准备事项

crewai>=0.80.0
openai>=1.0.0
pydantic>=2.0.0
tdaimemory  # TDAI Memory SDK

pip3 install -r requirements.txt

# config.py
# 模型服务配置 (兼容 OpenAI SDK)
MODEL_CONFIG = {
    "base_url": "https://api.hunyuan.cloud.tencent.com/v1/",   # 请填写实际模型服务请求地址
    "api_key": "your_model_api_key",  # 请填写对应的模型服务API Key
    "model": "model_name"   # 请填写模型名称
}

# Memory 服务配置
MEMORY_CONFIG = {
    "endpoint": "https://memory.tdai.tencentyun.com",
    "api_key": "your_memory_api_key",
    "memory_id": "tdai-mem-xxxxxx",
    "actor_id": "user_a"
}

# memory_tool.py
import json
from tdaimemory import MemoryClient
from config import MEMORY_CONFIG

# 创建全局 Memory 客户端
_memory_client = MemoryClient(
    endpoint=MEMORY_CONFIG["endpoint"],
    api_key=MEMORY_CONFIG["api_key"],
    memory_id=MEMORY_CONFIG["memory_id"]
)

def search_user_memory(query: str, actor_id: str, limit: int = 10, memory_type: str = "persona") -> str:
    """搜索用户的历史记忆"""
    try:
        result = _memory_client.search_records(
            actor_id=actor_id,
            query=query,
            type=memory_type,
            limit=limit,
            enable_rerank=True
        )
        
        if not result.get("records"):
            return "未找到相关的用户记忆信息。"
        
        formatted_records = []
        for record in result.get("records", []):
            formatted_records.append({
                "content": record.get("record_content", ""),
                "type": record.get("type", ""),
                "updated_time": record.get("updated_time", "")
            })
        
        return json.dumps({
            "total_count": result.get("total_count", 0),
            "records": formatted_records
        }, ensure_ascii=False, indent=2)
        
    except Exception as e:
        return f"搜索记忆时发生错误: {str(e)}"