Use AWS Bedrock for ingestion and inference

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Self-hosted only
AWS Bedrock support is available for self-hosted SeekrFlow deployments on AWS/EKS. It is not available on SeekrFlow SaaS.

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:

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"
)

For a full walkthrough of vector database setup and file ingestion, see 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`.

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Note
AWS credentials can also be provided via IAM roles, instance profiles, or any standard boto3 credential chain.

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.

⚠️
Current limitations

Available for agents only. Model Chat is not supported.

Reasoning and speed optimization parameters are ignored.

Combining ingestion and inference

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