/vector_stores/search - Search Vector Store

Search a vector store for relevant chunks based on a query and file attributes filter. This is useful for retrieval-augmented generation (RAG) use cases.

Overview

Usage

LiteLLM Python SDK

Non-streaming example

response = await litellm.vector_stores.asearch(
    vector_store_id="vs_abc123",
    query="What is the capital of France?"
)
print(response)

Synchronous example

response = litellm.vector_stores.search(
    vector_store_id="vs_abc123",
    query="What is the capital of France?"
)
print(response)

With filters and ranking options

response = await litellm.vector_stores.asearch(
    vector_store_id="vs_abc123",
    query="What is the capital of France?",
    filters={
        "file_ids": ["file-abc123", "file-def456"]
    },
    max_num_results=5,
    ranking_options={
        "score_threshold": 0.7
    },
    rewrite_query=True
)
print(response)

Searching with multiple queries

response = await litellm.vector_stores.asearch(
    vector_store_id="vs_abc123",
    query=[
        "What is the capital of France?",
        "What is the population of Paris?"
    ],
    max_num_results=10
)
print(response)

Using OpenAI provider explicitly

# Set API key
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"

response = await litellm.vector_stores.asearch(
    vector_store_id="vs_abc123",
    query="What is the capital of France?",
    custom_llm_provider="openai"
)
print(response)

Using Azure AI Search

# Set credentials
os.environ["AZURE_SEARCH_API_KEY"] = "your-search-api-key"

response = await litellm.vector_stores.asearch(
    vector_store_id="my-vector-index",
    query="What is the capital of France?",
    custom_llm_provider="azure_ai",
    azure_search_service_name="your-search-service",
    litellm_embedding_model="azure/text-embedding-3-large",
    litellm_embedding_config={
        "api_base": "your-embedding-endpoint",
        "api_key": "your-embedding-api-key",
    },
    api_key=os.getenv("AZURE_SEARCH_API_KEY"),
)
print(response)

See full Azure AI vector store documentation

Using Milvus

# Set credentials
os.environ["MILVUS_API_KEY"] = "your-milvus-api-key"
os.environ["MILVUS_API_BASE"] = "https://your-milvus-instance.milvus.io"

response = await litellm.vector_stores.asearch(
    vector_store_id="my-collection-name",
    query="What is the capital of France?",
    custom_llm_provider="milvus",
    litellm_embedding_model="azure/text-embedding-3-large",
    litellm_embedding_config={
        "api_base": "your-embedding-endpoint",
        "api_key": "your-embedding-api-key",
    },
    milvus_text_field="book_intro",
    api_key=os.getenv("MILVUS_API_KEY"),
)
print(response)

See full Milvus vector store documentation

Using Gemini File Search

# Set credentials
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

response = await litellm.vector_stores.asearch(
    vector_store_id="fileSearchStores/your-store-id",
    query="What is the capital of France?",
    custom_llm_provider="gemini",
    max_num_results=5
)
print(response)

With Metadata Filter:

response = await litellm.vector_stores.asearch(
    vector_store_id="fileSearchStores/your-store-id",
    query="What is LiteLLM?",
    custom_llm_provider="gemini",
    filters={"author": "John Doe", "category": "documentation"},
    max_num_results=5
)
print(response)

See full Gemini File Search documentation

LiteLLM Proxy Server

1. Setup config.yaml

model_list:
  - model_name: gpt-4o
    litellm_params:
      model: openai/gpt-4o
      api_key: os.environ/OPENAI_API_KEY

general_settings:
  # Vector store settings can be added here if needed

2. Start proxy

litellm --config /path/to/config.yaml

3. Test it with OpenAI SDK!

from openai import OpenAI

# Point OpenAI SDK to LiteLLM proxy
client = OpenAI(
    base_url="http://0.0.0.0:4000",
    api_key="sk-1234",  # Your LiteLLM API key
)

search_results = client.beta.vector_stores.search(
    vector_store_id="vs_abc123",
    query="What is the capital of France?",
    max_num_results=5
)
print(search_results)

curl -L -X POST 'http://0.0.0.0:4000/v1/vector_stores/vs_abc123/search' \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer sk-1234' \\
-d '{
  "query": "What is the capital of France?",
  "filters": {
    "file_ids": ["file-abc123", "file-def456"]
  },
  "max_num_results": 5,
  "ranking_options": {
    "score_threshold": 0.7
  },
  "rewrite_query": true
}'

Setting Up Vector Stores

To use vector store search, configure your vector stores in the vector_store_registry. See the Vector Store Configuration Guide for:

• Provider-specific configuration (Bedrock, OpenAI, Azure, Vertex AI, PG Vector)
• Python SDK and Proxy setup examples
• Authentication and credential management

Using Vector Stores with Chat Completions

Pass vector_store_ids in chat completion requests to automatically retrieve relevant context. See Using Vector Stores with Chat Completions for implementation details.