3.20.4
Improvements & Enhancements
- Optimized and accelerated the synthetic data generation process for custom content policies
- Enabled GPU slicing for input guardrails running in EKS
Bug and Vulnerability Fixes
- Fixed bugs in custom output guardrail data generation
- Resolved inconsistencies in policy training status and header status
- Temporarily removed the
cancel training
feature. It will be re-enabled once the internal logic is fixed
3.20.3
Improvements & Enhancements
- Added new Dynamo AI logo to the UI
- Added minor upgrades to the UI policy creation workflow
- Added support for using custom LLM models during data generation in policy creation
- Added tracing for DynamoGuard inference
Bug and Vulnerability Fixes
- Fixed policy creation UI bugs
- Fixed finetuning job with new base image library
- Resolved keycloak running issues on HTTP servers
3.20.2
Bug and Vulnerability Fixes
- Added CUDA library to model images to support GKE nodes
3.20.1
Improvements & Enhancements
- Enabled inference API timeout to be configurable
- Improved DynamoGuard RAG hallucination models
Bug and Vulnerability Fixes
- Fixed bugs in DynamoEval progress bar and deep dive UI
- Updated Output guardrail thresholding to follow same scheme as input guardrails
- Updated the name of the Hallucination Guardrail submetrics.
3.20.0
New Features
- [Beta] View and update policy definitions while reviewing examples
- [Beta] Keyword blocklist policy: Block prompts and responses containing specific words or phrases
- [Beta] New Platform Admin user role for billing and platform management
- [Beta] New controller to enable dynamic model deployments in the cluster for input custom content policies
- [Alpha] Connect custom remote models as AI Systems to run DynamoEval tests on
- [Alpha] DynamoEval progress bar preview for PII extraction and bias/toxicity tests
- [Alpha] Connect custom models for data generation during the DynamoGuard policy creation process
- [Alpha] Duplicate default and custom content policies
Improvements & Enhancements
- Improved input guardrail model training process, resulting in more performant guardrails
- Improved output safety model