---
title: Exact kNN search
description: Run exact brute-force k-nearest neighbor (kNN) vector search in Elasticsearch using script_score queries for small datasets or precise scoring.
url: https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector/knn/exact-knn
applies_to:
  - Elastic Cloud Serverless: Generally available
  - Elastic Stack: Generally available
---

# 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](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector/knn/approximate-knn).
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.
   ```json

   {
     "mappings": {
       "properties": {
         "product-vector": {
           "type": "dense_vector",
           "dims": 5,
           "index": false
         },
         "price": {
           "type": "long"
         }
       }
     }
   }
   ```
2. Index your data.
   ```json

   { "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](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search) to run a `script_score` query containing a [vector function](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/tree/main/reference/query-languages/query-dsl/query-dsl-script-score-query#vector-functions).
   <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](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/tree/main/reference/query-languages/query-dsl/query-dsl-match-all-query) in this parameter to match all documents. However, matching all documents can significantly increase search latency.
   </tip>
   ```json

   {
     "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]
           }
         }
       }
     }
   }
   ```


## Resources

- [Approximate kNN search](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector/knn/approximate-knn): Learn how to map, index, and run fast, scalable approximate kNN search for most production workloads.
- [Build search queries](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector/knn/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](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector/knn/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](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector/knn/optimize-performance-accuracy): Learn how to tune search speed, recall, vector storage, quantization, and rescoring for approximate kNN search.
- [kNN search on Elasticsearch](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector/knn): Explore common use cases, prerequisites for kNN search, and a comparison of approximate and exact kNN methods.
- [Bring your own dense vectors](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector/bring-own-vectors): Follow a hands-on tutorial for ingesting dense vector embeddings and searching them in Elasticsearch.
- [Vector search in Elasticsearch](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/7138/solutions/search/vector): 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](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/tree/main/reference/query-languages/query-dsl/query-dsl-script-score-query): API reference for exact kNN search, including supported vector functions and scoring options.
- [`dense_vector` field type](https://docs-v3-preview.elastic.dev/elastic/elasticsearch/tree/main/reference/elasticsearch/mapping-reference/dense-vector): API reference for vector field mapping, including the `index` option used in exact kNN search.