🚀 I Built a Browser-Local AI Assistant in Next.js with WebLLM, WASM, ONNX Runtime, Web Workers, and RAG

This article provides a detailed overview of building a browser-based AI chat assistant using several cutting-edge technologies. Here's a summary and key takeaways:

1. Technologies Used:

   • WebLLM: For local model execution.
   • WASM (WebAssembly): For efficient computation in the browser.
   • ONNX Runtime Web: For embedding and reranking models.
   • Web Workers: To manage heavy AI tasks off the main thread.
   • Retrieval-Augmented Generation (RAG): For grounding responses to knowledge base data.

2. Architecture Overview:

   • The system is split into a frontend UI rendered by Next.js and a backend worker responsible for orchestrating the AI stack.
   • The worker handles initialization, caching, retrieval, scoring, reranking, and generation processes.
   • The main thread (UI) communicates with the worker via structured messages.

3. Key Steps in Prompt Lifecycle:

   • User interaction triggers prompt submission to the worker.
   • Worker performs dense/sparse retrieval, hybrid scoring, reranking, confidence checks, and context assembly before sending it to WebLLM for generation.
   • Final response is packaged with citations and returned

Read the full article at DEV Community

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