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fix(search): improve FT.HYBRID command implementation (#3171)
* fix(search): improve FT.HYBRID command implementation * fix: add jsdoc for ft.hybrid options * refactor(search): simplify FT.HYBRID command API * refactor(ft.hybrid): use discriminated union for HYBRID vector method * refactor(ft.hybrid): use discriminated union for HYBRID combine method * refactor(ft.hybrid): reuse AGGREGATE reducer types in HYBRID command * fix: improve FT.HYBRID command interface and parsing * fix: support LOAD '*' to load all fields in FT.HYBRID * docs(examples): add RediSearch hybrid search example
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‎examples/README.md‎

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

‎examples/search-hybrid.js‎

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

‎packages/search/lib/commands/AGGREGATE.ts‎

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@@ -87,7 +87,7 @@ interface RandomSampleReducer extends GroupByReducerWithProperty<FT_AGGREGATE_GR
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sampleSize: number;
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}
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type GroupByReducers = CountReducer | CountDistinctReducer | CountDistinctishReducer | SumReducer | MinReducer | MaxReducer | AvgReducer | StdDevReducer | QuantileReducer | ToListReducer | FirstValueReducer | RandomSampleReducer;
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export type GroupByReducers = CountReducer | CountDistinctReducer | CountDistinctishReducer | SumReducer | MinReducer | MaxReducer | AvgReducer | StdDevReducer | QuantileReducer | ToListReducer | FirstValueReducer | RandomSampleReducer;
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interface GroupByStep extends AggregateStep<FT_AGGREGATE_STEPS['GROUPBY']> {
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properties?: RediSearchProperty | Array<RediSearchProperty>;
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}
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}
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function parseGroupByReducer(parser: CommandParser, reducer: GroupByReducers) {
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export function parseGroupByReducer(parser: CommandParser, reducer: GroupByReducers) {
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parser.push('REDUCE', reducer.type);
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switch (reducer.type) {

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