---
title: Exact kNN search
description: Run exact brute-force k-nearest neighbor (kNN) vector search in Elasticsearch for small datasets or precise scoring.
url: https://www.elastic.co/elastic/docs-builder/docs/4300/solutions/search/vector/knn/exact-knn
products:
  - Elastic Documentation
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://www.elastic.co/elastic/docs-builder/docs/4300/solutions/search/vector/knn/approximate-knn).
Elasticsearch supports two query methods for exact kNN search:
- Use the [`dense_vector` query](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/4300/reference/query-languages/query-dsl/query-dsl-dense-vector-query) for standard exact vector scoring. See an [example](#exact-knn-dense-vector-query).
- Use the [`script_score` query](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/4300/reference/query-languages/query-dsl/query-dsl-script-score-query) when you need a custom scoring calculation. See an [example](#exact-knn-script-score-query).


## Map and index vectors

First, map and index the vectors that you want to search:
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 }
   ...
   ```


## Run an exact kNN search with the `dense_vector` query

<applies-to>
  - Elastic Cloud Serverless: Generally available
  - Elastic Stack: Planned
</applies-to>

Use the [search API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-search) to run a `dense_vector` query. The query scores every document that has a value for the specified vector field. To reduce the number of vectors that it scores, combine it with a filter in a `bool` query:
```json

{
  "query": {
    "bool": {
      "must": {
        "dense_vector": {
          "field": "product-vector",
          "query_vector": [-0.5, 90.0, -10, 14.8, -156.0]
        }
      },
      "filter": {
        "range": {
          "price": {
            "gte": 1000
          }
        }
      }
    }
  }
}
```

Because `product-vector` uses the default `float` element type and is not indexed, the query uses cosine similarity by default. You can use the `similarity_function` parameter to select a different similarity function. For indexed fields, the query uses the similarity configured in the field mapping by default.

## Run an exact kNN search with a `script_score` query

Use a `script_score` query if the `dense_vector` query isn't available in your Elastic Stack version. You can also use `script_score` when you need to customize the scoring calculation.
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/docs-builder/docs/4300/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.
```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]
        }
      }
    }
  }
}
```

The `dense_vector` and `script_score` examples can rank documents in the same order, but they don't return the same numeric scores. The `dense_vector` query applies the built-in score transformation for the selected similarity function. A `script_score` query returns the value calculated by your script, such as the cosine similarity plus `1.0` in this example.

## Resources

- [Tune approximate kNN search](https://www.elastic.co/elastic/docs-builder/docs/4300/deploy-manage/production-guidance/optimize-performance/approximate-knn-search): Production guidance for vector memory, node sizing, indexing, filesystem cache, and on-disk rescoring.
- [Profile kNN search](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/4300/reference/elasticsearch/rest-apis/search-profile#profiling-knn-search): Inspect query timing and vector operation counts to diagnose slow kNN searches.
- [`dense_vector` field type](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/4300/reference/elasticsearch/mapping-reference/dense-vector): API reference for vector field mapping, including `index`, `similarity`, `index_options`, and quantization parameters.
- [`knn` query](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/4300/reference/query-languages/query-dsl/query-dsl-knn-query): API reference for the `knn` query, including parameters, `query_vector_builder` options, and usage with `dense_vector` and `semantic_text` fields.