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https://github.com/meta-llama/llama-stack.git
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b90bb66f28
This adjusts the restoration of previous responses to prepend them to the list of Responses API inputs instead of our converted list of Chat Completion messages. This matches the expected behavior of the Responses API, and I misinterpreted the nuances here in the initial implementation. Signed-off-by: Ben Browning <bbrownin@redhat.com>
156 lines
4.7 KiB
Python
156 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 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 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 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 OpenAIResponseMessage(BaseModel):
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"""
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Corresponds to the various Message types in the Responses API.
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They are all under one type because the Responses API gives them all
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the same "type" value, and there is no way to tell them apart in certain
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scenarios.
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"""
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content: str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]
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role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
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type: Literal["message"] = "message"
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# The fields below are not used in all scenarios, but are required in others.
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id: str | None = None
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status: str | None = None
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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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OpenAIResponseMessage | 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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OpenAIResponseInput = Annotated[
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# Responses API allows output messages to be passed in as input
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OpenAIResponseOutputMessageWebSearchToolCall
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# Fallback to the generic message type as a last resort
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OpenAIResponseMessage,
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Field(union_mode="left_to_right"),
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]
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register_schema(OpenAIResponseInput, name="OpenAIResponseInput")
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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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class OpenAIResponseInputItemList(BaseModel):
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data: list[OpenAIResponseInput]
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object: Literal["list"] = "list"
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class OpenAIResponsePreviousResponseWithInputItems(BaseModel):
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input_items: OpenAIResponseInputItemList
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response: OpenAIResponseObject
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