Merge pull request #92 from redis/DOC-3046

RC: Amazon bedrock stage 3 changes

Author: cmilesb

content/integrate/amazon-bedrock/_index.md

@@ -11,19 +11,19 @@ categories:
 description: Shows how to use your Redis database with Amazon Bedrock to customize
   foundational models.
 group: cloud-service
+hideListLinks: true
 summary: With Amazon Bedrock, users can access foundational AI models from a variety
   of vendors through a single API, streamlining the process of leveraging generative
   artificial intelligence.
 type: integration
 weight: 3
-hideListLinks: true
 ---
 
-[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a service that allows you to securely customize foundational models (FMs) with your own data, and to use these models without having to build complex infrastructure management. With Amazon Bedrock, users can access FMs from a variety of vendors through a single API, streamlining the process of creating generative artificial intelligence (AI).
+[Amazon Bedrock](https://aws.amazon.com/bedrock/) streamlines GenAI deployment by offering foundational models (FMs) as a unified API, eliminating complex infrastructure management. It lets you create AI-powered [Agents](https://aws.amazon.com/bedrock/agents/) that execute complex tasks. Through [Knowledge Bases](https://aws.amazon.com/bedrock/knowledge-bases/) within Amazon Bedrock, you can seamlessly tether FMs to your proprietary data sources using retrieval-augmented generation (RAG). This direct integration amplifies the FM's intelligence based on your organization's resources.
 
-Amazon Bedrock allows you to choose Redis Cloud as the [vector database](https://redis.com/solutions/use-cases/vector-database/) for your knowledge base. After your database is set up and connected to Amazon Bedrock, it will import text data from an Amazon Simple Storage Service (S3) bucket into Redis Cloud and use it to extract relevant information when prompted.
+Amazon Bedrock lets you choose Redis Cloud as the [vector database](https://redis.io/solutions/vector-search/) for your agent's Knowledge Base. Once Redis Cloud is integrated with Amazon Bedrock, it automatically reads text documents from your Amazon Simple Storage Service (S3) buckets. This process lets the large language model (LLM) pinpoint and extract pertinent context in response to user queries, ensuring your AI agents are well-informed and grounded in their responses.
 
-For more information about the Redis integration with Amazon Bedrock, see the [Amazon Bedrock integration blog post](https://redis.com/blog/amazon-bedrock-integration-with-redis-enterprise/).
+For more information about the Redis integration with Amazon Bedrock, see the [Amazon Bedrock integration blog post](https://redis.io/blog/amazon-bedrock-integration-with-redis-enterprise/).
 
 To fully set up Bedrock with Redis Cloud, you will need to do the following:
 

content/integrate/amazon-bedrock/create-agent.md

@@ -1,5 +1,5 @@
 ---
-LinkTitle: Create agent
+LinkTitle: Create Bedrock agent
 Title: Create a Bedrock agent
 alwaysopen: false
 categories:

content/integrate/amazon-bedrock/create-knowledge-base.md

@@ -1,5 +1,5 @@
 ---
-LinkTitle: Create knowledge base
+LinkTitle: Create Bedrock knowledge base
 Title: Create a Bedrock knowledge base
 alwaysopen: false
 categories:

content/integrate/amazon-bedrock/set-up-redis.md

@@ -99,9 +99,9 @@ To set up a Redis Cloud instance for Bedrock, you need to:
 
     {{<image filename="images/rc/flexible-add-database-basic.png" width="75%" alt="The New Database dialog with basic settings." >}}
 
-    We selected **Search and query** and **JSON** for you already. You can remove **JSON** if you want.
+    We selected **Search and query** and **JSON** for you already. **Search and query** enables vector database features for your database. You can remove **JSON** if you want.
 
-1. Set the Memory limit of your database based on the amount of data that will be pulled from your Simple Storage Service (S3) [bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-buckets-s3.html). See [Find out the size of your S3 buckets](https://aws.amazon.com/blogs/storage/find-out-the-size-of-your-amazon-s3-buckets/) to find out how much training data is stored in your S3 bucket and pick the closest size, rounded up, from the table below. 
+1. Set the Memory limit of your database based on the amount of data that Bedrock will pull from your Simple Storage Service (S3) [bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-buckets-s3.html). See [Find out the size of your S3 buckets](https://aws.amazon.com/blogs/storage/find-out-the-size-of-your-amazon-s3-buckets/) to find out how much knowledge base data is stored in your S3 bucket and pick the closest size, rounded up, from the table below. 
 
     | Total Size of Documents in S3 | Database size without replication | Database size with replication |
 |-------------------------------|-----------------------------------|--------------------------------|
@@ -192,7 +192,7 @@ After you store this secret, you can view and copy the [Amazon Resource Name (AR
 
 ## Create a vector index in your database {#create-vector-index}
 
-After your database is set up, create an index with a vector field using [FT.CREATE]({{< relref "/commands" >}}/ft.create/) as your knowledge base for Amazon Bedrock. You can accomplish this using **RedisInsight** or `redis-cli`.
+After your Redis Cloud database is set up, create a search index with a vector field using [FT.CREATE]({{< relref "/commands" >}}/ft.create/) as your knowledge base for Amazon Bedrock. You can accomplish this using **RedisInsight** or `redis-cli`.
 
 ### RedisInsight