vector-search-setup

To set up vector search in Elasticsearch, you create indices with dense_vector mappings sized for your embedding model, index documents with generated embeddings, and run kNN or hybrid queries to retrieve semantically relevant results.

Yes, you can use Elasticsearch ingest pipelines or inference endpoints to generate embeddings at ingest time. This automates embedding creation for dense_vector fields, ensuring documents are properly indexed for kNN search without manual application-side processing.

The best way to combine keyword and vector search in Elasticsearch is using hybrid queries. This approach merges traditional keyword matching with kNN dense_vector retrieval, allowing you to tune parameters like size and num_candidates for optimal ranking.

Yes, Elasticsearch kNN search requires dense_vector mappings sized to match your specific embedding model's output dimensions, such as 384 or 768 dimensions. The mapping must be defined correctly during index provisioning for semantic search to function.

You tune Elasticsearch kNN search by adjusting the size and num_candidates query parameters. Size controls the number of returned results, while num_candidates balances search accuracy against performance by determining how many candidate vectors are evaluated per shard.

Yes, Elasticsearch vector search is designed for RAG retrieval pipelines. By indexing dense_vector embeddings and running kNN queries, you retrieve semantically relevant documents that serve as context for retrieval-augmented generation applications.