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Fix mypy issues

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Sameer Kankute 2026-04-07 20:51:16 +05:30
parent 58c3c52800
commit d0a6e6df8f
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4 changed files with 121 additions and 94 deletions
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2
enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
View file

@ -114,6 +114,7 @@ class PagerDutyAlerting(SlackAlerting):
user_api_key_max_budget=_meta.get("user_api_key_max_budget"),
user_api_key_budget_reset_at=_meta.get("user_api_key_budget_reset_at"),
user_api_key_org_id=_meta.get("user_api_key_org_id"),
user_api_key_org_alias=_meta.get("user_api_key_org_alias"),
user_api_key_team_id=_meta.get("user_api_key_team_id"),
user_api_key_project_id=_meta.get("user_api_key_project_id"),
user_api_key_project_alias=_meta.get("user_api_key_project_alias"),
@ -196,6 +197,7 @@ class PagerDutyAlerting(SlackAlerting):
else None
),
user_api_key_org_id=user_api_key_dict.org_id,
user_api_key_org_alias=user_api_key_dict.organization_alias,
user_api_key_team_id=user_api_key_dict.team_id,
user_api_key_project_id=user_api_key_dict.project_id,
user_api_key_project_alias=user_api_key_dict.project_alias,

6
litellm/a2a_protocol/providers/pydantic_ai_agents/config.py
View file

@ -21,11 +21,11 @@ class PydanticAIProviderConfig(BaseA2AProviderConfig):
request_id: str,
params: Dict[str, Any],
api_base: Optional[str] = None,
**kwargs,
**kwargs: Any,
) -> Dict[str, Any]:
"""Handle non-streaming request to Pydantic AI agent."""
if not api_base:
raise ValueError("api_base is required for Pydantic AI agents")
if api_base is None:
raise ValueError("api_base is required for PydanticAIProviderConfig")
return await PydanticAIHandler.handle_non_streaming(
request_id=request_id,
params=params,

1
litellm/litellm_core_utils/litellm_logging.py
View file

@ -5587,6 +5587,7 @@ def get_standard_logging_metadata(
user_api_key_budget_reset_at=None,
user_api_key_team_id=None,
user_api_key_org_id=None,
user_api_key_org_alias=None,
user_api_key_project_id=None,
user_api_key_project_alias=None,
user_api_key_user_id=None,

206
litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
View file

@ -3055,6 +3055,115 @@ class ModelResponseIterator:
self.cumulative_tool_call_index: int = 0
self.has_seen_tool_calls: bool = False
def _apply_stream_candidates(
self,
_candidates: List[Candidates],
model_response: Any,
) -> Tuple[List[dict], List[dict], List[dict], List[dict]]:
(
grounding_metadata,
url_context_metadata,
safety_ratings,
citation_metadata,
self.cumulative_tool_call_index,
) = VertexGeminiConfig._process_candidates(
_candidates,
model_response,
self.logging_obj.optional_params,
cumulative_tool_call_index=self.cumulative_tool_call_index,
)
# Track whether tool_calls have been seen across streaming chunks.
# Gemini sends tool_calls and finishReason in separate chunks,
# so we need to remember if earlier chunks contained tool_calls
# to correctly set finish_reason="tool_calls" per the OpenAI spec.
if not self.has_seen_tool_calls:
for choice in model_response.choices:
if (
hasattr(choice, "delta")
and choice.delta
and choice.delta.tool_calls
):
self.has_seen_tool_calls = True
break
# Handle final chunk with finishReason but no content.
# _process_candidates skips candidates without "content",
# so the finish_reason from the final chunk is lost.
if not model_response.choices and _candidates:
from litellm.types.utils import Delta, StreamingChoices
for candidate in _candidates:
finish_reason_str = candidate.get("finishReason")
if finish_reason_str is not None:
if self.has_seen_tool_calls:
mapped_finish_reason = "tool_calls"
else:
mapped_finish_reason = VertexGeminiConfig._check_finish_reason(
None, finish_reason_str
)
choice = StreamingChoices(
finish_reason=mapped_finish_reason,
index=candidate.get("index", 0),
delta=Delta(content=None, role=None),
logprobs=None,
enhancements=None,
)
model_response.choices.append(choice)
# Also handle the case where the final chunk has empty
# content (e.g. text:"") WITH finishReason. In this case
# _process_candidates DOES create a choice, but maps
# finishReason="STOP" to "stop" because the current chunk
# has no tool_calls. Override if we saw tool_calls earlier.
if self.has_seen_tool_calls:
for choice in model_response.choices:
if choice.finish_reason == "stop":
choice.finish_reason = "tool_calls"
setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata) # type: ignore
setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata) # type: ignore
setattr(model_response, "vertex_ai_safety_ratings", safety_ratings) # type: ignore
setattr(model_response, "vertex_ai_citation_metadata", citation_metadata) # type: ignore
return grounding_metadata, url_context_metadata, safety_ratings, citation_metadata
def _apply_stream_usage_metadata(
self,
processed_chunk: Any,
model_response: Any,
grounding_metadata: List[dict],
) -> Optional[Usage]:
if "usageMetadata" not in processed_chunk:
return None
usage = VertexGeminiConfig._calculate_usage(
completion_response=processed_chunk,
)
web_search_requests = VertexGeminiConfig._calculate_web_search_requests(
grounding_metadata
)
if web_search_requests is not None:
cast(
PromptTokensDetailsWrapper, usage.prompt_tokens_details
).web_search_requests = web_search_requests
traffic_type = processed_chunk.get("usageMetadata", {}).get("trafficType")
if traffic_type:
model_response._hidden_params.setdefault(
"provider_specific_fields", {}
)["traffic_type"] = traffic_type
service_tier = self.response_headers.get("x-gemini-service-tier")
if service_tier:
if service_tier.lower() == "standard":
setattr(model_response, "service_tier", "default")
else:
setattr(model_response, "service_tier", service_tier.lower())
return usage
def chunk_parser(self, chunk: dict) -> Optional["ModelResponseStream"]:
try:
verbose_logger.debug(f"RAW GEMINI CHUNK: {chunk}")
@ -3072,108 +3181,23 @@ class ModelResponseIterator:
if blocked_response is not None:
model_response = blocked_response
usage: Optional[Usage] = None
_candidates: Optional[List[Candidates]] = processed_chunk.get("candidates")
grounding_metadata: List[dict] = []
url_context_metadata: List[dict] = []
safety_ratings: List[dict] = []
citation_metadata: List[dict] = []
_candidates: Optional[List[Candidates]] = processed_chunk.get("candidates")
if _candidates:
(
grounding_metadata,
url_context_metadata,
safety_ratings,
citation_metadata,
self.cumulative_tool_call_index,
) = VertexGeminiConfig._process_candidates(
_candidates,
model_response,
self.logging_obj.optional_params,
cumulative_tool_call_index=self.cumulative_tool_call_index,
)
) = self._apply_stream_candidates(_candidates, model_response)
# Track whether tool_calls have been seen across streaming chunks.
# Gemini sends tool_calls and finishReason in separate chunks,
# so we need to remember if earlier chunks contained tool_calls
# to correctly set finish_reason="tool_calls" per the OpenAI spec.
if not self.has_seen_tool_calls:
for choice in model_response.choices:
if (
hasattr(choice, "delta")
and choice.delta
and choice.delta.tool_calls
):
self.has_seen_tool_calls = True
break
# Handle final chunk with finishReason but no content.
# _process_candidates skips candidates without "content",
# so the finish_reason from the final chunk is lost.
if not model_response.choices and _candidates:
from litellm.types.utils import Delta, StreamingChoices
for candidate in _candidates:
finish_reason_str = candidate.get("finishReason")
if finish_reason_str is not None:
if self.has_seen_tool_calls:
mapped_finish_reason = "tool_calls"
else:
mapped_finish_reason = (
VertexGeminiConfig._check_finish_reason(
None, finish_reason_str
)
)
choice = StreamingChoices(
finish_reason=mapped_finish_reason,
index=candidate.get("index", 0),
delta=Delta(content=None, role=None),
logprobs=None,
enhancements=None,
)
model_response.choices.append(choice)
# Also handle the case where the final chunk has empty
# content (e.g. text:"") WITH finishReason. In this case
# _process_candidates DOES create a choice, but maps
# finishReason="STOP" to "stop" because the current chunk
# has no tool_calls. Override if we saw tool_calls earlier.
if self.has_seen_tool_calls:
for choice in model_response.choices:
if choice.finish_reason == "stop":
choice.finish_reason = "tool_calls"
setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata) # type: ignore
setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata) # type: ignore
setattr(model_response, "vertex_ai_safety_ratings", safety_ratings) # type: ignore
setattr(model_response, "vertex_ai_citation_metadata", citation_metadata) # type: ignore
if "usageMetadata" in processed_chunk:
usage = VertexGeminiConfig._calculate_usage(
completion_response=processed_chunk,
)
web_search_requests = VertexGeminiConfig._calculate_web_search_requests(
grounding_metadata
)
if web_search_requests is not None:
cast(
PromptTokensDetailsWrapper, usage.prompt_tokens_details
).web_search_requests = web_search_requests
traffic_type = processed_chunk.get("usageMetadata", {}).get(
"trafficType"
)
if traffic_type:
model_response._hidden_params.setdefault(
"provider_specific_fields", {}
)["traffic_type"] = traffic_type
service_tier = self.response_headers.get("x-gemini-service-tier")
if service_tier:
if service_tier.lower() == "standard":
setattr(model_response, "service_tier", "default")
else:
setattr(model_response, "service_tier", service_tier.lower())
usage = self._apply_stream_usage_metadata(
processed_chunk, model_response, grounding_metadata
)
setattr(model_response, "usage", usage) # type: ignore