mirror of
https://github.com/meta-llama/llama-stack.git
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162 lines
4.7 KiB
Python
162 lines
4.7 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 Any, Dict, List, Literal, Optional, Union
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from pydantic import BaseModel, Field
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from typing_extensions import Annotated
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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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Union[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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Union[
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OpenAIResponseOutputMessage,
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OpenAIResponseOutputMessageWebSearchToolCall,
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],
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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: Optional[OpenAIResponseError] = 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: Optional[str] = None
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status: str
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temperature: Optional[float] = None
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top_p: Optional[float] = None
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truncation: Optional[str] = None
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user: Optional[str] = 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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Union[
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OpenAIResponseObjectStreamResponseCreated,
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OpenAIResponseObjectStreamResponseCompleted,
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],
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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: Optional[str] = None
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# TODO: handle file content types
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OpenAIResponseInputMessageContent = Annotated[
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Union[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: Union[str, List[OpenAIResponseInputMessageContent]]
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role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
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type: Optional[Literal["message"]] = "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: Optional[str] = Field(default="medium", pattern="^low|medium|high$")
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# TODO: add user_location
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@json_schema_type
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class OpenAIResponseInputToolFunction(BaseModel):
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type: Literal["function"] = "function"
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name: str
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description: Optional[str] = None
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parameters: Optional[Dict[str, Any]]
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strict: Optional[bool]
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class FileSearchRankingOptions(BaseModel):
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ranker: Optional[str] = None
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score_threshold: Optional[float] = Field(default=0.0, ge=0.0, le=1.0)
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@json_schema_type
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class OpenAIResponseInputToolFileSearch(BaseModel):
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type: Literal["file_search"] = "file_search"
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vector_store_id: List[str]
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ranking_options: Optional[FileSearchRankingOptions] = None
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# TODO: add filters
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OpenAIResponseInputTool = Annotated[
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Union[OpenAIResponseInputToolWebSearch, OpenAIResponseInputToolFileSearch, OpenAIResponseInputToolFunction],
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool")
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