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9f21e5b 2673f00 9f21e5b 9f52a95 9f21e5b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | import os
import tempfile
import faiss
import streamlit as st
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
from groq import Groq
# Load your embedding model (e.g., all-MiniLM)
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
# Initialize Groq client
client = Groq(api_key=os.environ.get("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"))
# Helper: Extract text from uploaded PDF
def extract_text_from_pdf(file):
pdf = PdfReader(file)
text = ""
for page in pdf.pages:
text += page.extract_text()
return text
# Helper: Chunk text
def chunk_text(text, chunk_size=500):
words = text.split()
return [" ".join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]
# Helper: Create FAISS vector store
def create_faiss_index(chunks):
embeddings = embedding_model.encode(chunks)
dim = embeddings.shape[1]
index = faiss.IndexFlatL2(dim)
index.add(embeddings)
return index, embeddings, chunks
# Helper: Retrieve top-k similar chunks
def retrieve_similar_chunks(query, index, chunks, embeddings, k=3):
query_vector = embedding_model.encode([query])
_, I = index.search(query_vector, k)
return [chunks[i] for i in I[0]]
# Streamlit UI
st.title("🧠 PDF-based RAG App using Groq")
uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
if uploaded_file:
text = extract_text_from_pdf(uploaded_file)
chunks = chunk_text(text)
index, embeddings, stored_chunks = create_faiss_index(chunks)
st.success("PDF processed and vector DB created.")
user_query = st.text_input("Ask a question based on the document:")
if user_query:
top_chunks = retrieve_similar_chunks(user_query, index, stored_chunks, embeddings)
# Build context for Groq
context = "\n".join(top_chunks)
prompt = f"Answer the question based on the context below:\n\nContext:\n{context}\n\nQuestion:\n{user_query}"
# Send to Groq
response = client.chat.completions.create(
model="llama3-8b-8192",
messages=[
{"role": "user", "content": prompt}
]
)
st.subheader("Answer:")
st.write(response.choices[0].message.content)
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