# Mem0 REST API Server

Mem0 provides a REST API server (written using FastAPI). Users can perform all operations through REST endpoints. The API also includes OpenAPI documentation, accessible at `/docs` when the server is running.

## Features

- **Create memories:** Create memories based on messages for a user, agent, or run.
- **Retrieve memories:** Get all memories for a given user, agent, or run.
- **Search memories:** Search stored memories based on a query.
- **Update memories:** Update an existing memory.
- **Delete memories:** Delete a specific memory or all memories for a user, agent, or run.
- **Reset memories:** Reset all memories for a user, agent, or run.
- **OpenAPI Documentation:** Accessible via `/docs` endpoint.

## Technology Stack

- **Vector Store**: ChromaDB - 高性能向量数据库
- **Graph Store**: SQLite - 轻量级图存储
- **LLM & Embedder**: Ollama - 本地大模型服务

## Quick Start

### 方式 1: Docker Compose（最简单）

```bash
# 1. 复制环境变量配置
cp .env.example .env

# 2. 编辑 .env 文件，根据需要修改配置
# vi .env 或 nano .env

# 3. 启动所有服务（包括 Ollama）
docker-compose --profile gpu up -d

# 4. 访问 API 文档
open http://localhost:8888/docs
```

**注意**: 首次启动需要下载 Ollama 模型，请等待约 60 秒。

### 方式 2: Docker 命令部署

```bash
# 1. 构建镜像
docker build -t mem0-server .

# 2. 运行容器（通过环境变量传入配置）
docker run -d \
  --name mem0-server \
  -p 8000:8000 \
  -v chroma_data:/app/chroma_db \
  -v sqlite_data:/app/graph_store.db \
  -v history_data:/app/history \
  --add-host=host.docker.internal:host-gateway \
  -e CHROMA_DB_PATH=/app/chroma_db \
  -e CHROMA_COLLECTION_NAME=memories \
  -e SQLITE_DB_PATH=/app/graph_store.db \
  -e OLLAMA_HOST=http://host.docker.internal:11434 \
  -e OLLAMA_LLM_MODEL=llama3.2 \
  -e OLLAMA_EMBEDDER_MODEL=nomic-embed-text \
  -e REDIS_HOST=host.docker.internal \
  -e REDIS_PORT=6379 \
  mem0-server
```

## 环境变量配置

所有配置都可以通过环境变量传入：

| 变量名 | 说明 | 默认值 |
|--------|------|--------|
| `CHROMA_DB_PATH` | ChromaDB 数据路径 | `/app/chroma_db` |
| `CHROMA_COLLECTION_NAME` | ChromaDB 集合名称 | `memories` |
| `SQLITE_DB_PATH` | SQLite 数据库路径 | `/app/graph_store.db` |
| `OLLAMA_HOST` | Ollama 服务地址 | `http://localhost:11434` |
| `OLLAMA_LLM_MODEL` | LLM 模型名称 | `llama3.2` |
| `OLLAMA_EMBEDDER_MODEL` | Embedder 模型名称 | `nomic-embed-text` |
| `HISTORY_DB_PATH` | 历史数据库路径 | `/app/history/history.db` |

详细部署文档请查看 [DEPLOYMENT.md](./DEPLOYMENT.md)

## Prerequisites

Before running the server, ensure you have:

1. **Ollama installed and running** on your host machine
   ```bash
   # Install Ollama from https://ollama.ai
   ollama pull llama3.2
   ollama pull nomic-embed-text
   ```

2. **Docker and Docker Compose** installed

## API Endpoints

- `POST /memories` - Create new memories
- `GET /memories` - Retrieve all memories
- `GET /memories/{memory_id}` - Get a specific memory
- `POST /search` - Search memories
- `PUT /memories/{memory_id}` - Update a memory
- `DELETE /memories/{memory_id}` - Delete a memory
- `DELETE /memories` - Delete all memories for an identifier
- `POST /reset` - Reset all memories
- `GET /docs` - OpenAPI documentation

# 验证
## 测试 ollama

curl http://localhost:11434/api/embeddings \
-d '{
"model":"nomic-embed-text",
"prompt":"hello world"
}'

## 测试 mem0
健康检查：

curl http://localhost:8000/health

返回：

{"status":"ok"}

创建记忆：

curl -X POST http://localhost:8000/v2/memories \
-H "Content-Type: application/json" \
-d '{
"user_id":"test",
"text":"测试用例创建"
}'

搜索记忆：

curl -X POST http://localhost:8000/v2/memories/search \
-H "Content-Type: application/json" \
-d '{
"user_id":"test",
"query":"测试"
}'


## 测试 neo4j