LangChain框架实战：构建个人专属知识库教程

将应用部署到云服务器（如 AWS、Azure 或 Google Cloud），并设置监控工具（如 Prometheus 或 Loggly）跟踪 API 使用情况和性能。使用 Flask 或 FastAPI 构建后端，前端通过 HTTP 请求与后端交互。捕获 API 可能返回的错误（如认证失败、超限等），并实现重试机制。根据用户反馈调整模型参数、改进上下文管理逻辑，或升级到更高版本的模型（如 GPT

ralkfwe947

440人浏览 · 2025-09-05 01:52:05

ralkfwe947 · 2025-09-05 01:52:05 发布

安装LangChain及相关依赖

确保Python环境（建议3.8+），安装LangChain核心库及常用扩展：

pip install langchain openai faiss-cpu tiktoken

若需使用本地模型（如Llama2），需额外安装llama-cpp-python：

pip install llama-cpp-python

准备知识库数据

将文档（PDF/TXT/Markdown等）统一存放于./data目录，支持以下预处理方式：

使用UnstructuredFileLoader拆分文本为片段
对长文本按固定长度分块（建议500-1000字符）
通过RecursiveCharacterTextSplitter保留上下文关联

示例代码：

from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

loader = DirectoryLoader('./data', glob="**/*.txt")
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
chunks = text_splitter.split_documents(docs)

构建向量数据库

选择嵌入模型（OpenAI或HuggingFace）和向量数据库（FAISS/Chroma）：

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
db = FAISS.from_documents(chunks, embeddings)
db.save_local("my_vectorstore")

设计检索增强生成链

结合检索器与LLM（如GPT-3.5或本地模型）：

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

retriever = db.as_retriever(search_kwargs={"k": 3})
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(temperature=0),
    chain_type="stuff",
    retriever=retriever
)

实现交互式查询

添加记忆功能支持多轮对话：

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
conversation = ConversationChain(llm=OpenAI(), memory=memory)

while True:
    query = input("User: ")
    if query.lower() == 'exit':
        break
    result = qa_chain({"query": query})
    print("AI:", result["result"])

高级优化技巧

混合检索：结合关键词搜索与语义搜索

from langchain.retrievers import BM25Retriever, EnsembleRetriever
bm25_retriever = BM25Retriever.from_documents(chunks)
ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, db.as_retriever()],
    weights=[0.4, 0.6]
)

查询重写：使用LLM优化原始查询

from langchain.chains.query_constructor.base import AttributeInfo
metadata_field_info = [AttributeInfo(name="source", description="文档来源", type="string")]

分级响应：设置置信度阈值，低置信时要求用户澄清

部署方案

本地API服务：使用FastAPI封装

from fastapi import FastAPI
app = FastAPI()
@app.post("/ask")
def ask_question(question: str):
    return qa_chain.run(question)

Gradio可视化：快速构建DEMO界面

import gradio as gr
demo = gr.Interface(fn=qa_chain.run, inputs="text", outputs="text")
demo.launch()

Docker容器化：制作生产环境镜像

FROM python:3.9
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["uvicorn", "main:app", "--host", "0.0.0.0"]

性能监控指标

检索召回率@K
响应延迟百分位（P90/P99）
每次查询的Token消耗
用户满意度评分（可集成反馈按钮）

常见问题排查：

处理OOV词：添加自定义术语表
解决幻觉回答：设置max_tokens和温度参数
优化分块策略：测试不同chunk_size对结果的影响

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