docs: updated how to create a mongodb vector index

examples/mongodb/2_load_and_index.ts

@@ -45,6 +45,39 @@ async function loadAndIndex() {
   await client.close();
 }
 
-loadAndIndex();
+/**
+ * This method is document in https://www.mongodb.com/docs/atlas/atlas-search/create-index/#create-an-fts-index-programmatically
+ * But, while testing a 'CommandNotFound' error occurred, so we're not using this here.
+ */
+async function createSearchIndex() {
+  const client = new MongoClient(mongoUri);
+  const database = client.db(databaseName);
+  const collection = database.collection(vectorCollectionName);
+
+  // define your Atlas Search index
+  const index = {
+    name: indexName,
+    definition: {
+      /* search index definition fields */
+      mappings: {
+        dynamic: true,
+        fields: [
+          {
+            type: "vector",
+            path: "embedding",
+            numDimensions: 1536,
+            similarity: "cosine",
+          },
+        ],
+      },
+    },
+  };
+  // run the helper method
+  const result = await collection.createSearchIndex(index);
+  console.log("Successfully created search index:", result);
+  await client.close();
+}
+
+loadAndIndex().catch(console.error);
 
 // you can't query your index yet because you need to create a vector search index in mongodb's UI now

examples/mongodb/README.md

@@ -76,7 +76,7 @@ Now if all has gone well you should be able to log in to the Mongo Atlas UI and
 
 Now it's time to create the vector search index so that you can query the data.
 It's not yet possible to programmatically create a vector search index using the [`createIndex`](https://www.mongodb.com/docs/manual/reference/method/db.collection.createIndex/) function, therefore we have to create one manually in the UI.
-To do so, first, click the Search tab, and then click "Create Search Index":
+To do so, first, click the 'Atlas Search' tab, and then click "Create Search Index":
 
 ![MongoDB Atlas create search index](./docs/4_search_tab.png)
 
@@ -88,16 +88,14 @@ Now under "database and collection" select `tiny_tweets_db` and within that sele
 
 ```json
 {
-  "mappings": {
-    "dynamic": true,
-    "fields": {
-      "embedding": {
-        "dimensions": 1536,
-        "similarity": "cosine",
-        "type": "knnVector"
-      }
+  "fields": [
+    {
+      "type": "vector",
+      "path": "embedding",
+      "numDimensions": 1536,
+      "similarity": "cosine"
     }
-  }
+  ]
 }
 ```