AWS bedrock documentation change
Summary
Added detailed documentation for Neptune Analytics graphs (GraphRAG) setup including vector index configuration, public access requirement, and metadata management fields. Clarified distance metric recommendation.
Security assessment
The change adds security-relevant documentation about requiring public access for Neptune Analytics graphs (a non-VPC deployment model), but this appears to be a feature requirement rather than addressing a specific vulnerability. The update primarily focuses on feature setup instructions rather than resolving security issues.
Diff
diff --git a/bedrock/latest/userguide/knowledge-base-setup.md index be4673471..2e86144d9 100644 --- a/bedrock/latest/userguide/knowledge-base-setup.md +++ b/bedrock/latest/userguide/knowledge-base-setup.md @@ -60 +60 @@ Cohere Embed Multilingual | 1,024 - * **Distance metric** – The metric used to measure the similarity between vectors. We recommend using **Euclidean**. + * **Distance metric** – The metric used to measure the similarity between vectors. We recommend using **Euclidean** for floating-point vector embeddings. @@ -113,0 +114,58 @@ Secret ARN | credentialsSecretArn | The ARN of the AWS Secrets Manager key for y +Neptune Analytics graphs (GraphRAG) + + + 1. To create a graph and vector store in Neptune Analytics in the AWS Management Console, follow the steps described in [Vector indexing in Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/vector-index.html) in the _Neptune Analytics User Guide_. + +###### Note + +To use Neptune GraphRAG, create an empty Neptune Analytics graph with a vector search index. The vector search index can only be created when the graph is created. When you create a [Neptune Analytics graph in the console](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/create-graph-using-console.html), you specify the index dimension under **Vector search settings** near the end of the process. + +Note the following considerations while creating the graph: + + 1. Give the graph a name of your choice. + + 2. Under **Data source** , choose **Create empty graph** , and specify the number of m-NCUs to be allocated. Each m-NCU has around one GiB of memory capacity and corresponding compute and networking. + +###### Note + +The capacity of your graph can be modified later. We recommend that you start with the smallest instance and later choose a different instance, if needed. + + 3. You can leave the default availability settings, and under **Network and Security** , you must enable public access. Neptune Analytics graphs behind a VPC are not supported. + + 4. Under **Vector search settings** , choose **Use vector dimension** and specify the number of dimensions in each vector. + +###### Note + +The number of dimensions in each vector must match the vector dimensions in the embeddings model. Refer to the following table to determine how many dimensions the vector should contain: + +Model | Dimensions +---|--- +Titan G1 Embeddings - Text | 1,536 +Titan V2 Embeddings - Text | 1,024 +Cohere Embed English | 1,024 +Cohere Embed Multilingual | 1,024 + + 5. Leave all other settings to their default and create the graph. + + 2. Once the graph is created, click it to take note of the **Resource ARN** and **Vector dimensions** for when you create the knowledge base. + + 3. Expand the **Metadata management** section and add two fields to configure the vector index to store additional metadata that's managed by Amazon Bedrock. The following table describes the fields and the values to specify for each field: + +Field description | Mapping field | Data type | Filterable +---|---|---|--- +Amazon Bedrock chunks the raw text from your data and stores the chunks in this field. | Name of your choice (for example, `text`) | String | True +Amazon Bedrock stores metadata related to your knowledge base in this field, such as the Amazon S3 location of the file that contains this text. | Name of your choice (for example, `bedrock-metadata`) | String | False + + 4. Take note of the names you choose for the vector index name, vector field name, and metadata management mapping field names for when you create your knowledge base. Then choose **Create**. + + + + +After the vector index is created, you can proceed to [create your knowledge base](./knowledge-base-create.html). The following table summarizes where you will enter each piece of information that you took note of. + +Field | Corresponding field in knowledge base setup (Console) | Corresponding field in knowledge base setup (API) | Description +---|---|---|--- +Graph ARN | Neptune Analytics Graph ARN | graphARN | The Amazon Resource Name (ARN) of the Neptune Analytics graph. +Metadata management (first mapping field) | Text field name | textField | The name of the field in which to store the raw text from your data sources. +Metadata management (second mapping field) | Bedrock-managed metadata field | metadataField | The name of the field in which to store metadata that Amazon Bedrock manages. +