Changelog

Version 1.0.251001

Bug Fixes

  • Fixed the output parser issue in SmartDataFrame's transform function.

Version 1.0.250930

Enhancements

  • Added MassiveAutomaticTimeSeriesFitAndSave, MassiveAutomaticTimeSeriesLoadModelAndPredict, and MassiveAutomaticTimeSeriesLoadModelAndScore tools to support massive time series model training, prediction, and scoring with group_key parameter.

  • Added MassiveTSOutlierDetection tool to support massive time series outlier detection with group_key parameter.

  • Added TSMakeFutureTableForMassiveForecastTool to create future tables for massive time series forecasting with group_key parameter.

  • Added MassiveTimeSeriesCheck tool to perform time series analysis and generate reports for multiple time series with group_key parameter.

  • Updated HANAMLToolkit to include the new massive time series tools.

API Changes

  • Modified SelectStatementToTableTool to include a 'force' parameter that allows overwriting existing tables.

  • Changed "Timeseries" to "TimeSeries" in class names for consistency.

Bug Fixes

  • Fixed an issue for text with special characters in the HANAVectorEmbeddings class.

Version 1.0.250923

Enhancements

  • Enhanced the outputs of tools when select_statement is too large by creating temporary tables with unique names.

  • Added additive_model_forecast_tools and intermittent_forecast df tools to the default tools in SmartDataFrame.

Version 1.0.250918

Enhancements

  • Added HANA table schema support for tools.

  • Improved output information for outlier detections.

  • Removed the hana_connection_context parameter from the HANAMLRAGAgent class and infer it from the tools.

  • Refine the default value of max_iterations parameter of HANAMLRAGAgent class parameters from 10 to 20.

  • Change the default value of vector_store_type parameter of HANAMLRAGAgent class from "faiss" to "hanadb".

  • Change the default value of long_term_db parameter of HANAMLRAGAgent class from sqlite to HANA DB.

  • Added the embedding_service parameter to the HANAMLRAGAgent class to allow users to pass their own embedding service. The default embedding service has been changed from GenAIHubEmbeddings to HANAVectorEmbeddings.

  • Added PAL CrossEncoder as the default cross-encoder model for reranking in the HANAMLRAGAgent class. If it is not available, it will fall back to sentence-transformers/all-MiniLM-L6-v2.

  • Added session_id parameter to the HANAMLRAGAgent class to support multiple sessions in long-term memory. By default, it is set to "global_session".

  • Removed the restriction to save memory into long term memory when the result is pandas data or large data. Now, all the results will be saved into long term memory with chunking and embeddings.

  • Deprecated the code template tool and python REPL tool in SmartDataFrame class. Users can use the tools from df_tools as default tools instead.

Version 1.0.250909

New Functions

  • Added TSMakeFutureTableTool to create a future table for time series forecasting.

  • Added SelectStatementToTableTool to execute a SELECT SQL statement and store the result in a new table.

Version 1.0.250904

Bug Fixes

  • Fixed the issue of calling code template tool.

Version 1.0.250707

Enhancements

  • Added vector_store_type parameter to HANAMLRAGAgent class to support different vector store types, including "hanadb" and "faiss".

  • Improved the HANAMLRAGAgent class to handle vector store initialization and updates more efficiently.

Bug Fixes

  • Fixed the parameter issues in HANAMLRAGAgent class by adding rerank_candidates and rerank_k parameters.

Version 1.0.250702

New Functions

  • Added HANAMLRagAgent class to enable Retrieval-Augmented Generation (RAG) capabilities, leveraging a hybrid short-term and long-term memory architecture with CrossEncoder reranking techniques.

Version 1.0.250630

New Functions

  • Added hdi artifacts tool.

Version 1.0.250617

New Functions

  • Added launch_mcp_server function to hanaml toolkit.

  • Added model deletion tool and chat history deletion tool to hanaml Agent.

Version 1.0.250530

Enhancements

  • Added unsupported tools check (classfication, regression).

  • BAS integration enhancements.

Version 1.0.250520

Enhancements

  • Added set_return_direct function in hanaml Agent.

Bug Fixes

  • Fixed the prompt in hanaml Agent to enable tool cal.

  • Fixed CAP generation temporary location in MacOS.

Version 1.0.250509

Enhancements

  • Output the inspect code for BAS integration.

Version 1.0.250506

Enhancements

  • Enhanced the seasonality detection for additive_model_forecast_tools.

  • Provide table meta information and supported algorithms in ts_check tool.

Version 1.0.250424

Enhancements

  • Added input table check and columns check to avoid stopping the reasoning.

  • Added samples to ts_check tool.

Bug Fixes

  • Fixed wrong report filename issue. (hana-ml appends _report.html to the file.)

Version 1.0.250411

Enhancements

  • Save observations to chat history in HANA ML agent. Added max_observations parameter to control the number of observations saved in the chat history.

  • Adjust the default value of fetch_data tool to return pandas indirectly to avoid chain stopping due to the tool call of fetch_data in the intermediate step.

Version 1.0.250410

Enhancements

  • Enhanced the HANA SQL agent to support case-sensitive SQL queries.

  • Added create_hana_sql_toolkit function to create a toolkit for HANA SQL.

  • Optimized the chat history management in HANA ML agent.

Bug Fixes

  • Fixed the accuracy_measure tool issue when evaluation_metric="spec".

Version 1.0.250407

Enhancements

  • Improved forecast_line_plot tool to automatically detect the confidence if it is not provided.

  • Serialized the tool's return if it is pandas DataFrame when return_direct is set to False.

Bug Fixes

  • Fixed the json serialization issue when the tool's return contains Timestamp.

Version 1.0.250403

New Functions

  • Added list_models tool to list all trained models in the model storage.

  • Added accuracy_measure tool to measure the accuracy of a model on a test dataset for time series forecasting.

Enhancements

  • Improved the intermittent_forecast tool to use CrostonTSB instead.

  • Added parameter return_direct to all tools and toolkit.

  • Improved the fetch_data tool to return a pandas DataFrame instead of a list of dictionaries. By default, the tool parameter return_direct is set to True, which means the tool will return a pandas DataFrame.