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refactor(caching): keep semantic tool prompt helpers within lint budgets

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Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
kerry 2026-09-30 16:53:53 +00:00
parent f462fbeb2e
commit 8b08e07f18
2 changed files with 15 additions and 7 deletions
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12
litellm/caching/redis_semantic_cache.py
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@ -276,7 +276,7 @@ class RedisSemanticCache(BaseCache):
@classmethod
def _collect_responses_input_text(cls, value: object, prompt_parts: list[str]) -> None:
value = cls._coerce_response_input_value(value)
value = cls._function_call_as_prompt(cls._coerce_response_input_value(value))
if value is None:
return
@ -292,10 +292,6 @@ class RedisSemanticCache(BaseCache):
return
if isinstance(value, dict):
if value.get("type") == "function_call":
prompt_parts.append(tool_call_str(value.get("name"), value.get("arguments")))
return
content = value.get("content")
if content is not None:
cls._collect_responses_input_text(content, prompt_parts)
@ -323,6 +319,12 @@ class RedisSemanticCache(BaseCache):
prompt_parts.append(stripped_text)
return
@staticmethod
def _function_call_as_prompt(value: object) -> object:
if isinstance(value, dict) and value.get("type") == "function_call":
return tool_call_str(value.get("name"), value.get("arguments"))
return value
@staticmethod
def _coerce_response_input_value(value: object) -> object:
model_dump: Final = getattr(value, "model_dump", None)

10
litellm/litellm_core_utils/prompt_templates/common_utils.py
View file

@ -205,7 +205,11 @@ def get_str_from_messages_with_tools(messages: object) -> str:
def tool_call_str(name: object, arguments: object) -> str:
return json.dumps({"name": name, "arguments": arguments}, separators=(",", ":"), default=str)
return f'{{"name":{_compact_json(name)},"arguments":{_compact_json(arguments)}}}'
def _compact_json(value: object) -> str:
return json.dumps(value, separators=(",", ":"), default=str)
def _message_str_with_tools(message: Mapping[str, object]) -> str:
@ -234,7 +238,9 @@ def _block_str_with_tools(block: Mapping[str, object]) -> str:
def _openai_tool_call_str(tool_call: Mapping[str, object]) -> str:
function: Final = _as_str_mapping(tool_call.get("function")) or {}
function: Final = _as_str_mapping(tool_call.get("function"))
if function is None:
return tool_call_str(None, None)
return tool_call_str(function.get("name"), function.get("arguments"))