iSharkFly-Docs
/
opensearch-docs-cn
mirror of https://github.com/iSharkFly-Docs/opensearch-docs-cn
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Add documentation for nested search in knn (#6237) 

* Add documentation for nested search in knn

Signed-off-by: Heemin Kim <heemin@amazon.com>

* Doc review

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Apply suggestions from code review

Co-authored-by: Nathan Bower <nbower@amazon.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>

---------

Signed-off-by: Heemin Kim <heemin@amazon.com>
Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>
Co-authored-by: Fanit Kolchina <kolchfa@amazon.com>
Co-authored-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>
Co-authored-by: Nathan Bower <nbower@amazon.com>
This commit is contained in:
Heemin Kim 2024-02-01 11:08:20 -08:00 committed by GitHub
parent 4b3f09ae11
commit cc5c60393d
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 351 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
_search-plugins/knn/approximate-knn.md
View File

@ -244,6 +244,10 @@ After data is ingested, it can be search just like any other `knn_vector` field!
To learn about using filters with k-NN search, see [k-NN search with filters]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/). To learn about using filters with k-NN search, see [k-NN search with filters]({{site.url}}{{site.baseurl}}/search-plugins/knn/filter-search-knn/).
### Using approximate k-NN with nested fields
To learn about using k-NN search with nested fields, see [k-NN search with nested fields]({{site.url}}{{site.baseurl}}/search-plugins/knn/nested-search-knn/).
## Spaces ## Spaces
A space corresponds to the function used to measure the distance between two points in order to determine the k-nearest neighbors. From the k-NN perspective, a lower score equates to a closer and better result. This is the opposite of how OpenSearch scores results, where a greater score equates to a better result. To convert distances to OpenSearch scores, we take 1 / (1 + distance). The k-NN plugin supports the following spaces. A space corresponds to the function used to measure the distance between two points in order to determine the k-nearest neighbors. From the k-NN perspective, a lower score equates to a closer and better result. This is the opposite of how OpenSearch scores results, where a greater score equates to a better result. To convert distances to OpenSearch scores, we take 1 / (1 + distance). The k-NN plugin supports the following spaces.

347
_search-plugins/knn/nested-search-knn.md Normal file
View File

@ -0,0 +1,347 @@
---
layout: default
title: k-NN search with nested fields
nav_order: 21
parent: k-NN search
grand_parent: Search methods
has_children: false
has_math: true
---
# k-NN search with nested fields
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.
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`.
Note that in the case of an approximate search, the results are approximations and not exact matches.
k-NN search with nested fields is supported by the HNSW algorithm for the Lucene and Faiss engines.
## Indexing and searching nested fields
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:
```json
PUT my-knn-index-1
{
"settings": {
"index": {
"knn": true
}
},
"mappings": {
"properties": {
"nested_field": {
"type": "nested",
"properties": {
"my_vector": {
"type": "knn_vector",
"dimension": 3,
"method": {
"name": "hnsw",
"space_type": "l2",
"engine": "lucene",
"parameters": {
"ef_construction": 100,
"m": 16
}
}
}
}
}
}
}
}
```
{% include copy-curl.html %}
After you create the index, add some data to it:
```json
PUT _bulk?refresh=true
{ "index": { "_index": "my-knn-index-1", "_id": "1" } }
{"nested_field":[{"my_vector":[1,1,1]},{"my_vector":[2,2,2]},{"my_vector":[3,3,3]}]}
{ "index": { "_index": "my-knn-index-1", "_id": "2" } }
{"nested_field":[{"my_vector":[10,10,10]},{"my_vector":[20,20,20]},{"my_vector":[30,30,30]}]}
```
{% include copy-curl.html %}
Then run a k-NN search on the data by using the `knn` query type:
```json
GET my-knn-index-1/_search
{
"query": {
"nested": {
"path": "nested_field",
"query": {
"knn": {
"nested_field.my_vector": {
"vector": [1,1,1],
"k": 2
}
}
}
}
}
}
```
{% include copy-curl.html %}
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:
```json
{
"took": 23,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "my-knn-index-1",
"_id": "1",
"_score": 1,
"_source": {
"nested_field": [
{
"my_vector": [
1,
1,
1
]
},
{
"my_vector": [
2,
2,
2
]
},
{
"my_vector": [
3,
3,
3
]
}
]
}
},
{
"_index": "my-knn-index-1",
"_id": "2",
"_score": 0.0040983604,
"_source": {
"nested_field": [
{
"my_vector": [
10,
10,
10
]
},
{
"my_vector": [
20,
20,
20
]
},
{
"my_vector": [
30,
30,
30
]
}
]
}
}
]
}
}
```
## k-NN search with filtering on nested fields
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.
The following example applies a filter to a top-level field.
First, create a k-NN index with a nested field:
```json
PUT my-knn-index-1
{
"settings": {
"index": {
"knn": true
}
},
"mappings": {
"properties": {
"nested_field": {
"type": "nested",
"properties": {
"my_vector": {
"type": "knn_vector",
"dimension": 3,
"method": {
"name": "hnsw",
"space_type": "l2",
"engine": "lucene",
"parameters": {
"ef_construction": 100,
"m": 16
}
}
}
}
}
}
}
}
```
{% include copy-curl.html %}
After you create the index, add some data to it:
```json
PUT _bulk?refresh=true
{ "index": { "_index": "my-knn-index-1", "_id": "1" } }
{"parking": false, "nested_field":[{"my_vector":[1,1,1]},{"my_vector":[2,2,2]},{"my_vector":[3,3,3]}]}
{ "index": { "_index": "my-knn-index-1", "_id": "2" } }
{"parking": true, "nested_field":[{"my_vector":[10,10,10]},{"my_vector":[20,20,20]},{"my_vector":[30,30,30]}]}
{ "index": { "_index": "my-knn-index-1", "_id": "3" } }
{"parking": true, "nested_field":[{"my_vector":[100,100,100]},{"my_vector":[200,200,200]},{"my_vector":[300,300,300]}]}
```
{% include copy-curl.html %}
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`:
```json
GET my-knn-index-1/_search
{
"query": {
"nested": {
"path": "nested_field",
"query": {
"knn": {
"nested_field.my_vector": {
"vector": [
1,
1,
1
],
"k": 3,
"filter": {
"term": {
"parking": true
}
}
}
}
}
}
}
}
```
{% include copy-curl.html %}
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:
```json
{
"took": 10,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 0.0040983604,
"hits": [
{
"_index": "my-knn-index-1",
"_id": "2",
"_score": 0.0040983604,
"_source": {
"parking": true,
"nested_field": [
{
"my_vector": [
10,
10,
10
]
},
{
"my_vector": [
20,
20,
20
]
},
{
"my_vector": [
30,
30,
30
]
}
]
}
},
{
"_index": "my-knn-index-1",
"_id": "3",
"_score": 3.400898E-5,
"_source": {
"parking": true,
"nested_field": [
{
"my_vector": [
100,
100,
100
]
},
{
"my_vector": [
200,
200,
200
]
},
{
"my_vector": [
300,
300,
300
]
}
]
}
}
]
}
}
```