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import os
import streamlit as st
import PyPDF2
from langchain_community.embeddings import HuggingFaceEmbeddings
import faiss
from groq import Groq
import numpy as np
# Load your environment variables
API = os.environ['GROQ_API_KEY'] = "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"
# Initialize Groq client
client = Groq(api_key=API)
# Initialize HuggingFace embedding model from langchain_community
embedding_model_name = "sentence-transformers/all-MiniLM-L6-v2"
embedding_model = HuggingFaceEmbeddings(model_name=embedding_model_name)
# Determine the vector size dynamically by generating a sample embedding
sample_embedding = embedding_model.embed_query("test")
dimension = len(sample_embedding)
# Initialize FAISS
index = faiss.IndexFlatL2(dimension)
# Streamlit front-end
st.title("RAG-based PDF Query Application")
# File uploader
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file is not None:
# Extract text from PDF
pdf_reader = PyPDF2.PdfReader(uploaded_file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
st.write("PDF Uploaded Successfully!")
# Create chunks
def create_chunks(text, chunk_size=500):
words = text.split()
chunks = [" ".join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]
return chunks
chunks = create_chunks(text)
st.write(f"Created {len(chunks)} chunks.")
# Generate embeddings
embeddings = [embedding_model.embed_query(chunk) for chunk in chunks]
embeddings = np.array(embeddings, dtype=np.float32) # Convert to float32
faiss.normalize_L2(embeddings)
index.add(embeddings)
st.write("Embeddings generated and stored in FAISS.")
# Query input
user_query = st.text_input("Enter your query:")
if user_query:
# Query embedding
query_embedding = embedding_model.embed_query(user_query)
query_embedding = np.array([query_embedding], dtype=np.float32) # Convert to float32
faiss.normalize_L2(query_embedding)
# Search for similar chunks
k = 3 # Number of nearest neighbors
distances, indices = index.search(query_embedding, k)
relevant_chunks = [chunks[i] for i in indices[0]]
# Pass to Groq API
prompt = "\n\n".join(relevant_chunks) + f"\n\nUser Query: {user_query}"
chat_completion = client.chat.completions.create(
messages=[
{"role": "user", "content": prompt}
],
model="llama3-8b-8192"
)
response = chat_completion.choices[0].message.content
# Display response
st.write("### Response")
st.write(response) |