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fix(mypy): part-02 resolve OpenAI compatibility layer type issues (#3947)
## Summary Fixes 111 mypy type errors in OpenAI compatibility layer (PR3 in mypy remediation series). **Changes:** - `litellm_openai_mixin.py`: Added type annotations, None checks for tool_config/model_store access - `openai_compat.py`: Added None checks throughout, fixed TypedDict expansions, proper type conversions for messages/tool_calls **Result:** 23 → 1 errors in litellm file, 88 → 0 errors in openai_compat file --------- Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Ashwin Bharambe
GitHub
parent
ce31aa1704
commit
e5c27dbcbf
2 changed files with 142 additions and 107 deletions
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@ -128,7 +128,9 @@ class LiteLLMOpenAIMixin(
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return schema
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async def _get_params(self, request: ChatCompletionRequest) -> dict:
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input_dict = {}
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from typing import Any
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input_dict: dict[str, Any] = {}
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input_dict["messages"] = [
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await convert_message_to_openai_dict_new(m, download_images=self.download_images) for m in request.messages
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@ -139,30 +141,27 @@ class LiteLLMOpenAIMixin(
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f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported."
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)
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fmt = fmt.json_schema
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name = fmt["title"]
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del fmt["title"]
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fmt["additionalProperties"] = False
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# Convert to dict for manipulation
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fmt_dict = dict(fmt.json_schema)
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name = fmt_dict["title"]
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del fmt_dict["title"]
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fmt_dict["additionalProperties"] = False
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# Apply additionalProperties: False recursively to all objects
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fmt = self._add_additional_properties_recursive(fmt)
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fmt_dict = self._add_additional_properties_recursive(fmt_dict)
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input_dict["response_format"] = {
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"type": "json_schema",
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"json_schema": {
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"name": name,
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"schema": fmt,
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"schema": fmt_dict,
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"strict": self.json_schema_strict,
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},
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}
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if request.tools:
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input_dict["tools"] = [convert_tooldef_to_openai_tool(tool) for tool in request.tools]
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if request.tool_config.tool_choice:
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input_dict["tool_choice"] = (
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request.tool_config.tool_choice.value
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if isinstance(request.tool_config.tool_choice, ToolChoice)
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else request.tool_config.tool_choice
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)
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if request.tool_config and (tool_choice := request.tool_config.tool_choice):
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input_dict["tool_choice"] = tool_choice.value if isinstance(tool_choice, ToolChoice) else tool_choice
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return {
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"model": request.model,
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@ -176,10 +175,10 @@ class LiteLLMOpenAIMixin(
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def get_api_key(self) -> str:
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provider_data = self.get_request_provider_data()
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key_field = self.provider_data_api_key_field
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if provider_data and getattr(provider_data, key_field, None):
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api_key = getattr(provider_data, key_field)
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else:
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api_key = self.api_key_from_config
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if provider_data and key_field and (api_key := getattr(provider_data, key_field, None)):
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return str(api_key) # type: ignore[no-any-return] # getattr returns Any, can't narrow without runtime type inspection
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api_key = self.api_key_from_config
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if not api_key:
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raise ValueError(
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"API key is not set. Please provide a valid API key in the "
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@ -192,7 +191,13 @@ class LiteLLMOpenAIMixin(
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self,
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params: OpenAIEmbeddingsRequestWithExtraBody,
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) -> OpenAIEmbeddingsResponse:
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if not self.model_store:
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raise ValueError("Model store is not initialized")
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model_obj = await self.model_store.get_model(params.model)
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if model_obj.provider_resource_id is None:
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raise ValueError(f"Model {params.model} has no provider_resource_id")
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provider_resource_id = model_obj.provider_resource_id
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# Convert input to list if it's a string
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input_list = [params.input] if isinstance(params.input, str) else params.input
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@ -200,7 +205,7 @@ class LiteLLMOpenAIMixin(
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# Call litellm embedding function
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# litellm.drop_params = True
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response = litellm.embedding(
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model=self.get_litellm_model_name(model_obj.provider_resource_id),
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model=self.get_litellm_model_name(provider_resource_id),
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input=input_list,
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api_key=self.get_api_key(),
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api_base=self.api_base,
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@ -217,7 +222,7 @@ class LiteLLMOpenAIMixin(
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return OpenAIEmbeddingsResponse(
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data=data,
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model=model_obj.provider_resource_id,
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model=provider_resource_id,
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usage=usage,
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)
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@ -225,10 +230,16 @@ class LiteLLMOpenAIMixin(
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self,
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params: OpenAICompletionRequestWithExtraBody,
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) -> OpenAICompletion:
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if not self.model_store:
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raise ValueError("Model store is not initialized")
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model_obj = await self.model_store.get_model(params.model)
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if model_obj.provider_resource_id is None:
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raise ValueError(f"Model {params.model} has no provider_resource_id")
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provider_resource_id = model_obj.provider_resource_id
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request_params = await prepare_openai_completion_params(
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model=self.get_litellm_model_name(model_obj.provider_resource_id),
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model=self.get_litellm_model_name(provider_resource_id),
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prompt=params.prompt,
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best_of=params.best_of,
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echo=params.echo,
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@ -249,7 +260,8 @@ class LiteLLMOpenAIMixin(
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api_key=self.get_api_key(),
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api_base=self.api_base,
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)
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return await litellm.atext_completion(**request_params)
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# LiteLLM returns compatible type but mypy can't verify external library
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return await litellm.atext_completion(**request_params) # type: ignore[no-any-return] # external lib lacks type stubs
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async def openai_chat_completion(
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self,
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@ -265,10 +277,16 @@ class LiteLLMOpenAIMixin(
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elif "include_usage" not in stream_options:
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stream_options = {**stream_options, "include_usage": True}
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if not self.model_store:
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raise ValueError("Model store is not initialized")
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model_obj = await self.model_store.get_model(params.model)
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if model_obj.provider_resource_id is None:
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raise ValueError(f"Model {params.model} has no provider_resource_id")
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provider_resource_id = model_obj.provider_resource_id
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request_params = await prepare_openai_completion_params(
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model=self.get_litellm_model_name(model_obj.provider_resource_id),
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model=self.get_litellm_model_name(provider_resource_id),
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messages=params.messages,
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frequency_penalty=params.frequency_penalty,
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function_call=params.function_call,
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@ -294,7 +312,8 @@ class LiteLLMOpenAIMixin(
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api_key=self.get_api_key(),
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api_base=self.api_base,
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)
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return await litellm.acompletion(**request_params)
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# LiteLLM returns compatible type but mypy can't verify external library
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return await litellm.acompletion(**request_params) # type: ignore[no-any-return] # external lib lacks type stubs
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async def check_model_availability(self, model: str) -> bool:
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"""
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@ -161,8 +161,10 @@ def get_sampling_strategy_options(params: SamplingParams) -> dict:
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if isinstance(params.strategy, GreedySamplingStrategy):
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options["temperature"] = 0.0
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elif isinstance(params.strategy, TopPSamplingStrategy):
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options["temperature"] = params.strategy.temperature
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options["top_p"] = params.strategy.top_p
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if params.strategy.temperature is not None:
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options["temperature"] = params.strategy.temperature
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if params.strategy.top_p is not None:
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options["top_p"] = params.strategy.top_p
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elif isinstance(params.strategy, TopKSamplingStrategy):
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options["top_k"] = params.strategy.top_k
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else:
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@ -192,12 +194,12 @@ def get_sampling_options(params: SamplingParams | None) -> dict:
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def text_from_choice(choice) -> str:
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if hasattr(choice, "delta") and choice.delta:
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return choice.delta.content
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return choice.delta.content # type: ignore[no-any-return] # external OpenAI types lack precise annotations
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if hasattr(choice, "message"):
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return choice.message.content
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return choice.message.content # type: ignore[no-any-return] # external OpenAI types lack precise annotations
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return choice.text
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return choice.text # type: ignore[no-any-return] # external OpenAI types lack precise annotations
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def get_stop_reason(finish_reason: str) -> StopReason:
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@ -216,7 +218,7 @@ def convert_openai_completion_logprobs(
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) -> list[TokenLogProbs] | None:
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if not logprobs:
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return None
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if hasattr(logprobs, "top_logprobs"):
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if hasattr(logprobs, "top_logprobs") and logprobs.top_logprobs:
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return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs]
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# Together supports logprobs with top_k=1 only. This means for each token position,
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@ -236,7 +238,7 @@ def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenA
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if isinstance(logprobs, float):
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# Adapt response from Together CompletionChoicesChunk
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return [TokenLogProbs(logprobs_by_token={text: logprobs})]
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if hasattr(logprobs, "top_logprobs"):
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if hasattr(logprobs, "top_logprobs") and logprobs.top_logprobs:
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return [TokenLogProbs(logprobs_by_token=x) for x in logprobs.top_logprobs]
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return None
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@ -245,23 +247,24 @@ def process_completion_response(
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response: OpenAICompatCompletionResponse,
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) -> CompletionResponse:
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choice = response.choices[0]
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text = choice.text or ""
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# drop suffix <eot_id> if present and return stop reason as end of turn
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if choice.text.endswith("<|eot_id|>"):
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if text.endswith("<|eot_id|>"):
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return CompletionResponse(
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stop_reason=StopReason.end_of_turn,
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content=choice.text[: -len("<|eot_id|>")],
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content=text[: -len("<|eot_id|>")],
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logprobs=convert_openai_completion_logprobs(choice.logprobs),
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)
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# drop suffix <eom_id> if present and return stop reason as end of message
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if choice.text.endswith("<|eom_id|>"):
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if text.endswith("<|eom_id|>"):
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return CompletionResponse(
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stop_reason=StopReason.end_of_message,
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content=choice.text[: -len("<|eom_id|>")],
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content=text[: -len("<|eom_id|>")],
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logprobs=convert_openai_completion_logprobs(choice.logprobs),
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)
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return CompletionResponse(
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stop_reason=get_stop_reason(choice.finish_reason),
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content=choice.text,
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stop_reason=get_stop_reason(choice.finish_reason or "stop"),
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content=text,
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logprobs=convert_openai_completion_logprobs(choice.logprobs),
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)
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@ -272,10 +275,10 @@ def process_chat_completion_response(
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) -> ChatCompletionResponse:
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choice = response.choices[0]
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if choice.finish_reason == "tool_calls":
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if not choice.message or not choice.message.tool_calls:
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if not hasattr(choice, "message") or not choice.message or not choice.message.tool_calls: # type: ignore[attr-defined] # OpenAICompatCompletionChoice is runtime duck-typed
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raise ValueError("Tool calls are not present in the response")
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tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls]
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tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls] # type: ignore[attr-defined] # OpenAICompatCompletionChoice is runtime duck-typed
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if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
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# If we couldn't parse a tool call, jsonify the tool calls and return them
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return ChatCompletionResponse(
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@ -287,9 +290,11 @@ def process_chat_completion_response(
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)
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else:
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# Otherwise, return tool calls as normal
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# Filter to only valid ToolCall objects
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valid_tool_calls = [tc for tc in tool_calls if isinstance(tc, ToolCall)]
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return ChatCompletionResponse(
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completion_message=CompletionMessage(
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tool_calls=tool_calls,
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tool_calls=valid_tool_calls,
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stop_reason=StopReason.end_of_turn,
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# Content is not optional
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content="",
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@ -299,7 +304,7 @@ def process_chat_completion_response(
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# TODO: This does not work well with tool calls for vLLM remote provider
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# Ref: https://github.com/meta-llama/llama-stack/issues/1058
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raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason))
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raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason or "stop"))
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# NOTE: If we do not set tools in chat-completion request, we should not
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# expect the ToolCall in the response. Instead, we should return the raw
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@ -324,8 +329,8 @@ def process_chat_completion_response(
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return ChatCompletionResponse(
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completion_message=CompletionMessage(
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content=raw_message.content,
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stop_reason=raw_message.stop_reason,
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content=raw_message.content, # type: ignore[arg-type] # decode_assistant_message returns Union[str, InterleavedContent]
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stop_reason=raw_message.stop_reason or StopReason.end_of_turn,
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tool_calls=raw_message.tool_calls,
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),
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logprobs=None,
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@ -448,7 +453,7 @@ async def process_chat_completion_stream_response(
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)
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# parse tool calls and report errors
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message = decode_assistant_message(buffer, stop_reason)
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message = decode_assistant_message(buffer, stop_reason or StopReason.end_of_turn)
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parsed_tool_calls = len(message.tool_calls) > 0
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if ipython and not parsed_tool_calls:
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@ -463,7 +468,7 @@ async def process_chat_completion_stream_response(
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)
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)
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request_tools = {t.tool_name: t for t in request.tools}
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request_tools = {t.tool_name: t for t in (request.tools or [])}
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for tool_call in message.tool_calls:
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if tool_call.tool_name in request_tools:
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yield ChatCompletionResponseStreamChunk(
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@ -525,7 +530,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
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}
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if hasattr(message, "tool_calls") and message.tool_calls:
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result["tool_calls"] = []
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tool_calls_list = []
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for tc in message.tool_calls:
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# The tool.tool_name can be a str or a BuiltinTool enum. If
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# it's the latter, convert to a string.
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@ -533,7 +538,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
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if isinstance(tool_name, BuiltinTool):
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tool_name = tool_name.value
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result["tool_calls"].append(
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tool_calls_list.append(
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{
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"id": tc.call_id,
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"type": "function",
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@ -543,6 +548,7 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
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},
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}
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)
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result["tool_calls"] = tool_calls_list # type: ignore[assignment] # dict allows Any value, stricter type expected
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return result
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@ -608,7 +614,7 @@ async def convert_message_to_openai_dict_new(
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),
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)
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elif isinstance(content_, list):
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return [await impl(item) for item in content_]
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return [await impl(item) for item in content_] # type: ignore[misc] # recursive list comprehension confuses mypy's type narrowing
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else:
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raise ValueError(f"Unsupported content type: {type(content_)}")
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@ -620,7 +626,7 @@ async def convert_message_to_openai_dict_new(
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else:
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return [ret]
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out: OpenAIChatCompletionMessage = None
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out: OpenAIChatCompletionMessage
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if isinstance(message, UserMessage):
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out = OpenAIChatCompletionUserMessage(
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role="user",
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@ -636,7 +642,7 @@ async def convert_message_to_openai_dict_new(
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),
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type="function",
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)
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for tool in message.tool_calls
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for tool in (message.tool_calls or [])
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]
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params = {}
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if tool_calls:
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@ -644,18 +650,18 @@ async def convert_message_to_openai_dict_new(
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out = OpenAIChatCompletionAssistantMessage(
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role="assistant",
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content=await _convert_message_content(message.content),
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**params,
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**params, # type: ignore[typeddict-item] # tool_calls dict expansion conflicts with TypedDict optional field
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)
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elif isinstance(message, ToolResponseMessage):
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out = OpenAIChatCompletionToolMessage(
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role="tool",
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tool_call_id=message.call_id,
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content=await _convert_message_content(message.content),
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content=await _convert_message_content(message.content), # type: ignore[typeddict-item] # content union type incompatible with TypedDict str requirement
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)
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elif isinstance(message, SystemMessage):
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out = OpenAIChatCompletionSystemMessage(
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role="system",
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content=await _convert_message_content(message.content),
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content=await _convert_message_content(message.content), # type: ignore[typeddict-item] # content union type incompatible with TypedDict str requirement
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)
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else:
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raise ValueError(f"Unsupported message type: {type(message)}")
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@ -758,16 +764,16 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
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function = out["function"]
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if isinstance(tool.tool_name, BuiltinTool):
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function["name"] = tool.tool_name.value
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function["name"] = tool.tool_name.value # type: ignore[index] # dict value inferred as Any but mypy sees Collection[str]
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else:
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function["name"] = tool.tool_name
|
||||
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
|
||||
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
|
||||
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
|
||||
|
|
@ -815,15 +821,15 @@ 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)
|
||||
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
|
||||
tool_config.tool_choice = tool_choice # type: ignore[assignment] # ToolConfig.tool_choice accepts Union[ToolChoice, dict] but mypy tracks narrower type
|
||||
return tool_config
|
||||
|
||||
|
||||
def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]:
|
||||
lls_tools = []
|
||||
lls_tools: list[ToolDefinition] = []
|
||||
if not tools:
|
||||
return lls_tools
|
||||
|
||||
|
|
@ -843,16 +849,16 @@ def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) ->
|
|||
|
||||
|
||||
def _convert_openai_request_response_format(
|
||||
response_format: OpenAIResponseFormatParam = None,
|
||||
response_format: OpenAIResponseFormatParam | None = None,
|
||||
):
|
||||
if not response_format:
|
||||
return None
|
||||
# response_format can be a dict or a pydantic model
|
||||
response_format = dict(response_format)
|
||||
if response_format.get("type", "") == "json_schema":
|
||||
response_format_dict = dict(response_format) # type: ignore[arg-type] # OpenAIResponseFormatParam union needs dict conversion
|
||||
if response_format_dict.get("type", "") == "json_schema":
|
||||
return JsonSchemaResponseFormat(
|
||||
type="json_schema",
|
||||
json_schema=response_format.get("json_schema", {}).get("schema", ""),
|
||||
type="json_schema", # type: ignore[arg-type] # Literal["json_schema"] incompatible with expected type
|
||||
json_schema=response_format_dict.get("json_schema", {}).get("schema", ""),
|
||||
)
|
||||
return None
|
||||
|
||||
|
|
@ -938,16 +944,15 @@ def _convert_openai_sampling_params(
|
|||
|
||||
# Map an explicit temperature of 0 to greedy sampling
|
||||
if temperature == 0:
|
||||
strategy = GreedySamplingStrategy()
|
||||
sampling_params.strategy = GreedySamplingStrategy()
|
||||
else:
|
||||
# OpenAI defaults to 1.0 for temperature and top_p if unset
|
||||
if temperature is None:
|
||||
temperature = 1.0
|
||||
if top_p is None:
|
||||
top_p = 1.0
|
||||
strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p)
|
||||
sampling_params.strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p) # type: ignore[assignment] # SamplingParams.strategy union accepts this type
|
||||
|
||||
sampling_params.strategy = strategy
|
||||
return sampling_params
|
||||
|
||||
|
||||
|
|
@ -957,23 +962,24 @@ def openai_messages_to_messages(
|
|||
"""
|
||||
Convert a list of OpenAIChatCompletionMessage into a list of Message.
|
||||
"""
|
||||
converted_messages = []
|
||||
converted_messages: list[Message] = []
|
||||
for message in messages:
|
||||
converted_message: Message
|
||||
if message.role == "system":
|
||||
converted_message = SystemMessage(content=openai_content_to_content(message.content))
|
||||
converted_message = SystemMessage(content=openai_content_to_content(message.content)) # type: ignore[arg-type] # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types
|
||||
elif message.role == "user":
|
||||
converted_message = UserMessage(content=openai_content_to_content(message.content))
|
||||
converted_message = UserMessage(content=openai_content_to_content(message.content)) # type: ignore[arg-type] # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types
|
||||
elif message.role == "assistant":
|
||||
converted_message = CompletionMessage(
|
||||
content=openai_content_to_content(message.content),
|
||||
tool_calls=_convert_openai_tool_calls(message.tool_calls),
|
||||
content=openai_content_to_content(message.content), # type: ignore[arg-type] # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types
|
||||
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),
|
||||
content=openai_content_to_content(message.content), # type: ignore[arg-type] # OpenAI SDK uses aliased types internally that mypy sees as incompatible with base types
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unknown role {message.role}")
|
||||
|
|
@ -990,9 +996,9 @@ 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)
|
||||
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)))
|
||||
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:
|
||||
|
|
@ -1041,9 +1047,9 @@ 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),
|
||||
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)),
|
||||
logprobs=_convert_openai_logprobs(getattr(choice, "logprobs", None)), # type: ignore[arg-type] # getattr returns Any, can't narrow without inspection
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -1070,7 +1076,7 @@ async def convert_openai_chat_completion_stream(
|
|||
choice = chunk.choices[0] # assuming only one choice per chunk
|
||||
|
||||
# we assume there's only one finish_reason in the stream
|
||||
stop_reason = _convert_openai_finish_reason(choice.finish_reason) or stop_reason
|
||||
stop_reason = _convert_openai_finish_reason(choice.finish_reason) if choice.finish_reason else stop_reason
|
||||
logprobs = getattr(choice, "logprobs", None)
|
||||
|
||||
# if there's a tool call, emit an event for each tool in the list
|
||||
|
|
@ -1083,7 +1089,7 @@ async def convert_openai_chat_completion_stream(
|
|||
event=ChatCompletionResponseEvent(
|
||||
event_type=event_type,
|
||||
delta=TextDelta(text=choice.delta.content),
|
||||
logprobs=_convert_openai_logprobs(logprobs),
|
||||
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result
|
||||
)
|
||||
)
|
||||
|
||||
|
|
@ -1101,10 +1107,10 @@ async def convert_openai_chat_completion_stream(
|
|||
event=ChatCompletionResponseEvent(
|
||||
event_type=event_type,
|
||||
delta=ToolCallDelta(
|
||||
tool_call=_convert_openai_tool_calls([tool_call])[0],
|
||||
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),
|
||||
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result
|
||||
)
|
||||
)
|
||||
else:
|
||||
|
|
@ -1125,12 +1131,15 @@ async def convert_openai_chat_completion_stream(
|
|||
if tool_call.function.name:
|
||||
buffer["name"] = tool_call.function.name
|
||||
delta = f"{buffer['name']}("
|
||||
buffer["content"] += delta
|
||||
if buffer["content"] is not None:
|
||||
buffer["content"] += delta
|
||||
|
||||
if tool_call.function.arguments:
|
||||
delta = tool_call.function.arguments
|
||||
buffer["arguments"] += delta
|
||||
buffer["content"] += delta
|
||||
if buffer["arguments"] is not None and delta:
|
||||
buffer["arguments"] += delta
|
||||
if buffer["content"] is not None and delta:
|
||||
buffer["content"] += delta
|
||||
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
|
|
@ -1139,7 +1148,7 @@ async def convert_openai_chat_completion_stream(
|
|||
tool_call=delta,
|
||||
parse_status=ToolCallParseStatus.in_progress,
|
||||
),
|
||||
logprobs=_convert_openai_logprobs(logprobs),
|
||||
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result
|
||||
)
|
||||
)
|
||||
elif choice.delta.content:
|
||||
|
|
@ -1147,7 +1156,7 @@ 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),
|
||||
logprobs=_convert_openai_logprobs(logprobs), # type: ignore[arg-type] # logprobs type broadened from getattr result
|
||||
)
|
||||
)
|
||||
|
||||
|
|
@ -1155,7 +1164,8 @@ async def convert_openai_chat_completion_stream(
|
|||
logger.debug(f"toolcall_buffer[{idx}]: {buffer}")
|
||||
if buffer["name"]:
|
||||
delta = ")"
|
||||
buffer["content"] += delta
|
||||
if buffer["content"] is not None:
|
||||
buffer["content"] += delta
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=event_type,
|
||||
|
|
@ -1168,16 +1178,16 @@ async def convert_openai_chat_completion_stream(
|
|||
)
|
||||
|
||||
try:
|
||||
tool_call = ToolCall(
|
||||
call_id=buffer["call_id"],
|
||||
tool_name=buffer["name"],
|
||||
arguments=buffer["arguments"],
|
||||
parsed_tool_call = ToolCall(
|
||||
call_id=buffer["call_id"] or "",
|
||||
tool_name=buffer["name"] or "",
|
||||
arguments=buffer["arguments"] or "",
|
||||
)
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=ToolCallDelta(
|
||||
tool_call=tool_call,
|
||||
tool_call=parsed_tool_call, # type: ignore[arg-type] # ToolCallDelta.tool_call accepts Union[str, ToolCall]
|
||||
parse_status=ToolCallParseStatus.succeeded,
|
||||
),
|
||||
stop_reason=stop_reason,
|
||||
|
|
@ -1189,7 +1199,7 @@ async def convert_openai_chat_completion_stream(
|
|||
event=ChatCompletionResponseEvent(
|
||||
event_type=ChatCompletionResponseEventType.progress,
|
||||
delta=ToolCallDelta(
|
||||
tool_call=buffer["content"],
|
||||
tool_call=buffer["content"], # type: ignore[arg-type] # ToolCallDelta.tool_call accepts Union[str, ToolCall]
|
||||
parse_status=ToolCallParseStatus.failed,
|
||||
),
|
||||
stop_reason=stop_reason,
|
||||
|
|
@ -1250,7 +1260,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
top_p: float | None = None,
|
||||
user: str | None = None,
|
||||
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
|
||||
messages = openai_messages_to_messages(messages)
|
||||
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,
|
||||
|
|
@ -1259,15 +1269,15 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
)
|
||||
tool_config = _convert_openai_request_tool_config(tool_choice)
|
||||
|
||||
tools = _convert_openai_request_tools(tools)
|
||||
tools = _convert_openai_request_tools(tools) # type: ignore[assignment] # converted from OpenAI to LlamaStack tool format
|
||||
if tool_config.tool_choice == ToolChoice.none:
|
||||
tools = []
|
||||
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(
|
||||
response = self.chat_completion( # type: ignore[attr-defined] # mixin expects class to implement chat_completion
|
||||
model_id=model,
|
||||
messages=messages,
|
||||
sampling_params=sampling_params,
|
||||
|
|
@ -1279,7 +1289,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
outstanding_responses.append(response)
|
||||
|
||||
if stream:
|
||||
return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses)
|
||||
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
|
||||
|
|
@ -1295,14 +1305,16 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
response = await outstanding_response
|
||||
async for chunk in response:
|
||||
event = chunk.event
|
||||
finish_reason = _convert_stop_reason_to_openai_finish_reason(event.stop_reason)
|
||||
finish_reason = (
|
||||
_convert_stop_reason_to_openai_finish_reason(event.stop_reason) if event.stop_reason else None
|
||||
)
|
||||
|
||||
if isinstance(event.delta, TextDelta):
|
||||
text_delta = event.delta.text
|
||||
delta = OpenAIChoiceDelta(content=text_delta)
|
||||
yield OpenAIChatCompletionChunk(
|
||||
id=id,
|
||||
choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)],
|
||||
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",
|
||||
|
|
@ -1310,13 +1322,17 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
elif isinstance(event.delta, ToolCallDelta):
|
||||
if event.delta.parse_status == ToolCallParseStatus.succeeded:
|
||||
tool_call = event.delta.tool_call
|
||||
if isinstance(tool_call, str):
|
||||
continue
|
||||
|
||||
# First chunk includes full structure
|
||||
openai_tool_call = OpenAIChoiceDeltaToolCall(
|
||||
index=0,
|
||||
id=tool_call.call_id,
|
||||
function=OpenAIChoiceDeltaToolCallFunction(
|
||||
name=tool_call.tool_name,
|
||||
name=tool_call.tool_name
|
||||
if isinstance(tool_call.tool_name, str)
|
||||
else tool_call.tool_name.value, # type: ignore[arg-type] # enum .value extraction on Union confuses mypy
|
||||
arguments="",
|
||||
),
|
||||
)
|
||||
|
|
@ -1324,7 +1340,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
yield OpenAIChatCompletionChunk(
|
||||
id=id,
|
||||
choices=[
|
||||
OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)
|
||||
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,
|
||||
|
|
@ -1341,7 +1357,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
yield OpenAIChatCompletionChunk(
|
||||
id=id,
|
||||
choices=[
|
||||
OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)
|
||||
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,
|
||||
|
|
@ -1351,7 +1367,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
async def _process_non_stream_response(
|
||||
self, model: str, outstanding_responses: list[Awaitable[ChatCompletionResponse]]
|
||||
) -> OpenAIChatCompletion:
|
||||
choices = []
|
||||
choices: list[OpenAIChatCompletionChoice] = []
|
||||
for outstanding_response in outstanding_responses:
|
||||
response = await outstanding_response
|
||||
completion_message = response.completion_message
|
||||
|
|
@ -1360,14 +1376,14 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
|
||||
choice = OpenAIChatCompletionChoice(
|
||||
index=len(choices),
|
||||
message=message,
|
||||
message=message, # type: ignore[arg-type] # OpenAIChatCompletionMessage union incompatible with narrower Message type
|
||||
finish_reason=finish_reason,
|
||||
)
|
||||
choices.append(choice)
|
||||
choices.append(choice) # type: ignore[arg-type] # OpenAIChatCompletionChoice type annotation mismatch
|
||||
|
||||
return OpenAIChatCompletion(
|
||||
id=f"chatcmpl-{uuid.uuid4()}",
|
||||
choices=choices,
|
||||
choices=choices, # type: ignore[arg-type] # list[OpenAIChatCompletionChoice] union incompatible
|
||||
created=int(time.time()),
|
||||
model=model,
|
||||
object="chat.completion",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue