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# from langchain.text_splitter import RecursiveCharacterTextSplitter | |
# from langchain.document_loaders import UnstructuredFileLoader, DirectoryLoader | |
from langchain.embeddings.openai import OpenAIEmbeddings | |
from langchain.vectorstores import Chroma | |
from langchain.chat_models import ChatOpenAI | |
from langchain.chains import RetrievalQA | |
import os, logging | |
# destination_folder = './data/' | |
# txt_dir_loader = DirectoryLoader(destination_folder, | |
# loader_cls=UnstructuredFileLoader) | |
# data = txt_dir_loader.load() | |
# text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, | |
# chunk_overlap=20) | |
# documents = text_splitter.split_documents(data) | |
print("LOGGING") | |
embeddings = OpenAIEmbeddings() | |
print("Got Embeddings") | |
persist_directory = "./vector_db" | |
# vectordb = Chroma.from_documents(documents=documents, | |
# embedding=embeddings, | |
# persist_directory=persist_directory) | |
# vectordb.persist() | |
# vectordb = None | |
vectordb = Chroma(persist_directory=persist_directory, | |
embedding_function=embeddings) | |
print("Loaded vector db") | |
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo") | |
print("Instatiated OpenAI LLM") | |
doc_retriever = vectordb.as_retriever() | |
print("Retrieved Docs") | |
hp_qa = RetrievalQA.from_chain_type(llm=llm, | |
chain_type="stuff", | |
retriever=doc_retriever) | |
print("Made hp_qa") | |
def answer_question(query): | |
return(hp_qa.run(query)) | |
if __name__ == "__main__": | |
import gradio as gr | |
# print(answer_question("Who is Harry's Father")) | |
gr.Interface( | |
answer_question, | |
[ | |
gr.inputs.Textbox(lines=2, label="Query"), | |
], | |
gr.outputs.Textbox(label="Response"), | |
title="Ask Harry Potter", | |
description=""" Ask Harry Potter is a tool that let's you ask a question with | |
the books' text as reference""" | |
).launch() |