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import os
import pickle
import time
import gradio as gr
from langchain import OpenAI
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredURLLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from dotenv import load_dotenv
load_dotenv() # take environment variables from .env (especially openai api key)
# Define the main function to process URLs and handle queries
def process_and_query(url1, url2, url3, query):
urls = [url1, url2, url3]
file_path = "faiss_store_openai.pkl"
llm = OpenAI(temperature=0.9, max_tokens=500)
# Load data
loader = UnstructuredURLLoader(urls=urls)
data = loader.load()
# Split data
text_splitter = RecursiveCharacterTextSplitter(
separators=['\n\n', '\n', '.', ','],
chunk_size=1000
)
docs = text_splitter.split_documents(data)
# Create embeddings and save it to FAISS index
embeddings = OpenAIEmbeddings()
vectorstore_openai = FAISS.from_documents(docs, embeddings)
# Save the FAISS index to a pickle file
with open(file_path, "wb") as f:
pickle.dump(vectorstore_openai, f)
# Process the query
if os.path.exists(file_path):
with open(file_path, "rb") as f:
vectorstore = pickle.load(f)
chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever())
result = chain({"question": query}, return_only_outputs=True)
answer = result["answer"]
# Extract and format sources
sources = result.get("sources", "")
sources_list = sources.split("\n") if sources else []
return answer, sources_list
# Define the Gradio interface
url1_input = gr.inputs.Textbox(label="URL 1")
url2_input = gr.inputs.Textbox(label="URL 2")
url3_input = gr.inputs.Textbox(label="URL 3")
query_input = gr.inputs.Textbox(label="Question")
output_text = gr.outputs.Textbox(label="Answer")
output_sources = gr.outputs.Textbox(label="Sources")
interface = gr.Interface(
fn=process_and_query,
inputs=[url1_input, url2_input, url3_input, query_input],
outputs=[output_text, output_sources],
title="RockyBot: News Research Tool 📈",
description="Enter up to three news article URLs and ask a question. The bot will process the articles and provide an answer along with the sources."
)
if __name__ == "__main__":
interface.launch()
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