RAG System Demo - Interactive Retrieval Augmented Generation

1. Initialize RAG Pipeline

Load the embedding model, retriever, generator, and 2000 Wikipedia articles. This may take a moment due to the large dataset.

Note: Loading 2000 real Wikipedia articles (approximately 5MB of data). Please wait for initialization to complete.

2. Document Collection

Dataset Loaded: 2000 real Wikipedia articles across diverse topics

Loaded 2000 real Wikipedia articles from various topics including science, history, arts, technology, and more. These articles provide a rich knowledge base for testing RAG capabilities.

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Total Articles

Wikipedia

Source

Real Data

Dataset Type

View Sample Articles (Click to expand)

Click to upload text or JSON files

Or use the 2000 Wikipedia articles

3. Document Chunking & Indexing

Chunking Strategy

Chunk Size (words)

Overlap (words)

4. Embedding Visualization

Visualize document chunks in vector space (dimensionality reduced to 2D/3D)

5. Query & Retrieval

Enter your question

Number of documents to retrieve (Top K)

Mode

6. Results

Sample Queries to Try

Try these queries against the 2000 Wikipedia articles to see RAG in action:

Retrieval Augmented Generation (RAG)

1. Initialize RAG Pipeline

2. Document Collection

3. Document Chunking & Indexing

Chunking Statistics

4. Embedding Visualization

5. Query & Retrieval

6. Results

Retrieved Documents

Generated Answer

WITH RAG

Answer

WITHOUT RAG

Answer

Sample Queries to Try