redis · nkaradzhov · Feb 11, 2026 · Feb 2, 2026 · Feb 3, 2026 · Feb 3, 2026
diff --git a/examples/README.md b/examples/README.md
@@ -23,6 +23,7 @@ This folder contains example scripts showing how to use Node Redis in different
 | `search-hashes.js`                       | Uses [RediSearch](https://redisearch.io) to index and search data in hashes.                                                                         |
 | `search-json.js`                         | Uses [RediSearch](https://redisearch.io/) and [RedisJSON](https://redisjson.io/) to index and search JSON data.                                      |
 | `search-knn.js`                          | Uses [RediSearch vector similarity]([https://redisearch.io/](https://redis.io/docs/stack/search/reference/vectors/)) to index and run KNN queries.   |
+| `search-hybrid.js`                       | Uses [RediSearch](https://redisearch.io) hybrid search to combine text search with vector similarity search.                                         |
 | `set-scan.js`                            | An example script that shows how to use the SSCAN iterator functionality.                                                                            |
 | `sorted-set.js`                          | Add members with scores to a Sorted Set and retrieve them using the ZSCAN iteractor functionality.                                                   |
 | `stream-producer.js`                     | Adds entries to a [Redis Stream](https://redis.io/topics/streams-intro) using the `XADD` command.                                                    |

diff --git a/examples/search-hybrid.js b/examples/search-hybrid.js
@@ -0,0 +1,173 @@
+// This example demonstrates how to use RediSearch hybrid search (FT.HYBRID).
+// Hybrid search combines text search with vector similarity search for more
+// comprehensive and relevant results.
+
+import {
+  createClient,
+  SCHEMA_FIELD_TYPE,
+  SCHEMA_VECTOR_FIELD_ALGORITHM,
+} from "redis";
+
+const client = createClient();
+
+await client.connect();
+
+// Helper function to create a Float32Array vector as a Buffer
+const createVectorBuffer = (values) => {
+  return Buffer.from(new Float32Array(values).buffer);
+};
+
+// Create an index with text, tag, numeric, and vector fields...
+const indexName = "idx:products";
+try {
+  // Documentation: https://redis.io/commands/ft.create/
+  await client.ft.create(
+    indexName,
+    {
+      description: SCHEMA_FIELD_TYPE.TEXT,
+      category: SCHEMA_FIELD_TYPE.TAG,
+      price: SCHEMA_FIELD_TYPE.NUMERIC,
+      embedding: {
+        type: SCHEMA_FIELD_TYPE.VECTOR,
+        ALGORITHM: SCHEMA_VECTOR_FIELD_ALGORITHM.FLAT,
+        TYPE: "FLOAT32",
+        DIM: 4,
+        DISTANCE_METRIC: "L2",
+      },
+    },
+    {
+      ON: "HASH",
+      PREFIX: "noderedis:products",
+    },
+  );
+} catch (e) {
+  if (e.message === "Index already exists") {
+    console.log("Index exists already, skipped creation.");
+  } else {
+    console.error(e);
+    process.exit(1);
+  }
+}
+
+// Add some sample product data with embeddings...
+await Promise.all([
+  client.hSet("noderedis:products:1", {
+    description: "comfortable red running shoes",
+    category: "footwear",
+    price: "79",
+    embedding: createVectorBuffer([1, 2, 7, 8]),
+  }),
+  client.hSet("noderedis:products:2", {
+    description: "stylish blue sneakers",
+    category: "footwear",
+    price: "89",
+    embedding: createVectorBuffer([1, 4, 7, 8]),
+  }),
+  client.hSet("noderedis:products:3", {
+    description: "elegant red dress",
+    category: "clothing",
+    price: "129",
+    embedding: createVectorBuffer([1, 2, 6, 5]),
+  }),
+  client.hSet("noderedis:products:4", {
+    description: "warm winter jacket",
+    category: "clothing",
+    price: "199",
+    embedding: createVectorBuffer([5, 6, 7, 8]),
+  }),
+]);
+
+// Perform a hybrid search combining text search with vector similarity
+// Documentation: https://redis.io/commands/ft.hybrid/
+const results = await client.ft.hybrid(indexName, {
+  // Text search component - full-text search on TEXT fields
+  SEARCH: {
+    query: "@description:red",
+    YIELD_SCORE_AS: "text_score",
+  },
+  // Vector similarity component
+  VSIM: {
+    field: "@embedding",
+    // Reference to the vector parameter (must match a key in PARAMS, prefixed with '$')
+    vector: "$query_vector",
+    YIELD_SCORE_AS: "vector_score",
+    // Search method configuration - KNN or RANGE
+    method: {
+      type: "KNN",
+      K: 10,
+    },
+  },
+  // Combine method: RRF (Reciprocal Rank Fusion) or LINEAR
+  COMBINE: {
+    method: { type: "RRF", CONSTANT: 60 },
+    YIELD_SCORE_AS: "combined_score",
+  },
+  // Fields to load from the documents
+  // - Use `'*'` to load all fields from documents
+  LOAD: ["@__key", "@description", "@category", "@price"],
+  // Sort by combined score
+  SORTBY: {
+    fields: [{ field: "@combined_score", direction: "DESC" }],
+  },
+  // Limit results
+  LIMIT: { offset: 0, count: 10 },
+  // Query parameters - the param name must match the vector reference in VSIM
+  // (e.g., '$query_vector' in VSIM.vector corresponds to 'query_vector' here)
+  PARAMS: {
+    query_vector: createVectorBuffer([1, 2, 6, 5]),
+  },
+});
+
+// results:
+// {
+//   totalResults: 4,
+//   executionTime: 0.879,
+//   warnings: [],
+//   results: [
+//     {
+//       text_score: '0.0404949945054',
+//       __key: 'noderedis:products:3',
+//       description: 'elegant red dress',
+//       category: 'clothing',
+//       price: '129',
+//       vector_score: '1',
+//       combined_score: '0.0327868852459'
+//     },
+//     {
+//       text_score: '0.0358374231755',
+//       __key: 'noderedis:products:1',
+//       description: 'comfortable red running shoes',
+//       category: 'footwear',
+//       price: '79',
+//       vector_score: '0.0909090909091',
+//       combined_score: '0.0322580645161'
+//     },
+//     {
+//       __key: 'noderedis:products:2',
+//       description: 'stylish blue sneakers',
+//       category: 'footwear',
+//       price: '89',
+//       vector_score: '0.0666666666667',
+//       combined_score: '0.015873015873'
+//     },
+//     {
+//       __key: 'noderedis:products:4',
+//       description: 'warm winter jacket',
+//       category: 'clothing',
+//       price: '199',
+//       vector_score: '0.0232558139535',
+//       combined_score: '0.015625'
+//     }
+//   ]
+// }
+
+console.log(`Results found: ${results.totalResults}`);
+console.log(`Execution time: ${results.executionTime}ms`);
+
+for (const doc of results.results) {
+  console.log(`${doc.__key} - ${doc.description} ($${doc.price})`);
+  console.log(`  Category: ${doc.category}`);
+  console.log(`  Combined score: ${doc.combined_score}`);
+}
+
+client.destroy();
diff --git a/packages/search/lib/commands/AGGREGATE.ts b/packages/search/lib/commands/AGGREGATE.ts
@@ -87,7 +87,7 @@ interface RandomSampleReducer extends GroupByReducerWithProperty<FT_AGGREGATE_GR
   sampleSize: number;
 }
 
-type GroupByReducers = CountReducer | CountDistinctReducer | CountDistinctishReducer | SumReducer | MinReducer | MaxReducer | AvgReducer | StdDevReducer | QuantileReducer | ToListReducer | FirstValueReducer | RandomSampleReducer;
+export type GroupByReducers = CountReducer | CountDistinctReducer | CountDistinctishReducer | SumReducer | MinReducer | MaxReducer | AvgReducer | StdDevReducer | QuantileReducer | ToListReducer | FirstValueReducer | RandomSampleReducer;
 
 interface GroupByStep extends AggregateStep<FT_AGGREGATE_STEPS['GROUPBY']> {
   properties?: RediSearchProperty | Array<RediSearchProperty>;
@@ -284,7 +284,7 @@ function pushLoadField(args: Array<RedisArgument>, toLoad: LoadField) {
   }
 }
 
-function parseGroupByReducer(parser: CommandParser, reducer: GroupByReducers) {
+export function parseGroupByReducer(parser: CommandParser, reducer: GroupByReducers) {
   parser.push('REDUCE', reducer.type);
 
   switch (reducer.type) {