mirror of
https://github.com/meta-llama/llama-stack.git
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2d9fd041eb
# What does this PR do? These are a couple of fixes to get an example LangChain app working with our OpenAI Responses API implementation. The Responses API spec requires an annotations array in `output[*].content[*].annotations` and we were not providing one. So, this adds that as an empty list, even though we don't do anything to populate it yet. This prevents an error from client libraries like Langchain that expect this field to always exist, even if an empty list. The other fix is `web_search_preview` is a valid name for the web search tool in the Responses API, but we only responded to `web_search` or `web_search_preview_2025_03_11`. ## Test Plan The existing Responses unit tests were expanded to test these cases, via: ``` pytest -sv tests/unit/providers/agents/meta_reference/test_openai_responses.py ``` The existing test_openai_responses.py integration tests still pass with this change, tested as below with Fireworks: ``` uv run llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ uv run pytest -sv tests/integration/agents/test_openai_responses.py \ --text-model accounts/fireworks/models/llama4-scout-instruct-basic ``` Lastly, this example LangChain app now works with Llama stack (tested with Ollama in the starter template in this case). This LangChain code is using the example snippets for using Responses API at https://python.langchain.com/docs/integrations/chat/openai/#responses-api ```python from langchain_openai import ChatOpenAI llm = ChatOpenAI( base_url="http://localhost:8321/v1/openai/v1", api_key="fake", model="ollama/meta-llama/Llama-3.2-3B-Instruct", ) tool = {"type": "web_search_preview"} llm_with_tools = llm.bind_tools([tool]) response = llm_with_tools.invoke("What was a positive news story from today?") print(response.content) ``` Signed-off-by: Ben Browning <bbrownin@redhat.com>
511 lines
16 KiB
Python
511 lines
16 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, Any, Literal
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from pydantic import BaseModel, Field
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from typing_extensions import TypedDict
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from llama_stack.apis.vector_io import SearchRankingOptions as FileSearchRankingOptions
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from llama_stack.schema_utils import json_schema_type, register_schema
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# NOTE(ashwin): this file is literally a copy of the OpenAI responses API schema. We should probably
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# take their YAML and generate this file automatically. Their YAML is available.
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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 OpenAIResponseAnnotationFileCitation(BaseModel):
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type: Literal["file_citation"] = "file_citation"
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file_id: str
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filename: str
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index: int
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@json_schema_type
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class OpenAIResponseAnnotationCitation(BaseModel):
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type: Literal["url_citation"] = "url_citation"
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end_index: int
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start_index: int
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title: str
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url: str
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@json_schema_type
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class OpenAIResponseAnnotationContainerFileCitation(BaseModel):
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type: Literal["container_file_citation"] = "container_file_citation"
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container_id: str
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end_index: int
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file_id: str
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filename: str
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start_index: int
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@json_schema_type
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class OpenAIResponseAnnotationFilePath(BaseModel):
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type: Literal["file_path"] = "file_path"
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file_id: str
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index: int
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OpenAIResponseAnnotations = Annotated[
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OpenAIResponseAnnotationFileCitation
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| OpenAIResponseAnnotationCitation
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| OpenAIResponseAnnotationContainerFileCitation
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| OpenAIResponseAnnotationFilePath,
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseAnnotations, name="OpenAIResponseAnnotations")
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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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annotations: list[OpenAIResponseAnnotations] = Field(default_factory=list)
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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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@json_schema_type
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class OpenAIResponseOutputMessageFileSearchToolCall(BaseModel):
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id: str
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queries: list[str]
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status: str
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type: Literal["file_search_call"] = "file_search_call"
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results: list[dict[str, Any]] | None = None
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@json_schema_type
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class OpenAIResponseOutputMessageFunctionToolCall(BaseModel):
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call_id: str
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name: str
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arguments: str
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type: Literal["function_call"] = "function_call"
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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 OpenAIResponseOutputMessageMCPCall(BaseModel):
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id: str
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type: Literal["mcp_call"] = "mcp_call"
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arguments: str
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name: str
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server_label: str
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error: str | None = None
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output: str | None = None
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class MCPListToolsTool(BaseModel):
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input_schema: dict[str, Any]
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name: str
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description: str | None = None
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@json_schema_type
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class OpenAIResponseOutputMessageMCPListTools(BaseModel):
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id: str
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type: Literal["mcp_list_tools"] = "mcp_list_tools"
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server_label: str
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tools: list[MCPListToolsTool]
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OpenAIResponseOutput = Annotated[
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OpenAIResponseMessage
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| OpenAIResponseOutputMessageWebSearchToolCall
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| OpenAIResponseOutputMessageFileSearchToolCall
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| OpenAIResponseOutputMessageFunctionToolCall
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| OpenAIResponseOutputMessageMCPCall
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| OpenAIResponseOutputMessageMCPListTools,
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseOutput, name="OpenAIResponseOutput")
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# This has to be a TypedDict because we need a "schema" field and our strong
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# typing code in the schema generator doesn't support Pydantic aliases. That also
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# means we can't use a discriminator field here, because TypedDicts don't support
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# default values which the strong typing code requires for discriminators.
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class OpenAIResponseTextFormat(TypedDict, total=False):
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"""Configuration for Responses API text format.
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:param type: Must be "text", "json_schema", or "json_object" to identify the format type
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:param name: The name of the response format. Only used for json_schema.
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:param schema: The JSON schema the response should conform to. In a Python SDK, this is often a `pydantic` model. Only used for json_schema.
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:param description: (Optional) A description of the response format. Only used for json_schema.
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:param strict: (Optional) Whether to strictly enforce the JSON schema. If true, the response must match the schema exactly. Only used for json_schema.
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"""
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type: Literal["text"] | Literal["json_schema"] | Literal["json_object"]
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name: str | None
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schema: dict[str, Any] | None
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description: str | None
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strict: bool | None
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@json_schema_type
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class OpenAIResponseText(BaseModel):
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format: OpenAIResponseTextFormat | None = None
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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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# Default to text format to avoid breaking the loading of old responses
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# before the field was added. New responses will have this set always.
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text: OpenAIResponseText = OpenAIResponseText(format=OpenAIResponseTextFormat(type="text"))
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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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@json_schema_type
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class OpenAIResponseObjectStreamResponseOutputItemAdded(BaseModel):
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response_id: str
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item: OpenAIResponseOutput
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output_index: int
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sequence_number: int
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type: Literal["response.output_item.added"] = "response.output_item.added"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseOutputItemDone(BaseModel):
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response_id: str
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item: OpenAIResponseOutput
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output_index: int
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sequence_number: int
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type: Literal["response.output_item.done"] = "response.output_item.done"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseOutputTextDelta(BaseModel):
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content_index: int
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delta: str
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.output_text.delta"] = "response.output_text.delta"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseOutputTextDone(BaseModel):
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content_index: int
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text: str # final text of the output item
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.output_text.done"] = "response.output_text.done"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta(BaseModel):
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delta: str
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.function_call_arguments.delta"] = "response.function_call_arguments.delta"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone(BaseModel):
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arguments: str # final arguments of the function call
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.function_call_arguments.done"] = "response.function_call_arguments.done"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseWebSearchCallInProgress(BaseModel):
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.web_search_call.in_progress"] = "response.web_search_call.in_progress"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseWebSearchCallSearching(BaseModel):
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.web_search_call.searching"] = "response.web_search_call.searching"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseWebSearchCallCompleted(BaseModel):
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.web_search_call.completed"] = "response.web_search_call.completed"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseMcpListToolsInProgress(BaseModel):
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sequence_number: int
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type: Literal["response.mcp_list_tools.in_progress"] = "response.mcp_list_tools.in_progress"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseMcpListToolsFailed(BaseModel):
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sequence_number: int
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type: Literal["response.mcp_list_tools.failed"] = "response.mcp_list_tools.failed"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseMcpListToolsCompleted(BaseModel):
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sequence_number: int
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type: Literal["response.mcp_list_tools.completed"] = "response.mcp_list_tools.completed"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta(BaseModel):
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delta: str
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.mcp_call.arguments.delta"] = "response.mcp_call.arguments.delta"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseMcpCallArgumentsDone(BaseModel):
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arguments: str # final arguments of the MCP call
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.mcp_call.arguments.done"] = "response.mcp_call.arguments.done"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseMcpCallInProgress(BaseModel):
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item_id: str
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output_index: int
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sequence_number: int
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type: Literal["response.mcp_call.in_progress"] = "response.mcp_call.in_progress"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseMcpCallFailed(BaseModel):
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sequence_number: int
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type: Literal["response.mcp_call.failed"] = "response.mcp_call.failed"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseMcpCallCompleted(BaseModel):
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sequence_number: int
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type: Literal["response.mcp_call.completed"] = "response.mcp_call.completed"
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OpenAIResponseObjectStream = Annotated[
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OpenAIResponseObjectStreamResponseCreated
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| OpenAIResponseObjectStreamResponseOutputItemAdded
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| OpenAIResponseObjectStreamResponseOutputItemDone
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| OpenAIResponseObjectStreamResponseOutputTextDelta
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| OpenAIResponseObjectStreamResponseOutputTextDone
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| OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta
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| OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone
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| OpenAIResponseObjectStreamResponseWebSearchCallInProgress
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| OpenAIResponseObjectStreamResponseWebSearchCallSearching
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| OpenAIResponseObjectStreamResponseWebSearchCallCompleted
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| OpenAIResponseObjectStreamResponseMcpListToolsInProgress
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| OpenAIResponseObjectStreamResponseMcpListToolsFailed
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| OpenAIResponseObjectStreamResponseMcpListToolsCompleted
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| OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta
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| OpenAIResponseObjectStreamResponseMcpCallArgumentsDone
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| OpenAIResponseObjectStreamResponseMcpCallInProgress
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| OpenAIResponseObjectStreamResponseMcpCallFailed
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| OpenAIResponseObjectStreamResponseMcpCallCompleted
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| 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 OpenAIResponseInputFunctionToolCallOutput(BaseModel):
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"""
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This represents the output of a function call that gets passed back to the model.
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"""
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call_id: str
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output: str
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type: Literal["function_call_output"] = "function_call_output"
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id: str | None = None
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status: str | None = None
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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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| OpenAIResponseOutputMessageFileSearchToolCall
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| OpenAIResponseOutputMessageFunctionToolCall
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| OpenAIResponseInputFunctionToolCallOutput
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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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# Must match type Literals of OpenAIResponseInputToolWebSearch below
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WebSearchToolTypes = ["web_search", "web_search_preview", "web_search_preview_2025_03_11"]
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@json_schema_type
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class OpenAIResponseInputToolWebSearch(BaseModel):
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# Must match values of WebSearchToolTypes above
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type: Literal["web_search"] | Literal["web_search_preview"] | Literal["web_search_preview_2025_03_11"] = (
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"web_search"
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)
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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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@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: str | None = None
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parameters: dict[str, Any] | None
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strict: bool | None = None
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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_ids: list[str]
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filters: dict[str, Any] | None = None
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max_num_results: int | None = Field(default=10, ge=1, le=50)
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ranking_options: FileSearchRankingOptions | None = None
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class ApprovalFilter(BaseModel):
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always: list[str] | None = None
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never: list[str] | None = None
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class AllowedToolsFilter(BaseModel):
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tool_names: list[str] | None = None
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@json_schema_type
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class OpenAIResponseInputToolMCP(BaseModel):
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type: Literal["mcp"] = "mcp"
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server_label: str
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server_url: str
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headers: dict[str, Any] | None = None
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require_approval: Literal["always"] | Literal["never"] | ApprovalFilter = "never"
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allowed_tools: list[str] | AllowedToolsFilter | None = None
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OpenAIResponseInputTool = Annotated[
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OpenAIResponseInputToolWebSearch
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| OpenAIResponseInputToolFileSearch
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| OpenAIResponseInputToolFunction
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| OpenAIResponseInputToolMCP,
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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 ListOpenAIResponseInputItem(BaseModel):
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data: list[OpenAIResponseInput]
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object: Literal["list"] = "list"
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@json_schema_type
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class OpenAIResponseObjectWithInput(OpenAIResponseObject):
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input: list[OpenAIResponseInput]
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@json_schema_type
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class ListOpenAIResponseObject(BaseModel):
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data: list[OpenAIResponseObjectWithInput]
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has_more: bool
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first_id: str
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last_id: str
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object: Literal["list"] = "list"
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