Build the decision system. Run it from training to serving.

Hotvect is an application framework and runtime for the code around developer-chosen ML libraries. Use CatBoost, TensorFlow, PyTorch, or custom ML code for the model; Hotvect connects it to typed request handling, feature computation, reusable components, rules, and final ranking or selection logic. Together, those parts form one callable, versioned algorithm.

Algorithm authors build an algorithm package containing the implementation and definition. Offline tooling trains or generates state and creates a separate parameter package when needed. A containing application loads the selected packages and calls the algorithm. Today, those packages are distributed as a JVM JAR and a ZIP respectively.

algorithm implementation + embedded definition
  → algorithm package
  → optional training, evaluation, or state generation
  → optional parameter package
  → application loads AlgorithmInstance
  → request → decision

The same implementation is exercised during offline prediction, evaluation, and application integration. Sharing the algorithm and parameter packages removes one source of offline/online drift; it does not make inputs, external services, or execution settings automatically identical.

Hotvect currently provides:

• public decision contracts for ranking, bulk scoring, TopK, and themed TopK;
• JVM feature transformation, including compile-time generated ranking transformers;
• simple and composite algorithm factories with named child dependencies;
• integration with CatBoost, TensorFlow, managed Python workers, and algorithm-owned custom runtimes;
• local and SageMaker train, audit, predict, evaluate, performance-test, and backtest workflows;
• dynamic algorithm-package and parameter-package loading for containing Java applications;
• optional read integration with an external Experiment Management Service for refreshed slot state and local variant assignment;
• local HTTP and browser debugging surfaces.

You need JDK 21, Maven, Make, Python 3.11–3.13, and uv.

From a source checkout:

cd python
make init
source .venv/bin/activate
hv --version

Then follow the documentation in this order:

1. Install and verify Hotvect
2. Run the example product algorithms
3. Understand how Hotvect works
4. Build your first algorithm, or tour an existing algorithm

The complete documentation starts at Hotvect documentation. The same version-matched Markdown is searchable from the installed CLI:

hv docs search "build first algorithm" --limit 3
hv docs read concepts/how-hotvect-works/index.md

For an existing algorithm:

inspect definition and factories
  → build the algorithm package
  → resolve data and child artifacts
  → run the smallest relevant audit, predict, train, or backtest
  → inspect result.json and stage outputs
  → expand only after the bounded proof succeeds

Use hv --help, hv-ext --help, and the CLI reference for exact commands. hv serve and hv worker serve are local debugging tools; production use embeds the online runtime in a containing application.

Hotvect owns the algorithm contract, artifact loading, feature/runtime integration, and offline lifecycle for one decision algorithm. It does not replace:

• a general data or job scheduler;
• a feature store or artifact registry;
• a model library;
• the external EMS control-plane server, release governance, or production monitoring;
• the HTTP, event, authentication, and traffic layers of a serving application.

Those systems can surround Hotvect or satisfy explicit algorithm dependencies while keeping their own operational contracts.