Development Of Multimodal Vector Database Using Embedding Vectors
• Developed a multimodal on-device vector search system to enable semantic retrieval across image, text, and audio data.
• Implemented CLIP-based embedding pipeline with FP16 compression and optimized HNSW indexing (hnswlib) for fast nearest-neighbor search on mobile devices.
• Achieved -95% Recall@10 with sub-millisecond search latency, enabling efficient and scalable on- device semantic search for large datasets.
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Posted Apr 17, 2026
Industry Collaboration Project - SAMSUNG PRISM
Development Of Multimodal Vector Database Using Embedding Vectors
• Developed a multimodal on-device vector se...