app.py

import streamlit as st
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_nomic.embeddings import NomicEmbeddings

from langchain_community.llms import HuggingFaceHub

from bs4 import BeautifulSoup
# from langchain_core.runnables import RunnablePassthrough
# from langchain_core.output_parsers import StrOutputParser
# from langchain_core.prompts import ChatPromptTemplate


def method_get_website_text(urls):
    # Convert string of URLs to list
    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


def method_get_text_chunks(text):
    #split the text into chunks
    text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=7500, chunk_overlap=100)
    doc_splits = text_splitter.split_documents(docs_list)
    return doc_splits


def method_get_vectorstore(doc_splits):
    #convert text chunks into embeddings and store in vector database

    # create the open-source embedding function
    embeddings = NomicEmbeddings(model="nomic-embed-text-v1.5")

    # create a vectorstore from the chunks
    vector_store = Chroma.from_documents(document_chunks, embeddings)
    return vectorstore

def get_context_retriever_chain(vector_store):
    # Initialize the retriever
    retriever = vector_store.as_retriever()
    
    # Initialize the language model
    llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-v0.1", model_kwargs={"temperature": 0.6, "max_length": 512})
    
    # Define the response template
    response_template = """Answer the question based only on the following context:
    {context}
    Question: {question}
    """
    
    return retriever, llm, response_template


# def get_context_retriever_chain(vector_store):
#     #llm = ChatOpenAI()
#     llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-v0.1", model_kwargs={"temperature":0.6, "max_length":512})

#     retriever = vector_store.as_retriever()
    
#     prompt = ChatPromptTemplate.from_messages([
#       MessagesPlaceholder(variable_name="chat_history"),
#       ("user", "{input}"),
#       ("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
#     ])
    
#     retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
    
#     return retriever_chain, llm

# def method_get_conversation_chain(retriever_chain, question):
#     # Use the retriever chain to generate a response to the user query
#     response = retriever_chain(question)
#     return response
    
# def method_get_conversation_chain(retriever_chain,llm,question):
#     retriever = vectorstore.as_retriever()

#     #perform the RAG 
    
#     after_rag_template = """Answer the question based only on the following context:
#     {context}
#     Question: {question}
#     """
#     after_rag_prompt = ChatPromptTemplate.from_template(after_rag_template)
#     after_rag_chain = (
#         {"context": retriever, "question": RunnablePassthrough()}
#         | after_rag_prompt
#         | model_local
#         | StrOutputParser()
#     )
#     return after_rag_chain.invoke(question) 

    
#     #llm = ChatOpenAI()
#     llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})

#     memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
#     conversation_chain = ConversationalRetrievalChain.from_llm(
#         llm=llm,
#         retriever=vectorstore.as_retriever(),
#         memory=memory
#     )
#     return conversation_chain


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
        question = st.text_input("Question")
        
        # Button to process input
        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
                vectorstore = method_get_vectorstore(doc_splits)
                st.write(doc_splits)
                # retriever_chain = get_context_retriever_chain(vector_store)
                # # create conversation chain
                # answer = method_get_conversation_chain(retriever_chain,question)
                # st.text_area("Answer", value=answer, height=300, disabled=True)

                
                # Get the retriever, LLM, and response template
                retriever, llm, response_template = get_context_retriever_chain(vectorstore)
                # Retrieve relevant context using the retriever
                context = retriever(question)
                # Generate response using the LLM
                llm_response = llm(question)
                # Apply the response template to format the final answer
                answer = response_template.format(context=context, question=question) + llm_response
                # Display the generated answer
                st.text_area("Answer", value=answer, height=300, disabled=True)

if __name__ == '__main__':
    main()