bot.py

# Import necessary modules from the langchain library
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.prompts.chat import SystemMessagePromptTemplate


def get_text_from_file(file_path):
    """
    Read and return the content of a text file.

    Args:
    - file_path (str): Path to the text file.

    Returns:
    - str: Content of the text file.
    """
    try:
        with open(file_path, 'r') as f:
            text = f.read()
        return text
    except FileNotFoundError:
        print("Error: The specified file was not found.")
        return ""
    except Exception as e:
        print(f"Error reading the file: {e}")
        return ""


def get_text_chunks(text):
    """
    Split the provided text into smaller chunks using CharacterTextSplitter.

    Args:
    - text (str): The text to be split.

    Returns:
    - list: List of text chunks.
    """
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks


def get_vectorstore(text_chunks):
    """
    Convert text chunks to vectors using OpenAIEmbeddings and store them in a FAISS vector store.

    Args:
    - text_chunks (list): List of text chunks.

    Returns:
    - FAISS vector store: The vector store containing text chunk vectors.
    """
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore


def get_conversation_chain(vectorstore):
    """
    Create a conversational retrieval chain using a provided vector store.

    Args:
    - vectorstore (FAISS vector store): The vector store containing text chunk vectors.

    Returns:
    - ConversationalRetrievalChain: The conversational retrieval chain for chatbot interaction.
    """
    template = """ 
    You are an AI assistant for answering questions about DreamSpace Academy.if the question is a greeting answer with greeting else Use the following pieces of context to answer the question at the end. 
    If you don't know the answer, just say 'Sorry, I don't know', don't try to make up an answer. 
    Use three sentences maximum and keep the answer as concise as possible.
    If the question is not about the DreamSpace Academy, politely inform them that you are tuned to only answer questions about DreamSpace Academy. 
    {context}
    Question: {question}
    Helpful Answer:"""

    llm = ChatOpenAI()

    memory = ConversationBufferMemory(
        memory_key='chat_history', return_messages=True)

    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory,
        verbose=True,
    )

    QA_CHAIN_PROMPT = PromptTemplate(
        input_variables=["context", "question"], template=template)

    # Modify the default prompt for the chain
    conversation_chain.combine_docs_chain.llm_chain.prompt.messages[0] = SystemMessagePromptTemplate(
        prompt=QA_CHAIN_PROMPT)

    return conversation_chain