【Langchain】+ 【baichuan】实现领域知识库【RAG】问答系统-Toy模板网

这篇具有很好参考价值的文章主要介绍了【Langchain】+ 【baichuan】实现领域知识库【RAG】问答系统。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

本项目使用Langchain 和 baichuan 大模型，结合领域百科词条数据（用xlsx保存），简单地实现了领域百科问答实现。文章来源地址https://www.toymoban.com/news/detail-832318.html

from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain_community.embeddings import OpenAIEmbeddings, SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma, FAISS
from langchain_community.llms import OpenAI, Baichuan
from langchain_community.chat_models import ChatOpenAI, ChatBaichuan
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain, RetrievalQA
#import langchain_community import chat_models
#print(chat_models.__all__)

import streamlit as st
import pandas as pd
import os
import warnings
import time
warnings.filterwarnings('ignore')




# 对存储了领域百科词条的xlsx文件进行解析
def get_xlsx_text(xlsx_file):
    df = pd.read_excel(xlsx_file, engine='openpyxl')
    text = ""
    for index, row in df.iterrows():
        text += row['title'].replace('\n', '')
        text += row['content'].replace('\n', '')
        text += '\n\n'
    return text

# Splits a given text into smaller chunks based on specified conditions
def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(
        separators="\n\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks


# 对切分的文本块构建编码向量并存储到FASISS
# Generates embeddings for given text chunks and creates a vector store using FAISS
def get_vectorstore(text_chunks):
    # embeddings = OpenAIEmbeddings() #有经济条件的可以使用　opanaiembending
    embeddings = SentenceTransformerEmbeddings(model_name='all-MiniLM-L6-v2')
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore
# Initializes a conversation chain with a given vector store

# 对切分的文本块构建编码向量并存储到Chroma
# Generates embeddings for given text chunks and creates a vector store using Chroma
def get_vectorstore_chroma(text_chunks):
    # embeddings = OpenAIEmbeddings()
    embeddings = SentenceTransformerEmbeddings(model_name='all-MiniLM-L6-v2')
    vectorstore = Chroma.from_texts(
        texts=text_chunks, embedding=embeddings)
    return vectorstore


def get_conversation_chain_baichuan(vectorstore):
    memory = ConversationBufferWindowMemory(
        memory_key='chat_history', return_message=True)　# 设置记忆存储器
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=Baichuan(temperature=temperature_input, model_name=model_select),
        retriever=vectorstore.as_retriever(),
        get_chat_history=lambda h: h,
        memory=memory
    )
    return conversation_chain


os.environ["http_proxy"] = "http://127.0.0.1:7890"
os.environ["https_proxy"] = "http://127.0.0.1:7890"
 # langchain 可以通过设置环境变量来设置参数
os.environ['BAICHUAN_API_KEY'] = 'sk-88888888888888888888888888888888'
temperature_input = 0.7
model_select = 'Baichuan2-Turbo-192K'
raw_text = get_xlsx_text('领域文件/twiki百科问答.xlsx')


text_chunks = get_text_chunks(raw_text)
vectorstore = get_vectorstore_chroma(text_chunks)
# Create conversation chain
qa = get_conversation_chain_baichuan(vectorstore)
questions = [
    "什么是森林经营项目?",
    "风电项目开发过程中需要的主要资料?",
    "什么是ESG"
]
for question in questions:
    result = qa(question)
    print(f"**Question**: {question} \n")
    print(f"**Answer__**: {result['answer']} \n")