From 2042a67f3c063626ffb6725adf3b0557114d5cbc Mon Sep 17 00:00:00 2001
From: Curran Kelleher <68416+curran@users.noreply.github.com>
Date: Wed, 18 Jan 2023 12:54:51 -0500
Subject: [PATCH] KNN example (#2352)

* Sketch KNN example

* Got KNN example to work. Closes #2351

* clean example

Co-authored-by: Leibale Eidelman <me@leibale.com>
---
 examples/search-knn.js | 91 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 examples/search-knn.js

diff --git a/examples/search-knn.js b/examples/search-knn.js
new file mode 100644
index 0000000000..ea20f52e3f
--- /dev/null
+++ b/examples/search-knn.js
@@ -0,0 +1,91 @@
+// This example demonstrates how to use RediSearch to index and query data
+// stored in Redis hashes using vector similarity search.
+//
+// Inspired by RediSearch Python tests:
+// https://github.com/RediSearch/RediSearch/blob/06e36d48946ea08bd0d8b76394a4e82eeb919d78/tests/pytests/test_vecsim.py#L96
+
+import { createClient, SchemaFieldTypes, VectorAlgorithms } from 'redis';
+
+const client = createClient();
+
+await client.connect();
+
+// Create an index...
+try {
+  // Documentation: https://redis.io/docs/stack/search/reference/vectors/
+  await client.ft.create('idx:knn-example', {
+    v: {
+      type: SchemaFieldTypes.VECTOR,
+      ALGORITHM: VectorAlgorithms.HNSW,
+      TYPE: 'FLOAT32',
+      DIM: 2,
+      DISTANCE_METRIC: 'COSINE'
+    }
+  }, {
+    ON: 'HASH',
+    PREFIX: 'noderedis:knn'
+  });
+} catch (e) {
+  if (e.message === 'Index already exists') {
+    console.log('Index exists already, skipped creation.');
+  } else {
+    // Something went wrong, perhaps RediSearch isn't installed...
+    console.error(e);
+    process.exit(1);
+  }
+}
+
+function float32Buffer(arr) {
+  return Buffer.from(new Float32Array(arr).buffer);
+}
+
+// Add some sample data...
+// https://redis.io/commands/hset/
+await Promise.all([
+  client.hSet('noderedis:knn:a', { v: float32Buffer([0.1, 0.1]) }),
+  client.hSet('noderedis:knn:b', { v: float32Buffer([0.1, 0.2]) }),
+  client.hSet('noderedis:knn:c', { v: float32Buffer([0.1, 0.3]) }),
+  client.hSet('noderedis:knn:d', { v: float32Buffer([0.1, 0.4]) }),
+]);
+// Perform a K-Nearest Neighbors vector similarity search
+// Documentation: https://redis.io/docs/stack/search/reference/vectors/#pure-knn-queries
+const results = await client.ft.search('idx:knn-example', '*=>[KNN 4 @v $BLOB AS dist]', {
+  PARAMS: {
+    BLOB: float32Buffer([0.1, 0.1])
+  },
+  SORTBY: 'dist',
+  DIALECT: 2,
+  RETURN: ['dist']
+});
+console.log(JSON.stringify(results, null, 2));
+// results:
+// {
+//   "total": 4,
+//   "documents": [
+//     {
+//       "id": "noderedis:knn:a",
+//       "value": {
+//         "dist": "5.96046447754e-08"
+//       }
+//     },
+//     {
+//       "id": "noderedis:knn:b",
+//       "value": {
+//         "dist": "0.0513167381287"
+//       }
+//     },
+//     {
+//       "id": "noderedis:knn:c",
+//       "value": {
+//         "dist": "0.10557281971"
+//       }
+//     },
+//     {
+//       "id": "noderedis:knn:d",
+//       "value": {
+//         "dist": "0.142507016659"
+//       }
+//     }
+//   ]
+// }
+await client.quit();