| name | synapse-action-development |
| description | Explains how to create Synapse plugin actions. Use when the user asks to "create an action", "write an action", uses "@action decorator", "BaseAction class", "function-based action", "class-based action", "Pydantic params", "ActionPipeline", "DataType", "input_type", "output_type", "semantic types", "YOLODataset", "ModelWeights", "pipeline chaining", or needs help with synapse plugin action development. |
Synapse Action Development
Synapse SDK provides two patterns for plugin actions: function-based (simple, stateless) and class-based (complex, stateful).
Quick Start: Function-Based Action
from pydantic import BaseModel
from synapse_sdk.plugins.decorators import action
from synapse_sdk.plugins.context import RuntimeContext
class TrainParams(BaseModel):
epochs: int = 10
learning_rate: float = 0.001
@action(name='train', description='Train a model', params=TrainParams)
def train(params: TrainParams, ctx: RuntimeContext) -> dict:
for epoch in range(params.epochs):
ctx.set_progress(epoch + 1, params.epochs)
return {'status': 'completed'}
Quick Start: Class-Based Action
from pydantic import BaseModel
from synapse_sdk.plugins.action import BaseAction
class InferParams(BaseModel):
model_path: str
threshold: float = 0.5
class InferAction(BaseAction[InferParams]):
action_name = 'inference'
def execute(self) -> dict:
self.set_progress(0, 100)
return {'predictions': []}
When to Use Each Pattern
| Criteria | Function-Based | Class-Based |
|---|
| Complexity | Simple, single-purpose | Complex, multi-step |
| State | Stateless | Can use helper methods |
| Semantic types | Limited | Full support |
Recommendation: Start with function-based. Use class-based when needing helper methods or semantic type declarations.
@action Decorator Parameters
| Parameter | Required | Description |
|---|
name | No | Action name (defaults to function name) |
description | No | Human-readable description |
params | No | Pydantic model for parameter validation |
result | No | Pydantic model for result validation |
category | No | PluginCategory for grouping |
Category Parameter Examples
from synapse_sdk.plugins.decorators import action
from synapse_sdk.plugins.constants import PluginCategory
@action(
name='train',
category=PluginCategory.NEURAL_NET,
description='Train object detection model'
)
def train(params, ctx):
...
@action(
name='export_coco',
category=PluginCategory.EXPORT,
description='Export to COCO format'
)
def export_coco(params, ctx):
...
@action(
name='auto_segment',
category=PluginCategory.SMART_TOOL,
description='Auto-segmentation tool'
)
def auto_segment(params, ctx):
...
@action(
name='pre_label',
category=PluginCategory.PRE_ANNOTATION,
description='Pre-label with model predictions'
)
def pre_label(params, ctx):
...
Available Categories: NEURAL_NET, EXPORT, UPLOAD, SMART_TOOL, PRE_ANNOTATION, POST_ANNOTATION, DATA_VALIDATION, CUSTOM
BaseAction Class Attributes
| Attribute | Description |
|---|
action_name | Action name for invocation |
category | PluginCategory |
input_type | Semantic input type for pipelines |
output_type | Semantic output type for pipelines |
params_model | Auto-extracted from generic |
result_model | Optional result schema |
Available Methods in BaseAction
self.params - Validated parametersself.ctx - RuntimeContextself.logger - Logger shortcutself.set_progress(current, total, category) - Progress trackingself.set_metrics(value, category) - Metrics recordingself.log(event, data, file) - Event logging
Additional Resources
For detailed patterns and advanced techniques:
