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import asyncio
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from contextlib import asynccontextmanager
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from logging.handlers import RotatingFileHandler
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import jieba # 导入 jieba 分词库
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import uvicorn
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from fastapi import FastAPI, Request, HTTPException
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from pydantic import BaseModel, Field, ValidationError
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from fastapi.staticfiles import StaticFiles
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from openai import OpenAI
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from sse_starlette.sse import EventSourceResponse
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from gensim.models import KeyedVectors
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from Config import Config
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from Config.Config import MS_MODEL_PATH, MS_MODEL_LIMIT, MS_HOST, MS_PORT, MS_MAX_CONNECTIONS, MS_NPROBE, DEEPSEEK_API_KEY, DEEPSEEK_URL, MS_COLLECTION_NAME
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from Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
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from Milvus.Utils.MilvusConnectionPool import *
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from Milvus.Utils.MilvusConnectionPool import MilvusConnectionPool
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from docx import Document
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from docx.shared import Inches
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from io import BytesIO
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import html2text
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# 初始化日志
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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handler = RotatingFileHandler('Logs/start.log', maxBytes=1024 * 1024, backupCount=5)
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handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
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logger.addHandler(handler)
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# 1. 加载预训练的 Word2Vec 模型
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model = KeyedVectors.load_word2vec_format(MS_MODEL_PATH, binary=False, limit=MS_MODEL_LIMIT)
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logger.info(f"模型加载成功，词向量维度: {model.vector_size}")
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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# 初始化Milvus连接池
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app.state.milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
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# 初始化集合管理器
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app.state.collection_manager = MilvusCollectionManager(MS_COLLECTION_NAME)
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app.state.collection_manager.load_collection()
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# 初始化DeepSeek客户端
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app.state.deepseek_client = OpenAI(
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api_key=Config.DEEPSEEK_API_KEY,
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base_url=Config.DEEPSEEK_URL
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)
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yield
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# 关闭Milvus连接池
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app.state.milvus_pool.close()
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app = FastAPI(lifespan=lifespan)
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# 挂载静态文件目录
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app.mount("/static", StaticFiles(directory="Static"), name="static")
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# 将文本转换为嵌入向量
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def text_to_embedding(text):
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words = jieba.lcut(text) # 使用 jieba 分词
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print(f"文本: {text}, 分词结果: {words}")
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embeddings = [model[word] for word in words if word in model]
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logger.info(f"有效词向量数量: {len(embeddings)}")
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if embeddings:
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avg_embedding = sum(embeddings) / len(embeddings)
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logger.info(f"生成的平均向量: {avg_embedding[:5]}...") # 打印前 5 维
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return avg_embedding
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else:
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logger.warning("未找到有效词，返回零向量")
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return [0.0] * model.vector_size
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async def generate_stream(client, milvus_pool, collection_manager, query):
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"""生成SSE流"""
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# 从连接池获取连接
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connection = milvus_pool.get_connection()
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try:
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# 1. 将查询文本转换为向量
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current_embedding = text_to_embedding(query)
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# 2. 搜索相关数据
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search_params = {
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"metric_type": "L2", # 使用 L2 距离度量方式
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"params": {"nprobe": MS_NPROBE} # 设置 IVF_FLAT 的 nprobe 参数
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}
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# 7. 将文本转换为嵌入向量
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results = collection_manager.search(current_embedding, search_params, limit=5) # 返回 2 条结果
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# 3. 处理搜索结果
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logger.info("最相关的历史对话:")
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context = ""
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if results:
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for hits in results:
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for hit in hits:
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try:
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# 查询非向量字段
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record = collection_manager.query_by_id(hit.id)
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logger.info(f"ID: {hit.id}")
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logger.info(f"标签: {record['tags']}")
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logger.info(f"用户问题: {record['user_input']}")
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# 获取完整内容
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full_content = record['tags'].get('full_content', record['user_input'])
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context = context + full_content
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logger.info(f"时间: {record['timestamp']}")
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logger.info(f"距离: {hit.distance}")
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logger.info("-" * 40) # 分隔线
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except Exception as e:
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logger.error(f"查询失败: {e}")
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else:
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logger.warning("未找到相关历史对话，请检查查询参数或数据。")
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prompt = f"""
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信息检索与回答助手
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根据以下关于'{query}'的相关信息：
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基本信息
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- 语言: 中文
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- 描述: 根据提供的材料检索信息并回答问题
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- 特点: 快速准确提取关键信息，清晰简洁地回答
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相关信息
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{context}
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回答要求
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1. 依托给定的资料，快速准确地回答问题，可以添加一些额外的信息，但请勿重复内容。
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2. 使用HTML格式返回，包含适当的段落、列表和标题标签
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3. 确保内容结构清晰，便于前端展示
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"""
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response = client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{"role": "system", "content": "你是一个专业的文档整理助手"},
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{"role": "user", "content": prompt}
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],
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temperature=0.3,
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stream=False
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)
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yield {"data": response.choices[0].message.content}
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except Exception as e:
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yield {"data": f"生成报告时出错: {str(e)}"}
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finally:
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# 释放连接
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milvus_pool.release_connection(connection)
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"""
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http://10.10.21.22:8000/static/ai.html
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小学数学中有哪些模型？
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"""
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class QueryRequest(BaseModel):
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query: str = Field(..., description="用户查询的问题")
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class SaveWordRequest(BaseModel):
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html: str = Field(..., description="要保存为Word的HTML内容")
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@app.post("/api/save-word")
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async def save_to_word(request: Request):
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try:
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data = await request.json()
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save_request = SaveWordRequest(**data)
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except ValidationError as e:
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logger.error(f"请求体验证失败: {e.errors()}")
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raise HTTPException(status_code=422, detail=e.errors())
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except Exception as e:
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logger.error(f"请求解析失败: {str(e)}")
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raise HTTPException(status_code=400, detail="无效的请求格式")
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# 转换HTML为纯文本
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h = html2text.HTML2Text()
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h.ignore_links = True
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plain_text = h.handle(save_request.html)
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# 创建Word文档
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doc = Document()
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doc.add_heading('小学数学问答', 0)
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doc.add_paragraph(plain_text)
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# 保存到内存中的字节流
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file_stream = BytesIO()
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doc.save(file_stream)
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file_stream.seek(0)
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return StreamingResponse(
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file_stream,
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media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
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headers={"Content-Disposition": "attachment; filename=小学数学问答.docx"}
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)
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@app.post("/api/rag")
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async def rag_stream(request: Request):
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try:
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data = await request.json()
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query_request = QueryRequest(**data)
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except ValidationError as e:
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logger.error(f"请求体验证失败: {e.errors()}")
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raise HTTPException(status_code=422, detail=e.errors())
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except Exception as e:
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logger.error(f"请求解析失败: {str(e)}")
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raise HTTPException(status_code=400, detail="无效的请求格式")
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"""RAG+DeepSeek接口"""
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async for chunk in generate_stream(
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request.app.state.deepseek_client,
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request.app.state.milvus_pool,
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request.app.state.collection_manager,
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query_request.query
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):
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return chunk
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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