Building a Simple RAG Application with Python

Retrieval-Augmented Generation (RAG) is revolutionizing how we interact with documents. Instead of reading through hundreds of pages, you can simply ask questions and get precise answers backed by your content.

What is RAG?

RAG combines the power of information retrieval with generative AI. It works by:

1. Chunking your documents into smaller pieces
2. Embedding these chunks into vector representations
3. Retrieving the most relevant chunks for your query
4. Generating answers using an LLM with the retrieved context

Building the Application

Here's how I built my RAG app that's now live on Hugging Face:

import streamlit as st
from qdrant_client import QdrantClient
from sentence_transformers import SentenceTransformer

def create_embeddings(text_chunks):
    model = SentenceTransformer('all-MiniLM-L6-v2')
    embeddings = model.encode(text_chunks)
    return embeddings

def retrieve_relevant_chunks(query, vector_db, top_k=3):
    query_embedding = model.encode([query])
    results = vector_db.search(
        collection_name="documents",
        query_vector=query_embedding[0],
        limit=top_k
    )
    return results

Key Challenges Solved

1. Document Chunking Strategy

Finding the right chunk size was crucial. Too small and you lose context, too large and retrieval becomes imprecise.

2. Vector Database Selection

I chose Qdrant for its simplicity and performance with small to medium datasets.

3. LLM Integration

Using a lightweight model ensures fast responses while maintaining quality.

Results

The application achieves:

• Sub-second response times
• High accuracy for factual questions
• Contextual awareness across document sections

Try it out on Hugging Face and let me know your thoughts!

Next Steps

I'm working on adding:

• Multi-document support
• Better chunk overlap strategies
• Integration with larger models

What would you like to see next in RAG applications?