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from langchain.embeddings.openai import OpenAIEmbeddings | |
from langchain.vectorstores import Chroma | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.chains.question_answering import load_qa_chain | |
from langchain.llms import OpenAI | |
import os | |
setup_complete = False | |
with open("guide1.txt") as f: | |
hitchhikersguide = f.read() | |
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = "\n") | |
texts = text_splitter.split_text(hitchhikersguide) | |
def get_api_key(input_1, input_2): | |
if len(input_1) > 0: | |
os.environ['OPENAI_API_KEY'] = input_1 | |
elif len(input_2) > 0: | |
os.environ['OPENAI_API_KEY'] = input_2 | |
return True | |
def setup_chain(): | |
global embeddings, docsearch, chain, setup_complete | |
embeddings = OpenAIEmbeddings() | |
docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever() | |
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff") | |
setup_complete = True | |
def make_inference(query): | |
if not setup_complete: | |
setup_chain() | |
docs = docsearch.get_relevant_documents(query) | |
return(chain.run(input_documents=docs, question=query)) | |
if __name__ == "__main__": | |
# make a gradio interface | |
import gradio as gr | |
gr.Interface( | |
make_inference, | |
[ | |
gr.inputs.Textbox(lines=2, label="Query"), | |
], | |
gr.outputs.Textbox(label="Response"), | |
title="Query Hitchhiker's Guide", | |
description="See title. What would Douglas Adams say if he saw you query The Hitchhiker's Guide to the Galaxy with AI?", | |
).launch(auth=get_api_key) |