Generative AI Toolkit for SAP HANA Cloud

About this project

Generative AI Client for SAP HANA Cloud is an extension of the existing HANA ML Python client library, mainly focusing on GenAI and related use cases. It includes many leading-edge GenAI related open source libraries and provides seamless integration with HANA ML, HANA vector engine, and other SAP GenAI Hub SDK, see our Introduction, Notebook and Documentation.

Requirements and Setup

The prerequisites for using the Generative AI Toolkit for SAP HANA Cloud are listed at Prerequisites.

The Generative AI Toolkit for SAP HANA Cloud is available as a Python package. You can install it via pip:

pip install hana-ai

ContextAgent

The toolkit includes a file-based ContextAgent for conversational forecasting and data-preparation workflows with Markdown-backed memory, tool calling, and runtime-selectable skills.

Core ContextAgent skills include:

• data_ingestion_and_dataset_preparation for CSV import and time-ordered train, test, and validation table creation
• timeseries_data_profiling for dataset reports and statistical checks before model selection
• timeseries_forecasting for single-series train, predict, score, and plot workflows
• prediction_result_analysis for predicted-versus-actual comparison and quality analysis
• outlier_detection_and_repair_prep for anomaly inspection before model training
• massive_forecasting for grouped forecasting across many related series
• model_lifecycle_and_artifacts for listing, deleting, and packaging saved models
• hana_dataframe_fallback for SQL and restricted Python fallback transformations

Tools

The toolkit exposes HANAML-oriented tools for data preparation, profiling, forecasting, evaluation, artifact generation, and grouped forecasting workflows.

Core forecasting and analysis tools include:

• import_csv_to_table, split_table_for_forecasting, fetch_data
• ts_dataset_report, ts_check, stationarity_test, trend_test, seasonality_test, white_noise_test
• automatic_timeseries_fit_and_save, automatic_timeseries_load_model_and_predict, automatic_timeseries_load_model_and_score
• additive_model_forecast_fit_and_save, additive_model_forecast_load_model_and_predict, intermittent_forecast
• ts_outlier_detection, ts_make_future_table, forecast_line_plot, accuracy_measure
• list_models, delete_models, cap_artifacts, hdi_artifacts
• SelectStatement_to_table, python_hanaml_exec

Grouped and massive forecasting tools include:

• massive_ts_check, massive_ts_outlier_detection
• massive_automatic_timeseries_fit_and_save, massive_automatic_timeseries_load_model_and_predict, massive_automatic_timeseries_load_model_and_score
• massive_additive_model_forecast_fit_and_save, massive_additive_model_forecast_load_model_and_predict
• ts_make_future_table_for_massive_forecast

See the ContextAgent notebook for an end-to-end example at nutest/testscripts/demo/e2e_scenarios/context_agent.ipynb.

Support, Feedback, Contributing

This project is open to feature requests/suggestions, bug reports etc. via GitHub issues. Contribution and feedback are encouraged and always welcome. For more information about how to contribute, the project structure, as well as additional contribution information, see our Contribution Guidelines.

Security / Disclosure

If you find any bug that may be a security problem, please follow our instructions at in our security policy on how to report it. Please do not create GitHub issues for security-related doubts or problems.

Code of Conduct

We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone. By participating in this project, you agree to abide by its Code of Conduct at all times.

Licensing

Copyright 2026 SAP SE or an SAP affiliate company and generative-ai-toolkit-for-sap-hana-cloud contributors. Please see our LICENSE for copyright and license information. Detailed information including third-party components and their licensing/copyright information is available via the REUSE tool.