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fix(azure): flatten top-level tool schema combinators on Azure chat completions
Azure's chat completions validator rejects tool parameters carrying a top-level anyOf/oneOf/allOf for every model family. AzureOpenAIConfig and the o-series config now flatten them via the shared helper moved to prompt_templates common_utils. Requests bridged to the Responses API for gpt-5.4+ with reasoning active keep the union, which that surface accepts
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mateo-berri
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7 changed files with 246 additions and 16 deletions
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@ -1246,6 +1246,19 @@ def flatten_top_level_schema_combinators(schema: Mapping[str, object]) -> Mappin
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return _flatten_schema_against_root(schema, schema, frozenset(), 0, {}) # mutable-ok: fresh per-call $ref memo
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def tool_with_flattened_parameters(tool: Mapping[str, object]) -> Mapping[str, object]:
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function: Final = tool.get("function")
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if not isinstance(function, dict):
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return tool
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parameters: Final = function.get("parameters")
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if not isinstance(parameters, dict):
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return tool
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flattened: Final = flatten_top_level_schema_combinators(parameters)
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if flattened is parameters:
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return tool
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return {**tool, "function": {**function, "parameters": flattened}} # mutable-ok: request tools are JSON dicts
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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
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from types import MappingProxyType
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from typing import TYPE_CHECKING, Any, Final
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from httpx._models import Headers, Response
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@ -6,6 +8,7 @@ import litellm
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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drop_tool_reference_parts_from_tool_messages,
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hoist_images_from_tool_messages,
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tool_with_flattened_parameters,
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)
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from litellm.litellm_core_utils.prompt_templates.factory import (
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convert_to_azure_openai_messages,
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@ -32,6 +35,19 @@ else:
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LoggingClass = Any
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_NO_TOOLS_UPDATE: Final[Mapping[str, object]] = MappingProxyType({})
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def flattened_tools_update(optional_params: Mapping[str, object]) -> Mapping[str, object]:
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tools: Final = optional_params.get("tools")
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if not isinstance(tools, list):
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return _NO_TOOLS_UPDATE
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flattened: Final = [ # mutable-ok: request tools are a JSON list
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tool_with_flattened_parameters(tool) if isinstance(tool, dict) else tool for tool in tools
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]
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return MappingProxyType({"tools": flattened})
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class AzureOpenAIConfig(BaseConfig):
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"""
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Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions
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@ -261,6 +277,7 @@ class AzureOpenAIConfig(BaseConfig):
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"model": model,
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"messages": azure_messages,
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**optional_params,
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**flattened_tools_update(optional_params),
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}
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def transform_response(
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@ -20,6 +20,7 @@ from litellm.types.llms.openai import AllMessageValues
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from litellm.utils import get_model_info, supports_reasoning
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from ...openai.chat.o_series_transformation import OpenAIOSeriesConfig
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from .gpt_transformation import flattened_tools_update
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class AzureOpenAIO1Config(OpenAIOSeriesConfig):
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@ -108,4 +109,8 @@ class AzureOpenAIO1Config(OpenAIOSeriesConfig):
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headers: dict,
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) -> dict:
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model = model.replace("o_series/", "") # handle o_series/my-random-deployment-name
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return super().transform_request(model, messages, optional_params, litellm_params, headers)
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flattened_params: Final = { # mutable-ok: transform_request's contract takes a plain JSON params dict
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**optional_params,
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**flattened_tools_update(optional_params),
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}
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return super().transform_request(model, messages, flattened_params, litellm_params, headers)
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@ -20,9 +20,9 @@ from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response impo
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)
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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drop_tool_reference_parts_from_tool_messages,
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flatten_top_level_schema_combinators,
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get_tool_call_names,
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hoist_images_from_tool_messages,
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tool_with_flattened_parameters,
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)
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from litellm.litellm_core_utils.prompt_templates.image_handling import (
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async_convert_url_to_base64,
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@ -70,19 +70,6 @@ else:
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_NO_TOOLS_UPDATE: Final[Mapping[str, object]] = MappingProxyType({})
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def _tool_with_flattened_parameters(tool: Mapping[str, object]) -> Mapping[str, object]:
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function: Final = tool.get("function")
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if not isinstance(function, dict):
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return tool
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parameters: Final = function.get("parameters")
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if not isinstance(parameters, dict):
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return tool
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flattened: Final = flatten_top_level_schema_combinators(parameters)
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if flattened is parameters:
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return tool
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return {**tool, "function": {**function, "parameters": flattened}} # mutable-ok: request tools are JSON dicts
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class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
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"""
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Reference: https://platform.openai.com/docs/api-reference/chat/create
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@ -462,7 +449,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
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):
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return _NO_TOOLS_UPDATE
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flattened: Final = [ # mutable-ok: request tools are a JSON list
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_tool_with_flattened_parameters(tool) if isinstance(tool, dict) else tool for tool in tools
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tool_with_flattened_parameters(tool) if isinstance(tool, dict) else tool for tool in tools
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]
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return MappingProxyType({"tools": flattened})
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@ -1433,3 +1433,77 @@ class TestFlattenTopLevelSchemaCombinators:
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flatten_top_level_schema_combinators(schema)
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assert schema == snapshot
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class TestToolWithFlattenedParameters:
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def _anyof_tool(self):
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return {
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"type": "function",
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"function": {
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"name": "automation_update",
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"description": "Update an automation",
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"parameters": {
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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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},
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}
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def test_flattens_anyof_parameters_into_new_tool(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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tool_with_flattened_parameters,
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)
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tool = self._anyof_tool()
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result = tool_with_flattened_parameters(tool)
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assert result is not tool
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parameters = result["function"]["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 result["function"]["name"] == "automation_update"
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assert tool == self._anyof_tool()
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def test_clean_parameters_return_the_same_tool_object(self):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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tool_with_flattened_parameters,
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)
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tool = {
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"type": "function",
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"function": {
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"name": "lookup",
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"parameters": {"type": "object", "properties": {"id": {"type": "string"}}, "required": ["id"]},
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},
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}
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assert tool_with_flattened_parameters(tool) is tool
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@pytest.mark.parametrize(
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"tool",
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[
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{"type": "function"},
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{"type": "function", "function": "not-a-dict"},
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{"type": "function", "function": {"name": "no_params"}},
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{"type": "function", "function": {"name": "bad_params", "parameters": "not-a-dict"}},
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],
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)
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def test_non_dict_function_or_parameters_return_the_same_tool_object(self, tool):
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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tool_with_flattened_parameters,
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)
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assert tool_with_flattened_parameters(tool) is tool
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@ -11,6 +11,7 @@ sys.path.insert(
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import litellm
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from litellm.litellm_core_utils.prompt_templates.common_utils import TOOL_RESULT_IMAGE_BOUNDARY
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from litellm.llms.azure.chat.gpt_5_transformation import AzureOpenAIGPT5Config
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from litellm.llms.azure.chat.gpt_transformation import AzureOpenAIConfig
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from litellm.utils import get_optional_params
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@ -195,3 +196,91 @@ def test_azure_gpt_5_takes_the_reasoning_path() -> None:
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assert "presence_penalty" not in mapped
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assert "logit_bias" not in mapped
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assert "reasoning_effort" in supported
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class TestAzureToolSchemaCombinatorFlattening:
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"""
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Regression tests for LIT-6510: Azure's chat completions validator rejects
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tool parameters carrying a top-level anyOf/oneOf/allOf for every model
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family, so AzureOpenAIConfig.transform_request must flatten them.
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"""
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@staticmethod
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def _anyof_tool():
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return {
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"type": "function",
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"function": {
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"name": "automation_update",
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"description": "Update an automation",
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"parameters": {
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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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},
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}
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def _transform(self, config, model, tools):
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return config.transform_request(
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model=model,
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messages=[{"role": "user", "content": "hi"}],
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optional_params={"tools": tools},
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litellm_params={"custom_llm_provider": "azure"},
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headers={},
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)
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def test_transform_request_flattens_top_level_anyof(self):
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request = self._transform(AzureOpenAIConfig(), "gpt-4o", [self._anyof_tool()])
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parameters = request["tools"][0]["function"]["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 request["tools"][0]["function"]["name"] == "automation_update"
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def test_gpt5_config_flattens_via_shared_transform(self):
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request = self._transform(AzureOpenAIGPT5Config(), "gpt-5.4-mini", [self._anyof_tool()])
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parameters = request["tools"][0]["function"]["parameters"]
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assert "anyOf" not in parameters
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assert set(parameters["properties"]) == {"id", "enabled", "schedule"}
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def test_caller_tool_dict_is_not_mutated(self):
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tool = self._anyof_tool()
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self._transform(AzureOpenAIConfig(), "gpt-4o", [tool])
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assert tool == self._anyof_tool()
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def test_clean_object_schema_passes_through_as_same_object(self):
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tool = {
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"type": "function",
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"function": {
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"name": "lookup",
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"parameters": {"type": "object", "properties": {"id": {"type": "string"}}, "required": ["id"]},
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},
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}
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request = self._transform(AzureOpenAIConfig(), "gpt-4o", [tool])
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assert request["tools"][0] is tool
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def test_non_dict_tool_entries_pass_through_unchanged(self):
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request = self._transform(AzureOpenAIConfig(), "gpt-4o", ["not-a-tool"])
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assert request["tools"] == ["not-a-tool"]
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def test_request_without_tools_is_unchanged(self):
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request = AzureOpenAIConfig().transform_request(
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model="gpt-4o",
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messages=[{"role": "user", "content": "hi"}],
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optional_params={"temperature": 0.2},
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litellm_params={"custom_llm_provider": "azure"},
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headers={},
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)
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assert "tools" not in request
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assert request["temperature"] == 0.2
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@ -23,3 +23,48 @@ async def test_azure_chat_o_series_transformation():
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)
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print(response)
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assert response["model"] == "web-interface-o1-mini"
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def test_azure_o_series_transform_request_flattens_top_level_anyof():
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"""Regression test for LIT-6510: the o-series super() chain ends in
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OpenAIGPTConfig, whose flatten gate skips provider 'azure', so
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AzureOpenAIO1Config must flatten tool schema combinators itself."""
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tool = {
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"type": "function",
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"function": {
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"name": "automation_update",
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"description": "Update an automation",
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"parameters": {
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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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},
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}
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optional_params = {"tools": [tool]}
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request = AzureOpenAIO1Config().transform_request(
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model="o3-mini",
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messages=[{"role": "user", "content": "hi"}],
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optional_params=optional_params,
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litellm_params={"custom_llm_provider": "azure"},
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headers={},
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)
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parameters = request["tools"][0]["function"]["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 "anyOf" in tool["function"]["parameters"]
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assert optional_params["tools"][0] is tool
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