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https://github.com/meta-llama/llama-stack.git
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467fb19345
This adds storing of input items with previous responses and then restores those input items to prepend to the user's messages list when using conversation state. I missed this in the initial implementation, but it makes sense that we have to store the input items from previous responses so that we can reconstruct the proper messages stack for multi-turn conversations - just the output from previous responses isn't enough context for the models to follow the turns and the original instructions. Signed-off-by: Ben Browning <bbrownin@redhat.com>
150 lines
4.3 KiB
Python
150 lines
4.3 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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@json_schema_type
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class OpenAIResponseInputItemMessage(OpenAIResponseInputMessage):
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id: str
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@json_schema_type
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class OpenAIResponseInputItemList(BaseModel):
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data: list[OpenAIResponseInputItemMessage]
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object: Literal["list"] = "list"
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@json_schema_type
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class OpenAIResponsePreviousResponseWithInputItems(BaseModel):
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input_items: OpenAIResponseInputItemList
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response: OpenAIResponseObject
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