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https://github.com/microsoft/agent-framework.git
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Python: openai updates (#388)
* openai updates * rebuild of openai structure * updated responses structure * renamed sample * added file id support to code interpreter * added hosted file ids to code interpretor * mypy fixes * removed default az cred from codebase * updated agent name setup * added kwargs to entra methods * and further kwargs * extra comment * updated all samples * readded custom get methods for responses * updated int tests with ad credential * missed one
This commit is contained in:
Eduard van Valkenburg
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GitHub
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GitHub
parent
19676978e9
commit
df9d85d1f0
@@ -7,9 +7,10 @@ from time import perf_counter
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from typing import TYPE_CHECKING, Annotated, Any, Generic, Protocol, TypeVar, get_args, get_origin, runtime_checkable
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from opentelemetry import metrics, trace
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from pydantic import BaseModel, Field, create_model
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from pydantic import BaseModel, Field, PrivateAttr, create_model
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from ._logging import get_logger
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from ._pydantic import AFBaseModel
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from .telemetry import GenAIAttributes, start_as_current_span
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if TYPE_CHECKING:
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@@ -22,6 +23,48 @@ logger = get_logger()
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__all__ = ["AIFunction", "AITool", "HostedCodeInterpreterTool", "ai_function"]
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def _parse_inputs(
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inputs: "AIContents | dict[str, Any] | str | list[AIContents | dict[str, Any] | str] | None",
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) -> list["AIContents"]:
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"""Parse the inputs for a tool, ensuring they are of type AIContents."""
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if inputs is None:
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return []
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from ._types import AIContent, DataContent, HostedFileContent, HostedVectorStoreContent, UriContent
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parsed_inputs: list["AIContents"] = []
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if not isinstance(inputs, list):
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inputs = [inputs]
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for input_item in inputs:
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if isinstance(input_item, str):
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# If it's a string, we assume it's a URI or similar identifier.
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# Convert it to a UriContent or similar type as needed.
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parsed_inputs.append(UriContent(uri=input_item, media_type="text/plain"))
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elif isinstance(input_item, dict):
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# If it's a dict, we assume it contains properties for a specific content type.
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# we check if the required keys are present to determine the type.
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# for instance, if it has "uri" and "media_type", we treat it as UriContent.
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# if is only has uri, then we treat it as DataContent.
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# etc.
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if "uri" in input_item:
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parsed_inputs.append(
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UriContent(**input_item) if "media_type" in input_item else DataContent(**input_item)
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)
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elif "file_id" in input_item:
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parsed_inputs.append(HostedFileContent(**input_item))
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elif "vector_store_id" in input_item:
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parsed_inputs.append(HostedVectorStoreContent(**input_item))
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elif "data" in input_item:
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parsed_inputs.append(DataContent(**input_item))
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else:
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raise ValueError(f"Unsupported input type: {input_item}")
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elif isinstance(input_item, AIContent):
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parsed_inputs.append(input_item)
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else:
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raise TypeError(f"Unsupported input type: {type(input_item).__name__}. Expected AIContents or dict.")
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return parsed_inputs
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@runtime_checkable
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class AITool(Protocol):
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"""Represents a generic tool that can be specified to an AI service.
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@@ -37,9 +80,9 @@ class AITool(Protocol):
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name: str
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"""The name of the tool."""
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description: str | None = None
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description: str
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"""A description of the tool, suitable for use in describing the purpose to a model."""
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additional_properties: dict[str, Any] | None = None
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additional_properties: dict[str, Any] | None
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"""Additional properties associated with the tool."""
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def __str__(self) -> str:
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@@ -51,48 +94,96 @@ ArgsT = TypeVar("ArgsT", bound=BaseModel)
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ReturnT = TypeVar("ReturnT")
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class AIFunction(AITool, Generic[ArgsT, ReturnT]):
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"""A AITool that is callable as code."""
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class AIToolBase(AFBaseModel):
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"""Base class for AI tools, providing common attributes and methods.
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Args:
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name: The name of the tool.
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description: A description of the tool.
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additional_properties: Additional properties associated with the tool.
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"""
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name: str = Field(..., kw_only=False)
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description: str = ""
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additional_properties: dict[str, Any] | None = None
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def __str__(self) -> str:
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"""Return a string representation of the tool."""
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if self.description:
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return f"{self.__class__.__name__}(name={self.name}, description={self.description})"
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return f"{self.__class__.__name__}(name={self.name})"
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class HostedCodeInterpreterTool(AIToolBase):
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"""Represents a hosted tool that can be specified to an AI service to enable it to execute generated code.
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This tool does not implement code interpretation itself. It serves as a marker to inform a service
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that it is allowed to execute generated code if the service is capable of doing so.
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"""
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inputs: list[Any] = Field(default_factory=list)
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def __init__(
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self,
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func: Callable[..., Awaitable[ReturnT] | ReturnT],
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name: str,
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description: str,
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input_model: type[ArgsT],
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*,
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inputs: "AIContents | dict[str, Any] | str | list[AIContents | dict[str, Any] | str] | None" = None,
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description: str | None = None,
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additional_properties: dict[str, Any] | None = None,
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**kwargs: Any,
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):
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"""Initialize a FunctionTool.
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) -> None:
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"""Initialize the HostedCodeInterpreterTool.
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Args:
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func: The function to wrap.
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name: The name of the tool.
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inputs: A list of contents that the tool can accept as input. Defaults to None.
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This should mostly be HostedFileContent or HostedVectorStoreContent.
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Can also be DataContent, depending on the service used.
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When supplying a list, it can contain:
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- AIContents instances
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- dicts with properties for AIContents (e.g., {"uri": "http://example.com", "media_type": "text/html"})
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- strings (which will be converted to UriContent with media_type "text/plain").
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If None, defaults to an empty list.
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description: A description of the tool.
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input_model: A Pydantic model that defines the input parameters for the function.
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**kwargs: Additional properties to set on the tool.
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stored in additional_properties.
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additional_properties: Additional properties associated with the tool.
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**kwargs: Additional keyword arguments to pass to the base class.
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"""
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self.name = name
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self.description = description
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self.input_model = input_model
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self.additional_properties: dict[str, Any] | None = kwargs
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self._func = func
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self.invocation_duration_histogram = meter.create_histogram(
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"agent_framework.function.invocation.duration",
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args: dict[str, Any] = {
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"name": "code_interpreter",
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}
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if inputs:
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args["inputs"] = _parse_inputs(inputs)
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if description is not None:
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args["description"] = description
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if additional_properties is not None:
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args["additional_properties"] = additional_properties
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if "name" in kwargs:
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raise ValueError("The 'name' argument is reserved for the HostedCodeInterpreterTool and cannot be set.")
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super().__init__(**args, **kwargs)
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class AIFunction(AIToolBase, Generic[ArgsT, ReturnT]):
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"""A AITool that is callable as code.
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Args:
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name: The name of the function.
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description: A description of the function.
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additional_properties: Additional properties to set on the function.
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func: The function to wrap. If None, returns a decorator.
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input_model: The Pydantic model that defines the input parameters for the function.
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"""
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func: Callable[..., Awaitable[ReturnT] | ReturnT]
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input_model: type[ArgsT]
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_invocation_duration_histogram: metrics.Histogram = PrivateAttr(
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default_factory=lambda: meter.create_histogram(
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GenAIAttributes.MEASUREMENT_FUNCTION_INVOCATION_DURATION.value,
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unit="s",
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description="Measures the duration of a function's execution",
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)
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def parameters(self) -> dict[str, Any]:
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"""Return the parameter json schemas of the input model."""
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return self.input_model.model_json_schema()
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)
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def __call__(self, *args: Any, **kwargs: Any) -> ReturnT | Awaitable[ReturnT]:
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"""Call the wrapped function with the provided arguments."""
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return self._func(*args, **kwargs)
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def __str__(self) -> str:
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return f"AIFunction(name={self.name}, description={self.description})"
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return self.func(*args, **kwargs)
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async def invoke(
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self,
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@@ -136,9 +227,24 @@ class AIFunction(AITool, Generic[ArgsT, ReturnT]):
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raise
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finally:
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duration = perf_counter() - starting_time_stamp
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self.invocation_duration_histogram.record(duration, attributes=attributes)
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self._invocation_duration_histogram.record(duration, attributes=attributes)
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logger.info("Function completed. Duration: %fs", duration)
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def parameters(self) -> dict[str, Any]:
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"""Create the json schema of the parameters."""
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return self.input_model.model_json_schema()
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def to_json_schema_spec(self) -> dict[str, Any]:
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"""Convert a AIFunction to the JSON Schema function specification format."""
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return {
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"type": "function",
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"function": {
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"name": self.name,
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"description": self.description,
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"parameters": self.parameters(),
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},
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}
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def _parse_annotation(annotation: Any) -> Any:
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"""Parse a type annotation and return the corresponding type.
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@@ -207,91 +313,13 @@ def ai_function(
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raise TypeError(f"Input model for {tool_name} must be a subclass of BaseModel, got {input_model}")
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return AIFunction[Any, ReturnT](
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func=f,
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name=tool_name,
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description=tool_desc,
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additional_properties=additional_properties or {},
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func=f,
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input_model=input_model,
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**(additional_properties if additional_properties is not None else {}),
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)
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return wrapper(func)
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return decorator(func) if func else decorator # type: ignore[reportReturnType, return-value]
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def _parse_inputs(
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inputs: "AIContents | dict[str, Any] | str | list[AIContents | dict[str, Any] | str] | None",
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) -> list["AIContents"]:
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"""Parse the inputs for a tool, ensuring they are of type AIContents."""
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if inputs is None:
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return []
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from ._types import AIContent, DataContent, HostedFileContent, HostedVectorStoreContent, UriContent
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parsed_inputs: list["AIContents"] = []
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if not isinstance(inputs, list):
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inputs = [inputs]
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for input_item in inputs:
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if isinstance(input_item, str):
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# If it's a string, we assume it's a URI or similar identifier.
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# Convert it to a UriContent or similar type as needed.
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parsed_inputs.append(UriContent(uri=input_item, media_type="text/plain"))
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elif isinstance(input_item, dict):
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# If it's a dict, we assume it contains properties for a specific content type.
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# we check if the required keys are present to determine the type.
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if "uri" in input_item:
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parsed_inputs.append(
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UriContent(**input_item) if "media_type" in input_item else DataContent(**input_item)
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)
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elif "file_id" in input_item:
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parsed_inputs.append(HostedFileContent(**input_item))
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elif "vector_store_id" in input_item:
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parsed_inputs.append(HostedVectorStoreContent(**input_item))
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elif "data" in input_item:
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parsed_inputs.append(DataContent(**input_item))
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else:
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raise ValueError(f"Unsupported input type: {input_item}")
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elif isinstance(input_item, AIContent):
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parsed_inputs.append(input_item)
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else:
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raise TypeError(f"Unsupported input type: {type(input_item).__name__}. Expected AIContents or dict.")
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return parsed_inputs
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class HostedCodeInterpreterTool(AITool):
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"""Represents a hosted tool that can be specified to an AI service to enable it to execute generated code.
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This tool does not implement code interpretation itself. It serves as a marker to inform a service
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that it is allowed to execute generated code if the service is capable of doing so.
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"""
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def __init__(
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self,
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name: str = "code_interpreter",
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inputs: "AIContents | dict[str, Any] | str | list[AIContents | dict[str, Any] | str] | None" = None,
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description: str | None = None,
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additional_properties: dict[str, Any] | None = None,
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):
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"""Initialize a HostedCodeInterpreterTool.
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Args:
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name: The name of the tool. Defaults to "code_interpreter".
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inputs: A list of contents that the tool can accept as input. Defaults to None.
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This should mostly be HostedFileContent or HostedVectorStoreContent.
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Can also be DataContent, depending on the service used.
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When supplying a list, it can contain:
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- AIContents instances
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- dicts with properties for AIContents (e.g., {"uri": "http://example.com", "media_type": "text/html"})
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- strings (which will be converted to UriContent with media_type "text/plain").
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If None, defaults to an empty list.
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description: A description of the tool.
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additional_properties: Additional properties associated with the tool, specific to the service used.
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"""
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self.name = name
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self.inputs = _parse_inputs(inputs)
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self.description = description
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self.additional_properties = additional_properties
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def __str__(self) -> str:
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"""Return a string representation of the tool."""
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return f"HostedCodeInterpreterTool(name={self.name})"
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