#!/usr/bin/env python3
"""
混合检索MCP服务
使用fastmcp框架实现，提供图片解析后的向量化入库和混合检索功能
"""

import sys
import os
import requests
from io import BytesIO
from typing import List, Dict, Any
from fastmcp import FastMCP
# 添加项目根目录到Python路径
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from PIL import Image
from utils.infinity_util import InfinityVectorDB
from model.multimodal_embedding import Embedding
from conf.settings import model_settings, ragflow_settings, vector_db_settings



# 初始化fastmcp应用
mcp = FastMCP("Multi_Vector_Search")

# 初始化向量数据库
vector_db = InfinityVectorDB()

# 初始化多模态嵌入模型
embedding_model = Embedding(
    model_name=model_settings.multimodal_embedding_model_name,
    api_key=model_settings.dashscope_api_key
)

@mcp.tool(name="hybrid_search")
def hybrid_search(request: Dict[str, Any]) -> Dict[str, Any]:
    """
    混合检索API
    使用文本查询和向量查询进行混合检索
    """
    try:
        # 解析请求参数
        text_query = request["text_query"]
        image_url = request["image"]
        topn = request.get("topn", 2)
        
        print(f"开始混合检索，数据库: {vector_db_settings.infinity_database}, 知识库id: {ragflow_settings.dataset_id}, 文本查询: {text_query}, 返回数量: {topn}")
        
        # 构建索引名称
        index_name = f"pdf_documents_{ragflow_settings.dataset_id}"        
        print(f"开始生成多模态嵌入，文本长度: {len(text_query)}")
        
        # 处理image_url为image: Image.Image
        if isinstance(image_url, str):
                # 下载图片
                response = requests.get(image_url)
                response.raise_for_status()  # 检查HTTP状态码
    
                # 将响应内容转换为字节流
                image_bytes = BytesIO(response.content)
    
                # 创建Image对象
                image = Image.open(image_bytes)
        
        # 生成多模态嵌入向量
        embedding = embedding_model.get_multimodal_embedding(text_query, image)
        
        print(f"多模态嵌入生成完成，向量长度: {len(embedding)}")
        
        # 执行混合检索
        result = vector_db.hybrid_search(
            index_name=index_name,
            match_method="dense",
            vector_field="dense_vector_1024",
            query_vector=embedding,
            element_type="float",
            metric_type="cosine",
            topn=topn,
            text_query=text_query,
            text_field="content"
        )
        
        print(f"混合检索完成，总命中数: {result.get('total', 0)}")
        
        # 返回成功响应
        return {
            "success": True,
            "message": "混合检索成功",
            "output": result.get("output", []),
            "total": result.get("total", topn)
        }
    except Exception as e:
        print(f"混合检索失败: {str(e)}")
        return {
            "success": False,
            "message": str(e)
        }


if __name__ == "__main__":
    mcp.run(transport="sse", host="0.0.0.0", port=18000)
    # 启动HTTP服务器，使用uvicorn运行FastAPI应用
    # import uvicorn
    # uvicorn.run(mcp.http_app, host="0.0.0.0", port=18000, transport="stdio")