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Merge 314516a9b0 into add7dea9aa

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Grainne McKnight 2026-11-03 18:06:29 +00:00 • committed by GitHub
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6
litellm/responses/litellm_completion_transformation/transformation.py
View file

@ -431,6 +431,12 @@ def tool_output_to_chat_content(
text_acc.append(txt)
if txt:
normalized_blocks.append({"type": "text", "text": txt})
elif part_type == "tool_reference":
match cast(Mapping[str, object], part): # cast-ok: dict checked above
case {"tool_name": str(tool_name)} if tool_name:
normalized_blocks.append({"type": "tool_reference", "tool_name": tool_name})
case _:
continue
elif (media := decode_responses_media(part)) is not None:
normalized_blocks.append(media)

56
tests/unit/responses/litellm_completion_transformation/test_function_call_output_normalization.py
View file

@ -7,15 +7,18 @@ we normalize it to text/image blocks or a string.
"""
import json
from collections.abc import Mapping
from copy import deepcopy
from typing import Final
from typing import Final, cast
import pytest
from litellm.exceptions import BadRequestError
from litellm.litellm_core_utils.prompt_templates.factory import convert_to_anthropic_tool_result
from litellm.responses.litellm_completion_transformation.transformation import (
LiteLLMCompletionResponsesConfig,
)
from litellm.types.llms.openai import ChatCompletionToolMessage, ToolMessageContentPart
def _normalize_media_content(kind: str, parts: list[dict[str, object]]) -> object:
@ -188,3 +191,54 @@ def test_computer_screenshot_output_remains_structured_media() -> None:
"image_url": {"url": "data:image/png;base64,AAAA", "detail": "auto"},
}
]
@pytest.mark.parametrize(
("output", "expected_content"),
[
(
[{"type": "tool_reference", "tool_name": "get_weather"}],
[{"type": "tool_reference", "tool_name": "get_weather"}],
),
(
[
{"type": "input_text", "text": "Loaded tools"},
{"type": "tool_reference", "tool_name": "get_weather"},
{"type": "input_text", "text": " and "},
{"type": "tool_reference", "tool_name": "get_forecast"},
],
[
{"type": "text", "text": "Loaded tools"},
{"type": "tool_reference", "tool_name": "get_weather"},
{"type": "text", "text": " and "},
{"type": "tool_reference", "tool_name": "get_forecast"},
],
),
(
[
{"type": "tool_reference"},
{"type": "tool_reference", "tool_name": None},
{"type": "tool_reference", "tool_name": ""},
{"type": "tool_reference", "tool_name": "get_weather"},
],
[{"type": "tool_reference", "tool_name": "get_weather"}],
),
],
ids=["reference-only", "interleaved-text-and-references", "malformed-references-omitted"],
)
def test_function_call_output_preserves_tool_references_for_anthropic(
output: list[Mapping[str, object]], expected_content: list[ToolMessageContentPart]
) -> None:
messages: Final = (
LiteLLMCompletionResponsesConfig._transform_responses_api_tool_call_output_to_chat_completion_message(
{"type": "function_call_output", "call_id": "call_load_tools", "output": output}
)
)
assert messages == [{"role": "tool", "tool_call_id": "call_load_tools", "content": expected_content}]
message: Final = cast(ChatCompletionToolMessage, messages[0]) # cast-ok: complete tool message asserted above
assert convert_to_anthropic_tool_result(message) == {
"type": "tool_result",
"tool_use_id": "call_load_tools",
"content": expected_content,
}