indexing

docs/plugins/firebase.md

@@ -81,6 +81,7 @@ const yourRetrieverRef = defineFirestoreRetriever({
   contentField: 'yourDataChunks',
   vectorField: 'embedding',
   embedder: textEmbeddingGecko,
+  distanceMeasure: 'COSINE', // 'EUCLIDEAN', 'DOT_PRODUCT', or 'COSINE' (default)
 });
 ```
 
@@ -97,8 +98,39 @@ const docs = await retrieve({
 For indexing, use an embedding generator along with the Admin SDK:
 
 ```js
+import { initializeApp } from "firebase-admin";
+import { getFirestore } from "firebase-admin/firestore";
+import { textEmbeddingGecko } from '@genkit-ai/vertexai';
+import { embed } from '@genkit-ai/ai/embedder';
+
+const app = initializeApp();
+const firestore = getFirestore(app);
+
+const indexConfig = {
+  collection: "yourCollection",
+  contentField: "yourDataChunks",
+  vectorField: "embedding",
+  embedder: textEmbeddingGecko,
+}
+
+async function indexToFirestore(content) {
+  const embedding = await embed({
+    embedder: indexConfig.embedder,
+    content,
+  });
+  await firestore.collection(indexConfig.collection).add({
+    [indexConfig.vectorField]: embedding,
+    [indexConfig.contentField]: content,
+  });
+}
 ```
 
+Firestore depends on indices to provide fast and efficient querying on
+collections. The prior example requires the `embedding` field to be indexed to
+work. To do so, invoke the function and Firestore will throw an error with a
+command to create an index. Execute that command and your index should be ready
+to use.
+
 See the [Retrieval-augmented generation](../rag.md) page for a general
 discussion on indexers and retrievers.