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import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_openai import OpenAIEmbeddings
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
from dotenv import load_dotenv
load_dotenv()
## load the GROQ And OpenAI API KEY
os.environ['OPENAI_API_KEY']=os.getenv("OPENAI_API_KEY")
groq_api_key=os.getenv('GROQ_API_KEY')
st.title("Chatgroq With Llama3 Demo")
llm=ChatGroq(groq_api_key=groq_api_key,
model_name="Llama3-8b-8192")
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}
"""
)
def vector_embedding():
if "vectors" not in st.session_state:
st.session_state.embeddings=OpenAIEmbeddings()
st.session_state.loader=PyPDFDirectoryLoader("./us_census") ## 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 OpenAI embeddings
prompt1=st.text_input("Enter Your Question From Doduments")
if st.button("Documents Embedding"):
vector_embedding()
st.write("Vector Store DB Is Ready")
import time
if prompt1:
document_chain=create_stuff_documents_chain(llm,prompt)
retriever=st.session_state.vectors.as_retriever()
retrieval_chain=create_retrieval_chain(retriever,document_chain)
start=time.process_time()
response=retrieval_chain.invoke({'input':prompt1})
print("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"]):
st.write(doc.page_content)
st.write("--------------------------------")
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