week5/community-contributions/rag_chat_example/run_rag_chat.py

import gradio as gr
from langchain_chroma import Chroma
from pathlib import Path
from utils import create_vector_db, Rag, get_chunks, get_conversation_chain, get_local_vector_db


def chat(question, history) -> str:

    """
    Get the chat data need for the gradio app

    :param question:
        The question being asked in the chat app.
    :type question: str
    :param history:
        A list of the conversation questions and answers.
    :type history: list
    :return:
        The answer from the current question.
    """

    result = conversation_chain.invoke({"question": question})
    answer = result['answer']

    # include source documents if they exist
    # grab the first one as that should be related to the answer
    source_doc = ""
    if result.get('source_documents'):
        source_doc = result['source_documents'][0]

    response = f"{answer}\n\n**Source:**\n{source_doc.metadata.get('source', 'Source')}" \
               if source_doc \
               else answer
    return response


def main():

    gr.ChatInterface(chat, type="messages").launch(inbrowser=True)


if __name__ == '__main__':

    create_new_db = False if Path('vector_db').exists() else True

    if create_new_db:
        folders = Path('knowledge_base').glob('*')
        chunks = get_chunks(folders=folders)
        vector_store = create_vector_db(chunks=chunks, db_name=Rag.DB_NAME.value, embeddings=Rag.EMBED_MODEL.value)
    else:
        client = get_local_vector_db(path='../rag_chat_example/vector_db')
        vector_store = Chroma(client=client, embedding_function=Rag.EMBED_MODEL.value)

    conversation_chain = get_conversation_chain(vectorstore=vector_store)

    main()