Skip to main content
Self-hosted onlyAWS 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 IDDimensionsLanguagesNotes
bedrock:amazon.titan-embed-text-v2:0256, 512, 1024100+Recommended. Best for RAG, document search, and reranking.
bedrock:amazon.titan-embed-text-v1153625+Legacy. Text retrieval and semantic similarity.
bedrock:amazon.titan-embed-g1-text-02153625+Legacy G1 model.
bedrock:amazon.titan-embed-image-v1256, 384, 1024N/AMultimodal (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:
MethodDescription
pymupdfText-based extraction. Fast and lightweight.
bedrock-visionVision-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

VariableRequiredDefaultDescription
AWS_ACCESS_KEY_IDYes*NoneAWS access key for Bedrock API access.
AWS_SECRET_ACCESS_KEYYes*NoneAWS secret key for Bedrock API access.
AWS_REGIONNous-east-1AWS region for all Bedrock API calls.
BEDROCK_VISION_MODELNoNoneBedrock vision model for PDF conversion. Must use the bedrock: prefix — for example, bedrock:us.anthropic.claude-3-5-sonnet-20241022-v2:0.
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.
Last modified on June 23, 2026