The challenge of making recommendations or performing similarity search when there is insufficient historical data for new users, items, or embeddings. In vector databases and RAG systems, cold start affects new documents without usage data, requiring strategies like content-based filtering and hybrid approaches.

An advanced RAG architecture where an AI agent autonomously decides which questions to ask, which tools to use, when to retrieve information, and how to aggregate results. Represents a major trend in 2026 for more intelligent and adaptive retrieval systems.

Agentic Search and Memory Retrieval technique by Supermemory using parallel reader agents and search agents that achieved ~99% accuracy on LongMemEval benchmark.

Combining dense vector embeddings with sparse representations in a single unified model. Captures both semantic meaning (dense) and exact term matching (sparse) for superior retrieval performance.

A search architecture that combines dense vector embeddings (semantic search) with sparse representations like BM25 (lexical search) to achieve better overall search quality. The industry standard approach for production RAG systems in 2026.

Retrieval-Augmented Generation extended to handle multiple modalities including text, images, video, and audio. Uses multimodal embeddings like Gemini Embedding 2 or CLIP to enable cross-modal search and generation.