9610677276

Committed 21 Jun 2024 08:29AM UTC coverage: 90.181% (+0.01%) from 90.171%

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fix: Prevent component pre-init hook from being called recursively (#7894)

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haystack/core/component/component.py

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0

"""
Attributes:

    component: Marks a class as a component. Any class decorated with `@component` can be used by a Pipeline.

All components must follow the contract below. This docstring is the source of truth for components contract.

<hr>

`@component` decorator

All component classes must be decorated with the `@component` decorator. This allows Canals to discover them.

<hr>

`__init__(self, **kwargs)`

Optional method.

Components may have an `__init__` method where they define:

- `self.init_parameters = {same parameters that the __init__ method received}`:
    In this dictionary you can store any state the components wish to be persisted when they are saved.
    These values will be given to the `__init__` method of a new instance when the pipeline is loaded.
    Note that by default the `@component` decorator saves the arguments automatically.
    However, if a component sets their own `init_parameters` manually in `__init__()`, that will be used instead.
    Note: all of the values contained here **must be JSON serializable**. Serialize them manually if needed.

Components should take only "basic" Python types as parameters of their `__init__` function, or iterables and
dictionaries containing only such values. Anything else (objects, functions, etc) will raise an exception at init
time. If there's the need for such values, consider serializing them to a string.

_(TODO explain how to use classes and functions in init. In the meantime see `test/components/test_accumulate.py`)_

The `__init__` must be extremely lightweight, because it's a frequent operation during the construction and
validation of the pipeline. If a component has some heavy state to initialize (models, backends, etc...) refer to
the `warm_up()` method.

<hr>

`warm_up(self)`

Optional method.

This method is called by Pipeline before the graph execution. Make sure to avoid double-initializations,
because Pipeline will not keep track of which components it called `warm_up()` on.

<hr>

`run(self, data)`

Mandatory method.

This is the method where the main functionality of the component should be carried out. It's called by
`Pipeline.run()`.

When the component should run, Pipeline will call this method with an instance of the dataclass returned by the
method decorated with `@component.input`. This dataclass contains:

- all the input values coming from other components connected to it,
- if any is missing, the corresponding value defined in `self.defaults`, if it exists.

`run()` must return a single instance of the dataclass declared through the method decorated with
`@component.output`.

"""

import inspect
import sys
from collections.abc import Callable
from contextlib import contextmanager
from contextvars import ContextVar
from copy import deepcopy
from dataclasses import dataclass
from types import new_class
from typing import Any, Dict, Optional, Protocol, runtime_checkable

from haystack import logging
from haystack.core.errors import ComponentError

from .sockets import Sockets
from .types import InputSocket, OutputSocket, _empty

logger = logging.getLogger(__name__)


@dataclass
class PreInitHookPayload:
    """
    Payload for the hook called before a component instance is initialized.

    :param callback:
        Receives the following inputs: component class and init parameter keyword args.
    :param in_progress:
        Flag to indicate if the hook is currently being executed.
        Used to prevent it from being called recursively (if the component's constructor
        instantiates another component).
    """

    callback: Callable
    in_progress: bool = False


_COMPONENT_PRE_INIT_HOOK: ContextVar[Optional[PreInitHookPayload]] = ContextVar("component_pre_init_hook", default=None)


@contextmanager
def _hook_component_init(callback: Callable):
    """
    Context manager to set a callback that will be invoked before a component's constructor is called.

    The callback receives the component class and the init parameters (as keyword arguments) and can modify the init
    parameters in place.

    :param callback:
        Callback function to invoke.
    """
    token = _COMPONENT_PRE_INIT_HOOK.set(PreInitHookPayload(callback))
    try:
        yield
    finally:
        _COMPONENT_PRE_INIT_HOOK.reset(token)


@runtime_checkable
class Component(Protocol):
    """
    Note this is only used by type checking tools.

    In order to implement the `Component` protocol, custom components need to
    have a `run` method. The signature of the method and its return value
    won't be checked, i.e. classes with the following methods:

        def run(self, param: str) -> Dict[str, Any]:
            ...

    and

        def run(self, **kwargs):
            ...

    will be both considered as respecting the protocol. This makes the type
    checking much weaker, but we have other places where we ensure code is
    dealing with actual Components.

    The protocol is runtime checkable so it'll be possible to assert:

        isinstance(MyComponent, Component)
    """

    # This is the most reliable way to define the protocol for the `run` method.
    # Defining a method doesn't work as different Components will have different
    # arguments. Even defining here a method with `**kwargs` doesn't work as the
    # expected signature must be identical.
    # This makes most Language Servers and type checkers happy and shows less errors.
    # NOTE: This check can be removed when we drop Python 3.8 support.
    if sys.version_info >= (3, 9):
        run: Callable[..., Dict[str, Any]]
    else:
        run: Callable


class ComponentMeta(type):
    @staticmethod
    def positional_to_kwargs(cls_type, args) -> Dict[str, Any]:
        """
        Convert positional arguments to keyword arguments based on the signature of the `__init__` method.
        """
        init_signature = inspect.signature(cls_type.__init__)
        init_params = {name: info for name, info in init_signature.parameters.items() if name != "self"}

        out = {}
        for arg, (name, info) in zip(args, init_params.items()):
            if info.kind == inspect.Parameter.VAR_POSITIONAL:
                raise ComponentError(
                    "Pre-init hooks do not support components with variadic positional args in their init method"
                )

            assert info.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.POSITIONAL_ONLY)
            out[name] = arg
        return out

    def __call__(cls, *args, **kwargs):
        """
        This method is called when clients instantiate a Component and runs before __new__ and __init__.
        """
        # This will call __new__ then __init__, giving us back the Component instance
        pre_init_hook = _COMPONENT_PRE_INIT_HOOK.get()
        if pre_init_hook is None or pre_init_hook.in_progress:
            instance = super().__call__(*args, **kwargs)
        else:
            try:
                pre_init_hook.in_progress = True
                named_positional_args = ComponentMeta.positional_to_kwargs(cls, args)
                assert (
                    set(named_positional_args.keys()).intersection(kwargs.keys()) == set()
                ), "positional and keyword arguments overlap"
                kwargs.update(named_positional_args)
                pre_init_hook.callback(cls, kwargs)
                instance = super().__call__(**kwargs)
            finally:
                pre_init_hook.in_progress = False

        # Before returning, we have the chance to modify the newly created
        # Component instance, so we take the chance and set up the I/O sockets

        # If `component.set_output_types()` was called in the component constructor,
        # `__haystack_output__` is already populated, no need to do anything.
        if not hasattr(instance, "__haystack_output__"):
            # If that's not the case, we need to populate `__haystack_output__`
            #
            # If the `run` method was decorated, it has a `_output_types_cache` field assigned
            # that stores the output specification.
            # We deepcopy the content of the cache to transfer ownership from the class method
            # to the actual instance, so that different instances of the same class won't share this data.
            instance.__haystack_output__ = Sockets(
                instance, deepcopy(getattr(instance.run, "_output_types_cache", {})), OutputSocket
            )

        # Create the sockets if set_input_types() wasn't called in the constructor.
        # If it was called and there are some parameters also in the `run()` method, these take precedence.
        if not hasattr(instance, "__haystack_input__"):
            instance.__haystack_input__ = Sockets(instance, {}, InputSocket)
        run_signature = inspect.signature(getattr(cls, "run"))
        for param in list(run_signature.parameters)[1:]:  # First is 'self' and it doesn't matter.
            if run_signature.parameters[param].kind not in (
                inspect.Parameter.VAR_POSITIONAL,
                inspect.Parameter.VAR_KEYWORD,
            ):  # ignore variable args
                socket_kwargs = {"name": param, "type": run_signature.parameters[param].annotation}
                if run_signature.parameters[param].default != inspect.Parameter.empty:
                    socket_kwargs["default_value"] = run_signature.parameters[param].default
                instance.__haystack_input__[param] = InputSocket(**socket_kwargs)

        # Since a Component can't be used in multiple Pipelines at the same time
        # we need to know if it's already owned by a Pipeline when adding it to one.
        # We use this flag to check that.
        instance.__haystack_added_to_pipeline__ = None

        # Only Components with variadic inputs can be greedy. If the user set the greedy flag
        # to True, but the component doesn't have a variadic input, we set it to False.
        # We can have this information only at instance creation time, so we do it here.
        is_variadic = any(socket.is_variadic for socket in instance.__haystack_input__._sockets_dict.values())
        if not is_variadic and cls.__haystack_is_greedy__:
            logger.warning(
                "Component '{component}' has no variadic input, but it's marked as greedy. "
                "This is not supported and can lead to unexpected behavior.",
                component=cls.__name__,
            )

        return instance


def _component_repr(component: Component) -> str:
    """
    All Components override their __repr__ method with this one.

    It prints the component name and the input/output sockets.
    """
    result = object.__repr__(component)
    if pipeline := getattr(component, "__haystack_added_to_pipeline__"):
        # This Component has been added in a Pipeline, let's get the name from there.
        result += f"\n{pipeline.get_component_name(component)}"

    # We're explicitly ignoring the type here because we're sure that the component
    # has the __haystack_input__ and __haystack_output__ attributes at this point
    return f"{result}\n{component.__haystack_input__}\n{component.__haystack_output__}"  # type: ignore[attr-defined]


class _Component:
    """
    See module's docstring.

    Args:
        class_: the class that Canals should use as a component.
        serializable: whether to check, at init time, if the component can be saved with
        `save_pipelines()`.

    Returns:
        A class that can be recognized as a component.

    Raises:
        ComponentError: if the class provided has no `run()` method or otherwise doesn't respect the component contract.
    """

    def __init__(self):
        self.registry = {}

    def set_input_type(self, instance, name: str, type: Any, default: Any = _empty):  # noqa: A002
        """
        Add a single input socket to the component instance.

        :param instance: Component instance where the input type will be added.
        :param name: name of the input socket.
        :param type: type of the input socket.
        :param default: default value of the input socket, defaults to _empty
        """
        if not hasattr(instance, "__haystack_input__"):
            instance.__haystack_input__ = Sockets(instance, {}, InputSocket)
        instance.__haystack_input__[name] = InputSocket(name=name, type=type, default_value=default)

    def set_input_types(self, instance, **types):
        """
        Method that specifies the input types when 'kwargs' is passed to the run method.

        Use as:

        ```python
        @component
        class MyComponent:

            def __init__(self, value: int):
                component.set_input_types(self, value_1=str, value_2=str)
                ...

            @component.output_types(output_1=int, output_2=str)
            def run(self, **kwargs):
                return {"output_1": kwargs["value_1"], "output_2": ""}
        ```

        Note that if the `run()` method also specifies some parameters, those will take precedence.

        For example:

        ```python
        @component
        class MyComponent:

            def __init__(self, value: int):
                component.set_input_types(self, value_1=str, value_2=str)
                ...

            @component.output_types(output_1=int, output_2=str)
            def run(self, value_0: str, value_1: Optional[str] = None, **kwargs):
                return {"output_1": kwargs["value_1"], "output_2": ""}
        ```

        would add a mandatory `value_0` parameters, make the `value_1`
        parameter optional with a default None, and keep the `value_2`
        parameter mandatory as specified in `set_input_types`.

        """
        instance.__haystack_input__ = Sockets(
            instance, {name: InputSocket(name=name, type=type_) for name, type_ in types.items()}, InputSocket
        )

    def set_output_types(self, instance, **types):
        """
        Method that specifies the output types when the 'run' method is not decorated with 'component.output_types'.

        Use as:

        ```python
        @component
        class MyComponent:

            def __init__(self, value: int):
                component.set_output_types(self, output_1=int, output_2=str)
                ...

            # no decorators here
            def run(self, value: int):
                return {"output_1": 1, "output_2": "2"}
        ```
        """
        instance.__haystack_output__ = Sockets(
            instance, {name: OutputSocket(name=name, type=type_) for name, type_ in types.items()}, OutputSocket
        )

    def output_types(self, **types):
        """
        Decorator factory that specifies the output types of a component.

        Use as:

        ```python
        @component
        class MyComponent:
            @component.output_types(output_1=int, output_2=str)
            def run(self, value: int):
                return {"output_1": 1, "output_2": "2"}
        ```
        """

        def output_types_decorator(run_method):
            """
            Decorator that sets the output types of the decorated method.

            This happens at class creation time, and since we don't have the decorated
            class available here, we temporarily store the output types as an attribute of
            the decorated method. The ComponentMeta metaclass will use this data to create
            sockets at instance creation time.
            """
            setattr(
                run_method,
                "_output_types_cache",
                {name: OutputSocket(name=name, type=type_) for name, type_ in types.items()},
            )
            return run_method

        return output_types_decorator

    def _component(self, cls, is_greedy: bool = False):
        """
        Decorator validating the structure of the component and registering it in the components registry.
        """
        logger.debug("Registering {component} as a component", component=cls)

        # Check for required methods and fail as soon as possible
        if not hasattr(cls, "run"):
            raise ComponentError(f"{cls.__name__} must have a 'run()' method. See the docs for more information.")

        def copy_class_namespace(namespace):
            """
            This is the callback that `typing.new_class` will use to populate the newly created class.

            Simply copy the whole namespace from the decorated class.
            """
            for key, val in dict(cls.__dict__).items():
                # __dict__ and __weakref__ are class-bound, we should let Python recreate them.
                if key in ("__dict__", "__weakref__"):
                    continue
                namespace[key] = val

        # Recreate the decorated component class so it uses our metaclass.
        # We must explicitly redefine the type of the class to make sure language servers
        # and type checkers understand that the class is of the correct type.
        # mypy doesn't like that we do this though so we explicitly ignore the type check.
        cls: cls.__name__ = new_class(cls.__name__, cls.__bases__, {"metaclass": ComponentMeta}, copy_class_namespace)  # type: ignore[no-redef]

        # Save the component in the class registry (for deserialization)
        class_path = f"{cls.__module__}.{cls.__name__}"
        if class_path in self.registry:
            # Corner case, but it may occur easily in notebooks when re-running cells.
            logger.debug(
                "Component {component} is already registered. Previous imported from '{module_name}', \
                new imported from '{new_module_name}'",
                component=class_path,
                module_name=self.registry[class_path],
                new_module_name=cls,
            )
        self.registry[class_path] = cls
        logger.debug("Registered Component {component}", component=cls)

        # Override the __repr__ method with a default one
        cls.__repr__ = _component_repr

        # The greedy flag can be True only if the component has a variadic input.
        # At this point of the lifetime of the component, we can't reliably know if it has a variadic input.
        # So we set it to whatever the user specified, during the instance creation we'll change it if needed
        # since we'll have access to the input sockets and check if any of them is variadic.
        setattr(cls, "__haystack_is_greedy__", is_greedy)

        return cls

    def __call__(self, cls: Optional[type] = None, is_greedy: bool = False):
        # We must wrap the call to the decorator in a function for it to work
        # correctly with or without parens
        def wrap(cls):
            return self._component(cls, is_greedy=is_greedy)

        if cls:
            # Decorator is called without parens
            return wrap(cls)

        # Decorator is called with parens
        return wrap


component = _Component()

1	# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
2	#
3	# SPDX-License-Identifier: Apache-2.0
4
5	"""
6	Attributes:
7
8	component: Marks a class as a component. Any class decorated with `@component` can be used by a Pipeline.
9
10	All components must follow the contract below. This docstring is the source of truth for components contract.
11
12	<hr>
13
14	`@component` decorator
15
16	All component classes must be decorated with the `@component` decorator. This allows Canals to discover them.
17
18	<hr>
19
20	`__init__(self, **kwargs)`
21
22	Optional method.
23
24	Components may have an `__init__` method where they define:
25
26	- `self.init_parameters = {same parameters that the __init__ method received}`:
27	In this dictionary you can store any state the components wish to be persisted when they are saved.
28	These values will be given to the `__init__` method of a new instance when the pipeline is loaded.
29	Note that by default the `@component` decorator saves the arguments automatically.
30	However, if a component sets their own `init_parameters` manually in `__init__()`, that will be used instead.
31	Note: all of the values contained here must be JSON serializable. Serialize them manually if needed.
32
33	Components should take only "basic" Python types as parameters of their `__init__` function, or iterables and
34	dictionaries containing only such values. Anything else (objects, functions, etc) will raise an exception at init
35	time. If there's the need for such values, consider serializing them to a string.
36
37	_(TODO explain how to use classes and functions in init. In the meantime see `test/components/test_accumulate.py`)_
38
39	The `__init__` must be extremely lightweight, because it's a frequent operation during the construction and
40	validation of the pipeline. If a component has some heavy state to initialize (models, backends, etc...) refer to
41	the `warm_up()` method.
42
43	<hr>
44
45	`warm_up(self)`
46
47	Optional method.
48
49	This method is called by Pipeline before the graph execution. Make sure to avoid double-initializations,
50	because Pipeline will not keep track of which components it called `warm_up()` on.
51
52	<hr>
53
54	`run(self, data)`
55
56	Mandatory method.
57
58	This is the method where the main functionality of the component should be carried out. It's called by
59	`Pipeline.run()`.
60
61	When the component should run, Pipeline will call this method with an instance of the dataclass returned by the
62	method decorated with `@component.input`. This dataclass contains:
63
64	- all the input values coming from other components connected to it,
65	- if any is missing, the corresponding value defined in `self.defaults`, if it exists.
66
67	`run()` must return a single instance of the dataclass declared through the method decorated with
68	`@component.output`.
69
70	"""
71
72	import inspect	1✔
73	import sys	1✔
74	from collections.abc import Callable	1✔
75	from contextlib import contextmanager	1✔
76	from contextvars import ContextVar	1✔
77	from copy import deepcopy	1✔
78	from dataclasses import dataclass	1✔
79	from types import new_class	1✔
80	from typing import Any, Dict, Optional, Protocol, runtime_checkable	1✔
81
82	from haystack import logging	1✔
83	from haystack.core.errors import ComponentError	1✔
84
85	from .sockets import Sockets	1✔
86	from .types import InputSocket, OutputSocket, _empty	1✔
87
88	logger = logging.getLogger(__name__)	1✔
89
90
91	@dataclass	1✔
92	class PreInitHookPayload:	1✔
93	"""
94	Payload for the hook called before a component instance is initialized.
95
96	:param callback:
97	Receives the following inputs: component class and init parameter keyword args.
98	:param in_progress:
99	Flag to indicate if the hook is currently being executed.
100	Used to prevent it from being called recursively (if the component's constructor
101	instantiates another component).
102	"""
103
104	callback: Callable	1✔
105	in_progress: bool = False	1✔
106
107
108	_COMPONENT_PRE_INIT_HOOK: ContextVar[Optional[PreInitHookPayload]] = ContextVar("component_pre_init_hook", default=None)	1✔
109
110
111	@contextmanager	1✔
112	def _hook_component_init(callback: Callable):	1✔
113	"""
114	Context manager to set a callback that will be invoked before a component's constructor is called.
115
116	The callback receives the component class and the init parameters (as keyword arguments) and can modify the init
117	parameters in place.
118
119	:param callback:
120	Callback function to invoke.
121	"""
122	token = _COMPONENT_PRE_INIT_HOOK.set(PreInitHookPayload(callback))	1✔
123	try:	1✔
124	yield	1✔
125	finally:
126	_COMPONENT_PRE_INIT_HOOK.reset(token)	1✔
127
128
129	@runtime_checkable	1✔
130	class Component(Protocol):	1✔
131	"""
132	Note this is only used by type checking tools.
133
134	In order to implement the `Component` protocol, custom components need to
135	have a `run` method. The signature of the method and its return value
136	won't be checked, i.e. classes with the following methods:
137
138	def run(self, param: str) -> Dict[str, Any]:
139	...
140
141	and
142
143	def run(self, **kwargs):
144	...
145
146	will be both considered as respecting the protocol. This makes the type
147	checking much weaker, but we have other places where we ensure code is
148	dealing with actual Components.
149
150	The protocol is runtime checkable so it'll be possible to assert:
151
152	isinstance(MyComponent, Component)
153	"""
154
155	# This is the most reliable way to define the protocol for the `run` method.
156	# Defining a method doesn't work as different Components will have different
157	# arguments. Even defining here a method with `**kwargs` doesn't work as the
158	# expected signature must be identical.
159	# This makes most Language Servers and type checkers happy and shows less errors.
160	# NOTE: This check can be removed when we drop Python 3.8 support.
161	if sys.version_info >= (3, 9):	1✔
162	run: Callable[..., Dict[str, Any]]	×
163	else:
164	run: Callable	1✔
165
166
167	class ComponentMeta(type):	1✔
168	@staticmethod	1✔
169	def positional_to_kwargs(cls_type, args) -> Dict[str, Any]:	1✔
170	"""
171	Convert positional arguments to keyword arguments based on the signature of the `__init__` method.
172	"""
173	init_signature = inspect.signature(cls_type.__init__)	1✔
174	init_params = {name: info for name, info in init_signature.parameters.items() if name != "self"}	1✔
175
176	out = {}	1✔
177	for arg, (name, info) in zip(args, init_params.items()):	1✔
178	if info.kind == inspect.Parameter.VAR_POSITIONAL:	1✔
179	raise ComponentError(	1✔
180	"Pre-init hooks do not support components with variadic positional args in their init method"
181	)
182
183	assert info.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.POSITIONAL_ONLY)	1✔
184	out[name] = arg	1✔
185	return out	1✔
186
187	def __call__(cls, args, *kwargs):	1✔
188	"""
189	This method is called when clients instantiate a Component and runs before __new__ and __init__.
190	"""
191	# This will call __new__ then __init__, giving us back the Component instance
192	pre_init_hook = _COMPONENT_PRE_INIT_HOOK.get()	1✔
193	if pre_init_hook is None or pre_init_hook.in_progress:	1✔
194	instance = super().__call__(args, *kwargs)	1✔
195	else:
196	try:	1✔
197	pre_init_hook.in_progress = True	1✔
198	named_positional_args = ComponentMeta.positional_to_kwargs(cls, args)	1✔
199	assert (	1✔
200	set(named_positional_args.keys()).intersection(kwargs.keys()) == set()
201	), "positional and keyword arguments overlap"
202	kwargs.update(named_positional_args)	1✔
203	pre_init_hook.callback(cls, kwargs)	1✔
204	instance = super().__call__(**kwargs)	1✔
205	finally:
206	pre_init_hook.in_progress = False	1✔
207
208	# Before returning, we have the chance to modify the newly created
209	# Component instance, so we take the chance and set up the I/O sockets
210
211	# If `component.set_output_types()` was called in the component constructor,
212	# `__haystack_output__` is already populated, no need to do anything.
213	if not hasattr(instance, "__haystack_output__"):	1✔
214	# If that's not the case, we need to populate `__haystack_output__`
215	#
216	# If the `run` method was decorated, it has a `_output_types_cache` field assigned
217	# that stores the output specification.
218	# We deepcopy the content of the cache to transfer ownership from the class method
219	# to the actual instance, so that different instances of the same class won't share this data.
220	instance.__haystack_output__ = Sockets(	1✔
221	instance, deepcopy(getattr(instance.run, "_output_types_cache", {})), OutputSocket
222	)
223
224	# Create the sockets if set_input_types() wasn't called in the constructor.
225	# If it was called and there are some parameters also in the `run()` method, these take precedence.
226	if not hasattr(instance, "__haystack_input__"):	1✔
227	instance.__haystack_input__ = Sockets(instance, {}, InputSocket)	1✔
228	run_signature = inspect.signature(getattr(cls, "run"))	1✔
229	for param in list(run_signature.parameters)[1:]: # First is 'self' and it doesn't matter.	1✔
230	if run_signature.parameters[param].kind not in (	1✔
231	inspect.Parameter.VAR_POSITIONAL,
232	inspect.Parameter.VAR_KEYWORD,
233	): # ignore variable args
234	socket_kwargs = {"name": param, "type": run_signature.parameters[param].annotation}	1✔
235	if run_signature.parameters[param].default != inspect.Parameter.empty:	1✔
236	socket_kwargs["default_value"] = run_signature.parameters[param].default	1✔
237	instance.__haystack_input__[param] = InputSocket(**socket_kwargs)	1✔
238
239	# Since a Component can't be used in multiple Pipelines at the same time
240	# we need to know if it's already owned by a Pipeline when adding it to one.
241	# We use this flag to check that.
242	instance.__haystack_added_to_pipeline__ = None	1✔
243
244	# Only Components with variadic inputs can be greedy. If the user set the greedy flag
245	# to True, but the component doesn't have a variadic input, we set it to False.
246	# We can have this information only at instance creation time, so we do it here.
247	is_variadic = any(socket.is_variadic for socket in instance.__haystack_input__._sockets_dict.values())	1✔
248	if not is_variadic and cls.__haystack_is_greedy__:	1✔
249	logger.warning(	1✔
250	"Component '{component}' has no variadic input, but it's marked as greedy. "
251	"This is not supported and can lead to unexpected behavior.",
252	component=cls.__name__,
253	)
254
255	return instance	1✔
256
257
258	def _component_repr(component: Component) -> str:	1✔
259	"""
260	All Components override their __repr__ method with this one.
261
262	It prints the component name and the input/output sockets.
263	"""
264	result = object.__repr__(component)	1✔
265	if pipeline := getattr(component, "__haystack_added_to_pipeline__"):	1✔
266	# This Component has been added in a Pipeline, let's get the name from there.
267	result += f"\n{pipeline.get_component_name(component)}"	1✔
268
269	# We're explicitly ignoring the type here because we're sure that the component
270	# has the __haystack_input__ and __haystack_output__ attributes at this point
271	return f"{result}\n{component.__haystack_input__}\n{component.__haystack_output__}" # type: ignore[attr-defined]	1✔
272
273
274	class _Component:	1✔
275	"""
276	See module's docstring.
277
278	Args:
279	class_: the class that Canals should use as a component.
280	serializable: whether to check, at init time, if the component can be saved with
281	`save_pipelines()`.
282
283	Returns:
284	A class that can be recognized as a component.
285
286	Raises:
287	ComponentError: if the class provided has no `run()` method or otherwise doesn't respect the component contract.
288	"""
289
290	def __init__(self):	1✔
291	self.registry = {}	1✔
292
293	def set_input_type(self, instance, name: str, type: Any, default: Any = _empty): # noqa: A002	1✔
294	"""
295	Add a single input socket to the component instance.
296
297	:param instance: Component instance where the input type will be added.
298	:param name: name of the input socket.
299	:param type: type of the input socket.
300	:param default: default value of the input socket, defaults to _empty
301	"""
302	if not hasattr(instance, "__haystack_input__"):	1✔
303	instance.__haystack_input__ = Sockets(instance, {}, InputSocket)	×
304	instance.__haystack_input__[name] = InputSocket(name=name, type=type, default_value=default)	1✔
305
306	def set_input_types(self, instance, **types):	1✔
307	"""
308	Method that specifies the input types when 'kwargs' is passed to the run method.
309
310	Use as:
311
312	```python
313	@component
314	class MyComponent:
315
316	def __init__(self, value: int):
317	component.set_input_types(self, value_1=str, value_2=str)
318	...
319
320	@component.output_types(output_1=int, output_2=str)
321	def run(self, **kwargs):
322	return {"output_1": kwargs["value_1"], "output_2": ""}
323	```
324
325	Note that if the `run()` method also specifies some parameters, those will take precedence.
326
327	For example:
328
329	```python
330	@component
331	class MyComponent:
332
333	def __init__(self, value: int):
334	component.set_input_types(self, value_1=str, value_2=str)
335	...
336
337	@component.output_types(output_1=int, output_2=str)
338	def run(self, value_0: str, value_1: Optional[str] = None, **kwargs):
339	return {"output_1": kwargs["value_1"], "output_2": ""}
340	```
341
342	would add a mandatory `value_0` parameters, make the `value_1`
343	parameter optional with a default None, and keep the `value_2`
344	parameter mandatory as specified in `set_input_types`.
345
346	"""
347	instance.__haystack_input__ = Sockets(	1✔
348	instance, {name: InputSocket(name=name, type=type_) for name, type_ in types.items()}, InputSocket
349	)
350
351	def set_output_types(self, instance, **types):	1✔
352	"""
353	Method that specifies the output types when the 'run' method is not decorated with 'component.output_types'.
354
355	Use as:
356
357	```python
358	@component
359	class MyComponent:
360
361	def __init__(self, value: int):
362	component.set_output_types(self, output_1=int, output_2=str)
363	...
364
365	# no decorators here
366	def run(self, value: int):
367	return {"output_1": 1, "output_2": "2"}
368	```
369	"""
370	instance.__haystack_output__ = Sockets(	1✔
371	instance, {name: OutputSocket(name=name, type=type_) for name, type_ in types.items()}, OutputSocket
372	)
373
374	def output_types(self, **types):	1✔
375	"""
376	Decorator factory that specifies the output types of a component.
377
378	Use as:
379
380	```python
381	@component
382	class MyComponent:
383	@component.output_types(output_1=int, output_2=str)
384	def run(self, value: int):
385	return {"output_1": 1, "output_2": "2"}
386	```
387	"""
388
389	def output_types_decorator(run_method):	1✔
390	"""
391	Decorator that sets the output types of the decorated method.
392
393	This happens at class creation time, and since we don't have the decorated
394	class available here, we temporarily store the output types as an attribute of
395	the decorated method. The ComponentMeta metaclass will use this data to create
396	sockets at instance creation time.
397	"""
398	setattr(	1✔
399	run_method,
400	"_output_types_cache",
401	{name: OutputSocket(name=name, type=type_) for name, type_ in types.items()},
402	)
403	return run_method	1✔
404
405	return output_types_decorator	1✔
406
407	def _component(self, cls, is_greedy: bool = False):	1✔
408	"""
409	Decorator validating the structure of the component and registering it in the components registry.
410	"""
411	logger.debug("Registering {component} as a component", component=cls)	1✔
412
413	# Check for required methods and fail as soon as possible
414	if not hasattr(cls, "run"):	1✔
415	raise ComponentError(f"{cls.__name__} must have a 'run()' method. See the docs for more information.")	1✔
416
417	def copy_class_namespace(namespace):	1✔
418	"""
419	This is the callback that `typing.new_class` will use to populate the newly created class.
420
421	Simply copy the whole namespace from the decorated class.
422	"""
423	for key, val in dict(cls.__dict__).items():	1✔
424	# __dict__ and __weakref__ are class-bound, we should let Python recreate them.
425	if key in ("__dict__", "__weakref__"):	1✔
426	continue	1✔
427	namespace[key] = val	1✔
428
429	# Recreate the decorated component class so it uses our metaclass.
430	# We must explicitly redefine the type of the class to make sure language servers
431	# and type checkers understand that the class is of the correct type.
432	# mypy doesn't like that we do this though so we explicitly ignore the type check.
433	cls: cls.__name__ = new_class(cls.__name__, cls.__bases__, {"metaclass": ComponentMeta}, copy_class_namespace) # type: ignore[no-redef]	1✔
434
435	# Save the component in the class registry (for deserialization)
436	class_path = f"{cls.__module__}.{cls.__name__}"	1✔
437	if class_path in self.registry:	1✔
438	# Corner case, but it may occur easily in notebooks when re-running cells.
439	logger.debug(	1✔
440	"Component {component} is already registered. Previous imported from '{module_name}', \
441	new imported from '{new_module_name}'",
442	component=class_path,
443	module_name=self.registry[class_path],
444	new_module_name=cls,
445	)
446	self.registry[class_path] = cls	1✔
447	logger.debug("Registered Component {component}", component=cls)	1✔
448
449	# Override the __repr__ method with a default one
450	cls.__repr__ = _component_repr	1✔
451
452	# The greedy flag can be True only if the component has a variadic input.
453	# At this point of the lifetime of the component, we can't reliably know if it has a variadic input.
454	# So we set it to whatever the user specified, during the instance creation we'll change it if needed
455	# since we'll have access to the input sockets and check if any of them is variadic.
456	setattr(cls, "__haystack_is_greedy__", is_greedy)	1✔
457
458	return cls	1✔
459
460	def __call__(self, cls: Optional[type] = None, is_greedy: bool = False):	1✔
461	# We must wrap the call to the decorator in a function for it to work
462	# correctly with or without parens
463	def wrap(cls):	1✔
464	return self._component(cls, is_greedy=is_greedy)	1✔
465
466	if cls:	1✔
467	# Decorator is called without parens
468	return wrap(cls)	1✔
469
470	# Decorator is called with parens
471	return wrap	1✔
472
473
474	component = _Component()	1✔

deepset-ai / haystack / 9610677276

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