> ## Documentation Index
> Fetch the complete documentation index at: https://docs.seekr.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Use AWS Bedrock for ingestion and inference

> Configure SeekrFlow to use AWS Bedrock for document ingestion and agent inference on self-hosted EKS deployments.

<Note>
  **Self-hosted only**

  AWS Bedrock support is available for self-hosted SeekrFlow deployments on AWS/EKS. It is not available on SeekrFlow SaaS.
</Note>

For self-hosted EKS deployments, SeekrFlow can route document ingestion and agent inference through AWS Bedrock instead of SeekrFlow's built-in models. This lets you run agent inference, embedding generation, and document conversion on AWS.

## Prerequisites

* AWS credentials with Bedrock Runtime permissions (`bedrock-runtime:InvokeModel`, `bedrock-runtime:Converse`)
* Desired Titan embedding and Claude models enabled in the AWS Console for your region
* EKS deployment (agent inference is EKS-only)

## Ingestion

### Bedrock embedding models

When you create a vector database with a `bedrock:`-prefixed model name, SeekrFlow routes all embedding requests to AWS Bedrock instead of SeekrFlow's built-in models. The `bedrock:` prefix is stripped before making API calls to AWS.

| Model ID                                | Dimensions     | Languages | Notes                                                      |
| --------------------------------------- | -------------- | --------- | ---------------------------------------------------------- |
| `bedrock:amazon.titan-embed-text-v2:0`  | 256, 512, 1024 | 100+      | Recommended. Best for RAG, document search, and reranking. |
| `bedrock:amazon.titan-embed-text-v1`    | 1536           | 25+       | Legacy. Text retrieval and semantic similarity.            |
| `bedrock:amazon.titan-embed-g1-text-02` | 1536           | 25+       | Legacy G1 model.                                           |
| `bedrock:amazon.titan-embed-image-v1`   | 256, 384, 1024 | N/A       | Multimodal (text + image) embeddings.                      |

To create a vector database with a Bedrock embedding model:

<CodeGroup>
  ```python Python theme={null}
  from seekrai import SeekrFlow

  client = SeekrFlow()

  vector_db = client.vector_database.create(
      model="bedrock:amazon.titan-embed-text-v2:0",
      name="my-bedrock-db",
      description="Vector database using Bedrock embeddings"
  )
  ```
</CodeGroup>

For a full walkthrough of vector database setup and file ingestion, see [Create and populate a vector database](/flow/sdk/data-engine/create-and-populate-a-vector-database).

### Bedrock vision PDF conversion

In Bedrock mode, SeekrFlow supports two PDF conversion methods:

| Method           | Description                                                                                                                                                                |
| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `pymupdf`        | Text-based extraction. Fast and lightweight.                                                                                                                               |
| `bedrock-vision` | Vision-based extraction using a Bedrock Claude model. Renders each page as an image and extracts content using the model. Best for scanned documents and image-heavy PDFs. |

Other conversion methods (bookmark, seekr-saas, smoldocling, etc.) are not available in Bedrock mode.

To enable vision-based conversion, set the `BEDROCK_VISION_MODEL` environment variable. When set, the ingestion pipeline automatically restricts available conversion methods to `pymupdf` and `bedrock-vision`.

### Environment variables

| Variable                | Required | Default     | Description                                                                                                                                    |
| ----------------------- | -------- | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `AWS_ACCESS_KEY_ID`     | Yes\*    | None        | AWS access key for Bedrock API access.                                                                                                         |
| `AWS_SECRET_ACCESS_KEY` | Yes\*    | None        | AWS secret key for Bedrock API access.                                                                                                         |
| `AWS_REGION`            | No       | `us-east-1` | AWS region for all Bedrock API calls.                                                                                                          |
| `BEDROCK_VISION_MODEL`  | No       | None        | Bedrock vision model for PDF conversion. Must use the `bedrock:` prefix — for example, `bedrock:us.anthropic.claude-3-5-sonnet-20241022-v2:0`. |

<Info>
  AWS credentials can also be provided via IAM roles, instance profiles, or any standard boto3 credential chain.
</Info>

## Agent inference

When Bedrock is configured, Claude 4.5 is available for all intra-agent inference. Bedrock models appear alongside other models in the agent model-selection UI and are configured via Helm at deploy time.

<Note>
  **Current limitations**

  * Available for agents only. Model Chat is not supported.
  * Reasoning and speed optimization parameters are ignored.
</Note>

## Combining ingestion and inference

You can use Bedrock for ingestion, inference, or both, including fully Bedrock-backed RAG agents.