diff --git a/.circleci/config.yml b/.circleci/config.yml index 62e5b77dc65..2a156798017 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" diff --git a/README.md b/README.md index c8a073432c9..df2350b6c9e 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ Discord - + Slack @@ -408,7 +408,7 @@ 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", 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[{"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/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/http_handler_config.md b/docs/my-website/docs/completion/http_handler_config.md new file mode 100644 index 00000000000..d4a25ce2043 --- /dev/null +++ b/docs/my-website/docs/completion/http_handler_config.md @@ -0,0 +1,145 @@ +# Custom HTTP Handler + +Configure custom aiohttp sessions for better performance and control in LiteLLM completions. + +## Overview + +You can now inject custom `aiohttp.ClientSession` instances into LiteLLM for: +- Custom connection pooling and timeouts +- Corporate proxy and SSL configurations +- Performance optimization +- Request monitoring + +## Basic Usage + +### Default (No Changes Required) +```python +import litellm + +# Works exactly as before +response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + +### Custom Session +```python +import aiohttp +import litellm +from litellm.llms.custom_httpx.aiohttp_handler import BaseLLMAIOHTTPHandler + +# Create optimized session +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=180), + connector=aiohttp.TCPConnector(limit=300, limit_per_host=75) +) + +# Replace global handler +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) + +# All completions now use your session +response = await litellm.acompletion(model="gpt-3.5-turbo", messages=[...]) +``` + +## Common Patterns + +### FastAPI Integration +```python +from contextlib import asynccontextmanager +from fastapi import FastAPI +import aiohttp +import litellm + +@asynccontextmanager +async def lifespan(app: FastAPI): + # Startup + session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=180), + connector=aiohttp.TCPConnector(limit=300) + ) + litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler( + client_session=session + ) + yield + # Shutdown + await session.close() + +app = FastAPI(lifespan=lifespan) + +@app.post("/chat") +async def chat(messages: list[dict]): + return await litellm.acompletion(model="gpt-3.5-turbo", messages=messages) +``` + +### Corporate Proxy +```python +import ssl + +# Custom SSL context +ssl_context = ssl.create_default_context() +ssl_context.load_cert_chain('cert.pem', 'key.pem') + +# Proxy session +session = aiohttp.ClientSession( + connector=aiohttp.TCPConnector(ssl=ssl_context), + trust_env=True # Use environment proxy settings +) + +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) +``` + +### High Performance +```python +# Optimized for high throughput +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=300), + connector=aiohttp.TCPConnector( + limit=1000, # High connection limit + limit_per_host=200, # Per host limit + ttl_dns_cache=600, # DNS cache + keepalive_timeout=60, # Keep connections alive + enable_cleanup_closed=True + ) +) + +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) +``` + +## Constructor Options + +```python +BaseLLMAIOHTTPHandler( + client_session=None, # Custom aiohttp.ClientSession + transport=None, # Advanced transport control + connector=None, # Custom aiohttp.BaseConnector +) +``` + +## Resource Management + +- **User sessions**: You manage the lifecycle (call `await session.close()`) +- **Auto-created sessions**: Automatically cleaned up by the handler +- **100% backward compatible**: Existing code works unchanged + +## Configuration Tips + +### Development +```python +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=60), + connector=aiohttp.TCPConnector(limit=50) +) +``` + +### Production +```python +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=300), + connector=aiohttp.TCPConnector( + limit=1000, + limit_per_host=200, + keepalive_timeout=60 + ) +) +``` \ No newline at end of file diff --git a/docs/my-website/docs/langchain/langchain.md b/docs/my-website/docs/langchain/langchain.md index 78425a73b99..c67375ce1be 100644 --- a/docs/my-website/docs/langchain/langchain.md +++ b/docs/my-website/docs/langchain/langchain.md @@ -162,3 +162,321 @@ Get more details [here](../observability/lunary_integration.md) ## Use LangChain ChatLiteLLM + Langfuse Checkout this section [here](../observability/langfuse_integration#use-langchain-chatlitellm--langfuse) for more details on how to integrate Langfuse with ChatLiteLLM. + +## Using Tags with LangChain and LiteLLM + +Tags are a powerful feature in LiteLLM that allow you to categorize, filter, and track your LLM requests. When using LangChain with LiteLLM, you can pass tags through the `extra_body` parameter in the metadata. + +### Basic Tag Usage + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +os.environ['OPENAI_API_KEY'] = "sk-your-key-here" + +chat = ChatOpenAI( + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["production", "customer-support", "high-priority"] + } + } +) + +messages = [ + SystemMessage(content="You are a helpful customer support assistant."), + HumanMessage(content="How do I reset my password?") +] + +response = chat.invoke(messages) +print(response) +``` + + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +os.environ['ANTHROPIC_API_KEY'] = "sk-ant-your-key-here" + +chat = ChatOpenAI( + model="claude-3-sonnet-20240229", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["research", "analysis", "claude-model"] + } + } +) + +messages = [ + SystemMessage(content="You are a research analyst."), + HumanMessage(content="Analyze this market trend...") +] + +response = chat.invoke(messages) +print(response) +``` + + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +# No API key needed when using proxy +chat = ChatOpenAI( + openai_api_base="http://localhost:4000", # Your proxy URL + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["proxy", "team-alpha", "feature-flagged"], + "generation_name": "customer-onboarding", + "trace_user_id": "user-12345" + } + } +) + +messages = [ + SystemMessage(content="You are an onboarding assistant."), + HumanMessage(content="Welcome our new customer!") +] + +response = chat.invoke(messages) +print(response) +``` + + + + +### Advanced Tag Patterns + +#### Dynamic Tags Based on Context + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +def create_chat_with_tags(user_type: str, feature: str): + """Create a chat instance with dynamic tags based on context""" + + # Build tags dynamically + tags = ["langchain-integration"] + + if user_type == "premium": + tags.extend(["premium-user", "high-priority"]) + elif user_type == "enterprise": + tags.extend(["enterprise", "custom-sla"]) + else: + tags.append("standard-user") + + # Add feature-specific tags + if feature == "code-review": + tags.extend(["development", "code-analysis"]) + elif feature == "content-gen": + tags.extend(["marketing", "content-creation"]) + + return ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": tags, + "user_type": user_type, + "feature": feature, + "trace_user_id": f"user-{user_type}-{feature}" + } + } + ) + +# Usage examples +premium_chat = create_chat_with_tags("premium", "code-review") +enterprise_chat = create_chat_with_tags("enterprise", "content-gen") + +messages = [HumanMessage(content="Help me with this task")] +response = premium_chat.invoke(messages) +``` + +#### Tags for Cost Tracking and Analytics + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +# Tags for cost tracking +cost_tracking_chat = ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": [ + "cost-center-marketing", + "budget-q4-2024", + "project-launch-campaign", + "high-cost-model" # Flag for expensive models + ], + "department": "marketing", + "project_id": "campaign-2024-q4", + "cost_threshold": "high" + } + } +) + +messages = [ + SystemMessage(content="You are a marketing copywriter."), + HumanMessage(content="Create compelling ad copy for our new product launch.") +] + +response = cost_tracking_chat.invoke(messages) +``` + +#### Tags for A/B Testing + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage +import random + +def create_ab_test_chat(test_variant: str = None): + """Create chat instance for A/B testing with appropriate tags""" + + if test_variant is None: + test_variant = random.choice(["variant-a", "variant-b"]) + + return ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7 if test_variant == "variant-a" else 0.9, # Different temp for variants + extra_body={ + "metadata": { + "tags": [ + "ab-test-experiment-1", + f"variant-{test_variant}", + "temperature-test", + "user-experience" + ], + "experiment_id": "ab-test-001", + "variant": test_variant, + "test_group": "temperature-optimization" + } + } + ) + +# Run A/B test +variant_a_chat = create_ab_test_chat("variant-a") +variant_b_chat = create_ab_test_chat("variant-b") + +test_message = [HumanMessage(content="Explain quantum computing in simple terms")] + +response_a = variant_a_chat.invoke(test_message) +response_b = variant_b_chat.invoke(test_message) +``` + +### Tag Best Practices + +#### 1. **Consistent Naming Convention** +```python +# ✅ Good: Consistent, descriptive tags +tags = ["production", "api-v2", "customer-support", "urgent"] + +# ❌ Avoid: Inconsistent or unclear tags +tags = ["prod", "v2", "support", "urgent123"] +``` + +#### 2. **Hierarchical Tags** +```python +# ✅ Good: Hierarchical structure +tags = ["env:production", "team:backend", "service:api", "priority:high"] + +# This allows for easy filtering and grouping +``` + +#### 3. **Include Context Information** +```python +extra_body={ + "metadata": { + "tags": ["production", "user-onboarding"], + "user_id": "user-12345", + "session_id": "session-abc123", + "feature_flag": "new-onboarding-flow", + "environment": "production" + } +} +``` + +#### 4. **Tag Categories** +Consider organizing tags into categories: +- **Environment**: `production`, `staging`, `development` +- **Team/Service**: `backend`, `frontend`, `api`, `worker` +- **Feature**: `authentication`, `payment`, `notification` +- **Priority**: `critical`, `high`, `medium`, `low` +- **User Type**: `premium`, `enterprise`, `free` + +### Using Tags with LiteLLM Proxy + +When using tags with LiteLLM Proxy, you can: + +1. **Filter requests** based on tags +2. **Track costs** by tags in spend reports +3. **Apply routing rules** based on tags +4. **Monitor usage** with tag-based analytics + +#### Example Proxy Configuration with Tags + +```yaml +# config.yaml +model_list: + - model_name: gpt-4o + litellm_params: + model: gpt-4o + api_key: your-key + +# Tag-based routing rules +tag_routing: + - tags: ["premium", "high-priority"] + models: ["gpt-4o", "claude-3-opus"] + - tags: ["standard"] + models: ["gpt-3.5-turbo", "claude-3-haiku"] +``` + +### Monitoring and Analytics + +Tags enable powerful analytics capabilities: + +```python +# Example: Get spend reports by tags +import requests + +response = requests.get( + "http://localhost:4000/global/spend/report", + headers={"Authorization": "Bearer sk-your-key"}, + params={ + "start_date": "2024-01-01", + "end_date": "2024-12-31", + "group_by": "tags" + } +) + +spend_by_tags = response.json() +``` + +This documentation covers the essential patterns for using tags effectively with LangChain and LiteLLM, enabling better organization, tracking, and analytics of your LLM requests. diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 45cec48cd7e..18c99051709 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -195,70 +195,169 @@ 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 +380,6 @@ 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. 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/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/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/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 365eb8acb04..ee857f700a3 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -21,7 +21,7 @@ litellm_settings: failure_callback: ["sentry"] # list of failure callbacks callbacks: ["otel"] # list of callbacks - runs on success and failure service_callbacks: ["datadog", "prometheus"] # logs redis, postgres failures on datadog, prometheus - turn_off_message_logging: boolean # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged. + turn_off_message_logging: boolean # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged. Useful for privacy/compliance when handling sensitive data. redact_user_api_key_info: boolean # Redact information about the user api key (hashed token, user_id, team id, etc.), from logs. Currently supported for Langfuse, OpenTelemetry, Logfire, ArizeAI logging. langfuse_default_tags: ["cache_hit", "cache_key", "proxy_base_url", "user_api_key_alias", "user_api_key_user_id", "user_api_key_user_email", "user_api_key_team_alias", "semantic-similarity", "proxy_base_url"] # default tags for Langfuse Logging @@ -131,7 +131,7 @@ general_settings: | failure_callback | array of strings | List of failure callbacks [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) | | callbacks | array of strings | List of callbacks - runs on success and failure [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) | | service_callbacks | array of strings | System health monitoring - Logs redis, postgres failures on specified services (e.g. datadog, prometheus) [Doc Metrics](prometheus) | -| turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged [Proxy Logging](logging) | +| turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged. Useful for privacy/compliance when handling sensitive data [Proxy Logging](logging) | | modify_params | boolean | If true, allows modifying the parameters of the request before it is sent to the LLM provider | | enable_preview_features | boolean | If true, enables preview features - e.g. Azure O1 Models with streaming support.| | redact_user_api_key_info | boolean | If true, redacts information about the user api key from logs [Proxy Logging](logging#redacting-userapikeyinfo) | @@ -473,6 +473,7 @@ 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 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/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/logging.md b/docs/my-website/docs/proxy/logging.md index 5d3f8417222..ff2591daad2 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -60,7 +60,7 @@ components in your system, including in logging tools. ### Redact Messages, Response Content -Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to your logging provider, but request metadata - e.g. spend, will still be tracked. +Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to your logging provider, but request metadata - e.g. spend, will still be tracked. Useful for privacy/compliance when handling sensitive data. 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/user_keys.md b/docs/my-website/docs/proxy/user_keys.md index ecf6f2d0532..21e1d3dbf40 100644 --- a/docs/my-website/docs/proxy/user_keys.md +++ b/docs/my-website/docs/proxy/user_keys.md @@ -357,6 +357,106 @@ assert user.age == 25 +## Using Tags for Categorization and Tracking + +Tags allow you to categorize, filter, and track your LLM requests. Add tags to your metadata for better organization and analytics. + + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello!"}], + extra_body={ + "metadata": { + "tags": ["production", "customer-support", "urgent"], + "generation_name": "support-bot", + "trace_user_id": "user-123" + } + } +) +``` + + + + + +```python +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", + model="gpt-4o", + extra_body={ + "metadata": { + "tags": ["langchain-integration", "content-gen"], + "trace_user_id": "user-456" + } + } +) + +response = chat.invoke([HumanMessage(content="Generate a blog post")]) +``` + + + + + +```bash +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": "Hello!"}], + "metadata": { + "tags": ["api-test", "development"], + "trace_user_id": "test-user" + } +}' +``` + + + + + +```js +const { OpenAI } = require('openai'); + +const openai = new OpenAI({ + apiKey: "sk-1234", + baseURL: "http://0.0.0.0:4000" +}); + +async function main() { + const response = await openai.chat.completions.create({ + messages: [{ role: 'user', content: 'Hello!' }], + model: 'gpt-3.5-turbo', + metadata: { + tags: ["javascript-client", "api-test"], + trace_user_id: "js-user-789" + } + }); +} +``` + + + + +### Tag Benefits + +- **Cost Tracking**: Monitor spending by project/team/feature +- **Analytics**: Filter requests by tags in logs and dashboards +- **Routing**: Use tags for conditional model routing +- **Debugging**: Easier troubleshooting with categorized requests + ### Response Format ```json 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/sidebars.js b/docs/my-website/sidebars.js index dfaa7b2bd96..8c831536b42 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -141,6 +141,7 @@ const sidebars = { "proxy/clientside_auth", "proxy/request_headers", "proxy/response_headers", + "proxy/forward_client_headers", "proxy/model_discovery", ], }, @@ -261,6 +262,7 @@ const sidebars = { "completion/input", "completion/output", "completion/usage", + "completion/http_handler_config", ], }, "response_api", @@ -409,6 +411,7 @@ const sidebars = { items: [ "providers/bedrock", "providers/bedrock_agents", + "providers/bedrock_batches", "providers/bedrock_vector_store", ] }, 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/batches/main.py b/litellm/batches/main.py index 0d250779da3..e5c7a2e4b6e 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 diff --git a/litellm/constants.py b/litellm/constants.py index 75c25d9ea9e..c0ce0f265b5 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 @@ -949,7 +951,9 @@ 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)) 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/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/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/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/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 19d7c5512ba..2f21d280899 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -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, @@ -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}") @@ -907,13 +909,19 @@ 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 +934,12 @@ 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 +996,14 @@ 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 +1011,8 @@ 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 @@ -1714,12 +1734,16 @@ 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: @@ -3367,6 +3391,7 @@ 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 @@ -3594,6 +3619,7 @@ 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 @@ -4504,7 +4530,7 @@ 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( diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py index 3f83719dd32..16860ad6852 100644 --- a/litellm/litellm_core_utils/logging_worker.py +++ b/litellm/litellm_core_utils/logging_worker.py @@ -1,10 +1,21 @@ import asyncio import contextlib -from typing import Coroutine, Optional +import contextvars +from typing import Coroutine, Optional, 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 +24,84 @@ 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 +109,42 @@ 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 +155,3 @@ class LoggingWorker: # Global instance for backward compatibility GLOBAL_LOGGING_WORKER = LoggingWorker() - diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 2203cac11d0..83b4985b239 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -1937,7 +1937,7 @@ class CustomStreamWrapper: ) ## Map to OpenAI Exception try: - exception_type( + raise exception_type( model=self.model, custom_llm_provider=self.custom_llm_provider, original_exception=e, 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/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index ce580ebc624..3338450f790 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -124,15 +124,13 @@ 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 +149,7 @@ 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, 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/custom_httpx/aiohttp_handler.py b/litellm/llms/custom_httpx/aiohttp_handler.py index d9fc85877c3..c7a04a49fc2 100644 --- a/litellm/llms/custom_httpx/aiohttp_handler.py +++ b/litellm/llms/custom_httpx/aiohttp_handler.py @@ -17,6 +17,7 @@ from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, _get_httpx_client, ) +from litellm.llms.custom_httpx.aiohttp_transport import LiteLLMAiohttpTransport from litellm.types.llms.openai import FileTypes from litellm.types.utils import HttpHandlerRequestFields, ImageResponse, LlmProviders from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager @@ -32,8 +33,71 @@ DEFAULT_TIMEOUT = 600 class BaseLLMAIOHTTPHandler: - def __init__(self): - self.client_session: Optional[aiohttp.ClientSession] = None + def __init__( + self, + client_session: Optional[aiohttp.ClientSession] = None, + transport: Optional[LiteLLMAiohttpTransport] = None, + connector: Optional[aiohttp.BaseConnector] = None, + ): + self.client_session = client_session + self._owns_session = ( + client_session is None + ) # Track if we own the session for cleanup + + self.transport = transport + self._owns_transport = ( + transport is None + ) # Track if we own the transport for cleanup + + self.connector = connector + self._owns_connector = ( + connector is None + ) # Track if we own the connector for cleanup + + def _get_or_create_transport(self) -> Optional[LiteLLMAiohttpTransport]: + """Get existing transport or create a new one if needed.""" + if self.transport: + return self.transport + + # Create a transport using AsyncHTTPHandler's logic + try: + self.transport = AsyncHTTPHandler._create_aiohttp_transport() + self._owns_transport = True + return self.transport + except Exception: + # If transport creation fails, return None (will use direct session) + return None + + def _get_connector(self) -> Optional[aiohttp.BaseConnector]: + """Get or create a connector for the client session.""" + if self.connector: + return self.connector + elif self.transport and hasattr(self.transport, "client"): + # Extract connector from transport if available + client = self.transport.client + if callable(client): + # If client is a factory, we can't extract connector directly + return None + elif hasattr(client, "connector"): + return client.connector + return None + + def _create_client_session_with_transport(self) -> ClientSession: + """Create a new client session using transport or connector configuration.""" + connector = self._get_connector() + + if self.transport and hasattr(self.transport, "_get_valid_client_session"): + # Use transport's session creation if available + session = self.transport._get_valid_client_session() + return session + elif connector: + # Use provided connector + session = aiohttp.ClientSession(connector=connector) + return session + else: + # Default session creation + session = aiohttp.ClientSession() + return session def _get_async_client_session( self, dynamic_client_session: Optional[ClientSession] = None @@ -43,15 +107,33 @@ class BaseLLMAIOHTTPHandler: elif self.client_session: return self.client_session else: - # init client session, and then return new session - self.client_session = aiohttp.ClientSession() + # Create client session using transport/connector if available + self.client_session = self._create_client_session_with_transport() + self._owns_session = True # We created this session, so we own it return self.client_session async def close(self): - """Close the aiohttp client session if it exists.""" - if self.client_session and not self.client_session.closed: + """Close the aiohttp client session and transport if we own them.""" + # Close client session if we own it + if ( + self.client_session + and not self.client_session.closed + and self._owns_session + ): await self.client_session.close() + # Close transport if we own it + if ( + self.transport + and self._owns_transport + and hasattr(self.transport, "aclose") + ): + try: + await self.transport.aclose() + except Exception: + # Ignore errors during transport cleanup + pass + async def _make_common_async_call( self, async_client_session: Optional[ClientSession], diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 13133a56aad..a4f40e90df5 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -2201,7 +2201,6 @@ class BaseLLMHTTPHandler: litellm_params=litellm_params, optional_params={}, ) - if _is_async: return self.async_create_file( transformed_request=transformed_request, @@ -2218,6 +2217,7 @@ class BaseLLMHTTPHandler: sync_httpx_client = _get_httpx_client() else: sync_httpx_client = client + if isinstance(transformed_request, dict) and "method" in transformed_request: # Handle pre-signed requests (e.g., from Bedrock S3 uploads) @@ -2283,11 +2283,15 @@ class BaseLLMHTTPHandler: 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( @@ -2408,15 +2412,19 @@ 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 +2433,7 @@ 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 +2443,7 @@ 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={}, @@ -2495,7 +2503,7 @@ class BaseLLMHTTPHandler: 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, @@ -2512,6 +2520,7 @@ 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 @@ -2572,7 +2581,7 @@ class BaseLLMHTTPHandler: 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, 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 d3df5bbf361..a1370074238 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -169,12 +169,20 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): if tool is None: return None + # Build DatabricksFunction explicitly to avoid parameter conflicts + function_params: DatabricksFunction = { + "name": tool["name"], + "parameters": cast(dict, tool.get("input_schema") or {}) + } + + # 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=DatabricksFunction( - name=tool["name"], - parameters=cast(dict, tool.get("input_schema") or {}), - ), + function=function_params, ) def _map_openai_to_dbrx_tool(self, model: str, tools: List) -> List[DatabricksTool]: @@ -331,8 +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)}") @@ -361,19 +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/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/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 392d47f9822..1d52f74b7b9 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, } diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index c795367e486..1066a8f0363 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( 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/main.py b/litellm/main.py index 1bb84cccdee..6c81d3eded9 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -150,9 +150,9 @@ 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 @@ -358,7 +358,9 @@ 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 +506,9 @@ 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 +903,9 @@ 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 +1122,12 @@ 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 +1333,7 @@ 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, @@ -2712,9 +2721,7 @@ 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 +2737,7 @@ 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(): @@ -3712,7 +3719,9 @@ 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 @@ -3854,7 +3863,7 @@ def embedding( # noqa: PLR0915 max_retries = kwargs.get("max_retries", None) litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore mock_response: Optional[List[float]] = kwargs.get("mock_response", None) # type: ignore - azure_ad_token_provider = kwargs.pop("azure_ad_token_provider", None) + azure_ad_token_provider = kwargs.get("azure_ad_token_provider", None) aembedding = kwargs.get("aembedding", None) extra_headers = kwargs.get("extra_headers", None) headers = kwargs.get("headers", None) @@ -5780,7 +5789,14 @@ 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..46331c7cff7 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -6,6 +6,7 @@ "input_cost_per_token": 0.0, "output_cost_per_token": 0.0, "output_cost_per_reasoning_token": 0.0, + "input_cost_per_audio_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, @@ -15389,16 +15390,6 @@ "litellm_provider": "ollama", "mode": "completion" }, - "deepinfra/Austism/chronos-hermes-13b-v2": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 1.3e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/Gryphe/MythoMax-L2-13b": { "max_tokens": 4096, "max_input_tokens": 4096, @@ -15409,26 +15400,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/Gryphe/MythoMax-L2-13b-turbo": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 1.3e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/KoboldAI/LLaMA2-13B-Tiefighter": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 1e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/NousResearch/Hermes-3-Llama-3.1-405B": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -15447,78 +15418,18 @@ "output_cost_per_token": 2.8e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/NovaSky-AI/Sky-T1-32B-Preview": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 1.2e-07, - "output_cost_per_token": 1.8e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Phind/Phind-CodeLlama-34B-v2": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 6e-07, - "output_cost_per_token": 6e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/Qwen/QVQ-72B-Preview": { - "max_tokens": 32000, - "max_input_tokens": 32000, - "max_output_tokens": 32000, - "input_cost_per_token": 2.5e-07, - "output_cost_per_token": 5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", "supports_tool_choice": false }, "deepinfra/Qwen/QwQ-32B": { "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 7.5e-08, - "output_cost_per_token": 1.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/Qwen/QwQ-32B-Preview": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 1.2e-07, - "output_cost_per_token": 1.8e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Qwen/Qwen2-72B-Instruct": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 3.5e-07, + "input_cost_per_token": 1.5e-07, "output_cost_per_token": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true }, - "deepinfra/Qwen/Qwen2-7B-Instruct": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 5.5e-08, - "output_cost_per_token": 5.5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/Qwen/Qwen2.5-72B-Instruct": { "max_tokens": 32768, "max_input_tokens": 32768, @@ -15539,26 +15450,6 @@ "mode": "chat", "supports_tool_choice": false }, - "deepinfra/Qwen/Qwen2.5-Coder-32B-Instruct": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 6e-08, - "output_cost_per_token": 1.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Qwen/Qwen2.5-Coder-7B": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 2.5e-08, - "output_cost_per_token": 5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, "deepinfra/Qwen/Qwen2.5-VL-32B-Instruct": { "max_tokens": 128000, "max_input_tokens": 128000, @@ -15645,30 +15536,11 @@ "max_output_tokens": 262144, "input_cost_per_token": 3e-07, "output_cost_per_token": 1.2e-06, + "cache_read_input_token_cost": 2.4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true }, - "deepinfra/Sao10K/L3-70B-Euryale-v2.1": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 7e-07, - "output_cost_per_token": 8e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Sao10K/L3-8B-Lunaris-v1": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 3e-08, - "output_cost_per_token": 6e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, "deepinfra/Sao10K/L3-8B-Lunaris-v1-Turbo": { "max_tokens": 8192, "max_input_tokens": 8192, @@ -15715,6 +15587,7 @@ "max_output_tokens": 200000, "input_cost_per_token": 3.3e-06, "output_cost_per_token": 1.65e-05, + "cache_read_input_token_cost": 3.3e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true @@ -15739,67 +15612,15 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/bigcode/starcoder2-15b-instruct-v0.1": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1.5e-07, - "output_cost_per_token": 1.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/cognitivecomputations/dolphin-2.6-mixtral-8x7b": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 2.4e-07, - "output_cost_per_token": 2.4e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/cognitivecomputations/dolphin-2.9.1-llama-3-70b": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 3.5e-07, - "output_cost_per_token": 4e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/deepinfra/airoboros-70b": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 7e-07, - "output_cost_per_token": 9e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/deepseek-ai/DeepSeek-Prover-V2-671B": { - "max_tokens": 163840, - "max_input_tokens": 163840, - "max_output_tokens": 163840, - "input_cost_per_token": 5e-07, - "output_cost_per_token": 2.18e-06, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false, - "supports_reasoning": true - }, "deepinfra/deepseek-ai/DeepSeek-R1": { "max_tokens": 163840, "max_input_tokens": 163840, "max_output_tokens": 163840, - "input_cost_per_token": 4.5e-07, - "output_cost_per_token": 2.15e-06, + "input_cost_per_token": 7e-07, + "output_cost_per_token": 2.4e-06, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true, - "supports_reasoning": true + "supports_tool_choice": true }, "deepinfra/deepseek-ai/DeepSeek-R1-0528": { "max_tokens": 163840, @@ -15807,10 +15628,10 @@ "max_output_tokens": 163840, "input_cost_per_token": 5e-07, "output_cost_per_token": 2.15e-06, + "cache_read_input_token_cost": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true, - "supports_reasoning": true + "supports_tool_choice": true }, "deepinfra/deepseek-ai/DeepSeek-R1-0528-Turbo": { "max_tokens": 32768, @@ -15820,8 +15641,7 @@ "output_cost_per_token": 3e-06, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true, - "supports_reasoning": true + "supports_tool_choice": true }, "deepinfra/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": { "max_tokens": 131072, @@ -15831,8 +15651,7 @@ "output_cost_per_token": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": false, - "supports_reasoning": true + "supports_tool_choice": false }, "deepinfra/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B": { "max_tokens": 131072, @@ -15842,19 +15661,17 @@ "output_cost_per_token": 1.5e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true, - "supports_reasoning": true + "supports_tool_choice": true }, "deepinfra/deepseek-ai/DeepSeek-R1-Turbo": { - "max_tokens": 163840, - "max_input_tokens": 163840, - "max_output_tokens": 163840, + "max_tokens": 40960, + "max_input_tokens": 40960, + "max_output_tokens": 40960, "input_cost_per_token": 1e-06, "output_cost_per_token": 3e-06, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true, - "supports_reasoning": true + "supports_tool_choice": true }, "deepinfra/deepseek-ai/DeepSeek-V3": { "max_tokens": 163840, @@ -15864,8 +15681,7 @@ "output_cost_per_token": 8.9e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true, - "supports_reasoning": true + "supports_tool_choice": true }, "deepinfra/deepseek-ai/DeepSeek-V3-0324": { "max_tokens": 163840, @@ -15873,63 +15689,23 @@ "max_output_tokens": 163840, "input_cost_per_token": 2.8e-07, "output_cost_per_token": 8.8e-07, + "cache_read_input_token_cost": 2.24e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true, - "supports_reasoning": true - }, - "deepinfra/deepseek-ai/DeepSeek-V3-0324-Turbo": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 1e-06, - "output_cost_per_token": 3e-06, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true, - "supports_reasoning": true + "supports_tool_choice": true }, "deepinfra/deepseek-ai/DeepSeek-V3.1": { "max_tokens": 163840, "max_input_tokens": 163840, "max_output_tokens": 163840, - "input_cost_per_token": 3e-07, + "input_cost_per_token": 2.7e-07, "output_cost_per_token": 1e-06, + "cache_read_input_token_cost": 2.16e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": false, + "supports_tool_choice": true, "supports_reasoning": true }, - "deepinfra/google/codegemma-7b-it": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 7e-08, - "output_cost_per_token": 7e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/google/gemini-1.5-flash": { - "max_tokens": 1000000, - "max_input_tokens": 1000000, - "max_output_tokens": 1000000, - "input_cost_per_token": 7.5e-08, - "output_cost_per_token": 3e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/google/gemini-1.5-flash-8b": { - "max_tokens": 1000000, - "max_input_tokens": 1000000, - "max_output_tokens": 1000000, - "input_cost_per_token": 3.75e-08, - "output_cost_per_token": 1.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/google/gemini-2.0-flash-001": { "max_tokens": 1000000, "max_input_tokens": 1000000, @@ -15960,36 +15736,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/google/gemma-1.1-7b-it": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 7e-08, - "output_cost_per_token": 7e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/google/gemma-2-27b-it": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 2.7e-07, - "output_cost_per_token": 2.7e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/google/gemma-2-9b-it": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 3e-08, - "output_cost_per_token": 6e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, "deepinfra/google/gemma-3-12b-it": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -16014,48 +15760,8 @@ "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 2e-08, - "output_cost_per_token": 4e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/lizpreciatior/lzlv_70b_fp16_hf": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 3.5e-07, - "output_cost_per_token": 4e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/mattshumer/Reflection-Llama-3.1-70B": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 3.5e-07, - "output_cost_per_token": 4e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/meta-llama/Llama-2-13b-chat-hf": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 1.3e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/meta-llama/Llama-2-70b-chat-hf": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 6.4e-07, - "output_cost_per_token": 8e-07, + "input_cost_per_token": 4e-08, + "output_cost_per_token": 8e-08, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true @@ -16070,16 +15776,6 @@ "mode": "chat", 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"https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-01-25": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 8192, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-flash-2025-07-28": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 2.5e-07, "output_cost_per_token": 2e-06} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "output_cost_per_reasoning_token": 5e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo-2025-04-28": { + "max_tokens": 1000000, + "max_input_tokens": 1000000, + "max_output_tokens": 16384, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "output_cost_per_reasoning_token": 5e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo-2024-11-01": { + "max_tokens": 1000000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwq-plus": { + "max_tokens": 131072, + "max_input_tokens": 98304, + "max_output_tokens": 8192, + "input_cost_per_token": 8e-07, + "output_cost_per_token": 2.4e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "moonshot/moonshot-v1-8k": { "max_tokens": 8192, @@ -20998,4 +20778,4 @@ "notes": "Volcengine Doubao embedding model - text-240715 version with 2560 dimensions" } } -} \ No newline at end of file +} diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 34a0d604f39..b35b3ad93d5 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -241,6 +241,9 @@ class MCPServerManager: 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), ) @@ -716,8 +719,8 @@ 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, diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 38619112ccc..d0461f91e9e 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -215,9 +215,9 @@ 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() @@ -279,33 +279,15 @@ if MCP_AVAILABLE: ############ Helper Functions ########################## ######################################################## - async def _get_tools_from_mcp_servers( - user_api_key_auth: Optional[UserAPIKeyAuth], - mcp_auth_header: Optional[str], + async def _get_allowed_mcp_servers_from_mcp_server_names( mcp_servers: Optional[List[str]], - mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, - ) -> List[MCPTool]: + allowed_mcp_servers: List[str], + ) -> List[str]: """ - Helper method to fetch tools from MCP servers based on server filtering criteria. - - Args: - user_api_key_auth: User authentication info for access control - mcp_auth_header: Optional auth header for MCP server (deprecated) - mcp_servers: Optional list of server names/aliases to filter by - mcp_server_auth_headers: Optional dict of server-specific auth headers {server_alias: auth_value} - - Returns: - List[MCPTool]: Combined list of tools from filtered servers + Get the filtered MCP servers from the MCP server names """ - if not 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) - - filtered_server_ids = set() - + 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: @@ -336,6 +318,40 @@ if MCP_AVAILABLE: 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. + + Args: + user_api_key_auth: User authentication info for access control + mcp_auth_header: Optional auth header for MCP server (deprecated) + mcp_servers: Optional list of server names/aliases to filter by + mcp_server_auth_headers: Optional dict of server-specific auth headers {server_alias: auth_value} + + Returns: + List[MCPTool]: Combined list of tools from filtered servers + """ + if not 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) + + if mcp_servers is not None: + 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 = [] @@ -556,20 +572,25 @@ if MCP_AVAILABLE: except Exception as e: return [TextContent(text=f"Error: {str(e)}", type="text")] - async def extract_mcp_auth_context(scope, path): + def _get_mcp_servers_in_path(path: str) -> Optional[List[str]]: """ - 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) + Get the MCP servers from the path """ import re - - mcp_servers_from_path = None + mcp_servers_from_path: Optional[List[str]] = 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()] + 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) + """ + mcp_servers_from_path = _get_mcp_servers_in_path(path) if mcp_servers_from_path is not None: ( user_api_key_auth, diff --git a/litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/FMlZjJYLUentCU02Wj6R_/_buildManifest.js similarity index 100% rename from litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_buildManifest.js rename to litellm/proxy/_experimental/out/_next/static/FMlZjJYLUentCU02Wj6R_/_buildManifest.js diff --git a/litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/FMlZjJYLUentCU02Wj6R_/_ssgManifest.js similarity index 100% rename from litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_ssgManifest.js rename to litellm/proxy/_experimental/out/_next/static/FMlZjJYLUentCU02Wj6R_/_ssgManifest.js diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js b/litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js new file mode 100644 index 00000000000..e9bbcb3d1f6 --- /dev/null +++ b/litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return s.Z},xv:function(){return r.Z},zx:function(){return a.Z}});var a=n(20831),s=n(12514),r=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,endpointType:p,selectedModel:u,selectedSdk:g}=e,x="session"===n?s:r,h=window.location.origin,f=i||"Your prompt here",_=f.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),b=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let j=u||"your-model-name",y="azure"===g?'import openai\n\nclient = openai.AzureOpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n azure_endpoint="').concat(h,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(h,'"\n)');switch(p){case a.KP.CHAT:{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.chat.completions.create(\n model="'.concat(j,'",\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(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. 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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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