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import os
import streamlit as st
from dotenv import load_dotenv
from embedding import get_embedding
from vector import VectorStore
from parse import PDFTextExtractor
from chunk import SimpleTextChunker
from llm import ask_llm
# Load environment variables
load_dotenv()
# Initialize VectorStore
if "store" not in st.session_state:
st.session_state["store"] = VectorStore()
st.title("📚 RAG Note Assistant - Upload & Ask")
PDF_FOLDER = "pdf_folder"
os.makedirs(PDF_FOLDER, exist_ok=True)
# upload PDF files
uploaded_files = st.file_uploader("Upload new PDF documents", accept_multiple_files=True, type=["pdf"])
if uploaded_files:
for file in uploaded_files:
file_path = os.path.join(PDF_FOLDER, file.name)
with open(file_path, "wb") as f:
f.write(file.getbuffer())
# Extract text from the uploaded PDF
extractor = PDFTextExtractor(PDF_FOLDER)
document = extractor.extract_text_from_pdf(file_path)
# Chunk the extracted text
chunker = SimpleTextChunker(chunk_size=500, chunk_overlap=100)
chunks = chunker.process_document(document)
# Generate embeddings and upsert into Pinecone
embeddings = [get_embedding(chunk["content"]) for chunk in chunks]
st.session_state["store"].add(embeddings, chunks)
st.success(f" '{file.name}' has been successfully added to the knowledge base!")
# ask question
question = st.text_input("Enter your question")
if st.button("Submit"):
if not question.strip():
st.warning(" Please enter a valid question.")
else:
# Generate query embedding
query_embedding = get_embedding(question)
# Perform similarity search
relevant_chunks = st.session_state["store"].search(query_embedding)
if not relevant_chunks:
st.warning(" No relevant content found in the knowledge base. Please upload related documents first.")
else:
# Combine retrieved chunks into context
context = "\n".join([chunk["text"] for chunk in relevant_chunks])
# Ask the LLM for the answer
with st.spinner('AI is thinking...'):
answer = ask_llm(question, context)
st.markdown("### 🤖 AI Answer")
st.write(answer)
st.markdown("### 📖 Reference Chunks")
st.write(context)
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