mirror of
https://github.com/microsoft/semantic-kernel.git
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Eduard van ValkenburgandEvan Mattson
de54193a0f
### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Implements the OpenAI Realtime API with Semantic Kernel ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Adds a RealtimeClientBase Adds RealtimeEvents Adds OpenAI and Azure Realtime clients Closes: #10108 ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [x] I didn't break anyone 😄 --------- Co-authored-by: Evan Mattson <[email protected]>
89 lines
3.6 KiB
Python
89 lines
3.6 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import mimetypes
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from typing import Any, ClassVar, Literal, TypeVar
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from numpy import ndarray
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from pydantic import Field
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from semantic_kernel.contents.binary_content import BinaryContent
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from semantic_kernel.contents.const import AUDIO_CONTENT_TAG, ContentTypes
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from semantic_kernel.utils.feature_stage_decorator import experimental
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_T = TypeVar("_T", bound="AudioContent")
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@experimental
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class AudioContent(BinaryContent):
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"""Audio Content class.
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This can be created either the bytes data or a data uri, additionally it can have a uri.
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The uri is a reference to the source, and might or might not point to the same thing as the data.
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Use the .from_audio_file method to create an instance from an audio file.
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This reads the file and guesses the mime_type.
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If both data_uri and data is provided, data will be used and a warning is logged.
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Args:
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uri (Url | None): The reference uri of the content.
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data_uri (DataUrl | None): The data uri of the content.
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data (str | bytes | None): The data of the content.
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data_format (str | None): The format of the data (e.g. base64).
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mime_type (str | None): The mime type of the audio, only used with data.
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kwargs (Any): Any additional arguments:
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inner_content (Any): The inner content of the response,
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this should hold all the information from the response so even
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when not creating a subclass a developer can leverage the full thing.
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ai_model_id (str | None): The id of the AI model that generated this response.
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metadata (dict[str, Any]): Any metadata that should be attached to the response.
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"""
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content_type: Literal[ContentTypes.AUDIO_CONTENT] = Field(default=AUDIO_CONTENT_TAG, init=False) # type: ignore
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tag: ClassVar[str] = AUDIO_CONTENT_TAG
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def __init__(
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self,
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uri: str | None = None,
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data_uri: str | None = None,
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data: str | bytes | ndarray | None = None,
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data_format: str | None = None,
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mime_type: str | None = None,
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**kwargs: Any,
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):
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"""Create an Audio Content object, either from a data_uri or data.
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Args:
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uri: The reference uri of the content.
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data_uri: The data uri of the content.
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data: The data of the content.
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data_format: The format of the data (e.g. base64).
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mime_type: The mime type of the audio, only used with data.
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kwargs: Any additional arguments:
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inner_content: The inner content of the response,
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this should hold all the information from the response so even
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when not creating a subclass a developer
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can leverage the full thing.
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ai_model_id: The id of the AI model that generated this response.
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metadata: Any metadata that should be attached to the response.
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"""
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super().__init__(
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uri=uri,
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data_uri=data_uri,
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data=data,
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data_format=data_format,
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mime_type=mime_type,
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**kwargs,
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)
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@classmethod
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def from_audio_file(cls: type[_T], path: str) -> _T:
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"""Create an instance from an audio file."""
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mime_type = mimetypes.guess_type(path)[0]
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with open(path, "rb") as audio_file:
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return cls(data=audio_file.read(), data_format="base64", mime_type=mime_type, uri=path)
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def to_dict(self) -> dict[str, Any]:
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"""Convert the instance to a dictionary."""
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return {"type": "audio_url", "audio_url": {"uri": str(self)}}
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