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import streamlit as st | |
import os | |
from langchain_groq import ChatGroq | |
from langchain.text_splitter import RecursiveCharacterTextSplitter | |
from langchain.chains.combine_documents import create_stuff_documents_chain | |
from langchain_core.prompts import ChatPromptTemplate | |
from langchain.chains import create_retrieval_chain | |
from langchain_community.vectorstores import FAISS | |
from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader | |
from bs4 import BeautifulSoup as Soup | |
import time | |
from langchain.embeddings import HuggingFaceEmbeddings | |
st.sidebar.title("OpenRAG") | |
st.sidebar.markdown( | |
""" | |
OpenRAG is a tool that enhances the speed and efficiency of retrieving information from educational websites, | |
including the scrap it out component, allowing quick access to precise answers. | |
""" | |
) | |
st.sidebar.markdown( | |
""" | |
Whether for academic research, professional inquiries, or personal curiosity, OpenRAG's Scrap it out feature is poised | |
to revolutionize the way users engage with online educational resources. Experience the unparalleled convenience and effectiveness of Scrap it out | |
β your gateway to rapid, reliable information retrieval. | |
""" | |
) | |
st.sidebar.markdown( | |
""" | |
Enjoy Using Scrap it out!! | |
""" | |
) | |
st.title("Scrap it out π¦ ") | |
st.text("") | |
url_link = st.text_input("Input your website link here") | |
# Check if website needs to be loaded (initial load or new URL) | |
if url_link and ("vector" not in st.session_state or url_link != st.session_state.get("loaded_url")): | |
with st.spinner("Loading..."): | |
st.session_state.embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
st.session_state.loader = RecursiveUrlLoader(url=url_link, max_depth=10, extractor=lambda x: Soup(x, "html.parser").text) | |
st.session_state.docs = st.session_state.loader.load() | |
st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100) | |
st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs) | |
st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings) | |
st.session_state["loaded_url"] = url_link # Store the loaded URL | |
st.success("Loaded!") | |
# Rest of the code for LLM and user interaction remains the same | |
llm = ChatGroq(model_name="mixtral-8x7b-32768", groq_api_key="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") | |
prompt = ChatPromptTemplate.from_template( | |
""" | |
Answer the questions based on the provided context only. | |
Please provide the most accurate response based on the question. | |
<context> | |
{context} | |
<context> | |
Questions;{input} | |
""" | |
) | |
if url_link: | |
document_chain = create_stuff_documents_chain(llm, prompt) | |
retriever = st.session_state.vectors.as_retriever() | |
retrieval_chain = create_retrieval_chain(retriever, document_chain) | |
st.text("") | |
query = st.text_input("Input your question here") | |
if query: | |
start = time.process_time() | |
response = (retrieval_chain.invoke({"input":query})) | |
print("Response time: ", time.process_time() - start) | |
st.write(response['answer']) | |
st.write("Response time: ", time.process_time() - start) | |
with st.expander("NOT THE EXPECTED RESPONSE? CHECK OUT HERE"): | |
for i, doc in enumerate(response["context"]): | |
st.write(doc.page_content) | |
st.write("----------------------------------") |