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@@ -369,6 +369,7 @@ nav:
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- Errors: reference/errors.md
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- Types: reference/types.md
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- Constants: reference/constants.md
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- Functional API: reference/func.md
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- LangGraph Platform:
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- Server API: "cloud/reference/api/api_ref.md"
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- CLI: "cloud/reference/cli.md"
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@@ -112,6 +112,46 @@ def entrypoint(
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store: Optional[BaseStore] = None,
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config_schema: Optional[type[Any]] = None,
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) -> Callable[[types.FunctionType], Pregel]:
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"""Define a LangGraph workflow using the `entrypoint` decorator.
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The decorated function must accept a single parameter, which serves as the input
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to the function. This input parameter can be of any type. Use a dictionary
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to pass multiple parameters to the function.
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The decorated function also has access to these optional parameters:
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- `writer`: A `StreamWriter` instance for writing data to a stream.
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- `config`: A configuration object for accessing workflow settings.
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- `previous`: The previous return value for the given thread (available only when
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a checkpointer is provided).
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The entrypoint decorator can be applied to sync functions, async functions,
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generator functions, and async generator functions.
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For generator functions, the `previous` parameter will represent a list of
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the values previously yielded by the generator. During a run any values yielded
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by the generator, will be written to the `custom` stream.
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Args:
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checkpointer: Specify a checkpointer to create a workflow that can persist
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its state across runs.
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store: A generalized key-value store. Some implementations may support
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semantic search capabilities through an optional `index` configuration.
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config_schema: Specifies the schema for the configuration object that will be
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passed to the workflow.
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Returns:
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A decorator that converts a function into a Pregel graph.
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Example:
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```python
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@entrypoint()
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def my_workflow(data: str) -> str:
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return data.upper()
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```
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"""
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def _imp(func: types.FunctionType) -> Pregel:
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"""Convert a function into a Pregel graph.
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