Qdrant Knowledge Graph
About
Integrates a knowledge graph with semantic search capabilities, enabling efficient storage, retrieval, and querying of structured information for context-aware applications.
Details
- Author
- delorenj
- Repository
- delorenj/mcp-qdrant-memory
- GitHub stars
- 8
- Downloads
- 3,067
- Categories
- Database, Other, Knowledge Base, AI, Developer Tools, Design, File Management, Search, Infrastructure
- Tags
- #visualization
Jump to
- Graph-based knowledge representation with entities and relations
- File-based persistence (memory.json)
- Semantic search using Qdrant vector database
- OpenAI embeddings for semantic similarity
- HTTPS support with reverse proxy compatibility
- Docker support for easy deployment
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 Knowledge GraphCommand (node, npx, python, etc.)/bin/zshArguments-
Argument 1
-c -
Argument 2
cd /path/to/server && node dist/index.js
Environment-
QDRANT_URL
http://your-qdrant-server:6333 -
OPENAI_API_KEY
your-openai-api-key -
QDRANT_API_KEY
your-qdrant-api-key -
QDRANT_COLLECTION_NAME
your-collection-name
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
The following environment variables are required:
QDRANT_API_KEY=your-qdrant-api-key
1. Install dependencies:
npm install
2. Build the server:
npm run build
1. Build the Docker image:
docker build -t mcp-qdrant-memory .
2. Run the Docker container with required environment variables:
docker run -d \
-e OPENAI_API_KEY=your-openai-api-key \
-e QDRANT_URL=http://your-qdrant-server:6333 \
-e QDRANT_COLLECTION_NAME=your-collection-name \
-e QDRANT_API_KEY=your-qdrant-api-key \
--name mcp-qdrant-memory \
mcp-qdrant-memory
// Create entities
await client.callTool("create_entities", {
entities: [{
name: "Project",
entityType: "Task",
observations: ["A new development project"]
}]
});
// Search similar concepts
const results = await client.callTool("search_similar", {
query: "development tasks",
limit: 5
});
```
The server supports connecting to Qdrant through HTTPS and reverse proxies. This is particularly useful when:
- Running Qdrant behind a reverse proxy like Nginx or Apache
- Using self-signed certificates
- Requiring custom SSL/TLS configurations
create_entities
Create multiple new entities.
create_relations
Create relations between entities.
add_observations
Add observations to entities.
delete_entities
Delete entities and their relations.
delete_observations
Delete specific observations.
delete_relations
Delete specific relations.
read_graph
Get the full knowledge graph.
search_similar
Search for semantically similar entities and relations. Parameters: query (string), limit (optional number, default: 10).
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"qdrant knowledge graph": {
"env": {
"QDRANT_URL": "http://your-qdrant-server:6333",
"OPENAI_API_KEY": "your-openai-api-key",
"QDRANT_API_KEY": "your-qdrant-api-key",
"QDRANT_COLLECTION_NAME": "your-collection-name"
},
"args": [
"-c",
"cd /path/to/server && node dist/index.js"
],
"shell": true,
"command": "/bin/zsh"
}
}
}Linux
{
"env": {
"QDRANT_URL": "http://your-qdrant-server:6333",
"OPENAI_API_KEY": "your-openai-api-key",
"QDRANT_API_KEY": "your-qdrant-api-key",
"QDRANT_COLLECTION_NAME": "your-collection-name"
},
"args": [
"-c",
"cd /path/to/server && node dist/index.js"
],
"shell": true,
"command": "/bin/zsh"
}Macos
{
"env": {
"QDRANT_URL": "http://your-qdrant-server:6333",
"OPENAI_API_KEY": "your-openai-api-key",
"QDRANT_API_KEY": "your-qdrant-api-key",
"QDRANT_COLLECTION_NAME": "your-collection-name"
},
"args": [
"-c",
"cd /path/to/server && node dist/index.js"
],
"shell": true,
"command": "/bin/zsh"
}Windows
{
"env": {
"QDRANT_URL": "http://your-qdrant-server:6333",
"OPENAI_API_KEY": "your-openai-api-key",
"QDRANT_API_KEY": "your-qdrant-api-key",
"QDRANT_COLLECTION_NAME": "your-collection-name"
},
"args": [
"/c",
"cd /path/to/server && node dist/index.js"
],
"shell": true,
"command": "cmd"
}MCP Memory Server with Qdrant Persistence
This MCP server provides a knowledge graph implementation with semantic search capabilities powered by Qdrant vector database.
Features
- Graph-based knowledge representation with entities and relations
- File-based persistence (memory.json)
- Semantic search using Qdrant vector database
- OpenAI embeddings for semantic similarity
- HTTPS support with reverse proxy compatibility
- Docker support for easy deployment
Environment Variables
The following environment variables are required:
# OpenAI API key for generating embeddings
OPENAI_API_KEY=your-openai-api-key
Qdrant server URL (supports both HTTP and HTTPS)
QDRANT_URL=https://your-qdrant-server
Qdrant API key (if authentication is enabled)
QDRANT_API_KEY=your-qdrant-api-key
Name of the Qdrant collection to use
QDRANT_COLLECTION_NAME=your-collection-name
Setup
Local Setup
1. Install dependencies:
npm install
2. Build the server:
npm run build
Docker Setup
1. Build the Docker image:
docker build -t mcp-qdrant-memory .
2. Run the Docker container with required environment variables:
docker run -d \
-e OPENAI_API_KEY=your-openai-api-key \
-e QDRANT_URL=http://your-qdrant-server:6333 \
-e QDRANT_COLLECTION_NAME=your-collection-name \
-e QDRANT_API_KEY=your-qdrant-api-key \
--name mcp-qdrant-memory \
mcp-qdrant-memory
Add to MCP settings:
{
"mcpServers": {
"memory": {
"command": "/bin/zsh",
"args": ["-c", "cd /path/to/server && node dist/index.js"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"QDRANT_API_KEY": "your-qdrant-api-key",
"QDRANT_URL": "http://your-qdrant-server:6333",
"QDRANT_COLLECTION_NAME": "your-collection-name"
},
"alwaysAllow": [
"create_entities",
"create_relations",
"add_observations",
"delete_entities",
"delete_observations",
"delete_relations",
"read_graph",
"search_similar"
]
}
}
}
Tools
Entity Management
-create_entities: Create multiple new entities
- create_relations: Create relations between entities
- add_observations: Add observations to entities
- delete_entities: Delete entities and their relations
- delete_observations: Delete specific observations
- delete_relations: Delete specific relations
- read_graph: Get the full knowledge graph
Semantic Search
-search_similar: Search for semantically similar entities and relations
interface SearchParams {
query: string; // Search query text
limit?: number; // Max results (default: 10)
}
Implementation Details
The server maintains two forms of persistence:
1. File-based (memory.json):
- Complete knowledge graph structure
- Fast access to full graph
- Used for graph operations
2. Qdrant Vector DB:
- Semantic embeddings of entities and relations
- Enables similarity search
- Automatically synchronized with file storage
Synchronization
When entities or relations are modified:
1. Changes are written to memory.json
2. Embeddings are generated using OpenAI
3. Vectors are stored in Qdrant
4. Both storage systems remain consistent
Search Process
When searching:
1. Query text is converted to embedding
2. Qdrant performs similarity search
3. Results include both entities and relations
4. Results are ranked by semantic similarity
Example Usage
// Create entities
await client.callTool("create_entities", {
entities: [{
name: "Project",
entityType: "Task",
observations: ["A new development project"]
}]
});
// Search similar concepts
const results = await client.callTool("search_similar", {
query: "development tasks",
limit: 5
});
HTTPS and Reverse Proxy Configuration
The server supports connecting to Qdrant through HTTPS and reverse proxies. This is particularly useful when:
- Running Qdrant behind a reverse proxy like Nginx or Apache
- Using self-signed certificates
- Requiring custom SSL/TLS configurations
Setting up with a Reverse Proxy
1. Configure your reverse proxy (example using Nginx):
server {
listen 443 ssl;
server_name qdrant.yourdomain.com;
ssl_certificate /path/to/cert.pem;
ssl_certificate_key /path/to/key.pem;
location / {
proxy_pass http://localhost:6333;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
}
}
2. Update your environment variables:
QDRANT_URL=https://qdrant.yourdomain.com
Security Considerations
The server implements robust HTTPS handling with:
- Custom SSL/TLS configuration
- Proper certificate verification options
- Connection pooling and keepalive
- Automatic retry with exponential backoff
- Configurable timeouts
Troubleshooting HTTPS Connections
If you experience connection issues:
1. Verify your certificates:
openssl s_client -connect qdrant.yourdomain.com:443
2. Test direct connectivity:
curl -v https://qdrant.yourdomain.com/collections
3. Check for any proxy settings:
env | grep -i proxy
Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request
License
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