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import gradio as gr | |
import os | |
from langchain.chains import RetrievalQA | |
from langchain.llms import OpenAI | |
from langchain.document_loaders import PyPDFLoader | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.embeddings import OpenAIEmbeddings | |
from langchain.vectorstores import Chroma | |
def qa_system(pdf_file, openai_key, prompt, chain_type, k): | |
os.environ["OPENAI_API_KEY"] = openai_key | |
# load document | |
loader = PyPDFLoader(pdf_file.name) | |
documents = loader.load() | |
# split the documents into chunks | |
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | |
texts = text_splitter.split_documents(documents) | |
# select which embeddings we want to use | |
embeddings = OpenAIEmbeddings() | |
# create the vectorestore to use as the index | |
db = Chroma.from_documents(texts, embeddings) | |
# expose this index in a retriever interface | |
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k}) | |
# create a chain to answer questions | |
qa = RetrievalQA.from_chain_type( | |
llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True) | |
# get the result | |
result = qa({"query": prompt}) | |
return result['result'], [doc.page_content for doc in result["source_documents"]] | |
# define the Gradio interface | |
input_file = gr.inputs.File(label="PDF File") | |
openai_key = gr.inputs.Textbox(label="OpenAI API Key", type="password") | |
prompt = gr.inputs.Textbox(label="Question Prompt") | |
chain_type = gr.inputs.Radio(['stuff', 'map_reduce', "refine", "map_rerank"], label="Chain Type") | |
k = gr.inputs.Slider(minimum=1, maximum=5, default=1, label="Number of Relevant Chunks") | |
output_text = gr.outputs.Textbox(label="Answer") | |
output_docs = gr.outputs.Textbox(label="Relevant Source Text") | |
gr.Interface(qa_system, inputs=[input_file, openai_key, prompt, chain_type, k], outputs=[output_text, output_docs], | |
title="Question Answering with PDF File and OpenAI", | |
description="Upload a PDF file, enter your OpenAI API key, type a question prompt, select a chain type, and choose the number of relevant chunks to use for the answer.").launch(debug = True) | |