hhofmann
77 lines
1.4 KiB
Markdown
77 lines
1.4 KiB
Markdown
# Mem0 Docker with Qdrant
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Dockerized memory service using mem0 with Qdrant vector database for semantic memory storage.
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## Quick Start
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1. Copy the example environment file:
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```bash
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cp example.env .env
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```
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2. Edit `.env` with your configuration:
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- `MEM0_PORT`: Port for the mem0 API
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- `QDRANT_HOST`: Qdrant host (default: qdrant)
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- `QDRANT_PORT`: Qdrant port (default: 6333)
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- `EMBEDDING_URL`: llama.cpp embedding endpoint URL
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- `EMBEDDING_DIMS`: Embedding dimension size
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3. Start the services:
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```bash
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docker-compose up -d
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```
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## API Endpoints
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### Health Check
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```
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GET /health
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```
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### Add Memory
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```
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POST /add
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Content-Type: application/json
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{
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"message": "Memory text to store",
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"user_id": "default",
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"metadata": {}
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}
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```
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### Search Memories
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```
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POST /search
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Content-Type: application/json
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{
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"query": "Search query",
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"user_id": "default",
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"limit": 5
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}
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```
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### Get All Memories
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```
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GET /memories?user_id=default
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```
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### Delete Memory
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```
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DELETE /delete/{memory_id}
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```
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## Files
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- `docker-compose.yml`: Docker Compose configuration
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- `Dockerfile`: Container build instructions
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- `main.py`: FastAPI memory API server
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- `mem0_server.py`: Alternative HTTP server implementation
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- `requirements.txt`: Python dependencies
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- `example.env`: Environment variable template
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## Configuration
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The service requires an external llama.cpp embedding endpoint. Configure the `EMBEDDING_URL` to point to your embedding service.
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