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import streamlit as st
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import os
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from langchain_groq import ChatGroq
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from langchain_openai import OpenAIEmbeddings
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.chains.combine_documents import create_stuff_documents_chain
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from langchain_core.prompts import ChatPromptTemplate
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from langchain.chains import create_retrieval_chain
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from langchain_community.vectorstores import FAISS
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from langchain_community.document_loaders import PyPDFDirectoryLoader
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from dotenv import load_dotenv
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load_dotenv()
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os.environ['OPENAI_API_KEY']=os.getenv("OPENAI_API_KEY")
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groq_api_key=os.getenv('GROQ_API_KEY')
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st.title("Chatgroq With Llama3 Demo")
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llm=ChatGroq(groq_api_key=groq_api_key,
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model_name="Llama3-8b-8192")
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prompt=ChatPromptTemplate.from_template(
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"""
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Answer the questions based on the provided context only.
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Please provide the most accurate response based on the question
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<context>
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{context}
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<context>
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Questions:{input}
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"""
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)
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def vector_embedding():
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if "vectors" not in st.session_state:
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st.session_state.embeddings=OpenAIEmbeddings()
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st.session_state.loader=PyPDFDirectoryLoader("./us_census")
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st.session_state.docs=st.session_state.loader.load()
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st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)
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st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:20])
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st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings)
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prompt1=st.text_input("Enter Your Question From Doduments")
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if st.button("Documents Embedding"):
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vector_embedding()
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st.write("Vector Store DB Is Ready")
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import time
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if prompt1:
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document_chain=create_stuff_documents_chain(llm,prompt)
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retriever=st.session_state.vectors.as_retriever()
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retrieval_chain=create_retrieval_chain(retriever,document_chain)
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start=time.process_time()
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response=retrieval_chain.invoke({'input':prompt1})
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print("Response time :",time.process_time()-start)
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st.write(response['answer'])
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with st.expander("Document Similarity Search"):
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for i, doc in enumerate(response["context"]):
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st.write(doc.page_content)
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st.write("--------------------------------")
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