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import streamlit as st | |
import os | |
import google.generativeai as genai | |
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI | |
# from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings | |
# from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace | |
# from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings | |
from langchain_community.document_loaders import PyPDFDirectoryLoader | |
from langchain.text_splitter import RecursiveCharacterTextSplitter | |
from langchain.chains.combine_documents import create_stuff_documents_chain | |
from langchain_core.prompts import ChatPromptTemplate | |
from langchain_core.output_parsers import StrOutputParser | |
from langchain.chains import create_retrieval_chain | |
from langchain_community.vectorstores import FAISS | |
import time | |
import asyncio | |
from dotenv import load_dotenv | |
load_dotenv() | |
# Load environment variables | |
# huggingfacehub_api_token = os.getenv("HF_TOKEN") | |
# # Initialize HuggingFace endpoint and LLM | |
# repo_id = "meta-llama/Meta-Llama-3-8B-Instruct" | |
# llm_endpoint = HuggingFaceEndpoint( | |
# repo_id=repo_id, | |
# max_length=128, | |
# temperature=0.7, | |
# huggingfacehub_api_token=huggingfacehub_api_token | |
# ) | |
# llm = ChatHuggingFace(llm=llm_endpoint) | |
genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
# Ensure that an event loop exists | |
async def initialize_llm(): | |
return ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0.5, verbose=True) | |
llm = asyncio.run(initialize_llm()) | |
# Function for vector embedding | |
def vector_embedding(): | |
if "vectors" not in st.session_state: | |
st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
st.session_state.loader = PyPDFDirectoryLoader("./analysis-pdf") | |
st.session_state.docs = st.session_state.loader.load() | |
st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=50) | |
st.session_state.final_docs = st.session_state.text_splitter.split_documents(st.session_state.docs[:30]) | |
st.session_state.vectors = FAISS.from_documents(st.session_state.final_docs, st.session_state.embeddings) | |
st.title("Gemini RAG DEMO") | |
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> | |
Question: {input} | |
""" | |
) | |
question_prompt = st.text_input("Enter Your Question From Documents") | |
if st.button("Document Embedding"): | |
vector_embedding() | |
st.write("Vector Store DB is Ready!") | |
if question_prompt: | |
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 = time.process_time() | |
response = retrieval_chain.invoke({"input": question_prompt}) | |
print("Response time :", time.process_time() - start_time) | |
st.write(response['answer']) | |
with st.expander("Document Similarity Search"): | |
for i, doc in enumerate(response["context"]): | |
st.write(doc.page_content) | |
st.write("---------------------------") | |