Support Bot Langchain

support_cust.py

import json

from colorama import Fore
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.conversational_retrieval.base import BaseConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.prompts import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma

chat_history = []
def generate_response(support_qa: BaseConversationalRetrievalChain, prompt):
    response = support_qa({"question": prompt, "chat_history": chat_history})
    chat_history.append((prompt, response["answer"]))
    print( json.dumps(chat_history))
    return response['answer']

if __name__ == '__main__':
    print("status: loading document")
    loader = TextLoader("./docs/doc.txt")
    pages = loader.load_and_split()

    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len,
    )

    docs = text_splitter.split_documents(pages)

    # Split documents into chunks
    gds_data_split = docs
    print(len(gds_data_split))

    # Define embedding model
    OPENAI_API_KEY = "sk-key"

    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
    support_data = gds_data_split
    support_store = Chroma.from_documents(
        support_data, embeddings, collection_name="support"
    )
    print("status: configure llm")
    llm = ChatOpenAI(
        model_name="gpt-3.5-turbo",
        temperature=0,
        openai_api_key=OPENAI_API_KEY,
        max_tokens=1024,
    )
    _template = """
    {chat_history}
    {question}"""
    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)

    support_template = """As a TechROVA marketing bot, your goal is to provide accurate and helpful information about TechROVA products,
    a software company selling softwares to clients.
    You should answer user inquiries based on the context provided and avoid making up answers.
    If you don't know the answer, go through the previous chats first and if not, then use your own training data.
    
    {context}

    Question: {question}"""

    SUPPORT_PROMPT = PromptTemplate(
        template=support_template, input_variables=["context", "question"]
    )
    print("status: confiure chain")
    support_qa = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=support_store.as_retriever(search_kwargs={"k": 3}),
        verbose=True,
        return_source_documents=True,
        qa_prompt=SUPPORT_PROMPT,
        condense_question_prompt=CONDENSE_QUESTION_PROMPT,
    )

    while True:
        user_input = input("-> ")
        response = generate_response(support_qa, user_input)
        print(Fore.RED + response)