Add README.md with documentation

README.md

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+# 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:
+```bash
+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:
+```bash
+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.