---
title: "/rag/query"
description: "RAG Query endpoint: **Search Vector Store → (Rerank) → LLM Completion**"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/rag-query
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:37:26.215Z
license: CC-BY-4.0
attribution: "/rag/query — Claudary (https://claudary.paisolsolutions.com/skills/rag-query)"
---

# /rag/query
RAG Query endpoint: **Search Vector Store → (Rerank) → LLM Completion**

## Overview

# /rag/query

RAG Query endpoint: **Search Vector Store → (Rerank) → LLM Completion**

| Feature | Supported |
|---------|-----------|
| Logging | Yes |
| Streaming | Yes |
| Reranking | Yes (optional) |
| Supported Providers | `openai`, `bedrock`, `vertex_ai` |

## Quick Start

```bash showLineNumbers title="RAG Query with OpenAI"
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
        }
    }'
```

## How It Works

The RAG query endpoint performs the following steps:

1. **Extract Query**: Extracts the query text from the last user message
2. **Search Vector Store**: Searches the specified vector store for relevant context
3. **Rerank (Optional)**: Reranks the search results using a reranking model
4. **Generate Response**: Calls the LLM with the retrieved context prepended to the messages

## Response

The response follows the standard OpenAI chat completion format, with additional search metadata:

```json
{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1703123456,
  "model": "gpt-4o-mini",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "LiteLLM is a unified interface for 100+ LLMs..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 150,
    "completion_tokens": 50,
    "total_tokens": 200
  },
  "_hidden_params": {
    "search_results": {...},
    "rerank_results": {...}
  }
}
```

## With Reranking

Add a `rerank` configuration to improve result quality:

```bash showLineNumbers title="RAG Query with Reranking"
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
        }
    }'
```

## Streaming

Enable streaming for real-time responses:

```bash showLineNumbers title="RAG Query with Streaming"
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"
        },
        "stream": true
    }'
```

## Request Parameters

### Top-Level

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `model` | string | Yes | The LLM model to use for generation |
| `messages` | array | Yes | Array of chat messages (OpenAI format) |
| `retrieval_config` | object | Yes | Vector store search configuration |
| `rerank` | object | No | Reranking configuration |
| `stream` | boolean | No | Enable streaming (default: `false`) |

### retrieval_config

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `vector_store_id` | string | **required** | ID of the vector store to search |
| `custom_llm_provider` | string | `"openai"` | Vector store provider |
| `top_k` | integer | `10` | Number of results to retrieve |

### rerank

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `enabled` | boolean | `false` | Enable reranking |
| `model` | string | - | Reranking model (e.g., `cohere/rerank-english-v3.0`) |
| `top_n` | integer | `5` | Number of results after reranking |

## End-to-End Example

### 1. Ingest a Document

First, ingest a document using the [/rag/ingest](./rag_ingest.md) endpoint:

```bash showLineNumbers title="Step 1: Ingest"
curl -X POST "http://localhost:4000/v1/rag/ingest" \\
    -H "Authorization: Bearer sk-1234" \\
    -H "Content-Type: application/json" \\
    -d "{
        \\"file\\": {
            \\"filename\\": \\"company_docs.txt\\",
            \\"content\\": \\"$(base64 -i company_docs.txt)\\",
            \\"content_type\\": \\"text/plain\\"
        },
        \\"ingest_options\\": {
            \\"vector_store\\": {
                \\"custom_llm_provider\\": \\"openai\\"
            }
        }
    }"
```

Response:
```json
{
  "id": "ingest_abc123",
  "status": "completed",
  "vector_store_id": "vs_xyz789",
  "file_id": "file-123"
}
```

### 2. Query with RAG

Now query the ingested documents:

```bash showLineNumbers title="Step 2: Query"
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 products does the company offer?"}
        ],
        "retrieval_config": {
            "vector_store_id": "vs_xyz789",
            "custom_llm_provider": "openai",
            "top_k": 5
        }
    }'
```

Response:
```json
{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "model": "gpt-4o-mini",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Based on the company documents, the company offers..."
      },
      "finish_reason": "stop"
    }
  ]
}
```

## Provider Examples

### Bedrock

```bash showLineNumbers title="RAG Query with Bedrock"
curl -X POST "http://localhost:4000/v1/rag/query" \\
    -H "Authorization: Bearer sk-1234" \\
    -H "Content-Type: application/json" \\
    -d '{
        "model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
        "messages": [{"role": "user", "content": "What is LiteLLM?"}],
        "retrieval_config": {
            "vector_store_id": "KNOWLEDGE_BASE_ID",
            "custom_llm_provider": "bedrock",
            "top_k": 5
        }
    }'
```

### Vertex AI

```bash showLineNumbers title="RAG Query with Vertex AI"
curl -X POST "http://localhost:4000/v1/rag/query" \\
    -H "Authorization: Bearer sk-1234" \\
    -H "Content-Type: application/json" \\
    -d '{
        "model": "vertex_ai/gemini-1.5-pro",
        "messages": [{"role": "user", "content": "What is LiteLLM?"}],
        "retrieval_config": {
            "vector_store_id": "your-corpus-id",
            "custom_llm_provider": "vertex_ai",
            "top_k": 5
        }
    }'
```

## Python SDK

```python showLineNumbers title="Using litellm.aquery()"
import litellm

response = await litellm.aquery(
    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,
    },
    rerank={
        "enabled": True,
        "model": "cohere/rerank-english-v3.0",
        "top_n": 3,
    },
)

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

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/rag-query) · https://claudary.paisolsolutions.com