# Create a managed knowledge base
When you create a managed knowledge base, Amazon Bedrock AgentCore manages the storage, indexing, and retrieval infrastructure for you. By default, a service-managed embedding model is used and no model selection or configuration is required. You can optionally provide your own Bedrock embedding model instead. You can also optionally provide a KMS key for encryption of the managed vector store.
After you create the knowledge base, connect it to a data source and start ingestion. For details on connecting a data source, see [Connect a data source](kb-managed-connect-ds.md). To sync a data source, use the `StartIngestionJob` API. For details, see [Sync your data with your Amazon Bedrock knowledge base](kb-data-source-sync-ingest.md).
**Note**
After you create and sync a managed knowledge base, use the `Retrieve` API to query it. In the `retrievalConfiguration` field, specify `managedSearchConfiguration`. The `vectorSearchConfiguration` field applies only to custom knowledge bases. For request examples and additional considerations, see [Query a knowledge base and retrieve data](kb-test-retrieve.md).
To learn how to create a managed knowledge base, choose the tab for your preferred method:
------
#### [ Console ]
**To create a managed knowledge base**
1. Sign in to the AWS Management Console and navigate to Amazon Bedrock AgentCore > **Built-in tools** > **Knowledge Base**.
1. Choose **Create Managed Knowledge Base**.
1. (Optional) Expand **Additional configurations** of **Knowledge Base details** section to configure the following:
+ Add a description.
+ Choose an embedding model type:
+ **Managed** (default): A service-managed embedding model is used. No model selection or configuration is required.
+ **Custom**: Select a Bedrock embedding model. Choose the model to open the model selector, which shows available providers (Amazon, Cohere) and models.
+ **Custom multimodal embedding model**: Select a multimodal embedding model. Currently, only TwelveLabs Marengo Embed 3.0 is supported. This model natively processes image, audio, and video files. When you choose this model, you must also provide a **Multimodal storage destination** – the Amazon S3 bucket that is used for processing and ingesting your multimodal content. Amazon Bedrock Knowledge Bases creates an `aws/` prefix folder within your bucket for easy access. For more information, see [Native multimodal processing](kb-managed-native-multimodal.md).
+ Configure IAM permissions: choose **Create and use a new service role** (recommended) or select an existing role.
+ Configure AWS KMS encryption for the managed vector store (AWS managed key by default, or select a custom KMS key).
1. Under **Data source**, provide a data source name.
1. Select your data source type from the dropdown: Amazon S3, Confluence, Custom, Google Drive, OneDrive, SharePoint, or Web Crawler.
1. Configure the data source connection settings for your selected data source type.
1. (Optional) Expand **Content parsing and chunking** to configure the following:
+ Parsing strategy is set to **Managed parser** by default.
+ Select a text chunking strategy from the dropdown:
+ **Default chunking** (recommended): Splits text into fixed-size chunks.
+ **Fixed-size chunking**: Splits text into your set approximate token size.
+ **No chunking**: For pre-processed or pre-split documents.
1. (Optional) Expand **Advanced configurations** to configure advanced indexing. Under **Content indexing**, the default indexes text-based content from common documents. Enable advanced indexing for additional modalities:
+ **Visual content in documents**: Processes standalone image files (.png, .jpg, .jpeg, .jpe, .tif, .tiff, .gif, .bmp, .webp, .svg, .jp2, .heic) and embedded visuals in .pdf, .docx, .ppt, .pptx files.
+ **Audio files**: Processes .mp3, .wav, .m4a, .flac, .ogg files.
+ **Video files**: Processes .mp4, .mov, .m4v files.
Optionally set a maximum file size (MB) and configure the document deletion safeguard.
1. (Optional) Configure log delivery to send knowledge base ingestion logs to a destination such as CloudWatch Logs, Amazon S3, or Firehose.
1. Choose **Create Knowledge Base**.
1. Wait for the knowledge base and data source to be created (2–5 minutes). If you create a managed knowledge base with a customer managed key, the creation may take longer.
------
#### [ API ]
The following is an example of creating a managed knowledge base and configuring your data source using the API with the AWS CLI or supported SDK, such as Python. After you call [CreateKnowledgeBase](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_CreateKnowledgeBase.html), you call [CreateDataSource](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_CreateDataSource.html) to create your data source with your connection information in `dataSourceConfiguration`.
To learn about customizations that you can apply to ingestion by including the optional `vectorIngestionConfiguration` field, see [Customize ingestion for a data source](kb-managed-customize-ingestion.md).
**AWS Command Line Interface**
**Step 1: Create the knowledge base**
With a managed embedding model (default):
```
aws bedrock-agent create-knowledge-base \
--name "{{my-managed-kb}}" \
--role-arn "{{arn:aws:iam::123456789012:role/BedrockKBRole}}" \
--description "{{My managed knowledge base}}" \
--knowledge-base-configuration file://kb-config.json
kb-config.json
{
"type": "MANAGED",
"managedKnowledgeBaseConfiguration": {
"embeddingModelType": "MANAGED"
}
}
```
With a custom embedding model (customer-provided Bedrock model):
```
aws bedrock-agent create-knowledge-base \
--name "{{my-custom-embed-kb}}" \
--role-arn "{{arn:aws:iam::123456789012:role/BedrockKBRole}}" \
--description "{{My managed knowledge base with custom embedding}}" \
--knowledge-base-configuration file://kb-config.json
kb-config.json
{
"type": "MANAGED",
"managedKnowledgeBaseConfiguration": {
"embeddingModelType": "CUSTOM",
"embeddingModelArn": "{{arn:aws:bedrock:us-west-2::foundation-model/amazon.titan-embed-text-v2:0}}",
"embeddingModelConfiguration": {
"bedrockEmbeddingModelConfiguration": {
"dimensions": 1024
}
}
}
}
```
**Note**
When `embeddingModelType` is omitted, it defaults to `MANAGED`. When using `MANAGED`, you must not specify `embeddingModelArn` or `embeddingModelConfiguration`. When using `CUSTOM`, both fields are required.
With a custom multimodal embedding model (TwelveLabs Marengo Embed 3.0):
A multimodal embedding model uses the same `CUSTOM` `embeddingModelType` as a text embedding model. The difference is that `bedrockEmbeddingModelConfiguration` accepts an additional `modelConfiguration` field, in which you pass model-specific settings, and that you must specify a `supplementalDataStorageConfiguration` for your multimodal storage destination.
```
aws bedrock-agent create-knowledge-base \
--name "{{my-multimodal-embed-kb}}" \
--role-arn "{{arn:aws:iam::123456789012:role/BedrockKBRole}}" \
--description "{{My managed knowledge base with multimodal embedding}}" \
--knowledge-base-configuration file://kb-config.json
kb-config.json
{
"type": "MANAGED",
"managedKnowledgeBaseConfiguration": {
"embeddingModelType": "CUSTOM",
"embeddingModelArn": "{{arn:aws:bedrock:us-east-1::foundation-model/twelvelabs.marengo-embed-3-0-v1:0}}",
"embeddingModelConfiguration": {
"bedrockEmbeddingModelConfiguration": {
"embeddingDataType": "FLOAT",
"modelConfiguration": {
"version": "1",
"audio": {
"segmentation": {
"method": "dynamic",
"dynamic": {
"minDurationSec": 4
}
}
},
"video": {
"segmentation": {
"method": "fixed",
"fixed": {
"durationSec": 6
}
}
}
}
}
},
"supplementalDataStorageConfiguration": {
"storageLocations": [
{
"s3Location": {
"uri": "{{s3://amzn-s3-demo-bucket}}"
},
"type": "S3"
}
]
}
}
}
```
The `modelConfiguration` field is a JSON object whose contents are specific to the embedding model that you choose. The `version` field is required and must be set to `1`. For each of the `audio` and `video` modalities, you can set a `segmentation` `method` of either `dynamic`, which takes a `minDurationSec` value, or `fixed`, which takes a `durationSec` value. For the settings that TwelveLabs Marengo Embed 3.0 accepts, see [TwelveLabs Marengo Embed 3.0](model-parameters-marengo-3.md).
**Note**
You must provide a `supplementalDataStorageConfiguration` when you use the TwelveLabs Marengo Embed 3.0 model.
**Step 2: Create a data source**
```
aws bedrock-agent create-data-source \
--name "{{S3-connector}}" \
--description "{{S3 data source connector for Amazon Bedrock to use content in S3}}" \
--knowledge-base-id "{{your-knowledge-base-id}}" \
--data-source-configuration file://bedrock-s3-managed-connector-configuration.json \
--data-deletion-policy "DELETE" \
--vector-ingestion-configuration '{"parsingConfiguration":{"parsingStrategy":"SMART_PARSING"}}'
bedrock-s3-managed-connector-configuration.json
{
"type": "MANAGED_KNOWLEDGE_BASE_CONNECTOR",
"managedKnowledgeBaseConnectorConfiguration": {
"mediaExtractionConfiguration": {
"imageExtractionConfiguration": {
"imageExtractionStatus": "ENABLED"
}
},
"connectorParameters": {
"type": "S3",
"version": "1",
"connectionConfiguration": {
"bucketName": "{{your-test-s3-bucket}}",
"bucketOwnerAccountId": "{{123456789012}}"
},
"deletionProtectionConfiguration": {
"enableDeletionProtection": false
}
}
}
}
```
------
## Embedding model options
Managed knowledge bases support the following embedding model types:
+ **Managed embedding** (default) – A service-managed embedding model is used automatically. You don't need to select a model, configure dimensions, or manage Bedrock service limits for embedding. The service handles model selection, hosting, and scaling transparently.
+ **Custom embedding** – You provide your own Bedrock embedding model ARN. When using a custom embedding model, you must specify the model dimensions (1024) and float32 embedding data type. The following Bedrock embedding models are supported:
+ Amazon Titan Text Embeddings V2
+ Cohere Embed English v3
+ Cohere Embed Multilingual v3
+ Cohere Embed v4
+ Amazon Nova Multimodal Embeddings
+ **Custom multimodal embedding** – You provide the ARN of a multimodal embedding model, which natively processes image, audio, and video files in addition to text. Currently, TwelveLabs Marengo Embed 3.0 (`twelvelabs.marengo-embed-3-0-v1:0`) is the only supported multimodal embedding model. This option requires a multimodal storage destination.
**Note**
You cannot change the embedding model type after creating the knowledge base. To switch between managed and custom embedding, you must create a new knowledge base.
**Important**
If you create a knowledge base with a custom embedding model, the managed reranker is not available for that knowledge base. To use the managed reranker, create your knowledge base with the default managed embedding model.
## Supported data source connectors
Managed knowledge bases support the following data source connectors:
+ Amazon S3
+ Box
+ Confluence Cloud
+ Confluence Data Center
+ Microsoft SharePoint Online
+ Google Drive
+ Microsoft OneDrive
+ ServiceNow
+ Web Crawler
+ Custom connector
For information about configuring data source connectors, see [Connect a data source](kb-managed-connect-ds.md).