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embeddings\nOPENAI_API_KEY=your-openai-api-key\n\n# Qdrant server URL (supports both HTTP and HTTPS)\nQDRANT_URL=https://your-qdrant-server\n\n# Qdrant API key (if authentication is enabled)\nQDRANT_API_KEY=your-qdrant-api-key\n\n# Name of the Qdrant collection to use\nQDRANT_COLLECTION_NAME=your-collection-name\n```\n\n## Setup\n\n### Local Setup\n\n1. Install dependencies:\n```bash\nnpm install\n```\n\n2. Build the server:\n```bash\nnpm run build\n```\n\n### Docker Setup\n\n1. Build the Docker image:\n```bash\ndocker build -t mcp-qdrant-memory .\n```\n\n2. Run the Docker container with required environment variables:\n```bash\ndocker run -d \\\n  -e OPENAI_API_KEY=your-openai-api-key \\\n  -e QDRANT_URL=http://your-qdrant-server:6333 \\\n  -e QDRANT_COLLECTION_NAME=your-collection-name \\\n  -e QDRANT_API_KEY=your-qdrant-api-key \\\n  --name mcp-qdrant-memory \\\n  mcp-qdrant-memory\n```\n\n### Add to MCP settings:\n```json\n{\n  \"mcpServers\": {\n    \"memory\": {\n      \"command\": \"/bin/zsh\",\n      \"args\": [\"-c\", \"cd /path/to/server && node dist/index.js\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\",\n        \"QDRANT_API_KEY\": \"your-qdrant-api-key\",\n        \"QDRANT_URL\": \"http://your-qdrant-server:6333\",\n        \"QDRANT_COLLECTION_NAME\": \"your-collection-name\"\n      },\n      \"alwaysAllow\": [\n        \"create_entities\",\n        \"create_relations\",\n        \"add_observations\",\n        \"delete_entities\",\n        \"delete_observations\",\n        \"delete_relations\",\n        \"read_graph\",\n        \"search_similar\"\n      ]\n    }\n  }\n}\n```\n\n## Tools\n\n### Entity Management\n- `create_entities`: Create multiple new entities\n- `create_relations`: Create relations between entities\n- `add_observations`: Add observations to entities\n- `delete_entities`: Delete entities and their relations\n- `delete_observations`: Delete specific observations\n- `delete_relations`: Delete specific relations\n- `read_graph`: Get the full knowledge graph\n\n### Semantic Search\n- `search_similar`: Search for semantically similar entities and relations\n  ```typescript\n  interface SearchParams {\n    query: string;     // Search query text\n    limit?: number;    // Max results (default: 10)\n  }\n  ```\n\n## Implementation Details\n\nThe server maintains two forms of persistence:\n\n1. File-based (memory.json):\n   - Complete knowledge graph structure\n   - Fast access to full graph\n   - Used for graph operations\n\n2. Qdrant Vector DB:\n   - Semantic embeddings of entities and relations\n   - Enables similarity search\n   - Automatically synchronized with file storage\n\n### Synchronization\n\nWhen entities or relations are modified:\n1. Changes are written to memory.json\n2. Embeddings are generated using OpenAI\n3. Vectors are stored in Qdrant\n4. Both storage systems remain consistent\n\n### Search Process\n\nWhen searching:\n1. Query text is converted to embedding\n2. Qdrant performs similarity search\n3. Results include both entities and relations\n4. Results are ranked by semantic similarity\n\n## Example Usage\n\n```typescript\n// Create entities\nawait client.callTool(\"create_entities\", {\n  entities: [{\n    name: \"Project\",\n    entityType: \"Task\",\n    observations: [\"A new development project\"]\n  }]\n});\n\n// Search similar concepts\nconst results = await client.callTool(\"search_similar\", {\n  query: \"development tasks\",\n  limit: 5\n});\n```\n\n## HTTPS and Reverse Proxy Configuration\n\nThe server supports connecting to Qdrant through HTTPS and reverse proxies. This is particularly useful when:\n- Running Qdrant behind a reverse proxy like Nginx or Apache\n- Using self-signed certificates\n- Requiring custom SSL/TLS configurations\n\n### Setting up with a Reverse Proxy\n\n1. Configure your reverse proxy (example using Nginx):\n```nginx\nserver {\n    listen 443 ssl;\n    server_name qdrant.yourdomain.com;\n\n    ssl_certificate /path/to/cert.pem;\n    ssl_certificate_key /path/to/key.pem;\n\n    location / {\n        proxy_pass http://localhost:6333;\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n    }\n}\n```\n\n2. Update your environment variables:\n```bash\nQDRANT_URL=https://qdrant.yourdomain.com\n```\n\n### Security Considerations\n\nThe server implements robust HTTPS handling with:\n- Custom SSL/TLS configuration\n- Proper certificate verification options\n- Connection pooling and keepalive\n- Automatic retry with exponential backoff\n- Configurable timeouts\n\n### Troubleshooting HTTPS Connections\n\nIf you experience connection issues:\n\n1. Verify your certificates:\n```bash\nopenssl s_client -connect qdrant.yourdomain.com:443\n```\n\n2. Test direct connectivity:\n```bash\ncurl -v https://qdrant.yourdomain.com/collections\n```\n\n3. Check for any proxy settings:\n```bash\nenv | grep -i proxy\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Submit a pull request\n\n## License\n\nMIT","readmeFilename":"README.md"}