DOC-4039: add TCEs to the query pages - reissue (#2846)

2025-08-06 02:15:48 +03:00 · 2024-10-23 07:48:36 -07:00
parent 49fdb79897
commit c1edc2e718
8 changed files with 4617 additions and 81 deletions
--- a/doctests/query-vector.js
+++ b/doctests/query-vector.js
@@ -0,0 +1,111 @@
+// EXAMPLE: query_vector
+// HIDE_START
+import assert from 'node:assert';
+import fs from 'node:fs';
+import { createClient } from 'redis';
+import { SchemaFieldTypes, VectorAlgorithms } from '@redis/search';
+import { pipeline } from '@xenova/transformers';
+
+function float32Buffer(arr) {
+  const floatArray = new Float32Array(arr);
+  const float32Buffer = Buffer.from(floatArray.buffer);
+  return float32Buffer;
+}
+
+async function embedText(sentence) {
+  let modelName = 'Xenova/all-MiniLM-L6-v2';
+  let pipe = await pipeline('feature-extraction', modelName);
+
+  let vectorOutput = await pipe(sentence, {
+      pooling: 'mean',
+      normalize: true,
+  });
+
+  const embedding = Object.values(vectorOutput?.data);
+
+  return embedding;
+}
+
+let query = 'Bike for small kids';
+let vector_query = float32Buffer(await embedText('That is a very happy person'));
+
+const client = createClient();
+await client.connect().catch(console.error);
+
+// create index
+await client.ft.create('idx:bicycle', {
+    '$.description': {
+      type: SchemaFieldTypes.TEXT,
+      AS: 'description'
+    },
+    '$.description_embeddings': {
+        type: SchemaFieldTypes.VECTOR,
+        TYPE: 'FLOAT32',
+        ALGORITHM: VectorAlgorithms.FLAT,
+        DIM: 384,
+        DISTANCE_METRIC: 'COSINE',
+        AS: 'vector'
+    }
+}, {
+    ON: 'JSON',
+    PREFIX: 'bicycle:'
+});
+
+// load data
+const bicycles = JSON.parse(fs.readFileSync('data/query_vector.json', 'utf8'));
+
+await Promise.all(
+  bicycles.map((bicycle, bid) => {
+      return client.json.set(`bicycle:${bid}`, '$', bicycle);
+  })
+);
+// HIDE_END
+
+// STEP_START vector1
+const res1 = await client.ft.search('idx:bicycle', 
+  '*=>[KNN 3 @vector $query_vector AS score]', {
+    PARAMS: { query_vector: vector_query },
+    RETURN: ['description'],
+    DIALECT: 2
+  }
+);
+console.log(res1.total); // >>> 3
+console.log(res1); // >>>
+//{
+//  total: 3,
+//  documents: [
+//    { id: 'bicycle:0', value: [Object: null prototype] {} },
+//    { id: 'bicycle:2', value: [Object: null prototype] {} },
+//    { id: 'bicycle:9', value: [Object: null prototype] {} }
+//  ]
+//}
+// REMOVE_START
+assert.strictEqual(res1.total, 3);
+// REMOVE_END
+// STEP_END
+
+// STEP_START vector2
+const res2 = await client.ft.search('idx:bicycle', 
+  '@vector:[VECTOR_RANGE 0.9 $query_vector]=>{$YIELD_DISTANCE_AS: vector_dist}', {
+    PARAMS: { query_vector: vector_query },
+    SORTBY: 'vector_dist',
+    RETURN: ['vector_dist', 'description'],
+    DIALECT: 2
+  }
+);
+console.log(res2.total); // >>> 1
+console.log(res2); // >>>
+//{
+//  total: 1,
+//  documents: [ { id: 'bicycle:0', value: [Object: null prototype] } ]
+//}
+// REMOVE_START
+assert.strictEqual(res2.total, 1);
+// REMOVE_END
+// STEP_END
+
+// REMOVE_START
+// destroy index and data
+await client.ft.dropIndex('idx:bicycle', { DD: true });
+await client.disconnect();
+// REMOVE_END