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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
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from openai import OpenAI
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from sse_starlette.sse import EventSourceResponse
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from starlette.staticfiles import StaticFiles
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from gensim.models import KeyedVectors
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from Config import Config
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from Milvus.Config.MulvusConfig import *
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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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# 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_path = MS_MODEL_PATH # 替换为你的 Word2Vec 模型路径
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model = KeyedVectors.load_word2vec_format(model_path, binary=False, limit=MS_MODEL_LIMIT)
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print(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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# 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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print(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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print(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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print("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=10) # 返回 2 条结果
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# 3. 处理搜索结果
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print("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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print(f"ID: {hit.id}")
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print(f"会话 ID: {record['person_id']}")
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print(f"用户问题: {record['user_input']}")
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context=context+record['user_input']
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print(f"大模型回复: {record['model_response']}")
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print(f"时间: {record['timestamp']}")
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print(f"距离: {hit.distance}")
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print("-" * 40) # 分隔线
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except Exception as e:
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print(f"查询失败: {e}")
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else:
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print("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"""根据以下关于'{query}'的相关信息,整理一份结构化的报告:
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要求:
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1. 分章节组织内容
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2. 包含关键数据和事实
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3. 语言简洁专业
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相关信息:
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{context}"""
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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=True
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)
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for chunk in response:
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if chunk.choices[0].delta.content:
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yield {"data": chunk.choices[0].delta.content}
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await asyncio.sleep(0.01)
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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/api/rag?query=小学数学中有哪些模型
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"""
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@app.get("/api/rag")
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async def rag_stream(query: str, request: Request):
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"""RAG+DeepSeek流式接口"""
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return EventSourceResponse(
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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
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)
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)
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app.mount("/static", StaticFiles(directory="Static"), name="static")
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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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