diff --git a/src/core/index.js b/src/core/index.js
index dcf0305..669c334 100644
--- a/src/core/index.js
+++ b/src/core/index.js
@@ -8,6 +8,8 @@ exports.Aggregation = require('./aggregation');
 
 exports.Query = require('./query');
 
+exports.KNN = require('./knn');
+
 exports.Suggester = require('./suggester');
 
 exports.Script = require('./script');
diff --git a/src/core/knn.js b/src/core/knn.js
new file mode 100644
index 0000000..c5970e4
--- /dev/null
+++ b/src/core/knn.js
@@ -0,0 +1,133 @@
+'use strict';
+
+const { recursiveToJSON, checkType } = require('./util');
+const Query = require('./query');
+
+/**
+ * Class representing a k-Nearest Neighbors (k-NN) query.
+ * This class extends the Query class to support the specifics of k-NN search, including setting up the field,
+ * query vector, number of neighbors (k), and number of candidates.
+ *
+ * NOTE: kNN search was added to Elasticsearch in v8.0
+ *
+ * [Elasticsearch reference](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html)
+ */
+class KNN {
+    /**
+     * Creates an instance of KNN.
+     */
+    constructor(field, k, numCandidates) {
+        if (k > numCandidates)
+            throw new Error('KNN numCandidates cannot be less than k');
+        this._body = {};
+        this._body.field = field;
+        this._body.k = k;
+        this._body.filter = [];
+        this._body.num_candidates = numCandidates;
+    }
+
+    /**
+     * Sets the query vector for the k-NN search.
+     * @param {Array<number>} vector - The query vector.
+     * @returns {KNN} Returns the instance of KNN for method chaining.
+     */
+    queryVector(vector) {
+        if (this._body.query_vector_builder)
+            throw new Error(
+                'cannot provide both query_vector_builder and query_vector'
+            );
+        this._body.query_vector = vector;
+        return this;
+    }
+
+    /**
+     * Sets the query vector builder for the k-NN search.
+     * This method configures a query vector builder using a specified model ID and model text.
+     * It's important to note that either a direct query vector or a query vector builder can be
+     * provided, but not both.
+     *
+     * @param {string} modelId - The ID of the model to be used for generating the query vector.
+     * @param {string} modelText - The text input based on which the query vector is generated.
+     * @returns {KNN} Returns the instance of KNN for method chaining.
+     * @throws {Error} Throws an error if both query_vector_builder and query_vector are provided.
+     *
+     * Usage example:
+     * let knn = new KNN();
+     * knn.queryVectorBuilder('model_123', 'Sample model text');
+     */
+    queryVectorBuilder(modelId, modelText) {
+        if (this._body.query_vector)
+            throw new Error(
+                'cannot provide both query_vector_builder and query_vector'
+            );
+        this._body.query_vector_builder = {
+            text_embeddings: {
+                model_id: modelId,
+                model_text: modelText
+            }
+        };
+        return this;
+    }
+
+    /**
+     * Adds one or more filter queries to the k-NN search.
+     *
+     * This method is designed to apply filters to the k-NN search. It accepts either a single
+     * query or an array of queries. Each query acts as a filter, refining the search results
+     * according to the specified conditions. These queries must be instances of the `Query` class.
+     * If any provided query is not an instance of `Query`, a TypeError is thrown.
+     *
+     * @param {Query|Query[]} queries - A single `Query` instance or an array of `Query` instances for filtering.
+     * @returns {KNN} Returns `this` to allow method chaining.
+     * @throws {TypeError} If any of the provided queries is not an instance of `Query`.
+     *
+     * Usage example:
+     * let knn = new KNN();
+     * knn.filter(new TermQuery('field', 'value')); // Applying a single filter query
+     * knn.filter([new TermQuery('field1', 'value1'), new TermQuery('field2', 'value2')]); // Applying multiple filter queries
+     */
+    filter(queries) {
+        const queryArray = Array.isArray(queries) ? queries : [queries];
+        queryArray.forEach(query => {
+            checkType(query, Query);
+            this._body.filter.push(query);
+        });
+        return this;
+    }
+
+    /**
+     * Sets the field to perform the k-NN search on.
+     * @param {number} boost - The number of the boost
+     * @returns {KNN} Returns the instance of KNN for method chaining.
+     */
+    boost(boost) {
+        this._body.boost = boost;
+        return this;
+    }
+
+    /**
+     * Sets the field to perform the k-NN search on.
+     * @param {number} similarity - The number of the similarity
+     * @returns {KNN} Returns the instance of KNN for method chaining.
+     */
+    similarity(similarity) {
+        this._body.similarity = similarity;
+        return this;
+    }
+
+    /**
+     * Override default `toJSON` to return DSL representation for the `query`
+     *
+     * @override
+     * @returns {Object} returns an Object which maps to the elasticsearch query DSL
+     */
+    toJSON() {
+        if (!this._body.query_vector && !this._body.query_vector_builder)
+            throw new Error(
+                'either query_vector_builder or query_vector must be provided'
+            );
+        return recursiveToJSON(this._body);
+    }
+}
+
+module.exports = KNN;
diff --git a/src/core/request-body-search.js b/src/core/request-body-search.js
index c1a90d1..79ecb87 100644
--- a/src/core/request-body-search.js
+++ b/src/core/request-body-search.js
@@ -10,7 +10,8 @@ const Query = require('./query'),
     Rescore = require('./rescore'),
     Sort = require('./sort'),
     Highlight = require('./highlight'),
-    InnerHits = require('./inner-hits');
+    InnerHits = require('./inner-hits'),
+    KNN = require('./knn');
 
 const { checkType, setDefault, recursiveToJSON } = require('./util');
 
@@ -69,6 +70,7 @@ class RequestBodySearch {
     constructor() {
         // Maybe accept some optional parameter?
         this._body = {};
+        this._knn = [];
         this._aggs = [];
         this._suggests = [];
         this._suggestText = null;
@@ -87,6 +89,21 @@ class RequestBodySearch {
         return this;
     }
 
+    /**
+     * Sets knn on the search request body.
+     *
+     * @param {Knn|Knn[]} knn
+     * @returns {RequestBodySearch} returns `this` so that calls can be chained.
+     */
+    kNN(knn) {
+        const knns = Array.isArray(knn) ? knn : [knn];
+        knns.forEach(_knn => {
+            checkType(_knn, KNN);
+            this._knn.push(_knn);
+        });
+        return this;
+    }
+
     /**
      * Sets aggregation on the request body.
      * Alias for method `aggregation`
@@ -785,6 +802,8 @@ class RequestBodySearch {
     toJSON() {
         const dsl = recursiveToJSON(this._body);
 
+        if (!isEmpty(this._knn)) dsl.knn = this._knn;
+
         if (!isEmpty(this._aggs)) dsl.aggs = recMerge(this._aggs);
 
         if (!isEmpty(this._suggests) || !isNil(this._suggestText)) {
diff --git a/src/index.d.ts b/src/index.d.ts
index 9a01a76..9ccc120 100644
--- a/src/index.d.ts
+++ b/src/index.d.ts
@@ -18,6 +18,13 @@ declare namespace esb {
          */
         query(query: Query): this;
 
+        /**
+         * Sets knn on the request body.
+         *
+         * @param {KNN|KNN[]} knn
+         */
+        kNN(knn: KNN | KNN[]): this;
+
         /**
          * Sets aggregation on the request body.
          * Alias for method `aggregation`
@@ -3074,7 +3081,7 @@ declare namespace esb {
 
         /**
          * Sets the script used to compute the score of documents returned by the query.
-         * 
+         *
          * @param {Script} script A valid `Script` object
          */
         script(script: Script): this;
@@ -3268,6 +3275,86 @@ declare namespace esb {
      */
     export function distanceFeatureQuery(field?: string): DistanceFeatureQuery;
 
+    /**
+     * The `rank_feature` query boosts the relevance score on the numeric value of
+     * document with a rank_feature/rank_features field.
+     *
+     * [Elasticsearch reference](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-rank-feature-query.html)
+     *
+     * NOTE: This query was added in elasticsearch v7.0.
+     *
+     * @example
+     * const query = new RankFeatureQuery('rank_feature_field');
+     *   query
+     *       .linear()
+     *       .toJSON();
+     * @param {string} field The field inside the document to be used in the query
+     * @return {RankFeatureQuery}
+     */
+    export class RankFeatureQuery extends Query {
+        constructor(field?: string);
+
+        /**
+         * Sets the field for the `distance_feature` query
+         * @param {string} fieldName Name of the field inside the document
+         * @returns {DistanceFeatureQuery} Instance of the query
+         */
+        field(fieldName: string): RankFeatureQuery;
+
+        /**
+         * Linear function to boost relevance scores based on the value of the rank feature field
+         * @returns {RankFeatureQuery}
+         */
+        linear(): RankFeatureQuery;
+
+        /**
+         * Saturation function to boost relevance scores based on the value of the rank feature field.
+         * Uses a default pivot value computed by Elasticsearch.
+         * @returns {RankFeatureQuery}
+         */
+        saturation(): RankFeatureQuery;
+
+        /**
+         * Saturation function to boost relevance scores based on the value of the rank feature field.
+         * @param {number} pivot
+         * @returns {RankFeatureQuery}
+         */
+        saturationPivot(pivot: number): RankFeatureQuery;
+
+        /**
+         * The log function gives a score equal to log(scaling_factor + S), where S
+         * is the value of the rank feature field and scaling_factor is a configurable
+         * scaling factor.
+         * @param {number} scaling_factor
+         * @returns {RankFeatureQuery}
+         */
+        log(scalingFactor: number): RankFeatureQuery;
+
+        /**
+         * The sigmoid function extends the saturation function with a configurable exponent.
+         * @param {number} pivot
+         * @param {number} exponent
+         * @returns {RankFeatureQuery}
+         */
+        sigmoid(pivot: number, exponent: number): RankFeatureQuery;
+    }
+
+    /**
+     * The `rank_feature` query boosts the relevance score on the numeric value of
+     * document with a rank_feature/rank_features field.
+     *
+     * [Elasticsearch reference](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-rank-feature-query.html)
+     *
+     * @example
+     * const query = new RankFeatureQuery('rank_feature_field');
+     *   query
+     *       .linear()
+     *       .toJSON();
+     * @param {string} field The field inside the document to be used in the query
+     * @return {RankFeatureQuery}
+     */
+    export function rankFeatureQuery(field?: string): RankFeatureQuery;
+
     /**
      * Interface-like class used to group and identify various implementations of Span queries.
      *
@@ -3614,6 +3701,84 @@ declare namespace esb {
         spanQry?: SpanQueryBase
     ): SpanFieldMaskingQuery;
 
+    /**
+     * Knn performs k-nearest neighbor (KNN) searches.
+     * This class allows configuring the KNN search with various parameters such as field, query vector,
+     * number of nearest neighbors (k), number of candidates, boost factor, and similarity metric.
+     *
+     * NOTE: Only available in Elasticsearch v8.0+
+     */
+    export class KNN {
+        /**
+         * Creates an instance of Knn, initializing the internal state for the k-NN search.
+         *
+         * @param {string} field - (Optional) The field against which to perform the k-NN search.
+         * @param {number} k - (Optional) The number of nearest neighbors to retrieve.
+         * @param {number} numCandidates - (Optional) The number of candidate neighbors to consider during the search.
+         * @throws {Error} If the number of candidates (numCandidates) is less than the number of neighbors (k).
+         */
+        constructor(field: string, k: number, numCandidates: number);
+
+        /**
+         * Sets the query vector for the KNN search, an array of numbers representing the reference point.
+         *
+         * @param {number[]} vector
+         */
+        queryVector(vector: number[]): this;
+
+        /**
+         * Sets the query vector builder for the k-NN search.
+         * This method configures a query vector builder using a specified model ID and model text.
+         * Note that either a direct query vector or a query vector builder can be provided, but not both.
+         *
+         * @param {string} modelId - The ID of the model used for generating the query vector.
+         * @param {string} modelText - The text input based on which the query vector is generated.
+         * @returns {KNN} Returns the instance of Knn for method chaining.
+         * @throws {Error} If both query_vector_builder and query_vector are provided.
+         */
+        queryVectorBuilder(modelId: string, modelText: string): this;
+
+        /**
+         * Adds one or more filter queries to the k-NN search.
+         * This method is designed to apply filters to the k-NN search. It accepts either a single
+         * query or an array of queries. Each query acts as a filter, refining the search results
+         * according to the specified conditions. These queries must be instances of the `Query` class.
+         *
+         * @param {Query|Query[]} queries - A single `Query` instance or an array of `Query` instances for filtering.
+         * @returns {KNN} Returns `this` to allow method chaining.
+         * @throws {TypeError} If any of the provided queries is not an instance of `Query`.
+         */
+        filter(queries: Query | Query[]): this;
+
+        /**
+         * Applies a boost factor to the query to influence the relevance score of returned documents.
+         *
+         * @param {number} boost
+         */
+        boost(boost: number): this;
+
+        /**
+         * Sets the similarity metric used in the KNN algorithm to calculate similarity.
+         *
+         * @param {number} similarity
+         */
+        similarity(similarity: number): this;
+
+        /**
+         * Override default `toJSON` to return DSL representation for the `query`
+         *
+         * @override
+         */
+        toJSON(): object;
+    }
+
+    /**
+     * Factory function to instantiate a new Knn object.
+     *
+     * @returns {KNN}
+     */
+    export function kNN(field: string, k: number, numCandidates: number): KNN;
+
     /**
      * Base class implementation for all aggregation types.
      *
diff --git a/src/index.js b/src/index.js
index af7f821..a3bb1c0 100644
--- a/src/index.js
+++ b/src/index.js
@@ -14,6 +14,7 @@ const {
     InnerHits,
     SearchTemplate,
     Query,
+    KNN,
     util: { constructorWrapper }
 } = require('./core');
 
@@ -333,6 +334,13 @@ exports.spanWithinQuery = constructorWrapper(SpanWithinQuery);
 
 exports.SpanFieldMaskingQuery = SpanFieldMaskingQuery;
 exports.spanFieldMaskingQuery = constructorWrapper(SpanFieldMaskingQuery);
+
+/* ============ ============ ============ */
+/* ======== KNN ======== */
+/* ============ ============ ============ */
+exports.KNN = KNN;
+exports.kNN = constructorWrapper(KNN);
+
 /* ============ ============ ============ */
 /* ======== Metrics Aggregations ======== */
 /* ============ ============ ============ */
diff --git a/test/core-test/knn.test.js b/test/core-test/knn.test.js
new file mode 100644
index 0000000..6f717a0
--- /dev/null
+++ b/test/core-test/knn.test.js
@@ -0,0 +1,87 @@
+import test from 'ava';
+import { KNN, TermQuery } from '../../src';
+
+test('knn can be instantiated', t => {
+    const knn = new KNN('my_field', 5, 10).queryVector([1, 2, 3]);
+    const json = knn.toJSON();
+    t.truthy(json);
+});
+
+test('knn throws error if numCandidates is less than k', t => {
+    const error = t.throws(() =>
+        new KNN('my_field', 10, 5).queryVector([1, 2, 3])
+    );
+    t.is(error.message, 'KNN numCandidates cannot be less than k');
+});
+
+test('knn queryVector sets correctly', t => {
+    const vector = [1, 2, 3];
+    const knn = new KNN('my_field', 5, 10).queryVector(vector);
+    const json = knn.toJSON();
+    t.deepEqual(json.query_vector, vector);
+});
+
+test('knn queryVectorBuilder sets correctly', t => {
+    const modelId = 'model_123';
+    const modelText = 'Sample model text';
+    const knn = new KNN('my_field', 5, 10).queryVectorBuilder(
+        modelId,
+        modelText
+    );
+    const json = knn.toJSON();
+    t.deepEqual(json.query_vector_builder.text_embeddings, {
+        model_id: modelId,
+        model_text: modelText
+    });
+});
+
+test('knn filter method adds queries correctly', t => {
+    const knn = new KNN('my_field', 5, 10).queryVector([1, 2, 3]);
+    const query = new TermQuery('field', 'value');
+    knn.filter(query);
+    const json = knn.toJSON();
+    t.deepEqual(json.filter, [query.toJSON()]);
+});
+
+test('knn boost method sets correctly', t => {
+    const boostValue = 1.5;
+    const knn = new KNN('my_field', 5, 10)
+        .boost(boostValue)
+        .queryVector([1, 2, 3]);
+    const json = knn.toJSON();
+    t.is(json.boost, boostValue);
+});
+
+test('knn similarity method sets correctly', t => {
+    const similarityValue = 0.8;
+    const knn = new KNN('my_field', 5, 10)
+        .similarity(similarityValue)
+        .queryVector([1, 2, 3]);
+    const json = knn.toJSON();
+    t.is(json.similarity, similarityValue);
+});
+
+test('knn toJSON method returns correct DSL', t => {
+    const knn = new KNN('my_field', 5, 10)
+        .queryVector([1, 2, 3])
+        .filter(new TermQuery('field', 'value'));
+
+    const expectedDSL = {
+        field: 'my_field',
+        k: 5,
+        num_candidates: 10,
+        query_vector: [1, 2, 3],
+        filter: [{ term: { field: 'value' } }]
+    };
+
+    t.deepEqual(knn.toJSON(), expectedDSL);
+});
+
+test('knn toJSON throws error if neither query_vector nor query_vector_builder is provided', t => {
+    const knn = new KNN('my_field', 5, 10);
+    const error = t.throws(() => knn.toJSON());
+    t.is(
+        error.message,
+        'either query_vector_builder or query_vector must be provided'
+    );
+});