app_BACKUP.py

# JB:
# LangChainDeprecationWarning: Importing embeddings from langchain is deprecated. 
# Importing from langchain will no longer be supported as of langchain==0.2.0.
# Please import from langchain-community instead:
# `from langchain_community.embeddings import FastEmbedEmbeddings`.
# To install langchain-community run `pip install -U langchain-community`.
from langchain_community.embeddings import FastEmbedEmbeddings

import os
import streamlit as st
from langchain_groq import ChatGroq
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.embeddings import OllamaEmbeddings

# JB:
from langchain.embeddings import FastEmbedEmbeddings

# from langchain_community.vectorstores import FAISS
# from langchain.vectorstores import Chroma
from langchain_community.vectorstores import Chroma

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
import time
from dotenv import load_dotenv

load_dotenv()  #

# groq_api_key = os.environ['GROQ_API_KEY']
groq_api_key = "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" # os.environ['GROQ_API_KEY']
print("groq_api_key: ", groq_api_key)


if "vector" not in st.session_state:

    # st.session_state.embeddings = OllamaEmbeddings() # ORIGINAL
    st.session_state.embeddings = FastEmbedEmbeddings() # JB


    st.session_state.loader = WebBaseLoader("https://paulgraham.com/greatwork.html")
    st.session_state.docs = st.session_state.loader.load()

    st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    st.session_state.documents = st.session_state.text_splitter.split_documents( st.session_state.docs)
    # st.session_state.vector = FAISS.from_documents(st.session_state.documents, st.session_state.embeddings) # ORIGINAL
    # ZIE: 
    # ZIE VOOR EEN APP MET CHROMADB:
    # https://github.com/vndee/local-rag-example/blob/main/rag.py
    # https://raw.githubusercontent.com/vndee/local-rag-example/main/rag.py
    # Chroma.from_documents(documents=chunks, embedding=FastEmbedEmbeddings())
    st.session_state.vector = Chroma.from_documents(st.session_state.documents, st.session_state.embeddings) # JB


# st.title("Chat with Docs - Groq Edition :) ")
st.title("Literature Based Research (LBR) - Alexander Unzicker and Jan Bours - Chat with Docs - Groq Edition (Very Fast!) ")


llm = ChatGroq(
            groq_api_key=groq_api_key, 
            model_name='mixtral-8x7b-32768'
    )

prompt = ChatPromptTemplate.from_template("""
Answer the following question based only on the provided context. 
Think step by step before providing a detailed answer. 
I will tip you $200 if the user finds the answer helpful. 
<context>
{context}
</context>
Question: {input}""")

document_chain = create_stuff_documents_chain(llm, prompt)

retriever = st.session_state.vector.as_retriever()
retrieval_chain = create_retrieval_chain(retriever, document_chain)

prompt = st.text_input("Input your prompt here")


# If the user hits enter
if prompt:
    # Then pass the prompt to the LLM
    start = time.process_time()
    response = retrieval_chain.invoke({"input": prompt})
    print(f"Response time: {time.process_time() - start}")

    st.write(response["answer"])

    # With a streamlit expander
    with st.expander("Document Similarity Search"):
        # Find the relevant chunks
        for i, doc in enumerate(response["context"]):
            # print(doc)
            # st.write(f"Source Document # {i+1} : {doc.metadata['source'].split('/')[-1]}")
            st.write(doc.page_content)
            st.write("--------------------------------")