# Copyright (c) "Neo4j"
# Neo4j Sweden AB [https://neo4j.com]
# #
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# #
# https://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import inspect
from typing import Any, Union, get_type_hints
from pydantic import BaseModel
from neo4j_graphrag.experimental.pipeline.exceptions import PipelineDefinitionError
from neo4j_graphrag.experimental.pipeline.types.context import RunContext
from neo4j_graphrag.utils.validation import issubclass_safe
class DataModel(BaseModel):
"""Input or Output data model for Components"""
pass
class ComponentMeta(type):
def __new__(
meta, name: str, bases: tuple[type, ...], attrs: dict[str, Any]
) -> type:
# extract required inputs and outputs from the run method signature
run_method = attrs.get("run")
run_context_method = attrs.get("run_with_context")
run = run_context_method if run_context_method is not None else run_method
if run is None:
raise RuntimeError(
f"You must implement either `run` or `run_with_context` in Component '{name}'"
)
sig = inspect.signature(run)
attrs["component_inputs"] = {
param.name: {
"has_default": param.default != inspect.Parameter.empty,
"annotation": param.annotation,
}
for param in sig.parameters.values()
if param.name not in ("self", "kwargs", "context_")
}
# extract returned fields from the run method return type hint
return_model = get_type_hints(run).get("return")
if return_model is None:
raise PipelineDefinitionError(
f"The run method return type must be annotated in {name}"
)
# the type hint must be a subclass of DataModel
if not issubclass_safe(return_model, DataModel):
raise PipelineDefinitionError(
f"The run method must return a subclass of DataModel in {name}"
)
attrs["component_outputs"] = {
f: {
"has_default": field.is_required(),
"annotation": field.annotation,
}
for f, field in return_model.model_fields.items()
}
return type.__new__(meta, name, bases, attrs)
[docs]
class Component(metaclass=ComponentMeta):
"""Interface that needs to be implemented
by all components.
"""
# these variables are filled by the metaclass
# added here for the type checker
# DO NOT CHANGE
component_inputs: dict[str, dict[str, Union[str, bool]]]
component_outputs: dict[str, dict[str, Union[str, bool, type]]]
[docs]
async def run(self, *args: Any, **kwargs: Any) -> DataModel:
"""Run the component and return its result.
Note: if `run_with_context` is implemented, this method will not be used.
"""
raise NotImplementedError(
"You must implement the `run` or `run_with_context` method. "
)
[docs]
async def run_with_context(
self, context_: RunContext, *args: Any, **kwargs: Any
) -> DataModel:
"""This method is called by the pipeline orchestrator.
The `context_` parameter contains information about
the pipeline run: the `run_id` and a `notify` function
that can be used to send events from the component to
the pipeline callback.
This feature will be moved to the `run` method in a future
release.
It defaults to calling the `run` method to prevent any breaking change.
"""
# default behavior to prevent a breaking change
return await self.run(*args, **kwargs)