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import os
import streamlit as st
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
from groq import Groq
GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)
# Initialize embedding model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2') # Open-source lightweight model
# Initialize FAISS index
dimension = 384 # Embedding size of the model
index = faiss.IndexFlatL2(dimension)
# Helper Functions
def extract_text_from_pdf(file):
"""Extract text from a PDF file."""
reader = PdfReader(file)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
def create_chunks(text, chunk_size=500):
"""Split text into chunks of a specified size."""
words = text.split()
return [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
def generate_embeddings(chunks):
"""Generate embeddings for the given chunks."""
return embedding_model.encode(chunks, convert_to_numpy=True)
def query_groq(prompt):
"""Query the Groq model for a response."""
response = client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model="llama3-8b-8192"
)
return response.choices[0].message.content
# Streamlit App
st.title("RAG-Based Document Query Application")
st.write("Upload a PDF, ask questions, and get AI-powered answers!")
# Step 1: PDF Upload
uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
if uploaded_file:
with st.spinner("Processing the PDF..."):
# Extract text
pdf_text = extract_text_from_pdf(uploaded_file)
st.success("PDF processed successfully!")
# Step 2: Chunking
chunks = create_chunks(pdf_text)
st.info(f"Document split into {len(chunks)} chunks.")
# Step 3: Embedding Creation
embeddings = generate_embeddings(chunks)
index.add(np.array(embeddings))
st.success("Embeddings stored in FAISS database.")
# Step 4: Query
user_query = st.text_input("Ask a question:")
if user_query:
with st.spinner("Searching and generating a response..."):
# Embed user query
query_embedding = embedding_model.encode([user_query], convert_to_numpy=True)
# Search in FAISS
distances, indices = index.search(np.array(query_embedding), k=5)
relevant_chunks = [chunks[i] for i in indices[0]]
# Combine retrieved chunks as context
context = " ".join(relevant_chunks)
# Query Groq model
prompt = f"Context: {context}\n\nQuestion: {user_query}\n\nAnswer:"
answer = query_groq(prompt)
# Display response
st.write("### Answer:")
st.write(answer)
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