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https://github.com/meta-llama/llama-stack.git
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9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Annotated, Literal
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from pydantic import BaseModel, Field
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from llama_stack.schema_utils import json_schema_type, register_schema
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@json_schema_type
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class OpenAIResponseError(BaseModel):
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code: str
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message: str
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@json_schema_type
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class OpenAIResponseOutputMessageContentOutputText(BaseModel):
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text: str
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type: Literal["output_text"] = "output_text"
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OpenAIResponseOutputMessageContent = Annotated[
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OpenAIResponseOutputMessageContentOutputText,
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseOutputMessageContent, name="OpenAIResponseOutputMessageContent")
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@json_schema_type
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class OpenAIResponseOutputMessage(BaseModel):
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id: str
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content: list[OpenAIResponseOutputMessageContent]
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role: Literal["assistant"] = "assistant"
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status: str
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type: Literal["message"] = "message"
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@json_schema_type
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class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
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id: str
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status: str
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type: Literal["web_search_call"] = "web_search_call"
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OpenAIResponseOutput = Annotated[
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OpenAIResponseOutputMessage | OpenAIResponseOutputMessageWebSearchToolCall,
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseOutput, name="OpenAIResponseOutput")
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@json_schema_type
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class OpenAIResponseObject(BaseModel):
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created_at: int
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error: OpenAIResponseError | None = None
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id: str
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model: str
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object: Literal["response"] = "response"
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output: list[OpenAIResponseOutput]
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parallel_tool_calls: bool = False
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previous_response_id: str | None = None
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status: str
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temperature: float | None = None
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top_p: float | None = None
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truncation: str | None = None
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user: str | None = None
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@json_schema_type
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class OpenAIResponseObjectStreamResponseCreated(BaseModel):
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response: OpenAIResponseObject
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type: Literal["response.created"] = "response.created"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseCompleted(BaseModel):
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response: OpenAIResponseObject
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type: Literal["response.completed"] = "response.completed"
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OpenAIResponseObjectStream = Annotated[
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OpenAIResponseObjectStreamResponseCreated | OpenAIResponseObjectStreamResponseCompleted,
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream")
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@json_schema_type
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class OpenAIResponseInputMessageContentText(BaseModel):
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text: str
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type: Literal["input_text"] = "input_text"
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@json_schema_type
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class OpenAIResponseInputMessageContentImage(BaseModel):
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detail: Literal["low"] | Literal["high"] | Literal["auto"] = "auto"
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type: Literal["input_image"] = "input_image"
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# TODO: handle file_id
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image_url: str | None = None
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# TODO: handle file content types
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OpenAIResponseInputMessageContent = Annotated[
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OpenAIResponseInputMessageContentText | OpenAIResponseInputMessageContentImage,
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseInputMessageContent, name="OpenAIResponseInputMessageContent")
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@json_schema_type
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class OpenAIResponseInputMessage(BaseModel):
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content: str | list[OpenAIResponseInputMessageContent]
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role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
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type: Literal["message"] | None = "message"
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@json_schema_type
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class OpenAIResponseInputToolWebSearch(BaseModel):
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type: Literal["web_search"] | Literal["web_search_preview_2025_03_11"] = "web_search"
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# TODO: actually use search_context_size somewhere...
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search_context_size: str | None = Field(default="medium", pattern="^low|medium|high$")
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# TODO: add user_location
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OpenAIResponseInputTool = Annotated[
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OpenAIResponseInputToolWebSearch,
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool")
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