Mem0 Docker with Qdrant

Dockerized memory service using mem0 with Qdrant vector database for semantic memory storage.

Quick Start

1. Copy the example environment file:

cp example.env .env

2. Edit .env with your configuration:

   • MEM0_PORT: Port for the mem0 API
   • QDRANT_HOST: Qdrant host (default: qdrant)
   • QDRANT_PORT: Qdrant port (default: 6333)
   • EMBEDDING_URL: llama.cpp embedding endpoint URL
   • EMBEDDING_DIMS: Embedding dimension size

3. Start the services:

docker-compose up -d

API Endpoints

Health Check

GET /health

Add Memory

POST /add
Content-Type: application/json

{
  "message": "Memory text to store",
  "user_id": "default",
  "metadata": {}
}

Search Memories

POST /search
Content-Type: application/json

{
  "query": "Search query",
  "user_id": "default",
  "limit": 5
}

Get All Memories

GET /memories?user_id=default

Delete Memory

DELETE /delete/{memory_id}

Files

• docker-compose.yml: Docker Compose configuration
• Dockerfile: Container build instructions
• main.py: FastAPI memory API server
• mem0_server.py: Alternative HTTP server implementation
• requirements.txt: Python dependencies
• example.env: Environment variable template

Configuration

The service requires an external llama.cpp embedding endpoint. Configure the EMBEDDING_URL to point to your embedding service.