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import streamlit as st
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_nomic.embeddings import NomicEmbeddings
from langchain_community.llms import HuggingFaceHub
from langchain_community.embeddings import HuggingFaceEmbeddings
from bs4 import BeautifulSoup
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
# Convert string of URLs to list
def method_get_website_text(urls):
urls_list = urls.split("\n")
docs = [WebBaseLoader(url).load() for url in urls_list]
docs_list = [item for sublist in docs for item in sublist]
return docs_list
#split the text into chunks
def method_get_text_chunks(text):
#text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=7500, chunk_overlap=100)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=7500, chunk_overlap=100)
doc_splits = text_splitter.split_documents(text)
return doc_splits
#convert text chunks into embeddings and store in vector database
def method_get_vectorstore(document_chunks):
embeddings = HuggingFaceEmbeddings()
#embeddings = NomicEmbeddings(model="nomic-embed-text-v1.5")
# create a vectorstore from the chunks
vector_store = Chroma.from_documents(document_chunks, embeddings)
return vector_store
def get_context_retriever_chain(vector_store,question):
# Initialize the retriever
retriever = vector_store.as_retriever()
# Define the RAG template
after_rag_template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
# Create the RAG prompt template
after_rag_prompt = ChatPromptTemplate.from_template(after_rag_template)
# Initialize the Hugging Face language model (LLM)
llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-Instruct-v0.2", model_kwargs={"temperature":0.6, "max_length":1024})
# Construct the RAG pipeline
after_rag_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| after_rag_prompt
| llm
| StrOutputParser()
)
return after_rag_chain.invoke(question)
def main():
st.set_page_config(page_title="Chat with websites", page_icon="🤖")
st.title("Chat with websites")
# sidebar
with st.sidebar:
st.header("Settings")
website_url = st.text_input("Website URL")
if website_url is None or website_url == "":
st.info("Please enter a website URL")
else:
# Input fields
st.markdown("*Your are gonna interact with the below Website:*")
st.info("Click on the Start button")
# Button to pre-process input
if st.button("Start"):
with st.spinner('Tokenizing and Embedding the Website Data'):
# get pdf text
raw_text = method_get_website_text(website_url)
# get the text chunks
doc_splits = method_get_text_chunks(raw_text)
# create vector store
vector_store = method_get_vectorstore(doc_splits)
# Input fields
question = st.text_input("Question")
# Button to process input and get output
if st.button('Query Documents'):
with st.spinner('Processing...'):
# # get pdf text
# raw_text = method_get_website_text(website_url)
# # get the text chunks
# doc_splits = method_get_text_chunks(raw_text)
# # create vector store
# vector_store = method_get_vectorstore(doc_splits)
# Generate response using the RAG pipeline
answer = get_context_retriever_chain(vector_store,question)
# Display the generated answer
split_string = "Question: " + str(question)
result = answer.split(split_string)[-1]
st.text_area("Answer", value=result, height=300, disabled=True)
if __name__ == '__main__':
main() |