Teams building retrieval for retrieval augmented generation (RAG) applications and agentic AI usually have to make many configuration decisions at index time, and that work grows at production scale. We're introducing these capabilities to take the setup and index tuning off your plate, so vector search works out of the box.

VectorDB index mode delivers high-performance vector search with a single setting; there’s no manual configuration required. It applies defaults already optimized for vectors, tuning quantization, merge policy, and cache loading for you, so vector search is simpler to stand up and performs out of the box.

Auto-calibration for DiskBBQ vector search automatically configures quantization depth, preconditioning, and oversampling based on statistical analysis of the vectors in the index. The tuning that normally takes expertise and experimentation now happens on its own, using unique algorithms developed at Elastic.

Multimodal semantic search makes image search easier for developers. Searching images normally means setting up embeddings, ingesting and embedding images, and embedding the query. The new semantic field simplifies these steps, so users can search images as easily as they search text with semantic_text.

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Articles by Sarah Leslie

Principal Product Marketing Launch Ops & Programs Manager

Elastic 9.5: Columnar, VectorDB index mode & auto-calibration, and AI-driven alert triage

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