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RAG System Visualizer

Learn how Retrieval-Augmented Generation works through interactive animations

Educational Platform
Interactive Learning
AI-Powered

What is RAG?

Retrieval

Find relevant information from a knowledge base using vector similarity search

Smart Search

Augmented

Enhance the query with retrieved context to provide accurate information

Context Enhanced

Generation

Generate accurate responses using advanced language models with context

AI Powered

RAG Process Flow

Document
→
Chunking
→
Embeddings
→
Vector DB

Document Indexing Process

Click "Start Demo" to see how documents are processed into vectors

1. Document Loading

DSA.pdf
Arrays are fundamental data structures... Binary trees provide efficient searching... Hash tables offer O(1) average access...

2. Text Chunking

3. Vector Embeddings

"Arrays are fundamental..."
↓
Embedding Model
↓

4. Vector Database Storage

Pinecone Vector DB

Query Processing & Retrieval

Binary Search Hash Tables Merge Sort

1. Query Vectorization

"What is the time complexity of binary search?"
→

2. Similarity Search

Query Vector

3. Context Retrieval

4. Response Generation

Context + Query

→
Gemini LLM
→

Try RAG System Live

Hello! I'm a RAG-powered DSA assistant. Ask me anything about data structures and algorithms!

Process Monitor

Query Embedding
Idle
Context Retrieval
Idle
Response Generation
Idle