import streamlit as st import os from langchain_groq import ChatGroq from langchain.embeddings import HuggingFaceEmbeddings 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 import PyPDFDirectoryLoader import time # Retrieve API keys from environment variables huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN") groq_api_key = os.getenv("GROQ_API_KEY") # Check if keys are retrieved correctly if not huggingfacehub_api_token: st.error("HUGGINGFACEHUB_API_TOKEN environment variable is not set") st.stop() if not groq_api_key: st.error("GROQ_API_KEY environment variable is not set") st.stop() # Initialize ChatGroq LLM with error handling try: llm = ChatGroq(api_key=groq_api_key, model_name="Llama3-8b-8192") except Exception as e: st.error(f"Failed to initialize ChatGroq LLM: {e}") st.stop() st.title("DataScience Chatgroq With Llama3") prompt = ChatPromptTemplate.from_template( """ Answer the questions based on the provided context only. Please provide the most accurate response based on the question. {context} Questions: {input} """ ) def vector_embedding(): if "vectors" not in st.session_state: st.session_state.embeddings = HuggingFaceEmbeddings() st.session_state.loader = PyPDFDirectoryLoader("./Data_Science") # Data Ingestion st.session_state.docs = st.session_state.loader.load() # Document Loading st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) # Chunk Creation st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:20]) # Splitting st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings) # Vector HuggingFace embeddings st.write("Vector Store DB Is Ready") else: st.write("Vectors already initialized.") prompt1 = st.text_input("Enter Your Question From Documents") if st.button("Documents Embedding"): vector_embedding() if prompt1: if "vectors" not in st.session_state: st.error("Vectors are not initialized. Please click 'Documents Embedding' first.") else: document_chain = create_stuff_documents_chain(llm, prompt) retriever = st.session_state.vectors.as_retriever() retrieval_chain = create_retrieval_chain(retriever, document_chain) try: start = time.process_time() response = retrieval_chain.invoke({'input': prompt1}) st.write("Response time: ", time.process_time() - start) st.write(response['answer']) with st.expander("Document Similarity Search"): for i, doc in enumerate(response["context"]): st.write(doc.page_content) st.write("--------------------------------") except Exception as e: st.error(f"Failed to retrieve the answer: {e}")