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🔌 Building a Retrieval-Augmented Generation Assistant for EV Charging Support

How Retrieval-Augmented Generation (RAG) combines a knowledge base with a large language model to answer technical support questions about EV charging stations, illustrated through a working prototype architecture.

AI & Machine Learning3DAdvanced60 FPS

A 3D vector space full of EV-charging support documents, where a query is embedded, matched against nearby chunks, and fed into a language model that generates a grounded answer on a live terminal panel.

🔬 What It Demonstrates

Documents cluster by topic in embedding space; the closer a chunk sits to the query point, the more semantically similar it is. Retrieval pulls only the nearest chunks above a similarity threshold into the model's context.

🎮 How to Use

Pick a sample support question, tune top-K and the similarity threshold, and press Ask. Watch retrieval lines light up, particles carry chunks to the language-model core, and the answer type out on the terminal.

💡 Did You Know?

Production RAG systems for support desks often re-embed and re-index documents within minutes of a manual update, so answers stay current without retraining the underlying model.

⚙ Under the hood

large language modelsretrieval augmented generationev chargingartificial intelligenceknowledge basechatbotThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install