feat: update landing page for MVI

docs/vector-index/index.md

@@ -5,27 +5,107 @@ sidebar_label: Momento Vector Index
 description: <TBD>
 ---
 
-# What is Momento Vector Index?
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Getting started with Momento Vector Index
+
+If you're looking to jump in and get started with Momento Vector Index with an SDK, you've come to the right place.
+
+## Step 1: Create your API key
+
+To use Momento Vector Index you will need a Super User Token for AWS us-west-2.
+Go to the [Momento console](https://console.gomomento.com/tokens) and follow the instructions to log in with your email
+address, Google account, or GitHub account.
+
+![Image of Momento console landing page](@site/static/img/getting-started/console.png)
+
+In the console, select the [API Keys](https://console.gomomento.com/tokens) menu option.
+
+Once on the API key page, select the information that matches where your caches live:
+
+1. Cloud provider - AWS
+2. Region - us-west-2
+3. Key Type - Super User
+4. (Optional) Expiration date
+
+![Image showing the fields to create a new API key](@site/static/img/getting-started/select-provider-region-us-west-2.png)
 
-Momento Vector Index (MVI) is a scalable, developer-friendly vector index service designed for real-time storage and retrieval of vector data for use in AI-powered applications.
+## Step 2: Create an index and insert some vectors!
 
-![Main image about vector index service.](@site/static/img/vector-index/mvi.jpg)
+<Tabs>
+   <TabItem value="node" label="Node.js" default>
 
-MVI's serverless and API-driven approach to vector indexes aligns with modern development needs, providing compute resources as required without the burden of infrastructure or maintenance. The combination of simple self-service and consumption-based pricing reflects an intentional design that considers both initial development and future scaling. It's a solution that offers flexibility and convenience for developers at various stages of the development lifecycle.
+```javascript
+import {
+    CredentialProvider,
+    PreviewVectorIndexClient,
+    VectorIndexConfigurations,
+} from "@gomomento/sdk";
+
+async function run(){
+    const mviClient = new PreviewVectorIndexClient({
+        configuration: VectorIndexConfigurations.Laptop.latest(),
+        credentialProvider: CredentialProvider.fromEnvironmentVariable({
+            environmentVariableName: 'MOMENTO_API_KEY',
+        })
+    });
+    await mviClient.createIndex('test-index', 2);
+    await mviClient.upsertItemBatch('test-index', [
+        {
+            id: 'example_item_1',
+            vector: [1.0, 2.0],
+            metadata: {key1: 'value1'},
+        },
+        {
+            id: 'example_item_2',
+            vector: [3.0, 4.0],
+            metadata: {key2: 'value2'},
+        },
+    ]);
+}
+```
+
+   </TabItem>
+</Tabs>
+
+# What is Momento Vector Index?
+
+Momento Vector Index (MVI) is a scalable, developer-friendly vector index service designed for real-time storage and
+retrieval of vector data for use in AI-powered applications.
+
+MVI's serverless and API-driven approach to vector indexes aligns with modern development needs, providing compute
+resources as required without the burden of infrastructure or maintenance. The combination of simple self-service and
+consumption-based pricing reflects an intentional design that considers both initial development and future scaling.
+It's a solution that offers flexibility and convenience for developers at various stages of the development lifecycle.
 
 ## What is MVI’s role in your application?
 
 Build with Momento Vector Indexes with use cases such as:
 
 ### Semantic search
-MVI improves your search features by understanding context and meaning. By using vector indexing, your application delivers more relevant and nuanced search results. This feature shines in areas like e-commerce, personalized recommendations, fraud detection, and information retrieval.
+
+MVI improves your search features by understanding context and meaning. By using vector indexing, your application
+delivers more relevant and nuanced search results. This feature shines in areas like e-commerce, personalized
+recommendations, fraud detection, and information retrieval.
 
 ### Multimodal search
-MVI also supports multimodal search, letting users search across different types of data such as text, images, videos, and audio. Users can start a search in one modality and target any other, creating a flexible and comprehensive search experience.
+
+MVI also supports multimodal search, letting users search across different types of data such as text, images, videos,
+and audio. Users can start a search in one modality and target any other, creating a flexible and comprehensive search
+experience.
 
 ### Additional applications
-While semantic and multimodal search are the main uses, MVI can be valuable for sentiment analysis, specifically in assessing user feelings toward a business across multiple dimensions. By identifying emotional patterns in customer reviews, social media mentions, or survey responses, MVI helps you gain a multi-faceted understanding of customer sentiment. This can inform business strategies, customer engagement, and service improvements.
+
+While semantic and multimodal search are the main uses, MVI can be valuable for sentiment analysis, specifically in
+assessing user feelings toward a business across multiple dimensions. By identifying emotional patterns in customer
+reviews, social media mentions, or survey responses, MVI helps you gain a multi-faceted understanding of customer
+sentiment. This can inform business strategies, customer engagement, and service improvements.
 
 ## Why is it called a vector index, not a vector database?
 
-A vector index excels in specialized tasks, offering rapid and efficient search capabilities using vectors and metadata. Unlike a general-purpose database, which is designed for a wide range of tasks including complex queries, joins, and transactions, our vector index is streamlined for performance. It's not only fast and cost-effective but also lean, allowing for easy integration into both early-stage development and production environments. Additionally, you can store and filter through arbitrary metadata, like the original text and images, making it even more useful.
+A vector index excels in specialized tasks, offering rapid and efficient search capabilities using vectors and metadata.
+Unlike a general-purpose database, which is designed for a wide range of tasks including complex queries, joins, and
+transactions, our vector index is streamlined for performance. It's not only fast and cost-effective but also lean,
+allowing for easy integration into both early-stage development and production environments. Additionally, you can store
+and filter through arbitrary metadata, like the original text and images, making it even more useful.