The following are the release notes for TetraScience AI Services versions 1.2.x. TetraScience AI Services empower developers and scientists to transform data into decisions through seamless, secure APIs, and enable intelligent automation across scientific, lab, and enterprise workflows.

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NOTE

TetraScience AI Services are currently part of a limited availability release and must be activated in coordination with TetraScience along with related consuming applications. For more information, contact your customer account leader.

v1.2.0

Release date: 30 April 2026

TetraScience has released its first general availability version of TetraScience AI Services version 1.2.0. This release introduces an Invoke a Training Notebook API, Knowledge Base and Vector Store support, a centralized model and artifact repository, cross-environment model promotion, model aliases for lifecycle management, enhanced LLM endpoint controls, and improved System Log coverage.

Here are the details for what's new in TetraScience AI Services v1.2.0.

Prerequisites

TetraScience AI Services v1.2.0 requires the following:

• Tetra Data Platform (TDP) v4.4.1 or later

New Functionality

New functionality includes features not previously available in TetraScience AI Services.

Invoke a Training Notebook API

TetraScience AI Services now provides an Invoke a Training Notebook API for model training that allows users to invoke any training notebook within their Scientific AI Workflow directly through the AI Services API. The new POST /v1/inference/invoke/* endpoint accepts arbitrary JSON payloads and Amazon Simple Storage Service (Amazon S3) input files (using file IDs), allowing users to trigger model training, data prefetching, and other custom notebook tasks without leaving the platform.

For more information, see Invoke a Training Notebook in the Run an Inference (AI Services v1.2.x) guide.

Knowledge Base and Vector Store Support

TetraScience AI Services now supports the creation and management of vectorized knowledge bases, powered by Databricks Vector Search. This capability enables AI use cases that require retrieval-augmented generation (RAG) and semantic search over enterprise knowledge bases.

Key capabilities include:

• Vector store creation and management: Create, update, and delete vector stores scoped to your organization with role-based access control.
• Customizable vectorization: Configure how knowledge base content is vectorized at creation time, including the embedding model, chunking strategy, chunk size, and chunk overlap, to optimize retrieval accuracy for your data and use cases.
• Text-based and vector-based querying: Query vector stores using natural language text directly, without needing to manually generate embeddings.
• Knowledge base file uploads: Upload and manage knowledge base files through the AI Asset Files API, with support for multipart uploads for large files.
• Permission model: Organization-level vector stores are accessible to all users within the organization, with role-based permissions for create, read, update, and delete operations.

The following new API endpoints support this functionality:

Model Promotion (Beta)

Customers can now promote AI assets (models and tables) between TDP environments (for example, from a development environment to a production environment) using Databricks Delta Sharing. This new beta release feature provides controlled, auditable model deployment across your organization's environments without manual data copying.

Model Promotion is available as part of a beta release and must be enabled in coordination with TetraScience. For more information, see Promote Assets Between Environments in the TetraScience AI Services User Guide (v1.2.x).

Model Aliases

AI Workflows can now use model aliases to reference model versions by lifecycle stage instead of by version number. Supported aliases include dev, staging, canary, champion, prod, and rollback. Updating an alias automatically updates endpoint routing without requiring redeployment, enabling fast and safe model promotions and rollbacks.

For more information, see Model Aliases in the TetraScience AI Services User Guide (v1.2.x).

Enhancements

Enhancements are modifications to existing functionality that improve performance or usability, but don't alter the function or intended use of the system.

Multipart Upload Support for AI Assets

The AI Asset Files API now supports multipart uploads with pre-signed URLs for large model weights and knowledge base files. This enables reliable upload of files that exceed single-request size limits, with automatic recovery from orphaned upload sessions.

System Log Improvements

Activating, cancelling, and changing the version of an AI Workflow are now captured in the System Log, providing a complete compliance record for GxP validation.

For more information, see System Log.

Bug Fixes

The following bugs are now fixed.

• The AI Asset upload API no longer returns an incorrect S3 path containing an extra /schemas segment, which previously caused uploaded assets to be unreachable from Databricks.
• The concurrent upload slot counter now automatically recovers from orphaned upload sessions, preventing organizations from being permanently blocked from uploading after sessions expire without being completed or aborted.
• The real-time inference curl command now correctly includes the namespace when an AI Workflow is installed in the common namespace.
• The AI Services UI now displays the correct version number.

Deprecated Features

There are no deprecated features in this release. For more information about TDP deprecations, see Tetra Product Deprecation Notices.

Known and Possible Issues

The following are known limitations of TetraScience AI Services v1.2.0:

• Task Script README Parsing: The platform uses task script README files to determine input configurations. Parsing might be inconsistent due to varying README file formats.
• AI Agent Accuracy: AI-generated information cannot be guaranteed to be accurate. Agents may hallucinate or provide incorrect information.
• Databricks Workspace Mapping: Initially, one TDP organization maps to one Databricks workspace only.
• Analyst Policy Install Workflow UI Access: Users assigned a role with an Analyst policy can view, but not use, the Install Workflow button even though the policy doesn't allow them to install an AI workflow or run an inference. A fix for this issue is scheduled for a future AI Services UI release.

Upgrade Considerations

To upgrade to the latest AI Services version, see Update the AI Services UI version in the TetraScience AI Services User Guide (v1.2.x).

During the upgrade, there might be a brief downtime when users won't be able to access AI Services functionality.

After the upgrade, verify the following:

• Existing AI Workflow installations continue to function correctly
• Inference requests complete successfully
• Knowledge base and vector store operations function as expected
• Model training API endpoints respond correctly
• The AI Services UI displays correctly

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Security

TetraScience continually monitors and tests the codebase to identify potential security issues. Security updates are applied on an ongoing basis.

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Quality Management

TetraScience is committed to creating quality software. Software is developed and tested following the ISO 9001-certified TetraScience Quality Management System.

For instructions on how to validate core AI Services functionality programmatically, see Test TetraScience AI Services Programmatically in the TetraConnect Hub. For access, see Access the TetraConnect Hub.

For more information, see the TetraScience AI Services documentation.

Other Release Notes

To view other TetraScience AI Services release notes, see TetraScience AI Services Release Notes.