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import os
import time
import streamlit as st
from langchain_groq import ChatGroq
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import FAISS
from dotenv import load_dotenv
from sentence_transformers import SentenceTransformer
# Load environment variables
load_dotenv()
groq_api_key = os.getenv("GROQ_API_KEY")
# Streamlit Title
st.title("ChatGroq RAG with PDF")
# Initialize LLM
llm = ChatGroq(groq_api_key=groq_api_key, model="llama3-8b-8192")
# Define Prompt Template
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}
"""
)
# Initialize Embedding Model
# Embedding Function
def vector_embedding():
if "vectors" not in st.session_state:
st.session_state.loader = PyPDFDirectoryLoader("./pdf")
st.session_state.docs = st.session_state.loader.load()
st.session_state.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=200
)
st.session_state.final_document = st.session_state.text_splitter.split_documents(
st.session_state.docs
)
model_name = "sentence-transformers/all-mpnet-base-v2"
st.session_state.embeddings = HuggingFaceEmbeddings(model_name=model_name)
#model = SentenceTransformer("jxm/cde-small-v1", trust_remote_code=True)
#st.session_state.embeddings = HuggingFaceEmbeddings(model=model)
st.session_state.vectors = FAISS.from_documents(
st.session_state.final_document, st.session_state.embeddings
)
# UI for User Input
prompt1 = st.text_input("Enter Your Question from Documents")
# Embed Documents Button
if st.button("Document Embedding"):
with st.spinner("Embedding documents..."):
vector_embedding()
st.success("Vector Store created.")
# Handle Queries
if prompt1.strip():
if "vectors" not in st.session_state or st.session_state.vectors is None:
st.error("Please embed the documents first by clicking the 'Document Embedding' button.")
else:
with st.spinner("Fetching response..."):
start = time.time()
document_chain = create_stuff_documents_chain(llm, prompt)
retriever = st.session_state.vectors.as_retriever()
retrieval_chain = create_retrieval_chain(retriever, document_chain)
response = retrieval_chain.invoke({"input": prompt1})
end = time.time()
st.write(response['answer'])
st.write(f"Response generated in {end - start:.2f} seconds.")
with st.expander("Document Similarity Search"):
context = response.get('context', [])
if not context:
st.write("No similar documents found.")
else:
for i, doc in enumerate(context):
st.write(doc.page_content)
st.write("-----------------------------------------------")
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