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MCP Qdrant Server with OpenAI Embeddings

by amansingh0311
GitHub
Python
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Integrations

  • Provides semantic search capabilities using OpenAI embeddings to convert text into vector representations for search queries

MCP Qdrant Server with OpenAI Embeddings

This MCP server provides vector search capabilities using Qdrant vector database and OpenAI embeddings.

Features

  • Semantic search in Qdrant collections using OpenAI embeddings
  • List available collections
  • View collection information

Prerequisites

  • Python 3.10+ installed
  • Qdrant instance (local or remote)
  • OpenAI API key

Installation

  1. Clone this repository:
    git clone https://github.com/yourusername/mcp-qdrant-openai.git cd mcp-qdrant-openai
  2. Install dependencies:
    pip install -r requirements.txt

Configuration

Set the following environment variables:

  • OPENAI_API_KEY: Your OpenAI API key
  • QDRANT_URL: URL to your Qdrant instance (default: "http://localhost:6333")
  • QDRANT_API_KEY: Your Qdrant API key (if applicable)

Usage

Run the server directly

python mcp_qdrant_server.py

Run with MCP CLI

mcp dev mcp_qdrant_server.py

Installing in Claude Desktop

mcp install mcp_qdrant_server.py --name "Qdrant-OpenAI"

Available Tools

query_collection

Search a Qdrant collection using semantic search with OpenAI embeddings.

  • collection_name: Name of the Qdrant collection to search
  • query_text: The search query in natural language
  • limit: Maximum number of results to return (default: 5)
  • model: OpenAI embedding model to use (default: text-embedding-3-small)

list_collections

List all available collections in the Qdrant database.

collection_info

Get information about a specific collection.

  • collection_name: Name of the collection to get information about

Example Usage in Claude Desktop

Once installed in Claude Desktop, you can use the tools like this:

What collections are available in my Qdrant database? Search for documents about climate change in my "documents" collection. Show me information about the "articles" collection.

This server cannot be installed

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hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

This server enables semantic search capabilities using Qdrant vector database and OpenAI embeddings, allowing users to query collections, list available collections, and view collection information.

  1. Features
    1. Prerequisites
      1. Installation
        1. Configuration
          1. Usage
            1. Run the server directly
            2. Run with MCP CLI
            3. Installing in Claude Desktop
          2. Available Tools
            1. query_collection
            2. list_collections
            3. collection_info
          3. Example Usage in Claude Desktop

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