---
title: "Rag Query"
method: POST
path: "/v1/rag/query"
tags: ["rag"]
---

# Rag Query

`POST /v1/rag/query`

RAG Query endpoint - search vector store, optionally rerank, and generate LLM response.

This endpoint:
1. Extracts the query from the last user message
2. Searches the vector store for relevant context
3. Optionally reranks the results
4. Generates an LLM response with the retrieved context

## Example Request:
```bash
curl -X POST "http://localhost:4000/v1/rag/query" \
    -H "Authorization: Bearer sk-1234" \
    -H "Content-Type: application/json" \
    -d '{
        "model": "gpt-4o-mini",
        "messages": [{"role": "user", "content": "What is LiteLLM?"}],
        "retrieval_config": {
            "vector_store_id": "vs_abc123",
            "custom_llm_provider": "openai",
            "top_k": 5
        }
    }'
```

## With Reranking:
```bash
curl -X POST "http://localhost:4000/v1/rag/query" \
    -H "Authorization: Bearer sk-1234" \
    -H "Content-Type: application/json" \
    -d '{
        "model": "gpt-4o-mini",
        "messages": [{"role": "user", "content": "What is LiteLLM?"}],
        "retrieval_config": {
            "vector_store_id": "vs_abc123",
            "custom_llm_provider": "openai",
            "top_k": 10
        },
        "rerank": {
            "enabled": true,
            "model": "cohere/rerank-english-v3.0",
            "top_n": 3
        }
    }'
```

## Response `200`

Successful Response

- unknown

---

[API](https://skmtc.net/flock/apis/litellm-api.md) · [All operations](https://skmtc.net/flock/apis/litellm-api/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/flock/litellm-api/revisions/8fbaab4fc7c5/schema)