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add comments explaining the suppressions

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This commit is contained in:
Ashwin Bharambe 2025-10-28 13:32:21 -07:00
parent a8d51a1a8b
commit 9032ba9097
2 changed files with 54 additions and 54 deletions
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6
src/llama_stack/providers/utils/inference/litellm_openai_mixin.py
View file

@ -176,7 +176,7 @@ class LiteLLMOpenAIMixin(
provider_data = self.get_request_provider_data()
key_field = self.provider_data_api_key_field
if provider_data and key_field and (api_key := getattr(provider_data, key_field, None)):
return str(api_key) # type: ignore[no-any-return]
return str(api_key) # type: ignore[no-any-return] # getattr returns Any, can't narrow without runtime type inspection
api_key = self.api_key_from_config
if not api_key:
@ -259,7 +259,7 @@ class LiteLLMOpenAIMixin(
api_base=self.api_base,
)
# LiteLLM returns compatible type but mypy can't verify external library
return await litellm.atext_completion(**request_params) # type: ignore[no-any-return]
return await litellm.atext_completion(**request_params) # type: ignore[no-any-return] # external lib lacks type stubs
async def openai_chat_completion(
self,
@ -310,7 +310,7 @@ class LiteLLMOpenAIMixin(
api_base=self.api_base,
)
# LiteLLM returns compatible type but mypy can't verify external library
return await litellm.acompletion(**request_params) # type: ignore[no-any-return]
return await litellm.acompletion(**request_params) # type: ignore[no-any-return] # external lib lacks type stubs
async def check_model_availability(self, model: str) -> bool:
"""

102
src/llama_stack/providers/utils/inference/openai_compat.py
View file

@ -194,12 +194,12 @@ def get_sampling_options(params: SamplingParams | None) -> dict:
def text_from_choice(choice) -> str:
if hasattr(choice, "delta") and choice.delta:
return choice.delta.content # type: ignore[no-any-return]
return choice.delta.content # type: ignore[no-any-return] # external OpenAI types lack precise annotations
if hasattr(choice, "message"):
return choice.message.content # type: ignore[no-any-return]
return choice.message.content # type: ignore[no-any-return] # external OpenAI types lack precise annotations
return choice.text # type: ignore[no-any-return]
return choice.text # type: ignore[no-any-return] # external OpenAI types lack precise annotations
def get_stop_reason(finish_reason: str) -> StopReason:
@ -275,10 +275,10 @@ def process_chat_completion_response(
) -> ChatCompletionResponse:
choice = response.choices[0]
if choice.finish_reason == "tool_calls":
if not hasattr(choice, "message") or not choice.message or not choice.message.tool_calls: # type: ignore[attr-defined]
if not hasattr(choice, "message") or not choice.message or not choice.message.tool_calls: # type: ignore[attr-defined] # OpenAICompatCompletionChoice is runtime duck-typed
raise ValueError("Tool calls are not present in the response")
tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls] # type: ignore[attr-defined]
tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls] # type: ignore[attr-defined] # OpenAICompatCompletionChoice is runtime duck-typed
if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
# If we couldn't parse a tool call, jsonify the tool calls and return them
return ChatCompletionResponse(
@ -329,7 +329,7 @@ def process_chat_completion_response(
return ChatCompletionResponse(
completion_message=CompletionMessage(
content=raw_message.content, # type: ignore[arg-type]
content=raw_message.content, # type: ignore[arg-type] # decode_assistant_message returns Union[str, InterleavedContent]
stop_reason=raw_message.stop_reason or StopReason.end_of_turn,
tool_calls=raw_message.tool_calls,
),
@ -530,7 +530,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
}
if hasattr(message, "tool_calls") and message.tool_calls:
result["tool_calls"] = [] # type: ignore[assignment]
result["tool_calls"] = [] # type: ignore[assignment] # dict allows Any value, stricter type expected
for tc in message.tool_calls:
# The tool.tool_name can be a str or a BuiltinTool enum. If
# it's the latter, convert to a string.
@ -538,7 +538,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
if isinstance(tool_name, BuiltinTool):
tool_name = tool_name.value
result["tool_calls"].append( # type: ignore[union-attr]
result["tool_calls"].append( # type: ignore[union-attr] # reassigned as list above, mypy can't track
{
"id": tc.call_id,
"type": "function",
@ -613,7 +613,7 @@ async def convert_message_to_openai_dict_new(
),
)
elif isinstance(content_, list):
return [await impl(item) for item in content_] # type: ignore[misc]
return [await impl(item) for item in content_] # type: ignore[misc] # recursive list comprehension confuses mypy's type narrowing
else:
raise ValueError(f"Unsupported content type: {type(content_)}")
@ -649,18 +649,18 @@ async def convert_message_to_openai_dict_new(
out = OpenAIChatCompletionAssistantMessage(
role="assistant",
content=await _convert_message_content(message.content),
**params, # type: ignore[typeddict-item]
**params, # type: ignore[typeddict-item] # tool_calls dict expansion conflicts with TypedDict optional field
)
elif isinstance(message, ToolResponseMessage):
out = OpenAIChatCompletionToolMessage(
role="tool",
tool_call_id=message.call_id,
content=await _convert_message_content(message.content), # type: ignore[typeddict-item]
content=await _convert_message_content(message.content), # type: ignore[typeddict-item] # content union type incompatible with TypedDict str requirement
)
elif isinstance(message, SystemMessage):
out = OpenAIChatCompletionSystemMessage(
role="system",
content=await _convert_message_content(message.content), # type: ignore[typeddict-item]
content=await _convert_message_content(message.content), # type: ignore[typeddict-item] # content union type incompatible with TypedDict str requirement
)
else:
raise ValueError(f"Unsupported message type: {type(message)}")
@ -763,16 +763,16 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
function = out["function"]
if isinstance(tool.tool_name, BuiltinTool):
function["name"] = tool.tool_name.value # type: ignore[index]
function["name"] = tool.tool_name.value # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str]
else:
function["name"] = tool.tool_name # type: ignore[index]
function["name"] = tool.tool_name # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str]
if tool.description:
function["description"] = tool.description # type: ignore[index]
function["description"] = tool.description # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str]
if tool.input_schema:
# Pass through the entire JSON Schema as-is
function["parameters"] = tool.input_schema # type: ignore[index]
function["parameters"] = tool.input_schema # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str]
# NOTE: OpenAI does not support output_schema, so we drop it here
# It's stored in LlamaStack for validation and other provider usage
@ -820,10 +820,10 @@ def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None
tool_config = ToolConfig()
if tool_choice:
try:
tool_choice = ToolChoice(tool_choice) # type: ignore[assignment]
tool_choice = ToolChoice(tool_choice) # type: ignore[assignment] # reassigning to enum narrows union but mypy can't track after exception
except ValueError:
pass
tool_config.tool_choice = tool_choice # type: ignore[assignment]
tool_config.tool_choice = tool_choice # type: ignore[assignment] # ToolConfig.tool_choice accepts Union[ToolChoice, dict] but mypy tracks narrower type
return tool_config
@ -853,11 +853,11 @@ def _convert_openai_request_response_format(
if not response_format:
return None
# response_format can be a dict or a pydantic model
response_format = dict(response_format) # type: ignore[assignment]
if response_format.get("type", "") == "json_schema": # type: ignore[union-attr]
response_format = dict(response_format) # type: ignore[assignment] # OpenAIResponseFormatParam union needs dict conversion
if response_format.get("type", "") == "json_schema": # type: ignore[union-attr] # narrowed to dict but mypy doesn't track .get()
return JsonSchemaResponseFormat(
type="json_schema", # type: ignore[arg-type]
json_schema=response_format.get("json_schema", {}).get("schema", ""), # type: ignore[union-attr]
type="json_schema", # type: ignore[arg-type] # Literal["json_schema"] incompatible with expected type
json_schema=response_format.get("json_schema", {}).get("schema", ""), # type: ignore[union-attr] # chained .get() on reassigned dict confuses mypy
)
return None
@ -950,7 +950,7 @@ def _convert_openai_sampling_params(
temperature = 1.0
if top_p is None:
top_p = 1.0
sampling_params.strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p) # type: ignore[assignment]
sampling_params.strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p) # type: ignore[assignment] # SamplingParams.strategy union accepts this type
return sampling_params
@ -965,20 +965,20 @@ def openai_messages_to_messages(
for message in messages:
converted_message: Message
if message.role == "system":
converted_message = SystemMessage(content=openai_content_to_content(message.content)) # type: ignore[arg-type]
converted_message = SystemMessage(content=openai_content_to_content(message.content)) # type: ignore[arg-type] # OpenAI content union broader than Message content union
elif message.role == "user":
converted_message = UserMessage(content=openai_content_to_content(message.content)) # type: ignore[arg-type]
converted_message = UserMessage(content=openai_content_to_content(message.content)) # type: ignore[arg-type] # OpenAI content union broader than Message content union
elif message.role == "assistant":
converted_message = CompletionMessage(
content=openai_content_to_content(message.content), # type: ignore[arg-type]
tool_calls=_convert_openai_tool_calls(message.tool_calls) if message.tool_calls else [], # type: ignore[arg-type]
content=openai_content_to_content(message.content), # type: ignore[arg-type] # OpenAI content union broader than Message content union
tool_calls=_convert_openai_tool_calls(message.tool_calls) if message.tool_calls else [], # type: ignore[arg-type] # OpenAI tool_calls type incompatible with conversion function
stop_reason=StopReason.end_of_turn,
)
elif message.role == "tool":
converted_message = ToolResponseMessage(
role="tool",
call_id=message.tool_call_id,
content=openai_content_to_content(message.content), # type: ignore[arg-type]
content=openai_content_to_content(message.content), # type: ignore[arg-type] # OpenAI content union broader than Message content union
)
else:
raise ValueError(f"Unknown role {message.role}")
@ -995,9 +995,9 @@ def openai_content_to_content(content: str | Iterable[OpenAIChatCompletionConten
return [openai_content_to_content(c) for c in content]
elif hasattr(content, "type"):
if content.type == "text":
return TextContentItem(type="text", text=content.text) # type: ignore[attr-defined]
return TextContentItem(type="text", text=content.text) # type: ignore[attr-defined] # Iterable narrowed by hasattr check but mypy doesn't track
elif content.type == "image_url":
return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url))) # type: ignore[attr-defined]
return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url))) # type: ignore[attr-defined] # Iterable narrowed by hasattr check but mypy doesn't track
else:
raise ValueError(f"Unknown content type: {content.type}")
else:
@ -1046,9 +1046,9 @@ def convert_openai_chat_completion_choice(
completion_message=CompletionMessage(
content=choice.message.content or "", # CompletionMessage content is not optional
stop_reason=_convert_openai_finish_reason(choice.finish_reason),
tool_calls=_convert_openai_tool_calls(choice.message.tool_calls) if choice.message.tool_calls else [], # type: ignore[arg-type]
tool_calls=_convert_openai_tool_calls(choice.message.tool_calls) if choice.message.tool_calls else [], # type: ignore[arg-type] # OpenAI tool_calls Optional type broadens union
),
logprobs=_convert_openai_logprobs(getattr(choice, "logprobs", None)), # type: ignore[arg-type]
logprobs=_convert_openai_logprobs(getattr(choice, "logprobs", None)), # type: ignore[arg-type] # getattr returns Any, can't narrow without inspection
)
@ -1088,7 +1088,7 @@ async def convert_openai_chat_completion_stream(
event=ChatCompletionResponseEvent(
event_type=event_type,
delta=TextDelta(text=choice.delta.content),
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type]
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result
)
)
@ -1106,10 +1106,10 @@ async def convert_openai_chat_completion_stream(
event=ChatCompletionResponseEvent(
event_type=event_type,
delta=ToolCallDelta(
tool_call=_convert_openai_tool_calls([tool_call])[0], # type: ignore[arg-type, list-item]
tool_call=_convert_openai_tool_calls([tool_call])[0], # type: ignore[arg-type, list-item] # delta tool_call type differs from complete tool_call
parse_status=ToolCallParseStatus.succeeded,
),
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type]
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result
)
)
else:
@ -1147,7 +1147,7 @@ async def convert_openai_chat_completion_stream(
tool_call=delta,
parse_status=ToolCallParseStatus.in_progress,
),
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type]
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result
)
)
elif choice.delta.content:
@ -1155,7 +1155,7 @@ async def convert_openai_chat_completion_stream(
event=ChatCompletionResponseEvent(
event_type=event_type,
delta=TextDelta(text=choice.delta.content or ""),
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type]
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result
)
)
@ -1186,7 +1186,7 @@ async def convert_openai_chat_completion_stream(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
tool_call=parsed_tool_call, # type: ignore[arg-type]
tool_call=parsed_tool_call, # type: ignore[arg-type] # ToolCallDelta.tool_call accepts Union[str, ToolCall]
parse_status=ToolCallParseStatus.succeeded,
),
stop_reason=stop_reason,
@ -1198,7 +1198,7 @@ async def convert_openai_chat_completion_stream(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
tool_call=buffer["content"], # type: ignore[arg-type]
tool_call=buffer["content"], # type: ignore[arg-type] # ToolCallDelta.tool_call accepts Union[str, ToolCall]
parse_status=ToolCallParseStatus.failed,
),
stop_reason=stop_reason,
@ -1259,7 +1259,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
top_p: float | None = None,
user: str | None = None,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
messages = openai_messages_to_messages(messages) # type: ignore[assignment]
messages = openai_messages_to_messages(messages) # type: ignore[assignment] # converted from OpenAI to LlamaStack message format
response_format = _convert_openai_request_response_format(response_format)
sampling_params = _convert_openai_sampling_params(
max_tokens=max_tokens,
@ -1268,15 +1268,15 @@ class OpenAIChatCompletionToLlamaStackMixin:
)
tool_config = _convert_openai_request_tool_config(tool_choice)
tools = _convert_openai_request_tools(tools) # type: ignore[assignment]
tools = _convert_openai_request_tools(tools) # type: ignore[assignment] # converted from OpenAI to LlamaStack tool format
if tool_config.tool_choice == ToolChoice.none:
tools = [] # type: ignore[assignment]
tools = [] # type: ignore[assignment] # empty list narrows return type but mypy tracks broader type
outstanding_responses = []
# "n" is the number of completions to generate per prompt
n = n or 1
for _i in range(0, n):
response = self.chat_completion( # type: ignore[attr-defined]
response = self.chat_completion( # type: ignore[attr-defined] # mixin expects class to implement chat_completion
model_id=model,
messages=messages,
sampling_params=sampling_params,
@ -1288,7 +1288,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
outstanding_responses.append(response)
if stream:
return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses) # type: ignore[no-any-return]
return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses) # type: ignore[no-any-return] # mixin async generator return type too complex for mypy
return await OpenAIChatCompletionToLlamaStackMixin._process_non_stream_response(
self, model, outstanding_responses
@ -1313,7 +1313,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
delta = OpenAIChoiceDelta(content=text_delta)
yield OpenAIChatCompletionChunk(
id=id,
choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)], # type: ignore[arg-type]
choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)], # type: ignore[arg-type] # finish_reason Optional[str] incompatible with Literal union
created=int(time.time()),
model=model,
object="chat.completion.chunk",
@ -1331,7 +1331,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
function=OpenAIChoiceDeltaToolCallFunction(
name=tool_call.tool_name
if isinstance(tool_call.tool_name, str)
else tool_call.tool_name.value, # type: ignore[arg-type]
else tool_call.tool_name.value, # type: ignore[arg-type] # enum .value extraction on Union confuses mypy
arguments="",
),
)
@ -1339,7 +1339,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
yield OpenAIChatCompletionChunk(
id=id,
choices=[
OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) # type: ignore[arg-type]
OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) # type: ignore[arg-type] # finish_reason Optional[str] incompatible with Literal union
],
created=int(time.time()),
model=model,
@ -1356,7 +1356,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
yield OpenAIChatCompletionChunk(
id=id,
choices=[
OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) # type: ignore[arg-type]
OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) # type: ignore[arg-type] # finish_reason Optional[str] incompatible with Literal union
],
created=int(time.time()),
model=model,
@ -1375,14 +1375,14 @@ class OpenAIChatCompletionToLlamaStackMixin:
choice = OpenAIChatCompletionChoice(
index=len(choices),
message=message, # type: ignore[arg-type]
message=message, # type: ignore[arg-type] # OpenAIChatCompletionMessage union incompatible with narrower Message type
finish_reason=finish_reason,
)
choices.append(choice) # type: ignore[arg-type]
choices.append(choice) # type: ignore[arg-type] # OpenAIChatCompletionChoice type annotation mismatch
return OpenAIChatCompletion(
id=f"chatcmpl-{uuid.uuid4()}",
choices=choices, # type: ignore[arg-type]
choices=choices, # type: ignore[arg-type] # list[OpenAIChatCompletionChoice] union incompatible
created=int(time.time()),
model=model,
object="chat.completion",