LCA-LLM/LCA_RAG/RAGnoUI.py

'''
支持上传文件进行分析，记录历史上下文，
'''
import time
import tempfile
from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate
from langchain.prompts import MessagesPlaceholder
from langchain_community.document_loaders import TextLoader, PyPDFLoader, CSVLoader, JSONLoader
from langchain_core.messages import AIMessage, HumanMessage
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_openai import ChatOpenAI
from langchain_community.chat_models.tongyi import ChatTongyi
# from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
from langchain_community.chat_models import QianfanChatEndpoint
from langchain_community.chat_models import ChatZhipuAI
from langchain.retrievers import ContextualCompressionRetriever
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter, RecursiveJsonSplitter
import requests
import os
import re
import pandas as pd


os.environ["CUDA_VISIBLE_DEVICES"] = "1"
device = "cuda"
memory = 6

def init_chain():
    try:
        # 加载环境变量并初始化模型

        # 使用Tongyi
        # os.environ["DASHSCOPE_API_KEY"] = 'sk-c5f441f863f44094b0ddb96c831b5002'
        # llm = ChatTongyi(
        #     streaming=True,
        #     model='llama3-70b-instruct'
        # )
        
        # 使用Qianfan
        # ak = "beiDmKGviUVjTyGqb8U0dc2u"
        # sk = "i3GexGV2B5Poi00RVAFUoZ61Ylj0P7tI"
        # llm = QianfanChatEndpoint(
        #     streaming=True,
        #     model = 'ERNIE-3.5-8K',
        #     api_key=ak,
        #     secret_key=sk
        # )
        
        # 使用智谱
        # os.environ["ZHIPUAI_API_KEY"] = "434790cf952335f18b6347e7b6de9777.V50p55zfk8Ye4ojV"
        # llm = ChatZhipuAI(
        #     streaming=True,
        #     model = "glm-4"
        # )

        # deepseek
        # pip3 install langchain_openai
# python3 deepseek_v2_langchain.py

        llm = ChatOpenAI(
            model='deepseek-chat', 
            openai_api_key='sk-c47f0552d66549ac92c72d64ebdc4d05', 
            openai_api_base='https://api.deepseek.com',
        )

        embedding = HuggingFaceEmbeddings(model_name="/home/zhangxj/models/acge_text_embedding", model_kwargs={"device": device})
        retriever = Chroma(persist_directory="chromaDB", embedding_function=embedding,)

        retriever = retriever.as_retriever(search_type="mmr", search_kwargs={"k": 10})

        instruct_system_prompt = (
            "你是生命周期领域富有经验和知识的专家。"
            "使用以下检索到的上下文来回答问题。"
            "{context}"
            "答案最多使用1句话并保持非常简洁，不能换行。"
        )
        # instr = "你是生命周期领域富有经验和知识的专家。根据你所掌握的知识只用1句话回答问题。不要列出几点来回答，不需要换行"
        instruct_prompt = ChatPromptTemplate.from_messages(
            [
                ("system", instruct_system_prompt),
                ("human", "{input}"),
            ]
        )

        # Create a chain for passing a list of Documents to a model.
        qa_chain = create_stuff_documents_chain(llm, instruct_prompt) #
        rag_chain = create_retrieval_chain(retriever, qa_chain)

        return rag_chain,retriever
    
    except Exception as e:
        print(f"Error in init_chain: {e}")
        return None

def user_in(uin, rag_chain):
    try:
        result = rag_chain.invoke({"input": uin})['answer']
        # print(rag_chain)
        # result = rag_chain(uin)
        return result
    except Exception as e:
        print(f"Error in user_in: {e}")
        return "An error occurred while processing your request."

def retrieve_and_output(retriever,query):
    try:
        retrieved_docs = retriever.get_relevant_documents(query)
        # 输出检索到的内容
        print("Retrieved content:")
        for doc in retrieved_docs:
            print(doc.page_content)
    except Exception as e:
        print(f"Error in retrieve_and_output: {e}")

def main():
    
    question  = []
    with open("/home/zhangxj/WorkFile/LCA-GPT/QA/split/question/ques0.txt","r",encoding="utf-8") as file:
        for line in file.readlines():
            question.append(line.strip())
    
    rag,retriever= init_chain()
    
    answers = []
    for ques in question:
        # print(ques)
        response = user_in(ques, rag)
        retrieve_and_output(retriever,ques)
        print(response) 
        # print(len(response))
        answers.append(response.strip())
        # if len(answers) == 3:
        #     break
    with open("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/RAGpred.txt","w",encoding="utf-8") as file:
        for ans in answers:
            line = re.sub(r'\s+', '', ans)
            file.write(line+'\n')
    data = {"ans":answers}
    df = pd.DataFrame(data)
    df.to_csv("/home/zhangxj/WorkFile/LCA-GPT/QA/eval/rag.csv",index=False)

if __name__ == "__main__":
    main()