13674921125

Committed 05 Mar 2025 11:26AM UTC coverage: 90.081% (+0.007%) from 90.074%

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Merge 4500e0dbc into b77f2bad7

Pull Request Pull Request #8978: build: drop Python 3.8 support

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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 Haystack 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, Type, 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.
    run: Callable[..., Dict[str, Any]]


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

    @staticmethod
    def _parse_and_set_output_sockets(instance: Any):
        has_async_run = hasattr(instance, "run_async")

        # 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 either of the run methods were decorated, they'll have a field assigned that
            # stores the output specification. If both run methods were decorated, we ensure that
            # outputs are the same. 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.

            run_output_types = getattr(instance.run, "_output_types_cache", {})
            async_run_output_types = getattr(instance.run_async, "_output_types_cache", {}) if has_async_run else {}

            if has_async_run and run_output_types != async_run_output_types:
                raise ComponentError("Output type specifications of 'run' and 'run_async' methods must be the same")
            output_types_cache = run_output_types

            instance.__haystack_output__ = Sockets(instance, deepcopy(output_types_cache), OutputSocket)

    @staticmethod
    def _parse_and_set_input_sockets(component_cls: Type, instance: Any):
        def inner(method, sockets):
            from inspect import Parameter

            run_signature = inspect.signature(method)

            for param_name, param_info in run_signature.parameters.items():
                if param_name == "self" or param_info.kind in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):
                    continue

                socket_kwargs = {"name": param_name, "type": param_info.annotation}
                if param_info.default != Parameter.empty:
                    socket_kwargs["default_value"] = param_info.default

                new_socket = InputSocket(**socket_kwargs)

                # Also ensure that new sockets don't override existing ones.
                existing_socket = sockets.get(param_name)
                if existing_socket is not None and existing_socket != new_socket:
                    raise ComponentError(
                        "set_input_types()/set_input_type() cannot override the parameters of the 'run' method"
                    )

                sockets[param_name] = new_socket

            return run_signature

        # Create the sockets if set_input_types() wasn't called in the constructor.
        if not hasattr(instance, "__haystack_input__"):
            instance.__haystack_input__ = Sockets(instance, {}, InputSocket)

        inner(getattr(component_cls, "run"), instance.__haystack_input__)

        # Ensure that the sockets are the same for the async method, if it exists.
        async_run = getattr(component_cls, "run_async", None)
        if async_run is not None:
            run_sockets = Sockets(instance, {}, InputSocket)
            async_run_sockets = Sockets(instance, {}, InputSocket)

            # Can't use the sockets from above as they might contain
            # values set with set_input_types().
            run_sig = inner(getattr(component_cls, "run"), run_sockets)
            async_run_sig = inner(async_run, async_run_sockets)

            if async_run_sockets != run_sockets or run_sig != async_run_sig:
                raise ComponentError("Parameters of 'run' and 'run_async' methods must be the same")

    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
        has_async_run = hasattr(instance, "run_async")
        if has_async_run and not inspect.iscoroutinefunction(instance.run_async):
            raise ComponentError(f"Method 'run_async' of component '{cls.__name__}' must be a coroutine")
        instance.__haystack_supports_async__ = has_async_run

        ComponentMeta._parse_and_set_input_sockets(cls, instance)
        ComponentMeta._parse_and_set_output_sockets(instance)

        # 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

        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__", None):
        # 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{getattr(component, '__haystack_input__', '<invalid_input_sockets>')}"
        f"\n{getattr(component, '__haystack_output__', '<invalid_output_sockets>')}"
    )


def _component_run_has_kwargs(component_cls: Type) -> bool:
    run_method = getattr(component_cls, "run", None)
    if run_method is None:
        return False
    else:
        return any(
            param.kind == inspect.Parameter.VAR_KEYWORD for param in inspect.signature(run_method).parameters.values()
        )


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

    Args:
        cls: the class that should be used as a component.

    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,  # noqa: A002
        default: Any = _empty,
    ):
        """
        Add a single input socket to the component instance.

        Replaces any existing input socket with the same name.

        :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 _component_run_has_kwargs(instance.__class__):
            raise ComponentError(
                "Cannot set input types on a component that doesn't have a kwargs parameter in the 'run' method"
            )

        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`.

        """
        if not _component_run_has_kwargs(instance.__class__):
            raise ComponentError(
                "Cannot set input types on a component that doesn't have a kwargs parameter in the 'run' method"
            )

        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"}
        ```
        """
        has_decorator = hasattr(instance.run, "_output_types_cache")
        if has_decorator:
            raise ComponentError(
                "Cannot call `set_output_types` on a component that already has "
                "the 'output_types' decorator on its `run` method"
            )

        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.
            """
            method_name = run_method.__name__
            if method_name not in ("run", "run_async"):
                raise ComponentError("'output_types' decorator can only be used on 'run' and 'run_async' methods")

            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: Any):
        """
        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.
        new_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"{new_cls.__module__}.{new_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=new_cls,
            )
        self.registry[class_path] = new_cls
        logger.debug("Registered Component {component}", component=new_cls)

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

        return new_cls

    def __call__(self, cls: Optional[type] = None):
        # 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)

        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 Haystack 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, Type, 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	run: Callable[..., Dict[str, Any]]	1✔
161
162
163	class ComponentMeta(type):	1✔
164	@staticmethod	1✔
165	def _positional_to_kwargs(cls_type, args) -> Dict[str, Any]:	1✔
166	"""
167	Convert positional arguments to keyword arguments based on the signature of the `__init__` method.
168	"""
169	init_signature = inspect.signature(cls_type.__init__)	1✔
170	init_params = {name: info for name, info in init_signature.parameters.items() if name != "self"}	1✔
171
172	out = {}	1✔
173	for arg, (name, info) in zip(args, init_params.items()):	1✔
174	if info.kind == inspect.Parameter.VAR_POSITIONAL:	1✔
175	raise ComponentError(	1✔
176	"Pre-init hooks do not support components with variadic positional args in their init method"
177	)
178
179	assert info.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.POSITIONAL_ONLY)	1✔
180	out[name] = arg	1✔
181	return out	1✔
182
183	@staticmethod	1✔
184	def _parse_and_set_output_sockets(instance: Any):	1✔
185	has_async_run = hasattr(instance, "run_async")	1✔
186
187	# If `component.set_output_types()` was called in the component constructor,
188	# `__haystack_output__` is already populated, no need to do anything.
189	if not hasattr(instance, "__haystack_output__"):	1✔
190	# If that's not the case, we need to populate `__haystack_output__`
191	#
192	# If either of the run methods were decorated, they'll have a field assigned that
193	# stores the output specification. If both run methods were decorated, we ensure that
194	# outputs are the same. We deepcopy the content of the cache to transfer ownership from
195	# the class method to the actual instance, so that different instances of the same class
196	# won't share this data.
197
198	run_output_types = getattr(instance.run, "_output_types_cache", {})	1✔
199	async_run_output_types = getattr(instance.run_async, "_output_types_cache", {}) if has_async_run else {}	1✔
200
201	if has_async_run and run_output_types != async_run_output_types:	1✔
202	raise ComponentError("Output type specifications of 'run' and 'run_async' methods must be the same")	1✔
203	output_types_cache = run_output_types	1✔
204
205	instance.__haystack_output__ = Sockets(instance, deepcopy(output_types_cache), OutputSocket)	1✔
206
207	@staticmethod	1✔
208	def _parse_and_set_input_sockets(component_cls: Type, instance: Any):	1✔
209	def inner(method, sockets):	1✔
210	from inspect import Parameter	1✔
211
212	run_signature = inspect.signature(method)	1✔
213
214	for param_name, param_info in run_signature.parameters.items():	1✔
215	if param_name == "self" or param_info.kind in (Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD):	1✔
216	continue	1✔
217
218	socket_kwargs = {"name": param_name, "type": param_info.annotation}	1✔
219	if param_info.default != Parameter.empty:	1✔
220	socket_kwargs["default_value"] = param_info.default	1✔
221
222	new_socket = InputSocket(**socket_kwargs)	1✔
223
224	# Also ensure that new sockets don't override existing ones.
225	existing_socket = sockets.get(param_name)	1✔
226	if existing_socket is not None and existing_socket != new_socket:	1✔
227	raise ComponentError(	1✔
228	"set_input_types()/set_input_type() cannot override the parameters of the 'run' method"
229	)
230
231	sockets[param_name] = new_socket	1✔
232
233	return run_signature	1✔
234
235	# Create the sockets if set_input_types() wasn't called in the constructor.
236	if not hasattr(instance, "__haystack_input__"):	1✔
237	instance.__haystack_input__ = Sockets(instance, {}, InputSocket)	1✔
238
239	inner(getattr(component_cls, "run"), instance.__haystack_input__)	1✔
240
241	# Ensure that the sockets are the same for the async method, if it exists.
242	async_run = getattr(component_cls, "run_async", None)	1✔
243	if async_run is not None:	1✔
244	run_sockets = Sockets(instance, {}, InputSocket)	1✔
245	async_run_sockets = Sockets(instance, {}, InputSocket)	1✔
246
247	# Can't use the sockets from above as they might contain
248	# values set with set_input_types().
249	run_sig = inner(getattr(component_cls, "run"), run_sockets)	1✔
250	async_run_sig = inner(async_run, async_run_sockets)	1✔
251
252	if async_run_sockets != run_sockets or run_sig != async_run_sig:	1✔
253	raise ComponentError("Parameters of 'run' and 'run_async' methods must be the same")	1✔
254
255	def __call__(cls, args, *kwargs):	1✔
256	"""
257	This method is called when clients instantiate a Component and runs before __new__ and __init__.
258	"""
259	# This will call __new__ then __init__, giving us back the Component instance
260	pre_init_hook = _COMPONENT_PRE_INIT_HOOK.get()	1✔
261	if pre_init_hook is None or pre_init_hook.in_progress:	1✔
262	instance = super().__call__(args, *kwargs)	1✔
263	else:
264	try:	1✔
265	pre_init_hook.in_progress = True	1✔
266	named_positional_args = ComponentMeta._positional_to_kwargs(cls, args)	1✔
267	assert set(named_positional_args.keys()).intersection(kwargs.keys()) == set(), (	1✔
268	"positional and keyword arguments overlap"
269	)
270	kwargs.update(named_positional_args)	1✔
271	pre_init_hook.callback(cls, kwargs)	1✔
272	instance = super().__call__(**kwargs)	1✔
273	finally:
274	pre_init_hook.in_progress = False	1✔
275
276	# Before returning, we have the chance to modify the newly created
277	# Component instance, so we take the chance and set up the I/O sockets
278	has_async_run = hasattr(instance, "run_async")	1✔
279	if has_async_run and not inspect.iscoroutinefunction(instance.run_async):	1✔
280	raise ComponentError(f"Method 'run_async' of component '{cls.__name__}' must be a coroutine")	1✔
281	instance.__haystack_supports_async__ = has_async_run	1✔
282
283	ComponentMeta._parse_and_set_input_sockets(cls, instance)	1✔
284	ComponentMeta._parse_and_set_output_sockets(instance)	1✔
285
286	# Since a Component can't be used in multiple Pipelines at the same time
287	# we need to know if it's already owned by a Pipeline when adding it to one.
288	# We use this flag to check that.
289	instance.__haystack_added_to_pipeline__ = None	1✔
290
291	return instance	1✔
292
293
294	def _component_repr(component: Component) -> str:	1✔
295	"""
296	All Components override their __repr__ method with this one.
297
298	It prints the component name and the input/output sockets.
299	"""
300	result = object.__repr__(component)	1✔
301	if pipeline := getattr(component, "__haystack_added_to_pipeline__", None):	1✔
302	# This Component has been added in a Pipeline, let's get the name from there.
303	result += f"\n{pipeline.get_component_name(component)}"	1✔
304
305	# We're explicitly ignoring the type here because we're sure that the component
306	# has the __haystack_input__ and __haystack_output__ attributes at this point
307	return (	1✔
308	f"{result}\n{getattr(component, '__haystack_input__', '<invalid_input_sockets>')}"
309	f"\n{getattr(component, '__haystack_output__', '<invalid_output_sockets>')}"
310	)
311
312
313	def _component_run_has_kwargs(component_cls: Type) -> bool:	1✔
314	run_method = getattr(component_cls, "run", None)	1✔
315	if run_method is None:	1✔
316	return False	×
317	else:
318	return any(	1✔
319	param.kind == inspect.Parameter.VAR_KEYWORD for param in inspect.signature(run_method).parameters.values()
320	)
321
322
323	class _Component:	1✔
324	"""
325	See module's docstring.
326
327	Args:
328	cls: the class that should be used as a component.
329
330	Returns:
331	A class that can be recognized as a component.
332
333	Raises:
334	ComponentError: if the class provided has no `run()` method or otherwise doesn't respect the component contract.
335	"""
336
337	def __init__(self):	1✔
338	self.registry = {}	1✔
339
340	def set_input_type(	1✔
341	self,
342	instance,
343	name: str,
344	type: Any, # noqa: A002
345	default: Any = _empty,
346	):
347	"""
348	Add a single input socket to the component instance.
349
350	Replaces any existing input socket with the same name.
351
352	:param instance: Component instance where the input type will be added.
353	:param name: name of the input socket.
354	:param type: type of the input socket.
355	:param default: default value of the input socket, defaults to _empty
356	"""
357	if not _component_run_has_kwargs(instance.__class__):	1✔
358	raise ComponentError(	1✔
359	"Cannot set input types on a component that doesn't have a kwargs parameter in the 'run' method"
360	)
361
362	if not hasattr(instance, "__haystack_input__"):	1✔
363	instance.__haystack_input__ = Sockets(instance, {}, InputSocket)	1✔
364	instance.__haystack_input__[name] = InputSocket(name=name, type=type, default_value=default)	1✔
365
366	def set_input_types(self, instance, **types):	1✔
367	"""
368	Method that specifies the input types when 'kwargs' is passed to the run method.
369
370	Use as:
371
372	```python
373	@component
374	class MyComponent:
375
376	def __init__(self, value: int):
377	component.set_input_types(self, value_1=str, value_2=str)
378	...
379
380	@component.output_types(output_1=int, output_2=str)
381	def run(self, **kwargs):
382	return {"output_1": kwargs["value_1"], "output_2": ""}
383	```
384
385	Note that if the `run()` method also specifies some parameters, those will take precedence.
386
387	For example:
388
389	```python
390	@component
391	class MyComponent:
392
393	def __init__(self, value: int):
394	component.set_input_types(self, value_1=str, value_2=str)
395	...
396
397	@component.output_types(output_1=int, output_2=str)
398	def run(self, value_0: str, value_1: Optional[str] = None, **kwargs):
399	return {"output_1": kwargs["value_1"], "output_2": ""}
400	```
401
402	would add a mandatory `value_0` parameters, make the `value_1`
403	parameter optional with a default None, and keep the `value_2`
404	parameter mandatory as specified in `set_input_types`.
405
406	"""
407	if not _component_run_has_kwargs(instance.__class__):	1✔
408	raise ComponentError(	1✔
409	"Cannot set input types on a component that doesn't have a kwargs parameter in the 'run' method"
410	)
411
412	instance.__haystack_input__ = Sockets(	1✔
413	instance, {name: InputSocket(name=name, type=type_) for name, type_ in types.items()}, InputSocket
414	)
415
416	def set_output_types(self, instance, **types):	1✔
417	"""
418	Method that specifies the output types when the 'run' method is not decorated with 'component.output_types'.
419
420	Use as:
421
422	```python
423	@component
424	class MyComponent:
425
426	def __init__(self, value: int):
427	component.set_output_types(self, output_1=int, output_2=str)
428	...
429
430	# no decorators here
431	def run(self, value: int):
432	return {"output_1": 1, "output_2": "2"}
433	```
434	"""
435	has_decorator = hasattr(instance.run, "_output_types_cache")	1✔
436	if has_decorator:	1✔
437	raise ComponentError(	1✔
438	"Cannot call `set_output_types` on a component that already has "
439	"the 'output_types' decorator on its `run` method"
440	)
441
442	instance.__haystack_output__ = Sockets(	1✔
443	instance, {name: OutputSocket(name=name, type=type_) for name, type_ in types.items()}, OutputSocket
444	)
445
446	def output_types(self, **types):	1✔
447	"""
448	Decorator factory that specifies the output types of a component.
449
450	Use as:
451
452	```python
453	@component
454	class MyComponent:
455	@component.output_types(output_1=int, output_2=str)
456	def run(self, value: int):
457	return {"output_1": 1, "output_2": "2"}
458	```
459	"""
460
461	def output_types_decorator(run_method):	1✔
462	"""
463	Decorator that sets the output types of the decorated method.
464
465	This happens at class creation time, and since we don't have the decorated
466	class available here, we temporarily store the output types as an attribute of
467	the decorated method. The ComponentMeta metaclass will use this data to create
468	sockets at instance creation time.
469	"""
470	method_name = run_method.__name__	1✔
471	if method_name not in ("run", "run_async"):	1✔
472	raise ComponentError("'output_types' decorator can only be used on 'run' and 'run_async' methods")	1✔
473
474	setattr(	1✔
475	run_method,
476	"_output_types_cache",
477	{name: OutputSocket(name=name, type=type_) for name, type_ in types.items()},
478	)
479	return run_method	1✔
480
481	return output_types_decorator	1✔
482
483	def _component(self, cls: Any):	1✔
484	"""
485	Decorator validating the structure of the component and registering it in the components registry.
486	"""
487	logger.debug("Registering {component} as a component", component=cls)	1✔
488
489	# Check for required methods and fail as soon as possible
490	if not hasattr(cls, "run"):	1✔
491	raise ComponentError(f"{cls.__name__} must have a 'run()' method. See the docs for more information.")	1✔
492
493	def copy_class_namespace(namespace):	1✔
494	"""
495	This is the callback that `typing.new_class` will use to populate the newly created class.
496
497	Simply copy the whole namespace from the decorated class.
498	"""
499	for key, val in dict(cls.__dict__).items():	1✔
500	# __dict__ and __weakref__ are class-bound, we should let Python recreate them.
501	if key in ("__dict__", "__weakref__"):	1✔
502	continue	1✔
503	namespace[key] = val	1✔
504
505	# Recreate the decorated component class so it uses our metaclass.
506	# We must explicitly redefine the type of the class to make sure language servers
507	# and type checkers understand that the class is of the correct type.
508	# mypy doesn't like that we do this though so we explicitly ignore the type check.
509	new_cls: cls.__name__ = new_class(	1✔
510	cls.__name__, cls.__bases__, {"metaclass": ComponentMeta}, copy_class_namespace
511	) # type: ignore[no-redef]
512
513	# Save the component in the class registry (for deserialization)
514	class_path = f"{new_cls.__module__}.{new_cls.__name__}"	1✔
515	if class_path in self.registry:	1✔
516	# Corner case, but it may occur easily in notebooks when re-running cells.
517	logger.debug(	1✔
518	"Component {component} is already registered. Previous imported from '{module_name}', \
519	new imported from '{new_module_name}'",
520	component=class_path,
521	module_name=self.registry[class_path],
522	new_module_name=new_cls,
523	)
524	self.registry[class_path] = new_cls	1✔
525	logger.debug("Registered Component {component}", component=new_cls)	1✔
526
527	# Override the __repr__ method with a default one
528	new_cls.__repr__ = _component_repr	1✔
529
530	return new_cls	1✔
531
532	def __call__(self, cls: Optional[type] = None):	1✔
533	# We must wrap the call to the decorator in a function for it to work
534	# correctly with or without parens
535	def wrap(cls):	1✔
536	return self._component(cls)	1✔
537
538	if cls:	1✔
539	# Decorator is called without parens
540	return wrap(cls)	1✔
541
542	# Decorator is called with parens
543	return wrap	1✔
544
545
546	component = _Component()	1✔

deepset-ai / haystack / 13674921125

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