synapse-action-development

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)
        # Implementation here
        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

# Training action
@action(
    name='train',
    category=PluginCategory.NEURAL_NET,
    description='Train object detection model'
)
def train(params, ctx):
    ...

# Export action
@action(
    name='export_coco',
    category=PluginCategory.EXPORT,
    description='Export to COCO format'
)
def export_coco(params, ctx):
    ...

# Smart tool (AI-assisted annotation)
@action(
    name='auto_segment',
    category=PluginCategory.SMART_TOOL,
    description='Auto-segmentation tool'
)
def auto_segment(params, ctx):
    ...

# Pre-annotation
@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 parameters
• self.ctx - RuntimeContext
• self.logger - Logger shortcut
• self.set_progress(current, total, category) - Progress tracking
• self.set_metrics(value, category) - Metrics recording
• self.log(event, data, file) - Event logging

Additional Resources

For detailed patterns and advanced techniques:

• references/patterns.md - Parameter validation, semantic types
• references/categories.md - Available PluginCategory values
• references/types-hierarchy.md - Semantic type system (DataType, Dataset, Model)
• references/pipelines.md - ActionPipeline for chaining actions