LocalMind: Zero-Server Multimodal RAG via Browser-Native AI

Retrieval-Augmented Generation (RAG) systems have traditionally required server infrastructure for embedding computation, vector storage, and language model inference creating barriers of cost, privacy risk, and network dependency. This paper presents LocalMind, a fully browser-native multimodal RAG system that performs the complete pipeline — document ingestion, cross-modal embedding, vector retrieval, and language model generation entirely within a web browser with zero server infrastructure and zero ongoing cost. LocalMind accepts documents across five modalities (PDF, image, audio, video, and plain text), embeds all content into a unified 768-dimensional vector space using aligned text and vision embedding models (nomic-embed-text-v1.5 and nomic-embed-vision-v1.5), persists vectors in IndexedDB across sessions, and generates answers using Gemma 3n E2B via WebGPU acceleration. Empirical evaluation on a 179-chunk corpus with 20 benchmark queries demonstrates mean end-to-end query latency of 4,597ms, vector search overhead of only 0.2ms, and peak JS heap usage of 34.9MB. Text-to-text retrieval achieved 100% top-1 accuracy. Text-to-image retrieval achieved 0%, revealing a critical cross-modal alignment gap that is analysed in detail. The complete system is released as open source. #RAG #browser-native #AI #WebGPU #on-device-inference #multimodal #embeddings #IndexedDB #Transformers.js #WebLLM #Gemma3n #privacy-preserving