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import openai
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from retriever import *
from chain import *
import gradio as gr
def chatbot(query):
llm_response = qa_chain.run({"query": query})
return llm_response
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)
inputs = gr.inputs.Textbox(lines=7, label="Coversational Interface with Chat history")
outputs = gr.outputs.Textbox(label="Reply")
gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="Retrieval Augmented Question Answering",
show_progress = True, theme="compact").launch(share = True, debug=True) |