Fix VectorSearch typos

src/content/docs/docs/integrations/vectorsearch-bigquery.mdx

@@ -1,6 +1,6 @@
 ---
-title: Vector Search using Bigquery
-description: Learn how to use the GCP Vector search using Bigquery with Genkit.
+title: Vertex AI Vector Search with Bigquery
+description: Learn how to use Vertex AI Vector Search with Bigquery using Genkit.
 ---
 
 import LanguageSelector from '../../../../components/LanguageSelector.astro';
@@ -15,14 +15,14 @@ import LanguageContent from '../../../../components/LanguageContent.astro';
 <LanguageContent lang="python">
 
 :::note[Feature unavailable for Python]
-Vertex AI Vectorsearch with Bigquery database integration is not currently available for Python. Please use Go for Vertex AI Vectorsearch with Bigquery database functionality.
+Vertex AI Vector Search with Bigquery database integration is not currently available for Python.
 :::
 
 </LanguageContent>
 
 <LanguageContent lang="js">
 
-Vector search provided by Google Cloud services allows you to index and retrieve documents. The documents are stored in Bigquery and the corresponding document IDs are indexed using the vector search index provided by GCP. These are suitable for production use cases.
+Vertex AI Vector Search allows you to index and retrieve documents. The documents are stored in Bigquery and the corresponding document IDs are indexed using the vector search index provided by Vertex AI. These are suitable for production use cases.
 
 ## Installation
 
@@ -32,10 +32,10 @@ npm install @genkit-ai/vertexai
 
 ## Configuration
 
-1. Create a vector search index in GCP. Details on creating vector search index can be found at [Create your Vector Search Index](https://cloud.google.com/vertex-ai/docs/vector-search/create-manage-index#create-index)
+1. Create a Vertex AI Vector Search index. Details on creating an index can be found at [Create your Vector Search Index](https://cloud.google.com/vertex-ai/docs/vector-search/create-manage-index#create-index)
 2. Create a Bigquery Dataset and a Table within that dataset to store the documents that will be indexed. More information to create Bigquery datasets is available [here](https://cloud.google.com/bigquery/docs/datasets)
 
-To use the GCP vector search with Bigquery, initialize it and define a retriever with an embedder. You can also use a custom indexer and retriever for indexing and retrieving documents from the Bigquery dataset:
+To use Vertex AI Vector Search with Bigquery, initialize it and define a retriever with an embedder. You can also use a custom indexer and retriever for indexing and retrieving documents from the Bigquery dataset:
 
 ```ts
 import { BigQuery } from '@google-cloud/bigquery';
@@ -147,7 +147,7 @@ async ({ query, k }) => {
 
 <LanguageContent lang="go">
 
-Vector search provided by Google Cloud services allows you to index and retrieve documents. The documents are stored in Bigquery and the corresponding document IDs are indexed using the vector search index provided by GCP. These are suitable for production use cases.
+Vertex AI Vector Search allows you to index and retrieve documents. The documents are stored in Bigquery and the corresponding document IDs are indexed using the vector search index provided by Vertex AI. These are suitable for production use cases.
 
 ## Installation
 
@@ -159,10 +159,10 @@ import "github.com/firebase/genkit/go/plugins/vertexai/vectorsearch"
 
 ## Configuration
 
-1. Create a vector search index in GCP. Details on creating vector search index can be found at [Create your Vector Search Index](https://cloud.google.com/vertex-ai/docs/vector-search/create-manage-index#create-index)
+1. Create a Vertex AI Vector Search index. Details on creating an index can be found at [Create your Vector Search Index](https://cloud.google.com/vertex-ai/docs/vector-search/create-manage-index#create-index)
 2. Create a Bigquery Dataset and a Table within that dataset to store the documents that will be indexed. More information to create Bigquery datasets is available [here](https://cloud.google.com/bigquery/docs/datasets)
 
-To use the GCP vector search with Bigquery, initialize it and define a retriever with an embedder. You can also use a custom indexer and retriever for indexing and retrieving documents from the Bigquery dataset:
+To use Vertex AI Vector Search with Bigquery, initialize it and define a retriever with an embedder. You can also use a custom indexer and retriever for indexing and retrieving documents from the Bigquery dataset:
 
 ```go
 import (
@@ -289,4 +289,4 @@ if err != nil {
 }
 ```
 
-</LanguageContent>
+</LanguageContent>

src/content/docs/docs/integrations/vectorsearch-firestore.mdx

@@ -1,6 +1,6 @@
 ---
-title: Vector Search using Firestore
-description: Learn how to use the GCP Vector search using Firestore with Genkit.
+title: Vertex AI Vector Search with Firestore
+description: Learn how to use the Vertex AI Vector Search with Firestore using Genkit.
 ---
 
 import LanguageSelector from '../../../../components/LanguageSelector.astro';
@@ -15,14 +15,14 @@ import LanguageContent from '../../../../components/LanguageContent.astro';
 <LanguageContent lang="python">
 
 :::note[Feature unavailable for Python]
-Vertex AI Vectorsearch with Firestore database integration is not currently available for Python. Please use Go for Vertex AI Vectorsearch with Firestore database functionality.
+Vertex AI Vector Search with Firestore database integration is not currently available for Python.
 :::
 
 </LanguageContent>
 
 <LanguageContent lang="js">
 
-Vector search provided by Google Cloud services allows you to index and retrieve documents. The documents are stored in Firestore and the corresponding document IDs are indexed using the vector search index provided by GCP. These are suitable for production use cases.
+Vertex AI Vector Search allows you to index and retrieve documents. The documents are stored in Firestore and the corresponding document IDs are indexed using the vector search index provided by Vertex AI. These are suitable for production use cases.
 
 ## Installation
 
@@ -34,10 +34,10 @@ npm install @genkit-ai/vertexai
 
 ## Configuration
 
-1. Create a vector search index in GCP. Details on creating vector search index can be found at [Create your Vector Search Index](https://cloud.google.com/vertex-ai/docs/vector-search/create-manage-index#create-index)
+1. Create a vector search index in Vertex AI. Details on creating vector search index can be found at [Create your Vector Search Index](https://cloud.google.com/vertex-ai/docs/vector-search/create-manage-index#create-index)
 2. Create a Firestore Dataset and a Collection within that dataset to store the documents that will be indexed. More information to create Firestore datasets is available [here](https://firebase.google.com/docs/firestore/quickstart#create)
 
-To use the GCP vector search with Firestore, initialize it and define a retriever with an embedder. You can also use a custom indexer and retriever for indexing and retrieving documents from the Firestore dataset:
+To use the Vertex AI Vector search with Firestore, initialize it and define a retriever with an embedder. You can also use a custom indexer and retriever for indexing and retrieving documents from the Firestore dataset:
 
 ```ts
 import { initializeApp } from 'firebase-admin/app';
@@ -176,7 +176,7 @@ async ({ query, k, restricts, numericRestricts }) => {
 
 <LanguageContent lang="go">
 
-Vector search provided by Google Cloud services allows you to index and retrieve documents. The documents are stored in Firestore and the corresponding document IDs are indexed using the vector search index provided by GCP. These are suitable for production use cases.
+Vertex AI Vector Search allows you to index and retrieve documents. The documents are stored in Firestore and the corresponding document IDs are indexed using the vector search index provided by Vertex AI. These are suitable for production use cases.
 
 ## Installation
 
@@ -188,7 +188,7 @@ import "github.com/firebase/genkit/go/plugins/vertexai/vectorsearch"
 
 ## Configuration
 
-1. Create a vector search index in GCP. Details on creating vector search index can be found at [Create your Vector Search Index](https://cloud.google.com/vertex-ai/docs/vector-search/create-manage-index#create-index)
+1. Create a vector search index in Vertex AI. Details on creating vector search index can be found at [Create your Vector Search Index](https://cloud.google.com/vertex-ai/docs/vector-search/create-manage-index#create-index)
 2. Create a Firestore Dataset and a Collection within that dataset to store the documents that will be indexed. More information to create Firestore datasets is available [here](https://firebase.google.com/docs/firestore/quickstart#create)
 
 To use the GCP vector search with Firestore, initialize it and define a retriever with an embedder. You can also use a custom indexer and retriever for indexing and retrieving documents from the Firestore dataset:
@@ -316,4 +316,4 @@ if err != nil {
 }
 ```
 
-</LanguageContent>
+</LanguageContent>

src/sidebar.ts

@@ -66,8 +66,8 @@ const DOCS_SIDEBAR = [
       { label: "AlloyDB for PostgreSQL", slug: "docs/integrations/alloydb" },
       { label: "Cloud SQL PostgreSQL", slug: "docs/integrations/cloud-sql-postgresql" },
       { label: "Cloud Firestore", slug: "docs/integrations/cloud-firestore" },
-      { label: "Vertex AI Vectosearch with Bigquery", slug: "docs/integrations/vectorsearch-bigquery" },
-      { label: "Vertex AI Vectosearch with Firestore", slug: "docs/integrations/vectorsearch-firestore" },
+      { label: "Vertex AI VectorSearch with Bigquery", slug: "docs/integrations/vectorsearch-bigquery" },
+      { label: "Vertex AI VectorSearch with Firestore", slug: "docs/integrations/vectorsearch-firestore" },
     ],
   },
   {