Rasa Pro Services Change Log

All notable changes to Rasa Pro Services will be documented in this page. This product adheres to Semantic Versioning starting with version 3.3 (initial version).

[3.4.0] - 2024-12-12#

Rasa Pro Services 3.4.0 (2024-12-12)

Improvements#

• Added Python 3.10 and Python 3.11 support to Rasa Analytics

Bugfixes#

• MTS and MRS: Add retry capability to Kubernetes API calls on 5xx errors.

Miscellaneous internal changes#

Miscellaneous internal changes.

[3.3.5] - 2024-10-29#

Rasa Pro Services 3.3.5 (2024-10-29)

Bugfixes#

• MTS and MRS: Add retry capability to Kubernetes API calls on 5xx errors.

[3.3.4] - 2024-10-02#

Rasa Pro Services 3.3.4 (2024-10-02)

Bugfixes#

• Return status code 503 when the Analytics service is unavailable during a healthcheck request. This allows the user to implement liveness probes that could automatically restart the service upon failure.

[3.3.3] - 2024-09-25#

Rasa Pro Services 3.3.3 (2024-09-25)

Bugfixes#

• MTS: Fix bug (ATO-2257) when training pod's status is not caught when MTS consumer job restarts.
• [MTS] Update certifi to 2023.7.22 to resolve vulnerability CVE-2023-37920.

[3.3.2] - 2024-05-28#

Rasa Pro Services 3.3.2 (2024-05-28)

Improvements#

• MRS: Upload rasa pod logs to remote storage for user-friendly access to logs in case the running of the assistant fails.

[3.3.1] - 2024-05-27#

Rasa Pro Services 3.3.1 (2024-05-27)

Bugfixes#

• Allow users to specify image pull secrets for MTS / MRS

[3.3.0] - 2024-04-03#

Rasa Pro Services 3.3.0 (2024-04-03)

Improvements#

• Align column names representing the flow_id as defined in the yaml file across rasa_flow_status, rasa_llm_command and rasa_dialogue_stack_frame tables:
  • rasa_llm_command table: flow_name column has been renamed to flow_identifier.
  • rasa_dialogue_stack_frame table: active_flow column has been renamed to active_flow_identifier.
• MTS: Handle MTS Job Consumer restarts when rasa training pod continues to run. If the TrainingManager finds a running training job when it restarts, it will check the status of the pod:
  • If the pod is pending or running, it will watch the pod until training is complete.
  • If the pod has completed, it will upload the logs and trained model (only if it is present).
• MTS: Accept nlu in the config data of CALM assistants in order to use nlu_triggers.
• MTS and MRS: Support debug logs in rasa pods.

Miscellaneous internal changes#

Miscellaneous internal changes.

[3.2.3] - 2023-12-20#

Rasa Pro Services 3.2.3 (2023-12-20)

Improvements#

• MTS: Setup alembic schema migration mechanism for the database of the model training orchestrator. Add initial table creation migration file.
• [MTS] Add capability to configure log level for MTS orchestrator. Log level can be configured through LOG_LEVEL environment variable. Default log level is INFO.

Bugfixes#

• Fix telemetry reporting in shipped Docker images.

[3.2.2] - 2023-12-05#

Rasa Pro Services 3.2.2 (2023-12-05)

Bugfixes#

• Remove obsolete component RemoteGCSFetcher from MTS orchestrator.

[3.2.1] - 2023-12-01#

Rasa Pro Services 3.2.1 (2023-12-01)

Improvements#

• Align column names representing the flow_id as defined in the yaml file across rasa_flow_status, rasa_llm_command and rasa_dialogue_stack_frame tables:
  • rasa_llm_command table: flow_name column has been renamed to flow_identifier.
  • rasa_dialogue_stack_frame table: active_flow column has been renamed to active_flow_identifier.
• Add environment variables to control resource requirements and limits for Rasa pod. MTS and MRS job consumers can now be configured to use specify resource requirements and limits for the Rasa pod. This can be done by setting the following environment variables in the Rasa pod:
  • RASA_REQUESTS_CPU
  • RASA_REQUESTS_MEMORY
  • RASA_LIMITS_CPU
  • RASA_LIMITS_MEMORY

[3.2.0] - 2023-11-22#

Rasa Pro Services 3.2.0 (2023-11-22)

Features#

• Added new table rasa_dialogue_stack_frame to store active flow names and steps for each event sequence in the conversation.
• Add new table rasa_llm_command to store LLM generated commands for each user message. Add new column in the _rasa_raw_event table to store the serialized LLM generated commands.
• Add new table rasa_flow_status to store the transformations of rasa flow events. Add new columns in the _rasa_raw_event table to store the flow_id and step_id of these events where applicable.

[3.1.1] - 2023-07-17#

Rasa Pro Services 3.1.1 (2023-07-17)

Miscellaneous internal changes#

Miscellaneous internal changes.

[3.1.0] - 2023-07-03#

Rasa Pro Services 3.1.0 (2023-07-03)

Features#

• Added Real Time Processing of Markers. Markers can now be evaluated real time by the Analytics Data Pipeline. We've added event handlers for evaluation all events from Kafka to extract markers. The extracted markers are saved into rasa_marker database table. These markers are evaluated with the patterns stored in rasa_pattern table.

  Added API endpoints to create patterns in rasa_pattern table. This endpoint is used by Rasa Plus for rasa markers upload command.

Miscellaneous internal changes#

Miscellaneous internal changes.

[3.0.2] - 2023-06-13#

Rasa Pro Services 3.0.2 (2023-06-13)

Improvements#

• Adds an environment variable to control logging level of the application.

Miscellaneous internal changes#

Miscellaneous internal changes.

[3.0.1] - 2022-10-26#

Rasa Pro Services 3.0.1 (2023-10-26)

Miscellaneous internal changes#

Miscellaneous internal changes.

[3.0.0] - 2022-10-24#

Rasa Pro Services 3.0.0 (2023-10-24)

Features#

• Analytics Data Pipeline helps visualize and process Rasa assistant metrics in the tooling (BI tools, data warehouses) of your choice. Visualizations and analysis of the production assistant and its conversations allow you to assess ROI and improve the performance of the assistant over time.