HOOPS AI 1.1

Release 1.1 (June 15, 2026)

What’s New in HOOPS AI 1.1.0

HOOPS AI 1.1 introduces the SIGNAL architecture for HOOPS Embeddings, designed to deliver more robust geometric representation, improved shape matching, and higher-quality CAD similarity results.

  • Python 3.12 and PyTorch 2.9 - HOOPS AI now targets Python 3.12 and PyTorch 2.9. This brings support for the latest NVIDIA GPU architectures (Ada Lovelace, Blackwell) and improved performance across the board.

  • Migration from DGL to PyTorch Geometric - DGL (Deep Graph Library) has been removed in favour of PyTorch Geometric. DGL is no longer actively maintained for the use cases covered by HOOPS AI. PyTorch Geometric is the actively developed standard for graph neural networks in the PyTorch ecosystem.

  • Context Layer - Introduced the Context Layer, a new capability for inferring engineering context from nearest CADSearch neighbors. Supports prediction of metadata such as Material, Manufacturing, Label, Cost, Weight, Lead Time, and other user-defined attributes.

Machine Learning and Retrieval

HOOPS Embeddings SIGNAL Architecture

  • Preview of the SIGNAL architecture for HOOPS Embeddings.

  • Enhanced retrieval quality based on internal benchmark results.

  • Added a second-stage reranking step to improve the relevance of top search results.

  • The reranker refines the order of the top candidates to improve the accuracy and relevance of CADSearch results for a given query.

Context Layer (Preview)

  • Introduced the Context Layer, a new capability for inferring engineering context from nearest CADSearch neighbors.

  • Supports prediction of metadata such as Material, Manufacturing, Label, Cost, Weight, Lead Time, and other user-defined attributes.

  • Consumes VectorHit results from CADSearch and retrieves neighbor metadata through a user-supplied ContextProvider layer.

  • Added ContextPrediction objects for each requested context key. Apply a status policy to deliver ready-to-propose, needs-review, insufficient-evidence.

Breaking Changes

Python and PyTorch

HOOPS AI 1.1.0 requires Python 3.12 and PyTorch 2.9. Earlier Python versions (3.11 and below) are not supported.

DGL to PyTorch Geometric

DGL is no longer a dependency. If you have custom models or data loaders that use DGL graph objects (dgl.DGLGraph), they must be migrated to PyTorch Geometric (torch_geometric.data.Data).

The built-in HOOPS AI models and pipeline components have been updated and require no changes on your side if you are using the standard flow.

Environment Setup

The installation workflow has changed. Conda is no longer required. HOOPS AI 1.1.0 uses a standard Python virtual environment.

See Install HOOPS AI for full installation instructions.

Supported Platforms

See Supported Platforms for the full platform and driver requirements.