20304787960

Committed 17 Dec 2025 01:39PM UTC coverage: 92.156% (+0.002%) from 92.154%

Build # 20304787960

Build Type

Pull #10256

github

Committed by

web-flow

Commit Message

Merge b69f7a4d9 into 99d506b3e

Pull Request Pull Request #10256: feat: Component tool state validation

Run Details

14157 of 15362 relevant lines covered (92.16%)

0.92 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

94.51

haystack/tools/component_tool.py

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

from typing import Any, Callable, Optional, Union, get_args, get_origin

from pydantic import Field, TypeAdapter, create_model

from haystack import logging
from haystack.core.component import Component
from haystack.core.serialization import (
    component_from_dict,
    component_to_dict,
    generate_qualified_class_name,
    import_class_by_name,
)
from haystack.tools import Tool
from haystack.tools.errors import SchemaGenerationError
from haystack.tools.from_function import _remove_title_from_schema
from haystack.tools.parameters_schema_utils import _get_component_param_descriptions, _resolve_type
from haystack.tools.tool import _deserialize_outputs_to_state, _serialize_outputs_to_state
from haystack.utils.callable_serialization import deserialize_callable, serialize_callable

logger = logging.getLogger(__name__)


class ComponentTool(Tool):
    """
    A Tool that wraps Haystack components, allowing them to be used as tools by LLMs.

    ComponentTool automatically generates LLM-compatible tool schemas from component input sockets,
    which are derived from the component's `run` method signature and type hints.


    Key features:
    - Automatic LLM tool calling schema generation from component input sockets
    - Type conversion and validation for component inputs
    - Support for types:
        - Dataclasses
        - Lists of dataclasses
        - Basic types (str, int, float, bool, dict)
        - Lists of basic types
    - Automatic name generation from component class name
    - Description extraction from component docstrings

    To use ComponentTool, you first need a Haystack component - either an existing one or a new one you create.
    You can create a ComponentTool from the component by passing the component to the ComponentTool constructor.
    Below is an example of creating a ComponentTool from an existing SerperDevWebSearch component.

    ## Usage Example:

    ```python
    from haystack import component, Pipeline
    from haystack.tools import ComponentTool
    from haystack.components.websearch import SerperDevWebSearch
    from haystack.utils import Secret
    from haystack.components.tools.tool_invoker import ToolInvoker
    from haystack.components.generators.chat import OpenAIChatGenerator
    from haystack.dataclasses import ChatMessage

    # Create a SerperDev search component
    search = SerperDevWebSearch(api_key=Secret.from_env_var("SERPERDEV_API_KEY"), top_k=3)

    # Create a tool from the component
    tool = ComponentTool(
        component=search,
        name="web_search",  # Optional: defaults to "serper_dev_web_search"
        description="Search the web for current information on any topic"  # Optional: defaults to component docstring
    )

    # Create pipeline with OpenAIChatGenerator and ToolInvoker
    pipeline = Pipeline()
    pipeline.add_component("llm", OpenAIChatGenerator(tools=[tool]))
    pipeline.add_component("tool_invoker", ToolInvoker(tools=[tool]))

    # Connect components
    pipeline.connect("llm.replies", "tool_invoker.messages")

    message = ChatMessage.from_user("Use the web search tool to find information about Nikola Tesla")

    # Run pipeline
    result = pipeline.run({"llm": {"messages": [message]}})

    print(result)
    ```

    """

    def __init__(
        self,
        component: Component,
        name: Optional[str] = None,
        description: Optional[str] = None,
        parameters: Optional[dict[str, Any]] = None,
        *,
        outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]] = None,
        inputs_from_state: Optional[dict[str, str]] = None,
        outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]] = None,
    ) -> None:
        """
        Create a Tool instance from a Haystack component.

        :param component: The Haystack component to wrap as a tool.
        :param name: Optional name for the tool (defaults to snake_case of component class name).
        :param description: Optional description (defaults to component's docstring).
        :param parameters:
            A JSON schema defining the parameters expected by the Tool.
            Will fall back to the parameters defined in the component's run method signature if not provided.
        :param outputs_to_string:
            Optional dictionary defining how a tool outputs should be converted into a string.
            If the source is provided only the specified output key is sent to the handler.
            If the source is omitted the whole tool result is sent to the handler.
            Example:
            ```python
            {
                "source": "docs", "handler": format_documents
            }
            ```
        :param inputs_from_state:
            Optional dictionary mapping state keys to tool parameter names.
            Example: `{"repository": "repo"}` maps state's "repository" to tool's "repo" parameter.
        :param outputs_to_state:
            Optional dictionary defining how tool outputs map to keys within state as well as optional handlers.
            If the source is provided only the specified output key is sent to the handler.
            Example:
            ```python
            {
                "documents": {"source": "docs", "handler": custom_handler}
            }
            ```
            If the source is omitted the whole tool result is sent to the handler.
            Example:
            ```python
            {
                "documents": {"handler": custom_handler}
            }
            ```
        :raises ValueError: If the component is invalid or schema generation fails.
        """
        if not isinstance(component, Component):
            message = (
                f"Object {component!r} is not a Haystack component. "
                "Use ComponentTool only with Haystack component instances."
            )
            raise ValueError(message)

        if getattr(component, "__haystack_added_to_pipeline__", None):
            msg = (
                "Component has been added to a pipeline and can't be used to create a ComponentTool. "
                "Create ComponentTool from a non-pipeline component instead."
            )
            raise ValueError(msg)

        self._unresolved_parameters = parameters
        # Create the tools schema from the component run method parameters
        tool_schema = parameters or self._create_tool_parameters_schema(component, inputs_from_state or {})

        def component_invoker(**kwargs):
            """
            Invokes the component using keyword arguments provided by the LLM function calling/tool-generated response.

            :param kwargs: The keyword arguments to invoke the component with.
            :returns: The result of the component invocation.
            """
            converted_kwargs = {}
            input_sockets = component.__haystack_input__._sockets_dict  # type: ignore[attr-defined]
            for param_name, param_value in kwargs.items():
                param_type = input_sockets[param_name].type

                # Check if the type (or list element type) has from_dict
                target_type = get_args(param_type)[0] if get_origin(param_type) is list else param_type
                if hasattr(target_type, "from_dict"):
                    if isinstance(param_value, list):
                        resolved_param_value = [
                            target_type.from_dict(item) if isinstance(item, dict) else item for item in param_value
                        ]
                    elif isinstance(param_value, dict):
                        resolved_param_value = target_type.from_dict(param_value)
                    else:
                        resolved_param_value = param_value
                else:
                    # Let TypeAdapter handle both single values and lists
                    type_adapter = TypeAdapter(param_type)
                    resolved_param_value = type_adapter.validate_python(param_value)

                converted_kwargs[param_name] = resolved_param_value
            logger.debug(f"Invoking component {type(component)} with kwargs: {converted_kwargs}")
            return component.run(**converted_kwargs)

        # Generate a name for the tool if not provided
        if not name:
            class_name = component.__class__.__name__
            # Convert camelCase/PascalCase to snake_case
            name = "".join(
                [
                    "_" + c.lower() if c.isupper() and i > 0 and not class_name[i - 1].isupper() else c.lower()
                    for i, c in enumerate(class_name)
                ]
            ).lstrip("_")

        description = description or component.__doc__ or name

        # Store component before calling super().__init__() so _get_valid_outputs() can access it
        self._component = component
        self._is_warmed_up = False

        # Create the Tool instance with the component invoker as the function to be called and the schema
        super().__init__(
            name=name,
            description=description,
            parameters=tool_schema,
            function=component_invoker,
            inputs_from_state=inputs_from_state,
            outputs_to_state=outputs_to_state,
            outputs_to_string=outputs_to_string,
        )

    def _get_valid_inputs(self) -> set[str]:
        """
        Return valid input parameter names from the component's input sockets.

        Used to validate `inputs_from_state` against the component's actual inputs.
        This ensures users don't reference non-existent component inputs.

        :returns: Set of component input socket names.
        """
        return set(self._component.__haystack_input__._sockets_dict.keys())  # type: ignore[attr-defined]

    def _get_valid_outputs(self) -> set[str]:
        """
        Return valid output names from the component's output sockets.

        Used to validate `outputs_to_state` against the component's actual outputs.
        This ensures users don't reference non-existent component outputs.

        :returns: Set of component output socket names.
        """
        return set(self._component.__haystack_output__._sockets_dict.keys())  # type: ignore[attr-defined]

    def warm_up(self):
        """
        Prepare the ComponentTool for use.
        """
        if not self._is_warmed_up:
            if hasattr(self._component, "warm_up"):
                self._component.warm_up()
            self._is_warmed_up = True

    def to_dict(self) -> dict[str, Any]:
        """
        Serializes the ComponentTool to a dictionary.
        """
        serialized: dict[str, Any] = {
            "component": component_to_dict(obj=self._component, name=self.name),
            "name": self.name,
            "description": self.description,
            "parameters": self._unresolved_parameters,
            "inputs_from_state": self.inputs_from_state,
            "outputs_to_state": _serialize_outputs_to_state(self.outputs_to_state) if self.outputs_to_state else None,
        }

        if self.outputs_to_string is not None and self.outputs_to_string.get("handler") is not None:
            # This is soft-copied as to not modify the attributes in place
            serialized["outputs_to_string"] = self.outputs_to_string.copy()
            serialized["outputs_to_string"]["handler"] = serialize_callable(self.outputs_to_string["handler"])
        else:
            serialized["outputs_to_string"] = None

        return {"type": generate_qualified_class_name(type(self)), "data": serialized}

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> "ComponentTool":
        """
        Deserializes the ComponentTool from a dictionary.
        """
        inner_data = data["data"]
        component_class = import_class_by_name(inner_data["component"]["type"])
        component = component_from_dict(cls=component_class, data=inner_data["component"], name=inner_data["name"])

        if "outputs_to_state" in inner_data and inner_data["outputs_to_state"]:
            inner_data["outputs_to_state"] = _deserialize_outputs_to_state(inner_data["outputs_to_state"])

        if (
            inner_data.get("outputs_to_string") is not None
            and inner_data["outputs_to_string"].get("handler") is not None
        ):
            inner_data["outputs_to_string"]["handler"] = deserialize_callable(
                inner_data["outputs_to_string"]["handler"]
            )

        return cls(
            component=component,
            name=inner_data["name"],
            description=inner_data["description"],
            parameters=inner_data.get("parameters", None),
            outputs_to_string=inner_data.get("outputs_to_string", None),
            inputs_from_state=inner_data.get("inputs_from_state", None),
            outputs_to_state=inner_data.get("outputs_to_state", None),
        )

    def _create_tool_parameters_schema(self, component: Component, inputs_from_state: dict[str, Any]) -> dict[str, Any]:
        """
        Creates an OpenAI tools schema from a component's run method parameters.

        :param component: The component to create the schema from.
        :raises SchemaGenerationError: If schema generation fails
        :returns: OpenAI tools schema for the component's run method parameters.
        """
        component_run_description, param_descriptions = _get_component_param_descriptions(component)

        # collect fields (types and defaults) and descriptions from function parameters
        fields: dict[str, Any] = {}

        for input_name, socket in component.__haystack_input__._sockets_dict.items():  # type: ignore[attr-defined]
            if inputs_from_state is not None and input_name in list(inputs_from_state.values()):
                continue
            input_type = socket.type
            description = param_descriptions.get(input_name, f"Input '{input_name}' for the component.")

            # if the parameter has not a default value, Pydantic requires an Ellipsis (...)
            # to explicitly indicate that the parameter is required
            default = ... if socket.is_mandatory else socket.default_value
            resolved_type = _resolve_type(input_type)
            fields[input_name] = (resolved_type, Field(default=default, description=description))

        parameters_schema: dict[str, Any] = {}
        try:
            model = create_model(component.run.__name__, __doc__=component_run_description, **fields)
            parameters_schema = model.model_json_schema()
        except Exception as e:
            raise SchemaGenerationError(
                f"Failed to create JSON schema for the run method of Component '{component.__class__.__name__}'"
            ) from e

        # we don't want to include title keywords in the schema, as they contain redundant information
        # there is no programmatic way to prevent Pydantic from adding them, so we remove them later
        # see https://github.com/pydantic/pydantic/discussions/8504
        _remove_title_from_schema(parameters_schema)

        return parameters_schema

1	# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
2	#
3	# SPDX-License-Identifier: Apache-2.0
4
5	from typing import Any, Callable, Optional, Union, get_args, get_origin	1✔
6
7	from pydantic import Field, TypeAdapter, create_model	1✔
8
9	from haystack import logging	1✔
10	from haystack.core.component import Component	1✔
11	from haystack.core.serialization import (	1✔
12	component_from_dict,
13	component_to_dict,
14	generate_qualified_class_name,
15	import_class_by_name,
16	)
17	from haystack.tools import Tool	1✔
18	from haystack.tools.errors import SchemaGenerationError	1✔
19	from haystack.tools.from_function import _remove_title_from_schema	1✔
20	from haystack.tools.parameters_schema_utils import _get_component_param_descriptions, _resolve_type	1✔
21	from haystack.tools.tool import _deserialize_outputs_to_state, _serialize_outputs_to_state	1✔
22	from haystack.utils.callable_serialization import deserialize_callable, serialize_callable	1✔
23
24	logger = logging.getLogger(__name__)	1✔
25
26
27	class ComponentTool(Tool):	1✔
28	"""
29	A Tool that wraps Haystack components, allowing them to be used as tools by LLMs.
30
31	ComponentTool automatically generates LLM-compatible tool schemas from component input sockets,
32	which are derived from the component's `run` method signature and type hints.
33
34
35	Key features:
36	- Automatic LLM tool calling schema generation from component input sockets
37	- Type conversion and validation for component inputs
38	- Support for types:
39	- Dataclasses
40	- Lists of dataclasses
41	- Basic types (str, int, float, bool, dict)
42	- Lists of basic types
43	- Automatic name generation from component class name
44	- Description extraction from component docstrings
45
46	To use ComponentTool, you first need a Haystack component - either an existing one or a new one you create.
47	You can create a ComponentTool from the component by passing the component to the ComponentTool constructor.
48	Below is an example of creating a ComponentTool from an existing SerperDevWebSearch component.
49
50	## Usage Example:
51
52	```python
53	from haystack import component, Pipeline
54	from haystack.tools import ComponentTool
55	from haystack.components.websearch import SerperDevWebSearch
56	from haystack.utils import Secret
57	from haystack.components.tools.tool_invoker import ToolInvoker
58	from haystack.components.generators.chat import OpenAIChatGenerator
59	from haystack.dataclasses import ChatMessage
60
61	# Create a SerperDev search component
62	search = SerperDevWebSearch(api_key=Secret.from_env_var("SERPERDEV_API_KEY"), top_k=3)
63
64	# Create a tool from the component
65	tool = ComponentTool(
66	component=search,
67	name="web_search", # Optional: defaults to "serper_dev_web_search"
68	description="Search the web for current information on any topic" # Optional: defaults to component docstring
69	)
70
71	# Create pipeline with OpenAIChatGenerator and ToolInvoker
72	pipeline = Pipeline()
73	pipeline.add_component("llm", OpenAIChatGenerator(tools=[tool]))
74	pipeline.add_component("tool_invoker", ToolInvoker(tools=[tool]))
75
76	# Connect components
77	pipeline.connect("llm.replies", "tool_invoker.messages")
78
79	message = ChatMessage.from_user("Use the web search tool to find information about Nikola Tesla")
80
81	# Run pipeline
82	result = pipeline.run({"llm": {"messages": [message]}})
83
84	print(result)
85	```
86
87	"""
88
89	def __init__(	1✔
90	self,
91	component: Component,
92	name: Optional[str] = None,
93	description: Optional[str] = None,
94	parameters: Optional[dict[str, Any]] = None,
95	*,
96	outputs_to_string: Optional[dict[str, Union[str, Callable[[Any], str]]]] = None,
97	inputs_from_state: Optional[dict[str, str]] = None,
98	outputs_to_state: Optional[dict[str, dict[str, Union[str, Callable]]]] = None,
99	) -> None:
100	"""
101	Create a Tool instance from a Haystack component.
102
103	:param component: The Haystack component to wrap as a tool.
104	:param name: Optional name for the tool (defaults to snake_case of component class name).
105	:param description: Optional description (defaults to component's docstring).
106	:param parameters:
107	A JSON schema defining the parameters expected by the Tool.
108	Will fall back to the parameters defined in the component's run method signature if not provided.
109	:param outputs_to_string:
110	Optional dictionary defining how a tool outputs should be converted into a string.
111	If the source is provided only the specified output key is sent to the handler.
112	If the source is omitted the whole tool result is sent to the handler.
113	Example:
114	```python
115	{
116	"source": "docs", "handler": format_documents
117	}
118	```
119	:param inputs_from_state:
120	Optional dictionary mapping state keys to tool parameter names.
121	Example: `{"repository": "repo"}` maps state's "repository" to tool's "repo" parameter.
122	:param outputs_to_state:
123	Optional dictionary defining how tool outputs map to keys within state as well as optional handlers.
124	If the source is provided only the specified output key is sent to the handler.
125	Example:
126	```python
127	{
128	"documents": {"source": "docs", "handler": custom_handler}
129	}
130	```
131	If the source is omitted the whole tool result is sent to the handler.
132	Example:
133	```python
134	{
135	"documents": {"handler": custom_handler}
136	}
137	```
138	:raises ValueError: If the component is invalid or schema generation fails.
139	"""
140	if not isinstance(component, Component):	1✔
141	message = (	1✔
142	f"Object {component!r} is not a Haystack component. "
143	"Use ComponentTool only with Haystack component instances."
144	)
145	raise ValueError(message)	1✔
146
147	if getattr(component, "__haystack_added_to_pipeline__", None):	1✔
148	msg = (	1✔
149	"Component has been added to a pipeline and can't be used to create a ComponentTool. "
150	"Create ComponentTool from a non-pipeline component instead."
151	)
152	raise ValueError(msg)	1✔
153
154	self._unresolved_parameters = parameters	1✔
155	# Create the tools schema from the component run method parameters
156	tool_schema = parameters or self._create_tool_parameters_schema(component, inputs_from_state or {})	1✔
157
158	def component_invoker(**kwargs):	1✔
159	"""
160	Invokes the component using keyword arguments provided by the LLM function calling/tool-generated response.
161
162	:param kwargs: The keyword arguments to invoke the component with.
163	:returns: The result of the component invocation.
164	"""
165	converted_kwargs = {}	1✔
166	input_sockets = component.__haystack_input__._sockets_dict # type: ignore[attr-defined]	1✔
167	for param_name, param_value in kwargs.items():	1✔
168	param_type = input_sockets[param_name].type	1✔
169
170	# Check if the type (or list element type) has from_dict
171	target_type = get_args(param_type)[0] if get_origin(param_type) is list else param_type	1✔
172	if hasattr(target_type, "from_dict"):	1✔
173	if isinstance(param_value, list):	1✔
174	resolved_param_value = [	1✔
175	target_type.from_dict(item) if isinstance(item, dict) else item for item in param_value
176	]
177	elif isinstance(param_value, dict):	×
178	resolved_param_value = target_type.from_dict(param_value)	×
179	else:
180	resolved_param_value = param_value	×
181	else:
182	# Let TypeAdapter handle both single values and lists
183	type_adapter = TypeAdapter(param_type)	1✔
184	resolved_param_value = type_adapter.validate_python(param_value)	1✔
185
186	converted_kwargs[param_name] = resolved_param_value	1✔
187	logger.debug(f"Invoking component {type(component)} with kwargs: {converted_kwargs}")	1✔
188	return component.run(**converted_kwargs)	1✔
189
190	# Generate a name for the tool if not provided
191	if not name:	1✔
192	class_name = component.__class__.__name__	1✔
193	# Convert camelCase/PascalCase to snake_case
194	name = "".join(	1✔
195	[
196	"_" + c.lower() if c.isupper() and i > 0 and not class_name[i - 1].isupper() else c.lower()
197	for i, c in enumerate(class_name)
198	]
199	).lstrip("_")
200
201	description = description or component.__doc__ or name	1✔
202
203	# Store component before calling super().__init__() so _get_valid_outputs() can access it
204	self._component = component	1✔
205	self._is_warmed_up = False	1✔
206
207	# Create the Tool instance with the component invoker as the function to be called and the schema
208	super().__init__(	1✔
209	name=name,
210	description=description,
211	parameters=tool_schema,
212	function=component_invoker,
213	inputs_from_state=inputs_from_state,
214	outputs_to_state=outputs_to_state,
215	outputs_to_string=outputs_to_string,
216	)
217
218	def _get_valid_inputs(self) -> set[str]:	1✔
219	"""
220	Return valid input parameter names from the component's input sockets.
221
222	Used to validate `inputs_from_state` against the component's actual inputs.
223	This ensures users don't reference non-existent component inputs.
224
225	:returns: Set of component input socket names.
226	"""
227	return set(self._component.__haystack_input__._sockets_dict.keys()) # type: ignore[attr-defined]	1✔
228
229	def _get_valid_outputs(self) -> set[str]:	1✔
230	"""
231	Return valid output names from the component's output sockets.
232
233	Used to validate `outputs_to_state` against the component's actual outputs.
234	This ensures users don't reference non-existent component outputs.
235
236	:returns: Set of component output socket names.
237	"""
238	return set(self._component.__haystack_output__._sockets_dict.keys()) # type: ignore[attr-defined]	1✔
239
240	def warm_up(self):	1✔
241	"""
242	Prepare the ComponentTool for use.
243	"""
244	if not self._is_warmed_up:	1✔
245	if hasattr(self._component, "warm_up"):	1✔
246	self._component.warm_up()	1✔
247	self._is_warmed_up = True	1✔
248
249	def to_dict(self) -> dict[str, Any]:	1✔
250	"""
251	Serializes the ComponentTool to a dictionary.
252	"""
253	serialized: dict[str, Any] = {	1✔
254	"component": component_to_dict(obj=self._component, name=self.name),
255	"name": self.name,
256	"description": self.description,
257	"parameters": self._unresolved_parameters,
258	"inputs_from_state": self.inputs_from_state,
259	"outputs_to_state": _serialize_outputs_to_state(self.outputs_to_state) if self.outputs_to_state else None,
260	}
261
262	if self.outputs_to_string is not None and self.outputs_to_string.get("handler") is not None:	1✔
263	# This is soft-copied as to not modify the attributes in place
264	serialized["outputs_to_string"] = self.outputs_to_string.copy()	1✔
265	serialized["outputs_to_string"]["handler"] = serialize_callable(self.outputs_to_string["handler"])	1✔
266	else:
267	serialized["outputs_to_string"] = None	1✔
268
269	return {"type": generate_qualified_class_name(type(self)), "data": serialized}	1✔
270
271	@classmethod	1✔
272	def from_dict(cls, data: dict[str, Any]) -> "ComponentTool":	1✔
273	"""
274	Deserializes the ComponentTool from a dictionary.
275	"""
276	inner_data = data["data"]	1✔
277	component_class = import_class_by_name(inner_data["component"]["type"])	1✔
278	component = component_from_dict(cls=component_class, data=inner_data["component"], name=inner_data["name"])	1✔
279
280	if "outputs_to_state" in inner_data and inner_data["outputs_to_state"]:	1✔
281	inner_data["outputs_to_state"] = _deserialize_outputs_to_state(inner_data["outputs_to_state"])	1✔
282
283	if (	1✔
284	inner_data.get("outputs_to_string") is not None
285	and inner_data["outputs_to_string"].get("handler") is not None
286	):
287	inner_data["outputs_to_string"]["handler"] = deserialize_callable(	1✔
288	inner_data["outputs_to_string"]["handler"]
289	)
290
291	return cls(	1✔
292	component=component,
293	name=inner_data["name"],
294	description=inner_data["description"],
295	parameters=inner_data.get("parameters", None),
296	outputs_to_string=inner_data.get("outputs_to_string", None),
297	inputs_from_state=inner_data.get("inputs_from_state", None),
298	outputs_to_state=inner_data.get("outputs_to_state", None),
299	)
300
301	def _create_tool_parameters_schema(self, component: Component, inputs_from_state: dict[str, Any]) -> dict[str, Any]:	1✔
302	"""
303	Creates an OpenAI tools schema from a component's run method parameters.
304
305	:param component: The component to create the schema from.
306	:raises SchemaGenerationError: If schema generation fails
307	:returns: OpenAI tools schema for the component's run method parameters.
308	"""
309	component_run_description, param_descriptions = _get_component_param_descriptions(component)	1✔
310
311	# collect fields (types and defaults) and descriptions from function parameters
312	fields: dict[str, Any] = {}	1✔
313
314	for input_name, socket in component.__haystack_input__._sockets_dict.items(): # type: ignore[attr-defined]	1✔
315	if inputs_from_state is not None and input_name in list(inputs_from_state.values()):	1✔
316	continue	1✔
317	input_type = socket.type	1✔
318	description = param_descriptions.get(input_name, f"Input '{input_name}' for the component.")	1✔
319
320	# if the parameter has not a default value, Pydantic requires an Ellipsis (...)
321	# to explicitly indicate that the parameter is required
322	default = ... if socket.is_mandatory else socket.default_value	1✔
323	resolved_type = _resolve_type(input_type)	1✔
324	fields[input_name] = (resolved_type, Field(default=default, description=description))	1✔
325
326	parameters_schema: dict[str, Any] = {}	1✔
327	try:	1✔
328	model = create_model(component.run.__name__, __doc__=component_run_description, **fields)	1✔
329	parameters_schema = model.model_json_schema()	1✔
330	except Exception as e:	×
331	raise SchemaGenerationError(	×
332	f"Failed to create JSON schema for the run method of Component '{component.__class__.__name__}'"
333	) from e
334
335	# we don't want to include title keywords in the schema, as they contain redundant information
336	# there is no programmatic way to prevent Pydantic from adding them, so we remove them later
337	# see https://github.com/pydantic/pydantic/discussions/8504
338	_remove_title_from_schema(parameters_schema)	1✔
339
340	return parameters_schema	1✔

deepset-ai / haystack / 20304787960

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous