Track file and vector database dependencies across your environment

New usage endpoints surface file and vector database dependencies across tools, agents, and data jobs in your SeekrFlow environment.

File usage endpoints return the downstream dependencies of a given file:

• GET /files/{file_id}/vector-dbs – Vector databases that ingested this file.
• GET /files/{file_id}/data-jobs – Data jobs that reference this file.
• GET /files/{file_id}/derived-files – Derived files produced from this file.

Vector database usage endpoints return what depends on a given database:

• GET /vectordb/{database_id}/tools – File search tools that reference this database.
• GET /vectordb/{database_id}/data-jobs – Data jobs with this database set as the vector database.

With the GET /tools/{tool_id}/agents endpoint, you can trace the full dependency chain from a file or vector database to the agents that surface it. Use these endpoints to understand usage before deleting or migrating resources, or to audit data lineage across your workflows.

For more information, see File usage.

AWS Bedrock support extended to ingestion pipelines

SeekrFlow ingestion pipelines now support AWS Bedrock for embedding generation and PDF-to-Markdown conversion.

• Embedding generation for vector databases and RAG workflows can now run on AWS Bedrock.
• PDF-to-Markdown conversion uses Bedrock-compatible vision models, supporting AI-ready data jobs for synthetic dataset generation and downstream fine-tuning.
• Using a bedrock:-prefixed model automatically routes embedding requests to AWS Bedrock.
• Vector databases created with Bedrock models use Titan embeddings for indexing and retrieval.

Manage the full AI-ready data pipeline through a single job ID

The Data Jobs API and SDK introduce a unified way to manage the full AI-ready data pipeline — from file upload and ingestion through alignment — under a single job ID, replacing fragmented multi-call workflows.

• Create and manage data jobs via REST endpoints (/v1/flow/data-jobs) and Python SDK helpers (client.data_jobs.*).
• Attach files with automatic ingestion using accuracy- or speed-optimized modes.
• Remove files or failed records before alignment to keep workflows clean.
• Edit job metadata, system prompt, and vector database ID at any time.
• Start, cancel, and monitor alignment with pre-flight validation and structured error responses.
• Track per-file ingestion status, queue position, and suggested fixes through a single job record.
• Supports three job types: principle files, context-grounded files, and context-grounded vector DB.