forked from phoenix-oss/llama-stack-mirror
8dfce2f596
# What does this PR do? This provides an initial [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) implementation. The API is not yet complete, and this is more a proof-of-concept to show how we can store responses in our key-value stores and use them to support the Responses API concepts like `previous_response_id`. ## Test Plan I've added a new `tests/integration/openai_responses/test_openai_responses.py` as part of a test-driven development for this new API. I'm only testing this locally with the remote-vllm provider for now, but it should work with any of our inference providers since the only API it requires out of the inference provider is the `openai_chat_completion` endpoint. ``` VLLM_URL="http://localhost:8000/v1" \ INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack build --template remote-vllm --image-type venv --run ``` ``` LLAMA_STACK_CONFIG="http://localhost:8321" \ python -m pytest -v \ tests/integration/openai_responses/test_openai_responses.py \ --text-model "meta-llama/Llama-3.2-3B-Instruct" ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
140 lines
4 KiB
Python
140 lines
4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
|
|
from typing import List, Literal, Optional, Union
|
|
|
|
from pydantic import BaseModel, Field
|
|
from typing_extensions import Annotated
|
|
|
|
from llama_stack.schema_utils import json_schema_type, register_schema
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseError(BaseModel):
|
|
code: str
|
|
message: str
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseOutputMessageContentOutputText(BaseModel):
|
|
text: str
|
|
type: Literal["output_text"] = "output_text"
|
|
|
|
|
|
OpenAIResponseOutputMessageContent = Annotated[
|
|
Union[OpenAIResponseOutputMessageContentOutputText,],
|
|
Field(discriminator="type"),
|
|
]
|
|
register_schema(OpenAIResponseOutputMessageContent, name="OpenAIResponseOutputMessageContent")
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseOutputMessage(BaseModel):
|
|
id: str
|
|
content: List[OpenAIResponseOutputMessageContent]
|
|
role: Literal["assistant"] = "assistant"
|
|
status: str
|
|
type: Literal["message"] = "message"
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
|
|
id: str
|
|
status: str
|
|
type: Literal["web_search_call"] = "web_search_call"
|
|
|
|
|
|
OpenAIResponseOutput = Annotated[
|
|
Union[
|
|
OpenAIResponseOutputMessage,
|
|
OpenAIResponseOutputMessageWebSearchToolCall,
|
|
],
|
|
Field(discriminator="type"),
|
|
]
|
|
register_schema(OpenAIResponseOutput, name="OpenAIResponseOutput")
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseObject(BaseModel):
|
|
created_at: int
|
|
error: Optional[OpenAIResponseError] = None
|
|
id: str
|
|
model: str
|
|
object: Literal["response"] = "response"
|
|
output: List[OpenAIResponseOutput]
|
|
parallel_tool_calls: bool = False
|
|
previous_response_id: Optional[str] = None
|
|
status: str
|
|
temperature: Optional[float] = None
|
|
top_p: Optional[float] = None
|
|
truncation: Optional[str] = None
|
|
user: Optional[str] = None
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseObjectStreamResponseCreated(BaseModel):
|
|
response: OpenAIResponseObject
|
|
type: Literal["response.created"] = "response.created"
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseObjectStreamResponseCompleted(BaseModel):
|
|
response: OpenAIResponseObject
|
|
type: Literal["response.completed"] = "response.completed"
|
|
|
|
|
|
OpenAIResponseObjectStream = Annotated[
|
|
Union[
|
|
OpenAIResponseObjectStreamResponseCreated,
|
|
OpenAIResponseObjectStreamResponseCompleted,
|
|
],
|
|
Field(discriminator="type"),
|
|
]
|
|
register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream")
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseInputMessageContentText(BaseModel):
|
|
text: str
|
|
type: Literal["input_text"] = "input_text"
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseInputMessageContentImage(BaseModel):
|
|
detail: Literal["low"] | Literal["high"] | Literal["auto"] = "auto"
|
|
type: Literal["input_image"] = "input_image"
|
|
# TODO: handle file_id
|
|
image_url: Optional[str] = None
|
|
|
|
|
|
# TODO: handle file content types
|
|
OpenAIResponseInputMessageContent = Annotated[
|
|
Union[OpenAIResponseInputMessageContentText, OpenAIResponseInputMessageContentImage],
|
|
Field(discriminator="type"),
|
|
]
|
|
register_schema(OpenAIResponseInputMessageContent, name="OpenAIResponseInputMessageContent")
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseInputMessage(BaseModel):
|
|
content: Union[str, List[OpenAIResponseInputMessageContent]]
|
|
role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
|
|
type: Optional[Literal["message"]] = "message"
|
|
|
|
|
|
@json_schema_type
|
|
class OpenAIResponseInputToolWebSearch(BaseModel):
|
|
type: Literal["web_search"] | Literal["web_search_preview_2025_03_11"] = "web_search"
|
|
# TODO: actually use search_context_size somewhere...
|
|
search_context_size: Optional[str] = Field(default="medium", pattern="^low|medium|high$")
|
|
# TODO: add user_location
|
|
|
|
|
|
OpenAIResponseInputTool = Annotated[
|
|
Union[OpenAIResponseInputToolWebSearch,],
|
|
Field(discriminator="type"),
|
|
]
|
|
register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool")
|