"""
conda activate rag
pip install fastapi uvicorn sse-starlette
"""
import asyncio

from elasticsearch import Elasticsearch
from fastapi import FastAPI
from openai import OpenAI
from sse_starlette.sse import EventSourceResponse

from Config import Config

app = FastAPI()

# 初始化ES连接
es = Elasticsearch(
    hosts=Config.ES_CONFIG['hosts'],
    basic_auth=Config.ES_CONFIG['basic_auth'],
    verify_certs=Config.ES_CONFIG['verify_certs']
)

# 初始化DeepSeek客户端
client = OpenAI(
    api_key=Config.DEEPSEEK_API_KEY,
    base_url=Config.DEEPSEEK_URL
)

def search_related_data(query):
    """搜索与查询相关的数据"""
    # 向量搜索
    vector_results = es.search(
        index=Config.ES_CONFIG['default_index'],
        body={
            "query": {
                "match": {
                    "content": {
                        "query": query,
                        "analyzer": "ik_smart"
                    }
                }
            },
            "size": 5
        }
    )
    
    # 文本精确搜索
    text_results = es.search(
        index="raw_texts",
        body={
            "query": {
                "match": {
                    "text.keyword": query
                }
            },
            "size": 5
        }
    )
    
    # 合并结果
    context = ""
    for hit in vector_results['hits']['hits']:
        context += f"向量相似度结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
    
    for hit in text_results['hits']['hits']:
        context += f"文本精确匹配结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
    
    return context

async def generate_stream(query):
    """生成SSE流"""
    context = search_related_data(query)
    
    prompt = f"""根据以下关于'{query}'的相关信息，整理一份结构化的报告：
要求：
1. 分章节组织内容
2. 包含关键数据和事实
3. 语言简洁专业

相关信息：
{context}"""
    
    try:
        response = client.chat.completions.create(
            model="deepseek-chat",
            messages=[
                {"role": "system", "content": "你是一个专业的文档整理助手"},
                {"role": "user", "content": prompt}
            ],
            temperature=0.3,
            stream=True
        )
        
        for chunk in response:
            if chunk.choices[0].delta.content:
                yield {"data": chunk.choices[0].delta.content}
            await asyncio.sleep(0.01)
    except Exception as e:
        yield {"data": f"生成报告时出错: {str(e)}"}

@app.get("/api/rag")
async def rag_stream(query: str):
    """RAG+DeepSeek流式接口"""
    return EventSourceResponse(generate_stream(query))

"""
http://localhost:8000/api/rag?query=整理云南省初中在校生情况文档
http://10.10.21.20:8000/api/rag?query=整理云南省初中在校生情况文档
"""
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)