feat: Add Tool.from_component

haystack_experimental/components/tools/openai/component_caller.py

@@ -0,0 +1,145 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <[email protected]>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import MISSING, fields, is_dataclass
+from inspect import getdoc
+from typing import Any, Callable, Dict, Union, get_args, get_origin
+
+from docstring_parser import parse
+from haystack import logging
+from haystack.core.component import Component
+from pydantic.fields import FieldInfo
+
+from haystack_experimental.util.utils import is_pydantic_v2_model
+
+logger = logging.getLogger(__name__)
+
+
+def extract_component_parameters(component: Component) -> Dict[str, Any]:
+    """
+    Extracts parameters from a Haystack component and converts them to OpenAI tools JSON format.
+
+    :param component: The component to extract parameters from.
+    :returns: A dictionary representing the component's input parameters schema.
+    """
+    properties = {}
+    required = []
+
+    param_descriptions = get_param_descriptions(component.run)
+
+    for input_name, socket in component.__haystack_input__._sockets_dict.items():
+        input_type = socket.type
+        description = param_descriptions.get(input_name, f"Input '{input_name}' for the component.")
+
+        try:
+            property_schema = create_property_schema(input_type, description)
+        except ValueError as e:
+            raise ValueError(f"Error processing input '{input_name}': {e}")
+
+        properties[input_name] = property_schema
+
+        # Use socket.is_mandatory() to check if the input is required
+        if socket.is_mandatory:
+            required.append(input_name)
+
+    parameters_schema = {"type": "object", "properties": properties}
+
+    if required:
+        parameters_schema["required"] = required
+
+    return parameters_schema
+
+
+def get_param_descriptions(method: Callable) -> Dict[str, str]:
+    """
+    Extracts parameter descriptions from the method's docstring using docstring_parser.
+
+    :param method: The method to extract parameter descriptions from.
+    :returns: A dictionary mapping parameter names to their descriptions.
+    """
+    docstring = getdoc(method)
+    if not docstring:
+        return {}
+
+    parsed_doc = parse(docstring)
+    param_descriptions = {}
+    for param in parsed_doc.params:
+        if not param.description:
+            logger.warning(
+                "Missing description for parameter '%s'. Please add a description in the component's "
+                "run() method docstring using the format ':param %s: <description>'. "
+                "This description is used to generate the Tool and helps the LLM understand how to use this parameter.",
+                param.arg_name,
+                param.arg_name,
+            )
+        param_descriptions[param.arg_name] = param.description.strip() if param.description else ""
+    return param_descriptions
+
+
+# ruff: noqa: PLR0912
+def create_property_schema(python_type: Any, description: str, default: Any = None) -> Dict[str, Any]:
+    """
+    Creates a property schema for a given Python type, recursively if necessary.
+
+    :param python_type: The Python type to create a property schema for.
+    :param description: The description of the property.
+    :param default: The default value of the property.
+    :returns: A dictionary representing the property schema.
+    """
+    nullable = is_nullable_type(python_type)
+    if nullable:
+        non_none_types = [t for t in get_args(python_type) if t is not type(None)]
+        python_type = non_none_types[0] if non_none_types else str
+
+    origin = get_origin(python_type)
+    if origin is list:
+        item_type = get_args(python_type)[0] if get_args(python_type) else Any
+        # recursively call create_property_schema for the item type
+        items_schema = create_property_schema(item_type, "")
+        items_schema.pop("description", None)
+        schema = {"type": "array", "description": description, "items": items_schema}
+    elif is_dataclass(python_type) or is_pydantic_v2_model(python_type):
+        schema = {"type": "object", "description": description, "properties": {}}
+        required_fields = []
+
+        if is_dataclass(python_type):
+            # Get the actual class if python_type is an instance otherwise use the type
+            cls = python_type if isinstance(python_type, type) else python_type.__class__
+            for field in fields(cls):
+                field_description = f"Field '{field.name}' of '{cls.__name__}'."
+                if isinstance(schema["properties"], dict):
+                    schema["properties"][field.name] = create_property_schema(field.type, field_description)
+
+        else:  # Pydantic model
+            for m_name, m_field in python_type.model_fields.items():
+                field_description = f"Field '{m_name}' of '{python_type.__name__}'."
+                if isinstance(schema["properties"], dict):
+                    schema["properties"][m_name] = create_property_schema(m_field.annotation, field_description)
+                if m_field.is_required():
+                    required_fields.append(m_name)
+
+        if required_fields:
+            schema["required"] = required_fields
+    else:
+        # Basic types
+        type_mapping = {str: "string", int: "integer", float: "number", bool: "boolean", dict: "object"}
+        schema = {"type": type_mapping.get(python_type, "string"), "description": description}
+
+    if default is not None:
+        schema["default"] = default
+
+    return schema
+
+
+def is_nullable_type(python_type: Any) -> bool:
+    """
+    Checks if the type is a Union with NoneType (i.e., Optional).
+
+    :param python_type: The Python type to check.
+    :returns: True if the type is a Union with NoneType, False otherwise.
+    """
+    origin = get_origin(python_type)
+    if origin is Union:
+        return type(None) in get_args(python_type)
+    return False

haystack_experimental/dataclasses/tool.py

@@ -4,17 +4,22 @@
 
 import inspect
 from dataclasses import asdict, dataclass
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Callable, Dict, Optional, get_args, get_origin
 
+from haystack import logging
+from haystack.core.component import Component
 from haystack.lazy_imports import LazyImport
 from haystack.utils import deserialize_callable, serialize_callable
-from pydantic import create_model
+from pydantic import TypeAdapter, create_model
 
 with LazyImport(message="Run 'pip install jsonschema'") as jsonschema_import:
     from jsonschema import Draft202012Validator
     from jsonschema.exceptions import SchemaError
 
 
+logger = logging.getLogger(__name__)
+
+
 class ToolInvocationError(Exception):
     """
     Exception raised when a Tool invocation fails.
@@ -198,6 +203,66 @@ def get_weather(
 
         return Tool(name=name or function.__name__, description=tool_description, parameters=schema, function=function)
 
+    @classmethod
+    def from_component(cls, component: Component, name: str, description: str) -> "Tool":
+        """
+        Create a Tool instance from a Haystack component.
+
+        :param component: The Haystack component to be converted into a Tool.
+        :param name: Name for the tool.
+        :param description: Description of the tool.
+        :returns: The Tool created from the Component.
+        :raises ValueError: If the component is invalid or schema generation fails.
+        """
+
+        if not isinstance(component, Component):
+            raise ValueError(
+                f"{component} is not a Haystack component!" "Can only create a Tool from a Haystack component instance."
+            )
+
+        if getattr(component, "__haystack_added_to_pipeline__", None):
+            msg = (
+                "Component has been added in a Pipeline and can't be used to create a Tool. "
+                "Create Tool from a non-pipeline component instead."
+            )
+            raise ValueError(msg)
+
+        from haystack_experimental.components.tools.openai.component_caller import extract_component_parameters
+
+        # Extract the parameters schema from the component
+        parameters = extract_component_parameters(component)
+
+        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
+            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):
+                        param_value = [target_type.from_dict(item) for item in param_value if isinstance(item, dict)]
+                    elif isinstance(param_value, dict):
+                        param_value = target_type.from_dict(param_value)
+                else:
+                    # Let TypeAdapter handle both single values and lists
+                    type_adapter = TypeAdapter(param_type)
+                    param_value = type_adapter.validate_python(param_value)
+
+                converted_kwargs[param_name] = param_value
+            logger.debug(f"Invoking component {type(component)} with kwargs: {converted_kwargs}")
+            return component.run(**converted_kwargs)
+
+        # Return a new Tool instance with the component invoker as the function to be called
+        return Tool(name=name, description=description, parameters=parameters, function=component_invoker)
+
 
 def _remove_title_from_schema(schema: Dict[str, Any]):
     """

haystack_experimental/util/utils.py

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import List, Union
+from typing import Any, List, Union
 
 
 def expand_page_range(page_range: List[Union[str, int]]) -> List[int]:
@@ -41,3 +41,13 @@ def expand_page_range(page_range: List[Union[str, int]]) -> List[int]:
         raise ValueError("No valid page numbers or ranges found in the input list")
 
     return expanded_page_range
+
+
+def is_pydantic_v2_model(instance: Any) -> bool:
+    """
+    Checks if the instance is a Pydantic v2 model.
+
+    :param instance: The instance to check.
+    :returns: True if the instance is a Pydantic v2 model, False otherwise.
+    """
+    return hasattr(instance, "model_validate")