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참고/neo4j-graphrag-python-main/examples/customize/build_graph/components/custom_component.py

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+"""This examples shows how to create a custom component
+that can be added to a Pipeline with:
+
+c = MyComponent(min_value=0, max_value=10)
+pipe = Pipeline()
+pipe.add_component(c, name="my_component")
+"""
+
+import random
+
+from neo4j_graphrag.experimental.pipeline import Component, DataModel
+from pydantic import BaseModel, validate_call
+
+
+class ComponentInputModel(BaseModel):
+    """A class to model the component inputs.
+    This is not required, inputs can also be passed individually.
+
+    Note: can also inherit from DataModel.
+    """
+
+    text: str
+
+
+class ComponentResultModel(DataModel):
+    """A class to model the component outputs.
+    Each component must have such a description of the output,
+    so that the parameter mapping can be validated before the
+    pipeline run starts.
+    """
+
+    value: int
+    text: str
+
+
+class MyComponent(Component):
+    """Multiplies an input text by a random number
+    between `min_value` and `max_value`
+    """
+
+    def __init__(self, min_value: int, max_value: int) -> None:
+        self.min_value = min_value
+        self.max_value = max_value
+
+    # this decorator is required when a Pydantic model is used in the inputs
+    @validate_call
+    async def run(self, inputs: ComponentInputModel) -> ComponentResultModel:
+        # logic here
+        random_value = random.randint(self.min_value, self.max_value)
+        return ComponentResultModel(
+            value=random_value,
+            text=inputs.text * random_value,
+        )
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    c = MyComponent(min_value=0, max_value=10)
+    print(
+        asyncio.run(
+            c.run(
+                inputs={"text": "Hello"}  # type: ignore
+            )
+        )
+    )