Portia AI provides a mechanism for extending functionality at multiple levels:
Basic Extension Approach::
- Customization Tools: Inherit base class implementation
__call__method, registered to theDefaultToolRegistryclass CustomTool(Tool): def __call__(self, task): # 实现自定义逻辑 return result - Implementation Hooks: By
ExecutionHookSubclasses inject logic at specific stagesclass AuditHook(ExecutionHook): def before_payment(self, amount): return audit_approval(amount) - state processor: Realization
StateHandlerInterface to customize state persistence
Typical Extension Scenarios::
- Enterprise ERP Integration: Interfacing with SAP/Oracle and other system implementations:
- Automated order processing
- Inventory Status Synchronization
- Financial reconciliation process - Industry-Specific Features: For example, in the medical field:
- Checking reservation conflicts
- Processing of insurance claims
- Generate compliance reports - Enhanced Monitoring::
- Adding Slack Notification Hooks
- Integrated Prometheus metrics monitoring
- Implementation of a customized logging system
Suggested extensions: refer to the "Advanced Customization" section of the official documentation, and the community also provides a wealth of sample projects.
This answer comes from the articlePortia AI: A Python Toolkit for Building Intelligent Automated WorkflowsThe
May not be reproduced without permission:AI productivity tools " How can developers extend custom functionality with Portia AI? What are some typical extension scenarios?
