knn query

A knn search query can be used for searching over text fields defined as searchable with semantic search with a pre-computed embedding.

Note: The knn query works with both server-side inference (using the model parameter) and pre-embedded documents (using the dim parameter). For text-based queries that require server-side embedding computation, use the semantic query instead.

Unlike semantic query, the knn query does NOT run embedding inference, and expects the query embedding provided in the request:

{
  "query": {
    "knn": {
      "field": "title",
      "query_vector": [1,2,3,4,5],
      "k": 10,
      "num_candidates": 15
    }
  }
}

Fields: * field: a text or text[] field name with semantic search enabled in the index mapping. * query_vector: a text query embedding. * k: an optional parameter of how many neighbor documents to fetch. By default, equals to the request.size field. * num_candidates: an optional parameter for the number of nearest neighbor candidates to consider per shard while doing knn search. Cannot exceed 10,000. Increasing num_candidates tends to improve the accuracy of the final results. Defaults to 1.5 * k if k is set, or 1.5 * size if k is not set.

Note: For text[] fields, the query uses multi-vector search where the highest-scoring item in the array determines the document score. See text field types for details.

For a case when you would like Nixiesearch to embed the query, see the semantic query.