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fix(responses): drop a malformed prompt_cache_breakpoint under drop_params in the chat-to-Responses bridge

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mateo-berri 2026-10-07 14:13:19 -07:00
parent d89bd9ee29
commit f2c6adefc5
2 changed files with 96 additions and 9 deletions
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41
litellm/completion_extras/litellm_responses_transformation/transformation.py
View file

@ -19,7 +19,7 @@ from openai.types.responses.response_input_param import (
from openai.types.responses.tool_choice_custom_param import ToolChoiceCustomParam
from openai.types.responses.tool_choice_function_param import ToolChoiceFunctionParam
from openai.types.responses.tool_param import FunctionToolParam
from pydantic import BaseModel
from pydantic import BaseModel, TypeAdapter, ValidationError
import litellm
from litellm import ModelResponse
@ -45,6 +45,7 @@ from litellm.types.llms.openai import (
ChatCompletionToolCallChunk,
ChatCompletionToolCallFunctionChunk,
ChatCompletionToolParamFunctionChunk,
PromptCacheBreakpoint,
Reasoning,
ResponsesAPIOptionalRequestParams,
ResponsesAPIResponse,
@ -205,6 +206,19 @@ def _map_incomplete_reason_to_finish_reason(incomplete_reason: str | None) -> Li
return "length"
_PROMPT_CACHE_BREAKPOINT: Final = TypeAdapter(PromptCacheBreakpoint)
def _prompt_cache_breakpoint_for_wire(marker: object, drop_params: bool) -> object:
if marker is None or not drop_params:
return marker
try:
return _PROMPT_CACHE_BREAKPOINT.validate_python(marker)
except ValidationError:
verbose_logger.debug("Chat provider: dropping malformed prompt_cache_breakpoint %r under drop_params", marker)
return None
def _input_file_from_file_value(file_value: object) -> dict[str, object]:
if not isinstance(file_value, dict):
return {"type": "input_file"}
@ -364,7 +378,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
return None, index
def convert_chat_completion_messages_to_responses_api(
self, messages: list["AllMessageValues"]
self, messages: list["AllMessageValues"], drop_params: bool = False
) -> tuple[list[object], str | None]:
input_items: Final[list[object]] = []
instructions: str | None = None
@ -405,6 +419,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
"content": self._convert_content_to_responses_format(
content,
role,
drop_params=drop_params,
),
}
)
@ -423,6 +438,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
tool_output = self._convert_content_to_responses_format(
content,
"user", # Use "user" role to get input_* types
drop_params=drop_params,
)
else:
# Fallback: convert unexpected types to input_text
@ -450,7 +466,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
{
"type": "message",
"role": "assistant",
"content": self._convert_content_to_responses_format(content, "assistant"),
"content": self._convert_content_to_responses_format(
content, "assistant", drop_params=drop_params
),
}
)
for tool_call in tool_calls:
@ -484,7 +502,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
{
"type": "message",
"role": role,
"content": self._convert_content_to_responses_format(content, cast(str, role)),
"content": self._convert_content_to_responses_format(
content, cast(str, role), drop_params=drop_params
),
}
)
elif role == "assistant":
@ -597,7 +617,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
(
input_items,
instructions,
) = self.convert_chat_completion_messages_to_responses_api(messages)
) = self.convert_chat_completion_messages_to_responses_api(
messages, drop_params=bool(litellm_params.get("drop_params") or litellm.drop_params)
)
# OpenAI's Responses API rejects an empty input. For a system-only
# request, carry the system message as a system-role input item instead
@ -1072,6 +1094,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
]
| None,
role: str,
drop_params: bool = False,
) -> list[dict[str, object]]:
"""Convert chat completion content to responses API format"""
from litellm.types.llms.openai import ChatCompletionImageObject
@ -1098,7 +1121,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
if original_type == "text":
converted = with_prompt_cache_breakpoint(
self._convert_content_str_to_input_text(item.get("text", ""), role),
item.get("prompt_cache_breakpoint"),
_prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
)
result.append(converted)
verbose_logger.debug("Chat provider: text -> %s", converted)
@ -1111,7 +1134,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
cast(ChatCompletionImageObject, item), role
),
),
item.get("prompt_cache_breakpoint"),
_prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
)
result.append(converted)
verbose_logger.debug("Chat provider: image_url -> %s", converted)
@ -1127,7 +1150,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
_input_file_from_file_value(
cast("ChatCompletionFileObject", item).get("file"), # cast-ok: type tag checked
),
item.get("prompt_cache_breakpoint"),
_prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
)
result.append(converted)
verbose_logger.debug("Chat provider: file -> %s", converted)
@ -1151,7 +1174,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
# Default to input_text for unknown types
converted = with_prompt_cache_breakpoint(
self._convert_content_str_to_input_text(str(item.get("text", item)), role),
item.get("prompt_cache_breakpoint"),
_prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
)
result.append(converted)
verbose_logger.debug("Chat provider: unknown(%s) -> %s", original_type, converted)

64
tests/unit/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_transformation.py
View file

@ -4642,3 +4642,67 @@ def test_convert_chat_completion_messages_to_responses_api_keeps_prompt_cache_br
content = response[0]["content"]
assert [block["type"] for block in content] == ["input_text", "input_text"]
assert content[1]["prompt_cache_breakpoint"] == breakpoint_marker
_HAND_WRITTEN_PROMPT_CACHE_BREAKPOINT_BLOCKS: Final = (
{"type": "text", "text": "a string marker", "prompt_cache_breakpoint": "explicit"},
{"type": "text", "text": "an unknown mode", "prompt_cache_breakpoint": {"mode": "bogus"}},
{
"type": "input_audio",
"input_audio": {"data": "Zm9v", "format": "wav"},
"prompt_cache_breakpoint": ["explicit"],
},
{"type": "text", "text": "an extra key", "prompt_cache_breakpoint": {"mode": "explicit", "ttl": "1h"}},
{"type": "text", "text": "well formed", "prompt_cache_breakpoint": {"mode": "explicit"}},
)
def test_convert_chat_completion_messages_to_responses_api_drops_malformed_prompt_cache_breakpoint_under_drop_params():
"""Under drop_params a marker the Responses API would reject is dropped like any other unsupported value.
A well-formed marker still reaches the wire, and an extra key is dropped from an otherwise valid one.
"""
handler = LiteLLMResponsesTransformationHandler()
messages = [{"role": "user", "content": list(_HAND_WRITTEN_PROMPT_CACHE_BREAKPOINT_BLOCKS)}]
response, _ = handler.convert_chat_completion_messages_to_responses_api(messages, drop_params=True)
content = response[0]["content"]
assert [block.get("prompt_cache_breakpoint") for block in content] == [
None,
None,
None,
{"mode": "explicit"},
{"mode": "explicit"},
]
assert all("prompt_cache_breakpoint" not in block for block in content[:3])
def test_convert_chat_completion_messages_to_responses_api_keeps_malformed_prompt_cache_breakpoint_by_default():
"""Without drop_params every marker goes out as written, and the provider judges a malformed one."""
handler = LiteLLMResponsesTransformationHandler()
messages = [{"role": "user", "content": list(_HAND_WRITTEN_PROMPT_CACHE_BREAKPOINT_BLOCKS)}]
response, _ = handler.convert_chat_completion_messages_to_responses_api(messages)
content = response[0]["content"]
assert [block["prompt_cache_breakpoint"] for block in content] == [
block["prompt_cache_breakpoint"] for block in _HAND_WRITTEN_PROMPT_CACHE_BREAKPOINT_BLOCKS
]
def test_transform_request_drop_params_in_litellm_params_gates_the_prompt_cache_breakpoint_carry():
"""The per-request drop_params flag travels in litellm_params and has to reach the content converter."""
handler = LiteLLMResponsesTransformationHandler()
messages = [{"role": "user", "content": [{"type": "text", "text": "hi", "prompt_cache_breakpoint": "explicit"}]}]
result = handler.transform_request(
model="gpt-6.1-sol",
messages=messages,
optional_params={},
litellm_params={"drop_params": True},
headers={},
litellm_logging_obj=Mock(),
)
assert "prompt_cache_breakpoint" not in result["input"][0]["content"][0]