Using Vector Stores (Knowledge Bases)

Use Vector Stores with any LiteLLM supported model

LiteLLM integrates with vector stores, allowing your models to access your organization's data for more accurate and contextually relevant responses.

Supported Vector Stores

• Bedrock Knowledge Bases

Quick Start

In order to use a vector store with LiteLLM, you need to

• Initialize litellm.vector_store_registry
• Pass tools with vector_store_ids to the completion request. Where vector_store_ids is a list of vector store ids you initialized in litellm.vector_store_registry

LiteLLM Python SDK

LiteLLM's allows you to use vector stores in the OpenAI API spec by passing a tool with vector_store_ids you want to use

Basic Bedrock Knowledge Base Usage

import os
import litellm

from litellm.vector_stores.vector_store_registry import VectorStoreRegistry, LiteLLM_ManagedVectorStore

# Init vector store registry
litellm.vector_store_registry = VectorStoreRegistry(
    vector_stores=[
        LiteLLM_ManagedVectorStore(
            vector_store_id="T37J8R4WTM",
            custom_llm_provider="bedrock"
        )
    ]
)


# Make a completion request with vector_store_ids parameter
response = await litellm.acompletion(
    model="anthropic/claude-3-5-sonnet", 
    messages=[{"role": "user", "content": "What is litellm?"}],
    tools=[
        {
            "type": "file_search",
            "vector_store_ids": ["T37J8R4WTM"]
        }
    ],
)

print(response.choices[0].message.content)

LiteLLM Proxy

1. Configure your vector_store_registry

In order to use a vector store with LiteLLM, you need to configure your vector_store_registry. This tells litellm which vector stores to use and api provider to use for the vector store.

config.yaml

config.yaml

model_list:
  - model_name: claude-3-5-sonnet
    litellm_params:
      model: anthropic/claude-3-5-sonnet
      api_key: os.environ/ANTHROPIC_API_KEY

vector_store_registry:
  - vector_store_name: "bedrock-litellm-website-knowledgebase"
    litellm_params:
      vector_store_id: "T37J8R4WTM"
      custom_llm_provider: "bedrock"
      vector_store_description: "Bedrock vector store for the Litellm website knowledgebase"
      vector_store_metadata:
        source: "https://www.litellm.com/docs"

LiteLLM UI

On the LiteLLM UI, Navigate to Experimental > Vector Stores > Create Vector Store. On this page you can create a vector store with a name, vector store id and credentials.

2. Make a request with vector_store_ids parameter

Curl

Curl Request to LiteLLM Proxy

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -d '{
    "model": "claude-3-5-sonnet",
    "messages": [{"role": "user", "content": "What is litellm?"}],
    "tools": [
        {
            "type": "file_search",
            "vector_store_ids": ["T37J8R4WTM"]
        }
    ]
  }'

OpenAI Python SDK

OpenAI Python SDK Request

from openai import OpenAI

# Initialize client with your LiteLLM proxy URL
client = OpenAI(
    base_url="http://localhost:4000",
    api_key="your-litellm-api-key"
)

# Make a completion request with vector_store_ids parameter
response = client.chat.completions.create(
    model="claude-3-5-sonnet",
    messages=[{"role": "user", "content": "What is litellm?"}],
    tools=[
        {
            "type": "file_search",
            "vector_store_ids": ["T37J8R4WTM"]
        }
    ]
)

print(response.choices[0].message.content)

Advanced

Logging Vector Store Usage

LiteLLM allows you to view your vector store usage in the LiteLLM UI on the Logs page.

After completing a request with a vector store, navigate to the Logs page on LiteLLM. Here you should be able to see the query sent to the vector store and corresponding response with scores.

LiteLLM Logs Page: Vector Store Usage

Listing available vector stores

You can list all available vector stores using the /vector_store/list endpoint

Request:

List all available vector stores

curl -X GET "http://localhost:4000/vector_store/list" \
  -H "Authorization: Bearer $LITELLM_API_KEY"

Response:

The response will be a list of all vector stores that are available to use with LiteLLM.

{
  "object": "list",
  "data": [
    {
      "vector_store_id": "T37J8R4WTM",
      "custom_llm_provider": "bedrock",
      "vector_store_name": "bedrock-litellm-website-knowledgebase",
      "vector_store_description": "Bedrock vector store for the Litellm website knowledgebase",
      "vector_store_metadata": {
        "source": "https://www.litellm.com/docs"
      },
      "created_at": "2023-05-03T18:21:36.462Z",
      "updated_at": "2023-05-03T18:21:36.462Z",
      "litellm_credential_name": "bedrock_credentials"
    }
  ],
  "total_count": 1,
  "current_page": 1,
  "total_pages": 1
}

Always on for a model

Use this if you want vector stores to be used by default for a specific model.

In this config, we add vector_store_ids to the claude-3-5-sonnet-with-vector-store model. This means that any request to the claude-3-5-sonnet-with-vector-store model will always use the vector store with the id T37J8R4WTM defined in the vector_store_registry.

Always on for a model

model_list:
  - model_name: claude-3-5-sonnet-with-vector-store
    litellm_params:
      model: anthropic/claude-3-5-sonnet
      vector_store_ids: ["T37J8R4WTM"]

vector_store_registry:
  - vector_store_name: "bedrock-litellm-website-knowledgebase"
    litellm_params:
      vector_store_id: "T37J8R4WTM"
      custom_llm_provider: "bedrock"
      vector_store_description: "Bedrock vector store for the Litellm website knowledgebase"
      vector_store_metadata:
        source: "https://www.litellm.com/docs"

How It Works

If your request includes a vector_store_ids parameter where any of the vector store ids are found in the vector_store_registry, LiteLLM will automatically use the vector store for the request.

1. You make a completion request with the vector_store_ids parameter and any of the vector store ids are found in the litellm.vector_store_registry
2. LiteLLM automatically:
   • Uses your last message as the query to retrieve relevant information from the Knowledge Base
   • Adds the retrieved context to your conversation
   • Sends the augmented messages to the model

Example Transformation

When you pass vector_store_ids=["YOUR_KNOWLEDGE_BASE_ID"], your request flows through these steps:

1. Original Request to LiteLLM:

{
    "model": "anthropic/claude-3-5-sonnet",
    "messages": [
        {"role": "user", "content": "What is litellm?"}
    ],
    "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"]
}

2. Request to AWS Bedrock Knowledge Base:

{
    "retrievalQuery": {
        "text": "What is litellm?"
    }
}

This is sent to: https://bedrock-agent-runtime.{aws_region}.amazonaws.com/knowledgebases/YOUR_KNOWLEDGE_BASE_ID/retrieve

3. Final Request to LiteLLM:

{
    "model": "anthropic/claude-3-5-sonnet",
    "messages": [
        {"role": "user", "content": "What is litellm?"},
        {"role": "user", "content": "Context: \n\nLiteLLM is an open-source SDK to simplify LLM API calls across providers (OpenAI, Claude, etc). It provides a standardized interface with robust error handling, streaming, and observability tools."}
    ]
}

This process happens automatically whenever you include the vector_store_ids parameter in your request.

API Reference

LiteLLM Completion Knowledge Base Parameters

When using the Knowledge Base integration with LiteLLM, you can include the following parameters:

Parameter	Type	Description
vector_store_ids	List[str]	List of Knowledge Base IDs to query

VectorStoreRegistry

The VectorStoreRegistry is a central component for managing vector stores in LiteLLM. It acts as a registry where you can configure and access your vector stores.

What is VectorStoreRegistry?

VectorStoreRegistry is a class that:

• Maintains a collection of vector stores that LiteLLM can use
• Allows you to register vector stores with their credentials and metadata
• Makes vector stores accessible via their IDs in your completion requests

Using VectorStoreRegistry in Python

from litellm.vector_stores.vector_store_registry import VectorStoreRegistry, LiteLLM_ManagedVectorStore

# Initialize the vector store registry with one or more vector stores
litellm.vector_store_registry = VectorStoreRegistry(
    vector_stores=[
        LiteLLM_ManagedVectorStore(
            vector_store_id="YOUR_VECTOR_STORE_ID",  # Required: Unique ID for referencing this store
            custom_llm_provider="bedrock"            # Required: Provider (e.g., "bedrock")
        )
    ]
)

LiteLLM_ManagedVectorStore Parameters

Each vector store in the registry is configured using a LiteLLM_ManagedVectorStore object with these parameters:

Parameter	Type	Required	Description
vector_store_id	str	Yes	Unique identifier for the vector store
custom_llm_provider	str	Yes	The provider of the vector store (e.g., "bedrock")
vector_store_name	str	No	A friendly name for the vector store
vector_store_description	str	No	Description of what the vector store contains
vector_store_metadata	dict or str	No	Additional metadata about the vector store
litellm_credential_name	str	No	Name of the credentials to use for this vector store

Configuring VectorStoreRegistry in config.yaml

For the LiteLLM Proxy, you can configure the same registry in your config.yaml file:

Vector store configuration in config.yaml

vector_store_registry:
  - vector_store_name: "bedrock-litellm-website-knowledgebase"  # Optional friendly name
    litellm_params:
      vector_store_id: "T37J8R4WTM"                            # Required: Unique ID  
      custom_llm_provider: "bedrock"                           # Required: Provider
      vector_store_description: "Bedrock vector store for the Litellm website knowledgebase"
      vector_store_metadata:
        source: "https://www.litellm.com/docs"

The litellm_params section accepts all the same parameters as the LiteLLM_ManagedVectorStore constructor in the Python SDK.