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欢迎来到LangChain实战课
https://time.geekbang.org/column/intro/100617601
作者 黄佳
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此笔记来自于 黄佳 的极客时间 LangChain 实战课。如有侵权请联系删除。
课程链接
课程github
pip install pypdf
pip install docx2txt
pip install qdrant-client # qdrant 向量库
import os
os.environ["OPENAI_API_KEY"] = "你的OpenAI API Key"
# 1.Load 导入Document Loaders
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import Docx2txtLoader
from langchain.document_loaders import TextLoader
# 加载Documents
base_dir = "./OneFlower" # 文档的存放目录
documents = []
for file in os.listdir(base_dir):
# 构建完整的文件路径
file_path = os.path.join(base_dir, file)
if file.endswith(".pdf"):
loader = PyPDFLoader(file_path)
documents.extend(loader.load())
elif file.endswith(".docx"):
loader = Docx2txtLoader(file_path)
documents.extend(loader.load())
elif file.endswith(".txt"):
loader = TextLoader(file_path)
documents.extend(loader.load())
# 2.Split 将Documents切分成块以便后续进行嵌入和向量存储
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=10)
chunked_documents = text_splitter.split_documents(documents)
# 3.Store 将分割嵌入并存储在矢量数据库Qdrant中
from langchain.vectorstores import Qdrant
from langchain.embeddings import OpenAIEmbeddings
vectorstore = Qdrant.from_documents(
documents=chunked_documents, # 以分块的文档
embedding=OpenAIEmbeddings(), # 用OpenAI的Embedding Model做嵌入
location=":memory:", # in-memory 存储
collection_name="my_documents",
) # 指定collection_name
# 4. Retrieval 准备模型和Retrieval链
import logging # 导入Logging工具
from langchain.chat_models import ChatOpenAI # ChatOpenAI模型
from langchain.retrievers.multi_query import (
MultiQueryRetriever,
) # MultiQueryRetriever工具
from langchain.chains import RetrievalQA # RetrievalQA链
# 设置Logging
logging.basicConfig()
logging.getLogger("langchain.retrievers.multi_query").setLevel(logging.INFO)
# 实例化一个大模型工具 - OpenAI的GPT-3.5
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
# 实例化一个MultiQueryRetriever
retriever_from_llm = MultiQueryRetriever.from_llm(
retriever=vectorstore.as_retriever(), llm=llm
)
# 实例化一个RetrievalQA链
qa_chain = RetrievalQA.from_chain_type(llm, retriever=retriever_from_llm)
# 5. Output 问答系统的UI实现
from flask import Flask, request, render_template
app = Flask(__name__) # Flask APP
@app.route("/", methods=["GET", "POST"])
def home():
if request.method == "POST":
# 接收用户输入作为问题
question = request.form.get("question")
# RetrievalQA链 - 读入问题,生成答案
result = qa_chain({"query": question})
# 把大模型的回答结果返回网页进行渲染
return render_template("index.html", result=result)
return render_template("index.html")
if __name__ == "__main__":
app.run(host="0.0.0.0", debug=True, port=5000)