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https://github.com/BerriAI/litellm.git
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fix(openai): flatten top-level anyOf/oneOf/allOf in Responses API tool schemas
OpenAI's function-calling validator rejects tool parameters carrying oneOf/anyOf/allOf/enum/const/not at the top level, while the ChatGPT backend Codex talks to natively accepts them, so an MCP tool declaring a top-level union 400s through the proxy. Merge the branches into the object schema for OpenAI itself only, walking the namespace-nested tools current Codex builds send, on both /v1/responses and /v1/responses/compact
This commit is contained in:
mateo-berri
parent
fa25ff2a2e
commit
9b8ad46f37
4 changed files with 416 additions and 4 deletions
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@ -10,6 +10,7 @@ from collections.abc import Iterable, Mapping, Sequence
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from itertools import groupby
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from os import PathLike
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from pathlib import Path
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from types import MappingProxyType
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from typing import TYPE_CHECKING, Any, Final, Literal, TypeVar, cast
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from openai.types.chat.chat_completion_custom_tool_param import (
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@ -1089,6 +1090,91 @@ def sanitize_input_schema_for_anthropic(input_schema: dict) -> "AnthropicInputSc
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return AnthropicInputSchema(**filtered)
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_TOP_LEVEL_SCHEMA_COMBINATORS: Final = ("allOf", "anyOf", "oneOf")
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_OPENAI_REJECTED_TOP_LEVEL_SCHEMA_KEYS: Final = ("enum", "const", "not")
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_EMPTY_SCHEMA: Final[Mapping[str, object]] = MappingProxyType({})
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def _schema_properties(schema: Mapping[str, object]) -> Mapping[str, object]:
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properties: Final = schema.get("properties")
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return properties if isinstance(properties, dict) else _EMPTY_SCHEMA
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def _schema_branches(schema: Mapping[str, object], combinator: str) -> tuple[Mapping[str, object], ...]:
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branches: Final = schema.get(combinator)
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if not isinstance(branches, list):
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return ()
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return tuple(branch for branch in branches if isinstance(branch, dict))
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def _schema_required_names(schema: Mapping[str, object]) -> frozenset[str]:
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required: Final = schema.get("required")
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if not isinstance(required, list):
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return frozenset()
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return frozenset(name for name in required if isinstance(name, str))
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def _combinator_required_names(combinator: str, branches: tuple[Mapping[str, object], ...]) -> frozenset[str]:
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branch_names: Final = tuple(_schema_required_names(branch) for branch in branches)
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if not branch_names:
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return frozenset()
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if combinator == "allOf":
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return branch_names[0].union(*branch_names[1:])
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return branch_names[0].intersection(*branch_names[1:])
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def flatten_top_level_schema_combinators(schema: Mapping[str, object]) -> Mapping[str, object]:
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"""Merge top-level ``allOf``/``anyOf``/``oneOf`` branches into an object tool schema.
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OpenAI's function-calling validator rejects tool ``parameters`` carrying
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'oneOf'/'anyOf'/'allOf'/'enum'/'const'/'not' at the top level (nested uses
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are accepted), while lenient backends such as the ChatGPT backend Codex
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talks to natively accept them, so an MCP tool declaring a top-level union
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400s through LiteLLM. Branch properties merge without clobbering (the
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top-level schema wins, then earlier branches); a missing ``required``
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becomes the intersection of the branch lists for anyOf/oneOf and their
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union for allOf. Non-object schemas pass through unchanged and the input
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is never mutated.
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"""
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branch_groups: Final = tuple(
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(combinator, _schema_branches(schema, combinator))
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for combinator in _TOP_LEVEL_SCHEMA_COMBINATORS
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if isinstance(schema.get(combinator), list)
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)
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dropped: Final = (
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*(combinator for combinator, _ in branch_groups),
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*(key for key in _OPENAI_REJECTED_TOP_LEVEL_SCHEMA_KEYS if key in schema),
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)
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if not dropped:
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return schema
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branches: Final = tuple(branch for _, group in branch_groups for branch in group)
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is_object_schema: Final = schema.get("type") == "object" or (
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"type" not in schema and branches != () and all("properties" in branch for branch in branches)
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)
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if not is_object_schema:
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return schema
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merged_properties: Final = { # mutable-ok: tool parameters are JSON dicts
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name: value for source in (*reversed(branches), schema) for name, value in _schema_properties(source).items()
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}
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fallback_required: Final = frozenset(
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name for combinator, group in branch_groups for name in _combinator_required_names(combinator, group)
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)
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kept: Final = MappingProxyType({key: value for key, value in schema.items() if key not in dropped})
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required_update: Final = (
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MappingProxyType({"required": sorted(fallback_required)})
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if "required" not in kept and fallback_required
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else _EMPTY_SCHEMA
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)
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return { # mutable-ok: tool parameters are JSON dicts
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**kept,
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"type": "object",
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"properties": merged_properties,
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**required_update,
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}
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def _get_image_mime_type_from_url(url: str) -> str | None:
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"""
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Get mime type for common image URLs
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@ -1,3 +1,5 @@
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from collections.abc import Mapping, Sequence
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from types import MappingProxyType
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from typing import TYPE_CHECKING, Any, Final, cast, get_type_hints
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import httpx
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@ -29,6 +31,8 @@ if TYPE_CHECKING:
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else:
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LiteLLMLoggingObj = Any
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_NO_TOOL_UPDATE: Final[Mapping[str, object]] = MappingProxyType({})
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class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
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@property
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@ -167,8 +171,9 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
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input = self._validate_input_param(input)
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tools = response_api_optional_request_params.get("tools")
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input, tools = self.remove_cache_control_flag_from_input_and_tools(model=model, input=input, tools=tools)
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if tools is not None:
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response_api_optional_request_params["tools"] = tools
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sanitized_tools: Final = self._flatten_tool_schema_combinators_for_openai(tools)
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if sanitized_tools is not None:
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response_api_optional_request_params["tools"] = sanitized_tools
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final_request_params: Final = dict(
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ResponsesAPIRequestParams(model=model, input=input, **response_api_optional_request_params)
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)
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@ -207,6 +212,54 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
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return input, tools
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def _flatten_tool_schema_combinators_for_openai(
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self,
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tools: list[ALL_RESPONSES_API_TOOL_PARAMS] | None, # mutable-ok: request tools are a JSON list
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) -> list[ALL_RESPONSES_API_TOOL_PARAMS] | None: # mutable-ok: request tools are a JSON list
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"""Flatten top-level schema combinators for OpenAI itself only.
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OpenAI-compatible backends reusing this config (and the ChatGPT backend
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Codex talks to natively) accept them. Codex wraps MCP tools inside
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namespace entries, so nested ``tools`` arrays are walked too.
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"""
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if tools is None or self.custom_llm_provider != LlmProviders.OPENAI:
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return tools
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flattened: Final = [ # mutable-ok: request tools are a JSON list
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self._flattened_tool_entry(tool) for tool in tools
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]
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return cast("list[ALL_RESPONSES_API_TOOL_PARAMS]", flattened) # cast-ok: dict spread keeps each tool's shape
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@staticmethod
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def _flattened_tool_entry(
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entry: Mapping[str, object],
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) -> dict[str, object]: # mutable-ok: request tools are JSON dicts
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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parameters: Final = entry.get("parameters")
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nested_tools: Final = entry.get("tools")
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parameters_update: Final = (
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MappingProxyType({"parameters": flatten_top_level_schema_combinators(parameters)})
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if isinstance(parameters, dict)
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else _NO_TOOL_UPDATE
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)
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tools_update: Final = (
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MappingProxyType({"tools": OpenAIResponsesAPIConfig._flattened_nested_tools(nested_tools)})
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if isinstance(nested_tools, list)
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else _NO_TOOL_UPDATE
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)
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return {**entry, **parameters_update, **tools_update} # mutable-ok: request tools are JSON dicts
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@staticmethod
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def _flattened_nested_tools(
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nested_tools: Sequence[object],
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) -> list[object]: # mutable-ok: namespace tools are a JSON list
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return [ # mutable-ok: namespace tools are a JSON list
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OpenAIResponsesAPIConfig._flattened_tool_entry(item) if isinstance(item, dict) else item
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for item in nested_tools
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]
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def _validate_input_param(self, input: str | ResponseInputParam) -> str | ResponseInputParam:
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"""
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Ensure all input fields if pydantic are converted to dict
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@ -646,8 +699,9 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
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input = self._validate_input_param(input)
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tools = response_api_optional_request_params.get("tools")
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input, tools = self.remove_cache_control_flag_from_input_and_tools(model=model, input=input, tools=tools)
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if tools is not None:
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response_api_optional_request_params["tools"] = tools
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sanitized_tools: Final = self._flatten_tool_schema_combinators_for_openai(tools)
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if sanitized_tools is not None:
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response_api_optional_request_params["tools"] = sanitized_tools
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data: Final = dict(ResponsesAPIRequestParams(model=model, input=input, **response_api_optional_request_params))
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return url, data
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@ -1094,3 +1094,140 @@ def test_drop_tool_reference_parts_leaves_non_tool_messages_alone():
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assert result[0] == user_message
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assert result[2]["content"] == ""
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class TestFlattenTopLevelSchemaCombinators:
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def _customer_anyof_schema(self):
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return {
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"type": "object",
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"anyOf": [
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{
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"properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}},
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"required": ["id", "enabled"],
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},
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{
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"properties": {"id": {"type": "string"}, "schedule": {"type": "string"}},
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"required": ["id", "schedule"],
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},
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],
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"properties": {"id": {"type": "string"}},
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"required": ["id"],
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}
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def test_merges_anyof_branches_into_object_schema(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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result = flatten_top_level_schema_combinators(self._customer_anyof_schema())
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assert "anyOf" not in result
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assert result["type"] == "object"
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assert set(result["properties"]) == {"id", "enabled", "schedule"}
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assert result["properties"]["enabled"] == {"type": "boolean"}
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assert result["required"] == ["id"]
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def test_typeless_anyof_of_object_branches_gets_intersected_required(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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schema = {
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"anyOf": [
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{"properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}}, "required": ["id", "enabled"]},
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{"properties": {"id": {"type": "string"}, "schedule": {"type": "string"}}, "required": ["id", "schedule"]},
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]
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}
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result = flatten_top_level_schema_combinators(schema)
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assert result["type"] == "object"
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assert "anyOf" not in result
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assert result["required"] == ["id"]
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def test_allof_required_is_the_union_of_branches(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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schema = {
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"type": "object",
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"allOf": [
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{"properties": {"id": {"type": "string"}}, "required": ["id"]},
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{"properties": {"enabled": {"type": "boolean"}}, "required": ["enabled"]},
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],
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}
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result = flatten_top_level_schema_combinators(schema)
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assert "allOf" not in result
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assert result["required"] == ["enabled", "id"]
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assert set(result["properties"]) == {"id", "enabled"}
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def test_top_level_schema_wins_property_collisions(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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schema = {
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"type": "object",
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"anyOf": [
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{"properties": {"id": {"type": "integer"}}},
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{"properties": {"id": {"type": "number"}}},
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],
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"properties": {"id": {"type": "string"}},
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}
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result = flatten_top_level_schema_combinators(schema)
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assert result["properties"]["id"] == {"type": "string"}
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def test_drops_openai_rejected_scalar_keys_on_object_schema(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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schema = {
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"type": "object",
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"properties": {"id": {"type": "string"}},
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"enum": [{"id": "a"}],
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"const": {"id": "a"},
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"not": {"required": ["other"]},
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}
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result = flatten_top_level_schema_combinators(schema)
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assert "enum" not in result
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assert "const" not in result
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assert "not" not in result
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assert result["properties"] == {"id": {"type": "string"}}
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def test_non_object_union_passes_through_unchanged(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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schema = {"anyOf": [{"type": "string"}, {"type": "number"}]}
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assert flatten_top_level_schema_combinators(schema) is schema
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def test_schema_without_rejected_keys_is_returned_as_is(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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schema = {"type": "object", "properties": {"nested": {"anyOf": [{"type": "string"}, {"type": "null"}]}}}
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assert flatten_top_level_schema_combinators(schema) is schema
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def test_input_schema_is_never_mutated(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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flatten_top_level_schema_combinators,
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)
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schema = self._customer_anyof_schema()
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snapshot = json.loads(json.dumps(schema))
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flatten_top_level_schema_combinators(schema)
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assert schema == snapshot
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@ -1626,3 +1626,138 @@ class TestResponsesSurfaceSharesTheEffortRule:
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drop_params=True,
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)
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assert ("temperature" in mapped) is temperature_survives
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class TestFlattenToolSchemaCombinatorsWiring:
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"""Regression tests for MCP tools with a top-level anyOf schema (Codex Desktop).
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OpenAI's /v1/responses rejects function tool parameters carrying
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'oneOf'/'anyOf'/'allOf'/'enum'/'const'/'not' at the top level, while the
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ChatGPT backend Codex uses natively accepts them, so those tools 400'd
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through the proxy with "Invalid schema for function ...".
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"""
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def _anyof_parameters(self):
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return {
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"type": "object",
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"anyOf": [
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{
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"properties": {"id": {"type": "string"}, "enabled": {"type": "boolean"}},
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"required": ["id", "enabled"],
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},
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{
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"properties": {"id": {"type": "string"}, "schedule": {"type": "string"}},
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"required": ["id", "schedule"],
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},
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],
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"properties": {"id": {"type": "string"}},
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"required": ["id"],
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}
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def _flat_function_tool(self):
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return {
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"type": "function",
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"name": "mcp__codex_app__automation_update",
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"description": "Update an automation",
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"parameters": self._anyof_parameters(),
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"strict": False,
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}
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def _codex_namespace_tool(self):
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return {
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"type": "namespace",
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"name": "mcp__codex_app",
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"tools": [
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{
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"name": "automation_update",
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"description": "Update an automation",
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"parameters": self._anyof_parameters(),
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"strict": False,
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}
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],
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}
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def test_openai_flattens_top_level_anyof_on_flat_function_tool(self):
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result = OpenAIResponsesAPIConfig().transform_responses_api_request(
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model="gpt-4o",
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input="hi",
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response_api_optional_request_params={"tools": [self._flat_function_tool()]},
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litellm_params=GenericLiteLLMParams(),
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headers={},
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)
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parameters = result["tools"][0]["parameters"]
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assert "anyOf" not in parameters
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assert parameters["type"] == "object"
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assert set(parameters["properties"]) == {"id", "enabled", "schedule"}
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assert parameters["required"] == ["id"]
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assert json.loads(json.dumps(result["tools"])) == result["tools"]
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def test_openai_flattens_anyof_inside_codex_namespace_tools(self):
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result = OpenAIResponsesAPIConfig().transform_responses_api_request(
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model="gpt-4o",
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input="hi",
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response_api_optional_request_params={"tools": [self._codex_namespace_tool()]},
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litellm_params=GenericLiteLLMParams(),
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headers={},
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)
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nested_parameters = result["tools"][0]["tools"][0]["parameters"]
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assert "anyOf" not in nested_parameters
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assert set(nested_parameters["properties"]) == {"id", "enabled", "schedule"}
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assert json.loads(json.dumps(result["tools"])) == result["tools"]
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def test_openai_compact_request_flattens_top_level_anyof(self):
|
||||
_, data = OpenAIResponsesAPIConfig().transform_compact_response_api_request(
|
||||
model="gpt-4o",
|
||||
input="hi",
|
||||
response_api_optional_request_params={"tools": [self._flat_function_tool()]},
|
||||
api_base="https://api.openai.com/v1/responses",
|
||||
litellm_params=GenericLiteLLMParams(),
|
||||
headers={},
|
||||
)
|
||||
|
||||
assert "anyOf" not in data["tools"][0]["parameters"]
|
||||
|
||||
def test_openai_leaves_tools_without_rejected_keys_alone(self):
|
||||
clean_tool = {
|
||||
"type": "function",
|
||||
"name": "get_weather",
|
||||
"parameters": {"type": "object", "properties": {"city": {"type": "string"}}},
|
||||
}
|
||||
|
||||
result = OpenAIResponsesAPIConfig().transform_responses_api_request(
|
||||
model="gpt-4o",
|
||||
input="hi",
|
||||
response_api_optional_request_params={"tools": [clean_tool]},
|
||||
litellm_params=GenericLiteLLMParams(),
|
||||
headers={},
|
||||
)
|
||||
|
||||
assert result["tools"][0]["parameters"] == {"type": "object", "properties": {"city": {"type": "string"}}}
|
||||
|
||||
def test_openai_does_not_mutate_caller_tool_dicts(self):
|
||||
tool = self._flat_function_tool()
|
||||
|
||||
OpenAIResponsesAPIConfig().transform_responses_api_request(
|
||||
model="gpt-4o",
|
||||
input="hi",
|
||||
response_api_optional_request_params={"tools": [tool]},
|
||||
litellm_params=GenericLiteLLMParams(),
|
||||
headers={},
|
||||
)
|
||||
|
||||
assert "anyOf" in tool["parameters"]
|
||||
|
||||
def test_non_openai_subclass_does_not_flatten(self):
|
||||
from litellm.llms.hosted_vllm.responses.transformation import HostedVLLMResponsesAPIConfig
|
||||
|
||||
result = HostedVLLMResponsesAPIConfig().transform_responses_api_request(
|
||||
model="hosted_vllm/qwen",
|
||||
input="hi",
|
||||
response_api_optional_request_params={"tools": [self._flat_function_tool()]},
|
||||
litellm_params=GenericLiteLLMParams(),
|
||||
headers={},
|
||||
)
|
||||
|
||||
assert "anyOf" in result["tools"][0]["parameters"]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue