app.py

import openai
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from retriever import *
from chain import *
import gradio as gr

def load_embeddings_database_from_disk(persistence_directory, embeddings_generator):
    """
    Load a Chroma vector database from disk.

    This function loads a Chroma vector database from the specified directory on disk.
    It expects the same persistence_directory and embedding function as used when creating the database.
    
    Args:
        persistence_directory (str): The directory where the database is stored on disk.
        embeddings_generator (obj): The embeddings generator function that was used when creating the database.

    Returns:
        vector_database (obj): The loaded Chroma vector database.
    """

    # Load the Chroma vector database from the persistence directory.
    # The embedding_function parameter should be the same as the one used when the database was created.
    vector_database = Chroma(persist_directory=persistence_directory, embedding_function=embeddings_generator)

    return vector_database


# Specify the directory where the database will be stored when it's persisted.
persistence_directory = 'db'
# Create and persist the embeddings for the documents.
embeddings_generator = OpenAIEmbeddings(openai_api_key = openai.api_key)
# Load the Chroma vector database from disk.
vector_database = load_embeddings_database_from_disk(persistence_directory, embeddings_generator)
topk_documents = 2
# Creating the retriever on top documents.
retriever = initialize_document_retriever(topk_documents, vector_database)
qa_chain = create_question_answering_chain(retriever)


def add_text(history, text):
    history = history + [(text, None)]
    return history, gr.update(value="", interactive=False)


def bot(query):
    llm_response = qa_chain.run({"query": query[-1][0]})
    query[-1][1] = llm_response
    return query


with gr.Blocks() as demo:
    chatbot = gr.Chatbot([], elem_id="Retrieval Augmented Question Answering").style(height=750)

    with gr.Row():
        with gr.Column(scale=0.95):
            txt = gr.Textbox(
                show_label=False,
                placeholder="Enter text and press enter",
            ).style(container=False)

    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
        bot, chatbot, chatbot
    )
    txt_msg.then(lambda: gr.update(interactive=True), None, txt, queue=False)


demo.launch()