Amazon Bedrock
Last updated
Last updated
Before starting, you'll need:
An AWS account.
An AWS Identity and Access Management role with permissions for AWS Bedrock.
Access to the Foundation Models you'll use.
Click AI Platforms from the main menu.
Select AWS Bedrock.
Enter the name, access key, secret key, and select your region using the AWS Bedrock Parameters table below as a guide, then click Create AWS Bedrock Integration.
| Field | Description | Required |
|---|---|---|
Name | A descriptive name to identify the integration within Vectorize. | Yes |
Access key | Your Amazon access key. | Yes |
Secret key | Your Amazon secret key. | Yes |
Region | The Amazon region to use. | Yes |
You can think of the Amazon Bedrock integration as having two parts to it. The first is authorization for your region. This part is re-usable across pipelines and allows you to connect to this same integration in different pipelines without providing the credentials and specifying the region every time.
The second part is the configuration that's specific to your RAG Pipeline, which allows you to specify which foundation model to use.
If you haven't yet built a connector to your source database, go to configure a source connector and select your source data platform.
OR
If you're ready to start creating pipelines for vectorizing data, head to Pipeline Basics.
