"""
Mem0 记忆层客户端封装模块
负责 Mem0 的初始化、检索和存储
"""

import os
from typing import Optional, List, Dict, Any
from mem0 import AsyncMemory

# 本地模块
from app.config import QDRANT_URL, QDRANT_COLLECTION_NAME, VLLM_EMBEDDING_URL
from app.logger import info, warning, error


class Mem0Client:
    """Mem0 异步客户端封装类"""
    
    def __init__(self, llm_instance):
        """
        初始化 Mem0 客户端
        
        Args:
            llm_instance: LangChain LLM 实例（用于事实提取）
        """
        self.llm = llm_instance
        self.mem0: Optional[AsyncMemory] = None
        self._initialized = False
    
    async def initialize(self):
        """异步初始化 Mem0 客户端"""
        if self._initialized:
            return
        
        try:
            # 检查 Qdrant 是否可达 (可选)
            import requests
            try:
                resp = requests.get(f"{QDRANT_URL}/collections", timeout=2)
                if resp.status_code == 200:
                    info(f"✅ Qdrant 服务正常: {QDRANT_URL}")
            except Exception:
                warning(f"⚠️ 无法连接到 Qdrant: {QDRANT_URL}，Mem0 将尝试自动连接")

            config = {
                # 向量存储：复用 Qdrant 实例
                "vector_store": {
                    "provider": "qdrant",
                    "config": {
                        "collection_name": QDRANT_COLLECTION_NAME,
                        "host": QDRANT_URL.split("://")[1].split(":")[0] if "://" in QDRANT_URL else "localhost",
                        "port": int(QDRANT_URL.split(":")[-1]) if ":" in QDRANT_URL.split("://")[-1] else 6333,
                        "embedding_model_dims": 768,  # embeddinggemma-300m 输出 768 维
                    }
                },
                # 事实提取 LLM：直接复用传入的 LangChain 实例
                "llm": {
                    "provider": "langchain",
                    "config": {
                        "model": self.llm  # 直接传入 LangChain 模型实例
                    }
                },
                # Embedding：指向 vLLM 服务
                "embedder": {
                    "provider": "openai",
                    "embedding_dims": 768,  # 关键：将维度参数提升到顶层
                    "config": {
                        "model": "google/embeddinggemma-300m",
                        "api_key": "EMPTY",
                        "api_base": VLLM_EMBEDDING_URL,
                        # 注意：不要在此处传递 dimensions 参数，避免与 vLLM v0.7.2 不兼容
                    }
                },
                "version": "v1.1"
            }

            self.mem0 = AsyncMemory.from_config(config)
            self._initialized = True
            info(f"✅ Mem0 初始化成功 (Embedding: vLLM@8002, Vector: Qdrant, LLM: 复用现有实例)")

        except Exception as e:
            error(f"❌ Mem0 初始化失败: {e}")
            import traceback
            traceback.print_exc()
            self.mem0 = None
    
    async def search_memories(self, query: str, user_id: str, limit: int = 5) -> List[str]:
        """
        检索相关记忆
        
        Args:
            query: 查询文本
            user_id: 用户 ID
            limit: 返回结果数量限制
            
        Returns:
            List[str]: 记忆事实列表
        """
        if not self.mem0:
            warning("⚠️ Mem0 未初始化，跳过记忆检索")
            return []
        
        try:
            memories = await self.mem0.search(query, user_id=user_id, limit=limit)
            
            if memories and "results" in memories:
                facts = [m["memory"] for m in memories["results"] if m.get("memory")]
                if facts:
                    info(f"🔍 [记忆检索] Mem0 返回 {len(facts)} 条记忆")
                    return facts
            
            info("🔍 [记忆检索] 未找到相关记忆")
            return []
            
        except Exception as e:
            warning(f"⚠️ Mem0 检索失败: {e}")
            return []
    
    async def add_memories(self, messages: List[Dict[str, str]], user_id: str) -> bool:
        """
        添加记忆（自动提取事实并存储）
        
        Args:
            messages: 消息列表，格式为 [{"role": "user/assistant/system", "content": "..."}]
            user_id: 用户 ID
            
        Returns:
            bool: 是否成功
        """
        if not self.mem0:
            warning("⚠️ Mem0 未初始化，跳过记忆添加")
            return False
        
        try:
            result = await self.mem0.add(
                messages,
                user_id=user_id,
                metadata={"type": "conversation"}
            )
            info(f"📝 [记忆添加] 已提交给 Mem0 进行事实提取")
            return True
            
        except Exception as e:
            error(f"❌ Mem0 记忆添加失败: {e}")
            return False