rag.py

from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI

# Function to chat with text files
def chat_with_text_files(user_input, texts):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    split_texts = text_splitter.split_documents(texts)
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.from_documents(split_texts, embeddings)
    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    qa_chain = load_qa_chain(OpenAI(model_name="gpt-3.5-turbo"), chain_type="stuff", memory=memory)
    qa = ConversationalRetrievalChain(combine_docs_chain=qa_chain, retriever=vectorstore.as_retriever(), memory=memory)
    response = qa({"question": user_input})
    return response["answer"]