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import streamlit as st | |
import os | |
from langchain_groq import ChatGroq | |
from langchain_openai import OpenAIEmbeddings | |
from langchain_community.embeddings import HuggingFaceBgeEmbeddings | |
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 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=HuggingFaceBgeEmbeddings(model_name="BAAI/bge-small-en-v1.5", | |
model_kwargs={'device':'cpu'}, | |
encode_kwargs={'normalize_embeddings':True}) | |
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 Huggingface embeddings | |
prompt1=st.text_input("Enter Your Question From Documents") | |
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("--------------------------------") | |