diff --git a/.circleci/config.yml b/.circleci/config.yml index 62e5b77dc65..caaa91a9dd0 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -671,6 +671,7 @@ jobs: pip install mypy pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" + pip install "google-genai==1.22.0" pip install pyarrow pip install "boto3==1.36.0" pip install "aioboto3==13.4.0" @@ -1457,6 +1458,7 @@ jobs: # - run: python ./tests/documentation_tests/test_general_setting_keys.py - run: python ./tests/code_coverage_tests/check_licenses.py - run: python ./tests/code_coverage_tests/router_code_coverage.py + - run: python ./tests/code_coverage_tests/test_chat_completion_imports.py - run: python ./tests/code_coverage_tests/info_log_check.py - run: python ./tests/code_coverage_tests/test_ban_set_verbose.py - run: python ./tests/code_coverage_tests/code_qa_check_tests.py @@ -2824,8 +2826,8 @@ jobs: source "$NVM_DIR/bash_completion" # Install and use Node version - nvm install v18.17.0 - nvm use v18.17.0 + nvm install v20 + nvm use v20 cd ui/litellm-dashboard @@ -2878,7 +2880,26 @@ jobs: name: Install Playwright Browsers command: | npx playwright install + - run: + name: Run UI unit tests (Vitest) + command: | + # Use Node 20 (several deps require >=20) + export NVM_DIR="/opt/circleci/.nvm" + source "$NVM_DIR/nvm.sh" + nvm install 20 + nvm use 20 + cd ui/litellm-dashboard + npm ci || npm install + + # CI run, with both LCOV (Codecov) and HTML (artifact you can click) + CI=true npm run test -- --run --coverage \ + --coverage.provider=v8 \ + --coverage.reporter=lcov \ + --coverage.reporter=html \ + --coverage.reportsDirectory=coverage/html + + - run: name: Build Docker image command: docker build -t my-app:latest -f ./docker/Dockerfile.database . diff --git a/.gitignore b/.gitignore index ed8c88c8990..c2ac5137cbe 100644 --- a/.gitignore +++ b/.gitignore @@ -95,4 +95,5 @@ test.py litellm_config.yaml .cursor .vscode/launch.json -litellm/proxy/to_delete_loadtest_work/* \ No newline at end of file +litellm/proxy/to_delete_loadtest_work/* +update_model_cost_map.py diff --git a/Dockerfile b/Dockerfile index addc109e10c..f85582f992d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -41,9 +41,6 @@ RUN pip uninstall jwt -y RUN pip uninstall PyJWT -y RUN pip install PyJWT==2.9.0 --no-cache-dir -# Build Admin UI -RUN chmod +x docker/build_admin_ui.sh && ./docker/build_admin_ui.sh - # Runtime stage FROM $LITELLM_RUNTIME_IMAGE AS runtime diff --git a/README.md b/README.md index c8a073432c9..f74889fbb27 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ Discord - + Slack @@ -37,7 +37,7 @@ LiteLLM manages: - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing) - Set Budgets & Rate limits per project, api key, model [LiteLLM Proxy Server (LLM Gateway)](https://docs.litellm.ai/docs/simple_proxy) -[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#openai-proxy---docs)
+[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#litellm-proxy-server-llm-gateway---docs)
[**Jump to Supported LLM Providers**](https://github.com/BerriAI/litellm?tab=readme-ov-file#supported-providers-docs) 🚨 **Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published. [More information about the release cycle here](https://docs.litellm.ai/docs/proxy/release_cycle) @@ -316,6 +316,7 @@ curl 'http://0.0.0.0:4000/key/generate' \ | [google AI Studio - gemini](https://docs.litellm.ai/docs/providers/gemini) | ✅ | ✅ | ✅ | ✅ | | | | [mistral ai api](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [cloudflare AI Workers](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | ✅ | | | +| [CompactifAI](https://docs.litellm.ai/docs/providers/compactifai) | ✅ | ✅ | ✅ | ✅ | | | | [cohere](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [anthropic](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | ✅ | | | | [empower](https://docs.litellm.ai/docs/providers/empower) | ✅ | ✅ | ✅ | ✅ | @@ -345,6 +346,7 @@ curl 'http://0.0.0.0:4000/key/generate' \ | [Featherless AI](https://docs.litellm.ai/docs/providers/featherless_ai) | ✅ | ✅ | ✅ | ✅ | | | | [Nebius AI Studio](https://docs.litellm.ai/docs/providers/nebius) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [Heroku](https://docs.litellm.ai/docs/providers/heroku) | ✅ | ✅ | | | | | +| [OVHCloud AI Endpoints](https://docs.litellm.ai/docs/providers/ovhcloud) | ✅ | ✅ | | | | | [**Read the Docs**](https://docs.litellm.ai/docs/) @@ -408,7 +410,7 @@ All these checks must pass before your PR can be merged. - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- [Community Slack 💭](https://join.slack.com/share/enQtOTE0ODczMzk2Nzk4NC01YjUxNjY2YjBlYTFmNDRiZTM3NDFiYTM3MzVkODFiMDVjOGRjMmNmZTZkZTMzOWQzZGQyZWIwYjQ0MWExYmE3) +- [Community Slack 💭](https://www.litellm.ai/support) - Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py new file mode 100644 index 00000000000..615baa422eb --- /dev/null +++ b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py @@ -0,0 +1,25 @@ +from openai import OpenAI + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234", +) + +BEDROCK_BATCH_MODEL = "bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0" + +# Upload file +batch_input_file = client.files.create( + file=open("./bedrock_batch_completions.jsonl", "rb"), + purpose="batch", + extra_body={"target_model_names": BEDROCK_BATCH_MODEL} +) +print(batch_input_file) + +# Create batch +batch = client.batches.create( + input_file_id=batch_input_file.id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "Test batch job"}, +) +print(batch) \ No newline at end of file diff --git a/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl new file mode 100644 index 00000000000..adef9ac2dd5 --- /dev/null +++ b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl @@ -0,0 +1,128 @@ +{"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}} +{"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 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"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}} +{"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}} +{"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}} +{"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}} +{"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}} +{"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}} +{"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/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py b/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py new file mode 100644 index 00000000000..351b0920eb8 --- /dev/null +++ b/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py @@ -0,0 +1,36 @@ +""" +Use LiteLLM Proxy MCP Gateway to call MCP tools. + +When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers. +""" +import openai + +client = openai.OpenAI( + api_key="sk-1234", # paste your litellm proxy api key here + base_url="http://localhost:4000" # paste your litellm proxy base url here +) +print("Making API request to Responses API with MCP tools") + +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy", + "require_approval": "never" + } + ], + stream=True, + tool_choice="required" +) + +for chunk in response: + print("response chunk: ", chunk) diff --git a/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md new file mode 100644 index 00000000000..f24bc8f129e --- /dev/null +++ b/cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md @@ -0,0 +1,256 @@ +# LiteLLM Release Notes Generation Instructions + +This document provides comprehensive instructions for AI agents to generate release notes for LiteLLM following the established format and style. + +## Required Inputs + +1. **Release Version** (e.g., `v1.76.3-stable`) +2. **PR Diff/Changelog** - List of PRs with titles and contributors +3. **Previous Version Commit Hash** - To compare model pricing changes +4. **Reference Release Notes** - Previous release notes to follow style/format + +## Step-by-Step Process + +### 1. Initial Setup and Analysis + +```bash +# Check git diff for model pricing changes +git diff HEAD -- model_prices_and_context_window.json +``` + +**Key Analysis Points:** +- New models added (look for new entries) +- Deprecated models removed (look for deleted entries) +- Pricing updates (look for cost changes) +- Feature support changes (tool calling, reasoning, etc.) + +### 2. Release Notes Structure + +Follow this exact structure based on `docs/my-website/release_notes/v1.76.1-stable/index.md`: + +```markdown +--- +title: "v1.76.X-stable - [Key Theme]" +slug: "v1-76-X" +date: YYYY-MM-DDTHH:mm:ss +authors: [standard author block] +hide_table_of_contents: false +--- + +## Deploy this version +[Docker and pip installation tabs] + +## Key Highlights +[3-5 bullet points of major features] + +## Major Changes +[Critical changes users need to know] + +## Performance Improvements +[Performance-related changes] + +## New Models / Updated Models +[Detailed model tables and provider updates] + +## LLM API Endpoints +[API-related features and fixes] + +## Management Endpoints / UI +[Admin interface and management changes] + +## Logging / Guardrail Integrations +[Observability and security features] + +## Performance / Loadbalancing / Reliability improvements +[Infrastructure improvements] + +## General Proxy Improvements +[Other proxy-related changes] + +## New Contributors +[List of first-time contributors] + +## Full Changelog +[Link to GitHub comparison] +``` + +### 3. Categorization Rules + +**Performance Improvements:** +- RPS improvements +- Memory optimizations +- CPU usage optimizations +- Timeout controls +- Worker configuration + +**New Models/Updated Models:** +- Extract from model_prices_and_context_window.json diff +- Create tables with: Provider, Model, Context Window, Input Cost, Output Cost, Features +- Group by provider +- Note pricing corrections +- Highlight deprecated models + +**Provider Features:** +- Group by provider (Gemini, OpenAI, Anthropic, etc.) +- Link to provider docs: `../../docs/providers/[provider_name]` +- Separate features from bug fixes + +**API Endpoints:** +- Images API +- Video Generation (if applicable) +- Responses API +- Passthrough endpoints +- General chat completions + +**UI/Management:** +- Authentication changes +- Dashboard improvements +- Team management +- Key management + +**Integrations:** +- Logging providers (Datadog, Braintrust, etc.) +- Guardrails +- Cost tracking +- Observability + +### 4. Documentation Linking Strategy + +**Link to docs when:** +- New provider support added +- Significant feature additions +- API endpoint changes +- Integration additions + +**Link format:** `../../docs/[category]/[specific_doc]` + +**Common doc paths:** +- `../../docs/providers/[provider]` - Provider-specific docs +- `../../docs/image_generation` - Image generation +- `../../docs/video_generation` - Video generation (if exists) +- `../../docs/response_api` - Responses API +- `../../docs/proxy/logging` - Logging integrations +- `../../docs/proxy/guardrails` - Guardrails +- `../../docs/pass_through/[provider]` - Passthrough endpoints + +### 5. Model Table Generation + +From git diff analysis, create tables like: + +```markdown +| 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 | +``` + +**Extract from JSON:** +- `max_input_tokens` → Context Window +- `input_cost_per_token` × 1,000,000 → Input cost +- `output_cost_per_token` × 1,000,000 → Output cost +- `supports_*` fields → Features +- Special pricing fields (per image, per second) for generation models + +### 6. PR Categorization Logic + +**By Keywords in PR Title:** +- `[Perf]`, `Performance`, `RPS` → Performance Improvements +- `[Bug]`, `[Bug Fix]`, `Fix` → Bug Fixes section +- `[Feat]`, `[Feature]`, `Add support` → Features section +- `[Docs]` → Documentation (usually exclude from main sections) +- Provider names (Gemini, OpenAI, etc.) → Group under provider + +**By PR Content Analysis:** +- New model additions → New Models section +- UI changes → Management Endpoints/UI +- Logging/observability → Logging/Guardrail Integrations +- Rate limiting/budgets → Performance/Reliability +- Authentication → Management Endpoints + +### 7. Writing Style Guidelines + +**Tone:** +- Professional but accessible +- Focus on user impact +- Highlight breaking changes clearly +- Use active voice + +**Formatting:** +- Use consistent markdown formatting +- Include PR links: `[PR #XXXXX](https://github.com/BerriAI/litellm/pull/XXXXX)` +- Use code blocks for configuration examples +- Bold important terms and section headers + +**Warnings/Notes:** +- Add warning boxes for breaking changes +- Include migration instructions when needed +- Provide override options for default changes + +### 8. Quality Checks + +**Before finalizing:** +- Verify all PR links work +- Check documentation links are valid +- Ensure model pricing is accurate +- Confirm provider names are consistent +- Review for typos and formatting issues + +### 9. Common Patterns to Follow + +**Performance Changes:** +```markdown +- **+400 RPS Performance Boost** - Description - [PR #XXXXX](link) +``` + +**New Models:** +Always include pricing table and feature highlights + +**Breaking Changes:** +```markdown +:::warning +This release has a known issue... +::: +``` + +**Provider Features:** +```markdown +- **[Provider Name](../../docs/providers/provider)** + - Feature description - [PR #XXXXX](link) +``` + +### 10. Missing Documentation Check + +**Review for missing docs:** +- New providers without documentation +- New API endpoints without examples +- Complex features without guides +- Integration setup instructions + +**Flag for documentation needs:** +- New provider integrations +- Significant API changes +- Complex configuration options +- Migration requirements + +## Example Command Workflow + +```bash +# 1. Get model changes +git diff HEAD -- model_prices_and_context_window.json + +# 2. Analyze PR list for categorization +# 3. Create release notes following template +# 4. Link to appropriate documentation +# 5. Review for missing documentation needs +``` + +## Output Requirements + +- Follow exact markdown structure from reference +- Include all PR links and contributors +- Provide accurate model pricing tables +- Link to relevant documentation +- Highlight breaking changes with warnings +- Include deployment instructions +- End with full changelog link + +This process ensures consistent, comprehensive release notes that help users understand changes and upgrade smoothly. diff --git a/docs/my-website/docs/batches.md b/docs/my-website/docs/batches.md index d5fbc53c080..1bd4c700ae7 100644 --- a/docs/my-website/docs/batches.md +++ b/docs/my-website/docs/batches.md @@ -7,7 +7,7 @@ Covers Batches, Files | Feature | Supported | Notes | |-------|-------|-------| -| Supported Providers | OpenAI, Azure, Vertex | - | +| Supported Providers | OpenAI, Azure, Vertex, Bedrock | - | | ✨ Cost Tracking | ✅ | LiteLLM Enterprise only | | Logging | ✅ | Works across all logging integrations | @@ -178,6 +178,7 @@ print("list_batches_response=", list_batches_response) ### [Azure OpenAI](./providers/azure#azure-batches-api) ### [OpenAI](#quick-start) ### [Vertex AI](./providers/vertex#batch-apis) +### [Bedrock](./providers/bedrock_batches) ## How Cost Tracking for Batches API Works diff --git a/docs/my-website/docs/completion/input.md b/docs/my-website/docs/completion/input.md index 9699d97b352..91d9cc72cf3 100644 --- a/docs/my-website/docs/completion/input.md +++ b/docs/my-website/docs/completion/input.md @@ -65,6 +65,7 @@ Use `litellm.get_supported_openai_params()` for an updated list of params for ea | Github | ✅| ✅ | ✅ | ✅| ✅ | ✅ | ✅ | ✅| ✅ | ✅| ✅|| || ✅ | ✅ (model dependent) | ✅ (model dependent) || || | Novita AI| ✅| ✅ || ✅| ✅ | ✅ | ✅ | ✅| ✅ | ✅| || ✅||| |||| || | Bytez | ✅| ✅ || ✅| ✅ | | | ✅|| || || || || || || +| OVHCloud AI Endpoints | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | :::note diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 45cec48cd7e..80b4c32d0ab 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -114,7 +114,6 @@ mcp_servers: description: "My custom MCP server" auth_type: "api_key" auth_value: "abc123" - spec_version: "2025-03-26" ``` **Configuration Options:** @@ -195,70 +194,169 @@ litellm_settings: ## Using your MCP +### Use on LiteLLM UI + +Follow this walkthrough to use your MCP on LiteLLM UI + + + +### Use with Responses API + +Replace `http://localhost:4000` with your LiteLLM Proxy base URL. + +Demo Video Using Responses API with LiteLLM Proxy: [Demo video here](https://www.loom.com/share/34587e618c5c47c0b0d67b4e4d02718f?sid=2caf3d45-ead4-4490-bcc1-8d6dd6041c02) + + - - -#### Connect via OpenAI Responses API - -Use the OpenAI Responses API to connect to your LiteLLM MCP server: + ```bash title="cURL Example" showLineNumbers -curl --location 'https://api.openai.com/v1/responses' \ +curl --location 'http://localhost:4000/v1/responses' \ --header 'Content-Type: application/json' \ ---header "Authorization: Bearer $OPENAI_API_KEY" \ +--header "Authorization: Bearer sk-1234" \ --data '{ - "model": "gpt-4o", + "model": "gpt-5", + "input": [ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], "tools": [ { "type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy", - "require_approval": "never", - "headers": { - "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" - } + "require_approval": "never" } ], - "input": "Run available tools", + "stream": true, "tool_choice": "required" }' ``` + - +```python title="Python SDK Example" showLineNumbers +""" +Use LiteLLM Proxy MCP Gateway to call MCP tools. -#### Connect via LiteLLM Proxy Responses API +When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers. +""" +import openai -Use this when calling LiteLLM Proxy for LLM API requests to `/v1/responses` endpoint. +client = openai.OpenAI( + api_key="sk-1234", # paste your litellm proxy api key here + base_url="http://localhost:4000" # paste your litellm proxy base url here +) +print("Making API request to Responses API with MCP tools") -```bash title="cURL Example" showLineNumbers -curl --location '/v1/responses' \ ---header 'Content-Type: application/json' \ ---header "Authorization: Bearer $LITELLM_API_KEY" \ ---data '{ - "model": "gpt-4o", - "tools": [ +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ { "type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy", - "require_approval": "never", - "headers": { - "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" - } + "require_approval": "never" } ], - "input": "Run available tools", + stream=True, + tool_choice="required" +) + +for chunk in response: + print("response chunk: ", chunk) +``` + + + + +#### Specifying MCP Tools + +You can specify which MCP tools are available by using the `allowed_tools` parameter. This allows you to restrict access to specific tools within an MCP server. + +To get the list of allowed tools when using LiteLLM MCP Gateway, you can naigate to the LiteLLM UI on MCP Servers > MCP Tools > Click the Tool > Copy Tool Name. + + + + +```bash title="cURL Example with allowed_tools" showLineNumbers +curl --location 'http://localhost:4000/v1/responses' \ +--header 'Content-Type: application/json' \ +--header "Authorization: Bearer sk-1234" \ +--data '{ + "model": "gpt-5", + "input": [ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + "tools": [ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ], + "stream": true, "tool_choice": "required" }' ``` + - +```python title="Python SDK Example with allowed_tools" showLineNumbers +import openai -#### Connect via Cursor IDE +client = openai.OpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) + +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ], + stream=True, + tool_choice="required" +) + +print(response) +``` + + + + +### Use with Cursor IDE Use tools directly from Cursor IDE with LiteLLM MCP: @@ -281,9 +379,6 @@ Use tools directly from Cursor IDE with LiteLLM MCP: } ``` - - - #### How it works when server_url="litellm_proxy" When server_url="litellm_proxy", LiteLLM bridges non-MCP providers to your MCP tools. @@ -620,7 +715,6 @@ mcp_servers: url: https://mcp.deepwiki.com/mcp transport: "http" auth_type: "none" - spec_version: "2025-03-26" access_groups: ["dev_group"] ``` diff --git a/docs/my-website/docs/observability/helicone_integration.md b/docs/my-website/docs/observability/helicone_integration.md index 9b807b8d0f6..22ea051f7cd 100644 --- a/docs/my-website/docs/observability/helicone_integration.md +++ b/docs/my-website/docs/observability/helicone_integration.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Helicone - OSS LLM Observability Platform :::tip @@ -9,9 +12,68 @@ https://github.com/BerriAI/litellm [Helicone](https://helicone.ai/) is an open source observability platform that proxies your LLM requests and provides key insights into your usage, spend, latency and more. -## Using Helicone with LiteLLM +## Quick Start -LiteLLM provides `success_callbacks` and `failure_callbacks`, allowing you to easily log data to Helicone based on the status of your responses. + + + +Use just 1 line of code to instantly log your responses **across all providers** with Helicone: + +```python +import os +from litellm import completion + +## Set env variables +os.environ["HELICONE_API_KEY"] = "your-helicone-key" +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +# Set callbacks +litellm.success_callback = ["helicone"] + +# OpenAI call +response = completion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hi 👋 - I'm OpenAI"}], +) + +print(response) +``` + + + + +Add Helicone to your LiteLLM proxy configuration: + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + +# Add Helicone callback +litellm_settings: + success_callback: ["helicone"] + +# Set Helicone API key +environment_variables: + HELICONE_API_KEY: "your-helicone-key" +``` + +Start the proxy: +```bash +litellm --config config.yaml +``` + + + + +## Integration Methods + +There are two main approaches to integrate Helicone with LiteLLM: + +1. **Callbacks**: Log to Helicone while using any provider +2. **Proxy Mode**: Use Helicone as a proxy for advanced features ### Supported LLM Providers @@ -26,27 +88,16 @@ Helicone can log requests across [various LLM providers](https://docs.helicone.a - Replicate - And more -### Integration Methods +## Method 1: Using Callbacks -There are two main approaches to integrate Helicone with LiteLLM: +Log requests to Helicone while using any LLM provider directly. -1. Using callbacks -2. Using Helicone as a proxy - -Let's explore each method in detail. - -### Approach 1: Use Callbacks - -Use just 1 line of code to instantly log your responses **across all providers** with Helicone: - -```python -litellm.success_callback = ["helicone"] -``` - -Complete Code + + ```python import os +import litellm from litellm import completion ## Set env variables @@ -66,28 +117,78 @@ response = completion( print(response) ``` -### Approach 2: Use Helicone as a proxy + + + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + - model_name: claude-3 + litellm_params: + model: anthropic/claude-3-sonnet-20240229 + api_key: os.environ/ANTHROPIC_API_KEY + +# Add Helicone logging +litellm_settings: + success_callback: ["helicone"] + +# Environment variables +environment_variables: + HELICONE_API_KEY: "your-helicone-key" + OPENAI_API_KEY: "your-openai-key" + ANTHROPIC_API_KEY: "your-anthropic-key" +``` + +Start the proxy: +```bash +litellm --config config.yaml +``` + +Make requests to your proxy: +```python +import openai + +client = openai.OpenAI( + api_key="anything", # proxy doesn't require real API key + base_url="http://localhost:4000" +) + +response = client.chat.completions.create( + model="gpt-4", # This gets logged to Helicone + messages=[{"role": "user", "content": "Hello!"}] +) +``` + + + + +## Method 2: Using Helicone as a Proxy Helicone's proxy provides [advanced functionality](https://docs.helicone.ai/getting-started/proxy-vs-async) like caching, rate limiting, LLM security through [PromptArmor](https://promptarmor.com/) and more. -To use Helicone as a proxy for your LLM requests: + + -1. Set Helicone as your base URL via: litellm.api_base -2. Pass in Helicone request headers via: litellm.metadata - -Complete Code: +Set Helicone as your base URL and pass authentication headers: ```python import os import litellm from litellm import completion +# Configure LiteLLM to use Helicone proxy litellm.api_base = "https://oai.hconeai.com/v1" litellm.headers = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", } -response = litellm.completion( +# Set your OpenAI API key +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +response = completion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "How does a court case get to the Supreme Court?"}] ) @@ -136,36 +237,119 @@ litellm.metadata = { } ``` -### Session Tracking and Tracing + + + +## Session Tracking and Tracing Track multi-step and agentic LLM interactions using session IDs and paths: -```python -litellm.metadata = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API - "Helicone-Session-Id": "session-abc-123", # The session ID you want to track - "Helicone-Session-Path": "parent-trace/child-trace", # The path of the session -} -``` - -- `Helicone-Session-Id`: Use this to specify the unique identifier for the session you want to track. This allows you to group related requests together. -- `Helicone-Session-Path`: This header defines the path of the session, allowing you to represent parent and child traces. For example, "parent/child" represents a child trace of a parent trace. - -By using these two headers, you can effectively group and visualize multi-step LLM interactions, gaining insights into complex AI workflows. - -### Retry and Fallback Mechanisms - -Set up retry mechanisms and fallback options: + + ```python +import litellm + +litellm.api_base = "https://oai.hconeai.com/v1" litellm.metadata = { - "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API - "Helicone-Retry-Enabled": "true", # Enable retry mechanism - "helicone-retry-num": "3", # Set number of retries - "helicone-retry-factor": "2", # Set exponential backoff factor - "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', # Set fallback models + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "parent-trace/child-trace", } + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Start a conversation"}] +) ``` + + + +```python +import openai + +client = openai.OpenAI( + api_key="anything", + base_url="http://localhost:4000" +) + +# First request in session +response1 = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "Hello"}], + extra_headers={ + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "conversation/greeting" + } +) + +# Follow-up request in same session +response2 = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "Tell me more"}], + extra_headers={ + "Helicone-Session-Id": "session-abc-123", + "Helicone-Session-Path": "conversation/follow-up" + } +) +``` + + + + +- `Helicone-Session-Id`: Unique identifier for the session to group related requests +- `Helicone-Session-Path`: Hierarchical path to represent parent/child traces (e.g., "parent/child") + +## Retry and Fallback Mechanisms + + + + +```python +import litellm + +litellm.api_base = "https://oai.hconeai.com/v1" +litellm.metadata = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", + "Helicone-Retry-Enabled": "true", + "helicone-retry-num": "3", + "helicone-retry-factor": "2", # Exponential backoff + "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', +} + +response = litellm.completion( + model="gpt-4", + messages=[{"role": "user", "content": "Hello"}] +) +``` + + + + +```yaml title="config.yaml" +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + api_base: "https://oai.hconeai.com/v1" + +default_litellm_params: + headers: + Helicone-Auth: "Bearer ${HELICONE_API_KEY}" + Helicone-Retry-Enabled: "true" + helicone-retry-num: "3" + helicone-retry-factor: "2" + Helicone-Fallbacks: '["gpt-3.5-turbo", "gpt-4"]' + +environment_variables: + HELICONE_API_KEY: "your-helicone-key" + OPENAI_API_KEY: "your-openai-key" +``` + + + + > **Supported Headers** - For a full list of supported Helicone headers and their descriptions, please refer to the [Helicone documentation](https://docs.helicone.ai/getting-started/quick-start). > By utilizing these headers and metadata options, you can gain deeper insights into your LLM usage, optimize performance, and better manage your AI workflows with Helicone and LiteLLM. diff --git a/docs/my-website/docs/observability/posthog_integration.md b/docs/my-website/docs/observability/posthog_integration.md new file mode 100644 index 00000000000..7e6a0e1076b --- /dev/null +++ b/docs/my-website/docs/observability/posthog_integration.md @@ -0,0 +1,216 @@ +# PostHog - Tracking LLM Usage Analytics + +## What is PostHog? + +PostHog is an open-source product analytics platform that helps you track and analyze how users interact with your product. For LLM applications, PostHog provides specialized AI features to track model usage, performance, and user interactions with your AI features. + +## Usage with LiteLLM Proxy (LLM Gateway) + +**Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +litellm_settings: + success_callback: ["posthog"] + failure_callback: ["posthog"] +``` + +**Step 2**: Set required environment variables + +```shell +export POSTHOG_API_KEY="your-posthog-api-key" +# Optional, defaults to https://app.posthog.com +export POSTHOG_API_URL="https://app.posthog.com" # optional +``` + +**Step 3**: Start the proxy, make a test request + +Start proxy + +```shell +litellm --config config.yaml --debug +``` + +Test Request + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + "metadata": { + "user_id": "user-123", + "custom_field": "custom_value" + } +}' +``` + +## Usage with LiteLLM Python SDK + +### Quick Start + +Use just 2 lines of code, to instantly log your responses **across all providers** with PostHog: + +```python +litellm.success_callback = ["posthog"] +litellm.failure_callback = ["posthog"] # logs errors to posthog +``` +```python +import litellm +import os + +# from PostHog +os.environ["POSTHOG_API_KEY"] = "" +# Optional, defaults to https://app.posthog.com +os.environ["POSTHOG_API_URL"] = "" # optional + +# LLM API Keys +os.environ['OPENAI_API_KEY']="" + +# set posthog as a callback, litellm will send the data to posthog +litellm.success_callback = ["posthog"] + +# openai call +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi - i'm openai"} + ], + metadata = { + "user_id": "user-123", # set posthog user ID + } +) +``` + +### Advanced + +#### Set User ID and Custom Metadata + +Pass `user_id` in `metadata` to associate events with specific users in PostHog: + +**With LiteLLM Python SDK:** + +```python +import litellm + +litellm.success_callback = ["posthog"] + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hello world"} + ], + metadata={ + "user_id": "user-123", # Add user ID for PostHog tracking + "custom_field": "custom_value" # Add custom metadata + } +) +``` + +**With LiteLLM Proxy using OpenAI Python SDK:** + +```python +import openai + +client = openai.OpenAI( + api_key="sk-1234", # Your LiteLLM Proxy API key + base_url="http://0.0.0.0:4000" # Your LiteLLM Proxy URL +) + +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hello world"} + ], + extra_body={ + "metadata": { + "user_id": "user-123", # Add user ID for PostHog tracking + "project_name": "my-project", # Add custom metadata + "environment": "production" + } + } +) +``` + +#### Disable Logging for Specific Calls + +Use the `no-log` flag to prevent logging for specific calls: + +```python +import litellm + +litellm.success_callback = ["posthog"] + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "This won't be logged"} + ], + metadata={"no-log": True} +) +``` + +## What's Logged to PostHog? + +When LiteLLM logs to PostHog, it captures detailed information about your LLM usage: + +### For Completion Calls +- **Model Information**: Provider, model name, model parameters +- **Usage Metrics**: Input tokens, output tokens, total cost +- **Performance**: Latency, completion time +- **Content**: Input messages, model responses (respects privacy settings) +- **Metadata**: Custom fields, user ID, trace information + +### For Embedding Calls +- **Model Information**: Provider, model name +- **Usage Metrics**: Input tokens, total cost +- **Performance**: Latency +- **Content**: Input text (respects privacy settings) +- **Metadata**: Custom fields, user ID, trace information + +### For Errors +- **Error Details**: Error type, error message, stack trace +- **Context**: Model, provider, input that caused the error +- **Timing**: When the error occurred, request duration + +## Environment Variables + +| Variable | Required | Description | +|----------|----------|-------------| +| `POSTHOG_API_KEY` | Yes | Your PostHog project API key | +| `POSTHOG_API_URL` | No | PostHog API URL (defaults to https://app.posthog.com) | + +## Troubleshooting + +### 1. Missing API Key +``` +Error: POSTHOG_API_KEY is not set +``` + +Set your PostHog API key: +```python +import os +os.environ["POSTHOG_API_KEY"] = "your-api-key" +``` + +### 2. Custom PostHog Instance +If you're using a self-hosted PostHog instance: +```python +import os +os.environ["POSTHOG_API_URL"] = "https://your-posthog-instance.com" +``` + +### 3. Events Not Appearing +- Check that your API key is correct +- Verify network connectivity to PostHog +- Events may take a few minutes to appear in PostHog dashboard \ No newline at end of file diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index c191b742268..13f017333d3 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -889,6 +889,19 @@ curl http://0.0.0.0:4000/v1/chat/completions \ Example of using [Bedrock Guardrails with LiteLLM](https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-use-converse-api.html) +### Selective Content Moderation with `guarded_text` + +LiteLLM supports selective content moderation using the `guarded_text` content type. This allows you to wrap only specific content that should be moderated by Bedrock Guardrails, rather than evaluating the entire conversation. + +**How it works:** +- Content with `type: "guarded_text"` gets automatically wrapped in `guardrailConverseContent` blocks +- Only the wrapped content is evaluated by Bedrock Guardrails +- Regular content with `type: "text"` bypasses guardrail evaluation + +:::note +If `guarded_text` is not used, the entire conversation history will be sent to the guardrail for evaluation, which can increase latency and costs. +::: + @@ -915,6 +928,24 @@ response = completion( "trace": "disabled", # The trace behavior for the guardrail. Can either be "disabled" or "enabled" }, ) + +# Selective guardrail usage with guarded_text - only specific content is evaluated +response_guard = completion( + model="anthropic.claude-v2", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is the main topic of this legal document?"}, + {"type": "guarded_text", "text": "This document contains sensitive legal information that should be moderated by guardrails."} + ] + } + ], + guardrailConfig={ + "guardrailIdentifier": "gr-abc123", + "guardrailVersion": "DRAFT" + } +) ``` @@ -993,7 +1024,20 @@ response = client.chat.completions.create(model="bedrock-claude-v1", messages = temperature=0.7 ) -print(response) +# For adding selective guardrail usage with guarded_text +response_guard = client.chat.completions.create(model="bedrock-claude-v1", messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What is the main topic of this legal document?"}, + {"type": "guarded_text", "text": "This document contains sensitive legal information that should be moderated by guardrails."} + ] + } +], +temperature=0.7 +) + +print(response_guard) ``` @@ -1777,6 +1821,7 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re | Mistral 7B Instruct | `completion(model='bedrock/mistral.mistral-7b-instruct-v0:2', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Mixtral 8x7B Instruct | `completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | + ## Bedrock Embedding ### API keys @@ -1798,11 +1843,29 @@ response = embedding( print(response) ``` +#### Titan V2 - encoding_format support +```python +from litellm import embedding +# Float format (default) +response = embedding( + model="bedrock/amazon.titan-embed-text-v2:0", + input=["good morning from litellm"], + encoding_format="float" # Returns float array +) + +# Binary format +response = embedding( + model="bedrock/amazon.titan-embed-text-v2:0", + input=["good morning from litellm"], + encoding_format="base64" # Returns base64 encoded binary +) +``` + ## Supported AWS Bedrock Embedding Models | Model Name | Usage | Supported Additional OpenAI params | |----------------------|---------------------------------------------|-----| -| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) | +| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | `dimensions`, `encoding_format` | | Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53) | Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) | | Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) @@ -1891,6 +1954,39 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/images/generations' \ +### Using Inference Profiles with Image Generation + +For AWS Bedrock Application Inference Profiles with image generation, use the `model_id` parameter to specify the inference profile ARN: + + + + +```python +from litellm import image_generation + +response = image_generation( + model="bedrock/amazon.nova-canvas-v1:0", + model_id="arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0", + prompt="A cute baby sea otter" +) +print(f"response: {response}") +``` + + + + +```yaml +model_list: + - model_name: nova-canvas-inference-profile + litellm_params: + model: bedrock/amazon.nova-canvas-v1:0 + model_id: arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0 + aws_region_name: "eu-west-1" +``` + + + + ## Supported AWS Bedrock Image Generation Models | Model Name | Function Call | diff --git a/docs/my-website/docs/providers/bedrock_batches.md b/docs/my-website/docs/providers/bedrock_batches.md new file mode 100644 index 00000000000..57487f7d2c9 --- /dev/null +++ b/docs/my-website/docs/providers/bedrock_batches.md @@ -0,0 +1,180 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Bedrock Batches + +Use Amazon Bedrock Batch Inference API through LiteLLM. + +| Property | Details | +|----------|---------| +| Description | Amazon Bedrock Batch Inference allows you to run inference on large datasets asynchronously | +| Provider Doc | [AWS Bedrock Batch Inference ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) | + +## Overview + +Use this to: + +- Run batch inference on large datasets with Bedrock models +- Control batch model access by key/user/team (same as chat completion models) +- Manage S3 storage for batch input/output files + +## (Proxy Admin) Usage + +Here's how to give developers access to your Bedrock Batch models. + +### 1. Setup config.yaml + +- Specify `mode: batch` for each model: Allows developers to know this is a batch model +- Configure S3 bucket and AWS credentials for batch operations + +```yaml showLineNumbers title="litellm_config.yaml" +model_list: + - model_name: "bedrock-batch-claude" + litellm_params: + model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0 + ######################################################### + ########## batch specific params ######################## + s3_bucket_name: litellm-proxy + s3_region_name: us-west-2 + s3_access_key_id: os.environ/AWS_ACCESS_KEY_ID + s3_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_batch_role_arn: arn:aws:iam::888602223428:role/service-role/AmazonBedrockExecutionRoleForAgents_BB9HNW6V4CV + model_info: + mode: batch # 👈 SPECIFY MODE AS BATCH, to tell user this is a batch model +``` + +**Required Parameters:** + +| Parameter | Description | +|-----------|-------------| +| `s3_bucket_name` | S3 bucket for batch input/output files | +| `s3_region_name` | AWS region for S3 bucket | +| `s3_access_key_id` | AWS access key for S3 bucket | +| `s3_secret_access_key` | AWS secret key for S3 bucket | +| `aws_batch_role_arn` | IAM role ARN for Bedrock batch operations. Bedrock Batch APIs require an IAM role ARN to be set. | +| `mode: batch` | Indicates to LiteLLM this is a batch model | + +### 2. Create Virtual Key + +```bash showLineNumbers title="create_virtual_key.sh" +curl -L -X POST 'https://{PROXY_BASE_URL}/key/generate' \ +-H 'Authorization: Bearer ${PROXY_API_KEY}' \ +-H 'Content-Type: application/json' \ +-d '{"models": ["bedrock-batch-claude"]}' +``` + +You can now use the virtual key to access the batch models (See Developer flow). + +## (Developer) Usage + +Here's how to create a LiteLLM managed file and execute Bedrock Batch CRUD operations with the file. + +### 1. Create request.jsonl + +- Check models available via `/model_group/info` +- See all models with `mode: batch` +- Set `model` in .jsonl to the model from `/model_group/info` + +```json showLineNumbers title="bedrock_batch_completions.jsonl" +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are an unhelpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} +``` + +Expectation: + +- LiteLLM translates this to the bedrock deployment specific value (e.g. `bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0`) + +### 2. Upload File + +Specify `target_model_names: ""` to enable LiteLLM managed files and request validation. + +model-name should be the same as the model-name in the request.jsonl + + + + +```python showLineNumbers title="bedrock_batch.py" +from openai import OpenAI + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234", +) + +# Upload file +batch_input_file = client.files.create( + file=open("./bedrock_batch_completions.jsonl", "rb"), # {"model": "bedrock-batch-claude"} <-> {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0"} + purpose="batch", + extra_body={"target_model_names": "bedrock-batch-claude"} +) +print(batch_input_file) +``` + + + + +```bash showLineNumbers title="Upload File" +curl http://localhost:4000/v1/files \ + -H "Authorization: Bearer sk-1234" \ + -F purpose="batch" \ + -F file="@bedrock_batch_completions.jsonl" \ + -F extra_body='{"target_model_names": "bedrock-batch-claude"}' +``` + + + + +**Where is the file written?**: + +The file is written to S3 bucket specified in your config and prepared for Bedrock batch inference. + +### 3. Create the batch + + + + +```python showLineNumbers title="bedrock_batch.py" +... +# Create batch +batch = client.batches.create( + input_file_id=batch_input_file.id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "Test batch job"}, +) +print(batch) +``` + + + + +```bash showLineNumbers title="Create Batch Request" +curl http://localhost:4000/v1/batches \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "input_file_id": "file-abc123", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "metadata": {"description": "Test batch job"} + }' +``` + + + + +## FAQ + +### Where are my files written? + +When a `target_model_names` is specified, the file is written to the S3 bucket configured in your Bedrock batch model configuration. + +### What models are supported? + +LiteLLM only supports Bedrock Anthropic Models for Batch API. If you want other bedrock models file an issue [here](https://github.com/BerriAI/litellm/issues/new/choose). + +## Further Reading + +- [AWS Bedrock Batch Inference Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) +- [LiteLLM Managed Batches](../proxy/managed_batches) +- [LiteLLM Authentication to Bedrock](https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication) diff --git a/docs/my-website/docs/providers/bedrock_embedding.md b/docs/my-website/docs/providers/bedrock_embedding.md new file mode 100644 index 00000000000..95ee8d3d228 --- /dev/null +++ b/docs/my-website/docs/providers/bedrock_embedding.md @@ -0,0 +1,95 @@ +# Bedrock Embedding + +## Supported Embedding Models + +| Provider | LiteLLM Route | AWS Documentation | +|----------|---------------|-------------------| +| Amazon Titan | `bedrock/amazon.*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | +| Cohere | `bedrock/cohere.*` | [Cohere Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-embed.html) | +| TwelveLabs | `bedrock/us.twelvelabs.*` | [TwelveLabs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-twelvelabs.html) | + +### API keys +This can be set as env variables or passed as **params to litellm.embedding()** +```python +import os +os.environ["AWS_ACCESS_KEY_ID"] = "" # Access key +os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key +os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2 +``` + +## Usage +### LiteLLM Python SDK +```python +from litellm import embedding +response = embedding( + model="bedrock/amazon.titan-embed-text-v1", + input=["good morning from litellm"], +) +print(response) +``` + +### LiteLLM Proxy Server + +#### 1. Setup config.yaml +```yaml +model_list: + - model_name: titan-embed-v1 + litellm_params: + model: bedrock/amazon.titan-embed-text-v1 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: us-east-1 + - model_name: titan-embed-v2 + litellm_params: + model: bedrock/amazon.titan-embed-text-v2:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: us-east-1 +``` + +#### 2. Start Proxy +```bash +litellm --config /path/to/config.yaml +``` + +#### 3. Use with OpenAI Python SDK +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.embeddings.create( + input=["good morning from litellm"], + model="titan-embed-v1" +) +print(response) +``` + +#### 4. Use with LiteLLM Python SDK +```python +import litellm +response = litellm.embedding( + model="titan-embed-v1", # model alias from config.yaml + input=["good morning from litellm"], + api_base="http://0.0.0.0:4000", + api_key="anything" +) +print(response) +``` + +## Supported AWS Bedrock Embedding Models + +| Model Name | Usage | Supported Additional OpenAI params | +|----------------------|---------------------------------------------|-----| +| Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) | +| Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53) +| Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) | +| TwelveLabs Marengo Embed 2.7 | `embedding(model="bedrock/us.twelvelabs.marengo-embed-2-7-v1:0", input=input)` | Supports multimodal input (text, video, audio, image) | +| Cohere Embeddings - English | `embedding(model="bedrock/cohere.embed-english-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) +| Cohere Embeddings - Multilingual | `embedding(model="bedrock/cohere.embed-multilingual-v3", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/cohere_transformation.py#L18) + +### Advanced - [Drop Unsupported Params](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage) + +### Advanced - [Pass model/provider-specific Params](https://docs.litellm.ai/docs/completion/provider_specific_params#proxy-usage) \ No newline at end of file diff --git a/docs/my-website/docs/providers/compactifai.md b/docs/my-website/docs/providers/compactifai.md new file mode 100644 index 00000000000..1aa81463071 --- /dev/null +++ b/docs/my-website/docs/providers/compactifai.md @@ -0,0 +1,223 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# CompactifAI +https://docs.compactif.ai/ + +CompactifAI offers highly compressed versions of leading language models, delivering up to **70% lower inference costs**, **4x throughput gains**, and **low-latency inference** with minimal quality loss (under 5%). CompactifAI's OpenAI-compatible API makes integration straightforward, enabling developers to build ultra-efficient, scalable AI applications with superior concurrency and resource efficiency. + +| Property | Details | +|-------|-------| +| Description | CompactifAI offers compressed versions of leading language models with up to 70% cost reduction and 4x throughput gains | +| Provider Route on LiteLLM | `compactifai/` (add this prefix to the model name - e.g. `compactifai/cai-llama-3-1-8b-slim`) | +| Provider Doc | [CompactifAI ↗](https://docs.compactif.ai/) | +| API Endpoint for Provider | https://api.compactif.ai/v1 | +| Supported Endpoints | `/chat/completions`, `/completions` | + +## Supported OpenAI Parameters + +CompactifAI is fully OpenAI-compatible and supports the following parameters: + +``` +"stream", +"stop", +"temperature", +"top_p", +"max_tokens", +"presence_penalty", +"frequency_penalty", +"logit_bias", +"user", +"response_format", +"seed", +"tools", +"tool_choice", +"parallel_tool_calls", +"extra_headers" +``` + +## API Key Setup + +CompactifAI API keys are available through AWS Marketplace subscription: + +1. Subscribe via [AWS Marketplace](https://aws.amazon.com/marketplace) +2. Complete subscription verification (24-hour review process) +3. Access MultiverseIAM dashboard with provided credentials +4. Retrieve your API key from the dashboard + +```python +import os + +os.environ["COMPACTIFAI_API_KEY"] = "your-api-key" +``` + +## Usage + + + + +```python +from litellm import completion +import os + +os.environ['COMPACTIFAI_API_KEY'] = "your-api-key" + +response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[ + {"role": "user", "content": "Hello from LiteLLM!"} + ], +) +print(response) +``` + + + + +```yaml +model_list: + - model_name: llama-2-compressed + litellm_params: + model: compactifai/cai-llama-3-1-8b-slim + api_key: os.environ/COMPACTIFAI_API_KEY +``` + + + + +## Streaming + +```python +from litellm import completion +import os + +os.environ['COMPACTIFAI_API_KEY'] = "your-api-key" + +response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[ + {"role": "user", "content": "Write a short story"} + ], + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Advanced Usage + +### Custom Parameters + +```python +from litellm import completion + +response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[{"role": "user", "content": "Explain quantum computing"}], + temperature=0.7, + max_tokens=500, + top_p=0.9, + stop=["Human:", "AI:"] +) +``` + +### Function Calling + +CompactifAI supports OpenAI-compatible function calling: + +```python +from litellm import completion + +functions = [ + { + "name": "get_weather", + "description": "Get current weather information", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state" + } + }, + "required": ["location"] + } + } +] + +response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[{"role": "user", "content": "What's the weather in San Francisco?"}], + tools=[{"type": "function", "function": f} for f in functions], + tool_choice="auto" +) +``` + +### Async Usage + +```python +import asyncio +from litellm import acompletion + +async def async_call(): + response = await acompletion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[{"role": "user", "content": "Hello async world!"}] + ) + return response + +# Run async function +response = asyncio.run(async_call()) +print(response) +``` + +## Available Models + +CompactifAI offers compressed versions of popular models. Use the `/models` endpoint to get the latest list: + +```python +import httpx + +headers = {"Authorization": f"Bearer {your_api_key}"} +response = httpx.get("https://api.compactif.ai/v1/models", headers=headers) +models = response.json() +``` + +Common model formats: +- `compactifai/cai-llama-3-1-8b-slim` +- `compactifai/mistral-7b-compressed` +- `compactifai/codellama-7b-compressed` + +## Benefits + +- **Cost Efficient**: Up to 70% lower inference costs compared to standard models +- **High Performance**: 4x throughput gains with minimal quality loss (under 5%) +- **Low Latency**: Optimized for fast response times +- **Drop-in Replacement**: Full OpenAI API compatibility +- **Scalable**: Superior concurrency and resource efficiency + +## Error Handling + +CompactifAI returns standard OpenAI-compatible error responses: + +```python +from litellm import completion +from litellm.exceptions import AuthenticationError, RateLimitError + +try: + response = completion( + model="compactifai/cai-llama-3-1-8b-slim", + messages=[{"role": "user", "content": "Hello"}] + ) +except AuthenticationError: + print("Invalid API key") +except RateLimitError: + print("Rate limit exceeded") +``` + +## Support + +- Documentation: https://docs.compactif.ai/ +- LinkedIn: [MultiverseComputing](https://www.linkedin.com/company/multiversecomputing) +- Analysis: [Artificial Analysis Provider Comparison](https://artificialanalysis.ai/providers/compactifai) \ No newline at end of file diff --git a/docs/my-website/docs/providers/dashscope.md b/docs/my-website/docs/providers/dashscope.md index eb18fa32a47..565776d6c4c 100644 --- a/docs/my-website/docs/providers/dashscope.md +++ b/docs/my-website/docs/providers/dashscope.md @@ -1,4 +1,4 @@ -# Dashscope +# Dashscope (Qwen API) https://dashscope.console.aliyun.com/ **We support ALL Qwen models, just set `dashscope/` as a prefix when sending completion requests** diff --git a/docs/my-website/docs/providers/ovhcloud.md b/docs/my-website/docs/providers/ovhcloud.md new file mode 100644 index 00000000000..6c42208f2cc --- /dev/null +++ b/docs/my-website/docs/providers/ovhcloud.md @@ -0,0 +1,380 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# 🆕 OVHCloud AI Endpoints +Leading French Cloud provider in Europe with data sovereignty and privacy. + +You can explore the last models we made available in our [catalog](https://endpoints.ai.cloud.ovh.net/catalog). + +:::tip + +We support ALL OVHCloud AI Endpoints models, just set `model=ovhcloud/` as a prefix when sending litellm requests. +For the complete models catalog, visit https://endpoints.ai.cloud.ovh.net/catalog. ** + +::: + +## Sample usage +### Chat completion +You can define your API key by setting the `OVHCLOUD_API_KEY` environment variable or by overriding the `api_key` parameter. You can generate a key on the [OVHCloud Manager](https://www.ovh.com/manager). + +```python +from litellm import completion +import os + +# Our API is free but ratelimited for calls without an API key. +os.environ['OVHCLOUD_API_KEY'] = "your-api-key" + +response = completion( + model = "ovhcloud/Meta-Llama-3_3-70B-Instruct", + messages = [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + max_tokens = 10, + stop = [], + temperature = 0.2, + top_p = 0.9, + user = "user", + api_key = "your-api-key" # Optional if set through the enviromnent variable. +) + +print(response) +``` + +### Streaming +Set the parameter `stream` to `True` to stream a response. +```python +from litellm import completion +import os + +os.environ['OVHCLOUD_API_KEY'] = "your-api-key" + +response = completion( + model = "ovhcloud/Meta-Llama-3_3-70B-Instruct", + messages = [ + { + "role": "user", + "content": "Hello, how are you?", + } + ], + max_tokens = 10, + stop = [], + temperature = 0.2, + top_p = 0.9, + user = "user", + api_key = "your-api-key" # Optional if set through the enviromnent variable, + stream = True +) + +for part in response: + print(response) +``` + +### Tool Calling + +```python +from litellm import completion +import json + +def get_current_weather(location, unit="celsius"): + if unit == "celsius": + return {"location": location, "temperature": "22", "unit": "celsius"} + else: + return {"location": location, "temperature": "72", "unit": "fahrenheit"} + +def print_message(role, content, is_tool_call=False, function_name=None): + if role == "user": + print(f"🧑 User: {content}") + elif role == "assistant": + if is_tool_call: + print(f"🤖 Assistant: I will call the function '{function_name}' to get some informations.") + else: + print(f"🤖 Assistant: {content}") + elif role == "tool": + print(f"🔧 Tool ({function_name}): {content}") + print() + +messages = [{"role": "user", "content": "What's the weather like in Paris?"}] +model = "ovhcloud/Meta-Llama-3_3-70B-Instruct" + +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 country, e.g. Montréal, Canada", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } +] + +print("🌟 Beginning of the conversation") + +# Initial user message +print_message("user", messages[0]["content"]) + +# First request to the model +print("📡 Sending first request to the model...") +response = completion( + model=model, + messages=messages, + tools=tools, + tool_choice="auto", +) + +response_message = response.choices[0].message +tool_calls = response_message.tool_calls + +if tool_calls: + available_functions = { + "get_current_weather": get_current_weather, + } + + # Display the tool calls suggested by the model + for tool_call in tool_calls: + print_message("assistant", "", is_tool_call=True, function_name=tool_call.function.name) + print(f" 📋 Arguments: {tool_call.function.arguments}") + print() + + # Add assistant message with tool calls to the conversation history + assistant_message = { + "role": "assistant", + "content": response_message.content, + "tool_calls": [ + { + "id": tool_call.id, + "type": "function", + "function": { + "name": tool_call.function.name, + "arguments": tool_call.function.arguments + } + } for tool_call in tool_calls + ] + } + + messages.append(assistant_message) + + # Execute each tool call and add the results to the conversation history + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + + print(f"🔧 Executing function '{function_name}'...") + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + + # Display tool response + print_message("tool", json.dumps(function_response, indent=2), function_name=function_name) + + messages.append({ + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": json.dumps(function_response), + }) + + print("📡 Sending second request to the model with results...") + + # Second request with function results + second_response = completion( + model=model, + messages=messages + ) + + # Display final response + final_content = second_response.choices[0].message.content + print_message("assistant", final_content) + +else: + print("❌ No function call detected") + print_message("assistant", response_message.content) +``` + +### Vision Example + +```python +from base64 import b64encode +from mimetypes import guess_type +import litellm + +# Auxiliary function to get b64 images +def data_url_from_image(file_path): + mime_type, _ = guess_type(file_path) + if mime_type is None: + raise ValueError("Could not determine MIME type of the file") + + with open(file_path, "rb") as image_file: + encoded_string = b64encode(image_file.read()).decode("utf-8") + + data_url = f"data:{mime_type};base64,{encoded_string}" + return data_url + +response = litellm.completion( + model = "ovhcloud/Mistral-Small-3.2-24B-Instruct-2506", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What's in this image?" + }, + { + "type": "image_url", + "image_url": { + "url": data_url_from_image("your_image.jpg"), + "format": "image/jpeg" + } + } + ] + } + ], + stream=False +) + +print(response.choices[0].message.content) +``` + + +### Structured Output + +```python +from litellm import completion + +response = completion( + model="ovhcloud/Meta-Llama-3_3-70B-Instruct", + messages=[ + { + "role": "system", + "content": ( + "You are a specialist in extracting structured data from unstructured text. " + "Your task is to identify relevant entities and categories, then format them " + "according to the requested structure." + ), + }, + { + "role": "user", + "content": "Room 12 contains books, a desk, and a lamp." + }, + ], + response_format={ + "type": "json_schema", + "json_schema": { + "title": "data", + "name": "data_extraction", + "schema": { + "type": "object", + "properties": { + "section": {"type": "string"}, + "products": { + "type": "array", + "items": {"type": "string"} + } + }, + "required": ["section", "products"], + "additionalProperties": False + }, + "strict": False + } + }, + stream=False +) + +print(response.choices[0].message.content) +``` + +### Embeddings + +```python +from litellm import embedding + +response = embedding( + model="ovhcloud/BGE-M3", + input=["sample text to embed", "another sample text to embed"] +) + +print(response.data) +``` + +## Usage with LiteLLM Proxy Server + +Here's how to call a OVHCloud AI Endpoints model with the LiteLLM Proxy Server + +1. Modify the config.yaml + + ```yaml + model_list: + - model_name: my-model + litellm_params: + model: ovhcloud/ # add ovhcloud/ prefix to route as OVHCloud provider + api_key: api-key # api key to send your model + ``` + + +2. Start the proxy + + ```bash + $ litellm --config /path/to/config.yaml + ``` + +3. Send Request to LiteLLM Proxy Server + + + + + + ```python + import openai + client = openai.OpenAI( + api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys + base_url="http://0.0.0.0:4000" # litellm-proxy-base url + ) + + response = client.chat.completions.create( + model="my-model", + messages = [ + { + "role": "user", + "content": "what llm are you" + } + ], + ) + + print(response) + ``` + + + + + ```shell + curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "my-model", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' + ``` + + + diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index fda0cee8626..cb90b7434e7 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -2509,150 +2509,6 @@ print("response from proxy", response) -## **Batch APIs** - -Just add the following Vertex env vars to your environment. - -```bash -# GCS Bucket settings, used to store batch prediction files in -export GCS_BUCKET_NAME = "litellm-testing-bucket" # the bucket you want to store batch prediction files in -export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file - -# Vertex /batch endpoint settings, used for LLM API requests -export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file -export VERTEXAI_LOCATION="us-central1" # can be any vertex location -export VERTEXAI_PROJECT="my-test-project" -``` - -### Usage - - -#### 1. Create a file of batch requests for vertex - -LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)** - -Each `body` in the file should be an **OpenAI API request** - -Create a file called `vertex_batch_completions.jsonl` in the current working directory, the `model` should be the Vertex AI model name -``` -{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "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": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} -``` - - -#### 2. Upload a File of batch requests - -For `vertex_ai` litellm will upload the file to the provided `GCS_BUCKET_NAME` - -```python -import os -oai_client = OpenAI( - api_key="sk-1234", # litellm proxy API key - base_url="http://localhost:4000" # litellm proxy base url -) -file_name = "vertex_batch_completions.jsonl" # -_current_dir = os.path.dirname(os.path.abspath(__file__)) -file_path = os.path.join(_current_dir, file_name) -file_obj = oai_client.files.create( - file=open(file_path, "rb"), - purpose="batch", - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use vertex_ai for this file upload -) -``` - -**Expected Response** - -```json -{ - "id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "bytes": 416, - "created_at": 1733392026, - "filename": "litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "object": "file", - "purpose": "batch", - "status": "uploaded", - "status_details": null -} -``` - - - -#### 3. Create a batch - -```python -batch_input_file_id = file_obj.id # use `file_obj` from step 2 -create_batch_response = oai_client.batches.create( - completion_window="24h", - endpoint="/v1/chat/completions", - input_file_id=batch_input_file_id, # example input_file_id = "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/c2b1b785-252b-448c-b180-033c4c63b3ce" - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request -) -``` - -**Expected Response** - -```json -{ - "id": "3814889423749775360", - "completion_window": "24hrs", - "created_at": 1733392026, - "endpoint": "", - "input_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a", - "object": "batch", - "status": "validating", - "cancelled_at": null, - "cancelling_at": null, - "completed_at": null, - "error_file_id": null, - "errors": null, - "expired_at": null, - "expires_at": null, - "failed_at": null, - "finalizing_at": null, - "in_progress_at": null, - "metadata": null, - "output_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001", - "request_counts": null -} -``` - -#### 4. Retrieve a batch - -```python -retrieved_batch = oai_client.batches.retrieve( - batch_id=create_batch_response.id, - extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request -) -``` - -**Expected Response** - -```json -{ - "id": "3814889423749775360", - "completion_window": "24hrs", - "created_at": 1736500100, - "endpoint": "", - "input_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/7b2e47f5-3dd4-436d-920f-f9155bbdc952", - "object": "batch", - "status": "completed", - "cancelled_at": null, - "cancelling_at": null, - "completed_at": null, - "error_file_id": null, - "errors": null, - "expired_at": null, - "expires_at": null, - "failed_at": null, - "finalizing_at": null, - "in_progress_at": null, - "metadata": null, - "output_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001", - "request_counts": null -} -``` - - ## **Fine Tuning APIs** @@ -2758,6 +2614,44 @@ curl http://localhost:4000/v1/fine_tuning/jobs \ +## Labels + + +Google enables you to add custom metadata to its `generateContent` and `streamGenerateContent` calls. +This mechanism is useful in Vertex AI because it allows costs and usage tracking over multiple +different applications or users. + + +### Usage + +You can use that feature through LiteLLM by sending `labels` or `metadata` field in your requests. + +If the client sets the `labels` field in the request to the LiteLLM, +the LiteLLM will pass the `labels` field to the Vertex AI backend. + +If the client sets the `metadata` field in the request to the LiteLLM and the `labels` field is not set, +the LiteLLM will create the `labels` field filled with `metadata` key/value pairs for all string values and +pass it to the Vertex AI backend. + + +Here is an example JSON request demonstrating the labels usage: + +```json +{ + "model": "gemini-2.0-flash-lite", + "messages": [ + { "role": "user", "content": "respond in 20 words. who are you?" } + ], + "labels": { + "client_app": "acme_comp_financial_app", + "department": "finance", + "project": "acme_ai" + } +} +``` + + + ## Extra ### Using `GOOGLE_APPLICATION_CREDENTIALS` diff --git a/docs/my-website/docs/providers/vertex_batch.md b/docs/my-website/docs/providers/vertex_batch.md new file mode 100644 index 00000000000..4eaa0d69d4b --- /dev/null +++ b/docs/my-website/docs/providers/vertex_batch.md @@ -0,0 +1,264 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +## **Batch APIs** + +Just add the following Vertex env vars to your environment. + +```bash +# GCS Bucket settings, used to store batch prediction files in +export GCS_BUCKET_NAME="my-batch-bucket" # the bucket you want to store batch prediction files in +export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file + +# Vertex /batch endpoint settings, used for LLM API requests +export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file +export VERTEXAI_LOCATION="us-central1" # can be any vertex location +export VERTEXAI_PROJECT="my-project" +``` + +### Usage + +Follow this complete workflow: create JSONL file → upload file → create batch → retrieve batch status → get file content + +#### 1. Create a JSONL file of batch requests + +LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)**. + +Each `body` in the file should be an **OpenAI API request**. + +Create a file called `batch_requests.jsonl` with your requests: +```jsonl +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "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": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +``` + +#### 2. Upload the file + +Upload your JSONL file. For `vertex_ai`, the file will be stored in your configured GCS bucket provided by `GCS_BUCKET_NAME`. + + + + +```python showLineNumbers title="upload_file.py" +from openai import OpenAI + +oai_client = OpenAI( + api_key="sk-1234", # litellm proxy API key + base_url="http://localhost:4000" # litellm proxy base url +) + +file_obj = oai_client.files.create( + file=open("batch_requests.jsonl", "rb"), + purpose="batch", + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"File uploaded with ID: {file_obj.id}") +``` + + + + +```bash showLineNumbers title="Upload File" +curl --request POST \ + --url http://localhost:4000/v1/files \ + --header 'Content-Type: multipart/form-data' \ + --form purpose=batch \ + --form file=@batch_requests.jsonl \ + --form custom_llm_provider=vertex_ai +``` + + + + +**Expected Response:** + +```json +{ + "id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "bytes": 416, + "created_at": 1758303684, + "filename": "litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "file", + "purpose": "batch", + "status": "uploaded", + "expires_at": null, + "status_details": null +} +``` + +#### 3. Create a batch + +Create a batch job using the uploaded file ID. + + + + +```python showLineNumbers title="create_batch.py" +batch_input_file_id = file_obj.id # from step 2 +create_batch_response = oai_client.batches.create( + completion_window="24h", + endpoint="/v1/chat/completions", + input_file_id=batch_input_file_id, # e.g. "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd" + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"Batch created with ID: {create_batch_response.id}") +``` + + + + +```bash showLineNumbers title="Create Batch Request" +curl --request POST \ + --url http://localhost:4000/v1/batches \ + --header 'Content-Type: application/json' \ + --data '{ + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "custom_llm_provider": "vertex_ai" +}' +``` + + + + +**Expected Response:** + +```json +{ + "id": "7814463557919047680", + "completion_window": "24hrs", + "created_at": 1758328011, + "endpoint": "", + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "batch", + "status": "validating", + "cancelled_at": null, + "cancelling_at": null, + "completed_at": null, + "error_file_id": null, + "errors": null, + "expired_at": null, + "expires_at": null, + "failed_at": null, + "finalizing_at": null, + "in_progress_at": null, + "metadata": null, + "output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite", + "request_counts": null, + "usage": null +} +``` + +#### 4. Retrieve batch status + +Check the status of your batch job. The batch will progress through states: `validating` → `in_progress` → `completed`. + + + + +```python showLineNumbers title="retrieve_batch.py" +retrieved_batch = oai_client.batches.retrieve( + batch_id=create_batch_response.id, # Created batch id, e.g. 7814463557919047680 + extra_body={"custom_llm_provider": "vertex_ai"} +) + +print(f"Batch status: {retrieved_batch.status}") +if retrieved_batch.status == "completed": + print(f"Output file: {retrieved_batch.output_file_id}") +``` + + + + +```bash showLineNumbers title="Retrieve Batch Status" +curl --request GET \ + --url 'http://localhost:4000/batches/7814463557919047680?provider=vertex_ai' \ + --header 'Authorization: Bearer sk-1234' +``` + + + + +**Expected Response (when completed):** + +```json +{ + "id": "7814463557919047680", + "completion_window": "24hrs", + "created_at": 1758328011, + "endpoint": "", + "input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd", + "object": "batch", + "status": "completed", + "cancelled_at": null, + "cancelling_at": null, + "completed_at": null, + "error_file_id": null, + "errors": null, + "expired_at": null, + "expires_at": null, + "failed_at": null, + "finalizing_at": null, + "in_progress_at": null, + "metadata": null, + "output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/prediction-model-2025-09-19T21:26:51.569037Z/predictions.jsonl", + "request_counts": null, + "usage": null +} +``` + +#### 5. Get file content + +Once the batch is completed, retrieve the results using the `output_file_id` from the batch response. + +**Important:** The `output_file_id` must be URL encoded when used in the request path. + + + + +```python showLineNumbers title="get_file_content.py" +import urllib.parse +import json + +output_file_id = retrieved_batch.output_file_id +# URL encode the file ID +encoded_file_id = urllib.parse.quote_plus(output_file_id) + +# Get file content +file_content = oai_client.files.content( + file_id=encoded_file_id, + extra_body={"custom_llm_provider": "vertex_ai"} +) + +# Process the results +for line in file_content.text.strip().split('\n'): + result = json.loads(line) + print(f"Request: {result['request']}") + print(f"Response: {result['response']}") + print("---") +``` + + + + +```bash showLineNumbers title="Get File Content" +# Note: The file ID must be URL encoded +curl --request GET \ + --url 'http://localhost:4000/files/gs%253A%252F%252Fmy-batch-bucket%252Flitellm-vertex-files%252Fpublishers%252Fgoogle%252Fmodels%252Fgemini-2.5-flash-lite%252Fprediction-model-2025-09-19T21%253A26%253A51.569037Z%252Fpredictions.jsonl/content?provider=vertex_ai' \ + --header 'Authorization: Bearer sk-1234' +``` + + + + +**Expected Response:** + +The response contains JSONL format with one result per line: + +```jsonl +{"status":"","processed_time":"2025-09-19T21:29:47.352+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are a helpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.48079710006713866,"content":{"parts":[{"text":"Hello there! It's nice to meet you"}],"role":"model"},"finishReason":"MAX_TOKENS"}],"createTime":"2025-09-19T21:29:47.484619Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaIvKHdvshMIP_aOtuAg","usageMetadata":{"candidatesTokenCount":10,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":10}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":19,"trafficType":"ON_DEMAND"}}} +{"status":"","processed_time":"2025-09-19T21:29:47.358+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are an unhelpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.6168075137668185,"content":{"parts":[{"text":"I am unable to assist with this request."}],"role":"model"},"finishReason":"STOP"}],"createTime":"2025-09-19T21:29:47.470889Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaOneHISShMIP28nA8QQ","usageMetadata":{"candidatesTokenCount":9,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":9}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":18,"trafficType":"ON_DEMAND"}}} +``` diff --git a/docs/my-website/docs/providers/vllm.md b/docs/my-website/docs/providers/vllm.md index 5472f0602f4..1a37f2f10e7 100644 --- a/docs/my-website/docs/providers/vllm.md +++ b/docs/my-website/docs/providers/vllm.md @@ -8,9 +8,9 @@ LiteLLM supports all models on VLLM. | Property | Details | |-------|-------| | Description | vLLM is a fast and easy-to-use library for LLM inference and serving. [Docs](https://docs.vllm.ai/en/latest/index.html) | -| Provider Route on LiteLLM | `hosted_vllm/` (for OpenAI compatible server), `vllm/` (for vLLM sdk usage) | +| Provider Route on LiteLLM | `hosted_vllm/` (for OpenAI compatible server), `vllm/` ([DEPRECATED] for vLLM sdk usage) | | Provider Doc | [vLLM ↗](https://docs.vllm.ai/en/latest/index.html) | -| Supported Endpoints | `/chat/completions`, `/embeddings`, `/completions`, `/rerank` | +| Supported Endpoints | `/chat/completions`, `/embeddings`, `/completions`, `/rerank`, `/audio/transcriptions` | # Quick Start diff --git a/docs/my-website/docs/proxy/admin_ui_sso.md b/docs/my-website/docs/proxy/admin_ui_sso.md index 823301d4c38..32bf97410cb 100644 --- a/docs/my-website/docs/proxy/admin_ui_sso.md +++ b/docs/my-website/docs/proxy/admin_ui_sso.md @@ -4,6 +4,10 @@ import TabItem from '@theme/TabItem'; # ✨ SSO for Admin UI +:::info +From v1.76.0, SSO is now Free for up to 5 users. +::: + :::info ✨ SSO is on LiteLLM Enterprise diff --git a/docs/my-website/docs/proxy/budget_reset_and_tz.md b/docs/my-website/docs/proxy/budget_reset_and_tz.md index 541ff6a2f0a..340e33afe18 100644 --- a/docs/my-website/docs/proxy/budget_reset_and_tz.md +++ b/docs/my-website/docs/proxy/budget_reset_and_tz.md @@ -29,5 +29,6 @@ Common timezone values: - `US/Pacific` - Pacific Time - `Europe/London` - UK Time - `Asia/Kolkata` - Indian Standard Time (IST) +- `Asia/Bangkok` - Indochina Time (ICT) - `Asia/Tokyo` - Japan Standard Time - `Australia/Sydney` - Australian Eastern Time diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 82669b10cd5..974e95a07bd 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -93,6 +93,8 @@ callback_settings: general_settings: completion_model: string + store_prompts_in_spend_logs: boolean + forward_client_headers_to_llm_api: boolean disable_spend_logs: boolean # turn off writing each transaction to the db disable_master_key_return: boolean # turn off returning master key on UI (checked on '/user/info' endpoint) disable_retry_on_max_parallel_request_limit_error: boolean # turn off retries when max parallel request limit is reached @@ -121,6 +123,35 @@ general_settings: alerting: ["slack", "email"] alerting_threshold: 0 use_client_credentials_pass_through_routes: boolean # use client credentials for all pass through routes like "/vertex-ai", /bedrock/. When this is True Virtual Key auth will not be applied on these endpoints + +router_settings: + routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle" - RECOMMENDED for best performance + redis_host: # string + redis_password: # string + redis_port: # string + enable_pre_call_checks: true # bool - Before call is made check if a call is within model context window + allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. + cooldown_time: 30 # (in seconds) how long to cooldown model if fails/min > allowed_fails + disable_cooldowns: True # bool - Disable cooldowns for all models + enable_tag_filtering: True # bool - Use tag based routing for requests + retry_policy: { # Dict[str, int]: retry policy for different types of exceptions + "AuthenticationErrorRetries": 3, + "TimeoutErrorRetries": 3, + "RateLimitErrorRetries": 3, + "ContentPolicyViolationErrorRetries": 4, + "InternalServerErrorRetries": 4 + } + allowed_fails_policy: { + "BadRequestErrorAllowedFails": 1000, # Allow 1000 BadRequestErrors before cooling down a deployment + "AuthenticationErrorAllowedFails": 10, # int + "TimeoutErrorAllowedFails": 12, # int + "RateLimitErrorAllowedFails": 10000, # int + "ContentPolicyViolationErrorAllowedFails": 15, # int + "InternalServerErrorAllowedFails": 20, # int + } + content_policy_fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for content policy violations + fallbacks=[{"claude-2": ["my-fallback-model"]}] # List[Dict[str, List[str]]]: Fallback model for all errors + ``` ### litellm_settings - Reference @@ -473,6 +504,7 @@ router_settings: | EMAIL_SIGNATURE | Custom HTML footer/signature for all emails. Can include HTML tags for formatting and links. | EMAIL_SUBJECT_INVITATION | Custom subject template for invitation emails. | EMAIL_SUBJECT_KEY_CREATED | Custom subject template for key creation emails. +| EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING | Flag to enable new multi-instance rate limiting. **Default is False** | FIREWORKS_AI_4_B | Size parameter for Fireworks AI 4B model. Default is 4 | FIREWORKS_AI_16_B | Size parameter for Fireworks AI 16B model. Default is 16 | FIREWORKS_AI_56_B_MOE | Size parameter for Fireworks AI 56B MOE model. Default is 56 @@ -658,6 +690,8 @@ router_settings: | PILLAR_API_KEY | API key for Pillar API Guardrails | PILLAR_ON_FLAGGED_ACTION | Action to take when content is flagged ('block' or 'monitor') | POD_NAME | Pod name for the server, this will be [emitted to `datadog` logs](https://docs.litellm.ai/docs/proxy/logging#datadog) as `POD_NAME` +| POSTHOG_API_KEY | API key for PostHog analytics integration +| POSTHOG_API_URL | Base URL for PostHog API (defaults to https://us.i.posthog.com) | PREDIBASE_API_BASE | Base URL for Predibase API | PRESIDIO_ANALYZER_API_BASE | Base URL for Presidio Analyzer service | PRESIDIO_ANONYMIZER_API_BASE | Base URL for Presidio Anonymizer service @@ -738,3 +772,4 @@ router_settings: | WEBHOOK_URL | URL for receiving webhooks from external services | SPEND_LOG_RUN_LOOPS | Constant for setting how many runs of 1000 batch deletes should spend_log_cleanup task run | | SPEND_LOG_CLEANUP_BATCH_SIZE | Number of logs deleted per batch during cleanup. Default is 1000 | +| COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY | Maximum size for CoroutineChecker in-memory cache. Default is 1000 | \ No newline at end of file diff --git a/docs/my-website/docs/proxy/debugging.md b/docs/my-website/docs/proxy/debugging.md index 5cca6541763..fbcac24a4d6 100644 --- a/docs/my-website/docs/proxy/debugging.md +++ b/docs/my-website/docs/proxy/debugging.md @@ -11,13 +11,13 @@ The proxy also supports json logs. [See here](#json-logs) **via cli** -```bash +```bash showLineNumbers $ litellm --debug ``` **via env** -```python +```python showLineNumbers os.environ["LITELLM_LOG"] = "INFO" ``` @@ -25,25 +25,25 @@ os.environ["LITELLM_LOG"] = "INFO" **via cli** -```bash +```bash showLineNumbers $ litellm --detailed_debug ``` **via env** -```python +```python showLineNumbers os.environ["LITELLM_LOG"] = "DEBUG" ``` ### Debug Logs Run the proxy with `--detailed_debug` to view detailed debug logs -```shell +```shell showLineNumbers litellm --config /path/to/config.yaml --detailed_debug ``` When making requests you should see the POST request sent by LiteLLM to the LLM on the Terminal output -```shell +```shell showLineNumbers POST Request Sent from LiteLLM: curl -X POST \ https://api.openai.com/v1/chat/completions \ @@ -51,25 +51,63 @@ https://api.openai.com/v1/chat/completions \ -d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "this is a test request, write a short poem"}]}' ``` +## Debug single request + +Pass in `litellm_request_debug=True` in the request body + +```bash showLineNumbers +curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model":"fake-openai-endpoint", + "messages": [{"role": "user","content": "How many r in the word strawberry?"}], + "litellm_request_debug": true +}' +``` + +This will emit the raw request sent by LiteLLM to the API Provider and raw response received from the API Provider for **just** this request in the logs. + + +```bash showLineNumbers +INFO: Uvicorn running on http://0.0.0.0:4000 (Press CTRL+C to quit) +20:14:06 - LiteLLM:WARNING: litellm_logging.py:938 - + +POST Request Sent from LiteLLM: +curl -X POST \ +https://exampleopenaiendpoint-production.up.railway.app/chat/completions \ +-H 'Authorization: Be****ey' -H 'Content-Type: application/json' \ +-d '{'model': 'fake', 'messages': [{'role': 'user', 'content': 'How many r in the word strawberry?'}], 'stream': False}' + + +20:14:06 - LiteLLM:WARNING: litellm_logging.py:1015 - RAW RESPONSE: +{"id":"chatcmpl-817fc08f0d6c451485d571dab39b26a1","object":"chat.completion","created":1677652288,"model":"gpt-3.5-turbo-0301","system_fingerprint":"fp_44709d6fcb","choices":[{"index":0,"message":{"role":"assistant","content":"\n\nHello there, how may I assist you today?"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":9,"completion_tokens":12,"total_tokens":21}} + + +INFO: 127.0.0.1:56155 - "POST /chat/completions HTTP/1.1" 200 OK + +``` + + ## JSON LOGS Set `JSON_LOGS="True"` in your env: -```bash +```bash showLineNumbers export JSON_LOGS="True" ``` **OR** Set `json_logs: true` in your yaml: -```yaml +```yaml showLineNumbers litellm_settings: json_logs: true ``` Start proxy -```bash +```bash showLineNumbers $ litellm ``` @@ -80,7 +118,7 @@ The proxy will now all logs in json format. Turn off fastapi's default 'INFO' logs 1. Turn on 'json logs' -```yaml +```yaml showLineNumbers litellm_settings: json_logs: true ``` @@ -89,20 +127,20 @@ litellm_settings: Only get logs if an error occurs. -```bash +```bash showLineNumbers LITELLM_LOG="ERROR" ``` 3. Start proxy -```bash +```bash showLineNumbers $ litellm ``` Expected Output: -```bash +```bash showLineNumbers # no info statements ``` @@ -119,14 +157,14 @@ This can be caused due to all your models hitting rate limit errors, causing the How to control this? - Adjust the cooldown time -```yaml +```yaml showLineNumbers router_settings: cooldown_time: 0 # 👈 KEY CHANGE ``` - Disable Cooldowns [NOT RECOMMENDED] -```yaml +```yaml showLineNumbers router_settings: disable_cooldowns: True ``` diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index cdb6f7018fc..6a11d069fb0 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -13,6 +13,7 @@ To start using Litellm, run the following commands in a shell: ```bash # Get the code curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/docker-compose.yml +curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/prometheus.yml # Add the master key - you can change this after setup echo 'LITELLM_MASTER_KEY="sk-1234"' > .env diff --git a/docs/my-website/docs/proxy/forward_client_headers.md b/docs/my-website/docs/proxy/forward_client_headers.md new file mode 100644 index 00000000000..5477ffe87aa --- /dev/null +++ b/docs/my-website/docs/proxy/forward_client_headers.md @@ -0,0 +1,212 @@ +# Forward Client Headers to LLM API + +Control which model groups can forward client headers to the underlying LLM provider APIs. + +## Overview + +By default, LiteLLM does not forward client headers to LLM provider APIs for security reasons. However, you can selectively enable header forwarding for specific model groups using the `forward_client_headers_to_llm_api` setting. + +## Configuration + +## Enable Globally + +```yaml +general_settings: + forward_client_headers_to_llm_api: true +``` + +## Enable for a Model Group + +Add the `forward_client_headers_to_llm_api` setting under `model_group_settings` in your configuration: + +```yaml +model_list: + - model_name: gpt-4o-mini + litellm_params: + model: openai/gpt-4o-mini + api_key: "your-api-key" + - model_name: "wildcard-models/*" + litellm_params: + model: "openai/*" + api_key: "your-api-key" + +litellm_settings: + model_group_settings: + forward_client_headers_to_llm_api: + - gpt-4o-mini + - wildcard-models/* +``` + +## Supported Model Patterns + +The configuration supports various model matching patterns: + +### 1. Exact Model Names +```yaml +forward_client_headers_to_llm_api: + - gpt-4o-mini + - claude-3-sonnet +``` + +### 2. Wildcard Patterns +```yaml +forward_client_headers_to_llm_api: + - "openai/*" # All OpenAI models + - "anthropic/*" # All Anthropic models + - "wildcard-group/*" # All models in wildcard-group +``` + +### 3. Team Model Aliases +If your team has model aliases configured, the forwarding will work with both the original model name and the alias. + +## Forwarded Headers + +When enabled for a model group, LiteLLM forwards the following types of headers: + +### Custom Headers (x- prefix) +- Any header starting with `x-` (except `x-stainless-*` which can cause OpenAI SDK issues) +- Examples: `x-custom-header`, `x-request-id`, `x-trace-id` + +### Provider-Specific Headers +- **Anthropic**: `anthropic-beta` headers +- **OpenAI**: `openai-organization` (when enabled via `forward_openai_org_id: true`) + +### User Information Headers (Optional) +When `add_user_information_to_llm_headers` is enabled, LiteLLM adds: +- `x-litellm-user-id` +- `x-litellm-org-id` +- Other user metadata as `x-litellm-*` headers + +## Security Considerations + +⚠️ **Important Security Notes:** + +1. **Sensitive Data**: Only enable header forwarding for trusted model groups, as headers may contain sensitive information +2. **API Keys**: Never include API keys or secrets in forwarded headers +3. **PII**: Be cautious about forwarding headers that might contain personally identifiable information +4. **Provider Limits**: Some providers have restrictions on custom headers + +## Example Use Cases + +### 1. Request Tracing +Forward tracing headers to track requests across your system: + +```bash +curl -X POST "https://your-proxy.com/v1/chat/completions" \ + -H "Authorization: Bearer your-key" \ + -H "x-trace-id: abc123" \ + -H "x-request-source: mobile-app" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Hello"}] + }' +``` + +### 2. Custom Metadata +Pass custom metadata to your LLM provider: + +```bash +curl -X POST "https://your-proxy.com/v1/chat/completions" \ + -H "Authorization: Bearer your-key" \ + -H "x-customer-id: customer-123" \ + -H "x-environment: production" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Hello"}] + }' +``` + +### 3. Anthropic Beta Features +Enable beta features for Anthropic models: + +```bash +curl -X POST "https://your-proxy.com/v1/chat/completions" \ + -H "Authorization: Bearer your-key" \ + -H "anthropic-beta: tools-2024-04-04" \ + -d '{ + "model": "claude-3-sonnet", + "messages": [{"role": "user", "content": "Hello"}] + }' +``` + +## Complete Configuration Example + +```yaml +model_list: + # Fixed model with header forwarding + - model_name: byok-fixed-gpt-4o-mini + litellm_params: + model: openai/gpt-4o-mini + api_base: "https://your-openai-endpoint.com" + api_key: "your-api-key" + + # Wildcard model group with header forwarding + - model_name: "byok-wildcard/*" + litellm_params: + model: "openai/*" + api_base: "https://your-openai-endpoint.com" + api_key: "your-api-key" + + # Standard model without header forwarding + - model_name: standard-gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: "your-api-key" + +litellm_settings: + # Enable user info headers globally (optional) + add_user_information_to_llm_headers: true + + model_group_settings: + forward_client_headers_to_llm_api: + - byok-fixed-gpt-4o-mini + - byok-wildcard/* + # Note: standard-gpt-4 is NOT included, so no headers forwarded + +general_settings: + # Enable OpenAI organization header forwarding (optional) + forward_openai_org_id: true +``` + +## Testing Header Forwarding + +To test if headers are being forwarded: + +1. **Enable Debug Logging**: Set `set_verbose: true` in your config +2. **Check Provider Logs**: Monitor your LLM provider's request logs +3. **Use Webhook Sites**: For testing, you can use webhook.site URLs as api_base to see forwarded headers + +## Troubleshooting + +### Headers Not Being Forwarded + +1. **Check Model Name**: Ensure the model name in your request matches the configuration +2. **Verify Pattern Matching**: Wildcard patterns must match exactly +3. **Review Logs**: Enable verbose logging to see header processing + +### Provider Errors + +1. **Invalid Headers**: Some providers reject unknown headers +2. **Header Limits**: Providers may have limits on header count/size +3. **Authentication**: Ensure forwarded headers don't conflict with authentication + +## Related Features + +- [Request Headers](./request_headers.md) - Complete list of supported request headers +- [Response Headers](./response_headers.md) - Headers returned by LiteLLM +- [Team Model Aliases](./team_model_add.md) - Configure model aliases for teams +- [Model Access Control](./model_access.md) - Control which users can access which models + +## API Reference + +The header forwarding is controlled by the `ModelGroupSettings` configuration: + +```python +class ModelGroupSettings(BaseModel): + forward_client_headers_to_llm_api: Optional[List[str]] = None +``` + +Where each string in the list can be: +- An exact model name (e.g., `"gpt-4o-mini"`) +- A wildcard pattern (e.g., `"openai/*"`) +- A model group name (e.g., `"my-model-group/*"`) diff --git a/docs/my-website/docs/proxy/guardrails/noma_security.md b/docs/my-website/docs/proxy/guardrails/noma_security.md index 3a50841d65e..4aebb29eb57 100644 --- a/docs/my-website/docs/proxy/guardrails/noma_security.md +++ b/docs/my-website/docs/proxy/guardrails/noma_security.md @@ -135,6 +135,7 @@ guardrails: # application_id: "my-app" # monitor_mode: false # block_failures: true + # anonymize_input: false ``` ### Required Parameters @@ -147,6 +148,7 @@ guardrails: - **`application_id`**: Your application identifier (defaults to `"litellm"`) - **`monitor_mode`**: If `true`, logs violations without blocking (defaults to `false`) - **`block_failures`**: If `true`, blocks requests when guardrail API failures occur (defaults to `true`) +- **`anonymize_input`**: If `true`, replaces sensitive content with anonymized version (defaults to `false`) ## Environment Variables @@ -158,6 +160,7 @@ export NOMA_API_BASE="https://api.noma.security/" # Optional export NOMA_APPLICATION_ID="my-app" # Optional export NOMA_MONITOR_MODE="false" # Optional export NOMA_BLOCK_FAILURES="true" # Optional +export NOMA_ANONYMIZE_INPUT="false" # Optional ``` ## Advanced Configuration @@ -190,6 +193,20 @@ guardrails: block_failures: false # Allow requests to proceed if guardrail API fails ``` +### Content Anonymization + +Enable anonymization to replace sensitive content instead of blocking: + +```yaml +guardrails: + - guardrail_name: "noma-anonymize" + litellm_params: + guardrail: noma + mode: "pre_call" + api_key: os.environ/NOMA_API_KEY + anonymize_input: true # Replace sensitive data with anonymized version +``` + ### Multiple Guardrails Apply different configurations for input and output: diff --git a/docs/my-website/docs/proxy/guardrails/tool_permission.md b/docs/my-website/docs/proxy/guardrails/tool_permission.md new file mode 100644 index 00000000000..9ed05ed46a8 --- /dev/null +++ b/docs/my-website/docs/proxy/guardrails/tool_permission.md @@ -0,0 +1,153 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Tool Permission Guardrail + +LiteLLM provides a Tool Permission Guardrail that lets you control which **tool calls** a model is allowed to invoke, using configurable allow/deny rules. This offers fine-grained, provider-agnostic control over tool execution (e.g., OpenAI Chat Completions `tool_calls`, Anthropic Messages `tool_use`, MCP tools). + +## Quick Start +### 1. Define Guardrails on your LiteLLM config.yaml + +Define your guardrails under the `guardrails` section +```yaml +guardrails: + - guardrail_name: "tool-permission-guardrail" + litellm_params: + guardrail: tool_permission + mode: "post_call" + rules: + - id: "allow_bash" + tool_name: "Bash" + decision: "allow" + - id: "allow_github_mcp" + tool_name: "mcp__github_*" + decision: "allow" + - id: "allow_aws_documentation" + tool_name: "mcp__aws-documentation_*_documentation" + decision: "allow" + - id: "deny_read_commands" + tool_name: "Read" + decision: "Deny" + default_action: "deny" # Fallback when no rule matches: "allow" or "deny" + on_disallowed_action: "block" # How to handle disallowed tools: "block" or "rewrite" +``` + +#### Rule Structure + +```yaml +- id: "unique_rule_id" # Unique identifier for the rule + tool_name: "pattern" # Tool name or pattern to match + decision: "allow" # "allow" or "deny" +``` + +#### Supported values for `mode` + +- `pre_call` Run **before** LLM call, on **input** +- `post_call` Run **after** LLM call, on **input & output** + +### 2. Start the Proxy + +```shell +litellm --config config.yaml --port 4000 +``` + +## Examples + + + + +**Block requset** + +```bash +# Test +curl -X POST "http://localhost:4000/v1/chat/completions" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-master-key-here" \ + -d '{ + "model": "gpt-5-mini", + "messages": [{"role": "user","content": "What is the weather like in Tokyo today?"}], + "tools": [ + { + "type":"function", + "function": { + "name":"get_current_weather", + "description": "Get the current weather in a given location" + } + } + ] + }' +``` + +**Expected response (Denied):** + +```json +{ + "error": + { + "message": "Guardrail raised an exception, Guardrail: tool-permission-guardrail, Message: Tool 'get_current_weather' denied by default action", + "type": "None", + "param": "None", + "code": "500" + } +} +``` + + + + +**Rewrite requset** + +```bash +# Test +curl -X POST "http://localhost:4000/v1/chat/completions" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-master-key-here" \ + -d '{ + "model": "gpt-5-mini", + "messages": [{"role": "user","content": "What is the weather like in Tokyo today?"}], + "tools": [ + { + "type":"function", + "function": { + "name":"get_current_weather", + "description": "Get the current weather in a given location" + } + } + ] + }' +``` + +**Expected response:** + +```json +{ + "id": "chatcmpl-xxxxxxxxxxxxxxx", + "created": 1757716050, + "model": "gpt-5-mini-2025-08-07", + "object": "chat.completion", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "I can’t fetch live weather — I don’t have real‑time internet access.", + "role": "assistant", + "annotations": [] + }, + "provider_specific_fields": {} + } + ], + "usage": { + "prompt_tokens": 112, + "total_tokens": 735, + "completion_tokens_details": { + "reasoning_tokens": 384, + }, + }, + "service_tier": "default" +} +``` + + + diff --git a/docs/my-website/docs/proxy/logging_spec.md b/docs/my-website/docs/proxy/logging_spec.md index a39a62318e7..5166b86ae17 100644 --- a/docs/my-website/docs/proxy/logging_spec.md +++ b/docs/my-website/docs/proxy/logging_spec.md @@ -61,6 +61,11 @@ Inherits from `StandardLoggingUserAPIKeyMetadata` and adds: | `requester_metadata` | `Optional[dict]` | Additional requester metadata | | `vector_store_request_metadata` | `Optional[List[StandardLoggingVectorStoreRequest]]` | Vector store request metadata | | `requester_custom_headers` | Dict[str, str] | Any custom (`x-`) headers sent by the client to the proxy. | +| `prompt_management_metadata` | `Optional[StandardLoggingPromptManagementMetadata]` | Prompt management and versioning metadata | +| `mcp_tool_call_metadata` | `Optional[StandardLoggingMCPToolCall]` | MCP (Model Context Protocol) tool call information and cost tracking | +| `applied_guardrails` | `Optional[List[str]]` | List of applied guardrail names | +| `usage_object` | `Optional[dict]` | Raw usage object from the LLM provider | +| `cold_storage_object_key` | `Optional[str]` | S3/GCS object key for cold storage retrieval | | `guardrail_information` | `Optional[StandardLoggingGuardrailInformation]` | Guardrail information | @@ -145,4 +150,82 @@ A literal type with two possible values: | `duration` | `Optional[float]` | Duration of the guardrail in seconds | | `masked_entity_count` | `Optional[Dict[str, int]]` | Count of masked entities | +## StandardLoggingPromptManagementMetadata +Used for tracking prompt versioning and management information. + +| Field | Type | Description | +|-------|------|-------------| +| `prompt_id` | `str` | **Required**. Unique identifier for the prompt template or version | +| `prompt_variables` | `Optional[dict]` | Variables/parameters used in the prompt template (e.g., `{"user_name": "John", "context": "support"}`) | +| `prompt_integration` | `str` | **Required**. Integration or system managing the prompt (e.g., `"langfuse"`, `"promptlayer"`, `"custom"`) | + +## StandardLoggingMCPToolCall + +Used to track Model Context Protocol (MCP) tool calls within LiteLLM requests. This provides detailed logging for external tool integrations. + +| Field | Type | Description | +|-------|------|-------------| +| `name` | `str` | **Required**. The name of the tool being called (e.g., `"get_weather"`, `"search_database"`) | +| `arguments` | `dict` | **Required**. Arguments passed to the tool as key-value pairs | +| `result` | `Optional[dict]` | The response/result returned by the tool execution (populated by custom logging hooks) | +| `mcp_server_name` | `Optional[str]` | Name of the MCP server that handled the tool call (e.g., `"weather-service"`, `"database-connector"`) | +| `mcp_server_logo_url` | `Optional[str]` | URL for the MCP server's logo (used for UI display in LiteLLM dashboard) | +| `namespaced_tool_name` | `Optional[str]` | Fully qualified tool name including server prefix (e.g., `"deepwiki-mcp/get_page_content"`, `"github-mcp/create_issue"`) | +| `mcp_server_cost_info` | `Optional[MCPServerCostInfo]` | Cost tracking information for the tool call | + +### MCPServerCostInfo + +Cost tracking structure for MCP server tool calls: + +| Field | Type | Description | +|-------|------|-------------| +| `default_cost_per_query` | `Optional[float]` | Default cost in USD for any tool call to this MCP server | +| `tool_name_to_cost_per_query` | `Optional[Dict[str, float]]` | Per-tool cost mapping for granular pricing (e.g., `{"search": 0.01, "create": 0.05}`) | + +### Usage + +```python +# Basic MCP tool call metadata +mcp_tool_call = { + "name": "search_documents", + "arguments": { + "query": "machine learning tutorials", + "limit": 10, + "filter": "type:pdf" + }, + "mcp_server_name": "document-search-service", + "namespaced_tool_name": "docs-mcp/search_documents", + "mcp_server_cost_info": { + "default_cost_per_query": 0.02, + "tool_name_to_cost_per_query": { + "search_documents": 0.02, + "get_document": 0.01 + } + } +} + +# optional result field (via custom logging hooks) +mcp_tool_call_with_result = { + "name": "search_documents", + "arguments": { + "query": "machine learning tutorials", + "limit": 10, + "filter": "type:pdf" + }, + "result": { + "documents": [...], + "total_found": 42, + "search_time_ms": 150 + }, + "mcp_server_name": "document-search-service", + "namespaced_tool_name": "docs-mcp/search_documents", + "mcp_server_cost_info": { + "default_cost_per_query": 0.02, + "tool_name_to_cost_per_query": { + "search_documents": 0.02, + "get_document": 0.01 + } + } +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/request_headers.md b/docs/my-website/docs/proxy/request_headers.md index eea66e5fa93..090c201f884 100644 --- a/docs/my-website/docs/proxy/request_headers.md +++ b/docs/my-website/docs/proxy/request_headers.md @@ -2,6 +2,10 @@ Special headers that are supported by LiteLLM. +## Header Forwarding + +By default, LiteLLM does not forward client headers to LLM provider APIs. However, you can selectively enable header forwarding for specific model groups. [Learn more about configuring header forwarding](./forward_client_headers.md). + ## LiteLLM Headers `x-litellm-timeout` Optional[float]: The timeout for the request in seconds. @@ -21,11 +25,15 @@ Special headers that are supported by LiteLLM. `anthropic-version` Optional[str]: The version of the Anthropic API to use. `anthropic-beta` Optional[str]: The beta version of the Anthropic API to use. - For `/v1/messages` endpoint, this will always be forward the header to the underlying model. - - For `/chat/completions` endpoint, this will only be forwarded if `forward_client_headers_to_llm_api` is true. + - For `/chat/completions` endpoint, this will only be forwarded if the model is configured in `forward_client_headers_to_llm_api`. [Learn more](./forward_client_headers.md) ## OpenAI Headers `openai-organization` Optional[str]: The organization to use for the OpenAI API. (currently needs to be enabled via `general_settings::forward_openai_org_id: true`) +## Custom Headers + +Custom headers starting with `x-` can be forwarded to LLM provider APIs when the model is configured in `forward_client_headers_to_llm_api`. [Learn more about header forwarding configuration](./forward_client_headers.md). + diff --git a/docs/my-website/docs/proxy/team_budgets.md b/docs/my-website/docs/proxy/team_budgets.md index 66ba679c65e..38474066411 100644 --- a/docs/my-website/docs/proxy/team_budgets.md +++ b/docs/my-website/docs/proxy/team_budgets.md @@ -10,8 +10,30 @@ import TabItem from '@theme/TabItem'; - You must set up a Postgres database (e.g. Supabase, Neon, etc.) - To enable team member rate limits, set the environment variable `EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING=true` **before starting the proxy server**. Without this, team member rate limits will not be enforced. + +## Default Budget for Auto-Generated JWT Teams + +When using JWT authentication with `team_id_upsert: true`, you can automatically assign a default budget to any newly created team. + +This is configured in `default_team_settings` in your `config.yaml`. + +**Example:** +```yaml +# in your config.yaml + +litellm_jwtauth: + team_id_upsert: true + team_id_jwt_field: "team_id" + # ... other jwt settings + +litellm_settings: + default_team_settings: + - team_id: "default-settings" + max_budget: 100.0 +``` Track spend, set budgets for your Internal Team + ## Setting Monthly Team Budgets ### 1. Create a team diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md index b6a9c6a6b92..9d2b3757ee2 100644 --- a/docs/my-website/docs/troubleshoot.md +++ b/docs/my-website/docs/troubleshoot.md @@ -2,7 +2,7 @@ [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -[Community Slack 💭](https://join.slack.com/share/enQtOTE0ODczMzk2Nzk4NC01YjUxNjY2YjBlYTFmNDRiZTM3NDFiYTM3MzVkODFiMDVjOGRjMmNmZTZkZTMzOWQzZGQyZWIwYjQ0MWExYmE3) +[Community Slack 💭](https://litellmossslack.slack.com/) Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ diff --git a/docs/my-website/docs/tutorials/openweb_ui.md b/docs/my-website/docs/tutorials/openweb_ui.md index ecf1e289da3..38f1ec38260 100644 --- a/docs/my-website/docs/tutorials/openweb_ui.md +++ b/docs/my-website/docs/tutorials/openweb_ui.md @@ -89,16 +89,20 @@ To track spend and usage for each Open WebUI user, configure both Open WebUI and 2. **Configure LiteLLM to Parse User Headers** - Add the following to your LiteLLM `config.yaml` to specify a header to use for user tracking: + Add the following to your LiteLLM `config.yaml` to specify the request header mapping for user tracking: ```yaml general_settings: - user_header_name: X-OpenWebUI-User-Id + user_header_mappings: + - header_name: X-OpenWebUI-User-Id + litellm_user_role: internal_user + - header_name: X-OpenWebUI-User-Email + litellm_user_role: customer ``` ⓘ Available tracking options - You can use any of the following headers for `user_header_name`: + You can use any of the following headers in `header_name` in `user_header_mappings` : - `X-OpenWebUI-User-Id` - `X-OpenWebUI-User-Email` - `X-OpenWebUI-User-Name` @@ -109,6 +113,12 @@ To track spend and usage for each Open WebUI user, configure both Open WebUI and - Users can modify their own usernames - Administrators can modify both usernames and emails of any account +This video walks through on how we can map the openweb ui headers to LiteLLM user roles + + + +
+
## Render `thinking` content on Open WebUI diff --git a/docs/my-website/img/mcp_tools.png b/docs/my-website/img/mcp_tools.png new file mode 100644 index 00000000000..825dbf6ed8c Binary files /dev/null and b/docs/my-website/img/mcp_tools.png differ diff --git a/docs/my-website/release_notes/v1.77.2-stable/index.md b/docs/my-website/release_notes/v1.77.2-stable/index.md new file mode 100644 index 00000000000..bd12f46e48d --- /dev/null +++ b/docs/my-website/release_notes/v1.77.2-stable/index.md @@ -0,0 +1,161 @@ +--- +title: "[Pre-Release] v1.77.2-stable - Bedrock Batches API" +slug: "v1-77-2" +date: 2025-09-13T10:00:00 +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 yet live. + +::: + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.77.2.rc.2 +``` + + + + + +``` showLineNumbers title="pip install litellm" +``` + + + + +--- + +## Key Highlights + +- **Bedrock Batches API** - Support for creating Batch Inference Jobs on Bedrock using LiteLLM's unified batch API (OpenAI compatible) +- **Qwen API Tiered Pricing** - Cost tracking support for Dashscope (Qwen) models with multiple pricing tiers + +## New Models / Updated Models + +#### New Model Support + +| Provider | Model | Context Window | Pricing ($/1M tokens) | Features | +| ----------- | ------------------------------- | -------------- | --------------------- | -------- | +| DeepInfra | `deepinfra/deepseek-ai/DeepSeek-R1` | 164K | **Input:** $0.70
**Output:** $2.40 | Chat completions, tool calling | +| Heroku | `heroku/claude-4-sonnet` | 8K | Contact provider for pricing | Function calling, tool choice | +| Heroku | `heroku/claude-3-7-sonnet` | 8K | Contact provider for pricing | Function calling, tool choice | +| Heroku | `heroku/claude-3-5-sonnet-latest` | 8K | Contact provider for pricing | Function calling, tool choice | +| Heroku | `heroku/claude-3-5-haiku` | 4K | Contact provider for pricing | Function calling, tool choice | +| Dashscope | `dashscope/qwen-plus-latest` | 1M | **Tiered Pricing:**
• 0-256K tokens: $0.40 / $1.20
• 256K-1M tokens: $1.20 / $3.60 | Function calling, reasoning | +| Dashscope | `dashscope/qwen3-max-preview` | 262K | **Tiered Pricing:**
• 0-32K tokens: $1.20 / $6.00
• 32K-128K tokens: $2.40 / $12.00
• 128K-252K tokens: $3.00 / $15.00 | Function calling, reasoning | +| Dashscope | `dashscope/qwen-flash` | 1M | **Tiered Pricing:**
• 0-256K tokens: $0.05 / $0.40
• 256K-1M tokens: $0.25 / $2.00 | Function calling, reasoning | +| Dashscope | `dashscope/qwen3-coder-plus` | 1M | **Tiered Pricing:**
• 0-32K tokens: $1.00 / $5.00
• 32K-128K tokens: $1.80 / $9.00
• 128K-256K tokens: $3.00 / $15.00
• 256K-1M tokens: $6.00 / $60.00 | Function calling, reasoning, caching | +| Dashscope | `dashscope/qwen3-coder-flash` | 1M | **Tiered Pricing:**
• 0-32K tokens: $0.30 / $1.50
• 32K-128K tokens: $0.50 / $2.50
• 128K-256K tokens: $0.80 / $4.00
• 256K-1M tokens: $1.60 / $9.60 | Function calling, reasoning, caching | + +--- + +#### Features + +- **[Bedrock](../../docs/providers/bedrock_batches)** + - Bedrock Batches API - batch processing support with file upload and request transformation - [PR #14518](https://github.com/BerriAI/litellm/pull/14518), [PR #14522](https://github.com/BerriAI/litellm/pull/14522) +- **[VLLM](../../docs/providers/vllm)** + - Added transcription endpoint support - [PR #14523](https://github.com/BerriAI/litellm/pull/14523) +- **[Ollama](../../docs/providers/ollama)** + - `ollama_chat/` - images, thinking, and content as list handling - [PR #14523](https://github.com/BerriAI/litellm/pull/14523) +- **General** + - New debug flag for detailed request/response logging [PR #14482](https://github.com/BerriAI/litellm/pull/14482) + +#### Bug Fixes + +- **[Azure OpenAI](../../docs/providers/azure)** + - Fixed extra_body injection causing payload rejection in image generation - [PR #14475](https://github.com/BerriAI/litellm/pull/14475) +- **[LM Studio](../../docs/providers/lm-studio)** + - Resolved illegal Bearer header value issue - [PR #14512](https://github.com/BerriAI/litellm/pull/14512) + +--- + +## LLM API Endpoints + +#### Bug Fixes + +- **[/messages](../../docs/anthropic_unified)** + - Don't send content block after message w/ finish reason + usage block - [PR #14477](https://github.com/BerriAI/litellm/pull/14477) +- **[/generateContent](../../docs/generateContent)** + - Gemini CLI Integration - Fixed token count errors - [PR #14451](https://github.com/BerriAI/litellm/pull/14451), [PR #14417](https://github.com/BerriAI/litellm/pull/14417) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +#### Features + +- **[Qwen API Tiered Pricing](../../docs/providers/dashscope)** - Added comprehensive tiered cost tracking for Dashscope/Qwen models - [PR #14471](https://github.com/BerriAI/litellm/pull/14471), [PR #14479](https://github.com/BerriAI/litellm/pull/14479) + +#### Bug Fixes + +- **Provider Budgets** - Fixed provider budget calculations - [PR #14459](https://github.com/BerriAI/litellm/pull/14459) + +--- + +## Management Endpoints / UI + +#### Features + +- **User Headers Mapping** - New X-LiteLLM Users mapping feature for enhanced user tracking - [PR #14485](https://github.com/BerriAI/litellm/pull/14485) +- **Key Unblocking** - Support for hashed tokens in `/key/unblock` endpoint - [PR #14477](https://github.com/BerriAI/litellm/pull/14477) +- **Model Group Header Forwarding** - Enhanced wildcard model support with documentation - [PR #14528](https://github.com/BerriAI/litellm/pull/14528) + +#### Bug Fixes + +- **Log Tab Key Alias** - Fixed filtering inaccuracies for failed logs - [PR #14469](https://github.com/BerriAI/litellm/pull/14469), [PR #14529](https://github.com/BerriAI/litellm/pull/14529) + +--- + +## Logging / Guardrail Integrations + +#### Features + +- **Noma Integration** - Added non-blocking monitor mode with anonymize input support - [PR #14401](https://github.com/BerriAI/litellm/pull/14401) + +--- + +## Performance / Loadbalancing / Reliability improvements + +#### Performance +- Removed dynamic creation of static values - [PR #14538](https://github.com/BerriAI/litellm/pull/14538) +- Using `_PROXY_MaxParallelRequestsHandler_v3` by default for optimal throughput - [PR #14450](https://github.com/BerriAI/litellm/pull/14450) +- Improved execution context propagation into logging tasks - [PR #14455](https://github.com/BerriAI/litellm/pull/14455) + +--- + + + +## New Contributors +* @Sameerlite made their first contribution in [PR #14460](https://github.com/BerriAI/litellm/pull/14460) +* @holzman made their first contribution in [PR #14459](https://github.com/BerriAI/litellm/pull/14459) +* @sashank5644 made their first contribution in [PR #14469](https://github.com/BerriAI/litellm/pull/14469) +* @TomAlon made their first contribution in [PR #14401](https://github.com/BerriAI/litellm/pull/14401) +* @AlexsanderHamir made their first contribution in [PR #14538](https://github.com/BerriAI/litellm/pull/14538) + +--- + +## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.77.1.dev.2...v1.77.2.dev)** diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 72b38596433..0829d78de07 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -49,6 +49,7 @@ const sidebars = { "proxy/guardrails/secret_detection", "proxy/guardrails/custom_guardrail", "proxy/guardrails/prompt_injection", + "proxy/guardrails/tool_permission", ].sort(), ], }, @@ -141,6 +142,7 @@ const sidebars = { "proxy/clientside_auth", "proxy/request_headers", "proxy/response_headers", + "proxy/forward_client_headers", "proxy/model_discovery", ], }, @@ -390,6 +392,7 @@ const sidebars = { "providers/vertex", "providers/vertex_partner", "providers/vertex_image", + "providers/vertex_batch", ] }, { @@ -409,7 +412,9 @@ const sidebars = { label: "Bedrock", items: [ "providers/bedrock", + "providers/bedrock_embedding", "providers/bedrock_agents", + "providers/bedrock_batches", "providers/bedrock_vector_store", ] }, @@ -450,6 +455,7 @@ const sidebars = { "providers/elevenlabs", "providers/fireworks_ai", "providers/clarifai", + "providers/compactifai", "providers/vllm", "providers/llamafile", "providers/infinity", @@ -485,7 +491,8 @@ const sidebars = { "providers/bytez", "providers/heroku", "providers/oci", - "providers/datarobot", + "providers/datarobot", + "providers/ovhcloud", ], }, { diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py index 1028a443a42..d4964b9667e 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py @@ -109,6 +109,9 @@ class PagerDutyAlerting(SlackAlerting): error_llm_provider=error_info.get("llm_provider"), user_api_key_hash=_meta.get("user_api_key_hash"), user_api_key_alias=_meta.get("user_api_key_alias"), + user_api_key_spend=_meta.get("user_api_key_spend"), + user_api_key_max_budget=_meta.get("user_api_key_max_budget"), + user_api_key_budget_reset_at=_meta.get("user_api_key_budget_reset_at"), user_api_key_org_id=_meta.get("user_api_key_org_id"), user_api_key_team_id=_meta.get("user_api_key_team_id"), user_api_key_user_id=_meta.get("user_api_key_user_id"), @@ -191,6 +194,9 @@ class PagerDutyAlerting(SlackAlerting): error_llm_provider="HangingRequest", user_api_key_hash=user_api_key_dict.api_key, user_api_key_alias=user_api_key_dict.key_alias, + user_api_key_spend=user_api_key_dict.spend, + user_api_key_max_budget=user_api_key_dict.max_budget, + user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None, user_api_key_org_id=user_api_key_dict.org_id, user_api_key_team_id=user_api_key_dict.team_id, user_api_key_user_id=user_api_key_dict.user_id, diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index efee1a7783e..58943dc2dd4 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -1,8 +1,11 @@ # used for /metrics endpoint on LiteLLM Proxy #### What this does #### # On success, log events to Prometheus +import os import sys +import tempfile from datetime import datetime, timedelta +from pathlib import Path from typing import ( TYPE_CHECKING, Any, @@ -16,6 +19,64 @@ from typing import ( cast, ) + +# CRITICAL: Set up multiprocess mode BEFORE importing prometheus_client +# This must happen at module import time, not at class instantiation time +def _setup_early_multiprocess_mode(): + """Setup multiprocess mode at import time if needed.""" + try: + # Check if we're in a multiprocess environment + num_workers = os.environ.get("NUM_WORKERS", "1") + is_multiprocess = False + + try: + if int(num_workers) > 1: + is_multiprocess = True + except (ValueError, TypeError): + pass + + # Check for gunicorn worker environment variables + if os.environ.get("GUNICORN_CMD_ARGS") or os.environ.get("GUNICORN_WORKER_ID"): + is_multiprocess = True + + # Check if PROMETHEUS_MULTIPROC_DIR is explicitly set (admin override) + if os.environ.get("PROMETHEUS_MULTIPROC_DIR"): + is_multiprocess = True + + if is_multiprocess: + existing_dir = os.environ.get("PROMETHEUS_MULTIPROC_DIR") + if not existing_dir: + # Set up multiprocess directory + multiproc_dir = os.path.join( + tempfile.gettempdir(), "litellm_prometheus_multiproc" + ) + os.environ["PROMETHEUS_MULTIPROC_DIR"] = multiproc_dir + + # Ensure the directory exists + Path(multiproc_dir).mkdir(parents=True, exist_ok=True) + + verbose_logger.info( + f"Prometheus multiprocess mode auto-enabled with directory: {multiproc_dir}" + ) + else: + # Directory already set, just ensure it exists + Path(existing_dir).mkdir(parents=True, exist_ok=True) + verbose_logger.info( + f"Using existing Prometheus multiprocess directory: {existing_dir}" + ) + + except PermissionError as e: + verbose_logger.warning( + f"Warning: Unable to create Prometheus multiprocess directory due to permission error. " + f"Running in non-root environment. Prometheus metrics may not work correctly in multiprocess mode. Error: {e}" + ) + except Exception as e: + verbose_logger.warning(f"Warning: Failed to setup early multiprocess mode: {e}") + + +# Set up multiprocess mode before any prometheus imports +_setup_early_multiprocess_mode() + import litellm from litellm._logging import print_verbose, verbose_logger from litellm.integrations.custom_logger import CustomLogger @@ -44,6 +105,18 @@ class PrometheusLogger(CustomLogger): # Always initialize label_filters, even for non-premium users self.label_filters = self._parse_prometheus_config() + # Initialize multiprocess mode for Prometheus metrics to handle multiple workers + self._setup_multiprocess_mode() + + # Debug: Check if multiprocess mode is active + multiproc_dir = os.environ.get("PROMETHEUS_MULTIPROC_DIR") + if multiproc_dir: + verbose_logger.info( + f"Prometheus multiprocess mode active with directory: {multiproc_dir}" + ) + else: + verbose_logger.info("Prometheus running in single-process mode") + if premium_user is not True: verbose_logger.warning( f"🚨🚨🚨 Prometheus Metrics is on LiteLLM Enterprise\n🚨 {CommonProxyErrors.not_premium_user.value}" @@ -102,7 +175,9 @@ class PrometheusLogger(CustomLogger): # "team", # "team_alias", # ], - labelnames=self.get_labels_for_metric("litellm_llm_api_time_to_first_token_metric"), + labelnames=self.get_labels_for_metric( + "litellm_llm_api_time_to_first_token_metric" + ), buckets=LATENCY_BUCKETS, ) @@ -132,47 +207,52 @@ class PrometheusLogger(CustomLogger): labelnames=self.get_labels_for_metric("litellm_output_tokens_metric"), ) - # Remaining Budget for Team + # Remaining Budget for Team (use 'mostrecent' for multiprocess mode) self.litellm_remaining_team_budget_metric = self._gauge_factory( "litellm_remaining_team_budget_metric", "Remaining budget for team", labelnames=self.get_labels_for_metric( "litellm_remaining_team_budget_metric" ), + multiprocess_mode="mostrecent", ) - # Max Budget for Team + # Max Budget for Team (use 'mostrecent' for multiprocess mode) self.litellm_team_max_budget_metric = self._gauge_factory( "litellm_team_max_budget_metric", "Maximum budget set for team", labelnames=self.get_labels_for_metric("litellm_team_max_budget_metric"), + multiprocess_mode="mostrecent", ) - # Team Budget Reset At + # Team Budget Reset At (use 'mostrecent' for multiprocess mode) self.litellm_team_budget_remaining_hours_metric = self._gauge_factory( "litellm_team_budget_remaining_hours_metric", "Remaining days for team budget to be reset", labelnames=self.get_labels_for_metric( "litellm_team_budget_remaining_hours_metric" ), + multiprocess_mode="mostrecent", ) - # Remaining Budget for API Key + # Remaining Budget for API Key (use 'mostrecent' for multiprocess mode) self.litellm_remaining_api_key_budget_metric = self._gauge_factory( "litellm_remaining_api_key_budget_metric", "Remaining budget for api key", labelnames=self.get_labels_for_metric( "litellm_remaining_api_key_budget_metric" ), + multiprocess_mode="mostrecent", ) - # Max Budget for API Key + # Max Budget for API Key (use 'mostrecent' for multiprocess mode) self.litellm_api_key_max_budget_metric = self._gauge_factory( "litellm_api_key_max_budget_metric", "Maximum budget set for api key", labelnames=self.get_labels_for_metric( "litellm_api_key_max_budget_metric" ), + multiprocess_mode="mostrecent", ) self.litellm_api_key_budget_remaining_hours_metric = self._gauge_factory( @@ -181,36 +261,40 @@ class PrometheusLogger(CustomLogger): labelnames=self.get_labels_for_metric( "litellm_api_key_budget_remaining_hours_metric" ), + multiprocess_mode="mostrecent", ) ######################################## # LiteLLM Virtual API KEY metrics ######################################## - # Remaining MODEL RPM limit for API Key + # Remaining MODEL RPM limit for API Key (use 'mostrecent' for multiprocess mode) self.litellm_remaining_api_key_requests_for_model = self._gauge_factory( "litellm_remaining_api_key_requests_for_model", "Remaining Requests API Key can make for model (model based rpm limit on key)", labelnames=["hashed_api_key", "api_key_alias", "model"], + multiprocess_mode="mostrecent", ) - # Remaining MODEL TPM limit for API Key + # Remaining MODEL TPM limit for API Key (use 'mostrecent' for multiprocess mode) self.litellm_remaining_api_key_tokens_for_model = self._gauge_factory( "litellm_remaining_api_key_tokens_for_model", "Remaining Tokens API Key can make for model (model based tpm limit on key)", labelnames=["hashed_api_key", "api_key_alias", "model"], + multiprocess_mode="mostrecent", ) ######################################## # LLM API Deployment Metrics / analytics ######################################## - # Remaining Rate Limit for model + # Remaining Rate Limit for model (use 'mostrecent' for multiprocess mode) self.litellm_remaining_requests_metric = self._gauge_factory( "litellm_remaining_requests", "LLM Deployment Analytics - remaining requests for model, returned from LLM API Provider", labelnames=self.get_labels_for_metric( "litellm_remaining_requests_metric" ), + multiprocess_mode="mostrecent", ) self.litellm_remaining_tokens_metric = self._gauge_factory( @@ -219,6 +303,7 @@ class PrometheusLogger(CustomLogger): labelnames=self.get_labels_for_metric( "litellm_remaining_tokens_metric" ), + multiprocess_mode="mostrecent", ) self.litellm_overhead_latency_metric = self._histogram_factory( @@ -229,25 +314,27 @@ class PrometheusLogger(CustomLogger): ), buckets=LATENCY_BUCKETS, ) - # llm api provider budget metrics + # llm api provider budget metrics (use 'mostrecent' for multiprocess mode) self.litellm_provider_remaining_budget_metric = self._gauge_factory( "litellm_provider_remaining_budget_metric", "Remaining budget for provider - used when you set provider budget limits", labelnames=["api_provider"], + multiprocess_mode="mostrecent", ) - # Metric for deployment state + # Metric for deployment state (use 'mostrecent' for multiprocess mode) 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=self.get_labels_for_metric("litellm_deployment_state") + labelnames=self.get_labels_for_metric("litellm_deployment_state"), + multiprocess_mode="mostrecent", ) 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=self.get_labels_for_metric("litellm_deployment_cooled_down") + labelnames=self.get_labels_for_metric("litellm_deployment_cooled_down"), ) self.litellm_deployment_success_responses = self._counter_factory( @@ -318,6 +405,105 @@ class PrometheusLogger(CustomLogger): print_verbose(f"Got exception on init prometheus client {str(e)}") raise e + def _setup_multiprocess_mode(self): + """ + Setup Prometheus multiprocess mode to handle multiple workers properly. + This ensures that metrics are aggregated correctly across all worker processes. + """ + import os + import tempfile + from pathlib import Path + + try: + # Check if we're in a multiprocess environment (multiple workers) + if not self._is_multiprocess_environment(): + verbose_logger.debug( + "Single process environment detected, skipping multiprocess setup" + ) + return + + # Set up multiprocess directory if not already configured + multiproc_dir = os.environ.get("PROMETHEUS_MULTIPROC_DIR") + if not multiproc_dir: + # Create a temp directory for multiprocess metrics + multiproc_dir = os.path.join( + tempfile.gettempdir(), "litellm_prometheus_multiproc" + ) + os.environ["PROMETHEUS_MULTIPROC_DIR"] = multiproc_dir + verbose_logger.debug(f"Set PROMETHEUS_MULTIPROC_DIR to {multiproc_dir}") + + # Ensure the directory exists + Path(multiproc_dir).mkdir(parents=True, exist_ok=True) + + # Force the prometheus_client to recognize multiprocess mode + # This is important because the environment variable must be set BEFORE importing prometheus_client + try: + from prometheus_client import multiprocess + + # This will trigger the multiprocess mode if the env var is set + verbose_logger.debug( + "Prometheus multiprocess module imported successfully" + ) + except Exception as e: + verbose_logger.warning( + f"Failed to import prometheus multiprocess module: {e}" + ) + + verbose_logger.info( + f"Prometheus multiprocess mode enabled with directory: {multiproc_dir}" + ) + + except Exception as e: + verbose_logger.warning(f"Failed to setup Prometheus multiprocess mode: {e}") + + def _is_multiprocess_environment(self) -> bool: + """ + Detect if we're running in a multiprocess environment (uvicorn/gunicorn with multiple workers). + """ + import os + + # Check for common environment variables that indicate multiple workers + num_workers = os.environ.get("NUM_WORKERS", "1") + try: + if int(num_workers) > 1: + return True + except (ValueError, TypeError): + pass + + # Check for gunicorn worker environment variables + if os.environ.get("GUNICORN_CMD_ARGS") or os.environ.get("GUNICORN_WORKER_ID"): + return True + + # Check if PROMETHEUS_MULTIPROC_DIR is explicitly set (admin override) + if os.environ.get("PROMETHEUS_MULTIPROC_DIR"): + return True + + return False + + @staticmethod + def cleanup_multiprocess_metrics(): + """ + Clean up multiprocess metrics directory on startup. + This should be called once during application startup to prevent stale metrics. + """ + import os + from pathlib import Path + + multiproc_dir = os.environ.get("PROMETHEUS_MULTIPROC_DIR") + if multiproc_dir and os.path.exists(multiproc_dir): + try: + # Remove all files in the directory but keep the directory itself + for file_path in Path(multiproc_dir).glob("*"): + if file_path.is_file(): + file_path.unlink() + verbose_logger.info( + f"Cleaned up Prometheus multiprocess metrics directory: {multiproc_dir}" + ) + except Exception as e: + verbose_logger.warning( + f"Failed to cleanup Prometheus multiprocess directory: {e}" + ) + def _parse_prometheus_config(self) -> Dict[str, List[str]]: """Parse prometheus metrics configuration for label filtering and enabled metrics""" import litellm @@ -727,7 +913,16 @@ class PrometheusLogger(CustomLogger): metric_name = args[0] if args else kwargs.get("name", "") if self._is_metric_enabled(metric_name): - return metric_class(*args, **kwargs) + # Handle multiprocess_mode parameter for Gauge metrics + if metric_class.__name__ == "Gauge" and "multiprocess_mode" in kwargs: + # Pass through multiprocess_mode to the Gauge constructor + return metric_class(*args, **kwargs) + else: + # For Counter and Histogram, remove multiprocess_mode if present + filtered_kwargs = { + k: v for k, v in kwargs.items() if k != "multiprocess_mode" + } + return metric_class(*args, **filtered_kwargs) else: return NoOpMetric() @@ -845,13 +1040,6 @@ class PrometheusLogger(CustomLogger): # increment total LLM requests and spend metric self._increment_top_level_request_and_spend_metrics( - end_user_id=end_user_id, - user_api_key=user_api_key, - user_api_key_alias=user_api_key_alias, - model=model, - user_api_team=user_api_team, - user_api_team_alias=user_api_team_alias, - user_id=user_id, response_cost=response_cost, enum_values=enum_values, ) @@ -1018,13 +1206,6 @@ class PrometheusLogger(CustomLogger): def _increment_top_level_request_and_spend_metrics( self, - end_user_id: Optional[str], - user_api_key: Optional[str], - user_api_key_alias: Optional[str], - model: Optional[str], - user_api_team: Optional[str], - user_api_team_alias: Optional[str], - user_id: Optional[str], response_cost: float, enum_values: UserAPIKeyLabelValues, ): @@ -1039,20 +1220,11 @@ class PrometheusLogger(CustomLogger): _labels = prometheus_label_factory( supported_enum_labels=self.get_labels_for_metric( - metric_name="litellm_proxy_total_requests_metric" + metric_name="litellm_spend_metric" ), enum_values=enum_values, ) - - self.litellm_spend_metric.labels( - end_user_id, - user_api_key, - user_api_key_alias, - model, - user_api_team, - user_api_team_alias, - user_id, - ).inc(response_cost) + self.litellm_spend_metric.labels(**_labels).inc(response_cost) def _set_virtual_key_rate_limit_metrics( self, @@ -2179,13 +2351,14 @@ class PrometheusLogger(CustomLogger): def _mount_metrics_endpoint(premium_user: bool): """ Mount the Prometheus metrics endpoint with optional authentication. + Uses multiprocess collector when running with multiple workers. Args: premium_user (bool): Whether the user is a premium user - require_auth (bool, optional): Whether to require authentication for the metrics endpoint. - Defaults to False. """ - from prometheus_client import make_asgi_app + import os + + from prometheus_client import CollectorRegistry, make_asgi_app from litellm._logging import verbose_proxy_logger from litellm.proxy._types import CommonProxyErrors @@ -2196,14 +2369,34 @@ class PrometheusLogger(CustomLogger): f"Prometheus metrics are only available for premium users. {CommonProxyErrors.not_premium_user.value}" ) - # Create metrics ASGI app - metrics_app = make_asgi_app() + # Check if we're in multiprocess mode + multiproc_dir = os.environ.get("PROMETHEUS_MULTIPROC_DIR") + + if multiproc_dir: + # Use multiprocess collector for worker aggregation + try: + from prometheus_client import multiprocess + + registry = CollectorRegistry() + multiprocess.MultiProcessCollector(registry) + metrics_app = make_asgi_app(registry) + verbose_proxy_logger.info( + f"Starting Prometheus Metrics on /metrics with multiprocess collector (directory: {multiproc_dir})" + ) + except Exception as e: + verbose_proxy_logger.warning( + f"Failed to setup multiprocess collector, falling back to default: {e}" + ) + metrics_app = make_asgi_app() + else: + # Use default single-process collector + metrics_app = make_asgi_app() + verbose_proxy_logger.debug( + "Starting Prometheus Metrics on /metrics (single process mode)" + ) # Mount the metrics app to the app app.mount("/metrics", metrics_app) - verbose_proxy_logger.debug( - "Starting Prometheus Metrics on /metrics (no authentication)" - ) def prometheus_label_factory( @@ -2280,7 +2473,9 @@ def get_custom_labels_from_metadata(metadata: dict) -> Dict[str, str]: return result -def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: str) -> bool: +def _tag_matches_wildcard_configured_pattern( + tags: List[str], configured_tag: str +) -> bool: """ Check if any of the request tags matches a wildcard configured pattern @@ -2305,6 +2500,7 @@ def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: st import re from litellm.router_utils.pattern_match_deployments import PatternMatchRouter + pattern_router = PatternMatchRouter() regex_pattern = pattern_router._pattern_to_regex(configured_tag) return any(re.match(pattern=regex_pattern, string=tag) for tag in tags) @@ -2313,11 +2509,11 @@ def _tag_matches_wildcard_configured_pattern(tags: List[str], configured_tag: st def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: """ Get custom labels from tags based on admin configuration. - + Supports both exact matches and wildcard patterns: - Exact match: "prod" matches "prod" exactly - - Wildcard pattern: "User-Agent: curl/*" matches "User-Agent: curl/7.68.0" - + - Wildcard pattern: "User-Agent: curl/*" matches "User-Agent: curl/7.68.0" + Reuses PatternMatchRouter for wildcard pattern matching. Returns dict of label_name: "true" if the tag matches the configured tag, "false" otherwise @@ -2331,9 +2527,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: "tag_Service_web_app_v1": "false", } """ - import re - - from litellm.router_utils.pattern_match_deployments import PatternMatchRouter from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name configured_tags = litellm.custom_prometheus_tags @@ -2341,21 +2534,22 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: return {} result: Dict[str, str] = {} - pattern_router = PatternMatchRouter() for configured_tag in configured_tags: label_name = _sanitize_prometheus_label_name(f"tag_{configured_tag}") - + # Check for exact match first (backwards compatibility) if configured_tag in tags: result[label_name] = "true" continue - + # Use PatternMatchRouter for wildcard pattern matching - if "*" in configured_tag and _tag_matches_wildcard_configured_pattern(tags=tags, configured_tag=configured_tag): + if "*" in configured_tag and _tag_matches_wildcard_configured_pattern( + tags=tags, configured_tag=configured_tag + ): result[label_name] = "true" continue - + # No match found result[label_name] = "false" diff --git a/enterprise/litellm_enterprise/types/proxy/proxy_server.py b/enterprise/litellm_enterprise/types/proxy/proxy_server.py index 497be59c4b9..f1a1f2639ed 100644 --- a/enterprise/litellm_enterprise/types/proxy/proxy_server.py +++ b/enterprise/litellm_enterprise/types/proxy/proxy_server.py @@ -1,4 +1,6 @@ -from typing import Literal, TypedDict +from typing import Literal + +from typing_extensions import TypedDict class CustomAuthSettings(TypedDict): diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index 217bb753f42..1d1fa64549c 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-enterprise" -version = "0.1.19" +version = "0.1.20" description = "Package for LiteLLM Enterprise features" authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.1.19" +version = "0.1.20" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-enterprise==", diff --git a/litellm-js/spend-logs/package-lock.json b/litellm-js/spend-logs/package-lock.json index 2f9e2248351..95f8acdec3a 100644 --- a/litellm-js/spend-logs/package-lock.json +++ b/litellm-js/spend-logs/package-lock.json @@ -6,7 +6,7 @@ "": { "dependencies": { "@hono/node-server": "^1.10.1", - "hono": "^4.6.5" + "hono": "^4.9.7" }, "devDependencies": { "@types/node": "^20.11.17", @@ -463,9 +463,10 @@ } }, "node_modules/hono": { - "version": "4.6.5", - "resolved": "https://registry.npmjs.org/hono/-/hono-4.6.5.tgz", - "integrity": "sha512-qsmN3V5fgtwdKARGLgwwHvcdLKursMd+YOt69eGpl1dUCJb8mCd7hZfyZnBYjxCegBG7qkJRQRUy2oO25yHcyQ==", + "version": "4.9.7", + "resolved": "https://registry.npmjs.org/hono/-/hono-4.9.7.tgz", + "integrity": "sha512-t4Te6ERzIaC48W3x4hJmBwgNlLhmiEdEE5ViYb02ffw4ignHNHa5IBtPjmbKstmtKa8X6C35iWwK4HaqvrzG9w==", + "license": "MIT", "engines": { "node": ">=16.9.0" } diff --git a/litellm-js/spend-logs/package.json b/litellm-js/spend-logs/package.json index 9e51f1018a6..5370f7a0eca 100644 --- a/litellm-js/spend-logs/package.json +++ b/litellm-js/spend-logs/package.json @@ -4,7 +4,7 @@ }, "dependencies": { "@hono/node-server": "^1.10.1", - "hono": "^4.6.5" + "hono": "^4.9.7" }, "devDependencies": { "@types/node": "^20.11.17", diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl new file mode 100644 index 00000000000..c035bb44215 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz new file mode 100644 index 00000000000..85069c622b0 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz differ diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql new file mode 100644 index 00000000000..5686876b37c --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20250918083359_drop_spec_version_column_from_mcp_table/migration.sql @@ -0,0 +1,8 @@ +/* + Warnings: + + - You are about to drop the column `spec_version` on the `LiteLLM_MCPServerTable` table. All the data in the column will be lost. + +*/ +-- AlterTable +ALTER TABLE "public"."LiteLLM_MCPServerTable" DROP COLUMN "spec_version"; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index b8f2201d6b5..2b1e20820f9 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -171,7 +171,6 @@ model LiteLLM_MCPServerTable { description String? url String? transport String @default("sse") - spec_version String @default("2025-03-26") auth_type String? created_at DateTime? @default(now()) @map("created_at") created_by String? diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 0cb9c35fa62..1c368d58077 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.2.18" +version = "0.2.19" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.2.18" +version = "0.2.19" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index f6be2bc6f00..038787f5cce 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_EMBEDDING_PROVIDERS_LITERAL, BEDROCK_CONVERSE_MODELS, DEFAULT_MAX_TOKENS, DEFAULT_SOFT_BUDGET, @@ -116,6 +117,7 @@ _custom_logger_compatible_callbacks_literal = Literal[ "logfire", "literalai", "dynamic_rate_limiter", + "dynamic_rate_limiter_v3", "langsmith", "prometheus", "otel", @@ -147,6 +149,7 @@ _custom_logger_compatible_callbacks_literal = Literal[ "vector_store_pre_call_hook", "dotprompt", "cloudzero", + "posthog", ] configured_cold_storage_logger: Optional[ _custom_logger_compatible_callbacks_literal @@ -241,6 +244,7 @@ gradient_ai_api_key: Optional[str] = None nebius_key: Optional[str] = None heroku_key: Optional[str] = None cometapi_key: Optional[str] = None +ovhcloud_key: Optional[str] = None common_cloud_provider_auth_params: dict = { "params": ["project", "region_name", "token"], "providers": ["vertex_ai", "bedrock", "watsonx", "azure", "vertex_ai_beta"], @@ -520,6 +524,8 @@ cometapi_models: Set = set() oci_models: Set = set() vercel_ai_gateway_models: Set = set() volcengine_models: Set = set() +ovhcloud_models: Set = set() +ovhcloud_embedding_models: Set = set() def is_bedrock_pricing_only_model(key: str) -> bool: @@ -734,6 +740,10 @@ def add_known_models(): oci_models.add(key) elif value.get("litellm_provider") == "volcengine": volcengine_models.add(key) + elif value.get("litellm_provider") == "ovhcloud": + ovhcloud_models.add(key) + elif value.get("litellm_provider") == "ovhcloud-embedding-models": + ovhcloud_embedding_models.add(key) add_known_models() @@ -828,6 +838,7 @@ model_list = list( | heroku_models | vercel_ai_gateway_models | volcengine_models + | ovhcloud_models ) model_list_set = set(model_list) @@ -909,6 +920,7 @@ models_by_provider: dict = { "cometapi": cometapi_models, "oci": oci_models, "volcengine": volcengine_models, + "ovhcloud": ovhcloud_models | ovhcloud_embedding_models, } # mapping for those models which have larger equivalents @@ -943,6 +955,7 @@ all_embedding_models = ( | fireworks_ai_embedding_models | nebius_embedding_models | sambanova_embedding_models + | ovhcloud_embedding_models ) ####### IMAGE GENERATION MODELS ################### @@ -1013,6 +1026,7 @@ from .llms.openai_like.chat.handler import OpenAILikeChatConfig from .llms.aiohttp_openai.chat.transformation import AiohttpOpenAIChatConfig from .llms.galadriel.chat.transformation import GaladrielChatConfig from .llms.github.chat.transformation import GithubChatConfig +from .llms.compactifai.chat.transformation import CompactifAIChatConfig from .llms.empower.chat.transformation import EmpowerChatConfig from .llms.huggingface.chat.transformation import HuggingFaceChatConfig from .llms.huggingface.embedding.transformation import HuggingFaceEmbeddingConfig @@ -1033,7 +1047,6 @@ from .llms.databricks.chat.transformation import DatabricksConfig from .llms.databricks.embed.transformation import DatabricksEmbeddingConfig from .llms.predibase.chat.transformation import PredibaseConfig from .llms.replicate.chat.transformation import ReplicateConfig -from .llms.cohere.completion.transformation import CohereTextConfig as CohereConfig from .llms.snowflake.chat.transformation import SnowflakeConfig from .llms.cohere.rerank.transformation import CohereRerankConfig from .llms.cohere.rerank_v2.transformation import CohereRerankV2Config @@ -1254,6 +1267,8 @@ from .llms.morph.chat.transformation import MorphChatConfig from .llms.lambda_ai.chat.transformation import LambdaAIChatConfig from .llms.hyperbolic.chat.transformation import HyperbolicChatConfig from .llms.vercel_ai_gateway.chat.transformation import VercelAIGatewayConfig +from .llms.ovhcloud.chat.transformation import OVHCloudChatConfig +from .llms.ovhcloud.embedding.transformation import OVHCloudEmbeddingConfig from .main import * # type: ignore from .integrations import * from .llms.custom_httpx.async_client_cleanup import close_litellm_async_clients diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 0d250779da3..37b9aff4efb 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -19,6 +19,7 @@ from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast import httpx import litellm +from litellm._logging import verbose_logger 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 @@ -38,6 +39,7 @@ from litellm.utils import ( ProviderConfigManager, client, get_litellm_params, + get_llm_provider, supports_httpx_timeout, ) @@ -49,6 +51,45 @@ base_llm_http_handler = BaseLLMHTTPHandler() ################################################# +def _resolve_timeout( + optional_params: GenericLiteLLMParams, + kwargs: Dict[str, Any], + custom_llm_provider: str, + default_timeout: float = 600.0, +) -> float: + """ + Resolve timeout value from various sources and handle httpx.Timeout objects. + + Args: + optional_params: GenericLiteLLMParams object containing timeout + kwargs: Additional kwargs that may contain request_timeout + custom_llm_provider: Provider name for httpx timeout support check + default_timeout: Default timeout value to use + + Returns: + Resolved timeout as float + """ + timeout = optional_params.timeout or kwargs.get("request_timeout", default_timeout) or default_timeout + + # Handle httpx.Timeout objects + if isinstance(timeout, httpx.Timeout): + if supports_httpx_timeout(custom_llm_provider) is False: + # Extract read timeout for providers that don't support httpx.Timeout + read_timeout = timeout.read or default_timeout + return float(read_timeout) + else: + # For providers that support httpx.Timeout, we still need to return a float + # This case might need to be handled differently based on the actual use case + return float(timeout.read or default_timeout) + + # Handle None case + if timeout is None: + return float(default_timeout) + + # Handle numeric values (int, float, string representations) + return float(timeout) + + @client async def acreate_batch( completion_window: Literal["24h"], @@ -118,13 +159,23 @@ def create_batch( litellm_call_id = kwargs.get("litellm_call_id", None) proxy_server_request = kwargs.get("proxy_server_request", None) model_info = kwargs.get("model_info", None) + model: Optional[str] = kwargs.get("model", None) + try: + if model is not None: + model, _, _, _ = get_llm_provider( + model=model, + custom_llm_provider=None, + ) + except Exception as e: + verbose_logger.exception(f"litellm.batches.main.py::create_batch() - Error inferring custom_llm_provider - {str(e)}") + _is_async = kwargs.pop("acreate_batch", False) is True 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 + timeout = _resolve_timeout(optional_params, kwargs, custom_llm_provider) litellm_logging_obj.update_environment_variables( - model=None, + model=model, user=None, optional_params=optional_params.model_dump(), litellm_params={ @@ -138,18 +189,6 @@ def create_batch( }, custom_llm_provider=custom_llm_provider, ) - - if ( - timeout is not None - and isinstance(timeout, httpx.Timeout) - and supports_httpx_timeout(custom_llm_provider) is False - ): - read_timeout = timeout.read or 600 - timeout = read_timeout # default 10 min timeout - elif timeout is not None and not isinstance(timeout, httpx.Timeout): - timeout = float(timeout) # type: ignore - elif timeout is None: - timeout = 600.0 _create_batch_request = CreateBatchRequest( @@ -160,10 +199,13 @@ 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 model is not None: + provider_config = ProviderConfigManager.get_provider_batches_config( + model=model, + provider=LlmProviders(custom_llm_provider), + ) + else: + provider_config = None if provider_config is not None: response = base_llm_http_handler.create_batch( provider_config=provider_config, @@ -179,6 +221,7 @@ def create_batch( and isinstance(client, (HTTPHandler, AsyncHTTPHandler)) else None, timeout=timeout, + model=model, ) return response api_base: Optional[str] = None @@ -297,7 +340,7 @@ def create_batch( @client async def aretrieve_batch( batch_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, @@ -335,11 +378,129 @@ async def aretrieve_batch( except Exception as e: raise e +def _handle_retrieve_batch_providers_without_provider_config( + batch_id: str, + optional_params: GenericLiteLLMParams, + timeout: Union[float, httpx.Timeout], + litellm_params: dict, + _retrieve_batch_request: RetrieveBatchRequest, + _is_async: bool, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", +): + 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 + api_base = ( + optional_params.api_base + or litellm.api_base + or os.getenv("OPENAI_BASE_URL") + or os.getenv("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + organization = ( + optional_params.organization + or litellm.organization + or os.getenv("OPENAI_ORGANIZATION", None) + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + optional_params.api_key + or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or os.getenv("OPENAI_API_KEY") + ) + + response = openai_batches_instance.retrieve_batch( + _is_async=_is_async, + retrieve_batch_data=_retrieve_batch_request, + api_base=api_base, + api_key=api_key, + organization=organization, + timeout=timeout, + max_retries=optional_params.max_retries, + ) + elif custom_llm_provider == "azure": + api_base = ( + optional_params.api_base + or litellm.api_base + or get_secret_str("AZURE_API_BASE") + ) + api_version = ( + optional_params.api_version + or litellm.api_version + or get_secret_str("AZURE_API_VERSION") + ) + + api_key = ( + optional_params.api_key + or litellm.api_key + or litellm.azure_key + or get_secret_str("AZURE_OPENAI_API_KEY") + or get_secret_str("AZURE_API_KEY") + ) + + extra_body = optional_params.get("extra_body", {}) + if extra_body is not None: + extra_body.pop("azure_ad_token", None) + else: + get_secret_str("AZURE_AD_TOKEN") # type: ignore + + response = azure_batches_instance.retrieve_batch( + _is_async=_is_async, + api_base=api_base, + api_key=api_key, + api_version=api_version, + timeout=timeout, + max_retries=optional_params.max_retries, + retrieve_batch_data=_retrieve_batch_request, + litellm_params=litellm_params, + ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + vertex_ai_project = ( + optional_params.vertex_project + or litellm.vertex_project + or get_secret_str("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.vertex_location + or litellm.vertex_location + or get_secret_str("VERTEXAI_LOCATION") + ) + vertex_credentials = optional_params.vertex_credentials or get_secret_str( + "VERTEXAI_CREDENTIALS" + ) + + response = vertex_ai_batches_instance.retrieve_batch( + _is_async=_is_async, + batch_id=batch_id, + api_base=api_base, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + vertex_credentials=vertex_credentials, + timeout=timeout, + max_retries=optional_params.max_retries, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( + custom_llm_provider + ), + model="n/a", + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + content="Unsupported provider", + request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + return response @client def retrieve_batch( batch_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, @@ -387,115 +548,59 @@ def retrieve_batch( ) _is_async = kwargs.pop("aretrieve_batch", False) is True - 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 - api_base = ( - optional_params.api_base - or litellm.api_base - or os.getenv("OPENAI_BASE_URL") - or os.getenv("OPENAI_API_BASE") - or "https://api.openai.com/v1" - ) - organization = ( - optional_params.organization - or litellm.organization - or os.getenv("OPENAI_ORGANIZATION", None) - or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 - ) - # set API KEY - api_key = ( - optional_params.api_key - or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there - or litellm.openai_key - or os.getenv("OPENAI_API_KEY") - ) - - response = openai_batches_instance.retrieve_batch( - _is_async=_is_async, - retrieve_batch_data=_retrieve_batch_request, - api_base=api_base, - api_key=api_key, - organization=organization, - timeout=timeout, - max_retries=optional_params.max_retries, - ) - elif custom_llm_provider == "azure": - api_base = ( - optional_params.api_base - or litellm.api_base - or get_secret_str("AZURE_API_BASE") - ) - api_version = ( - optional_params.api_version - or litellm.api_version - or get_secret_str("AZURE_API_VERSION") - ) - - api_key = ( - optional_params.api_key - or litellm.api_key - or litellm.azure_key - or get_secret_str("AZURE_OPENAI_API_KEY") - or get_secret_str("AZURE_API_KEY") - ) - - extra_body = optional_params.get("extra_body", {}) - if extra_body is not None: - extra_body.pop("azure_ad_token", None) - else: - get_secret_str("AZURE_AD_TOKEN") # type: ignore - - response = azure_batches_instance.retrieve_batch( - _is_async=_is_async, - api_base=api_base, - api_key=api_key, - api_version=api_version, - timeout=timeout, - max_retries=optional_params.max_retries, - retrieve_batch_data=_retrieve_batch_request, - litellm_params=litellm_params, - ) - elif custom_llm_provider == "vertex_ai": - api_base = optional_params.api_base or "" - vertex_ai_project = ( - optional_params.vertex_project - or litellm.vertex_project - or get_secret_str("VERTEXAI_PROJECT") - ) - vertex_ai_location = ( - optional_params.vertex_location - or litellm.vertex_location - or get_secret_str("VERTEXAI_LOCATION") - ) - vertex_credentials = optional_params.vertex_credentials or get_secret_str( - "VERTEXAI_CREDENTIALS" - ) - - response = vertex_ai_batches_instance.retrieve_batch( - _is_async=_is_async, - batch_id=batch_id, - api_base=api_base, - vertex_project=vertex_ai_project, - vertex_location=vertex_ai_location, - vertex_credentials=vertex_credentials, - timeout=timeout, - max_retries=optional_params.max_retries, + client = kwargs.get("client", None) + + # Try to use provider config first (for providers like bedrock) + model: Optional[str] = kwargs.get("model", None) + if model is not None: + provider_config = ProviderConfigManager.get_provider_batches_config( + model=model, + provider=LlmProviders(custom_llm_provider), ) else: - raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( - custom_llm_provider - ), - model="n/a", - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=400, - content="Unsupported provider", - request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + provider_config = None + + if provider_config is not None: + response = base_llm_http_handler.retrieve_batch( + batch_id=batch_id, + provider_config=provider_config, + litellm_params=litellm_params, + headers=extra_headers or {}, + api_base=optional_params.api_base, + api_key=optional_params.api_key, + logging_obj=litellm_logging_obj or LiteLLMLoggingObj( + model=model or "bedrock/unknown", + messages=[], + stream=False, + call_type="batch_retrieve", + start_time=None, + litellm_call_id="batch_retrieve_" + batch_id, + function_id="batch_retrieve", ), + _is_async=_is_async, + client=client + if client is not None + and isinstance(client, (HTTPHandler, AsyncHTTPHandler)) + else None, + timeout=timeout, + model=model, ) - return response + return response + + + ######################################################### + # Handle providers without provider config + ######################################################### + return _handle_retrieve_batch_providers_without_provider_config( + batch_id=batch_id, + custom_llm_provider=custom_llm_provider, + optional_params=optional_params, + litellm_params=litellm_params, + _retrieve_batch_request=_retrieve_batch_request, + _is_async=_is_async, + timeout=timeout, + ) + except Exception as e: raise e diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index 2540043f636..c09a407d782 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -19,6 +19,7 @@ from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast import litellm from litellm._logging import print_verbose, verbose_logger from litellm.litellm_core_utils.core_helpers import _get_parent_otel_span_from_kwargs +from litellm.litellm_core_utils.coroutine_checker import coroutine_checker from litellm.types.caching import RedisPipelineIncrementOperation from litellm.types.services import ServiceTypes @@ -140,7 +141,7 @@ class RedisCache(BaseCache): self.redis_flush_size = redis_flush_size self.redis_version = "Unknown" try: - if not inspect.iscoroutinefunction(self.redis_client): + if not coroutine_checker.is_async_callable(self.redis_client): self.redis_version = self.redis_client.info()["redis_version"] # type: ignore except Exception: pass diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index f2eeaf04554..6ec49ce0620 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -2,7 +2,9 @@ Handler for transforming /chat/completions api requests to litellm.responses requests """ -from typing import TYPE_CHECKING, Any, Coroutine, TypedDict, Union +from typing import TYPE_CHECKING, Any, Coroutine, Union + +from typing_extensions import TypedDict if TYPE_CHECKING: from litellm import CustomStreamWrapper, LiteLLMLoggingObj, ModelResponse diff --git a/litellm/constants.py b/litellm/constants.py index 75c25d9ea9e..9b44613b855 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", os.cpu_count() or 4) + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) ) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" @@ -60,7 +60,9 @@ 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) + os.getenv( + "DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512 + ) ) # Generic fallback for unknown models @@ -177,7 +179,7 @@ NON_LLM_CONNECTION_TIMEOUT = int( os.getenv("NON_LLM_CONNECTION_TIMEOUT", 15) ) # timeout for adjacent services (e.g. jwt auth) MAX_EXCEPTION_MESSAGE_LENGTH = int(os.getenv("MAX_EXCEPTION_MESSAGE_LENGTH", 2000)) -MAX_STRING_LENGTH_PROMPT_IN_DB = int(os.getenv("MAX_STRING_LENGTH_PROMPT_IN_DB", 1000)) +MAX_STRING_LENGTH_PROMPT_IN_DB = int(os.getenv("MAX_STRING_LENGTH_PROMPT_IN_DB", 2048)) BEDROCK_MAX_POLICY_SIZE = int(os.getenv("BEDROCK_MAX_POLICY_SIZE", 75)) REPLICATE_POLLING_DELAY_SECONDS = float( os.getenv("REPLICATE_POLLING_DELAY_SECONDS", 0.5) @@ -311,6 +313,7 @@ LITELLM_CHAT_PROVIDERS = [ "morph", "lambda_ai", "vercel_ai_gateway", + "ovhcloud", ] LITELLM_EMBEDDING_PROVIDERS_SUPPORTING_INPUT_ARRAY_OF_TOKENS = [ @@ -766,6 +769,12 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "deepseek_r1", ] +BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[ + "cohere", + "amazon", + "twelvelabs", +] + BEDROCK_CONVERSE_MODELS = [ "openai.gpt-oss-20b-1:0", "openai.gpt-oss-120b-1:0", @@ -819,6 +828,7 @@ bedrock_embedding_models: set = set( "amazon.titan-embed-text-v1", "cohere.embed-english-v3", "cohere.embed-multilingual-v3", + "twelvelabs.marengo-embed-2-7-v1:0", ] ) @@ -949,7 +959,9 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" 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)) +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)) @@ -1019,6 +1031,7 @@ SENTRY_DENYLIST = [ "FIREWORKS_API_KEY", "FIREWORKS_AI_API_KEY", "FIREWORKSAI_API_KEY", + "OVHCLOUD_API_KEY", # Database and Connection Strings "database_url", "redis_url", @@ -1057,3 +1070,8 @@ SENTRY_PII_DENYLIST = [ "SMTP_SENDER_EMAIL", "TEST_EMAIL_ADDRESS", ] + +# CoroutineChecker cache configuration +COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY = int( + os.getenv("COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY", 1000) +) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 01f3e2472f8..5d8f5faadf1 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -344,6 +344,11 @@ def cost_per_token( # noqa: PLR0915 return perplexity_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "xai": return xai_cost_per_token(model=model, usage=usage_block) + elif custom_llm_provider == "dashscope": + from litellm.llms.dashscope.cost_calculator import ( + cost_per_token as dashscope_cost_per_token, + ) + return dashscope_cost_per_token(model=model, usage=usage_block) else: model_info = _cached_get_model_info_helper( model=model, custom_llm_provider=custom_llm_provider diff --git a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py index 3035c5065c5..13af0a30fe0 100644 --- a/litellm/endpoints/speech/speech_to_completion_bridge/handler.py +++ b/litellm/endpoints/speech/speech_to_completion_bridge/handler.py @@ -2,7 +2,9 @@ Handler for transforming /chat/completions api requests to litellm.responses requests """ -from typing import TYPE_CHECKING, Optional, TypedDict, Union +from typing import TYPE_CHECKING, Optional, Union + +from typing_extensions import TypedDict if TYPE_CHECKING: from litellm import LiteLLMLoggingObj diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index c97da6624ac..ecb58e18223 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -19,8 +19,6 @@ from litellm._logging import verbose_logger from litellm.types.mcp import ( MCPAuth, MCPAuthType, - MCPSpecVersion, - MCPSpecVersionType, MCPStdioConfig, MCPTransport, MCPTransportType, @@ -48,7 +46,6 @@ class MCPClient: auth_value: Optional[str] = None, timeout: float = 60.0, stdio_config: Optional[MCPStdioConfig] = None, - protocol_version: MCPSpecVersionType = MCPSpecVersion.jun_2025, ): self.server_url: str = server_url self.transport_type: MCPTransport = transport_type @@ -62,7 +59,6 @@ class MCPClient: self._session_ctx = None self._task: Optional[asyncio.Task] = None self.stdio_config: Optional[MCPStdioConfig] = stdio_config - self.protocol_version: MCPSpecVersionType = protocol_version # handle the basic auth value if provided if auth_value: @@ -84,22 +80,24 @@ class MCPClient: """Initialize the transport and session.""" if self._session: return # Already connected - + try: if self.transport_type == MCPTransport.stdio: # For stdio transport, use stdio_client with command-line parameters if not self.stdio_config: raise ValueError("stdio_config is required for stdio transport") - + server_params = StdioServerParameters( command=self.stdio_config.get("command", ""), args=self.stdio_config.get("args", []), - env=self.stdio_config.get("env", {}) + env=self.stdio_config.get("env", {}), ) - + self._transport_ctx = stdio_client(server_params) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() elif self.transport_type == MCPTransport.sse: @@ -110,7 +108,9 @@ class MCPClient: headers=headers, ) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() else: # http @@ -121,7 +121,9 @@ class MCPClient: headers=headers, ) self._transport = await self._transport_ctx.__aenter__() - self._session_ctx = ClientSession(self._transport[0], self._transport[1]) + self._session_ctx = ClientSession( + self._transport[0], self._transport[1] + ) self._session = await self._session_ctx.__aenter__() await self._session.initialize() except ValueError as e: @@ -184,8 +186,10 @@ class MCPClient: def _get_auth_headers(self) -> dict: """Generate authentication headers based on auth type.""" - headers = {} - + headers = { + "MCP-Protocol-Version": "2025-06-18" + } + if self._mcp_auth_value: if self.auth_type == MCPAuth.bearer_token: headers["Authorization"] = f"Bearer {self._mcp_auth_value}" @@ -196,18 +200,8 @@ class MCPClient: elif self.auth_type == MCPAuth.authorization: headers["Authorization"] = self._mcp_auth_value - # Handle protocol version - it might be a string or enum - if hasattr(self.protocol_version, 'value'): - # It's an enum - protocol_version_str = self.protocol_version.value - else: - # It's a string - protocol_version_str = str(self.protocol_version) - - headers["MCP-Protocol-Version"] = protocol_version_str return headers - async def list_tools(self) -> List[MCPTool]: """List available tools from the server.""" if not self._session: @@ -216,7 +210,7 @@ class MCPClient: except Exception as e: verbose_logger.warning(f"MCP client connection failed: {str(e)}") return [] - + if self._session is None: verbose_logger.warning("MCP client session is not initialized") return [] @@ -245,17 +239,20 @@ class MCPClient: except Exception as e: verbose_logger.warning(f"MCP client connection failed: {str(e)}") return MCPCallToolResult( - content=[TextContent(type="text", text=f"{str(e)}")], - isError=True + content=[TextContent(type="text", text=f"{str(e)}")], isError=True ) if self._session is None: verbose_logger.warning("MCP client session is not initialized") return MCPCallToolResult( - content=[TextContent(type="text", text="MCP client session is not initialized")], + content=[ + TextContent( + type="text", text="MCP client session is not initialized" + ) + ], isError=True, ) - + try: tool_result = await self._session.call_tool( name=call_tool_request_params.name, @@ -270,8 +267,8 @@ class MCPClient: await self.disconnect() # Return a default error result instead of raising return MCPCallToolResult( - content=[TextContent(type="text", text=f"{str(e)}")], # Empty content for error case + content=[ + TextContent(type="text", text=f"{str(e)}") + ], # Empty content for error case isError=True, ) - - diff --git a/litellm/experimental_mcp_client/tools.py b/litellm/experimental_mcp_client/tools.py index bfbd3f96a5c..b716e3171e7 100644 --- a/litellm/experimental_mcp_client/tools.py +++ b/litellm/experimental_mcp_client/tools.py @@ -17,22 +17,60 @@ from litellm.types.utils import ChatCompletionMessageToolCall ######################################################## def transform_mcp_tool_to_openai_tool(mcp_tool: MCPTool) -> ChatCompletionToolParam: """Convert an MCP tool to an OpenAI tool.""" + normalized_parameters = _normalize_mcp_input_schema(mcp_tool.inputSchema) + return ChatCompletionToolParam( type="function", function=FunctionDefinition( name=mcp_tool.name, description=mcp_tool.description or "", - parameters=mcp_tool.inputSchema, + parameters=normalized_parameters, strict=False, ), ) +def _normalize_mcp_input_schema(input_schema: dict) -> dict: + """ + Normalize MCP input schema to ensure it's valid for OpenAI function calling. + + OpenAI requires that function parameters have: + - type: 'object' + - properties: dict (can be empty) + - additionalProperties: false (recommended) + """ + if not input_schema: + return { + "type": "object", + "properties": {}, + "additionalProperties": False + } + + # Make a copy to avoid modifying the original + normalized_schema = dict(input_schema) + + # Ensure type is 'object' + if "type" not in normalized_schema: + normalized_schema["type"] = "object" + + # Ensure properties exists (can be empty) + if "properties" not in normalized_schema: + normalized_schema["properties"] = {} + + # Add additionalProperties if not present (recommended by OpenAI) + if "additionalProperties" not in normalized_schema: + normalized_schema["additionalProperties"] = False + + return normalized_schema + + def transform_mcp_tool_to_openai_responses_api_tool(mcp_tool: MCPTool) -> FunctionToolParam: """Convert an MCP tool to an OpenAI Responses API tool.""" + normalized_parameters = _normalize_mcp_input_schema(mcp_tool.inputSchema) + return FunctionToolParam( name=mcp_tool.name, - parameters=mcp_tool.inputSchema, + parameters=normalized_parameters, strict=False, type="function", description=mcp_tool.description or "", diff --git a/litellm/files/main.py b/litellm/files/main.py index 299e52895bf..18be2c702bf 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -731,7 +731,7 @@ def file_list( async def afile_content( file_id: str, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -887,6 +887,32 @@ def file_content( client=client, litellm_params=litellm_params_dict, ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + vertex_ai_project = ( + optional_params.vertex_project + or litellm.vertex_project + or get_secret_str("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.vertex_location + or litellm.vertex_location + or get_secret_str("VERTEXAI_LOCATION") + ) + vertex_credentials = optional_params.vertex_credentials or get_secret_str( + "VERTEXAI_CREDENTIALS" + ) + + response = vertex_ai_files_instance.file_content( + _is_async=_is_async, + file_content_request=_file_content_request, + api_base=api_base, + vertex_credentials=vertex_credentials, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + timeout=timeout, + max_retries=optional_params.max_retries, + ) else: raise litellm.exceptions.BadRequestError( message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai'.".format( diff --git a/litellm/files/utils.py b/litellm/files/utils.py new file mode 100644 index 00000000000..a56a29467d9 --- /dev/null +++ b/litellm/files/utils.py @@ -0,0 +1,27 @@ +from typing import Optional + +from litellm.types.llms.openai import CreateFileRequest +from litellm.types.utils import ExtractedFileData + + +class FilesAPIUtils: + """ + Utils for files API interface on litellm + """ + @staticmethod + def is_batch_jsonl_file(create_file_data: CreateFileRequest, extracted_file_data: ExtractedFileData) -> bool: + """ + Check if the file is a batch jsonl file + """ + return ( + create_file_data.get("purpose") == "batch" + and FilesAPIUtils.valid_content_type(extracted_file_data.get("content_type")) + and extracted_file_data.get("content") is not None + ) + + @staticmethod + def valid_content_type(content_type: Optional[str]) -> bool: + """ + Check if the content type is valid + """ + return content_type in set(["application/jsonl", "application/octet-stream"]) diff --git a/litellm/google_genai/main.py b/litellm/google_genai/main.py index 87970885355..b480a85c85e 100644 --- a/litellm/google_genai/main.py +++ b/litellm/google_genai/main.py @@ -224,6 +224,9 @@ async def agenerate_content( loop = asyncio.get_event_loop() kwargs["agenerate_content"] = True + # Handle generationConfig parameter from kwargs for backward compatibility + if "generationConfig" in kwargs and config is None: + config = kwargs.pop("generationConfig") # 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( @@ -288,6 +291,9 @@ def generate_content( try: _is_async = kwargs.pop("agenerate_content", False) is True + # Handle generationConfig parameter from kwargs for backward compatibility + if "generationConfig" in kwargs and config is None: + config = kwargs.pop("generationConfig") # Check for mock response first litellm_params = GenericLiteLLMParams(**kwargs) if litellm_params.mock_response and isinstance( @@ -374,6 +380,9 @@ async def agenerate_content_stream( try: kwargs["agenerate_content_stream"] = True + # Handle generationConfig parameter from kwargs for backward compatibility + if "generationConfig" in kwargs and config is None: + config = kwargs.pop("generationConfig") # 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( @@ -461,6 +470,9 @@ def generate_content_stream( # Remove any async-related flags since this is the sync function _is_async = kwargs.pop("agenerate_content_stream", False) + # Handle generationConfig parameter from kwargs for backward compatibility + if "generationConfig" in kwargs and config is None: + config = kwargs.pop("generationConfig") # Setup the call setup_result = GenerateContentHelper.setup_generate_content_call( model=model, diff --git a/litellm/integrations/SlackAlerting/budget_alert_types.py b/litellm/integrations/SlackAlerting/budget_alert_types.py index beebee8b6bf..1e9ad286e37 100644 --- a/litellm/integrations/SlackAlerting/budget_alert_types.py +++ b/litellm/integrations/SlackAlerting/budget_alert_types.py @@ -31,7 +31,7 @@ class SoftBudgetAlert(BaseBudgetAlertType): return "Soft Budget Crossed: " def get_id(self, user_info: CallInfo) -> str: - return "default_id" + return user_info.token or "default_id" class UserBudgetAlert(BaseBudgetAlertType): diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 40d2137a7f1..6b77557cd3d 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -357,6 +357,7 @@ class CustomGuardrail(CustomLogger): end_time: Optional[float] = None, duration: Optional[float] = None, masked_entity_count: Optional[Dict[str, int]] = None, + guardrail_provider: Optional[str] = None, ) -> None: """ Builds `StandardLoggingGuardrailInformation` and adds it to the request metadata so it can be used for logging to DataDog, Langfuse, etc. @@ -367,6 +368,7 @@ class CustomGuardrail(CustomLogger): slg = StandardLoggingGuardrailInformation( guardrail_name=self.guardrail_name, + guardrail_provider=guardrail_provider, guardrail_mode=( GuardrailMode(**self.event_hook.model_dump()) # type: ignore if isinstance(self.event_hook, Mode) @@ -487,7 +489,8 @@ class CustomGuardrail(CustomLogger): """ Update the guardrails litellm params in memory """ - pass + for key, value in vars(litellm_params).items(): + setattr(self, key, value) def log_guardrail_information(func): diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 200f2f283de..2702192f637 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -64,7 +64,7 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): asyncio.create_task(self.periodic_flush()) self.flush_lock = asyncio.Lock() self.log_queue: List[LLMObsPayload] = [] - + ######################################################### # Handle datadog_llm_observability_params set as litellm.datadog_llm_observability_params ######################################################### @@ -83,22 +83,25 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): """ dict_datadog_llm_obs_params: Dict = {} if litellm.datadog_llm_observability_params is not None: - if isinstance(litellm.datadog_llm_observability_params, DatadogLLMObsInitParams): - dict_datadog_llm_obs_params = litellm.datadog_llm_observability_params.model_dump() + if isinstance( + litellm.datadog_llm_observability_params, DatadogLLMObsInitParams + ): + dict_datadog_llm_obs_params = ( + litellm.datadog_llm_observability_params.model_dump() + ) elif isinstance(litellm.datadog_llm_observability_params, Dict): # only allow params that are of DatadogLLMObsInitParams - dict_datadog_llm_obs_params = DatadogLLMObsInitParams(**litellm.datadog_llm_observability_params).model_dump() + dict_datadog_llm_obs_params = DatadogLLMObsInitParams( + **litellm.datadog_llm_observability_params + ).model_dump() return dict_datadog_llm_obs_params - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): try: verbose_logger.debug( f"DataDogLLMObs: Logging success event for model {kwargs.get('model', 'unknown')}" ) - payload = self.create_llm_obs_payload( - kwargs, start_time, end_time - ) + payload = self.create_llm_obs_payload(kwargs, start_time, end_time) verbose_logger.debug(f"DataDogLLMObs: Payload: {payload}") self.log_queue.append(payload) @@ -108,15 +111,13 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): verbose_logger.exception( f"DataDogLLMObs: Error logging success event - {str(e)}" ) - + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): try: verbose_logger.debug( f"DataDogLLMObs: Logging failure event for model {kwargs.get('model', 'unknown')}" ) - payload = self.create_llm_obs_payload( - kwargs, start_time, end_time - ) + payload = self.create_llm_obs_payload(kwargs, start_time, end_time) verbose_logger.debug(f"DataDogLLMObs: Payload: {payload}") self.log_queue.append(payload) @@ -147,10 +148,22 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): ), ), } - verbose_logger.debug("payload %s", json.dumps(payload, indent=4)) + + # serialize datetime objects - for budget reset time in spend metrics + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + + try: + verbose_logger.debug("payload %s", safe_dumps(payload)) + except Exception as debug_error: + verbose_logger.debug( + "payload serialization failed: %s", str(debug_error) + ) + + json_payload = safe_dumps(payload) + response = await self.async_client.post( url=self.intake_url, - json=payload, + content=json_payload, headers={ "DD-API-KEY": self.DD_API_KEY, "Content-Type": "application/json", @@ -184,7 +197,6 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): messages = standard_logging_payload["messages"] messages = self._ensure_string_content(messages=messages) - response_obj = standard_logging_payload.get("response") metadata = kwargs.get("litellm_params", {}).get("metadata", {}) @@ -193,10 +205,12 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): messages ) ) - output_meta = OutputMeta(messages=self._get_response_messages( - response_obj=response_obj, - call_type=standard_logging_payload.get("call_type") - )) + output_meta = OutputMeta( + messages=self._get_response_messages( + standard_logging_payload=standard_logging_payload, + call_type=standard_logging_payload.get("call_type"), + ) + ) error_info = self._assemble_error_info(standard_logging_payload) @@ -214,7 +228,9 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): output_tokens=float(standard_logging_payload.get("completion_tokens", 0)), total_tokens=float(standard_logging_payload.get("total_tokens", 0)), total_cost=float(standard_logging_payload.get("response_cost", 0)), - time_to_first_token=self._get_time_to_first_token_seconds(standard_logging_payload), + time_to_first_token=self._get_time_to_first_token_seconds( + standard_logging_payload + ), ) payload: LLMObsPayload = LLMObsPayload( @@ -251,27 +267,35 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): except Exception: pass return None - - def _assemble_error_info(self, standard_logging_payload: StandardLoggingPayload) -> Optional[DDLLMObsError]: + + def _assemble_error_info( + self, standard_logging_payload: StandardLoggingPayload + ) -> Optional[DDLLMObsError]: """ Assemble error information for failure cases according to DD LLM Obs API spec """ # Handle error information for failure cases according to DD LLM Obs API spec error_info: Optional[DDLLMObsError] = None - + if standard_logging_payload.get("status") == "failure": # Try to get structured error information first - error_information: Optional[StandardLoggingPayloadErrorInformation] = standard_logging_payload.get("error_information") - + error_information: Optional[ + StandardLoggingPayloadErrorInformation + ] = standard_logging_payload.get("error_information") + if error_information: error_info = DDLLMObsError( - message=error_information.get("error_message") or standard_logging_payload.get("error_str") or "Unknown error", + message=error_information.get("error_message") + or standard_logging_payload.get("error_str") + or "Unknown error", type=error_information.get("error_class"), - stack=error_information.get("traceback") + stack=error_information.get("traceback"), ) return error_info - def _get_time_to_first_token_seconds(self, standard_logging_payload: StandardLoggingPayload) -> float: + def _get_time_to_first_token_seconds( + self, standard_logging_payload: StandardLoggingPayload + ) -> float: """ Get the time to first token in seconds @@ -280,7 +304,9 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): For non streaming calls, CompletionStartTime is time we get the response back """ start_time: Optional[float] = standard_logging_payload.get("startTime") - completion_start_time: Optional[float] = standard_logging_payload.get("completionStartTime") + completion_start_time: Optional[float] = standard_logging_payload.get( + "completionStartTime" + ) end_time: Optional[float] = standard_logging_payload.get("endTime") if completion_start_time is not None and start_time is not None: @@ -290,19 +316,43 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): else: return 0.0 - def _get_response_messages( - self, response_obj: Any, call_type: Optional[str] + self, standard_logging_payload: StandardLoggingPayload, call_type: Optional[str] ) -> List[Any]: """ Get the messages from the response object for now this handles logging /chat/completions responses """ + + response_obj = standard_logging_payload.get("response") if response_obj is None: return [] - - if call_type in [CallTypes.completion.value, CallTypes.acompletion.value]: + + # edge case: handle response_obj is a string representation of a dict + if isinstance(response_obj, str): + try: + import ast + + response_obj = ast.literal_eval(response_obj) + except (ValueError, SyntaxError): + try: + # fallback to json parsing + response_obj = json.loads(str(response_obj)) + except json.JSONDecodeError: + return [] + + if call_type in [ + CallTypes.completion.value, + CallTypes.acompletion.value, + CallTypes.text_completion.value, + CallTypes.atext_completion.value, + CallTypes.generate_content.value, + CallTypes.agenerate_content.value, + CallTypes.generate_content_stream.value, + CallTypes.agenerate_content_stream.value, + CallTypes.anthropic_messages.value, + ]: try: # Safely extract message from response_obj, handle failure cases if isinstance(response_obj, dict) and "choices" in response_obj: @@ -315,102 +365,104 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): return [] return [] - def _get_datadog_span_kind(self, call_type: Optional[str]) -> Literal["llm", "tool", "task", "embedding", "retrieval"]: + def _get_datadog_span_kind( + self, call_type: Optional[str] + ) -> Literal["llm", "tool", "task", "embedding", "retrieval"]: """ Map liteLLM call_type to appropriate DataDog LLM Observability span kind. - + Available DataDog span kinds: "llm", "tool", "task", "embedding", "retrieval" """ if call_type is None: return "llm" - + # Embedding operations if call_type in [CallTypes.embedding.value, CallTypes.aembedding.value]: return "embedding" - - # LLM completion operations + + # LLM completion operations if call_type in [ - CallTypes.completion.value, + CallTypes.completion.value, CallTypes.acompletion.value, - CallTypes.text_completion.value, + CallTypes.text_completion.value, CallTypes.atext_completion.value, - CallTypes.generate_content.value, + CallTypes.generate_content.value, CallTypes.agenerate_content.value, - CallTypes.generate_content_stream.value, + CallTypes.generate_content_stream.value, CallTypes.agenerate_content_stream.value, - CallTypes.anthropic_messages.value + CallTypes.anthropic_messages.value, ]: return "llm" - + # Tool operations if call_type in [CallTypes.call_mcp_tool.value]: return "tool" - + # Retrieval operations if call_type in [ - CallTypes.get_assistants.value, + CallTypes.get_assistants.value, CallTypes.aget_assistants.value, - CallTypes.get_thread.value, + CallTypes.get_thread.value, CallTypes.aget_thread.value, - CallTypes.get_messages.value, + CallTypes.get_messages.value, CallTypes.aget_messages.value, - CallTypes.afile_retrieve.value, + CallTypes.afile_retrieve.value, CallTypes.file_retrieve.value, - CallTypes.afile_list.value, + CallTypes.afile_list.value, CallTypes.file_list.value, - CallTypes.afile_content.value, + CallTypes.afile_content.value, CallTypes.file_content.value, - CallTypes.retrieve_batch.value, + CallTypes.retrieve_batch.value, CallTypes.aretrieve_batch.value, - CallTypes.retrieve_fine_tuning_job.value, + CallTypes.retrieve_fine_tuning_job.value, CallTypes.aretrieve_fine_tuning_job.value, - CallTypes.responses.value, + CallTypes.responses.value, CallTypes.aresponses.value, - CallTypes.alist_input_items.value + CallTypes.alist_input_items.value, ]: return "retrieval" - + # Task operations (batch, fine-tuning, file operations, etc.) if call_type in [ - CallTypes.create_batch.value, + CallTypes.create_batch.value, CallTypes.acreate_batch.value, - CallTypes.create_fine_tuning_job.value, + CallTypes.create_fine_tuning_job.value, CallTypes.acreate_fine_tuning_job.value, - CallTypes.cancel_fine_tuning_job.value, + CallTypes.cancel_fine_tuning_job.value, CallTypes.acancel_fine_tuning_job.value, - CallTypes.list_fine_tuning_jobs.value, + CallTypes.list_fine_tuning_jobs.value, CallTypes.alist_fine_tuning_jobs.value, - CallTypes.create_assistants.value, + CallTypes.create_assistants.value, CallTypes.acreate_assistants.value, - CallTypes.delete_assistant.value, + CallTypes.delete_assistant.value, CallTypes.adelete_assistant.value, - CallTypes.create_thread.value, + CallTypes.create_thread.value, CallTypes.acreate_thread.value, - CallTypes.add_message.value, + CallTypes.add_message.value, CallTypes.a_add_message.value, - CallTypes.run_thread.value, + CallTypes.run_thread.value, CallTypes.arun_thread.value, - CallTypes.run_thread_stream.value, + CallTypes.run_thread_stream.value, CallTypes.arun_thread_stream.value, - CallTypes.file_delete.value, + CallTypes.file_delete.value, CallTypes.afile_delete.value, - CallTypes.create_file.value, + CallTypes.create_file.value, CallTypes.acreate_file.value, - CallTypes.image_generation.value, + CallTypes.image_generation.value, CallTypes.aimage_generation.value, - CallTypes.image_edit.value, + CallTypes.image_edit.value, CallTypes.aimage_edit.value, - CallTypes.moderation.value, + CallTypes.moderation.value, CallTypes.amoderation.value, - CallTypes.transcription.value, + CallTypes.transcription.value, CallTypes.atranscription.value, - CallTypes.speech.value, + CallTypes.speech.value, CallTypes.aspeech.value, - CallTypes.rerank.value, - CallTypes.arerank.value + CallTypes.rerank.value, + CallTypes.arerank.value, ]: return "task" - + # Default fallback for unknown or passthrough operations return "llm" @@ -443,7 +495,10 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): "cache_hit": standard_logging_payload.get("cache_hit", "unknown"), "cache_key": standard_logging_payload.get("cache_key", "unknown"), "saved_cache_cost": standard_logging_payload.get("saved_cache_cost", 0), - "guardrail_information": standard_logging_payload.get("guardrail_information", None), + "guardrail_information": standard_logging_payload.get( + "guardrail_information", None + ), + "is_streamed_request": self._get_stream_value_from_payload(standard_logging_payload), } ######################################################### @@ -452,22 +507,38 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): latency_metrics = self._get_latency_metrics(standard_logging_payload) _metadata.update({"latency_metrics": dict(latency_metrics)}) + ######################################################### + # Add spend metrics to metadata + ######################################################### + spend_metrics = self._get_spend_metrics(standard_logging_payload) + _metadata.update({"spend_metrics": dict(spend_metrics)}) + + ## extract tool calls and add to metadata + tool_call_metadata = self._extract_tool_call_metadata(standard_logging_payload) + _metadata.update(tool_call_metadata) + _standard_logging_metadata: dict = ( dict(standard_logging_payload.get("metadata", {})) or {} ) _metadata.update(_standard_logging_metadata) return _metadata - def _get_latency_metrics(self, standard_logging_payload: StandardLoggingPayload) -> DDLLMObsLatencyMetrics: + def _get_latency_metrics( + self, standard_logging_payload: StandardLoggingPayload + ) -> DDLLMObsLatencyMetrics: """ Get the latency metrics from the standard logging payload """ latency_metrics: DDLLMObsLatencyMetrics = DDLLMObsLatencyMetrics() # Add latency metrics to metadata # Time to first token (convert from seconds to milliseconds for consistency) - time_to_first_token_seconds = self._get_time_to_first_token_seconds(standard_logging_payload) + time_to_first_token_seconds = self._get_time_to_first_token_seconds( + standard_logging_payload + ) if time_to_first_token_seconds > 0: - latency_metrics["time_to_first_token_ms"] = time_to_first_token_seconds * 1000 + latency_metrics["time_to_first_token_ms"] = ( + time_to_first_token_seconds * 1000 + ) # LiteLLM overhead time hidden_params = standard_logging_payload.get("hidden_params", {}) @@ -476,11 +547,233 @@ class DataDogLLMObsLogger(DataDogLogger, CustomBatchLogger): latency_metrics["litellm_overhead_time_ms"] = litellm_overhead_ms # Guardrail overhead latency - guardrail_info: Optional[StandardLoggingGuardrailInformation] = standard_logging_payload.get("guardrail_information") + guardrail_info: Optional[ + StandardLoggingGuardrailInformation + ] = standard_logging_payload.get("guardrail_information") if guardrail_info is not None: - _guardrail_duration_seconds: Optional[float] = guardrail_info.get("duration") + _guardrail_duration_seconds: Optional[float] = guardrail_info.get( + "duration" + ) if _guardrail_duration_seconds is not None: # Convert from seconds to milliseconds for consistency - latency_metrics["guardrail_overhead_time_ms"] = _guardrail_duration_seconds * 1000 - - return latency_metrics \ No newline at end of file + latency_metrics["guardrail_overhead_time_ms"] = ( + _guardrail_duration_seconds * 1000 + ) + + return latency_metrics + + def _get_stream_value_from_payload(self, standard_logging_payload: StandardLoggingPayload) -> bool: + """ + Extract the stream value from standard logging payload. + + The stream field in StandardLoggingPayload is only set to True for completed streaming responses. + For non-streaming requests, it's None. The original stream parameter is in model_parameters. + + Returns: + bool: True if this was a streaming request, False otherwise + """ + # Check top-level stream field first (only True for completed streaming) + stream_value = standard_logging_payload.get("stream") + if stream_value is True: + return True + + # Fallback to model_parameters.stream for original request parameters + model_params = standard_logging_payload.get("model_parameters", {}) + if isinstance(model_params, dict): + stream_value = model_params.get("stream") + if stream_value is True: + return True + + # Default to False for non-streaming requests + return False + + def _get_spend_metrics( + self, standard_logging_payload: StandardLoggingPayload + ) -> DDLLMObsSpendMetrics: + """ + Get the spend metrics from the standard logging payload + """ + spend_metrics: DDLLMObsSpendMetrics = DDLLMObsSpendMetrics() + + # send response cost + spend_metrics["response_cost"] = standard_logging_payload.get( + "response_cost", 0.0 + ) + + # Get budget information from metadata + metadata = standard_logging_payload.get("metadata", {}) + + # API key max budget + user_api_key_max_budget = metadata.get("user_api_key_max_budget") + if user_api_key_max_budget is not None: + spend_metrics["user_api_key_max_budget"] = float(user_api_key_max_budget) + + # API key spend + user_api_key_spend = metadata.get("user_api_key_spend") + if user_api_key_spend is not None: + try: + spend_metrics["user_api_key_spend"] = float(user_api_key_spend) + except (ValueError, TypeError): + verbose_logger.debug( + f"Invalid user_api_key_spend value: {user_api_key_spend}" + ) + + # API key budget reset datetime + user_api_key_budget_reset_at = metadata.get("user_api_key_budget_reset_at") + if user_api_key_budget_reset_at is not None: + try: + from datetime import datetime, timezone + + budget_reset_at = None + if isinstance(user_api_key_budget_reset_at, str): + # Handle ISO format strings that might have 'Z' suffix + iso_string = user_api_key_budget_reset_at.replace("Z", "+00:00") + budget_reset_at = datetime.fromisoformat(iso_string) + elif isinstance(user_api_key_budget_reset_at, datetime): + budget_reset_at = user_api_key_budget_reset_at + + if budget_reset_at is not None: + # Preserve timezone info if already present + if budget_reset_at.tzinfo is None: + budget_reset_at = budget_reset_at.replace(tzinfo=timezone.utc) + + # Convert to ISO string format for JSON serialization + # This prevents circular reference issues and ensures proper timezone representation + iso_string = budget_reset_at.isoformat() + spend_metrics["user_api_key_budget_reset_at"] = iso_string + + # Debug logging to verify the conversion + verbose_logger.debug( + f"Converted budget_reset_at to ISO format: {iso_string}" + ) + except Exception as e: + verbose_logger.debug(f"Error processing budget reset datetime: {e}") + verbose_logger.debug(f"Original value: {user_api_key_budget_reset_at}") + + return spend_metrics + + def _process_input_messages_preserving_tool_calls( + self, messages: List[Any] + ) -> List[Dict[str, Any]]: + """ + Process input messages while preserving tool_calls and tool message types. + + This bypasses the lossy string conversion when tool calls are present, + allowing complex nested tool_calls objects to be preserved for Datadog. + """ + processed = [] + for msg in messages: + if isinstance(msg, dict): + # Preserve messages with tool_calls or tool role as-is + if "tool_calls" in msg or msg.get("role") == "tool": + processed.append(msg) + else: + # For regular messages, still apply string conversion + converted = ( + handle_any_messages_to_chat_completion_str_messages_conversion( + [msg] + ) + ) + processed.extend(converted) + else: + # For non-dict messages, apply string conversion + converted = ( + handle_any_messages_to_chat_completion_str_messages_conversion( + [msg] + ) + ) + processed.extend(converted) + return processed + + @staticmethod + def _tool_calls_kv_pair(tool_calls: List[Dict[str, Any]]) -> Dict[str, Any]: + """ + Extract tool call information into key-value pairs for Datadog metadata. + + Similar to OpenTelemetry's implementation but adapted for Datadog's format. + """ + kv_pairs: Dict[str, Any] = {} + for idx, tool_call in enumerate(tool_calls): + try: + # Extract tool call ID + tool_id = tool_call.get("id") + if tool_id: + kv_pairs[f"tool_calls.{idx}.id"] = tool_id + + # Extract tool call type + tool_type = tool_call.get("type") + if tool_type: + kv_pairs[f"tool_calls.{idx}.type"] = tool_type + + # Extract function information + function = tool_call.get("function") + if function: + function_name = function.get("name") + if function_name: + kv_pairs[f"tool_calls.{idx}.function.name"] = function_name + + function_arguments = function.get("arguments") + if function_arguments: + # Store arguments as JSON string for Datadog + if isinstance(function_arguments, str): + kv_pairs[ + f"tool_calls.{idx}.function.arguments" + ] = function_arguments + else: + import json + + kv_pairs[ + f"tool_calls.{idx}.function.arguments" + ] = json.dumps(function_arguments) + except (KeyError, TypeError, ValueError) as e: + verbose_logger.debug( + f"DataDogLLMObs: Error processing tool call {idx}: {str(e)}" + ) + continue + + return kv_pairs + + def _extract_tool_call_metadata( + self, standard_logging_payload: StandardLoggingPayload + ) -> Dict[str, Any]: + """ + Extract tool call information from both input messages and response for Datadog metadata. + """ + tool_call_metadata: Dict[str, Any] = {} + + try: + # Extract tool calls from input messages + messages = standard_logging_payload.get("messages", []) + if messages and isinstance(messages, list): + for message in messages: + if isinstance(message, dict) and "tool_calls" in message: + tool_calls = message.get("tool_calls") + if tool_calls: + input_tool_calls_kv = self._tool_calls_kv_pair(tool_calls) + # Prefix with "input_" to distinguish from response tool calls + for key, value in input_tool_calls_kv.items(): + tool_call_metadata[f"input_{key}"] = value + + # Extract tool calls from response + response_obj = standard_logging_payload.get("response") + if response_obj and isinstance(response_obj, dict): + choices = response_obj.get("choices", []) + for choice in choices: + if isinstance(choice, dict): + message = choice.get("message") + if message and isinstance(message, dict): + tool_calls = message.get("tool_calls") + if tool_calls: + response_tool_calls_kv = self._tool_calls_kv_pair( + tool_calls + ) + # Prefix with "output_" to distinguish from input tool calls + for key, value in response_tool_calls_kv.items(): + tool_call_metadata[f"output_{key}"] = value + + except Exception as e: + verbose_logger.debug( + f"DataDogLLMObs: Error extracting tool call metadata: {str(e)}" + ) + + return tool_call_metadata diff --git a/litellm/integrations/humanloop.py b/litellm/integrations/humanloop.py index 9f43d806266..8e60d3736e0 100644 --- a/litellm/integrations/humanloop.py +++ b/litellm/integrations/humanloop.py @@ -4,9 +4,10 @@ Humanloop integration https://humanloop.com/ """ -from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union, cast +from typing import Any, Dict, List, Optional, Tuple, Union, cast import httpx +from typing_extensions import TypedDict import litellm from litellm.caching import DualCache diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 9c3f07fa1a5..24577731384 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -15,7 +15,7 @@ from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info from litellm.llms.custom_httpx.http_handler import _get_httpx_client from litellm.secret_managers.main import str_to_bool from litellm.types.integrations.langfuse import * -from litellm.types.llms.openai import HttpxBinaryResponseContent +from litellm.types.llms.openai import HttpxBinaryResponseContent, ResponsesAPIResponse from litellm.types.utils import ( EmbeddingResponse, ImageResponse, @@ -196,6 +196,7 @@ class LangFuseLogger: TranscriptionResponse, RerankResponse, HttpxBinaryResponseContent, + ResponsesAPIResponse, ], start_time: Optional[datetime] = None, end_time: Optional[datetime] = None, @@ -305,6 +306,7 @@ class LangFuseLogger: TranscriptionResponse, RerankResponse, HttpxBinaryResponseContent, + ResponsesAPIResponse, ], prompt: dict, level: str, @@ -369,6 +371,11 @@ class LangFuseLogger: ): input = prompt output = response_obj.results + elif response_obj is not None and isinstance( + response_obj, litellm.ResponsesAPIResponse + ): + input = prompt + output = self._get_responses_api_content_for_langfuse(response_obj) elif ( kwargs.get("call_type") is not None and kwargs.get("call_type") == "_arealtime" @@ -768,6 +775,19 @@ class LangFuseLogger: else: return None + @staticmethod + def _get_responses_api_content_for_langfuse( + response_obj: ResponsesAPIResponse, + ): + """ + Get the responses API content for Langfuse logging + """ + if hasattr(response_obj, 'output') and response_obj.output: + # ResponsesAPIResponse.output is a list of strings + return response_obj.output + else: + return None + @staticmethod def _get_langfuse_tags( standard_logging_object: Optional[StandardLoggingPayload], diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 7035aa3a819..7783b704b46 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -39,6 +39,7 @@ class LangsmithLogger(CustomBatchLogger): langsmith_api_key: Optional[str] = None, langsmith_project: Optional[str] = None, langsmith_base_url: Optional[str] = None, + langsmith_sampling_rate: Optional[float] = None, **kwargs, ): self.flush_lock = asyncio.Lock() @@ -49,7 +50,8 @@ class LangsmithLogger(CustomBatchLogger): langsmith_base_url=langsmith_base_url, ) self.sampling_rate: float = ( - float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore + langsmith_sampling_rate + or float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore if os.getenv("LANGSMITH_SAMPLING_RATE") is not None and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore else 1.0 @@ -76,26 +78,14 @@ class LangsmithLogger(CustomBatchLogger): langsmith_base_url: Optional[str] = None, ) -> LangsmithCredentialsObject: _credentials_api_key = langsmith_api_key or os.getenv("LANGSMITH_API_KEY") - if _credentials_api_key is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_api_key=None." - ) _credentials_project = ( langsmith_project or os.getenv("LANGSMITH_PROJECT") or "litellm-completion" ) - if _credentials_project is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_project=None." - ) _credentials_base_url = ( langsmith_base_url or os.getenv("LANGSMITH_BASE_URL") or "https://api.smith.langchain.com" ) - if _credentials_base_url is None: - raise Exception( - "Invalid Langsmith API Key given. _credentials_base_url=None." - ) return LangsmithCredentialsObject( LANGSMITH_API_KEY=_credentials_api_key, @@ -200,12 +190,7 @@ class LangsmithLogger(CustomBatchLogger): def log_success_event(self, kwargs, response_obj, start_time, end_time): try: - sampling_rate = ( - float(os.getenv("LANGSMITH_SAMPLING_RATE")) # type: ignore - if os.getenv("LANGSMITH_SAMPLING_RATE") is not None - and os.getenv("LANGSMITH_SAMPLING_RATE").strip().isdigit() # type: ignore - else 1.0 - ) + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -219,6 +204,7 @@ class LangsmithLogger(CustomBatchLogger): kwargs, response_obj, ) + credentials = self._get_credentials_to_use_for_request(kwargs=kwargs) data = self._prepare_log_data( kwargs=kwargs, @@ -245,7 +231,7 @@ class LangsmithLogger(CustomBatchLogger): async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): try: - sampling_rate = self.sampling_rate + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -286,7 +272,7 @@ class LangsmithLogger(CustomBatchLogger): ) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): - sampling_rate = self.sampling_rate + sampling_rate = self._get_sampling_rate_to_use_for_request(kwargs=kwargs) random_sample = random.random() if random_sample > sampling_rate: verbose_logger.info( @@ -417,6 +403,17 @@ class LangsmithLogger(CustomBatchLogger): for queue_object in self.log_queue: credentials = queue_object["credentials"] + # if credential missing, skip - log warning + if ( + credentials["LANGSMITH_API_KEY"] is None + or credentials["LANGSMITH_PROJECT"] is None + ): + verbose_logger.warning( + "Langsmith Logging - credentials missing - api_key: %s, project: %s", + credentials["LANGSMITH_API_KEY"], + credentials["LANGSMITH_PROJECT"], + ) + continue key = CredentialsKey( api_key=credentials["LANGSMITH_API_KEY"], project=credentials["LANGSMITH_PROJECT"], @@ -432,6 +429,19 @@ class LangsmithLogger(CustomBatchLogger): return log_queue_by_credentials + def _get_sampling_rate_to_use_for_request(self, kwargs: Dict[str, Any]) -> float: + standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( + kwargs.get("standard_callback_dynamic_params", None) + ) + sampling_rate: float = self.sampling_rate + if standard_callback_dynamic_params is not None: + _sampling_rate = standard_callback_dynamic_params.get( + "langsmith_sampling_rate" + ) + if _sampling_rate is not None: + sampling_rate = float(_sampling_rate) + return sampling_rate + def _get_credentials_to_use_for_request( self, kwargs: Dict[str, Any] ) -> LangsmithCredentialsObject: @@ -442,9 +452,9 @@ class LangsmithLogger(CustomBatchLogger): Otherwise, use the default credentials. """ - standard_callback_dynamic_params: Optional[ - StandardCallbackDynamicParams - ] = kwargs.get("standard_callback_dynamic_params", None) + standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( + kwargs.get("standard_callback_dynamic_params", None) + ) if standard_callback_dynamic_params is not None: credentials = self.get_credentials_from_env( langsmith_api_key=standard_callback_dynamic_params.get( diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py index 8cbfb9e6535..9f90d2384d8 100644 --- a/litellm/integrations/opik/opik.py +++ b/litellm/integrations/opik/opik.py @@ -3,6 +3,7 @@ Opik Logger that logs LLM events to an Opik server """ import asyncio +from datetime import timezone import json import traceback from typing import Dict, List @@ -291,8 +292,8 @@ class OpikLogger(CustomBatchLogger): "project_name": project_name, "id": trace_id, "name": trace_name, - "start_time": start_time.isoformat() + "Z", - "end_time": end_time.isoformat() + "Z", + "start_time": start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), + "end_time": end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), "input": input_data, "output": output_data, "metadata": metadata, @@ -312,8 +313,8 @@ class OpikLogger(CustomBatchLogger): "parent_span_id": parent_span_id, "name": span_name, "type": "llm", - "start_time": start_time.isoformat() + "Z", - "end_time": end_time.isoformat() + "Z", + "start_time": start_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), + "end_time": end_time.astimezone(timezone.utc).isoformat().replace("+00:00", "Z"), "input": input_data, "output": output_data, "metadata": metadata, diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py new file mode 100644 index 00000000000..d321135f289 --- /dev/null +++ b/litellm/integrations/posthog.py @@ -0,0 +1,333 @@ +""" +PostHog Integration - sends LLM analytics events to PostHog + +Follows PostHog's LLM Analytics format: https://posthog.com/docs/llm-analytics/manual-capture + +async_log_success_event: stores batch of events in memory and flushes to PostHog +async_log_failure_event: logs failed LLM calls with error information + +For batching specific details see CustomBatchLogger class +""" + +import asyncio +import os +import uuid +from typing import Any, Dict, Optional + + +from litellm._logging import verbose_logger +from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.llms.custom_httpx.http_handler import ( + _get_httpx_client, + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.integrations.posthog import ( + POSTHOG_MAX_BATCH_SIZE, + PostHogEventPayload, +) +from litellm.types.utils import StandardLoggingPayload + + +class PostHogLogger(CustomBatchLogger): + def __init__(self, **kwargs): + """ + Initializes the PostHog logger, checks if the correct env variables are set + + Required environment variables: + `POSTHOG_API_KEY` - your PostHog API key + `POSTHOG_API_URL` - your PostHog API URL (defaults to https://app.posthog.com) + """ + try: + verbose_logger.debug("PostHog: in init posthog logger") + if os.getenv("POSTHOG_API_KEY", None) is None: + raise Exception("POSTHOG_API_KEY is not set, set 'POSTHOG_API_KEY=<>'") + + self.async_client = get_async_httpx_client( + llm_provider=httpxSpecialProvider.LoggingCallback + ) + self.sync_client = _get_httpx_client() + + self.POSTHOG_API_KEY = os.getenv("POSTHOG_API_KEY") + posthog_api_url = os.getenv("POSTHOG_API_URL", "https://us.i.posthog.com") + self.posthog_host = posthog_api_url.rstrip('/') + self.capture_url = f"{self.posthog_host}/batch/" + + self._async_initialized = False + self.flush_lock = None + self.log_queue = [] + + super().__init__( + **kwargs, flush_lock=None, batch_size=POSTHOG_MAX_BATCH_SIZE + ) + + except Exception as e: + verbose_logger.exception( + f"PostHog: Got exception on init PostHog client {str(e)}" + ) + raise e + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + "PostHog: Sync logging - Enters logging function for model %s", kwargs + ) + + event_payload = self.create_posthog_event_payload(kwargs) + + headers = { + "Content-Type": "application/json", + } + + payload = self._create_posthog_payload([event_payload]) + + response = self.sync_client.post( + url=self.capture_url, + json=payload, + headers=headers, + ) + response.raise_for_status() + + if response.status_code != 200: + raise Exception( + f"Response from PostHog API status_code: {response.status_code}, text: {response.text}" + ) + + verbose_logger.debug("PostHog: Sync event successfully sent") + + except Exception as e: + verbose_logger.exception(f"PostHog Sync Layer Error - {str(e)}") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + "PostHog: Async logging - Enters logging function for model %s", kwargs + ) + self._ensure_async_setup() # Lazy initialization + await self._log_async_event(kwargs, response_obj, start_time, end_time) + except Exception as e: + verbose_logger.exception(f"PostHog Layer Error - {str(e)}") + pass + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + "PostHog: Async logging - Enters logging function for model %s", kwargs + ) + self._ensure_async_setup() # Lazy initialization + await self._log_async_event(kwargs, response_obj, start_time, end_time) + except Exception as e: + verbose_logger.exception(f"PostHog Layer Error - {str(e)}") + pass + + async def _log_async_event(self, kwargs, response_obj=None, start_time=0.0, end_time=0.0): + # Note: response_obj, start_time, end_time not used - all data comes from kwargs + event_payload = self.create_posthog_event_payload(kwargs) + + self.log_queue.append(event_payload) + verbose_logger.debug( + f"PostHog, event added to queue. Will flush in {self.flush_interval} seconds..." + ) + + if len(self.log_queue) >= self.batch_size: + await self.flush_queue() + + def create_posthog_event_payload(self, kwargs: Dict[str, Any]) -> PostHogEventPayload: + """ + Helper function to create a PostHog event payload for logging + + Args: + kwargs (Dict[str, Any]): request kwargs containing standard_logging_object + + Returns: + PostHogEventPayload: defined in types.py + """ + standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object", None + ) + if standard_logging_object is None: + raise ValueError("standard_logging_object not found in kwargs") + + call_type = standard_logging_object.get("call_type", "") + event_name = "$ai_embedding" if call_type == "embedding" else "$ai_generation" + + properties = self._create_posthog_properties( + standard_logging_object=standard_logging_object, + kwargs=kwargs, + event_name=event_name, + ) + + distinct_id = self._get_distinct_id(standard_logging_object, kwargs) + + return PostHogEventPayload( + event=event_name, + properties=properties, + distinct_id=distinct_id, + ) + + def _create_posthog_properties( + self, + standard_logging_object: StandardLoggingPayload, + kwargs: Dict[str, Any], + event_name: str, + ) -> Dict[str, Any]: + """Create PostHog properties following LLM Analytics spec""" + properties = {} + + # Core model information + properties["$ai_model"] = self._safe_get(standard_logging_object, "model", "") + properties["$ai_provider"] = self._safe_get(standard_logging_object, "custom_llm_provider", "") + + # Input/Output data + messages = self._safe_get(standard_logging_object, "messages") + if messages is not None: + properties["$ai_input"] = messages + + if event_name == "$ai_generation": + response = self._safe_get(standard_logging_object, "response") + if response is not None: + properties["$ai_output_choices"] = response + + # Token information + properties["$ai_input_tokens"] = self._safe_get(standard_logging_object, "prompt_tokens", 0) + if event_name == "$ai_generation": + properties["$ai_output_tokens"] = self._safe_get(standard_logging_object, "completion_tokens", 0) + + # Cost and performance + response_cost = self._safe_get(standard_logging_object, "response_cost") + if response_cost is not None: + properties["$ai_total_cost_usd"] = response_cost + + properties["$ai_latency"] = self._safe_get(standard_logging_object, "response_time", 0.0) + + # Error handling + if self._safe_get(standard_logging_object, "status") == "failure": + properties["$ai_is_error"] = True + error_str = self._safe_get(standard_logging_object, "error_str") + if error_str is not None: + properties["$ai_error"] = error_str + + # Add trace properties + self._add_trace_properties(properties, kwargs) + + # Add custom metadata fields + self._add_custom_metadata_properties(properties, kwargs) + + return properties + + def _add_trace_properties(self, properties: Dict[str, Any], kwargs: Dict[str, Any]): + standard_logging_object = self._safe_get(kwargs, "standard_logging_object", {}) + + trace_id = self._safe_get(standard_logging_object, "trace_id", self._safe_uuid()) + properties["$ai_trace_id"] = trace_id + + span_id = self._safe_get(standard_logging_object, "id", self._safe_uuid()) + properties["$ai_span_id"] = span_id + + metadata = self._extract_metadata(kwargs) + parent_id = metadata.get("parent_run_id") or metadata.get("parent_id") + if parent_id: + properties["$ai_parent_id"] = parent_id + + def _add_custom_metadata_properties(self, properties: Dict[str, Any], kwargs: Dict[str, Any]): + """Add custom metadata fields to PostHog properties""" + metadata = self._extract_metadata(kwargs) + if not isinstance(metadata, dict): + return + + litellm_internal_fields = { + "endpoint", "caching_groups", "user_api_key_hash", "user_api_key_alias", + "user_api_key_team_id", "user_api_key_user_id", "user_api_key_org_id", + "user_api_key_team_alias", "user_api_key_end_user_id", "user_api_key_user_email", + "user_api_key", "user_api_end_user_max_budget", "litellm_api_version", + "global_max_parallel_requests", "user_api_key_team_max_budget", "user_api_key_team_spend", + "user_api_key_spend", "user_api_key_max_budget", "user_api_key_model_max_budget", + "user_api_key_metadata", "headers", "litellm_parent_otel_span", "requester_ip_address", + "model_group", "model_group_size", "deployment", "model_info", "api_base", + "caching_groups", "hidden_params", "parent_run_id", "parent_id", "user_id" + } + + for key, value in metadata.items(): + if key not in litellm_internal_fields: + properties[key] = value + + def _get_distinct_id( + self, standard_logging_object: StandardLoggingPayload, kwargs: Dict[str, Any] + ) -> str: + metadata = self._extract_metadata(kwargs) + user_id = self._safe_get(metadata, "user_id") + if user_id: + return str(user_id) + end_user = self._safe_get(standard_logging_object, "end_user") + if end_user: + return str(end_user) + trace_id = self._safe_get(standard_logging_object, "trace_id") + if trace_id: + return str(trace_id) + + return self._safe_uuid() + + async def async_send_batch(self): + """ + Sends the in memory logs queue to PostHog API + + Raises: + Raises a NON Blocking verbose_logger.exception if an error occurs + """ + try: + if not self.log_queue: + return + + verbose_logger.debug( + f"PostHog: Sending batch of {len(self.log_queue)} events" + ) + + headers = { + "Content-Type": "application/json", + } + + payload = self._create_posthog_payload(list(self.log_queue)) + + response = await self.async_client.post( + url=self.capture_url, + json=payload, + headers=headers, + ) + response.raise_for_status() + + if response.status_code != 200: + raise Exception( + f"Response from PostHog API status_code: {response.status_code}, text: {response.text}" + ) + + verbose_logger.debug( + f"PostHog: Batch of {len(self.log_queue)} events successfully sent" + ) + except Exception as e: + verbose_logger.exception(f"PostHog Error sending batch API - {str(e)}") + + def _ensure_async_setup(self): + if not self._async_initialized: + try: + self.flush_lock = asyncio.Lock() + asyncio.create_task(self.periodic_flush()) + self._async_initialized = True + verbose_logger.debug("PostHog: Async components initialized") + except Exception as e: + verbose_logger.error(f"PostHog: Failed to initialize async components: {str(e)}") + raise + + def _extract_metadata(self, kwargs: Dict[str, Any]) -> Dict[str, Any]: + litellm_params = kwargs.get("litellm_params", {}) or {} + return litellm_params.get("metadata", {}) or {} + + def _safe_uuid(self) -> str: + return str(uuid.uuid4()) + + def _create_posthog_payload(self, events: list) -> Dict[str, Any]: + return {"api_key": self.POSTHOG_API_KEY, "batch": events} + + def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any: + if obj is None or not hasattr(obj, 'get'): + return default + return obj.get(key, default) diff --git a/litellm/integrations/prompt_management_base.py b/litellm/integrations/prompt_management_base.py index 34b4455f564..7754ca435ca 100644 --- a/litellm/integrations/prompt_management_base.py +++ b/litellm/integrations/prompt_management_base.py @@ -1,5 +1,7 @@ from abc import ABC, abstractmethod -from typing import Any, Dict, List, Optional, Tuple, TypedDict +from typing import Any, Dict, List, Optional, Tuple + +from typing_extensions import TypedDict from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import StandardCallbackDynamicParams diff --git a/litellm/integrations/s3_v2.py b/litellm/integrations/s3_v2.py index efe18cb68ad..a65500c80dc 100644 --- a/litellm/integrations/s3_v2.py +++ b/litellm/integrations/s3_v2.py @@ -203,7 +203,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): start_time=start_time, end_time=end_time, ) - + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): await self._async_log_event_base( kwargs=kwargs, @@ -212,7 +212,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): end_time=end_time, ) pass - async def _async_log_event_base(self, kwargs, response_obj, start_time, end_time): try: @@ -242,7 +241,6 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): verbose_logger.exception(f"s3 Layer Error - {str(e)}") pass - async def async_upload_data_to_s3( self, batch_logging_element: s3BatchLoggingElement ): @@ -277,8 +275,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): # Prepare the URL url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}" - if self.s3_endpoint_url: - url = self.s3_endpoint_url + "/" + batch_logging_element.s3_object_key + if self.s3_endpoint_url and self.s3_bucket_name: + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + batch_logging_element.s3_object_key + ) # Convert JSON to string json_string = safe_dumps(batch_logging_element.payload) @@ -420,8 +424,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): # Prepare the URL url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}" - if self.s3_endpoint_url: - url = self.s3_endpoint_url + "/" + batch_logging_element.s3_object_key + if self.s3_endpoint_url and self.s3_bucket_name: + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + batch_logging_element.s3_object_key + ) # Convert JSON to string json_string = safe_dumps(batch_logging_element.payload) @@ -462,14 +472,13 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): except Exception as e: verbose_logger.exception(f"Error uploading to s3: {str(e)}") - async def _download_object_from_s3(self, s3_object_key: str) -> Optional[dict]: """ Download and parse JSON object from S3. - + Args: s3_object_key: The S3 object key to download - + Returns: Optional[dict]: The parsed JSON object or None if not found/error """ @@ -481,7 +490,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): from botocore.awsrequest import AWSRequest except ImportError: raise ImportError("Missing boto3 to call S3. Run 'pip install boto3'.") - + try: from litellm.litellm_core_utils.asyncify import asyncify @@ -506,8 +515,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): # Prepare the URL url = f"https://{self.s3_bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{s3_object_key}" - if self.s3_endpoint_url: - url = self.s3_endpoint_url + "/" + s3_object_key + if self.s3_endpoint_url and self.s3_bucket_name: + url = ( + self.s3_endpoint_url + + "/" + + self.s3_bucket_name + + "/" + + s3_object_key + ) # Prepare the request for GET operation # For GET requests, we need x-amz-content-sha256 with hash of empty string @@ -533,12 +548,14 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): response = await self.async_httpx_client.get(url, headers=signed_headers) if response.status_code != 200: - verbose_logger.exception("S3 object not found, saw response=", response.text) + verbose_logger.exception( + "S3 object not found, saw response=", response.text + ) return None - + # Parse JSON response return response.json() - + except Exception as e: verbose_logger.exception(f"Error downloading from S3: {str(e)}") return None @@ -551,11 +568,11 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): Get the proxy server request from cold storage Allows fetching a dict of the proxy server request from s3 or GCS bucket. - + Args: request_id: The unique request ID to search for start_time: The start time of the request (datetime or ISO string) - + Returns: Optional[dict]: The request data dictionary or None if not found """ @@ -564,5 +581,7 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM): downloaded_object = await self._download_object_from_s3(object_key) return downloaded_object except Exception as e: - verbose_logger.exception(f"Error retrieving object {object_key} from cold storage: {str(e)}") - return None \ No newline at end of file + verbose_logger.exception( + f"Error retrieving object {object_key} from cold storage: {str(e)}" + ) + return None diff --git a/litellm/litellm_core_utils/cached_imports.py b/litellm/litellm_core_utils/cached_imports.py new file mode 100644 index 00000000000..c3ab292e9c5 --- /dev/null +++ b/litellm/litellm_core_utils/cached_imports.py @@ -0,0 +1,56 @@ +""" +Cached imports module for LiteLLM. + +This module provides cached import functionality to avoid repeated imports +inside functions that are critical to performance. +""" + +from typing import TYPE_CHECKING, Callable, Optional, Type + +# Type annotations for cached imports +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.litellm_core_utils.coroutine_checker import CoroutineChecker + +# Global cache variables +_LiteLLMLogging: Optional[Type["Logging"]] = None +_coroutine_checker: Optional["CoroutineChecker"] = None +_set_callbacks: Optional[Callable] = None + + +def get_litellm_logging_class() -> Type["Logging"]: + """Get the cached LiteLLM Logging class, initializing if needed.""" + global _LiteLLMLogging + if _LiteLLMLogging is not None: + return _LiteLLMLogging + from litellm.litellm_core_utils.litellm_logging import Logging + _LiteLLMLogging = Logging + return _LiteLLMLogging + + +def get_coroutine_checker() -> "CoroutineChecker": + """Get the cached coroutine checker instance, initializing if needed.""" + global _coroutine_checker + if _coroutine_checker is not None: + return _coroutine_checker + from litellm.litellm_core_utils.coroutine_checker import coroutine_checker + _coroutine_checker = coroutine_checker + return _coroutine_checker + + +def get_set_callbacks() -> Callable: + """Get the cached set_callbacks function, initializing if needed.""" + global _set_callbacks + if _set_callbacks is not None: + return _set_callbacks + from litellm.litellm_core_utils.litellm_logging import set_callbacks + _set_callbacks = set_callbacks + return _set_callbacks + + +def clear_cached_imports() -> None: + """Clear all cached imports. Useful for testing or memory management.""" + global _LiteLLMLogging, _coroutine_checker, _set_callbacks + _LiteLLMLogging = None + _coroutine_checker = None + _set_callbacks = None diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index 4aeb9d4d640..7423e55b626 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -228,9 +228,11 @@ def safe_deep_copy(data): """ Safe Deep Copy - The LiteLLM Request has some object that can-not be pickled / deep copied - - Use this function to safely deep copy the LiteLLM Request + The LiteLLM request may contain objects that cannot be pickled/deep-copied + (e.g., tracing spans, locks, clients). + + This helper deep-copies each top-level key independently; on failure keeps + original ref """ import copy @@ -255,9 +257,22 @@ def safe_deep_copy(data): "litellm_parent_otel_span" ) data["litellm_metadata"]["litellm_parent_otel_span"] = "placeholder" - new_data = copy.deepcopy(data) - # Step 2: re-add the litellm_parent_otel_span after doing a deep copy + # Step 2: Per-key deepcopy with fallback + if isinstance(data, dict): + new_data = {} + for k, v in data.items(): + try: + new_data[k] = copy.deepcopy(v) + except Exception: + new_data[k] = v + else: + try: + new_data = copy.deepcopy(data) + except Exception: + new_data = data + + # Step 3: re-add the litellm_parent_otel_span after doing a deep copy if isinstance(data, dict) and litellm_parent_otel_span is not None: if "metadata" in data and "litellm_parent_otel_span" in data["metadata"]: data["metadata"]["litellm_parent_otel_span"] = litellm_parent_otel_span @@ -268,4 +283,4 @@ def safe_deep_copy(data): data["litellm_metadata"][ "litellm_parent_otel_span" ] = litellm_parent_otel_span - return new_data + return new_data \ No newline at end of file diff --git a/litellm/litellm_core_utils/coroutine_checker.py b/litellm/litellm_core_utils/coroutine_checker.py new file mode 100644 index 00000000000..368aee62ed0 --- /dev/null +++ b/litellm/litellm_core_utils/coroutine_checker.py @@ -0,0 +1,63 @@ +# CoroutineChecker utility for checking if functions/callables are coroutines or coroutine functions + +import inspect +from typing import Any +from weakref import WeakKeyDictionary +from litellm.constants import ( + COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY, +) + + +class CoroutineChecker: + """Utility class for checking coroutine status of functions and callables. + + Simple bounded cache using WeakKeyDictionary to avoid memory leaks. + """ + + def __init__(self): + self._cache = WeakKeyDictionary() + self._max_size = COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY + + def is_async_callable(self, callback: Any) -> bool: + """Fast, cached check for whether a callback is an async function. + Falls back gracefully if the object cannot be weak-referenced or cached. + 2.59x speedup. + """ + # Fast path: check cache first (most common case) + try: + cached = self._cache.get(callback) + if cached is not None: + return cached + except Exception: + pass + + # Determine target - optimized path for common cases + target = callback + if not inspect.isfunction(target) and not inspect.ismethod(target): + try: + call_attr = getattr(target, "__call__", None) + if call_attr is not None: + target = call_attr + except Exception: + pass + + # Compute result + try: + result = inspect.iscoroutinefunction(target) + except Exception: + result = False + + # Cache the result with size enforcement + try: + # Simple size enforcement: clear cache if it gets too large + if len(self._cache) >= self._max_size: + self._cache.clear() + + self._cache[callback] = result + except Exception: + pass + + return result + +# Global instance for backward compatibility and convenience +coroutine_checker = CoroutineChecker() diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py index af51fe9ab79..bb8fa580e3c 100644 --- a/litellm/litellm_core_utils/custom_logger_registry.py +++ b/litellm/litellm_core_utils/custom_logger_registry.py @@ -33,6 +33,7 @@ from litellm.integrations.mlflow import MlflowLogger from litellm.integrations.openmeter import OpenMeterLogger from litellm.integrations.opentelemetry import OpenTelemetry from litellm.integrations.opik.opik import OpikLogger +from litellm.integrations.posthog import PostHogLogger try: from litellm_enterprise.integrations.prometheus import PrometheusLogger @@ -46,6 +47,7 @@ from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook i VectorStorePreCallHook, ) from litellm.proxy.hooks.dynamic_rate_limiter import _PROXY_DynamicRateLimitHandler +from litellm.proxy.hooks.dynamic_rate_limiter_v3 import _PROXY_DynamicRateLimitHandlerV3 class CustomLoggerRegistry: @@ -85,9 +87,11 @@ class CustomLoggerRegistry: "s3_v2": S3Logger, "aws_sqs": SQSLogger, "dynamic_rate_limiter": _PROXY_DynamicRateLimitHandler, + "dynamic_rate_limiter_v3": _PROXY_DynamicRateLimitHandlerV3, "vector_store_pre_call_hook": VectorStorePreCallHook, "dotprompt": DotpromptManager, "cloudzero": CloudZeroLogger, + "posthog": PostHogLogger, } try: diff --git a/litellm/litellm_core_utils/duration_parser.py b/litellm/litellm_core_utils/duration_parser.py index 08e5323c30c..9a317cfcf0d 100644 --- a/litellm/litellm_core_utils/duration_parser.py +++ b/litellm/litellm_core_utils/duration_parser.py @@ -158,6 +158,7 @@ def _setup_timezone( "US/Eastern": timezone(timedelta(hours=-4)), # EDT "US/Pacific": timezone(timedelta(hours=-7)), # PDT "Asia/Kolkata": timezone(timedelta(hours=5, minutes=30)), # IST + "Asia/Bangkok": timezone(timedelta(hours=7)), # ICT (Indochina Time) "Europe/London": timezone(timedelta(hours=1)), # BST "UTC": timezone.utc, } diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 25ae0269ab3..7698c9f2fa0 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -6,6 +6,7 @@ import httpx import litellm from litellm._logging import verbose_logger +from litellm.types.utils import LlmProviders from ..exceptions import ( APIConnectionError, @@ -556,7 +557,7 @@ def exception_type( # type: ignore # noqa: PLR0915 model=model, llm_provider="anthropic", ) - elif "overloaded_error" in error_str: + elif "overloaded_error" in error_str or "Overloaded" in error_str: exception_mapping_worked = True raise InternalServerError( message="AnthropicError - {}".format(error_str), @@ -762,7 +763,7 @@ def exception_type( # type: ignore # noqa: PLR0915 error_str += "XXXXXXX" + '"' raise AuthenticationError( - message=f"{custom_llm_provider}Exception: Authentication Error - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", llm_provider=custom_llm_provider, model=model, response=getattr(original_exception, "response", None), @@ -771,14 +772,14 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "model's maximum context limit" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( - message=f"{custom_llm_provider}Exception: Context Window Error - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", model=model, llm_provider=custom_llm_provider, ) elif "token_quota_reached" in error_str: exception_mapping_worked = True raise RateLimitError( - message=f"{custom_llm_provider}Exception: Rate Limit Errror - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", llm_provider=custom_llm_provider, model=model, response=getattr(original_exception, "response", None), @@ -789,14 +790,14 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif "model_no_support_for_function" in error_str: exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", llm_provider=custom_llm_provider, model=model, ) @@ -804,7 +805,7 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 500: exception_mapping_worked = True raise litellm.InternalServerError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) @@ -814,28 +815,28 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise AuthenticationError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 400: exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 404: exception_mapping_worked = True raise NotFoundError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) elif original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -846,7 +847,7 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise BadRequestError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -854,7 +855,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 429: exception_mapping_worked = True raise RateLimitError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -862,7 +863,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 503: exception_mapping_worked = True raise ServiceUnavailableError( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -870,7 +871,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif original_exception.status_code == 504: # gateway timeout error exception_mapping_worked = True raise Timeout( - message=f"{custom_llm_provider}Exception - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -1168,9 +1169,9 @@ def exception_type( # type: ignore # noqa: PLR0915 exception_status_code=original_exception.status_code, ) elif ( - custom_llm_provider == "vertex_ai" - or custom_llm_provider == "vertex_ai_beta" - or custom_llm_provider == "gemini" + custom_llm_provider == LlmProviders.VERTEX_AI + or custom_llm_provider == LlmProviders.VERTEX_AI_BETA + or custom_llm_provider == LlmProviders.GEMINI ): if ( "Vertex AI API has not been used in project" in error_str @@ -1178,9 +1179,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise BadRequestError( - message=f"litellm.BadRequestError: VertexAIException - {error_str}", + message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=400, request=httpx.Request( @@ -1193,7 +1194,7 @@ def exception_type( # type: ignore # noqa: PLR0915 if "400 Request payload size exceeds" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( - message=f"VertexException - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", model=model, llm_provider=custom_llm_provider, ) @@ -1203,9 +1204,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"litellm.InternalServerError: VertexAIException - {error_str}", + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=500, content=str(original_exception), @@ -1216,7 +1217,7 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "API key not valid." in error_str: exception_mapping_worked = True raise AuthenticationError( - message=f"{custom_llm_provider}Exception - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", model=model, llm_provider=custom_llm_provider, litellm_debug_info=extra_information, @@ -1224,9 +1225,9 @@ def exception_type( # type: ignore # noqa: PLR0915 elif "403" in error_str: exception_mapping_worked = True raise BadRequestError( - message=f"VertexAIException BadRequestError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, response=httpx.Response( status_code=403, request=httpx.Request( @@ -1243,9 +1244,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise ContentPolicyViolationError( - message=f"VertexAIException ContentPolicyViolationError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=400, @@ -1264,9 +1265,9 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise RateLimitError( - message=f"litellm.RateLimitError: VertexAIException - {error_str}", + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=429, @@ -1282,18 +1283,18 @@ def exception_type( # type: ignore # noqa: PLR0915 ): exception_mapping_worked = True raise litellm.InternalServerError( - message=f"litellm.InternalServerError: VertexAIException - {error_str}", + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, ) if hasattr(original_exception, "status_code"): if original_exception.status_code == 400: exception_mapping_worked = True raise BadRequestError( - message=f"VertexAIException BadRequestError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=400, @@ -1306,21 +1307,21 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 401: exception_mapping_worked = True raise AuthenticationError( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) if original_exception.status_code == 404: exception_mapping_worked = True raise NotFoundError( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) if original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) @@ -1328,9 +1329,9 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 429: exception_mapping_worked = True raise RateLimitError( - message=f"litellm.RateLimitError: VertexAIException - {error_str}", + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=429, @@ -1343,9 +1344,9 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 500: exception_mapping_worked = True raise litellm.InternalServerError( - message=f"VertexAIException InternalServerError - {error_str}", + message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", model=model, - llm_provider="vertex_ai", + llm_provider=custom_llm_provider, litellm_debug_info=extra_information, response=httpx.Response( status_code=500, @@ -1356,68 +1357,10 @@ def exception_type( # type: ignore # noqa: PLR0915 if original_exception.status_code == 503: exception_mapping_worked = True raise ServiceUnavailableError( - message=f"VertexAIException - {original_exception.message}", + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", llm_provider=custom_llm_provider, model=model, ) - elif custom_llm_provider == "palm" or custom_llm_provider == "gemini": - if "503 Getting metadata" in error_str: - # auth errors look like this - # 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate. - exception_mapping_worked = True - raise BadRequestError( - message="GeminiException - Invalid api key", - model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), - ) - if ( - "504 Deadline expired before operation could complete." in error_str - or "504 Deadline Exceeded" in error_str - ): - exception_mapping_worked = True - raise Timeout( - message=f"GeminiException - {original_exception.message}", - model=model, - llm_provider="palm", - exception_status_code=original_exception.status_code, - ) - if "400 Request payload size exceeds" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"GeminiException - {error_str}", - model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), - ) - if ( - "500 An internal error has occurred." in error_str - or "list index out of range" in error_str - ): - exception_mapping_worked = True - raise APIError( - status_code=getattr(original_exception, "status_code", 500), - message=f"GeminiException - {original_exception.message}", - llm_provider="palm", - model=model, - request=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"GeminiException - {error_str}", - model=model, - llm_provider="palm", - response=getattr(original_exception, "response", None), - ) - # Dailed: Error occurred: 400 Request payload size exceeds the limit: 20000 bytes elif custom_llm_provider == "cloudflare": if "Authentication error" in error_str: exception_mapping_worked = True @@ -1449,6 +1392,14 @@ def exception_type( # type: ignore # noqa: PLR0915 model=model, response=getattr(original_exception, "response", None), ) + elif "invalid type: parameter" in error_str: + exception_mapping_worked = True + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) elif "too many tokens" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index c354dea0241..c167c202e5d 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -62,6 +62,7 @@ def get_litellm_params( use_litellm_proxy: Optional[bool] = None, api_version: Optional[str] = None, max_retries: Optional[int] = None, + litellm_request_debug: Optional[bool] = None, **kwargs, ) -> dict: litellm_params = { @@ -118,5 +119,6 @@ def get_litellm_params( "vertex_credentials": kwargs.get("vertex_credentials"), "vertex_project": kwargs.get("vertex_project"), "use_litellm_proxy": use_litellm_proxy, + "litellm_request_debug": litellm_request_debug, } return litellm_params diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index d5009fb0ca6..414ccb7ab83 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -372,6 +372,10 @@ def get_llm_provider( # noqa: PLR0915 custom_llm_provider = "cometapi" elif model.startswith("oci/"): custom_llm_provider = "oci" + elif model.startswith("compactifai/"): + custom_llm_provider = "compactifai" + elif model.startswith("ovhcloud/"): + custom_llm_provider = "ovhcloud" if not custom_llm_provider: if litellm.suppress_debug_info is False: print() # noqa diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index 86535943762..d77f53bd798 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -94,9 +94,7 @@ def get_supported_openai_params( # noqa: PLR0915 return litellm.VLLMConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "deepseek": return litellm.DeepSeekChatConfig().get_supported_openai_params(model=model) - elif custom_llm_provider == "cohere": - return litellm.CohereConfig().get_supported_openai_params(model=model) - elif custom_llm_provider == "cohere_chat": + elif custom_llm_provider == "cohere_chat" or custom_llm_provider == "cohere": return litellm.CohereChatConfig().get_supported_openai_params(model=model) elif custom_llm_provider == "maritalk": return litellm.MaritalkConfig().get_supported_openai_params(model=model) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 19d7c5512ba..059fd9f1fcf 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -138,6 +138,7 @@ from ..integrations.logfire_logger import LogfireLevel, LogfireLogger from ..integrations.lunary import LunaryLogger from ..integrations.openmeter import OpenMeterLogger from ..integrations.opik.opik import OpikLogger +from ..integrations.posthog import PostHogLogger from ..integrations.prompt_layer import PromptLayerLogger from ..integrations.s3 import S3Logger from ..integrations.s3_v2 import S3Logger as S3V2Logger @@ -193,7 +194,6 @@ _in_memory_loggers: List[Any] = [] sentry_sdk_instance = None capture_exception = None add_breadcrumb = None -posthog = None slack_app = None alerts_channel = None heliconeLogger = None @@ -245,6 +245,7 @@ class Logging(LiteLLMLoggingBaseClass): global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app custom_pricing: bool = False stream_options = None + litellm_request_debug: bool = False def __init__( self, @@ -299,9 +300,9 @@ class Logging(LiteLLMLoggingBaseClass): self.litellm_trace_id: str = litellm_trace_id or str(uuid.uuid4()) self.function_id = function_id self.streaming_chunks: List[Any] = [] # for generating complete stream response - self.sync_streaming_chunks: List[Any] = ( - [] - ) # for generating complete stream response + self.sync_streaming_chunks: List[ + Any + ] = [] # for generating complete stream response self.log_raw_request_response = log_raw_request_response # Initialize dynamic callbacks @@ -470,6 +471,7 @@ class Logging(LiteLLMLoggingBaseClass): **self.litellm_params, **scrub_sensitive_keys_in_metadata(litellm_params), } + self.litellm_request_debug = litellm_params.get("litellm_request_debug", False) self.logger_fn = litellm_params.get("logger_fn", None) verbose_logger.debug(f"self.optional_params: {self.optional_params}") @@ -670,24 +672,23 @@ class Logging(LiteLLMLoggingBaseClass): if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook( non_default_params ): - self.model_call_details["prompt_integration"] = ( - anthropic_cache_control_logger.__class__.__name__ - ) + self.model_call_details[ + "prompt_integration" + ] = anthropic_cache_control_logger.__class__.__name__ return anthropic_cache_control_logger ######################################################### # Vector Store / Knowledge Base hooks ######################################################### if litellm.vector_store_registry is not None: - vector_store_custom_logger = _init_custom_logger_compatible_class( logging_integration="vector_store_pre_call_hook", internal_usage_cache=None, llm_router=None, ) - self.model_call_details["prompt_integration"] = ( - vector_store_custom_logger.__class__.__name__ - ) + self.model_call_details[ + "prompt_integration" + ] = vector_store_custom_logger.__class__.__name__ return vector_store_custom_logger return None @@ -739,9 +740,9 @@ class Logging(LiteLLMLoggingBaseClass): model ): # if model name was changes pre-call, overwrite the initial model call name with the new one self.model_call_details["model"] = model - self.model_call_details["litellm_params"]["api_base"] = ( - self._get_masked_api_base(additional_args.get("api_base", "")) - ) + self.model_call_details["litellm_params"][ + "api_base" + ] = self._get_masked_api_base(additional_args.get("api_base", "")) def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915 # Log the exact input to the LLM API @@ -770,10 +771,10 @@ class Logging(LiteLLMLoggingBaseClass): try: # [Non-blocking Extra Debug Information in metadata] if turn_off_message_logging is True: - _metadata["raw_request"] = ( - "redacted by litellm. \ + _metadata[ + "raw_request" + ] = "redacted by litellm. \ 'litellm.turn_off_message_logging=True'" - ) else: curl_command = self._get_request_curl_command( api_base=additional_args.get("api_base", ""), @@ -784,32 +785,32 @@ class Logging(LiteLLMLoggingBaseClass): _metadata["raw_request"] = str(curl_command) # split up, so it's easier to parse in the UI - self.model_call_details["raw_request_typed_dict"] = ( - RawRequestTypedDict( - raw_request_api_base=str( - additional_args.get("api_base") or "" - ), - raw_request_body=self._get_raw_request_body( - additional_args.get("complete_input_dict", {}) - ), - raw_request_headers=self._get_masked_headers( - additional_args.get("headers", {}) or {}, - ignore_sensitive_headers=True, - ), - error=None, - ) + self.model_call_details[ + "raw_request_typed_dict" + ] = RawRequestTypedDict( + raw_request_api_base=str( + additional_args.get("api_base") or "" + ), + raw_request_body=self._get_raw_request_body( + additional_args.get("complete_input_dict", {}) + ), + raw_request_headers=self._get_masked_headers( + additional_args.get("headers", {}) or {}, + ignore_sensitive_headers=True, + ), + error=None, ) except Exception as e: - self.model_call_details["raw_request_typed_dict"] = ( - RawRequestTypedDict( - error=str(e), - ) + self.model_call_details[ + "raw_request_typed_dict" + ] = RawRequestTypedDict( + error=str(e), ) - _metadata["raw_request"] = ( - "Unable to Log \ + _metadata[ + "raw_request" + ] = "Unable to Log \ raw request: {}".format( - str(e) - ) + str(e) ) if getattr(self, "logger_fn", None) and callable(self.logger_fn): try: @@ -907,13 +908,19 @@ class Logging(LiteLLMLoggingBaseClass): Prints the RAW curl command sent from LiteLLM """ - if _is_debugging_on(): + if _is_debugging_on() or self.litellm_request_debug: if json_logs: masked_headers = self._get_masked_headers(headers) - verbose_logger.debug( - "POST Request Sent from LiteLLM", - extra={"api_base": {api_base}, **masked_headers}, - ) + if self.litellm_request_debug: + verbose_logger.warning( # .warning ensures this shows up in all environments + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) + else: + verbose_logger.debug( + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) else: headers = additional_args.get("headers", {}) if headers is None: @@ -926,7 +933,12 @@ class Logging(LiteLLMLoggingBaseClass): additional_args=additional_args, data=data, ) - verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") + if self.litellm_request_debug: + verbose_logger.warning( + f"\033[92m{curl_command}\033[0m\n" + ) # .warning ensures this shows up in all environments + else: + verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") def _get_request_body(self, data: dict) -> str: return str(data) @@ -983,8 +995,14 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["additional_args"] = additional_args self.model_call_details["log_event_type"] = "post_api_call" + if self.litellm_request_debug: + attr = "warning" + else: + attr = "debug" + if json_logs: - verbose_logger.debug( + callattr = getattr(verbose_logger, attr) + callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details @@ -992,7 +1010,8 @@ class Logging(LiteLLMLoggingBaseClass): ), ) else: - print_verbose( + callattr = getattr(verbose_logger, attr) + callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details @@ -1092,13 +1111,13 @@ class Logging(LiteLLMLoggingBaseClass): for callback in callbacks: try: if isinstance(callback, CustomLogger): - response: Optional[MCPPostCallResponseObject] = ( - await callback.async_post_mcp_tool_call_hook( - kwargs=kwargs, - response_obj=post_mcp_tool_call_response_obj, - start_time=start_time, - end_time=end_time, - ) + response: Optional[ + MCPPostCallResponseObject + ] = await callback.async_post_mcp_tool_call_hook( + kwargs=kwargs, + response_obj=post_mcp_tool_call_response_obj, + start_time=start_time, + end_time=end_time, ) ###################################################################### # if any of the callbacks modify the response, use the modified response @@ -1218,9 +1237,9 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( f"response_cost_failure_debug_information: {debug_info}" ) - self.model_call_details["response_cost_failure_debug_information"] = ( - debug_info - ) + self.model_call_details[ + "response_cost_failure_debug_information" + ] = debug_info return None try: @@ -1245,9 +1264,9 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( f"response_cost_failure_debug_information: {debug_info}" ) - self.model_call_details["response_cost_failure_debug_information"] = ( - debug_info - ) + self.model_call_details[ + "response_cost_failure_debug_information" + ] = debug_info return None @@ -1391,9 +1410,9 @@ class Logging(LiteLLMLoggingBaseClass): end_time = datetime.datetime.now() if self.completion_start_time is None: self.completion_start_time = end_time - self.model_call_details["completion_start_time"] = ( - self.completion_start_time - ) + self.model_call_details[ + "completion_start_time" + ] = self.completion_start_time self.model_call_details["log_event_type"] = "successful_api_call" self.model_call_details["end_time"] = end_time self.model_call_details["cache_hit"] = cache_hit @@ -1446,39 +1465,39 @@ class Logging(LiteLLMLoggingBaseClass): "response_cost" ] else: - self.model_call_details["response_cost"] = ( - self._response_cost_calculator(result=logging_result) - ) + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator(result=logging_result) ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=logging_result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=logging_result, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) elif isinstance(result, dict) or isinstance(result, list): ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=result, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=result, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) elif standard_logging_object is not None: - self.model_call_details["standard_logging_object"] = ( - standard_logging_object - ) + self.model_call_details[ + "standard_logging_object" + ] = standard_logging_object else: # streaming chunks + image gen. self.model_call_details["response_cost"] = None @@ -1577,7 +1596,6 @@ class Logging(LiteLLMLoggingBaseClass): ) if complete_streaming_response is not None: - self.success_handler(result=complete_streaming_response) return @@ -1630,23 +1648,23 @@ class Logging(LiteLLMLoggingBaseClass): verbose_logger.debug( "Logging Details LiteLLM-Success Call streaming complete" ) - self.model_call_details["complete_streaming_response"] = ( - complete_streaming_response - ) - self.model_call_details["response_cost"] = ( - self._response_cost_calculator(result=complete_streaming_response) - ) + self.model_call_details[ + "complete_streaming_response" + ] = complete_streaming_response + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator(result=complete_streaming_response) ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=complete_streaming_response, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=complete_streaming_response, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_success_callbacks, @@ -1714,12 +1732,16 @@ class Logging(LiteLLMLoggingBaseClass): response_obj=result, start_time=start_time, end_time=end_time, - litellm_call_id=current_call_id - if ( - current_call_id := litellm_params.get("litellm_call_id") - ) - is not None - else str(uuid.uuid4()), + litellm_call_id=( + current_call_id + if ( + current_call_id := litellm_params.get( + "litellm_call_id" + ) + ) + is not None + else str(uuid.uuid4()) + ), print_verbose=print_verbose, ) if callback == "wandb" and weightsBiasesLogger is not None: @@ -1970,10 +1992,10 @@ class Logging(LiteLLMLoggingBaseClass): ) else: if self.stream and complete_streaming_response: - self.model_call_details["complete_response"] = ( - self.model_call_details.get( - "complete_streaming_response", {} - ) + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} ) result = self.model_call_details["complete_response"] openMeterLogger.log_success_event( @@ -2012,10 +2034,10 @@ class Logging(LiteLLMLoggingBaseClass): ) else: if self.stream and complete_streaming_response: - self.model_call_details["complete_response"] = ( - self.model_call_details.get( - "complete_streaming_response", {} - ) + self.model_call_details[ + "complete_response" + ] = self.model_call_details.get( + "complete_streaming_response", {} ) result = self.model_call_details["complete_response"] @@ -2117,10 +2139,12 @@ class Logging(LiteLLMLoggingBaseClass): result.usage = batch_usage elif not is_base64_unified_file_id: # only run for non-unified file ids - response_cost, batch_usage, batch_models = ( - await _handle_completed_batch( - batch=result, custom_llm_provider=self.custom_llm_provider - ) + ( + response_cost, + batch_usage, + batch_models, + ) = await _handle_completed_batch( + batch=result, custom_llm_provider=self.custom_llm_provider ) result._hidden_params["response_cost"] = response_cost @@ -2151,9 +2175,9 @@ class Logging(LiteLLMLoggingBaseClass): if complete_streaming_response is not None: print_verbose("Async success callbacks: Got a complete streaming response") - self.model_call_details["async_complete_streaming_response"] = ( - complete_streaming_response - ) + self.model_call_details[ + "async_complete_streaming_response" + ] = complete_streaming_response try: if self.model_call_details.get("cache_hit", False) is True: @@ -2164,10 +2188,10 @@ class Logging(LiteLLMLoggingBaseClass): model_call_details=self.model_call_details ) # base_model defaults to None if not set on model_info - self.model_call_details["response_cost"] = ( - self._response_cost_calculator( - result=complete_streaming_response - ) + self.model_call_details[ + "response_cost" + ] = self._response_cost_calculator( + result=complete_streaming_response ) verbose_logger.debug( @@ -2180,16 +2204,16 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["response_cost"] = None ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj=complete_streaming_response, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="success", - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=complete_streaming_response, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="success", + standard_built_in_tools_params=self.standard_built_in_tools_params, ) callbacks = self.get_combined_callback_list( dynamic_success_callbacks=self.dynamic_async_success_callbacks, @@ -2402,18 +2426,18 @@ class Logging(LiteLLMLoggingBaseClass): ## STANDARDIZED LOGGING PAYLOAD - self.model_call_details["standard_logging_object"] = ( - get_standard_logging_object_payload( - kwargs=self.model_call_details, - init_response_obj={}, - start_time=start_time, - end_time=end_time, - logging_obj=self, - status="failure", - error_str=str(exception), - original_exception=exception, - standard_built_in_tools_params=self.standard_built_in_tools_params, - ) + self.model_call_details[ + "standard_logging_object" + ] = get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj={}, + start_time=start_time, + end_time=end_time, + logging_obj=self, + status="failure", + error_str=str(exception), + original_exception=exception, + standard_built_in_tools_params=self.standard_built_in_tools_params, ) return start_time, end_time @@ -3044,7 +3068,7 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 """ Globally sets the callback client """ - global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger + global sentry_sdk_instance, capture_exception, add_breadcrumb, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, supabaseClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger, deepevalLogger try: for callback in callback_list: @@ -3083,19 +3107,6 @@ def set_callbacks(callback_list, function_id=None): # noqa: PLR0915 ) capture_exception = sentry_sdk_instance.capture_exception add_breadcrumb = sentry_sdk_instance.add_breadcrumb - elif callback == "posthog": - try: - from posthog import Posthog - except ImportError: - print_verbose("Package 'posthog' is missing. Installing it...") - subprocess.check_call( - [sys.executable, "-m", "pip", "install", "posthog"] - ) - from posthog import Posthog - posthog = Posthog( - project_api_key=os.environ.get("POSTHOG_API_KEY"), - host=os.environ.get("POSTHOG_API_URL"), - ) elif callback == "slack": try: from slack_bolt import App @@ -3190,6 +3201,14 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 _openmeter_logger = OpenMeterLogger() _in_memory_loggers.append(_openmeter_logger) return _openmeter_logger # type: ignore + elif logging_integration == "posthog": + for callback in _in_memory_loggers: + if isinstance(callback, PostHogLogger): + return callback # type: ignore + + _posthog_logger = PostHogLogger() + _in_memory_loggers.append(_posthog_logger) + return _posthog_logger # type: ignore elif logging_integration == "braintrust": from litellm.integrations.braintrust_logging import BraintrustLogger @@ -3302,9 +3321,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 endpoint=arize_config.endpoint, ) - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - f"space_id={arize_config.space_key},api_key={arize_config.api_key}" - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = f"space_id={arize_config.space_key},api_key={arize_config.api_key}" for callback in _in_memory_loggers: if ( isinstance(callback, ArizeLogger) @@ -3328,9 +3347,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 # auth can be disabled on local deployments of arize phoenix if arize_phoenix_config.otlp_auth_headers is not None: - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - arize_phoenix_config.otlp_auth_headers - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = arize_phoenix_config.otlp_auth_headers for callback in _in_memory_loggers: if ( @@ -3367,6 +3386,7 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 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 @@ -3424,6 +3444,30 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 dynamic_rate_limiter_obj.update_variables(llm_router=llm_router) _in_memory_loggers.append(dynamic_rate_limiter_obj) return dynamic_rate_limiter_obj # type: ignore + elif logging_integration == "dynamic_rate_limiter_v3": + from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( + _PROXY_DynamicRateLimitHandlerV3, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): + return callback # type: ignore + + if internal_usage_cache is None: + raise Exception( + "Internal Error: Cache cannot be empty - internal_usage_cache={}".format( + internal_usage_cache + ) + ) + + dynamic_rate_limiter_obj_v3 = _PROXY_DynamicRateLimitHandlerV3( + internal_usage_cache=internal_usage_cache + ) + + if llm_router is not None and isinstance(llm_router, litellm.Router): + dynamic_rate_limiter_obj_v3.update_variables(llm_router=llm_router) + _in_memory_loggers.append(dynamic_rate_limiter_obj_v3) + return dynamic_rate_limiter_obj_v3 # type: ignore elif logging_integration == "langtrace": if "LANGTRACE_API_KEY" not in os.environ: raise ValueError("LANGTRACE_API_KEY not found in environment variables") @@ -3437,9 +3481,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 exporter="otlp_http", endpoint="https://langtrace.ai/api/trace", ) - os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( - f"api_key={os.getenv('LANGTRACE_API_KEY')}" - ) + os.environ[ + "OTEL_EXPORTER_OTLP_TRACES_HEADERS" + ] = f"api_key={os.getenv('LANGTRACE_API_KEY')}" for callback in _in_memory_loggers: if ( isinstance(callback, OpenTelemetry) @@ -3594,6 +3638,7 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 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 @@ -3686,6 +3731,14 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if isinstance(callback, _PROXY_DynamicRateLimitHandler): return callback # type: ignore + elif logging_integration == "dynamic_rate_limiter_v3": + from litellm.proxy.hooks.dynamic_rate_limiter_v3 import ( + _PROXY_DynamicRateLimitHandlerV3, + ) + + for callback in _in_memory_loggers: + if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3): + return callback # type: ignore elif logging_integration == "langtrace": from litellm.integrations.opentelemetry import OpenTelemetry @@ -3884,22 +3937,25 @@ class StandardLoggingPayloadSetup: clean_metadata = StandardLoggingMetadata( user_api_key_hash=None, user_api_key_alias=None, + user_api_key_spend=None, + user_api_key_max_budget=None, + user_api_key_budget_reset_at=None, user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_user_id=None, user_api_key_team_alias=None, user_api_key_user_email=None, + user_api_key_end_user_id=None, + user_api_key_request_route=None, spend_logs_metadata=None, requester_ip_address=None, requester_metadata=None, - user_api_key_end_user_id=None, prompt_management_metadata=prompt_management_metadata, applied_guardrails=applied_guardrails, mcp_tool_call_metadata=mcp_tool_call_metadata, vector_store_request_metadata=vector_store_request_metadata, usage_object=usage_object, requester_custom_headers=None, - user_api_key_request_route=None, cold_storage_object_key=None, ) if isinstance(metadata, dict): @@ -4088,10 +4144,10 @@ class StandardLoggingPayloadSetup: for key in StandardLoggingHiddenParams.__annotations__.keys(): if key in hidden_params: if key == "additional_headers": - clean_hidden_params["additional_headers"] = ( - StandardLoggingPayloadSetup.get_additional_headers( - hidden_params[key] - ) + clean_hidden_params[ + "additional_headers" + ] = StandardLoggingPayloadSetup.get_additional_headers( + hidden_params[key] ) else: clean_hidden_params[key] = hidden_params[key] # type: ignore @@ -4124,15 +4180,28 @@ class StandardLoggingPayloadSetup: from litellm.integrations.s3 import get_s3_object_key # Only generate object key if cold storage is configured - if litellm.configured_cold_storage_logger is None: + configured_cold_storage_logger = litellm.configured_cold_storage_logger + if configured_cold_storage_logger is None: return None try: # Generate file name in same format as litellm.utils.get_logging_id s3_file_name = f"time-{start_time.strftime('%H-%M-%S-%f')}_{response_id}" + # Get the actual s3_path from the configured cold storage logger instance + s3_path = "" # default value + + # Try to get the actual logger instance from the logger name + try: + custom_logger = litellm.logging_callback_manager.get_active_custom_logger_for_callback_name(configured_cold_storage_logger) + if custom_logger and hasattr(custom_logger, 's3_path') and custom_logger.s3_path: + s3_path = custom_logger.s3_path + except Exception: + # If any error occurs in getting the logger instance, use default empty s3_path + pass + s3_object_key = get_s3_object_key( - s3_path="", # Use empty path as default + s3_path=s3_path, # Use actual s3_path from logger configuration team_alias_prefix="", # Don't split by team alias for cold storage start_time=start_time, s3_file_name=s3_file_name, @@ -4504,7 +4573,7 @@ def get_standard_logging_object_payload( def emit_standard_logging_payload(payload: StandardLoggingPayload): if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): - print(json.dumps(payload, indent=4)) # noqa + print(json.dumps(payload, indent=4)) # noqa def get_standard_logging_metadata( @@ -4527,6 +4596,9 @@ def get_standard_logging_metadata( clean_metadata = StandardLoggingMetadata( user_api_key_hash=None, user_api_key_alias=None, + user_api_key_spend=None, + user_api_key_max_budget=None, + user_api_key_budget_reset_at=None, user_api_key_team_id=None, user_api_key_org_id=None, user_api_key_user_id=None, @@ -4546,14 +4618,10 @@ def get_standard_logging_metadata( cold_storage_object_key=None, ) if isinstance(metadata, dict): - # Filter the metadata dictionary to include only the specified keys - clean_metadata = StandardLoggingMetadata( - **{ # type: ignore - key: metadata[key] - for key in StandardLoggingMetadata.__annotations__.keys() - if key in metadata - } - ) + # Update the clean_metadata with values from input metadata that match StandardLoggingMetadata fields + for key in StandardLoggingMetadata.__annotations__.keys(): + if key in metadata: + clean_metadata[key] = metadata[key] # type: ignore if metadata.get("user_api_key") is not None: if is_valid_sha256_hash(str(metadata.get("user_api_key"))): @@ -4576,9 +4644,9 @@ def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]): ): for k, v in metadata["user_api_key_metadata"].items(): if k == "logging": # prevent logging user logging keys - cleaned_user_api_key_metadata[k] = ( - "scrubbed_by_litellm_for_sensitive_keys" - ) + cleaned_user_api_key_metadata[ + k + ] = "scrubbed_by_litellm_for_sensitive_keys" else: cleaned_user_api_key_metadata[k] = v diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index c851ec06a6b..60a31198415 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -1,11 +1,12 @@ # What is this? ## Helper utilities for cost_per_token() -from typing import Any, Literal, Optional, Tuple, cast +from typing import Any, Literal, Optional, Tuple, TypedDict, cast import litellm from litellm._logging import verbose_logger from litellm.types.utils import ( + CacheCreationTokenDetails, CallTypes, ImageResponse, ModelInfo, @@ -113,20 +114,34 @@ def _generic_cost_per_character( return prompt_cost, completion_cost -def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, float, float, float]: +def _get_token_base_cost( + model_info: ModelInfo, usage: Usage +) -> Tuple[float, float, float, float, float]: """ Return prompt cost, completion cost, and cache costs for a given model and usage. If input_tokens > threshold and `input_cost_per_token_above_[x]k_tokens` or `input_cost_per_token_above_[x]_tokens` is set, then we use the corresponding threshold cost for all token types. - + Returns: Tuple[float, float, float, float] - (prompt_cost, completion_cost, cache_creation_cost, cache_read_cost) """ - prompt_base_cost = cast(float, _get_cost_per_unit(model_info, "input_cost_per_token")) - completion_base_cost = cast(float, _get_cost_per_unit(model_info, "output_cost_per_token")) - cache_creation_cost = cast(float, _get_cost_per_unit(model_info, "cache_creation_input_token_cost")) - cache_read_cost = cast(float, _get_cost_per_unit(model_info, "cache_read_input_token_cost")) + prompt_base_cost = cast( + float, _get_cost_per_unit(model_info, "input_cost_per_token") + ) + completion_base_cost = cast( + float, _get_cost_per_unit(model_info, "output_cost_per_token") + ) + cache_creation_cost = cast( + float, _get_cost_per_unit(model_info, "cache_creation_input_token_cost") + ) + cache_creation_cost_above_1hr = cast( + float, + _get_cost_per_unit(model_info, "cache_creation_input_token_cost_above_1hr"), + ) + cache_read_cost = cast( + float, _get_cost_per_unit(model_info, "cache_read_input_token_cost") + ) ## CHECK IF ABOVE THRESHOLD threshold: Optional[float] = None @@ -140,34 +155,57 @@ def _get_token_base_cost(model_info: ModelInfo, usage: Usage) -> Tuple[float, fl ) if usage.prompt_tokens > threshold: - prompt_base_cost = cast(float, _get_cost_per_unit(model_info, key, prompt_base_cost)) - completion_base_cost = cast(float, _get_cost_per_unit( - model_info, - f"output_cost_per_token_above_{threshold_str}_tokens", - completion_base_cost, - )) - + prompt_base_cost = cast( + float, _get_cost_per_unit(model_info, key, prompt_base_cost) + ) + completion_base_cost = cast( + float, + _get_cost_per_unit( + model_info, + f"output_cost_per_token_above_{threshold_str}_tokens", + completion_base_cost, + ), + ) + # Apply tiered pricing to cache costs - cache_creation_tiered_key = f"cache_creation_input_token_cost_above_{threshold_str}_tokens" - cache_read_tiered_key = f"cache_read_input_token_cost_above_{threshold_str}_tokens" - + cache_creation_tiered_key = ( + f"cache_creation_input_token_cost_above_{threshold_str}_tokens" + ) + cache_read_tiered_key = ( + f"cache_read_input_token_cost_above_{threshold_str}_tokens" + ) + if cache_creation_tiered_key in model_info: - cache_creation_cost = cast(float, _get_cost_per_unit( - model_info, cache_creation_tiered_key, cache_creation_cost - )) - + cache_creation_cost = cast( + float, + _get_cost_per_unit( + model_info, + cache_creation_tiered_key, + cache_creation_cost, + ), + ) + if cache_read_tiered_key in model_info: - cache_read_cost = cast(float, _get_cost_per_unit( - model_info, cache_read_tiered_key, cache_read_cost - )) - + cache_read_cost = cast( + float, + _get_cost_per_unit( + model_info, cache_read_tiered_key, cache_read_cost + ), + ) + break except (IndexError, ValueError): continue except Exception: continue - return prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost + return ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ) def calculate_cost_component( @@ -195,7 +233,9 @@ def calculate_cost_component( return 0.0 -def _get_cost_per_unit(model_info: ModelInfo, cost_key: str, default_value: Optional[float] = 0.0) -> Optional[float]: +def _get_cost_per_unit( + model_info: ModelInfo, cost_key: str, default_value: Optional[float] = 0.0 +) -> Optional[float]: # Sometimes the cost per unit is a string (e.g.: If a value like "3e-7" was read from the config.yaml) cost_per_unit = model_info.get(cost_key) if isinstance(cost_per_unit, float): @@ -210,7 +250,196 @@ def _get_cost_per_unit(model_info: ModelInfo, cost_key: str, default_value: Opti f"litellm.litellm_core_utils.llm_cost_calc.utils.py::calculate_cost_per_component(): Exception occured - {cost_per_unit}\nDefaulting to 0.0" ) return default_value - + + +def calculate_cache_writing_cost( + cache_creation_tokens: int, + cache_creation_token_details: Optional[CacheCreationTokenDetails], + cache_creation_cost_above_1hr: float, + cache_creation_cost: float, +) -> float: + """ + Adjust cost of cache creation tokens based on the cache creation token details. + """ + total_cost: float = 0.0 + if cache_creation_token_details is not None: + # get the number of 5m and 1h cache creation tokens + cache_creation_tokens_5m = ( + cache_creation_token_details.ephemeral_5m_input_tokens + ) + cache_creation_tokens_1h = ( + cache_creation_token_details.ephemeral_1h_input_tokens + ) + # add the number of 5m and 1h cache creation tokens to the cache creation tokens + total_cost += ( + cache_creation_tokens_5m * cache_creation_cost + if cache_creation_tokens_5m is not None + else 0.0 + ) + total_cost += ( + cache_creation_tokens_1h * cache_creation_cost_above_1hr + if cache_creation_tokens_1h is not None + else 0.0 + ) + else: + total_cost += cache_creation_tokens * cache_creation_cost + return total_cost + + +class PromptTokensDetailsResult(TypedDict): + cache_hit_tokens: int + cache_creation_tokens: int + cache_creation_token_details: Optional[CacheCreationTokenDetails] + text_tokens: int + audio_tokens: int + character_count: int + image_count: int + video_length_seconds: int + + +def _parse_prompt_tokens_details(usage: Usage) -> PromptTokensDetailsResult: + cache_hit_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "cached_tokens", 0)) + or 0 + ) + cache_creation_tokens = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "cache_creation_tokens", 0), + ) + or 0 + ) + cache_creation_token_details = ( + cast( + Optional[CacheCreationTokenDetails], + getattr(usage.prompt_tokens_details, "cache_creation_token_details", None), + ) + or None + ) + text_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "text_tokens", None)) + or 0 # default to prompt tokens, if this field is not set + ) + audio_tokens = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "audio_tokens", 0)) + or 0 + ) + character_count = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "character_count", 0), + ) + or 0 + ) + image_count = ( + cast(Optional[int], getattr(usage.prompt_tokens_details, "image_count", 0)) or 0 + ) + video_length_seconds = ( + cast( + Optional[int], + getattr(usage.prompt_tokens_details, "video_length_seconds", 0), + ) + or 0 + ) + + return PromptTokensDetailsResult( + cache_hit_tokens=cache_hit_tokens, + cache_creation_tokens=cache_creation_tokens, + cache_creation_token_details=cache_creation_token_details, + text_tokens=text_tokens, + audio_tokens=audio_tokens, + character_count=character_count, + image_count=image_count, + video_length_seconds=video_length_seconds, + ) + + +class CompletionTokensDetailsResult(TypedDict): + audio_tokens: int + text_tokens: int + reasoning_tokens: int + + +def _parse_completion_tokens_details(usage: Usage) -> CompletionTokensDetailsResult: + audio_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "audio_tokens", 0), + ) + or 0 + ) + text_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "text_tokens", None), + ) + or 0 # default to completion tokens, if this field is not set + ) + reasoning_tokens = ( + cast( + Optional[int], + getattr(usage.completion_tokens_details, "reasoning_tokens", 0), + ) + or 0 + ) + + return CompletionTokensDetailsResult( + audio_tokens=audio_tokens, + text_tokens=text_tokens, + reasoning_tokens=reasoning_tokens, + ) + + +def _calculate_input_cost( + prompt_tokens_details: PromptTokensDetailsResult, + model_info: ModelInfo, + prompt_base_cost: float, + cache_read_cost: float, + cache_creation_cost: float, + cache_creation_cost_above_1hr: float, +) -> float: + """ + Calculates the input cost for a given model, prompt tokens, and completion tokens. + """ + prompt_cost = float(prompt_tokens_details["text_tokens"]) * prompt_base_cost + + ### CACHE READ COST - Now uses tiered pricing + prompt_cost += float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost + + ### AUDIO COST + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_audio_token", prompt_tokens_details["audio_tokens"] + ) + + ### CACHE WRITING COST - Now uses tiered pricing + prompt_cost += calculate_cache_writing_cost( + cache_creation_tokens=prompt_tokens_details["cache_creation_tokens"], + cache_creation_token_details=prompt_tokens_details[ + "cache_creation_token_details" + ], + cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, + cache_creation_cost=cache_creation_cost, + ) + + ### CHARACTER COST + + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_character", prompt_tokens_details["character_count"] + ) + + ### IMAGE COUNT COST + prompt_cost += calculate_cost_component( + model_info, "input_cost_per_image", prompt_tokens_details["image_count"] + ) + + ### VIDEO LENGTH COST + prompt_cost += calculate_cost_component( + model_info, + "input_cost_per_video_per_second", + prompt_tokens_details["video_length_seconds"], + ) + + return prompt_cost def generic_cost_per_token( @@ -236,83 +465,45 @@ def generic_cost_per_token( ### Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing) prompt_cost = 0.0 ### PROCESSING COST - text_tokens = usage.prompt_tokens - cache_hit_tokens = 0 - audio_tokens = 0 - character_count = 0 - image_count = 0 - video_length_seconds = 0 + prompt_tokens_details = PromptTokensDetailsResult( + cache_hit_tokens=0, + cache_creation_tokens=0, + cache_creation_token_details=None, + text_tokens=usage.prompt_tokens, + audio_tokens=0, + character_count=0, + image_count=0, + video_length_seconds=0, + ) if usage.prompt_tokens_details: - cache_hit_tokens = ( - cast( - Optional[int], getattr(usage.prompt_tokens_details, "cached_tokens", 0) - ) - or 0 - ) - text_tokens = ( - cast( - Optional[int], getattr(usage.prompt_tokens_details, "text_tokens", None) - ) - or 0 # default to prompt tokens, if this field is not set - ) - audio_tokens = ( - cast(Optional[int], getattr(usage.prompt_tokens_details, "audio_tokens", 0)) - or 0 - ) - character_count = ( - cast( - Optional[int], - getattr(usage.prompt_tokens_details, "character_count", 0), - ) - or 0 - ) - image_count = ( - cast(Optional[int], getattr(usage.prompt_tokens_details, "image_count", 0)) - or 0 - ) - video_length_seconds = ( - cast( - Optional[int], - getattr(usage.prompt_tokens_details, "video_length_seconds", 0), - ) - or 0 - ) + prompt_tokens_details = _parse_prompt_tokens_details(usage) ## EDGE CASE - text tokens not set inside PromptTokensDetails - if text_tokens == 0: - text_tokens = usage.prompt_tokens - cache_hit_tokens - audio_tokens - prompt_base_cost, completion_base_cost, cache_creation_cost, cache_read_cost = _get_token_base_cost( - model_info=model_info, usage=usage - ) + if prompt_tokens_details["text_tokens"] == 0: + text_tokens = ( + usage.prompt_tokens + - prompt_tokens_details["cache_hit_tokens"] + - prompt_tokens_details["audio_tokens"] + - prompt_tokens_details["cache_creation_tokens"] + ) + prompt_tokens_details["text_tokens"] = text_tokens - prompt_cost = float(text_tokens) * prompt_base_cost + ( + prompt_base_cost, + completion_base_cost, + cache_creation_cost, + cache_creation_cost_above_1hr, + cache_read_cost, + ) = _get_token_base_cost(model_info=model_info, usage=usage) - ### CACHE READ COST - Now uses tiered pricing - prompt_cost += float(cache_hit_tokens) * cache_read_cost - - ### AUDIO COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_audio_token", audio_tokens - ) - - ### CACHE WRITING COST - Now uses tiered pricing - prompt_cost += float(usage._cache_creation_input_tokens or 0) * cache_creation_cost - - ### CHARACTER COST - - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_character", character_count - ) - - ### IMAGE COUNT COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_image", image_count - ) - - ### VIDEO LENGTH COST - prompt_cost += calculate_cost_component( - model_info, "input_cost_per_video_per_second", video_length_seconds + prompt_cost = _calculate_input_cost( + prompt_tokens_details=prompt_tokens_details, + model_info=model_info, + prompt_base_cost=prompt_base_cost, + cache_read_cost=cache_read_cost, + cache_creation_cost=cache_creation_cost, + cache_creation_cost_above_1hr=cache_creation_cost_above_1hr, ) ## CALCULATE OUTPUT COST @@ -321,27 +512,10 @@ def generic_cost_per_token( reasoning_tokens = 0 is_text_tokens_total = False if usage.completion_tokens_details is not None: - audio_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "audio_tokens", 0), - ) - or 0 - ) - text_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "text_tokens", None), - ) - or 0 # default to completion tokens, if this field is not set - ) - reasoning_tokens = ( - cast( - Optional[int], - getattr(usage.completion_tokens_details, "reasoning_tokens", 0), - ) - or 0 - ) + completion_tokens_details = _parse_completion_tokens_details(usage) + audio_tokens = completion_tokens_details["audio_tokens"] + text_tokens = completion_tokens_details["text_tokens"] + reasoning_tokens = completion_tokens_details["reasoning_tokens"] if text_tokens == 0: text_tokens = usage.completion_tokens @@ -350,8 +524,12 @@ def generic_cost_per_token( ## TEXT COST completion_cost = float(text_tokens) * completion_base_cost - _output_cost_per_audio_token = _get_cost_per_unit(model_info, "output_cost_per_audio_token", None) - _output_cost_per_reasoning_token = _get_cost_per_unit(model_info, "output_cost_per_reasoning_token", None) + _output_cost_per_audio_token = _get_cost_per_unit( + model_info, "output_cost_per_audio_token", None + ) + _output_cost_per_reasoning_token = _get_cost_per_unit( + model_info, "output_cost_per_reasoning_token", None + ) ## AUDIO COST if not is_text_tokens_total and audio_tokens is not None and audio_tokens > 0: @@ -397,7 +575,7 @@ class CostCalculatorUtils: ]: return True return False - + @staticmethod def route_image_generation_cost_calculator( model: str, diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py index 8dc3061460a..ce054b91cc9 100644 --- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py +++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py @@ -1,6 +1,5 @@ import asyncio import json -import re import time import traceback import uuid @@ -9,6 +8,9 @@ from typing import Dict, Iterable, List, Literal, Optional, Tuple, Union import litellm from litellm._logging import verbose_logger from litellm.constants import RESPONSE_FORMAT_TOOL_NAME +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + _extract_reasoning_content, +) from litellm.types.llms.databricks import DatabricksTool from litellm.types.llms.openai import ( ChatCompletionThinkingBlock, @@ -274,49 +276,6 @@ def _handle_invalid_parallel_tool_calls( return tool_calls -def _parse_content_for_reasoning( - message_text: Optional[str], -) -> Tuple[Optional[str], Optional[str]]: - """ - Parse the content for reasoning - - Returns: - - reasoning_content: The content of the reasoning - - content: The content of the message - """ - if not message_text: - return None, message_text - - reasoning_match = re.match( - r"<(?:think|thinking)>(.*?)(.*)", message_text, re.DOTALL - ) - - if reasoning_match: - return reasoning_match.group(1), reasoning_match.group(2) - - return None, message_text - - -def _extract_reasoning_content(message: dict) -> Tuple[Optional[str], Optional[str]]: - """ - Extract reasoning content and main content from a message. - - Args: - message (dict): The message dictionary that may contain reasoning_content - - Returns: - tuple[Optional[str], Optional[str]]: A tuple of (reasoning_content, content) - """ - message_content = message.get("content") - if "reasoning_content" in message: - return message["reasoning_content"], message["content"] - elif "reasoning" in message: - return message["reasoning"], message["content"] - elif isinstance(message_content, str): - return _parse_content_for_reasoning(message_content) - return None, message_content - - class LiteLLMResponseObjectHandler: @staticmethod def convert_to_image_response( diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py index 3f83719dd32..3c475f133a8 100644 --- a/litellm/litellm_core_utils/logging_worker.py +++ b/litellm/litellm_core_utils/logging_worker.py @@ -1,10 +1,23 @@ import asyncio import contextlib +import contextvars from typing import Coroutine, Optional +from typing_extensions import TypedDict + from litellm._logging import verbose_logger +class LoggingTask(TypedDict): + """ + A logging task with its associated context to ensure logging is executed in + the original task's context. + """ + + coroutine: Coroutine + context: contextvars.Context + + class LoggingWorker: """ A simple, async logging worker that processes log coroutines in the background. @@ -13,77 +26,84 @@ class LoggingWorker: This leads to a +200 RPS performance improvement when using LiteLLM Python SDK or Proxy Server. - Use this to queue coroutine tasks that are not critical to the main flow of the application. e.g Success/Error callbacks, logging, etc. """ + LOGGING_WORKER_MAX_QUEUE_SIZE = 50_000 LOGGING_WORKER_MAX_TIME_PER_COROUTINE = 20.0 MAX_ITERATIONS_TO_CLEAR_QUEUE = 200 MAX_TIME_TO_CLEAR_QUEUE = 5.0 - + def __init__( - self, - timeout: float = LOGGING_WORKER_MAX_TIME_PER_COROUTINE, + self, + timeout: float = LOGGING_WORKER_MAX_TIME_PER_COROUTINE, max_queue_size: int = LOGGING_WORKER_MAX_QUEUE_SIZE, ): self.timeout = timeout self.max_queue_size = max_queue_size - self._queue: Optional[asyncio.Queue] = None + self._queue: Optional[asyncio.Queue[LoggingTask]] = None self._worker_task: Optional[asyncio.Task] = None - + def _ensure_queue(self) -> None: """Initialize the queue if it doesn't exist.""" if self._queue is None: self._queue = asyncio.Queue(maxsize=self.max_queue_size) - + def start(self) -> None: """Start the logging worker. Idempotent - safe to call multiple times.""" self._ensure_queue() if self._worker_task is None or self._worker_task.done(): self._worker_task = asyncio.create_task(self._worker_loop()) - + async def _worker_loop(self) -> None: """Main worker loop that processes log coroutines sequentially.""" try: if self._queue is None: return - + while True: # Process one coroutine at a time to keep event loop load predictable - coroutine = await self._queue.get() + task = await self._queue.get() try: - await asyncio.wait_for(coroutine, timeout=self.timeout) + # Run the coroutine in its original context + await asyncio.wait_for( + task["context"].run(asyncio.create_task, task["coroutine"]), + timeout=self.timeout, + ) except Exception as e: verbose_logger.exception(f"LoggingWorker error: {e}") pass finally: self._queue.task_done() - + except asyncio.CancelledError: verbose_logger.debug("LoggingWorker cancelled during shutdown") # Attempt to clear remaining items to prevent "never awaited" warnings await self.clear_queue() - + def enqueue(self, coroutine: Coroutine) -> None: """ - Add a coroutine to the logging queue. + Add a coroutine to the logging queue. Hot path: never blocks, drops logs if queue is full. """ if self._queue is None: return - + try: - self._queue.put_nowait(coroutine) + # Capture the current context when enqueueing + task = LoggingTask(coroutine=coroutine, context=contextvars.copy_context()) + self._queue.put_nowait(task) except asyncio.QueueFull as e: verbose_logger.exception(f"LoggingWorker queue is full: {e}") # Drop logs on overload to protect request throughput pass - + def ensure_initialized_and_enqueue(self, async_coroutine: Coroutine): """ Ensure the logging worker is initialized and enqueue the coroutine. """ self.start() self.enqueue(async_coroutine) - + async def stop(self) -> None: """Stop the logging worker and clean up resources.""" if self._worker_task: @@ -91,34 +111,42 @@ class LoggingWorker: with contextlib.suppress(Exception): await self._worker_task self._worker_task = None - + async def flush(self) -> None: """Flush the logging queue.""" if self._queue is None: return while not self._queue.empty(): await self._queue.join() - + async def clear_queue(self): """ Clear the queue with a maximum time limit. """ if self._queue is None: return - + start_time = asyncio.get_event_loop().time() - + for _ in range(self.MAX_ITERATIONS_TO_CLEAR_QUEUE): # Check if we've exceeded the maximum time - if asyncio.get_event_loop().time() - start_time >= self.MAX_TIME_TO_CLEAR_QUEUE: - verbose_logger.warning(f"clear_queue exceeded max_time of {self.MAX_TIME_TO_CLEAR_QUEUE}s, stopping early") + if ( + asyncio.get_event_loop().time() - start_time + >= self.MAX_TIME_TO_CLEAR_QUEUE + ): + verbose_logger.warning( + f"clear_queue exceeded max_time of {self.MAX_TIME_TO_CLEAR_QUEUE}s, stopping early" + ) break - + try: - coroutine = self._queue.get_nowait() + task = self._queue.get_nowait() # Await the coroutine to properly execute and avoid "never awaited" warnings try: - await asyncio.wait_for(coroutine, timeout=self.timeout) + await asyncio.wait_for( + task["context"].run(asyncio.create_task, task["coroutine"]), + timeout=self.timeout, + ) except Exception: # Suppress errors during cleanup pass @@ -129,4 +157,3 @@ class LoggingWorker: # Global instance for backward compatibility GLOBAL_LOGGING_WORKER = LoggingWorker() - diff --git a/litellm/litellm_core_utils/object_pooling.py b/litellm/litellm_core_utils/object_pooling.py new file mode 100644 index 00000000000..846e6536f80 --- /dev/null +++ b/litellm/litellm_core_utils/object_pooling.py @@ -0,0 +1,137 @@ +""" +Generic object pooling utilities for LiteLLM. + +This module provides a flexible object pooling system that can be used +to pool any type of object, reducing memory allocation overhead and +improving performance for frequently created/destroyed objects. + +Memory Management Strategy: +- Balanced eviction-based memory control to optimize reuse ratio +- Moderate eviction frequency (300s) to maintain high object reuse +- Conservative eviction weight (0.3) to avoid destroying useful objects +- Lower pre-warm count (5) to reduce initial memory footprint +- Always keeps at least one object available for high availability +- Unlimited pools when maxsize is not specified (eviction controls actual usage) +""" + +from typing import Any, Callable, Optional, Type, TypeVar + +from pond import Pond, PooledObject, PooledObjectFactory + +T = TypeVar('T') + +class GenericPooledObjectFactory(PooledObjectFactory): + """Generic factory class for creating pooled objects of any type.""" + + def __init__( + self, + object_class: Type[T], + pooled_maxsize: Optional[int] = None, # None = unlimited pool with eviction-based memory control + least_one: bool = True, # Always keep at least one for high concurrency + initializer: Optional[Callable[[T], None]] = None + ): + # Only pass maxsize to Pond if user specified it - otherwise let Pond handle unlimited pools + if pooled_maxsize is not None: + super().__init__(pooled_maxsize=pooled_maxsize, least_one=least_one) + else: + super().__init__(least_one=least_one) + self.object_class = object_class + self.initializer = initializer + self._user_maxsize = pooled_maxsize # Store original user preference + + def createInstance(self) -> PooledObject: + """Create a new instance wrapped in a PooledObject.""" + # Create a properly initialized instance + obj = self.object_class() + return PooledObject(obj) + + def destroy(self, pooled_object: PooledObject): + """Destroy the pooled object.""" + if hasattr(pooled_object.keeped_object, '__dict__'): + pooled_object.keeped_object.__dict__.clear() + del pooled_object + + def reset(self, pooled_object: PooledObject, **kwargs: Any) -> PooledObject: + """Reset the pooled object to a clean state.""" + obj = pooled_object.keeped_object + # Reset the object by calling its reset method if it exists + if hasattr(obj, 'reset') and callable(getattr(obj, 'reset')): + obj.reset() + else: + # Fallback: clear all attributes to reset the object + if hasattr(obj, '__dict__'): + obj.__dict__.clear() + return pooled_object + + def validate(self, pooled_object: PooledObject) -> bool: + """Validate if the pooled object is still usable.""" + return pooled_object.keeped_object is not None + +# Global pond instances +_pools: dict[str, Pond] = {} + +def get_object_pool( + pool_name: str, + object_class: Type[T], + pooled_maxsize: Optional[int] = None, # None = unlimited pool with eviction-based memory control + least_one: bool = True, # Always keep at least one + borrowed_timeout: int = 10, # Longer timeout for high concurrency + time_between_eviction_runs: int = 300, # Less frequent eviction to maintain high reuse ratio + eviction_weight: float = 0.3, # Less aggressive eviction for better reuse + prewarm_count: int = 5 # Lower pre-warm count to reduce initial memory usage +) -> Pond: + """Get or create a global object pool instance with balanced eviction-based memory control. + + Memory is controlled through moderate eviction to balance reuse ratio and memory usage: + - Moderate eviction frequency (300s) to maintain high object reuse ratio + - Conservative eviction weight (0.3) to avoid destroying useful objects + - Lower pre-warm count (5) to reduce initial memory footprint + + Args: + pool_name: Unique name for the pool + object_class: The class type to pool + pooled_maxsize: Maximum number of objects in the pool (None = truly unlimited) + least_one: Whether to keep at least one object in the pool (default: True) + borrowed_timeout: Timeout for borrowing objects (seconds, default: 10) + time_between_eviction_runs: Time between eviction runs (seconds, default: 300) + eviction_weight: Weight for eviction algorithm (default: 0.3, conservative) + prewarm_count: Number of objects to pre-warm the pool with (default: 5) + + Returns: + Pond instance for the specified object type + """ + + if pool_name in _pools: + return _pools[pool_name] + + # Create new pond + pond = Pond( + borrowed_timeout=borrowed_timeout, + time_between_eviction_runs=time_between_eviction_runs, + thread_daemon=True, + eviction_weight=eviction_weight + ) + + # Register the factory with user's maxsize preference + factory = GenericPooledObjectFactory( + object_class=object_class, + pooled_maxsize=pooled_maxsize, + least_one=least_one + ) + pond.register(factory, name=f"{pool_name}Factory") + + # Pre-warm the pool + _prewarm_pool(pond, pool_name, prewarm_count) + + _pools[pool_name] = pond + return pond + +def _prewarm_pool(pond: Pond, pool_name: str, prewarm_count: int = 20) -> None: + """Pre-warm the pool with initial objects for high concurrency.""" + for _ in range(prewarm_count): + try: + pooled_obj = pond.borrow(name=f"{pool_name}Factory") + pond.recycle(pooled_obj, name=f"{pool_name}Factory") + except Exception: + # If pre-warming fails, just continue + break \ No newline at end of file diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index a99883ef7b6..19d5932ff28 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -14,6 +14,7 @@ from typing import ( Literal, Mapping, Optional, + Tuple, Union, cast, ) @@ -869,3 +870,63 @@ def convert_prefix_message_to_non_prefix_messages( else: new_messages.append(message) return new_messages + + +def _extract_reasoning_content(message: dict) -> Tuple[Optional[str], Optional[str]]: + """ + Extract reasoning content and main content from a message. + + Args: + message (dict): The message dictionary that may contain reasoning_content + + Returns: + tuple[Optional[str], Optional[str]]: A tuple of (reasoning_content, content) + """ + message_content = message.get("content") + if "reasoning_content" in message: + return message["reasoning_content"], message["content"] + elif "reasoning" in message: + return message["reasoning"], message["content"] + elif isinstance(message_content, str): + return _parse_content_for_reasoning(message_content) + return None, message_content + + +def _parse_content_for_reasoning( + message_text: Optional[str], +) -> Tuple[Optional[str], Optional[str]]: + """ + Parse the content for reasoning + + Returns: + - reasoning_content: The content of the reasoning + - content: The content of the message + """ + if not message_text: + return None, message_text + + reasoning_match = re.match( + r"<(?:think|thinking)>(.*?)(.*)", message_text, re.DOTALL + ) + + if reasoning_match: + return reasoning_match.group(1), reasoning_match.group(2) + + return None, message_text + + +def extract_images_from_message(message: AllMessageValues) -> List[str]: + """ + Extract images from a message + """ + images = [] + message_content = message.get("content") + if isinstance(message_content, list): + for m in message_content: + image_url = m.get("image_url") + if image_url: + if isinstance(image_url, str): + images.append(image_url) + elif isinstance(image_url, dict) and "url" in image_url: + images.append(image_url["url"]) + return images diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 2adddd52e74..b9cc5e50c3b 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -16,8 +16,8 @@ from litellm import verbose_logger from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client from litellm.types.files import get_file_extension_from_mime_type from litellm.types.llms.anthropic import * -from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock from litellm.types.llms.bedrock import CachePointBlock +from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock from litellm.types.llms.custom_http import httpxSpecialProvider from litellm.types.llms.ollama import OllamaVisionModelObject from litellm.types.llms.openai import ( @@ -1067,10 +1067,10 @@ def convert_to_gemini_tool_call_invoke( if tool_calls is not None: for tool in tool_calls: if "function" in tool: - gemini_function_call: Optional[VertexFunctionCall] = ( - _gemini_tool_call_invoke_helper( - function_call_params=tool["function"] - ) + gemini_function_call: Optional[ + VertexFunctionCall + ] = _gemini_tool_call_invoke_helper( + function_call_params=tool["function"] ) if gemini_function_call is not None: _parts_list.append( @@ -1589,9 +1589,9 @@ def anthropic_messages_pt( # noqa: PLR0915 ) if "cache_control" in _content_element: - _anthropic_content_element["cache_control"] = ( - _content_element["cache_control"] - ) + _anthropic_content_element[ + "cache_control" + ] = _content_element["cache_control"] user_content.append(_anthropic_content_element) elif m.get("type", "") == "text": m = cast(ChatCompletionTextObject, m) @@ -1629,9 +1629,9 @@ def anthropic_messages_pt( # noqa: PLR0915 ) if "cache_control" in _content_element: - _anthropic_content_text_element["cache_control"] = ( - _content_element["cache_control"] - ) + _anthropic_content_text_element[ + "cache_control" + ] = _content_element["cache_control"] user_content.append(_anthropic_content_text_element) @@ -2482,8 +2482,7 @@ class BedrockImageProcessor: if is_document: return BedrockImageProcessor._get_document_format( - mime_type=mime_type, - supported_doc_formats=supported_doc_formats + mime_type=mime_type, supported_doc_formats=supported_doc_formats ) else: @@ -2495,12 +2494,9 @@ class BedrockImageProcessor: f"Unsupported image format: {image_format}. Supported formats: {supported_image_and_video_formats}" ) return image_format - + @staticmethod - def _get_document_format( - mime_type: str, - supported_doc_formats: List[str] - ) -> str: + def _get_document_format(mime_type: str, supported_doc_formats: List[str]) -> str: """ Get the document format from the mime type @@ -2519,13 +2515,9 @@ class BedrockImageProcessor: The document format """ valid_extensions: Optional[List[str]] = None - potential_extensions = mimetypes.guess_all_extensions( - mime_type, strict=False - ) + potential_extensions = mimetypes.guess_all_extensions(mime_type, strict=False) valid_extensions = [ - ext[1:] - for ext in potential_extensions - if ext[1:] in supported_doc_formats + ext[1:] for ext in potential_extensions if ext[1:] in supported_doc_formats ] # Fallback to types/files.py if mimetypes doesn't return valid extensions @@ -2680,16 +2672,21 @@ def _convert_to_bedrock_tool_call_invoke( id = tool["id"] name = tool["function"].get("name", "") arguments = tool["function"].get("arguments", "") - arguments_dict = json.loads(arguments) if arguments else {} + if not arguments or not arguments.strip(): + arguments_dict = {} + else: + arguments_dict = json.loads(arguments) bedrock_tool = BedrockToolUseBlock( input=arguments_dict, name=name, toolUseId=id ) bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool) _parts_list.append(bedrock_content_block) - + # Check for cache_control and add a separate cachePoint block if tool.get("cache_control", None) is not None: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) _parts_list.append(cache_point_block) return _parts_list except Exception as e: @@ -2751,7 +2748,7 @@ def _convert_to_bedrock_tool_call_result( for content in content_list: if content["type"] == "text": content_str += content["text"] - + message.get("name", "") id = str(message.get("tool_call_id", str(uuid.uuid4()))) @@ -2760,7 +2757,7 @@ def _convert_to_bedrock_tool_call_result( content=[tool_result_content_block], toolUseId=id, ) - + content_block = BedrockContentBlock(toolResult=tool_result) return content_block @@ -3125,6 +3122,12 @@ class BedrockConverseMessagesProcessor: if element["type"] == "text": _part = BedrockContentBlock(text=element["text"]) _parts.append(_part) + elif element["type"] == "guarded_text": + # Wrap guarded_text in guardContent block + _part = BedrockContentBlock( + guardContent={"text": {"text": element["text"]}} + ) + _parts.append(_part) elif element["type"] == "image_url": format: Optional[str] = None if isinstance(element["image_url"], dict): @@ -3196,26 +3199,29 @@ class BedrockConverseMessagesProcessor: current_message = messages[msg_i] tool_call_result = _convert_to_bedrock_tool_call_result(current_message) tool_content.append(tool_call_result) - + # Check if we need to add a separate cachePoint block has_cache_control = False - + # Check for message-level cache_control if current_message.get("cache_control", None) is not None: has_cache_control = True # Check for content-level cache_control in list content elif isinstance(current_message.get("content"), list): for content_element in current_message["content"]: - if (isinstance(content_element, dict) and - content_element.get("cache_control", None) is not None): + if ( + isinstance(content_element, dict) + and content_element.get("cache_control", None) is not None + ): has_cache_control = True break - + # Add a separate cachePoint block if cache_control is present if has_cache_control: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) tool_content.append(cache_point_block) - msg_i += 1 if tool_content: @@ -3296,7 +3302,7 @@ class BedrockConverseMessagesProcessor: image_url=image_url ) assistants_parts.append(assistants_part) - # Add cache point block for assistant content elements + # Add cache point block for assistant content elements _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( message_block=cast( @@ -3308,8 +3314,12 @@ class BedrockConverseMessagesProcessor: if _cache_point_block is not None: assistants_parts.append(_cache_point_block) assistant_content.extend(assistants_parts) - elif _assistant_content is not None and isinstance(_assistant_content, str): - assistant_content.append(BedrockContentBlock(text=_assistant_content)) + elif _assistant_content is not None and isinstance( + _assistant_content, str + ): + assistant_content.append( + BedrockContentBlock(text=_assistant_content) + ) # Add cache point block for assistant string content _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( @@ -3493,6 +3503,12 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 if element["type"] == "text": _part = BedrockContentBlock(text=element["text"]) _parts.append(_part) + elif element["type"] == "guarded_text": + # Wrap guarded_text in guardContent block + _part = BedrockContentBlock( + guardContent={"text": {"text": element["text"]}} + ) + _parts.append(_part) elif element["type"] == "image_url": format: Optional[str] = None if isinstance(element["image_url"], dict): @@ -3562,29 +3578,33 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 while msg_i < len(messages) and messages[msg_i]["role"] == "tool": tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i]) current_message = messages[msg_i] - + # Add the tool result first tool_content.append(tool_call_result) - + # Check if we need to add a separate cachePoint block has_cache_control = False - + # Check for message-level cache_control if current_message.get("cache_control", None) is not None: has_cache_control = True # Check for content-level cache_control in list content elif isinstance(current_message.get("content"), list): for content_element in current_message["content"]: - if (isinstance(content_element, dict) and - content_element.get("cache_control", None) is not None): + if ( + isinstance(content_element, dict) + and content_element.get("cache_control", None) is not None + ): has_cache_control = True break - + # Add a separate cachePoint block if cache_control is present if has_cache_control: - cache_point_block = BedrockContentBlock(cachePoint=CachePointBlock(type="default")) + cache_point_block = BedrockContentBlock( + cachePoint=CachePointBlock(type="default") + ) tool_content.append(cache_point_block) - + msg_i += 1 if tool_content: # if last message was a 'user' message, then add a blank assistant message (bedrock requires alternating roles) @@ -3849,10 +3869,9 @@ def function_call_prompt(messages: list, functions: list): if isinstance(message["content"], str): message["content"] += f""" {function_prompt}""" else: - message["content"].append({ - "type": "text", - "text": f""" {function_prompt}""" - }) + message["content"].append( + {"type": "text", "text": f""" {function_prompt}"""} + ) function_added_to_prompt = True if function_added_to_prompt is False: diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 83b4985b239..322691e28b4 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -1024,6 +1024,8 @@ class CustomStreamWrapper: return def chunk_creator(self, chunk: Any): # type: ignore # noqa: PLR0915 + if hasattr(chunk, 'id'): + self.response_id = chunk.id model_response = self.model_response_creator() response_obj: Dict[str, Any] = {} try: diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index ce874bfde9a..e54aeaf995f 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -45,7 +45,10 @@ from litellm.types.llms.openai import ( OpenAIMcpServerTool, OpenAIWebSearchOptions, ) -from litellm.types.utils import CompletionTokensDetailsWrapper +from litellm.types.utils import ( + CacheCreationTokenDetails, + CompletionTokensDetailsWrapper, +) from litellm.types.utils import Message as LitellmMessage from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse from litellm.utils import ( @@ -200,8 +203,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) _allowed_properties = set(AnthropicInputSchema.__annotations__.keys()) - input_schema_filtered = {k: v for k, v in _input_schema.items() if k in _allowed_properties} - input_anthropic_schema: AnthropicInputSchema = AnthropicInputSchema(**input_schema_filtered) + input_schema_filtered = { + k: v for k, v in _input_schema.items() if k in _allowed_properties + } + input_anthropic_schema: AnthropicInputSchema = AnthropicInputSchema( + **input_schema_filtered + ) _tool = AnthropicMessagesTool( name=tool["function"]["name"], @@ -816,12 +823,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _usage = usage_object cache_creation_input_tokens: int = 0 cache_read_input_tokens: int = 0 + cache_creation_token_details: Optional[CacheCreationTokenDetails] = None web_search_requests: Optional[int] = None if ( "cache_creation_input_tokens" in _usage and _usage["cache_creation_input_tokens"] is not None ): cache_creation_input_tokens = _usage["cache_creation_input_tokens"] + prompt_tokens += cache_creation_input_tokens if ( "cache_read_input_tokens" in _usage and _usage["cache_read_input_tokens"] is not None @@ -837,8 +846,20 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): int, _usage["server_tool_use"]["web_search_requests"] ) + if "cache_creation" in _usage and _usage["cache_creation"] is not None: + cache_creation_token_details = CacheCreationTokenDetails( + ephemeral_5m_input_tokens=_usage["cache_creation"].get( + "ephemeral_5m_input_tokens" + ), + ephemeral_1h_input_tokens=_usage["cache_creation"].get( + "ephemeral_1h_input_tokens" + ), + ) + prompt_tokens_details = PromptTokensDetailsWrapper( cached_tokens=cache_read_input_tokens, + cache_creation_tokens=cache_read_input_tokens, + cache_creation_token_details=cache_creation_token_details, ) completion_token_details = ( CompletionTokensDetailsWrapper( diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 06ebb5079d9..68b5341e954 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -107,10 +107,8 @@ class AnthropicModelInfo(BaseLLMModelInfo): user_anthropic_beta_headers: Optional[List[str]] = None, ) -> dict: betas = set() - # Note: prompt-caching-2024-07-31 header is no longer required for prompt caching - # as per current Anthropic documentation. It's now generally available. - # if prompt_caching_set: - # betas.add("prompt-caching-2024-07-31") + if prompt_caching_set: + betas.add("prompt-caching-2024-07-31") if computer_tool_used: betas.add("computer-use-2024-10-22") # if pdf_used: @@ -178,11 +176,6 @@ class AnthropicModelInfo(BaseLLMModelInfo): mcp_server_used=mcp_server_used, ) - # For Vertex AI requests, remove any user-provided anthropic-beta headers - # since Vertex AI rejects them and they're no longer required for prompt caching - if optional_params.get("is_vertex_request", False): - headers = {k: v for k, v in headers.items() if k != "anthropic-beta"} - headers = {**headers, **anthropic_headers} return headers diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index aa95183bb6c..e4191a945f3 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -28,10 +28,6 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): TextBlock, ) - def __init__(self, completion_stream: Any, model: str): - super().__init__(completion_stream) - self.model = model - sent_first_chunk: bool = False sent_content_block_start: bool = False sent_content_block_finish: bool = False @@ -39,6 +35,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): sent_last_message: bool = False holding_chunk: Optional[Any] = None holding_stop_reason_chunk: Optional[Any] = None + queued_usage_chunk: bool = False current_content_block_index: int = 0 current_content_block_start: ContentBlockContentBlockDict = TextBlock( type="text", @@ -47,6 +44,10 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): pending_new_content_block: bool = False chunk_queue: deque = deque() # Queue for buffering multiple chunks + def __init__(self, completion_stream: Any, model: str): + super().__init__(completion_stream) + self.model = model + def __next__(self): from .transformation import LiteLLMAnthropicMessagesAdapter @@ -217,77 +218,83 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): # Queue the merged chunk and reset self.chunk_queue.append(merged_chunk) + self.queued_usage_chunk = True self.holding_stop_reason_chunk = None return self.chunk_queue.popleft() # Check if this processed chunk has a stop_reason - hold it for next chunk - if should_start_new_block and not self.sent_content_block_finish: - # Queue the sequence: content_block_stop -> content_block_start -> current_chunk + if not self.queued_usage_chunk: + if should_start_new_block and not self.sent_content_block_finish: + # Queue the sequence: content_block_stop -> content_block_start -> current_chunk - # 1. Stop current content block - self.chunk_queue.append( - { - "type": "content_block_stop", - "index": max(self.current_content_block_index - 1, 0), - } - ) + # 1. Stop current content block + self.chunk_queue.append( + { + "type": "content_block_stop", + "index": max(self.current_content_block_index - 1, 0), + } + ) - # 2. Start new content block - self.chunk_queue.append( - { - "type": "content_block_start", - "index": self.current_content_block_index, - "content_block": self.current_content_block_start, - } - ) + # 2. Start new content block + self.chunk_queue.append( + { + "type": "content_block_start", + "index": self.current_content_block_index, + "content_block": self.current_content_block_start, + } + ) - # 3. Queue the current chunk (don't lose it!) - self.chunk_queue.append(processed_chunk) - - # Reset state for new block - self.sent_content_block_finish = False - - # Return the first queued item - return self.chunk_queue.popleft() - - if ( - processed_chunk["type"] == "message_delta" - and self.sent_content_block_finish is False - ): - # Queue both the content_block_stop and the holding chunk - self.chunk_queue.append( - { - "type": "content_block_stop", - "index": self.current_content_block_index, - } - ) - self.sent_content_block_finish = True - if processed_chunk.get("delta", {}).get("stop_reason") is not None: - - self.holding_stop_reason_chunk = processed_chunk - else: + # 3. Queue the current chunk (don't lose it!) self.chunk_queue.append(processed_chunk) - return self.chunk_queue.popleft() - elif self.holding_chunk is not None: - # Queue both chunks - self.chunk_queue.append(self.holding_chunk) - self.chunk_queue.append(processed_chunk) - self.holding_chunk = None - return self.chunk_queue.popleft() - else: - # Queue the current chunk - self.chunk_queue.append(processed_chunk) - return self.chunk_queue.popleft() + + # Reset state for new block + self.sent_content_block_finish = False + + # Return the first queued item + return self.chunk_queue.popleft() + + if ( + processed_chunk["type"] == "message_delta" + and self.sent_content_block_finish is False + ): + # Queue both the content_block_stop and the holding chunk + self.chunk_queue.append( + { + "type": "content_block_stop", + "index": self.current_content_block_index, + } + ) + self.sent_content_block_finish = True + if ( + processed_chunk.get("delta", {}).get("stop_reason") + is not None + ): + + self.holding_stop_reason_chunk = processed_chunk + else: + self.chunk_queue.append(processed_chunk) + return self.chunk_queue.popleft() + elif self.holding_chunk is not None: + # Queue both chunks + self.chunk_queue.append(self.holding_chunk) + self.chunk_queue.append(processed_chunk) + self.holding_chunk = None + return self.chunk_queue.popleft() + else: + # Queue the current chunk + self.chunk_queue.append(processed_chunk) + return self.chunk_queue.popleft() # Handle any remaining held chunks after stream ends - if self.holding_stop_reason_chunk is not None: - self.chunk_queue.append(self.holding_stop_reason_chunk) - self.holding_stop_reason_chunk = None + if not self.queued_usage_chunk: + if self.holding_stop_reason_chunk is not None: + self.chunk_queue.append(self.holding_stop_reason_chunk) + self.holding_stop_reason_chunk = None - if self.holding_chunk is not None: - self.chunk_queue.append(self.holding_chunk) - self.holding_chunk = None + if self.holding_chunk is not None: + self.chunk_queue.append(self.holding_chunk) + self.holding_chunk = None if not self.sent_last_message: self.sent_last_message = True diff --git a/litellm/llms/azure/responses/transformation.py b/litellm/llms/azure/responses/transformation.py index 488a711669d..0050bd163d1 100644 --- a/litellm/llms/azure/responses/transformation.py +++ b/litellm/llms/azure/responses/transformation.py @@ -1,5 +1,7 @@ from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Tuple +import httpx + from litellm._logging import verbose_logger from litellm.llms.azure.common_utils import BaseAzureLLM from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig @@ -194,3 +196,66 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig): params["order"] = order verbose_logger.debug(f"list input items url={url}") return url, params + + ######################################################### + ########## CANCEL RESPONSE API TRANSFORMATION ########## + ######################################################### + def transform_cancel_response_api_request( + self, + response_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform the cancel response API request into a URL and data + + Azure OpenAI API expects the following request: + - POST /openai/responses/{response_id}/cancel?api-version=xxx + + This function handles URLs with query parameters by inserting the response_id + at the correct location (before any query parameters). + """ + from urllib.parse import urlparse, urlunparse + + # Parse the URL to separate its components + parsed_url = urlparse(api_base) + + # Insert the response_id and /cancel at the end of the path component + # Remove trailing slash if present to avoid double slashes + path = parsed_url.path.rstrip("/") + new_path = f"{path}/{response_id}/cancel" + + # Reconstruct the URL with all original components but with the modified path + cancel_url = urlunparse( + ( + parsed_url.scheme, # http, https + parsed_url.netloc, # domain name, port + new_path, # path with response_id and /cancel added + parsed_url.params, # parameters + parsed_url.query, # query string + parsed_url.fragment, # fragment + ) + ) + + data: Dict = {} + verbose_logger.debug(f"cancel response url={cancel_url}") + return cancel_url, data + + def transform_cancel_response_api_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ResponsesAPIResponse: + """ + Transform the cancel response API response into a ResponsesAPIResponse + """ + try: + raw_response_json = raw_response.json() + except Exception: + from litellm.llms.azure.chat.gpt_transformation import AzureOpenAIError + + raise AzureOpenAIError( + message=raw_response.text, status_code=raw_response.status_code + ) + return ResponsesAPIResponse(**raw_response_json) diff --git a/litellm/llms/base_llm/audio_transcription/transformation.py b/litellm/llms/base_llm/audio_transcription/transformation.py index 179b8d0fb02..3574996e48e 100644 --- a/litellm/llms/base_llm/audio_transcription/transformation.py +++ b/litellm/llms/base_llm/audio_transcription/transformation.py @@ -1,6 +1,6 @@ from abc import ABC, abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union +from typing import TYPE_CHECKING, Any, List, Optional, Union import httpx @@ -23,12 +23,13 @@ else: class AudioTranscriptionRequestData: """ Structured data for audio transcription requests. - + Attributes: data: The request data (form data for multipart, json data for regular requests) files: Optional files dict for multipart form data content_type: Optional content type override """ + data: Union[dict, bytes] files: Optional[dict] = None content_type: Optional[str] = None @@ -66,13 +67,11 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC): audio_file: FileTypes, optional_params: dict, litellm_params: dict, - ) -> Union[AudioTranscriptionRequestData, Dict]: + ) -> AudioTranscriptionRequestData: raise NotImplementedError( "AudioTranscriptionConfig needs a request transformation for audio transcription models" ) - - def transform_audio_transcription_response( self, raw_response: httpx.Response, @@ -110,7 +109,6 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC): raise NotImplementedError( "AudioTranscriptionConfig does not need a response transformation for audio transcription models" ) - def get_provider_specific_params( self, @@ -141,7 +139,7 @@ class BaseAudioTranscriptionConfig(BaseConfig, ABC): provider_specific_params[key] = value return provider_specific_params - + def _should_exclude_param( self, param_name: str, diff --git a/litellm/llms/base_llm/batches/transformation.py b/litellm/llms/base_llm/batches/transformation.py index 1d3e54fae67..9e67689fcd9 100644 --- a/litellm/llms/base_llm/batches/transformation.py +++ b/litellm/llms/base_llm/batches/transformation.py @@ -158,6 +158,48 @@ class BaseBatchesConfig(ABC): """ pass + @abstractmethod + def transform_retrieve_batch_request( + self, + batch_id: str, + optional_params: dict, + litellm_params: dict, + ) -> Union[bytes, str, Dict[str, Any]]: + """ + Transform the batch retrieval request to provider-specific format. + + Args: + batch_id: Batch ID to retrieve + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed request data + """ + pass + + @abstractmethod + def transform_retrieve_batch_response( + self, + model: Optional[str], + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform provider-specific batch retrieval 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] diff --git a/litellm/llms/base_llm/responses/transformation.py b/litellm/llms/base_llm/responses/transformation.py index 4da4f7652e0..facabbda72a 100644 --- a/litellm/llms/base_llm/responses/transformation.py +++ b/litellm/llms/base_llm/responses/transformation.py @@ -217,3 +217,28 @@ class BaseResponsesAPIConfig(ABC): ) -> bool: """Returns True if litellm should fake a stream for the given model and stream value""" return False + + ######################################################### + ########## CANCEL RESPONSE API TRANSFORMATION ########## + ######################################################### + @abstractmethod + def transform_cancel_response_api_request( + self, + response_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + pass + + @abstractmethod + def transform_cancel_response_api_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ResponsesAPIResponse: + pass + + ######################################################### + ########## END CANCEL RESPONSE API TRANSFORMATION ####### + ######################################################### diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index ce196757f94..8211addaf95 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -20,7 +20,11 @@ from pydantic import BaseModel from litellm._logging import verbose_logger from litellm.caching.caching import DualCache -from litellm.constants import BEDROCK_INVOKE_PROVIDERS_LITERAL, BEDROCK_MAX_POLICY_SIZE +from litellm.constants import ( + BEDROCK_EMBEDDING_PROVIDERS_LITERAL, + BEDROCK_INVOKE_PROVIDERS_LITERAL, + BEDROCK_MAX_POLICY_SIZE, +) from litellm.litellm_core_utils.dd_tracing import tracer from litellm.secret_managers.main import get_secret, get_secret_str @@ -66,6 +70,7 @@ class BaseAWSLLM: "aws_web_identity_token", "aws_sts_endpoint", "aws_bedrock_runtime_endpoint", + "aws_external_id", ] def get_cache_key(self, credential_args: Dict[str, Optional[str]]) -> str: @@ -88,6 +93,7 @@ class BaseAWSLLM: aws_role_name: Optional[str] = None, aws_web_identity_token: Optional[str] = None, aws_sts_endpoint: Optional[str] = None, + aws_external_id: Optional[str] = None, ): """ Return a boto3.Credentials object @@ -103,6 +109,7 @@ class BaseAWSLLM: aws_role_name, aws_web_identity_token, aws_sts_endpoint, + aws_external_id, ] # Iterate over parameters and update if needed @@ -127,6 +134,7 @@ class BaseAWSLLM: aws_role_name, aws_web_identity_token, aws_sts_endpoint, + aws_external_id, ) = params_to_check verbose_logger.debug( @@ -139,7 +147,8 @@ class BaseAWSLLM: "aws_profile_name=%s\n" "aws_role_name=%s\n" "aws_web_identity_token=%s\n" - "aws_sts_endpoint=%s", + "aws_sts_endpoint=%s\n" + "aws_external_id=%s", aws_access_key_id, aws_secret_access_key, aws_session_token, @@ -149,6 +158,7 @@ class BaseAWSLLM: aws_role_name, aws_web_identity_token, aws_sts_endpoint, + aws_external_id, ) # create cache key for non-expiring auth flows @@ -177,34 +187,45 @@ class BaseAWSLLM: aws_session_name=aws_session_name, aws_region_name=aws_region_name, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) elif aws_role_name is not None: # Check if we're in IRSA and trying to assume the same role we already have current_role_arn = os.getenv("AWS_ROLE_ARN") web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE") - + # In IRSA environments, we should skip role assumption if we're already running as the target role # This is true when: # 1. We have AWS_ROLE_ARN set (current role) # 2. We have AWS_WEB_IDENTITY_TOKEN_FILE set (IRSA environment) # 3. The current role matches the requested role - if (current_role_arn and web_identity_token_file and - current_role_arn == aws_role_name): - verbose_logger.debug("Using IRSA same-role optimization: calling _auth_with_env_vars") + if ( + current_role_arn + and web_identity_token_file + and current_role_arn == aws_role_name + ): + verbose_logger.debug( + "Using IRSA same-role optimization: calling _auth_with_env_vars" + ) # We're already running as this role via IRSA, no need to assume it again # Use the default boto3 credentials (which will use the IRSA credentials) credentials, _cache_ttl = self._auth_with_env_vars() else: - verbose_logger.debug("Using role assumption: calling _auth_with_aws_role") + verbose_logger.debug( + "Using role assumption: calling _auth_with_aws_role" + ) # If aws_session_name is not provided, generate a default one if aws_session_name is None: - aws_session_name = f"litellm-session-{int(datetime.now().timestamp())}" + aws_session_name = ( + f"litellm-session-{int(datetime.now().timestamp())}" + ) credentials, _cache_ttl = self._auth_with_aws_role( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, aws_session_token=aws_session_token, aws_role_name=aws_role_name, aws_session_name=aws_session_name, + aws_external_id=aws_external_id, ) elif aws_profile_name is not None: ### CHECK SESSION ### @@ -310,6 +331,40 @@ class BaseAWSLLM: return provider return None + @staticmethod + def get_bedrock_embedding_provider( + model: str, + ) -> Optional[BEDROCK_EMBEDDING_PROVIDERS_LITERAL]: + """ + Helper function to get the bedrock embedding provider from the model + + Handles scenarios like: + 1. model=cohere.embed-english-v3:0 -> Returns `cohere` + 2. model=amazon.titan-embed-text-v1 -> Returns `amazon` + 3. model=us.twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` + 4. model=twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` + """ + # Handle regional models like us.twelvelabs.marengo-embed-2-7-v1:0 + if "." in model: + parts = model.split(".") + # Check if the second part (after potential region) is a known provider + if len(parts) >= 2: + potential_provider = parts[1] # e.g., "twelvelabs" from "us.twelvelabs.marengo-embed-2-7-v1:0" + if potential_provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): + return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, potential_provider) + + # Check if the first part is a known provider (standard format) + potential_provider = parts[0] # e.g., "cohere" from "cohere.embed-english-v3:0" + if potential_provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): + return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, potential_provider) + + # Fallback: check if any provider name appears in the model string + for provider in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): + if provider in model: + return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, provider) + + return None + def _get_aws_region_name( self, optional_params: dict, @@ -406,6 +461,7 @@ class BaseAWSLLM: aws_session_name: str, aws_region_name: Optional[str], aws_sts_endpoint: Optional[str], + aws_external_id: Optional[str] = None, ) -> Tuple[Credentials, Optional[int]]: """ Authenticate with AWS Web Identity Token @@ -438,13 +494,19 @@ class BaseAWSLLM: # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html - sts_response = sts_client.assume_role_with_web_identity( - RoleArn=aws_role_name, - RoleSessionName=aws_session_name, - WebIdentityToken=oidc_token, - DurationSeconds=3600, - Policy='{"Version":"2012-10-17","Statement":[{"Sid":"BedrockLiteLLM","Effect":"Allow","Action":["bedrock:InvokeModel","bedrock:InvokeModelWithResponseStream"],"Resource":"*","Condition":{"Bool":{"aws:SecureTransport":"true"},"StringLike":{"aws:UserAgent":"litellm/*"}}}]}', - ) + assume_role_params = { + "RoleArn": aws_role_name, + "RoleSessionName": aws_session_name, + "WebIdentityToken": oidc_token, + "DurationSeconds": 3600, + "Policy": '{"Version":"2012-10-17","Statement":[{"Sid":"BedrockLiteLLM","Effect":"Allow","Action":["bedrock:InvokeModel","bedrock:InvokeModelWithResponseStream"],"Resource":"*","Condition":{"Bool":{"aws:SecureTransport":"true"},"StringLike":{"aws:UserAgent":"litellm/*"}}}]}', + } + + # Add ExternalId parameter if provided + if aws_external_id is not None: + assume_role_params["ExternalId"] = aws_external_id + + sts_response = sts_client.assume_role_with_web_identity(**assume_role_params) iam_creds_dict = { "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"], @@ -464,90 +526,131 @@ class BaseAWSLLM: iam_creds = session.get_credentials() return iam_creds, self._get_default_ttl_for_boto3_credentials() - def _handle_irsa_cross_account(self, irsa_role_arn: str, aws_role_name: str, - aws_session_name: str, region: str, web_identity_token_file: str) -> dict: + def _handle_irsa_cross_account( + self, + irsa_role_arn: str, + aws_role_name: str, + aws_session_name: str, + region: str, + web_identity_token_file: str, + aws_external_id: Optional[str] = None, + ) -> dict: """Handle cross-account role assumption for IRSA.""" import boto3 - + verbose_logger.debug("Cross-account role assumption detected") - + # Read the web identity token - with open(web_identity_token_file, 'r') as f: + with open(web_identity_token_file, "r") as f: web_identity_token = f.read().strip() - + # Create an STS client without credentials with tracer.trace("boto3.client(sts) for manual IRSA"): - sts_client = boto3.client('sts', region_name=region) - + sts_client = boto3.client("sts", region_name=region) + # Manually assume the IRSA role with the session name - verbose_logger.debug(f"Manually assuming IRSA role {irsa_role_arn} with session {aws_session_name}") + verbose_logger.debug( + f"Manually assuming IRSA role {irsa_role_arn} with session {aws_session_name}" + ) irsa_response = sts_client.assume_role_with_web_identity( RoleArn=irsa_role_arn, RoleSessionName=aws_session_name, - WebIdentityToken=web_identity_token + WebIdentityToken=web_identity_token, ) - + # Extract the credentials from the IRSA assumption irsa_creds = irsa_response["Credentials"] - + # Create a new STS client with the IRSA credentials with tracer.trace("boto3.client(sts) with manual IRSA credentials"): sts_client_with_creds = boto3.client( - 'sts', + "sts", region_name=region, aws_access_key_id=irsa_creds["AccessKeyId"], aws_secret_access_key=irsa_creds["SecretAccessKey"], - aws_session_token=irsa_creds["SessionToken"] + aws_session_token=irsa_creds["SessionToken"], ) - + # Get current caller identity for debugging try: caller_identity = sts_client_with_creds.get_caller_identity() - verbose_logger.debug(f"Current identity after manual IRSA assumption: {caller_identity.get('Arn', 'unknown')}") + verbose_logger.debug( + f"Current identity after manual IRSA assumption: {caller_identity.get('Arn', 'unknown')}" + ) except Exception as e: verbose_logger.debug(f"Failed to get caller identity: {e}") - - # Now assume the target role - verbose_logger.debug(f"Attempting to assume target role: {aws_role_name} with session: {aws_session_name}") - return sts_client_with_creds.assume_role( - RoleArn=aws_role_name, RoleSessionName=aws_session_name - ) - def _handle_irsa_same_account(self, aws_role_name: str, aws_session_name: str, region: str) -> dict: + # Now assume the target role + verbose_logger.debug( + f"Attempting to assume target role: {aws_role_name} with session: {aws_session_name}" + ) + assume_role_params = { + "RoleArn": aws_role_name, + "RoleSessionName": aws_session_name, + } + + # Add ExternalId parameter if provided + if aws_external_id is not None: + assume_role_params["ExternalId"] = aws_external_id + + return sts_client_with_creds.assume_role(**assume_role_params) + + def _handle_irsa_same_account( + self, + aws_role_name: str, + aws_session_name: str, + region: str, + aws_external_id: Optional[str] = None, + ) -> dict: """Handle same-account role assumption for IRSA.""" import boto3 - + verbose_logger.debug("Same account role assumption, using automatic IRSA") with tracer.trace("boto3.client(sts) with automatic IRSA"): sts_client = boto3.client("sts", region_name=region) - + # Get current caller identity for debugging try: caller_identity = sts_client.get_caller_identity() - verbose_logger.debug(f"Current IRSA identity: {caller_identity.get('Arn', 'unknown')}") + verbose_logger.debug( + f"Current IRSA identity: {caller_identity.get('Arn', 'unknown')}" + ) except Exception as e: verbose_logger.debug(f"Failed to get caller identity: {e}") - - # Assume the role - verbose_logger.debug(f"Attempting to assume role: {aws_role_name} with session: {aws_session_name}") - return sts_client.assume_role( - RoleArn=aws_role_name, RoleSessionName=aws_session_name - ) - def _extract_credentials_and_ttl(self, sts_response: dict) -> Tuple[Credentials, Optional[int]]: + # Assume the role + verbose_logger.debug( + f"Attempting to assume role: {aws_role_name} with session: {aws_session_name}" + ) + assume_role_params = { + "RoleArn": aws_role_name, + "RoleSessionName": aws_session_name, + } + + # Add ExternalId parameter if provided + if aws_external_id is not None: + assume_role_params["ExternalId"] = aws_external_id + + return sts_client.assume_role(**assume_role_params) + + def _extract_credentials_and_ttl( + self, sts_response: dict + ) -> Tuple[Credentials, Optional[int]]: """Extract credentials and TTL from STS response.""" from botocore.credentials import Credentials - + sts_credentials = sts_response["Credentials"] credentials = Credentials( access_key=sts_credentials["AccessKeyId"], secret_key=sts_credentials["SecretAccessKey"], token=sts_credentials["SessionToken"], ) - + expiration_time = sts_credentials["Expiration"] - ttl = int((expiration_time - datetime.now(expiration_time.tzinfo)).total_seconds()) - + ttl = int( + (expiration_time - datetime.now(expiration_time.tzinfo)).total_seconds() + ) + return credentials, ttl @tracer.wrap() @@ -558,6 +661,7 @@ class BaseAWSLLM: aws_session_token: Optional[str], aws_role_name: str, aws_session_name: str, + aws_external_id: Optional[str] = None, ) -> Tuple[Credentials, Optional[int]]: """ Authenticate with AWS Role @@ -568,34 +672,51 @@ class BaseAWSLLM: # Check if we're in an EKS/IRSA environment web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE") irsa_role_arn = os.getenv("AWS_ROLE_ARN") - + # If we have IRSA environment variables and no explicit credentials, # we need to use the web identity token flow - if (web_identity_token_file and irsa_role_arn and - aws_access_key_id is None and aws_secret_access_key is None): + if ( + web_identity_token_file + and irsa_role_arn + and aws_access_key_id is None + and aws_secret_access_key is None + ): # For cross-account role assumption with specific session names, # we need to manually assume the IRSA role first with the correct session name - verbose_logger.debug(f"IRSA detected: using web identity token from {web_identity_token_file}") - + verbose_logger.debug( + f"IRSA detected: using web identity token from {web_identity_token_file}" + ) + try: # Get region from environment - region = os.getenv("AWS_REGION") or os.getenv("AWS_DEFAULT_REGION") or "us-east-1" - + region = ( + os.getenv("AWS_REGION") + or os.getenv("AWS_DEFAULT_REGION") + or "us-east-1" + ) + # Check if we need to do cross-account role assumption if aws_role_name != irsa_role_arn: sts_response = self._handle_irsa_cross_account( - irsa_role_arn, aws_role_name, aws_session_name, region, web_identity_token_file + irsa_role_arn, + aws_role_name, + aws_session_name, + region, + web_identity_token_file, + aws_external_id, ) else: sts_response = self._handle_irsa_same_account( - aws_role_name, aws_session_name, region + aws_role_name, aws_session_name, region, aws_external_id ) - + return self._extract_credentials_and_ttl(sts_response) - + except Exception as e: verbose_logger.debug(f"Failed to assume role via IRSA: {e}") - if "AccessDenied" in str(e) and "is not authorized to perform: sts:AssumeRole" in str(e): + if "AccessDenied" in str( + e + ) and "is not authorized to perform: sts:AssumeRole" in str(e): # Provide a more helpful error message for trust policy issues verbose_logger.error( f"Access denied when trying to assume role {aws_role_name}. " @@ -604,7 +725,7 @@ class BaseAWSLLM: ) # Re-raise the exception instead of falling through raise - + # In EKS/IRSA environments, use ambient credentials (no explicit keys needed) # This allows the web identity token to work automatically if aws_access_key_id is None and aws_secret_access_key is None: @@ -619,9 +740,16 @@ class BaseAWSLLM: aws_session_token=aws_session_token, ) - sts_response = sts_client.assume_role( - RoleArn=aws_role_name, RoleSessionName=aws_session_name - ) + assume_role_params = { + "RoleArn": aws_role_name, + "RoleSessionName": aws_session_name, + } + + # Add ExternalId parameter if provided + if aws_external_id is not None: + assume_role_params["ExternalId"] = aws_external_id + + sts_response = sts_client.assume_role(**assume_role_params) # Extract the credentials from the response and convert to Session Credentials sts_credentials = sts_response["Credentials"] @@ -743,14 +871,14 @@ class BaseAWSLLM: ) # Determine proxy_endpoint_url - if env_aws_bedrock_runtime_endpoint and isinstance( - env_aws_bedrock_runtime_endpoint, str - ): - proxy_endpoint_url = env_aws_bedrock_runtime_endpoint - elif aws_bedrock_runtime_endpoint is not None and isinstance( + if aws_bedrock_runtime_endpoint is not None and isinstance( aws_bedrock_runtime_endpoint, str ): proxy_endpoint_url = aws_bedrock_runtime_endpoint + elif env_aws_bedrock_runtime_endpoint and isinstance( + env_aws_bedrock_runtime_endpoint, str + ): + proxy_endpoint_url = env_aws_bedrock_runtime_endpoint else: proxy_endpoint_url = endpoint_url @@ -800,6 +928,7 @@ class BaseAWSLLM: aws_bedrock_runtime_endpoint = optional_params.pop( "aws_bedrock_runtime_endpoint", None ) # https://bedrock-runtime.{region_name}.amazonaws.com + aws_external_id = optional_params.pop("aws_external_id", None) credentials: Credentials = self.get_credentials( aws_access_key_id=aws_access_key_id, @@ -811,6 +940,7 @@ class BaseAWSLLM: aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) return Boto3CredentialsInfo( @@ -915,6 +1045,7 @@ class BaseAWSLLM: aws_profile_name = optional_params.get("aws_profile_name", None) aws_web_identity_token = optional_params.get("aws_web_identity_token", None) aws_sts_endpoint = optional_params.get("aws_sts_endpoint", None) + aws_external_id = optional_params.get("aws_external_id", None) aws_region_name = self._get_aws_region_name( optional_params=optional_params, model=model ) @@ -929,6 +1060,7 @@ class BaseAWSLLM: aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) sigv4 = SigV4Auth(credentials, service_name, aws_region_name) diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index ce580ebc624..2f3d00dddda 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -7,7 +7,6 @@ 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, @@ -124,15 +123,13 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): "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.") + if not model: + raise ValueError("Could not determine Bedrock model ID. Please pass `model` in your request body.") # 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") + job_name = self.common_utils.generate_unique_job_name(model, prefix="litellm") output_key = f"litellm-batch-outputs/{job_name}/" # Build input data config @@ -151,7 +148,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): # Create Bedrock batch request with proper typing bedrock_request: BedrockCreateBatchRequest = { - "modelId": bedrock_model_id, + "modelId": model, "jobName": job_name, "inputDataConfig": input_data_config, "outputDataConfig": output_data_config, @@ -202,19 +199,23 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): # Extract information from typed Bedrock response job_arn = response_data.get("jobArn", "") - status: BedrockBatchJobStatus = response_data.get("status", "Submitted") + status_str: str = str(response_data.get("status", "Submitted")) # Map Bedrock status to OpenAI-compatible status - status_mapping: Dict[BedrockBatchJobStatus, str] = { + status_mapping: Dict[str, str] = { "Submitted": "validating", + "Validating": "validating", + "Scheduled": "in_progress", "InProgress": "in_progress", + "PartiallyCompleted": "completed", "Completed": "completed", "Failed": "failed", "Stopping": "cancelling", - "Stopped": "cancelled" + "Stopped": "cancelled", + "Expired": "expired", } - openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status, "validating")) + openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status_str, "validating")) # Get original request data from litellm_params if available original_request = litellm_params.get("original_batch_request", {}) @@ -231,7 +232,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): 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, + in_progress_at=int(time.time()) if status_str == "InProgress" else None, expires_at=None, finalizing_at=None, completed_at=None, @@ -243,6 +244,203 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): metadata=original_request.get("metadata", {}), ) + def transform_retrieve_batch_request( + self, + batch_id: str, + optional_params: dict, + litellm_params: dict, + ) -> Dict[str, Any]: + """ + Transform batch retrieval request for Bedrock. + + Args: + batch_id: Bedrock job ARN + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed request data for Bedrock GetModelInvocationJob API + """ + # For Bedrock, batch_id should be the full job ARN + # The GetModelInvocationJob API expects the full ARN as the identifier + if not batch_id.startswith("arn:aws:bedrock:"): + raise ValueError(f"Invalid batch_id format. Expected ARN, got: {batch_id}") + + # Extract the job identifier from the ARN - use the full ARN path part + # ARN format: arn:aws:bedrock:region:account:model-invocation-job/job-name + arn_parts = batch_id.split(":") + if len(arn_parts) < 6: + raise ValueError(f"Invalid ARN format: {batch_id}") + + region = arn_parts[3] + # arn_parts[5] contains "model-invocation-job/{jobId}" + + # Build the endpoint URL for GetModelInvocationJob + # AWS API format: GET /model-invocation-job/{jobIdentifier} + # Use the FULL ARN as jobIdentifier and URL-encode it (includes ':' and '/') + import urllib.parse as _ul + encoded_arn = _ul.quote(batch_id, safe="") + endpoint_url = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}" + + # Use common utility for AWS signing + signed_headers, _ = self.common_utils.sign_aws_request( + service_name="bedrock", + data={}, # GET request has no body + endpoint_url=endpoint_url, + optional_params=optional_params, + method="GET" + ) + + # Return pre-signed request format + return { + "method": "GET", + "url": endpoint_url, + "headers": signed_headers, + "data": None + } + + def _parse_timestamps_and_status(self, response_data, status_str: str): + """Helper to parse timestamps based on status.""" + import datetime + def parse_timestamp(ts_str: Optional[str]) -> Optional[int]: + if not ts_str: + return None + try: + dt = datetime.datetime.fromisoformat(ts_str.replace('Z', '+00:00')) + return int(dt.timestamp()) + except Exception: + return None + + created_at = parse_timestamp(str(response_data.get("submitTime")) if response_data.get("submitTime") is not None else None) + in_progress_states = {"InProgress", "Validating", "Scheduled"} + in_progress_at = ( + parse_timestamp(str(response_data.get("lastModifiedTime")) if response_data.get("lastModifiedTime") is not None else None) + if status_str in in_progress_states + else None + ) + completed_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str in {"Completed", "PartiallyCompleted"} else None + failed_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str == "Failed" else None + cancelled_at = parse_timestamp(str(response_data.get("endTime")) if response_data.get("endTime") is not None else None) if status_str == "Stopped" else None + expires_at = parse_timestamp(str(response_data.get("jobExpirationTime")) if response_data.get("jobExpirationTime") is not None else None) + + return created_at, in_progress_at, completed_at, failed_at, cancelled_at, expires_at + + def _extract_file_configs(self, response_data): + """Helper to extract input and output file configurations.""" + # Extract input file ID + input_file_id = "" + input_data_config = response_data.get("inputDataConfig", {}) + if isinstance(input_data_config, dict): + s3_input_config = input_data_config.get("s3InputDataConfig", {}) + if isinstance(s3_input_config, dict): + input_file_id = s3_input_config.get("s3Uri", "") + + # Extract output file ID + output_file_id = None + output_data_config = response_data.get("outputDataConfig", {}) + if isinstance(output_data_config, dict): + s3_output_config = output_data_config.get("s3OutputDataConfig", {}) + if isinstance(s3_output_config, dict): + output_file_id = s3_output_config.get("s3Uri", "") + + return input_file_id, output_file_id + + def _extract_errors_and_metadata(self, response_data, raw_response): + """Helper to extract errors and enriched metadata.""" + # Extract errors + message = response_data.get("message") + errors = None + if message: + from openai.types.batch import Errors + from openai.types.batch_error import BatchError + errors = Errors( + data=[BatchError(message=message, code=str(raw_response.status_code))], + object="list" + ) + + # Enrich metadata with useful Bedrock fields + enriched_metadata_raw: Dict[str, Any] = { + "jobName": response_data.get("jobName"), + "clientRequestToken": response_data.get("clientRequestToken"), + "modelId": response_data.get("modelId"), + "roleArn": response_data.get("roleArn"), + "timeoutDurationInHours": response_data.get("timeoutDurationInHours"), + "vpcConfig": response_data.get("vpcConfig"), + } + import json as _json + enriched_metadata: Dict[str, str] = {} + for _k, _v in enriched_metadata_raw.items(): + if _v is None: + continue + if isinstance(_v, (dict, list)): + try: + enriched_metadata[_k] = _json.dumps(_v) + except Exception: + enriched_metadata[_k] = str(_v) + else: + enriched_metadata[_k] = str(_v) + + return errors, enriched_metadata + + def transform_retrieve_batch_response( + self, + model: Optional[str], + raw_response: Response, + logging_obj: Any, + litellm_params: dict, + ) -> LiteLLMBatch: + """ + Transform Bedrock batch retrieval response to LiteLLM format. + """ + from litellm.types.llms.bedrock import BedrockGetBatchResponse + try: + response_data: BedrockGetBatchResponse = raw_response.json() + except Exception as e: + raise ValueError(f"Failed to parse Bedrock batch response: {e}") + + job_arn = response_data.get("jobArn", "") + status_str: str = str(response_data.get("status", "Submitted")) + + # Map Bedrock status to OpenAI-compatible status + status_mapping: Dict[str, str] = { + "Submitted": "validating", "Validating": "validating", "Scheduled": "in_progress", + "InProgress": "in_progress", "PartiallyCompleted": "completed", "Completed": "completed", + "Failed": "failed", "Stopping": "cancelling", "Stopped": "cancelled", "Expired": "expired" + } + openai_status = cast(Literal["validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled"], status_mapping.get(status_str, "validating")) + + # Parse timestamps + created_at, in_progress_at, completed_at, failed_at, cancelled_at, expires_at = self._parse_timestamps_and_status(response_data, status_str) + + # Extract file configurations + input_file_id, output_file_id = self._extract_file_configs(response_data) + + # Extract errors and metadata + errors, enriched_metadata = self._extract_errors_and_metadata(response_data, raw_response) + + return LiteLLMBatch( + id=job_arn, + object="batch", + endpoint="/v1/chat/completions", + errors=errors, + input_file_id=input_file_id, + completion_window="24h", + status=openai_status, + output_file_id=output_file_id, + error_file_id=None, + created_at=created_at or int(time.time()), + in_progress_at=in_progress_at, + expires_at=expires_at, + finalizing_at=None, + completed_at=completed_at, + failed_at=failed_at, + expired_at=None, + cancelling_at=None, + cancelled_at=cancelled_at, + request_counts=None, + metadata=enriched_metadata, + ) + def get_error_class( self, error_message: str, status_code: int, headers: Union[Dict, Headers] ) -> BaseLLMException: diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index 15a5002f0e4..54c603e5960 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -307,6 +307,7 @@ class BedrockConverseLLM(BaseAWSLLM): ) # https://bedrock-runtime.{region_name}.amazonaws.com aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None) + aws_external_id = optional_params.pop("aws_external_id", None) optional_params.pop("aws_region_name", None) litellm_params[ @@ -323,6 +324,7 @@ class BedrockConverseLLM(BaseAWSLLM): aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, aws_sts_endpoint=aws_sts_endpoint, + aws_external_id=aws_external_id, ) ### SET RUNTIME ENDPOINT ### diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index fda9220ff7d..d99cff6b6bc 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -14,7 +14,7 @@ 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 -from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( +from litellm.litellm_core_utils.prompt_templates.common_utils import ( _parse_content_for_reasoning, ) from litellm.litellm_core_utils.prompt_templates.factory import ( @@ -102,6 +102,61 @@ class AmazonConverseConfig(BaseConfig): "performanceConfig": PerformanceConfigBlock, } + @staticmethod + def _convert_consecutive_user_messages_to_guarded_text( + messages: List[AllMessageValues], optional_params: dict + ) -> List[AllMessageValues]: + """ + Convert consecutive user messages at the end to guarded_text type if guardrailConfig is present + and no guarded_text is already present in those messages. + """ + # Check if guardrailConfig is present + if "guardrailConfig" not in optional_params: + return messages + + # Find all consecutive user messages at the end + consecutive_user_message_indices = [] + for i in range(len(messages) - 1, -1, -1): + if messages[i].get("role") == "user": + consecutive_user_message_indices.append(i) + else: + break + + if not consecutive_user_message_indices: + return messages + + # Process each consecutive user message + messages_copy = copy.deepcopy(messages) + for user_message_index in consecutive_user_message_indices: + user_message = messages_copy[user_message_index] + content = user_message.get("content", []) + + if isinstance(content, list): + has_guarded_text = any( + isinstance(item, dict) and item.get("type") == "guarded_text" + for item in content + ) + if has_guarded_text: + continue # Skip this message if it already has guarded_text + + # Convert text elements to guarded_text + new_content = [] + for item in content: + if isinstance(item, dict) and item.get("type") == "text": + new_item = {"type": "guarded_text", "text": item["text"]} # type: ignore + new_content.append(new_item) + else: + new_content.append(item) + + messages_copy[user_message_index]["content"] = new_content # type: ignore + elif isinstance(content, str): + # If content is a string, convert it to guarded_text + messages_copy[user_message_index]["content"] = [ # type: ignore + {"type": "guarded_text", "text": content} # type: ignore + ] + + return messages_copy + @classmethod def get_config(cls): return { @@ -397,7 +452,11 @@ class AmazonConverseConfig(BaseConfig): for param, value in non_default_params.items(): if param == "response_format" and isinstance(value, dict): 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 + value=value, + model=model, + optional_params=optional_params, + non_default_params=non_default_params, + is_thinking_enabled=is_thinking_enabled, ) if param == "max_tokens" or param == "max_completion_tokens": optional_params["maxTokens"] = value @@ -446,11 +505,11 @@ class AmazonConverseConfig(BaseConfig): ) return optional_params - + def _translate_response_format_param( - self, - value: dict, - model: str, + self, + value: dict, + model: str, optional_params: dict, non_default_params: dict, is_thinking_enabled: bool, @@ -497,14 +556,13 @@ class AmazonConverseConfig(BaseConfig): ) 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( @@ -766,6 +824,11 @@ class AmazonConverseConfig(BaseConfig): headers: Optional[dict] = None, ) -> RequestObject: messages, system_content_blocks = self._transform_system_message(messages) + + # Convert last user message to guarded_text if guardrailConfig is present + messages = self._convert_consecutive_user_messages_to_guarded_text( + messages, optional_params + ) ## TRANSFORMATION ## _data: CommonRequestObject = self._transform_request_helper( @@ -818,6 +881,11 @@ class AmazonConverseConfig(BaseConfig): ) -> RequestObject: messages, system_content_blocks = self._transform_system_message(messages) + # Convert last user message to guarded_text if guardrailConfig is present + messages = self._convert_consecutive_user_messages_to_guarded_text( + messages, optional_params + ) + _data: CommonRequestObject = self._transform_request_helper( model=model, system_content_blocks=system_content_blocks, @@ -991,7 +1059,9 @@ class AmazonConverseConfig(BaseConfig): return message, returned_finish_reason - def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[ + def _translate_message_content( + self, content_blocks: List[ContentBlock] + ) -> Tuple[ str, List[ChatCompletionToolCallChunk], Optional[List[BedrockConverseReasoningContentBlock]], @@ -1006,9 +1076,9 @@ class AmazonConverseConfig(BaseConfig): """ content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( - None - ) + reasoningContentBlocks: Optional[ + List[BedrockConverseReasoningContentBlock] + ] = None for idx, content in enumerate(content_blocks): """ - Content is either a tool response or text @@ -1129,9 +1199,9 @@ class AmazonConverseConfig(BaseConfig): chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( - None - ) + reasoningContentBlocks: Optional[ + List[BedrockConverseReasoningContentBlock] + ] = None if message is not None: ( @@ -1144,12 +1214,12 @@ class AmazonConverseConfig(BaseConfig): chat_completion_message["provider_specific_fields"] = { "reasoningContentBlocks": reasoningContentBlocks, } - chat_completion_message["reasoning_content"] = ( - self._transform_reasoning_content(reasoningContentBlocks) - ) - chat_completion_message["thinking_blocks"] = ( - self._transform_thinking_blocks(reasoningContentBlocks) - ) + chat_completion_message[ + "reasoning_content" + ] = self._transform_reasoning_content(reasoningContentBlocks) + chat_completion_message[ + "thinking_blocks" + ] = self._transform_thinking_blocks(reasoningContentBlocks) chat_completion_message["content"] = content_str if ( json_mode is True @@ -1167,7 +1237,6 @@ class AmazonConverseConfig(BaseConfig): # 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 diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py index d7ceec1f1c1..0fe84b0ce0c 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py @@ -3,7 +3,7 @@ from typing import Any, List, Optional, cast from httpx import Response from litellm import verbose_logger -from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( +from litellm.litellm_core_utils.prompt_templates.common_utils import ( _parse_content_for_reasoning, ) from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 831a6da93b3..c7f7acf331d 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -738,19 +738,24 @@ class CommonBatchFilesUtils: ) # Prepare the request data - if isinstance(data, dict): - import json - request_data = json.dumps(data) + method_upper = method.upper() + if method_upper == "GET": + # GET requests should be signed with an empty payload + request_data = "" + headers = {} else: - request_data = data - - # Prepare headers - headers = {"Content-Type": "application/json"} + if isinstance(data, dict): + import json + request_data = json.dumps(data) + else: + request_data = data + # Prepare headers for non-GET requests + 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 + method=method_upper, url=endpoint_url, data=request_data, headers=headers ) sigv4.add_auth(request) prepped = request.prepare() diff --git a/litellm/llms/bedrock/count_tokens/handler.py b/litellm/llms/bedrock/count_tokens/handler.py new file mode 100644 index 00000000000..d4355c0c360 --- /dev/null +++ b/litellm/llms/bedrock/count_tokens/handler.py @@ -0,0 +1,123 @@ +""" +AWS Bedrock CountTokens API handler. + +Simplified handler leveraging existing LiteLLM Bedrock infrastructure. +""" + +from typing import Any, Dict + +from fastapi import HTTPException + +import litellm +from litellm._logging import verbose_logger +from litellm.llms.bedrock.count_tokens.transformation import BedrockCountTokensConfig +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client + + +class BedrockCountTokensHandler(BedrockCountTokensConfig): + """ + Simplified handler for AWS Bedrock CountTokens API requests. + + Uses existing LiteLLM infrastructure for authentication and request handling. + """ + + async def handle_count_tokens_request( + self, + request_data: Dict[str, Any], + litellm_params: Dict[str, Any], + resolved_model: str, + ) -> Dict[str, Any]: + """ + Handle a CountTokens request using existing LiteLLM patterns. + + Args: + request_data: The incoming request payload + litellm_params: LiteLLM configuration parameters + resolved_model: The actual model ID resolved from router + + Returns: + Dictionary containing token count response + """ + try: + # Validate the request + self.validate_count_tokens_request(request_data) + + verbose_logger.debug( + f"Processing CountTokens request for resolved model: {resolved_model}" + ) + + # Get AWS region using existing LiteLLM function + aws_region_name = self._get_aws_region_name( + optional_params=litellm_params, + model=resolved_model, + model_id=None, + ) + + verbose_logger.debug(f"Retrieved AWS region: {aws_region_name}") + + # Transform request to Bedrock format (supports both Converse and InvokeModel) + bedrock_request = self.transform_anthropic_to_bedrock_count_tokens( + request_data=request_data + ) + + verbose_logger.debug(f"Transformed request: {bedrock_request}") + + # Get endpoint URL using simplified function + endpoint_url = self.get_bedrock_count_tokens_endpoint( + resolved_model, aws_region_name + ) + + verbose_logger.debug(f"Making request to: {endpoint_url}") + + # Use existing _sign_request method from BaseAWSLLM + headers = {"Content-Type": "application/json"} + signed_headers, signed_body = self._sign_request( + service_name="bedrock", + headers=headers, + optional_params=litellm_params, + request_data=bedrock_request, + api_base=endpoint_url, + model=resolved_model, + ) + + async_client = get_async_httpx_client(llm_provider=litellm.LlmProviders.BEDROCK) + + response = await async_client.post( + endpoint_url, + headers=signed_headers, + data=signed_body, + timeout=30.0, + ) + + verbose_logger.debug(f"Response status: {response.status_code}") + + if response.status_code != 200: + error_text = response.text + verbose_logger.error(f"AWS Bedrock error: {error_text}") + raise HTTPException( + status_code=400, + detail={"error": f"AWS Bedrock error: {error_text}"}, + ) + + bedrock_response = response.json() + + verbose_logger.debug(f"Bedrock response: {bedrock_response}") + + # Transform response back to expected format + final_response = self.transform_bedrock_response_to_anthropic( + bedrock_response + ) + + verbose_logger.debug(f"Final response: {final_response}") + + return final_response + + except HTTPException: + # Re-raise HTTP exceptions as-is + raise + except Exception as e: + verbose_logger.error(f"Error in CountTokens handler: {str(e)}") + raise HTTPException( + status_code=500, + detail={"error": f"CountTokens processing error: {str(e)}"}, + ) diff --git a/litellm/llms/bedrock/count_tokens/transformation.py b/litellm/llms/bedrock/count_tokens/transformation.py new file mode 100644 index 00000000000..d46ed3aa452 --- /dev/null +++ b/litellm/llms/bedrock/count_tokens/transformation.py @@ -0,0 +1,213 @@ +""" +AWS Bedrock CountTokens API transformation logic. + +This module handles the transformation of requests from Anthropic Messages API format +to AWS Bedrock's CountTokens API format and vice versa. +""" + +from typing import Any, Dict, List + +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.llms.bedrock.common_utils import BedrockModelInfo + + +class BedrockCountTokensConfig(BaseAWSLLM): + """ + Configuration and transformation logic for AWS Bedrock CountTokens API. + + AWS Bedrock CountTokens API Specification: + - Endpoint: POST /model/{modelId}/count-tokens + - Input formats: 'invokeModel' or 'converse' + - Response: {"inputTokens": } + """ + + def _detect_input_type(self, request_data: Dict[str, Any]) -> str: + """ + Detect whether to use 'converse' or 'invokeModel' input format. + + Args: + request_data: The original request data + + Returns: + 'converse' or 'invokeModel' + """ + # If the request has messages in the expected Anthropic format, use converse + if "messages" in request_data and isinstance(request_data["messages"], list): + return "converse" + + # For raw text or other formats, use invokeModel + # This handles cases where the input is prompt-based or already in raw Bedrock format + return "invokeModel" + + def transform_anthropic_to_bedrock_count_tokens( + self, + request_data: Dict[str, Any], + ) -> Dict[str, Any]: + """ + Transform request to Bedrock CountTokens format. + Supports both Converse and InvokeModel input types. + + Input (Anthropic format): + { + "model": "claude-3-5-sonnet", + "messages": [{"role": "user", "content": "Hello!"}] + } + + Output (Bedrock CountTokens format for Converse): + { + "input": { + "converse": { + "messages": [...], + "system": [...] (if present) + } + } + } + + Output (Bedrock CountTokens format for InvokeModel): + { + "input": { + "invokeModel": { + "body": "{...raw model input...}" + } + } + } + """ + input_type = self._detect_input_type(request_data) + + if input_type == "converse": + return self._transform_to_converse_format(request_data.get("messages", [])) + else: + return self._transform_to_invoke_model_format(request_data) + + def _transform_to_converse_format( + self, messages: List[Dict[str, Any]] + ) -> Dict[str, Any]: + """Transform to Converse input format.""" + # Extract system messages if present + system_messages = [] + user_messages = [] + + for message in messages: + if message.get("role") == "system": + system_messages.append({"text": message.get("content", "")}) + else: + # Transform message content to Bedrock format + transformed_message: Dict[str, Any] = {"role": message.get("role"), "content": []} + + # Handle content - ensure it's in the correct array format + content = message.get("content", "") + if isinstance(content, str): + # String content -> convert to text block + transformed_message["content"].append({"text": content}) + elif isinstance(content, list): + # Already in blocks format - use as is + transformed_message["content"] = content + + user_messages.append(transformed_message) + + # Build the converse input format + converse_input = {"messages": user_messages} + + # Add system messages if present + if system_messages: + converse_input["system"] = system_messages + + # Build the complete request + return {"input": {"converse": converse_input}} + + def _transform_to_invoke_model_format( + self, request_data: Dict[str, Any] + ) -> Dict[str, Any]: + """Transform to InvokeModel input format.""" + import json + + # For InvokeModel, we need to provide the raw body that would be sent to the model + # Remove the 'model' field from the body as it's not part of the model input + body_data = {k: v for k, v in request_data.items() if k != "model"} + + return {"input": {"invokeModel": {"body": json.dumps(body_data)}}} + + def get_bedrock_count_tokens_endpoint( + self, model: str, aws_region_name: str + ) -> str: + """ + Construct the AWS Bedrock CountTokens API endpoint using existing LiteLLM functions. + + Args: + model: The resolved model ID from router lookup + aws_region_name: AWS region (e.g., "eu-west-1") + + Returns: + Complete endpoint URL for CountTokens API + """ + # Use existing LiteLLM function to get the base model ID (removes region prefix) + model_id = BedrockModelInfo.get_base_model(model) + + # Remove bedrock/ prefix if present + if model_id.startswith("bedrock/"): + model_id = model_id[8:] # Remove "bedrock/" prefix + + base_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com" + endpoint = f"{base_url}/model/{model_id}/count-tokens" + + return endpoint + + def transform_bedrock_response_to_anthropic( + self, bedrock_response: Dict[str, Any] + ) -> Dict[str, Any]: + """ + Transform Bedrock CountTokens response to Anthropic format. + + Input (Bedrock response): + { + "inputTokens": 123 + } + + Output (Anthropic format): + { + "input_tokens": 123 + } + """ + input_tokens = bedrock_response.get("inputTokens", 0) + + return {"input_tokens": input_tokens} + + def validate_count_tokens_request(self, request_data: Dict[str, Any]) -> None: + """ + Validate the incoming count tokens request. + Supports both Converse and InvokeModel input formats. + + Args: + request_data: The request payload + + Raises: + ValueError: If the request is invalid + """ + if not request_data.get("model"): + raise ValueError("model parameter is required") + + input_type = self._detect_input_type(request_data) + + if input_type == "converse": + # Validate Converse format (messages-based) + messages = request_data.get("messages", []) + if not messages: + raise ValueError("messages parameter is required for Converse input") + + if not isinstance(messages, list): + raise ValueError("messages must be a list") + + for i, message in enumerate(messages): + if not isinstance(message, dict): + raise ValueError(f"Message {i} must be a dictionary") + + if "role" not in message: + raise ValueError(f"Message {i} must have a 'role' field") + + if "content" not in message: + raise ValueError(f"Message {i} must have a 'content' field") + else: + # For InvokeModel format, we need at least some content to count tokens + # The content structure varies by model, so we do minimal validation + if len(request_data) <= 1: # Only has 'model' field + raise ValueError("Request must contain content to count tokens") diff --git a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py index 8056e9e9b2c..ff748b58e8e 100644 --- a/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py +++ b/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py @@ -10,7 +10,7 @@ Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-tit """ import types -from typing import List, Optional +from typing import List, Optional, Union from litellm.types.llms.bedrock import ( AmazonTitanV2EmbeddingRequest, @@ -30,9 +30,7 @@ class AmazonTitanV2Config: normalize: Optional[bool] = None dimensions: Optional[int] = None - def __init__( - self, normalize: Optional[bool] = None, dimensions: Optional[int] = None - ) -> None: + def __init__(self, normalize: Optional[bool] = None, dimensions: Optional[int] = None) -> None: locals_ = locals().copy() for key, value in locals_.items(): if key != "self" and value is not None: @@ -57,32 +55,56 @@ class AmazonTitanV2Config: } def get_supported_openai_params(self) -> List[str]: - return ["dimensions"] + return ["dimensions", "encoding_format"] - def map_openai_params( - self, non_default_params: dict, optional_params: dict - ) -> dict: + def map_openai_params(self, non_default_params: dict, optional_params: dict) -> dict: for k, v in non_default_params.items(): if k == "dimensions": optional_params["dimensions"] = v + elif k == "encoding_format": + # Map OpenAI encoding_format to AWS embeddingTypes + if v == "float": + optional_params["embeddingTypes"] = ["float"] + elif v == "base64": + # base64 maps to binary format in AWS + optional_params["embeddingTypes"] = ["binary"] + else: + # For any other encoding format, default to float + optional_params["embeddingTypes"] = ["float"] return optional_params - def _transform_request( - self, input: str, inference_params: dict - ) -> AmazonTitanV2EmbeddingRequest: + def _transform_request(self, input: str, inference_params: dict) -> AmazonTitanV2EmbeddingRequest: return AmazonTitanV2EmbeddingRequest(inputText=input, **inference_params) # type: ignore - def _transform_response( - self, response_list: List[dict], model: str - ) -> EmbeddingResponse: + def _transform_response(self, response_list: List[dict], model: str) -> EmbeddingResponse: total_prompt_tokens = 0 transformed_responses: List[Embedding] = [] for index, response in enumerate(response_list): _parsed_response = AmazonTitanV2EmbeddingResponse(**response) # type: ignore + + # According to AWS docs, embeddingsByType is always present + # If binary was requested (encoding_format="base64"), use binary data + # Otherwise, use float data from embeddingsByType or fallback to embedding field + embedding_data: Union[List[float], List[int]] + + if ("embeddingsByType" in _parsed_response and + "binary" in _parsed_response["embeddingsByType"]): + # Use binary data if available (for encoding_format="base64") + embedding_data = _parsed_response["embeddingsByType"]["binary"] + elif ("embeddingsByType" in _parsed_response and + "float" in _parsed_response["embeddingsByType"]): + # Use float data from embeddingsByType + embedding_data = _parsed_response["embeddingsByType"]["float"] + elif "embedding" in _parsed_response: + # Fallback to legacy embedding field + embedding_data = _parsed_response["embedding"] + else: + raise ValueError(f"No embedding data found in response: {response}") + transformed_responses.append( Embedding( - embedding=_parsed_response["embedding"], + embedding=embedding_data, index=index, object="embedding", ) diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index 0824905f511..d4dd716a1f4 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -4,12 +4,13 @@ Handles embedding calls to Bedrock's `/invoke` endpoint import copy import json -from typing import Any, Callable, List, Optional, Tuple, Union import urllib.parse +from typing import Any, Callable, List, Optional, Tuple, Union, get_args import httpx import litellm +from litellm.constants import BEDROCK_EMBEDDING_PROVIDERS_LITERAL from litellm.llms.cohere.embed.handler import embedding as cohere_embedding from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, @@ -18,7 +19,11 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, ) from litellm.secret_managers.main import get_secret -from litellm.types.llms.bedrock import AmazonEmbeddingRequest, CohereEmbeddingRequest +from litellm.types.llms.bedrock import ( + AmazonEmbeddingRequest, + CohereEmbeddingRequest, + TwelveLabsMarengoEmbeddingRequest, +) from litellm.types.utils import EmbeddingResponse from ..base_aws_llm import BaseAWSLLM @@ -29,6 +34,7 @@ from .amazon_titan_multimodal_transformation import ( ) from .amazon_titan_v2_transformation import AmazonTitanV2Config from .cohere_transformation import BedrockCohereEmbeddingConfig +from .twelvelabs_marengo_transformation import TwelveLabsMarengoEmbeddingConfig class BedrockEmbedding(BaseAWSLLM): @@ -145,6 +151,44 @@ class BedrockEmbedding(BaseAWSLLM): raise BedrockError(status_code=408, message="Timeout error occurred.") return response.json() + + def _transform_response( + self, response_list: List[dict], model: str, provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL + ) -> Optional[EmbeddingResponse]: + """ + Transforms the response from the Bedrock embedding provider to the OpenAI format. + """ + returned_response: Optional[EmbeddingResponse] = None + if model == "amazon.titan-embed-image-v1": + returned_response = ( + AmazonTitanMultimodalEmbeddingG1Config()._transform_response( + response_list=response_list, model=model + ) + ) + elif model == "amazon.titan-embed-text-v1": + returned_response = AmazonTitanG1Config()._transform_response( + response_list=response_list, model=model + ) + elif model == "amazon.titan-embed-text-v2:0": + returned_response = AmazonTitanV2Config()._transform_response( + response_list=response_list, model=model + ) + elif provider == "twelvelabs": + returned_response = TwelveLabsMarengoEmbeddingConfig()._transform_response( + response_list=response_list, model=model + ) + + + ########################################################## + # Validate returned response + ########################################################## + if returned_response is None: + raise Exception( + "Unable to map model response to known provider format. model={}".format( + model + ) + ) + return returned_response def _single_func_embeddings( self, @@ -157,6 +201,7 @@ class BedrockEmbedding(BaseAWSLLM): aws_region_name: str, model: str, logging_obj: Any, + provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL, api_key: Optional[str] = None, ): responses: List[dict] = [] @@ -164,16 +209,16 @@ class BedrockEmbedding(BaseAWSLLM): headers = {"Content-Type": "application/json"} if extra_headers is not None: headers = {"Content-Type": "application/json", **extra_headers} - + prepped = self.get_request_headers( - credentials=credentials, - aws_region_name=aws_region_name, - extra_headers=extra_headers, - endpoint_url=endpoint_url, - data=json.dumps(data), - headers=headers, - api_key=api_key - ) + credentials=credentials, + aws_region_name=aws_region_name, + extra_headers=extra_headers, + endpoint_url=endpoint_url, + data=json.dumps(data), + headers=headers, + api_key=api_key, + ) ## LOGGING logging_obj.pre_call( @@ -203,32 +248,9 @@ class BedrockEmbedding(BaseAWSLLM): responses.append(response) - returned_response: Optional[EmbeddingResponse] = None - - ## TRANSFORM RESPONSE ## - if model == "amazon.titan-embed-image-v1": - returned_response = ( - AmazonTitanMultimodalEmbeddingG1Config()._transform_response( - response_list=responses, model=model - ) - ) - elif model == "amazon.titan-embed-text-v1": - returned_response = AmazonTitanG1Config()._transform_response( - response_list=responses, model=model - ) - elif model == "amazon.titan-embed-text-v2:0": - returned_response = AmazonTitanV2Config()._transform_response( - response_list=responses, model=model - ) - - if returned_response is None: - raise Exception( - "Unable to map model response to known provider format. model={}".format( - model - ) - ) - - return returned_response + return self._transform_response( + response_list=responses, model=model, provider=provider + ) async def _async_single_func_embeddings( self, @@ -241,6 +263,7 @@ class BedrockEmbedding(BaseAWSLLM): aws_region_name: str, model: str, logging_obj: Any, + provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL, api_key: Optional[str] = None, ): responses: List[dict] = [] @@ -248,16 +271,16 @@ class BedrockEmbedding(BaseAWSLLM): headers = {"Content-Type": "application/json"} if extra_headers is not None: headers = {"Content-Type": "application/json", **extra_headers} - + prepped = self.get_request_headers( - credentials=credentials, - aws_region_name=aws_region_name, - extra_headers=extra_headers, - endpoint_url=endpoint_url, - data=json.dumps(data), - headers=headers, - api_key=api_key, - ) + credentials=credentials, + aws_region_name=aws_region_name, + extra_headers=extra_headers, + endpoint_url=endpoint_url, + data=json.dumps(data), + headers=headers, + api_key=api_key, + ) ## LOGGING logging_obj.pre_call( @@ -286,33 +309,10 @@ class BedrockEmbedding(BaseAWSLLM): ) responses.append(response) - - returned_response: Optional[EmbeddingResponse] = None - ## TRANSFORM RESPONSE ## - if model == "amazon.titan-embed-image-v1": - returned_response = ( - AmazonTitanMultimodalEmbeddingG1Config()._transform_response( - response_list=responses, model=model - ) - ) - elif model == "amazon.titan-embed-text-v1": - returned_response = AmazonTitanG1Config()._transform_response( - response_list=responses, model=model - ) - elif model == "amazon.titan-embed-text-v2:0": - returned_response = AmazonTitanV2Config()._transform_response( - response_list=responses, model=model - ) - - if returned_response is None: - raise Exception( - "Unable to map model response to known provider format. model={}".format( - model - ) - ) - - return returned_response + return self._transform_response( + response_list=responses, model=model, provider=provider + ) def embeddings( self, @@ -336,7 +336,7 @@ class BedrockEmbedding(BaseAWSLLM): ### TRANSFORMATION ### unencoded_model_id = ( optional_params.pop("model_id", None) or model - ) # default to model if not passed + ) # default to model if not passed modelId = urllib.parse.quote(unencoded_model_id, safe="") aws_region_name = self._get_aws_region_name( optional_params=optional_params, @@ -344,7 +344,12 @@ class BedrockEmbedding(BaseAWSLLM): model_id=unencoded_model_id, ) - provider = model.split(".")[0] + provider = self.get_bedrock_embedding_provider(model) + if provider is None: + raise Exception( + f"Unable to determine bedrock embedding provider for model: {model}. " + f"Supported providers: {list(get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL))}" + ) inference_params = copy.deepcopy(optional_params) inference_params = { k: v @@ -394,6 +399,15 @@ class BedrockEmbedding(BaseAWSLLM): ) ) batch_data.append(transformed_request) + elif provider == "twelvelabs": + batch_data = [] + for i in input: + twelvelabs_request: ( + TwelveLabsMarengoEmbeddingRequest + ) = TwelveLabsMarengoEmbeddingConfig()._transform_request( + input=i, inference_params=inference_params + ) + batch_data.append(twelvelabs_request) ### SET RUNTIME ENDPOINT ### endpoint_url, proxy_endpoint_url = self.get_runtime_endpoint( @@ -422,8 +436,9 @@ class BedrockEmbedding(BaseAWSLLM): model=model, logging_obj=logging_obj, api_key=api_key, + provider=provider, ) - return self._single_func_embeddings( + returned_response = self._single_func_embeddings( client=( client if client is not None and isinstance(client, HTTPHandler) @@ -438,14 +453,18 @@ class BedrockEmbedding(BaseAWSLLM): model=model, logging_obj=logging_obj, api_key=api_key, + provider=provider, ) + if returned_response is None: + raise Exception("Unable to map Bedrock request to provider") + return returned_response elif data is None: raise Exception("Unable to map Bedrock request to provider") headers = {"Content-Type": "application/json"} if extra_headers is not None: headers = {"Content-Type": "application/json", **extra_headers} - + prepped = self.get_request_headers( credentials=credentials, aws_region_name=aws_region_name, diff --git a/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py b/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py new file mode 100644 index 00000000000..fdad8a65043 --- /dev/null +++ b/litellm/llms/bedrock/embed/twelvelabs_marengo_transformation.py @@ -0,0 +1,140 @@ +""" +Transformation logic from OpenAI /v1/embeddings format to Bedrock TwelveLabs Marengo /invoke format. + +Why separate file? Make it easy to see how transformation works + +Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-marengo.html +""" + +from typing import List + +from litellm.types.llms.bedrock import ( + TwelveLabsMarengoEmbeddingRequest, +) +from litellm.types.utils import Embedding, EmbeddingResponse, Usage +from litellm.utils import get_base64_str, is_base64_encoded + + +class TwelveLabsMarengoEmbeddingConfig: + """ + Reference - https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-marengo.html + + Supports text and image inputs for Phase 1. + Video and audio support will be added in Phase 2. + """ + + def __init__(self) -> None: + pass + + def get_supported_openai_params(self) -> List[str]: + return ["encoding_format", "textTruncate", "embeddingOption"] + + def map_openai_params( + self, non_default_params: dict, optional_params: dict + ) -> dict: + for k, v in non_default_params.items(): + if k == "encoding_format": + # TwelveLabs doesn't have encoding_format, but we can map it to embeddingOption + if v == "float": + optional_params["embeddingOption"] = ["visual-text", "visual-image"] + elif k == "textTruncate": + optional_params["textTruncate"] = v + elif k == "embeddingOption": + optional_params["embeddingOption"] = v + return optional_params + + def _transform_request( + self, input: str, inference_params: dict + ) -> TwelveLabsMarengoEmbeddingRequest: + """ + Transform OpenAI-style input to TwelveLabs Marengo format. + Phase 1: Supports text and image inputs only. + """ + # Check if input is base64 encoded image + is_encoded = is_base64_encoded(input) + + if is_encoded: + # Image input + b64_str = get_base64_str(input) + transformed_request = TwelveLabsMarengoEmbeddingRequest( + inputType="image", mediaSource={"base64String": b64_str} + ) + else: + # Text input + transformed_request = TwelveLabsMarengoEmbeddingRequest( + inputType="text", inputText=input + ) + + # Set default textTruncate if not specified + if "textTruncate" not in inference_params: + transformed_request["textTruncate"] = "end" + + # Apply any additional inference parameters + for k, v in inference_params.items(): + if k not in [ + "inputType", + "inputText", + "mediaSource", + ]: # Don't override core fields + transformed_request[k] = v # type: ignore + + return transformed_request + + def _transform_response( + self, response_list: List[dict], model: str + ) -> EmbeddingResponse: + """ + Transform TwelveLabs response to OpenAI format. + Handles the actual TwelveLabs response format: {"data": [{"embedding": [...]}]} + """ + embeddings: List[Embedding] = [] + total_tokens = 0 + + for response in response_list: + # TwelveLabs response format has a "data" field containing the embeddings + if "data" in response and isinstance(response["data"], list): + for item in response["data"]: + if "embedding" in item: + # Single embedding response + embedding = Embedding( + embedding=item["embedding"], + index=len(embeddings), + object="embedding", + ) + embeddings.append(embedding) + + # Estimate token count (rough approximation) + if "inputTextTokenCount" in item: + total_tokens += item["inputTextTokenCount"] + else: + # Rough estimate: 1 token per 4 characters for text, or use embedding size + total_tokens += len(item["embedding"]) // 4 + elif "embedding" in response: + # Direct embedding response (fallback for other formats) + embedding = Embedding( + embedding=response["embedding"], + index=len(embeddings), + object="embedding", + ) + embeddings.append(embedding) + + # Estimate token count (rough approximation) + if "inputTextTokenCount" in response: + total_tokens += response["inputTextTokenCount"] + else: + # Rough estimate: 1 token per 4 characters for text + total_tokens += len(response.get("inputText", "")) // 4 + elif "embeddings" in response: + # Multiple embeddings response (from video/audio) + for i, emb in enumerate(response["embeddings"]): + embedding = Embedding( + embedding=emb["embedding"], + index=len(embeddings), + object="embedding", + ) + embeddings.append(embedding) + total_tokens += len(emb["embedding"]) // 4 # Rough estimate + + usage = Usage(prompt_tokens=total_tokens, total_tokens=total_tokens) + + return EmbeddingResponse(data=embeddings, model=model, usage=usage) diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index 83bbad7e1e8..d493deddb62 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -6,6 +6,8 @@ from typing import Any, Dict, List, Optional, Tuple, Union from httpx import Headers, Response +from litellm._logging import verbose_logger +from litellm.files.utils import FilesAPIUtils 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 ( @@ -21,6 +23,7 @@ from litellm.types.llms.openai import ( PathLike, ) from litellm.types.utils import ExtractedFileData, LlmProviders +from litellm.utils import get_llm_provider from ..base_aws_llm import BaseAWSLLM from ..common_utils import BedrockError @@ -111,6 +114,10 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): # Remove bedrock/ prefix if present if _model.startswith("bedrock/"): _model = _model[8:] + + # Replace colons with hyphens for Bedrock S3 URI compliance + _model = _model.replace(":", "-") + object_name = f"litellm-bedrock-files-{_model}-{uuid.uuid4()}.jsonl" return object_name @@ -191,24 +198,6 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): ) -> 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, @@ -218,11 +207,12 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ Transform OpenAI request body to Bedrock-compatible modelInput parameters using existing transformation logic """ + from litellm.types.utils import LlmProviders _model = openai_request_body.get("model", "") messages = openai_request_body.get("messages", []) # Use existing Anthropic transformation logic for Anthropic models - if provider == "anthropic": + if provider == LlmProviders.ANTHROPIC: from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import ( AmazonAnthropicClaudeConfig, ) @@ -231,16 +221,22 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): # 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"]} + mapped_params = anthropic_config.map_openai_params( + non_default_params={}, + optional_params=optional_params, + model=_model, + drop_params=False + ) # Transform using existing Anthropic logic bedrock_params = anthropic_config.transform_request( model=_model, messages=messages, - optional_params=optional_params, + optional_params=mapped_params, litellm_params={}, headers={} ) - + return bedrock_params else: # For other providers, use basic mapping @@ -278,9 +274,17 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): # Extract the request body from OpenAI format openai_body = _openai_jsonl_content.get("body", {}) model = openai_body.get("model", "") + + try: + model, _, _, _ = get_llm_provider( + model=model, + custom_llm_provider=None, + ) + except Exception as e: + verbose_logger.exception(f"litellm.llms.bedrock.files.transformation.py::_transform_openai_jsonl_content_to_bedrock_jsonl_content() - Error inferring custom_llm_provider - {str(e)}") # Determine provider from model name - provider = self._get_bedrock_provider_from_model(model) + provider = self.get_bedrock_invoke_provider(model) # Transform to Bedrock modelInput format model_input = self._map_openai_to_bedrock_params( @@ -315,11 +319,13 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): extracted_file_data = extract_file_data(file_data) extracted_file_data_content = extracted_file_data.get("content") + if extracted_file_data_content is None: + raise ValueError("file content is required") + # 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 + if FilesAPIUtils.is_batch_jsonl_file( + create_file_data=create_file_data, + extracted_file_data=extracted_file_data, ): ## Transform JSONL content to Bedrock format original_file_content = self._get_content_from_openai_file( @@ -357,6 +363,8 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): api_base=api_base, optional_params=optional_params, ) + + litellm_params["upload_url"] = api_base # Return a dict that tells the HTTP handler exactly what to do return { @@ -440,6 +448,56 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): return dict(aws_request.headers), signed_body + def _convert_https_url_to_s3_uri(self, https_url: str) -> tuple[str, str]: + """ + Convert HTTPS S3 URL to s3:// URI format. + + Args: + https_url: HTTPS S3 URL (e.g., "https://s3.us-west-2.amazonaws.com/bucket/key") + + Returns: + Tuple of (s3_uri, filename) + + Example: + Input: "https://s3.us-west-2.amazonaws.com/litellm-proxy/file.jsonl" + Output: ("s3://litellm-proxy/file.jsonl", "file.jsonl") + """ + import re + + # Match HTTPS S3 URL patterns + # Pattern 1: https://s3.region.amazonaws.com/bucket/key + # Pattern 2: https://bucket.s3.region.amazonaws.com/key + + pattern1 = r"https://s3\.([^.]+)\.amazonaws\.com/([^/]+)/(.+)" + pattern2 = r"https://([^.]+)\.s3\.([^.]+)\.amazonaws\.com/(.+)" + + match1 = re.match(pattern1, https_url) + match2 = re.match(pattern2, https_url) + + if match1: + # Pattern: https://s3.region.amazonaws.com/bucket/key + region, bucket, key = match1.groups() + s3_uri = f"s3://{bucket}/{key}" + elif match2: + # Pattern: https://bucket.s3.region.amazonaws.com/key + bucket, region, key = match2.groups() + s3_uri = f"s3://{bucket}/{key}" + else: + # Fallback: try to extract bucket and key from URL path + from urllib.parse import urlparse + parsed = urlparse(https_url) + path_parts = parsed.path.lstrip('/').split('/', 1) + if len(path_parts) >= 2: + bucket, key = path_parts[0], path_parts[1] + s3_uri = f"s3://{bucket}/{key}" + else: + raise ValueError(f"Unable to parse S3 URL: {https_url}") + + # Extract filename from key + filename = key.split("/")[-1] if "/" in key else key + + return s3_uri, filename + def transform_create_file_response( self, model: Optional[str], @@ -452,21 +510,18 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ # 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 - + # Use the actual upload URL that was used for the S3 upload + upload_url = litellm_params.get("upload_url") + file_id: str = "" + filename: str = "" + if upload_url: + # Convert HTTPS S3 URL to s3:// URI format + file_id, filename = self._convert_https_url_to_s3_uri(upload_url) + return OpenAIFileObject( purpose="batch", # Default purpose for Bedrock files id=file_id, diff --git a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py b/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py index 3ef7a40e9a9..cd33e62af16 100644 --- a/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py +++ b/litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py @@ -7,12 +7,12 @@ from litellm.types.llms.bedrock import ( AmazonNovaCanvasColorGuidedGenerationParams, AmazonNovaCanvasColorGuidedRequest, AmazonNovaCanvasImageGenerationConfig, + AmazonNovaCanvasInpaintingParams, + AmazonNovaCanvasInpaintingRequest, AmazonNovaCanvasRequestBase, AmazonNovaCanvasTextToImageParams, AmazonNovaCanvasTextToImageRequest, AmazonNovaCanvasTextToImageResponse, - AmazonNovaCanvasInpaintingParams, - AmazonNovaCanvasInpaintingRequest, ) from litellm.types.utils import ImageResponse @@ -67,6 +67,11 @@ class AmazonNovaCanvasConfig: """ task_type = optional_params.pop("taskType", "TEXT_IMAGE") image_generation_config = optional_params.pop("imageGenerationConfig", {}) + + # Extract model_id parameter to prevent "extraneous key" error from Bedrock API + # Following the same pattern as chat completions and embeddings + unencoded_model_id = optional_params.pop("model_id", None) # noqa: F841 + image_generation_config = {**image_generation_config, **optional_params} if task_type == "TEXT_IMAGE": text_to_image_params: Dict[str, Any] = image_generation_config.pop( diff --git a/litellm/llms/bedrock/image/image_handler.py b/litellm/llms/bedrock/image/image_handler.py index 55d94675d14..0103f190d36 100644 --- a/litellm/llms/bedrock/image/image_handler.py +++ b/litellm/llms/bedrock/image/image_handler.py @@ -233,7 +233,17 @@ class BedrockImageGeneration(BaseAWSLLM): Returns: dict: The request body to use for the Bedrock Image Generation API """ - provider = model.split(".")[0] + # Use the existing ARN-aware provider detection method + bedrock_provider = self.get_bedrock_invoke_provider(model) + + if bedrock_provider == "amazon" or bedrock_provider == "nova": + # Handle Amazon Nova Canvas models + provider = "amazon" + elif bedrock_provider == "stability": + provider = "stability" + else: + # Fallback to original logic for backward compatibility + provider = model.split(".")[0] inference_params = copy.deepcopy(optional_params) inference_params.pop( "user", None diff --git a/litellm/llms/cohere/completion/handler.py b/litellm/llms/cohere/completion/handler.py deleted file mode 100644 index 6a77951146f..00000000000 --- a/litellm/llms/cohere/completion/handler.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Cohere /generate API - uses `llm_http_handler.py` to make httpx requests - -Request/Response transformation is handled in `transformation.py` -""" diff --git a/litellm/llms/cohere/completion/transformation.py b/litellm/llms/cohere/completion/transformation.py deleted file mode 100644 index f96ef89d3c5..00000000000 --- a/litellm/llms/cohere/completion/transformation.py +++ /dev/null @@ -1,265 +0,0 @@ -import time -from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union - -import httpx - -import litellm -from litellm.litellm_core_utils.prompt_templates.common_utils import ( - convert_content_list_to_str, -) -from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException -from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import Choices, Message, ModelResponse, Usage - -from ..common_utils import CohereError -from ..common_utils import ModelResponseIterator as CohereModelResponseIterator -from ..common_utils import validate_environment as cohere_validate_environment - -if TYPE_CHECKING: - from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj - - LiteLLMLoggingObj = _LiteLLMLoggingObj -else: - LiteLLMLoggingObj = Any - - -class CohereTextConfig(BaseConfig): - """ - Reference: https://docs.cohere.com/reference/generate - - The class `CohereConfig` provides configuration for the Cohere's API interface. Below are the parameters: - - - `num_generations` (integer): Maximum number of generations returned. Default is 1, with a minimum value of 1 and a maximum value of 5. - - - `max_tokens` (integer): Maximum number of tokens the model will generate as part of the response. Default value is 20. - - - `truncate` (string): Specifies how the API handles inputs longer than maximum token length. Options include NONE, START, END. Default is END. - - - `temperature` (number): A non-negative float controlling the randomness in generation. Lower temperatures result in less random generations. Default is 0.75. - - - `preset` (string): Identifier of a custom preset, a combination of parameters such as prompt, temperature etc. - - - `end_sequences` (array of strings): The generated text gets cut at the beginning of the earliest occurrence of an end sequence, which will be excluded from the text. - - - `stop_sequences` (array of strings): The generated text gets cut at the end of the earliest occurrence of a stop sequence, which will be included in the text. - - - `k` (integer): Limits generation at each step to top `k` most likely tokens. Default is 0. - - - `p` (number): Limits generation at each step to most likely tokens with total probability mass of `p`. Default is 0. - - - `frequency_penalty` (number): Reduces repetitiveness of generated tokens. Higher values apply stronger penalties to previously occurred tokens. - - - `presence_penalty` (number): Reduces repetitiveness of generated tokens. Similar to frequency_penalty, but this penalty applies equally to all tokens that have already appeared. - - - `return_likelihoods` (string): Specifies how and if token likelihoods are returned with the response. Options include GENERATION, ALL and NONE. - - - `logit_bias` (object): Used to prevent the model from generating unwanted tokens or to incentivize it to include desired tokens. e.g. {"hello_world": 1233} - """ - - num_generations: Optional[int] = None - max_tokens: Optional[int] = None - truncate: Optional[str] = None - temperature: Optional[int] = None - preset: Optional[str] = None - end_sequences: Optional[list] = None - stop_sequences: Optional[list] = None - k: Optional[int] = None - p: Optional[int] = None - frequency_penalty: Optional[int] = None - presence_penalty: Optional[int] = None - return_likelihoods: Optional[str] = None - logit_bias: Optional[dict] = None - - def __init__( - self, - num_generations: Optional[int] = None, - max_tokens: Optional[int] = None, - truncate: Optional[str] = None, - temperature: Optional[int] = None, - preset: Optional[str] = None, - end_sequences: Optional[list] = None, - stop_sequences: Optional[list] = None, - k: Optional[int] = None, - p: Optional[int] = None, - frequency_penalty: Optional[int] = None, - presence_penalty: Optional[int] = None, - return_likelihoods: Optional[str] = None, - logit_bias: Optional[dict] = 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 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: - return cohere_validate_environment( - headers=headers, - model=model, - messages=messages, - optional_params=optional_params, - api_key=api_key, - ) - - def get_error_class( - self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] - ) -> BaseLLMException: - return CohereError(status_code=status_code, message=error_message) - - def get_supported_openai_params(self, model: str) -> List: - return [ - "stream", - "temperature", - "max_tokens", - "logit_bias", - "top_p", - "frequency_penalty", - "presence_penalty", - "stop", - "n", - "extra_headers", - ] - - def map_openai_params( - self, - non_default_params: dict, - optional_params: dict, - model: str, - drop_params: bool, - ) -> dict: - for param, value in non_default_params.items(): - if param == "stream": - optional_params["stream"] = value - elif param == "temperature": - optional_params["temperature"] = value - elif param == "max_tokens": - optional_params["max_tokens"] = value - elif param == "n": - optional_params["num_generations"] = value - elif param == "logit_bias": - optional_params["logit_bias"] = value - elif param == "top_p": - optional_params["p"] = value - elif param == "frequency_penalty": - optional_params["frequency_penalty"] = value - elif param == "presence_penalty": - optional_params["presence_penalty"] = value - elif param == "stop": - optional_params["stop_sequences"] = value - return optional_params - - def transform_request( - self, - model: str, - messages: List[AllMessageValues], - optional_params: dict, - litellm_params: dict, - headers: dict, - ) -> dict: - prompt = " ".join( - convert_content_list_to_str(message=message) for message in messages - ) - - ## Load Config - config = litellm.CohereConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - - ## Handle Tool Calling - if "tools" in optional_params: - _is_function_call = True - tool_calling_system_prompt = self._construct_cohere_tool_for_completion_api( - tools=optional_params["tools"] - ) - optional_params["tools"] = tool_calling_system_prompt - - data = { - "model": model, - "prompt": prompt, - **optional_params, - } - - return data - - def transform_response( - self, - model: str, - raw_response: httpx.Response, - model_response: ModelResponse, - logging_obj: LiteLLMLoggingObj, - request_data: dict, - messages: List[AllMessageValues], - optional_params: dict, - litellm_params: dict, - encoding: Any, - api_key: Optional[str] = None, - json_mode: Optional[bool] = None, - ) -> ModelResponse: - prompt = " ".join( - convert_content_list_to_str(message=message) for message in messages - ) - completion_response = raw_response.json() - choices_list = [] - for idx, item in enumerate(completion_response["generations"]): - if len(item["text"]) > 0: - message_obj = Message(content=item["text"]) - else: - message_obj = Message(content=None) - choice_obj = Choices( - finish_reason=item["finish_reason"], - index=idx + 1, - message=message_obj, - ) - choices_list.append(choice_obj) - model_response.choices = choices_list # type: ignore - - ## CALCULATING USAGE - prompt_tokens = len(encoding.encode(prompt)) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) - - model_response.created = int(time.time()) - model_response.model = model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - setattr(model_response, "usage", usage) - return model_response - - def _construct_cohere_tool_for_completion_api( - self, - tools: Optional[List] = None, - ) -> dict: - if tools is None: - tools = [] - return {"tools": tools} - - def get_model_response_iterator( - self, - streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], - sync_stream: bool, - json_mode: Optional[bool] = False, - ): - return CohereModelResponseIterator( - streaming_response=streaming_response, - sync_stream=sync_stream, - json_mode=json_mode, - ) diff --git a/litellm/llms/compactifai/__init__.py b/litellm/llms/compactifai/__init__.py new file mode 100644 index 00000000000..16b0c04cdab --- /dev/null +++ b/litellm/llms/compactifai/__init__.py @@ -0,0 +1 @@ +# CompactifAI provider for LiteLLM \ No newline at end of file diff --git a/litellm/llms/compactifai/chat/__init__.py b/litellm/llms/compactifai/chat/__init__.py new file mode 100644 index 00000000000..d1a4463166b --- /dev/null +++ b/litellm/llms/compactifai/chat/__init__.py @@ -0,0 +1 @@ +# CompactifAI chat completions \ No newline at end of file diff --git a/litellm/llms/compactifai/chat/transformation.py b/litellm/llms/compactifai/chat/transformation.py new file mode 100644 index 00000000000..5cb8cd9a4ab --- /dev/null +++ b/litellm/llms/compactifai/chat/transformation.py @@ -0,0 +1,100 @@ +""" +CompactifAI chat completion transformation +""" + +from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union + +import httpx + +from litellm.secret_managers.main import get_secret_str +from litellm.types.utils import ModelResponse +from litellm.llms.openai.common_utils import OpenAIError +from litellm.llms.base_llm.chat.transformation import BaseLLMException + +from ...openai.chat.gpt_transformation import OpenAIGPTConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class CompactifAIChatConfig(OpenAIGPTConfig): + """ + Configuration class for CompactifAI chat completions. + Since CompactifAI is OpenAI-compatible, we extend OpenAIGPTConfig. + """ + + def _get_openai_compatible_provider_info( + self, + api_base: Optional[str], + api_key: Optional[str], + ) -> Tuple[Optional[str], Optional[str]]: + """ + Get API base and key for CompactifAI provider. + """ + api_base = api_base or "https://api.compactif.ai/v1" + dynamic_api_key = api_key or get_secret_str("COMPACTIFAI_API_KEY") or "" + return api_base, dynamic_api_key + + def transform_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ModelResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + messages: List, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ModelResponse: + """ + Transform CompactifAI response to LiteLLM format. + Since CompactifAI is OpenAI-compatible, we can use the standard OpenAI transformation. + """ + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=raw_response.text, + additional_args={"complete_input_dict": request_data}, + ) + + ## RESPONSE OBJECT + response_json = raw_response.json() + + # Handle JSON mode if needed + if json_mode: + for choice in response_json["choices"]: + message = choice.get("message") + if message and message.get("tool_calls"): + # Convert tool calls to content for JSON mode + tool_calls = message.get("tool_calls", []) + if len(tool_calls) == 1: + message["content"] = tool_calls[0]["function"].get("arguments", "") + message["tool_calls"] = None + + returned_response = ModelResponse(**response_json) + + # Set model name with provider prefix + returned_response.model = f"compactifai/{model}" + + return returned_response + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + """ + Get the appropriate error class for CompactifAI errors. + Since CompactifAI is OpenAI-compatible, we use OpenAI error handling. + """ + return OpenAIError( + status_code=status_code, + message=error_message, + headers=headers, + ) \ No newline at end of file diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 13133a56aad..dc64fea1a33 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -118,7 +118,6 @@ class BaseLLMHTTPHandler: response: Optional[httpx.Response] = None for i in range(max(max_retry_on_unprocessable_entity_error, 1)): try: - response = await async_httpx_client.post( url=api_base, headers=headers, @@ -2221,7 +2220,9 @@ class BaseLLMHTTPHandler: 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())( + upload_response = getattr( + sync_httpx_client, transformed_request["method"].lower() + )( url=transformed_request["url"], headers=transformed_request["headers"], data=transformed_request["data"], @@ -2233,8 +2234,8 @@ class BaseLLMHTTPHandler: # 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') - + 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": @@ -2282,12 +2283,16 @@ class BaseLLMHTTPHandler: e=e, provider_config=provider_config, ) + + # Store the upload URL in litellm_params for the transformation method + litellm_params_with_url = dict(litellm_params) + litellm_params_with_url["upload_url"] = api_base return provider_config.transform_create_file_response( model=None, raw_response=upload_response, logging_obj=logging_obj, - litellm_params=litellm_params, + litellm_params=litellm_params_with_url, ) async def async_create_file( @@ -2310,7 +2315,7 @@ class BaseLLMHTTPHandler: ) else: async_httpx_client = client - + ######################################################### # Debug Logging ######################################################### @@ -2326,7 +2331,9 @@ class BaseLLMHTTPHandler: 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())( + upload_response = await getattr( + async_httpx_client, transformed_request["method"].lower() + )( url=transformed_request["url"], headers=transformed_request["headers"], data=transformed_request["data"], @@ -2338,8 +2345,8 @@ class BaseLLMHTTPHandler: # 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') - + 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": @@ -2408,15 +2415,19 @@ class BaseLLMHTTPHandler: _is_async: bool = False, client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, + model: Optional[str] = None, ) -> Union["LiteLLMBatch", Coroutine[Any, Any, "LiteLLMBatch"]]: """ Creates a batch using provider-specific batch creation process """ # get config from model, custom llm provider + if model is None: + raise ValueError("model is required for create_batch") + headers = provider_config.validate_environment( api_key=api_key, headers=headers, - model="", + model=model, messages=[], optional_params={}, litellm_params=litellm_params, @@ -2425,7 +2436,7 @@ class BaseLLMHTTPHandler: api_base = provider_config.get_complete_batch_url( api_base=api_base, api_key=api_key, - model="", + model=model, optional_params={}, litellm_params=litellm_params, data=create_batch_data, @@ -2435,7 +2446,7 @@ class BaseLLMHTTPHandler: # Get the transformed request data transformed_request = provider_config.transform_create_batch_request( - model="", + model=model, create_batch_data=create_batch_data, litellm_params=litellm_params, optional_params={}, @@ -2460,9 +2471,14 @@ class BaseLLMHTTPHandler: sync_httpx_client = client try: - if isinstance(transformed_request, dict) and "method" in transformed_request: + 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())( + batch_response = getattr( + sync_httpx_client, transformed_request["method"].lower() + )( url=transformed_request["url"], headers=transformed_request["headers"], data=transformed_request["data"], @@ -2492,15 +2508,107 @@ class BaseLLMHTTPHandler: ) # Store original request for response transformation - litellm_params_with_request = {**litellm_params, "original_batch_request": create_batch_data} - + litellm_params_with_request = { + **litellm_params, + "original_batch_request": create_batch_data, + } + return provider_config.transform_create_batch_response( - model=None, + model=model, raw_response=batch_response, logging_obj=logging_obj, litellm_params=litellm_params_with_request, ) + def retrieve_batch( + self, + batch_id: str, + 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, + model: Optional[str] = None, + ) -> Union["LiteLLMBatch", Coroutine[Any, Any, "LiteLLMBatch"]]: + """ + Retrieve a batch using provider-specific configuration. + """ + # Transform the request using provider config + transformed_request = provider_config.transform_retrieve_batch_request( + batch_id=batch_id, + optional_params=litellm_params, + litellm_params=litellm_params, + ) + + if _is_async: + return self.async_retrieve_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, + batch_id=batch_id, + model=model, + ) + + 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) + method = transformed_request["method"].lower() + request_kwargs = { + "url": transformed_request["url"], + "headers": transformed_request["headers"], + } + + # Only add data for non-GET requests + if method != "get" and transformed_request.get("data") is not None: + request_kwargs["data"] = transformed_request["data"] + + batch_response = getattr(sync_httpx_client, method)(**request_kwargs) + elif isinstance(transformed_request, dict) and api_base: + # For other providers that use JSON requests + batch_response = sync_httpx_client.get( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + params=transformed_request, + ) + else: + # Handle other request types if needed + if not api_base: + raise ValueError("api_base is required for non-pre-signed requests") + batch_response = sync_httpx_client.get( + url=api_base, + headers=headers, + ) + except Exception as e: + verbose_logger.exception(f"Error retrieving batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + return provider_config.transform_retrieve_batch_response( + model=model, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params, + ) + async def async_create_batch( self, transformed_request: Union[bytes, str, dict], @@ -2512,6 +2620,7 @@ class BaseLLMHTTPHandler: client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, create_batch_data: Optional["CreateBatchRequest"] = None, + model: Optional[str] = None, ): """ Async version of create_batch @@ -2522,7 +2631,7 @@ class BaseLLMHTTPHandler: ) else: async_httpx_client = client - + ######################################################### # Debug Logging ######################################################### @@ -2537,9 +2646,14 @@ class BaseLLMHTTPHandler: ) try: - if isinstance(transformed_request, dict) and "method" in transformed_request: + 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())( + batch_response = await getattr( + async_httpx_client, transformed_request["method"].lower() + )( url=transformed_request["url"], headers=transformed_request["headers"], data=transformed_request["data"], @@ -2569,15 +2683,254 @@ class BaseLLMHTTPHandler: ) # Store original request for response transformation (for async version) - litellm_params_with_request = {**litellm_params, "original_batch_request": create_batch_data or {}} - + litellm_params_with_request = { + **litellm_params, + "original_batch_request": create_batch_data or {}, + } + return provider_config.transform_create_batch_response( - model=None, + model=model, raw_response=batch_response, logging_obj=logging_obj, litellm_params=litellm_params_with_request, ) + async def async_retrieve_batch( + self, + transformed_request: Union[bytes, str, dict], + litellm_params: dict, + provider_config: "BaseBatchesConfig", + headers: dict, + api_base: Optional[str], + logging_obj: "LiteLLMLoggingObj", + client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + batch_id: Optional[str] = None, + model: Optional[str] = None, + ): + """ + Async version of retrieve_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, + "batch_id": batch_id, + }, + ) + + try: + if ( + isinstance(transformed_request, dict) + and "method" in transformed_request + ): + # Handle pre-signed requests (e.g., from Bedrock with AWS auth) + method = transformed_request["method"].lower() + request_kwargs = { + "url": transformed_request["url"], + "headers": transformed_request["headers"], + } + + # Only add data for non-GET requests + if method != "get" and transformed_request.get("data") is not None: + request_kwargs["data"] = transformed_request["data"] + + batch_response = await getattr(async_httpx_client, method)(**request_kwargs) + elif isinstance(transformed_request, dict) and api_base: + # For other providers that use JSON requests + batch_response = await async_httpx_client.get( + url=api_base, + headers={**headers, "Content-Type": "application/json"}, + params=transformed_request, + ) + else: + # Handle other request types if needed + if not api_base: + raise ValueError("api_base is required for non-pre-signed requests") + batch_response = await async_httpx_client.get( + url=api_base, + headers=headers, + ) + except Exception as e: + verbose_logger.exception(f"Error retrieving batch: {e}") + raise self._handle_error( + e=e, + provider_config=provider_config, + ) + + return provider_config.transform_retrieve_batch_response( + model=model, + raw_response=batch_response, + logging_obj=logging_obj, + litellm_params=litellm_params, + ) + + def cancel_response_api_handler( + self, + response_id: str, + responses_api_provider_config: BaseResponsesAPIConfig, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + custom_llm_provider: Optional[str], + extra_headers: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + ) -> Union[ResponsesAPIResponse, Coroutine[Any, Any, ResponsesAPIResponse]]: + """ + Async version of the responses API handler. + Uses async HTTP client to make requests. + """ + if _is_async: + return self.async_cancel_response_api_handler( + response_id=response_id, + responses_api_provider_config=responses_api_provider_config, + litellm_params=litellm_params, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + extra_headers=extra_headers, + extra_body=extra_body, + timeout=timeout, + client=client, + ) + if client is None or not isinstance(client, HTTPHandler): + sync_httpx_client = _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + else: + sync_httpx_client = client + + headers = responses_api_provider_config.validate_environment( + headers=extra_headers or {}, model="None", litellm_params=litellm_params + ) + + if extra_headers: + headers.update(extra_headers) + + api_base = responses_api_provider_config.get_complete_url( + api_base=litellm_params.api_base, + litellm_params=dict(litellm_params), + ) + + url, data = responses_api_provider_config.transform_cancel_response_api_request( + response_id=response_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + ## LOGGING + logging_obj.pre_call( + input=response_id, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = sync_httpx_client.post( + url=url, headers=headers, json=data, timeout=timeout + ) + + except Exception as e: + raise self._handle_error( + e=e, + provider_config=responses_api_provider_config, + ) + + return responses_api_provider_config.transform_cancel_response_api_response( + raw_response=response, + logging_obj=logging_obj, + ) + + async def async_cancel_response_api_handler( + self, + response_id: str, + responses_api_provider_config: BaseResponsesAPIConfig, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + custom_llm_provider: Optional[str], + extra_headers: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + _is_async: bool = False, + ) -> ResponsesAPIResponse: + """ + Async version of the cancel response API handler. + Uses async HTTP client to make requests. + """ + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + else: + async_httpx_client = client + + headers = responses_api_provider_config.validate_environment( + headers=extra_headers or {}, model="None", litellm_params=litellm_params + ) + + if extra_headers: + headers.update(extra_headers) + + api_base = responses_api_provider_config.get_complete_url( + api_base=litellm_params.api_base, + litellm_params=dict(litellm_params), + ) + + url, data = responses_api_provider_config.transform_cancel_response_api_request( + response_id=response_id, + api_base=api_base, + litellm_params=litellm_params, + headers=headers, + ) + + ## LOGGING + logging_obj.pre_call( + input=response_id, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": url, + "headers": headers, + }, + ) + + try: + response = await async_httpx_client.post( + url=url, headers=headers, json=data, timeout=timeout + ) + + except Exception as e: + raise self._handle_error( + e=e, + provider_config=responses_api_provider_config, + ) + + return responses_api_provider_config.transform_cancel_response_api_response( + raw_response=response, + logging_obj=logging_obj, + ) + def list_files(self): """ Lists all files @@ -2738,10 +3091,7 @@ class BaseLLMHTTPHandler: _is_async: bool = False, fake_stream: bool = False, litellm_metadata: Optional[Dict[str, Any]] = None, - ) -> Union[ - ImageResponse, - Coroutine[Any, Any, ImageResponse], - ]: + ) -> Union[ImageResponse, Coroutine[Any, Any, ImageResponse],]: """ Handles image edit requests. @@ -2931,10 +3281,7 @@ class BaseLLMHTTPHandler: fake_stream: bool = False, litellm_metadata: Optional[Dict[str, Any]] = None, api_key: Optional[str] = None, - ) -> Union[ - ImageResponse, - Coroutine[Any, Any, ImageResponse], - ]: + ) -> Union[ImageResponse, Coroutine[Any, Any, ImageResponse],]: """ Handles image generation requests. When _is_async=True, returns a coroutine instead of making the call directly. @@ -3168,15 +3515,16 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url, request_body = ( - vector_store_provider_config.transform_search_vector_store_request( - vector_store_id=vector_store_id, - query=query, - vector_store_search_optional_params=vector_store_search_optional_params, - api_base=api_base, - litellm_logging_obj=logging_obj, - litellm_params=dict(litellm_params), - ) + ( + url, + request_body, + ) = vector_store_provider_config.transform_search_vector_store_request( + vector_store_id=vector_store_id, + query=query, + vector_store_search_optional_params=vector_store_search_optional_params, + api_base=api_base, + litellm_logging_obj=logging_obj, + litellm_params=dict(litellm_params), ) all_optional_params: Dict[str, Any] = dict(litellm_params) all_optional_params.update(vector_store_search_optional_params or {}) @@ -3267,15 +3615,16 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url, request_body = ( - vector_store_provider_config.transform_search_vector_store_request( - vector_store_id=vector_store_id, - query=query, - vector_store_search_optional_params=vector_store_search_optional_params, - api_base=api_base, - litellm_logging_obj=logging_obj, - litellm_params=dict(litellm_params), - ) + ( + url, + request_body, + ) = vector_store_provider_config.transform_search_vector_store_request( + vector_store_id=vector_store_id, + query=query, + vector_store_search_optional_params=vector_store_search_optional_params, + api_base=api_base, + litellm_logging_obj=logging_obj, + litellm_params=dict(litellm_params), ) all_optional_params: Dict[str, Any] = dict(litellm_params) @@ -3349,11 +3698,12 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url, request_body = ( - vector_store_provider_config.transform_create_vector_store_request( - vector_store_create_optional_params=vector_store_create_optional_params, - api_base=api_base, - ) + ( + url, + request_body, + ) = vector_store_provider_config.transform_create_vector_store_request( + vector_store_create_optional_params=vector_store_create_optional_params, + api_base=api_base, ) logging_obj.pre_call( @@ -3424,11 +3774,12 @@ class BaseLLMHTTPHandler: litellm_params=dict(litellm_params), ) - url, request_body = ( - vector_store_provider_config.transform_create_vector_store_request( - vector_store_create_optional_params=vector_store_create_optional_params, - api_base=api_base, - ) + ( + url, + request_body, + ) = vector_store_provider_config.transform_create_vector_store_request( + vector_store_create_optional_params=vector_store_create_optional_params, + api_base=api_base, ) logging_obj.pre_call( @@ -3507,13 +3858,14 @@ class BaseLLMHTTPHandler: sync_httpx_client = client # Get headers and URL from the provider config - headers, api_base = ( - generate_content_provider_config.sync_get_auth_token_and_url( - api_base=litellm_params.api_base, - model=model, - litellm_params=dict(litellm_params), - stream=stream, - ) + ( + headers, + api_base, + ) = generate_content_provider_config.sync_get_auth_token_and_url( + api_base=litellm_params.api_base, + model=model, + litellm_params=dict(litellm_params), + stream=stream, ) if extra_headers: @@ -3613,13 +3965,14 @@ class BaseLLMHTTPHandler: async_httpx_client = client # Get headers and URL from the provider config - headers, api_base = ( - await generate_content_provider_config.get_auth_token_and_url( - model=model, - litellm_params=dict(litellm_params), - stream=stream, - api_base=litellm_params.api_base, - ) + ( + headers, + api_base, + ) = await generate_content_provider_config.get_auth_token_and_url( + model=model, + litellm_params=dict(litellm_params), + stream=stream, + api_base=litellm_params.api_base, ) if extra_headers: diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py index 0f4490cb3df..107eb7f5adf 100644 --- a/litellm/llms/dashscope/cost_calculator.py +++ b/litellm/llms/dashscope/cost_calculator.py @@ -1,21 +1,155 @@ """ -Cost calculator for DeepSeek Chat models. +Cost calculator for Dashscope Chat models. -Handles prompt caching scenario. +Handles tiered pricing and prompt caching scenarios. """ -from typing import Tuple +from dataclasses import dataclass +from typing import List, Optional, Tuple -from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token -from litellm.types.utils import Usage +from litellm.types.utils import ModelInfo, Usage +from litellm.utils import get_model_info + + +@dataclass +class TokenBreakdown: + """Token breakdown for cost calculation.""" + text_tokens: int + cached_tokens: int + completion_tokens: int + reasoning_tokens: int + + +def _extract_token_breakdown(usage: Usage) -> TokenBreakdown: + """Extract token counts from usage, handling cached and reasoning tokens.""" + cached_tokens = 0 + if usage.prompt_tokens_details and hasattr(usage.prompt_tokens_details, "cached_tokens"): + cached_tokens = usage.prompt_tokens_details.cached_tokens or 0 + + text_tokens = usage.prompt_tokens - cached_tokens + + reasoning_tokens = 0 + if (hasattr(usage, "completion_tokens_details") and + usage.completion_tokens_details and + hasattr(usage.completion_tokens_details, "reasoning_tokens")): + reasoning_tokens = usage.completion_tokens_details.reasoning_tokens or 0 + + completion_tokens = (usage.completion_tokens or 0) - reasoning_tokens + + return TokenBreakdown(text_tokens, cached_tokens, completion_tokens, reasoning_tokens) + + +def _calculate_tiered_cost( + tokens: int, + tiered_pricing: List[dict], + cost_key: str, + fallback_cost_key: Optional[str] = None +) -> float: + """Calculate cost using tiered pricing structure. + + Finds the appropriate tier based on token count and applies that tier's rate to all tokens. + """ + if not tiered_pricing or tokens <= 0: + return 0.0 + + # Find the appropriate tier for the token count + for tier in tiered_pricing: + tier_range = tier.get("range", []) + if len(tier_range) != 2: + continue + + range_start, range_end = tier_range + + # Check if tokens fall within this tier's range + if range_start <= tokens <= range_end: + cost_per_token = tier.get(cost_key) or tier.get(fallback_cost_key, 0) + return tokens * cost_per_token + + # If no tier matches, use the last tier (highest tier) + if tiered_pricing: + last_tier = tiered_pricing[-1] + cost_per_token = last_tier.get(cost_key) or last_tier.get(fallback_cost_key, 0) + return tokens * cost_per_token + + return 0.0 + + +def _calculate_flat_cost(tokens: int, cost_per_token: float) -> float: + """Calculate cost using flat pricing.""" + return tokens * cost_per_token + + +def _calculate_prompt_cost(breakdown: TokenBreakdown, model_info: ModelInfo, tiered_pricing: Optional[List[dict]]) -> float: + """Calculate total prompt cost including cached tokens.""" + if tiered_pricing: + text_cost = _calculate_tiered_cost( + tokens=breakdown.text_tokens, + tiered_pricing=tiered_pricing, + cost_key="input_cost_per_token" + ) + cache_cost = _calculate_tiered_cost( + tokens=breakdown.cached_tokens, + tiered_pricing=tiered_pricing, + cost_key="cache_read_input_token_cost" + ) + return text_cost + cache_cost + + input_cost = model_info.get("input_cost_per_token", 0.0) + cache_cost = model_info.get("cache_read_input_token_cost", input_cost) or input_cost + + return (_calculate_flat_cost(tokens=breakdown.text_tokens, cost_per_token=input_cost) + + _calculate_flat_cost(tokens=breakdown.cached_tokens, cost_per_token=cache_cost)) + + +def _calculate_completion_cost(breakdown: TokenBreakdown, model_info: ModelInfo, tiered_pricing: Optional[List[dict]]) -> float: + """Calculate total completion cost including reasoning tokens.""" + if tiered_pricing: + completion_cost = _calculate_tiered_cost( + tokens=breakdown.completion_tokens, + tiered_pricing=tiered_pricing, + cost_key="output_cost_per_token" + ) + reasoning_cost = _calculate_tiered_cost( + tokens=breakdown.reasoning_tokens, + tiered_pricing=tiered_pricing, + cost_key="output_cost_per_reasoning_token", + fallback_cost_key="output_cost_per_token" + ) + return completion_cost + reasoning_cost + + output_cost = model_info.get("output_cost_per_token", 0.0) + reasoning_cost = model_info.get("output_cost_per_reasoning_token", output_cost) or output_cost + + return (_calculate_flat_cost(tokens=breakdown.completion_tokens, cost_per_token=output_cost) + + _calculate_flat_cost(tokens=breakdown.reasoning_tokens, cost_per_token=reasoning_cost)) def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: """ - Calculates the cost per token for a given model, prompt tokens, and completion tokens. - - Follows the same logic as Anthropic's cost per token calculation. + Calculate cost per token for Dashscope models. + + Supports both tiered and flat pricing with cached and reasoning tokens. + + Args: + model: Model name without provider prefix + usage: LiteLLM Usage block + + Returns: + Tuple[float, float] - (prompt_cost_in_usd, completion_cost_in_usd) """ - return generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="deepseek" + model_info = get_model_info(model=model, custom_llm_provider="dashscope") + breakdown = _extract_token_breakdown(usage) + tiered_pricing = model_info.get("tiered_pricing") if isinstance(model_info.get("tiered_pricing"), list) else None + + prompt_cost = _calculate_prompt_cost( + breakdown=breakdown, + model_info=model_info, + tiered_pricing=tiered_pricing ) + completion_cost = _calculate_completion_cost( + breakdown=breakdown, + model_info=model_info, + tiered_pricing=tiered_pricing + ) + + return prompt_cost, completion_cost diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 3522b6006d7..a1370074238 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -169,19 +169,20 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): if tool is None: return None - function_data: DatabricksFunction = { + # Build DatabricksFunction explicitly to avoid parameter conflicts + function_params: DatabricksFunction = { "name": tool["name"], - "parameters": cast(dict, tool.get("input_schema") or {}), + "parameters": cast(dict, tool.get("input_schema") or {}) } - - if tool.get("description"): - description = tool.get("description") - if isinstance(description, (dict, str)): - function_data["description"] = description + + # Only add description if it exists + description = tool.get("description") + if description is not None: + function_params["description"] = cast(Union[dict, str], description) return DatabricksTool( type="function", - function=function_data, + function=function_params, ) def _map_openai_to_dbrx_tool(self, model: str, tools: List) -> List[DatabricksTool]: @@ -338,8 +339,9 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): elif isinstance(content, list): content_str = "" for item in content: - if item["type"] == "text": - content_str += item["text"] + if item.get("type") == "text": + text_value = item.get("text", "") + content_str += str(text_value) if text_value is not None else "" return content_str else: raise Exception(f"Unsupported content type: {type(content)}") @@ -368,19 +370,21 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): reasoning_content: Optional[str] = None if isinstance(content, list): for item in content: - if item["type"] == "reasoning": - for sum in item["summary"]: - if reasoning_content is None: - reasoning_content = "" - reasoning_content += sum["text"] - thinking_block = ChatCompletionThinkingBlock( - type="thinking", - thinking=sum.get("text", ""), - signature=sum.get("signature", ""), - ) - if thinking_blocks is None: - thinking_blocks = [] - thinking_blocks.append(thinking_block) + if item.get("type") == "reasoning": + summary_list = item.get("summary", []) + if isinstance(summary_list, list): + for sum in summary_list: + if reasoning_content is None: + reasoning_content = "" + reasoning_content += sum["text"] + thinking_block = ChatCompletionThinkingBlock( + type="thinking", + thinking=sum.get("text", ""), + signature=sum.get("signature", ""), + ) + if thinking_blocks is None: + thinking_blocks = [] + thinking_blocks.append(thinking_block) return reasoning_content, thinking_blocks @staticmethod diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py index 37217ebfaab..e889126883c 100644 --- a/litellm/llms/gemini/chat/transformation.py +++ b/litellm/llms/gemini/chat/transformation.py @@ -1,10 +1,13 @@ -from typing import List, Optional +from typing import List, Optional, cast from litellm.litellm_core_utils.prompt_templates.factory import ( convert_generic_image_chunk_to_openai_image_obj, convert_to_anthropic_image_obj, ) -from litellm.types.llms.openai import AllMessageValues +from litellm.litellm_core_utils.prompt_templates.image_handling import ( + convert_url_to_base64, +) +from litellm.types.llms.openai import AllMessageValues, ChatCompletionFileObject from litellm.types.llms.vertex_ai import ContentType, PartType from litellm.utils import supports_reasoning @@ -99,7 +102,8 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): self, messages: List[AllMessageValues] ) -> List[ContentType]: """ - Google AI Studio Gemini does not support image urls in messages. + Google AI Studio Gemini does not support HTTP/HTTPS URLs for files. + Convert them to base64 data instead. """ for message in messages: _message_content = message.get("content") @@ -124,4 +128,16 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): image_obj ) ) + elif element.get("type") == "file": + file_element = cast(ChatCompletionFileObject, element) + file_id = file_element["file"].get("file_id") + if file_id and ("http://" in file_id or "https://" in file_id): + # Convert HTTP/HTTPS file URL to base64 data + try: + base64_data = convert_url_to_base64(file_id) + file_element["file"]["file_data"] = base64_data # type: ignore + file_element["file"].pop("file_id", None) # type: ignore + except Exception: + # If conversion fails, leave as is and let the API handle it + pass return _gemini_convert_messages_with_history(messages=messages) diff --git a/litellm/llms/gemini/count_tokens/handler.py b/litellm/llms/gemini/count_tokens/handler.py index bcc8ab9553d..4d6c7fd8864 100644 --- a/litellm/llms/gemini/count_tokens/handler.py +++ b/litellm/llms/gemini/count_tokens/handler.py @@ -11,7 +11,39 @@ if TYPE_CHECKING: else: GenerateContentContentListUnionDict = Any + class GoogleAIStudioTokenCounter: + def _clean_contents_for_gemini_api(self, contents: Any) -> Any: + """ + Clean up contents to remove unsupported fields for the Gemini API. + + The Google Gemini API doesn't recognize the 'id' field in function responses, + so we need to remove it to prevent 400 Bad Request errors. + + Args: + contents: The contents to clean up + + Returns: + Cleaned contents with unsupported fields removed + """ + import copy + + from google.genai.types import FunctionResponse + + cleaned_contents = copy.deepcopy(contents) + + for content in cleaned_contents: + parts = content["parts"] + for part in parts: + if "functionResponse" in part: + function_response_data = part["functionResponse"] + function_response_part = FunctionResponse(**function_response_data) + function_response_part.id = None + part["functionResponse"] = function_response_part.model_dump( + exclude_none=True + ) + + return cleaned_contents def _construct_url(self, model: str, api_base: Optional[str] = None) -> str: """ @@ -20,7 +52,6 @@ class GoogleAIStudioTokenCounter: base_url = api_base or "https://generativelanguage.googleapis.com" return f"{base_url}/v1beta/models/{model}:countTokens" - async def validate_environment( self, api_base: Optional[str] = None, @@ -33,7 +64,8 @@ class GoogleAIStudioTokenCounter: Returns a Tuple of headers and url for the Google Gen AI Studio countTokens endpoint. """ from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig - headers = GoogleGenAIConfig().validate_environment( + + headers = GoogleGenAIConfig().validate_environment( api_key=api_key, headers=headers, model=model, @@ -54,7 +86,7 @@ class GoogleAIStudioTokenCounter: ) -> Dict[str, Any]: """ Count tokens using Google Gen AI Studio countTokens endpoint. - + Args: contents: The content to count tokens for (Google Gen AI format) Example: [{"parts": [{"text": "Hello world"}]}] @@ -63,7 +95,7 @@ class GoogleAIStudioTokenCounter: api_base: Optional API base URL (defaults to Google Gen AI Studio) timeout: Optional timeout for the request **kwargs: Additional parameters - + Returns: Dict containing token count information from Google Gen AI Studio API. Example response: @@ -77,14 +109,13 @@ class GoogleAIStudioTokenCounter: } ] } - + Raises: ValueError: If API key is missing litellm.APIError: If the API call fails litellm.APIConnectionError: If the connection fails Exception: For any other unexpected errors """ - # Set up API base URL # Prepare headers headers, url = await self.validate_environment( @@ -94,46 +125,40 @@ class GoogleAIStudioTokenCounter: model=model, litellm_params=kwargs, ) - - # Prepare request body - request_body = { - "contents": contents - } - + + # Prepare request body - clean up contents to remove unsupported fields + cleaned_contents = self._clean_contents_for_gemini_api(contents) + request_body = {"contents": cleaned_contents} + async_httpx_client = get_async_httpx_client( llm_provider=LlmProviders.GEMINI, ) try: response = await async_httpx_client.post( - url=url, - headers=headers, - json=request_body + url=url, headers=headers, json=request_body ) - + # Check for HTTP errors response.raise_for_status() - + # Parse response result = response.json() return result - + except httpx.HTTPStatusError as e: error_msg = f"Google Gen AI Studio API error: {e.response.status_code} - {e.response.text}" raise litellm.APIError( message=error_msg, llm_provider="gemini", model=model, - status_code=e.response.status_code + status_code=e.response.status_code, ) from e except httpx.RequestError as e: error_msg = f"Request to Google Gen AI Studio failed: {str(e)}" raise litellm.APIConnectionError( - message=error_msg, - llm_provider="gemini", - model=model + message=error_msg, llm_provider="gemini", model=model ) from e except Exception as e: error_msg = f"Unexpected error during token counting: {str(e)}" raise Exception(error_msg) from e - diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py index e57364fd288..f136bd0a404 100644 --- a/litellm/llms/gemini/image_generation/transformation.py +++ b/litellm/llms/gemini/image_generation/transformation.py @@ -85,17 +85,25 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): ) -> str: """ Get the complete url for the request - - Google AI API format: https://generativelanguage.googleapis.com/v1beta/models/{model}:predict + + Gemini 2.5 Flash Image Preview: :generateContent + Other Imagen models: :predict """ complete_url: str = ( - api_base - or get_secret_str("GEMINI_API_BASE") + api_base + or get_secret_str("GEMINI_API_BASE") or self.DEFAULT_BASE_URL ) complete_url = complete_url.rstrip("/") - complete_url = f"{complete_url}/models/{model}:predict" + + # Gemini 2.5 Flash Image Preview uses generateContent endpoint + if "2.5-flash-image-preview" in model: + complete_url = f"{complete_url}/models/{model}:generateContent" + else: + # All other Imagen models use predict endpoint + complete_url = f"{complete_url}/models/{model}:predict" + return complete_url def validate_environment( @@ -128,35 +136,52 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): headers: dict, ) -> dict: """ - Transform the image generation request to Google AI Imagen format - - Google AI API format: + Transform the image generation request to Gemini format + + For Gemini 2.5 Flash Image Preview, use the standard Gemini format with response_modalities: { - "instances": [ + "contents": [ { - "prompt": "Robot holding a red skateboard" + "parts": [ + {"text": "Generate an image of..."} + ] } ], - "parameters": { - "sampleCount": 4, - "aspectRatio": "1:1", - "personGeneration": "allow_adult" + "generationConfig": { + "response_modalities": ["IMAGE", "TEXT"] } } """ - from litellm.types.llms.gemini import ( - GeminiImageGenerationInstance, - GeminiImageGenerationParameters, - ) - request_body: GeminiImageGenerationRequest = GeminiImageGenerationRequest( - instances=[ - GeminiImageGenerationInstance( - prompt=prompt - ) - ], - parameters=GeminiImageGenerationParameters(**optional_params) - ) - return request_body.model_dump(exclude_none=True) + # For Gemini 2.5 Flash Image Preview, use standard Gemini format + if "2.5-flash-image-preview" in model: + request_body: dict = { + "contents": [ + { + "parts": [ + {"text": prompt} + ] + } + ], + "generationConfig": { + "response_modalities": ["IMAGE", "TEXT"] + } + } + return request_body + else: + # For other Imagen models, use the original Imagen format + from litellm.types.llms.gemini import ( + GeminiImageGenerationInstance, + GeminiImageGenerationParameters, + ) + request_body_obj: GeminiImageGenerationRequest = GeminiImageGenerationRequest( + instances=[ + GeminiImageGenerationInstance( + prompt=prompt + ) + ], + parameters=GeminiImageGenerationParameters(**optional_params) + ) + return request_body_obj.model_dump(exclude_none=True) def transform_image_generation_response( self, @@ -185,14 +210,30 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): if not model_response.data: model_response.data = [] - - # Google AI returns predictions with generated images - predictions = response_data.get("predictions", []) - for prediction in predictions: - # Google AI returns base64 encoded images in the prediction - model_response.data.append(ImageObject( - b64_json=prediction.get("bytesBase64Encoded", None), - url=None, # Google AI returns base64, not URLs - )) - + + # Handle different response formats based on model + if "2.5-flash-image-preview" in model: + # Gemini 2.5 Flash Image Preview returns in candidates format + candidates = response_data.get("candidates", []) + for candidate in candidates: + content = candidate.get("content", {}) + parts = content.get("parts", []) + for part in parts: + # Look for inlineData with image + if "inlineData" in part: + inline_data = part["inlineData"] + if "data" in inline_data: + model_response.data.append(ImageObject( + b64_json=inline_data["data"], + url=None, + )) + else: + # Original Imagen format - predictions with generated images + predictions = response_data.get("predictions", []) + for prediction in predictions: + # Google AI returns base64 encoded images in the prediction + model_response.data.append(ImageObject( + b64_json=prediction.get("bytesBase64Encoded", None), + url=None, # Google AI returns base64, not URLs + )) return model_response \ No newline at end of file diff --git a/litellm/llms/hosted_vllm/transcriptions/transformation.py b/litellm/llms/hosted_vllm/transcriptions/transformation.py new file mode 100644 index 00000000000..5eeb892d846 --- /dev/null +++ b/litellm/llms/hosted_vllm/transcriptions/transformation.py @@ -0,0 +1,72 @@ +""" +Transformation logic for Hosted VLLM rerank +""" + +from typing import Optional, Union + +import httpx + +from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, +) +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.openai.transcriptions.whisper_transformation import ( + OpenAIWhisperAudioTranscriptionConfig, +) +from litellm.types.utils import FileTypes + + +class HostedVLLMAudioTranscriptionError(BaseLLMException): + def __init__( + self, + status_code: int, + message: str, + headers: Optional[Union[dict, httpx.Headers]] = None, + ): + super().__init__(status_code=status_code, message=message, headers=headers) + + +class HostedVLLMAudioTranscriptionConfig(OpenAIWhisperAudioTranscriptionConfig): + def __init__(self) -> None: + pass + + 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: + if api_base: + # Remove trailing slashes and ensure clean base URL + api_base = api_base.rstrip("/") + if not api_base.endswith("/v1/audio/transcriptions"): + api_base = f"{api_base}/v1/audio/transcriptions" + return api_base + raise ValueError("api_base must be provided for Hosted VLLM rerank") + + def transform_audio_transcription_request( + self, + model: str, + audio_file: FileTypes, + optional_params: dict, + litellm_params: dict, + ) -> AudioTranscriptionRequestData: + """ + Transform the audio transcription request + """ + + data = {"model": model, "file": audio_file, **optional_params} + + if "response_format" not in data or ( + data["response_format"] == "text" or data["response_format"] == "json" + ): + data["response_format"] = ( + "verbose_json" # ensures 'duration' is received - used for cost calculation + ) + + return AudioTranscriptionRequestData( + data=data, + ) diff --git a/litellm/llms/huggingface/rerank/transformation.py b/litellm/llms/huggingface/rerank/transformation.py index 3f5c44fec05..9c3234c3512 100644 --- a/litellm/llms/huggingface/rerank/transformation.py +++ b/litellm/llms/huggingface/rerank/transformation.py @@ -1,8 +1,9 @@ import os import uuid -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, TypedDict, Union +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import httpx +from typing_extensions import TypedDict import litellm from litellm.llms.base_llm.chat.transformation import BaseLLMException diff --git a/litellm/llms/lm_studio/chat/transformation.py b/litellm/llms/lm_studio/chat/transformation.py index f7a2cc0f28a..7b188ff33f8 100644 --- a/litellm/llms/lm_studio/chat/transformation.py +++ b/litellm/llms/lm_studio/chat/transformation.py @@ -15,8 +15,8 @@ class LMStudioChatConfig(OpenAIGPTConfig): ) -> Tuple[Optional[str], Optional[str]]: api_base = api_base or get_secret_str("LM_STUDIO_API_BASE") # type: ignore dynamic_api_key = ( - api_key or get_secret_str("LM_STUDIO_API_KEY") or " " - ) # vllm does not require an api key + api_key or get_secret_str("LM_STUDIO_API_KEY") or "fake-api-key" + ) # LM Studio does not require an api key, but OpenAI client requires non-None value return api_base, dynamic_api_key def map_openai_params( diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 3be373ca5e5..6755cab22e0 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -378,7 +378,7 @@ class OCIChatConfig(BaseConfig): or not oci_compartment_id ): raise Exception( - "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, " + "Missing required parameters: oci_user, oci_fingerprint, oci_tenancy, oci_compartment_id " "and at least one of oci_key or oci_key_file." ) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index ee0d3acef70..3527a579218 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -16,9 +16,18 @@ from httpx._models import Headers, Response from pydantic import BaseModel import litellm +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + _extract_reasoning_content, + convert_content_list_to_str, + extract_images_from_message, +) from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException -from litellm.types.llms.ollama import OllamaToolCall, OllamaToolCallFunction +from litellm.types.llms.ollama import ( + OllamaChatCompletionMessage, + OllamaToolCall, + OllamaToolCallFunction, +) from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionAssistantToolCall, @@ -299,7 +308,23 @@ class OllamaChatConfig(BaseConfig): ) new_tools.append(ollama_tool_call) cast(dict, m)["tool_calls"] = new_tools - new_messages.append(m) + reasoning_content, parsed_content = _extract_reasoning_content( + cast(dict, m) + ) + content_str = convert_content_list_to_str(cast(AllMessageValues, m)) + images = extract_images_from_message(cast(AllMessageValues, m)) + + ollama_message = OllamaChatCompletionMessage( + role=cast(str, m.get("role")), + ) + if reasoning_content is not None: + ollama_message["thinking"] = reasoning_content + if content_str is not None: + ollama_message["content"] = content_str + if images is not None: + ollama_message["images"] = images + + new_messages.append(ollama_message) # Load Config config = self.get_config() @@ -361,7 +386,7 @@ class OllamaChatConfig(BaseConfig): 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 ( + from litellm.litellm_core_utils.prompt_templates.common_utils import ( _parse_content_for_reasoning, ) diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 71bcf0bb3f7..bfb0b7f1877 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -229,7 +229,7 @@ class OllamaConfig(BaseConfig): model = model.split("/", 1)[1] api_base = get_secret_str("OLLAMA_API_BASE") or "http://localhost:11434" api_key = self.get_api_key() - headers = { "Authorization": f"Bearer {api_key}" } if api_key else {} + headers = {"Authorization": f"Bearer {api_key}"} if api_key else {} try: response = litellm.module_level_client.post( @@ -279,7 +279,7 @@ 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 ( + from litellm.litellm_core_utils.prompt_templates.common_utils import ( _parse_content_for_reasoning, ) diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 392d47f9822..25078267571 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -272,6 +272,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ResponsesAPIStreamEvents.WEB_SEARCH_CALL_IN_PROGRESS: WebSearchCallInProgressEvent, ResponsesAPIStreamEvents.WEB_SEARCH_CALL_SEARCHING: WebSearchCallSearchingEvent, ResponsesAPIStreamEvents.WEB_SEARCH_CALL_COMPLETED: WebSearchCallCompletedEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_IN_PROGRESS: MCPListToolsInProgressEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_COMPLETED: MCPListToolsCompletedEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_FAILED: MCPListToolsFailedEvent, + ResponsesAPIStreamEvents.MCP_CALL_IN_PROGRESS: MCPCallInProgressEvent, + ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DELTA: MCPCallArgumentsDeltaEvent, + ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DONE: MCPCallArgumentsDoneEvent, + ResponsesAPIStreamEvents.MCP_CALL_COMPLETED: MCPCallCompletedEvent, + ResponsesAPIStreamEvents.MCP_CALL_FAILED: MCPCallFailedEvent, ResponsesAPIStreamEvents.ERROR: ErrorEvent, } @@ -417,3 +425,39 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): raise OpenAIError( message=raw_response.text, status_code=raw_response.status_code ) + + ######################################################### + ########## CANCEL RESPONSE API TRANSFORMATION ########## + ######################################################### + def transform_cancel_response_api_request( + self, + response_id: str, + api_base: str, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[str, Dict]: + """ + Transform the cancel response API request into a URL and data + + OpenAI API expects the following request + - POST /v1/responses/{response_id}/cancel + """ + url = f"{api_base}/{response_id}/cancel" + data: Dict = {} + return url, data + + def transform_cancel_response_api_response( + self, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ResponsesAPIResponse: + """ + Transform the cancel response API response into a ResponsesAPIResponse + """ + try: + raw_response_json = raw_response.json() + except Exception: + raise OpenAIError( + message=raw_response.text, status_code=raw_response.status_code + ) + return ResponsesAPIResponse(**raw_response_json) diff --git a/litellm/llms/openai/transcriptions/gpt_transformation.py b/litellm/llms/openai/transcriptions/gpt_transformation.py index 796e10f5153..34621c44e22 100644 --- a/litellm/llms/openai/transcriptions/gpt_transformation.py +++ b/litellm/llms/openai/transcriptions/gpt_transformation.py @@ -1,5 +1,8 @@ from typing import List +from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, +) from litellm.types.llms.openai import OpenAIAudioTranscriptionOptionalParams from litellm.types.utils import FileTypes @@ -27,8 +30,12 @@ class OpenAIGPTAudioTranscriptionConfig(OpenAIWhisperAudioTranscriptionConfig): audio_file: FileTypes, optional_params: dict, litellm_params: dict, - ) -> dict: + ) -> AudioTranscriptionRequestData: """ Transform the audio transcription request """ - return {"model": model, "file": audio_file, **optional_params} + data = {"model": model, "file": audio_file, **optional_params} + + return AudioTranscriptionRequestData( + data=data, + ) diff --git a/litellm/llms/openai/transcriptions/handler.py b/litellm/llms/openai/transcriptions/handler.py index 4fe48dd3c6c..19b303bb968 100644 --- a/litellm/llms/openai/transcriptions/handler.py +++ b/litellm/llms/openai/transcriptions/handler.py @@ -1,4 +1,4 @@ -from typing import Optional, Union +from typing import Optional, Union, cast import httpx from openai import AsyncOpenAI, OpenAI @@ -34,6 +34,7 @@ class OpenAIAudioTranscription(OpenAIChatCompletion): - call openai_aclient.audio.transcriptions.create by default """ try: + raw_response = ( await openai_aclient.audio.transcriptions.with_raw_response.create( **data, timeout=timeout @@ -93,15 +94,14 @@ class OpenAIAudioTranscription(OpenAIChatCompletion): Handle audio transcription request """ if provider_config is not None: - data = provider_config.transform_audio_transcription_request( + transformed_data = provider_config.transform_audio_transcription_request( model=model, audio_file=audio_file, optional_params=optional_params, litellm_params=litellm_params, ) - if not isinstance(data, dict): - raise ValueError("OpenAI transformation route requires a dict") + data = cast(dict, transformed_data.data) else: data = {"model": model, "file": audio_file, **optional_params} diff --git a/litellm/llms/openai/transcriptions/whisper_transformation.py b/litellm/llms/openai/transcriptions/whisper_transformation.py index c0ccc71579f..fa507e1bc26 100644 --- a/litellm/llms/openai/transcriptions/whisper_transformation.py +++ b/litellm/llms/openai/transcriptions/whisper_transformation.py @@ -1,8 +1,9 @@ from typing import List, Optional, Union -from httpx import Headers +from httpx import Headers, Response from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, BaseAudioTranscriptionConfig, ) from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -11,12 +12,40 @@ from litellm.types.llms.openai import ( AllMessageValues, OpenAIAudioTranscriptionOptionalParams, ) -from litellm.types.utils import FileTypes +from litellm.types.utils import FileTypes, TranscriptionResponse from ..common_utils import OpenAIError class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): + 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: + """ + OPTIONAL + + Get the complete url for the request + + Some providers need `model` in `api_base` + """ + ## get the api base, attach the endpoint - v1/audio/transcriptions + # strip trailing slash if present + api_base = api_base.rstrip("/") if api_base else "" + + # if endswith "/v1" + if api_base and api_base.endswith("/v1"): + api_base = f"{api_base}/audio/transcriptions" + else: + api_base = f"{api_base}/v1/audio/transcriptions" + + return api_base or "" + def get_supported_openai_params( self, model: str ) -> List[OpenAIAudioTranscriptionOptionalParams]: @@ -72,21 +101,22 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): audio_file: FileTypes, optional_params: dict, litellm_params: dict, - ) -> dict: + ) -> AudioTranscriptionRequestData: """ Transform the audio transcription request """ - data = {"model": model, "file": audio_file, **optional_params} if "response_format" not in data or ( data["response_format"] == "text" or data["response_format"] == "json" ): - data[ - "response_format" - ] = "verbose_json" # ensures 'duration' is received - used for cost calculation + data["response_format"] = ( + "verbose_json" # ensures 'duration' is received - used for cost calculation + ) - return data + return AudioTranscriptionRequestData( + data=data, + ) def get_error_class( self, error_message: str, status_code: int, headers: Union[dict, Headers] @@ -96,3 +126,25 @@ class OpenAIWhisperAudioTranscriptionConfig(BaseAudioTranscriptionConfig): message=error_message, headers=headers, ) + + def transform_audio_transcription_response( + self, + raw_response: Response, + ) -> TranscriptionResponse: + try: + raw_response_json = raw_response.json() + except Exception as e: + raise ValueError( + f"Error transforming response to json: {str(e)}\nResponse: {raw_response.text}" + ) + + if any( + key in raw_response_json + for key in TranscriptionResponse.model_fields.keys() + ): + return TranscriptionResponse(**raw_response_json) + else: + raise ValueError( + "Invalid response format. Received response does not match the expected format. Got: ", + raw_response_json, + ) diff --git a/litellm/llms/ovhcloud/chat/transformation.py b/litellm/llms/ovhcloud/chat/transformation.py new file mode 100644 index 00000000000..6bdc28620ff --- /dev/null +++ b/litellm/llms/ovhcloud/chat/transformation.py @@ -0,0 +1,141 @@ +""" +Support for OVHCloud AI Endpoints `/v1/chat/completions` endpoint. + +Our unified API follows the OpenAI standard. +More information on our website: https://endpoints.ai.cloud.ovh.net +""" +from typing import Optional, Union, List + +import httpx +from litellm import ModelResponseStream, OpenAIGPTConfig, get_model_info, verbose_logger +from litellm.llms.ovhcloud.utils import OVHCloudException +from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai import AllMessageValues + +class OVHCloudChatConfig(OpenAIGPTConfig): + @property + def custom_llm_provider(self) -> Optional[str]: + return "ovhcloud" + + def get_supported_openai_params(self, model: str) -> list: + """ + Details about function calling support can be found here: + https://help.ovhcloud.com/csm/en-gb-public-cloud-ai-endpoints-function-calling?id=kb_article_view&sysparm_article=KB0071907 + """ + supports_function_calling: Optional[bool] = None + try: + model_info = get_model_info(model, custom_llm_provider="ovhcloud") + supports_function_calling = model_info.get( + "supports_function_calling", False + ) + except Exception as e: + verbose_logger.debug(f"Error getting supported OpenAI params: {e}") + pass + + optional_params = super().get_supported_openai_params(model) + if supports_function_calling is not True: + verbose_logger.debug( + "You can see our models supporting function_calling in our catalog: https://endpoints.ai.cloud.ovh.net/catalog " + ) + optional_params.remove("tools") + optional_params.remove("tool_choice") + optional_params.remove("function_call") + optional_params.remove("response_format") + return optional_params + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + api_base = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" if api_base is None else api_base.rstrip("/") + complete_url = f"{api_base}/chat/completions" + return complete_url + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return OVHCloudException( + message=error_message, + status_code=status_code, + headers=headers, + ) + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + mapped_openai_params = super().map_openai_params( + non_default_params, optional_params, model, drop_params + ) + return mapped_openai_params + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + extra_body = optional_params.pop("extra_body", {}) + response = super().transform_request( + model, messages, optional_params, litellm_params, headers + ) + response.update(extra_body) + return response + +class OVHCloudChatCompletionStreamingHandler(BaseModelResponseIterator): + """ + Handler for OVHCloud AI Endpoints streaming chat completion responses + """ + + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + """ + Parse individual chunks from streaming response + """ + try: + if "error" in chunk: + error_chunk = chunk["error"] + error_message = "OVHCloud Error: {}".format( + error_chunk.get("message", "Unknown error") + ) + raise OVHCloudException( + message=error_message, + status_code=error_chunk.get("code", 400), + headers={"Content-Type": "application/json"}, + ) + + new_choices = [] + for choice in chunk["choices"]: + if "delta" in choice and "reasoning" in choice["delta"]: + choice["delta"]["reasoning_content"] = choice["delta"].get("reasoning") + new_choices.append(choice) + + return ModelResponseStream( + id=chunk["id"], + object="chat.completion.chunk", + created=chunk["created"], + usage=chunk.get("usage"), + model=chunk["model"], + choices=new_choices, + ) + except KeyError as e: + raise OVHCloudException( + message=f"KeyError: {e}, Got unexpected response from CometAPI: {chunk}", + status_code=400, + headers={"Content-Type": "application/json"}, + ) + except Exception as e: + raise e \ No newline at end of file diff --git a/litellm/llms/ovhcloud/embedding/transformation.py b/litellm/llms/ovhcloud/embedding/transformation.py new file mode 100644 index 00000000000..1266f74c0a2 --- /dev/null +++ b/litellm/llms/ovhcloud/embedding/transformation.py @@ -0,0 +1,122 @@ +""" +This is OpenAI compatible - no transformation is applied + +""" +from typing import List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse, Usage + +from ..utils import OVHCloudException + + +class OVHCloudEmbeddingConfig(BaseEmbeddingConfig): + def __init__(self) -> None: + pass + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + api_base = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" if api_base is None else api_base.rstrip("/") + complete_url = f"{api_base}/embeddings" + return complete_url + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + if api_key is None: + api_key = get_secret_str("OVHCLOUD_API_KEY") + + default_headers = { + "Authorization": f"Bearer {api_key}", + "accept": "application/json", + "Content-Type": "application/json", + } + + if "Authorization" in headers: + default_headers["Authorization"] = headers["Authorization"] + + return {**default_headers, **headers} + + def get_supported_openai_params(self, model: str): + return [] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ): + supported_openai_params = self.get_supported_openai_params(model) + for param, value in non_default_params.items(): + if param in supported_openai_params: + optional_params[param] = value + return optional_params + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + return {"input": input, "model": model, **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: + try: + raw_response_json = raw_response.json() + except Exception: + raise OVHCloudException( + message=raw_response.text, + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + model_response.model = raw_response_json.get("model") + model_response.data = raw_response_json.get("data") + model_response.object = raw_response_json.get("object") + + usage = Usage( + prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0), + total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0), + ) + + model_response.usage = usage + return model_response + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return OVHCloudException( + message=error_message, status_code=status_code, headers=headers + ) diff --git a/litellm/llms/ovhcloud/utils.py b/litellm/llms/ovhcloud/utils.py new file mode 100644 index 00000000000..9ae4dfb1efd --- /dev/null +++ b/litellm/llms/ovhcloud/utils.py @@ -0,0 +1,6 @@ +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class OVHCloudException(BaseLLMException): + """OVHCloud AI Endpoints exception handling class""" + pass \ No newline at end of file diff --git a/litellm/llms/vertex_ai/batches/transformation.py b/litellm/llms/vertex_ai/batches/transformation.py index a97f312d486..5b6d21b5948 100644 --- a/litellm/llms/vertex_ai/batches/transformation.py +++ b/litellm/llms/vertex_ai/batches/transformation.py @@ -114,7 +114,14 @@ class VertexAIBatchTransformation: """ Gets the output file id from the Vertex AI Batch response """ - output_file_id: str = "" + + output_file_id: str = ( + response.get("outputInfo", OutputInfo()).get("gcsOutputDirectory", "") + + "/predictions.jsonl" + ) + if output_file_id != "/predictions.jsonl": + return output_file_id + output_config = response.get("outputConfig") if output_config is None: return output_file_id diff --git a/litellm/llms/vertex_ai/files/handler.py b/litellm/llms/vertex_ai/files/handler.py index a666a2c37fb..6636bccd6a3 100644 --- a/litellm/llms/vertex_ai/files/handler.py +++ b/litellm/llms/vertex_ai/files/handler.py @@ -1,5 +1,6 @@ import asyncio -from typing import Any, Coroutine, Optional, Union +import urllib.parse +from typing import Any, Coroutine, Optional, Tuple, Union import httpx @@ -9,7 +10,12 @@ from litellm.integrations.gcs_bucket.gcs_bucket_base import ( GCSLoggingConfig, ) from litellm.llms.custom_httpx.http_handler import get_async_httpx_client -from litellm.types.llms.openai import CreateFileRequest, OpenAIFileObject +from litellm.types.llms.openai import ( + CreateFileRequest, + FileContentRequest, + HttpxBinaryResponseContent, + OpenAIFileObject, +) from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES from .transformation import VertexAIJsonlFilesTransformation @@ -105,3 +111,136 @@ class VertexAIFilesHandler(GCSBucketBase): max_retries=max_retries, ) ) + + def _extract_bucket_and_object_from_file_id(self, file_id: str) -> Tuple[str, str]: + """ + Extract bucket name and object path from URL-encoded file_id. + + Expected format: gs%3A%2F%2Fbucket-name%2Fpath%2Fto%2Ffile + Which decodes to: gs://bucket-name/path/to/file + + Returns: + tuple: (bucket_name, url_encoded_object_path) + - bucket_name: "bucket-name" + - url_encoded_object_path: "path%2Fto%2Ffile" + """ + decoded_path = urllib.parse.unquote(file_id) + + if decoded_path.startswith("gs://"): + full_path = decoded_path[5:] # Remove 'gs://' prefix + else: + full_path = decoded_path + + if "/" in full_path: + bucket_name, object_path = full_path.split("/", 1) + else: + bucket_name = full_path + object_path = "" + + encoded_object_path = urllib.parse.quote(object_path, safe="") + + return bucket_name, encoded_object_path + + async def afile_content( + self, + file_content_request: FileContentRequest, + vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES], + vertex_project: Optional[str], + vertex_location: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + ) -> HttpxBinaryResponseContent: + """ + Download file content from GCS bucket for VertexAI files. + + Args: + file_content_request: Contains file_id (URL-encoded GCS path) + vertex_credentials: VertexAI credentials + vertex_project: VertexAI project ID + vertex_location: VertexAI location + timeout: Request timeout + max_retries: Max retry attempts + + Returns: + HttpxBinaryResponseContent: Binary content wrapped in compatible response format + """ + file_id = file_content_request.get("file_id") + if not file_id: + raise ValueError("file_id is required in file_content_request") + + bucket_name, encoded_object_path = self._extract_bucket_and_object_from_file_id( + file_id + ) + + download_kwargs = { + "standard_callback_dynamic_params": {"gcs_bucket_name": bucket_name} + } + + file_content = await self.download_gcs_object( + object_name=encoded_object_path, **download_kwargs + ) + + if file_content is None: + decoded_path = urllib.parse.unquote(file_id) + raise ValueError(f"Failed to download file from GCS: {decoded_path}") + + decoded_path = urllib.parse.unquote(file_id) + mock_response = httpx.Response( + status_code=200, + content=file_content, + headers={"content-type": "application/octet-stream"}, + request=httpx.Request(method="GET", url=decoded_path), + ) + + return HttpxBinaryResponseContent(response=mock_response) + + def file_content( + self, + _is_async: bool, + file_content_request: FileContentRequest, + api_base: Optional[str], + vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES], + vertex_project: Optional[str], + vertex_location: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + ) -> Union[ + HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent] + ]: + """ + Download file content from GCS bucket for VertexAI files. + Supports both sync and async operations. + + Args: + _is_async: Whether to run asynchronously + file_content_request: Contains file_id (URL-encoded GCS path) + api_base: API base (unused for GCS operations) + vertex_credentials: VertexAI credentials + vertex_project: VertexAI project ID + vertex_location: VertexAI location + timeout: Request timeout + max_retries: Max retry attempts + + Returns: + HttpxBinaryResponseContent or Coroutine: Binary content wrapped in compatible response format + """ + if _is_async: + return self.afile_content( + file_content_request=file_content_request, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=vertex_location, + timeout=timeout, + max_retries=max_retries, + ) + else: + return asyncio.run( + self.afile_content( + file_content_request=file_content_request, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=vertex_location, + timeout=timeout, + max_retries=max_retries, + ) + ) diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index c795367e486..f2e5a5b5d25 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -6,6 +6,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union from httpx import Headers, Response +from litellm.files.utils import FilesAPIUtils 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 ( @@ -260,10 +261,13 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): raise ValueError("file is required") extracted_file_data = extract_file_data(file_data) extracted_file_data_content = extracted_file_data.get("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 + + if extracted_file_data_content is None: + raise ValueError("file content is required") + + if FilesAPIUtils.is_batch_jsonl_file( + create_file_data=create_file_data, + extracted_file_data=extracted_file_data, ): ## 1. If jsonl, check if there's a model name file_content = self._get_content_from_openai_file( @@ -279,7 +283,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): openai_jsonl_content ) ) - return json.dumps(vertex_jsonl_content) + return "\n".join(json.dumps(item) for item in vertex_jsonl_content) elif isinstance(extracted_file_data_content, bytes): return extracted_file_data_content else: diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 327b269d1d4..c59e3bb24e8 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -28,6 +28,7 @@ from litellm.types.files import ( get_file_type_from_extension, is_gemini_1_5_accepted_file_type, ) +from litellm.types.utils import LlmProviders from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionAssistantMessage, @@ -492,7 +493,8 @@ def _transform_request_body( data["generationConfig"] = generation_config if cached_content is not None: data["cachedContent"] = cached_content - if labels is not None: + # Only add labels for Vertex AI endpoints (not Google GenAI/AI Studio) and only if non-empty + if labels and custom_llm_provider != LlmProviders.GEMINI: data["labels"] = labels except Exception as e: raise e @@ -647,3 +649,5 @@ def _transform_system_message( return SystemInstructions(parts=system_content_blocks), messages return None, messages + + diff --git a/litellm/llms/vertex_ai/google_genai/transformation.py b/litellm/llms/vertex_ai/google_genai/transformation.py index 47933811196..574000e6bca 100644 --- a/litellm/llms/vertex_ai/google_genai/transformation.py +++ b/litellm/llms/vertex_ai/google_genai/transformation.py @@ -1,7 +1,7 @@ """ Transformation for Calling Google models in their native format. """ -from typing import Literal, Optional, Union +from typing import Dict, Literal, Optional, Union from litellm.llms.gemini.google_genai.transformation import GoogleGenAIConfig from litellm.types.router import GenericLiteLLMParams @@ -11,20 +11,20 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig): """ Configuration for calling Google models in their native format. """ + HEADER_NAME = "Authorization" BEARER_PREFIX = "Bearer" - + @property def custom_llm_provider(self) -> Literal["gemini", "vertex_ai"]: return "vertex_ai" - def validate_environment( - self, + self, api_key: Optional[str], headers: Optional[dict], model: str, - litellm_params: Optional[Union[GenericLiteLLMParams, dict]] + litellm_params: Optional[Union[GenericLiteLLMParams, dict]], ) -> dict: default_headers = { "Content-Type": "application/json", @@ -36,4 +36,65 @@ class VertexAIGoogleGenAIConfig(GoogleGenAIConfig): default_headers.update(headers) return default_headers - \ No newline at end of file + + def _camel_to_snake(self, camel_str: str) -> str: + """Convert camelCase to snake_case""" + import re + + return re.sub(r"(? dict: + """ + Transform the generate content request for Vertex AI. + Since Vertex AI natively supports Google GenAI format, we can pass most fields directly. + """ + # Build the request in Google GenAI format that Vertex AI expects + result = { + "model": model, + "contents": contents, + } + + # Add tools if provided + if tools: + result["tools"] = tools + + # Add systemInstruction if provided + if system_instruction: + result["systemInstruction"] = system_instruction + + # Handle generationConfig - Vertex AI expects it in the same format + if generate_content_config_dict: + result["generationConfig"] = generate_content_config_dict + + return result diff --git a/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py b/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py index 18bc72db46a..9d9015c2b91 100644 --- a/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py +++ b/litellm/llms/vertex_ai/text_to_speech/text_to_speech_handler.py @@ -1,6 +1,7 @@ -from typing import Optional, TypedDict, Union +from typing import Optional, Union import httpx +from typing_extensions import TypedDict import litellm from litellm.llms.custom_httpx.http_handler import ( diff --git a/litellm/llms/vertex_ai/vertex_embeddings/types.py b/litellm/llms/vertex_ai/vertex_embeddings/types.py index c0c53b170c4..7f85ea46f31 100644 --- a/litellm/llms/vertex_ai/vertex_embeddings/types.py +++ b/litellm/llms/vertex_ai/vertex_embeddings/types.py @@ -3,7 +3,9 @@ Types for Vertex Embeddings Requests """ from enum import Enum -from typing import List, Optional, TypedDict, Union +from typing import List, Optional, Union + +from typing_extensions import TypedDict class TaskType(str, Enum): diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py index 76998e76698..0f0bc776cc9 100644 --- a/litellm/llms/vertex_ai/vertex_llm_base.py +++ b/litellm/llms/vertex_ai/vertex_llm_base.py @@ -239,6 +239,7 @@ class VertexBase: stream=stream, auth_header=None, url=default_api_base, + model=model, ) return api_base @@ -292,6 +293,7 @@ class VertexBase: stream: Optional[bool], auth_header: Optional[str], url: str, + model: Optional[str] = None, ) -> Tuple[Optional[str], str]: """ for cloudflare ai gateway - https://github.com/BerriAI/litellm/issues/4317 @@ -301,7 +303,12 @@ class VertexBase: """ if api_base: if custom_llm_provider == "gemini": - url = "{}:{}".format(api_base, endpoint) + # For Gemini (Google AI Studio), construct the full path like other providers + if model is None: + raise ValueError( + "Model parameter is required for Gemini custom API base URLs" + ) + url = "{}/models/{}:{}".format(api_base, model, endpoint) if gemini_api_key is None: raise ValueError( "Missing gemini_api_key, please set `GEMINI_API_KEY`" @@ -373,6 +380,7 @@ class VertexBase: endpoint=endpoint, stream=stream, url=url, + model=model, ) def _handle_reauthentication( @@ -384,19 +392,19 @@ class VertexBase: ) -> Tuple[str, str]: """ Handle reauthentication when credentials refresh fails. - + This method clears the cached credentials and attempts to reload them once. It should only be called when "Reauthentication is needed" error occurs. - + Args: credentials: The original credentials project_id: The project ID credential_cache_key: The cache key to clear error: The original error that triggered reauthentication - + Returns: Tuple of (access_token, project_id) - + Raises: The original error if reauthentication fails """ @@ -404,11 +412,11 @@ class VertexBase: f"Handling reauthentication for project_id: {project_id}. " f"Clearing cache and retrying once." ) - + # Clear the cached credentials if credential_cache_key in self._credentials_project_mapping: del self._credentials_project_mapping[credential_cache_key] - + # Retry once with _retry_reauth=True to prevent infinite recursion try: return self.get_access_token( @@ -438,12 +446,12 @@ class VertexBase: 3. Check if loaded credentials have expired 4. If expired, refresh credentials 5. Return access token and project id - + Args: credentials: The credentials to use for authentication project_id: The Google Cloud project ID _retry_reauth: Internal flag to prevent infinite recursion during reauthentication - + Returns: Tuple of (access_token, project_id) """ diff --git a/litellm/llms/volcengine/chat/transformation.py b/litellm/llms/volcengine/chat/transformation.py index 216570a1aba..6df1cd38267 100644 --- a/litellm/llms/volcengine/chat/transformation.py +++ b/litellm/llms/volcengine/chat/transformation.py @@ -4,6 +4,9 @@ from litellm.llms.openai_like.chat.transformation import OpenAILikeChatConfig class VolcEngineChatConfig(OpenAILikeChatConfig): + """ + Reference: https://www.volcengine.com/docs/82379/1494384 + """ frequency_penalty: Optional[int] = None function_call: Optional[Union[str, dict]] = None functions: Optional[list] = None @@ -81,20 +84,22 @@ class VolcEngineChatConfig(OpenAILikeChatConfig): ) if "thinking" in optional_params: + """ + The `thinking` parameters of VolcEngine model has different default values. + See the docs for details. + Refrence: https://www.volcengine.com/docs/82379/1449737#0002 + """ thinking_value = optional_params.pop("thinking") - # Handle disabled thinking case - don't add to extra_body if disabled + # Handle using thinking params case - add to extra_body if value is legal if ( thinking_value is not None and isinstance(thinking_value, dict) - and thinking_value.get("type") == "disabled" + and thinking_value.get("type", None) in ["enabled", "disabled", "auto"] # legal values, see docs ): - # Skip adding thinking parameter when it's disabled - pass + # Add thinking parameter to extra_body for all legal cases + optional_params.setdefault("extra_body", {})["thinking"] = thinking_value else: - # Add thinking parameter to extra_body for all other cases - optional_params.setdefault("extra_body", {})[ - "thinking" - ] = thinking_value - + # Skip adding thinking parameter when it's not set or has invalid value + pass return optional_params diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py index 78c20ac5731..b01f6c18466 100644 --- a/litellm/llms/xai/chat/transformation.py +++ b/litellm/llms/xai/chat/transformation.py @@ -80,6 +80,8 @@ class XAIChatConfig(OpenAIGPTConfig): return False elif "grok-4" in model: return False + elif "grok-code-fast" in model: + return False return True def _supports_frequency_penalty(self, model: str) -> bool: diff --git a/litellm/main.py b/litellm/main.py index d7395eb1457..44f591d49ae 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -116,6 +116,7 @@ from litellm.utils import ( from ._logging import verbose_logger from .caching.caching import disable_cache, enable_cache, update_cache +from .litellm_core_utils.core_helpers import safe_deep_copy from .litellm_core_utils.fallback_utils import ( async_completion_with_fallbacks, completion_with_fallbacks, @@ -150,9 +151,9 @@ from .llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from .llms.custom_llm import CustomLLM, custom_chat_llm_router from .llms.databricks.embed.handler import DatabricksEmbeddingHandler from .llms.deprecated_providers import aleph_alpha, palm +from .llms.gemini.common_utils import get_api_key_from_env from .llms.groq.chat.handler import GroqChatCompletion from .llms.heroku.chat.transformation import HerokuChatConfig -from .llms.gemini.common_utils import get_api_key_from_env from .llms.huggingface.embedding.handler import HuggingFaceEmbedding from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion from .llms.oci.chat.transformation import OCIChatConfig @@ -164,6 +165,7 @@ from .llms.openai.openai import OpenAIChatCompletion from .llms.openai.transcriptions.handler import OpenAIAudioTranscription from .llms.openai_like.chat.handler import OpenAILikeChatHandler from .llms.openai_like.embedding.handler import OpenAILikeEmbeddingHandler +from .llms.ovhcloud.chat.transformation import OVHCloudChatConfig from .llms.petals.completion import handler as petals_handler from .llms.predibase.chat.handler import PredibaseChatCompletion from .llms.replicate.chat.handler import completion as replicate_chat_completion @@ -259,6 +261,7 @@ sagemaker_chat_completion = SagemakerChatHandler() bytez_transformation = BytezChatConfig() heroku_transformation = HerokuChatConfig() oci_transformation = OCIChatConfig() +ovhcloud_transformation = OVHCloudChatConfig() ####### COMPLETION ENDPOINTS ################ @@ -358,7 +361,9 @@ async def acompletion( logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, deployment_id=None, - reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = 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, @@ -504,7 +509,9 @@ async def acompletion( } if custom_llm_provider is None: _, custom_llm_provider, _, _ = get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider, 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 @@ -899,7 +906,9 @@ def completion( # type: ignore # noqa: PLR0915 logit_bias: Optional[dict] = None, user: Optional[str] = None, # openai v1.0+ new params - reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = 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, @@ -1116,10 +1125,12 @@ def completion( # type: ignore # noqa: PLR0915 ) if provider_specific_header is not None: - headers.update(ProviderSpecificHeaderUtils.get_provider_specific_headers( - provider_specific_header=provider_specific_header, - custom_llm_provider=custom_llm_provider, - )) + headers.update( + ProviderSpecificHeaderUtils.get_provider_specific_headers( + provider_specific_header=provider_specific_header, + custom_llm_provider=custom_llm_provider, + ) + ) if model_response is not None and hasattr(model_response, "_hidden_params"): model_response._hidden_params["custom_llm_provider"] = custom_llm_provider @@ -1325,6 +1336,7 @@ def completion( # type: ignore # noqa: PLR0915 azure_scope=kwargs.get("azure_scope"), max_retries=max_retries, timeout=timeout, + litellm_request_debug=kwargs.get("litellm_request_debug", False), ) cast(LiteLLMLoggingObj, logging).update_environment_variables( model=model, @@ -2383,47 +2395,7 @@ def completion( # type: ignore # noqa: PLR0915 ) return response response = model_response - elif custom_llm_provider == "cohere": - cohere_key = ( - api_key - or litellm.cohere_key - or get_secret("COHERE_API_KEY") - or get_secret("CO_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("COHERE_API_BASE") - or "https://api.cohere.ai/v1/generate" - ) - - headers = headers or litellm.headers or {} - if headers is None: - headers = {} - - if extra_headers is not None: - headers.update(extra_headers) - - response = base_llm_http_handler.completion( - model=model, - stream=stream, - messages=messages, - acompletion=acompletion, - api_base=api_base, - model_response=model_response, - optional_params=optional_params, - litellm_params=litellm_params, - custom_llm_provider="cohere", - timeout=timeout, - headers=headers, - encoding=encoding, - api_key=cohere_key, - logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements - client=client, - ) - elif custom_llm_provider == "cohere_chat": + elif custom_llm_provider == "cohere_chat" or custom_llm_provider == "cohere": cohere_key = ( api_key or litellm.cohere_key @@ -2538,6 +2510,37 @@ def completion( # type: ignore # noqa: PLR0915 encoding=encoding, stream=stream, ) + elif custom_llm_provider == "compactifai": + api_key = ( + api_key + or get_secret_str("COMPACTIFAI_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or "https://api.compactif.ai/v1" + ) + + ## COMPLETION CALL + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, + client=client, + custom_llm_provider=custom_llm_provider, + encoding=encoding, + stream=stream, + provider_config=provider_config, + ) elif custom_llm_provider == "oobabooga": custom_llm_provider = "oobabooga" model_response = oobabooga.completion( @@ -2712,9 +2715,7 @@ def completion( # type: ignore # noqa: PLR0915 ) api_key = ( - api_key - or litellm.api_key - or get_secret("VERCEL_AI_GATEWAY_API_KEY") + api_key or litellm.api_key or get_secret("VERCEL_AI_GATEWAY_API_KEY") ) vercel_site_url = get_secret("VERCEL_SITE_URL") or "https://litellm.ai" @@ -2730,7 +2731,7 @@ def completion( # type: ignore # noqa: PLR0915 vercel_headers.update(_headers) headers = vercel_headers - + ## Load Config config = litellm.VercelAIGatewayConfig.get_config() for k, v in config.items(): @@ -2807,8 +2808,7 @@ def completion( # type: ignore # noqa: PLR0915 ) api_base = api_base or litellm.api_base or get_secret("GEMINI_API_BASE") - - new_params = deepcopy(optional_params) + new_params = safe_deep_copy(optional_params or {}) response = vertex_chat_completion.completion( # type: ignore model=model, messages=messages, @@ -2852,7 +2852,7 @@ def completion( # type: ignore # noqa: PLR0915 api_base = api_base or litellm.api_base or get_secret("VERTEXAI_API_BASE") - new_params = deepcopy(optional_params) + new_params = safe_deep_copy(optional_params or {}) if vertex_partner_models_chat_completion.is_vertex_partner_model(model): model_response = vertex_partner_models_chat_completion.completion( model=model, @@ -3498,6 +3498,42 @@ def completion( # type: ignore # noqa: PLR0915 pass + elif custom_llm_provider == "ovhcloud" or model in litellm.ovhcloud_models: + api_key = ( + api_key + or litellm.ovhcloud_key + or get_secret_str("OVHCLOUD_API_KEY") + or litellm.api_key + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret_str("OVHCLOUD_API_BASE") + or "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" + ) + + response = base_llm_http_handler.completion( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + timeout=timeout, # type: ignore + client=client, + custom_llm_provider=custom_llm_provider, + encoding=encoding, + stream=stream, + provider_config=ovhcloud_transformation, + ) + + pass + elif custom_llm_provider == "custom": url = litellm.api_base or api_base or "" if url is None or url == "": @@ -3712,7 +3748,9 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse: func_with_context = partial(ctx.run, func) _, custom_llm_provider, _, _ = get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider, api_base=kwargs.get("api_base", None) + model=model, + custom_llm_provider=custom_llm_provider, + api_base=kwargs.get("api_base", None), ) # Await normally @@ -4564,6 +4602,28 @@ def embedding( # noqa: PLR0915 aembedding=aembedding, headers=headers, ) + elif custom_llm_provider == "ovhcloud": + api_key = api_key or litellm.api_key or get_secret_str("OVHCLOUD_API_KEY") + api_base = ( + api_base + or litellm.api_base + or get_secret_str("OVHCLOUD_API_BASE") + or "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" + ) + response = base_llm_http_handler.embedding( + model=model, + input=input, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + litellm_params={}, + ) elif custom_llm_provider in litellm._custom_providers: custom_handler: Optional[CustomLLM] = None for item in litellm.custom_provider_map: @@ -5258,7 +5318,10 @@ def transcription( model_response = litellm.utils.TranscriptionResponse() model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider, api_base=api_base + model=model, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, ) # type: ignore if dynamic_api_key is not None: @@ -5274,6 +5337,7 @@ def transcription( custom_llm_provider=custom_llm_provider, **non_default_params, ) + litellm_params_dict = get_litellm_params(**kwargs) litellm_logging_obj.update_environment_variables( @@ -5338,9 +5402,8 @@ def transcription( max_retries=max_retries, litellm_params=litellm_params_dict, ) - elif ( - custom_llm_provider == "openai" - or custom_llm_provider in litellm.openai_compatible_providers + elif custom_llm_provider == "openai" or ( + custom_llm_provider in litellm.openai_compatible_providers ): api_base = ( api_base @@ -5355,6 +5418,7 @@ def transcription( or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 ) # set API KEY + api_key = api_key or litellm.api_key or litellm.openai_key or get_secret("OPENAI_API_KEY") # type: ignore response = openai_audio_transcriptions.audio_transcriptions( model=model, @@ -5371,10 +5435,7 @@ def transcription( provider_config=provider_config, litellm_params=litellm_params_dict, ) - elif custom_llm_provider in [ - LlmProviders.DEEPGRAM.value, - LlmProviders.ELEVENLABS.value, - ]: + elif provider_config is not None: response = base_llm_http_handler.audio_transcriptions( model=model, audio_file=file, @@ -5780,7 +5841,14 @@ async def ahealth_check( input=input or ["test"], ), "audio_speech": lambda: litellm.aspeech( - **{**_filter_model_params(model_params), **({"voice": "alloy"} if "voice" not in _filter_model_params(model_params) else {})}, + **{ + **_filter_model_params(model_params), + **( + {"voice": "alloy"} + if "voice" not in _filter_model_params(model_params) + else {} + ), + }, input=prompt or "test", ), "audio_transcription": lambda: litellm.atranscription( diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 168cbeeade0..43ea3af320f 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1,2569 +1,881 @@ { - "sample_spec": { - "max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.", - "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens", - "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens", - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "output_cost_per_reasoning_token": 0.0, - "litellm_provider": "one of https://docs.litellm.ai/docs/providers", - "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank", - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_vision": true, - "supports_audio_input": true, - "supports_audio_output": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_reasoning": true, - "supports_web_search": true, - "search_context_cost_per_query": { - "search_context_size_low": 0.0, - "search_context_size_medium": 0.0, - "search_context_size_high": 0.0 - }, - "file_search_cost_per_1k_calls": 0.0, - "file_search_cost_per_gb_per_day": 0.0, - "vector_store_cost_per_gb_per_day": 0.0, - "computer_use_input_cost_per_1k_tokens": 0.0, - "computer_use_output_cost_per_1k_tokens": 0.0, - "code_interpreter_cost_per_session": 0.0, - "supported_regions": [ - "global", - "us-west-2", - "eu-west-1", - "ap-southeast-1", - "ap-northeast-1" - ], - "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD" - }, - "omni-moderation-latest": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "omni-moderation-latest-intents": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "omni-moderation-2024-09-26": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 0, - "input_cost_per_token": 0.0, - "output_cost_per_token": 0.0, - "litellm_provider": "openai", - "mode": "moderation" - }, - "gpt-4": { - "max_tokens": 4096, - "max_input_tokens": 8192, - "max_output_tokens": 4096, - "input_cost_per_token": 3e-05, - "output_cost_per_token": 6e-05, - "litellm_provider": "openai", - "mode": "chat", - "supports_function_calling": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true - }, - "gpt-4.1": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 2e-06, - "output_cost_per_token": 8e-06, - "input_cost_per_token_batches": 1e-06, - "output_cost_per_token_batches": 4e-06, - "cache_read_input_token_cost": 5e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-2025-04-14": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 2e-06, - "output_cost_per_token": 8e-06, - "input_cost_per_token_batches": 1e-06, - "output_cost_per_token_batches": 4e-06, - "cache_read_input_token_cost": 5e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_native_streaming": true - }, - "gpt-4.1-mini": { - "max_tokens": 32768, - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "input_cost_per_token": 4e-07, - "output_cost_per_token": 1.6e-06, - "input_cost_per_token_batches": 2e-07, - "output_cost_per_token_batches": 8e-07, - "cache_read_input_token_cost": 1e-07, - "litellm_provider": "openai", - "mode": "chat", - "supported_endpoints": [ - "/v1/chat/completions", - "/v1/batch", - "/v1/responses" - ], - "supported_modalities": [ - "text", - "image" - ], - "supported_output_modalities": [ - "text" - ], - "supports_pdf_input": true, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_prompt_caching": true, - 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+ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "output_vector_size": 1024, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-claude-3-7-sonnet": { + "input_cost_per_token": 2.5e-06, + "input_dbu_cost_per_token": 3.571e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Claude 3.7 conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.7857e-05, + "output_db_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-gte-large-en": { + "input_cost_per_token": 1.2999e-07, + "input_dbu_cost_per_token": 1.857e-06, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "output_vector_size": 1024, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-llama-2-70b-chat": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "output_dbu_cost_per_token": 2.1429e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-llama-4-maverick": { + "input_cost_per_token": 5e-06, + "input_dbu_cost_per_token": 7.143e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Databricks documentation now provides both DBU costs (_dbu_cost_per_token) and dollar costs(_cost_per_token)." + }, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_dbu_cost_per_token": 0.00021429, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-1-405b-instruct": { + "input_cost_per_token": 5e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.500002e-05, + "output_db_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-3-70b-instruct": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.99999e-06, + "output_dbu_cost_per_token": 4.2857e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-70b-instruct": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.99999e-06, + "output_dbu_cost_per_token": 4.2857e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mixtral-8x7b-instruct": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.9902e-07, + "output_dbu_cost_per_token": 1.4286e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mpt-30b-instruct": { + "input_cost_per_token": 9.9902e-07, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.9902e-07, + "output_dbu_cost_per_token": 1.4286e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mpt-7b-instruct": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "davinci-002": { + "input_cost_per_token": 2e-06, + "litellm_provider": "text-completion-openai", + "max_input_tokens": 16384, + "max_output_tokens": 4096, + "max_tokens": 16384, + "mode": "completion", + "output_cost_per_token": 2e-06 + }, + "deepgram/base": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-conversationalai": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-finance": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-general": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-meeting": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-phonecall": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-video": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-voicemail": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-finance": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-general": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-meeting": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-phonecall": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-atc": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-automotive": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-conversationalai": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-drivethru": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-finance": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-general": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-meeting": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-phonecall": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-video": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-voicemail": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-3": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-3-general": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-3-medical": { + "input_cost_per_second": 8.667e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0052/60 seconds = $0.00008667 per second (multilingual)", + "original_pricing_per_minute": 0.0052 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-general": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-phonecall": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/whisper": { + "input_cost_per_second": 0.0001, + "litellm_provider": "deepgram", + "metadata": { + "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" + }, + "mode": "audio_transcription", + 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Defaulting to base model pricing", + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:gpt-4o-2024-08-06": { + "input_cost_per_token": 3.75e-06, + "input_cost_per_token_batches": 1.875e-06, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_batches": 7.5e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "ft:gpt-4o-2024-11-20": { + "cache_creation_input_token_cost": 1.875e-06, + "input_cost_per_token": 3.75e-06, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "ft:gpt-4o-mini-2024-07-18": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 3e-07, + "input_cost_per_token_batches": 1.5e-07, + "litellm_provider": "openai", + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "output_cost_per_token_batches": 6e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "gemini-1.0-pro": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 32760, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#google_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true }, "gemini-1.0-pro-001": { - "max_tokens": 8192, + "deprecation_date": "2025-04-09", + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 32760, "max_output_tokens": 8192, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", + "max_tokens": 8192, "mode": "chat", - "supports_function_calling": true, + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.0-ultra": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 2048, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", "supports_function_calling": true, - "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.0-ultra-001": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 2048, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, - "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": true, - "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_parallel_function_calling": true, + "supports_tool_choice": true }, "gemini-1.0-pro-002": { - "max_tokens": 8192, + "deprecation_date": "2025-04-09", + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", "max_input_tokens": 32760, "max_output_tokens": 8192, - "input_cost_per_image": 0.0025, - "input_cost_per_video_per_second": 0.002, - "input_cost_per_token": 5e-07, - "input_cost_per_character": 1.25e-07, - "output_cost_per_token": 1.5e-06, + "max_tokens": 8192, + "mode": "chat", "output_cost_per_character": 3.75e-07, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_function_calling": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 1.25e-06, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 2.5e-06, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 5e-06, - "output_cost_per_character": 1.25e-06, - "output_cost_per_token_above_128k_tokens": 1e-05, - "output_cost_per_character_above_128k_tokens": 2.5e-06, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_vision": true, - "supports_pdf_input": true, - "supports_system_messages": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_parallel_function_calling": true - }, - "gemini-1.5-pro-002": { - "max_tokens": 8192, - "max_input_tokens": 2097152, - "max_output_tokens": 8192, - "input_cost_per_image": 0.00032875, - "input_cost_per_audio_per_second": 3.125e-05, - "input_cost_per_video_per_second": 0.00032875, - "input_cost_per_token": 1.25e-06, - "input_cost_per_character": 3.125e-07, - "input_cost_per_image_above_128k_tokens": 0.0006575, - "input_cost_per_video_per_second_above_128k_tokens": 0.0006575, - "input_cost_per_audio_per_second_above_128k_tokens": 6.25e-05, - "input_cost_per_token_above_128k_tokens": 2.5e-06, - "input_cost_per_character_above_128k_tokens": 6.25e-07, - "output_cost_per_token": 5e-06, - "output_cost_per_character": 1.25e-06, - 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"input_cost_per_character_above_128k_tokens": 2.5e-07, - "input_cost_per_image_above_128k_tokens": 4e-05, - "input_cost_per_video_per_second_above_128k_tokens": 4e-05, - "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, - "output_cost_per_token": 4.6875e-09, - "output_cost_per_character": 1.875e-08, - "output_cost_per_token_above_128k_tokens": 9.375e-09, - "output_cost_per_character_above_128k_tokens": 3.75e-08, - "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "supports_system_messages": true, - "supports_function_calling": true, - "supports_vision": true, - "supports_response_schema": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true - }, - "gemini-1.5-flash-002": { - "max_tokens": 8192, - "max_input_tokens": 1048576, - "max_output_tokens": 8192, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, - 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"input_cost_per_token": 5e-07, "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", - "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true }, "gemini-1.0-pro-vision": { - "max_tokens": 2048, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-vision-models", + "max_images_per_prompt": 16, "max_input_tokens": 16384, "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, + "max_tokens": 2048, "max_video_length": 2, - "input_cost_per_token": 5e-07, - "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", + "max_videos_per_prompt": 1, "mode": "chat", - "supports_function_calling": true, - "supports_vision": true, + "output_cost_per_token": 1.5e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_vision": true }, "gemini-1.0-pro-vision-001": { - "max_tokens": 2048, + "deprecation_date": "2025-04-09", + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "litellm_provider": "vertex_ai-vision-models", + "max_images_per_prompt": 16, "max_input_tokens": 16384, "max_output_tokens": 2048, - "max_images_per_prompt": 16, - "max_videos_per_prompt": 1, + "max_tokens": 2048, "max_video_length": 2, - "input_cost_per_token": 5e-07, + "max_videos_per_prompt": 1, + "mode": "chat", "output_cost_per_token": 1.5e-06, - "input_cost_per_image": 0.0025, - "litellm_provider": "vertex_ai-vision-models", - "mode": "chat", + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", "supports_function_calling": true, - "supports_vision": true, - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "deprecation_date": "2025-04-09", + "supports_parallel_function_calling": true, "supports_tool_choice": true, - "supports_parallel_function_calling": true + "supports_vision": true }, - "medlm-medium": { - "max_tokens": 8192, - "max_input_tokens": 32768, - "max_output_tokens": 8192, - "input_cost_per_character": 5e-07, - "output_cost_per_character": 1e-06, + "gemini-1.0-ultra": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, "litellm_provider": "vertex_ai-language-models", - "mode": "chat", - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", - "supports_tool_choice": true - }, - "medlm-large": { - "max_tokens": 1024, "max_input_tokens": 8192, - "max_output_tokens": 1024, - "input_cost_per_character": 5e-06, - "output_cost_per_character": 1.5e-05, - "litellm_provider": "vertex_ai-language-models", + "max_output_tokens": 2048, + "max_tokens": 8192, "mode": "chat", - "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, + "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, "supports_tool_choice": true }, - "gemini-2.5-pro-exp-03-25": { - "max_tokens": 65535, - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_images_per_prompt": 3000, - "max_videos_per_prompt": 10, - "max_video_length": 1, + "gemini-1.0-ultra-001": { + "input_cost_per_character": 1.25e-07, + "input_cost_per_image": 0.0025, + "input_cost_per_token": 5e-07, + "input_cost_per_video_per_second": 0.002, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 8192, + "max_output_tokens": 2048, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_character": 3.75e-07, + "output_cost_per_token": 1.5e-06, + "source": "As of Jun, 2024. There is no available doc on vertex ai pricing gemini-1.0-ultra-001. Using gemini-1.0-pro pricing. Got max_tokens info here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_tool_choice": true + }, + "gemini-1.5-flash": { + "input_cost_per_audio_per_second": 2e-06, + "input_cost_per_audio_per_second_above_128k_tokens": 4e-06, + "input_cost_per_character": 1.875e-08, + "input_cost_per_character_above_128k_tokens": 2.5e-07, + "input_cost_per_image": 2e-05, + "input_cost_per_image_above_128k_tokens": 4e-05, + "input_cost_per_token": 7.5e-08, + "input_cost_per_token_above_128k_tokens": 1e-06, + "input_cost_per_video_per_second": 2e-05, + "input_cost_per_video_per_second_above_128k_tokens": 4e-05, + "litellm_provider": "vertex_ai-language-models", "max_audio_length_hours": 8.4, "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, "max_pdf_size_mb": 30, - "input_cost_per_token": 1.25e-06, - 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Assumed 50/50 split for input/output." - } - }, - "nscale/meta-llama/Llama-3.1-8B-Instruct": { - "input_cost_per_token": 3e-08, - "output_cost_per_token": 3e-08, - "litellm_provider": "nscale", - "mode": "chat", - "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models", - "metadata": { - "notes": "Pricing listed as $0.06/1M tokens total. Assumed 50/50 split for input/output." - } - }, - "nscale/meta-llama/Llama-3.3-70B-Instruct": { + "groq/gemma2-9b-it": { "input_cost_per_token": 2e-07, + "litellm_provider": "groq", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", "output_cost_per_token": 2e-07, - "litellm_provider": "nscale", - "mode": "chat", - "source": "https://docs.nscale.com/docs/inference/serverless-models/current#chat-models", - "metadata": { - "notes": "Pricing listed as $0.40/1M tokens total. Assumed 50/50 split for input/output." - } - }, - "nscale/black-forest-labs/FLUX.1-schnell": { - "mode": "image_generation", - "input_cost_per_pixel": 1.3e-09, - "output_cost_per_pixel": 0.0, - "litellm_provider": "nscale", - "supported_endpoints": [ - "/v1/images/generations" - ], - "source": "https://docs.nscale.com/docs/inference/serverless-models/current#image-models" - }, - "nscale/stabilityai/stable-diffusion-xl-base-1.0": { - "mode": "image_generation", - "input_cost_per_pixel": 3e-09, - "output_cost_per_pixel": 0.0, - "litellm_provider": "nscale", - "supported_endpoints": [ - "/v1/images/generations" - ], - "source": "https://docs.nscale.com/docs/inference/serverless-models/current#image-models" - }, - "featherless_ai/featherless-ai/Qwerky-72B": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 4096, - "litellm_provider": "featherless_ai", - "mode": "chat" - }, - "featherless_ai/featherless-ai/Qwerky-QwQ-32B": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 4096, - "litellm_provider": "featherless_ai", - "mode": "chat" - }, - "deepgram/nova-3": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-3-general": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-3-medical": { - "mode": "audio_transcription", - "input_cost_per_second": 8.667e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0052, - "calculation": "$0.0052/60 seconds = $0.00008667 per second (multilingual)" - } - }, - "deepgram/nova-2": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-general": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-meeting": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-voicemail": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-finance": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-conversationalai": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-video": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-drivethru": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-automotive": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-2-atc": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-general": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/nova-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 7.167e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0043, - "calculation": "$0.0043/60 seconds = $0.00007167 per second" - } - }, - "deepgram/enhanced": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-general": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-meeting": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/enhanced-finance": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00024167, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0145, - "calculation": "$0.0145/60 seconds = $0.00024167 per second" - } - }, - "deepgram/base": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-general": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-meeting": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-phonecall": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-voicemail": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-finance": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-conversationalai": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/base-video": { - "mode": "audio_transcription", - "input_cost_per_second": 0.00020833, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "original_pricing_per_minute": 0.0125, - "calculation": "$0.0125/60 seconds = $0.00020833 per second" - } - }, - "deepgram/whisper": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-tiny": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-base": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-small": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-medium": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "deepgram/whisper-large": { - "mode": "audio_transcription", - "input_cost_per_second": 0.0001, - "output_cost_per_second": 0.0, - "litellm_provider": "deepgram", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://deepgram.com/pricing", - "metadata": { - "notes": "Deepgram's hosted OpenAI Whisper models - pricing may differ from native Deepgram models" - } - }, - "elevenlabs/scribe_v1": { - "mode": "audio_transcription", - "input_cost_per_second": 6.11e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "elevenlabs", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://elevenlabs.io/pricing", - "metadata": { - "original_pricing_per_hour": 0.22, - "calculation": "$0.22/hour = $0.00366/minute = $0.0000611 per second (enterprise pricing)", - "notes": "ElevenLabs Scribe v1 - state-of-the-art speech recognition model with 99 language support" - } - }, - "elevenlabs/scribe_v1_experimental": { - "mode": "audio_transcription", - "input_cost_per_second": 6.11e-05, - "output_cost_per_second": 0.0, - "litellm_provider": "elevenlabs", - "supported_endpoints": [ - "/v1/audio/transcriptions" - ], - "source": "https://elevenlabs.io/pricing", - "metadata": { - "original_pricing_per_hour": 0.22, - "calculation": "$0.22/hour = $0.00366/minute = $0.0000611 per second (enterprise pricing)", - "notes": "ElevenLabs Scribe v1 experimental - enhanced version of the main Scribe model" - } - }, - "bedrock/us-gov-east-1/amazon.titan-embed-text-v1": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "output_vector_size": 1536, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 0.0, - "litellm_provider": "bedrock", - "mode": "embedding" - }, - "bedrock/us-gov-east-1/amazon.titan-embed-text-v2:0": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "output_vector_size": 1024, - "input_cost_per_token": 2e-07, - "output_cost_per_token": 0.0, - "litellm_provider": "bedrock", - "mode": "embedding" - }, - "bedrock/us-gov-east-1/amazon.titan-text-express-v1": { - "max_tokens": 8000, - "max_input_tokens": 42000, - "max_output_tokens": 8000, - "input_cost_per_token": 1.3e-06, - "output_cost_per_token": 1.7e-06, - "litellm_provider": "bedrock", - "mode": "chat" - }, - "bedrock/us-gov-east-1/amazon.titan-text-lite-v1": { - "max_tokens": 4000, - "max_input_tokens": 42000, - "max_output_tokens": 4000, - "input_cost_per_token": 3e-07, - "output_cost_per_token": 4e-07, - "litellm_provider": "bedrock", - "mode": "chat" - }, - "bedrock/us-gov-east-1/amazon.titan-text-premier-v1:0": { - "max_tokens": 32000, - "max_input_tokens": 42000, - "max_output_tokens": 32000, - "input_cost_per_token": 5e-07, - "output_cost_per_token": 1.5e-06, - "litellm_provider": "bedrock", - "mode": "chat" - }, - "bedrock/us-gov-east-1/anthropic.claude-3-5-sonnet-20240620-v1:0": { - "max_tokens": 8192, - "max_input_tokens": 200000, - "max_output_tokens": 8192, - "input_cost_per_token": 3.6e-06, - "output_cost_per_token": 1.8e-05, - "litellm_provider": "bedrock", - "mode": "chat", - "supports_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_pdf_input": true, - "supports_tool_choice": true - }, - "bedrock/us-gov-east-1/anthropic.claude-3-haiku-20240307-v1:0": { - "max_tokens": 4096, - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "input_cost_per_token": 3e-07, - "output_cost_per_token": 1.5e-06, - "litellm_provider": "bedrock", - "mode": "chat", - "supports_function_calling": true, - "supports_response_schema": true, - "supports_vision": true, - "supports_pdf_input": true, - "supports_tool_choice": true - }, - 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true, + "supports_tool_choice": true + }, + "sambanova/Meta-Llama-3.2-1B-Instruct": { + "input_cost_per_token": 4e-08, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 8e-08, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/Meta-Llama-3.2-3B-Instruct": { + "input_cost_per_token": 8e-08, + "litellm_provider": "sambanova", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 1.6e-07, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/Meta-Llama-3.3-70B-Instruct": { + "input_cost_per_token": 6e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "sambanova/Meta-Llama-Guard-3-8B": { + "input_cost_per_token": 3e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 3e-07, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/QwQ-32B": { + "input_cost_per_token": 5e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 16384, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1e-06, + "source": "https://cloud.sambanova.ai/plans/pricing" + }, + "sambanova/Qwen2-Audio-7B-Instruct": { + "input_cost_per_token": 5e-07, + "litellm_provider": "sambanova", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 0.0001, + "source": "https://cloud.sambanova.ai/plans/pricing", + "supports_audio_input": true + }, + 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"max_tokens": 128000, + "mode": "chat" + }, + "snowflake/mixtral-8x7b": { + "litellm_provider": "snowflake", + "max_input_tokens": 32000, + "max_output_tokens": 8192, + "max_tokens": 32000, + "mode": "chat" + }, + "snowflake/reka-core": { + "litellm_provider": "snowflake", + "max_input_tokens": 32000, + "max_output_tokens": 8192, + "max_tokens": 32000, + "mode": "chat" + }, + "snowflake/reka-flash": { + "litellm_provider": "snowflake", + "max_input_tokens": 100000, + "max_output_tokens": 8192, + "max_tokens": 100000, + "mode": "chat" + }, + "snowflake/snowflake-arctic": { + "litellm_provider": "snowflake", + "max_input_tokens": 4096, + "max_output_tokens": 8192, + "max_tokens": 4096, + "mode": "chat" + }, + "snowflake/snowflake-llama-3.1-405b": { + "litellm_provider": "snowflake", + "max_input_tokens": 8000, + "max_output_tokens": 8192, + "max_tokens": 8000, + "mode": "chat" + }, + "snowflake/snowflake-llama-3.3-70b": { + "litellm_provider": "snowflake", + "max_input_tokens": 8000, + "max_output_tokens": 8192, + "max_tokens": 8000, + "mode": "chat" + }, + "stability.sd3-5-large-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.08 + }, + "stability.sd3-large-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.08 + }, + "stability.stable-image-core-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.04 + }, + "stability.stable-image-core-v1:1": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.04 + }, + "stability.stable-image-ultra-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.14 + }, + 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"https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-bison32k": { + "input_cost_per_character": 2.5e-07, + "input_cost_per_token": 1.25e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "max_tokens": 1024, + "mode": "completion", + "output_cost_per_character": 5e-07, + "output_cost_per_token": 1.25e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-bison32k@002": { + "input_cost_per_character": 2.5e-07, + "input_cost_per_token": 1.25e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "max_tokens": 1024, + "mode": "completion", + "output_cost_per_character": 5e-07, + "output_cost_per_token": 1.25e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-bison@001": { + "input_cost_per_character": 2.5e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "max_tokens": 1024, + "mode": "completion", + "output_cost_per_character": 5e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-bison@002": { + "input_cost_per_character": 2.5e-07, + "litellm_provider": "vertex_ai-text-models", + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "max_tokens": 1024, + "mode": "completion", + "output_cost_per_character": 5e-07, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "text-completion-codestral/codestral-2405": { + "input_cost_per_token": 0.0, + "litellm_provider": "text-completion-codestral", + "max_input_tokens": 32000, + "max_output_tokens": 8191, + "max_tokens": 8191, + "mode": "completion", + "output_cost_per_token": 0.0, + "source": "https://docs.mistral.ai/capabilities/code_generation/" + }, + 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"source": "https://x.ai/api#pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_tool_choice": true, + "supports_web_search": true }, - "aiml/flux-pro": { - "output_cost_per_image": 0.053, - "litellm_provider": "aiml", - "mode": "image_generation", - "supported_endpoints": [ - "/v1/images/generations" - ], - "source": "https://docs.aimlapi.com/", - "metadata": { - "notes": "Flux Dev - Development version optimized for experimentation" - } + "xai/grok-3-mini-fast-beta": { + "input_cost_per_token": 6e-07, + "litellm_provider": "xai", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://x.ai/api#pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_tool_choice": true, + "supports_web_search": true }, - "aiml/dall-e-3": { - "output_cost_per_image": 0.042, - "litellm_provider": "aiml", - "mode": "image_generation", - "supported_endpoints": [ - "/v1/images/generations" - ], - "source": "https://docs.aimlapi.com/", - "metadata": { - "notes": "DALL-E 3 via AI/ML API - High-quality text-to-image generation" - } + "xai/grok-3-mini-fast-latest": { + "input_cost_per_token": 6e-07, + "litellm_provider": "xai", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://x.ai/api#pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_tool_choice": true, + "supports_web_search": true }, - "aiml/dall-e-2": { - "output_cost_per_image": 0.021, - "litellm_provider": "aiml", - "mode": "image_generation", - "supported_endpoints": [ - "/v1/images/generations" - ], - "source": "https://docs.aimlapi.com/", - "metadata": { - "notes": "DALL-E 2 via AI/ML API - Reliable text-to-image generation" - } + "xai/grok-3-mini-latest": { + "input_cost_per_token": 3e-07, + "litellm_provider": "xai", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 5e-07, + "source": "https://x.ai/api#pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_tool_choice": true, + "supports_web_search": true }, - "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" - } + "xai/grok-4": { + "input_cost_per_token": 3e-06, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_web_search": true }, - "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" - } + "xai/grok-4-0709": { + "input_cost_per_token": 3e-06, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_web_search": true }, - "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" - } + "xai/grok-4-latest": { + "input_cost_per_token": 3e-06, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_web_search": true }, - "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" - } + "xai/grok-beta": { + "input_cost_per_token": 5e-06, + "litellm_provider": "xai", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true }, - "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" - } + "xai/grok-code-fast": { + "cache_read_input_token_cost": 2e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "xai/grok-code-fast-1": { + "cache_read_input_token_cost": 2e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "xai/grok-code-fast-1-0825": { + "cache_read_input_token_cost": 2e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "xai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://docs.x.ai/docs/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "xai/grok-vision-beta": { + "input_cost_per_image": 5e-06, + "input_cost_per_token": 5e-06, + "litellm_provider": "xai", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true } } \ No newline at end of file diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py index a075de13fb1..6afed97fe93 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py +++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py @@ -28,12 +28,16 @@ class MCPRequestHandler: LITELLM_MCP_SERVERS_HEADER_NAME = SpecialHeaders.mcp_servers.value LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME = SpecialHeaders.mcp_access_groups.value - + # MCP Protocol Version header MCP_PROTOCOL_VERSION_HEADER_NAME = "MCP-Protocol-Version" @staticmethod - async def process_mcp_request(scope: Scope) -> Tuple[UserAPIKeyAuth, Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str]]: + async def process_mcp_request( + scope: Scope, + ) -> Tuple[ + UserAPIKeyAuth, Optional[str], Optional[List[str]], Optional[Dict[str, str]] + ]: """ Process and validate MCP request headers from the ASGI scope. This includes: @@ -49,7 +53,6 @@ class MCPRequestHandler: mcp_auth_header: Optional[str] MCP auth header to be passed to the MCP server (deprecated) mcp_servers: Optional[List[str]] List of MCP servers and access groups to use mcp_server_auth_headers: Optional[Dict[str, str]] Server-specific auth headers in format {server_alias: auth_value} - mcp_protocol_version: Optional[str] MCP protocol version from request header Raises: HTTPException: If headers are invalid or missing required headers @@ -58,39 +61,50 @@ class MCPRequestHandler: litellm_api_key = ( MCPRequestHandler.get_litellm_api_key_from_headers(headers) or "" ) - + # Get the old mcp_auth_header for backward compatibility mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers) - - # Get the new server-specific auth headers - mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) - # Get MCP protocol version from header - mcp_protocol_version = headers.get(MCPRequestHandler.MCP_PROTOCOL_VERSION_HEADER_NAME) + # Get the new server-specific auth headers + mcp_server_auth_headers = ( + MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) + ) # Parse MCP servers from header - mcp_servers_header = headers.get(MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME) + mcp_servers_header = headers.get( + MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME + ) verbose_logger.debug(f"Raw MCP servers header: {mcp_servers_header}") mcp_servers = None if mcp_servers_header is not None: try: - mcp_servers = [s.strip() for s in mcp_servers_header.split(",") if s.strip()] + mcp_servers = [ + s.strip() for s in mcp_servers_header.split(",") if s.strip() + ] verbose_logger.debug(f"Parsed MCP servers: {mcp_servers}") except Exception as e: verbose_logger.debug(f"Error parsing mcp_servers header: {e}") mcp_servers = None - if mcp_servers_header == "" or (mcp_servers is not None and len(mcp_servers) == 0): + if mcp_servers_header == "" or ( + mcp_servers is not None and len(mcp_servers) == 0 + ): mcp_servers = [] # Create a proper Request object with mock body method to avoid ASGI receive channel issues request = Request(scope=scope) + async def mock_body(): return b"{}" + request.body = mock_body # type: ignore validated_user_api_key_auth = await user_api_key_auth( api_key=litellm_api_key, request=request ) - return validated_user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version - + return ( + validated_user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + ) @staticmethod def _get_mcp_auth_header_from_headers(headers: Headers) -> Optional[str]: @@ -104,10 +118,12 @@ class MCPRequestHandler: Support this auth: https://docs.litellm.ai/docs/mcp#using-your-mcp-with-client-side-credentials If you want to use a different header name, you can set the `LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME` in the secret manager or `mcp_client_side_auth_header_name` in the general settings. - + DEPRECATED: This method is deprecated in favor of server-specific auth headers using the format x-mcp-{{server_alias}}-{{header_name}} instead. """ - mcp_client_side_auth_header_name: str = MCPRequestHandler._get_mcp_client_side_auth_header_name() + mcp_client_side_auth_header_name: str = ( + MCPRequestHandler._get_mcp_client_side_auth_header_name() + ) auth_header = headers.get(mcp_client_side_auth_header_name) if auth_header: verbose_logger.warning( @@ -115,42 +131,49 @@ class MCPRequestHandler: f"Please use server-specific auth headers in the format 'x-mcp-{{server_alias}}-{{header_name}}' instead." ) return auth_header - + @staticmethod def _get_mcp_server_auth_headers_from_headers(headers: Headers) -> Dict[str, str]: """ Parse server-specific MCP auth headers from the request headers. - + Looks for headers in the format: x-mcp-{server_alias}-{header_name} Examples: - x-mcp-github-authorization: Bearer token123 - x-mcp-zapier-x-api-key: api_key_456 - x-mcp-deepwiki-authorization: Basic base64_encoded_creds - + Returns: Dict[str, str]: Mapping of server alias to auth value """ server_auth_headers = {} prefix = "x-mcp-" - + for header_name, header_value in headers.items(): if header_name.lower().startswith(prefix): # Skip the access groups header as it's not a server auth header - if header_name.lower() == MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME.lower() or header_name.lower() == MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME.lower(): + if ( + header_name.lower() + == MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME.lower() + or header_name.lower() + == MCPRequestHandler.LITELLM_MCP_SERVERS_HEADER_NAME.lower() + ): continue - + # Extract server_alias and header_name from x-mcp-{server_alias}-{header_name} - remaining = header_name[len(prefix):].lower() - if '-' in remaining: + remaining = header_name[len(prefix) :].lower() + if "-" in remaining: # Split on the last dash to separate server_alias from header_name - parts = remaining.rsplit('-', 1) + parts = remaining.rsplit("-", 1) if len(parts) == 2: server_alias, auth_header_name = parts server_auth_headers[server_alias] = header_value - verbose_logger.debug(f"Found server auth header: {server_alias} -> {auth_header_name}: {header_value[:10]}...") - + verbose_logger.debug( + f"Found server auth header: {server_alias} -> {auth_header_name}: {header_value[:10]}..." + ) + return server_auth_headers - + @staticmethod def _get_mcp_client_side_auth_header_name() -> str: """ @@ -162,13 +185,21 @@ class MCPRequestHandler: """ from litellm.proxy.proxy_server import general_settings from litellm.secret_managers.main import get_secret_str - MCP_CLIENT_SIDE_AUTH_HEADER_NAME: str = MCPRequestHandler.LITELLM_MCP_AUTH_HEADER_NAME - if get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") is not None: - MCP_CLIENT_SIDE_AUTH_HEADER_NAME = get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") or MCP_CLIENT_SIDE_AUTH_HEADER_NAME - elif general_settings.get("mcp_client_side_auth_header_name") is not None: - MCP_CLIENT_SIDE_AUTH_HEADER_NAME = general_settings.get("mcp_client_side_auth_header_name") or MCP_CLIENT_SIDE_AUTH_HEADER_NAME - return MCP_CLIENT_SIDE_AUTH_HEADER_NAME + MCP_CLIENT_SIDE_AUTH_HEADER_NAME: str = ( + MCPRequestHandler.LITELLM_MCP_AUTH_HEADER_NAME + ) + if get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") is not None: + MCP_CLIENT_SIDE_AUTH_HEADER_NAME = ( + get_secret_str("LITELLM_MCP_CLIENT_SIDE_AUTH_HEADER_NAME") + or MCP_CLIENT_SIDE_AUTH_HEADER_NAME + ) + elif general_settings.get("mcp_client_side_auth_header_name") is not None: + MCP_CLIENT_SIDE_AUTH_HEADER_NAME = ( + general_settings.get("mcp_client_side_auth_header_name") + or MCP_CLIENT_SIDE_AUTH_HEADER_NAME + ) + return MCP_CLIENT_SIDE_AUTH_HEADER_NAME @staticmethod def get_litellm_api_key_from_headers(headers: Headers) -> Optional[str]: @@ -229,10 +260,14 @@ class MCPRequestHandler: try: allowed_mcp_servers: List[str] = [] allowed_mcp_servers_for_key = ( - await MCPRequestHandler._get_allowed_mcp_servers_for_key(user_api_key_auth) + await MCPRequestHandler._get_allowed_mcp_servers_for_key( + user_api_key_auth + ) ) allowed_mcp_servers_for_team = ( - await MCPRequestHandler._get_allowed_mcp_servers_for_team(user_api_key_auth) + await MCPRequestHandler._get_allowed_mcp_servers_for_team( + user_api_key_auth + ) ) ######################################################### @@ -274,7 +309,9 @@ class MCPRequestHandler: try: key_object_permission = ( await prisma_client.db.litellm_objectpermissiontable.find_unique( - where={"object_permission_id": user_api_key_auth.object_permission_id}, + where={ + "object_permission_id": user_api_key_auth.object_permission_id + }, ) ) if key_object_permission is None: @@ -282,17 +319,21 @@ class MCPRequestHandler: # Get direct MCP servers direct_mcp_servers = key_object_permission.mcp_servers or [] - + # Get MCP servers from access groups - access_group_servers = await MCPRequestHandler._get_mcp_servers_from_access_groups( - key_object_permission.mcp_access_groups or [] + access_group_servers = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + key_object_permission.mcp_access_groups or [] + ) ) - + # Combine both lists all_servers = direct_mcp_servers + access_group_servers return list(set(all_servers)) except Exception as e: - verbose_logger.warning(f"Failed to get allowed MCP servers for key: {str(e)}") + verbose_logger.warning( + f"Failed to get allowed MCP servers for key: {str(e)}" + ) return [] @staticmethod @@ -318,10 +359,10 @@ class MCPRequestHandler: return [] try: - team_obj: Optional[LiteLLM_TeamTable] = ( - await prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": user_api_key_auth.team_id}, - ) + team_obj: Optional[ + LiteLLM_TeamTable + ] = await prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": user_api_key_auth.team_id}, ) if team_obj is None: verbose_logger.debug("team_obj is None") @@ -333,21 +374,27 @@ class MCPRequestHandler: # Get direct MCP servers direct_mcp_servers = object_permissions.mcp_servers or [] - + # Get MCP servers from access groups - access_group_servers = await MCPRequestHandler._get_mcp_servers_from_access_groups( - object_permissions.mcp_access_groups or [] + access_group_servers = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + object_permissions.mcp_access_groups or [] + ) ) - + # Combine both lists all_servers = direct_mcp_servers + access_group_servers return list(set(all_servers)) except Exception as e: - verbose_logger.warning(f"Failed to get allowed MCP servers for team: {str(e)}") + verbose_logger.warning( + f"Failed to get allowed MCP servers for team: {str(e)}" + ) return [] @staticmethod - def _get_config_server_ids_for_access_groups(config_mcp_servers, access_groups: List[str]) -> Set[str]: + def _get_config_server_ids_for_access_groups( + config_mcp_servers, access_groups: List[str] + ) -> Set[str]: """ Helper to get server_ids from config-loaded servers that match any of the given access groups. """ @@ -359,7 +406,9 @@ class MCPRequestHandler: return server_ids @staticmethod - async def _get_db_server_ids_for_access_groups(prisma_client, access_groups: List[str]) -> Set[str]: + async def _get_db_server_ids_for_access_groups( + prisma_client, access_groups: List[str] + ) -> Set[str]: """ Helper to get server_ids from DB servers that match any of the given access groups. """ @@ -367,21 +416,19 @@ class MCPRequestHandler: if access_groups and prisma_client is not None: try: mcp_servers = await prisma_client.db.litellm_mcpservertable.find_many( - where={ - "mcp_access_groups": { - "hasSome": access_groups - } - } + where={"mcp_access_groups": {"hasSome": access_groups}} ) for server in mcp_servers: server_ids.add(server.server_id) except Exception as e: - verbose_logger.debug(f"Error getting MCP servers from access groups: {e}") + verbose_logger.debug( + f"Error getting MCP servers from access groups: {e}" + ) return server_ids @staticmethod async def _get_mcp_servers_from_access_groups( - access_groups: List[str] + access_groups: List[str], ) -> List[str]: """ Resolve MCP access groups to server IDs by querying BOTH the MCP server table (DB) AND config-loaded servers @@ -390,22 +437,28 @@ class MCPRequestHandler: try: # Import here to avoid circular import - from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager - + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + # Use the new helper for config-loaded servers server_ids = MCPRequestHandler._get_config_server_ids_for_access_groups( global_mcp_server_manager.config_mcp_servers, access_groups ) # Use the new helper for DB servers - db_server_ids = await MCPRequestHandler._get_db_server_ids_for_access_groups( - prisma_client, access_groups + db_server_ids = ( + await MCPRequestHandler._get_db_server_ids_for_access_groups( + prisma_client, access_groups + ) ) server_ids.update(db_server_ids) return list(server_ids) except Exception as e: - verbose_logger.warning(f"Failed to get MCP servers from access groups: {str(e)}") + verbose_logger.warning( + f"Failed to get MCP servers from access groups: {str(e)}" + ) return [] @staticmethod @@ -418,8 +471,8 @@ class MCPRequestHandler: from typing import List access_groups: List[str] = [] - access_groups_for_key = ( - await MCPRequestHandler._get_mcp_access_groups_for_key(user_api_key_auth) + access_groups_for_key = await MCPRequestHandler._get_mcp_access_groups_for_key( + user_api_key_auth ) access_groups_for_team = ( await MCPRequestHandler._get_mcp_access_groups_for_team(user_api_key_auth) @@ -482,10 +535,10 @@ class MCPRequestHandler: verbose_logger.debug("prisma_client is None") return [] - team_obj: Optional[LiteLLM_TeamTable] = ( - await prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": user_api_key_auth.team_id}, - ) + team_obj: Optional[ + LiteLLM_TeamTable + ] = await prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": user_api_key_auth.team_id}, ) if team_obj is None: verbose_logger.debug("team_obj is None") @@ -502,10 +555,14 @@ class MCPRequestHandler: """ Extract and parse the x-mcp-access-groups header as a list of strings. """ - mcp_access_groups_header = headers.get(MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME) + mcp_access_groups_header = headers.get( + MCPRequestHandler.LITELLM_MCP_ACCESS_GROUPS_HEADER_NAME + ) if mcp_access_groups_header is not None: try: - return [s.strip() for s in mcp_access_groups_header.split(",") if s.strip()] + return [ + s.strip() for s in mcp_access_groups_header.split(",") if s.strip() + ] except Exception: return None return None @@ -516,4 +573,4 @@ class MCPRequestHandler: Extract and parse the x-mcp-access-groups header from an ASGI scope. """ headers = MCPRequestHandler._safe_get_headers_from_scope(scope) - return MCPRequestHandler.get_mcp_access_groups_from_headers(headers) \ No newline at end of file + return MCPRequestHandler.get_mcp_access_groups_from_headers(headers) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 34a0d604f39..d0eadb36ba3 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -34,8 +34,6 @@ from litellm.proxy._experimental.mcp_server.utils import ( from litellm.proxy._types import ( LiteLLM_MCPServerTable, MCPAuthType, - MCPSpecVersion, - MCPSpecVersionType, MCPTransport, MCPTransportType, UserAPIKeyAuth, @@ -70,38 +68,6 @@ def _deserialize_env_dict(env_data: Any) -> Optional[Dict[str, str]]: return env_data -def _convert_protocol_version_to_enum( - protocol_version: Optional[str | MCPSpecVersionType], -) -> MCPSpecVersionType: - """ - Convert string protocol version to MCPSpecVersion enum. - - Args: - protocol_version: String protocol version, enum, or None - - Returns: - MCPSpecVersionType: The enum value - """ - if not protocol_version: - return cast(MCPSpecVersionType, MCPSpecVersion.jun_2025) - - # If it's already an MCPSpecVersion enum, return it - if isinstance(protocol_version, MCPSpecVersion): - return cast(MCPSpecVersionType, protocol_version) - - # If it's a string, try to match it to enum values - if isinstance(protocol_version, str): - for version in MCPSpecVersion: - if version.value == protocol_version: - return cast(MCPSpecVersionType, version) - - # If no match found, return default - verbose_logger.warning( - f"Unknown protocol version '{protocol_version}', using default" - ) - return cast(MCPSpecVersionType, MCPSpecVersion.jun_2025) - - class MCPServerManager: def __init__(self): self.registry: Dict[str, MCPServer] = {} @@ -113,8 +79,7 @@ class MCPServerManager: "name": "zapier_mcp_server", "url": "https://actions.zapier.com/mcp/sk-ak-2ew3bofIeQIkNoeKIdXrF1Hhhp/sse" "transport": "sse", - "auth_type": "api_key", - "spec_version": "2025-03-26" + "auth_type": "api_key" }, "uuid-2": { "name": "google_drive_mcp_server", @@ -223,7 +188,6 @@ class MCPServerManager: server_name=server_name, url=server_config.get("url", None) or "", transport=server_config.get("transport", MCPTransport.http), - spec_version=server_config.get("spec_version", MCPSpecVersion.jun_2025), auth_type=server_config.get("auth_type", None), alias=alias, ) @@ -239,8 +203,10 @@ class MCPServerManager: env=server_config.get("env", None) or {}, # TODO: utility fn the default values transport=server_config.get("transport", MCPTransport.http), - spec_version=server_config.get("spec_version", MCPSpecVersion.jun_2025), auth_type=server_config.get("auth_type", None), + authentication_token=server_config.get( + "authentication_token", server_config.get("auth_value", None) + ), mcp_info=mcp_info, access_groups=server_config.get("access_groups", None), ) @@ -284,7 +250,6 @@ class MCPServerManager: server_name=getattr(mcp_server, "server_name", None), url=mcp_server.url, transport=cast(MCPTransportType, mcp_server.transport), - spec_version=_convert_protocol_version_to_enum(mcp_server.spec_version), auth_type=cast(MCPAuthType, mcp_server.auth_type), mcp_info=MCPInfo( server_name=mcp_server.server_name or mcp_server.server_id, @@ -347,7 +312,6 @@ class MCPServerManager: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ List all tools available across all MCP Servers. @@ -387,7 +351,6 @@ class MCPServerManager: tools = await self._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) list_tools_result.extend(tools) verbose_logger.info( @@ -411,7 +374,6 @@ class MCPServerManager: self, server: MCPServer, mcp_auth_header: Optional[str] = None, - protocol_version: Optional[str] = None, ) -> MCPClient: """ Create an MCPClient instance for the given server. @@ -419,18 +381,12 @@ class MCPServerManager: Args: server (MCPServer): The server configuration mcp_auth_header: MCP auth header to be passed to the MCP server. This is optional and will be used if provided. - protocol_version: Optional MCP protocol version to use. If not provided, uses server's default. Returns: MCPClient: Configured MCP client instance """ transport = server.transport or MCPTransport.sse - # Convert protocol version string to enum - protocol_version_enum = _convert_protocol_version_to_enum( - protocol_version or server.spec_version - ) - # Handle stdio transport if transport == MCPTransport.stdio: # For stdio, we need to get the stdio config from the server @@ -447,7 +403,6 @@ class MCPServerManager: auth_value=mcp_auth_header or server.authentication_token, timeout=60.0, stdio_config=stdio_config, - protocol_version=protocol_version_enum, ) else: # For HTTP/SSE transports @@ -458,14 +413,12 @@ class MCPServerManager: auth_type=server.auth_type, auth_value=mcp_auth_header or server.authentication_token, timeout=60.0, - protocol_version=protocol_version_enum, ) async def _get_tools_from_server( self, server: MCPServer, mcp_auth_header: Optional[str] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ Helper method to get tools from a single MCP server with prefixed names. @@ -480,22 +433,18 @@ class MCPServerManager: verbose_logger.debug(f"Connecting to url: {server.url}") verbose_logger.info(f"_get_tools_from_server for {server.name}...") - protocol_version = ( - mcp_protocol_version if mcp_protocol_version else server.spec_version - ) client = None try: client = self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, - protocol_version=protocol_version, ) tools = await self._fetch_tools_with_timeout(client, server.name) - + prefixed_tools = self._create_prefixed_tools(tools, server) - + return prefixed_tools except Exception as e: @@ -527,7 +476,7 @@ class MCPServerManager: async def _list_tools_task(): try: await client.connect() - + tools = await client.list_tools() verbose_logger.debug(f"Tools from {server_name}: {tools}") return tools @@ -606,7 +555,6 @@ class MCPServerManager: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, proxy_logging_obj: Optional[ProxyLogging] = None, ) -> CallToolResult: """ @@ -657,32 +605,54 @@ class MCPServerManager: "arguments": arguments, "server_name": server_name_from_prefix, "user_api_key_auth": user_api_key_auth, - "user_api_key_user_id": getattr(user_api_key_auth, 'user_id', None) if user_api_key_auth else None, - "user_api_key_team_id": getattr(user_api_key_auth, 'team_id', None) if user_api_key_auth else None, - "user_api_key_end_user_id": getattr(user_api_key_auth, 'end_user_id', None) if user_api_key_auth else None, - "user_api_key_hash": getattr(user_api_key_auth, 'api_key_hash', None) if user_api_key_auth else None, + "user_api_key_user_id": getattr(user_api_key_auth, "user_id", None) + if user_api_key_auth + else None, + "user_api_key_team_id": getattr(user_api_key_auth, "team_id", None) + if user_api_key_auth + else None, + "user_api_key_end_user_id": getattr( + user_api_key_auth, "end_user_id", None + ) + if user_api_key_auth + else None, + "user_api_key_hash": getattr(user_api_key_auth, "api_key_hash", None) + if user_api_key_auth + else None, } - + # Create MCP request object for processing - mcp_request_obj = proxy_logging_obj._create_mcp_request_object_from_kwargs(pre_hook_kwargs) - + mcp_request_obj = proxy_logging_obj._create_mcp_request_object_from_kwargs( + pre_hook_kwargs + ) + # Convert to LLM format for existing guardrail compatibility - synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(mcp_request_obj, pre_hook_kwargs) - + synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format( + mcp_request_obj, pre_hook_kwargs + ) + try: # Use standard pre_call_hook with call_type="mcp_call" modified_data = await proxy_logging_obj.pre_call_hook( - user_api_key_dict=user_api_key_auth, #type: ignore + user_api_key_dict=user_api_key_auth, # type: ignore data=synthetic_llm_data, - call_type="mcp_call" #type: ignore + call_type="mcp_call", # type: ignore ) if modified_data: # Convert response back to MCP format and apply modifications - modified_kwargs = proxy_logging_obj._convert_mcp_hook_response_to_kwargs(modified_data, pre_hook_kwargs) + modified_kwargs = ( + proxy_logging_obj._convert_mcp_hook_response_to_kwargs( + modified_data, pre_hook_kwargs + ) + ) if modified_kwargs.get("arguments") != arguments: arguments = modified_kwargs["arguments"] - - except (BlockedPiiEntityError, GuardrailRaisedException, HTTPException) as e: + + except ( + BlockedPiiEntityError, + GuardrailRaisedException, + HTTPException, + ) as e: # Re-raise guardrail exceptions to properly fail the MCP call verbose_logger.error( f"Guardrail blocked MCP tool call pre call: {str(e)}" @@ -703,11 +673,9 @@ class MCPServerManager: client = self._create_mcp_client( server=mcp_server, mcp_auth_header=server_auth_header, - protocol_version=mcp_protocol_version, ) async with client: - # Use the original tool name (without prefix) for the actual call call_tool_params = MCPCallToolRequestParams( name=original_tool_name, @@ -716,9 +684,9 @@ class MCPServerManager: tasks = [] if proxy_logging_obj: # Create synthetic LLM data for during hook processing - from litellm.types.mcp import MCPDuringCallRequestObject from litellm.types.llms.base import HiddenParams - + from litellm.types.mcp import MCPDuringCallRequestObject + request_obj = MCPDuringCallRequestObject( tool_name=name, arguments=arguments, @@ -726,28 +694,29 @@ class MCPServerManager: start_time=start_time.timestamp() if start_time else None, hidden_params=HiddenParams(), ) - + during_hook_kwargs = { "name": name, "arguments": arguments, "server_name": server_name_from_prefix, "user_api_key_auth": user_api_key_auth, } - - synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format(request_obj, during_hook_kwargs) - + + synthetic_llm_data = proxy_logging_obj._convert_mcp_to_llm_format( + request_obj, during_hook_kwargs + ) + during_hook_task = asyncio.create_task( proxy_logging_obj.during_call_hook( user_api_key_dict=user_api_key_auth, data=synthetic_llm_data, - call_type="mcp_call" #type: ignore + call_type="mcp_call", # type: ignore ) ) tasks.append(during_hook_task) tasks.append(asyncio.create_task(client.call_tool(call_tool_params))) try: - mcp_responses = await asyncio.gather(*tasks) # If proxy_logging_obj is None, the tool call result is at index 0 @@ -836,19 +805,21 @@ class MCPServerManager: ) verbose_logger.info("Loading MCP servers from database into registry...") - + # perform authz check to filter the mcp servers user has access to prisma_client = get_prisma_client_or_throw( "Database not connected. Connect a database to your proxy" ) db_mcp_servers = await get_all_mcp_servers(prisma_client) verbose_logger.info(f"Found {len(db_mcp_servers)} MCP servers in database") - + # ensure the global_mcp_server_manager is up to date with the db for server in db_mcp_servers: - verbose_logger.debug(f"Adding server to registry: {server.server_id} ({server.server_name})") + verbose_logger.debug( + f"Adding server to registry: {server.server_id} ({server.server_name})" + ) self.add_update_server(server) - + verbose_logger.info(f"Registry now contains {len(self.get_registry())} servers") def get_mcp_server_by_id(self, server_id: str) -> Optional[MCPServer]: @@ -866,7 +837,6 @@ class MCPServerManager: server_name: str, url: str, transport: str, - spec_version: str, auth_type: Optional[str] = None, alias: Optional[str] = None, ) -> str: @@ -882,7 +852,6 @@ class MCPServerManager: server_name: Name of the server url: Server URL transport: Transport type (sse, http, etc.) - spec_version: MCP spec version auth_type: Authentication type (optional) alias: Server alias (optional) @@ -890,7 +859,9 @@ class MCPServerManager: A deterministic server ID string """ # Create a string from all the identifying parameters - params_string = f"{server_name}|{url}|{transport}|{spec_version}|{auth_type or ''}|{alias or ''}" + params_string = ( + f"{server_name}|{url}|{transport}|{auth_type or ''}|{alias or ''}" + ) # Generate SHA-256 hash hash_object = hashlib.sha256(params_string.encode("utf-8")) @@ -1047,11 +1018,12 @@ class MCPServerManager: alias=_server_config.alias, url=_server_config.url, transport=_server_config.transport, - spec_version=_server_config.spec_version, auth_type=_server_config.auth_type, created_at=datetime.datetime.now(), updated_at=datetime.datetime.now(), - description=_server_config.mcp_info.get("description") if _server_config.mcp_info else None, + description=_server_config.mcp_info.get("description") + if _server_config.mcp_info + else None, mcp_info=_server_config.mcp_info, mcp_access_groups=_server_config.access_groups or [], # Stdio-specific fields @@ -1108,7 +1080,6 @@ class MCPServerManager: description=server.description, url=server.url, transport=server.transport, - spec_version=server.spec_version, auth_type=server.auth_type, created_at=server.created_at, created_by=server.created_by, diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index 048b25fa35a..2a9174717d1 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -23,7 +23,6 @@ router = APIRouter( if MCP_AVAILABLE: from litellm.experimental_mcp_client.client import MCPTool from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( - _convert_protocol_version_to_enum, global_mcp_server_manager, ) from litellm.proxy._experimental.mcp_server.server import ( @@ -34,18 +33,24 @@ if MCP_AVAILABLE: ######################################################## ############ MCP Server REST API Routes ################# def _get_server_auth_header( - server, mcp_server_auth_headers: Optional[Dict[str, str]], mcp_auth_header: Optional[str] + server, + mcp_server_auth_headers: Optional[Dict[str, str]], + mcp_auth_header: Optional[str], ) -> Optional[str]: """Helper function to get server-specific auth header with case-insensitive matching.""" if mcp_server_auth_headers and server.alias: normalized_server_alias = server.alias.lower() - normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()} + normalized_headers = { + k.lower(): v for k, v in mcp_server_auth_headers.items() + } server_auth = normalized_headers.get(normalized_server_alias) if server_auth is not None: return server_auth elif mcp_server_auth_headers and server.server_name: normalized_server_name = server.server_name.lower() - normalized_headers = {k.lower(): v for k, v in mcp_server_auth_headers.items()} + normalized_headers = { + k.lower(): v for k, v in mcp_server_auth_headers.items() + } server_auth = normalized_headers.get(normalized_server_name) if server_auth is not None: return server_auth @@ -63,12 +68,11 @@ if MCP_AVAILABLE: for tool in tools ] - async def _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version): + async def _get_tools_for_single_server(server, server_auth_header): """Helper function to get tools for a single server.""" tools = await global_mcp_server_manager._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) return _create_tool_response_objects(tools, server.mcp_info) @@ -104,17 +108,20 @@ if MCP_AVAILABLE: from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( MCPRequestHandler, ) - + try: # Extract auth headers from request headers = request.headers - mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers) - mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) - mcp_protocol_version = headers.get(MCPRequestHandler.MCP_PROTOCOL_VERSION_HEADER_NAME) - + mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers( + headers + ) + mcp_server_auth_headers = ( + MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) + ) + list_tools_result = [] error_message = None - + # If server_id is specified, only query that specific server if server_id: server = global_mcp_server_manager.get_mcp_server_by_id(server_id) @@ -122,49 +129,67 @@ if MCP_AVAILABLE: return { "tools": [], "error": "server_not_found", - "message": f"Server with id {server_id} not found" + "message": f"Server with id {server_id} not found", } - - server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header) - + + server_auth_header = _get_server_auth_header( + server, mcp_server_auth_headers, mcp_auth_header + ) + try: - list_tools_result = await _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version) + list_tools_result = await _get_tools_for_single_server( + server, server_auth_header + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from {server.name}: {e}") + verbose_logger.exception( + f"Error getting tools from {server.name}: {e}" + ) return { "tools": [], "error": "server_error", - "message": f"Failed to get tools from server {server.name}: {str(e)}" + "message": f"Failed to get tools from server {server.name}: {str(e)}", } else: # Query all servers errors = [] for server in global_mcp_server_manager.get_registry().values(): - server_auth_header = _get_server_auth_header(server, mcp_server_auth_headers, mcp_auth_header) - + server_auth_header = _get_server_auth_header( + server, mcp_server_auth_headers, mcp_auth_header + ) + try: - tools_result = await _get_tools_for_single_server(server, server_auth_header, mcp_protocol_version) + tools_result = await _get_tools_for_single_server( + server, server_auth_header + ) list_tools_result.extend(tools_result) except Exception as e: - verbose_logger.exception(f"Error getting tools from {server.name}: {e}") + verbose_logger.exception( + f"Error getting tools from {server.name}: {e}" + ) errors.append(f"{server.name}: {str(e)}") continue - + if errors and not list_tools_result: - error_message = "Failed to get tools from servers: " + "; ".join(errors) - + error_message = "Failed to get tools from servers: " + "; ".join( + errors + ) + return { "tools": list_tools_result, "error": "partial_failure" if error_message else None, - "message": error_message if error_message else "Successfully retrieved tools" + "message": error_message + if error_message + else "Successfully retrieved tools", } - + except Exception as e: - verbose_logger.exception("Unexpected error in list_tool_rest_api: %s", str(e)) + verbose_logger.exception( + "Unexpected error in list_tool_rest_api: %s", str(e) + ) return { "tools": [], "error": "unexpected_error", - "message": f"An unexpected error occurred: {str(e)}" + "message": f"An unexpected error occurred: {str(e)}", } @router.post("/tools/call", dependencies=[Depends(user_api_key_auth)]) @@ -196,9 +221,9 @@ if MCP_AVAILABLE: detail={ "error": "blocked_pii_entity", "message": str(e), - "entity_type": getattr(e, 'entity_type', None), - "guardrail_name": getattr(e, 'guardrail_name', None) - } + "entity_type": getattr(e, "entity_type", None), + "guardrail_name": getattr(e, "guardrail_name", None), + }, ) except GuardrailRaisedException as e: verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}") @@ -207,8 +232,8 @@ if MCP_AVAILABLE: detail={ "error": "guardrail_violation", "message": str(e), - "guardrail_name": getattr(e, 'guardrail_name', None) - } + "guardrail_name": getattr(e, "guardrail_name", None), + }, ) except HTTPException as e: # Re-raise HTTPException as-is to preserve status code and detail @@ -220,10 +245,10 @@ if MCP_AVAILABLE: status_code=500, detail={ "error": "internal_server_error", - "message": f"An unexpected error occurred: {str(e)}" - } + "message": f"An unexpected error occurred: {str(e)}", + }, ) - + ######################################################## # MCP Connection testing routes # /health -> Test if we can connect to the MCP server @@ -234,15 +259,15 @@ if MCP_AVAILABLE: from litellm.proxy.management_endpoints.mcp_management_endpoints import ( NewMCPServerRequest, ) - + async def _execute_with_mcp_client(request: NewMCPServerRequest, operation): """ Common helper to create MCP client, execute operation, and ensure proper cleanup. - + Args: request: MCP server configuration operation: Async function that takes a client and returns the operation result - + Returns: Operation result or error response """ @@ -254,15 +279,14 @@ if MCP_AVAILABLE: name=request.alias or request.server_name or "", url=request.url, transport=request.transport, - spec_version=_convert_protocol_version_to_enum(request.spec_version), auth_type=request.auth_type, mcp_info=request.mcp_info, ), mcp_auth_header=None, ) - + return await operation(client) - + except Exception as e: verbose_logger.error(f"Error in MCP operation: {e}", exc_info=True) return {"status": "error", "message": "An internal error has occurred."} @@ -273,6 +297,7 @@ if MCP_AVAILABLE: await client.disconnect() except Exception as e: verbose_logger.warning(f"Error disconnecting MCP client: {e}") + @router.post("/test/connection") async def test_connection( request: NewMCPServerRequest, @@ -280,13 +305,13 @@ if MCP_AVAILABLE: """ Test if we can connect to the provided MCP server before adding it """ + async def _test_connection_operation(client): await client.connect() return {"status": "ok"} - + return await _execute_with_mcp_client(request, _test_connection_operation) - - + @router.post("/test/tools/list") async def test_tools_list( request: NewMCPServerRequest, @@ -295,13 +320,16 @@ if MCP_AVAILABLE: """ Preview tools available from MCP server before adding it """ + async def _list_tools_operation(client): list_tools_result: List[MCPTool] = await client.list_tools() - model_dumped_tools: List[dict] = [tool.model_dump() for tool in list_tools_result] + model_dumped_tools: List[dict] = [ + tool.model_dump() for tool in list_tools_result + ] return { "tools": model_dumped_tools, "error": None, - "message": "Successfully retrieved tools" + "message": "Successfully retrieved tools", } - + return await _execute_with_mcp_client(request, _list_tools_operation) diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 38619112ccc..3e45fccdc28 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -130,7 +130,9 @@ if MCP_AVAILABLE: await _sse_session_manager_cm.__aenter__() _SESSION_MANAGERS_INITIALIZED = True - verbose_logger.info("MCP Server started with StreamableHTTP and SSE session managers!") + verbose_logger.info( + "MCP Server started with StreamableHTTP and SSE session managers!" + ) async def shutdown_session_managers(): """Shutdown the session managers.""" @@ -171,11 +173,18 @@ if MCP_AVAILABLE: """ try: # Get user authentication from context variable - user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version = ( - get_auth_context() + ( + user_api_key_auth, + mcp_auth_header, + mcp_servers, + mcp_server_auth_headers, + ) = get_auth_context() + verbose_logger.debug( + f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}" + ) + verbose_logger.debug( + f"MCP list_tools - MCP servers from context: {mcp_servers}" ) - verbose_logger.debug(f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}") - verbose_logger.debug(f"MCP list_tools - MCP servers from context: {mcp_servers}") verbose_logger.debug( f"MCP list_tools - MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) @@ -186,9 +195,10 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) - verbose_logger.info(f"MCP list_tools - Successfully returned {len(tools)} tools") + verbose_logger.info( + f"MCP list_tools - Successfully returned {len(tools)} tools" + ) return tools except Exception as e: verbose_logger.exception(f"Error in list_tools endpoint: {str(e)}") @@ -215,14 +225,21 @@ if MCP_AVAILABLE: """ from fastapi import Request + from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException from litellm.proxy.litellm_pre_call_utils import add_litellm_data_to_request from litellm.proxy.proxy_server import proxy_config - from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException # Validate arguments - user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, mcp_protocol_version = get_auth_context() + ( + user_api_key_auth, + mcp_auth_header, + _, + mcp_server_auth_headers, + ) = get_auth_context() - verbose_logger.debug(f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}") + verbose_logger.debug( + f"MCP mcp_server_tool_call - User API Key Auth from context: {user_api_key_auth}" + ) try: # Create a body date for logging body_data = {"name": name, "arguments": arguments} @@ -249,17 +266,22 @@ if MCP_AVAILABLE: user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, **data, # for logging ) except BlockedPiiEntityError as e: verbose_logger.error(f"BlockedPiiEntityError in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent(text=f"Error: Blocked PII entity detected - {str(e)}", type="text")] + return [ + TextContent( + text=f"Error: Blocked PII entity detected - {str(e)}", type="text" + ) + ] except GuardrailRaisedException as e: verbose_logger.error(f"GuardrailRaisedException in MCP tool call: {str(e)}") # Return error as text content for MCP protocol - return [TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text")] + return [ + TextContent(text=f"Error: Guardrail violation - {str(e)}", type="text") + ] except HTTPException as e: verbose_logger.error(f"HTTPException in MCP tool call: {str(e)}") # Return error as text content for MCP protocol @@ -279,12 +301,62 @@ if MCP_AVAILABLE: ############ Helper Functions ########################## ######################################################## + async def _get_allowed_mcp_servers_from_mcp_server_names( + mcp_servers: Optional[List[str]], + allowed_mcp_servers: List[str], + ) -> List[str]: + """ + Get the filtered MCP servers from the MCP server names + """ + from typing import Set + + filtered_server_ids: Set[str] = set() + # Filter servers based on mcp_servers parameter if provided + if mcp_servers is not None: + for server_or_group in mcp_servers: + server_name_matched = False + + for server_id in allowed_mcp_servers: + server = global_mcp_server_manager.get_mcp_server_by_id(server_id) + + if server: + match_list = [ + s.lower() + for s in [server.alias, server.server_name, server_id] + if s is not None + ] + + if server_or_group.lower() in match_list: + filtered_server_ids.add(server_id) + server_name_matched = True + break + + if not server_name_matched: + try: + access_group_server_ids = ( + await MCPRequestHandler._get_mcp_servers_from_access_groups( + [server_or_group] + ) + ) + # Only include servers that the user has access to + for server_id in access_group_server_ids: + if server_id in allowed_mcp_servers: + filtered_server_ids.add(server_id) + except Exception as e: + verbose_logger.debug( + f"Could not resolve '{server_or_group}' as access group: {e}" + ) + + if filtered_server_ids: + allowed_mcp_servers = list(filtered_server_ids) + + return allowed_mcp_servers + async def _get_tools_from_mcp_servers( user_api_key_auth: Optional[UserAPIKeyAuth], mcp_auth_header: Optional[str], mcp_servers: Optional[List[str]], mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ Helper method to fetch tools from MCP servers based on server filtering criteria. @@ -302,40 +374,15 @@ if MCP_AVAILABLE: return [] # Get allowed MCP servers based on user permissions - allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth) + allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers( + user_api_key_auth + ) - filtered_server_ids = set() - - # Filter servers based on mcp_servers parameter if provided if mcp_servers is not None: - for server_or_group in mcp_servers: - server_name_matched = False - - for server_id in allowed_mcp_servers: - server = global_mcp_server_manager.get_mcp_server_by_id(server_id) - - if server: - match_list = [s.lower() for s in [server.alias, server.server_name, server_id] if s is not None] - - if server_or_group.lower() in match_list: - filtered_server_ids.add(server_id) - server_name_matched = True - break - - if not server_name_matched: - try: - access_group_server_ids = await MCPRequestHandler._get_mcp_servers_from_access_groups( - [server_or_group] - ) - # Only include servers that the user has access to - for server_id in access_group_server_ids: - if server_id in allowed_mcp_servers: - filtered_server_ids.add(server_id) - except Exception as e: - verbose_logger.debug(f"Could not resolve '{server_or_group}' as access group: {e}") - - if filtered_server_ids: - allowed_mcp_servers = list(filtered_server_ids) + allowed_mcp_servers = await _get_allowed_mcp_servers_from_mcp_server_names( + mcp_servers=mcp_servers, + allowed_mcp_servers=allowed_mcp_servers, + ) # Get tools from each allowed server all_tools = [] @@ -359,15 +406,20 @@ if MCP_AVAILABLE: tools = await global_mcp_server_manager._get_tools_from_server( server=server, mcp_auth_header=server_auth_header, - mcp_protocol_version=mcp_protocol_version, ) all_tools.extend(tools) - verbose_logger.debug(f"Successfully fetched {len(tools)} tools from server {server.name}") + verbose_logger.debug( + f"Successfully fetched {len(tools)} tools from server {server.name}" + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from server {server.name}: {str(e)}") + verbose_logger.exception( + f"Error getting tools from server {server.name}: {str(e)}" + ) # Continue with other servers instead of failing completely - verbose_logger.info(f"Successfully fetched {len(all_tools)} tools total from all MCP servers") + verbose_logger.info( + f"Successfully fetched {len(all_tools)} tools total from all MCP servers" + ) return all_tools async def _list_mcp_tools( @@ -375,7 +427,6 @@ if MCP_AVAILABLE: mcp_auth_header: Optional[str] = None, mcp_servers: Optional[List[str]] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> List[MCPTool]: """ List all available MCP tools. @@ -399,11 +450,14 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) - verbose_logger.debug(f"Successfully fetched {len(managed_tools)} tools from managed MCP servers") + verbose_logger.debug( + f"Successfully fetched {len(managed_tools)} tools from managed MCP servers" + ) except Exception as e: - verbose_logger.exception(f"Error getting tools from managed MCP servers: {str(e)}") + verbose_logger.exception( + f"Error getting tools from managed MCP servers: {str(e)}" + ) # Continue with empty managed tools list instead of failing completely # Get tools from local registry @@ -414,10 +468,16 @@ if MCP_AVAILABLE: # Convert local tools to MCPTool format for tool in local_tools_raw: # Convert from litellm.types.mcp_server.tool_registry.MCPTool to mcp.types.Tool - mcp_tool = MCPTool(name=tool.name, description=tool.description, inputSchema=tool.input_schema) + mcp_tool = MCPTool( + name=tool.name, + description=tool.description, + inputSchema=tool.input_schema, + ) local_tools.append(mcp_tool) except Exception as e: - verbose_logger.exception(f"Error getting tools from local registry: {str(e)}") + verbose_logger.exception( + f"Error getting tools from local registry: {str(e)}" + ) # Continue with empty local tools list instead of failing completely # Combine all tools @@ -432,7 +492,6 @@ if MCP_AVAILABLE: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, **kwargs: Any, ) -> List[Union[TextContent, ImageContent, EmbeddedResource]]: """ @@ -440,35 +499,46 @@ if MCP_AVAILABLE: """ start_time = datetime.now() if arguments is None: - raise HTTPException(status_code=400, detail="Request arguments are required") + raise HTTPException( + status_code=400, detail="Request arguments are required" + ) # Remove prefix from tool name for logging and processing - original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp(name) - - standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = _get_standard_logging_mcp_tool_call( - name=original_tool_name, # Use original name for logging - arguments=arguments, - server_name=server_name_from_prefix, + original_tool_name, server_name_from_prefix = get_server_name_prefix_tool_mcp( + name + ) + + standard_logging_mcp_tool_call: StandardLoggingMCPToolCall = ( + _get_standard_logging_mcp_tool_call( + name=original_tool_name, # Use original name for logging + arguments=arguments, + server_name=server_name_from_prefix, + ) + ) + litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get( + "litellm_logging_obj", None ) - litellm_logging_obj: Optional[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj", None) if litellm_logging_obj: - litellm_logging_obj.model_call_details["mcp_tool_call_metadata"] = standard_logging_mcp_tool_call + litellm_logging_obj.model_call_details[ + "mcp_tool_call_metadata" + ] = standard_logging_mcp_tool_call litellm_logging_obj.model = f"MCP: {name}" # Try managed server tool first (pass the full prefixed name) # Primary and recommended way to use MCP servers ######################################################### - mcp_server: Optional[MCPServer] = global_mcp_server_manager._get_mcp_server_from_tool_name(name) + mcp_server: Optional[ + MCPServer + ] = global_mcp_server_manager._get_mcp_server_from_tool_name(name) if mcp_server: - standard_logging_mcp_tool_call["mcp_server_cost_info"] = (mcp_server.mcp_info or {}).get( - "mcp_server_cost_info" - ) + standard_logging_mcp_tool_call["mcp_server_cost_info"] = ( + mcp_server.mcp_info or {} + ).get("mcp_server_cost_info") response = await _handle_managed_mcp_tool( name=name, # Pass the full name (potentially prefixed) arguments=arguments, user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, litellm_logging_obj=litellm_logging_obj, ) @@ -521,7 +591,6 @@ if MCP_AVAILABLE: user_api_key_auth: Optional[UserAPIKeyAuth] = None, mcp_auth_header: Optional[str] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, litellm_logging_obj: Optional[Any] = None, ) -> List[Union[TextContent, ImageContent, EmbeddedResource]]: """Handle tool execution for managed server tools""" @@ -556,27 +625,62 @@ if MCP_AVAILABLE: except Exception as e: return [TextContent(text=f"Error: {str(e)}", type="text")] + def _get_mcp_servers_in_path(path: str) -> Optional[List[str]]: + """ + Get the MCP servers from the path + """ + import re + + mcp_servers_from_path: Optional[List[str]] = None + # Match /mcp// + # Where can be comma-separated list of server names + # Server names can contain slashes (e.g., "custom_solutions/user_123") + mcp_path_match = re.match(r"^/mcp/([^?#]+?)(/[^?#]*)?(?:\?.*)?(?:#.*)?$", path) + if mcp_path_match: + mcp_servers_str = mcp_path_match.group(1) + optional_path = mcp_path_match.group(2) + + if mcp_servers_str: + # First, try to split by comma for comma-separated lists + if ',' in mcp_servers_str: + # For comma-separated lists, we need to handle the case where the last item + # might include the path (e.g., "zapier,group1/tools" -> ["zapier", "group1/tools"]) + parts = [s.strip() for s in mcp_servers_str.split(",") if s.strip()] + + # If there's an optional path AND the last part contains a slash that matches the optional path, + # remove the path portion from the last server name + if optional_path and len(parts) > 0 and '/' in parts[-1]: + last_part = parts[-1] + # Check if the last part ends with the optional path + if optional_path and last_part.endswith(optional_path.lstrip('/')): + # Remove the path portion from the last server name + parts[-1] = last_part[:-len(optional_path.lstrip('/'))] + + mcp_servers_from_path = parts + else: + # For single server, it might be just a name or contain slashes + # We need to determine where the server name ends and the path begins + # This is tricky - let's use the original logic but handle comma cases differently + single_server_match = re.match(r"^([^/]+(?:/[^/]+)?)(?:/.*)?$", mcp_servers_str) + if single_server_match: + server_name = single_server_match.group(1) + mcp_servers_from_path = [server_name] + else: + mcp_servers_from_path = [mcp_servers_str] + return mcp_servers_from_path + async def extract_mcp_auth_context(scope, path): """ Extracts mcp_servers from the path and processes the MCP request for auth context. Returns: (user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers) """ - import re - - mcp_servers_from_path = None - mcp_path_match = re.match(r"^/mcp/([^/]+)(/.*)?$", path) - if mcp_path_match: - mcp_servers_str = mcp_path_match.group(1) - if mcp_servers_str: - mcp_servers_from_path = [s.strip() for s in mcp_servers_str.split(",") if s.strip()] - + mcp_servers_from_path = _get_mcp_servers_in_path(path) if mcp_servers_from_path is not None: ( user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, - mcp_protocol_version, ) = await MCPRequestHandler.process_mcp_request(scope) mcp_servers = mcp_servers_from_path else: @@ -585,11 +689,12 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await MCPRequestHandler.process_mcp_request(scope) - return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers, mcp_protocol_version + return user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers - async def handle_streamable_http_mcp(scope: Scope, receive: Receive, send: Send) -> None: + async def handle_streamable_http_mcp( + scope: Scope, receive: Receive, send: Send + ) -> None: """Handle MCP requests through StreamableHTTP.""" try: path = scope.get("path", "") @@ -598,20 +703,19 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await extract_mcp_auth_context(scope, path) - verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}") + verbose_logger.debug( + f"MCP request mcp_servers (header/path): {mcp_servers}" + ) verbose_logger.debug( f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) - verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}") # Set the auth context variable for easy access in MCP functions set_auth_context( user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) # Ensure session managers are initialized @@ -635,7 +739,9 @@ if MCP_AVAILABLE: ) await error_response(scope, receive, send) except Exception as response_error: - verbose_logger.exception(f"Failed to send error response: {response_error}") + verbose_logger.exception( + f"Failed to send error response: {response_error}" + ) # If we can't send a proper response, re-raise the original error raise e @@ -648,19 +754,18 @@ if MCP_AVAILABLE: mcp_auth_header, mcp_servers, mcp_server_auth_headers, - mcp_protocol_version, ) = await extract_mcp_auth_context(scope, path) - verbose_logger.debug(f"MCP request mcp_servers (header/path): {mcp_servers}") + verbose_logger.debug( + f"MCP request mcp_servers (header/path): {mcp_servers}" + ) verbose_logger.debug( f"MCP server auth headers: {list(mcp_server_auth_headers.keys()) if mcp_server_auth_headers else None}" ) - verbose_logger.debug(f"MCP protocol version: {mcp_protocol_version}") set_auth_context( user_api_key_auth=user_api_key_auth, mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) if not _SESSION_MANAGERS_INITIALIZED: @@ -682,7 +787,9 @@ if MCP_AVAILABLE: ) await error_response(scope, receive, send) except Exception as response_error: - verbose_logger.exception(f"Failed to send error response: {response_error}") + verbose_logger.exception( + f"Failed to send error response: {response_error}" + ) # If we can't send a proper response, re-raise the original error raise e @@ -718,7 +825,6 @@ if MCP_AVAILABLE: mcp_auth_header: Optional[str] = None, mcp_servers: Optional[List[str]] = None, mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, ) -> None: """ Set the UserAPIKeyAuth in the auth context variable. @@ -734,13 +840,17 @@ if MCP_AVAILABLE: mcp_auth_header=mcp_auth_header, mcp_servers=mcp_servers, mcp_server_auth_headers=mcp_server_auth_headers, - mcp_protocol_version=mcp_protocol_version, ) auth_context_var.set(auth_user) - def get_auth_context() -> Tuple[ - Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]], Optional[str] - ]: + def get_auth_context() -> ( + Tuple[ + Optional[UserAPIKeyAuth], + Optional[str], + Optional[List[str]], + Optional[Dict[str, str]], + ] + ): """ Get the UserAPIKeyAuth from the auth context variable. @@ -755,9 +865,8 @@ if MCP_AVAILABLE: auth_user.mcp_auth_header, auth_user.mcp_servers, auth_user.mcp_server_auth_headers, - auth_user.mcp_protocol_version, ) - return None, None, None, None, None + return None, None, None, None ######################################################## ############ End of Auth Context Functions ############# diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/117-6a9f6a591033d4a0.js 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n={method:"GET",headers:{[f]:"Bearer ".concat(e)}},l=await fetch(r,n);if(!l.ok){let e=await l.json(),t=ov(e);throw m(t),Error(t)}let c=await l.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eI=async(e,t,o,a)=>{try{let r=s?"".concat(s,"/global/activity/exceptions/deployment"):"/global/activity/exceptions/deployment";t&&o&&(r+="?start_date=".concat(t,"&end_date=").concat(o)),a&&(r+="&model_group=".concat(a));let n={method:"GET",headers:{[f]:"Bearer ".concat(e)}},l=await fetch(r,n);if(!l.ok){let e=await l.json(),t=ov(e);throw m(t),Error(t)}let c=await l.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eM=async e=>{try{let t=s?"".concat(s,"/global/spend/models?limit=5"):"/global/spend/models?limit=5",o=await fetch(t,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok){let e=await o.json(),t=ov(e);throw m(t),Error(t)}let a=await o.json();return console.log(a),a}catch(e){throw console.error("Failed to create key:",e),e}},ez=async(e,t)=>{try{let o=s?"".concat(s,"/v2/key/info"):"/v2/key/info",a=await fetch(o,{method:"POST",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"},body:JSON.stringify({keys:t})});if(!a.ok){let e=await a.text();if(e.includes("Invalid proxy server token passed"))throw Error("Invalid proxy server token passed");throw m(e),Error("Network response was not ok")}let r=await a.json();return console.log(r),r}catch(e){throw console.error("Failed to create key:",e),e}},eL=async(e,t,o)=>{try{console.log("Sending model connection test request:",JSON.stringify(t));let r=s?"".concat(s,"/health/test_connection"):"/health/test_connection",n=await fetch(r,{method:"POST",headers:{"Content-Type":"application/json",[f]:"Bearer ".concat(e)},body:JSON.stringify({litellm_params:t,mode:o})}),l=n.headers.get("content-type");if(!l||!l.includes("application/json")){let e=await n.text();throw console.error("Received non-JSON response:",e),Error("Received non-JSON response (".concat(n.status,": ").concat(n.statusText,"). 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u=s?"".concat(s,"/key/list"):"/key/list";console.log("in keyListCall");let h=new URLSearchParams;o&&h.append("team_id",o.toString()),t&&h.append("organization_id",t.toString()),a&&h.append("key_alias",a),n&&h.append("key_hash",n),r&&h.append("user_id",r.toString()),l&&h.append("page",l.toString()),c&&h.append("size",c.toString()),i&&h.append("sort_by",i),d&&h.append("sort_order",d),h.append("return_full_object","true"),h.append("include_team_keys","true");let p=h.toString();p&&(u+="?".concat(p));let g=await fetch(u,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!g.ok){let e=await g.json(),t=ov(e);throw m(t),Error(t)}let w=await g.json();return console.log("/team/list API Response:",w),w}catch(e){throw console.error("Failed to create key:",e),e}},eZ=async(e,t)=>{try{let o=s?"".concat(s,"/spend/users"):"/spend/users";console.log("in spendUsersCall:",o);let a=await fetch("".concat(o,"?user_id=").concat(t),{method:"GET",headers:{[f]:"Bearer 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or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(j,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(_,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(j,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(y,"\n").concat(t)}},49817:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return l}}),(r=a||(a={})).IMAGE_GENERATION="image_generation",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",(i=s||(s={})).IMAGE="image",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages";let o={image_generation:"image",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=o[e];return console.log("endpointType:",t),t}return"chat"}},29488:function(e,t,n){n.d(t,{Hc:function(){return 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extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(v,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(v,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(f,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(b).length>0,n="";if(e){let e=JSON.stringify({metadata:b},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(v,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(v,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(f,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===u?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(v,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===u?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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