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c427af8 | 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 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import os
import streamlit as st
import faiss
import numpy as np
import fitz # PyMuPDF for PDF text extraction
from sentence_transformers import SentenceTransformer
from groq import Groq
# Set up API key for Groq LLM
GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "")
if not GROQ_API_KEY:
st.error("🚨 Groq API Key is missing! Set `GROQ_API_KEY` in the environment.")
st.stop()
# Initialize Groq Client
client = Groq(api_key=GROQ_API_KEY)
# Load Embedding Model
embed_model = SentenceTransformer("all-MiniLM-L6-v2")
# Initialize FAISS Index
embedding_size = 384 # Dimension of embeddings from MiniLM
index = faiss.IndexFlatL2(embedding_size)
documents = [] # To store text chunks
# Function to extract text from PDF
def extract_text_from_pdf(pdf_file):
try:
doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
text = "\n".join([page.get_text("text") for page in doc])
return text
except Exception as e:
st.error(f"❌ Error extracting text: {e}")
return ""
# Function to split text into chunks
def chunk_text(text, chunk_size=512):
words = text.split()
return [" ".join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
# Function to store document embeddings in FAISS
def store_embeddings(chunks):
global documents, index
embeddings = embed_model.encode(chunks)
index.add(np.array(embeddings).astype("float32"))
documents.extend(chunks)
# Function to retrieve relevant chunks from FAISS
def retrieve_relevant_chunks(query, top_k=3):
if index.ntotal == 0:
return []
query_embedding = embed_model.encode([query]).astype("float32")
distances, indices = index.search(query_embedding, top_k)
return [documents[i] for i in indices[0] if i < len(documents)]
# Function to query Groq API with retrieved context
def ask_groq(question, context):
prompt = f"Context: {context}\nQuestion: {question}\nAnswer:"
try:
response = client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model="llama-3.3-70b-versatile",
)
return response.choices[0].message.content
except Exception as e:
return f"❌ Error generating response: {e}"
# Streamlit UI
st.set_page_config(page_title="RAG Q&A with Groq", page_icon="📄", layout="wide")
st.title("📄 RAG-based Q&A with Open Source LLM & FAISS")
st.write("Upload a **PDF document**, then ask questions based on its content!")
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file:
with st.spinner("Extracting text from PDF..."):
pdf_text = extract_text_from_pdf(uploaded_file)
if pdf_text:
with st.spinner("Chunking and embedding document..."):
chunks = chunk_text(pdf_text)
store_embeddings(chunks)
st.success("✅ Document processed! You can now ask questions.")
question = st.text_input("Ask a question from the document:", "")
if st.button("Get Answer"):
if question:
if index.ntotal == 0:
st.warning("⚠️ No document uploaded! Please upload a PDF first.")
else:
with st.spinner("Retrieving relevant context..."):
context = " ".join(retrieve_relevant_chunks(question))
with st.spinner("Generating answer using Groq LLM..."):
answer = ask_groq(question, context)
st.success("Answer:")
st.write(answer)
else:
st.warning("⚠️ Please enter a question!")
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