简介
这里不对 langchain 和 chatGPT 进行介绍，仅对实现过程进行整理
环境
Python >=3.8
Flask2.2.3
Jinja23.1.2
langchain0.0.143
openai0.27.4
实现 总结功能
使用 langchain 和 openai 接口实现总结功能
实现逻辑：通过
text_splitter
将pdf 分块，送入 langchain 的
summarize_chain
中进行处理
同样也可以使用
OpenAIEmbeddings
来实现，文档地址：langchain 官方文档
创建文件：
summarize.py
from langchain import PromptTemplate
from langchain.callbacks import get_openai_callback
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
defsummarize_docs(docs, doc_url, llm):print(f'You have {len(docs)} document(s) in your {doc_url} data')print(f'There are {len(docs[0].page_content)} characters in your document')
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
split_docs = text_splitter.split_documents(docs)print(f'You have {len(split_docs)} split document(s)')
prompt_template ="""Write a concise summary of the following:
{text}
CONCISE SUMMARY IN CHINESE:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
chain = load_summarize_chain(llm, chain_type="map_reduce", verbose=False, return_intermediate_steps=True,
map_prompt=PROMPT, combine_prompt=PROMPT)
response =""with get_openai_callback()as cb:
response = chain({"input_documents": split_docs}, return_only_outputs=True)print(f"Total Tokens: {cb.total_tokens}")print(f"Prompt Tokens: {cb.prompt_tokens}")print(f"Completion Tokens: {cb.completion_tokens}")print(f"Successful Requests: {cb.successful_requests}")print(f"Total Cost (USD): ${cb.total_cost}")return response
创建接口
使用 Flask 框架创建简单的接口
创建文件
server.py
import os
from flask import Flask, request, make_response, render_template
from langchain import OpenAI
from langchain.document_loaders import PyPDFLoader
from summarize import summarize_docs
app = Flask(__name__)@app.route('/summarize', methods=['POST'])defsummarize():
index_path ="./upload"if'file'notin request.files:return"Please send a POST request with a file",400
uploaded_file = request.files["file"]
filename = uploaded_file.filename
filepath = os.path.join(index_path, os.path.basename(filename))
uploaded_file.save(filepath)
llm = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY, model_name="text-davinci-003",
openai_api_base=OPENAI_API_BASE)
loader = PyPDFLoader(filepath)
pages = loader.load_and_split()
result = summarize_docs(pages, filepath, llm)return make_response(str(result.get("output_text"))),200if __name__ =='__main__':ifnot os.path.exists('./upload'):
os.makedirs('./upload')# 这里输入 openai 的 key
os.environ["OPENAI_API_KEY"]="sk-XXXXXXXXXXXXXXXXXXXXXXXXX"
OPENAI_API_KEY = os.environ['OPENAI_API_KEY']# 如果有域名代理可以使用这里# OPENAI_API_BASE = 'https://XXXX/v1'# 指定项目地址和端口号，这里需要和 web.xml 中对应
app.run(port=19100, host='127.0.0.1')
创建页面
在
server.py
中添加路由地址
@app.route('/')defindex():
msg ="welcome to pdf summarize."return render_template("web.html", data=msg)
创建目录
templates
, 并创建 html 文件
web.html
:
<!DOCTYPEhtml><html><head><metacharset="UTF-8"><title>文件上传</title><style>body{font-family: Arial, sans-serif;margin: 0;padding: 0;background-color: #f5f5f5;}.container{max-width: 600px;margin: 0 auto;padding: 20px;background-color: #fff;border-radius: 10px;box-shadow: 0 0 10px rgba(0, 0, 0, .2);}h1{margin-top: 0;font-size: 32px;color: #333;text-align: center;}form{display: flex;flex-direction: column;align-items: center;}input[type="file"]{margin-bottom: 20px;font-size: 16px;color: #333;padding: 10px;border: 1px solid #ccc;border-radius: 5px;background-color: #fff;box-shadow: 0 0 5px rgba(0, 0, 0, .1);}button{padding: 10px;background-color: #4CAF50;color: #fff;border: none;border-radius: 5px;cursor: pointer;transition: background-color .2s;}button:hover{background-color: #3e8e41;}.result{margin-top: 20px;padding: 20px;background-color: #f1f1f1;border-radius: 5px;white-space: pre-wrap;}.progress{margin-top: 20px;width: 100%;height: 20px;background-color: #f1f1f1;border-radius: 5px;overflow: hidden;box-shadow: 0 0 5px rgba(0, 0, 0, .1);}.bar{width: 0;height: 100%;background-color: #4CAF50;transition: width .2s;}</style></head><body><divclass="container"><h1>文件上传</h1><!-- 指定项目地址和端口号，这里需要和启动地址、断开和接口对应 --><formid="upload-form"method="POST"action="http://127.0.0.1:19100/summarize"enctype="multipart/form-data"><inputtype="file"name="file"><buttontype="submit">生成摘要</button></form><divclass="progress"><divclass="bar"></div></div><h2>返回结果</h2><div>目前响应时间较长，700k 文件响应时间为22秒，请耐心等待</div><divclass="result"><divid="result-text"></div></div><div>页面生成 power by openai chatGPT-3.5</div></div><script>const form = document.querySelector('#upload-form');const progressBar = document.querySelector('.bar');
form.addEventListener('submit',async(event)=>{
event.preventDefault();const formData =newFormData(form);const xhr =newXMLHttpRequest();
xhr.upload.addEventListener('progress',(event)=>{const percent =(event.loaded / event.total)*100;
progressBar.style.width = percent +'%';});
xhr.onreadystatechange=()=>{if(xhr.readyState === XMLHttpRequest.DONE&& xhr.status ===200){
progressBar.style.width ='0';
document.querySelector('#result-text').textContent = xhr.responseText;}};
xhr.open(form.method, form.action);
xhr.send(formData);});</script></body></html>
运行展示
完成后整体项目结构如下：
运行效果如下：
本文转载自: https://blog.csdn.net/assember/article/details/130249517
版权归原作者 assember 所有， 如有侵权，请联系我们删除。
版权归原作者 assember 所有， 如有侵权，请联系我们删除。