# -*-coding:utf-8 -*- import gradio as gr import os import json from glob import glob import requests from langchain import FAISS from langchain.embeddings import CohereEmbeddings, OpenAIEmbeddings from langchain import VectorDBQA from langchain.chat_models import ChatOpenAI from prompts import MyTemplate from build_index.run import process_files from langchain.prompts.chat import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ) from langchain.prompts import PromptTemplate from langchain.chains.llm import LLMChain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains import QAGenerationChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain # Streaming endpoint API_URL = "https://api.openai.com/v1/chat/completions" cohere_key = '5IRbILAbjTI0VcqTsktBfKsr13Lych9iBAFbLpkj' faiss_store = './output/' docsearch = None def process(files, openai_api_key, max_tokens, n_sample): """ 对文档处理进行摘要，构建问题，构建文档索引 """ os.environ['OPENAI_API_KEY'] = openai_api_key print('Displaying uploading files ') print(glob('/tmp/*')) docs = process_files([i.name for i in files], 'openai', max_tokens) print('Display Faiss index') print(glob('./output/*')) question = get_question(docs, openai_api_key, max_tokens, n_sample) summary = get_summary(docs, openai_api_key, max_tokens, n_sample) return question, summary def get_question(docs, openai_api_key, max_tokens, n_sample=5): q_list = [] llm = ChatOpenAI(openai_api_key=openai_api_key, max_tokens=max_tokens, temperature=0) # 基于文档进行QA生成 prompt = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template(MyTemplate['qa_sys_template']), HumanMessagePromptTemplate.from_template(MyTemplate['qa_user_template']), ] ) chain = QAGenerationChain.from_llm(llm, prompt=prompt) print('Generating Question from template') for i in range(n_sample): qa = chain.run(docs[i].page_content)[0] print(qa) q_list.append(f"问题{i + 1}: {qa['question']}") return '\n'.join(q_list) def get_summary(docs, openai_api_key, max_tokens, n_sample=5, verbose=None): llm = ChatOpenAI(openai_api_key=openai_api_key, max_tokens=max_tokens) print('Generating Summary from template') map_prompt = PromptTemplate(template=MyTemplate['summary_template'], input_variables=["text"]) combine_prompt = PromptTemplate(template=MyTemplate['summary_template'], input_variables=["text"]) map_chain = LLMChain(llm=llm, prompt=map_prompt, verbose=verbose) reduce_chain = LLMChain(llm=llm, prompt=combine_prompt, verbose=verbose) combine_document_chain = StuffDocumentsChain( llm_chain=reduce_chain, document_variable_name='text', verbose=verbose, ) chain = MapReduceDocumentsChain( llm_chain=map_chain, combine_document_chain=combine_document_chain, document_variable_name='text', collapse_document_chain=None, verbose=verbose ) summary = chain.run(docs[:n_sample]) print(summary) return summary def predict(inputs, openai_api_key, max_tokens, chat_counter, chatbot=[], history=[]): global docsearch print(f"chat_counter - {chat_counter}") print(f'Histroy - {history}') # History: Original Input and Output in flatten list print(f'chatbot - {chatbot}') # Chat Bot： 上一轮回复的[[user, AI]] history.append(inputs) if docsearch is None: print(f'loading faiss store from {faiss_store}') docsearch = FAISS.load_local(faiss_store, OpenAIEmbeddings(openai_api_key=openai_api_key)) else: print('faiss already loaded') # 构建模板 llm = ChatOpenAI(openai_api_key=openai_api_key, max_tokens=max_tokens) messages_combine = [ SystemMessagePromptTemplate.from_template(MyTemplate['chat_combine_template']), HumanMessagePromptTemplate.from_template("{question}") ] p_chat_combine = ChatPromptTemplate.from_messages(messages_combine) messages_reduce = [ SystemMessagePromptTemplate.from_template(MyTemplate['chat_reduce_template']), HumanMessagePromptTemplate.from_template("{question}") ] p_chat_reduce = ChatPromptTemplate.from_messages(messages_reduce) chain = VectorDBQA.from_chain_type(llm=llm, chain_type="map_reduce", vectorstore=docsearch, k=4, chain_type_kwargs={"question_prompt": p_chat_reduce, "combine_prompt": p_chat_combine} ) result = chain({"query": inputs}) print(result) result = result['result'] # 生成返回值 history.append(result) chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)] chat_counter += 1 yield chat, history, chat_counter def reset_textbox(): return gr.update(value='') with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""") as demo: gr.HTML("""

🚀Smart Doc Reader🚀

""") with gr.Column(elem_id="col_container"): openai_api_key = gr.Textbox(type='password', label="输入 API Key") with gr.Accordion("Parameters", open=True): with gr.Row(): max_tokens = gr.Slider(minimum=100, maximum=2000, value=1000, step=100, interactive=True, label="字数") chat_counter = gr.Number(value=0, precision=0, label='对话轮数') n_sample = gr.Slider(minimum=3, maximum=5, value=3, step=1, interactive=True, label="问题数") # 输入文件，进行摘要和问题生成 with gr.Row(): with gr.Column(): files = gr.File(file_count="multiple", file_types=[".pdf"], label='上传pdf文件') run = gr.Button('文档内容解读') with gr.Column(): summary = gr.Textbox(type='text', label="一眼看尽 - 文档概览") question = gr.Textbox(type='text', label='推荐问题 - 问别的也行哟') chatbot = gr.Chatbot(elem_id='chatbot') inputs = gr.Textbox(placeholder="这篇文档是关于什么的", label="针对文档你有哪些问题？") state = gr.State([]) with gr.Row(): clear = gr.Button("清空") start = gr.Button("提问") run.click(process, [files, openai_api_key, max_tokens, n_sample], [question, summary]) inputs.submit(predict, [inputs, openai_api_key, max_tokens, chat_counter, chatbot, state], [chatbot, state, chat_counter], ) start.click(predict, [inputs, openai_api_key, max_tokens, chat_counter, chatbot, state], [chatbot, state, chat_counter], ) # 每次对话结束都重置对话 clear.click(reset_textbox, [], [inputs], queue=False) inputs.submit(reset_textbox, [], [inputs]) demo.queue().launch(debug=True)