Exact kNN search

Exact kNN search computes similarity between the query vector and every matching document, so results are fully accurate but latency increases with corpus size. Use it for small datasets, pre-filtered subsets, or when you need precise scoring without approximate indexing. For most production workloads, prefer Approximate kNN search.

To run an exact kNN search, use a script_score query with a vector function.

1. Explicitly map one or more dense_vector fields. If you don’t intend to use the field for approximate kNN, set the index mapping option to false. This can significantly improve indexing speed.

   				PUT product-index
   					{
     "mappings": {
       "properties": {
         "product-vector": {
           "type": "dense_vector",
           "dims": 5,
           "index": false
         },
         "price": {
           "type": "long"
         }
       }
     }
   }
   		

2. Index your data.

   				POST product-index/_bulk?refresh=true
   					{ "index": { "_id": "1" } }
   { "product-vector": [230.0, 300.33, -34.8988, 15.555, -200.0], "price": 1599 }
   { "index": { "_id": "2" } }
   { "product-vector": [-0.5, 100.0, -13.0, 14.8, -156.0], "price": 799 }
   { "index": { "_id": "3" } }
   { "product-vector": [0.5, 111.3, -13.0, 14.8, -156.0], "price": 1099 }
   ...
   		

3. Use the search API to run a script_score query containing a vector function.

   Tip

   Specify a filter query in the script_score.query parameter to limit the number of matched documents passed to the vector function. If needed, you can use a match_all query in this parameter to match all documents. However, matching all documents can significantly increase search latency.

   				POST product-index/_search
   					{
     "query": {
       "script_score": {
         "query" : {
           "bool" : {
             "filter" : {
               "range" : {
                 "price" : {
                   "gte": 1000
                 }
               }
             }
           }
         },
         "script": {
           "source": "cosineSimilarity(params.queryVector, 'product-vector') + 1.0",
           "params": {
             "queryVector": [-0.5, 90.0, -10, 14.8, -156.0]
           }
         }
       }
     }
   }
   		

• Approximate kNN search: Learn how to map, index, and run fast, scalable approximate kNN search for most production workloads.
• Build search queries: Learn how to construct approximate kNN queries for filtering, hybrid retrieval, semantic search, multiple vector fields, and similarity thresholds.
• Nested kNN search: Learn how to run approximate kNN search on nested vectors for passage retrieval, filtering, inner hits, and chunked content.
• Optimize performance and accuracy: Learn how to tune search speed, recall, vector storage, quantization, and rescoring for approximate kNN search.
• kNN search on Elasticsearch: Explore common use cases, prerequisites for kNN search, and a comparison of approximate and exact kNN methods.
• Bring your own dense vectors: Follow a hands-on tutorial for ingesting dense vector embeddings and searching them in Elasticsearch.
• Vector search in Elasticsearch: Learn the core concepts and terminology for vector search in Elasticsearch, including embeddings, field types, and how vector retrieval fits with other search strategies.
• script_score query: API reference for exact kNN search, including supported vector functions and scoring options.
• dense_vector field type: API reference for vector field mapping, including the index option used in exact kNN search.