Intelligence Per Watt

Multi-Vector Retrieval Algorithm that reduces multi-vector similarity search to single-vector similarity search via Fixed Dimensional Encodings. Achieves 10% improved recall with 90% lower latency compared to existing approaches.

Contrastive Sparse Representation learning approach for ultra-sparse embeddings that achieves 7x speedup over Matryoshka Representation Learning with 300x improvements in compute and memory efficiency.

Local-first framework for building on-device personal AI agents with tools, memory, and learning capabilities. Runs entirely on-device with five composable primitives: intelligence, engine, agents, tools & memory, and learning.

Key metrics for evaluating vector search and retrieval systems including recall, precision, NDCG, MRR, and MAP. Understanding these metrics is essential for optimizing RAG systems, tuning vector indexes, and comparing embedding models for production deployments.

Retrieval Augmented Generation Assessment framework for reference-free evaluation of RAG pipelines. RAGAS provides automated metrics for retrieval quality, context relevance, and generation faithfulness.

Performance measurement framework for vector search and RAG systems including recall, precision, nDCG, MRR, and context relevance metrics to evaluate retrieval quality and relevance.