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Fix greptile comments

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This commit is contained in:
Sameer Kankute
2026-03-18 16:37:52 +05:30
parent 35b3ed58a8
commit b32f5ea379
2 changed files with 124 additions and 6 deletions
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litellm/responses/main.py
+58 -5
View File
@@ -464,6 +464,49 @@ async def aresponses(
# Update local_vars with detected provider (fixes #19782)
local_vars["custom_llm_provider"] = custom_llm_provider
#########################################################
# ASYNC PROMPT MANAGEMENT
#########################################################
litellm_logging_obj = kwargs.get("litellm_logging_obj", None)
prompt_id = cast(Optional[str], kwargs.get("prompt_id", None))
prompt_variables = cast(Optional[dict], kwargs.get("prompt_variables", None))
if isinstance(litellm_logging_obj, LiteLLMLoggingObj) and litellm_logging_obj.should_run_prompt_management_hooks(
prompt_id=prompt_id, non_default_params=kwargs
):
if isinstance(input, str):
client_input: List[AllMessageValues] = [
{"role": "user", "content": input}
]
else:
client_input = [
item # type: ignore[misc]
for item in input
if isinstance(item, dict) and "role" in item
]
(
model,
merged_input,
merged_optional_params,
) = await litellm_logging_obj.async_get_chat_completion_prompt(
model=model,
messages=client_input,
non_default_params=kwargs,
prompt_id=prompt_id,
prompt_variables=prompt_variables,
prompt_label=kwargs.get("prompt_label", None),
prompt_version=kwargs.get("prompt_version", None),
)
input = cast(Union[str, ResponseInputParam], merged_input)
if "/" in model:
_, custom_llm_provider, _, _ = litellm.get_llm_provider(
model=model
)
local_vars["custom_llm_provider"] = custom_llm_provider
for k, v in merged_optional_params.items():
if k in local_vars:
local_vars[k] = v
func = partial(
responses,
input=input,
@@ -633,11 +676,16 @@ def responses(
if isinstance(litellm_logging_obj, LiteLLMLoggingObj) and litellm_logging_obj.should_run_prompt_management_hooks(
prompt_id=prompt_id, non_default_params=kwargs
):
client_input: List[AllMessageValues] = (
[{"role": "user", "content": input}]
if isinstance(input, str)
else cast(List[AllMessageValues], list(input))
)
if isinstance(input, str):
client_input: List[AllMessageValues] = [
{"role": "user", "content": input}
]
else:
client_input = [
item # type: ignore[misc]
for item in input
if isinstance(item, dict) and "role" in item
]
(
model,
merged_input,
@@ -654,6 +702,11 @@ def responses(
input = cast(Union[str, ResponseInputParam], merged_input)
local_vars["input"] = input
local_vars["model"] = model
if "/" in model:
_, custom_llm_provider, _, _ = litellm.get_llm_provider(
model=model
)
local_vars["custom_llm_provider"] = custom_llm_provider
for k, v in merged_optional_params.items():
local_vars[k] = v
tests/test_litellm/responses/test_responses_prompt_management.py
+66 -1
View File
@@ -158,7 +158,7 @@ class TestResponsesAPIPromptManagement:
]
# Simulate get_chat_completion_prompt returning merged optional params
# that include a template-defined temperature
merged_kwargs = {"temperature": 0.2, "prompt_id": "t", "litellm_logging_obj": None}
merged_kwargs = {"temperature": 0.2}
logging_obj = MagicMock()
logging_obj.__class__ = LiteLLMLoggingObj
@@ -209,3 +209,68 @@ class TestResponsesAPIPromptManagement:
# The model passed to the downstream handler should be the overridden one
handler_call_kwargs = mock_handler.call_args.kwargs
assert handler_call_kwargs.get("model") == "openai/gpt-4o-mini"
def test_non_message_input_items_filtered(self):
"""[F] Non-message items in ResponseInputParam (e.g. function_call_output) are
filtered out before being passed to the prompt hook, avoiding malformed merges."""
template_messages: List[AllMessageValues] = [
{"role": "system", "content": "You are helpful."}, # type: ignore[list-item]
]
mixed_input = [
{"role": "user", "content": "Hello"},
{"type": "function_call_output", "call_id": "abc", "output": "42"},
]
logging_obj = _make_logging_obj(
merged_model="openai/gpt-4o",
merged_messages=template_messages + [{"role": "user", "content": "Hello"}], # type: ignore[operator]
)
patches = _patch_responses_dispatch()
with patches[0], patches[1], patches[2], patches[3]:
import litellm
litellm.responses(
input=mixed_input, # type: ignore[arg-type]
model="gpt-4o",
prompt_id="filter-test",
litellm_logging_obj=logging_obj,
)
call_kwargs = logging_obj.get_chat_completion_prompt.call_args.kwargs
passed_messages = call_kwargs["messages"]
assert all(isinstance(m, dict) and "role" in m for m in passed_messages)
assert len(passed_messages) == 1
def test_model_override_re_resolves_provider(self):
"""[G] When the prompt template overrides the model to a different provider,
custom_llm_provider is re-resolved so downstream routing uses the correct provider."""
template_messages: List[AllMessageValues] = [
{"role": "user", "content": "Hi"}, # type: ignore[list-item]
]
logging_obj = _make_logging_obj(
merged_model="anthropic/claude-3-5-sonnet",
merged_messages=template_messages,
)
patches = _patch_responses_dispatch()
with (
patch(
"litellm.responses.main.litellm.get_llm_provider",
side_effect=[
("gpt-4o", "openai", None, None),
("claude-3-5-sonnet", "anthropic", None, None),
],
),
patches[1],
patches[2],
patches[3] as mock_handler,
):
import litellm
litellm.responses(
input="Hi",
model="gpt-4o",
prompt_id="cross-provider",
litellm_logging_obj=logging_obj,
)
handler_call_kwargs = mock_handler.call_args.kwargs
assert handler_call_kwargs.get("custom_llm_provider") == "anthropic"