Qdrant with OpenAI Embeddings
About
Connects AI systems to Qdrant vector databases for semantic search using OpenAI embeddings, enabling contextual document retrieval and knowledge base querying.
Details
- Author
- amansingh0311
- Repository
- amansingh0311/mcp-qdrant-openai
- GitHub stars
- 2
- Categories
- Developer Tools, Design, Workplace, File Management, AI, Search, Knowledge Base, Frontend, Infrastructure
Jump to
- Semantic search in Qdrant collections using OpenAI embeddings
- List available collections
- View collection information
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Qdrant with OpenAI EmbeddingsCommand (node, npx, python, etc.)pythonArguments-
Argument 1
mcp_qdrant_server.py
Environment-
QDRANT_URL
http://localhost:6333 -
OPENAI_API_KEY
OPENAI_API_KEY -
QDRANT_API_KEY
QDRANT_API_KEY
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
To install Qdrant Vector Search Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @amansingh0311/mcp-qdrant-openai --client claude
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
mcp install mcp_qdrant_server.py --name "Qdrant-OpenAI"
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.
query_collection
Search a Qdrant collection using semantic search with OpenAI embeddings. Parameters: collection_name (string), query_text (string), limit (optional integer, default: 5), model (optional string, default: text-embedding-3-small)
list_collections
List all available collections in the Qdrant database.
collection_info
Get information about a specific collection. Parameters: collection_name (string)
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"qdrant with openai embeddings": {
"env": {
"QDRANT_URL": "http://localhost:6333",
"OPENAI_API_KEY": "OPENAI_API_KEY",
"QDRANT_API_KEY": "QDRANT_API_KEY"
},
"args": [
"mcp_qdrant_server.py"
],
"command": "python"
}
}
}Linux
{
"env": {
"QDRANT_URL": "http://localhost:6333",
"OPENAI_API_KEY": "OPENAI_API_KEY",
"QDRANT_API_KEY": "QDRANT_API_KEY"
},
"args": [
"mcp_qdrant_server.py"
],
"command": "python"
}Macos
{
"env": {
"QDRANT_URL": "http://localhost:6333",
"OPENAI_API_KEY": "OPENAI_API_KEY",
"QDRANT_API_KEY": "QDRANT_API_KEY"
},
"args": [
"mcp_qdrant_server.py"
],
"command": "python"
}Windows
{
"env": {
"QDRANT_URL": "http://localhost:6333",
"OPENAI_API_KEY": "OPENAI_API_KEY",
"QDRANT_API_KEY": "QDRANT_API_KEY"
},
"args": [
"mcp_qdrant_server.py"
],
"command": "python"
}
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
Installing via Smithery
To install Qdrant Vector Search Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @amansingh0311/mcp-qdrant-openai --client claude
Manual 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.
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