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import os
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import subprocess
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import tempfile
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import urllib.parse
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import uuid
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from contextlib import asynccontextmanager
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from io import BytesIO
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from logging.handlers import RotatingFileHandler
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from typing import List
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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 fastapi.staticfiles import StaticFiles
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from gensim.models import KeyedVectors
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from pydantic import BaseModel, Field, ValidationError
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from starlette.responses import StreamingResponse
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from Config.Config import MS_MODEL_PATH, MS_MODEL_LIMIT, MS_HOST, MS_PORT, MS_MAX_CONNECTIONS, MS_NPROBE, \
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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 Util.ALiYunUtil import ALiYunUtil
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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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# 将HTML文件转换为Word文件
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def html_to_word_pandoc(html_file, output_file):
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subprocess.run(['pandoc', html_file, '-o', output_file])
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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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# 初始化阿里云大模型工具
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app.state.aliyun_util = ALiYunUtil()
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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, documents):
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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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# 动态生成expr表达式
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if documents:
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conditions = [f"array_contains(tags['tags'], '{doc}')" for doc in documents]
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expr = " OR ".join(conditions)
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else:
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expr = "" # 如果没有选择文档，返回空字符串
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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,
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search_params,
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expr=expr, # 使用in操作符
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limit=5) # 返回 5 条结果
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# 3. 处理搜索结果
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logger.info("最相关的知识库内容:")
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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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if hit.distance < 0.88: # 设置距离阈值
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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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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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# 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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else:
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logger.warning(f"距离太远，忽略此结果: {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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logger.info(f"时间: {record['timestamp']}")
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logger.info(f"距离: {hit.distance}")
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continue
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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. 如果未提供相关信息，请不要回答。
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3. 如果发现相关信息与原来的问题契合度低，也不要回答
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4. 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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5. 确保内容结构清晰，便于前端展示
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"""
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# 调用阿里云大模型
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if len(context) > 0:
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html_content = client.chat(prompt)
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yield {"data": html_content}
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else:
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yield {"data": "没有在知识库中找到相关的信息，无法回答此问题。"}
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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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知识库中有的内容：
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小学数学中有哪些模型？
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帮我写一下 “如何理解点、线、面、体、角”的教学设计
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知识库中没有的内容：
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你知道黄海是谁吗？
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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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documents: List[str] = Field(..., description="用户上传的文档")
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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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temp_html = None
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output_file = None
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try:
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# Parse request data
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try:
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data = await request.json()
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html_content = data.get('html_content', '')
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if not html_content:
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raise ValueError("Empty HTML content")
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except Exception as e:
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logger.error(f"Request parsing failed: {str(e)}")
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raise HTTPException(status_code=400, detail=f"Invalid request: {str(e)}")
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# 创建临时HTML文件
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temp_html = os.path.join(tempfile.gettempdir(), uuid.uuid4().hex + ".html")
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with open(temp_html, "w", encoding="utf-8") as f:
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f.write(html_content)
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# 使用pandoc转换
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output_file = os.path.join(tempfile.gettempdir(), "小学数学问答.docx")
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subprocess.run(['pandoc', temp_html, '-o', output_file], check=True)
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# 读取生成的Word文件
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with open(output_file, "rb") as f:
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stream = BytesIO(f.read())
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# 返回响应
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encoded_filename = urllib.parse.quote("小学数学问答.docx")
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return StreamingResponse(
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stream,
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media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
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headers={"Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}"})
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Unexpected error: {str(e)}")
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raise HTTPException(status_code=500, detail="Internal server error")
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finally:
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# 清理临时文件
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try:
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if temp_html and os.path.exists(temp_html):
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os.remove(temp_html)
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if output_file and os.path.exists(output_file):
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os.remove(output_file)
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except Exception as e:
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logger.warning(f"Failed to clean up temp files: {str(e)}")
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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+ALiYun接口"""
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async for chunk in generate_stream(
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request.app.state.aliyun_util,
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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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query_request.documents
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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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