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import streamlit as st
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters 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 PyPDFLoader
import os
from dotenv import load_dotenv
import tempfile
import time
load_dotenv()
## Langsmith Tracking
os.environ['LANGCHAIN_API_KEY'] = os.getenv('LANGCHAIN_API_KEY')
os.environ['LANGCHAIN_TRACING_V2'] = 'true'
os.environ['LANGCHAIN_PROJECT'] = "Simple Q&A RAG With Groq"
os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
os.environ["HF_TOKEN"] = os.getenv('HF_TOKEN')
os.environ["TOKENIZERS_PARALLELISM"] = "false"
llm = ChatGroq(model="llama-3.1-70b-Versatile")
prompt = ChatPromptTemplate.from_template(
"""
Answer the question based on provided context only.
Please provide the most accurate response based on the question
<context>
{context}
</context>
Question: {input}
"""
)
def create_vector_embeddings(pdf_file_path):
st.session_state.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
st.session_state.loader = PyPDFLoader(pdf_file_path)
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_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
uploaded_file = st.file_uploader("Upload a PDF", type="pdf", key="pdf_uploader")
user_prompt = st.text_input("Enter your Query about PDF here:")
if st.button("Document Embedding") and uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(uploaded_file.getvalue())
tmp_file_path = tmp_file.name
create_vector_embeddings(tmp_file_path)
st.write("Vector Database is ready")
if user_prompt and "vectors" in st.session_state:
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": user_prompt})
st.write(f"Response Time: {time.process_time() - start}")
st.write(response["answer"])
with st.expander("Document Similarity Search"):
for i, doc in enumerate(response["context"]):
st.write(doc.page_content)
st.write("---------------------------------------") |