feat: add kNN search, query support

src/search/knn/mod.rs

@@ -0,0 +1,282 @@
+//! A k-nearest neighbor (kNN) search finds the k nearest vectors to a query vector, as measured by a similarity metric.
+//!
+//! Common use cases for kNN include:
+//! - Relevance ranking based on natural language processing (NLP) algorithms
+//! - Product recommendations and recommendation engines
+//! - Similarity search for images or videos
+//!
+//! <https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html#approximate-knn>
+
+use crate::search::*;
+use crate::util::*;
+use serde::Serialize;
+
+/// Performs a k-nearest neighbor (kNN) search and returns the matching documents.
+///
+/// The kNN search API performs a k-nearest neighbor (kNN) search on a `dense_vector` field. Given a query vector, it
+/// finds the _k_ closest vectors and returns those documents as search hits.
+///
+/// Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like most kNN algorithms, HNSW is an
+/// approximate method that sacrifices result accuracy for improved search speed. This means the results returned are
+/// not always the true _k_ closest neighbors.
+///
+/// The kNN search API supports restricting the search using a filter. The search will return the top `k` documents
+/// that also match the filter query.
+///
+/// To create a knn search with a query vector or query vector builder:
+/// ```
+/// # use elasticsearch_dsl::*;
+/// # let search =
+/// Search::new()
+///     .knn(Knn::query_vector("test1", vec![1.0, 2.0, 3.0]))
+///     .knn(Knn::query_vector_builder("test3", TextEmbedding::new("my-text-embedding-model", "The opposite of pink")));
+/// ```
+/// <https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-knn-query.html>
+#[derive(Debug, Clone, PartialEq, Serialize)]
+pub struct Knn {
+    field: String,
+
+    #[serde(skip_serializing_if = "ShouldSkip::should_skip")]
+    query_vector: Option<Vec<f32>>,
+
+    #[serde(skip_serializing_if = "ShouldSkip::should_skip")]
+    query_vector_builder: Option<QueryVectorBuilder>,
+
+    #[serde(skip_serializing_if = "ShouldSkip::should_skip")]
+    k: Option<u32>,
+
+    #[serde(skip_serializing_if = "ShouldSkip::should_skip")]
+    num_candidates: Option<u32>,
+
+    #[serde(skip_serializing_if = "ShouldSkip::should_skip")]
+    filter: Option<Box<Query>>,
+
+    #[serde(skip_serializing_if = "ShouldSkip::should_skip")]
+    similarity: Option<f32>,
+
+    #[serde(skip_serializing_if = "ShouldSkip::should_skip")]
+    boost: Option<f32>,
+
+    #[serde(skip_serializing_if = "ShouldSkip::should_skip")]
+    _name: Option<String>,
+}
+
+impl Knn {
+    /// Creates an instance of [`Knn`] search with query vector
+    ///
+    /// - `field` - The name of the vector field to search against. Must be a dense_vector field with indexing enabled.
+    /// - `query_vector` - Query vector. Must have the same number of dimensions as the vector field you are searching
+    /// against.
+    pub fn query_vector<T>(field: T, query_vector: Vec<f32>) -> Self
+    where
+        T: ToString,
+    {
+        Self {
+            field: field.to_string(),
+            query_vector: Some(query_vector),
+            query_vector_builder: None,
+            k: None,
+            num_candidates: None,
+            filter: None,
+            similarity: None,
+            boost: None,
+            _name: None,
+        }
+    }
+    /// Creates an instance of [`Knn`] search with query vector builder
+    ///
+    /// - `field` - The name of the vector field to search against. Must be a dense_vector field with indexing enabled.
+    /// - `query_vector_builder` - A configuration object indicating how to build a query_vector before executing the request.
+    pub fn query_vector_builder<T, U>(field: T, query_vector_builder: U) -> Self
+    where
+        T: ToString,
+        U: Into<QueryVectorBuilder>,
+    {
+        Self {
+            field: field.to_string(),
+            query_vector: None,
+            query_vector_builder: Some(query_vector_builder.into()),
+            k: None,
+            num_candidates: None,
+            filter: None,
+            similarity: None,
+            boost: None,
+            _name: None,
+        }
+    }
+
+    /// Number of nearest neighbors to return as top hits. This value must be less than `num_candidates`.
+    ///
+    /// Defaults to `size`.
+    pub fn k(mut self, k: u32) -> Self {
+        self.k = Some(k);
+        self
+    }
+
+    /// The number of nearest neighbor candidates to consider per shard. Cannot exceed 10,000. Elasticsearch collects
+    /// `num_candidates` results from each shard, then merges them to find the top results. Increasing `num_candidates`
+    /// tends to improve the accuracy of the final results. Defaults to `Math.min(1.5 * size, 10_000)`.
+    pub fn num_candidates(mut self, num_candidates: u32) -> Self {
+        self.num_candidates = Some(num_candidates);
+        self
+    }
+
+    /// Query to filter the documents that can match. The kNN search will return the top documents that also match
+    /// this filter. The value can be a single query or a list of queries. If `filter` is not provided, all documents
+    /// are allowed to match.
+    ///
+    /// The filter is a pre-filter, meaning that it is applied **during** the approximate kNN search to ensure that
+    /// `num_candidates` matching documents are returned.
+    pub fn filter<T>(mut self, filter: T) -> Self
+    where
+        T: Into<Query>,
+    {
+        self.filter = Some(Box::new(filter.into()));
+        self
+    }
+
+    /// The minimum similarity required for a document to be considered a match. The similarity value calculated
+    /// relates to the raw similarity used. Not the document score. The matched documents are then scored according
+    /// to similarity and the provided boost is applied.
+    pub fn similarity(mut self, similarity: f32) -> Self {
+        self.similarity = Some(similarity);
+        self
+    }
+
+    add_boost_and_name!();
+}
+
+/// A configuration object indicating how to build a query_vector before executing the request.
+///
+/// Currently, the only supported builder is [`TextEmbedding`].
+///
+/// <https://www.elastic.co/guide/en/elasticsearch/reference/8.13/knn-search.html#knn-semantic-search>
+#[derive(Debug, Clone, PartialEq, Serialize)]
+#[serde(rename_all = "snake_case")]
+pub enum QueryVectorBuilder {
+    /// The natural language processing task to perform.
+    TextEmbedding(TextEmbedding),
+}
+
+/// The natural language processing task to perform.
+#[derive(Debug, Clone, PartialEq, Serialize)]
+pub struct TextEmbedding {
+    model_id: String,
+    model_text: String,
+}
+
+impl From<TextEmbedding> for QueryVectorBuilder {
+    fn from(embedding: TextEmbedding) -> Self {
+        Self::TextEmbedding(embedding)
+    }
+}
+
+impl TextEmbedding {
+    /// Creates an instance of [`TextEmbedding`]
+    /// - `model_id` - The ID of the text embedding model to use to generate the dense vectors from the query string.
+    /// Use the same model that generated the embeddings from the input text in the index you search against. You can
+    /// use the value of the deployment_id instead in the model_id argument.
+    /// - `model_text` - The query string from which the model generates the dense vector representation.
+    pub fn new<T, U>(model_id: T, model_text: U) -> Self
+    where
+        T: ToString,
+        U: ToString,
+    {
+        Self {
+            model_id: model_id.to_string(),
+            model_text: model_text.to_string(),
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn serialization() {
+        assert_serialize(
+            Search::new()
+                .knn(Knn::query_vector("test1", vec![1.0, 2.0, 3.0]))
+                .knn(
+                    Knn::query_vector("test2", vec![4.0, 5.0, 6.0])
+                        .k(3)
+                        .num_candidates(100)
+                        .filter(Query::term("field", "value"))
+                        .similarity(0.5)
+                        .boost(2.0)
+                        .name("test2"),
+                )
+                .knn(Knn::query_vector_builder(
+                    "test3",
+                    TextEmbedding::new("my-text-embedding-model", "The opposite of pink"),
+                ))
+                .knn(
+                    Knn::query_vector_builder(
+                        "test4",
+                        TextEmbedding::new("my-text-embedding-model", "The opposite of blue"),
+                    )
+                    .k(5)
+                    .num_candidates(200)
+                    .filter(Query::term("field", "value"))
+                    .similarity(0.7)
+                    .boost(2.1)
+                    .name("test4"),
+                ),
+            json!({
+                "knn": [
+                    {
+                        "field": "test1",
+                        "query_vector": [1.0, 2.0, 3.0]
+                    },
+                    {
+                        "field": "test2",
+                        "query_vector": [4.0, 5.0, 6.0],
+                        "k": 3,
+                        "num_candidates": 100,
+                        "filter": {
+                            "term": {
+                                "field": {
+                                    "value": "value"
+                                }
+                            }
+                        },
+                        "similarity": 0.5,
+                        "boost": 2.0,
+                        "_name": "test2"
+                    },
+                    {
+                        "field": "test3",
+                        "query_vector_builder": {
+                            "text_embedding": {
+                                "model_id": "my-text-embedding-model",
+                                "model_text": "The opposite of pink"
+                            }
+                        }
+                    },
+                    {
+                        "field": "test4",
+                        "query_vector_builder": {
+                            "text_embedding": {
+                                "model_id": "my-text-embedding-model",
+                                "model_text": "The opposite of blue"
+                            }
+                        },
+                        "k": 5,
+                        "num_candidates": 200,
+                        "filter": {
+                            "term": {
+                                "field": {
+                                    "value": "value"
+                                }
+                            }
+                        },
+                        "similarity": 0.7,
+                        "boost": 2.1,
+                        "_name": "test4"
+                    }
+                ]
+            }),
+        );
+    }
+}

src/search/mod.rs

@@ -15,6 +15,7 @@ mod response;
 // Public modules
 pub mod aggregations;
 pub mod highlight;
+pub mod knn;
 pub mod params;
 pub mod queries;
 pub mod request;
@@ -27,6 +28,7 @@ pub mod suggesters;
 // Public re-exports
 pub use self::aggregations::*;
 pub use self::highlight::*;
+pub use self::knn::*;
 pub use self::params::*;
 pub use self::queries::params::*;
 pub use self::queries::*;

src/search/queries/mod.rs

@@ -204,6 +204,7 @@ query!(
     SpanOr(SpanOrQuery),
     SpanTerm(SpanTermQuery),
     SpanWithin(SpanWithinQuery),
+    Knn(KnnQuery),
 );
 
 #[cfg(test)]