diff --git a/_search-plugins/knn/approximate-knn.md b/_search-plugins/knn/approximate-knn.md
index d57d164050..13505b046c 100644
--- a/_search-plugins/knn/approximate-knn.md
+++ b/_search-plugins/knn/approximate-knn.md
@@ -242,30 +242,8 @@ POST _bulk
After data is ingested, it can be search just like any other `knn_vector` field!
### Using approximate k-NN with filters
-If you use the `knn` query alongside filters or other clauses (e.g. `bool`, `must`, `match`), you might receive fewer than `k` results. In this example, `post_filter` reduces the number of results from 2 to 1:
-```json
-GET my-knn-index-1/_search
-{
- "size": 2,
- "query": {
- "knn": {
- "my_vector2": {
- "vector": [2, 3, 5, 6],
- "k": 2
- }
- }
- },
- "post_filter": {
- "range": {
- "price": {
- "gte": 5,
- "lte": 10
- }
- }
- }
-}
-```
+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/).
## Spaces
diff --git a/_search-plugins/knn/filter-search-knn.md b/_search-plugins/knn/filter-search-knn.md
index 2e886ad5b2..7e926a970e 100644
--- a/_search-plugins/knn/filter-search-knn.md
+++ b/_search-plugins/knn/filter-search-knn.md
@@ -11,12 +11,24 @@ has_math: true
To refine k-NN results, you can filter a k-NN search using one of the following methods:
-- [Scoring script filter](#scoring-script-filter): This approach involves pre-filtering a document set and then running an exact k-NN search on the filtered subset. It does not scale for large filtered subsets.
+- [Efficient k-NN filtering](#efficient-k-nn-filtering): This approach applies filtering _during_ the k-NN search, as opposed to before or after the k-NN search, which ensures that `k` results are returned (if there are at least `k` results in total). This approach is supported by the following engines:
+ - Lucene engine with a Hierarchical Navigable Small World (HNSW) algorithm (k-NN plugin versions 2.4 and later)
+ - Faiss engine with an HNSW algorithm (k-NN plugin versions 2.9 or later)
-- [Boolean filter](#boolean-filter-with-ann-search): This approach runs an [approximate nearest neighbor (ANN)]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn) search and then applies a filter to the results. Because of post-filtering, it may return significantly fewer than `k` results for a restrictive filter.
+- [Post-filtering](#post-filtering): Because it is performed after the k-NN search, this approach may return significantly fewer than `k` results for a restrictive filter. You can use the following two filtering strategies for this approach:
+ - [Boolean post-filter](#boolean-filter-with-ann-search): This approach runs an [approximate nearest neighbor (ANN)]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/) search and then applies a filter to the results. The two query parts are executed independently, and then the results are combined based on the query operator (`should`, `must`, and so on) provided in the query.
+ - [The `post_filter` parameter](#post-filter-parameter): This approach runs an [ANN]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/) search on the full dataset and then applies the filter to the k-NN results.
-- [Lucene k-NN filter](#using-a-lucene-k-nn-filter): This approach applies filtering _during_ the k-NN search, as opposed to before or after the k-NN search, which ensures that `k` results are returned. You can only use this method with the Hierarchical Navigable Small World (HNSW) algorithm implemented by the Lucene search engine in k-NN plugin versions 2.4 and later.
+- [Scoring script filter](#scoring-script-filter): This approach involves pre-filtering a document set and then running an exact k-NN search on the filtered subset. It may have high latency and does not scale when filtered subsets are large.
+The following table summarizes the preceding filtering use cases.
+
+Filter | When the filter is applied | Type of search | Supported engines and methods | Where to place the `filter` clause
+:--- | :--- | :--- | :---
+Efficient k-NN filtering | During search (a hybrid of pre- and post-filtering) | Approximate | - `lucene` (`hnsw`)
- `faiss` (`hnsw`) | Inside the k-NN query clause.
+Boolean filter | After search (post-filtering) | Approximate | - `lucene`
- `nmslib`
- `faiss` | Outside the k-NN query clause. Must be a leaf clause.
+The `post_filter` parameter | After search (post-filtering) | Approximate | - `lucene`
- `nmslib`
- `faiss` | Outside the k-NN query clause.
+Scoring script filter | Before search (pre-filtering) | Exact | N/A | Inside the script score query clause.
## Filtered search optimization
@@ -31,176 +43,17 @@ Once you've estimated the number of documents in your index, the restrictiveness
| Number of documents in an index | Percentage of documents the filter returns | k | Filtering method to use for higher recall | Filtering method to use for lower latency |
| :-- | :-- | :-- | :-- | :-- |
| 10M | 2.5 | 100 | Scoring script | Scoring script |
-| 10M | 38 | 100 | Lucene filter | Boolean filter |
-| 10M | 80 | 100 | Scoring script | Lucene filter |
-| 1M | 2.5 | 100 | Lucene filter | Scoring script |
-| 1M | 38 | 100 | Lucene filter | Lucene filter/scoring script |
-| 1M | 80 | 100 | Boolean filter | Lucene filter |
-
-## Scoring script filter
-
-A scoring script filter first filters the documents and then uses a brute-force exact k-NN search on the results. For example, the following query searches for hotels with a rating between 8 and 10, inclusive, that provide parking and then performs a k-NN search to return the 3 hotels that are closest to the specified `location`:
-
-```json
-POST /hotels-index/_search
-{
- "size": 3,
- "query": {
- "script_score": {
- "query": {
- "bool": {
- "filter": {
- "bool": {
- "must": [
- {
- "range": {
- "rating": {
- "gte": 8,
- "lte": 10
- }
- }
- },
- {
- "term": {
- "parking": "true"
- }
- }
- ]
- }
- }
- }
- },
- "script": {
- "source": "knn_score",
- "lang": "knn",
- "params": {
- "field": "location",
- "query_value": [
- 5.0,
- 4.0
- ],
- "space_type": "l2"
- }
- }
- }
- }
-}
-```
-{% include copy-curl.html %}
-
-## Boolean filter with ANN search
-
-A Boolean filter consists of a Boolean query that contains a k-NN query and a filter. For example, the following query searches for hotels that are closest to the specified `location` and then filters the results to return hotels with a rating between 8 and 10, inclusive, that provide parking:
+| 10M | 38 | 100 | Efficient k-NN filtering | Boolean filter |
+| 10M | 80 | 100 | Scoring script | Efficient k-NN filtering |
+| 1M | 2.5 | 100 | Efficient k-NN filtering | Scoring script |
+| 1M | 38 | 100 | Efficient k-NN filtering | Efficient k-NN filtering/scoring script |
+| 1M | 80 | 100 | Efficient k-NN filtering | Boolean filter |
-```json
-POST /hotels-index/_search
-{
- "size": 3,
- "query": {
- "bool": {
- "filter": {
- "bool": {
- "must": [
- {
- "range": {
- "rating": {
- "gte": 8,
- "lte": 10
- }
- }
- },
- {
- "term": {
- "parking": "true"
- }
- }
- ]
- }
- },
- "must": [
- {
- "knn": {
- "location": {
- "vector": [
- 5,
- 4
- ],
- "k": 20
- }
- }
- }
- ]
- }
- }
-}
-```
-
-The response includes documents containing the matching hotels:
-
-```json
-{
- "took" : 95,
- "timed_out" : false,
- "_shards" : {
- "total" : 1,
- "successful" : 1,
- "skipped" : 0,
- "failed" : 0
- },
- "hits" : {
- "total" : {
- "value" : 5,
- "relation" : "eq"
- },
- "max_score" : 0.72992706,
- "hits" : [
- {
- "_index" : "hotels-index",
- "_id" : "3",
- "_score" : 0.72992706,
- "_source" : {
- "location" : [
- 4.9,
- 3.4
- ],
- "parking" : "true",
- "rating" : 9
- }
- },
- {
- "_index" : "hotels-index",
- "_id" : "6",
- "_score" : 0.3012048,
- "_source" : {
- "location" : [
- 6.4,
- 3.4
- ],
- "parking" : "true",
- "rating" : 9
- }
- },
- {
- "_index" : "hotels-index",
- "_id" : "5",
- "_score" : 0.24154587,
- "_source" : {
- "location" : [
- 3.3,
- 4.5
- ],
- "parking" : "true",
- "rating" : 8
- }
- }
- ]
- }
-}
-```
+## Efficient k-NN filtering
-The location of the `filter` clause matters when it's used with a k-NN query clause. If the `filter` clause is outside the k-NN query clause, it must be a leaf clause. In this case, the filter is applied after the k-NN search and works exactly like the `post_filter` keyword. If the `filter` clause is within the k-NN query clause, it works as a hybrid of pre- and post-filtering (this option is only supported for the Lucene search engine).
+You can perform efficient k-NN filtering with the `lucene` or `faiss` engines.
-## Lucene k-NN filter implementation
+### Lucene k-NN filter implementation
k-NN plugin version 2.2 introduced support for running k-NN searches with the Lucene engine using HNSW graphs. Starting with version 2.4, which is based on Lucene version 9.4, you can use Lucene filters for k-NN searches.
@@ -216,7 +69,7 @@ The following flow chart outlines the Lucene algorithm.
For more information about the Lucene filtering implementation and the underlying `KnnVectorQuery`, see the [Apache Lucene documentation](https://issues.apache.org/jira/browse/LUCENE-10382).
-## Using a Lucene k-NN filter
+### Using a Lucene k-NN filter
Consider a dataset that includes 12 documents containing hotel information. The following image shows all hotels on an xy coordinate plane by location. Additionally, the points for hotels that have a rating between 8 and 10, inclusive, are depicted with orange dots, and hotels that provide parking are depicted with green circles. The search point is colored in red:
@@ -224,7 +77,7 @@ Consider a dataset that includes 12 documents containing hotel information. The
In this example, you will create an index and search for the three hotels with high ratings and parking that are the closest to the search location.
-### Step 1: Create a new index
+**Step 1: Create a new index**
Before you can run a k-NN search with a filter, you need to create an index with a `knn_vector` field. For this field, you need to specify `lucene` as the engine and `hnsw` as the `method` in the mapping.
@@ -262,7 +115,7 @@ PUT /hotels-index
```
{% include copy-curl.html %}
-### Step 2: Add data to your index
+**Step 2: Add data to your index**
Next, add data to your index.
@@ -297,7 +150,7 @@ POST /_bulk
```
{% include copy-curl.html %}
-### Step 3: Search your data with a filter
+**Step 3: Search your data with a filter**
Now you can create a k-NN search with filters. In the k-NN query clause, include the point of interest that is used to search for nearest neighbors, the number of nearest neighbors to return (`k`), and a filter with the restriction criteria. Depending on how restrictive you want your filter to be, you can add multiple query clauses to a single request.
@@ -404,7 +257,195 @@ The response returns the three hotels that are nearest to the search point and h
}
```
-Note that there are multiple ways to construct a filter that returns hotels that provide parking, for example:
+For more ways to construct a filter, see [Constructing a filter](#constructing-a-filter).
+
+### Faiss k-NN filter implementation
+
+Starting with k-NN plugin version 2.9, you can use `faiss` filters for k-NN searches.
+
+When you specify a Faiss filter for a k-NN search, the Faiss algorithm decides whether to perform an exact k-NN search with pre-filtering or an approximate search with modified post-filtering. The algorithm uses the following variables:
+
+- N: The number of documents in the index.
+- P: The number of documents in the document subset after the filter is applied (P <= N).
+- k: The maximum number of vectors to return in the response.
+
+The following flow chart outlines the Faiss algorithm.
+
+
+
+### Using a Faiss efficient filter
+
+Consider an index that contains information about different shirts for an e-commerce application. You want to find the top-rated shirts that are similar to the one you already have but would like to restrict the results by shirt size.
+
+In this example, you will create an index and search for shirts that are similar to the shirt you provide.
+
+**Step 1: Create a new index**
+
+Before you can run a k-NN search with a filter, you need to create an index with a `knn_vector` field. For this field, you need to specify `faiss` and `hnsw` as the `method` in the mapping.
+
+The following request creates an index that contains vector representations of shirts:
+
+```json
+PUT /products-shirts
+{
+ "settings": {
+ "index": {
+ "knn": true
+ }
+ },
+ "mappings": {
+ "properties": {
+ "item_vector": {
+ "type": "knn_vector",
+ "dimension": 3,
+ "method": {
+ "name": "hnsw",
+ "space_type": "l2",
+ "engine": "faiss"
+ }
+ }
+ }
+ }
+}
+```
+{% include copy-curl.html %}
+
+**Step 2: Add data to your index**
+
+Next, add data to your index.
+
+The following request adds 12 documents that contain information about shirts, including their vector representation, size, and rating:
+
+```json
+POST /_bulk?refresh
+{ "index": { "_index": "products-shirts", "_id": "1" } }
+{ "item_vector": [5.2, 4.4, 8.4], "size" : "large", "rating" : 5 }
+{ "index": { "_index": "products-shirts", "_id": "2" } }
+{ "item_vector": [5.2, 3.9, 2.9], "size" : "small", "rating" : 4 }
+{ "index": { "_index": "products-shirts", "_id": "3" } }
+{ "item_vector": [4.9, 3.4, 2.2], "size" : "xlarge", "rating" : 9 }
+{ "index": { "_index": "products-shirts", "_id": "4" } }
+{ "item_vector": [4.2, 4.6, 5.5], "size" : "large", "rating" : 6}
+{ "index": { "_index": "products-shirts", "_id": "5" } }
+{ "item_vector": [3.3, 4.5, 8.8], "size" : "medium", "rating" : 8 }
+{ "index": { "_index": "products-shirts", "_id": "6" } }
+{ "item_vector": [6.4, 3.4, 6.6], "size" : "small", "rating" : 9 }
+{ "index": { "_index": "products-shirts", "_id": "7" } }
+{ "item_vector": [4.2, 6.2, 4.6], "size" : "small", "rating" : 5 }
+{ "index": { "_index": "products-shirts", "_id": "8" } }
+{ "item_vector": [2.4, 4.0, 3.0], "size" : "small", "rating" : 8 }
+{ "index": { "_index": "products-shirts", "_id": "9" } }
+{ "item_vector": [1.4, 3.2, 9.0], "size" : "small", "rating" : 5 }
+{ "index": { "_index": "products-shirts", "_id": "10" } }
+{ "item_vector": [7.0, 9.9, 9.0], "size" : "xlarge", "rating" : 9 }
+{ "index": { "_index": "products-shirts", "_id": "11" } }
+{ "item_vector": [3.0, 2.3, 2.0], "size" : "large", "rating" : 6 }
+{ "index": { "_index": "products-shirts", "_id": "12" } }
+{ "item_vector": [5.0, 1.0, 4.0], "size" : "large", "rating" : 3 }
+
+```
+{% include copy-curl.html %}
+
+**Step 3: Search your data with a filter**
+
+Now you can create a k-NN search with filters. In the k-NN query clause, include the vector representation of the shirt that is used to search for similar ones, the number of nearest neighbors to return (`k`), and a filter by size and rating.
+
+The following request searches for size small shirts rated between 7 and 10, inclusive:
+
+```json
+POST /products-shirts/_search
+{
+ "size": 2,
+ "query": {
+ "knn": {
+ "item_vector": {
+ "vector": [
+ 2, 4, 3
+ ],
+ "k": 10,
+ "filter": {
+ "bool": {
+ "must": [
+ {
+ "range": {
+ "rating": {
+ "gte": 7,
+ "lte": 10
+ }
+ }
+ },
+ {
+ "term": {
+ "size": "small"
+ }
+ }
+ ]
+ }
+ }
+ }
+ }
+ }
+}
+```
+{% include copy-curl.html %}
+
+The response returns the two matching documents:
+
+```json
+{
+ "took": 2,
+ "timed_out": false,
+ "_shards": {
+ "total": 1,
+ "successful": 1,
+ "skipped": 0,
+ "failed": 0
+ },
+ "hits": {
+ "total": {
+ "value": 2,
+ "relation": "eq"
+ },
+ "max_score": 0.8620689,
+ "hits": [
+ {
+ "_index": "products-shirts",
+ "_id": "8",
+ "_score": 0.8620689,
+ "_source": {
+ "item_vector": [
+ 2.4,
+ 4,
+ 3
+ ],
+ "size": "small",
+ "rating": 8
+ }
+ },
+ {
+ "_index": "products-shirts",
+ "_id": "6",
+ "_score": 0.029691212,
+ "_source": {
+ "item_vector": [
+ 6.4,
+ 3.4,
+ 6.6
+ ],
+ "size": "small",
+ "rating": 9
+ }
+ }
+ ]
+ }
+}
+```
+
+For more ways to construct a filter, see [Constructing a filter](#constructing-a-filter).
+
+### Constructing a filter
+
+There are multiple ways to construct a filter for the same condition. For example, you can use the following constructs to create a filter that returns hotels that provide parking:
- A `term` query clause in the `should` clause
- A `wildcard` query clause in the `should` clause
@@ -466,4 +507,196 @@ POST /hotels-index/_search
}
}
```
+{% include copy-curl.html %}
+
+## Post-filtering
+
+You can achieve post-filtering with a Boolean filter or by providing the `post_filter` parameter.
+
+### Boolean filter with ANN search
+
+A Boolean filter consists of a Boolean query that contains a k-NN query and a filter. For example, the following query searches for hotels that are closest to the specified `location` and then filters the results to return hotels with a rating between 8 and 10, inclusive, that provide parking:
+
+```json
+POST /hotels-index/_search
+{
+ "size": 3,
+ "query": {
+ "bool": {
+ "filter": {
+ "bool": {
+ "must": [
+ {
+ "range": {
+ "rating": {
+ "gte": 8,
+ "lte": 10
+ }
+ }
+ },
+ {
+ "term": {
+ "parking": "true"
+ }
+ }
+ ]
+ }
+ },
+ "must": [
+ {
+ "knn": {
+ "location": {
+ "vector": [
+ 5,
+ 4
+ ],
+ "k": 20
+ }
+ }
+ }
+ ]
+ }
+ }
+}
+```
+
+The response includes documents containing the matching hotels:
+
+```json
+{
+ "took" : 95,
+ "timed_out" : false,
+ "_shards" : {
+ "total" : 1,
+ "successful" : 1,
+ "skipped" : 0,
+ "failed" : 0
+ },
+ "hits" : {
+ "total" : {
+ "value" : 5,
+ "relation" : "eq"
+ },
+ "max_score" : 0.72992706,
+ "hits" : [
+ {
+ "_index" : "hotels-index",
+ "_id" : "3",
+ "_score" : 0.72992706,
+ "_source" : {
+ "location" : [
+ 4.9,
+ 3.4
+ ],
+ "parking" : "true",
+ "rating" : 9
+ }
+ },
+ {
+ "_index" : "hotels-index",
+ "_id" : "6",
+ "_score" : 0.3012048,
+ "_source" : {
+ "location" : [
+ 6.4,
+ 3.4
+ ],
+ "parking" : "true",
+ "rating" : 9
+ }
+ },
+ {
+ "_index" : "hotels-index",
+ "_id" : "5",
+ "_score" : 0.24154587,
+ "_source" : {
+ "location" : [
+ 3.3,
+ 4.5
+ ],
+ "parking" : "true",
+ "rating" : 8
+ }
+ }
+ ]
+ }
+}
+```
+
+### post-filter parameter
+
+If you use the `knn` query alongside filters or other clauses (for example, `bool`, `must`, `match`), you might receive fewer than `k` results. In this example, `post_filter` reduces the number of results from 2 to 1:
+
+```json
+GET my-knn-index-1/_search
+{
+ "size": 2,
+ "query": {
+ "knn": {
+ "my_vector2": {
+ "vector": [2, 3, 5, 6],
+ "k": 2
+ }
+ }
+ },
+ "post_filter": {
+ "range": {
+ "price": {
+ "gte": 5,
+ "lte": 10
+ }
+ }
+ }
+}
+```
+
+## Scoring script filter
+
+A scoring script filter first filters the documents and then uses a brute-force exact k-NN search on the results. For example, the following query searches for hotels with a rating between 8 and 10, inclusive, that provide parking and then performs a k-NN search to return the 3 hotels that are closest to the specified `location`:
+
+```json
+POST /hotels-index/_search
+{
+ "size": 3,
+ "query": {
+ "script_score": {
+ "query": {
+ "bool": {
+ "filter": {
+ "bool": {
+ "must": [
+ {
+ "range": {
+ "rating": {
+ "gte": 8,
+ "lte": 10
+ }
+ }
+ },
+ {
+ "term": {
+ "parking": "true"
+ }
+ }
+ ]
+ }
+ }
+ }
+ },
+ "script": {
+ "source": "knn_score",
+ "lang": "knn",
+ "params": {
+ "field": "location",
+ "query_value": [
+ 5.0,
+ 4.0
+ ],
+ "space_type": "l2"
+ }
+ }
+ }
+ }
+}
+```
{% include copy-curl.html %}
\ No newline at end of file
diff --git a/images/faiss-algorithm.jpg b/images/faiss-algorithm.jpg
new file mode 100644
index 0000000000..e992c83f14
Binary files /dev/null and b/images/faiss-algorithm.jpg differ