348 lines
8.2 KiB
Markdown
348 lines
8.2 KiB
Markdown
---
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layout: default
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title: k-NN search with nested fields
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nav_order: 21
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parent: k-NN search
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grand_parent: Search methods
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has_children: false
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has_math: true
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---
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# k-NN search with nested fields
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Using [nested fields]({{site.url}}{{site.baseurl}}/field-types/nested/) in a k-nearest neighbors (k-NN) index, you can store multiple vectors in a single document. For example, if your document consists of various components, you can generate a vector value for each component and store each vector in a nested field.
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A k-NN document search operates at the field level. For a document with nested fields, OpenSearch examines only the vector nearest to the query vector to decide whether to include the document in the results. For example, consider an index containing documents `A` and `B`. Document `A` is represented by vectors `A1` and `A2`, and document `B` is represented by vector `B1`. Further, the similarity order for a query Q is `A1`, `A2`, `B1`. If you search using query Q with a k value of 2, the search will return both documents `A` and `B` instead of only document `A`.
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Note that in the case of an approximate search, the results are approximations and not exact matches.
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k-NN search with nested fields is supported by the HNSW algorithm for the Lucene and Faiss engines.
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## Indexing and searching nested fields
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To use k-NN search with nested fields, you must create a k-NN index by setting `index.knn` to `true`. Create a nested field by setting its `type` to `nested` and specify one or more fields of the `knn_vector` data type within the nested field. In this example, the `knn_vector` field `my_vector` is nested inside the `nested_field` field:
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```json
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PUT my-knn-index-1
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{
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"settings": {
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"index": {
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"knn": true
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}
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},
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"mappings": {
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"properties": {
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"nested_field": {
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"type": "nested",
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"properties": {
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"my_vector": {
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"type": "knn_vector",
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"dimension": 3,
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"method": {
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"name": "hnsw",
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"space_type": "l2",
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"engine": "lucene",
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"parameters": {
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"ef_construction": 100,
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"m": 16
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}
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}
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}
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}
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}
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}
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}
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}
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```
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{% include copy-curl.html %}
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After you create the index, add some data to it:
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```json
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PUT _bulk?refresh=true
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{ "index": { "_index": "my-knn-index-1", "_id": "1" } }
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{"nested_field":[{"my_vector":[1,1,1]},{"my_vector":[2,2,2]},{"my_vector":[3,3,3]}]}
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{ "index": { "_index": "my-knn-index-1", "_id": "2" } }
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{"nested_field":[{"my_vector":[10,10,10]},{"my_vector":[20,20,20]},{"my_vector":[30,30,30]}]}
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```
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{% include copy-curl.html %}
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Then run a k-NN search on the data by using the `knn` query type:
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```json
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GET my-knn-index-1/_search
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{
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"query": {
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"nested": {
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"path": "nested_field",
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"query": {
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"knn": {
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"nested_field.my_vector": {
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"vector": [1,1,1],
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"k": 2
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}
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}
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}
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}
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}
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}
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```
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{% include copy-curl.html %}
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Even though all three vectors nearest to the query vector are in document 1, the query returns both documents 1 and 2 because k is set to 2:
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```json
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{
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"took": 23,
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"timed_out": false,
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"_shards": {
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"total": 1,
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"successful": 1,
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"skipped": 0,
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"failed": 0
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},
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"hits": {
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"total": {
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"value": 2,
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"relation": "eq"
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},
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"max_score": 1,
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"hits": [
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{
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"_index": "my-knn-index-1",
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"_id": "1",
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"_score": 1,
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"_source": {
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"nested_field": [
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{
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"my_vector": [
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1,
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1,
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1
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]
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},
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{
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"my_vector": [
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2,
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2,
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2
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]
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},
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{
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"my_vector": [
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3,
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3,
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3
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]
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}
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]
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}
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},
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{
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"_index": "my-knn-index-1",
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"_id": "2",
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"_score": 0.0040983604,
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"_source": {
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"nested_field": [
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{
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"my_vector": [
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10,
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10,
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10
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]
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},
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{
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"my_vector": [
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20,
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20,
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20
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]
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},
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{
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"my_vector": [
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30,
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30,
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30
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]
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}
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]
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}
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}
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]
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}
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}
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```
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## k-NN search with filtering on nested fields
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You can apply a filter to a k-NN search with nested fields. A filter can be applied to either a top-level field or a field inside a nested field.
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The following example applies a filter to a top-level field.
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First, create a k-NN index with a nested field:
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```json
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PUT my-knn-index-1
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{
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"settings": {
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"index": {
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"knn": true
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}
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},
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"mappings": {
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"properties": {
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"nested_field": {
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"type": "nested",
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"properties": {
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"my_vector": {
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"type": "knn_vector",
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"dimension": 3,
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"method": {
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"name": "hnsw",
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"space_type": "l2",
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"engine": "lucene",
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"parameters": {
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"ef_construction": 100,
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"m": 16
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}
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}
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}
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}
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}
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}
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}
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}
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```
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{% include copy-curl.html %}
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After you create the index, add some data to it:
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```json
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PUT _bulk?refresh=true
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{ "index": { "_index": "my-knn-index-1", "_id": "1" } }
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{"parking": false, "nested_field":[{"my_vector":[1,1,1]},{"my_vector":[2,2,2]},{"my_vector":[3,3,3]}]}
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{ "index": { "_index": "my-knn-index-1", "_id": "2" } }
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{"parking": true, "nested_field":[{"my_vector":[10,10,10]},{"my_vector":[20,20,20]},{"my_vector":[30,30,30]}]}
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{ "index": { "_index": "my-knn-index-1", "_id": "3" } }
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{"parking": true, "nested_field":[{"my_vector":[100,100,100]},{"my_vector":[200,200,200]},{"my_vector":[300,300,300]}]}
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```
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{% include copy-curl.html %}
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Then run a k-NN search on the data using the `knn` query type with a filter. The following query returns documents whose `parking` field is set to `true`:
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```json
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GET my-knn-index-1/_search
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{
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"query": {
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"nested": {
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"path": "nested_field",
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"query": {
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"knn": {
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"nested_field.my_vector": {
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"vector": [
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1,
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1,
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1
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],
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"k": 3,
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"filter": {
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"term": {
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"parking": true
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}
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}
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}
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}
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}
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}
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}
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}
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```
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{% include copy-curl.html %}
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Even though all three vectors nearest to the query vector are in document 1, the query returns documents 2 and 3 because document 1 is filtered out:
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```json
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{
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"took": 10,
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"timed_out": false,
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"_shards": {
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"total": 1,
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"successful": 1,
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"skipped": 0,
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"failed": 0
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},
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"hits": {
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"total": {
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"value": 2,
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"relation": "eq"
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},
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"max_score": 0.0040983604,
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"hits": [
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{
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"_index": "my-knn-index-1",
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"_id": "2",
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"_score": 0.0040983604,
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"_source": {
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"parking": true,
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"nested_field": [
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{
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"my_vector": [
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10,
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10,
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10
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]
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},
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{
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"my_vector": [
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20,
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20,
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20
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]
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},
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{
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"my_vector": [
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30,
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30,
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30
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]
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}
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]
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}
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},
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{
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"_index": "my-knn-index-1",
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"_id": "3",
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"_score": 3.400898E-5,
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"_source": {
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"parking": true,
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"nested_field": [
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{
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"my_vector": [
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100,
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100,
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100
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]
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},
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{
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"my_vector": [
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200,
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200,
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200
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]
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},
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{
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"my_vector": [
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300,
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300,
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300
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]
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}
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]
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}
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}
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]
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}
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}
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```
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