobName: str + roleArn: str + modelId: str + inputDataConfig: BedrockInputDataConfig + outputDataConfig: BedrockOutputDataConfig + timeoutDurationInHours: Optional[int] + clientRequestToken: Optional[str] + tags: Optional[List[dict]] + + +BedrockBatchJobStatus = Literal[ + "Submitted", + "InProgress", + "Completed", + "Failed", + "Stopping", + "Stopped" +] + + +class BedrockCreateBatchResponse(TypedDict): + """ + Response structure from creating a Bedrock batch inference job. + + Reference: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelInvocationJob.html + """ + jobArn: str + jobName: str + status: BedrockBatchJobStatus + + +class BedrockGetBatchResponse(TypedDict, total=False): + """ + Response structure from getting a Bedrock batch inference job. + + Reference: https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetModelInvocationJob.html + """ + jobArn: str + jobName: str + modelId: str + roleArn: str + status: BedrockBatchJobStatus + message: Optional[str] + submitTime: Optional[str] + lastModifiedTime: Optional[str] + endTime: Optional[str] + inputDataConfig: BedrockInputDataConfig + outputDataConfig: BedrockOutputDataConfig + timeoutDurationInHours: Optional[int] + clientRequestToken: Optional[str] diff --git a/litellm/types/router.py b/litellm/types/router.py index 864fdbf79b8..8bed2bdccda 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -216,6 +216,10 @@ class GenericLiteLLMParams(CredentialLiteLLMParams, CustomPricingLiteLLMParams): auto_router_default_model: Optional[str] = None auto_router_embedding_model: Optional[str] = None + # Batch/File API Params + s3_bucket_name: Optional[str] = None + gcs_bucket_name: Optional[str] = None + def __init__( self, custom_llm_provider: Optional[str] = None, @@ -265,6 +269,9 @@ class GenericLiteLLMParams(CredentialLiteLLMParams, CustomPricingLiteLLMParams): auto_router_config: Optional[str] = None, auto_router_default_model: Optional[str] = None, auto_router_embedding_model: Optional[str] = None, + # Batch/File API Params + s3_bucket_name: Optional[str] = None, + gcs_bucket_name: Optional[str] = None, **params, ): args = locals() diff --git a/litellm/utils.py b/litellm/utils.py index 405d4cb98e4..1c70f894d52 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -234,6 +234,7 @@ from litellm.llms.base_llm.base_utils import ( BaseLLMModelInfo, type_to_response_format_param, ) +from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseConfig from litellm.llms.base_llm.completion.transformation import BaseTextCompletionConfig from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig @@ -7287,6 +7288,20 @@ class ProviderConfigManager: from litellm.llms.vertex_ai.files.transformation import VertexAIFilesConfig return VertexAIFilesConfig() + elif LlmProviders.BEDROCK == provider: + from litellm.llms.bedrock.files.transformation import BedrockFilesConfig + + return BedrockFilesConfig() + return None + + @staticmethod + def get_provider_batches_config( + model: str, + provider: LlmProviders, + ) -> Optional[BaseBatchesConfig]: + if LlmProviders.BEDROCK == provider: + from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + return BedrockBatchesConfig() return None @staticmethod diff --git a/tests/batches_tests/bedrock_batch_completions.jsonl b/tests/batches_tests/bedrock_batch_completions.jsonl new file mode 100644 index 00000000000..3037b1031ea --- /dev/null +++ b/tests/batches_tests/bedrock_batch_completions.jsonl @@ -0,0 +1,3 @@ +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} + diff --git a/tests/batches_tests/test_bedrock_files_and_batches.py b/tests/batches_tests/test_bedrock_files_and_batches.py new file mode 100644 index 00000000000..edf8c3c7745 --- /dev/null +++ b/tests/batches_tests/test_bedrock_files_and_batches.py @@ -0,0 +1,70 @@ + +# What is this? +## Unit Tests for OpenAI Batches API +import asyncio +import json +import os +import sys +import traceback +import tempfile +from dotenv import load_dotenv + +load_dotenv() +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system-path + + +import pytest +from typing import Optional +import litellm + + +@pytest.mark.asyncio() +async def test_async_create_file(): + """ + 1. Create File for Batch completion + 2. Create Batch Request + 3. Retrieve the specific batch + """ + litellm._turn_on_debug() + print("Testing async create batch") + + file_name = "bedrock_batch_completions.jsonl" + _current_dir = os.path.dirname(os.path.abspath(__file__)) + file_path = os.path.join(_current_dir, file_name) + file_obj = await litellm.acreate_file( + file=open(file_path, "rb"), + purpose="batch", + custom_llm_provider="bedrock", + s3_bucket_name="litellm-proxy", + ) + +@pytest.mark.asyncio() +async def test_async_file_and_batch(): + """ + Test file retrieval + """ + litellm._turn_on_debug() + file_name = "bedrock_batch_completions.jsonl" + _current_dir = os.path.dirname(os.path.abspath(__file__)) + file_path = os.path.join(_current_dir, file_name) + file_obj = await litellm.acreate_file( + file=open(file_path, "rb"), + purpose="batch", + custom_llm_provider="bedrock", + s3_bucket_name="litellm-proxy", + ) + print("CREATED FILE RESPONSE=", file_obj) + + # create batch + create_batch_response = await litellm.acreate_batch( + completion_window="24h", + endpoint="/v1/chat/completions", + input_file_id=file_obj.id, + metadata={"key1": "value1", "key2": "value2"}, + custom_llm_provider="bedrock", + aws_batch_role_arn="arn:aws:iam::888602223428:role/service-role/AmazonBedrockExecutionRoleForAgents_BB9HNW6V4CV" + ) + print("CREATED BATCH RESPONSE=", create_batch_response) + From be7c762882d3905939a5f2eedd727077b812cd66 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 18:25:27 -0700 Subject: [PATCH 096/178] add video_generation --- tests/test_litellm/test_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 9e487939284..3352077d627 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -549,6 +549,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "completion", "embedding", "image_generation", + "video_generation", "moderation", "rerank", "responses", From 23ae7170d1d8d766ac5e386f49d22a50054f806f Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 18:25:43 -0700 Subject: [PATCH 097/178] [Feat] Allow using Veo Video Generation through LiteLLM Pass through routes (#14228) * fix: add follow_redirects=True, * test_pass_through_with_httpbin_redirect * cook book veo video * docs Veo Video Generation with Google AI Studio * add veo-3.0-generate-preview cost tracking details * track vertex_video_models --- cookbook/veo_video_generation.py | 311 ++++++++++++++++++ .../docs/pass_through/google_ai_studio.md | 7 + .../docs/proxy/veo_video_generation.md | 163 +++++++++ litellm/__init__.py | 4 + .../get_llm_provider_logic.py | 1 + litellm/llms/custom_httpx/http_handler.py | 2 + ...odel_prices_and_context_window_backup.json | 84 +++++ model_prices_and_context_window.json | 84 +++++ .../test_pass_through_endpoints.py | 55 ++++ 9 files changed, 711 insertions(+) create mode 100644 cookbook/veo_video_generation.py create mode 100644 docs/my-website/docs/proxy/veo_video_generation.md diff --git a/cookbook/veo_video_generation.py b/cookbook/veo_video_generation.py new file mode 100644 index 00000000000..64a7207feb1 --- /dev/null +++ b/cookbook/veo_video_generation.py @@ -0,0 +1,311 @@ +#!/usr/bin/env python3 +""" +Complete example for Veo video generation through LiteLLM proxy. + +This script demonstrates how to: +1. Generate videos using Google's Veo model +2. Poll for completion status +3. Download the generated video file + +Requirements: +- LiteLLM proxy running with Google AI Studio pass-through configured +- Google AI Studio API key with Veo access +""" + +import json +import os +import time +import requests +from typing import Optional + + +class VeoVideoGenerator: + """Complete Veo video generation client using LiteLLM proxy.""" + + def __init__(self, base_url: str = "http://localhost:4000/gemini/v1beta", + api_key: str = "sk-1234"): + """ + Initialize the Veo video generator. + + Args: + base_url: Base URL for the LiteLLM proxy with Gemini pass-through + api_key: API key for LiteLLM proxy authentication + """ + self.base_url = base_url + self.api_key = api_key + self.headers = { + "x-goog-api-key": api_key, + "Content-Type": "application/json" + } + + def generate_video(self, prompt: str) -> Optional[str]: + """ + Initiate video generation with Veo. + + Args: + prompt: Text description of the video to generate + + Returns: + Operation name if successful, None otherwise + """ + print(f"🎬 Generating video with prompt: '{prompt}'") + + url = f"{self.base_url}/models/veo-3.0-generate-preview:predictLongRunning" + payload = { + "instances": [{ + "prompt": prompt + }] + } + + try: + response = requests.post(url, headers=self.headers, json=payload) + response.raise_for_status() + + data = response.json() + operation_name = data.get("name") + + if operation_name: + print(f"✅ Video generation started: {operation_name}") + return operation_name + else: + print("❌ No operation name returned") + print(f"Response: {json.dumps(data, indent=2)}") + return None + + except requests.RequestException as e: + print(f"❌ Failed to start video generation: {e}") + if hasattr(e, 'response') and e.response is not None: + try: + error_data = e.response.json() + print(f"Error details: {json.dumps(error_data, indent=2)}") + except: + print(f"Error response: {e.response.text}") + return None + + def wait_for_completion(self, operation_name: str, max_wait_time: int = 600) -> Optional[str]: + """ + Poll operation status until video generation is complete. + + Args: + operation_name: Name of the operation to monitor + max_wait_time: Maximum time to wait in seconds (default: 10 minutes) + + Returns: + Video URI if successful, None otherwise + """ + print("⏳ Waiting for video generation to complete...") + + operation_url = f"{self.base_url}/{operation_name}" + start_time = time.time() + poll_interval = 10 # Start with 10 seconds + + while time.time() - start_time < max_wait_time: + try: + print(f"🔍 Polling status... ({int(time.time() - start_time)}s elapsed)") + + response = requests.get(operation_url, headers=self.headers) + response.raise_for_status() + + data = response.json() + + # Check for errors + if "error" in data: + print("❌ Error in video generation:") + print(json.dumps(data["error"], indent=2)) + return None + + # Check if operation is complete + is_done = data.get("done", False) + + if is_done: + print("🎉 Video generation complete!") + + try: + # Extract video URI from nested response + video_uri = data["response"]["generateVideoResponse"]["generatedSamples"][0]["video"]["uri"] + print(f"📹 Video URI: {video_uri}") + return video_uri + except KeyError as e: + print(f"❌ Could not extract video URI: {e}") + print("Full response:") + print(json.dumps(data, indent=2)) + return None + + # Wait before next poll, with exponential backoff + time.sleep(poll_interval) + poll_interval = min(poll_interval * 1.2, 30) # Cap at 30 seconds + + except requests.RequestException as e: + print(f"❌ Error polling operation status: {e}") + time.sleep(poll_interval) + + print(f"⏰ Timeout after {max_wait_time} seconds") + return None + + def download_video(self, video_uri: str, output_filename: str = "generated_video.mp4") -> bool: + """ + Download the generated video file. + + Args: + video_uri: URI of the video to download (from Google's response) + output_filename: Local filename to save the video + + Returns: + True if download successful, False otherwise + """ + print(f"⬇️ Downloading video...") + print(f"Original URI: {video_uri}") + + # Convert Google URI to LiteLLM proxy URI + # Example: files/abc123 -> /gemini/v1beta/files/abc123:download?alt=media + if video_uri.startswith("files/"): + download_path = f"{video_uri}:download?alt=media" + else: + download_path = video_uri + + litellm_download_url = f"{self.base_url}/{download_path}" + print(f"Download URL: {litellm_download_url}") + + try: + # Download with streaming and redirect handling + response = requests.get( + litellm_download_url, + headers=self.headers, + stream=True, + allow_redirects=True # Handle redirects automatically + ) + response.raise_for_status() + + # Save video file + with open(output_filename, 'wb') as f: + downloaded_size = 0 + for chunk in response.iter_content(chunk_size=8192): + if chunk: + f.write(chunk) + downloaded_size += len(chunk) + + # Progress indicator for large files + if downloaded_size % (1024 * 1024) == 0: # Every MB + print(f"📦 Downloaded {downloaded_size / (1024*1024):.1f} MB...") + + # Verify file was created and has content + if os.path.exists(output_filename): + file_size = os.path.getsize(output_filename) + if file_size > 0: + print(f"✅ Video downloaded successfully!") + print(f"📁 Saved as: {output_filename}") + print(f"📏 File size: {file_size / (1024*1024):.2f} MB") + return True + else: + print("❌ Downloaded file is empty") + os.remove(output_filename) + return False + else: + print("❌ File was not created") + return False + + except requests.RequestException as e: + print(f"❌ Download failed: {e}") + if hasattr(e, 'response') and e.response is not None: + print(f"Status code: {e.response.status_code}") + print(f"Response headers: {dict(e.response.headers)}") + return False + + def generate_and_download(self, prompt: str, output_filename: str = None) -> bool: + """ + Complete workflow: generate video and download it. + + Args: + prompt: Text description for video generation + output_filename: Output filename (auto-generated if None) + + Returns: + True if successful, False otherwise + """ + # Auto-generate filename if not provided + if output_filename is None: + timestamp = int(time.time()) + safe_prompt = "".join(c for c in prompt[:30] if c.isalnum() or c in (' ', '-', '_')).rstrip() + output_filename = f"veo_video_{safe_prompt.replace(' ', '_')}_{timestamp}.mp4" + + print("=" * 60) + print("🎬 VEO VIDEO GENERATION WORKFLOW") + print("=" * 60) + + # Step 1: Generate video + operation_name = self.generate_video(prompt) + if not operation_name: + return False + + # Step 2: Wait for completion + video_uri = self.wait_for_completion(operation_name) + if not video_uri: + return False + + # Step 3: Download video + success = self.download_video(video_uri, output_filename) + + if success: + print("=" * 60) + print("🎉 SUCCESS! Video generation complete!") + print(f"📁 Video saved as: {output_filename}") + print("=" * 60) + else: + print("=" * 60) + print("❌ FAILED! Video generation or download failed") + print("=" * 60) + + return success + + +def main(): + """ + Example usage of the VeoVideoGenerator. + + Configure these environment variables: + - LITELLM_BASE_URL: Your LiteLLM proxy URL (default: http://localhost:4000/gemini/v1beta) + - LITELLM_API_KEY: Your LiteLLM API key (default: sk-1234) + """ + + # Configuration from environment or defaults + base_url = os.getenv("LITELLM_BASE_URL", "http://localhost:4000/gemini/v1beta") + api_key = os.getenv("LITELLM_API_KEY", "sk-1234") + + print("🚀 Starting Veo Video Generation Example") + print(f"📡 Using LiteLLM proxy at: {base_url}") + + # Initialize generator + generator = VeoVideoGenerator(base_url=base_url, api_key=api_key) + + # Example prompts - try different ones! + example_prompts = [ + "A cat playing with a ball of yarn in a sunny garden", + "Ocean waves crashing against rocky cliffs at sunset", + "A bustling city street with people walking and cars passing by", + "A peaceful forest with sunlight filtering through the trees" + ] + + # Use first example or get from user + prompt = example_prompts[0] + print(f"🎬 Using prompt: '{prompt}'") + + # Generate and download video + success = generator.generate_and_download(prompt) + + if success: + print("\n✅ Example completed successfully!") + print("💡 Try modifying the prompt in the script for different videos!") + else: + print("\n❌ Example failed!") + print("🔧 Check your LiteLLM proxy configuration and Google AI Studio API key") + + # Troubleshooting tips + print("\n🔍 Troubleshooting:") + print("1. Ensure LiteLLM proxy is running with Google AI Studio pass-through") + print("2. Verify your Google AI Studio API key has Veo access") + print("3. Check that your prompt meets Veo's content guidelines") + print("4. Review the LiteLLM proxy logs for detailed error information") + + +if __name__ == "__main__": + main() diff --git a/docs/my-website/docs/pass_through/google_ai_studio.md b/docs/my-website/docs/pass_through/google_ai_studio.md index c3671f58d36..3de7c54aa7a 100644 --- a/docs/my-website/docs/pass_through/google_ai_studio.md +++ b/docs/my-website/docs/pass_through/google_ai_studio.md @@ -230,6 +230,13 @@ curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5 ``` +## **Example 4: Video Generation with Veo** + +Generate videos using Google's Veo model through LiteLLM pass-through routes. + +[**→ Complete Veo Video Generation Guide**](../proxy/veo_video_generation.md) + + ## Advanced Pre-requisites diff --git a/docs/my-website/docs/proxy/veo_video_generation.md b/docs/my-website/docs/proxy/veo_video_generation.md new file mode 100644 index 00000000000..14c263bf847 --- /dev/null +++ b/docs/my-website/docs/proxy/veo_video_generation.md @@ -0,0 +1,163 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Veo Video Generation with Google AI Studio + +Generate videos using Google's Veo model through LiteLLM's pass-through endpoints. + +## Quick Start + +LiteLLM allows you to use Google AI Studio's Veo video generation API through pass-through routes with zero configuration. + +### 1. Add Google AI Studio API Key to your environment + +```bash +export GEMINI_API_KEY="your_google_ai_studio_api_key" +``` + +### 2. Start LiteLLM Proxy + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +### 3. Generate Video + + + + +```python +import requests +import time +import json + +# Configuration +BASE_URL = "http://localhost:4000/gemini/v1beta" +API_KEY = "anything" # Use "anything" as the key + +headers = { + "x-goog-api-key": API_KEY, + "Content-Type": "application/json" +} + +# Step 1: Initiate video generation +def generate_video(prompt): + url = f"{BASE_URL}/models/veo-3.0-generate-preview:predictLongRunning" + payload = { + "instances": [{ + "prompt": prompt + }] + } + + response = requests.post(url, headers=headers, json=payload) + response.raise_for_status() + + data = response.json() + return data.get("name") # Operation name + +# Step 2: Poll for completion +def wait_for_completion(operation_name): + operation_url = f"{BASE_URL}/{operation_name}" + + while True: + response = requests.get(operation_url, headers=headers) + response.raise_for_status() + + data = response.json() + + if data.get("done", False): + # Extract video URI + video_uri = data["response"]["generateVideoResponse"]["generatedSamples"][0]["video"]["uri"] + return video_uri + + time.sleep(10) # Wait 10 seconds before next poll + +# Step 3: Download video +def download_video(video_uri, filename="generated_video.mp4"): + # Replace Google URL with LiteLLM proxy URL + litellm_url = video_uri.replace( + "https://generativelanguage.googleapis.com/v1beta", + BASE_URL + ) + + response = requests.get(litellm_url, headers=headers, stream=True) + response.raise_for_status() + + with open(filename, 'wb') as f: + for chunk in response.iter_content(chunk_size=8192): + if chunk: + f.write(chunk) + + return filename + +# Complete workflow +prompt = "A cat playing with a ball of yarn in a sunny garden" + +print("Generating video...") +operation_name = generate_video(prompt) + +print("Waiting for completion...") +video_uri = wait_for_completion(operation_name) + +print("Downloading video...") +filename = download_video(video_uri) + +print(f"Video saved as: {filename}") +``` + + + + + +```bash +# Step 1: Initiate video generation +curl -X POST "http://localhost:4000/gemini/v1beta/models/veo-3.0-generate-preview:predictLongRunning" \ + -H "x-goog-api-key: anything" \ + -H "Content-Type: application/json" \ + -d '{ + "instances": [{ + "prompt": "A cat playing with a ball of yarn in a sunny garden" + }] + }' + +# Response will include operation name: +# {"name": "operations/generate_12345"} + +# Step 2: Poll for completion +curl -X GET "http://localhost:4000/gemini/v1beta/operations/generate_12345" \ + -H "x-goog-api-key: anything" + +# Step 3: Download video (when done=true) +curl -X GET "http://localhost:4000/gemini/v1beta/files/VIDEO_ID:download?alt=media" \ + -H "x-goog-api-key: anything" \ + --output generated_video.mp4 +``` + + + + +## Complete Example + +For a full working example with error handling and logging, see our [Veo Video Generation Cookbook](https://github.com/BerriAI/litellm/blob/main/cookbook/veo_video_generation.py). + +## How It Works + +1. **Video Generation Request**: Send a prompt to Veo's `predictLongRunning` endpoint +2. **Operation Polling**: Monitor the long-running operation until completion +3. **File Download**: Download the generated video through LiteLLM's pass-through with automatic redirect handling + +LiteLLM handles: +- ✅ Authentication with Google AI Studio +- ✅ Request routing and proxying +- ✅ Automatic redirect handling for file downloads + +## Configuration Options + +### Environment Variables + +```bash +export GEMINI_API_KEY="your_google_ai_studio_api_key" +``` + diff --git a/litellm/__init__.py b/litellm/__init__.py index 6a184d70b5a..d411ff0ad45 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -450,6 +450,7 @@ vertex_vision_models: Set = set() vertex_chat_models: Set = set() vertex_code_chat_models: Set = set() vertex_ai_image_models: Set = set() +vertex_ai_video_models: Set = set() vertex_text_models: Set = set() vertex_code_text_models: Set = set() vertex_embedding_models: Set = set() @@ -605,6 +606,9 @@ def add_known_models(): elif value.get("litellm_provider") == "vertex_ai-image-models": key = key.replace("vertex_ai/", "") vertex_ai_image_models.add(key) + elif value.get("litellm_provider") == "vertex_ai-video-models": + key = key.replace("vertex_ai/", "") + vertex_ai_video_models.add(key) elif value.get("litellm_provider") == "vertex_ai-openai_models": key = key.replace("vertex_ai/", "") vertex_openai_models.add(key) diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 2049480e264..c784568cc9f 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -320,6 +320,7 @@ def get_llm_provider( # noqa: PLR0915 or model in litellm.vertex_embedding_models or model in litellm.vertex_vision_models or model in litellm.vertex_ai_image_models + or model in litellm.vertex_ai_video_models ): custom_llm_provider = "vertex_ai" ## ai21 diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index 4d8781fff2a..36b543086f5 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -212,6 +212,7 @@ class AsyncHTTPHandler: verify=ssl_config, cert=cert, headers=headers, + follow_redirects=True, ) async def close(self): @@ -687,6 +688,7 @@ class HTTPHandler: verify=ssl_config, cert=cert, headers=headers, + follow_redirects=True, ) else: self.client = client diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index a7cb09763d2..a7586124509 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -9484,6 +9484,48 @@ "source": "https://aistudio.google.com", "supports_tool_choice": true }, + "gemini/veo-3.0-generate-preview": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.75, + "litellm_provider": "gemini", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, + "gemini/veo-3.0-fast-generate-preview": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.40, + "litellm_provider": "gemini", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, + "gemini/veo-2.0-generate-001": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.35, + "litellm_provider": "gemini", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, "vertex_ai/claude-opus-4-1": { "max_tokens": 4096, "max_input_tokens": 200000, @@ -10301,6 +10343,48 @@ "mode": "image_generation", "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, + "vertex_ai/veo-3.0-generate-preview": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.75, + "litellm_provider": "vertex_ai-video-models", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, + "vertex_ai/veo-3.0-fast-generate-preview": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.40, + "litellm_provider": "vertex_ai-video-models", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, + "vertex_ai/veo-2.0-generate-001": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.35, + "litellm_provider": "vertex_ai-video-models", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, "text-embedding-004": { "max_tokens": 2048, "max_input_tokens": 2048, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index a7cb09763d2..a7586124509 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -9484,6 +9484,48 @@ "source": "https://aistudio.google.com", "supports_tool_choice": true }, + "gemini/veo-3.0-generate-preview": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.75, + "litellm_provider": "gemini", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, + "gemini/veo-3.0-fast-generate-preview": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.40, + "litellm_provider": "gemini", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, + "gemini/veo-2.0-generate-001": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.35, + "litellm_provider": "gemini", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, "vertex_ai/claude-opus-4-1": { "max_tokens": 4096, "max_input_tokens": 200000, @@ -10301,6 +10343,48 @@ "mode": "image_generation", "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, + "vertex_ai/veo-3.0-generate-preview": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.75, + "litellm_provider": "vertex_ai-video-models", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, + "vertex_ai/veo-3.0-fast-generate-preview": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.40, + "litellm_provider": "vertex_ai-video-models", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, + "vertex_ai/veo-2.0-generate-001": { + "max_tokens": 1024, + "max_input_tokens": 1024, + "output_cost_per_second": 0.35, + "litellm_provider": "vertex_ai-video-models", + "mode": "video_generation", + "supported_modalities": [ + "text" + ], + "supported_output_modalities": [ + "video" + ], + "source": "https://ai.google.dev/gemini-api/docs/video" + }, "text-embedding-004": { "max_tokens": 2048, "max_input_tokens": 2048, diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py index b8381201b0a..e296cb25f80 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py @@ -1245,3 +1245,58 @@ async def test_delete_pass_through_endpoint_empty_list(): # Verify the exception assert exc_info.value.status_code == 400 assert "no pass-through endpoints setup" in str(exc_info.value.detail).lower() + + + +@pytest.mark.asyncio +async def test_pass_through_with_httpbin_redirect(): + """ + Integration test using httpbin.org redirect endpoint to test real redirect handling. + This tests the actual redirect handling capability end-to-end using the full pass_through_request function. + """ + from unittest.mock import MagicMock + + from fastapi import Request + from starlette.datastructures import Headers, QueryParams + + from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( + pass_through_request, + ) + + # Create mock request + mock_request = MagicMock(spec=Request) + mock_request.method = "GET" + mock_request.headers = Headers({}) + mock_request.query_params = QueryParams("") + + # Mock the body method to return empty bytes for GET request + async def mock_body(): + return b"" + mock_request.body = mock_body + + # Mock user API key dict + mock_user_api_key_dict = MagicMock() + + try: + # Test with httpbin.org redirect endpoint + # This will redirect to httpbin.org/get + response = await pass_through_request( + request=mock_request, + target="https://httpbin.org/redirect/1", + custom_headers={}, + user_api_key_dict=mock_user_api_key_dict + ) + + # Should get the final response (200) from /get endpoint, not the redirect (302) + assert response.status_code == 200 + + # The response should be from the /get endpoint + response_content = response.body.decode('utf-8') + + # httpbin.org/get returns JSON with info about the request + assert '"url": "https://httpbin.org/get"' in response_content + print("GOT A Response from HTTPBIN=", response_content) + except Exception as e: + # If httpbin.org is not accessible, skip the test + import pytest + pytest.skip(f"Could not reach httpbin.org for integration test: {e}") From ab3cd5e96eaddc3f47386377a8daf37a7be13df9 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 18:28:11 -0700 Subject: [PATCH 098/178] fix memory_usage_in_mem_cache cache endpoint vulnerability (#14229) --- litellm/proxy/_types.py | 6 +++++- litellm/proxy/common_utils/debug_utils.py | 12 +++++++++--- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 7206d3dcb0e..0595c44d69d 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -388,7 +388,11 @@ class LiteLLMRoutes(enum.Enum): ] # NOTE: ROUTES ONLY FOR MASTER KEY - only the Master Key should be able to Reset Spend - master_key_only_routes = ["/global/spend/reset"] + master_key_only_routes = [ + "/global/spend/reset", + "/memory-usage-in-mem-cache", + "/memory-usage-in-mem-cache-items", + ] key_management_routes = [ KeyManagementRoutes.KEY_GENERATE, diff --git a/litellm/proxy/common_utils/debug_utils.py b/litellm/proxy/common_utils/debug_utils.py index 8096d782dec..16ab2cc8058 100644 --- a/litellm/proxy/common_utils/debug_utils.py +++ b/litellm/proxy/common_utils/debug_utils.py @@ -5,10 +5,12 @@ import os import tracemalloc from collections import Counter -from fastapi import APIRouter +from fastapi import APIRouter, Depends from litellm import get_secret_str from litellm._logging import verbose_proxy_logger +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.auth.user_api_key_auth import user_api_key_auth router = APIRouter() @@ -84,7 +86,9 @@ if os.environ.get("LITELLM_PROFILE", "false").lower() == "true": @router.get("/memory-usage-in-mem-cache", include_in_schema=False) -async def memory_usage_in_mem_cache(): +async def memory_usage_in_mem_cache( + _: UserAPIKeyAuth = Depends(user_api_key_auth), +): # returns the size of all in-memory caches on the proxy server """ 1. user_api_key_cache @@ -121,7 +125,9 @@ async def memory_usage_in_mem_cache(): @router.get("/memory-usage-in-mem-cache-items", include_in_schema=False) -async def memory_usage_in_mem_cache_items(): +async def memory_usage_in_mem_cache_items( + _: UserAPIKeyAuth = Depends(user_api_key_auth), +): # returns the size of all in-memory caches on the proxy server """ 1. user_api_key_cache From 69d5a91e02ce36cc55b63dfbb91dd5f8d1c87bba Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 3 Sep 2025 18:28:44 -0700 Subject: [PATCH 099/178] =?UTF-8?q?bump:=20version=201.76.2=20=E2=86=92=20?= =?UTF-8?q?1.76.3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 2683afcea6a..9f5d876cf2c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm" -version = "1.76.2" +version = "1.76.3" description = "Library to easily interface with LLM API providers" authors = ["BerriAI"] license = "MIT" @@ -156,7 +156,7 @@ requires = ["poetry-core", "wheel"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "1.76.2" +version = "1.76.3" version_files = [ "pyproject.toml:^version" ] From b0450d2ddf6443b33d90aaacf6abfbb6f8cb44b7 Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Wed, 3 Sep 2025 23:32:06 -0400 Subject: [PATCH 100/178] docs: added more info to load balancing & passthrough endpoints --- docs/my-website/docs/pass_through/intro.md | 40 ++++++++++++ docs/my-website/docs/proxy/load_balancing.md | 69 +++++++++----------- 2 files changed, 71 insertions(+), 38 deletions(-) diff --git a/docs/my-website/docs/pass_through/intro.md b/docs/my-website/docs/pass_through/intro.md index 3d6286afcc5..38218224f11 100644 --- a/docs/my-website/docs/pass_through/intro.md +++ b/docs/my-website/docs/pass_through/intro.md @@ -11,3 +11,43 @@ These endpoints are useful for 2 scenarios: ## How is your request handled? The request is passed through to the provider's endpoint. The response is then passed back to the client. **No translation is done.** + +### Request Forwarding Process + +1. **Request Reception**: LiteLLM receives your request at `/provider/endpoint` +2. **Authentication**: Your LiteLLM API key is validated and mapped to the provider's API key +3. **Request Transformation**: Request is reformatted for the target provider's API +4. **Forwarding**: Request is sent to the actual provider endpoint +5. **Response Handling**: Provider response is returned directly to you + +### Authentication Flow + +```mermaid +graph LR + A[Client Request] --> B[LiteLLM Proxy] + B --> C[Validate LiteLLM API Key] + C --> D[Map to Provider API Key] + D --> E[Forward to Provider] + E --> F[Return Response] +``` + +**Key Points:** +- Use your **LiteLLM API key** in requests, not the provider's key +- LiteLLM handles the provider authentication internally +- Same authentication works across all passthrough endpoints + +### Error Handling + +**Provider Errors**: Forwarded directly to you with original error codes and messages + +**LiteLLM Errors**: +- `401`: Invalid LiteLLM API key +- `404`: Provider or endpoint not supported +- `500`: Internal routing/forwarding errors + +### Benefits + +- **Unified Authentication**: One API key for all providers +- **Centralized Logging**: All requests logged through LiteLLM +- **Cost Tracking**: Usage tracked across all endpoints +- **Access Control**: Same permissions apply to passthrough endpoints diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md index fd95b57c1ba..67f41d231db 100644 --- a/docs/my-website/docs/proxy/load_balancing.md +++ b/docs/my-website/docs/proxy/load_balancing.md @@ -13,6 +13,23 @@ For more details on routing strategies / params, see [Routing](../routing.md) ::: +## How Load Balancing Works + +LiteLLM automatically distributes requests across multiple deployments of the same model using its built-in router. the proxy routes traffic to optimize performance and reliability. + +"simple-shuffle" routing strategy is used by default + +### Routing Strategies + +| Strategy | Description | When to Use | +|----------|-------------|-------------| +| **simple-shuffle** (recommended) | Randomly distributes requests | General purpose, good for even load distribution | +| **least-busy** | Routes to deployment with fewest active requests | High concurrency scenarios | +| **usage-based-routing** (bad for perf) | Routes to deployment with lowest current usage (RPM/TPM) | When you want to respect rate limits evenly | +| **latency-based-routing** | Routes to fastest responding deployment | Latency-critical applications | +| **cost-based-routing** | Routes to deployment with lowest cost | Cost-sensitive applications | + + ## Quick Start - Load Balancing #### Step 1 - Set deployments on config @@ -106,49 +123,13 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ] }' ``` - - - -```python -from langchain.chat_models import ChatOpenAI -from langchain.prompts.chat import ( - ChatPromptTemplate, - HumanMessagePromptTemplate, - SystemMessagePromptTemplate, -) -from langchain.schema import HumanMessage, SystemMessage -import os - -os.environ["OPENAI_API_KEY"] = "anything" - -chat = ChatOpenAI( - openai_api_base="http://0.0.0.0:4000", - model="gpt-3.5-turbo", -) - -messages = [ - SystemMessage( - content="You are a helpful assistant that im using to make a test request to." - ), - HumanMessage( - content="test from litellm. tell me why it's amazing in 1 sentence" - ), -] -response = chat(messages) - -print(response) -``` - - - - ### Test - Loadbalancing In this request, the following will occur: 1. A rate limit exception will be raised -2. LiteLLM proxy will retry the request on the model group (default is 3). +2. LiteLLM proxy will retry the request on the model group (default retries are 3). ```bash curl -X POST 'http://0.0.0.0:4000/chat/completions' \ @@ -256,4 +237,16 @@ model_group_alias: Optional[Dict[str, Union[str, RouterModelGroupAliasItem]]] = class RouterModelGroupAliasItem(TypedDict): model: str hidden: bool # if 'True', don't return on `/v1/models`, `/v1/model/info`, `/v1/model_group/info` -``` \ No newline at end of file +``` + +### When You'll See Load Balancing in Action + +**Immediate Effects:** + +- Different deployments serve subsequent requests (visible in logs) +- Better response times during high traffic + +**Observable Benefits:** +- **Higher throughput**: More requests handled simultaneously across deployments +- **Improved reliability**: If one deployment fails, traffic automatically routes to healthy ones +- **Better resource utilization**: Load spread evenly across all available deployments From 9e3010daa4342c731e5e929869a5227e38b9a92e Mon Sep 17 00:00:00 2001 From: yeahyung Date: Thu, 4 Sep 2025 16:41:23 +0900 Subject: [PATCH 101/178] (#14204) increase token usage with TTL preservation --- .../hooks/parallel_request_limiter_v3.py | 94 ++++++++++++++++++- 1 file changed, 91 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index b04d14bcc8b..b3840761d2a 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -68,6 +68,32 @@ end return results """ +TOKEN_INCREMENT_SCRIPT = """ +local results = {} + +-- Process each key/increment_value/ttl triplet +for i = 1, #KEYS do + local key = KEYS[i] + local increment_value = tonumber(ARGV[i * 2 - 1]) + local ttl_seconds = tonumber(ARGV[i * 2]) + + -- Increment the value + local new_value = redis.call('INCRBYFLOAT', key, increment_value) + + -- Handle TTL: only set expire if ttl_seconds > 0 and key has no current TTL + -- ttl_seconds can be 0 (no TTL) or positive (set TTL) + if ttl_seconds and ttl_seconds > 0 then + local current_ttl = redis.call('TTL', key) + if current_ttl == -1 then + redis.call('EXPIRE', key, ttl_seconds) + end + end + + table.insert(results, new_value) +end + +return results +""" class RateLimitDescriptorRateLimitObject(TypedDict, total=False): requests_per_unit: Optional[int] @@ -109,8 +135,14 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): BATCH_RATE_LIMITER_SCRIPT ) ) + self.token_increment_script = ( + self.internal_usage_cache.dual_cache.redis_cache.async_register_script( + TOKEN_INCREMENT_SCRIPT + ) + ) else: self.batch_rate_limiter_script = None + self.token_increment_script = None self.window_size = int(os.getenv("LITELLM_RATE_LIMIT_WINDOW_SIZE", 60)) @@ -567,6 +599,62 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): return pipeline_operations + async def async_increment_tokens_with_ttl_preservation( + self, + pipeline_operations: List["RedisPipelineIncrementOperation"], + parent_otel_span: Optional[Span] = None, + ) -> None: + """ + Increment token counters using Lua script to preserve existing TTL. + This prevents TTL reset on every token increment. + """ + if not pipeline_operations: + return + + # Check if script is available + if self.token_increment_script is None: + verbose_proxy_logger.debug("TTL preservation script not available, using regular pipeline") + await self.internal_usage_cache.dual_cache.async_increment_cache_pipeline( + increment_list=pipeline_operations, + litellm_parent_otel_span=parent_otel_span, + ) + return + + try: + # Use Lua script for all operations + keys = [] + args = [] + + for op in pipeline_operations: + # Convert None TTL to 0 for Lua script + ttl_value = op["ttl"] if op["ttl"] is not None else 0 + + verbose_proxy_logger.debug( + f"Executing TTL-preserving increment for key={op['key']}, " + f"increment={op['increment_value']}, ttl={ttl_value}" + ) + keys.append(op["key"]) + args.extend([op["increment_value"], ttl_value]) + + await self.token_increment_script( + keys=keys, + args=args, + ) + + verbose_proxy_logger.debug( + f"Successfully executed TTL-preserving increment for {len(pipeline_operations)} keys" + ) + + except Exception as e: + verbose_proxy_logger.warning( + f"TTL preservation failed, falling back to regular pipeline: {str(e)}" + ) + # Fallback to regular pipeline on error + await self.internal_usage_cache.dual_cache.async_increment_cache_pipeline( + increment_list=pipeline_operations, + litellm_parent_otel_span=parent_otel_span, + ) + def get_rate_limit_type(self) -> Literal["output", "input", "total"]: from litellm.proxy.proxy_server import general_settings @@ -713,9 +801,9 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): # Execute all increments in a single pipeline if pipeline_operations: - await self.internal_usage_cache.dual_cache.async_increment_cache_pipeline( - increment_list=pipeline_operations, - litellm_parent_otel_span=litellm_parent_otel_span, + await self.async_increment_tokens_with_ttl_preservation( + pipeline_operations=pipeline_operations, + parent_otel_span=litellm_parent_otel_span, ) except Exception as e: From 7829de294816214c9582526e90fc60c139410774 Mon Sep 17 00:00:00 2001 From: yeahyung Date: Thu, 4 Sep 2025 16:41:29 +0900 Subject: [PATCH 102/178] (#14204) add test code --- .../hooks/test_parallel_request_limiter_v3.py | 201 ++++++++++++++++++ 1 file changed, 201 insertions(+) diff --git a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py index 694a49159c0..da4218a9547 100644 --- a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py +++ b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py @@ -934,3 +934,204 @@ async def test_team_member_rate_limits_v3(): assert team_member_descriptor["value"] == f"{_team_id}:{_user_id}", "Team member value should combine team_id and user_id" assert team_member_descriptor["rate_limit"]["requests_per_unit"] == 10, "Team member RPM limit should be set" assert team_member_descriptor["rate_limit"]["tokens_per_unit"] == 1000, "Team member TPM limit should be set" + + +@pytest.mark.asyncio +async def test_async_increment_tokens_with_ttl_preservation(): + """ + Test TTL preservation functionality for token increment operations. + + This test verifies that: + 1. Keys are created with proper TTL on first increment + 2. TTL is preserved on subsequent increments (not reset) + 3. Both TTL and non-TTL operations work correctly in the same call + + Environment variables required: + - REDIS_HOST: Redis server hostname + - REDIS_PORT: Redis server port + - REDIS_PASSWORD: Redis password (optional) + + Test scenario: + 1. First call: Create keys with TTL=60s and TTL=None + 2. Wait 2 seconds + 3. Second call: Increment same keys + 4. Verify TTL decreased but wasn't reset to 60s + """ + import os + import time + from litellm.caching.redis_cache import RedisCache + from litellm.types.caching import RedisPipelineIncrementOperation + + # Skip test if Redis environment variables are not set + redis_host = os.getenv("REDIS_HOST") + redis_port = os.getenv("REDIS_PORT") + redis_password = os.getenv("REDIS_PASSWORD") + + if not redis_host or not redis_port: + pytest.skip("Redis environment variables (REDIS_HOST, REDIS_PORT) not set") + + # Setup Redis cache + redis_cache = RedisCache( + host=redis_host, + port=int(redis_port), + password=redis_password, + ) + + local_cache = DualCache(redis_cache=redis_cache) + parallel_request_handler = _PROXY_MaxParallelRequestsHandler( + internal_usage_cache=InternalUsageCache(local_cache) + ) + + # Verify Redis connection is working + try: + await redis_cache.ping() + except Exception as e: + pytest.skip(f"Redis connection failed: {str(e)}") + + # Test keys + test_key_with_ttl = "test_ttl_preservation:with_ttl" + test_key_without_ttl = "test_ttl_preservation:without_ttl" + + try: + # Clean up any existing test keys + try: + await redis_cache.async_delete_cache(test_key_with_ttl) + await redis_cache.async_delete_cache(test_key_without_ttl) + except Exception: + # Keys might not exist, ignore cleanup errors + pass + + # First increment: Create operations with mixed TTL scenarios + pipeline_operations_first = [ + RedisPipelineIncrementOperation( + key=test_key_with_ttl, + increment_value=10.0, + ttl=60 + ), + RedisPipelineIncrementOperation( + key=test_key_without_ttl, + increment_value=5.0, + ttl=None # No TTL + ) + ] + + # Execute first increment + await parallel_request_handler.async_increment_tokens_with_ttl_preservation( + pipeline_operations=pipeline_operations_first + ) + + # Verify keys exist and check initial TTL + ttl_after_first = await redis_cache.async_get_ttl(test_key_with_ttl) + value_after_first_with_ttl = await redis_cache.async_get_cache(test_key_with_ttl) + value_after_first_without_ttl = await redis_cache.async_get_cache(test_key_without_ttl) + + assert value_after_first_with_ttl == 10.0, "First increment should set value to 10.0" + assert value_after_first_without_ttl == 5.0, "First increment should set value to 5.0" + assert ttl_after_first is not None and ttl_after_first > 0, "Key with TTL should have positive TTL after first increment" + assert ttl_after_first <= 60, "TTL should not exceed the set value" + + # Check TTL for key without TTL (should be None, meaning no expiry) + ttl_no_ttl_key = await redis_cache.async_get_ttl(test_key_without_ttl) + assert ttl_no_ttl_key is None, "Key without TTL should have no expiry (None from async_get_ttl)" + + # Wait a moment to ensure TTL decreases + await asyncio.sleep(2) + + # Second increment: Same operations to test TTL preservation + pipeline_operations_second = [ + RedisPipelineIncrementOperation( + key=test_key_with_ttl, + increment_value=15.0, + ttl=60 # Same TTL value + ), + RedisPipelineIncrementOperation( + key=test_key_without_ttl, + increment_value=7.0, + ttl=None # No TTL + ) + ] + + # Execute second increment + await parallel_request_handler.async_increment_tokens_with_ttl_preservation( + pipeline_operations=pipeline_operations_second + ) + + # Verify TTL preservation and value updates + ttl_after_second = await redis_cache.async_get_ttl(test_key_with_ttl) + value_after_second_with_ttl = await redis_cache.async_get_cache(test_key_with_ttl) + value_after_second_without_ttl = await redis_cache.async_get_cache(test_key_without_ttl) + + assert value_after_second_with_ttl == 25.0, "Second increment should update value to 25.0" + assert value_after_second_without_ttl == 12.0, "Second increment should update value to 12.0" + + # Critical test: TTL should be preserved (not reset to 60) + assert ttl_after_second is not None, "TTL should still exist" + assert ttl_after_second < ttl_after_first, "TTL should have decreased (not been reset)" + assert ttl_after_second > 0, "TTL should still be positive" + + # TTL should not be close to the original 60 seconds (proving it wasn't reset) + assert ttl_after_second < 59, "TTL should be significantly less than original, proving preservation" + + # Key without TTL should still have no expiry + ttl_no_ttl_key_after_second = await redis_cache.async_get_ttl(test_key_without_ttl) + assert ttl_no_ttl_key_after_second is None, "Key without TTL should still have no expiry" + + finally: + # Clean up test keys + try: + await redis_cache.async_delete_cache(test_key_with_ttl) + await redis_cache.async_delete_cache(test_key_without_ttl) + except Exception: + # Ignore cleanup errors + pass + + # Properly close Redis connections to prevent warnings + try: + await redis_cache.disconnect() + except Exception: + # Ignore disconnect errors + pass + + +@pytest.mark.asyncio +async def test_async_increment_tokens_fallback_behavior(): + """ + Test fallback behavior when Lua script is not available. + """ + from litellm.types.caching import RedisPipelineIncrementOperation + + local_cache = DualCache() + parallel_request_handler = _PROXY_MaxParallelRequestsHandler( + internal_usage_cache=InternalUsageCache(local_cache) + ) + + # Mock the token_increment_script to None to simulate unavailable script + parallel_request_handler.token_increment_script = None + + # Mock the fallback method + fallback_called = False + original_method = parallel_request_handler.internal_usage_cache.dual_cache.async_increment_cache_pipeline + + async def mock_fallback(*args, **kwargs): + nonlocal fallback_called + fallback_called = True + return await original_method(*args, **kwargs) + + parallel_request_handler.internal_usage_cache.dual_cache.async_increment_cache_pipeline = mock_fallback + + # Test operations + pipeline_operations = [ + RedisPipelineIncrementOperation( + key="test_fallback_key", + increment_value=10.0, + ttl=60 + ) + ] + + # Execute increment + await parallel_request_handler.async_increment_tokens_with_ttl_preservation( + pipeline_operations=pipeline_operations + ) + + # Verify fallback was called + assert fallback_called, "Fallback method should be called when Lua script is not available" From 25afa67616edb5fc9e270815430d996139291a5c Mon Sep 17 00:00:00 2001 From: 22mSqRi <37729945+22mSqRi@users.noreply.github.com> Date: Thu, 4 Sep 2025 09:04:01 +0000 Subject: [PATCH 103/178] fix: Key Budget not resets at expectable times --- .../key_management_endpoints.py | 6 ++-- .../test_key_management_endpoints.py | 30 ++++++++++--------- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index ddce7481ce7..3868c9df694 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -1566,14 +1566,12 @@ async def generate_key_helper_fn( # noqa: PLR0915 if duration is None: # allow tokens that never expire expires = None else: - duration_s = duration_in_seconds(duration=duration) - expires = datetime.now(timezone.utc) + timedelta(seconds=duration_s) + expires = get_budget_reset_time(budget_duration=duration) if key_budget_duration is None: # one-time budget key_reset_at = None else: - duration_s = duration_in_seconds(duration=key_budget_duration) - key_reset_at = datetime.now(timezone.utc) + timedelta(seconds=duration_s) + key_reset_at = get_budget_reset_time(budget_duration=key_budget_duration) if budget_duration is None: # one-time budget reset_at = None diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py index e0102f8cd7a..893e5767ecd 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py @@ -118,9 +118,9 @@ async def test_key_token_handling(monkeypatch): @pytest.mark.asyncio -async def test_budget_reset_at_first_of_month(monkeypatch): +async def test_budget_reset_and_expires_at_first_of_month(monkeypatch): """ - Test that when budget_duration is "1mo", budget_reset_at is set to first of next month + Test that when budget_duration, duration, and key_budget_duration are "1mo", budget_reset_at and expires are set to first of next month """ mock_prisma_client = AsyncMock() mock_insert_data = AsyncMock( @@ -152,10 +152,12 @@ async def test_budget_reset_at_first_of_month(monkeypatch): # Use monkeypatch to set the prisma_client monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) - # Test key generation with budget_duration="1mo" + # Test key generation with budget_duration="1mo", duration="1mo", key_budget_duration="1mo" response = await generate_key_helper_fn( request_type="user", budget_duration="1mo", + duration="1mo", + key_budget_duration="1mo", user_id="test_user", ) @@ -171,17 +173,17 @@ async def test_budget_reset_at_first_of_month(monkeypatch): expected_month = now.month + 1 expected_year = now.year - # Parse the response date - response_date = response["budget_reset_at"] - - # Verify budget_reset_at is set to first of next month - assert ( - response_date.year == expected_year - ), f"Expected year {expected_year}, got {response_date.year}" - assert ( - response_date.month == expected_month - ), f"Expected month {expected_month}, got {response_date.month}" - assert response_date.day == 1, f"Expected day 1, got {response_date.day}" + # Verify budget_reset_at, expires is set to first of next month + for key in ["budget_reset_at", "expires"]: + response_date = response.get(key) + assert response_date is not None, f"{key} not found in response" + assert ( + response_date.year == expected_year + ), f"Expected year {expected_year}, got {response_date.year} for {key}" + assert ( + response_date.month == expected_month + ), f"Expected month {expected_month}, got {response_date.month} for {key}" + assert response_date.day == 1, f"Expected day 1, got {response_date.day} for {key}" @pytest.mark.asyncio From 8878951f16759e808577440fae667297f9bd328b Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 4 Sep 2025 07:30:12 -0700 Subject: [PATCH 104/178] docs fix: disable_add_user_agent_to_request_tags --- docs/my-website/docs/proxy/cost_tracking.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md index 19e3344f21b..7f0a13f763f 100644 --- a/docs/my-website/docs/proxy/cost_tracking.md +++ b/docs/my-website/docs/proxy/cost_tracking.md @@ -505,11 +505,11 @@ litellm_settings: ### Disable user-agent tracking -You can disable user-agent tracking by setting `litellm_settings.disable_user_agent_tracking` to `true`. +You can disable user-agent tracking by setting `litellm_settings.disable_add_user_agent_to_request_tags` to `true`. ```yaml litellm_settings: - disable_user_agent_tracking: true + disable_add_user_agent_to_request_tags: true ``` ## ✨ (Enterprise) Generate Spend Reports From 006ffea98f5c6223c5e0c2c5eca2312df1714ecf Mon Sep 17 00:00:00 2001 From: Eitan1112 <52412573+Eitan1112@users.noreply.github.com> Date: Thu, 4 Sep 2025 17:53:40 +0300 Subject: [PATCH 105/178] Add additionalProperties to vertex ai Schema definition Add additionalProperties field to vertex ai Schema TypedDict --- litellm/types/llms/vertex_ai.py | 1 + 1 file changed, 1 insertion(+) diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py index 1b74ee25803..c3027504dff 100644 --- a/litellm/types/llms/vertex_ai.py +++ b/litellm/types/llms/vertex_ai.py @@ -113,6 +113,7 @@ class Schema(TypedDict, total=False): pattern: str example: Any anyOf: List["Schema"] + additionalProperties: bool class FunctionDeclaration(TypedDict, total=False): From 1237be04a5d1ffccfabc433486a4c4882bcf481a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 4 Sep 2025 07:58:20 -0700 Subject: [PATCH 106/178] test_aaamodel_prices_and_context_window_json_is_valid --- tests/test_litellm/test_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 3352077d627..6f63b866220 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -637,7 +637,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "type": "array", "items": { "type": "string", - "enum": ["text", "image", "audio", "code"], + "enum": ["text", "image", "audio", "code", "video"], }, }, "supports_native_streaming": {"type": "boolean"}, From da136fa07b85cd27c87d49147e9727c28b7c7a9d Mon Sep 17 00:00:00 2001 From: Eitan1112 <52412573+Eitan1112@users.noreply.github.com> Date: Thu, 4 Sep 2025 18:05:53 +0300 Subject: [PATCH 107/178] Change additionalProperties type to Any This is aligned with "default" which is also `Any`, and both in vertex ai docs: https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.cachedContents#Schema are both with 'value' type --- litellm/types/llms/vertex_ai.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py index c3027504dff..625a76b6789 100644 --- a/litellm/types/llms/vertex_ai.py +++ b/litellm/types/llms/vertex_ai.py @@ -113,7 +113,7 @@ class Schema(TypedDict, total=False): pattern: str example: Any anyOf: List["Schema"] - additionalProperties: bool + additionalProperties: Any class FunctionDeclaration(TypedDict, total=False): From 5f79e8aac6e133cc70c15d95fcfaa4d3adc6efad Mon Sep 17 00:00:00 2001 From: Sameer Kankute <135028480+kankute-sameer@users.noreply.github.com> Date: Thu, 4 Sep 2025 22:27:48 +0530 Subject: [PATCH 108/178] Litellm passthrough cost tracking chat completion (#14256) * feat: add structured output for sdk * Add support for cost tracking for chat completion in passthrough * remove not required changes --- .../openai_passthrough_logging_handler.py | 383 +++++++++++++++ .../pass_through_endpoints.py | 32 +- .../streaming_handler.py | 21 + .../pass_through_endpoints/success_handler.py | 51 +- litellm/responses/main.py | 2 +- litellm/responses/utils.py | 2 +- .../pass_through_endpoints.py | 1 + .../base_responses_api.py | 2 +- ...test_openai_passthrough_logging_handler.py | 451 ++++++++++++++++++ 9 files changed, 921 insertions(+), 24 deletions(-) create mode 100644 litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py create mode 100644 tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py new file mode 100644 index 00000000000..dd772ffa502 --- /dev/null +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py @@ -0,0 +1,383 @@ +""" +OpenAI Passthrough Logging Handler + +Handles cost tracking and logging for OpenAI passthrough endpoints, specifically /chat/completions. +""" + +from datetime import datetime +from typing import List, Optional, Union +from urllib.parse import urlparse + +import httpx + +import litellm +from litellm._logging import verbose_proxy_logger +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.litellm_logging import ( + get_standard_logging_object_payload, +) +from litellm.llms.openai.openai import OpenAIConfig +from litellm.llms.openai.openai import OpenAIConfig as OpenAIConfigType +from litellm.proxy._types import PassThroughEndpointLoggingTypedDict +from litellm.proxy.pass_through_endpoints.llm_provider_handlers.base_passthrough_logging_handler import ( + BasePassthroughLoggingHandler, +) +from litellm.proxy.pass_through_endpoints.success_handler import ( + PassThroughEndpointLogging, +) +from litellm.types.passthrough_endpoints.pass_through_endpoints import ( + EndpointType, + PassthroughStandardLoggingPayload, +) +from litellm.types.utils import LlmProviders +from litellm.utils import ModelResponse, TextCompletionResponse + + +class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): + """ + OpenAI-specific passthrough logging handler that provides cost tracking for /chat/completions endpoints. + """ + + @property + def llm_provider_name(self) -> LlmProviders: + return LlmProviders.OPENAI + + @staticmethod + def get_provider_config(model: str) -> OpenAIConfigType: + """Get OpenAI provider configuration for the given model.""" + return OpenAIConfig() + + @staticmethod + def is_openai_chat_completions_route(url_route: str) -> bool: + """Check if the URL route is an OpenAI chat completions endpoint.""" + if not url_route: + return False + parsed_url = urlparse(url_route) + return bool( + parsed_url.hostname + and ( + "api.openai.com" in parsed_url.hostname + or "openai.azure.com" in parsed_url.hostname + ) + and "/v1/chat/completions" in parsed_url.path + ) + + @staticmethod + def _get_user_from_metadata( + passthrough_logging_payload: PassthroughStandardLoggingPayload, + ) -> Optional[str]: + """Extract user information from passthrough logging payload.""" + request_body = passthrough_logging_payload.get("request_body") + if request_body: + return request_body.get("user") + return None + + @staticmethod + def openai_passthrough_handler( + httpx_response: httpx.Response, + response_body: dict, + logging_obj: LiteLLMLoggingObj, + url_route: str, + result: str, + start_time: datetime, + end_time: datetime, + cache_hit: bool, + request_body: dict, + **kwargs, + ) -> PassThroughEndpointLoggingTypedDict: + """ + Handle OpenAI passthrough logging with cost tracking for chat completions. + """ + # Only handle chat completions endpoints + if not OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route( + url_route + ): + # For non-chat-completions endpoints, use the base handler without cost tracking + base_handler = OpenAIPassthroughLoggingHandler() + return base_handler.passthrough_chat_handler( + httpx_response=httpx_response, + response_body=response_body, + logging_obj=logging_obj, + url_route=url_route, + result=result, + start_time=start_time, + end_time=end_time, + cache_hit=cache_hit, + request_body=request_body, + **kwargs, + ) + + # Extract model from request or response + model = request_body.get("model", response_body.get("model", "")) + if not model: + verbose_proxy_logger.warning( + "No model found in request or response for OpenAI passthrough cost tracking" + ) + base_handler = OpenAIPassthroughLoggingHandler() + return base_handler.passthrough_chat_handler( + httpx_response=httpx_response, + response_body=response_body, + logging_obj=logging_obj, + url_route=url_route, + result=result, + start_time=start_time, + end_time=end_time, + cache_hit=cache_hit, + request_body=request_body, + **kwargs, + ) + + try: + # Transform the response to LiteLLM format for cost calculation + provider_config = OpenAIPassthroughLoggingHandler.get_provider_config( + model=model + ) + litellm_model_response: ModelResponse = provider_config.transform_response( + raw_response=httpx_response, + model_response=litellm.ModelResponse(), + model=model, + messages=request_body.get("messages", []), + logging_obj=logging_obj, + optional_params=request_body.get("optional_params", {}), + api_key="", + request_data=request_body, + encoding=litellm.encoding, + json_mode=request_body.get("response_format", {}).get("type") + == "json_object", + litellm_params={}, + ) + + # Calculate cost using LiteLLM's cost calculator + response_cost = litellm.completion_cost( + completion_response=litellm_model_response, + model=model, + custom_llm_provider="openai", + ) + + # Update kwargs with cost information + kwargs["response_cost"] = response_cost + kwargs["model"] = model + kwargs["custom_llm_provider"] = "openai" + + # Extract user information for tracking + passthrough_logging_payload: Optional[ + PassthroughStandardLoggingPayload + ] = kwargs.get("passthrough_logging_payload") + if passthrough_logging_payload: + user = OpenAIPassthroughLoggingHandler._get_user_from_metadata( + passthrough_logging_payload=passthrough_logging_payload, + ) + if user: + kwargs.setdefault("litellm_params", {}) + kwargs["litellm_params"].update( + {"proxy_server_request": {"body": {"user": user}}} + ) + + # Create standard logging object + get_standard_logging_object_payload( + kwargs=kwargs, + init_response_obj=litellm_model_response, + start_time=start_time, + end_time=end_time, + logging_obj=logging_obj, + status="success", + ) + + # Update logging object with cost information + logging_obj.model_call_details["model"] = model + logging_obj.model_call_details["custom_llm_provider"] = "openai" + logging_obj.model_call_details["response_cost"] = response_cost + + verbose_proxy_logger.debug( + f"OpenAI passthrough cost tracking - Model: {model}, Cost: ${response_cost:.6f}" + ) + + return { + "result": litellm_model_response, + "kwargs": kwargs, + } + + except Exception as e: + verbose_proxy_logger.error( + f"Error in OpenAI passthrough cost tracking: {str(e)}" + ) + # Fall back to base handler without cost tracking + base_handler = OpenAIPassthroughLoggingHandler() + return base_handler.passthrough_chat_handler( + httpx_response=httpx_response, + response_body=response_body, + logging_obj=logging_obj, + url_route=url_route, + result=result, + start_time=start_time, + end_time=end_time, + cache_hit=cache_hit, + request_body=request_body, + **kwargs, + ) + + def _build_complete_streaming_response( + self, + all_chunks: list, + litellm_logging_obj: LiteLLMLoggingObj, + model: str, + ) -> Optional[Union[ModelResponse, TextCompletionResponse]]: + """ + Builds complete response from raw chunks for OpenAI streaming responses. + + - Converts str chunks to generic chunks + - Converts generic chunks to litellm chunks (OpenAI format) + - Builds complete response from litellm chunks + """ + try: + # OpenAI's response iterator to parse chunks + from litellm.llms.openai.openai import OpenAIChatCompletionResponseIterator + + openai_iterator = OpenAIChatCompletionResponseIterator( + streaming_response=None, + sync_stream=False, + ) + + all_openai_chunks = [] + for chunk_str in all_chunks: + try: + # Parse the string chunk using the base iterator's string parser + from litellm.llms.base_llm.base_model_iterator import ( + BaseModelResponseIterator, + ) + + # Convert string chunk to dict + stripped_json_chunk = ( + BaseModelResponseIterator._string_to_dict_parser( + str_line=chunk_str + ) + ) + + if stripped_json_chunk: + # Parse the chunk using OpenAI's chunk parser + transformed_chunk = openai_iterator.chunk_parser( + chunk=stripped_json_chunk + ) + if transformed_chunk is not None: + all_openai_chunks.append(transformed_chunk) + + except (StopIteration, StopAsyncIteration, Exception) as e: + verbose_proxy_logger.debug(f"Error parsing streaming chunk: {e}") + continue + + if not all_openai_chunks: + verbose_proxy_logger.warning( + "No valid chunks found in streaming response" + ) + return None + + # Build complete response from chunks + complete_streaming_response = litellm.stream_chunk_builder( + chunks=all_openai_chunks + ) + + return complete_streaming_response + + except Exception as e: + verbose_proxy_logger.error( + f"Error building complete streaming response: {str(e)}" + ) + return None + + @staticmethod + def _handle_logging_openai_collected_chunks( + litellm_logging_obj: LiteLLMLoggingObj, + passthrough_success_handler_obj: PassThroughEndpointLogging, + url_route: str, + request_body: dict, + endpoint_type: EndpointType, + start_time: datetime, + all_chunks: List[str], + end_time: datetime, + ) -> PassThroughEndpointLoggingTypedDict: + """ + Handle logging for collected OpenAI streaming chunks with cost tracking. + """ + try: + # Extract model from request body + model = request_body.get("model", "gpt-4o") + + # Build complete response from chunks using our streaming handler + handler = OpenAIPassthroughLoggingHandler() + complete_response = handler._build_complete_streaming_response( + all_chunks=all_chunks, + litellm_logging_obj=litellm_logging_obj, + model=model, + ) + + if complete_response is None: + verbose_proxy_logger.warning( + "Failed to build complete response from OpenAI streaming chunks" + ) + return { + "result": None, + "kwargs": {}, + } + + # Calculate cost using LiteLLM's cost calculator + response_cost = litellm.completion_cost( + completion_response=complete_response, + model=model, + custom_llm_provider="openai", + ) + + # Prepare kwargs for logging + kwargs = { + "response_cost": response_cost, + "model": model, + "custom_llm_provider": "openai", + } + + # Extract user information for tracking + passthrough_logging_payload: Optional[ + PassthroughStandardLoggingPayload + ] = litellm_logging_obj.model_call_details.get( + "passthrough_logging_payload" + ) + if passthrough_logging_payload: + user = OpenAIPassthroughLoggingHandler._get_user_from_metadata( + passthrough_logging_payload=passthrough_logging_payload, + ) + if user: + kwargs.setdefault("litellm_params", {}) + kwargs["litellm_params"].update( + {"proxy_server_request": {"body": {"user": user}}} + ) + + # Create standard logging object + get_standard_logging_object_payload( + kwargs=kwargs, + init_response_obj=complete_response, + start_time=start_time, + end_time=end_time, + logging_obj=litellm_logging_obj, + status="success", + ) + + # Update logging object with cost information + litellm_logging_obj.model_call_details["model"] = model + litellm_logging_obj.model_call_details["custom_llm_provider"] = "openai" + litellm_logging_obj.model_call_details["response_cost"] = response_cost + + verbose_proxy_logger.debug( + f"OpenAI streaming passthrough cost tracking - Model: {model}, Cost: ${response_cost:.6f}" + ) + + return { + "result": complete_response, + "kwargs": kwargs, + } + + except Exception as e: + verbose_proxy_logger.error( + f"Error in OpenAI streaming passthrough cost tracking: {str(e)}" + ) + return { + "result": None, + "kwargs": {}, + } diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index adedcaf781d..fccc65b8bd4 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -314,6 +314,12 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils): return EndpointType.VERTEX_AI elif parsed_url.hostname == "api.anthropic.com": return EndpointType.ANTHROPIC + elif ( + parsed_url.hostname == "api.openai.com" + or parsed_url.hostname == "openai.azure.com" + or (parsed_url.hostname and "openai.com" in parsed_url.hostname) + ): + return EndpointType.OPENAI return EndpointType.GENERIC @staticmethod @@ -415,10 +421,10 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils): for field_name, field_value in form_data.items(): if isinstance(field_value, (StarletteUploadFile, UploadFile)): - files[field_name] = ( - await HttpPassThroughEndpointHelpers._build_request_files_from_upload_file( - upload_file=field_value - ) + files[ + field_name + ] = await HttpPassThroughEndpointHelpers._build_request_files_from_upload_file( + upload_file=field_value ) else: form_data_dict[field_name] = field_value @@ -497,9 +503,9 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils): "passthrough_logging_payload": passthrough_logging_payload, } - logging_obj.model_call_details["passthrough_logging_payload"] = ( - passthrough_logging_payload - ) + logging_obj.model_call_details[ + "passthrough_logging_payload" + ] = passthrough_logging_payload return kwargs @@ -531,10 +537,10 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils): subpath = subpath[1:] return base_target + subpath - + @staticmethod def _update_stream_param_based_on_request_body( - parsed_body: dict, + parsed_body: dict, stream: Optional[bool] = None, ) -> Optional[bool]: """ @@ -699,9 +705,11 @@ async def pass_through_request( # noqa: PLR0915 "headers": headers, }, ) - stream = HttpPassThroughEndpointHelpers._update_stream_param_based_on_request_body( - parsed_body=_parsed_body, - stream=stream, + stream = ( + HttpPassThroughEndpointHelpers._update_stream_param_based_on_request_body( + parsed_body=_parsed_body, + stream=stream, + ) ) if stream: diff --git a/litellm/proxy/pass_through_endpoints/streaming_handler.py b/litellm/proxy/pass_through_endpoints/streaming_handler.py index 08b49bac383..2d5b0a686ce 100644 --- a/litellm/proxy/pass_through_endpoints/streaming_handler.py +++ b/litellm/proxy/pass_through_endpoints/streaming_handler.py @@ -14,6 +14,9 @@ from litellm.types.utils import StandardPassThroughResponseObject from .llm_provider_handlers.anthropic_passthrough_logging_handler import ( AnthropicPassthroughLoggingHandler, ) +from .llm_provider_handlers.openai_passthrough_logging_handler import ( + OpenAIPassthroughLoggingHandler, +) from .llm_provider_handlers.vertex_passthrough_logging_handler import ( VertexPassthroughLoggingHandler, ) @@ -78,6 +81,7 @@ class PassThroughStreamingHandler: Supported endpoint types: - Anthropic - Vertex AI + - OpenAI """ all_chunks = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines( raw_bytes @@ -119,6 +123,23 @@ class PassThroughStreamingHandler: vertex_passthrough_logging_handler_result["result"] ) kwargs = vertex_passthrough_logging_handler_result["kwargs"] + elif endpoint_type == EndpointType.OPENAI: + openai_passthrough_logging_handler_result = ( + OpenAIPassthroughLoggingHandler._handle_logging_openai_collected_chunks( + litellm_logging_obj=litellm_logging_obj, + passthrough_success_handler_obj=passthrough_success_handler_obj, + url_route=url_route, + request_body=request_body, + endpoint_type=endpoint_type, + start_time=start_time, + all_chunks=all_chunks, + end_time=end_time, + ) + ) + standard_logging_response_object = ( + openai_passthrough_logging_handler_result["result"] + ) + kwargs = openai_passthrough_logging_handler_result["kwargs"] if standard_logging_response_object is None: standard_logging_response_object = StandardPassThroughResponseObject( diff --git a/litellm/proxy/pass_through_endpoints/success_handler.py b/litellm/proxy/pass_through_endpoints/success_handler.py index ce576d5ac71..5b54f94f8b5 100644 --- a/litellm/proxy/pass_through_endpoints/success_handler.py +++ b/litellm/proxy/pass_through_endpoints/success_handler.py @@ -162,9 +162,32 @@ class PassThroughEndpointLogging: cohere_passthrough_logging_handler_result["result"] ) kwargs = cohere_passthrough_logging_handler_result["kwargs"] - return_dict["standard_logging_response_object"] = ( - standard_logging_response_object - ) + elif self.is_openai_route(url_route): + from .llm_provider_handlers.openai_passthrough_logging_handler import ( + OpenAIPassthroughLoggingHandler, + ) + + openai_passthrough_logging_handler_result = ( + OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=httpx_response, + response_body=response_body or {}, + logging_obj=logging_obj, + url_route=url_route, + result=result, + start_time=start_time, + end_time=end_time, + cache_hit=cache_hit, + request_body=request_body, + **kwargs, + ) + ) + standard_logging_response_object = ( + openai_passthrough_logging_handler_result["result"] + ) + kwargs = openai_passthrough_logging_handler_result["kwargs"] + return_dict[ + "standard_logging_response_object" + ] = standard_logging_response_object return_dict["kwargs"] = kwargs return return_dict @@ -185,9 +208,9 @@ class PassThroughEndpointLogging: standard_logging_response_object: Optional[ PassThroughEndpointLoggingResultValues ] = None - logging_obj.model_call_details["passthrough_logging_payload"] = ( - passthrough_logging_payload - ) + logging_obj.model_call_details[ + "passthrough_logging_payload" + ] = passthrough_logging_payload if self.is_assemblyai_route(url_route): if ( AssemblyAIPassthroughLoggingHandler._should_log_request( @@ -286,6 +309,16 @@ class PassThroughEndpointLogging: return True return False + def is_openai_route(self, url_route: str): + """Check if the URL route is an OpenAI API route.""" + if not url_route: + return False + parsed_url = urlparse(url_route) + return parsed_url.hostname and ( + "api.openai.com" in parsed_url.hostname + or "openai.azure.com" in parsed_url.hostname + ) + def _set_cost_per_request( self, logging_obj: LiteLLMLoggingObj, @@ -305,8 +338,8 @@ class PassThroughEndpointLogging: kwargs["response_cost"] = passthrough_logging_payload.get( "cost_per_request" ) - logging_obj.model_call_details["response_cost"] = ( - passthrough_logging_payload.get("cost_per_request") - ) + logging_obj.model_call_details[ + "response_cost" + ] = passthrough_logging_payload.get("cost_per_request") return kwargs diff --git a/litellm/responses/main.py b/litellm/responses/main.py index 9584baf7368..46743d7bb88 100644 --- a/litellm/responses/main.py +++ b/litellm/responses/main.py @@ -1009,4 +1009,4 @@ def list_input_items( original_exception=e, completion_kwargs=local_vars, extra_kwargs=kwargs, - ) + ) \ No newline at end of file diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py index ac59d28a50d..20e1ee957c5 100644 --- a/litellm/responses/utils.py +++ b/litellm/responses/utils.py @@ -333,4 +333,4 @@ class ResponseAPILoggingUtils: prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, - ) + ) \ No newline at end of file diff --git a/litellm/types/passthrough_endpoints/pass_through_endpoints.py b/litellm/types/passthrough_endpoints/pass_through_endpoints.py index 8a91dd14ebd..39facdd8e6f 100644 --- a/litellm/types/passthrough_endpoints/pass_through_endpoints.py +++ b/litellm/types/passthrough_endpoints/pass_through_endpoints.py @@ -5,6 +5,7 @@ from typing import Optional, TypedDict class EndpointType(str, Enum): VERTEX_AI = "vertex-ai" ANTHROPIC = "anthropic" + OPENAI = "openai" GENERIC = "generic" diff --git a/tests/llm_responses_api_testing/base_responses_api.py b/tests/llm_responses_api_testing/base_responses_api.py index 5cb8295b1af..c177a76cda6 100644 --- a/tests/llm_responses_api_testing/base_responses_api.py +++ b/tests/llm_responses_api_testing/base_responses_api.py @@ -536,4 +536,4 @@ class BaseResponsesAPITest(ABC): # Validate final response structure validate_responses_api_response(final_response, final_chunk=True) assert final_response.output is not None - assert len(final_response.output) > 0 + assert len(final_response.output) > 0 \ No newline at end of file diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py new file mode 100644 index 00000000000..6d5e80910ba --- /dev/null +++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py @@ -0,0 +1,451 @@ +import json +import os +import sys +from datetime import datetime +from typing import Any, Dict, List +from unittest.mock import MagicMock, patch, AsyncMock + +import pytest +import httpx + +sys.path.insert( + 0, os.path.abspath("../../..") +) # Adds the parent directory to the system path + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.proxy.pass_through_endpoints.llm_provider_handlers.openai_passthrough_logging_handler import ( + OpenAIPassthroughLoggingHandler, +) +from litellm.proxy.pass_through_endpoints.success_handler import ( + PassThroughEndpointLogging, +) +from litellm.types.passthrough_endpoints.pass_through_endpoints import ( + PassthroughStandardLoggingPayload, +) + + +class TestOpenAIPassthroughLoggingHandler: + """Test the OpenAI passthrough logging handler for cost tracking.""" + + def setup_method(self): + """Set up test fixtures""" + self.start_time = datetime.now() + self.end_time = datetime.now() + self.handler = OpenAIPassthroughLoggingHandler() + + # Mock OpenAI chat completions response + self.mock_openai_response = { + "id": "chatcmpl-123", + "object": "chat.completion", + "created": 1677652288, + "model": "gpt-4o-2024-08-06", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello! How can I help you today?" + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 20, + "completion_tokens": 15, + "total_tokens": 35 + } + } + + def _create_mock_logging_obj(self) -> LiteLLMLoggingObj: + """Create a mock logging object""" + mock_logging_obj = MagicMock() + mock_logging_obj.model_call_details = {} + return mock_logging_obj + + def _create_mock_httpx_response(self, response_data: dict = None) -> httpx.Response: + """Create a mock httpx response""" + if response_data is None: + response_data = self.mock_openai_response + + mock_response = MagicMock(spec=httpx.Response) + mock_response.status_code = 200 + mock_response.text = json.dumps(response_data) + mock_response.json.return_value = response_data + mock_response.headers = {"content-type": "application/json"} + return mock_response + + def _create_passthrough_logging_payload(self, user: str = "test_user") -> PassthroughStandardLoggingPayload: + """Create a mock passthrough logging payload""" + return PassthroughStandardLoggingPayload( + url="https://api.openai.com/v1/chat/completions", + request_body={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}, + request_method="POST", + ) + + def test_llm_provider_name(self): + """Test that the handler returns the correct provider name""" + assert self.handler.llm_provider_name == "openai" + + def test_get_provider_config(self): + """Test that the handler returns an OpenAI config""" + config = OpenAIPassthroughLoggingHandler.get_provider_config(model="gpt-4o") + assert config is not None + # Verify it's an OpenAI config by checking if it has the expected methods + assert hasattr(config, 'transform_response') + + def test_is_openai_chat_completions_route(self): + """Test OpenAI chat completions route detection""" + # Positive cases + assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("https://api.openai.com/v1/chat/completions") == True + assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("https://openai.azure.com/v1/chat/completions") == True + + # Negative cases + assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("https://api.openai.com/v1/models") == False + assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("http://localhost:4000/openai/v1/chat/completions") == False + assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("https://api.anthropic.com/v1/messages") == False + assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("") == False + + @patch('litellm.completion_cost') + @patch('litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload') + def test_openai_passthrough_handler_success(self, mock_get_standard_logging, mock_completion_cost): + """Test successful cost tracking for OpenAI chat completions""" + # Arrange + mock_completion_cost.return_value = 0.000045 + mock_get_standard_logging.return_value = {"test": "logging_payload"} + + mock_httpx_response = self._create_mock_httpx_response() + mock_logging_obj = self._create_mock_logging_obj() + passthrough_payload = self._create_passthrough_logging_payload() + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "gpt-4o", + } + + # Act + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=self.mock_openai_response, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/chat/completions", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}, + **kwargs + ) + + # Assert + assert result is not None + assert "result" in result + assert "kwargs" in result + assert result["kwargs"]["response_cost"] == 0.000045 + assert result["kwargs"]["model"] == "gpt-4o" + assert result["kwargs"]["custom_llm_provider"] == "openai" + + # Verify cost calculation was called + mock_completion_cost.assert_called_once() + + # Verify logging object was updated + assert mock_logging_obj.model_call_details["response_cost"] == 0.000045 + assert mock_logging_obj.model_call_details["model"] == "gpt-4o" + assert mock_logging_obj.model_call_details["custom_llm_provider"] == "openai" + + @patch('litellm.completion_cost') + def test_openai_passthrough_handler_non_chat_completions(self, mock_completion_cost): + """Test that non-chat-completions routes fall back to base handler""" + # Arrange + mock_httpx_response = self._create_mock_httpx_response() + mock_logging_obj = self._create_mock_logging_obj() + passthrough_payload = self._create_passthrough_logging_payload() + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "gpt-4o", + } + + # Act - Use a non-chat-completions route + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body={"id": "file-123", "object": "file"}, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/files", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"purpose": "fine-tune"}, + **kwargs + ) + + # Assert - Should fall back to base handler for non-chat-completions + assert result is not None + assert "result" in result + assert "kwargs" in result + # Cost calculation may be called by the base handler fallback + # The important thing is that our specific OpenAI handler logic didn't run + + @patch('litellm.completion_cost') + @patch('litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload') + def test_openai_passthrough_handler_with_user_tracking(self, mock_get_standard_logging, mock_completion_cost): + """Test cost tracking with user information""" + # Arrange + mock_completion_cost.return_value = 0.000123 + mock_get_standard_logging.return_value = {"test": "logging_payload"} + + mock_httpx_response = self._create_mock_httpx_response() + mock_logging_obj = self._create_mock_logging_obj() + + # Create payload with user information + passthrough_payload = PassthroughStandardLoggingPayload( + url="https://api.openai.com/v1/chat/completions", + request_body={ + "model": "gpt-4o", + "messages": [{"role": "user", "content": "Hello"}], + "user": "test_user_123" + }, + request_method="POST", + ) + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "gpt-4o", + } + + # Act + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=self.mock_openai_response, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/chat/completions", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}], "user": "test_user_123"}, + **kwargs + ) + + # Assert + assert result is not None + assert "result" in result + assert "kwargs" in result + assert result["kwargs"]["response_cost"] == 0.000123 + + # Verify user information is included in litellm_params + assert "litellm_params" in result["kwargs"] + assert "proxy_server_request" in result["kwargs"]["litellm_params"] + assert "body" in result["kwargs"]["litellm_params"]["proxy_server_request"] + assert result["kwargs"]["litellm_params"]["proxy_server_request"]["body"]["user"] == "test_user_123" + + @patch('litellm.completion_cost') + def test_openai_passthrough_handler_cost_calculation_error(self, mock_completion_cost): + """Test error handling in cost calculation""" + # Arrange + mock_completion_cost.side_effect = Exception("Cost calculation failed") + + mock_httpx_response = self._create_mock_httpx_response() + mock_logging_obj = self._create_mock_logging_obj() + passthrough_payload = self._create_passthrough_logging_payload() + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "gpt-4o", + } + + # Act + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=self.mock_openai_response, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/chat/completions", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}, + **kwargs + ) + + # Assert - Should fall back to base handler when cost calculation fails + assert result is not None + assert "result" in result + assert "kwargs" in result + + def test_build_complete_streaming_response(self): + """Test the streaming response builder (placeholder implementation)""" + # This is a placeholder method that returns None for now + result = self.handler._build_complete_streaming_response( + all_chunks=["chunk1", "chunk2"], + litellm_logging_obj=self._create_mock_logging_obj(), + model="gpt-4o", + ) + + assert result is None # Placeholder implementation + + @patch('litellm.completion_cost') + @patch('litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload') + def test_different_models_cost_tracking(self, mock_get_standard_logging, mock_completion_cost): + """Test cost tracking for different OpenAI models""" + # Arrange + mock_get_standard_logging.return_value = {"test": "logging_payload"} + + test_cases = [ + ("gpt-4o", 0.000045), + ("gpt-4o-mini", 0.000015), + ("gpt-3.5-turbo", 0.000002), + ] + + for model, expected_cost in test_cases: + mock_completion_cost.return_value = expected_cost + + mock_httpx_response = self._create_mock_httpx_response() + mock_httpx_response.json.return_value = { + **self.mock_openai_response, + "model": model + } + + mock_logging_obj = self._create_mock_logging_obj() + passthrough_payload = self._create_passthrough_logging_payload() + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": model, + } + + # Act + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body={**self.mock_openai_response, "model": model}, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/chat/completions", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"model": model, "messages": [{"role": "user", "content": "Hello"}]}, + **kwargs + ) + + # Assert + assert result is not None + assert "result" in result + assert "kwargs" in result + assert result["kwargs"]["response_cost"] == expected_cost + assert result["kwargs"]["model"] == model + assert result["kwargs"]["custom_llm_provider"] == "openai" + + def test_static_methods(self): + """Test that static methods work correctly""" + # Test static method calls + assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("https://api.openai.com/v1/chat/completions") == True + assert OpenAIPassthroughLoggingHandler.get_provider_config("gpt-4o") is not None + + +class TestOpenAIPassthroughIntegration: + """Integration tests for OpenAI passthrough cost tracking""" + + def setup_method(self): + """Set up test fixtures""" + self.handler = PassThroughEndpointLogging() + + def test_is_openai_route_detection(self): + """Test OpenAI route detection in the main success handler""" + # Positive cases + assert self.handler.is_openai_route("https://api.openai.com/v1/chat/completions") == True + assert self.handler.is_openai_route("https://openai.azure.com/v1/chat/completions") == True + assert self.handler.is_openai_route("https://api.openai.com/v1/models") == True + + # Negative cases + assert self.handler.is_openai_route("http://localhost:4000/openai/v1/chat/completions") == False + assert self.handler.is_openai_route("https://api.anthropic.com/v1/messages") == False + assert self.handler.is_openai_route("https://api.assemblyai.com/v2/transcript") == False + assert self.handler.is_openai_route("") == False + + @patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.openai_passthrough_logging_handler.OpenAIPassthroughLoggingHandler.openai_passthrough_handler') + @pytest.mark.asyncio + async def test_success_handler_calls_openai_handler(self, mock_openai_handler): + """Test that the success handler calls our OpenAI handler for OpenAI routes""" + # Arrange + mock_openai_handler.return_value = { + "result": {"id": "chatcmpl-123"}, + "kwargs": { + "response_cost": 0.000045, + "model": "gpt-4o", + "custom_llm_provider": "openai" + } + } + + mock_httpx_response = MagicMock(spec=httpx.Response) + mock_httpx_response.text = '{"id": "chatcmpl-123", "choices": [{"message": {"content": "Hello"}}]}' + + mock_logging_obj = AsyncMock() + mock_logging_obj.model_call_details = {} + mock_logging_obj.async_success_handler = AsyncMock() + + passthrough_payload = PassthroughStandardLoggingPayload( + url="https://api.openai.com/v1/chat/completions", + request_body={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}, + request_method="POST", + ) + + # Act + result = await self.handler.pass_through_async_success_handler( + httpx_response=mock_httpx_response, + response_body={"id": "chatcmpl-123", "choices": [{"message": {"content": "Hello"}}]}, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/chat/completions", + result="", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + request_body={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}, + passthrough_logging_payload=passthrough_payload, + ) + + # Assert + mock_openai_handler.assert_called_once() + # The success handler returns None on success, which is expected + assert result is None + + @pytest.mark.asyncio + async def test_success_handler_falls_back_for_non_openai_routes(self): + """Test that non-OpenAI routes don't call our handler""" + # Arrange + mock_httpx_response = MagicMock(spec=httpx.Response) + mock_httpx_response.text = '{"status": "success"}' + mock_httpx_response.headers = {"content-type": "application/json"} + + mock_logging_obj = MagicMock() + mock_logging_obj.model_call_details = {} + + passthrough_payload = PassthroughStandardLoggingPayload( + url="https://api.anthropic.com/v1/messages", + request_body={"model": "claude-3-sonnet", "messages": [{"role": "user", "content": "Hello"}]}, + request_method="POST", + ) + + # Mock the _handle_logging method to capture calls + self.handler._handle_logging = AsyncMock() + + # Act + result = await self.handler.pass_through_async_success_handler( + httpx_response=mock_httpx_response, + response_body={"status": "success"}, + logging_obj=mock_logging_obj, + url_route="https://api.anthropic.com/v1/messages", + result="", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + request_body={"model": "claude-3-sonnet", "messages": [{"role": "user", "content": "Hello"}]}, + passthrough_logging_payload=passthrough_payload, + ) + + # Assert - Should call the base handler, not our OpenAI handler + self.handler._handle_logging.assert_called_once() + + +if __name__ == "__main__": + pytest.main([__file__]) From 99eceb8835a2faa1795ba5d885480c8d5f958497 Mon Sep 17 00:00:00 2001 From: tobias-mayr Date: Thu, 4 Sep 2025 18:29:06 +0100 Subject: [PATCH 109/178] feat: Add support for reasoning_effort='minimal' for Gemini models - Add DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET constant (128 tokens) - Update Gemini transformation to handle 'minimal' reasoning_effort - Maps 'minimal' to 128 tokens (Gemini's minimum thinking budget) - Maintains backward compatibility with existing reasoning_effort values - Fixes issue where Gemini API rejected 0 token thinking budget --- litellm/constants.py | 3 + .../exception_mapping_utils.py | 76 +++++++++++++++++-- .../vertex_and_google_ai_studio_gemini.py | 8 +- 3 files changed, 79 insertions(+), 8 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index 21e30bef32b..9f55d2a94ef 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -51,6 +51,9 @@ SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD = int( DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET", 0) ) +DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET = int( + os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET", 128) +) DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET", 1024) ) diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 25ae0269ab3..ad6b3dcaeb4 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -24,6 +24,55 @@ from ..exceptions import ( ) +def _is_operational_404(original_exception) -> bool: + """ + Determine if a 404 status code represents an operational issue rather than a missing model. + + Args: + original_exception: The exception with status_code 404 + + Returns: + True if this is an operational issue (rate limiting, cooldowns, etc.) + False if this is actually a missing model + """ + try: + # Import here to avoid circular imports + from litellm.types.router import RouterErrors + + # Check for known operational error patterns + error_message = str(original_exception).lower() + + # Check for router-specific operational errors + operational_patterns = [ + RouterErrors.no_deployments_available.value.lower(), + "no deployments available", + "no healthy deployment available", + "no healthy deployments available", + "deployment over user-defined ratelimit", + "crossed budget", + "cooldown", + "rate limit exceeded", + "too many requests" + ] + + for pattern in operational_patterns: + if pattern in error_message: + return True + + # Check if this is a RouterRateLimitError (which indicates operational issues) + if hasattr(original_exception, '__class__'): + exception_class_name = original_exception.__class__.__name__ + if "RouterRateLimitError" in exception_class_name: + return True + + return False + + except Exception: + # If we can't determine, default to treating it as a missing model + # This is safer than potentially hiding real model not found errors + return False + + class ExceptionCheckers: """ Helper class for checking various error conditions in exception strings. @@ -462,13 +511,26 @@ def exception_type( # type: ignore # noqa: PLR0915 ) elif original_exception.status_code == 404: exception_mapping_worked = True - raise NotFoundError( - message=f"NotFoundError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) + # Check if this is actually a "model not found" vs operational issue + if _is_operational_404(original_exception): + # This is operational (rate limiting, cooldowns), not a missing model + # The proxy will map this to 429 status code, which is correct + raise litellm.ServiceUnavailableError( + message=f"ServiceUnavailableError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + else: + # This is actually a missing model + raise NotFoundError( + message=f"NotFoundError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) elif original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( 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 37470a6ee09..4da99204165 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 @@ -30,6 +30,7 @@ from litellm.constants import ( DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET, + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET, ) from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.llms.custom_httpx.http_handler import ( @@ -423,7 +424,12 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): def _map_reasoning_effort_to_thinking_budget( reasoning_effort: str, ) -> GeminiThinkingConfig: - if reasoning_effort == "low": + if reasoning_effort == "minimal": + return { + "thinkingBudget": DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET, + "includeThoughts": True, + } + elif reasoning_effort == "low": return { "thinkingBudget": DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET, "includeThoughts": True, From fc9560573b4cfbd393372fce24345e1a375b97fd Mon Sep 17 00:00:00 2001 From: Sameer Kankute <135028480+kankute-sameer@users.noreply.github.com> Date: Thu, 4 Sep 2025 23:06:48 +0530 Subject: [PATCH 110/178] [BUG] Fix response api for reasoning item in input for litellm proxy (#14200) * fix response api for litellm proxy * Add test for checking if status is getting removed * add test in correct file * remove hardcoded fields * Make the handling simpler * fix lint error: --- .../llms/openai/responses/transformation.py | 68 ++++- .../base_responses_api.py | 56 +++- tests/llm_translation/test_openai.py | 2 +- .../test_openai_responses_transformation.py | 245 ++++++++++++++++++ 4 files changed, 367 insertions(+), 4 deletions(-) diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index e70cadddaf7..392d47f9822 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -1,6 +1,15 @@ -from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast, get_type_hints +from typing import ( + TYPE_CHECKING, + Any, + Dict, + Optional, + Union, + cast, + get_type_hints, +) import httpx +from openai.types.responses import ResponseReasoningItem from pydantic import BaseModel import litellm @@ -92,12 +101,67 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): # if it's pydantic, convert to dict if isinstance(item, BaseModel): validated_input.append(item.model_dump(exclude_none=True)) + elif isinstance(item, dict): + # Handle reasoning items specifically to filter out status=None + verbose_logger.debug(f"Handling reasoning item: {item}") + if item.get("type") == "reasoning": + # Type assertion since we know it's a dict at this point + dict_item = cast(Dict[str, Any], item) + filtered_item = self._handle_reasoning_item(dict_item) + else: + # For other dict items, just pass through + filtered_item = cast(Dict[str, Any], item) + validated_input.append(filtered_item) else: validated_input.append(item) - return validated_input + return validated_input # type: ignore # Input is expected to be either str or List, no single BaseModel expected return input + def _handle_reasoning_item(self, item: Dict[str, Any]) -> Dict[str, Any]: + """ + Handle reasoning items specifically to filter out status=None using OpenAI's model. + Issue: https://github.com/BerriAI/litellm/issues/13484 + OpenAI API does not accept ReasoningItem(status=None), so we need to: + 1. Check if the item is a reasoning type + 2. Create a ResponseReasoningItem object with the item data + 3. Convert it back to dict with exclude_none=True to filter None values + """ + verbose_logger.debug(f"Handling reasoning item: {item}") + if item.get("type") == "reasoning": + try: + # Ensure required fields are present for ResponseReasoningItem + item_data = dict(item) + if "id" not in item_data: + item_data["id"] = f"reasoning_{hash(str(item_data))}" + if "summary" not in item_data: + item_data["summary"] = ( + item_data.get("reasoning_content", "")[:100] + "..." + if len(item_data.get("reasoning_content", "")) > 100 + else item_data.get("reasoning_content", "") + ) + + # Create ResponseReasoningItem object from the item data + reasoning_item = ResponseReasoningItem(**item_data) + + # Convert back to dict with exclude_none=True to exclude None fields + dict_reasoning_item = reasoning_item.model_dump(exclude_none=True) + + return dict_reasoning_item + except Exception as e: + verbose_logger.debug( + f"Failed to create ResponseReasoningItem, falling back to manual filtering: {e}" + ) + # Fallback: manually filter out known None fields + filtered_item = { + k: v + for k, v in item.items() + if v is not None + or k not in {"status", "content", "encrypted_content"} + } + return filtered_item + return item + def transform_response_api_response( self, model: str, diff --git a/tests/llm_responses_api_testing/base_responses_api.py b/tests/llm_responses_api_testing/base_responses_api.py index c177a76cda6..d5d43e175c0 100644 --- a/tests/llm_responses_api_testing/base_responses_api.py +++ b/tests/llm_responses_api_testing/base_responses_api.py @@ -536,4 +536,58 @@ class BaseResponsesAPITest(ABC): # Validate final response structure validate_responses_api_response(final_response, final_chunk=True) assert final_response.output is not None - assert len(final_response.output) > 0 \ No newline at end of file + + def test_openai_responses_api_dict_input_filtering(self): + """ + Test that regular dict inputs with status fields are properly filtered + to replicate exclude_unset=True behavior for non-Pydantic objects. + """ + from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig + + # Test input with regular dict objects (like from JSON) + test_input = [ + { + "role": "user", + "content": "test" + }, + { + "id": "rs_123", + "summary": [{"text": "test", "type": "summary_text"}], + "type": "reasoning", + "content": None, # Should be filtered out + "encrypted_content": None, # Should be filtered out + "status": None # Should be filtered out + }, + { + "arguments": "{}", + "call_id": "call_123", + "name": "get_today", + "type": "function_call", + "id": "fc_123", + "status": "completed" # Should be preserved (not a default field) + } + ] + + config = OpenAIResponsesAPIConfig() + validated_input = config._validate_input_param(test_input) + + # Verify the results + assert len(validated_input) == 3 + + # Check reasoning item (index 1) + reasoning_item = validated_input[1] + assert reasoning_item["type"] == "reasoning" + assert "status" not in reasoning_item, "status field should be filtered out from reasoning item" + assert "content" not in reasoning_item, "content field should be filtered out from reasoning item" + assert "encrypted_content" not in reasoning_item, "encrypted_content field should be filtered out from reasoning item" + assert "id" in reasoning_item, "id field should be preserved" + assert "summary" in reasoning_item, "summary field should be preserved" + + # Check function call item (index 2) + function_call_item = validated_input[2] + assert function_call_item["type"] == "function_call" + assert "status" in function_call_item, "status field should be preserved in function call item" + assert function_call_item["status"] == "completed", "status value should be preserved" + + print("✅ OpenAI Responses API dict input filtering test passed") + diff --git a/tests/llm_translation/test_openai.py b/tests/llm_translation/test_openai.py index 619ae338e50..285a406b3fb 100644 --- a/tests/llm_translation/test_openai.py +++ b/tests/llm_translation/test_openai.py @@ -665,7 +665,6 @@ async def test_openai_gpt5_reasoning(): print("response: ", response) assert response.choices[0].message.content is not None - @pytest.mark.asyncio async def test_openai_safety_identifier_parameter(): """Test that safety_identifier parameter is correctly passed to the OpenAI API.""" @@ -723,3 +722,4 @@ def test_openai_safety_identifier_parameter_sync(): assert "safety_identifier" in request_body # Verify safety_identifier is correctly sent to the API assert request_body["safety_identifier"] == "user_code_123456" + diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py index 6d46a40f6c6..21232161d0c 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py @@ -668,3 +668,248 @@ def test_get_supported_openai_params(): assert "stream" in params assert "background" in params assert "stream" in params + + +class TestOpenAIFieldExclusionRegistry: + """Test suite for the OpenAI Field Exclusion Registry system""" + + def setup_method(self): + """Setup test fixtures""" + from litellm.llms.openai.responses.transformation import ( + OpenAIFieldExclusionRegistry, + OpenAIResponsesAPIConfig + ) + self.registry = OpenAIFieldExclusionRegistry + self.config = OpenAIResponsesAPIConfig() + + def test_registry_initialization(self): + """Test that the registry is properly initialized with ResponseReasoningItem""" + # Test that we can get excluded fields (should not be empty if ResponseReasoningItem is registered) + all_excluded_fields = self.registry.get_all_excluded_fields() + + # The registry should have at least some fields if ResponseReasoningItem was successfully registered + # If OpenAI SDK is not available, this might be empty, which is also valid + assert isinstance(all_excluded_fields, set), "get_all_excluded_fields should return a set" + + # If we have the OpenAI SDK available, we should have the expected fields + try: + from openai.types.responses import ResponseReasoningItem + reasoning_fields = self.registry.get_excluded_fields_for_model(ResponseReasoningItem) + expected_fields = {'status', 'content', 'encrypted_content'} + assert expected_fields.issubset(reasoning_fields), f"Expected fields {expected_fields} to be subset of {reasoning_fields}" + except ImportError: + # If OpenAI SDK is not available, that's fine - the registry should handle this gracefully + pytest.skip("OpenAI SDK not available, skipping ResponseReasoningItem specific tests") + + def test_register_model_functionality(self): + """Test that we can register new models to the registry""" + from pydantic import BaseModel + from typing import Optional + + # Create a test model with default None fields + class TestResponseModel(BaseModel): + id: str + type: str = "test" + status: Optional[str] = None + content: Optional[str] = None + required_field: str + + # Register the test model + self.registry.register_model(TestResponseModel) + + # Verify it was registered and fields are detected + excluded_fields = self.registry.get_excluded_fields_for_model(TestResponseModel) + expected_excluded = {'status', 'content'} # Fields with default None + + assert expected_excluded.issubset(excluded_fields), f"Expected {expected_excluded} to be in {excluded_fields}" + assert 'id' not in excluded_fields, "Required field 'id' should not be excluded" + assert 'required_field' not in excluded_fields, "Required field 'required_field' should not be excluded" + + def test_get_all_excluded_fields(self): + """Test that get_all_excluded_fields aggregates fields from all registered models""" + all_fields_before = self.registry.get_all_excluded_fields() + + # Create and register a test model + from pydantic import BaseModel + from typing import Optional + + class AnotherTestModel(BaseModel): + id: str + unique_field: Optional[str] = None + + self.registry.register_model(AnotherTestModel) + + all_fields_after = self.registry.get_all_excluded_fields() + + # The new fields should be included + assert 'unique_field' in all_fields_after, "New model's excluded field should be included" + assert len(all_fields_after) >= len(all_fields_before), "Should have at least as many fields as before" + + def test_convenience_registration_method(self): + """Test the convenience method for registering models""" + from pydantic import BaseModel + from typing import Optional + + class ConvenienceTestModel(BaseModel): + id: str + convenience_field: Optional[str] = None + + # Use the convenience method + self.config.register_model_for_field_exclusion(ConvenienceTestModel) + + # Verify it was registered + excluded_fields = self.registry.get_excluded_fields_for_model(ConvenienceTestModel) + assert 'convenience_field' in excluded_fields, "Field should be excluded after registration" + + def test_field_filtering_with_registry(self): + """Test that the field filtering works correctly with the registry""" + + # Test data that matches the structure of ResponseReasoningItem + test_input = [ + { + "role": "user", + "content": "test message" + }, + { + "id": "reasoning-123", + "type": "reasoning", + "status": None, # Should be filtered out + "content": None, # Should be filtered out + "encrypted_content": None, # Should be filtered out + "summary": [{"text": "This reasoning shows...", "type": "summary_text"}], + "role": "assistant" + }, + { + "id": "message-456", + "type": "message", + "status": "completed", # Should be preserved (not None) + "content": "Hello! How can I help?", # Should be preserved (not None) + "role": "assistant" + } + ] + + # Process the input through the validation + result = self.config._validate_input_param(test_input) + + # Verify the structure + assert len(result) == 3, "Should have 3 items" + + # Check the reasoning item (index 1) + reasoning_item = result[1] + assert reasoning_item["type"] == "reasoning" + assert reasoning_item["id"] == "reasoning-123" + assert "summary" in reasoning_item, "summary field should be preserved" + assert "role" in reasoning_item, "role field should be preserved" + + # These fields should be filtered out if they are in the registry + all_excluded_fields = self.registry.get_all_excluded_fields() + if 'status' in all_excluded_fields: + assert "status" not in reasoning_item, "status field should be filtered out" + if 'content' in all_excluded_fields: + assert "content" not in reasoning_item, "content field should be filtered out" + if 'encrypted_content' in all_excluded_fields: + assert "encrypted_content" not in reasoning_item, "encrypted_content field should be filtered out" + + # Check the message item (index 2) - non-None values should be preserved + message_item = result[2] + assert message_item["type"] == "message" + assert message_item["status"] == "completed", "Non-None status should be preserved" + assert message_item["content"] == "Hello! How can I help?", "Non-None content should be preserved" + + def test_field_filtering_with_empty_registry(self): + """Test that filtering works gracefully when no models are registered""" + # Create a fresh registry for this test + from litellm.llms.openai.responses.transformation import OpenAIFieldExclusionRegistry + + # Save the current state + original_models = OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION.copy() + + try: + # Clear the registry + OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION.clear() + + # Test data + test_input = [{ + "id": "test-123", + "status": None, + "content": None, + "other_field": "should be preserved" + }] + + # Process the input + result = self.config._validate_input_param(test_input) + + # With empty registry, nothing should be filtered (all fields preserved) + assert len(result) == 1 + item = result[0] + assert "status" in item, "With empty registry, status should be preserved" + assert "content" in item, "With empty registry, content should be preserved" + assert item["other_field"] == "should be preserved" + + finally: + # Restore the original state + OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION = original_models + + def test_pydantic_v1_v2_compatibility(self): + """Test that the registry works with both Pydantic v1 and v2""" + from pydantic import BaseModel + from typing import Optional + + class CompatibilityTestModel(BaseModel): + id: str + optional_field: Optional[str] = None + required_field: str = "default" + + # Register the model + self.registry.register_model(CompatibilityTestModel) + + # Get excluded fields + excluded_fields = self.registry.get_excluded_fields_for_model(CompatibilityTestModel) + + # Should work regardless of Pydantic version + assert isinstance(excluded_fields, set), "Should return a set" + assert 'optional_field' in excluded_fields, "Field with default None should be excluded" + + # Test that the model fields are accessible (works in both v1 and v2) + model_fields = getattr(CompatibilityTestModel, "model_fields", None) + if model_fields is None: + model_fields = getattr(CompatibilityTestModel, "__fields__", {}) + assert len(model_fields) > 0, "Should be able to access model fields" + + def test_non_registered_model_returns_empty_set(self): + """Test that non-registered models return empty excluded fields""" + from pydantic import BaseModel + + class UnregisteredModel(BaseModel): + id: str + some_field: str = None + + # Don't register this model + excluded_fields = self.registry.get_excluded_fields_for_model(UnregisteredModel) + + assert excluded_fields == set(), "Non-registered model should return empty set" + + @pytest.mark.parametrize("field_value", [None, "", 0, False, []]) + def test_only_none_values_are_filtered(self, field_value): + """Test that only None values are filtered, not other falsy values""" + test_input = [{ + "id": "test-123", + "status": field_value, + "content": "actual content", + "other_field": "preserved" + }] + + result = self.config._validate_input_param(test_input) + item = result[0] + + if field_value is None: + # Only None should be filtered (if status is in the registry) + all_excluded_fields = self.registry.get_all_excluded_fields() + if 'status' in all_excluded_fields: + assert "status" not in item, f"None value should be filtered out" + else: + assert item["status"] is None, f"If not in registry, None should be preserved" + else: + # Other falsy values should be preserved + assert "status" in item, f"Non-None value {field_value} should be preserved" + assert item["status"] == field_value, f"Value should be exactly {field_value}" From 0ade6cceff672a0a9001f0f185649b8c76c9c45d Mon Sep 17 00:00:00 2001 From: tobias-mayr Date: Thu, 4 Sep 2025 18:40:46 +0100 Subject: [PATCH 111/178] remove old code --- .../exception_mapping_utils.py | 82 +++---------------- 1 file changed, 10 insertions(+), 72 deletions(-) diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index ad6b3dcaeb4..f02c862f0fa 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -24,55 +24,6 @@ from ..exceptions import ( ) -def _is_operational_404(original_exception) -> bool: - """ - Determine if a 404 status code represents an operational issue rather than a missing model. - - Args: - original_exception: The exception with status_code 404 - - Returns: - True if this is an operational issue (rate limiting, cooldowns, etc.) - False if this is actually a missing model - """ - try: - # Import here to avoid circular imports - from litellm.types.router import RouterErrors - - # Check for known operational error patterns - error_message = str(original_exception).lower() - - # Check for router-specific operational errors - operational_patterns = [ - RouterErrors.no_deployments_available.value.lower(), - "no deployments available", - "no healthy deployment available", - "no healthy deployments available", - "deployment over user-defined ratelimit", - "crossed budget", - "cooldown", - "rate limit exceeded", - "too many requests" - ] - - for pattern in operational_patterns: - if pattern in error_message: - return True - - # Check if this is a RouterRateLimitError (which indicates operational issues) - if hasattr(original_exception, '__class__'): - exception_class_name = original_exception.__class__.__name__ - if "RouterRateLimitError" in exception_class_name: - return True - - return False - - except Exception: - # If we can't determine, default to treating it as a missing model - # This is safer than potentially hiding real model not found errors - return False - - class ExceptionCheckers: """ Helper class for checking various error conditions in exception strings. @@ -91,16 +42,16 @@ class ExceptionCheckers: """ if not isinstance(error_str, str): return False - + if "429" in error_str or "rate limit" in error_str.lower(): return True - + ####################################### # Mistral API returns this error string ######################################### if "service tier capacity exceeded" in error_str.lower(): return True - + return False @staticmethod @@ -511,26 +462,13 @@ def exception_type( # type: ignore # noqa: PLR0915 ) elif original_exception.status_code == 404: exception_mapping_worked = True - # Check if this is actually a "model not found" vs operational issue - if _is_operational_404(original_exception): - # This is operational (rate limiting, cooldowns), not a missing model - # The proxy will map this to 429 status code, which is correct - raise litellm.ServiceUnavailableError( - message=f"ServiceUnavailableError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - else: - # This is actually a missing model - raise NotFoundError( - message=f"NotFoundError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) + raise NotFoundError( + message=f"NotFoundError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) elif original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( From 2d30b55964324ee43d3c89e7b981d23aa5380c66 Mon Sep 17 00:00:00 2001 From: tobias-mayr Date: Thu, 4 Sep 2025 18:59:37 +0100 Subject: [PATCH 112/178] distinguish between gemini models --- litellm/constants.py | 22 +++++++++++++++---- .../vertex_and_google_ai_studio_gemini.py | 22 +++++++++++++++++-- 2 files changed, 38 insertions(+), 6 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index 9f55d2a94ef..25f26639494 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -51,9 +51,23 @@ SINGLE_DEPLOYMENT_TRAFFIC_FAILURE_THRESHOLD = int( DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_DISABLE_THINKING_BUDGET", 0) ) + +# Gemini model-specific minimal thinking budget constants +DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH = int( + os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH", 1) +) +DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO = int( + os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO", 128) +) +DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( + os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512) +) + +# Generic fallback for unknown models DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET", 128) ) + DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET", 1024) ) @@ -830,7 +844,7 @@ known_tokenizer_config = { "add_eos_token": False, "bos_token": { "__type": "AddedToken", - "content": "<｜begin▁of▁sentence｜>", + "content": " "lstrip": False, "normalized": True, "rstrip": False, @@ -839,7 +853,7 @@ known_tokenizer_config = { "clean_up_tokenization_spaces": False, "eos_token": { "__type": "AddedToken", - "content": "<｜end▁of▁sentence｜>", + "content": " "lstrip": False, "normalized": True, "rstrip": False, @@ -849,7 +863,7 @@ known_tokenizer_config = { "model_max_length": 16384, "pad_token": { "__type": "AddedToken", - "content": "<｜end▁of▁sentence｜>", + "content": " "lstrip": False, "normalized": True, "rstrip": False, @@ -858,7 +872,7 @@ known_tokenizer_config = { "sp_model_kwargs": {}, "unk_token": None, "tokenizer_class": "LlamaTokenizerFast", - "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '' in content %}{% set content = content.split('')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>\\n'}}{% endif %}", + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '赠送' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '赠送' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + ''}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '' in content %}{% set content = content.split('')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + ''}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜> 空' + message['content'] + ' 空'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n 空' + message['content'] + ' 空'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>\\n'}}{% endif %}", }, "status": "success", }, 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 4da99204165..ba1d64facce 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 @@ -31,6 +31,9 @@ from litellm.constants import ( DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET, DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET, + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH, + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO, + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE, ) from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.llms.custom_httpx.http_handler import ( @@ -423,10 +426,23 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): @staticmethod def _map_reasoning_effort_to_thinking_budget( reasoning_effort: str, + model: Optional[str] = None, ) -> GeminiThinkingConfig: if reasoning_effort == "minimal": + # Use model-specific minimum thinking budget or fallback + if model and "gemini-2.5-flash" in model.lower(): + budget = ( + DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH + ) + elif model and "gemini-2.5-pro" in model.lower(): + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO + elif model and "gemini-2.5-flash-lite" in model.lower(): + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE + else: + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET + return { - "thinkingBudget": DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET, + "thinkingBudget": budget, "includeThoughts": True, } elif reasoning_effort == "low": @@ -606,7 +622,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): optional_params["seed"] = value elif param == "reasoning_effort" and isinstance(value, str): optional_params["thinkingConfig"] = ( - VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(value) + VertexGeminiConfig._map_reasoning_effort_to_thinking_budget( + value, model + ) ) elif param == "thinking": optional_params["thinkingConfig"] = ( From d9304b74bd0aa602230755dfee66af5e4a9d6c21 Mon Sep 17 00:00:00 2001 From: tobias-mayr Date: Thu, 4 Sep 2025 19:03:25 +0100 Subject: [PATCH 113/178] fix accidental changes --- litellm/constants.py | 8 ++++---- litellm/litellm_core_utils/exception_mapping_utils.py | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index 25f26639494..746674f5306 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -844,7 +844,7 @@ known_tokenizer_config = { "add_eos_token": False, "bos_token": { "__type": "AddedToken", - "content": " + "content": "<｜begin▁of▁sentence｜>", "lstrip": False, "normalized": True, "rstrip": False, @@ -853,7 +853,7 @@ known_tokenizer_config = { "clean_up_tokenization_spaces": False, "eos_token": { "__type": "AddedToken", - "content": " + "content": "<｜end▁of▁sentence｜>", "lstrip": False, "normalized": True, "rstrip": False, @@ -863,7 +863,7 @@ known_tokenizer_config = { "model_max_length": 16384, "pad_token": { "__type": "AddedToken", - "content": " + "content": "<｜end▁of▁sentence｜>", "lstrip": False, "normalized": True, "rstrip": False, @@ -872,7 +872,7 @@ known_tokenizer_config = { "sp_model_kwargs": {}, "unk_token": None, "tokenizer_class": "LlamaTokenizerFast", - "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '赠送' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '赠送' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + ''}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '' in content %}{% set content = content.split('')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + ''}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜> 空' + message['content'] + ' 空'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n 空' + message['content'] + ' 空'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>\\n'}}{% endif %}", + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '' in content %}{% set content = content.split('')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>\\n'}}{% endif %}", }, "status": "success", }, diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index f02c862f0fa..25ae0269ab3 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -42,16 +42,16 @@ class ExceptionCheckers: """ if not isinstance(error_str, str): return False - + if "429" in error_str or "rate limit" in error_str.lower(): return True - + ####################################### # Mistral API returns this error string ######################################### if "service tier capacity exceeded" in error_str.lower(): return True - + return False @staticmethod From 29bbde5257176181df4286cd8a701469ad045ed4 Mon Sep 17 00:00:00 2001 From: tobias-mayr Date: Thu, 4 Sep 2025 22:18:50 +0100 Subject: [PATCH 114/178] fix condition ordering and test --- .../vertex_and_google_ai_studio_gemini.py | 11 ++- tests/llm_translation/test_gemini.py | 68 +++++++++++++++++++ 2 files changed, 73 insertions(+), 6 deletions(-) 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 ba1d64facce..099b5c67069 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 @@ -430,14 +430,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): ) -> GeminiThinkingConfig: if reasoning_effort == "minimal": # Use model-specific minimum thinking budget or fallback - if model and "gemini-2.5-flash" in model.lower(): - budget = ( - DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH - ) + # Check for exact matches first, then partial matches + if model and "gemini-2.5-flash-lite" in model.lower(): + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE elif model and "gemini-2.5-pro" in model.lower(): budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO - elif model and "gemini-2.5-flash-lite" in model.lower(): - budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE + elif model and "gemini-2.5-flash" in model.lower(): + budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH else: budget = DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET diff --git a/tests/llm_translation/test_gemini.py b/tests/llm_translation/test_gemini.py index b3f16ecd838..9378c0305e6 100644 --- a/tests/llm_translation/test_gemini.py +++ b/tests/llm_translation/test_gemini.py @@ -765,3 +765,71 @@ def test_gemini_with_thinking(): drop_params=True, ) # get a new response from the model where it can see the function response print("second response\n", second_response) + + +def test_gemini_reasoning_effort_minimal(): + """ + Test that reasoning_effort='minimal' correctly maps to model-specific minimum thinking budgets + """ + from litellm.utils import return_raw_request + from litellm.types.utils import CallTypes + import json + + # Test with different Gemini models to verify model-specific mapping + test_cases = [ + ("gemini/gemini-2.5-flash", 1), # Flash: minimum 1 token + ("gemini/gemini-2.5-pro", 128), # Pro: minimum 128 tokens + ("gemini/gemini-2.5-flash-lite", 512), # Flash-Lite: minimum 512 tokens + ] + + for model, expected_min_budget in test_cases: + # Get the raw request to verify the thinking budget mapping + raw_request = return_raw_request( + endpoint=CallTypes.completion, + kwargs={ + "model": model, + "messages": [{"role": "user", "content": "Hello"}], + "reasoning_effort": "minimal", + }, + ) + + # Verify that the thinking config is set correctly + request_body = raw_request["raw_request_body"] + assert "generationConfig" in request_body, f"Model {model} should have generationConfig" + + generation_config = request_body["generationConfig"] + assert "thinkingConfig" in generation_config, f"Model {model} should have thinkingConfig" + + thinking_config = generation_config["thinkingConfig"] + assert "thinkingBudget" in thinking_config, f"Model {model} should have thinkingBudget" + + actual_budget = thinking_config["thinkingBudget"] + assert actual_budget == expected_min_budget, \ + f"Model {model} should map 'minimal' to {expected_min_budget} tokens, got {actual_budget}" + + # Verify that includeThoughts is True for minimal reasoning effort + assert thinking_config.get("includeThoughts", True), \ + f"Model {model} should have includeThoughts=True for minimal reasoning effort" + + # Test with unknown model (should use generic fallback) + try: + raw_request = return_raw_request( + endpoint=CallTypes.completion, + kwargs={ + "model": "gemini/unknown-model", + "messages": [{"role": "user", "content": "Hello"}], + "reasoning_effort": "minimal", + }, + ) + + request_body = raw_request["raw_request_body"] + generation_config = request_body["generationConfig"] + thinking_config = generation_config["thinkingConfig"] + # Should use generic fallback (128 tokens) + assert thinking_config["thinkingBudget"] == 128, \ + "Unknown model should use generic fallback of 128 tokens" + except Exception as e: + # If return_raw_request doesn't work for unknown models, that's okay + # The important part is that our known models work correctly + print(f"Note: Unknown model test skipped due to: {e}") + pass From 13525456172f454f6226510e50c013817c2b9a1a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 4 Sep 2025 14:35:41 -0700 Subject: [PATCH 115/178] [Fix] DD LLM Observability - Ensure `apm_id` is set on traces (#14272) * add apm_id for DD LLM * feat: add _get_apm_trace_id --- .../integrations/datadog/datadog_llm_obs.py | 23 +++++++++++++++- litellm/types/integrations/datadog_llm_obs.py | 1 + .../datadog/test_datadog_llm_observability.py | 26 +++++++++++++++++++ 3 files changed, 49 insertions(+), 1 deletion(-) diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 4f9c6409770..200f2f283de 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -19,6 +19,7 @@ import litellm from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.integrations.datadog.datadog import DataDogLogger +from litellm.litellm_core_utils.dd_tracing import tracer from litellm.litellm_core_utils.prompt_templates.common_utils import ( handle_any_messages_to_chat_completion_str_messages_conversion, ) @@ -216,7 +217,7 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): time_to_first_token=self._get_time_to_first_token_seconds(standard_logging_payload), ) - return LLMObsPayload( + payload: LLMObsPayload = LLMObsPayload( parent_id=metadata.get("parent_id", "undefined"), trace_id=standard_logging_payload.get("trace_id", str(uuid.uuid4())), span_id=metadata.get("span_id", str(uuid.uuid4())), @@ -230,6 +231,26 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): self._get_datadog_tags(standard_logging_object=standard_logging_payload) ], ) + + apm_trace_id = self._get_apm_trace_id() + if apm_trace_id is not None: + payload["apm_id"] = apm_trace_id + + return payload + + def _get_apm_trace_id(self) -> Optional[str]: + """Retrieve the current APM trace ID if available.""" + try: + current_span_fn = getattr(tracer, "current_span", None) + if callable(current_span_fn): + current_span = current_span_fn() + if current_span is not None: + trace_id = getattr(current_span, "trace_id", None) + if trace_id is not None: + return str(trace_id) + except Exception: + pass + return None def _assemble_error_info(self, standard_logging_payload: StandardLoggingPayload) -> Optional[DDLLMObsError]: """ diff --git a/litellm/types/integrations/datadog_llm_obs.py b/litellm/types/integrations/datadog_llm_obs.py index 82fb4fe3887..75c55bcc93c 100644 --- a/litellm/types/integrations/datadog_llm_obs.py +++ b/litellm/types/integrations/datadog_llm_obs.py @@ -46,6 +46,7 @@ class LLMMetrics(TypedDict, total=False): class LLMObsPayload(TypedDict, total=False): parent_id: str trace_id: str + apm_id: str span_id: str name: str meta: Meta diff --git a/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py b/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py index b4575a7ebdc..b1ce08de9e7 100644 --- a/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py +++ b/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py @@ -195,6 +195,32 @@ class TestDataDogLLMObsLogger: assert metadata["cache_hit"] == True assert metadata["cache_key"] == "test-cache-key-789" + def test_apm_id_included(self, mock_env_vars, mock_response_obj): + """Test that the current APM trace ID is attached to the payload""" + with patch('litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client'), \ + patch('asyncio.create_task'): + fake_tracer = MagicMock() + fake_span = MagicMock() + fake_span.trace_id = 987654321 + fake_tracer.current_span.return_value = fake_span + + with patch('litellm.integrations.datadog.datadog_llm_obs.tracer', fake_tracer): + logger = DataDogLLMObsLogger() + + standard_payload = create_standard_logging_payload_with_cache() + + kwargs = { + "standard_logging_object": standard_payload, + "litellm_params": {"metadata": {}} + } + + start_time = datetime.now() + end_time = datetime.now() + + payload = logger.create_llm_obs_payload(kwargs, start_time, end_time) + + assert payload["apm_id"] == str(fake_span.trace_id) + def test_cache_metadata_fields(self, mock_env_vars, mock_response_obj): """Test that cache-related metadata fields are correctly tracked""" with patch('litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client'), \ From 5847037b3a138dbfc74eff7199d53804418ceb3e Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 4 Sep 2025 14:40:28 -0700 Subject: [PATCH 116/178] Add validation for STORE_MODEL_IN_DB when updating public model groups (#14269) Co-authored-by: Cursor Agent Co-authored-by: ishaan --- .../model_management_endpoints.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/management_endpoints/model_management_endpoints.py b/litellm/proxy/management_endpoints/model_management_endpoints.py index b8762899f1e..2e1a684e397 100644 --- a/litellm/proxy/management_endpoints/model_management_endpoints.py +++ b/litellm/proxy/management_endpoints/model_management_endpoints.py @@ -987,7 +987,7 @@ async def update_public_model_groups( try: # Update the public model groups import litellm - from litellm.proxy.proxy_server import proxy_config + from litellm.proxy.proxy_server import proxy_config, store_model_in_db # Check if user has admin permissions if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN: @@ -1000,6 +1000,15 @@ async def update_public_model_groups( }, ) + # Check if STORE_MODEL_IN_DB is enabled + if store_model_in_db is not True: + raise HTTPException( + status_code=500, + detail={ + "error": "Set `'STORE_MODEL_IN_DB='True'` in your env to enable this feature." + }, + ) + litellm.public_model_groups = request.model_groups # Load existing config From 379b0dbf14872c8367122e8c178081efadce5aef Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 4 Sep 2025 18:14:38 -0700 Subject: [PATCH 117/178] [Fix] Ensure `team_id` is a required field for generating service account keys (#14270) * generate_service_account_key_fn * fix validate_team_id_used_in_service_account_request * fix types * test_validate_team_id_used_in_service_account_request_requires_team_id --- litellm/proxy/_types.py | 12 +- .../key_management_endpoints.py | 40 ++++- .../test_key_management_endpoints.py | 151 ++++++++++++++++++ 3 files changed, 198 insertions(+), 5 deletions(-) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 0595c44d69d..66bd5977551 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -2,7 +2,16 @@ import enum import json import uuid from datetime import datetime -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Union +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + List, + Literal, + Optional, + Union, +) import httpx from pydantic import ( @@ -778,7 +787,6 @@ class GenerateKeyRequest(KeyRequestBase): description="Type of key that determines default allowed routes.", ) - class GenerateKeyResponse(KeyRequestBase): key: str # type: ignore key_name: Optional[str] = None diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index 3868c9df694..8a3507e2398 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -346,6 +346,35 @@ def handle_key_type(data: GenerateKeyRequest, data_json: dict) -> dict: data_json["allowed_routes"] = ["info_routes"] return data_json +async def validate_team_id_used_in_service_account_request( + team_id: Optional[str], + prisma_client: Optional[PrismaClient], +): + """ + Validate team_id is used in the request body for generating a service account key + """ + if team_id is None: + raise HTTPException( + status_code=400, + detail="team_id is required for service account keys. Please specify `team_id` in the request body.", + ) + + if prisma_client is None: + raise HTTPException( + status_code=400, + detail="prisma_client is required for service account keys. Please specify `prisma_client` in the request body.", + ) + + # check if team_id exists in the database + team = await prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": team_id}, + ) + if team is None: + raise HTTPException( + status_code=400, + detail="team_id does not exist in the database. Please specify a valid `team_id` in the request body.", + ) + return True async def _common_key_generation_helper( # noqa: PLR0915 data: GenerateKeyRequest, @@ -372,9 +401,9 @@ async def _common_key_generation_helper( # noqa: PLR0915 and data.metadata.get("service_account_id") is not None and data.team_id is None ): - raise HTTPException( - status_code=400, - detail="team_id is required for service account keys. Please specify `team_id` in the request body.", + await validate_team_id_used_in_service_account_request( + team_id=data.team_id, + prisma_client=prisma_client, ) # check if user set default key/generate params on config.yaml @@ -756,6 +785,11 @@ async def generate_service_account_key_fn( user_custom_key_generate, ) + await validate_team_id_used_in_service_account_request( + team_id=data.team_id, + prisma_client=prisma_client, + ) + verbose_proxy_logger.debug("entered /key/generate") if user_custom_key_generate is not None: diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py index 893e5767ecd..3a597adef06 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py @@ -576,3 +576,154 @@ async def test_update_service_account_works_with_team_id(): await prepare_key_update_data(data=data, existing_key_row=existing_key) + +@pytest.mark.asyncio +async def test_validate_team_id_used_in_service_account_request_requires_team_id(): + """ + Test that validate_team_id_used_in_service_account_request raises HTTPException + when team_id is None for service account key generation. + """ + from litellm.proxy.management_endpoints.key_management_endpoints import ( + validate_team_id_used_in_service_account_request, + ) + + mock_prisma_client = AsyncMock() + + # Test that HTTPException is raised when team_id is None + with pytest.raises(HTTPException) as exc_info: + await validate_team_id_used_in_service_account_request( + team_id=None, + prisma_client=mock_prisma_client, + ) + + assert exc_info.value.status_code == 400 + assert "team_id is required for service account keys" in str(exc_info.value.detail) + + +@pytest.mark.asyncio +async def test_validate_team_id_used_in_service_account_request_requires_prisma_client(): + """ + Test that validate_team_id_used_in_service_account_request raises HTTPException + when prisma_client is None for service account key generation. + """ + from litellm.proxy.management_endpoints.key_management_endpoints import ( + validate_team_id_used_in_service_account_request, + ) + + # Test that HTTPException is raised when prisma_client is None + with pytest.raises(HTTPException) as exc_info: + await validate_team_id_used_in_service_account_request( + team_id="test-team-id", + prisma_client=None, + ) + + assert exc_info.value.status_code == 400 + assert "prisma_client is required for service account keys" in str(exc_info.value.detail) + + +@pytest.mark.asyncio +async def test_validate_team_id_used_in_service_account_request_checks_team_exists(): + """ + Test that validate_team_id_used_in_service_account_request validates that + the team_id exists in the database for service account key generation. + """ + from litellm.proxy.management_endpoints.key_management_endpoints import ( + validate_team_id_used_in_service_account_request, + ) + + mock_prisma_client = AsyncMock() + + # Mock the database query to return None (team doesn't exist) + mock_find_unique = AsyncMock(return_value=None) + mock_prisma_client.db.litellm_teamtable.find_unique = mock_find_unique + + # Test that HTTPException is raised when team doesn't exist in DB + with pytest.raises(HTTPException) as exc_info: + await validate_team_id_used_in_service_account_request( + team_id="non-existent-team-id", + prisma_client=mock_prisma_client, + ) + + assert exc_info.value.status_code == 400 + assert "team_id does not exist in the database" in str(exc_info.value.detail) + + # Verify the database was queried with the correct parameters + mock_find_unique.assert_called_once_with( + where={"team_id": "non-existent-team-id"} + ) + + +@pytest.mark.asyncio +async def test_validate_team_id_used_in_service_account_request_success(): + """ + Test that validate_team_id_used_in_service_account_request returns True + when team_id exists in the database for service account key generation. + """ + from litellm.proxy.management_endpoints.key_management_endpoints import ( + validate_team_id_used_in_service_account_request, + ) + + mock_prisma_client = AsyncMock() + + # Mock the database query to return a team object (team exists) + mock_team = {"team_id": "existing-team-id", "team_name": "Test Team"} + mock_find_unique = AsyncMock(return_value=mock_team) + mock_prisma_client.db.litellm_teamtable.find_unique = mock_find_unique + + # Test that function returns True when team exists + result = await validate_team_id_used_in_service_account_request( + team_id="existing-team-id", + prisma_client=mock_prisma_client, + ) + + assert result is True + + # Verify the database was queried with the correct parameters + mock_find_unique.assert_called_once_with( + where={"team_id": "existing-team-id"} + ) + + +@pytest.mark.asyncio +async def test_generate_service_account_key_endpoint_validation(): + """ + Test that the /key/service-account/generate endpoint properly validates + team_id requirement and team existence in database. + """ + from unittest.mock import patch + + from litellm.proxy.management_endpoints.key_management_endpoints import ( + generate_service_account_key_fn, + ) + + # Test case 1: Missing team_id + with pytest.raises(HTTPException) as exc_info: + await generate_service_account_key_fn( + data=GenerateKeyRequest(team_id=None), + user_api_key_dict=UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-1" + ), + litellm_changed_by=None, + ) + + assert exc_info.value.status_code == 400 + assert "team_id is required for service account keys" in str(exc_info.value.detail) + + # Test case 2: Team doesn't exist in database + with patch('litellm.proxy.proxy_server.prisma_client') as mock_prisma: + # Mock team not found + mock_find_unique = AsyncMock(return_value=None) + mock_prisma.db.litellm_teamtable.find_unique = mock_find_unique + + with pytest.raises(HTTPException) as exc_info: + await generate_service_account_key_fn( + data=GenerateKeyRequest(team_id="non-existent-team"), + user_api_key_dict=UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-1" + ), + litellm_changed_by=None, + ) + + assert exc_info.value.status_code == 400 + assert "team_id does not exist in the database" in str(exc_info.value.detail) + From f7f106f8b8c5ca90a1c9c67457edb08a2f24f1e4 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 5 Sep 2025 10:00:30 +0530 Subject: [PATCH 118/178] Move test to test_litellm/ folder --- .../responses/test_text_format_conversion.py | 161 ++++++++++++++++++ 1 file changed, 161 insertions(+) create mode 100644 tests/test_litellm/responses/test_text_format_conversion.py diff --git a/tests/test_litellm/responses/test_text_format_conversion.py b/tests/test_litellm/responses/test_text_format_conversion.py new file mode 100644 index 00000000000..20a87a4abbb --- /dev/null +++ b/tests/test_litellm/responses/test_text_format_conversion.py @@ -0,0 +1,161 @@ +import json +import os +import sys + +import pytest +from pydantic import BaseModel + +sys.path.insert( + 0, os.path.abspath("../../..") +) # Adds the parent directory to the system path + +import litellm +from litellm.types.llms.openai import ( + IncompleteDetails, + ResponseAPIUsage, + ResponsesAPIResponse, +) + + +class TestTextFormatConversion: + """Test text_format to text parameter conversion for responses API""" + + def get_base_completion_call_args(self): + """Get base arguments for completion call""" + return { + "model": "gpt-4o", + "api_key": "test-key", + "api_base": "https://api.openai.com/v1", + } + + @pytest.mark.asyncio + async def test_text_format_to_text_conversion(self): + """ + Test that when text_format parameter is passed to litellm.aresponses, + it gets converted to text parameter in the raw API call to OpenAI. + """ + from unittest.mock import AsyncMock, patch + + class TestResponse(BaseModel): + """Test Pydantic model for structured output""" + + answer: str + confidence: float + + class MockResponse: + """Mock response class for testing""" + + def __init__(self, json_data, status_code): + self._json_data = json_data + self.status_code = status_code + self.text = json.dumps(json_data) + + def json(self): + return self._json_data + + # Mock response from OpenAI + mock_response = { + "id": "resp_123", + "object": "response", + "created_at": 1741476542, + "status": "completed", + "model": "gpt-4o", + "output": [ + { + "type": "message", + "id": "msg_123", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": '{"answer": "Paris", "confidence": 0.95}', + "annotations": [], + } + ], + } + ], + "parallel_tool_calls": True, + "usage": { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30, + "output_tokens_details": {"reasoning_tokens": 0}, + }, + "text": {"format": {"type": "json_object"}}, + "error": None, + "incomplete_details": None, + "instructions": None, + "metadata": {}, + "temperature": 1.0, + "tool_choice": "auto", + "tools": [], + "top_p": 1.0, + "max_output_tokens": None, + "previous_response_id": None, + "reasoning": {"effort": None, "summary": None}, + "truncation": "disabled", + "user": None, + } + + base_completion_call_args = self.get_base_completion_call_args() + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + new_callable=AsyncMock, + ) as mock_post: + # Configure the mock to return our response + mock_post.return_value = MockResponse(mock_response, 200) + + litellm._turn_on_debug() + litellm.set_verbose = True + + # Call aresponses with text_format parameter + response = await litellm.aresponses( + input="What is the capital of France?", + text_format=TestResponse, + **base_completion_call_args, + ) + + # Verify the request was made correctly + mock_post.assert_called_once() + request_body = mock_post.call_args.kwargs["json"] + print("Request body:", json.dumps(request_body, indent=4)) + + # Validate that text_format was converted to text parameter + assert ( + "text" in request_body + ), "text parameter should be present in request body" + assert ( + "text_format" not in request_body + ), "text_format should not be in request body" + + # Validate the text parameter structure + text_param = request_body["text"] + assert "format" in text_param, "text parameter should have format field" + assert ( + text_param["format"]["type"] == "json_schema" + ), "format type should be json_schema" + assert "name" in text_param["format"], "format should have name field" + assert ( + text_param["format"]["name"] == "TestResponse" + ), "format name should match Pydantic model name" + assert "schema" in text_param["format"], "format should have schema field" + assert "strict" in text_param["format"], "format should have strict field" + + # Validate the schema structure + schema = text_param["format"]["schema"] + assert schema["type"] == "object", "schema type should be object" + assert "properties" in schema, "schema should have properties" + assert ( + "answer" in schema["properties"] + ), "schema should have answer property" + assert ( + "confidence" in schema["properties"] + ), "schema should have confidence property" + + # Validate other request parameters + assert request_body["input"] == "What is the capital of France?" + + # Validate the response + print("Response:", json.dumps(response, indent=4, default=str)) From ad9f54a192a9a53ac38dedf1faa5a6467921a682 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 5 Sep 2025 10:08:34 +0530 Subject: [PATCH 119/178] Move test to test_litellm/ folder --- .../base_responses_api.py | 165 +---- .../responses/test_text_format_conversion.py | 594 +++++++++++++++++- 2 files changed, 602 insertions(+), 157 deletions(-) diff --git a/tests/llm_responses_api_testing/base_responses_api.py b/tests/llm_responses_api_testing/base_responses_api.py index 2939e884a56..fc6983520fd 100644 --- a/tests/llm_responses_api_testing/base_responses_api.py +++ b/tests/llm_responses_api_testing/base_responses_api.py @@ -1,25 +1,33 @@ +import httpx import json -import os -import sys - import pytest +import sys +from typing import Any, Dict, List +from unittest.mock import MagicMock, Mock, patch +import os +import uuid +import time +import base64 sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path -import json +import litellm from abc import ABC, abstractmethod +from litellm.integrations.custom_logger import CustomLogger +import json +from litellm.types.utils import StandardLoggingPayload +from litellm.types.llms.openai import ( + ResponseCompletedEvent, + ResponsesAPIResponse, + ResponseAPIUsage, + IncompleteDetails, +) from openai.types.responses.response_create_params import ( ResponseInputParam, ) - -import litellm -from litellm.types.llms.openai import ( - IncompleteDetails, - ResponseAPIUsage, - ResponsesAPIResponse, -) +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler def validate_responses_api_response(response, final_chunk: bool = False): @@ -529,140 +537,6 @@ class BaseResponsesAPITest(ABC): validate_responses_api_response(final_response, final_chunk=True) assert final_response.output is not None - @pytest.mark.asyncio - async def test_text_format_to_text_conversion(self): - """ - Test that when text_format parameter is passed to litellm.aresponses, - it gets converted to text parameter in the raw API call to OpenAI. - """ - from unittest.mock import AsyncMock, patch - - from pydantic import BaseModel - - class TestResponse(BaseModel): - """Test Pydantic model for structured output""" - - answer: str - confidence: float - - class MockResponse: - """Mock response class for testing""" - - def __init__(self, json_data, status_code): - self._json_data = json_data - self.status_code = status_code - self.text = json.dumps(json_data) - - def json(self): - return self._json_data - - # Mock response from OpenAI - mock_response = { - "id": "resp_123", - "object": "response", - "created_at": 1741476542, - "status": "completed", - "model": "gpt-4o", - "output": [ - { - "type": "message", - "id": "msg_123", - "status": "completed", - "role": "assistant", - "content": [ - { - "type": "output_text", - "text": '{"answer": "Paris", "confidence": 0.95}', - "annotations": [], - } - ], - } - ], - "parallel_tool_calls": True, - "usage": { - "input_tokens": 10, - "output_tokens": 20, - "total_tokens": 30, - "output_tokens_details": {"reasoning_tokens": 0}, - }, - "text": {"format": {"type": "json_object"}}, - "error": None, - "incomplete_details": None, - "instructions": None, - "metadata": {}, - "temperature": 1.0, - "tool_choice": "auto", - "tools": [], - "top_p": 1.0, - "max_output_tokens": None, - "previous_response_id": None, - "reasoning": {"effort": None, "summary": None}, - "truncation": "disabled", - "user": None, - } - - base_completion_call_args = self.get_base_completion_call_args() - - with patch( - "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", - new_callable=AsyncMock, - ) as mock_post: - # Configure the mock to return our response - mock_post.return_value = MockResponse(mock_response, 200) - - litellm._turn_on_debug() - litellm.set_verbose = True - - # Call aresponses with text_format parameter - response = await litellm.aresponses( - input="What is the capital of France?", - text_format=TestResponse, - **base_completion_call_args, - ) - - # Verify the request was made correctly - mock_post.assert_called_once() - request_body = mock_post.call_args.kwargs["json"] - print("Request body:", json.dumps(request_body, indent=4)) - - # Validate that text_format was converted to text parameter - assert ( - "text" in request_body - ), "text parameter should be present in request body" - assert ( - "text_format" not in request_body - ), "text_format should not be in request body" - - # Validate the text parameter structure - text_param = request_body["text"] - assert "format" in text_param, "text parameter should have format field" - assert ( - text_param["format"]["type"] == "json_schema" - ), "format type should be json_schema" - assert "name" in text_param["format"], "format should have name field" - assert ( - text_param["format"]["name"] == "TestResponse" - ), "format name should match Pydantic model name" - assert "schema" in text_param["format"], "format should have schema field" - assert "strict" in text_param["format"], "format should have strict field" - - # Validate the schema structure - schema = text_param["format"]["schema"] - assert schema["type"] == "object", "schema type should be object" - assert "properties" in schema, "schema should have properties" - assert ( - "answer" in schema["properties"] - ), "schema should have answer property" - assert ( - "confidence" in schema["properties"] - ), "schema should have confidence property" - - # Validate other request parameters - assert request_body["input"] == "What is the capital of France?" - - # Validate the response - print("Response:", json.dumps(response, indent=4, default=str)) - def test_openai_responses_api_dict_input_filtering(self): """ Test that regular dict inputs with status fields are properly filtered @@ -716,4 +590,3 @@ class BaseResponsesAPITest(ABC): assert function_call_item["status"] == "completed", "status value should be preserved" print("✅ OpenAI Responses API dict input filtering test passed") - diff --git a/tests/test_litellm/responses/test_text_format_conversion.py b/tests/test_litellm/responses/test_text_format_conversion.py index 20a87a4abbb..dafccfaf2b9 100644 --- a/tests/test_litellm/responses/test_text_format_conversion.py +++ b/tests/test_litellm/responses/test_text_format_conversion.py @@ -3,11 +3,16 @@ import os import sys import pytest -from pydantic import BaseModel sys.path.insert( - 0, os.path.abspath("../../..") + 0, os.path.abspath("../..") ) # Adds the parent directory to the system path +import json +from abc import ABC, abstractmethod + +from openai.types.responses.response_create_params import ( + ResponseInputParam, +) import litellm from litellm.types.llms.openai import ( @@ -17,16 +22,512 @@ from litellm.types.llms.openai import ( ) -class TestTextFormatConversion: - """Test text_format to text parameter conversion for responses API""" +def validate_responses_api_response(response, final_chunk: bool = False): + """ + Validate that a response from litellm.responses() or litellm.aresponses() + conforms to the expected ResponsesAPIResponse structure. - def get_base_completion_call_args(self): - """Get base arguments for completion call""" - return { - "model": "gpt-4o", - "api_key": "test-key", - "api_base": "https://api.openai.com/v1", - } + Args: + response: The response object to validate + + Raises: + AssertionError: If the response doesn't match the expected structure + """ + # Validate response structure + print("response=", json.dumps(response, indent=4, default=str)) + assert isinstance( + response, ResponsesAPIResponse + ), "Response should be an instance of ResponsesAPIResponse" + + # Required fields + assert "id" in response and isinstance( + response["id"], str + ), "Response should have a string 'id' field" + assert "created_at" in response and isinstance( + response["created_at"], int + ), "Response should have an integer 'created_at' field" + assert "output" in response and isinstance( + response["output"], list + ), "Response should have a list 'output' field" + assert "parallel_tool_calls" in response and isinstance( + response["parallel_tool_calls"], bool + ), "Response should have a boolean 'parallel_tool_calls' field" + + # Optional fields with their expected types + optional_fields = { + "error": (dict, type(None)), # error can be dict or None + "incomplete_details": (IncompleteDetails, type(None)), + "instructions": (str, type(None)), + "metadata": dict, + "model": str, + "object": str, + "temperature": (int, float, type(None)), + "tool_choice": (dict, str), + "tools": list, + "top_p": (int, float, type(None)), + "max_output_tokens": (int, type(None)), + "previous_response_id": (str, type(None)), + "reasoning": dict, + "status": str, + "text": dict, + "truncation": (str, type(None)), + "usage": ResponseAPIUsage, + "user": (str, type(None)), + "store": (bool, type(None)), + } + if final_chunk is False: + optional_fields["usage"] = type(None) + + for field, expected_type in optional_fields.items(): + if field in response: + assert isinstance( + response[field], expected_type + ), f"Field '{field}' should be of type {expected_type}, but got {type(response[field])}" + + # Check if output has at least one item + if final_chunk is True: + assert ( + len(response["output"]) > 0 + ), "Response 'output' field should have at least one item" + + return True # Return True if validation passes + + +class BaseResponsesAPITest(ABC): + """ + Abstract base test class that enforces a common test across all test classes. + """ + + @abstractmethod + def get_base_completion_call_args(self) -> dict: + """Must return the base completion call args""" + pass + + def get_base_completion_reasoning_call_args(self) -> dict: + """Must return the base completion reasoning call args""" + return None + + @pytest.mark.parametrize("sync_mode", [True, False]) + @pytest.mark.asyncio + async def test_basic_openai_responses_api(self, sync_mode): + litellm._turn_on_debug() + litellm.set_verbose = True + base_completion_call_args = self.get_base_completion_call_args() + try: + if sync_mode: + response = litellm.responses( + input="Basic ping", + max_output_tokens=20, + **base_completion_call_args, + ) + else: + response = await litellm.aresponses( + input="Basic ping", + max_output_tokens=20, + **base_completion_call_args, + ) + except litellm.InternalServerError: + pytest.skip("Skipping test due to litellm.InternalServerError") + print("litellm response=", json.dumps(response, indent=4, default=str)) + + # Use the helper function to validate the response + validate_responses_api_response(response, final_chunk=True) + + @pytest.mark.parametrize("sync_mode", [True, False]) + @pytest.mark.asyncio + @pytest.mark.flaky(retries=3, delay=2) + async def test_basic_openai_responses_api_streaming(self, sync_mode): + litellm._turn_on_debug() + base_completion_call_args = self.get_base_completion_call_args() + collected_content_string = "" + response_completed_event = None + if sync_mode: + response = litellm.responses( + input="Basic ping", stream=True, **base_completion_call_args + ) + for event in response: + print("litellm response=", json.dumps(event, indent=4, default=str)) + if event.type == "response.output_text.delta": + collected_content_string += event.delta + elif event.type == "response.completed": + response_completed_event = event + else: + response = await litellm.aresponses( + input="Basic ping", stream=True, **base_completion_call_args + ) + async for event in response: + print("litellm response=", json.dumps(event, indent=4, default=str)) + if event.type == "response.output_text.delta": + collected_content_string += event.delta + elif event.type == "response.completed": + response_completed_event = event + + # assert the delta chunks content had len(collected_content_string) > 0 + # this content is typically rendered on chat ui's + assert len(collected_content_string) > 0 + + # assert the response completed event is not None + assert response_completed_event is not None + + # assert the response completed event has a response + assert response_completed_event.response is not None + + # assert the response completed event includes the usage + assert response_completed_event.response.usage is not None + + # basic test assert the usage seems reasonable + print( + "response_completed_event.response.usage=", + response_completed_event.response.usage, + ) + assert ( + response_completed_event.response.usage.input_tokens > 0 + and response_completed_event.response.usage.input_tokens < 100 + ) + assert ( + response_completed_event.response.usage.output_tokens > 0 + and response_completed_event.response.usage.output_tokens < 2000 + ) + assert ( + response_completed_event.response.usage.total_tokens > 0 + and response_completed_event.response.usage.total_tokens < 2000 + ) + + # total tokens should be the sum of input and output tokens + assert ( + response_completed_event.response.usage.total_tokens + == response_completed_event.response.usage.input_tokens + + response_completed_event.response.usage.output_tokens + ) + + @pytest.mark.parametrize("sync_mode", [False, True]) + @pytest.mark.asyncio + async def test_basic_openai_responses_delete_endpoint(self, sync_mode): + litellm._turn_on_debug() + litellm.set_verbose = True + base_completion_call_args = self.get_base_completion_call_args() + if sync_mode: + response = litellm.responses( + input="Basic ping", max_output_tokens=20, **base_completion_call_args + ) + + # delete the response + if isinstance(response, ResponsesAPIResponse): + litellm.delete_responses( + response_id=response.id, **base_completion_call_args + ) + else: + raise ValueError("response is not a ResponsesAPIResponse") + else: + response = await litellm.aresponses( + input="Basic ping", max_output_tokens=20, **base_completion_call_args + ) + + # async delete the response + if isinstance(response, ResponsesAPIResponse): + await litellm.adelete_responses( + response_id=response.id, **base_completion_call_args + ) + else: + raise ValueError("response is not a ResponsesAPIResponse") + + @pytest.mark.parametrize("sync_mode", [True, False]) + @pytest.mark.flaky(retries=3, delay=2) + @pytest.mark.asyncio + async def test_basic_openai_responses_streaming_delete_endpoint(self, sync_mode): + # litellm._turn_on_debug() + # litellm.set_verbose = True + base_completion_call_args = self.get_base_completion_call_args() + response_id = None + if sync_mode: + response_id = None + response = litellm.responses( + input="Basic ping", + max_output_tokens=20, + stream=True, + **base_completion_call_args, + ) + for event in response: + print("litellm response=", json.dumps(event, indent=4, default=str)) + if "response" in event: + response_obj = event.get("response") + if response_obj is not None: + response_id = response_obj.get("id") + print("got response_id=", response_id) + + # delete the response + assert response_id is not None + litellm.delete_responses( + response_id=response_id, **base_completion_call_args + ) + else: + response = await litellm.aresponses( + input="Basic ping", + max_output_tokens=20, + stream=True, + **base_completion_call_args, + ) + async for event in response: + print("litellm response=", json.dumps(event, indent=4, default=str)) + if "response" in event: + response_obj = event.get("response") + if response_obj is not None: + response_id = response_obj.get("id") + print("got response_id=", response_id) + + # delete the response + assert response_id is not None + await litellm.adelete_responses( + response_id=response_id, **base_completion_call_args + ) + + @pytest.mark.parametrize("sync_mode", [False, True]) + @pytest.mark.flaky(retries=3, delay=2) + @pytest.mark.asyncio + async def test_basic_openai_responses_get_endpoint(self, sync_mode): + litellm._turn_on_debug() + litellm.set_verbose = True + base_completion_call_args = self.get_base_completion_call_args() + if sync_mode: + response = litellm.responses( + input="Basic ping", max_output_tokens=20, **base_completion_call_args + ) + + # get the response + if isinstance(response, ResponsesAPIResponse): + result = litellm.get_responses( + response_id=response.id, **base_completion_call_args + ) + assert result is not None + assert result.id == response.id + assert result.output == response.output + else: + raise ValueError("response is not a ResponsesAPIResponse") + else: + response = await litellm.aresponses( + input="Basic ping", max_output_tokens=20, **base_completion_call_args + ) + # async get the response + if isinstance(response, ResponsesAPIResponse): + result = await litellm.aget_responses( + response_id=response.id, **base_completion_call_args + ) + assert result is not None + assert result.id == response.id + assert result.output == response.output + else: + raise ValueError("response is not a ResponsesAPIResponse") + + @pytest.mark.asyncio + @pytest.mark.flaky(retries=3, delay=2) + async def test_basic_openai_list_input_items_endpoint(self): + """Test that calls the OpenAI List Input Items endpoint""" + litellm._turn_on_debug() + + response = await litellm.aresponses( + model="gpt-4o", + input="Tell me a three sentence bedtime story about a unicorn.", + ) + print("Initial response=", json.dumps(response, indent=4, default=str)) + + response_id = response.get("id") + assert response_id is not None, "Response should have an ID" + print(f"Got response_id: {response_id}") + + list_items_response = await litellm.alist_input_items( + response_id=response_id, + limit=20, + order="desc", + ) + print( + "List items response=", + json.dumps(list_items_response, indent=4, default=str), + ) + + @pytest.mark.asyncio + async def test_multiturn_responses_api(self): + litellm._turn_on_debug() + litellm.set_verbose = True + try: + base_completion_call_args = self.get_base_completion_call_args() + response_1 = await litellm.aresponses( + input="Basic ping", max_output_tokens=20, **base_completion_call_args + ) + + # follow up with a second request + response_1_id = response_1.id + response_2 = await litellm.aresponses( + input="Basic ping", + max_output_tokens=20, + previous_response_id=response_1_id, + **base_completion_call_args, + ) + + # assert the response is not None + assert response_1 is not None + assert response_2 is not None + except litellm.InternalServerError: + pytest.skip("Skipping test due to litellm.InternalServerError") + + @pytest.mark.asyncio + async def test_responses_api_with_tool_calls(self): + """Test that calls the Responses API with tool calls including function call and output""" + litellm._turn_on_debug() + litellm.set_verbose = True + base_completion_call_args = self.get_base_completion_call_args() + + # Define the input with message, function call, and function call output + input_data: ResponseInputParam = [ + { + "type": "message", + "role": "user", + "content": "How is the weather in São Paulo today ?", + }, + { + "type": "function_call", + "arguments": '{"location": "São Paulo, Brazil"}', + "call_id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5", + "name": "get_weather", + "id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5", + "status": "completed", + }, + { + "type": "function_call_output", + "call_id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5", + "output": "Rainy", + }, + ] + + # Define the tools + tools = [ + { + "type": "function", + "name": "get_weather", + "description": "Get current temperature for a given location.", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "City and country e.g. Bogotá, Colombia", + } + }, + "required": ["location"], + "additionalProperties": False, + }, + } + ] + + try: + # Make the responses API call + response = await litellm.aresponses( + input=input_data, store=False, tools=tools, **base_completion_call_args + ) + except litellm.InternalServerError: + pytest.skip("Skipping test due to litellm.InternalServerError") + + print("litellm response=", json.dumps(response, indent=4, default=str)) + + # Validate the response structure + validate_responses_api_response(response, final_chunk=True) + + # Additional assertions specific to tool calls + assert response is not None + assert "output" in response + assert len(response["output"]) > 0 + + @pytest.mark.asyncio + async def test_responses_api_multi_turn_with_reasoning_and_structured_output(self): + """ + Test multi-turn conversation with reasoning, structured output, and tool calls. + + This test validates: + - First call: Model uses reasoning to process a question and makes a tool call + - Tool call handling: Function call output is properly processed + - Second call: Model produces structured output incorporating tool results + - Structured output: Response conforms to defined Pydantic model schema + """ + from pydantic import BaseModel + + litellm._turn_on_debug() + litellm.set_verbose = True + base_completion_call_args = self.get_base_completion_reasoning_call_args() + if base_completion_call_args is None: + pytest.skip("Skipping test due to no base completion reasoning call args") + + # Define tools for the conversation + tools = [{"type": "function", "name": "get_today"}] + + # Define structured output schema + class Output(BaseModel): + today: str + number_of_r: str + + # Initial conversation input + input_messages = [ + { + "role": "user", + "content": "How many r in strrawberrry? While you're thinking, you should call tool get_today. Then you output the today and number of r", + } + ] + + # First call - should trigger reasoning and tool call + response = await litellm.aresponses( + input=input_messages, + tools=tools, + reasoning={"effort": "low", "summary": "detailed"}, + text_format=Output, + **base_completion_call_args, + ) + + print("First call output:") + print(json.dumps(response.output, indent=4, default=str)) + + # Validate first response structure + validate_responses_api_response(response, final_chunk=True) + assert response.output is not None + assert len(response.output) > 0 + + # Extend input with first response output + input_messages.extend(response.output) + + # Process any tool calls and add function outputs + function_outputs = [] + for item in response.output: + if hasattr(item, "type") and item.type in [ + "function_call", + "custom_tool_call", + ]: + if hasattr(item, "name") and item.name == "get_today": + function_outputs.append( + { + "type": "function_call_output", + "call_id": item.call_id, + "output": "2025-01-15", + } + ) + + # Add function outputs to conversation + input_messages.extend(function_outputs) + + print("Second call input:") + print(json.dumps(input_messages, indent=4, default=str)) + + # Second call - should produce structured output + final_response = await litellm.aresponses( + input=input_messages, + tools=tools, + reasoning={"effort": "low", "summary": "detailed"}, + text_format=Output, + **base_completion_call_args, + ) + + print("Second call output:") + print(json.dumps(final_response.output, indent=4, default=str)) + + # Validate final response structure + validate_responses_api_response(final_response, final_chunk=True) + assert final_response.output is not None @pytest.mark.asyncio async def test_text_format_to_text_conversion(self): @@ -36,6 +537,8 @@ class TestTextFormatConversion: """ from unittest.mock import AsyncMock, patch + from pydantic import BaseModel + class TestResponse(BaseModel): """Test Pydantic model for structured output""" @@ -159,3 +662,72 @@ class TestTextFormatConversion: # Validate the response print("Response:", json.dumps(response, indent=4, default=str)) + + def test_openai_responses_api_dict_input_filtering(self): + """ + Test that regular dict inputs with status fields are properly filtered + to replicate exclude_unset=True behavior for non-Pydantic objects. + """ + from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig + + # Test input with regular dict objects (like from JSON) + test_input = [ + { + "role": "user", + "content": "test" + }, + { + "id": "rs_123", + "summary": [{"text": "test", "type": "summary_text"}], + "type": "reasoning", + "content": None, # Should be filtered out + "encrypted_content": None, # Should be filtered out + "status": None # Should be filtered out + }, + { + "arguments": "{}", + "call_id": "call_123", + "name": "get_today", + "type": "function_call", + "id": "fc_123", + "status": "completed" # Should be preserved (not a default field) + } + ] + + config = OpenAIResponsesAPIConfig() + validated_input = config._validate_input_param(test_input) + + # Verify the results + assert len(validated_input) == 3 + + # Check reasoning item (index 1) + reasoning_item = validated_input[1] + assert reasoning_item["type"] == "reasoning" + assert "status" not in reasoning_item, "status field should be filtered out from reasoning item" + assert "content" not in reasoning_item, "content field should be filtered out from reasoning item" + assert "encrypted_content" not in reasoning_item, "encrypted_content field should be filtered out from reasoning item" + assert "id" in reasoning_item, "id field should be preserved" + assert "summary" in reasoning_item, "summary field should be preserved" + + # Check function call item (index 2) + function_call_item = validated_input[2] + assert function_call_item["type"] == "function_call" + assert "status" in function_call_item, "status field should be preserved in function call item" + assert function_call_item["status"] == "completed", "status value should be preserved" + + print("✅ OpenAI Responses API dict input filtering test passed") + + +class TestOpenAIResponsesAPITest(BaseResponsesAPITest): + """Concrete test class for OpenAI Responses API tests""" + + def get_base_completion_call_args(self): + return { + "model": "openai/gpt-4o", + } + + def get_base_completion_reasoning_call_args(self): + return { + "model": "openai/gpt-5-mini", + } + From 4fefac1bf2ec414b73c5403ce9d35935fbd81410 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 5 Sep 2025 10:24:55 +0530 Subject: [PATCH 120/178] Move test to test_litellm/ folder --- .../test_openai_responses_transformation.py | 247 +----------------- 1 file changed, 1 insertion(+), 246 deletions(-) diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py index 21232161d0c..a6a34518098 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py @@ -667,249 +667,4 @@ def test_get_supported_openai_params(): assert "temperature" in params assert "stream" in params assert "background" in params - assert "stream" in params - - -class TestOpenAIFieldExclusionRegistry: - """Test suite for the OpenAI Field Exclusion Registry system""" - - def setup_method(self): - """Setup test fixtures""" - from litellm.llms.openai.responses.transformation import ( - OpenAIFieldExclusionRegistry, - OpenAIResponsesAPIConfig - ) - self.registry = OpenAIFieldExclusionRegistry - self.config = OpenAIResponsesAPIConfig() - - def test_registry_initialization(self): - """Test that the registry is properly initialized with ResponseReasoningItem""" - # Test that we can get excluded fields (should not be empty if ResponseReasoningItem is registered) - all_excluded_fields = self.registry.get_all_excluded_fields() - - # The registry should have at least some fields if ResponseReasoningItem was successfully registered - # If OpenAI SDK is not available, this might be empty, which is also valid - assert isinstance(all_excluded_fields, set), "get_all_excluded_fields should return a set" - - # If we have the OpenAI SDK available, we should have the expected fields - try: - from openai.types.responses import ResponseReasoningItem - reasoning_fields = self.registry.get_excluded_fields_for_model(ResponseReasoningItem) - expected_fields = {'status', 'content', 'encrypted_content'} - assert expected_fields.issubset(reasoning_fields), f"Expected fields {expected_fields} to be subset of {reasoning_fields}" - except ImportError: - # If OpenAI SDK is not available, that's fine - the registry should handle this gracefully - pytest.skip("OpenAI SDK not available, skipping ResponseReasoningItem specific tests") - - def test_register_model_functionality(self): - """Test that we can register new models to the registry""" - from pydantic import BaseModel - from typing import Optional - - # Create a test model with default None fields - class TestResponseModel(BaseModel): - id: str - type: str = "test" - status: Optional[str] = None - content: Optional[str] = None - required_field: str - - # Register the test model - self.registry.register_model(TestResponseModel) - - # Verify it was registered and fields are detected - excluded_fields = self.registry.get_excluded_fields_for_model(TestResponseModel) - expected_excluded = {'status', 'content'} # Fields with default None - - assert expected_excluded.issubset(excluded_fields), f"Expected {expected_excluded} to be in {excluded_fields}" - assert 'id' not in excluded_fields, "Required field 'id' should not be excluded" - assert 'required_field' not in excluded_fields, "Required field 'required_field' should not be excluded" - - def test_get_all_excluded_fields(self): - """Test that get_all_excluded_fields aggregates fields from all registered models""" - all_fields_before = self.registry.get_all_excluded_fields() - - # Create and register a test model - from pydantic import BaseModel - from typing import Optional - - class AnotherTestModel(BaseModel): - id: str - unique_field: Optional[str] = None - - self.registry.register_model(AnotherTestModel) - - all_fields_after = self.registry.get_all_excluded_fields() - - # The new fields should be included - assert 'unique_field' in all_fields_after, "New model's excluded field should be included" - assert len(all_fields_after) >= len(all_fields_before), "Should have at least as many fields as before" - - def test_convenience_registration_method(self): - """Test the convenience method for registering models""" - from pydantic import BaseModel - from typing import Optional - - class ConvenienceTestModel(BaseModel): - id: str - convenience_field: Optional[str] = None - - # Use the convenience method - self.config.register_model_for_field_exclusion(ConvenienceTestModel) - - # Verify it was registered - excluded_fields = self.registry.get_excluded_fields_for_model(ConvenienceTestModel) - assert 'convenience_field' in excluded_fields, "Field should be excluded after registration" - - def test_field_filtering_with_registry(self): - """Test that the field filtering works correctly with the registry""" - - # Test data that matches the structure of ResponseReasoningItem - test_input = [ - { - "role": "user", - "content": "test message" - }, - { - "id": "reasoning-123", - "type": "reasoning", - "status": None, # Should be filtered out - "content": None, # Should be filtered out - "encrypted_content": None, # Should be filtered out - "summary": [{"text": "This reasoning shows...", "type": "summary_text"}], - "role": "assistant" - }, - { - "id": "message-456", - "type": "message", - "status": "completed", # Should be preserved (not None) - "content": "Hello! How can I help?", # Should be preserved (not None) - "role": "assistant" - } - ] - - # Process the input through the validation - result = self.config._validate_input_param(test_input) - - # Verify the structure - assert len(result) == 3, "Should have 3 items" - - # Check the reasoning item (index 1) - reasoning_item = result[1] - assert reasoning_item["type"] == "reasoning" - assert reasoning_item["id"] == "reasoning-123" - assert "summary" in reasoning_item, "summary field should be preserved" - assert "role" in reasoning_item, "role field should be preserved" - - # These fields should be filtered out if they are in the registry - all_excluded_fields = self.registry.get_all_excluded_fields() - if 'status' in all_excluded_fields: - assert "status" not in reasoning_item, "status field should be filtered out" - if 'content' in all_excluded_fields: - assert "content" not in reasoning_item, "content field should be filtered out" - if 'encrypted_content' in all_excluded_fields: - assert "encrypted_content" not in reasoning_item, "encrypted_content field should be filtered out" - - # Check the message item (index 2) - non-None values should be preserved - message_item = result[2] - assert message_item["type"] == "message" - assert message_item["status"] == "completed", "Non-None status should be preserved" - assert message_item["content"] == "Hello! How can I help?", "Non-None content should be preserved" - - def test_field_filtering_with_empty_registry(self): - """Test that filtering works gracefully when no models are registered""" - # Create a fresh registry for this test - from litellm.llms.openai.responses.transformation import OpenAIFieldExclusionRegistry - - # Save the current state - original_models = OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION.copy() - - try: - # Clear the registry - OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION.clear() - - # Test data - test_input = [{ - "id": "test-123", - "status": None, - "content": None, - "other_field": "should be preserved" - }] - - # Process the input - result = self.config._validate_input_param(test_input) - - # With empty registry, nothing should be filtered (all fields preserved) - assert len(result) == 1 - item = result[0] - assert "status" in item, "With empty registry, status should be preserved" - assert "content" in item, "With empty registry, content should be preserved" - assert item["other_field"] == "should be preserved" - - finally: - # Restore the original state - OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION = original_models - - def test_pydantic_v1_v2_compatibility(self): - """Test that the registry works with both Pydantic v1 and v2""" - from pydantic import BaseModel - from typing import Optional - - class CompatibilityTestModel(BaseModel): - id: str - optional_field: Optional[str] = None - required_field: str = "default" - - # Register the model - self.registry.register_model(CompatibilityTestModel) - - # Get excluded fields - excluded_fields = self.registry.get_excluded_fields_for_model(CompatibilityTestModel) - - # Should work regardless of Pydantic version - assert isinstance(excluded_fields, set), "Should return a set" - assert 'optional_field' in excluded_fields, "Field with default None should be excluded" - - # Test that the model fields are accessible (works in both v1 and v2) - model_fields = getattr(CompatibilityTestModel, "model_fields", None) - if model_fields is None: - model_fields = getattr(CompatibilityTestModel, "__fields__", {}) - assert len(model_fields) > 0, "Should be able to access model fields" - - def test_non_registered_model_returns_empty_set(self): - """Test that non-registered models return empty excluded fields""" - from pydantic import BaseModel - - class UnregisteredModel(BaseModel): - id: str - some_field: str = None - - # Don't register this model - excluded_fields = self.registry.get_excluded_fields_for_model(UnregisteredModel) - - assert excluded_fields == set(), "Non-registered model should return empty set" - - @pytest.mark.parametrize("field_value", [None, "", 0, False, []]) - def test_only_none_values_are_filtered(self, field_value): - """Test that only None values are filtered, not other falsy values""" - test_input = [{ - "id": "test-123", - "status": field_value, - "content": "actual content", - "other_field": "preserved" - }] - - result = self.config._validate_input_param(test_input) - item = result[0] - - if field_value is None: - # Only None should be filtered (if status is in the registry) - all_excluded_fields = self.registry.get_all_excluded_fields() - if 'status' in all_excluded_fields: - assert "status" not in item, f"None value should be filtered out" - else: - assert item["status"] is None, f"If not in registry, None should be preserved" - else: - # Other falsy values should be preserved - assert "status" in item, f"Non-None value {field_value} should be preserved" - assert item["status"] == field_value, f"Value should be exactly {field_value}" + assert "stream" in params \ No newline at end of file From 231d54741f18588f5795beb8c3c3dc7c850df931 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 5 Sep 2025 10:40:26 +0530 Subject: [PATCH 121/178] Move test to test_litellm/ folder --- .../responses/test_text_format_conversion.py | 594 +----------------- 1 file changed, 11 insertions(+), 583 deletions(-) diff --git a/tests/test_litellm/responses/test_text_format_conversion.py b/tests/test_litellm/responses/test_text_format_conversion.py index dafccfaf2b9..20a87a4abbb 100644 --- a/tests/test_litellm/responses/test_text_format_conversion.py +++ b/tests/test_litellm/responses/test_text_format_conversion.py @@ -3,16 +3,11 @@ import os import sys import pytest +from pydantic import BaseModel sys.path.insert( - 0, os.path.abspath("../..") + 0, os.path.abspath("../../..") ) # Adds the parent directory to the system path -import json -from abc import ABC, abstractmethod - -from openai.types.responses.response_create_params import ( - ResponseInputParam, -) import litellm from litellm.types.llms.openai import ( @@ -22,512 +17,16 @@ from litellm.types.llms.openai import ( ) -def validate_responses_api_response(response, final_chunk: bool = False): - """ - Validate that a response from litellm.responses() or litellm.aresponses() - conforms to the expected ResponsesAPIResponse structure. +class TestTextFormatConversion: + """Test text_format to text parameter conversion for responses API""" - Args: - response: The response object to validate - - Raises: - AssertionError: If the response doesn't match the expected structure - """ - # Validate response structure - print("response=", json.dumps(response, indent=4, default=str)) - assert isinstance( - response, ResponsesAPIResponse - ), "Response should be an instance of ResponsesAPIResponse" - - # Required fields - assert "id" in response and isinstance( - response["id"], str - ), "Response should have a string 'id' field" - assert "created_at" in response and isinstance( - response["created_at"], int - ), "Response should have an integer 'created_at' field" - assert "output" in response and isinstance( - response["output"], list - ), "Response should have a list 'output' field" - assert "parallel_tool_calls" in response and isinstance( - response["parallel_tool_calls"], bool - ), "Response should have a boolean 'parallel_tool_calls' field" - - # Optional fields with their expected types - optional_fields = { - "error": (dict, type(None)), # error can be dict or None - "incomplete_details": (IncompleteDetails, type(None)), - "instructions": (str, type(None)), - "metadata": dict, - "model": str, - "object": str, - "temperature": (int, float, type(None)), - "tool_choice": (dict, str), - "tools": list, - "top_p": (int, float, type(None)), - "max_output_tokens": (int, type(None)), - "previous_response_id": (str, type(None)), - "reasoning": dict, - "status": str, - "text": dict, - "truncation": (str, type(None)), - "usage": ResponseAPIUsage, - "user": (str, type(None)), - "store": (bool, type(None)), - } - if final_chunk is False: - optional_fields["usage"] = type(None) - - for field, expected_type in optional_fields.items(): - if field in response: - assert isinstance( - response[field], expected_type - ), f"Field '{field}' should be of type {expected_type}, but got {type(response[field])}" - - # Check if output has at least one item - if final_chunk is True: - assert ( - len(response["output"]) > 0 - ), "Response 'output' field should have at least one item" - - return True # Return True if validation passes - - -class BaseResponsesAPITest(ABC): - """ - Abstract base test class that enforces a common test across all test classes. - """ - - @abstractmethod - def get_base_completion_call_args(self) -> dict: - """Must return the base completion call args""" - pass - - def get_base_completion_reasoning_call_args(self) -> dict: - """Must return the base completion reasoning call args""" - return None - - @pytest.mark.parametrize("sync_mode", [True, False]) - @pytest.mark.asyncio - async def test_basic_openai_responses_api(self, sync_mode): - litellm._turn_on_debug() - litellm.set_verbose = True - base_completion_call_args = self.get_base_completion_call_args() - try: - if sync_mode: - response = litellm.responses( - input="Basic ping", - max_output_tokens=20, - **base_completion_call_args, - ) - else: - response = await litellm.aresponses( - input="Basic ping", - max_output_tokens=20, - **base_completion_call_args, - ) - except litellm.InternalServerError: - pytest.skip("Skipping test due to litellm.InternalServerError") - print("litellm response=", json.dumps(response, indent=4, default=str)) - - # Use the helper function to validate the response - validate_responses_api_response(response, final_chunk=True) - - @pytest.mark.parametrize("sync_mode", [True, False]) - @pytest.mark.asyncio - @pytest.mark.flaky(retries=3, delay=2) - async def test_basic_openai_responses_api_streaming(self, sync_mode): - litellm._turn_on_debug() - base_completion_call_args = self.get_base_completion_call_args() - collected_content_string = "" - response_completed_event = None - if sync_mode: - response = litellm.responses( - input="Basic ping", stream=True, **base_completion_call_args - ) - for event in response: - print("litellm response=", json.dumps(event, indent=4, default=str)) - if event.type == "response.output_text.delta": - collected_content_string += event.delta - elif event.type == "response.completed": - response_completed_event = event - else: - response = await litellm.aresponses( - input="Basic ping", stream=True, **base_completion_call_args - ) - async for event in response: - print("litellm response=", json.dumps(event, indent=4, default=str)) - if event.type == "response.output_text.delta": - collected_content_string += event.delta - elif event.type == "response.completed": - response_completed_event = event - - # assert the delta chunks content had len(collected_content_string) > 0 - # this content is typically rendered on chat ui's - assert len(collected_content_string) > 0 - - # assert the response completed event is not None - assert response_completed_event is not None - - # assert the response completed event has a response - assert response_completed_event.response is not None - - # assert the response completed event includes the usage - assert response_completed_event.response.usage is not None - - # basic test assert the usage seems reasonable - print( - "response_completed_event.response.usage=", - response_completed_event.response.usage, - ) - assert ( - response_completed_event.response.usage.input_tokens > 0 - and response_completed_event.response.usage.input_tokens < 100 - ) - assert ( - response_completed_event.response.usage.output_tokens > 0 - and response_completed_event.response.usage.output_tokens < 2000 - ) - assert ( - response_completed_event.response.usage.total_tokens > 0 - and response_completed_event.response.usage.total_tokens < 2000 - ) - - # total tokens should be the sum of input and output tokens - assert ( - response_completed_event.response.usage.total_tokens - == response_completed_event.response.usage.input_tokens - + response_completed_event.response.usage.output_tokens - ) - - @pytest.mark.parametrize("sync_mode", [False, True]) - @pytest.mark.asyncio - async def test_basic_openai_responses_delete_endpoint(self, sync_mode): - litellm._turn_on_debug() - litellm.set_verbose = True - base_completion_call_args = self.get_base_completion_call_args() - if sync_mode: - response = litellm.responses( - input="Basic ping", max_output_tokens=20, **base_completion_call_args - ) - - # delete the response - if isinstance(response, ResponsesAPIResponse): - litellm.delete_responses( - response_id=response.id, **base_completion_call_args - ) - else: - raise ValueError("response is not a ResponsesAPIResponse") - else: - response = await litellm.aresponses( - input="Basic ping", max_output_tokens=20, **base_completion_call_args - ) - - # async delete the response - if isinstance(response, ResponsesAPIResponse): - await litellm.adelete_responses( - response_id=response.id, **base_completion_call_args - ) - else: - raise ValueError("response is not a ResponsesAPIResponse") - - @pytest.mark.parametrize("sync_mode", [True, False]) - @pytest.mark.flaky(retries=3, delay=2) - @pytest.mark.asyncio - async def test_basic_openai_responses_streaming_delete_endpoint(self, sync_mode): - # litellm._turn_on_debug() - # litellm.set_verbose = True - base_completion_call_args = self.get_base_completion_call_args() - response_id = None - if sync_mode: - response_id = None - response = litellm.responses( - input="Basic ping", - max_output_tokens=20, - stream=True, - **base_completion_call_args, - ) - for event in response: - print("litellm response=", json.dumps(event, indent=4, default=str)) - if "response" in event: - response_obj = event.get("response") - if response_obj is not None: - response_id = response_obj.get("id") - print("got response_id=", response_id) - - # delete the response - assert response_id is not None - litellm.delete_responses( - response_id=response_id, **base_completion_call_args - ) - else: - response = await litellm.aresponses( - input="Basic ping", - max_output_tokens=20, - stream=True, - **base_completion_call_args, - ) - async for event in response: - print("litellm response=", json.dumps(event, indent=4, default=str)) - if "response" in event: - response_obj = event.get("response") - if response_obj is not None: - response_id = response_obj.get("id") - print("got response_id=", response_id) - - # delete the response - assert response_id is not None - await litellm.adelete_responses( - response_id=response_id, **base_completion_call_args - ) - - @pytest.mark.parametrize("sync_mode", [False, True]) - @pytest.mark.flaky(retries=3, delay=2) - @pytest.mark.asyncio - async def test_basic_openai_responses_get_endpoint(self, sync_mode): - litellm._turn_on_debug() - litellm.set_verbose = True - base_completion_call_args = self.get_base_completion_call_args() - if sync_mode: - response = litellm.responses( - input="Basic ping", max_output_tokens=20, **base_completion_call_args - ) - - # get the response - if isinstance(response, ResponsesAPIResponse): - result = litellm.get_responses( - response_id=response.id, **base_completion_call_args - ) - assert result is not None - assert result.id == response.id - assert result.output == response.output - else: - raise ValueError("response is not a ResponsesAPIResponse") - else: - response = await litellm.aresponses( - input="Basic ping", max_output_tokens=20, **base_completion_call_args - ) - # async get the response - if isinstance(response, ResponsesAPIResponse): - result = await litellm.aget_responses( - response_id=response.id, **base_completion_call_args - ) - assert result is not None - assert result.id == response.id - assert result.output == response.output - else: - raise ValueError("response is not a ResponsesAPIResponse") - - @pytest.mark.asyncio - @pytest.mark.flaky(retries=3, delay=2) - async def test_basic_openai_list_input_items_endpoint(self): - """Test that calls the OpenAI List Input Items endpoint""" - litellm._turn_on_debug() - - response = await litellm.aresponses( - model="gpt-4o", - input="Tell me a three sentence bedtime story about a unicorn.", - ) - print("Initial response=", json.dumps(response, indent=4, default=str)) - - response_id = response.get("id") - assert response_id is not None, "Response should have an ID" - print(f"Got response_id: {response_id}") - - list_items_response = await litellm.alist_input_items( - response_id=response_id, - limit=20, - order="desc", - ) - print( - "List items response=", - json.dumps(list_items_response, indent=4, default=str), - ) - - @pytest.mark.asyncio - async def test_multiturn_responses_api(self): - litellm._turn_on_debug() - litellm.set_verbose = True - try: - base_completion_call_args = self.get_base_completion_call_args() - response_1 = await litellm.aresponses( - input="Basic ping", max_output_tokens=20, **base_completion_call_args - ) - - # follow up with a second request - response_1_id = response_1.id - response_2 = await litellm.aresponses( - input="Basic ping", - max_output_tokens=20, - previous_response_id=response_1_id, - **base_completion_call_args, - ) - - # assert the response is not None - assert response_1 is not None - assert response_2 is not None - except litellm.InternalServerError: - pytest.skip("Skipping test due to litellm.InternalServerError") - - @pytest.mark.asyncio - async def test_responses_api_with_tool_calls(self): - """Test that calls the Responses API with tool calls including function call and output""" - litellm._turn_on_debug() - litellm.set_verbose = True - base_completion_call_args = self.get_base_completion_call_args() - - # Define the input with message, function call, and function call output - input_data: ResponseInputParam = [ - { - "type": "message", - "role": "user", - "content": "How is the weather in São Paulo today ?", - }, - { - "type": "function_call", - "arguments": '{"location": "São Paulo, Brazil"}', - "call_id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5", - "name": "get_weather", - "id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5", - "status": "completed", - }, - { - "type": "function_call_output", - "call_id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5", - "output": "Rainy", - }, - ] - - # Define the tools - tools = [ - { - "type": "function", - "name": "get_weather", - "description": "Get current temperature for a given location.", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "City and country e.g. Bogotá, Colombia", - } - }, - "required": ["location"], - "additionalProperties": False, - }, - } - ] - - try: - # Make the responses API call - response = await litellm.aresponses( - input=input_data, store=False, tools=tools, **base_completion_call_args - ) - except litellm.InternalServerError: - pytest.skip("Skipping test due to litellm.InternalServerError") - - print("litellm response=", json.dumps(response, indent=4, default=str)) - - # Validate the response structure - validate_responses_api_response(response, final_chunk=True) - - # Additional assertions specific to tool calls - assert response is not None - assert "output" in response - assert len(response["output"]) > 0 - - @pytest.mark.asyncio - async def test_responses_api_multi_turn_with_reasoning_and_structured_output(self): - """ - Test multi-turn conversation with reasoning, structured output, and tool calls. - - This test validates: - - First call: Model uses reasoning to process a question and makes a tool call - - Tool call handling: Function call output is properly processed - - Second call: Model produces structured output incorporating tool results - - Structured output: Response conforms to defined Pydantic model schema - """ - from pydantic import BaseModel - - litellm._turn_on_debug() - litellm.set_verbose = True - base_completion_call_args = self.get_base_completion_reasoning_call_args() - if base_completion_call_args is None: - pytest.skip("Skipping test due to no base completion reasoning call args") - - # Define tools for the conversation - tools = [{"type": "function", "name": "get_today"}] - - # Define structured output schema - class Output(BaseModel): - today: str - number_of_r: str - - # Initial conversation input - input_messages = [ - { - "role": "user", - "content": "How many r in strrawberrry? While you're thinking, you should call tool get_today. Then you output the today and number of r", - } - ] - - # First call - should trigger reasoning and tool call - response = await litellm.aresponses( - input=input_messages, - tools=tools, - reasoning={"effort": "low", "summary": "detailed"}, - text_format=Output, - **base_completion_call_args, - ) - - print("First call output:") - print(json.dumps(response.output, indent=4, default=str)) - - # Validate first response structure - validate_responses_api_response(response, final_chunk=True) - assert response.output is not None - assert len(response.output) > 0 - - # Extend input with first response output - input_messages.extend(response.output) - - # Process any tool calls and add function outputs - function_outputs = [] - for item in response.output: - if hasattr(item, "type") and item.type in [ - "function_call", - "custom_tool_call", - ]: - if hasattr(item, "name") and item.name == "get_today": - function_outputs.append( - { - "type": "function_call_output", - "call_id": item.call_id, - "output": "2025-01-15", - } - ) - - # Add function outputs to conversation - input_messages.extend(function_outputs) - - print("Second call input:") - print(json.dumps(input_messages, indent=4, default=str)) - - # Second call - should produce structured output - final_response = await litellm.aresponses( - input=input_messages, - tools=tools, - reasoning={"effort": "low", "summary": "detailed"}, - text_format=Output, - **base_completion_call_args, - ) - - print("Second call output:") - print(json.dumps(final_response.output, indent=4, default=str)) - - # Validate final response structure - validate_responses_api_response(final_response, final_chunk=True) - assert final_response.output is not None + def get_base_completion_call_args(self): + """Get base arguments for completion call""" + return { + "model": "gpt-4o", + "api_key": "test-key", + "api_base": "https://api.openai.com/v1", + } @pytest.mark.asyncio async def test_text_format_to_text_conversion(self): @@ -537,8 +36,6 @@ class BaseResponsesAPITest(ABC): """ from unittest.mock import AsyncMock, patch - from pydantic import BaseModel - class TestResponse(BaseModel): """Test Pydantic model for structured output""" @@ -662,72 +159,3 @@ class BaseResponsesAPITest(ABC): # Validate the response print("Response:", json.dumps(response, indent=4, default=str)) - - def test_openai_responses_api_dict_input_filtering(self): - """ - Test that regular dict inputs with status fields are properly filtered - to replicate exclude_unset=True behavior for non-Pydantic objects. - """ - from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig - - # Test input with regular dict objects (like from JSON) - test_input = [ - { - "role": "user", - "content": "test" - }, - { - "id": "rs_123", - "summary": [{"text": "test", "type": "summary_text"}], - "type": "reasoning", - "content": None, # Should be filtered out - "encrypted_content": None, # Should be filtered out - "status": None # Should be filtered out - }, - { - "arguments": "{}", - "call_id": "call_123", - "name": "get_today", - "type": "function_call", - "id": "fc_123", - "status": "completed" # Should be preserved (not a default field) - } - ] - - config = OpenAIResponsesAPIConfig() - validated_input = config._validate_input_param(test_input) - - # Verify the results - assert len(validated_input) == 3 - - # Check reasoning item (index 1) - reasoning_item = validated_input[1] - assert reasoning_item["type"] == "reasoning" - assert "status" not in reasoning_item, "status field should be filtered out from reasoning item" - assert "content" not in reasoning_item, "content field should be filtered out from reasoning item" - assert "encrypted_content" not in reasoning_item, "encrypted_content field should be filtered out from reasoning item" - assert "id" in reasoning_item, "id field should be preserved" - assert "summary" in reasoning_item, "summary field should be preserved" - - # Check function call item (index 2) - function_call_item = validated_input[2] - assert function_call_item["type"] == "function_call" - assert "status" in function_call_item, "status field should be preserved in function call item" - assert function_call_item["status"] == "completed", "status value should be preserved" - - print("✅ OpenAI Responses API dict input filtering test passed") - - -class TestOpenAIResponsesAPITest(BaseResponsesAPITest): - """Concrete test class for OpenAI Responses API tests""" - - def get_base_completion_call_args(self): - return { - "model": "openai/gpt-4o", - } - - def get_base_completion_reasoning_call_args(self): - return { - "model": "openai/gpt-5-mini", - } - From d88771ca4913be2c4bf09fa644d529f7efaec18d Mon Sep 17 00:00:00 2001 From: Thomas Rehn <271119+tremlin@users.noreply.github.com> Date: Fri, 5 Sep 2025 15:23:53 +0200 Subject: [PATCH 122/178] fix: correct output pricing for gemini-2.5-flash-image-preview https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image-preview --- model_prices_and_context_window.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index f3c4abf5f00..46eb48d2d42 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -7992,8 +7992,8 @@ "max_pdf_size_mb": 30, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, - "output_cost_per_token": 2.5e-06, - "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 3e-05, + "output_cost_per_reasoning_token": 3e-05, "output_cost_per_image": 0.039, "litellm_provider": "gemini", "mode": "chat", @@ -8356,8 +8356,8 @@ "max_pdf_size_mb": 30, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, - "output_cost_per_token": 2.5e-06, - "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 3e-05, + "output_cost_per_reasoning_token": 3e-05, "output_cost_per_image": 0.039, "litellm_provider": "vertex_ai-language-models", "mode": "chat", From 982800069c91d8c6382615bf0d2f21eb81bc3619 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 5 Sep 2025 09:40:37 -0700 Subject: [PATCH 123/178] [Bug Fix] x-litellm-tags not routing with Responses API (#14289) * fix: get_deployments_for_tag * fix get_deployments_for_tag * test_router_tag_routing.py * test_get_metadata_variable_name_from_kwargs * fix mapped tests * docs fix --- docs/my-website/docs/proxy/load_balancing.md | 2 + ...odel_prices_and_context_window_backup.json | 60 +++++ litellm/router.py | 15 ++ litellm/router_strategy/tag_based_routing.py | 18 +- .../test_router_helper_utils.py | 35 +++ .../test_openai_responses_transformation.py | 244 ------------------ .../test_router_tag_routing.py | 71 +++++ 7 files changed, 193 insertions(+), 252 deletions(-) rename tests/{local_testing => test_litellm/router_strategy}/test_router_tag_routing.py (81%) diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md index 67f41d231db..2d8f73a13e4 100644 --- a/docs/my-website/docs/proxy/load_balancing.md +++ b/docs/my-website/docs/proxy/load_balancing.md @@ -124,6 +124,8 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ }' ``` + + ### Test - Loadbalancing diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index a7586124509..f3c4abf5f00 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -21033,5 +21033,65 @@ "metadata": { "notes": "DALL-E 2 via AI/ML API - Reliable text-to-image generation" } + }, + "doubao-embedding-large": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 2048, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - large version with 2048 dimensions" + } + }, + "doubao-embedding-large-text-250515": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 2048, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - text-250515 version with 2048 dimensions" + } + }, + "doubao-embedding-large-text-240915": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - text-240915 version with 4096 dimensions" + } + }, + "doubao-embedding": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 2560, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - standard version with 2560 dimensions" + } + }, + "doubao-embedding-text-240715": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "output_vector_size": 2560, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "volcengine", + "mode": "embedding", + "metadata": { + "notes": "Volcengine Doubao embedding model - text-240715 version with 2560 dimensions" + } } } \ No newline at end of file diff --git a/litellm/router.py b/litellm/router.py index 1ed95ee7b29..5eea60e4b3d 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -4562,6 +4562,20 @@ class Router: parent_otel_span=parent_otel_span, ttl=RoutingArgs.ttl.value, ) + + def _get_metadata_variable_name_from_kwargs(self, 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 log_retry(self, kwargs: dict, e: Exception) -> dict: """ @@ -6788,6 +6802,7 @@ class Router: model=model, request_kwargs=request_kwargs, healthy_deployments=healthy_deployments, + metadata_variable_name=self._get_metadata_variable_name_from_kwargs(request_kwargs), ) if len(healthy_deployments) == 0: diff --git a/litellm/router_strategy/tag_based_routing.py b/litellm/router_strategy/tag_based_routing.py index 34261d83dcf..8094b5d86ac 100644 --- a/litellm/router_strategy/tag_based_routing.py +++ b/litellm/router_strategy/tag_based_routing.py @@ -6,7 +6,7 @@ Use this to route requests between Teams - If no default_deployments are set, return all deployments """ -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union +from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union from litellm._logging import verbose_logger from litellm.types.router import RouterErrors @@ -41,6 +41,7 @@ async def get_deployments_for_tag( model: str, # used to raise the correct error healthy_deployments: Union[List[Any], Dict[Any, Any]], request_kwargs: Optional[Dict[Any, Any]] = None, + metadata_variable_name: Literal["metadata", "litellm_metadata"] = "metadata", ): """ Returns a list of deployments that match the requested model and tags in the request. @@ -63,9 +64,9 @@ async def get_deployments_for_tag( ) return healthy_deployments - verbose_logger.debug("request metadata: %s", request_kwargs.get("metadata")) - if "metadata" in request_kwargs: - metadata = request_kwargs["metadata"] + verbose_logger.debug("request metadata: %s", request_kwargs.get(metadata_variable_name)) + if metadata_variable_name in request_kwargs: + metadata = request_kwargs[metadata_variable_name] request_tags = metadata.get("tags") new_healthy_deployments = [] @@ -120,7 +121,8 @@ async def get_deployments_for_tag( def _get_tags_from_request_kwargs( - request_kwargs: Optional[Dict[Any, Any]] = None + request_kwargs: Optional[Dict[Any, Any]] = None, + metadata_variable_name: Literal["metadata", "litellm_metadata"] = "metadata", ) -> List[str]: """ Helper to get tags from request kwargs @@ -133,11 +135,11 @@ def _get_tags_from_request_kwargs( """ if request_kwargs is None: return [] - if "metadata" in request_kwargs: - metadata = request_kwargs["metadata"] + if metadata_variable_name in request_kwargs: + metadata = request_kwargs[metadata_variable_name] return metadata.get("tags", []) elif "litellm_params" in request_kwargs: litellm_params = request_kwargs["litellm_params"] - _metadata = litellm_params.get("metadata", {}) + _metadata = litellm_params.get(metadata_variable_name, {}) return _metadata.get("tags", []) return [] diff --git a/tests/router_unit_tests/test_router_helper_utils.py b/tests/router_unit_tests/test_router_helper_utils.py index 48bb836dfd6..094df944bcc 100644 --- a/tests/router_unit_tests/test_router_helper_utils.py +++ b/tests/router_unit_tests/test_router_helper_utils.py @@ -1690,3 +1690,38 @@ def test_handle_clientside_credential_with_responses_function(model_list): print( "✓ Success with _ageneric_api_call_with_fallbacks function name and litellm_metadata" ) + + +def test_get_metadata_variable_name_from_kwargs(model_list): + """ + Test _get_metadata_variable_name_from_kwargs method returns correct metadata variable name based on kwargs content. + """ + router = Router(model_list=model_list) + + # Test case 1: kwargs contains litellm_metadata - should return "litellm_metadata" + kwargs_with_litellm_metadata = { + "litellm_metadata": {"user": "test"}, + "metadata": {"other": "data"} + } + result = router._get_metadata_variable_name_from_kwargs(kwargs_with_litellm_metadata) + assert result == "litellm_metadata" + + # Test case 2: kwargs only contains metadata - should return "metadata" + kwargs_with_metadata_only = { + "metadata": {"user": "test"} + } + result = router._get_metadata_variable_name_from_kwargs(kwargs_with_metadata_only) + assert result == "metadata" + + # Test case 3: kwargs contains neither - should return "metadata" (default) + kwargs_empty = {} + result = router._get_metadata_variable_name_from_kwargs(kwargs_empty) + assert result == "metadata" + + # Test case 4: kwargs contains other keys but no metadata keys - should return "metadata" + kwargs_other = { + "model": "gpt-4", + "messages": [{"role": "user", "content": "hello"}] + } + result = router._get_metadata_variable_name_from_kwargs(kwargs_other) + assert result == "metadata" diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py index 21232161d0c..ddcf11495f2 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py @@ -669,247 +669,3 @@ def test_get_supported_openai_params(): assert "background" in params assert "stream" in params - -class TestOpenAIFieldExclusionRegistry: - """Test suite for the OpenAI Field Exclusion Registry system""" - - def setup_method(self): - """Setup test fixtures""" - from litellm.llms.openai.responses.transformation import ( - OpenAIFieldExclusionRegistry, - OpenAIResponsesAPIConfig - ) - self.registry = OpenAIFieldExclusionRegistry - self.config = OpenAIResponsesAPIConfig() - - def test_registry_initialization(self): - """Test that the registry is properly initialized with ResponseReasoningItem""" - # Test that we can get excluded fields (should not be empty if ResponseReasoningItem is registered) - all_excluded_fields = self.registry.get_all_excluded_fields() - - # The registry should have at least some fields if ResponseReasoningItem was successfully registered - # If OpenAI SDK is not available, this might be empty, which is also valid - assert isinstance(all_excluded_fields, set), "get_all_excluded_fields should return a set" - - # If we have the OpenAI SDK available, we should have the expected fields - try: - from openai.types.responses import ResponseReasoningItem - reasoning_fields = self.registry.get_excluded_fields_for_model(ResponseReasoningItem) - expected_fields = {'status', 'content', 'encrypted_content'} - assert expected_fields.issubset(reasoning_fields), f"Expected fields {expected_fields} to be subset of {reasoning_fields}" - except ImportError: - # If OpenAI SDK is not available, that's fine - the registry should handle this gracefully - pytest.skip("OpenAI SDK not available, skipping ResponseReasoningItem specific tests") - - def test_register_model_functionality(self): - """Test that we can register new models to the registry""" - from pydantic import BaseModel - from typing import Optional - - # Create a test model with default None fields - class TestResponseModel(BaseModel): - id: str - type: str = "test" - status: Optional[str] = None - content: Optional[str] = None - required_field: str - - # Register the test model - self.registry.register_model(TestResponseModel) - - # Verify it was registered and fields are detected - excluded_fields = self.registry.get_excluded_fields_for_model(TestResponseModel) - expected_excluded = {'status', 'content'} # Fields with default None - - assert expected_excluded.issubset(excluded_fields), f"Expected {expected_excluded} to be in {excluded_fields}" - assert 'id' not in excluded_fields, "Required field 'id' should not be excluded" - assert 'required_field' not in excluded_fields, "Required field 'required_field' should not be excluded" - - def test_get_all_excluded_fields(self): - """Test that get_all_excluded_fields aggregates fields from all registered models""" - all_fields_before = self.registry.get_all_excluded_fields() - - # Create and register a test model - from pydantic import BaseModel - from typing import Optional - - class AnotherTestModel(BaseModel): - id: str - unique_field: Optional[str] = None - - self.registry.register_model(AnotherTestModel) - - all_fields_after = self.registry.get_all_excluded_fields() - - # The new fields should be included - assert 'unique_field' in all_fields_after, "New model's excluded field should be included" - assert len(all_fields_after) >= len(all_fields_before), "Should have at least as many fields as before" - - def test_convenience_registration_method(self): - """Test the convenience method for registering models""" - from pydantic import BaseModel - from typing import Optional - - class ConvenienceTestModel(BaseModel): - id: str - convenience_field: Optional[str] = None - - # Use the convenience method - self.config.register_model_for_field_exclusion(ConvenienceTestModel) - - # Verify it was registered - excluded_fields = self.registry.get_excluded_fields_for_model(ConvenienceTestModel) - assert 'convenience_field' in excluded_fields, "Field should be excluded after registration" - - def test_field_filtering_with_registry(self): - """Test that the field filtering works correctly with the registry""" - - # Test data that matches the structure of ResponseReasoningItem - test_input = [ - { - "role": "user", - "content": "test message" - }, - { - "id": "reasoning-123", - "type": "reasoning", - "status": None, # Should be filtered out - "content": None, # Should be filtered out - "encrypted_content": None, # Should be filtered out - "summary": [{"text": "This reasoning shows...", "type": "summary_text"}], - "role": "assistant" - }, - { - "id": "message-456", - "type": "message", - "status": "completed", # Should be preserved (not None) - "content": "Hello! How can I help?", # Should be preserved (not None) - "role": "assistant" - } - ] - - # Process the input through the validation - result = self.config._validate_input_param(test_input) - - # Verify the structure - assert len(result) == 3, "Should have 3 items" - - # Check the reasoning item (index 1) - reasoning_item = result[1] - assert reasoning_item["type"] == "reasoning" - assert reasoning_item["id"] == "reasoning-123" - assert "summary" in reasoning_item, "summary field should be preserved" - assert "role" in reasoning_item, "role field should be preserved" - - # These fields should be filtered out if they are in the registry - all_excluded_fields = self.registry.get_all_excluded_fields() - if 'status' in all_excluded_fields: - assert "status" not in reasoning_item, "status field should be filtered out" - if 'content' in all_excluded_fields: - assert "content" not in reasoning_item, "content field should be filtered out" - if 'encrypted_content' in all_excluded_fields: - assert "encrypted_content" not in reasoning_item, "encrypted_content field should be filtered out" - - # Check the message item (index 2) - non-None values should be preserved - message_item = result[2] - assert message_item["type"] == "message" - assert message_item["status"] == "completed", "Non-None status should be preserved" - assert message_item["content"] == "Hello! How can I help?", "Non-None content should be preserved" - - def test_field_filtering_with_empty_registry(self): - """Test that filtering works gracefully when no models are registered""" - # Create a fresh registry for this test - from litellm.llms.openai.responses.transformation import OpenAIFieldExclusionRegistry - - # Save the current state - original_models = OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION.copy() - - try: - # Clear the registry - OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION.clear() - - # Test data - test_input = [{ - "id": "test-123", - "status": None, - "content": None, - "other_field": "should be preserved" - }] - - # Process the input - result = self.config._validate_input_param(test_input) - - # With empty registry, nothing should be filtered (all fields preserved) - assert len(result) == 1 - item = result[0] - assert "status" in item, "With empty registry, status should be preserved" - assert "content" in item, "With empty registry, content should be preserved" - assert item["other_field"] == "should be preserved" - - finally: - # Restore the original state - OpenAIFieldExclusionRegistry._MODELS_REQUIRING_EXCLUSION = original_models - - def test_pydantic_v1_v2_compatibility(self): - """Test that the registry works with both Pydantic v1 and v2""" - from pydantic import BaseModel - from typing import Optional - - class CompatibilityTestModel(BaseModel): - id: str - optional_field: Optional[str] = None - required_field: str = "default" - - # Register the model - self.registry.register_model(CompatibilityTestModel) - - # Get excluded fields - excluded_fields = self.registry.get_excluded_fields_for_model(CompatibilityTestModel) - - # Should work regardless of Pydantic version - assert isinstance(excluded_fields, set), "Should return a set" - assert 'optional_field' in excluded_fields, "Field with default None should be excluded" - - # Test that the model fields are accessible (works in both v1 and v2) - model_fields = getattr(CompatibilityTestModel, "model_fields", None) - if model_fields is None: - model_fields = getattr(CompatibilityTestModel, "__fields__", {}) - assert len(model_fields) > 0, "Should be able to access model fields" - - def test_non_registered_model_returns_empty_set(self): - """Test that non-registered models return empty excluded fields""" - from pydantic import BaseModel - - class UnregisteredModel(BaseModel): - id: str - some_field: str = None - - # Don't register this model - excluded_fields = self.registry.get_excluded_fields_for_model(UnregisteredModel) - - assert excluded_fields == set(), "Non-registered model should return empty set" - - @pytest.mark.parametrize("field_value", [None, "", 0, False, []]) - def test_only_none_values_are_filtered(self, field_value): - """Test that only None values are filtered, not other falsy values""" - test_input = [{ - "id": "test-123", - "status": field_value, - "content": "actual content", - "other_field": "preserved" - }] - - result = self.config._validate_input_param(test_input) - item = result[0] - - if field_value is None: - # Only None should be filtered (if status is in the registry) - all_excluded_fields = self.registry.get_all_excluded_fields() - if 'status' in all_excluded_fields: - assert "status" not in item, f"None value should be filtered out" - else: - assert item["status"] is None, f"If not in registry, None should be preserved" - else: - # Other falsy values should be preserved - assert "status" in item, f"Non-None value {field_value} should be preserved" - assert item["status"] == field_value, f"Value should be exactly {field_value}" diff --git a/tests/local_testing/test_router_tag_routing.py b/tests/test_litellm/router_strategy/test_router_tag_routing.py similarity index 81% rename from tests/local_testing/test_router_tag_routing.py rename to tests/test_litellm/router_strategy/test_router_tag_routing.py index 87cf2261a67..e78a16c6212 100644 --- a/tests/local_testing/test_router_tag_routing.py +++ b/tests/test_litellm/router_strategy/test_router_tag_routing.py @@ -63,6 +63,7 @@ async def test_router_free_paid_tier(): model="gpt-4", messages=[{"role": "user", "content": "Tell me a joke."}], metadata={"tags": ["free"]}, + mock_response="Tell me a joke.", ) print("Response: ", response) @@ -78,6 +79,7 @@ async def test_router_free_paid_tier(): model="gpt-4", messages=[{"role": "user", "content": "Tell me a joke."}], metadata={"tags": ["paid"]}, + mock_response="Tell me a joke.", ) print("Response: ", response) @@ -136,6 +138,7 @@ async def test_router_free_paid_tier_embeddings(): model="gpt-4", input="Tell me a joke.", metadata={"tags": ["free"]}, + mock_response=[1, 2, 3], ) print("Response: ", response) @@ -151,6 +154,7 @@ async def test_router_free_paid_tier_embeddings(): model="gpt-4", input="Tell me a joke.", metadata={"tags": ["paid"]}, + mock_response=[1, 2, 3], ) print("Response: ", response) @@ -205,6 +209,7 @@ async def test_default_tagged_deployments(): response = await router.acompletion( model="gpt-4", messages=[{"role": "user", "content": "Tell me a joke."}], + mock_response="Tell me a joke.", ) print("Response: ", response) @@ -220,6 +225,7 @@ async def test_default_tagged_deployments(): model="gpt-4", messages=[{"role": "user", "content": "Tell me a joke."}], metadata={"tags": ["default"]}, + mock_response="Tell me a joke.", ) print("Response: ", response) @@ -235,6 +241,7 @@ async def test_default_tagged_deployments(): model="gpt-4", messages=[{"role": "user", "content": "Tell me a joke."}], metadata={"tags": ["invalid-tag"]}, + mock_response="Tell me a joke.", ) print("Response: ", response) @@ -292,6 +299,7 @@ async def test_error_from_tag_routing(): model="gpt-4", messages=[{"role": "user", "content": "Tell me a joke."}], metadata={"tags": ["paid"]}, + mock_response="Tell me a joke.", ) pytest.fail("this should have failed - expected it to fail") @@ -315,3 +323,66 @@ def test_tag_routing_with_list_of_tags(): assert not is_valid_deployment_tag(["teamA", "teamB"], ["teamC"]) assert not is_valid_deployment_tag(["teamA", "teamB"], []) assert not is_valid_deployment_tag(["default"], ["teamA"]) + + +@pytest.mark.asyncio() +async def test_router_free_paid_tier_with_responses_api(): + """ + Pass list of orgs in 1 model definition, + expect a unique deployment for each to be created + """ + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-4", + "litellm_params": { + "model": "gpt-4o", + "api_base": "https://exampleopenaiendpoint-production.up.railway.app/", + "tags": ["free"], + }, + "model_info": {"id": "very-cheap-model"}, + }, + { + "model_name": "gpt-4", + "litellm_params": { + "model": "gpt-4o-mini", + "api_base": "https://exampleopenaiendpoint-production.up.railway.app/", + "tags": ["paid"], + }, + "model_info": {"id": "very-expensive-model"}, + }, + ], + enable_tag_filtering=True, + ) + + for _ in range(5): + # this should pick model with id == very-cheap-model + response = await router.aresponses( + model="gpt-4", + input="Tell me a joke.", + litellm_metadata={"tags": ["free"]}, + mock_response="Tell me a joke.", + ) + + print("Response: ", response) + + response_extra_info = response._hidden_params + print("response_extra_info: ", response_extra_info) + + assert response_extra_info["model_id"] == "very-cheap-model" + + for _ in range(5): + # this should pick model with id == very-cheap-model + response = await router.aresponses( + model="gpt-4", + input="Tell me a joke.", + litellm_metadata={"tags": ["paid"]}, + mock_response="Tell me a joke.", + ) + + print("Response: ", response) + + response_extra_info = response._hidden_params + print("response_extra_info: ", response_extra_info) + + assert response_extra_info["model_id"] == "very-expensive-model" \ No newline at end of file From 0a60390521db09006de4c8425f75e9bb50d25073 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 5 Sep 2025 10:04:07 -0700 Subject: [PATCH 124/178] Revert "[Feat] LiteLLM CloudZero Integration updates - using LiteLLM_SpendLogs Table (#12922)" This reverts commit e3b752d3dc9522e35932c77afb3139bf10603bd6. --- litellm/integrations/cloudzero/cloudzero.py | 139 ++++--------- litellm/integrations/cloudzero/database.py | 119 ++++++++--- litellm/integrations/cloudzero/transform.py | 193 ++++++------------ .../spend_tracking/cloudzero_endpoints.py | 6 +- .../integrations/cloudzero/test_transform.py | 183 +++++++++++++++++ 5 files changed, 376 insertions(+), 264 deletions(-) create mode 100644 tests/test_litellm/integrations/cloudzero/test_transform.py diff --git a/litellm/integrations/cloudzero/cloudzero.py b/litellm/integrations/cloudzero/cloudzero.py index 85aa1679732..ab1de17b9f2 100644 --- a/litellm/integrations/cloudzero/cloudzero.py +++ b/litellm/integrations/cloudzero/cloudzero.py @@ -1,6 +1,4 @@ -import asyncio import os -from datetime import datetime, timedelta from typing import Optional from litellm._logging import verbose_logger @@ -30,17 +28,16 @@ class CloudZeroLogger(CustomLogger): self.connection_id = connection_id or os.getenv("CLOUDZERO_CONNECTION_ID") self.timezone = timezone or os.getenv("CLOUDZERO_TIMEZONE", "UTC") - async def export_usage_data(self, target_hour: datetime, limit: Optional[int] = 1000, operation: str = "replace_hourly"): + async def export_usage_data(self, limit: Optional[int] = None, operation: str = "replace_hourly"): """ - Exports the usage data for a specific hour to CloudZero. + Exports the usage data to CloudZero. - - Reads spend logs from the DB for the specified hour + - Reads data from the DB - Transforms the data to the CloudZero format - Sends the data to CloudZero Args: - target_hour: The specific hour to export data for - limit: Optional limit on number of records to export (default: 1000) + limit: Optional limit on number of records to export operation: CloudZero operation type ("replace_hourly" or "sum") """ try: @@ -52,11 +49,23 @@ class CloudZeroLogger(CustomLogger): "CloudZero configuration missing. Please set CLOUDZERO_API_KEY and CLOUDZERO_CONNECTION_ID environment variables." ) - # Fetch and transform data using helper - cbf_data = await self._fetch_cbf_data_for_hour(target_hour, limit) + # Initialize database connection and load data + database = LiteLLMDatabase() + verbose_logger.debug("CloudZero Logger: Loading usage data from database") + data = await database.get_usage_data(limit=limit) + + if data.is_empty(): + verbose_logger.info("CloudZero Logger: No usage data found to export") + return + + verbose_logger.debug(f"CloudZero Logger: Processing {len(data)} records") + + # Transform data to CloudZero CBF format + transformer = CBFTransformer() + cbf_data = transformer.transform(data) if cbf_data.is_empty(): - verbose_logger.info("CloudZero Logger: No usage data found to export") + verbose_logger.warning("CloudZero Logger: No valid data after transformation") return # Send data to CloudZero @@ -75,53 +84,33 @@ class CloudZeroLogger(CustomLogger): verbose_logger.error(f"CloudZero Logger: Error exporting usage data: {str(e)}") raise - async def _fetch_cbf_data_for_hour(self, target_hour: datetime, limit: Optional[int] = 1000): + async def dry_run_export_usage_data(self, limit: Optional[int] = 10000): """ - Helper method to fetch usage data for a specific hour and transform it to CloudZero CBF format. + Only prints the data that would be exported to CloudZero. Args: - target_hour: The specific hour to fetch data for - limit: Optional limit on number of records to fetch (default: 1000) - - Returns: - CBF formatted data ready for CloudZero ingestion - """ - # Initialize database connection and load data - database = LiteLLMDatabase() - verbose_logger.debug(f"CloudZero Logger: Loading spend logs for hour {target_hour}") - data = await database.get_usage_data_for_hour(target_hour=target_hour, limit=limit) - - if data.is_empty(): - verbose_logger.info("CloudZero Logger: No usage data found for the specified hour") - return data # Return empty data - - verbose_logger.debug(f"CloudZero Logger: Processing {len(data)} records") - - # Transform data to CloudZero CBF format - transformer = CBFTransformer() - cbf_data = transformer.transform(data) - - if cbf_data.is_empty(): - verbose_logger.warning("CloudZero Logger: No valid data after transformation") - - return cbf_data - - async def dry_run_export_usage_data(self, target_hour: datetime, limit: Optional[int] = 1000): - """ - Only prints the spend logs data for a specific hour that would be exported to CloudZero. - - Args: - target_hour: The specific hour to export data for - limit: Limit number of records to display (default: 1000) + limit: Limit number of records to display (default: 10000) """ try: verbose_logger.debug("CloudZero Logger: Starting dry run export") - # Fetch and transform data using helper - cbf_data = await self._fetch_cbf_data_for_hour(target_hour, limit) + # Initialize database connection and load data + database = LiteLLMDatabase() + verbose_logger.debug("CloudZero Logger: Loading usage data for dry run") + data = await database.get_usage_data(limit=limit) + + if data.is_empty(): + verbose_logger.warning("CloudZero Dry Run: No usage data found") + return + + verbose_logger.debug(f"CloudZero Dry Run: Processing {len(data)} records...") + + # Transform data to CloudZero CBF format + transformer = CBFTransformer() + cbf_data = transformer.transform(data) if cbf_data.is_empty(): - verbose_logger.warning("CloudZero Dry Run: No usage data found") + verbose_logger.warning("CloudZero Dry Run: No valid data after transformation") return # Display the transformed data on screen @@ -198,56 +187,4 @@ class CloudZeroLogger(CustomLogger): console.print(f" Unique Accounts: {unique_accounts}") console.print(f" Unique Services: {unique_services}") - console.print("\n[dim]💡 This is the CloudZero CBF format ready for AnyCost ingestion[/dim]") - - async def init_background_job(self, redis_cache=None): - """ - Initialize a background job that exports usage data every hour. - Uses PodLockManager to ensure only one instance runs the export at a time. - - Args: - redis_cache: Redis cache instance for pod locking - """ - from litellm.proxy.db.db_transaction_queue.pod_lock_manager import ( - PodLockManager, - ) - - lock_manager = PodLockManager(redis_cache=redis_cache) - cronjob_id = "cloudzero_hourly_export" - - async def hourly_export_task(): - while True: - try: - # Calculate the previous completed hour - now = datetime.utcnow() - target_hour = now.replace(minute=0, second=0, microsecond=0) - # Export data for the previous hour to ensure all data is available - target_hour = target_hour - timedelta(hours=1) - - # Try to acquire lock - lock_acquired = await lock_manager.acquire_lock(cronjob_id) - - if lock_acquired: - try: - verbose_logger.info(f"CloudZero Background Job: Starting export for hour {target_hour}") - await self.export_usage_data(target_hour) - verbose_logger.info(f"CloudZero Background Job: Completed export for hour {target_hour}") - finally: - # Always release the lock - await lock_manager.release_lock(cronjob_id) - else: - verbose_logger.debug("CloudZero Background Job: Another instance is already running the export") - - # Wait until the next hour - next_hour = (datetime.utcnow() + timedelta(hours=1)).replace(minute=0, second=0, microsecond=0) - sleep_seconds = (next_hour - datetime.utcnow()).total_seconds() - await asyncio.sleep(sleep_seconds) - - except Exception as e: - verbose_logger.error(f"CloudZero Background Job: Error in hourly export task: {str(e)}") - # Sleep for 5 minutes before retrying on error - await asyncio.sleep(300) - - # Start the background task - asyncio.create_task(hourly_export_task()) - verbose_logger.debug("CloudZero Background Job: Initialized hourly export task") \ No newline at end of file + console.print("\n[dim]💡 This is the CloudZero CBF format ready for AnyCost ingestion[/dim]") \ No newline at end of file diff --git a/litellm/integrations/cloudzero/database.py b/litellm/integrations/cloudzero/database.py index 6d12c5cfbd9..73a5c28e038 100644 --- a/litellm/integrations/cloudzero/database.py +++ b/litellm/integrations/cloudzero/database.py @@ -12,14 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. # -# CHANGELOG: 2025-07-23 - Added support for using LiteLLM_SpendLogs table for CBF mapping (ishaan-jaff) # CHANGELOG: 2025-01-19 - Refactored to use daily spend tables for proper CBF mapping (erik.peterson) # CHANGELOG: 2025-01-19 - Migrated from pandas to polars for database operations (erik.peterson) # CHANGELOG: 2025-01-19 - Initial database module for LiteLLM data extraction (erik.peterson) """Database connection and data extraction for LiteLLM.""" -from datetime import datetime, timedelta from typing import Any, Dict, Optional import polars as pl @@ -37,60 +35,123 @@ class LiteLLMDatabase: ) return prisma_client - async def get_usage_data_for_hour(self, target_hour: datetime, limit: Optional[int] = 1000) -> pl.DataFrame: - """Retrieve spend logs for a specific hour from LiteLLM_SpendLogs table with batching.""" + async def get_usage_data(self, limit: Optional[int] = None) -> pl.DataFrame: + """Retrieve consolidated usage data from LiteLLM daily spend tables.""" client = self._ensure_prisma_client() - # Calculate hour range - hour_start = target_hour.replace(minute=0, second=0, microsecond=0) - hour_end = hour_start + timedelta(hours=1) - - # Convert datetime objects to ISO format strings for PostgreSQL compatibility - hour_start_str = hour_start.isoformat() - hour_end_str = hour_end.isoformat() - - # Query to get spend logs for the specific hour + # Union query to combine user, team, and tag spend data query = """ - SELECT * - FROM "LiteLLM_SpendLogs" - WHERE "startTime" >= $1::timestamp - AND "startTime" < $2::timestamp - ORDER BY "startTime" ASC + WITH consolidated_spend AS ( + -- User spend data + SELECT + id, + date, + user_id as entity_id, + 'user' as entity_type, + api_key, + model, + model_group, + custom_llm_provider, + prompt_tokens, + completion_tokens, + spend, + api_requests, + successful_requests, + failed_requests, + cache_creation_input_tokens, + cache_read_input_tokens, + created_at, + updated_at + FROM "LiteLLM_DailyUserSpend" + + UNION ALL + + -- Team spend data + SELECT + id, + date, + team_id as entity_id, + 'team' as entity_type, + api_key, + model, + model_group, + custom_llm_provider, + prompt_tokens, + completion_tokens, + spend, + api_requests, + successful_requests, + failed_requests, + cache_creation_input_tokens, + cache_read_input_tokens, + created_at, + updated_at + FROM "LiteLLM_DailyTeamSpend" + + UNION ALL + + -- Tag spend data + SELECT + id, + date, + tag as entity_id, + 'tag' as entity_type, + api_key, + model, + model_group, + custom_llm_provider, + prompt_tokens, + completion_tokens, + spend, + api_requests, + successful_requests, + failed_requests, + cache_creation_input_tokens, + cache_read_input_tokens, + created_at, + updated_at + FROM "LiteLLM_DailyTagSpend" + ) + SELECT * FROM consolidated_spend + ORDER BY date DESC, created_at DESC """ if limit: query += f" LIMIT {limit}" try: - db_response = await client.db.query_raw(query, hour_start_str, hour_end_str) + db_response = await client.db.query_raw(query) # Convert the response to polars DataFrame - return pl.DataFrame(db_response) if db_response else pl.DataFrame() + return pl.DataFrame(db_response) except Exception as e: - raise Exception(f"Error retrieving spend logs for hour {target_hour}: {str(e)}") - + raise Exception(f"Error retrieving usage data: {str(e)}") async def get_table_info(self) -> Dict[str, Any]: - """Get information about the LiteLLM_SpendLogs table.""" + """Get information about the consolidated daily spend tables.""" client = self._ensure_prisma_client() try: - # Get row count from SpendLogs table - spend_logs_count = await self._get_table_row_count('LiteLLM_SpendLogs') + # Get combined row count from both tables + user_count = await self._get_table_row_count('LiteLLM_DailyUserSpend') + team_count = await self._get_table_row_count('LiteLLM_DailyTeamSpend') + tag_count = await self._get_table_row_count('LiteLLM_DailyTagSpend') - # Get column structure from spend logs table + # Get column structure from user spend table (representative) query = """ SELECT column_name, data_type, is_nullable FROM information_schema.columns - WHERE table_name = 'LiteLLM_SpendLogs' + WHERE table_name = 'LiteLLM_DailyUserSpend' ORDER BY ordinal_position; """ columns_response = await client.db.query_raw(query) return { 'columns': columns_response, - 'row_count': spend_logs_count, + 'row_count': user_count + team_count + tag_count, 'table_breakdown': { - 'spend_logs': spend_logs_count + 'user_spend': user_count, + 'team_spend': team_count, + 'tag_spend': tag_count } } except Exception as e: diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index 7091ea26b95..c8aba5dbe66 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # -# CHANGELOG: 2025-01-19 - Updated CBF transformation for LiteLLM_SpendLogs with hourly aggregation and team_id focus (ishaan-jaff) +# CHANGELOG: 2025-01-19 - Updated CBF transformation for daily spend tables and proper CloudZero mapping (erik.peterson) # CHANGELOG: 2025-01-19 - Migrated from pandas to polars for data transformation (erik.peterson) # CHANGELOG: 2025-01-19 - Initial CBF transformation module (erik.peterson) @@ -35,160 +35,90 @@ class CBFTransformer: self.czrn_generator = CZRNGenerator() def transform(self, data: pl.DataFrame) -> pl.DataFrame: - """Transform LiteLLM SpendLogs data to hourly aggregated CBF format.""" + """Transform LiteLLM data to CBF format, dropping records with zero successful_requests or invalid CZRNs.""" if data.is_empty(): return pl.DataFrame() - # Filter out records with zero spend or invalid team_id + # Filter out records with zero successful_requests first original_count = len(data) - filtered_data = data.filter( - (pl.col('spend') > 0) & - (pl.col('team_id').is_not_null()) & - (pl.col('team_id') != "") - ) - filtered_count = len(filtered_data) - zero_spend_dropped = original_count - filtered_count + if 'successful_requests' in data.columns: + filtered_data = data.filter(pl.col('successful_requests') > 0) + zero_requests_dropped = original_count - len(filtered_data) + else: + filtered_data = data + zero_requests_dropped = 0 - if filtered_data.is_empty(): - from rich.console import Console - console = Console() - console.print(f"[yellow]⚠️ Dropped all {original_count:,} records due to zero spend or missing team_id[/yellow]") - return pl.DataFrame() - - # Aggregate data to hourly level - hourly_aggregated = self._aggregate_to_hourly(filtered_data) - - # Transform aggregated data to CBF format cbf_data = [] czrn_dropped_count = 0 - - for row in hourly_aggregated.iter_rows(named=True): + filtered_count = len(filtered_data) + + for row in filtered_data.iter_rows(named=True): try: cbf_record = self._create_cbf_record(row) + # Only include the record if CZRN generation was successful cbf_data.append(cbf_record) except Exception: # Skip records that fail CZRN generation czrn_dropped_count += 1 continue - # Print summary of transformations + # Print summary of dropped records if any from rich.console import Console console = Console() - if zero_spend_dropped > 0: - console.print(f"[yellow]⚠️ Dropped {zero_spend_dropped:,} of {original_count:,} records with zero spend or missing team_id[/yellow]") + if zero_requests_dropped > 0: + console.print(f"[yellow]⚠️ Dropped {zero_requests_dropped:,} of {original_count:,} records with zero successful_requests[/yellow]") if czrn_dropped_count > 0: - console.print(f"[yellow]⚠️ Dropped {czrn_dropped_count:,} of {len(hourly_aggregated):,} aggregated records due to invalid CZRNs[/yellow]") + console.print(f"[yellow]⚠️ Dropped {czrn_dropped_count:,} of {filtered_count:,} filtered records due to invalid CZRNs[/yellow]") if len(cbf_data) > 0: - console.print(f"[green]✓ Successfully transformed {len(cbf_data):,} hourly aggregated records[/green]") + console.print(f"[green]✓ Successfully transformed {len(cbf_data):,} records[/green]") return pl.DataFrame(cbf_data) - def _aggregate_to_hourly(self, data: pl.DataFrame) -> pl.DataFrame: - """Aggregate spend logs to hourly level by team_id, key_name, model, and tags.""" - - # Extract hour from startTime, skip tags and metadata for now - data_with_hour = data.with_columns([ - pl.col('startTime').str.to_datetime().dt.truncate('1h').alias('usage_hour'), - pl.lit([]).cast(pl.List(pl.String)).alias('parsed_tags'), # Empty tags list for now - pl.lit("").alias('key_name') # Empty key name for now - ]) - - # Skip tag explosion for now - just add a null tag column - all_data = data_with_hour.with_columns([ - pl.lit(None, dtype=pl.String).alias('tag') - ]) - - # Group by hour, team_id, key_name, model, provider, and tag - aggregated = all_data.group_by([ - 'usage_hour', - 'team_id', - 'key_name', - 'model', - 'model_group', - 'custom_llm_provider', - 'tag' - ]).agg([ - pl.col('spend').sum().alias('total_spend'), - pl.col('total_tokens').sum().alias('total_tokens'), - pl.col('prompt_tokens').sum().alias('total_prompt_tokens'), - pl.col('completion_tokens').sum().alias('total_completion_tokens'), - pl.col('request_id').count().alias('request_count'), - pl.col('api_key').first().alias('api_key_sample'), # Keep one for reference - pl.col('status').filter(pl.col('status') == 'success').count().alias('successful_requests'), - pl.col('status').filter(pl.col('status') != 'success').count().alias('failed_requests') - ]) - return aggregated - - def _create_cbf_record(self, row: dict[str, Any]) -> CBFRecord: - """Create a single CBF record from aggregated hourly spend data.""" + """Create a single CBF record from LiteLLM daily spend row.""" - # Helper function to extract scalar values from polars data - def extract_scalar(value): - if hasattr(value, 'item') and not isinstance(value, (str, int, float, bool)): - return value.item() if value is not None else None - return value + # Parse date (daily spend tables use date strings like '2025-04-19') + usage_date = self._parse_date(row.get('date')) - # Use the aggregated hour as usage time - usage_time = self._parse_datetime(extract_scalar(row.get('usage_hour'))) - - # Use team_id as the primary entity_id - entity_id = str(extract_scalar(row.get('team_id', ''))) - key_name = str(extract_scalar(row.get('key_name', ''))) - model = str(extract_scalar(row.get('model', ''))) - model_group = str(extract_scalar(row.get('model_group', ''))) - provider = str(extract_scalar(row.get('custom_llm_provider', ''))) - tag = extract_scalar(row.get('tag')) - - # Calculate aggregated metrics - total_spend = float(extract_scalar(row.get('total_spend', 0.0)) or 0.0) - total_tokens = int(extract_scalar(row.get('total_tokens', 0)) or 0) - total_prompt_tokens = int(extract_scalar(row.get('total_prompt_tokens', 0)) or 0) - total_completion_tokens = int(extract_scalar(row.get('total_completion_tokens', 0)) or 0) - request_count = int(extract_scalar(row.get('request_count', 0)) or 0) - successful_requests = int(extract_scalar(row.get('successful_requests', 0)) or 0) - failed_requests = int(extract_scalar(row.get('failed_requests', 0)) or 0) + # Calculate total tokens + prompt_tokens = int(row.get('prompt_tokens', 0)) + completion_tokens = int(row.get('completion_tokens', 0)) + total_tokens = prompt_tokens + completion_tokens # Create CloudZero Resource Name (CZRN) as resource_id - # Create a mock row for CZRN generation with team_id as entity_id - czrn_row = { - 'entity_id': entity_id, - 'entity_type': 'team', - 'model': model, - 'custom_llm_provider': provider, - 'api_key': str(extract_scalar(row.get('api_key_sample', ''))) - } - resource_id = self.czrn_generator.create_from_litellm_data(czrn_row) + resource_id = self.czrn_generator.create_from_litellm_data(row) + + # Build dimensions for CloudZero + entity_id = str(row.get('entity_id', '')) + model = str(row.get('model', '')) + api_key_hash = str(row.get('api_key', ''))[:8] # First 8 chars for identification - # Build dimensions for CloudZero tracking dimensions = { - 'entity_type': 'team', + 'entity_type': str(row.get('entity_type', '')), # 'user' or 'team' 'entity_id': entity_id, - 'key_name': key_name, 'model': model, - 'model_group': model_group, - 'provider': provider, - 'request_count': str(request_count), - 'successful_requests': str(successful_requests), - 'failed_requests': str(failed_requests), + 'model_group': str(row.get('model_group', '')), + 'provider': str(row.get('custom_llm_provider', '')), + 'api_key_prefix': api_key_hash, + 'api_requests': str(row.get('api_requests', 0)), + 'successful_requests': str(row.get('successful_requests', 0)), + 'failed_requests': str(row.get('failed_requests', 0)), + 'cache_creation_tokens': str(row.get('cache_creation_input_tokens', 0)), + 'cache_read_tokens': str(row.get('cache_read_input_tokens', 0)), } - - # Add tag if present - if tag is not None and str(tag) not in ['', 'null', 'None']: - dimensions['tag'] = str(tag) # Extract CZRN components to populate corresponding CBF columns czrn_components = self.czrn_generator.extract_components(resource_id) - service_type, provider_czrn, region, owner_account_id, resource_type, cloud_local_id = czrn_components + service_type, provider, region, owner_account_id, resource_type, cloud_local_id = czrn_components # CloudZero CBF format with proper column names cbf_record = { # Required CBF fields - 'time/usage_start': usage_time.isoformat() if usage_time else None, # Required: ISO-formatted UTC datetime - 'cost/cost': total_spend, # Required: billed cost + 'time/usage_start': usage_date.isoformat() if usage_date else None, # Required: ISO-formatted UTC datetime + 'cost/cost': float(row.get('spend', 0.0)), # Required: billed cost 'resource/id': resource_id, # Required when resource tags are present # Usage metrics for token consumption @@ -206,41 +136,42 @@ class CBFTransformer: } # Add CZRN components that don't have direct CBF column mappings as resource tags - cbf_record['resource/tag:provider'] = provider_czrn # CZRN provider component + cbf_record['resource/tag:provider'] = provider # CZRN provider component cbf_record['resource/tag:model'] = cloud_local_id # CZRN cloud-local-id component (model) # Add resource tags for all dimensions (using resource/tag: format) for key, value in dimensions.items(): - # Ensure value is a scalar and not empty - if hasattr(value, 'item') and not isinstance(value, str): - value = value.item() if value is not None else None - if value is not None and str(value) not in ['', 'N/A', 'None', 'null']: # Only add non-empty tags + if value and value != 'N/A': # Only add non-empty tags cbf_record[f'resource/tag:{key}'] = str(value) # Add token breakdown as resource tags for analysis - if total_prompt_tokens > 0: - cbf_record['resource/tag:prompt_tokens'] = str(total_prompt_tokens) - if total_completion_tokens > 0: - cbf_record['resource/tag:completion_tokens'] = str(total_completion_tokens) + if prompt_tokens > 0: + cbf_record['resource/tag:prompt_tokens'] = str(prompt_tokens) + if completion_tokens > 0: + cbf_record['resource/tag:completion_tokens'] = str(completion_tokens) if total_tokens > 0: cbf_record['resource/tag:total_tokens'] = str(total_tokens) return CBFRecord(cbf_record) - def _parse_datetime(self, datetime_obj) -> Optional[datetime]: - """Parse datetime object to ensure proper format.""" - if datetime_obj is None: + def _parse_date(self, date_str) -> Optional[datetime]: + """Parse date string from daily spend tables (e.g., '2025-04-19').""" + if date_str is None: return None - if isinstance(datetime_obj, datetime): - return datetime_obj + if isinstance(date_str, datetime): + return date_str - if isinstance(datetime_obj, str): + if isinstance(date_str, str): try: - # Try to parse ISO format - return pl.Series([datetime_obj]).str.to_datetime().item() + # Parse date string and set to midnight UTC for daily aggregation + return pl.Series([date_str]).str.to_datetime("%Y-%m-%d").item() except Exception: - return None + try: + # Fallback: try ISO format parsing + return pl.Series([date_str]).str.to_datetime().item() + except Exception: + return None return None diff --git a/litellm/proxy/spend_tracking/cloudzero_endpoints.py b/litellm/proxy/spend_tracking/cloudzero_endpoints.py index 67de202aa7a..08f801c6468 100644 --- a/litellm/proxy/spend_tracking/cloudzero_endpoints.py +++ b/litellm/proxy/spend_tracking/cloudzero_endpoints.py @@ -302,7 +302,7 @@ async def init_cloudzero_background_job(): ) # Initialize the background job - await logger.init_background_job() + #await logger.init_background_job() _cloudzero_background_job_initialized = True verbose_proxy_logger.info("CloudZero background job initialized successfully") @@ -430,7 +430,7 @@ async def cloudzero_dry_run_export( try: # Import and initialize CloudZero logger with credentials - from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + from litellm.integrations.cloudzero.ll2cz.cloudzero import CloudZeroLogger # Initialize logger with credentials directly logger = CloudZeroLogger() @@ -490,7 +490,7 @@ async def cloudzero_export( settings = await _get_cloudzero_settings() # Import and initialize CloudZero logger with credentials - from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + from litellm.integrations.cloudzero.ll2cz.cloudzero import CloudZeroLogger # Initialize logger with credentials directly logger = CloudZeroLogger( diff --git a/tests/test_litellm/integrations/cloudzero/test_transform.py b/tests/test_litellm/integrations/cloudzero/test_transform.py new file mode 100644 index 00000000000..1f4db10cab8 --- /dev/null +++ b/tests/test_litellm/integrations/cloudzero/test_transform.py @@ -0,0 +1,183 @@ +import os +import sys +from datetime import datetime +from unittest.mock import MagicMock, patch + +import polars as pl +import pytest + +sys.path.insert(0, os.path.abspath("../../../..")) + +from litellm.integrations.cloudzero.transform import CBFTransformer +from litellm.types.integrations.cloudzero import CBFRecord + + +class TestCBFTransformer: + """Test suite for CBFTransformer class.""" + + def test_init(self): + """Test CBFTransformer initialization.""" + transformer = CBFTransformer() + assert hasattr(transformer, 'czrn_generator') + assert transformer.czrn_generator is not None + + def test_transform_empty_dataframe(self): + """Test transform method with empty DataFrame.""" + transformer = CBFTransformer() + empty_df = pl.DataFrame() + + result = transformer.transform(empty_df) + + assert result.is_empty() + assert isinstance(result, pl.DataFrame) + + def test_transform_with_zero_successful_requests(self): + """Test transform method filters out records with zero successful_requests.""" + transformer = CBFTransformer() + data = pl.DataFrame({ + 'date': ['2025-01-19'], + 'successful_requests': [0], + 'spend': [10.0], + 'entity_id': ['test_entity'], + 'model': ['gpt-4'] + }) + + result = transformer.transform(data) + + assert result.is_empty() + + def test_transform_with_valid_data(self): + """Test transform method with valid data.""" + transformer = CBFTransformer() + with patch.object(transformer, '_create_cbf_record') as mock_create: + mock_create.return_value = CBFRecord({'test': 'data'}) + + data = pl.DataFrame({ + 'date': ['2025-01-19'], + 'successful_requests': [5], + 'spend': [10.0], + 'entity_id': ['test_entity'], + 'model': ['gpt-4'] + }) + + result = transformer.transform(data) + + assert len(result) == 1 + mock_create.assert_called_once() + + def test_transform_handles_czrn_generation_failures(self): + """Test transform method handles CZRN generation failures gracefully.""" + transformer = CBFTransformer() + with patch.object(transformer, '_create_cbf_record') as mock_create: + mock_create.side_effect = Exception("CZRN generation failed") + + data = pl.DataFrame({ + 'date': ['2025-01-19'], + 'successful_requests': [5], + 'spend': [10.0], + 'entity_id': ['test_entity'], + 'model': ['gpt-4'] + }) + + result = transformer.transform(data) + + assert result.is_empty() + + def test_create_cbf_record(self): + """Test _create_cbf_record method with valid row data.""" + transformer = CBFTransformer() + with patch.object(transformer.czrn_generator, 'create_from_litellm_data') as mock_czrn, \ + patch.object(transformer.czrn_generator, 'extract_components') as mock_extract: + + mock_czrn.return_value = 'test-czrn' + mock_extract.return_value = ('service', 'provider', 'region', 'account', 'resource', 'local_id') + + row = { + 'date': '2025-01-19', + 'spend': 10.5, + 'prompt_tokens': 100, + 'completion_tokens': 50, + 'entity_id': 'test_entity', + 'model': 'gpt-4', + 'entity_type': 'user', + 'model_group': 'openai', + 'custom_llm_provider': 'openai', + 'api_key': 'sk-test123', + 'api_requests': 5, + 'successful_requests': 5, + 'failed_requests': 0 + } + + result = transformer._create_cbf_record(row) + + assert isinstance(result, CBFRecord) + assert result['cost/cost'] == 10.5 + assert result['usage/amount'] == 150 # 100 + 50 + assert result['usage/units'] == 'tokens' + assert result['resource/id'] == 'test-czrn' + + def test_create_cbf_record_minimal_data(self): + """Test _create_cbf_record method with minimal row data.""" + transformer = CBFTransformer() + with patch.object(transformer.czrn_generator, 'create_from_litellm_data') as mock_czrn, \ + patch.object(transformer.czrn_generator, 'extract_components') as mock_extract: + + mock_czrn.return_value = 'test-czrn' + mock_extract.return_value = ('service', 'provider', 'region', 'account', 'resource', 'local_id') + + row = { + 'date': '2025-01-19', + 'spend': 0.0 + } + + result = transformer._create_cbf_record(row) + + assert isinstance(result, CBFRecord) + assert result['cost/cost'] == 0.0 + assert result['usage/amount'] == 0 # no tokens + assert result['usage/units'] == 'tokens' + + def test_parse_date_with_valid_string(self): + """Test _parse_date method with valid date string.""" + transformer = CBFTransformer() + + result = transformer._parse_date('2025-01-19') + + assert isinstance(result, datetime) + assert result.year == 2025 + assert result.month == 1 + assert result.day == 19 + + def test_parse_date_with_datetime_object(self): + """Test _parse_date method with datetime object.""" + transformer = CBFTransformer() + dt = datetime(2025, 1, 19) + + result = transformer._parse_date(dt) + + assert result == dt + + def test_parse_date_with_none(self): + """Test _parse_date method with None.""" + transformer = CBFTransformer() + + result = transformer._parse_date(None) + + assert result is None + + def test_parse_date_with_invalid_string(self): + """Test _parse_date method with invalid date string.""" + transformer = CBFTransformer() + + result = transformer._parse_date('invalid-date') + + assert result is None + + def test_parse_date_with_iso_format(self): + """Test _parse_date method with ISO format string.""" + transformer = CBFTransformer() + + result = transformer._parse_date('2025-01-19T10:30:00Z') + + assert isinstance(result, datetime) + assert result.year == 2025 \ No newline at end of file From c051ab5b5afc719a753333b77fa1dcc498360fbc Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Fri, 5 Sep 2025 10:21:16 -0700 Subject: [PATCH 125/178] refactor: remove unused function --- litellm/llms/volcengine/__init__.py | 3 +- litellm/llms/volcengine/embedding/__init__.py | 3 +- litellm/llms/volcengine/embedding/handler.py | 208 ------------------ 3 files changed, 2 insertions(+), 212 deletions(-) delete mode 100644 litellm/llms/volcengine/embedding/handler.py diff --git a/litellm/llms/volcengine/__init__.py b/litellm/llms/volcengine/__init__.py index 0be9a4f428c..0887937bed5 100644 --- a/litellm/llms/volcengine/__init__.py +++ b/litellm/llms/volcengine/__init__.py @@ -4,12 +4,12 @@ Support for Volcengine (ByteDance) chat and embedding models """ from .chat.transformation import VolcEngineChatConfig -from .embedding import VolcEngineEmbeddingHandler, VolcEngineEmbeddingConfig from .common_utils import ( VolcEngineError, get_volcengine_base_url, get_volcengine_headers, ) +from .embedding import VolcEngineEmbeddingConfig # For backward compatibility, keep the old class name VolcEngineConfig = VolcEngineChatConfig @@ -17,7 +17,6 @@ VolcEngineConfig = VolcEngineChatConfig __all__ = [ "VolcEngineChatConfig", "VolcEngineConfig", # backward compatibility - "VolcEngineEmbeddingHandler", "VolcEngineEmbeddingConfig", "VolcEngineError", "get_volcengine_base_url", diff --git a/litellm/llms/volcengine/embedding/__init__.py b/litellm/llms/volcengine/embedding/__init__.py index 6063e88b740..7b3efc4f961 100644 --- a/litellm/llms/volcengine/embedding/__init__.py +++ b/litellm/llms/volcengine/embedding/__init__.py @@ -2,7 +2,6 @@ Volcengine Embedding Module """ -from .handler import VolcEngineEmbeddingHandler from .transformation import VolcEngineEmbeddingConfig -__all__ = ["VolcEngineEmbeddingHandler", "VolcEngineEmbeddingConfig"] +__all__ = ["VolcEngineEmbeddingConfig"] diff --git a/litellm/llms/volcengine/embedding/handler.py b/litellm/llms/volcengine/embedding/handler.py deleted file mode 100644 index 961495e72f1..00000000000 --- a/litellm/llms/volcengine/embedding/handler.py +++ /dev/null @@ -1,208 +0,0 @@ -""" -Volcengine Embedding Handler -Handles embedding requests to Volcengine's embedding API -""" - -from typing import Dict, List, Optional, Union - -import httpx -from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj -from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler -from litellm.types.utils import EmbeddingResponse -import litellm - -from .transformation import VolcEngineEmbeddingConfig -from ..common_utils import VolcEngineError - - -class VolcEngineEmbeddingHandler: - """Handler for Volcengine embedding API calls""" - - def __init__(self): - self.config = VolcEngineEmbeddingConfig() - - def _convert_to_litellm_response(self, transformed_response: Dict, model: str, input: Union[str, List[str]]) -> EmbeddingResponse: - """Convert transformed response to LiteLLM EmbeddingResponse""" - model_response = EmbeddingResponse() - model_response.object = transformed_response.get("object", "list") - model_response.data = transformed_response.get("data", []) - model_response.model = transformed_response.get("model", model) - - # Set usage information - usage_data = transformed_response.get("usage", {}) - if usage_data: - model_response.usage = litellm.Usage( - prompt_tokens=usage_data.get("prompt_tokens", 0), - completion_tokens=0, - total_tokens=usage_data.get("total_tokens", usage_data.get("prompt_tokens", 0)), - prompt_tokens_details=None, - completion_tokens_details=None, - ) - - return model_response - - def embedding( - self, - model: str, - input: Union[str, List[str]], - api_key: str, - api_base: Optional[str] = None, - encoding_format: Optional[str] = "float", - user: Optional[str] = None, - timeout: Optional[Union[float, httpx.Timeout]] = None, - extra_headers: Optional[Dict[str, str]] = None, - litellm_logging_obj: Optional[LiteLLMLoggingObj] = None, - **kwargs, - ) -> EmbeddingResponse: - """ - Synchronous embedding call to Volcengine API. - - Args: - model: Volcengine model ID (e.g., "doubao-embedding-text-240715") - input: Text or list of texts to embed - api_key: Volcengine API key - api_base: Optional custom API base URL - encoding_format: Response format (float, base64, null) - user: Optional user identifier - timeout: Request timeout - extra_headers: Optional additional headers - litellm_logging_obj: Optional logging object - **kwargs: Additional parameters - - Returns: - EmbeddingResponse object - """ - # Transform request to Volcengine format - request_data = self.config.transform_request( - model=model, - input=input, - api_key=api_key, - api_base=api_base, - encoding_format=encoding_format, - user=user, - extra_headers=extra_headers, - **kwargs, - ) - - # Make HTTP request - try: - client = HTTPHandler(timeout=timeout) - response = client.post( - url=request_data["url"], - headers=request_data["headers"], - json=request_data["data"], - ) - except Exception as e: - raise VolcEngineError( - status_code=500, - message=f"Network error during embedding request: {str(e)}", - ) - - # Handle HTTP errors - if response.status_code != 200: - error_message = f"Volcengine embedding request failed with status {response.status_code}" - try: - error_details = response.json() - if "error" in error_details: - error_message += f": {error_details['error']}" - elif "message" in error_details: - error_message += f": {error_details['message']}" - except Exception: - error_message += f": {response.text}" - - raise VolcEngineError( - status_code=response.status_code, - message=error_message, - headers=response.headers, - ) - - # Transform response to OpenAI format - transformed_response = self.config.transform_response( - response=response, model=model, input=input, encoding=encoding_format - ) - - # Convert to LiteLLM EmbeddingResponse - return self._convert_to_litellm_response(transformed_response, model, input) - - async def async_embedding( - self, - model: str, - input: Union[str, List[str]], - api_key: str, - api_base: Optional[str] = None, - encoding_format: Optional[str] = "float", - user: Optional[str] = None, - timeout: Optional[Union[float, httpx.Timeout]] = None, - extra_headers: Optional[Dict[str, str]] = None, - litellm_logging_obj: Optional[LiteLLMLoggingObj] = None, - **kwargs, - ) -> EmbeddingResponse: - """ - Asynchronous embedding call to Volcengine API. - - Args: - model: Volcengine model ID (e.g., "doubao-embedding-text-240715") - input: Text or list of texts to embed - api_key: Volcengine API key - api_base: Optional custom API base URL - encoding_format: Response format (float, base64, null) - user: Optional user identifier - timeout: Request timeout - extra_headers: Optional additional headers - litellm_logging_obj: Optional logging object - **kwargs: Additional parameters - - Returns: - EmbeddingResponse object - """ - # Transform request to Volcengine format - request_data = self.config.transform_request( - model=model, - input=input, - api_key=api_key, - api_base=api_base, - encoding_format=encoding_format, - user=user, - extra_headers=extra_headers, - **kwargs, - ) - - # Make async HTTP request - try: - client = AsyncHTTPHandler(timeout=timeout) - response = await client.post( - url=request_data["url"], - headers=request_data["headers"], - json=request_data["data"], - ) - except Exception as e: - raise VolcEngineError( - status_code=500, - message=f"Network error during embedding request: {str(e)}", - ) - - # Handle HTTP errors - if response.status_code != 200: - error_message = f"Volcengine embedding request failed with status {response.status_code}" - try: - error_details = response.json() - if "error" in error_details: - error_message += f": {error_details['error']}" - elif "message" in error_details: - error_message += f": {error_details['message']}" - except Exception: - error_message += f": {response.text}" - - raise VolcEngineError( - status_code=response.status_code, - message=error_message, - headers=response.headers, - ) - - # Transform response to OpenAI format - transformed_response = self.config.transform_response( - response=response, model=model, input=input, encoding=encoding_format - ) - - # Convert to LiteLLM EmbeddingResponse - return self._convert_to_litellm_response(transformed_response, model, input) From 31f806f7d021c25a1502c9846566b90623061cb0 Mon Sep 17 00:00:00 2001 From: Pierre-Emmanuel MERCIER <77622864+btpemercier@users.noreply.github.com> Date: Fri, 5 Sep 2025 19:35:11 +0200 Subject: [PATCH 126/178] feat: add redis ssl and username support (#11319) --- docs/my-website/docs/proxy/caching.md | 2 + litellm/_redis.py | 19 +++-- tests/test_litellm/test_redis.py | 109 ++++++++++++++++++++++++++ 3 files changed, 124 insertions(+), 6 deletions(-) create mode 100644 tests/test_litellm/test_redis.py diff --git a/docs/my-website/docs/proxy/caching.md b/docs/my-website/docs/proxy/caching.md index 1fb7385f689..49f0e199436 100644 --- a/docs/my-website/docs/proxy/caching.md +++ b/docs/my-website/docs/proxy/caching.md @@ -278,6 +278,8 @@ Set either `REDIS_URL` or the `REDIS_HOST` in your os environment, to enable cac REDIS_HOST = "" # REDIS_HOST='redis-18841.c274.us-east-1-3.ec2.cloud.redislabs.com' REDIS_PORT = "" # REDIS_PORT='18841' REDIS_PASSWORD = "" # REDIS_PASSWORD='liteLlmIsAmazing' + REDIS_USERNAME = "" # REDIS_USERNAME='my-redis-username' [OPTIONAL] if your redis server requires a username + REDIS_SSL = "True" # REDIS_SSL='True' to enable SSL by default is False ``` **Additional kwargs** diff --git a/litellm/_redis.py b/litellm/_redis.py index 8371ef5bbc7..8b64fe3dad9 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -174,14 +174,21 @@ def get_redis_url_from_environment(): raise ValueError( "Either 'REDIS_URL' or both 'REDIS_HOST' and 'REDIS_PORT' must be specified for Redis." ) - - if "REDIS_PASSWORD" in os.environ: - redis_password = f":{os.environ['REDIS_PASSWORD']}@" + + if "REDIS_SSL" in os.environ and os.environ["REDIS_SSL"].lower() == "true": + redis_protocol = "rediss" else: - redis_password = "" - + redis_protocol = "redis" + + # Build authentication part of URL + auth_part = "" + if "REDIS_USERNAME" in os.environ and "REDIS_PASSWORD" in os.environ: + auth_part = f"{os.environ['REDIS_USERNAME']}:{os.environ['REDIS_PASSWORD']}@" + elif "REDIS_PASSWORD" in os.environ: + auth_part = f"{os.environ['REDIS_PASSWORD']}@" + return ( - f"redis://{redis_password}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" + f"{redis_protocol}://{auth_part}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" ) diff --git a/tests/test_litellm/test_redis.py b/tests/test_litellm/test_redis.py new file mode 100644 index 00000000000..991126c2fef --- /dev/null +++ b/tests/test_litellm/test_redis.py @@ -0,0 +1,109 @@ +from litellm._redis import get_redis_url_from_environment +import os +import pytest + +def test_get_redis_url_from_environment_single_url(monkeypatch): + """Test when REDIS_URL is directly provided""" + # Set the environment variable + monkeypatch.setenv("REDIS_URL", "redis://redis-server:6379/0") + + # Call the function to get the Redis URL + redis_url = get_redis_url_from_environment() + + # Assert that the returned URL matches the expected value + assert redis_url == "redis://redis-server:6379/0" + +def test_get_redis_url_from_environment_host_port(monkeypatch): + """Test when REDIS_HOST and REDIS_PORT are provided""" + # Set the environment variables + monkeypatch.setenv("REDIS_HOST", "redis-server") + monkeypatch.setenv("REDIS_PORT", "6379") + + # Call the function to get the Redis URL + redis_url = get_redis_url_from_environment() + + # Assert that the returned URL matches the expected value + assert redis_url == "redis://redis-server:6379" + +def test_get_redis_url_from_environment_with_ssl(monkeypatch): + """Test when SSL is enabled""" + # Set the environment variables + monkeypatch.setenv("REDIS_HOST", "redis-server") + monkeypatch.setenv("REDIS_PORT", "6379") + monkeypatch.setenv("REDIS_SSL", "true") + + # Call the function to get the Redis URL + redis_url = get_redis_url_from_environment() + + # Assert that the returned URL uses rediss:// protocol + assert redis_url == "rediss://redis-server:6379" + +def test_get_redis_url_from_environment_with_username_password(monkeypatch): + """Test when username and password are provided""" + # Set the environment variables + monkeypatch.setenv("REDIS_HOST", "redis-server") + monkeypatch.setenv("REDIS_PORT", "6379") + monkeypatch.setenv("REDIS_USERNAME", "user") + monkeypatch.setenv("REDIS_PASSWORD", "password") + + # Call the function to get the Redis URL + redis_url = get_redis_url_from_environment() + + # Assert that the returned URL includes username:password@ + assert redis_url == "redis://user:password@redis-server:6379" + +def test_get_redis_url_from_environment_with_password_only(monkeypatch): + """Test when only password is provided""" + # Set the environment variables + monkeypatch.setenv("REDIS_HOST", "redis-server") + monkeypatch.setenv("REDIS_PORT", "6379") + monkeypatch.setenv("REDIS_PASSWORD", "password") + + # Call the function to get the Redis URL + redis_url = get_redis_url_from_environment() + + # Assert that the returned URL includes :password@ + assert redis_url == "redis://password@redis-server:6379" + +def test_get_redis_url_from_environment_with_all_options(monkeypatch): + """Test when all options are provided""" + # Set the environment variables + monkeypatch.setenv("REDIS_HOST", "redis-server") + monkeypatch.setenv("REDIS_PORT", "6379") + monkeypatch.setenv("REDIS_USERNAME", "user") + monkeypatch.setenv("REDIS_PASSWORD", "password") + monkeypatch.setenv("REDIS_SSL", "true") + + # Call the function to get the Redis URL + redis_url = get_redis_url_from_environment() + + # Assert that the returned URL includes all components + assert redis_url == "rediss://user:password@redis-server:6379" + +def test_get_redis_url_from_environment_missing_host_port(monkeypatch): + """Test error when required variables are missing""" + # Make sure these environment variables don't exist + monkeypatch.delenv("REDIS_URL", raising=False) + monkeypatch.delenv("REDIS_HOST", raising=False) + monkeypatch.delenv("REDIS_PORT", raising=False) + + # Call the function and expect a ValueError + with pytest.raises(ValueError) as excinfo: + get_redis_url_from_environment() + + # Check the error message + assert "Either 'REDIS_URL' or both 'REDIS_HOST' and 'REDIS_PORT' must be specified" in str(excinfo.value) + +def test_get_redis_url_from_environment_missing_port(monkeypatch): + """Test error when only REDIS_HOST is provided but REDIS_PORT is missing""" + # Make sure REDIS_URL doesn't exist and set only REDIS_HOST + monkeypatch.delenv("REDIS_URL", raising=False) + monkeypatch.delenv("REDIS_PORT", raising=False) + monkeypatch.setenv("REDIS_HOST", "redis-server") + + # Call the function and expect a ValueError + with pytest.raises(ValueError) as excinfo: + get_redis_url_from_environment() + + # Check the error message + assert "Either 'REDIS_URL' or both 'REDIS_HOST' and 'REDIS_PORT' must be specified" in str(excinfo.value) From 07ba3ff036812bbbbc7b6df32a4f44343c0d9e45 Mon Sep 17 00:00:00 2001 From: Sameer Kankute <135028480+kankute-sameer@users.noreply.github.com> Date: Sat, 6 Sep 2025 00:55:49 +0530 Subject: [PATCH 127/178] [Feat] Add pass through image gen and image editing on OpenAI (#14292) * add pass through image gen and image editing on OpenAI * fix lint --- litellm/litellm_core_utils/litellm_logging.py | 8 + .../openai_passthrough_logging_handler.py | 260 +++++++++++++--- ...test_openai_passthrough_logging_handler.py | 286 ++++++++++++++++++ 3 files changed, 512 insertions(+), 42 deletions(-) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 7bc7702684d..397858060de 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -1165,6 +1165,14 @@ class Logging(LiteLLMLoggingBaseClass): used for consistent cost calculation across response headers + logging integrations. """ + # Check if response_cost is already calculated and stored in model_call_details + # This is used by passthrough endpoints that calculate costs manually + if ( + hasattr(self, "model_call_details") + and self.model_call_details.get("response_cost") is not None + ): + return self.model_call_details["response_cost"] + if isinstance(result, BaseModel) and hasattr(result, "_hidden_params"): hidden_params = getattr(result, "_hidden_params", {}) if ( diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py index dd772ffa502..d230023a231 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py @@ -29,7 +29,7 @@ from litellm.types.passthrough_endpoints.pass_through_endpoints import ( EndpointType, PassthroughStandardLoggingPayload, ) -from litellm.types.utils import LlmProviders +from litellm.types.utils import LlmProviders, PassthroughCallTypes from litellm.utils import ModelResponse, TextCompletionResponse @@ -62,6 +62,36 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): and "/v1/chat/completions" in parsed_url.path ) + @staticmethod + def is_openai_image_generation_route(url_route: str) -> bool: + """Check if the URL route is an OpenAI image generation endpoint.""" + if not url_route: + return False + parsed_url = urlparse(url_route) + return bool( + parsed_url.hostname + and ( + "api.openai.com" in parsed_url.hostname + or "openai.azure.com" in parsed_url.hostname + ) + and "/v1/images/generations" in parsed_url.path + ) + + @staticmethod + def is_openai_image_editing_route(url_route: str) -> bool: + """Check if the URL route is an OpenAI image editing endpoint.""" + if not url_route: + return False + parsed_url = urlparse(url_route) + return bool( + parsed_url.hostname + and ( + "api.openai.com" in parsed_url.hostname + or "openai.azure.com" in parsed_url.hostname + ) + and "/v1/images/edits" in parsed_url.path + ) + @staticmethod def _get_user_from_metadata( passthrough_logging_payload: PassthroughStandardLoggingPayload, @@ -73,7 +103,79 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): return None @staticmethod - def openai_passthrough_handler( + def _calculate_image_generation_cost( + model: str, + response_body: dict, + request_body: dict, + ) -> float: + """Calculate cost for OpenAI image generation.""" + try: + # Extract parameters from request + n = request_body.get("n", 1) + try: + n = int(n) + except Exception: + n = 1 + size = request_body.get("size", "1024x1024") + quality = request_body.get("quality", None) + + # Use LiteLLM's default image cost calculator + from litellm.cost_calculator import default_image_cost_calculator + + cost = default_image_cost_calculator( + model=model, + custom_llm_provider="openai", + quality=quality, + n=n, + size=size, + optional_params=request_body, + ) + + return cost + except Exception as e: + verbose_proxy_logger.warning( + f"Error calculating image generation cost: {str(e)}" + ) + return 0.0 + + @staticmethod + def _calculate_image_editing_cost( + model: str, + response_body: dict, + request_body: dict, + ) -> float: + """Calculate cost for OpenAI image editing.""" + try: + # Extract parameters from request + n = request_body.get("n", 1) + # Image edit typically uses multipart/form-data (because of files), so all fields arrive as strings (e.g., n = "1"). + try: + n = int(n) + except Exception: + n = 1 + size = request_body.get("size", "1024x1024") + + # Use LiteLLM's default image cost calculator + from litellm.cost_calculator import default_image_cost_calculator + + cost = default_image_cost_calculator( + model=model, + custom_llm_provider="openai", + quality=None, # Image editing doesn't have quality parameter + n=n, + size=size, + optional_params=request_body, + ) + + return cost + except Exception as e: + verbose_proxy_logger.warning( + f"Error calculating image editing cost: {str(e)}" + ) + return 0.0 + + @staticmethod + def openai_passthrough_handler( # noqa: PLR0915 httpx_response: httpx.Response, response_body: dict, logging_obj: LiteLLMLoggingObj, @@ -86,13 +188,21 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): **kwargs, ) -> PassThroughEndpointLoggingTypedDict: """ - Handle OpenAI passthrough logging with cost tracking for chat completions. + Handle OpenAI passthrough logging with cost tracking for chat completions, image generation, and image editing. """ - # Only handle chat completions endpoints - if not OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route( - url_route - ): - # For non-chat-completions endpoints, use the base handler without cost tracking + # Check if this is a supported endpoint for cost tracking + is_chat_completions = ( + OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route(url_route) + ) + is_image_generation = ( + OpenAIPassthroughLoggingHandler.is_openai_image_generation_route(url_route) + ) + is_image_editing = ( + OpenAIPassthroughLoggingHandler.is_openai_image_editing_route(url_route) + ) + + if not (is_chat_completions or is_image_generation or is_image_editing): + # For unsupported endpoints, use the base handler without cost tracking base_handler = OpenAIPassthroughLoggingHandler() return base_handler.passthrough_chat_handler( httpx_response=httpx_response, @@ -128,31 +238,89 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): ) try: - # Transform the response to LiteLLM format for cost calculation - provider_config = OpenAIPassthroughLoggingHandler.get_provider_config( - model=model - ) - litellm_model_response: ModelResponse = provider_config.transform_response( - raw_response=httpx_response, - model_response=litellm.ModelResponse(), - model=model, - messages=request_body.get("messages", []), - logging_obj=logging_obj, - optional_params=request_body.get("optional_params", {}), - api_key="", - request_data=request_body, - encoding=litellm.encoding, - json_mode=request_body.get("response_format", {}).get("type") - == "json_object", - litellm_params={}, - ) + response_cost = 0.0 + litellm_model_response = None - # Calculate cost using LiteLLM's cost calculator - response_cost = litellm.completion_cost( - completion_response=litellm_model_response, - model=model, - custom_llm_provider="openai", - ) + if is_chat_completions: + # Handle chat completions with existing logic + provider_config = OpenAIPassthroughLoggingHandler.get_provider_config( + model=model + ) + litellm_model_response = provider_config.transform_response( + raw_response=httpx_response, + model_response=litellm.ModelResponse(), + model=model, + messages=request_body.get("messages", []), + logging_obj=logging_obj, + optional_params=request_body.get("optional_params", {}), + api_key="", + request_data=request_body, + encoding=litellm.encoding, + json_mode=request_body.get("response_format", {}).get("type") + == "json_object", + litellm_params={}, + ) + + # Calculate cost using LiteLLM's cost calculator + response_cost = litellm.completion_cost( + completion_response=litellm_model_response, + model=model, + custom_llm_provider="openai", + ) + elif is_image_generation: + # Handle image generation cost calculation + response_cost = ( + OpenAIPassthroughLoggingHandler._calculate_image_generation_cost( + model=model, + response_body=response_body, + request_body=request_body, + ) + ) + # Mark call type for downstream image-aware logic/metrics + try: + logging_obj.call_type = ( + PassthroughCallTypes.passthrough_image_generation.value + ) + except Exception: + pass + # Create a simple response object for logging + from litellm.types.utils import ImageResponse + + litellm_model_response = ImageResponse( + data=response_body.get("data", []), + model=model, + ) + # Set the calculated cost in _hidden_params to prevent recalculation + if not hasattr(litellm_model_response, "_hidden_params"): + litellm_model_response._hidden_params = {} + litellm_model_response._hidden_params["response_cost"] = response_cost + elif is_image_editing: + # Handle image editing cost calculation + response_cost = ( + OpenAIPassthroughLoggingHandler._calculate_image_editing_cost( + model=model, + response_body=response_body, + request_body=request_body, + ) + ) + # Mark call type for downstream image-aware logic/metrics + try: + logging_obj.call_type = ( + PassthroughCallTypes.passthrough_image_generation.value + ) + except Exception: + pass + # Create a simple response object for logging + from litellm.types.utils import ImageResponse + + litellm_model_response = ImageResponse( + data=response_body.get("data", []), + model=model, + ) + # Set the calculated cost in _hidden_params to prevent recalculation + if not hasattr(litellm_model_response, "_hidden_params"): + litellm_model_response._hidden_params = {} + litellm_model_response._hidden_params["response_cost"] = response_cost # Update kwargs with cost information kwargs["response_cost"] = response_cost @@ -174,26 +342,34 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): ) # Create standard logging object - get_standard_logging_object_payload( - kwargs=kwargs, - init_response_obj=litellm_model_response, - start_time=start_time, - end_time=end_time, - logging_obj=logging_obj, - status="success", - ) + if litellm_model_response is not None: + get_standard_logging_object_payload( + kwargs=kwargs, + init_response_obj=litellm_model_response, + start_time=start_time, + end_time=end_time, + logging_obj=logging_obj, + status="success", + ) # Update logging object with cost information logging_obj.model_call_details["model"] = model logging_obj.model_call_details["custom_llm_provider"] = "openai" logging_obj.model_call_details["response_cost"] = response_cost + endpoint_type = ( + "chat_completions" + if is_chat_completions + else "image_generation" + if is_image_generation + else "image_editing" + ) verbose_proxy_logger.debug( - f"OpenAI passthrough cost tracking - Model: {model}, Cost: ${response_cost:.6f}" + f"OpenAI passthrough cost tracking - Endpoint: {endpoint_type}, Model: {model}, Cost: ${response_cost:.6f}" ) return { - "result": litellm_model_response, + "result": litellm_model_response or response_body, "kwargs": kwargs, } diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py index 6d5e80910ba..6f808c9759c 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py @@ -105,6 +105,30 @@ class TestOpenAIPassthroughLoggingHandler: assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("https://api.anthropic.com/v1/messages") == False assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("") == False + def test_is_openai_image_generation_route(self): + """Test OpenAI image generation route detection""" + # Positive cases + assert OpenAIPassthroughLoggingHandler.is_openai_image_generation_route("https://api.openai.com/v1/images/generations") == True + assert OpenAIPassthroughLoggingHandler.is_openai_image_generation_route("https://openai.azure.com/v1/images/generations") == True + + # Negative cases + assert OpenAIPassthroughLoggingHandler.is_openai_image_generation_route("https://api.openai.com/v1/chat/completions") == False + assert OpenAIPassthroughLoggingHandler.is_openai_image_generation_route("https://api.openai.com/v1/images/edits") == False + assert OpenAIPassthroughLoggingHandler.is_openai_image_generation_route("http://localhost:4000/openai/v1/images/generations") == False + assert OpenAIPassthroughLoggingHandler.is_openai_image_generation_route("") == False + + def test_is_openai_image_editing_route(self): + """Test OpenAI image editing route detection""" + # Positive cases + assert OpenAIPassthroughLoggingHandler.is_openai_image_editing_route("https://api.openai.com/v1/images/edits") == True + assert OpenAIPassthroughLoggingHandler.is_openai_image_editing_route("https://openai.azure.com/v1/images/edits") == True + + # Negative cases + assert OpenAIPassthroughLoggingHandler.is_openai_image_editing_route("https://api.openai.com/v1/chat/completions") == False + assert OpenAIPassthroughLoggingHandler.is_openai_image_editing_route("https://api.openai.com/v1/images/generations") == False + assert OpenAIPassthroughLoggingHandler.is_openai_image_editing_route("http://localhost:4000/openai/v1/images/edits") == False + assert OpenAIPassthroughLoggingHandler.is_openai_image_editing_route("") == False + @patch('litellm.completion_cost') @patch('litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload') def test_openai_passthrough_handler_success(self, mock_get_standard_logging, mock_completion_cost): @@ -349,6 +373,34 @@ class TestOpenAIPassthroughIntegration: def setup_method(self): """Set up test fixtures""" self.handler = PassThroughEndpointLogging() + self.start_time = datetime.now() + self.end_time = datetime.now() + + def _create_mock_logging_obj(self) -> LiteLLMLoggingObj: + """Create a mock logging object""" + mock_logging_obj = MagicMock() + mock_logging_obj.model_call_details = {} + return mock_logging_obj + + def _create_mock_httpx_response(self, response_data: dict = None) -> httpx.Response: + """Create a mock httpx response""" + if response_data is None: + response_data = {"id": "test", "choices": [{"message": {"content": "Hello"}}]} + + mock_response = MagicMock(spec=httpx.Response) + mock_response.status_code = 200 + mock_response.text = json.dumps(response_data) + mock_response.json.return_value = response_data + mock_response.headers = {"content-type": "application/json"} + return mock_response + + def _create_passthrough_logging_payload(self, user: str = "test_user") -> PassthroughStandardLoggingPayload: + """Create a mock passthrough logging payload""" + return PassthroughStandardLoggingPayload( + url="https://api.openai.com/v1/chat/completions", + request_body={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}, + request_method="POST", + ) def test_is_openai_route_detection(self): """Test OpenAI route detection in the main success handler""" @@ -446,6 +498,240 @@ class TestOpenAIPassthroughIntegration: # Assert - Should call the base handler, not our OpenAI handler self.handler._handle_logging.assert_called_once() + @patch('litellm.cost_calculator.default_image_cost_calculator') + def test_calculate_image_generation_cost(self, mock_image_cost_calculator): + """Test image generation cost calculation""" + # Arrange + mock_image_cost_calculator.return_value = 0.040 + model = "dall-e-3" + response_body = { + "data": [ + { + "url": "https://example.com/image1.png", + "revised_prompt": "A beautiful sunset over the ocean" + } + ] + } + request_body = { + "model": "dall-e-3", + "prompt": "A beautiful sunset over the ocean", + "n": 1, + "size": "1024x1024", + "quality": "standard" + } + + # Act + cost = OpenAIPassthroughLoggingHandler._calculate_image_generation_cost( + model=model, + response_body=response_body, + request_body=request_body, + ) + + # Assert + assert cost == 0.040 + mock_image_cost_calculator.assert_called_once_with( + model=model, + custom_llm_provider="openai", + quality="standard", + n=1, + size="1024x1024", + optional_params=request_body, + ) + + @patch('litellm.cost_calculator.default_image_cost_calculator') + def test_calculate_image_editing_cost(self, mock_image_cost_calculator): + """Test image editing cost calculation""" + # Arrange + mock_image_cost_calculator.return_value = 0.020 + model = "dall-e-2" + response_body = { + "data": [ + { + "url": "https://example.com/edited_image.png", + "revised_prompt": "A beautiful sunset over the ocean with added clouds" + } + ] + } + request_body = { + "model": "dall-e-2", + "prompt": "Add clouds to the sky", + "n": 1, + "size": "1024x1024" + } + + # Act + cost = OpenAIPassthroughLoggingHandler._calculate_image_editing_cost( + model=model, + response_body=response_body, + request_body=request_body, + ) + + # Assert + assert cost == 0.020 + mock_image_cost_calculator.assert_called_once_with( + model=model, + custom_llm_provider="openai", + quality=None, # Image editing doesn't have quality parameter + n=1, + size="1024x1024", + optional_params=request_body, + ) + + def test_cost_calculation_preservation(self): + """Test that manually calculated costs are preserved and not overridden.""" + # Create a logging object + logging_obj = LiteLLMLoggingObj( + model="dall-e-3", + messages=[{"role": "user", "content": "Generate an image"}], + stream=False, + call_type="pass_through_endpoint", + start_time=self.start_time, + litellm_call_id="test_123", + function_id="test_fn", + ) + + # Set a manually calculated cost in model_call_details + test_cost = 0.040000 + logging_obj.model_call_details["response_cost"] = test_cost + logging_obj.model_call_details["model"] = "dall-e-3" + logging_obj.model_call_details["custom_llm_provider"] = "openai" + + # Create an ImageResponse with cost in _hidden_params + from litellm.types.utils import ImageResponse + image_response = ImageResponse( + data=[{"url": "https://example.com/image.png"}], + model="dall-e-3", + ) + image_response._hidden_params = {"response_cost": test_cost} + + # Test the _response_cost_calculator method + calculated_cost = logging_obj._response_cost_calculator(result=image_response) + + assert calculated_cost == test_cost, f"Expected {test_cost}, got {calculated_cost}" + + @patch('litellm.cost_calculator.default_image_cost_calculator') + def test_openai_passthrough_handler_image_generation(self, mock_image_cost_calculator): + """Test successful cost tracking for OpenAI image generation""" + # Arrange + mock_image_cost_calculator.return_value = 0.040 + + mock_image_response = { + "data": [ + { + "url": "https://example.com/image1.png", + "revised_prompt": "A beautiful sunset over the ocean" + } + ] + } + + mock_httpx_response = self._create_mock_httpx_response(mock_image_response) + mock_logging_obj = self._create_mock_logging_obj() + passthrough_payload = self._create_passthrough_logging_payload() + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "dall-e-3", + } + + request_body = { + "model": "dall-e-3", + "prompt": "A beautiful sunset over the ocean", + "n": 1, + "size": "1024x1024", + "quality": "standard" + } + + # Act + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=mock_image_response, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/images/generations", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body=request_body, + **kwargs + ) + + # Assert + assert result is not None + assert "result" in result + assert "kwargs" in result + assert result["kwargs"]["response_cost"] == 0.040 + assert result["kwargs"]["model"] == "dall-e-3" + assert result["kwargs"]["custom_llm_provider"] == "openai" + + # Verify cost calculation was called + mock_image_cost_calculator.assert_called_once() + + # Verify logging object was updated + assert mock_logging_obj.model_call_details["response_cost"] == 0.040 + assert mock_logging_obj.model_call_details["model"] == "dall-e-3" + assert mock_logging_obj.model_call_details["custom_llm_provider"] == "openai" + + @patch('litellm.cost_calculator.default_image_cost_calculator') + def test_openai_passthrough_handler_image_editing(self, mock_image_cost_calculator): + """Test successful cost tracking for OpenAI image editing""" + # Arrange + mock_image_cost_calculator.return_value = 0.020 + + mock_image_response = { + "data": [ + { + "url": "https://example.com/edited_image.png", + "revised_prompt": "A beautiful sunset over the ocean with added clouds" + } + ] + } + + mock_httpx_response = self._create_mock_httpx_response(mock_image_response) + mock_logging_obj = self._create_mock_logging_obj() + passthrough_payload = self._create_passthrough_logging_payload() + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "dall-e-2", + } + + request_body = { + "model": "dall-e-2", + "prompt": "Add clouds to the sky", + "n": 1, + "size": "1024x1024" + } + + # Act + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=mock_image_response, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/images/edits", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body=request_body, + **kwargs + ) + + # Assert + assert result is not None + assert "result" in result + assert "kwargs" in result + assert result["kwargs"]["response_cost"] == 0.020 + assert result["kwargs"]["model"] == "dall-e-2" + assert result["kwargs"]["custom_llm_provider"] == "openai" + + # Verify cost calculation was called + mock_image_cost_calculator.assert_called_once() + + # Verify logging object was updated + assert mock_logging_obj.model_call_details["response_cost"] == 0.020 + assert mock_logging_obj.model_call_details["model"] == "dall-e-2" + assert mock_logging_obj.model_call_details["custom_llm_provider"] == "openai" + if __name__ == "__main__": pytest.main([__file__]) From 5310bba35bf9f7784d2f04d210210afc5d43a88d Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 5 Sep 2025 21:29:41 -0700 Subject: [PATCH 128/178] [Feat] Litellm x CloudZero Integration - Cost Tracking (#14296) * fix: just pull LiteLLM_DailyUserSpend * get the team_id from user daily spend table * cloudzero_dry_run_export * fix CZ endpoints * trace entity_id * fix: get_usage_data * fix get_usage_data * fix _create_cbf_record * fix get_usage_data * ensure start and end time is used for exporting data * fix init_cloudzero_background_job * fix CloudZeroExportRequest * fix initialize_cloudzero_export_job * fix initialize_cloudzero_export_job * allow init with env + config.yaml for cloudzero * fix: init CZ through config.yaml * fix DRY run on CZ * TestCloudZeroDryRunEndpoint * fix: CLOUDZERO_EXPORT_INTERVAL_MINUTES * fix init_cloudzero_background_job * fix exporting data * fix transform * stash cloudzero docs * docs: CloudZero * ruff fix * fix rendering key alias * fix polars --- .circleci/config.yml | 1 + .../docs/observability/cloudzero.md | 209 ++++++++++++++++++ litellm/__init__.py | 1 + litellm/constants.py | 5 + litellm/integrations/cloudzero/cloudzero.py | 176 ++++++++++++++- .../cloudzero/cz_resource_names.py | 9 +- litellm/integrations/cloudzero/database.py | 145 +++++------- litellm/integrations/cloudzero/transform.py | 21 +- litellm/litellm_core_utils/litellm_logging.py | 14 +- litellm/proxy/proxy_config.yaml | 2 + litellm/proxy/proxy_server.py | 16 +- .../spend_tracking/cloudzero_endpoints.py | 146 ++++++------ litellm/types/proxy/cloudzero_endpoints.py | 7 +- .../cloudzero/test_dry_run_endpoint.py | 163 ++++++++++++++ 14 files changed, 719 insertions(+), 196 deletions(-) create mode 100644 docs/my-website/docs/observability/cloudzero.md create mode 100644 tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 7debc582915..2c2a2b6d6d3 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1292,6 +1292,7 @@ jobs: pip install "tokenizers==0.20.0" pip install "uvloop==0.21.0" pip install "fastuuid==0.12.0" + pip install "polars==1.31.0" pip install jsonschema - setup_litellm_enterprise_pip - run: diff --git a/docs/my-website/docs/observability/cloudzero.md b/docs/my-website/docs/observability/cloudzero.md new file mode 100644 index 00000000000..f213ef64e13 --- /dev/null +++ b/docs/my-website/docs/observability/cloudzero.md @@ -0,0 +1,209 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# CloudZero Integration + +LiteLLM provides an integration with CloudZero's AnyCost API, allowing you to export your LLM usage data to CloudZero for cost tracking analysis. + +## Overview + +| Property | Details | +|----------|---------| +| Description | Export LiteLLM usage data to CloudZero AnyCost API for cost tracking and analysis | +| callback name | `cloudzero`| +| Supported Operations | • Automatic hourly data export
• Manual data export
• Dry run testing
• Cost and token usage tracking | +| Data Format | CloudZero Billing Format (CBF) with proper resource tagging | +| Export Frequency | Hourly (configurable via `CLOUDZERO_EXPORT_INTERVAL_MINUTES`) | + +## Environment Variables + +| Variable | Required | Description | Example | +|----------|----------|-------------|---------| +| `CLOUDZERO_API_KEY` | Yes | Your CloudZero API key | `cz_api_xxxxxxxxxx` | +| `CLOUDZERO_CONNECTION_ID` | Yes | CloudZero connection ID for data submission | `conn_xxxxxxxxxx` | +| `CLOUDZERO_TIMEZONE` | No | Timezone for date handling (default: UTC) | `America/New_York` | +| `CLOUDZERO_EXPORT_INTERVAL_MINUTES` | No | Export frequency in minutes (default: 60) | `60` | + +## Setup + +### End to End Video Walkthrough +This video walks through the entire process of setting up LiteLLM with CloudZero integration and viewing LiteLLM exported usage data in CloudZero. + + + +### Step 1: Configure Environment Variables + +Set your CloudZero credentials in your environment: + +```bash +export CLOUDZERO_API_KEY="cz_api_xxxxxxxxxx" +export CLOUDZERO_CONNECTION_ID="conn_xxxxxxxxxx" +export CLOUDZERO_TIMEZONE="UTC" # Optional, defaults to UTC +``` + +### Step 2: Enable CloudZero Integration + +Add the CloudZero callback to your LiteLLM configuration YAML file: + + +```yaml +model_list: + - model_name: gpt-4o + litellm_params: + model: openai/gpt-4o + api_key: sk-xxxxxxx + +litellm_settings: + callbacks: ["cloudzero"] # Enable CloudZero integration +``` + +### Step 3: Start LiteLLM Proxy + +Start your LiteLLM proxy with the configuration: + +```bash +litellm --config /path/to/config.yaml +``` + +## Testing Your Setup + +### Dry Run Export + +Call the dry run endpoint to test your CloudZero configuration without sending data to CloudZero. This endpoint will not send any data to CloudZero, but will return the data that would be exported. + +```bash +curl -X POST "http://localhost:4000/cloudzero/dry-run" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "limit": 10 + }' | jq +``` + +**Expected Response:** +```json +{ + "message": "CloudZero dry run export completed successfully.", + "status": "success", + "dry_run_data": { + "usage_data": [...], + "cbf_data": [...], + "summary": { + "total_cost": 0.05, + "total_tokens": 1250, + "total_records": 10 + } + } +} +``` + +### Manual Export + +Call the export endpoint to send data immediately to CloudZero. We suggest setting a small `limit` to test the export. This will only export the last 10 records to CloudZero. Note: Cloudzero can take up to 15 minutes to process the exported data. + +```bash +curl -X POST "http://localhost:4000/cloudzero/export" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "limit": 10 + }' | jq +``` + +**Expected Response:** +```json +{ + "message": "CloudZero export completed successfully", + "status": "success" +} +``` + +## Data Export Details + +### Automatic Export Schedule + +- **Frequency**: Every 60 minutes (configurable via `CLOUDZERO_EXPORT_INTERVAL_MINUTES`) +- **Data Processing**: LiteLLM automatically processes and exports usage data hourly +- **CloudZero Processing**: CloudZero typically takes 10-15 minutes to process data from LiteLLM + +### Data Format + +LiteLLM exports data in CloudZero Billing Format (CBF) with the following structure: + +```json +{ + "time/usage_start": "2024-01-15T14:00:00Z", + "cost/cost": 0.002, + "usage/amount": 150, + "usage/units": "tokens", + "resource/id": "czrn:litellm:openai:cross-region:team-123:llm-usage:gpt-4o", + "resource/service": "litellm", + "resource/account": "team-123", + "resource/region": "cross-region", + "resource/usage_family": "llm-usage", + "resource/tag:provider": "openai", + "resource/tag:model": "gpt-4o", + "resource/tag:prompt_tokens": "100", + "resource/tag:completion_tokens": "50" +} +``` + +### Resource Tagging + +LiteLLM automatically creates comprehensive resource tags for cost attribution: + +- **Provider Tags**: `openai`, `anthropic`, `azure`, etc. +- **Model Tags**: Specific model names like `gpt-4o`, `claude-3-sonnet` +- **Team/User Tags**: Team IDs and user IDs for cost allocation +- **Token Breakdown**: Separate tracking of prompt and completion tokens +- **Usage Metrics**: Total tokens consumed per request + +## Advanced Configuration + +### Custom Export Frequency + +Change the export frequency (not recommended to go below 60 minutes): + +```bash +export CLOUDZERO_EXPORT_INTERVAL_MINUTES=120 # Export every 2 hours +``` + +### Custom Time Range Export + +Export data for a specific time range: + +```bash +curl -X POST "http://localhost:4000/cloudzero/export" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "start_time_utc": "2024-01-15T00:00:00Z", + "end_time_utc": "2024-01-15T23:59:59Z", + "operation": "replace_hourly" + }' | jq +``` + +## Troubleshooting + +### Common Issues + +1. **Missing Credentials Error** + ``` + CloudZero configuration missing. Please set CLOUDZERO_API_KEY and CLOUDZERO_CONNECTION_ID environment variables. + ``` + **Solution**: Ensure both environment variables are set with valid values. + +2. **Connection Issues** + - Verify your CloudZero API key is valid + - Check that the connection ID exists in your CloudZero account + - Ensure your proxy has internet access to reach CloudZero's API + +3. **No Data in CloudZero** + - CloudZero can take 10-15 minutes to process data + - Check that your LiteLLM proxy is generating usage data + - Use the dry-run endpoint to verify data is being formatted correctly + +## Related Links + +- [CloudZero Documentation](https://docs.cloudzero.com/) +- [CloudZero AnyCost API](https://docs.cloudzero.com/reference/anycost-api) diff --git a/litellm/__init__.py b/litellm/__init__.py index da5eb9d1b68..0ebea89941a 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -146,6 +146,7 @@ _custom_logger_compatible_callbacks_literal = Literal[ "aws_sqs", "vector_store_pre_call_hook", "dotprompt", + "cloudzero", ] configured_cold_storage_logger: Optional[_custom_logger_compatible_callbacks_literal] = None logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None diff --git a/litellm/constants.py b/litellm/constants.py index 21e30bef32b..089e73fc3b4 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -873,6 +873,9 @@ AZURE_STORAGE_MSFT_VERSION = "2019-07-07" PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES = int( os.getenv("PROMETHEUS_BUDGET_METRICS_REFRESH_INTERVAL_MINUTES", 5) ) +CLOUDZERO_EXPORT_INTERVAL_MINUTES = int( + os.getenv("CLOUDZERO_EXPORT_INTERVAL_MINUTES", 60) +) MCP_TOOL_NAME_PREFIX = "mcp_tool" MAXIMUM_TRACEBACK_LINES_TO_LOG = int(os.getenv("MAXIMUM_TRACEBACK_LINES_TO_LOG", 100)) @@ -927,6 +930,8 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" ########################### DB CRON JOB NAMES ########################### DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics" +CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data" +CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int(os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000)) SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) diff --git a/litellm/integrations/cloudzero/cloudzero.py b/litellm/integrations/cloudzero/cloudzero.py index ab1de17b9f2..727dabc0945 100644 --- a/litellm/integrations/cloudzero/cloudzero.py +++ b/litellm/integrations/cloudzero/cloudzero.py @@ -1,6 +1,8 @@ import os -from typing import Optional +from datetime import datetime +from typing import TYPE_CHECKING, Any, List, Optional, cast +import litellm from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger @@ -8,6 +10,11 @@ from .cz_stream_api import CloudZeroStreamer from .database import LiteLLMDatabase from .transform import CBFTransformer +if TYPE_CHECKING: + from apscheduler.schedulers.asyncio import AsyncIOScheduler +else: + AsyncIOScheduler = Any + class CloudZeroLogger(CustomLogger): """ @@ -27,8 +34,66 @@ class CloudZeroLogger(CustomLogger): self.api_key = api_key or os.getenv("CLOUDZERO_API_KEY") self.connection_id = connection_id or os.getenv("CLOUDZERO_CONNECTION_ID") self.timezone = timezone or os.getenv("CLOUDZERO_TIMEZONE", "UTC") + verbose_logger.debug(f"CloudZero Logger initialized with connection ID: {self.connection_id}, timezone: {self.timezone}") - async def export_usage_data(self, limit: Optional[int] = None, operation: str = "replace_hourly"): + async def initialize_cloudzero_export_job(self): + """ + Handler for initializing CloudZero export job. + + Runs when CloudZero logger starts up. + + - If redis cache is available, we use the pod lock manager to acquire a lock and export the data. + - Ensures only one pod exports the data at a time. + - If redis cache is not available, we export the data directly. + """ + from litellm.constants import ( + CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME, + ) + from litellm.proxy.proxy_server import proxy_logging_obj + pod_lock_manager = proxy_logging_obj.db_spend_update_writer.pod_lock_manager + + # if using redis, ensure only one pod exports the data at a time + if pod_lock_manager and pod_lock_manager.redis_cache: + if await pod_lock_manager.acquire_lock( + cronjob_id=CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME + ): + try: + await self._hourly_usage_data_export() + finally: + await pod_lock_manager.release_lock( + cronjob_id=CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME + ) + else: + # if not using redis, export the data directly + await self._hourly_usage_data_export() + + async def _hourly_usage_data_export(self): + """ + Exports the hourly usage data to CloudZero. + + Start time: 1 hour ago + End time: current time + """ + from datetime import timedelta, timezone + + from litellm.constants import CLOUDZERO_MAX_FETCHED_DATA_RECORDS + current_time_utc = datetime.now(timezone.utc) + one_hour_ago_utc = current_time_utc - timedelta(hours=1) + await self.export_usage_data( + limit=CLOUDZERO_MAX_FETCHED_DATA_RECORDS, + operation="replace_hourly", + start_time_utc=one_hour_ago_utc, + end_time_utc=current_time_utc + ) + + + async def export_usage_data( + self, + limit: Optional[int] = None, + operation: str = "replace_hourly", + start_time_utc: Optional[datetime] = None, + end_time_utc: Optional[datetime] = None + ): """ Exports the usage data to CloudZero. @@ -52,7 +117,11 @@ class CloudZeroLogger(CustomLogger): # Initialize database connection and load data database = LiteLLMDatabase() verbose_logger.debug("CloudZero Logger: Loading usage data from database") - data = await database.get_usage_data(limit=limit) + data = await database.get_usage_data( + limit=limit, + start_time_utc=start_time_utc, + end_time_utc=end_time_utc + ) if data.is_empty(): verbose_logger.info("CloudZero Logger: No usage data found to export") @@ -86,10 +155,13 @@ class CloudZeroLogger(CustomLogger): async def dry_run_export_usage_data(self, limit: Optional[int] = 10000): """ - Only prints the data that would be exported to CloudZero. + Returns the data that would be exported to CloudZero without actually sending it. Args: limit: Limit number of records to display (default: 10000) + + Returns: + dict: Contains usage_data, cbf_data, and summary statistics """ try: verbose_logger.debug("CloudZero Logger: Starting dry run export") @@ -101,23 +173,64 @@ class CloudZeroLogger(CustomLogger): if data.is_empty(): verbose_logger.warning("CloudZero Dry Run: No usage data found") - return + return { + "usage_data": [], + "cbf_data": [], + "summary": { + "total_records": 0, + "total_cost": 0, + "total_tokens": 0, + "unique_accounts": 0, + "unique_services": 0 + } + } verbose_logger.debug(f"CloudZero Dry Run: Processing {len(data)} records...") + # Convert usage data to dict format for response + usage_data_sample = data.head(50).to_dicts() # Return first 50 rows + # Transform data to CloudZero CBF format transformer = CBFTransformer() cbf_data = transformer.transform(data) if cbf_data.is_empty(): verbose_logger.warning("CloudZero Dry Run: No valid data after transformation") - return + return { + "usage_data": usage_data_sample, + "cbf_data": [], + "summary": { + "total_records": len(usage_data_sample), + "total_cost": sum(row.get('spend', 0) for row in usage_data_sample), + "total_tokens": sum(row.get('prompt_tokens', 0) + row.get('completion_tokens', 0) for row in usage_data_sample), + "unique_accounts": 0, + "unique_services": 0 + } + } - # Display the transformed data on screen - self._display_cbf_data_on_screen(cbf_data) + # Convert CBF data to dict format for response + cbf_data_dict = cbf_data.to_dicts() + + # Calculate summary statistics + total_cost = sum(record.get('cost/cost', 0) for record in cbf_data_dict) + unique_accounts = len(set(record.get('resource/account', '') for record in cbf_data_dict if record.get('resource/account'))) + unique_services = len(set(record.get('resource/service', '') for record in cbf_data_dict if record.get('resource/service'))) + total_tokens = sum(record.get('usage/amount', 0) for record in cbf_data_dict) verbose_logger.info(f"CloudZero Logger: Dry run completed for {len(cbf_data)} records") + return { + "usage_data": usage_data_sample, + "cbf_data": cbf_data_dict, + "summary": { + "total_records": len(cbf_data_dict), + "total_cost": total_cost, + "total_tokens": total_tokens, + "unique_accounts": unique_accounts, + "unique_services": unique_services + } + } + except Exception as e: verbose_logger.error(f"CloudZero Logger: Error in dry run export: {str(e)}") verbose_logger.error(f"CloudZero Dry Run Error: {str(e)}") @@ -144,6 +257,11 @@ class CloudZeroLogger(CustomLogger): cbf_table = Table(show_header=True, header_style="bold cyan", box=SIMPLE, padding=(0, 1)) cbf_table.add_column("time/usage_start", style="blue", no_wrap=False) cbf_table.add_column("cost/cost", style="green", justify="right", no_wrap=False) + cbf_table.add_column("entity_type", style="magenta", justify="right", no_wrap=False) + cbf_table.add_column("entity_id", style="magenta", justify="right", no_wrap=False) + cbf_table.add_column("team_id", style="cyan", no_wrap=False) + cbf_table.add_column("team_alias", style="cyan", no_wrap=False) + cbf_table.add_column("api_key_alias", style="yellow", no_wrap=False) cbf_table.add_column("usage/amount", style="yellow", justify="right", no_wrap=False) cbf_table.add_column("resource/id", style="magenta", no_wrap=False) cbf_table.add_column("resource/service", style="cyan", no_wrap=False) @@ -159,10 +277,20 @@ class CloudZeroLogger(CustomLogger): resource_service = str(record.get('resource/service', 'N/A')) resource_account = str(record.get('resource/account', 'N/A')) resource_region = str(record.get('resource/region', 'N/A')) + entity_type = str(record.get('entity_type', 'N/A')) + entity_id = str(record.get('entity_id', 'N/A')) + team_id = str(record.get('resource/tag:team_id', 'N/A')) + team_alias = str(record.get('resource/tag:team_alias', 'N/A')) + api_key_alias = str(record.get('resource/tag:api_key_alias', 'N/A')) cbf_table.add_row( time_usage_start, cost_cost, + entity_type, + entity_id, + team_id, + team_alias, + api_key_alias, usage_amount, resource_id, resource_service, @@ -187,4 +315,34 @@ class CloudZeroLogger(CustomLogger): console.print(f" Unique Accounts: {unique_accounts}") console.print(f" Unique Services: {unique_services}") - console.print("\n[dim]💡 This is the CloudZero CBF format ready for AnyCost ingestion[/dim]") \ No newline at end of file + console.print("\n[dim]💡 This is the CloudZero CBF format ready for AnyCost ingestion[/dim]") + + @staticmethod + async def init_cloudzero_background_job(scheduler: AsyncIOScheduler): + """ + Initialize the CloudZero background job. + + Starts the background job that exports the usage data to CloudZero every hour. + """ + from litellm.constants import CLOUDZERO_EXPORT_INTERVAL_MINUTES + from litellm.integrations.custom_logger import CustomLogger + + + prometheus_loggers: List[CustomLogger] = ( + litellm.logging_callback_manager.get_custom_loggers_for_type( + callback_type=CloudZeroLogger + ) + ) + # we need to get the initialized prometheus logger instance(s) and call logger.initialize_remaining_budget_metrics() on them + verbose_logger.debug("found %s cloudzero loggers", len(prometheus_loggers)) + if len(prometheus_loggers) > 0: + cloudzero_logger = cast(CloudZeroLogger, prometheus_loggers[0]) + verbose_logger.debug( + "Initializing remaining budget metrics as a cron job executing every %s minutes" + % CLOUDZERO_EXPORT_INTERVAL_MINUTES + ) + scheduler.add_job( + cloudzero_logger.initialize_cloudzero_export_job, + "interval", + minutes=CLOUDZERO_EXPORT_INTERVAL_MINUTES + ) \ No newline at end of file diff --git a/litellm/integrations/cloudzero/cz_resource_names.py b/litellm/integrations/cloudzero/cz_resource_names.py index 44147f9c210..f1098d20381 100644 --- a/litellm/integrations/cloudzero/cz_resource_names.py +++ b/litellm/integrations/cloudzero/cz_resource_names.py @@ -17,11 +17,16 @@ """CloudZero Resource Names (CZRN) generation and validation for LiteLLM resources.""" import re +from enum import Enum from typing import Any, cast import litellm +class CZEntityType(str, Enum): + TEAM = "team" + + class CZRNGenerator: """Generate CloudZero Resource Names (CZRNs) for LiteLLM resources.""" @@ -49,8 +54,8 @@ class CZRNGenerator: region = 'cross-region' # Use the actual entity_id (team_id or user_id) as the owner account - entity_id = row.get('entity_id', 'unknown') - owner_account_id = self._normalize_component(entity_id) + team_id = row.get('team_id', 'unknown') + owner_account_id = self._normalize_component(team_id) resource_type = 'llm-usage' diff --git a/litellm/integrations/cloudzero/database.py b/litellm/integrations/cloudzero/database.py index 73a5c28e038..71b4125ed75 100644 --- a/litellm/integrations/cloudzero/database.py +++ b/litellm/integrations/cloudzero/database.py @@ -18,6 +18,7 @@ """Database connection and data extraction for LiteLLM.""" +from datetime import datetime from typing import Any, Dict, Optional import polars as pl @@ -35,85 +36,54 @@ class LiteLLMDatabase: ) return prisma_client - async def get_usage_data(self, limit: Optional[int] = None) -> pl.DataFrame: - """Retrieve consolidated usage data from LiteLLM daily spend tables.""" + async def get_usage_data( + self, + limit: Optional[int] = None, + start_time_utc: Optional[datetime] = None, + end_time_utc: Optional[datetime] = None + ) -> pl.DataFrame: + """Retrieve usage data from LiteLLM daily user spend table.""" client = self._ensure_prisma_client() - # Union query to combine user, team, and tag spend data - query = """ - WITH consolidated_spend AS ( - -- User spend data - SELECT - id, - date, - user_id as entity_id, - 'user' as entity_type, - api_key, - model, - model_group, - custom_llm_provider, - prompt_tokens, - completion_tokens, - spend, - api_requests, - successful_requests, - failed_requests, - cache_creation_input_tokens, - cache_read_input_tokens, - created_at, - updated_at - FROM "LiteLLM_DailyUserSpend" - - UNION ALL - - -- Team spend data - SELECT - id, - date, - team_id as entity_id, - 'team' as entity_type, - api_key, - model, - model_group, - custom_llm_provider, - prompt_tokens, - completion_tokens, - spend, - api_requests, - successful_requests, - failed_requests, - cache_creation_input_tokens, - cache_read_input_tokens, - created_at, - updated_at - FROM "LiteLLM_DailyTeamSpend" - - UNION ALL - - -- Tag spend data - SELECT - id, - date, - tag as entity_id, - 'tag' as entity_type, - api_key, - model, - model_group, - custom_llm_provider, - prompt_tokens, - completion_tokens, - spend, - api_requests, - successful_requests, - failed_requests, - cache_creation_input_tokens, - cache_read_input_tokens, - created_at, - updated_at - FROM "LiteLLM_DailyTagSpend" - ) - SELECT * FROM consolidated_spend - ORDER BY date DESC, created_at DESC + # Build WHERE clause for time filtering + where_conditions = [] + if start_time_utc: + where_conditions.append(f"dus.created_at >= '{start_time_utc.isoformat()}'") + if end_time_utc: + where_conditions.append(f"dus.created_at <= '{end_time_utc.isoformat()}'") + + where_clause = "" + if where_conditions: + where_clause = "WHERE " + " AND ".join(where_conditions) + + # Query to get user spend data with team information + query = f""" + SELECT + dus.id, + dus.date, + dus.user_id, + dus.api_key, + dus.model, + dus.model_group, + dus.custom_llm_provider, + dus.prompt_tokens, + dus.completion_tokens, + dus.spend, + dus.api_requests, + dus.successful_requests, + dus.failed_requests, + dus.cache_creation_input_tokens, + dus.cache_read_input_tokens, + dus.created_at, + dus.updated_at, + vt.team_id, + vt.key_alias as api_key_alias, + tt.team_alias + FROM "LiteLLM_DailyUserSpend" dus + LEFT JOIN "LiteLLM_VerificationToken" vt ON dus.api_key = vt.token + LEFT JOIN "LiteLLM_TeamTable" tt ON vt.team_id = tt.team_id + {where_clause} + ORDER BY dus.date DESC, dus.created_at DESC """ if limit: @@ -121,22 +91,21 @@ class LiteLLMDatabase: try: db_response = await client.db.query_raw(query) - # Convert the response to polars DataFrame - return pl.DataFrame(db_response) + # Convert the response to polars DataFrame with full schema inference + # This prevents schema mismatch errors when data types vary across rows + return pl.DataFrame(db_response, infer_schema_length=None) except Exception as e: raise Exception(f"Error retrieving usage data: {str(e)}") async def get_table_info(self) -> Dict[str, Any]: - """Get information about the consolidated daily spend tables.""" + """Get information about the daily user spend table.""" client = self._ensure_prisma_client() try: - # Get combined row count from both tables + # Get row count from user spend table user_count = await self._get_table_row_count('LiteLLM_DailyUserSpend') - team_count = await self._get_table_row_count('LiteLLM_DailyTeamSpend') - tag_count = await self._get_table_row_count('LiteLLM_DailyTagSpend') - # Get column structure from user spend table (representative) + # Get column structure from user spend table query = """ SELECT column_name, data_type, is_nullable FROM information_schema.columns @@ -147,12 +116,8 @@ class LiteLLMDatabase: return { 'columns': columns_response, - 'row_count': user_count + team_count + tag_count, - 'table_breakdown': { - 'user_spend': user_count, - 'team_spend': team_count, - 'tag_spend': tag_count - } + 'row_count': user_count, + 'table_name': 'LiteLLM_DailyUserSpend' } except Exception as e: raise Exception(f"Error getting table info: {str(e)}") diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index c8aba5dbe66..e0263295388 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -24,7 +24,7 @@ from typing import Any, Optional import polars as pl from ...types.integrations.cloudzero import CBFRecord -from .cz_resource_names import CZRNGenerator +from .cz_resource_names import CZEntityType, CZRNGenerator class CBFTransformer: @@ -92,17 +92,26 @@ class CBFTransformer: resource_id = self.czrn_generator.create_from_litellm_data(row) # Build dimensions for CloudZero - entity_id = str(row.get('entity_id', '')) model = str(row.get('model', '')) api_key_hash = str(row.get('api_key', ''))[:8] # First 8 chars for identification - + + # Handle team information with fallbacks + team_id = row.get('team_id') + team_alias = row.get('team_alias') + + # Use team_alias if available, otherwise team_id, otherwise fallback to 'unknown' + entity_id = str(team_alias) if team_alias else (str(team_id) if team_id else 'unknown') + dimensions = { - 'entity_type': str(row.get('entity_type', '')), # 'user' or 'team' + 'entity_type': CZEntityType.TEAM.value, 'entity_id': entity_id, + 'team_id': str(team_id) if team_id else 'unknown', + 'team_alias': str(team_alias) if team_alias else 'unknown', 'model': model, 'model_group': str(row.get('model_group', '')), 'provider': str(row.get('custom_llm_provider', '')), 'api_key_prefix': api_key_hash, + 'api_key_alias': str(row.get('api_key_alias', '')), 'api_requests': str(row.get('api_requests', 0)), 'successful_requests': str(row.get('successful_requests', 0)), 'failed_requests': str(row.get('failed_requests', 0)), @@ -138,10 +147,10 @@ class CBFTransformer: # Add CZRN components that don't have direct CBF column mappings as resource tags cbf_record['resource/tag:provider'] = provider # CZRN provider component cbf_record['resource/tag:model'] = cloud_local_id # CZRN cloud-local-id component (model) - + # Add resource tags for all dimensions (using resource/tag: format) for key, value in dimensions.items(): - if value and value != 'N/A': # Only add non-empty tags + if value and value != 'N/A' and value != 'unknown': # Only add meaningful tags cbf_record[f'resource/tag:{key}'] = str(value) # Add token breakdown as resource tags for analysis diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 397858060de..7134f52c95a 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -3369,7 +3369,14 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 galileo_logger = GalileoObserve() _in_memory_loggers.append(galileo_logger) return galileo_logger # type: ignore - + elif logging_integration == "cloudzero": + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + for callback in _in_memory_loggers: + if isinstance(callback, CloudZeroLogger): + return callback # type: ignore + cloudzero_logger = CloudZeroLogger() + _in_memory_loggers.append(cloudzero_logger) + return cloudzero_logger # type: ignore elif logging_integration == "deepeval": for callback in _in_memory_loggers: if isinstance(callback, DeepEvalLogger): @@ -3589,6 +3596,11 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if isinstance(callback, GalileoObserve): return callback + elif logging_integration == "cloudzero": + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + for callback in _in_memory_loggers: + if isinstance(callback, CloudZeroLogger): + return callback elif logging_integration == "deepeval": for callback in _in_memory_loggers: if isinstance(callback, DeepEvalLogger): diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 72c69a28e95..7ee09105254 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -3,3 +3,5 @@ model_list: litellm_params: model: openai/* api_base: https://exampleopenaiendpoint-production-0ee2.up.railway.app/ +litellm_settings: + callbacks: ["cloudzero"] \ No newline at end of file diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 547aaf50788..9f1566b2e00 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -248,7 +248,9 @@ from litellm.proxy.management_endpoints.customer_endpoints import ( from litellm.proxy.management_endpoints.internal_user_endpoints import ( router as internal_user_router, ) -from litellm.proxy.management_endpoints.internal_user_endpoints import user_update +from litellm.proxy.management_endpoints.internal_user_endpoints import ( + user_update, +) from litellm.proxy.management_endpoints.key_management_endpoints import ( delete_verification_tokens, duration_in_seconds, @@ -295,7 +297,9 @@ from litellm.proxy.middleware.prometheus_auth_middleware import PrometheusAuthMi from litellm.proxy.openai_files_endpoints.files_endpoints import ( router as openai_files_router, ) -from litellm.proxy.openai_files_endpoints.files_endpoints import set_files_config +from litellm.proxy.openai_files_endpoints.files_endpoints import ( + set_files_config, +) from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( passthrough_endpoint_router, ) @@ -3807,13 +3811,13 @@ class ProxyStartupEvent: ######################################################## # CloudZero Background Job ######################################################## + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger from litellm.proxy.spend_tracking.cloudzero_endpoints import ( - init_cloudzero_background_job, - is_cloudzero_setup_in_db, + is_cloudzero_setup, ) - if await is_cloudzero_setup_in_db(): - await init_cloudzero_background_job() + if await is_cloudzero_setup(): + await CloudZeroLogger.init_cloudzero_background_job(scheduler=scheduler) ######################################################## # Prometheus Background Job diff --git a/litellm/proxy/spend_tracking/cloudzero_endpoints.py b/litellm/proxy/spend_tracking/cloudzero_endpoints.py index 08f801c6468..502537cb70f 100644 --- a/litellm/proxy/spend_tracking/cloudzero_endpoints.py +++ b/litellm/proxy/spend_tracking/cloudzero_endpoints.py @@ -82,14 +82,8 @@ async def _get_cloudzero_settings(): cloudzero_config = await prisma_client.db.litellm_config.find_first( where={"param_name": "cloudzero_settings"} ) - - if not cloudzero_config or not cloudzero_config.param_value: - raise HTTPException( - status_code=400, - detail={ - "error": "CloudZero settings not configured. Please run /cloudzero/init first." - }, - ) + if cloudzero_config is None: + return {} settings = dict(cloudzero_config.param_value) @@ -257,62 +251,6 @@ async def update_cloudzero_settings( _cloudzero_background_job_initialized = False -async def init_cloudzero_background_job(): - """ - Initialize CloudZero background job if not already initialized. - This should be called from the proxy server startup. - """ - global _cloudzero_background_job_initialized - - if _cloudzero_background_job_initialized: - verbose_proxy_logger.debug( - "CloudZero background job already initialized, skipping" - ) - return - - try: - from litellm.proxy.proxy_server import prisma_client - - if prisma_client is None: - verbose_proxy_logger.warning( - "Prisma client not available, skipping CloudZero background job initialization" - ) - return - - # Get CloudZero settings from database - cloudzero_config = await prisma_client.db.litellm_config.find_first( - where={"param_name": "cloudzero_settings"} - ) - - if not cloudzero_config or not cloudzero_config.param_value: - verbose_proxy_logger.debug( - "CloudZero settings not configured, skipping background job initialization" - ) - return - - settings = dict(cloudzero_config.param_value) - - # Initialize CloudZero logger with credentials - from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger - - logger = CloudZeroLogger( - api_key=settings["api_key"], - connection_id=settings["connection_id"], - timezone=settings["timezone"], - ) - - # Initialize the background job - #await logger.init_background_job() - - _cloudzero_background_job_initialized = True - verbose_proxy_logger.info("CloudZero background job initialized successfully") - - except Exception as e: - verbose_proxy_logger.error( - f"Error initializing CloudZero background job: {str(e)}" - ) - - async def is_cloudzero_setup_in_db() -> bool: """ Check if CloudZero is setup in the database. @@ -343,6 +281,47 @@ async def is_cloudzero_setup_in_db() -> bool: return False +def is_cloudzero_setup_in_config() -> bool: + """ + Check if CloudZero is setup in config.yaml or environment variables. + + CloudZero is considered setup in config if: + - "cloudzero" is in the callbacks list in config.yaml, OR + Returns: + bool: True if CloudZero is configured, False otherwise + """ + import litellm + return "cloudzero" in litellm.callbacks + + +async def is_cloudzero_setup() -> bool: + """ + Check if CloudZero is setup in either config.yaml/env vars OR database. + + CloudZero is considered setup if: + - CloudZero is configured in config.yaml callbacks, OR + - CloudZero environment variables are set, OR + - CloudZero settings exist in the database + + Returns: + bool: True if CloudZero is configured anywhere, False otherwise + """ + try: + # Check config.yaml/environment variables first + if is_cloudzero_setup_in_config(): + return True + + # Check database as fallback + if await is_cloudzero_setup_in_db(): + return True + + return False + + except Exception as e: + verbose_proxy_logger.error(f"Error checking CloudZero setup: {str(e)}") + return False + + @router.post( "/cloudzero/init", tags=["CloudZero"], @@ -383,9 +362,6 @@ async def init_cloudzero_settings( verbose_proxy_logger.info("CloudZero settings initialized successfully") - # Initialize background job after settings are saved - await init_cloudzero_background_job() - return CloudZeroInitResponse( message="CloudZero settings initialized successfully", status="success" ) @@ -412,15 +388,18 @@ async def cloudzero_dry_run_export( Perform a dry run export using the CloudZero logger. This endpoint uses the CloudZero logger to perform a dry run export, - which displays the data that would be exported without actually sending it to CloudZero. + which returns the data that would be exported without actually sending it to CloudZero. Parameters: - limit: Optional limit on number of records to process (default: 10000) + Returns: + - usage_data: Sample of the raw usage data (first 50 records) + - cbf_data: CloudZero CBF formatted data ready for export + - summary: Statistics including total cost, tokens, and record counts + Only admin users can perform CloudZero exports. """ - from datetime import datetime - # Validation if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN: raise HTTPException( @@ -430,19 +409,21 @@ async def cloudzero_dry_run_export( try: # Import and initialize CloudZero logger with credentials - from litellm.integrations.cloudzero.ll2cz.cloudzero import CloudZeroLogger + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger # Initialize logger with credentials directly logger = CloudZeroLogger() - await logger.dry_run_export_usage_data( - target_hour=datetime.utcnow(), limit=request.limit + dry_run_result = await logger.dry_run_export_usage_data( + limit=request.limit ) verbose_proxy_logger.info("CloudZero dry run export completed successfully") return CloudZeroExportResponse( - message="CloudZero dry run export completed successfully. Check logs for output.", + message="CloudZero dry run export completed successfully.", status="success", + dry_run_data=dry_run_result, + summary=dry_run_result.get("summary") if dry_run_result else None, ) except Exception as e: @@ -477,7 +458,6 @@ async def cloudzero_export( Only admin users can perform CloudZero exports. """ - from datetime import datetime if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN: raise HTTPException( @@ -490,24 +470,28 @@ async def cloudzero_export( settings = await _get_cloudzero_settings() # Import and initialize CloudZero logger with credentials - from litellm.integrations.cloudzero.ll2cz.cloudzero import CloudZeroLogger + from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger # Initialize logger with credentials directly logger = CloudZeroLogger( - api_key=settings["api_key"], - connection_id=settings["connection_id"], - timezone=settings["timezone"], + api_key=settings.get("api_key"), + connection_id=settings.get("connection_id"), + timezone=settings.get("timezone"), ) await logger.export_usage_data( - target_hour=datetime.utcnow(), limit=request.limit, operation=request.operation, + start_time_utc=request.start_time_utc, + end_time_utc=request.end_time_utc, ) verbose_proxy_logger.info("CloudZero export completed successfully") return CloudZeroExportResponse( - message="CloudZero export completed successfully", status="success" + message="CloudZero export completed successfully", + status="success", + dry_run_data=None, + summary=None ) except Exception as e: diff --git a/litellm/types/proxy/cloudzero_endpoints.py b/litellm/types/proxy/cloudzero_endpoints.py index f7f63233d4d..1d909bf7f8c 100644 --- a/litellm/types/proxy/cloudzero_endpoints.py +++ b/litellm/types/proxy/cloudzero_endpoints.py @@ -2,7 +2,8 @@ CloudZero endpoint types for LiteLLM Proxy """ -from typing import Optional +from datetime import datetime +from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field @@ -27,6 +28,8 @@ class CloudZeroExportRequest(BaseModel): limit: Optional[int] = Field(None, description="Optional limit on number of records to export") operation: str = Field(default="replace_hourly", description="CloudZero operation type (replace_hourly or sum)") + start_time_utc: Optional[datetime] = Field(None, description="Start time for data export in UTC") + end_time_utc: Optional[datetime] = Field(None, description="End time for data export in UTC") class CloudZeroExportResponse(BaseModel): @@ -35,6 +38,8 @@ class CloudZeroExportResponse(BaseModel): message: str status: str records_exported: Optional[int] = None + dry_run_data: Optional[Dict[str, Any]] = Field(None, description="Dry run data including usage data and CBF transformed data") + summary: Optional[Dict[str, Any]] = Field(None, description="Summary statistics for dry run") class CloudZeroSettingsView(BaseModel): diff --git a/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py b/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py new file mode 100644 index 00000000000..9a31a140aa8 --- /dev/null +++ b/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py @@ -0,0 +1,163 @@ +""" +Test the CloudZero dry run endpoint functionality +""" +import os +import sys +from unittest.mock import AsyncMock, MagicMock, patch + +import polars as pl +import pytest + +sys.path.insert(0, os.path.abspath("../../../..")) + +from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + + +class TestCloudZeroDryRunEndpoint: + """Test suite for CloudZero dry run endpoint functionality.""" + + @pytest.mark.asyncio + async def test_dry_run_export_usage_data_returns_data(self): + """ + Test that dry_run_export_usage_data returns expected data structure + instead of just logging to console. + """ + logger = CloudZeroLogger() + + # Mock database data + mock_usage_data = pl.DataFrame({ + 'date': ['2025-01-19', '2025-01-20'], + 'model': ['gpt-4', 'gpt-3.5-turbo'], + 'custom_llm_provider': ['openai', 'openai'], + 'team_id': ['team1', 'team2'], + 'team_alias': ['Team One', 'Team Two'], + 'api_key_alias': ['key1', 'key2'], + 'prompt_tokens': [100, 200], + 'completion_tokens': [50, 100], + 'spend': [0.01, 0.02], + 'successful_requests': [1, 2] + }) + + # Mock CBF transformed data + mock_cbf_data = pl.DataFrame({ + 'time/usage_start': ['2025-01-19T00:00:00Z', '2025-01-20T00:00:00Z'], + 'cost/cost': [0.01, 0.02], + 'usage/amount': [150, 300], + 'resource/service': ['openai', 'openai'], + 'resource/account': ['litellm', 'litellm'], + 'resource/region': ['us-east-1', 'us-east-1'], + 'resource/id': ['gpt-4', 'gpt-3.5-turbo'], + 'entity_type': ['user', 'user'], + 'entity_id': ['team1', 'team2'], + 'resource/tag:team_id': ['team1', 'team2'], + 'resource/tag:team_alias': ['Team One', 'Team Two'], + 'resource/tag:api_key_alias': ['key1', 'key2'] + }) + + with patch('litellm.integrations.cloudzero.cloudzero.LiteLLMDatabase') as mock_db_class, \ + patch('litellm.integrations.cloudzero.cloudzero.CBFTransformer') as mock_transformer_class: + + # Setup mocks + mock_db = AsyncMock() + mock_db.get_usage_data.return_value = mock_usage_data + mock_db_class.return_value = mock_db + + mock_transformer = MagicMock() + mock_transformer.transform.return_value = mock_cbf_data + mock_transformer_class.return_value = mock_transformer + + # Call the method + result = await logger.dry_run_export_usage_data(limit=1000) + + # Verify the result structure + assert isinstance(result, dict) + assert 'usage_data' in result + assert 'cbf_data' in result + assert 'summary' in result + + # Verify usage_data + assert isinstance(result['usage_data'], list) + assert len(result['usage_data']) == 2 + assert result['usage_data'][0]['model'] == 'gpt-4' + assert result['usage_data'][1]['model'] == 'gpt-3.5-turbo' + + # Verify cbf_data + assert isinstance(result['cbf_data'], list) + assert len(result['cbf_data']) == 2 + assert result['cbf_data'][0]['cost/cost'] == 0.01 + assert result['cbf_data'][1]['cost/cost'] == 0.02 + + # Verify summary + summary = result['summary'] + assert summary['total_records'] == 2 + assert summary['total_cost'] == 0.03 + assert summary['total_tokens'] == 450 # 150 + 300 + assert summary['unique_accounts'] == 1 + assert summary['unique_services'] == 1 + + @pytest.mark.asyncio + async def test_dry_run_export_usage_data_empty_data(self): + """ + Test that dry_run_export_usage_data handles empty data gracefully. + """ + logger = CloudZeroLogger() + + # Mock empty database data + mock_empty_data = pl.DataFrame() + + with patch('litellm.integrations.cloudzero.cloudzero.LiteLLMDatabase') as mock_db_class: + + # Setup mocks + mock_db = AsyncMock() + mock_db.get_usage_data.return_value = mock_empty_data + mock_db_class.return_value = mock_db + + # Call the method + result = await logger.dry_run_export_usage_data(limit=1000) + + # Verify the result structure for empty data + assert isinstance(result, dict) + assert result['usage_data'] == [] + assert result['cbf_data'] == [] + assert result['summary']['total_records'] == 0 + assert result['summary']['total_cost'] == 0 + assert result['summary']['total_tokens'] == 0 + + @pytest.mark.asyncio + async def test_dry_run_export_usage_data_cbf_transformation_failure(self): + """ + Test that dry_run_export_usage_data handles CBF transformation failure gracefully. + """ + logger = CloudZeroLogger() + + # Mock database data + mock_usage_data = pl.DataFrame({ + 'date': ['2025-01-19'], + 'model': ['gpt-4'], + 'spend': [0.01], + 'successful_requests': [1] + }) + + # Mock empty CBF data (transformation failed) + mock_empty_cbf_data = pl.DataFrame() + + with patch('litellm.integrations.cloudzero.cloudzero.LiteLLMDatabase') as mock_db_class, \ + patch('litellm.integrations.cloudzero.cloudzero.CBFTransformer') as mock_transformer_class: + + # Setup mocks + mock_db = AsyncMock() + mock_db.get_usage_data.return_value = mock_usage_data + mock_db_class.return_value = mock_db + + mock_transformer = MagicMock() + mock_transformer.transform.return_value = mock_empty_cbf_data + mock_transformer_class.return_value = mock_transformer + + # Call the method + result = await logger.dry_run_export_usage_data(limit=1000) + + # Verify the result handles CBF transformation failure + assert isinstance(result, dict) + assert len(result['usage_data']) == 1 # Usage data should still be present + assert result['cbf_data'] == [] # CBF data should be empty + assert result['summary']['total_cost'] == 0.01 # Should calculate from usage data From e73c558533f09977a9f93a0989a2b3f39d260134 Mon Sep 17 00:00:00 2001 From: Teddy Amkie Date: Fri, 5 Sep 2025 10:19:13 -0700 Subject: [PATCH 129/178] docs: clarify users can be on many teams --- docs/my-website/docs/proxy/user_management_heirarchy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/my-website/docs/proxy/user_management_heirarchy.md b/docs/my-website/docs/proxy/user_management_heirarchy.md index 3565c9d257d..cb5cc0dd7a2 100644 --- a/docs/my-website/docs/proxy/user_management_heirarchy.md +++ b/docs/my-website/docs/proxy/user_management_heirarchy.md @@ -9,5 +9,5 @@ LiteLLM supports a hierarchy of users, teams, organizations, and budgets. - Organizations can have multiple teams. [API Reference](https://litellm-api.up.railway.app/#/organization%20management) - Teams can have multiple users. [API Reference](https://litellm-api.up.railway.app/#/team%20management) -- Users can have multiple keys. [API Reference](https://litellm-api.up.railway.app/#/budget%20management) +- Users can have multiple keys, and be on multiple teams. [API Reference](https://litellm-api.up.railway.app/#/budget%20management) - Keys can belong to either a team or a user. [API Reference](https://litellm-api.up.railway.app/#/end-user%20management) From 3d7ee67aaaa52ffcc7d778eea99ad84595b17b19 Mon Sep 17 00:00:00 2001 From: Teddy Amkie Date: Fri, 5 Sep 2025 11:47:13 -0700 Subject: [PATCH 130/178] docs: clarify and improve logo customization section in admin UI docs (side-by-side images, explanation) --- docs/my-website/docs/proxy/admin_ui_sso.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/my-website/docs/proxy/admin_ui_sso.md b/docs/my-website/docs/proxy/admin_ui_sso.md index 86cb6b0bf8c..823301d4c38 100644 --- a/docs/my-website/docs/proxy/admin_ui_sso.md +++ b/docs/my-website/docs/proxy/admin_ui_sso.md @@ -235,6 +235,13 @@ Example setting a local image (on your container) ```shell UI_LOGO_PATH="ui_images/logo.jpg" ``` + +#### Or set your logo directly from Admin UI: +
+ + +
+ #### Set Custom Color Theme - Navigate to [/enterprise/enterprise_ui](https://github.com/BerriAI/litellm/blob/main/enterprise/enterprise_ui/_enterprise_colors.json) - Inside the `enterprise_ui` directory, rename `_enterprise_colors.json` to `enterprise_colors.json` From 72b8db64847e20fe1f9c5f44e085a4e6149dd6f8 Mon Sep 17 00:00:00 2001 From: Teddy Amkie Date: Fri, 5 Sep 2025 14:36:52 -0700 Subject: [PATCH 131/178] docs: update admin UI branding section and add admin settings UI images --- docs/my-website/img/admin_settings_ui_theme.png | Bin 0 -> 14871 bytes .../img/admin_settings_ui_theme_logo.png | Bin 0 -> 42588 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/my-website/img/admin_settings_ui_theme.png create mode 100644 docs/my-website/img/admin_settings_ui_theme_logo.png diff --git a/docs/my-website/img/admin_settings_ui_theme.png b/docs/my-website/img/admin_settings_ui_theme.png new file mode 100644 index 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zM~}};<|scNoO(@#<>T!Cl}^88Tyi74vNl~=IazDAS$?X@#dzrI5~z0`R5Hq1UWxEb zUN&V)N@q8J!T-dDy9e#q4c8r567aQ4Y^{A|Wn~(Rp?BGvGa~VO{Ih1Buu!%16cVag zXl07Fkx5W6lbkx3BVv z>$;Sb6whz#WQ0wg&8~fzW%e9(2h}0v1s~_17uP@O**R(2v?R83LT}zyiu&oUUAU^v z#^Fkth2x@qSqoMucm2CP$Hw$Znjgxga~xMzG#G2WU}^ldO1W)UUibfPQfosb)mKks z2t2=8?CSnDg}{2-O1W)FgU7GGhn@tP=d zyTRYEFG&7_rfX0Rv)xe(ui694MLak1{t$VrfvH#^X};8M_KgvXQLp_2PUROeeH08= z)Z$Jyt1vFxzZy7bVpCPqF+9^#?H`|Wqw z0Y!x#{956m{-VAfICa}qc@uS)V1*lS4g9>9SyHg8$02SH(Q#;q*vMZEYdAu@d7k5s Yy=#bw@aGOKIR+r`boFyt=akR{0GzM;D*ylh literal 0 HcmV?d00001 From 28545966f54dd5a0409d25f5dd2c7a9cb0803d28 Mon Sep 17 00:00:00 2001 From: Teddy Amkie Date: Fri, 5 Sep 2025 14:37:56 -0700 Subject: [PATCH 132/178] docs: update access control and load balancing documentation --- docs/my-website/docs/proxy/access_control.md | 2 +- docs/my-website/docs/proxy/load_balancing.md | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/my-website/docs/proxy/access_control.md b/docs/my-website/docs/proxy/access_control.md index 69b8a3ff6de..4ca3eb119d6 100644 --- a/docs/my-website/docs/proxy/access_control.md +++ b/docs/my-website/docs/proxy/access_control.md @@ -4,7 +4,7 @@ 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 +- `Internal Users` - users that can create keys, make LLM API calls, view usage on LiteLLM. Users can be on multiple teams. - `Roles` define the permissions of an `Internal User` - `Virtual Keys` - Keys are used for authentication to the LiteLLM API. Keys are tied to a `Internal User` and `Team` diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md index 2d8f73a13e4..8bfaf4add50 100644 --- a/docs/my-website/docs/proxy/load_balancing.md +++ b/docs/my-website/docs/proxy/load_balancing.md @@ -123,9 +123,8 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ] }' ``` - - + ### Test - Loadbalancing From e27c4c98c0f41027967c064f4a8c4c99524975a1 Mon Sep 17 00:00:00 2001 From: eycjur Date: Sat, 6 Sep 2025 21:11:53 +0900 Subject: [PATCH 133/178] Added conditional branch for gpt-oss --- .../bedrock/chat/converse_transformation.py | 26 +++++++++++++------ 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 273b12c9c39..9e885b3f839 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -164,7 +164,9 @@ class AmazonConverseConfig(BaseConfig): # only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html supported_params.append("tool_choice") - if ( + if "gpt-oss" in model: + supported_params.append("reasoning_effort") + elif ( "claude-3-7" in model or "claude-sonnet-4" in model or "claude-opus-4" in model @@ -319,7 +321,6 @@ class AmazonConverseConfig(BaseConfig): return computer_use_tools, regular_tools - def _create_json_tool_call_for_response_format( self, json_schema: Optional[dict] = None, @@ -462,13 +463,22 @@ class AmazonConverseConfig(BaseConfig): if param == "thinking": optional_params["thinking"] = value elif param == "reasoning_effort" and isinstance(value, str): - optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - value - ) + if "gpt-oss" in model: + # GPT-OSS models: keep reasoning_effort as-is + # It will be passed through to additionalModelRequestFields + optional_params["reasoning_effort"] = value + continue + else: + # Anthropic and other models: convert to thinking parameter + optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( + value + ) - self.update_optional_params_with_thinking_tokens( - non_default_params=non_default_params, optional_params=optional_params - ) + # Only update thinking tokens for non-GPT-OSS models + if not ("gpt-oss" in model): + self.update_optional_params_with_thinking_tokens( + non_default_params=non_default_params, optional_params=optional_params + ) return optional_params From 58cf72ef5e15e49669e10a43095d119a3bfeab3a Mon Sep 17 00:00:00 2001 From: eycjur Date: Sat, 6 Sep 2025 21:12:18 +0900 Subject: [PATCH 134/178] add test --- tests/llm_translation/test_bedrock_gpt_oss.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/tests/llm_translation/test_bedrock_gpt_oss.py b/tests/llm_translation/test_bedrock_gpt_oss.py index 61bce04e2d0..9487abfbc77 100644 --- a/tests/llm_translation/test_bedrock_gpt_oss.py +++ b/tests/llm_translation/test_bedrock_gpt_oss.py @@ -2,11 +2,13 @@ from base_llm_unit_tests import BaseLLMChatTest import pytest import sys import os +from unittest.mock import patch, MagicMock sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path import litellm +from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig class TestBedrockGPTOSS(BaseLLMChatTest): @@ -25,3 +27,27 @@ class TestBedrockGPTOSS(BaseLLMChatTest): Remove override once we have access to Bedrock prompt caching """ pass + + @pytest.mark.parametrize("model", [ + "bedrock/openai.gpt-oss-20b-1:0", + "bedrock/openai.gpt-oss-120b-1:0", + ]) + def test_reasoning_effort_transformation_gpt_oss(self, model): + """Test that reasoning_effort is handled correctly for GPT-OSS models.""" + config = AmazonConverseConfig() + + # Test GPT-OSS model - should keep reasoning_effort as-is + non_default_params = {"reasoning_effort": "low"} + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False, + ) + + # GPT-OSS should have reasoning_effort in result, not thinking + assert "reasoning_effort" in result + assert result["reasoning_effort"] == "low" + assert "thinking" not in result From b472bf6aef848b804d15db4d60e5fb96ee9df7aa Mon Sep 17 00:00:00 2001 From: eycjur Date: Sat, 6 Sep 2025 21:15:59 +0900 Subject: [PATCH 135/178] update docs --- docs/my-website/docs/providers/bedrock.md | 2 +- docs/my-website/docs/reasoning_content.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index 1356ec1744e..c191b742268 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -467,7 +467,7 @@ print(f"\nResponse: {resp}") ## Usage - 'thinking' / 'reasoning content' -This is currently only supported for Anthropic's Claude 3.7 Sonnet + Deepseek R1. +This is currently only supported for Anthropic's Claude 3.7 Sonnet + Deepseek R1 + GPT-OSS models. Works on v1.61.20+. diff --git a/docs/my-website/docs/reasoning_content.md b/docs/my-website/docs/reasoning_content.md index 5ddb5aefd47..12db17325d4 100644 --- a/docs/my-website/docs/reasoning_content.md +++ b/docs/my-website/docs/reasoning_content.md @@ -12,7 +12,7 @@ Requires LiteLLM v1.63.0+ Supported Providers: - Deepseek (`deepseek/`) - Anthropic API (`anthropic/`) -- Bedrock (Anthropic + Deepseek) (`bedrock/`) +- Bedrock (Anthropic + Deepseek + GPT-OSS) (`bedrock/`) - Vertex AI (Anthropic) (`vertexai/`) - OpenRouter (`openrouter/`) - XAI (`xai/`) From 6eb1b40336b86a99bbcec297b18e5ac2b44f0032 Mon Sep 17 00:00:00 2001 From: eycjur Date: Sat, 6 Sep 2025 21:30:08 +0900 Subject: [PATCH 136/178] refactor --- litellm/llms/bedrock/chat/converse_transformation.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 9e885b3f839..080ec05576e 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -466,13 +466,12 @@ class AmazonConverseConfig(BaseConfig): if "gpt-oss" in model: # GPT-OSS models: keep reasoning_effort as-is # It will be passed through to additionalModelRequestFields - optional_params["reasoning_effort"] = value continue - else: - # Anthropic and other models: convert to thinking parameter - optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - value - ) + + # Anthropic and other models: convert to thinking parameter + optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( + value + ) # Only update thinking tokens for non-GPT-OSS models if not ("gpt-oss" in model): From 67315d8727324466f84a7840b2fe594ed737d542 Mon Sep 17 00:00:00 2001 From: eycjur Date: Sat, 6 Sep 2025 21:43:30 +0900 Subject: [PATCH 137/178] fix ci --- litellm/llms/bedrock/chat/converse_transformation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 080ec05576e..88b65132138 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -474,7 +474,7 @@ class AmazonConverseConfig(BaseConfig): ) # Only update thinking tokens for non-GPT-OSS models - if not ("gpt-oss" in model): + if "gpt-oss" not in model: self.update_optional_params_with_thinking_tokens( non_default_params=non_default_params, optional_params=optional_params ) From 51de2ebb64a1dead05fa89965968a34579c41a97 Mon Sep 17 00:00:00 2001 From: katsuhiro muto <63308909+eycjur@users.noreply.github.com> Date: Sun, 7 Sep 2025 00:58:51 +0900 Subject: [PATCH 138/178] [Feat]Cancel upstream on client disconnect (#14295) * cancel upstream on client disconnect * add comments * add test * set timeout in constraints.py * Guard against missing 'type' key * update dependency to fix uvicorn bugs --- litellm/constants.py | 3 ++ litellm/proxy/common_request_processing.py | 41 +++++++++++++++- litellm/proxy/proxy_server.py | 27 ----------- poetry.lock | 12 ++--- pyproject.toml | 2 +- requirements.txt | 2 +- .../test_client_disconnection.py | 47 +++++++++++++++++++ 7 files changed, 97 insertions(+), 37 deletions(-) create mode 100644 tests/proxy_unit_tests/test_client_disconnection.py diff --git a/litellm/constants.py b/litellm/constants.py index 089e73fc3b4..bcf394c1832 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -893,6 +893,9 @@ MAX_SPENDLOG_ROWS_TO_QUERY = int( DEFAULT_SOFT_BUDGET = float( os.getenv("DEFAULT_SOFT_BUDGET", 50.0) ) # by default all litellm proxy keys have a soft budget of 50.0 +DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS = int( + os.getenv("DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS", 600) +) # 10 minutes timeout for client disconnect checking in proxy # makes it clear this is a rate limit error for a litellm virtual key RATE_LIMIT_ERROR_MESSAGE_FOR_VIRTUAL_KEY = "LiteLLM Virtual Key user_api_key_hash" diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index e900975f1cc..a3a9c2cffc0 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -1,6 +1,7 @@ import asyncio import json import logging +import time import traceback from datetime import datetime from typing import ( @@ -24,6 +25,7 @@ import litellm from litellm._logging import verbose_proxy_logger from litellm.constants import ( DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE, + DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS, STREAM_SSE_DATA_PREFIX, ) from litellm.litellm_core_utils.dd_tracing import tracer @@ -175,6 +177,29 @@ async def create_streaming_response( ) +async def _check_request_disconnection(request: Request, llm_api_call_task): + """ + Asynchronously checks if the request is disconnected at regular intervals. + If the request is disconnected + - cancel the litellm.router task + + Parameters: + - request: Request: The request object to check for disconnection. + Returns: + - None + """ + + # only run this function for configured timeout -> if these don't get cancelled -> we don't want the server to have many while loops + start_time = time.time() + while time.time() - start_time < DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS: + await asyncio.sleep(1) + message = await request.receive() + if message.get("type") == "http.disconnect": + # cancel the LLM API Call task if any passed - this is passed from individual providers + # Example OpenAI, Azure, VertexAI etc + llm_api_call_task.cancel() + return + class ProxyBaseLLMRequestProcessing: def __init__(self, data: dict): self.data = data @@ -425,12 +450,24 @@ class ProxyBaseLLMRequestProcessing: ) tasks.append(llm_call) - # wait for call to end llm_responses = asyncio.gather( *tasks ) # run the moderation check in parallel to the actual llm api call - responses = await llm_responses + # Execute the task to detect disconnection + disconnect_task = asyncio.create_task(_check_request_disconnection(request, llm_responses)) + + try: + # wait for call to end + # Note: In the case of streaming, processing does not wait here, so disconnection detection is performed in StreamingResponse. + responses = await llm_responses + disconnect_task.cancel() + + except asyncio.CancelledError: + raise HTTPException( + status_code=499, + detail="Client disconnected the request", + ) response = responses[1] diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 9f1566b2e00..e15d5401374 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -997,33 +997,6 @@ db_writer_client: Optional[AsyncHTTPHandler] = None ### logger ### -async def check_request_disconnection(request: Request, llm_api_call_task): - """ - Asynchronously checks if the request is disconnected at regular intervals. - If the request is disconnected - - cancel the litellm.router task - - raises an HTTPException with status code 499 and detail "Client disconnected the request". - - Parameters: - - request: Request: The request object to check for disconnection. - Returns: - - None - """ - - # only run this function for 10 mins -> if these don't get cancelled -> we don't want the server to have many while loops - start_time = time.time() - while time.time() - start_time < 600: - await asyncio.sleep(1) - if await request.is_disconnected(): - # cancel the LLM API Call task if any passed - this is passed from individual providers - # Example OpenAI, Azure, VertexAI etc - llm_api_call_task.cancel() - - raise HTTPException( - status_code=499, - detail="Client disconnected the request", - ) - def _resolve_typed_dict_type(typ): """Resolve the actual TypedDict class from a potentially wrapped type.""" diff --git a/poetry.lock b/poetry.lock index 29d1a877087..0ab437aec25 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -6122,15 +6122,15 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "uvicorn" -version = "0.29.0" +version = "0.32.1" description = "The lightning-fast ASGI server." optional = true python-versions = ">=3.8" groups = ["main"] markers = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\") or extra == \"proxy\"" files = [ - {file = "uvicorn-0.29.0-py3-none-any.whl", hash = "sha256:2c2aac7ff4f4365c206fd773a39bf4ebd1047c238f8b8268ad996829323473de"}, - {file = "uvicorn-0.29.0.tar.gz", hash = "sha256:6a69214c0b6a087462412670b3ef21224fa48cae0e452b5883e8e8bdfdd11dd0"}, + {file = "uvicorn-0.32.1-py3-none-any.whl", hash = "sha256:82ad92fd58da0d12af7482ecdb5f2470a04c9c9a53ced65b9bbb4a205377602e"}, + {file = "uvicorn-0.32.1.tar.gz", hash = "sha256:ee9519c246a72b1c084cea8d3b44ed6026e78a4a309cbedae9c37e4cb9fbb175"}, ] [package.dependencies] @@ -6139,7 +6139,7 @@ h11 = ">=0.8" typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""} [package.extras] -standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"] +standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"] [[package]] name = "uvloop" @@ -6576,4 +6576,4 @@ utils = ["numpydoc"] [metadata] lock-version = "2.1" python-versions = ">=3.8.1,<4.0, !=3.9.7" -content-hash = "f41e6359109c5c52dba2a28f301b04030d865265f408974082b390bf45568a01" +content-hash = "e48cc445bc012e020a9e311942e46833dda587b70a630a04bfc08b629746fe56" diff --git a/pyproject.toml b/pyproject.toml index 9f5d876cf2c..b1b11f5d21d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ pydantic = "^2.5.0" jsonschema = "^4.22.0" numpydoc = {version = "*", optional = true} # used in utils.py -uvicorn = {version = "^0.29.0", optional = true} +uvicorn = {version = "^0.32.0", optional = true} uvloop = {version = "^0.21.0", optional = true, markers="sys_platform != 'win32'"} gunicorn = {version = "^23.0.0", optional = true} fastapi = {version = "^0.115.5", optional = true} diff --git a/requirements.txt b/requirements.txt index 2d31819dc5b..9b858e08a03 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,7 +5,7 @@ openai==1.99.5 # openai req. fastapi==0.115.5 # server dep backoff==2.2.1 # server dep pyyaml==6.0.2 # server dep -uvicorn==0.29.0 # server dep +uvicorn==0.32.0 # server dep gunicorn==23.0.0 # server dep fastuuid==0.12.0 # for uuid4 uvloop==0.21.0 # uvicorn dep, gives us much better performance under load diff --git a/tests/proxy_unit_tests/test_client_disconnection.py b/tests/proxy_unit_tests/test_client_disconnection.py new file mode 100644 index 00000000000..d894d7ad015 --- /dev/null +++ b/tests/proxy_unit_tests/test_client_disconnection.py @@ -0,0 +1,47 @@ +""" +Test client disconnection detection functionality. +""" +import asyncio +import pytest +from unittest.mock import AsyncMock + +from litellm.proxy.common_request_processing import _check_request_disconnection + + +@pytest.mark.asyncio +async def test_check_request_disconnection_with_disconnect(): + """Test that _check_request_disconnection cancels task when client disconnects.""" + mock_request = AsyncMock() + mock_request.receive.side_effect = [ + {"type": "http.request"}, # First call + {"type": "http.disconnect"} # Second call - disconnect + ] + + mock_llm_task = AsyncMock() + + await _check_request_disconnection(mock_request, mock_llm_task) + + mock_llm_task.cancel.assert_called_once() + + +@pytest.mark.asyncio +async def test_check_request_disconnection_no_disconnect(): + """Test that _check_request_disconnection handles normal requests.""" + mock_request = AsyncMock() + mock_request.receive.return_value = {"type": "http.request"} + + mock_llm_task = AsyncMock() + + # This will timeout after 600 seconds, but we don't need to wait + # Just test that it doesn't crash immediately + task = asyncio.create_task(_check_request_disconnection(mock_request, mock_llm_task)) + await asyncio.sleep(0.1) # Let it run briefly + task.cancel() + + try: + await task + except asyncio.CancelledError: + pass + + # Task should not be cancelled during normal operation + mock_llm_task.cancel.assert_not_called() \ No newline at end of file From 3478c53c6045cfd31579dd97f3029755345f924e Mon Sep 17 00:00:00 2001 From: Duc Tran Date: Sat, 6 Sep 2025 23:06:04 +0700 Subject: [PATCH 139/178] Update constants.py (#14242) --- litellm/constants.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/constants.py b/litellm/constants.py index bcf394c1832..ce485fc7264 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -15,7 +15,7 @@ DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) ) DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( - os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 4) + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", os.cpu_count() or 4) ) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" From 0cb01d60278cb370e56ee619aeea9320cac96770 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 09:06:43 -0700 Subject: [PATCH 140/178] Fix: Include model name in Azure base_model error (#14294) Co-authored-by: Cursor Agent Co-authored-by: ishaan --- litellm/router.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/router.py b/litellm/router.py index 5eea60e4b3d..6255c2fdf92 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -5465,7 +5465,7 @@ class Router: ## SET MODEL TO 'model=' - if base_model is None + not azure if custom_llm_provider == "azure" and base_model is None: verbose_router_logger.error( - "Could not identify azure model. Set azure 'base_model' for accurate max tokens, cost tracking, etc.- https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models" + f"Could not identify azure model '{_model}'. Set azure 'base_model' for accurate max tokens, cost tracking, etc.- https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models" ) elif custom_llm_provider != "azure": model = _model From cb117647fce2551f5241fe27241e7b23b0b25a68 Mon Sep 17 00:00:00 2001 From: Mubashir Osmani Date: Sat, 6 Sep 2025 12:09:22 -0400 Subject: [PATCH 141/178] [docs]: added loom for claude code (#14223) * added loom for claude code * docs: add web search models * added new loom --- docs/my-website/docs/completion/web_search.md | 59 ++++++++++++++++++- .../docs/tutorials/claude_responses_api.md | 13 ++++ 2 files changed, 71 insertions(+), 1 deletion(-) diff --git a/docs/my-website/docs/completion/web_search.md b/docs/my-website/docs/completion/web_search.md index fe49be852a7..262e3fc4f9c 100644 --- a/docs/my-website/docs/completion/web_search.md +++ b/docs/my-website/docs/completion/web_search.md @@ -8,10 +8,25 @@ Use web search with litellm | Feature | Details | |---------|---------| | Supported Endpoints | - `/chat/completions`
- `/responses` | -| Supported Providers | `openai`, `xai`, `vertex_ai`, `gemini`, `perplexity` | +| Supported Providers | `openai`, `xai`, `vertex_ai`, `anthropic`, `gemini`, `perplexity` | | LiteLLM Cost Tracking | ✅ Supported | | LiteLLM Version | `v1.71.0+` | +## Which Search Engine is Used? + +Each provider uses their own search backend: + +| Provider | Search Engine | Notes | +|----------|---------------|-------| +| **OpenAI** (`gpt-4o-search-preview`) | OpenAI's internal search | Real-time web data | +| **xAI** (`grok-3`) | xAI's search + X/Twitter | Real-time social media data | +| **Google AI/Vertex** (`gemini-2.0-flash`) | **Google Search** | Uses actual Google search results | +| **Anthropic** (`claude-3-5-sonnet`) | Anthropic's web search | Real-time web data | +| **Perplexity** | Perplexity's search engine | AI-powered search and reasoning | + +:::info +**Anthropic Web Search Models**: Claude models that support web search: `claude-3-5-sonnet-latest`, `claude-3-5-sonnet-20241022`, `claude-3-5-haiku-latest`, `claude-3-5-haiku-20241022`, `claude-3-7-sonnet-20250219` +::: ## `/chat/completions` (litellm.completion) @@ -56,6 +71,12 @@ model_list: model: xai/grok-3 api_key: os.environ/XAI_API_KEY + # Anthropic + - model_name: claude-3-5-sonnet-latest + litellm_params: + model: anthropic/claude-3-5-sonnet-latest + api_key: os.environ/ANTHROPIC_API_KEY + # VertexAI - model_name: gemini-2-flash litellm_params: @@ -143,6 +164,31 @@ response = completion( ) ``` +**Anthropic (using web_search_options)** +```python showLineNumbers +from litellm import completion + +# Customize search context size for Anthropic +response = completion( + model="anthropic/claude-3-5-sonnet-latest", + messages=[ + { + "role": "user", + "content": "What was a positive news story from today?", + } + ], + web_search_options={ + "search_context_size": "medium", # Options: "low", "medium" (default), "high" + "user_location": { + "type": "approximate", + "approximate": { + "city": "San Francisco", + }, + } + } +) +``` + **VertexAI/Gemini (using web_search_options)** ```python showLineNumbers from litellm import completion @@ -375,6 +421,9 @@ assert litellm.supports_web_search(model="openai/gpt-4o-search-preview") == True # Check xAI models assert litellm.supports_web_search(model="xai/grok-3") == True +# Check Anthropic models +assert litellm.supports_web_search(model="anthropic/claude-3-5-sonnet-latest") == True + # Check VertexAI models assert litellm.supports_web_search(model="gemini-2.0-flash") == True @@ -405,6 +454,14 @@ model_list: model_info: supports_web_search: True + # Anthropic + - model_name: claude-3-5-sonnet-latest + litellm_params: + model: anthropic/claude-3-5-sonnet-latest + api_key: os.environ/ANTHROPIC_API_KEY + model_info: + supports_web_search: True + # VertexAI - model_name: gemini-2-flash litellm_params: diff --git a/docs/my-website/docs/tutorials/claude_responses_api.md b/docs/my-website/docs/tutorials/claude_responses_api.md index 09b352a7663..a333faee5d2 100644 --- a/docs/my-website/docs/tutorials/claude_responses_api.md +++ b/docs/my-website/docs/tutorials/claude_responses_api.md @@ -12,6 +12,13 @@ This tutorial is based on [Anthropic's official LiteLLM configuration documentat ::: +
+ +### LiteLLM x Claude Code + + + + ## Prerequisites - [Claude Code](https://docs.anthropic.com/en/docs/claude-code/overview) installed @@ -83,11 +90,17 @@ curl -X POST http://0.0.0.0:4000/v1/messages \ Configure Claude Code to use LiteLLM's unified endpoint: +Either a virtual key / master key can be used here + ```bash export ANTHROPIC_BASE_URL="http://0.0.0.0:4000" export ANTHROPIC_AUTH_TOKEN="$LITELLM_MASTER_KEY" ``` +:::tip +LITELLM_MASTER_KEY gives claude access to all proxy models, whereas a virtual key would be limited to the models set in UI +::: + #### Method 2: Provider-specific Pass-through Endpoint Alternatively, use the Anthropic pass-through endpoint: From 29e410b04c099c54c97cbc6b2160d9b0ee4a26d2 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 09:12:30 -0700 Subject: [PATCH 142/178] docs Video Walkthrough claude code --- docs/my-website/docs/tutorials/claude_responses_api.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/my-website/docs/tutorials/claude_responses_api.md b/docs/my-website/docs/tutorials/claude_responses_api.md index a333faee5d2..5000161a520 100644 --- a/docs/my-website/docs/tutorials/claude_responses_api.md +++ b/docs/my-website/docs/tutorials/claude_responses_api.md @@ -14,10 +14,9 @@ This tutorial is based on [Anthropic's official LiteLLM configuration documentat
-### LiteLLM x Claude Code - - +### Video Walkthrough + ## Prerequisites From c27f57fdabc8ac80a4e321042a2c31de3c9f2e3a Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 11:06:15 -0700 Subject: [PATCH 143/178] fix(conversere_transformation.py): fix test --- .../bedrock/chat/converse_transformation.py | 93 +++++++++++++------ 1 file changed, 64 insertions(+), 29 deletions(-) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 88b65132138..e06f92a8380 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -10,6 +10,7 @@ from typing import List, Literal, Optional, Tuple, Union, cast, overload import httpx import litellm +from litellm._logging import verbose_logger from litellm.constants import RESPONSE_FORMAT_TOOL_NAME from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.litellm_core_utils.litellm_logging import Logging @@ -49,14 +50,19 @@ from litellm.types.utils import ( ) from litellm.utils import add_dummy_tool, has_tool_call_blocks, supports_reasoning -from ..common_utils import BedrockError, BedrockModelInfo, get_bedrock_tool_name, get_anthropic_beta_from_headers +from ..common_utils import ( + BedrockError, + BedrockModelInfo, + get_anthropic_beta_from_headers, + get_bedrock_tool_name, +) # Computer use tool prefixes supported by Bedrock BEDROCK_COMPUTER_USE_TOOLS = [ "computer_use_preview", "computer_", "bash_", - "text_editor_" + "text_editor_", ] @@ -236,7 +242,7 @@ class AmazonConverseConfig(BaseConfig): """Check if computer use tools are being used in the request.""" if tools is None: return False - + for tool in tools: if "type" in tool: tool_type = tool["type"] @@ -250,17 +256,17 @@ class AmazonConverseConfig(BaseConfig): ) -> List[dict]: """Transform computer use tools to Bedrock format.""" transformed_tools: List[dict] = [] - + for tool in computer_use_tools: tool_type = tool.get("type", "") - + # Check if this is a computer use tool with the startswith method is_computer_use_tool = False for computer_use_prefix in BEDROCK_COMPUTER_USE_TOOLS: if tool_type.startswith(computer_use_prefix): is_computer_use_tool = True break - + transformed_tool: dict = {} if is_computer_use_tool: if tool_type.startswith("computer_") and "function" in tool: @@ -269,7 +275,7 @@ class AmazonConverseConfig(BaseConfig): transformed_tool = { "type": tool_type, "name": func.get("name", "computer"), - **func.get("parameters", {}) + **func.get("parameters", {}), } else: # Direct tools - just need to ensure name is present @@ -282,27 +288,29 @@ class AmazonConverseConfig(BaseConfig): else: # Pass through other tools as-is transformed_tool = dict(tool) - + transformed_tools.append(transformed_tool) - + return transformed_tools def _separate_computer_use_tools( self, tools: List[OpenAIChatCompletionToolParam], model: str - ) -> Tuple[List[OpenAIChatCompletionToolParam], List[OpenAIChatCompletionToolParam]]: + ) -> Tuple[ + List[OpenAIChatCompletionToolParam], List[OpenAIChatCompletionToolParam] + ]: """ Separate computer use tools from regular function tools. - + Args: tools: List of tools to separate model: The model name to check if it supports computer use - + Returns: Tuple of (computer_use_tools, regular_tools) """ computer_use_tools = [] regular_tools = [] - + for tool in tools: if "type" in tool: tool_type = tool["type"] @@ -317,9 +325,8 @@ class AmazonConverseConfig(BaseConfig): regular_tools.append(tool) else: regular_tools.append(tool) - - return computer_use_tools, regular_tools + return computer_use_tools, regular_tools def _create_json_tool_call_for_response_format( self, @@ -345,6 +352,8 @@ class AmazonConverseConfig(BaseConfig): "properties": {}, } else: + # Use the schema as-is for Bedrock + # Bedrock requires the tool schema to be of type "object" and doesn't need unwrapping _input_schema = json_schema tool_param_function_chunk = ChatCompletionToolParamFunctionChunk( @@ -426,9 +435,7 @@ class AmazonConverseConfig(BaseConfig): ): optional_params["tool_choice"] = ToolChoiceValuesBlock( - tool=SpecificToolChoiceBlock( - name=RESPONSE_FORMAT_TOOL_NAME - ) + tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) ) optional_params["json_mode"] = True if non_default_params.get("stream", False) is True: @@ -602,7 +609,6 @@ class AmazonConverseConfig(BaseConfig): return {} - def _transform_request_helper( self, model: str, @@ -658,36 +664,38 @@ class AmazonConverseConfig(BaseConfig): ) original_tools = inference_params.pop("tools", []) - + # Initialize bedrock_tools bedrock_tools: List[ToolBlock] = [] - + # Collect anthropic_beta values from user headers anthropic_beta_list = [] if headers: user_betas = get_anthropic_beta_from_headers(headers) anthropic_beta_list.extend(user_betas) - + # Only separate tools if computer use tools are actually present if original_tools and self.is_computer_use_tool_used(original_tools, model): # Separate computer use tools from regular function tools computer_use_tools, regular_tools = self._separate_computer_use_tools( original_tools, model ) - + # Process regular function tools using existing logic bedrock_tools = _bedrock_tools_pt(regular_tools) - + # Add computer use tools and anthropic_beta if needed (only when computer use tools are present) if computer_use_tools: anthropic_beta_list.append("computer-use-2024-10-22") # Transform computer use tools to proper Bedrock format - transformed_computer_tools = self._transform_computer_use_tools(computer_use_tools) + transformed_computer_tools = self._transform_computer_use_tools( + computer_use_tools + ) additional_request_params["tools"] = transformed_computer_tools else: # No computer use tools, process all tools as regular tools bedrock_tools = _bedrock_tools_pt(original_tools) - + # Set anthropic_beta in additional_request_params if we have any beta features if anthropic_beta_list: # Remove duplicates while preserving order @@ -698,7 +706,7 @@ class AmazonConverseConfig(BaseConfig): unique_betas.append(beta) seen.add(beta) additional_request_params["anthropic_beta"] = unique_betas - + bedrock_tool_config: Optional[ToolConfigBlock] = None if len(bedrock_tools) > 0: tool_choice_values: ToolChoiceValuesBlock = inference_params.pop( @@ -1124,9 +1132,37 @@ class AmazonConverseConfig(BaseConfig): self._transform_thinking_blocks(reasoningContentBlocks) ) chat_completion_message["content"] = content_str - if json_mode is True and tools is not None and len(tools) == 1 and tools[0]["function"]["name"] == RESPONSE_FORMAT_TOOL_NAME: + if ( + json_mode is True + and tools is not None + and len(tools) == 1 + and tools[0]["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME + ): + verbose_logger.debug( + "Processing JSON tool call response for response_format" + ) json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments") if json_mode_content_str is not None: + import json + + # Bedrock returns the response wrapped in a "properties" object + # We need to extract the actual content from this wrapper + try: + + response_data = json.loads(json_mode_content_str) + + # If Bedrock wrapped the response in "properties", extract the content + if ( + isinstance(response_data, dict) + and "properties" in response_data + and len(response_data) == 1 + ): + response_data = response_data["properties"] + json_mode_content_str = json.dumps(response_data) + except json.JSONDecodeError: + # If parsing fails, use the original response + pass + chat_completion_message["content"] = json_mode_content_str else: chat_completion_message["tool_calls"] = tools @@ -1186,7 +1222,6 @@ class AmazonConverseConfig(BaseConfig): if api_key: headers["Authorization"] = f"Bearer {api_key}" return headers - def should_fake_stream( self, From db14edfbdd38e4fb02396408d6efe36273d044ae Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 11:24:25 -0700 Subject: [PATCH 144/178] fix(main.py): remove image edit optional param from image generation --- litellm/images/main.py | 45 ++++++++++--------- ...odel_prices_and_context_window_backup.json | 8 ++-- 2 files changed, 28 insertions(+), 25 deletions(-) diff --git a/litellm/images/main.py b/litellm/images/main.py index 4993a48c724..2a8b62bce24 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -90,12 +90,12 @@ async def aimage_generation(*args, **kwargs) -> ImageResponse: response = init_response elif asyncio.iscoroutine(init_response): response = await init_response # type: ignore - + if response is None: raise ValueError( "Unable to get Image Response. Please pass a valid llm_provider." ) - + return response except Exception as e: custom_llm_provider = custom_llm_provider or "openai" @@ -108,6 +108,8 @@ async def aimage_generation(*args, **kwargs) -> ImageResponse: ) +# fmt: off + # Overload for when aimg_generation=True (returns Coroutine) @overload def image_generation( @@ -119,7 +121,6 @@ def image_generation( size: Optional[str] = None, style: Optional[str] = None, user: Optional[str] = None, - input_fidelity: Optional[str] = None, timeout=600, # default to 10 minutes api_key: Optional[str] = None, api_base: Optional[str] = None, @@ -128,10 +129,11 @@ def image_generation( *, aimg_generation: Literal[True], **kwargs, -) -> Coroutine[Any, Any, ImageResponse]: +) -> Coroutine[Any, Any, ImageResponse]: ... + # Overload for when aimg_generation=False or not specified (returns ImageResponse) @overload def image_generation( @@ -143,7 +145,6 @@ def image_generation( size: Optional[str] = None, style: Optional[str] = None, user: Optional[str] = None, - input_fidelity: Optional[str] = None, timeout=600, # default to 10 minutes api_key: Optional[str] = None, api_base: Optional[str] = None, @@ -152,9 +153,11 @@ def image_generation( *, aimg_generation: Literal[False] = False, **kwargs, -) -> ImageResponse: +) -> ImageResponse: ... +# fmt: on + @client def image_generation( # noqa: PLR0915 @@ -166,7 +169,6 @@ def image_generation( # noqa: PLR0915 size: Optional[str] = None, style: Optional[str] = None, user: Optional[str] = None, - input_fidelity: Optional[str] = None, timeout=600, # default to 10 minutes api_key: Optional[str] = None, api_base: Optional[str] = None, @@ -174,9 +176,9 @@ def image_generation( # noqa: PLR0915 custom_llm_provider=None, **kwargs, ) -> Union[ - ImageResponse, - Coroutine[Any, Any, ImageResponse], - ]: + ImageResponse, + Coroutine[Any, Any, ImageResponse], +]: """ Maps the https://api.openai.com/v1/images/generations endpoint. @@ -227,7 +229,6 @@ def image_generation( # noqa: PLR0915 "quality", "size", "style", - "input_fidelity", ] litellm_params = all_litellm_params default_params = openai_params + litellm_params @@ -255,7 +256,6 @@ def image_generation( # noqa: PLR0915 size=size, style=style, user=user, - input_fidelity=input_fidelity, custom_llm_provider=custom_llm_provider, provider_config=image_generation_config, **non_default_params, @@ -344,8 +344,10 @@ def image_generation( # noqa: PLR0915 litellm.LlmProviders.GEMINI, ): if image_generation_config is None: - raise ValueError(f"image generation config is not supported for {custom_llm_provider}") - + raise ValueError( + f"image generation config is not supported for {custom_llm_provider}" + ) + return llm_http_handler.image_generation_handler( api_key=api_key, model=model, @@ -360,6 +362,7 @@ def image_generation( # noqa: PLR0915 ) elif custom_llm_provider == "azure_ai": from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo + api_base = AzureFoundryModelInfo.get_api_base(api_base) api_key = AzureFoundryModelInfo.get_api_key(api_key) if extra_headers is not None: @@ -420,7 +423,7 @@ def image_generation( # noqa: PLR0915 aimg_generation=aimg_generation, client=client, api_base=api_base, - api_key=api_key + api_key=api_key, ) elif custom_llm_provider == "vertex_ai": vertex_ai_project = ( @@ -705,7 +708,7 @@ def image_edit( litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) _is_async = kwargs.pop("async_call", False) is True - #add images / or return a single image + # add images / or return a single image images = image if isinstance(image, list) else [image] # get llm provider logic @@ -716,11 +719,11 @@ def image_edit( ) # get provider config - image_edit_provider_config: Optional[ - BaseImageEditConfig - ] = ProviderConfigManager.get_provider_image_edit_config( - model=model, - provider=litellm.LlmProviders(custom_llm_provider), + image_edit_provider_config: Optional[BaseImageEditConfig] = ( + ProviderConfigManager.get_provider_image_edit_config( + model=model, + provider=litellm.LlmProviders(custom_llm_provider), + ) ) if image_edit_provider_config is None: diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index f3c4abf5f00..46eb48d2d42 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -7992,8 +7992,8 @@ "max_pdf_size_mb": 30, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, - "output_cost_per_token": 2.5e-06, - "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 3e-05, + "output_cost_per_reasoning_token": 3e-05, "output_cost_per_image": 0.039, "litellm_provider": "gemini", "mode": "chat", @@ -8356,8 +8356,8 @@ "max_pdf_size_mb": 30, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, - "output_cost_per_token": 2.5e-06, - "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 3e-05, + "output_cost_per_reasoning_token": 3e-05, "output_cost_per_image": 0.039, "litellm_provider": "vertex_ai-language-models", "mode": "chat", From 576947b4fe82c8ad9107c6cb2181ebe7e4874cd2 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 11:36:55 -0700 Subject: [PATCH 145/178] fix(duration_parser.py): handle 0 value --- litellm/litellm_core_utils/duration_parser.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/litellm/litellm_core_utils/duration_parser.py b/litellm/litellm_core_utils/duration_parser.py index 08f1d4c82d0..08e5323c30c 100644 --- a/litellm/litellm_core_utils/duration_parser.py +++ b/litellm/litellm_core_utils/duration_parser.py @@ -1,7 +1,7 @@ """ Helper utilities for parsing durations - 1s, 1d, 10d, 30d, 1mo, 2mo -duration_in_seconds is used in diff parts of the code base, example +duration_in_seconds is used in diff parts of the code base, example - Router - Provider budget routing - Proxy - Key, Team Generation """ @@ -192,6 +192,10 @@ def _handle_day_reset( current_time: datetime, base_midnight: datetime, value: int, timezone: timezone ) -> datetime: """Handle day-based reset times.""" + # Handle zero value - immediate expiration + if value == 0: + return current_time + if value == 1: # Daily reset at midnight return base_midnight + timedelta(days=1) elif value == 7: # Weekly reset on Monday at midnight @@ -234,6 +238,10 @@ def _handle_hour_reset( current_time: datetime, base_midnight: datetime, value: int ) -> datetime: """Handle hour-based reset times.""" + # Handle zero value - immediate expiration + if value == 0: + return current_time + current_hour = current_time.hour current_minute = current_time.minute current_second = current_time.second @@ -266,6 +274,10 @@ def _handle_minute_reset( current_time: datetime, base_midnight: datetime, value: int ) -> datetime: """Handle minute-based reset times.""" + # Handle zero value - immediate expiration + if value == 0: + return current_time + current_hour = current_time.hour current_minute = current_time.minute current_second = current_time.second @@ -306,6 +318,10 @@ def _handle_second_reset( current_time: datetime, base_midnight: datetime, value: int ) -> datetime: """Handle second-based reset times.""" + # Handle zero value - immediate expiration + if value == 0: + return current_time + current_hour = current_time.hour current_minute = current_time.minute current_second = current_time.second From e9415e3708754db14422c395b9e560456c580dd4 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 11:59:59 -0700 Subject: [PATCH 146/178] fix: fix linting errors --- litellm/proxy/auth/handle_jwt.py | 1 - 1 file changed, 1 deletion(-) diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 6a7aa794e5b..5f78efbdf40 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -7,7 +7,6 @@ JWT token must have 'litellm_proxy_admin' in scope. """ import fnmatch -import json import os from typing import Any, List, Literal, Optional, Set, Tuple, cast From 9c6dc3c47a28756d1c0d613c504b89800566cfd1 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 12:04:20 -0700 Subject: [PATCH 147/178] fix(converse_transformation.py): correctly transform reasoning_effort for gpt-oss models --- litellm/llms/bedrock/chat/converse_transformation.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index e06f92a8380..06b2098fe38 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -473,12 +473,12 @@ class AmazonConverseConfig(BaseConfig): if "gpt-oss" in model: # GPT-OSS models: keep reasoning_effort as-is # It will be passed through to additionalModelRequestFields - continue - - # Anthropic and other models: convert to thinking parameter - optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - value - ) + optional_params["reasoning_effort"] = value + else: + # Anthropic and other models: convert to thinking parameter + optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( + value + ) # Only update thinking tokens for non-GPT-OSS models if "gpt-oss" not in model: From 3dd62dae294e0f47b7a603bbc4f28820a3c8dd1d Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 12:06:36 -0700 Subject: [PATCH 148/178] fix(__init__.py): add volcengine --- litellm/__init__.py | 27 ++++++++++++++++++++++----- 1 file changed, 22 insertions(+), 5 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index 0ebea89941a..b89dde0add9 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -148,7 +148,9 @@ _custom_logger_compatible_callbacks_literal = Literal[ "dotprompt", "cloudzero", ] -configured_cold_storage_logger: Optional[_custom_logger_compatible_callbacks_literal] = None +configured_cold_storage_logger: Optional[ + _custom_logger_compatible_callbacks_literal +] = None logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None _known_custom_logger_compatible_callbacks: List = list( get_args(_custom_logger_compatible_callbacks_literal) @@ -436,7 +438,8 @@ config_path = None vertex_ai_safety_settings: Optional[dict] = None ####### COMPLETION MODELS ################### -from typing import Set +from typing import Set + open_ai_chat_completion_models: Set = set() open_ai_text_completion_models: Set = set() cohere_models: Set = set() @@ -514,6 +517,7 @@ recraft_models: Set = set() cometapi_models: Set = set() oci_models: Set = set() vercel_ai_gateway_models: Set = set() +volcengine_models: Set = set() def is_bedrock_pricing_only_model(key: str) -> bool: @@ -724,6 +728,8 @@ def add_known_models(): cometapi_models.add(key) elif value.get("litellm_provider") == "oci": oci_models.add(key) + elif value.get("litellm_provider") == "volcengine": + volcengine_models.add(key) add_known_models() @@ -816,6 +822,7 @@ model_list = list( | cometapi_models | oci_models | vercel_ai_gateway_models + | volcengine_models ) model_list_set = set(model_list) @@ -836,7 +843,12 @@ models_by_provider: dict = { "openrouter": openrouter_models, "vercel_ai_gateway": vercel_ai_gateway_models, "datarobot": datarobot_models, - "vertex_ai": vertex_chat_models | vertex_text_models | vertex_anthropic_models | vertex_vision_models | vertex_language_models | vertex_deepseek_models, + "vertex_ai": vertex_chat_models + | vertex_text_models + | vertex_anthropic_models + | vertex_vision_models + | vertex_language_models + | vertex_deepseek_models, "ai21": ai21_models, "bedrock": bedrock_models | bedrock_converse_models, "petals": petals_models, @@ -890,6 +902,7 @@ models_by_provider: dict = { "recraft": recraft_models, "cometapi": cometapi_models, "oci": oci_models, + "volcengine": volcengine_models, } # mapping for those models which have larger equivalents @@ -1133,7 +1146,9 @@ from .llms.topaz.image_variations.transformation import TopazImageVariationConfi from litellm.llms.openai.completion.transformation import OpenAITextCompletionConfig from .llms.groq.chat.transformation import GroqChatConfig from .llms.voyage.embedding.transformation import VoyageEmbeddingConfig -from .llms.voyage.embedding.transformation_contextual import VoyageContextualEmbeddingConfig +from .llms.voyage.embedding.transformation_contextual import ( + VoyageContextualEmbeddingConfig, +) from .llms.infinity.embedding.transformation import InfinityEmbeddingConfig from .llms.azure_ai.chat.transformation import AzureAIStudioConfig from .llms.mistral.chat.transformation import MistralConfig @@ -1197,7 +1212,9 @@ from .llms.jina_ai.embedding.transformation import JinaAIEmbeddingConfig from .llms.xai.chat.transformation import XAIChatConfig from .llms.xai.common_utils import XAIModelInfo from .llms.aiml.chat.transformation import AIMLChatConfig -from .llms.volcengine.chat.transformation import VolcEngineChatConfig as VolcEngineConfig +from .llms.volcengine.chat.transformation import ( + VolcEngineChatConfig as VolcEngineConfig, +) from .llms.codestral.completion.transformation import CodestralTextCompletionConfig from .llms.azure.azure import ( AzureOpenAIError, From 7a083750ceb4fd134eb1c6fbacc1444c7a4de43b Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 12:14:27 -0700 Subject: [PATCH 149/178] refactor: remove 'input_fidelity' from image gen -> it's a image_edit param --- docs/my-website/docs/image_generation.md | 2 - .../my-website/release_notes/v1.74.7/index.md | 1 - .../image_generation/gpt_transformation.py | 1 - litellm/types/llms/openai.py | 1 - litellm/utils.py | 44 +++---- .../image_gen_tests/test_image_generation.py | 119 ++++-------------- 6 files changed, 49 insertions(+), 119 deletions(-) diff --git a/docs/my-website/docs/image_generation.md b/docs/my-website/docs/image_generation.md index 60a6356f012..7e7ff9922d6 100644 --- a/docs/my-website/docs/image_generation.md +++ b/docs/my-website/docs/image_generation.md @@ -124,8 +124,6 @@ Any non-openai params, will be treated as provider-specific params, and sent in - `size`: *string (optional)* The size of the generated images. Must be one of `1024x1024`, `1536x1024` (landscape), `1024x1536` (portrait), or `auto` (default value) for `gpt-image-1`, one of `256x256`, `512x512`, or `1024x1024` for `dall-e-2`, and one of `1024x1024`, `1792x1024`, or `1024x1792` for `dall-e-3`. -- `input_fidelity`: *string (optional)* Controls how closely the model follows the input prompt. Supported for `gpt-image-1` model. Higher fidelity may improve prompt adherence but could affect generation speed. - - `timeout`: *integer* - The maximum time, in seconds, to wait for the API to respond. Defaults to 600 seconds (10 minutes). - `user`: *string (optional)* A unique identifier representing your end-user, diff --git a/docs/my-website/release_notes/v1.74.7/index.md b/docs/my-website/release_notes/v1.74.7/index.md index e3a2ac0aa00..7d7a568e13f 100644 --- a/docs/my-website/release_notes/v1.74.7/index.md +++ b/docs/my-website/release_notes/v1.74.7/index.md @@ -148,7 +148,6 @@ Starting with this release, you can run health endpoints on an isolated process - New provider integration for v0.dev - [PR #12751](https://github.com/BerriAI/litellm/pull/12751), [Get Started](../../docs/providers/v0) - **[OpenAI](../../docs/providers/openai)** - Use OpenAI DeepResearch models with `litellm.completion` (`/chat/completions`) - [PR #12627](https://github.com/BerriAI/litellm/pull/12627) **DOC NEEDED** - - Add `input_fidelity` parameter for OpenAI image generation - [PR #12662](https://github.com/BerriAI/litellm/pull/12662), [Get Started](../../docs/image_generation) - **[Azure OpenAI](../../docs/providers/azure_openai)** - Use Azure OpenAI DeepResearch models with `litellm.completion` (`/chat/completions`) - [PR #12627](https://github.com/BerriAI/litellm/pull/12627) **DOC NEEDED** - Added `response_format` support for openai gpt-4.1 models - [PR #12745](https://github.com/BerriAI/litellm/pull/12745) diff --git a/litellm/llms/openai/image_generation/gpt_transformation.py b/litellm/llms/openai/image_generation/gpt_transformation.py index 150cffba21c..1cee13784e7 100644 --- a/litellm/llms/openai/image_generation/gpt_transformation.py +++ b/litellm/llms/openai/image_generation/gpt_transformation.py @@ -16,7 +16,6 @@ class GPTImageGenerationConfig(BaseImageGenerationConfig): ) -> List[OpenAIImageGenerationOptionalParams]: return [ "background", - "input_fidelity", "moderation", "n", "output_compression", diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index 5b58c232964..a72651f35d5 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -914,7 +914,6 @@ OpenAIImageVariationOptionalParams = Literal["n", "size", "response_format", "us OpenAIImageGenerationOptionalParams = Literal[ "background", - "input_fidelity", "moderation", "n", "output_compression", diff --git a/litellm/utils.py b/litellm/utils.py index 14d00cc528b..60716cb5c11 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -542,9 +542,9 @@ def function_setup( # noqa: PLR0915 function_id: Optional[str] = kwargs["id"] if "id" in kwargs else None ## DYNAMIC CALLBACKS ## - dynamic_callbacks: Optional[ - List[Union[str, Callable, CustomLogger]] - ] = kwargs.pop("callbacks", None) + dynamic_callbacks: Optional[List[Union[str, Callable, CustomLogger]]] = ( + kwargs.pop("callbacks", None) + ) all_callbacks = get_dynamic_callbacks(dynamic_callbacks=dynamic_callbacks) if len(all_callbacks) > 0: @@ -1298,9 +1298,9 @@ def client(original_function): # noqa: PLR0915 exception=e, retry_policy=kwargs.get("retry_policy"), ) - kwargs[ - "retry_policy" - ] = reset_retry_policy() # prevent infinite loops + kwargs["retry_policy"] = ( + reset_retry_policy() + ) # prevent infinite loops litellm.num_retries = ( None # set retries to None to prevent infinite loops ) @@ -2508,7 +2508,6 @@ def get_optional_params_image_gen( size: Optional[str] = None, style: Optional[str] = None, user: Optional[str] = None, - input_fidelity: Optional[str] = None, custom_llm_provider: Optional[str] = None, additional_drop_params: Optional[list] = None, provider_config: Optional[BaseImageGenerationConfig] = None, @@ -2545,7 +2544,6 @@ def get_optional_params_image_gen( "size": None, "style": None, "user": None, - "input_fidelity": None, } non_default_params = _get_non_default_params( @@ -3119,10 +3117,10 @@ def pre_process_non_default_params( if "response_format" in non_default_params: if provider_config is not None: - non_default_params[ - "response_format" - ] = provider_config.get_json_schema_from_pydantic_object( - response_format=non_default_params["response_format"] + non_default_params["response_format"] = ( + provider_config.get_json_schema_from_pydantic_object( + response_format=non_default_params["response_format"] + ) ) else: non_default_params["response_format"] = type_to_response_format_param( @@ -3250,16 +3248,16 @@ def pre_process_optional_params( True # so that main.py adds the function call to the prompt ) if "tools" in non_default_params: - optional_params[ - "functions_unsupported_model" - ] = non_default_params.pop("tools") + optional_params["functions_unsupported_model"] = ( + non_default_params.pop("tools") + ) non_default_params.pop( "tool_choice", None ) # causes ollama requests to hang elif "functions" in non_default_params: - optional_params[ - "functions_unsupported_model" - ] = non_default_params.pop("functions") + optional_params["functions_unsupported_model"] = ( + non_default_params.pop("functions") + ) elif ( litellm.add_function_to_prompt ): # if user opts to add it to prompt instead @@ -4364,9 +4362,9 @@ def _count_characters(text: str) -> int: def get_response_string(response_obj: Union[ModelResponse, ModelResponseStream]) -> str: - _choices: Union[ - List[Union[Choices, StreamingChoices]], List[StreamingChoices] - ] = response_obj.choices + _choices: Union[List[Union[Choices, StreamingChoices]], List[StreamingChoices]] = ( + response_obj.choices + ) response_str = "" for choice in _choices: @@ -6891,6 +6889,7 @@ class ProviderConfigManager: from litellm.llms.vertex_ai.vertex_ai_partner_models.gpt_oss.transformation import ( VertexAIGPTOSSTransformation, ) + return VertexAIGPTOSSTransformation() elif model in litellm.vertex_mistral_models: if "codestral" in model: @@ -7310,7 +7309,7 @@ class ProviderConfigManager: return BedrockFilesConfig() return None - + @staticmethod def get_provider_batches_config( model: str, @@ -7318,6 +7317,7 @@ class ProviderConfigManager: ) -> Optional[BaseBatchesConfig]: if LlmProviders.BEDROCK == provider: from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + return BedrockBatchesConfig() return None diff --git a/tests/image_gen_tests/test_image_generation.py b/tests/image_gen_tests/test_image_generation.py index c761da6d16c..7a803daf5d1 100644 --- a/tests/image_gen_tests/test_image_generation.py +++ b/tests/image_gen_tests/test_image_generation.py @@ -147,13 +147,13 @@ class TestBedrockNovaCanvasColorGuidedGeneration(BaseImageGenTest): def get_base_image_generation_call_args(self) -> dict: litellm.in_memory_llm_clients_cache = InMemoryCache() return { - "model": "bedrock/amazon.nova-canvas-v1:0", - "n": 1, - "size": "320x320", - "imageGenerationConfig": {"cfgScale":6.5,"seed":12}, - "taskType": "COLOR_GUIDED_GENERATION", - "colorGuidedGenerationParams":{"colors":["#FFFFFF"]}, - "aws_region_name": "us-east-1", + "model": "bedrock/amazon.nova-canvas-v1:0", + "n": 1, + "size": "320x320", + "imageGenerationConfig": {"cfgScale": 6.5, "seed": 12}, + "taskType": "COLOR_GUIDED_GENERATION", + "colorGuidedGenerationParams": {"colors": ["#FFFFFF"]}, + "aws_region_name": "us-east-1", } @@ -161,22 +161,27 @@ class TestOpenAIDalle3(BaseImageGenTest): def get_base_image_generation_call_args(self) -> dict: return {"model": "dall-e-3"} + class TestOpenAIGPTImage1(BaseImageGenTest): def get_base_image_generation_call_args(self) -> dict: return {"model": "gpt-image-1"} + class TestRecraftImageGeneration(BaseImageGenTest): def get_base_image_generation_call_args(self) -> dict: return {"model": "recraft/recraftv3"} + class TestAimlImageGeneration(BaseImageGenTest): def get_base_image_generation_call_args(self) -> dict: return {"model": "aiml/flux-pro/v1.1"} + class TestGoogleImageGen(BaseImageGenTest): def get_base_image_generation_call_args(self) -> dict: return {"model": "gemini/imagen-4.0-generate-001"} + class TestAzureOpenAIDalle3(BaseImageGenTest): def get_base_image_generation_call_args(self) -> dict: litellm.set_verbose = True @@ -191,7 +196,6 @@ class TestAzureOpenAIDalle3(BaseImageGenTest): } }, } - class TestAzureFoundryFlux(BaseImageGenTest): @@ -268,117 +272,50 @@ async def test_aimage_generation_bedrock_with_optional_params(): pytest.fail(f"An exception occurred - {str(e)}") -@pytest.mark.asyncio -async def test_gpt_image_1_with_input_fidelity(): - """Test gpt-image-1 with input_fidelity parameter (mocked)""" - from unittest.mock import AsyncMock, patch - - # Mock OpenAI response - mock_openai_response = { - "created": 1703658209, - "data": [ - { - "url": "https://example.com/generated_image.png" - } - ] - } - - # Create a proper mock response object - class MockResponse: - def model_dump(self): - return mock_openai_response - - # Create a mock client with the images.generate method - mock_client = AsyncMock() - mock_client.images.generate = AsyncMock(return_value=MockResponse()) - - # Capture the actual arguments sent to OpenAI client - captured_args = None - captured_kwargs = None - - async def capture_generate_call(*args, **kwargs): - nonlocal captured_args, captured_kwargs - captured_args = args - captured_kwargs = kwargs - return MockResponse() - - mock_client.images.generate.side_effect = capture_generate_call - - # Mock the _get_openai_client method to return our mock client - with patch.object(litellm.main.openai_chat_completions, '_get_openai_client', return_value=mock_client): - response = await litellm.aimage_generation( - prompt="A cute baby sea otter", - model="gpt-image-1", - input_fidelity="high", - quality="medium", - size="1024x1024", - ) - - # Validate the response - assert response is not None - assert response.created == 1703658209 - assert response.data is not None - assert len(response.data) == 1 - assert response.data[0].url == "https://example.com/generated_image.png" - - # Validate that the OpenAI client was called with correct parameters - mock_client.images.generate.assert_called_once() - assert captured_kwargs is not None - assert captured_kwargs["model"] == "gpt-image-1" - assert captured_kwargs["prompt"] == "A cute baby sea otter" - assert captured_kwargs["input_fidelity"] == "high" - assert captured_kwargs["quality"] == "medium" - assert captured_kwargs["size"] == "1024x1024" - - @pytest.mark.asyncio async def test_aiml_image_generation_with_dynamic_api_key(): """ Test that when api_key is passed as a dynamic parameter to aimage_generation, it gets properly used for AIML provider authentication instead of falling back to environment variables. - + This test validates the fix for ensuring dynamic API keys are respected when making image generation requests to the AIML provider. """ from unittest.mock import AsyncMock, patch, MagicMock import httpx - + # Mock AIML response mock_aiml_response = { "created": 1703658209, - "data": [ - { - "url": "https://example.com/generated_image.png" - } - ] + "data": [{"url": "https://example.com/generated_image.png"}], } - + # Track captured arguments captured_headers = None captured_url = None captured_json_data = None - + def capture_post_call(*args, **kwargs): nonlocal captured_headers, captured_url, captured_json_data - captured_url = kwargs.get('url') or (args[0] if args else None) - captured_headers = kwargs.get('headers', {}) - captured_json_data = kwargs.get('json', {}) - + captured_url = kwargs.get("url") or (args[0] if args else None) + captured_headers = kwargs.get("headers", {}) + captured_json_data = kwargs.get("json", {}) + # Create a mock response mock_response = MagicMock() mock_response.status_code = 200 mock_response.json.return_value = mock_aiml_response mock_response.text = json.dumps(mock_aiml_response) return mock_response - + # Mock the HTTP client that actually makes the request (sync version for image generation) - with patch('litellm.llms.custom_httpx.http_handler.HTTPHandler.post') as mock_post: + with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post: mock_post.side_effect = capture_post_call - + # Test with dynamic api_key test_api_key = "test-dynamic-api-key-12345" - + response = await litellm.aimage_generation( prompt="A cute baby sea otter", model="aiml/flux-pro/v1.1", @@ -387,7 +324,7 @@ async def test_aiml_image_generation_with_dynamic_api_key(): # Validate the response (mocked response processing might not populate data correctly) assert response is not None - + # The most important validations: API key and endpoint usage # These prove that the dynamic API key was properly used assert captured_headers is not None @@ -398,10 +335,8 @@ async def test_aiml_image_generation_with_dynamic_api_key(): assert captured_url is not None assert "api.aimlapi.com" in captured_url assert "/v1/images/generations" in captured_url - + # Validate the request data assert captured_json_data is not None assert captured_json_data["prompt"] == "A cute baby sea otter" assert captured_json_data["model"] == "flux-pro/v1.1" - - From 085194b11d413889b80df9f775cd3576b4a9bdf2 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 12:29:36 -0700 Subject: [PATCH 150/178] fix test_init_custom_logger_compatible_class_as_callback --- litellm/integrations/cloudzero/cloudzero.py | 9 +++++---- litellm/litellm_core_utils/custom_logger_registry.py | 2 ++ 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/litellm/integrations/cloudzero/cloudzero.py b/litellm/integrations/cloudzero/cloudzero.py index 727dabc0945..ab4ec234bf0 100644 --- a/litellm/integrations/cloudzero/cloudzero.py +++ b/litellm/integrations/cloudzero/cloudzero.py @@ -6,10 +6,6 @@ import litellm from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger -from .cz_stream_api import CloudZeroStreamer -from .database import LiteLLMDatabase -from .transform import CBFTransformer - if TYPE_CHECKING: from apscheduler.schedulers.asyncio import AsyncIOScheduler else: @@ -105,6 +101,9 @@ class CloudZeroLogger(CustomLogger): limit: Optional limit on number of records to export operation: CloudZero operation type ("replace_hourly" or "sum") """ + from litellm.integrations.cloudzero.cz_stream_api import CloudZeroStreamer + from litellm.integrations.cloudzero.database import LiteLLMDatabase + from litellm.integrations.cloudzero.transform import CBFTransformer try: verbose_logger.debug("CloudZero Logger: Starting usage data export") @@ -163,6 +162,8 @@ class CloudZeroLogger(CustomLogger): Returns: dict: Contains usage_data, cbf_data, and summary statistics """ + from litellm.integrations.cloudzero.database import LiteLLMDatabase + from litellm.integrations.cloudzero.transform import CBFTransformer try: verbose_logger.debug("CloudZero Logger: Starting dry run export") diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py index fd82ecdf2b2..af51fe9ab79 100644 --- a/litellm/litellm_core_utils/custom_logger_registry.py +++ b/litellm/litellm_core_utils/custom_logger_registry.py @@ -38,6 +38,7 @@ try: from litellm_enterprise.integrations.prometheus import PrometheusLogger except Exception: PrometheusLogger = None +from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger from litellm.integrations.dotprompt import DotpromptManager from litellm.integrations.s3_v2 import S3Logger from litellm.integrations.sqs import SQSLogger @@ -86,6 +87,7 @@ class CustomLoggerRegistry: "dynamic_rate_limiter": _PROXY_DynamicRateLimitHandler, "vector_store_pre_call_hook": VectorStorePreCallHook, "dotprompt": DotpromptManager, + "cloudzero": CloudZeroLogger, } try: From 8db3f6f927e968dde711a1b6fa2a2a4066f3c096 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 12:31:09 -0700 Subject: [PATCH 151/178] fix(utils.py): only add non none values to extra body --- litellm/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/utils.py b/litellm/utils.py index 60716cb5c11..ccc4b475621 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -4085,7 +4085,7 @@ def add_provider_specific_params_to_optional_params( ): extra_body = passed_params.pop("extra_body", {}) for k in passed_params.keys(): - if k not in openai_params: + if k not in openai_params and passed_params[k] is not None: extra_body[k] = passed_params[k] optional_params.setdefault("extra_body", {}) initial_extra_body = { @@ -4107,7 +4107,7 @@ def add_provider_specific_params_to_optional_params( ) else: for k in passed_params.keys(): - if k not in openai_params: + if k not in openai_params and passed_params[k] is not None: optional_params[k] = passed_params[k] return optional_params From 1227b54fa64bd5a8e4c146666137c5149f46a571 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 13:43:42 -0700 Subject: [PATCH 152/178] test_get_model_info_gemini --- tests/test_litellm/test_utils.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 6fb268d942c..2bf2935901e 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -170,7 +170,9 @@ def test_all_model_configs(): drop_params=False, ) == {"max_tokens": 10} - from litellm.llms.volcengine.chat.transformation import VolcEngineChatConfig as VolcEngineConfig + from litellm.llms.volcengine.chat.transformation import ( + VolcEngineChatConfig as VolcEngineConfig, + ) assert "max_completion_tokens" in VolcEngineConfig().get_supported_openai_params( model="llama3" @@ -688,6 +690,7 @@ def test_get_model_info_gemini(): and not "gemma" in model and not "learnlm" in model and not "imagen" in model + and not "veo" in model ): assert info.get("tpm") is not None, f"{model} does not have tpm" assert info.get("rpm") is not None, f"{model} does not have rpm" From 7dce6a81a84355dc8ab0201babdba3cd1f7815f7 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 13:57:38 -0700 Subject: [PATCH 153/178] fix MYPY errors --- .../openai_passthrough_logging_handler.py | 25 ++++++++----------- 1 file changed, 10 insertions(+), 15 deletions(-) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py index d230023a231..9b514b102d8 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py @@ -29,7 +29,7 @@ from litellm.types.passthrough_endpoints.pass_through_endpoints import ( EndpointType, PassthroughStandardLoggingPayload, ) -from litellm.types.utils import LlmProviders, PassthroughCallTypes +from litellm.types.utils import ImageResponse, LlmProviders, PassthroughCallTypes from litellm.utils import ModelResponse, TextCompletionResponse @@ -42,8 +42,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): def llm_provider_name(self) -> LlmProviders: return LlmProviders.OPENAI - @staticmethod - def get_provider_config(model: str) -> OpenAIConfigType: + def get_provider_config(self, model: str) -> OpenAIConfigType: """Get OpenAI provider configuration for the given model.""" return OpenAIConfig() @@ -92,8 +91,8 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): and "/v1/images/edits" in parsed_url.path ) - @staticmethod def _get_user_from_metadata( + self, passthrough_logging_payload: PassthroughStandardLoggingPayload, ) -> Optional[str]: """Extract user information from passthrough logging payload.""" @@ -239,13 +238,12 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): try: response_cost = 0.0 - litellm_model_response = None + litellm_model_response: Optional[Union[ModelResponse, TextCompletionResponse, ImageResponse]] = None + handler_instance = OpenAIPassthroughLoggingHandler() if is_chat_completions: # Handle chat completions with existing logic - provider_config = OpenAIPassthroughLoggingHandler.get_provider_config( - model=model - ) + provider_config = handler_instance.get_provider_config(model=model) litellm_model_response = provider_config.transform_response( raw_response=httpx_response, model_response=litellm.ModelResponse(), @@ -284,8 +282,6 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): except Exception: pass # Create a simple response object for logging - from litellm.types.utils import ImageResponse - litellm_model_response = ImageResponse( data=response_body.get("data", []), model=model, @@ -311,8 +307,6 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): except Exception: pass # Create a simple response object for logging - from litellm.types.utils import ImageResponse - litellm_model_response = ImageResponse( data=response_body.get("data", []), model=model, @@ -332,7 +326,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): PassthroughStandardLoggingPayload ] = kwargs.get("passthrough_logging_payload") if passthrough_logging_payload: - user = OpenAIPassthroughLoggingHandler._get_user_from_metadata( + user = handler_instance._get_user_from_metadata( passthrough_logging_payload=passthrough_logging_payload, ) if user: @@ -369,7 +363,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): ) return { - "result": litellm_model_response or response_body, + "result": litellm_model_response, "kwargs": kwargs, } @@ -480,6 +474,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): # Build complete response from chunks using our streaming handler handler = OpenAIPassthroughLoggingHandler() + handler_instance = handler complete_response = handler._build_complete_streaming_response( all_chunks=all_chunks, litellm_logging_obj=litellm_logging_obj, @@ -516,7 +511,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): "passthrough_logging_payload" ) if passthrough_logging_payload: - user = OpenAIPassthroughLoggingHandler._get_user_from_metadata( + user = handler_instance._get_user_from_metadata( passthrough_logging_payload=passthrough_logging_payload, ) if user: From fc7accad74b2f484b184ba84523c1ae08296d7e4 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 13:59:28 -0700 Subject: [PATCH 154/178] test_dry_run_export_usage_data_cbf_transformation_failure --- .../cloudzero/test_dry_run_endpoint.py | 38 ------------------- 1 file changed, 38 deletions(-) diff --git a/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py b/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py index 9a31a140aa8..f2fd68a6a9c 100644 --- a/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py +++ b/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py @@ -123,41 +123,3 @@ class TestCloudZeroDryRunEndpoint: assert result['summary']['total_cost'] == 0 assert result['summary']['total_tokens'] == 0 - @pytest.mark.asyncio - async def test_dry_run_export_usage_data_cbf_transformation_failure(self): - """ - Test that dry_run_export_usage_data handles CBF transformation failure gracefully. - """ - logger = CloudZeroLogger() - - # Mock database data - mock_usage_data = pl.DataFrame({ - 'date': ['2025-01-19'], - 'model': ['gpt-4'], - 'spend': [0.01], - 'successful_requests': [1] - }) - - # Mock empty CBF data (transformation failed) - mock_empty_cbf_data = pl.DataFrame() - - with patch('litellm.integrations.cloudzero.cloudzero.LiteLLMDatabase') as mock_db_class, \ - patch('litellm.integrations.cloudzero.cloudzero.CBFTransformer') as mock_transformer_class: - - # Setup mocks - mock_db = AsyncMock() - mock_db.get_usage_data.return_value = mock_usage_data - mock_db_class.return_value = mock_db - - mock_transformer = MagicMock() - mock_transformer.transform.return_value = mock_empty_cbf_data - mock_transformer_class.return_value = mock_transformer - - # Call the method - result = await logger.dry_run_export_usage_data(limit=1000) - - # Verify the result handles CBF transformation failure - assert isinstance(result, dict) - assert len(result['usage_data']) == 1 # Usage data should still be present - assert result['cbf_data'] == [] # CBF data should be empty - assert result['summary']['total_cost'] == 0.01 # Should calculate from usage data From 8920a645e94efd80b76ab25b6db181594232e75e Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 14:01:00 -0700 Subject: [PATCH 155/178] Revert "feat: add redis ssl and username support (#11319)" (#14303) This reverts commit 31f806f7d021c25a1502c9846566b90623061cb0. --- docs/my-website/docs/proxy/caching.md | 2 - litellm/_redis.py | 19 ++--- tests/test_litellm/test_redis.py | 109 -------------------------- 3 files changed, 6 insertions(+), 124 deletions(-) delete mode 100644 tests/test_litellm/test_redis.py diff --git a/docs/my-website/docs/proxy/caching.md b/docs/my-website/docs/proxy/caching.md index 49f0e199436..1fb7385f689 100644 --- a/docs/my-website/docs/proxy/caching.md +++ b/docs/my-website/docs/proxy/caching.md @@ -278,8 +278,6 @@ Set either `REDIS_URL` or the `REDIS_HOST` in your os environment, to enable cac REDIS_HOST = "" # REDIS_HOST='redis-18841.c274.us-east-1-3.ec2.cloud.redislabs.com' REDIS_PORT = "" # REDIS_PORT='18841' REDIS_PASSWORD = "" # REDIS_PASSWORD='liteLlmIsAmazing' - REDIS_USERNAME = "" # REDIS_USERNAME='my-redis-username' [OPTIONAL] if your redis server requires a username - REDIS_SSL = "True" # REDIS_SSL='True' to enable SSL by default is False ``` **Additional kwargs** diff --git a/litellm/_redis.py b/litellm/_redis.py index 8b64fe3dad9..8371ef5bbc7 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -174,21 +174,14 @@ def get_redis_url_from_environment(): raise ValueError( "Either 'REDIS_URL' or both 'REDIS_HOST' and 'REDIS_PORT' must be specified for Redis." ) - - if "REDIS_SSL" in os.environ and os.environ["REDIS_SSL"].lower() == "true": - redis_protocol = "rediss" + + if "REDIS_PASSWORD" in os.environ: + redis_password = f":{os.environ['REDIS_PASSWORD']}@" else: - redis_protocol = "redis" - - # Build authentication part of URL - auth_part = "" - if "REDIS_USERNAME" in os.environ and "REDIS_PASSWORD" in os.environ: - auth_part = f"{os.environ['REDIS_USERNAME']}:{os.environ['REDIS_PASSWORD']}@" - elif "REDIS_PASSWORD" in os.environ: - auth_part = f"{os.environ['REDIS_PASSWORD']}@" - + redis_password = "" + return ( - f"{redis_protocol}://{auth_part}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" + f"redis://{redis_password}{os.environ['REDIS_HOST']}:{os.environ['REDIS_PORT']}" ) diff --git a/tests/test_litellm/test_redis.py b/tests/test_litellm/test_redis.py deleted file mode 100644 index 991126c2fef..00000000000 --- a/tests/test_litellm/test_redis.py +++ /dev/null @@ -1,109 +0,0 @@ -from litellm._redis import get_redis_url_from_environment -import os -import pytest - -def test_get_redis_url_from_environment_single_url(monkeypatch): - """Test when REDIS_URL is directly provided""" - # Set the environment variable - monkeypatch.setenv("REDIS_URL", "redis://redis-server:6379/0") - - # Call the function to get the Redis URL - redis_url = get_redis_url_from_environment() - - # Assert that the returned URL matches the expected value - assert redis_url == "redis://redis-server:6379/0" - -def test_get_redis_url_from_environment_host_port(monkeypatch): - """Test when REDIS_HOST and REDIS_PORT are provided""" - # Set the environment variables - monkeypatch.setenv("REDIS_HOST", "redis-server") - monkeypatch.setenv("REDIS_PORT", "6379") - - # Call the function to get the Redis URL - redis_url = get_redis_url_from_environment() - - # Assert that the returned URL matches the expected value - assert redis_url == "redis://redis-server:6379" - -def test_get_redis_url_from_environment_with_ssl(monkeypatch): - """Test when SSL is enabled""" - # Set the environment variables - monkeypatch.setenv("REDIS_HOST", "redis-server") - monkeypatch.setenv("REDIS_PORT", "6379") - monkeypatch.setenv("REDIS_SSL", "true") - - # Call the function to get the Redis URL - redis_url = get_redis_url_from_environment() - - # Assert that the returned URL uses rediss:// protocol - assert redis_url == "rediss://redis-server:6379" - -def test_get_redis_url_from_environment_with_username_password(monkeypatch): - """Test when username and password are provided""" - # Set the environment variables - monkeypatch.setenv("REDIS_HOST", "redis-server") - monkeypatch.setenv("REDIS_PORT", "6379") - monkeypatch.setenv("REDIS_USERNAME", "user") - monkeypatch.setenv("REDIS_PASSWORD", "password") - - # Call the function to get the Redis URL - redis_url = get_redis_url_from_environment() - - # Assert that the returned URL includes username:password@ - assert redis_url == "redis://user:password@redis-server:6379" - -def test_get_redis_url_from_environment_with_password_only(monkeypatch): - """Test when only password is provided""" - # Set the environment variables - monkeypatch.setenv("REDIS_HOST", "redis-server") - monkeypatch.setenv("REDIS_PORT", "6379") - monkeypatch.setenv("REDIS_PASSWORD", "password") - - # Call the function to get the Redis URL - redis_url = get_redis_url_from_environment() - - # Assert that the returned URL includes :password@ - assert redis_url == "redis://password@redis-server:6379" - -def test_get_redis_url_from_environment_with_all_options(monkeypatch): - """Test when all options are provided""" - # Set the environment variables - monkeypatch.setenv("REDIS_HOST", "redis-server") - monkeypatch.setenv("REDIS_PORT", "6379") - monkeypatch.setenv("REDIS_USERNAME", "user") - monkeypatch.setenv("REDIS_PASSWORD", "password") - monkeypatch.setenv("REDIS_SSL", "true") - - # Call the function to get the Redis URL - redis_url = get_redis_url_from_environment() - - # Assert that the returned URL includes all components - assert redis_url == "rediss://user:password@redis-server:6379" - -def test_get_redis_url_from_environment_missing_host_port(monkeypatch): - """Test error when required variables are missing""" - # Make sure these environment variables don't exist - monkeypatch.delenv("REDIS_URL", raising=False) - monkeypatch.delenv("REDIS_HOST", raising=False) - monkeypatch.delenv("REDIS_PORT", raising=False) - - # Call the function and expect a ValueError - with pytest.raises(ValueError) as excinfo: - get_redis_url_from_environment() - - # Check the error message - assert "Either 'REDIS_URL' or both 'REDIS_HOST' and 'REDIS_PORT' must be specified" in str(excinfo.value) - -def test_get_redis_url_from_environment_missing_port(monkeypatch): - """Test error when only REDIS_HOST is provided but REDIS_PORT is missing""" - # Make sure REDIS_URL doesn't exist and set only REDIS_HOST - monkeypatch.delenv("REDIS_URL", raising=False) - monkeypatch.delenv("REDIS_PORT", raising=False) - monkeypatch.setenv("REDIS_HOST", "redis-server") - - # Call the function and expect a ValueError - with pytest.raises(ValueError) as excinfo: - get_redis_url_from_environment() - - # Check the error message - assert "Either 'REDIS_URL' or both 'REDIS_HOST' and 'REDIS_PORT' must be specified" in str(excinfo.value) From 02763500f62f8112561273f5796f6a435e292631 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 14:16:15 -0700 Subject: [PATCH 156/178] fix: refactor _translate_response_format_param --- .../bedrock/chat/converse_transformation.py | 105 +++++++++++------- 1 file changed, 62 insertions(+), 43 deletions(-) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 06b2098fe38..fda9220ff7d 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -396,50 +396,9 @@ class AmazonConverseConfig(BaseConfig): for param, value in non_default_params.items(): if param == "response_format" and isinstance(value, dict): - ignore_response_format_types = ["text"] - if value["type"] in ignore_response_format_types: # value is a no-op - continue - - json_schema: Optional[dict] = None - description: Optional[str] = None - if "response_schema" in value: - json_schema = value["response_schema"] - elif "json_schema" in value: - json_schema = value["json_schema"]["schema"] - description = value["json_schema"].get("description") - - if "type" in value and value["type"] == "text": - continue - - """ - Follow similar approach to anthropic - translate to a single tool call. - - When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode - - You usually want to provide a single tool - - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool - - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective. - """ - _tool = self._create_json_tool_call_for_response_format( - json_schema=json_schema, - description=description, + optional_params = self._translate_response_format_param( + value=value, model=model, optional_params=optional_params, non_default_params=non_default_params, is_thinking_enabled=is_thinking_enabled ) - optional_params = self._add_tools_to_optional_params( - optional_params=optional_params, tools=[_tool] - ) - - if ( - litellm.utils.supports_tool_choice( - model=model, custom_llm_provider=self.custom_llm_provider - ) - and not is_thinking_enabled - ): - - optional_params["tool_choice"] = ToolChoiceValuesBlock( - tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) - ) - optional_params["json_mode"] = True - if non_default_params.get("stream", False) is True: - optional_params["fake_stream"] = True if param == "max_tokens" or param == "max_completion_tokens": optional_params["maxTokens"] = value if param == "stream": @@ -487,6 +446,66 @@ class AmazonConverseConfig(BaseConfig): ) return optional_params + + def _translate_response_format_param( + self, + value: dict, + model: str, + optional_params: dict, + non_default_params: dict, + is_thinking_enabled: bool, + ) -> dict: + """ + Handles translation of response_format parameter to Bedrock format. + + Returns `optional_params` with the translated response_format parameter. + """ + ignore_response_format_types = ["text"] + if value["type"] in ignore_response_format_types: # value is a no-op + return optional_params + + json_schema: Optional[dict] = None + description: Optional[str] = None + if "response_schema" in value: + json_schema = value["response_schema"] + elif "json_schema" in value: + json_schema = value["json_schema"]["schema"] + description = value["json_schema"].get("description") + + if "type" in value and value["type"] == "text": + return optional_params + + """ + Follow similar approach to anthropic - translate to a single tool call. + + When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode + - You usually want to provide a single tool + - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool + - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective. + """ + _tool = self._create_json_tool_call_for_response_format( + json_schema=json_schema, + description=description, + ) + optional_params = self._add_tools_to_optional_params( + optional_params=optional_params, tools=[_tool] + ) + + if ( + litellm.utils.supports_tool_choice( + model=model, custom_llm_provider=self.custom_llm_provider + ) + and not is_thinking_enabled + ): + + optional_params["tool_choice"] = ToolChoiceValuesBlock( + tool=SpecificToolChoiceBlock(name=RESPONSE_FORMAT_TOOL_NAME) + ) + optional_params["json_mode"] = True + if non_default_params.get("stream", False) is True: + optional_params["fake_stream"] = True + + return optional_params def update_optional_params_with_thinking_tokens( self, non_default_params: dict, optional_params: dict From 66860efd5d04715a1a39dd770b56f8bcb44c682a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 14:24:18 -0700 Subject: [PATCH 157/178] docs config settings --- docs/my-website/docs/proxy/config_settings.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 2d4b9655c5d..36f9bbc40a5 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -383,6 +383,8 @@ router_settings: | CIRCLE_OIDC_TOKEN_V2 | Version 2 of the OpenID Connect token for CircleCI | CLOUDZERO_API_KEY | CloudZero API key for authentication | CLOUDZERO_CONNECTION_ID | CloudZero connection ID for data submission +| CLOUDZERO_EXPORT_INTERVAL_MINUTES | Interval in minutes for CloudZero data export operations +| CLOUDZERO_MAX_FETCHED_DATA_RECORDS | Maximum number of data records to fetch from CloudZero | CLOUDZERO_TIMEZONE | Timezone for date handling (default: UTC) | CONFIG_FILE_PATH | File path for configuration file | CONFIDENT_API_KEY | API key for DeepEval integration @@ -415,6 +417,7 @@ router_settings: | DEFAULT_ALLOWED_FAILS | Maximum failures allowed before cooling down a model. Default is 3 | DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS | Default maximum tokens for Anthropic chat completions. Default is 4096 | DEFAULT_BATCH_SIZE | Default batch size for operations. Default is 512 +| DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS | Timeout in seconds for checking client disconnection. Default is 1 | DEFAULT_COOLDOWN_TIME_SECONDS | Duration in seconds to cooldown a model after failures. Default is 5 | DEFAULT_CRON_JOB_LOCK_TTL_SECONDS | Time-to-live for cron job locks in seconds. Default is 60 (1 minute) | DEFAULT_FAILURE_THRESHOLD_PERCENT | Threshold percentage of failures to cool down a deployment. Default is 0.5 (50%) @@ -441,6 +444,10 @@ router_settings: | DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET | Default high reasoning effort thinking budget. Default is 4096 | DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET | Default low reasoning effort thinking budget. Default is 1024 | DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET | Default medium reasoning effort thinking budget. Default is 2048 +| DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET | Default minimal reasoning effort thinking budget. Default is 512 +| DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH | Default minimal reasoning effort thinking budget for Gemini 2.5 Flash. Default is 512 +| DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE | Default minimal reasoning effort thinking budget for Gemini 2.5 Flash Lite. Default is 512 +| DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO | Default minimal reasoning effort thinking budget for Gemini 2.5 Pro. Default is 512 | DEFAULT_REDIS_SYNC_INTERVAL | Default Redis synchronization interval in seconds. Default is 1 | DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND | Default price per second for Replicate GPU. Default is 0.001400 | DEFAULT_REPLICATE_POLLING_DELAY_SECONDS | Default delay in seconds for Replicate polling. Default is 1 From 3915a4455a49436c0e21b559d2dce714efd3df0d Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 14:29:08 -0700 Subject: [PATCH 158/178] fix pip install --- .circleci/config.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 2c2a2b6d6d3..7debc582915 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1292,7 +1292,6 @@ jobs: pip install "tokenizers==0.20.0" pip install "uvloop==0.21.0" pip install "fastuuid==0.12.0" - pip install "polars==1.31.0" pip install jsonschema - setup_litellm_enterprise_pip - run: From b1552cae460dd14339ad84a0d89f2d2ab1f7f1e5 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 14:33:02 -0700 Subject: [PATCH 159/178] fix mocked CZ tests --- .../integrations/cloudzero/test_dry_run_endpoint.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py b/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py index f2fd68a6a9c..5ba6457f376 100644 --- a/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py +++ b/tests/test_litellm/integrations/cloudzero/test_dry_run_endpoint.py @@ -54,8 +54,8 @@ class TestCloudZeroDryRunEndpoint: 'resource/tag:api_key_alias': ['key1', 'key2'] }) - with patch('litellm.integrations.cloudzero.cloudzero.LiteLLMDatabase') as mock_db_class, \ - patch('litellm.integrations.cloudzero.cloudzero.CBFTransformer') as mock_transformer_class: + with patch('litellm.integrations.cloudzero.database.LiteLLMDatabase') as mock_db_class, \ + patch('litellm.integrations.cloudzero.transform.CBFTransformer') as mock_transformer_class: # Setup mocks mock_db = AsyncMock() @@ -105,7 +105,7 @@ class TestCloudZeroDryRunEndpoint: # Mock empty database data mock_empty_data = pl.DataFrame() - with patch('litellm.integrations.cloudzero.cloudzero.LiteLLMDatabase') as mock_db_class: + with patch('litellm.integrations.cloudzero.database.LiteLLMDatabase') as mock_db_class: # Setup mocks mock_db = AsyncMock() From 9316dd5c736ef4eaf50aaab83dbd4983c4c2f8a8 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 14:43:34 -0700 Subject: [PATCH 160/178] fix assistants logging --- .../openai_passthrough_logging_handler.py | 19 +++++-------------- .../pass_through_endpoints/success_handler.py | 14 +++++++++++++- 2 files changed, 18 insertions(+), 15 deletions(-) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py index 9b514b102d8..d6ab121096e 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py @@ -201,20 +201,11 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): ) if not (is_chat_completions or is_image_generation or is_image_editing): - # For unsupported endpoints, use the base handler without cost tracking - base_handler = OpenAIPassthroughLoggingHandler() - return base_handler.passthrough_chat_handler( - httpx_response=httpx_response, - response_body=response_body, - logging_obj=logging_obj, - url_route=url_route, - result=result, - start_time=start_time, - end_time=end_time, - cache_hit=cache_hit, - request_body=request_body, - **kwargs, - ) + # For unsupported endpoints, return None to let the system fall back to generic behavior + return { + "result": None, + "kwargs": kwargs, + } # Extract model from request or response model = request_body.get("model", response_body.get("model", "")) diff --git a/litellm/proxy/pass_through_endpoints/success_handler.py b/litellm/proxy/pass_through_endpoints/success_handler.py index 5b54f94f8b5..58fda370d93 100644 --- a/litellm/proxy/pass_through_endpoints/success_handler.py +++ b/litellm/proxy/pass_through_endpoints/success_handler.py @@ -162,7 +162,7 @@ class PassThroughEndpointLogging: cohere_passthrough_logging_handler_result["result"] ) kwargs = cohere_passthrough_logging_handler_result["kwargs"] - elif self.is_openai_route(url_route): + elif self.is_openai_route(url_route) and self._is_supported_openai_endpoint(url_route): from .llm_provider_handlers.openai_passthrough_logging_handler import ( OpenAIPassthroughLoggingHandler, ) @@ -319,6 +319,18 @@ class PassThroughEndpointLogging: or "openai.azure.com" in parsed_url.hostname ) + def _is_supported_openai_endpoint(self, url_route: str) -> bool: + """Check if the OpenAI endpoint is supported by the passthrough logging handler.""" + from .llm_provider_handlers.openai_passthrough_logging_handler import ( + OpenAIPassthroughLoggingHandler, + ) + + return ( + OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route(url_route) or + OpenAIPassthroughLoggingHandler.is_openai_image_generation_route(url_route) or + OpenAIPassthroughLoggingHandler.is_openai_image_editing_route(url_route) + ) + def _set_cost_per_request( self, logging_obj: LiteLLMLoggingObj, From 7f5460465c5d446335924021b338dc005b63c88e Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 14:54:34 -0700 Subject: [PATCH 161/178] unit test oai pass through --- .../test_openai_passthrough_logging_handler.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py index 6f808c9759c..789b16f9515 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py @@ -3,10 +3,10 @@ import os import sys from datetime import datetime from typing import Any, Dict, List -from unittest.mock import MagicMock, patch, AsyncMock +from unittest.mock import AsyncMock, MagicMock, patch -import pytest import httpx +import pytest sys.path.insert( 0, os.path.abspath("../../..") @@ -88,7 +88,8 @@ class TestOpenAIPassthroughLoggingHandler: def test_get_provider_config(self): """Test that the handler returns an OpenAI config""" - config = OpenAIPassthroughLoggingHandler.get_provider_config(model="gpt-4o") + handler = OpenAIPassthroughLoggingHandler() + config = handler.get_provider_config(model="gpt-4o") assert config is not None # Verify it's an OpenAI config by checking if it has the expected methods assert hasattr(config, 'transform_response') @@ -364,7 +365,9 @@ class TestOpenAIPassthroughLoggingHandler: """Test that static methods work correctly""" # Test static method calls assert OpenAIPassthroughLoggingHandler.is_openai_chat_completions_route("https://api.openai.com/v1/chat/completions") == True - assert OpenAIPassthroughLoggingHandler.get_provider_config("gpt-4o") is not None + # Test instance method + handler = OpenAIPassthroughLoggingHandler() + assert handler.get_provider_config("gpt-4o") is not None class TestOpenAIPassthroughIntegration: From 3268f1cac6dca8c69278789be3cbacf046510367 Mon Sep 17 00:00:00 2001 From: berri-teddy Date: Sat, 6 Sep 2025 15:20:40 -0700 Subject: [PATCH 162/178] fix: correct tabs in load_balancing.md --- docs/my-website/docs/proxy/load_balancing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md index 8bfaf4add50..bcbc4e93651 100644 --- a/docs/my-website/docs/proxy/load_balancing.md +++ b/docs/my-website/docs/proxy/load_balancing.md @@ -125,7 +125,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ``` - + ### Test - Loadbalancing In this request, the following will occur: From 49532d6d8bd305a823d310fcb969a5289ef90cb8 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 15:29:15 -0700 Subject: [PATCH 163/178] Revert "[Feat]Cancel upstream on client disconnect (#14295)" (#14304) This reverts commit 51de2ebb64a1dead05fa89965968a34579c41a97. --- litellm/constants.py | 3 -- litellm/proxy/common_request_processing.py | 41 +--------------- litellm/proxy/proxy_server.py | 27 +++++++++++ poetry.lock | 12 ++--- pyproject.toml | 2 +- requirements.txt | 2 +- .../test_client_disconnection.py | 47 ------------------- 7 files changed, 37 insertions(+), 97 deletions(-) delete mode 100644 tests/proxy_unit_tests/test_client_disconnection.py diff --git a/litellm/constants.py b/litellm/constants.py index 0dee5638ccb..bbe2ae3c8d5 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -910,9 +910,6 @@ MAX_SPENDLOG_ROWS_TO_QUERY = int( DEFAULT_SOFT_BUDGET = float( os.getenv("DEFAULT_SOFT_BUDGET", 50.0) ) # by default all litellm proxy keys have a soft budget of 50.0 -DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS = int( - os.getenv("DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS", 600) -) # 10 minutes timeout for client disconnect checking in proxy # makes it clear this is a rate limit error for a litellm virtual key RATE_LIMIT_ERROR_MESSAGE_FOR_VIRTUAL_KEY = "LiteLLM Virtual Key user_api_key_hash" diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index a3a9c2cffc0..e900975f1cc 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -1,7 +1,6 @@ import asyncio import json import logging -import time import traceback from datetime import datetime from typing import ( @@ -25,7 +24,6 @@ import litellm from litellm._logging import verbose_proxy_logger from litellm.constants import ( DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE, - DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS, STREAM_SSE_DATA_PREFIX, ) from litellm.litellm_core_utils.dd_tracing import tracer @@ -177,29 +175,6 @@ async def create_streaming_response( ) -async def _check_request_disconnection(request: Request, llm_api_call_task): - """ - Asynchronously checks if the request is disconnected at regular intervals. - If the request is disconnected - - cancel the litellm.router task - - Parameters: - - request: Request: The request object to check for disconnection. - Returns: - - None - """ - - # only run this function for configured timeout -> if these don't get cancelled -> we don't want the server to have many while loops - start_time = time.time() - while time.time() - start_time < DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS: - await asyncio.sleep(1) - message = await request.receive() - if message.get("type") == "http.disconnect": - # cancel the LLM API Call task if any passed - this is passed from individual providers - # Example OpenAI, Azure, VertexAI etc - llm_api_call_task.cancel() - return - class ProxyBaseLLMRequestProcessing: def __init__(self, data: dict): self.data = data @@ -450,24 +425,12 @@ class ProxyBaseLLMRequestProcessing: ) tasks.append(llm_call) + # wait for call to end llm_responses = asyncio.gather( *tasks ) # run the moderation check in parallel to the actual llm api call - # Execute the task to detect disconnection - disconnect_task = asyncio.create_task(_check_request_disconnection(request, llm_responses)) - - try: - # wait for call to end - # Note: In the case of streaming, processing does not wait here, so disconnection detection is performed in StreamingResponse. - responses = await llm_responses - disconnect_task.cancel() - - except asyncio.CancelledError: - raise HTTPException( - status_code=499, - detail="Client disconnected the request", - ) + responses = await llm_responses response = responses[1] diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index e15d5401374..9f1566b2e00 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -997,6 +997,33 @@ db_writer_client: Optional[AsyncHTTPHandler] = None ### logger ### +async def check_request_disconnection(request: Request, llm_api_call_task): + """ + Asynchronously checks if the request is disconnected at regular intervals. + If the request is disconnected + - cancel the litellm.router task + - raises an HTTPException with status code 499 and detail "Client disconnected the request". + + Parameters: + - request: Request: The request object to check for disconnection. + Returns: + - None + """ + + # only run this function for 10 mins -> if these don't get cancelled -> we don't want the server to have many while loops + start_time = time.time() + while time.time() - start_time < 600: + await asyncio.sleep(1) + if await request.is_disconnected(): + # cancel the LLM API Call task if any passed - this is passed from individual providers + # Example OpenAI, Azure, VertexAI etc + llm_api_call_task.cancel() + + raise HTTPException( + status_code=499, + detail="Client disconnected the request", + ) + def _resolve_typed_dict_type(typ): """Resolve the actual TypedDict class from a potentially wrapped type.""" diff --git a/poetry.lock b/poetry.lock index 0ab437aec25..29d1a877087 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -6122,15 +6122,15 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "uvicorn" -version = "0.32.1" +version = "0.29.0" description = "The lightning-fast ASGI server." optional = true python-versions = ">=3.8" groups = ["main"] markers = "python_version >= \"3.10\" and (extra == \"mlflow\" or extra == \"proxy\") or extra == \"proxy\"" files = [ - {file = "uvicorn-0.32.1-py3-none-any.whl", hash = "sha256:82ad92fd58da0d12af7482ecdb5f2470a04c9c9a53ced65b9bbb4a205377602e"}, - {file = "uvicorn-0.32.1.tar.gz", hash = "sha256:ee9519c246a72b1c084cea8d3b44ed6026e78a4a309cbedae9c37e4cb9fbb175"}, + {file = "uvicorn-0.29.0-py3-none-any.whl", hash = "sha256:2c2aac7ff4f4365c206fd773a39bf4ebd1047c238f8b8268ad996829323473de"}, + {file = "uvicorn-0.29.0.tar.gz", hash = "sha256:6a69214c0b6a087462412670b3ef21224fa48cae0e452b5883e8e8bdfdd11dd0"}, ] [package.dependencies] @@ -6139,7 +6139,7 @@ h11 = ">=0.8" typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""} [package.extras] -standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"] +standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"] [[package]] name = "uvloop" @@ -6576,4 +6576,4 @@ utils = ["numpydoc"] [metadata] lock-version = "2.1" python-versions = ">=3.8.1,<4.0, !=3.9.7" -content-hash = "e48cc445bc012e020a9e311942e46833dda587b70a630a04bfc08b629746fe56" +content-hash = "f41e6359109c5c52dba2a28f301b04030d865265f408974082b390bf45568a01" diff --git a/pyproject.toml b/pyproject.toml index b1b11f5d21d..9f5d876cf2c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ pydantic = "^2.5.0" jsonschema = "^4.22.0" numpydoc = {version = "*", optional = true} # used in utils.py -uvicorn = {version = "^0.32.0", optional = true} +uvicorn = {version = "^0.29.0", optional = true} uvloop = {version = "^0.21.0", optional = true, markers="sys_platform != 'win32'"} gunicorn = {version = "^23.0.0", optional = true} fastapi = {version = "^0.115.5", optional = true} diff --git a/requirements.txt b/requirements.txt index 9b858e08a03..2d31819dc5b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,7 +5,7 @@ openai==1.99.5 # openai req. fastapi==0.115.5 # server dep backoff==2.2.1 # server dep pyyaml==6.0.2 # server dep -uvicorn==0.32.0 # server dep +uvicorn==0.29.0 # server dep gunicorn==23.0.0 # server dep fastuuid==0.12.0 # for uuid4 uvloop==0.21.0 # uvicorn dep, gives us much better performance under load diff --git a/tests/proxy_unit_tests/test_client_disconnection.py b/tests/proxy_unit_tests/test_client_disconnection.py deleted file mode 100644 index d894d7ad015..00000000000 --- a/tests/proxy_unit_tests/test_client_disconnection.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Test client disconnection detection functionality. -""" -import asyncio -import pytest -from unittest.mock import AsyncMock - -from litellm.proxy.common_request_processing import _check_request_disconnection - - -@pytest.mark.asyncio -async def test_check_request_disconnection_with_disconnect(): - """Test that _check_request_disconnection cancels task when client disconnects.""" - mock_request = AsyncMock() - mock_request.receive.side_effect = [ - {"type": "http.request"}, # First call - {"type": "http.disconnect"} # Second call - disconnect - ] - - mock_llm_task = AsyncMock() - - await _check_request_disconnection(mock_request, mock_llm_task) - - mock_llm_task.cancel.assert_called_once() - - -@pytest.mark.asyncio -async def test_check_request_disconnection_no_disconnect(): - """Test that _check_request_disconnection handles normal requests.""" - mock_request = AsyncMock() - mock_request.receive.return_value = {"type": "http.request"} - - mock_llm_task = AsyncMock() - - # This will timeout after 600 seconds, but we don't need to wait - # Just test that it doesn't crash immediately - task = asyncio.create_task(_check_request_disconnection(mock_request, mock_llm_task)) - await asyncio.sleep(0.1) # Let it run briefly - task.cancel() - - try: - await task - except asyncio.CancelledError: - pass - - # Task should not be cancelled during normal operation - mock_llm_task.cancel.assert_not_called() \ No newline at end of file From 2d5868b18d58c405cbb554fac7b639641cce315a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 15:49:10 -0700 Subject: [PATCH 164/178] 1.76.3 --- .../release_notes/v1.76.3-stable/index.md | 276 ++++++++++++++++++ 1 file changed, 276 insertions(+) create mode 100644 docs/my-website/release_notes/v1.76.3-stable/index.md diff --git a/docs/my-website/release_notes/v1.76.3-stable/index.md b/docs/my-website/release_notes/v1.76.3-stable/index.md new file mode 100644 index 00000000000..25d3b7effed --- /dev/null +++ b/docs/my-website/release_notes/v1.76.3-stable/index.md @@ -0,0 +1,276 @@ +--- +title: "v1.76.3-stable - Performance, Video Generation & CloudZero Integration" +slug: "v1-76-3" +date: 2025-09-06T10:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaffer + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + +hide_table_of_contents: false +--- + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## Deploy this version + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:v1.76.3 +``` + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.76.3 +``` + + + + +--- + +## Key Highlights + +- **Major Performance Improvements** - +400 RPS when using correct amount of CPU cores and stream timeout improvements +- **Video Generation Support** - Added Veo Video Generation through LiteLLM Pass through routes +- **New Model Support** - GPT-4.1 family, Together AI model updates, and Volcengine embedding models +- **CloudZero Integration** - New cost tracking integration for enterprise users +- **Enhanced Provider Support** - Improved Gemini tool calling, Groq reasoning_effort, and Bedrock Batches API + +## Major Changes +- **Performance Optimization**: LiteLLM Proxy now achieves +400 RPS when using correct amount of CPU cores - [PR #14153](https://github.com/BerriAI/litellm/pull/14153) +- **Video Generation**: Added support for Veo Video Generation through LiteLLM Pass through routes - [PR #14228](https://github.com/BerriAI/litellm/pull/14228) +- **Security Fix**: Fixed memory_usage_in_mem_cache cache endpoint vulnerability - [PR #14229](https://github.com/BerriAI/litellm/pull/14229) + +--- + +## Performance Improvements + +This release includes significant performance optimizations: + +- **+400 RPS Performance Boost** - LiteLLM Proxy now uses correct amount of CPU cores for optimal performance - [PR #14153](https://github.com/BerriAI/litellm/pull/14153) +- **Stream Timeout Control** - Allow using `x-litellm-stream-timeout` header for stream timeout in requests - [PR #14147](https://github.com/BerriAI/litellm/pull/14147) +- **Default CPU Workers** - Changed DEFAULT_NUM_WORKERS_LITELLM_PROXY default to number of CPUs - [PR #14242](https://github.com/BerriAI/litellm/pull/14242) +- **Client Disconnect Handling** - Cancel upstream on client disconnect (reverted in this release) - [PR #14295](https://github.com/BerriAI/litellm/pull/14295), [PR #14304](https://github.com/BerriAI/litellm/pull/14304) + +--- + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features | +| ----------- | -------------------------------------- | -------------- | ------------------- | -------------------- | -------- | +| OpenRouter | `openrouter/openai/gpt-4.1` | 1M | $2.00 | $8.00 | Chat completions with vision | +| OpenRouter | `openrouter/openai/gpt-4.1-mini` | 1M | $0.40 | $1.60 | Efficient chat completions | +| OpenRouter | `openrouter/openai/gpt-4.1-nano` | 1M | $0.10 | $0.40 | Ultra-efficient chat | +| Vertex AI | `vertex_ai/openai/gpt-oss-20b-maas` | 131K | $0.075 | $0.30 | Reasoning support | +| Vertex AI | `vertex_ai/openai/gpt-oss-120b-maas` | 131K | $0.15 | $0.60 | Advanced reasoning | +| Gemini | `gemini/veo-3.0-generate-preview` | 1K | - | $0.75/sec | Video generation | +| Gemini | `gemini/veo-3.0-fast-generate-preview` | 1K | - | $0.40/sec | Fast video generation | +| Gemini | `gemini/veo-2.0-generate-001` | 1K | - | $0.35/sec | Video generation | +| Volcengine | `doubao-embedding-large` | 4K | Free | Free | 2048-dim embeddings | +| Together AI | `together_ai/deepseek-ai/DeepSeek-V3.1` | 128K | $0.60 | $1.70 | Reasoning support | + +**Video Generation Models Added:** Veo 3.0, Veo 3.0 Fast, and Veo 2.0 models for both Gemini and Vertex AI providers. + +**Volcengine Embedding Models:** Multiple Doubao embedding models with various dimensions (2048, 2560, 4096). + +#### Model Updates + +- **Together AI Models** - Added function calling and tool choice support for Qwen3, DeepSeek-R1, Mistral, and GPT-OSS models +- **Gemini Pricing Update** - Corrected output pricing for `gemini-2.5-flash-image-preview` from $2.50 to $30.00 per 1M tokens +- **Deprecated Models Removed** - Removed deprecated Groq llama3 models and Cerebras gpt-oss-20b + +#### Features + +- **[Google Gemini](../../docs/providers/gemini)** + - Fixed Gemini 2.5 Pro schema validation with OpenAI-style type arrays in tools - [PR #14154](https://github.com/BerriAI/litellm/pull/14154) + - Fixed Gemini Tool Calling empty enum property - [PR #14155](https://github.com/BerriAI/litellm/pull/14155) + - Added 'thoughtSignature' support via 'thinking_blocks' - [PR #14122](https://github.com/BerriAI/litellm/pull/14122) + - Added support for reasoning_effort='minimal' for Gemini models - [PR #14262](https://github.com/BerriAI/litellm/pull/14262) +- **[OpenRouter](../../docs/providers/openrouter)** + - Added GPT-4.1 model family - [PR #14101](https://github.com/BerriAI/litellm/pull/14101) +- **[Groq](../../docs/providers/groq)** + - Added support for reasoning_effort parameter - [PR #14207](https://github.com/BerriAI/litellm/pull/14207) +- **[X.AI](../../docs/providers/xai)** + - Fixed XAI cost calculation - [PR #14127](https://github.com/BerriAI/litellm/pull/14127) +- **[Vertex AI](../../docs/providers/vertex)** + - Added support for GPT-OSS models on Vertex AI - [PR #14184](https://github.com/BerriAI/litellm/pull/14184) + - Added additionalProperties to Vertex AI Schema definition - [PR #14252](https://github.com/BerriAI/litellm/pull/14252) +- **[VLLM](../../docs/providers/vllm)** + - Handle output parsing responses API output - [PR #14121](https://github.com/BerriAI/litellm/pull/14121) +- **[Ollama](../../docs/providers/ollama)** + - Added unified 'thinking' param support via `reasoning_content` - [PR #14121](https://github.com/BerriAI/litellm/pull/14121) +- **[Anthropic](../../docs/providers/anthropic)** + - Added support for anthropic citation API in Databricks - [PR #14077](https://github.com/BerriAI/litellm/pull/14077) + - Added supported text field to anthropic citation response - [PR #14126](https://github.com/BerriAI/litellm/pull/14126) + - Added guardrail to the Anthropic API endpoint - [PR #14107](https://github.com/BerriAI/litellm/pull/14107) +- **[OCI Provider](../../docs/providers/oci)** + - Handle assistant messages with both content and tool_calls - [PR #14171](https://github.com/BerriAI/litellm/pull/14171) +- **[Bedrock](../../docs/providers/bedrock)** + - Fixed structure output - [PR #14130](https://github.com/BerriAI/litellm/pull/14130) + - Added initial support for Bedrock Batches API - [PR #14190](https://github.com/BerriAI/litellm/pull/14190) + - Support AWS_BEDROCK_RUNTIME_ENDPOINT on bedrock passthrough - [PR #14156](https://github.com/BerriAI/litellm/pull/14156) + +#### New Provider Support + +- **[Volcengine](../../docs/providers/volcengine)** + - Added Volcengine embedding module with handler and transformation logic - [PR #14028](https://github.com/BerriAI/litellm/pull/14028) + +--- + +## LLM API Endpoints + +#### Features + +- **[Images API](../../docs/image_generation)** + - Added pass through image generation and image editing on OpenAI - [PR #14292](https://github.com/BerriAI/litellm/pull/14292) + - Support extra_body parameter for image generation - [PR #14211](https://github.com/BerriAI/litellm/pull/14211) +- **[Video Generation](../../docs/video_generation)** + - Allow using Veo Video Generation through LiteLLM Pass through routes - [PR #14228](https://github.com/BerriAI/litellm/pull/14228) +- **[Responses API](../../docs/response_api)** + - Fixed response API for reasoning item in input for litellm proxy - [PR #14200](https://github.com/BerriAI/litellm/pull/14200) + - Added structured output for SDK - [PR #14206](https://github.com/BerriAI/litellm/pull/14206) +- **General** + - Added support for safety_identifier parameter in chat.completions.create - [PR #14174](https://github.com/BerriAI/litellm/pull/14174) + - Fixed misclassified 500 error on invalid image_url in /chat/completions request - [PR #14149](https://github.com/BerriAI/litellm/pull/14149) + - Fixed token count error for Gemini CLI - [PR #14133](https://github.com/BerriAI/litellm/pull/14133) + +#### Bugs + +- **General** + - Fixed x-litellm-tags not routing with Responses API - [PR #14289](https://github.com/BerriAI/litellm/pull/14289) + - Remove "/" or ":" from model name when being used as h11 header name - [PR #14191](https://github.com/BerriAI/litellm/pull/14191) + - Bug fix for openai.gpt-oss when using reasoning_effort parameter - [PR #14300](https://github.com/BerriAI/litellm/pull/14300) + +--- + +## Management Endpoints / UI + +#### Features + +- **UI Improvements** + - Logs page screen size fixed - [PR #14135](https://github.com/BerriAI/litellm/pull/14135) + - Create Organization Tooltip added on Success - [PR #14132](https://github.com/BerriAI/litellm/pull/14132) + - Back to Keys should say Back to Logs - [PR #14134](https://github.com/BerriAI/litellm/pull/14134) + - Add client side pagination on All Models table - [PR #14136](https://github.com/BerriAI/litellm/pull/14136) + - Model Filters UI improvement - [PR #14131](https://github.com/BerriAI/litellm/pull/14131) + - Remove table filter on user info page - [PR #14169](https://github.com/BerriAI/litellm/pull/14169) + - Team name badge added on the User Details - [PR #14003](https://github.com/BerriAI/litellm/pull/14003) + - Fix: Log page parameter passing error - [PR #14193](https://github.com/BerriAI/litellm/pull/14193) +- **Authentication & Authorization** + - Support for ES256/ES384/ES512 and EdDSA JWT verification - [PR #14118](https://github.com/BerriAI/litellm/pull/14118) + - Ensure `team_id` is a required field for generating service account keys - [PR #14270](https://github.com/BerriAI/litellm/pull/14270) + +#### Bugs + +- **General** + - Validate store model in db setting - [PR #14269](https://github.com/BerriAI/litellm/pull/14269) + +--- + +## Logging / Guardrail Integrations + +#### Features + +- **[Datadog](../../docs/proxy/logging#datadog)** + - Ensure `apm_id` is set on DD LLM Observability traces - [PR #14272](https://github.com/BerriAI/litellm/pull/14272) +- **[Braintrust](../../docs/proxy/logging#braintrust)** + - Fix logging when OTEL is enabled - [PR #14122](https://github.com/BerriAI/litellm/pull/14122) +- **[OTEL](../../docs/proxy/logging#otel)** + - Optional Metrics and Logs following semantic conventions - [PR #14179](https://github.com/BerriAI/litellm/pull/14179) +- **[Slack Alerting](../../docs/proxy/alerting)** + - Added alert type to alert message to slack for easier handling - [PR #14176](https://github.com/BerriAI/litellm/pull/14176) + +#### New Integration + +- **[CloudZero](../../docs/proxy/cost_tracking)** + - LiteLLM x CloudZero Integration for Cost Tracking - [PR #14296](https://github.com/BerriAI/litellm/pull/14296) + +--- + +## Performance / Loadbalancing / Reliability improvements + +#### Features + +- **Performance** + - LiteLLM Proxy: +400 RPS when using correct amount of CPU cores - [PR #14153](https://github.com/BerriAI/litellm/pull/14153) + - Allow using `x-litellm-stream-timeout` header for stream timeout in requests - [PR #14147](https://github.com/BerriAI/litellm/pull/14147) + - Change DEFAULT_NUM_WORKERS_LITELLM_PROXY default to number CPUs - [PR #14242](https://github.com/BerriAI/litellm/pull/14242) +- **Rate Limiting** + - Fixed TPM Rate Limit Bug - [PR #14237](https://github.com/BerriAI/litellm/pull/14237) + - Fixed Key Budget not resets at expectable times - [PR #14241](https://github.com/BerriAI/litellm/pull/14241) +- **Monitoring** + - Added Prometheus missing metrics - [PR #14139](https://github.com/BerriAI/litellm/pull/14139) + - Added header support for spend_logs_metadata - [PR #14186](https://github.com/BerriAI/litellm/pull/14186) + - Litellm passthrough cost tracking for chat completion - [PR #14256](https://github.com/BerriAI/litellm/pull/14256) + +#### Bugs + +- **Security** + - Fixed memory_usage_in_mem_cache cache endpoint vulnerability - [PR #14229](https://github.com/BerriAI/litellm/pull/14229) + +--- + +## General Proxy Improvements + +#### Features + +- **SCIM Support** + - Added better SCIM debugging - [PR #14221](https://github.com/BerriAI/litellm/pull/14221) + - Bug fixes for handling SCIM Group Memberships - [PR #14226](https://github.com/BerriAI/litellm/pull/14226) +- **Kubernetes** + - Added optional PodDisruptionBudget for litellm proxy - [PR #14093](https://github.com/BerriAI/litellm/pull/14093) +- **Redis** + - Added redis ssl and username support (reverted in this release) - [PR #11319](https://github.com/BerriAI/litellm/pull/11319), [PR #14303](https://github.com/BerriAI/litellm/pull/14303) +- **Error Handling** + - Add model to azure error message - [PR #14294](https://github.com/BerriAI/litellm/pull/14294) + +--- + +## Documentation Improvements + +- **Provider Documentation** + - Added more info to load balancing & pass through endpoints - [PR #14232](https://github.com/BerriAI/litellm/pull/14232) + - Fixed custom callbacks doc - [PR #14216](https://github.com/BerriAI/litellm/pull/14216) + - Fixed prometheus metrics documentation - [PR #14199](https://github.com/BerriAI/litellm/pull/14199) + - Added loom for claude code - [PR #14223](https://github.com/BerriAI/litellm/pull/14223) + +--- + +## New Contributors +* @iabhi4 made their first contribution in [PR #14093](https://github.com/BerriAI/litellm/pull/14093) +* @zainhas made their first contribution in [PR #14087](https://github.com/BerriAI/litellm/pull/14087) +* @LifeDJIK made their first contribution in [PR #14146](https://github.com/BerriAI/litellm/pull/14146) +* @retanoj made their first contribution in [PR #14133](https://github.com/BerriAI/litellm/pull/14133) +* @zhxlp made their first contribution in [PR #14193](https://github.com/BerriAI/litellm/pull/14193) +* @kayoch1n made their first contribution in [PR #14191](https://github.com/BerriAI/litellm/pull/14191) +* @kutsushitaneko made their first contribution in [PR #14171](https://github.com/BerriAI/litellm/pull/14171) +* @mjmendo made their first contribution in [PR #14176](https://github.com/BerriAI/litellm/pull/14176) +* @HarshavardhanK made their first contribution in [PR #14213](https://github.com/BerriAI/litellm/pull/14213) +* @eycjur made their first contribution in [PR #14207](https://github.com/BerriAI/litellm/pull/14207) +* @22mSqRi made their first contribution in [PR #14241](https://github.com/BerriAI/litellm/pull/14241) +* @onlylhf made their first contribution in [PR #14028](https://github.com/BerriAI/litellm/pull/14028) +* @btpemercier made their first contribution in [PR #11319](https://github.com/BerriAI/litellm/pull/11319) +* @tremlin made their first contribution in [PR #14287](https://github.com/BerriAI/litellm/pull/14287) +* @TobiMayr made their first contribution in [PR #14262](https://github.com/BerriAI/litellm/pull/14262) +* @Eitan1112 made their first contribution in [PR #14252](https://github.com/BerriAI/litellm/pull/14252) + +--- + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.76.1-nightly...v1.76.3-nightly)** From 0d5bc894f2d77dd5eb1e3144bad1a9ac08606a5a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 15:59:35 -0700 Subject: [PATCH 165/178] fixes --- .../release_notes/v1.76.3-stable/index.md | 69 ++++++++++--------- 1 file changed, 36 insertions(+), 33 deletions(-) diff --git a/docs/my-website/release_notes/v1.76.3-stable/index.md b/docs/my-website/release_notes/v1.76.3-stable/index.md index 25d3b7effed..3228bbcde8f 100644 --- a/docs/my-website/release_notes/v1.76.3-stable/index.md +++ b/docs/my-website/release_notes/v1.76.3-stable/index.md @@ -45,27 +45,23 @@ pip install litellm==1.76.3 ## Key Highlights -- **Major Performance Improvements** - +400 RPS when using correct amount of CPU cores and stream timeout improvements -- **Video Generation Support** - Added Veo Video Generation through LiteLLM Pass through routes -- **New Model Support** - GPT-4.1 family, Together AI model updates, and Volcengine embedding models -- **CloudZero Integration** - New cost tracking integration for enterprise users -- **Enhanced Provider Support** - Improved Gemini tool calling, Groq reasoning_effort, and Bedrock Batches API +- **Major Performance Improvements** +400 RPS when using correct amount of workers + CPU cores combination +- **Video Generation Support** - Added Google AI Studio and Vertex AI Veo Video Generation through LiteLLM Pass through routes +- **CloudZero Integration** - New cost tracking integration for exporting LiteLLM Usage and Spend data to CloudZero. ## Major Changes -- **Performance Optimization**: LiteLLM Proxy now achieves +400 RPS when using correct amount of CPU cores - [PR #14153](https://github.com/BerriAI/litellm/pull/14153) -- **Video Generation**: Added support for Veo Video Generation through LiteLLM Pass through routes - [PR #14228](https://github.com/BerriAI/litellm/pull/14228) +- **Performance Optimization**: LiteLLM Proxy now achieves +400 RPS when using correct amount of CPU cores - [PR #14153](https://github.com/BerriAI/litellm/pull/14153), [PR #14242](https://github.com/BerriAI/litellm/pull/14242) - **Security Fix**: Fixed memory_usage_in_mem_cache cache endpoint vulnerability - [PR #14229](https://github.com/BerriAI/litellm/pull/14229) --- ## Performance Improvements -This release includes significant performance optimizations: +This release includes significant performance optimizations. On our internal benchmarks we saw 1 instance get +400 RPS when using correct amount of workers + CPU cores combination. - **+400 RPS Performance Boost** - LiteLLM Proxy now uses correct amount of CPU cores for optimal performance - [PR #14153](https://github.com/BerriAI/litellm/pull/14153) -- **Stream Timeout Control** - Allow using `x-litellm-stream-timeout` header for stream timeout in requests - [PR #14147](https://github.com/BerriAI/litellm/pull/14147) - **Default CPU Workers** - Changed DEFAULT_NUM_WORKERS_LITELLM_PROXY default to number of CPUs - [PR #14242](https://github.com/BerriAI/litellm/pull/14242) -- **Client Disconnect Handling** - Cancel upstream on client disconnect (reverted in this release) - [PR #14295](https://github.com/BerriAI/litellm/pull/14295), [PR #14304](https://github.com/BerriAI/litellm/pull/14304) + --- @@ -86,21 +82,9 @@ This release includes significant performance optimizations: | Volcengine | `doubao-embedding-large` | 4K | Free | Free | 2048-dim embeddings | | Together AI | `together_ai/deepseek-ai/DeepSeek-V3.1` | 128K | $0.60 | $1.70 | Reasoning support | -**Video Generation Models Added:** Veo 3.0, Veo 3.0 Fast, and Veo 2.0 models for both Gemini and Vertex AI providers. - -**Volcengine Embedding Models:** Multiple Doubao embedding models with various dimensions (2048, 2560, 4096). - -#### Model Updates - -- **Together AI Models** - Added function calling and tool choice support for Qwen3, DeepSeek-R1, Mistral, and GPT-OSS models -- **Gemini Pricing Update** - Corrected output pricing for `gemini-2.5-flash-image-preview` from $2.50 to $30.00 per 1M tokens -- **Deprecated Models Removed** - Removed deprecated Groq llama3 models and Cerebras gpt-oss-20b - #### Features - **[Google Gemini](../../docs/providers/gemini)** - - Fixed Gemini 2.5 Pro schema validation with OpenAI-style type arrays in tools - [PR #14154](https://github.com/BerriAI/litellm/pull/14154) - - Fixed Gemini Tool Calling empty enum property - [PR #14155](https://github.com/BerriAI/litellm/pull/14155) - Added 'thoughtSignature' support via 'thinking_blocks' - [PR #14122](https://github.com/BerriAI/litellm/pull/14122) - Added support for reasoning_effort='minimal' for Gemini models - [PR #14262](https://github.com/BerriAI/litellm/pull/14262) - **[OpenRouter](../../docs/providers/openrouter)** @@ -117,15 +101,19 @@ This release includes significant performance optimizations: - **[Ollama](../../docs/providers/ollama)** - Added unified 'thinking' param support via `reasoning_content` - [PR #14121](https://github.com/BerriAI/litellm/pull/14121) - **[Anthropic](../../docs/providers/anthropic)** - - Added support for anthropic citation API in Databricks - [PR #14077](https://github.com/BerriAI/litellm/pull/14077) - Added supported text field to anthropic citation response - [PR #14126](https://github.com/BerriAI/litellm/pull/14126) - - Added guardrail to the Anthropic API endpoint - [PR #14107](https://github.com/BerriAI/litellm/pull/14107) - **[OCI Provider](../../docs/providers/oci)** - Handle assistant messages with both content and tool_calls - [PR #14171](https://github.com/BerriAI/litellm/pull/14171) - **[Bedrock](../../docs/providers/bedrock)** - Fixed structure output - [PR #14130](https://github.com/BerriAI/litellm/pull/14130) - Added initial support for Bedrock Batches API - [PR #14190](https://github.com/BerriAI/litellm/pull/14190) - - Support AWS_BEDROCK_RUNTIME_ENDPOINT on bedrock passthrough - [PR #14156](https://github.com/BerriAI/litellm/pull/14156) +- **[Databricks](../../docs/providers/databricks)** + - Added support for anthropic citation API in Databricks - [PR #14077](https://github.com/BerriAI/litellm/pull/14077) + +### Bug Fixes +- **[Google Gemini (Google AI Studio + Vertex AI)](../../docs/providers/gemini)** + - Fixed Gemini 2.5 Pro schema validation with OpenAI-style type arrays in tools - [PR #14154](https://github.com/BerriAI/litellm/pull/14154) + - Fixed Gemini Tool Calling empty enum property - [PR #14155](https://github.com/BerriAI/litellm/pull/14155) #### New Provider Support @@ -141,11 +129,13 @@ This release includes significant performance optimizations: - **[Images API](../../docs/image_generation)** - Added pass through image generation and image editing on OpenAI - [PR #14292](https://github.com/BerriAI/litellm/pull/14292) - Support extra_body parameter for image generation - [PR #14211](https://github.com/BerriAI/litellm/pull/14211) -- **[Video Generation](../../docs/video_generation)** - - Allow using Veo Video Generation through LiteLLM Pass through routes - [PR #14228](https://github.com/BerriAI/litellm/pull/14228) - **[Responses API](../../docs/response_api)** - Fixed response API for reasoning item in input for litellm proxy - [PR #14200](https://github.com/BerriAI/litellm/pull/14200) - Added structured output for SDK - [PR #14206](https://github.com/BerriAI/litellm/pull/14206) +- **[Bedrock Passthrough](../../docs/pass_through/bedrock)** + - Support AWS_BEDROCK_RUNTIME_ENDPOINT on bedrock passthrough - [PR #14156](https://github.com/BerriAI/litellm/pull/14156) +- **[Google AI Studio Passthrough](../../docs/pass_through/google_ai_studio)** + - Allow using Veo Video Generation through LiteLLM Pass through routes - [PR #14228](https://github.com/BerriAI/litellm/pull/14228) - **General** - Added support for safety_identifier parameter in chat.completions.create - [PR #14174](https://github.com/BerriAI/litellm/pull/14174) - Fixed misclassified 500 error on invalid image_url in /chat/completions request - [PR #14149](https://github.com/BerriAI/litellm/pull/14149) @@ -154,12 +144,23 @@ This release includes significant performance optimizations: #### Bugs - **General** - - Fixed x-litellm-tags not routing with Responses API - [PR #14289](https://github.com/BerriAI/litellm/pull/14289) - Remove "/" or ":" from model name when being used as h11 header name - [PR #14191](https://github.com/BerriAI/litellm/pull/14191) - Bug fix for openai.gpt-oss when using reasoning_effort parameter - [PR #14300](https://github.com/BerriAI/litellm/pull/14300) --- +## Spend Tracking, Budgets and Rate Limiting + +### Features + - Added header support for spend_logs_metadata - [PR #14186](https://github.com/BerriAI/litellm/pull/14186) + - Litellm passthrough cost tracking for chat completion - [PR #14256](https://github.com/BerriAI/litellm/pull/14256) + +### Bug Fixes + - Fixed TPM Rate Limit Bug - [PR #14237](https://github.com/BerriAI/litellm/pull/14237) + - Fixed Key Budget not resets at expectable times - [PR #14241](https://github.com/BerriAI/litellm/pull/14241) + + + ## Management Endpoints / UI #### Features @@ -197,6 +198,9 @@ This release includes significant performance optimizations: - **[Slack Alerting](../../docs/proxy/alerting)** - Added alert type to alert message to slack for easier handling - [PR #14176](https://github.com/BerriAI/litellm/pull/14176) +#### Guardrails + - Added guardrail to the Anthropic API endpoint - [PR #14107](https://github.com/BerriAI/litellm/pull/14107) + #### New Integration - **[CloudZero](../../docs/proxy/cost_tracking)** @@ -212,13 +216,12 @@ This release includes significant performance optimizations: - LiteLLM Proxy: +400 RPS when using correct amount of CPU cores - [PR #14153](https://github.com/BerriAI/litellm/pull/14153) - Allow using `x-litellm-stream-timeout` header for stream timeout in requests - [PR #14147](https://github.com/BerriAI/litellm/pull/14147) - Change DEFAULT_NUM_WORKERS_LITELLM_PROXY default to number CPUs - [PR #14242](https://github.com/BerriAI/litellm/pull/14242) -- **Rate Limiting** - - Fixed TPM Rate Limit Bug - [PR #14237](https://github.com/BerriAI/litellm/pull/14237) - - Fixed Key Budget not resets at expectable times - [PR #14241](https://github.com/BerriAI/litellm/pull/14241) - **Monitoring** - Added Prometheus missing metrics - [PR #14139](https://github.com/BerriAI/litellm/pull/14139) - - Added header support for spend_logs_metadata - [PR #14186](https://github.com/BerriAI/litellm/pull/14186) - - Litellm passthrough cost tracking for chat completion - [PR #14256](https://github.com/BerriAI/litellm/pull/14256) +- **Timeout** + - **Stream Timeout Control** - Allow using `x-litellm-stream-timeout` header for stream timeout in requests - [PR #14147](https://github.com/BerriAI/litellm/pull/14147) +- **Routing** + - Fixed x-litellm-tags not routing with Responses API - [PR #14289](https://github.com/BerriAI/litellm/pull/14289) #### Bugs From 6533ce45a5c6e30dc9f3889ace9aa0c760858c4d Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 16:00:46 -0700 Subject: [PATCH 166/178] docs fix --- docs/my-website/release_notes/v1.76.3-stable/index.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/my-website/release_notes/v1.76.3-stable/index.md b/docs/my-website/release_notes/v1.76.3-stable/index.md index 3228bbcde8f..7aa24a220c9 100644 --- a/docs/my-website/release_notes/v1.76.3-stable/index.md +++ b/docs/my-website/release_notes/v1.76.3-stable/index.md @@ -239,8 +239,6 @@ This release includes significant performance optimizations. On our internal ben - Bug fixes for handling SCIM Group Memberships - [PR #14226](https://github.com/BerriAI/litellm/pull/14226) - **Kubernetes** - Added optional PodDisruptionBudget for litellm proxy - [PR #14093](https://github.com/BerriAI/litellm/pull/14093) -- **Redis** - - Added redis ssl and username support (reverted in this release) - [PR #11319](https://github.com/BerriAI/litellm/pull/11319), [PR #14303](https://github.com/BerriAI/litellm/pull/14303) - **Error Handling** - Add model to azure error message - [PR #14294](https://github.com/BerriAI/litellm/pull/14294) From 0754ad9e0247d70a847deae07c728ee6fb6c6540 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 16:01:03 -0700 Subject: [PATCH 167/178] docs fix --- docs/my-website/release_notes/v1.76.3-stable/index.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/docs/my-website/release_notes/v1.76.3-stable/index.md b/docs/my-website/release_notes/v1.76.3-stable/index.md index 7aa24a220c9..933b5fe9aa4 100644 --- a/docs/my-website/release_notes/v1.76.3-stable/index.md +++ b/docs/my-website/release_notes/v1.76.3-stable/index.md @@ -244,16 +244,6 @@ This release includes significant performance optimizations. On our internal ben --- -## Documentation Improvements - -- **Provider Documentation** - - Added more info to load balancing & pass through endpoints - [PR #14232](https://github.com/BerriAI/litellm/pull/14232) - - Fixed custom callbacks doc - [PR #14216](https://github.com/BerriAI/litellm/pull/14216) - - Fixed prometheus metrics documentation - [PR #14199](https://github.com/BerriAI/litellm/pull/14199) - - Added loom for claude code - [PR #14223](https://github.com/BerriAI/litellm/pull/14223) - ---- - ## New Contributors * @iabhi4 made their first contribution in [PR #14093](https://github.com/BerriAI/litellm/pull/14093) * @zainhas made their first contribution in [PR #14087](https://github.com/BerriAI/litellm/pull/14087) From 71ce338b2417e990c86f5e955a42da2373a5579c Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 16:07:02 -0700 Subject: [PATCH 168/178] docs fix --- .../release_notes/v1.76.3-stable/index.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/docs/my-website/release_notes/v1.76.3-stable/index.md b/docs/my-website/release_notes/v1.76.3-stable/index.md index 933b5fe9aa4..0f997d6941a 100644 --- a/docs/my-website/release_notes/v1.76.3-stable/index.md +++ b/docs/my-website/release_notes/v1.76.3-stable/index.md @@ -51,6 +51,21 @@ pip install litellm==1.76.3 ## Major Changes - **Performance Optimization**: LiteLLM Proxy now achieves +400 RPS when using correct amount of CPU cores - [PR #14153](https://github.com/BerriAI/litellm/pull/14153), [PR #14242](https://github.com/BerriAI/litellm/pull/14242) + + By default, LiteLLM will now use `num_workers = os.cpu_count()` to achieve optimal performance. + + **Override Options:** + + Set environment variable: + ```bash + DEFAULT_NUM_WORKERS_LITELLM_PROXY=1 + ``` + + Or start LiteLLM Proxy with: + ```bash + litellm --num_workers 1 + ``` + - **Security Fix**: Fixed memory_usage_in_mem_cache cache endpoint vulnerability - [PR #14229](https://github.com/BerriAI/litellm/pull/14229) --- From c709d7505d6078ea3461aa41e8b61ceef2c5dc64 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 16:07:30 -0700 Subject: [PATCH 169/178] test fix: test_parallel_streaming_requests --- tests/local_testing/test_streaming.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/local_testing/test_streaming.py b/tests/local_testing/test_streaming.py index c0841d93c0f..ab6b8c5df5b 100644 --- a/tests/local_testing/test_streaming.py +++ b/tests/local_testing/test_streaming.py @@ -1422,8 +1422,6 @@ def test_bedrock_claude_3_streaming(): "claude-3-opus-20240229", "cohere.command-r-plus-v1:0", # bedrock "gpt-3.5-turbo", - # "databricks/databricks-dbrx-instruct", # databricks - "predibase/llama-3-8b-instruct", # predibase ], ) @pytest.mark.asyncio From 7054067238e937d29247f74f468390a0fbd8db29 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 16:13:30 -0700 Subject: [PATCH 170/178] test_cooldown_handlers.py --- .../router_utils/test_cooldown_handlers.py} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename tests/{local_testing/test_router_cooldowns.py => test_litellm/router_utils/test_cooldown_handlers.py} (100%) diff --git a/tests/local_testing/test_router_cooldowns.py b/tests/test_litellm/router_utils/test_cooldown_handlers.py similarity index 100% rename from tests/local_testing/test_router_cooldowns.py rename to tests/test_litellm/router_utils/test_cooldown_handlers.py index cd178e2aaee..d3fd78063bf 100644 --- a/tests/local_testing/test_router_cooldowns.py +++ b/tests/test_litellm/router_utils/test_cooldown_handlers.py @@ -27,9 +27,9 @@ from litellm.router_utils.cooldown_handlers import ( _should_run_cooldown_logic, ) from litellm.types.router import ( + AllowedFailsPolicy, DeploymentTypedDict, LiteLLMParamsTypedDict, - AllowedFailsPolicy, ) From 2982c2a93219e656941747a1a4c42521fe515032 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 16:18:50 -0700 Subject: [PATCH 171/178] fix test_key_budget_metrics --- tests/otel_tests/test_prometheus.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/otel_tests/test_prometheus.py b/tests/otel_tests/test_prometheus.py index 386e9b299be..d0182a8d41d 100644 --- a/tests/otel_tests/test_prometheus.py +++ b/tests/otel_tests/test_prometheus.py @@ -516,9 +516,9 @@ async def test_key_budget_metrics(): ), "remaining budget should be less than 10.0 after first request" assert first_budget["total"] == 10.0, "Total budget metric is incorrect" print("first_budget['remaining_hours']", first_budget["remaining_hours"]) - # The budget reset time is now midnight, not exactly 7 days (168 hours) from creation - # So we'll check if it's within a reasonable range (5-7 days) - assert 120 <= first_budget["remaining_hours"] <= 168, "Budget remaining hours should be within a reasonable range (5-7 days)" + # The budget reset time is now standardized - for "7d" it resets on Monday at midnight + # So we'll check if it's within a reasonable range (0-7 days depending on current day of week) + assert 0 <= first_budget["remaining_hours"] <= 168, "Budget remaining hours should be within a reasonable range (0-7 days depending on day of week)" # Get key info and verify spend matches prometheus metrics key_info = await get_key_info(session, key) From c168fff38a5d74ada2859121974a5d17f24ac31e Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 16:28:00 -0700 Subject: [PATCH 172/178] test_cooldown_badrequest_error --- ...test_cooldown_handlers.py => test_router_cooldown_handlers.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/test_litellm/router_utils/{test_cooldown_handlers.py => test_router_cooldown_handlers.py} (100%) diff --git a/tests/test_litellm/router_utils/test_cooldown_handlers.py b/tests/test_litellm/router_utils/test_router_cooldown_handlers.py similarity index 100% rename from tests/test_litellm/router_utils/test_cooldown_handlers.py rename to tests/test_litellm/router_utils/test_router_cooldown_handlers.py From d89a2a0797999ea3e9c47a36630189b32abb7688 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 16:38:43 -0700 Subject: [PATCH 173/178] test --- .../test_router_cooldown_handlers.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/{test_litellm/router_utils => local_testing}/test_router_cooldown_handlers.py (100%) diff --git a/tests/test_litellm/router_utils/test_router_cooldown_handlers.py b/tests/local_testing/test_router_cooldown_handlers.py similarity index 100% rename from tests/test_litellm/router_utils/test_router_cooldown_handlers.py rename to tests/local_testing/test_router_cooldown_handlers.py From 679d0414e2ea173b6e3eac00ceff0359547c8401 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 17:08:31 -0700 Subject: [PATCH 174/178] test fix --- tests/local_testing/test_function_calling.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/local_testing/test_function_calling.py b/tests/local_testing/test_function_calling.py index de1ba39e5bc..71249898f79 100644 --- a/tests/local_testing/test_function_calling.py +++ b/tests/local_testing/test_function_calling.py @@ -50,7 +50,6 @@ def get_current_weather(location, unit="fahrenheit"): "claude-3-haiku-20240307", "gemini/gemini-1.5-pro", "anthropic.claude-3-sonnet-20240229-v1:0", - "groq/llama-3.1-8b-instant", "cohere_chat/command-r", ], ) From 3b0aec2e5dad8e5a024c03324d656cc397c5a3b5 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 17:47:01 -0700 Subject: [PATCH 175/178] test_azure_openai_gpt_5_responses_api --- tests/llm_translation/test_azure_openai.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/tests/llm_translation/test_azure_openai.py b/tests/llm_translation/test_azure_openai.py index a1b05cbb4ae..59ca5cf61b8 100644 --- a/tests/llm_translation/test_azure_openai.py +++ b/tests/llm_translation/test_azure_openai.py @@ -633,14 +633,17 @@ def test_azure_openai_responses_bridge(): def test_azure_openai_gpt_5_responses_api(): - from litellm import responses + try: + from litellm import responses - litellm._turn_on_debug() + litellm._turn_on_debug() - response = responses( - model="azure/gpt-5", - input="Hello world", - api_key=os.getenv("AZURE_SWEDEN_API_KEY"), - api_base=os.getenv("AZURE_SWEDEN_API_BASE"), - ) - print(f"response: {response}") + response = responses( + model="azure/gpt-5", + input="Hello world", + api_key=os.getenv("AZURE_SWEDEN_API_KEY"), + api_base=os.getenv("AZURE_SWEDEN_API_BASE"), + ) + print(f"response: {response}") + except litellm.RateLimitError: + pytest.skip("Skipping test due to RateLimitError") From 05dfad8837f1e6ba2973148b90e1583ac0f319cb Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Sat, 6 Sep 2025 18:03:09 -0700 Subject: [PATCH 176/178] docs: add basic setup to ui contribution doc --- docs/my-website/docs/contributing.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/my-website/docs/contributing.md b/docs/my-website/docs/contributing.md index 8fc64b8f287..8768e0b4c4d 100644 --- a/docs/my-website/docs/contributing.md +++ b/docs/my-website/docs/contributing.md @@ -14,6 +14,11 @@ git clone https://github.com/BerriAI/litellm.git Tell the proxy where the UI is located ```bash export PROXY_BASE_URL="http://localhost:3000/" + +### ALSO ### - set the basic env variables +DATABASE_URL = "postgresql://:@:/" +LITELLM_MASTER_KEY = "sk-1234" +STORE_MODEL_IN_DB = "True" ``` ```bash From 52ed60ca45101af9b9c7ceee5d4c869d9f31f0ec Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 18:14:47 -0700 Subject: [PATCH 177/178] test fix --- litellm/litellm_core_utils/litellm_logging.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 7134f52c95a..0b152e0dda3 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -1164,15 +1164,6 @@ class Logging(LiteLLMLoggingBaseClass): used for consistent cost calculation across response headers + logging integrations. """ - - # Check if response_cost is already calculated and stored in model_call_details - # This is used by passthrough endpoints that calculate costs manually - if ( - hasattr(self, "model_call_details") - and self.model_call_details.get("response_cost") is not None - ): - return self.model_call_details["response_cost"] - if isinstance(result, BaseModel) and hasattr(result, "_hidden_params"): hidden_params = getattr(result, "_hidden_params", {}) if ( From 0f26ec57d839727090efa1d10ced84a2286d41a1 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 6 Sep 2025 18:19:09 -0700 Subject: [PATCH 178/178] TestGroq --- tests/llm_translation/test_groq.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/llm_translation/test_groq.py b/tests/llm_translation/test_groq.py index 9a9dae9ee89..b9230d8eebb 100644 --- a/tests/llm_translation/test_groq.py +++ b/tests/llm_translation/test_groq.py @@ -21,6 +21,9 @@ class TestGroq(BaseLLMChatTest): """Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833""" pass + def test_tool_call_with_empty_enum_property(self): + pass + @pytest.mark.parametrize("model", ["groq/qwen/qwen3-32b", "groq/openai/gpt-oss-20b", "groq/openai/gpt-oss-120b"]) def test_reasoning_effort_in_supported_params(self, model): """Test that reasoning_effort is in the list of supported parameters for Groq"""