A high-performance, browser-native Retrieval-Augmented Generation (RAG) platform. This application enables local document processing and private AI interaction without external API dependencies or data exfiltration.
The platform utilizes a multi-layered WASM and WebGPU architecture for client-side inference:
- RAG Engine: barq-mesh-browser – Native WebAssembly mesh orchestrating parallel workers for high-speed document ingestion and hybrid retrieval.
- Vector Storage: barq-vweb – High-performance HNSW and BM25 indexing with local persistence.
- Compute Runtime: barq-wasm – SIMD-accelerated compute kernels for vector operations and similarity search.
- AI Inference: transformers.js / WebGPU – Running local LLMs (e.g., LFM2.5) directly on the client hardware.
- Private & Local: 100% of data processing, embedding, and inference occurs on-device.
- Hybrid Search: Unified BM25 and semantic search with RRF reranking for superior context relevance.
- Universal Ingestion: Parallel processing of PDF, DOCX, Markdown, and TXT files.
- State-of-the-Art Retrieval: Leverages
barq-mesh-browserfor near-instantaneous indexing of large document sets.
- Install dependencies:
npm install - Launch development server:
npm run dev - Build for production:
npm run build
- Modern browser with WebGPU and WebAssembly Threading support (Chrome/Edge 113+, Safari 17+).
Powered by the Barq Mesh ecosystem.