diff --git a/llama_stack/providers/utils/inference/openai_compat.py b/llama_stack/providers/utils/inference/openai_compat.py index a3e272d20..e50d4d561 100644 --- a/llama_stack/providers/utils/inference/openai_compat.py +++ b/llama_stack/providers/utils/inference/openai_compat.py @@ -15,17 +15,9 @@ from typing import Any from openai import AsyncStream from openai.types.chat import ( ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage, -) -from openai.types.chat import ( ChatCompletionChunk as OpenAIChatCompletionChunk, -) -from openai.types.chat import ( ChatCompletionContentPartImageParam as OpenAIChatCompletionContentPartImageParam, -) -from openai.types.chat import ( ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam, -) -from openai.types.chat import ( ChatCompletionContentPartTextParam as OpenAIChatCompletionContentPartTextParam, ) @@ -37,56 +29,15 @@ except ImportError: from openai.types.chat.chat_completion_message_tool_call import ( ChatCompletionMessageToolCall as OpenAIChatCompletionMessageFunctionToolCall, ) -from openai.types.chat import ( - ChatCompletionMessageParam as OpenAIChatCompletionMessage, -) -from openai.types.chat import ( - ChatCompletionMessageToolCall, -) -from openai.types.chat import ( - ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage, -) -from openai.types.chat import ( - ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage, -) -from openai.types.chat import ( - ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage, -) -from openai.types.chat.chat_completion import ( - Choice as OpenAIChoice, -) -from openai.types.chat.chat_completion import ( - ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs -) -from openai.types.chat.chat_completion_chunk import ( - Choice as OpenAIChatCompletionChunkChoice, -) -from openai.types.chat.chat_completion_chunk import ( - ChoiceDelta as OpenAIChoiceDelta, -) -from openai.types.chat.chat_completion_chunk import ( - ChoiceDeltaToolCall as OpenAIChoiceDeltaToolCall, -) -from openai.types.chat.chat_completion_chunk import ( - ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction, -) -from openai.types.chat.chat_completion_content_part_image_param import ( - ImageURL as OpenAIImageURL, -) -from openai.types.chat.chat_completion_message_tool_call import ( - Function as OpenAIFunction, -) -from pydantic import BaseModel - from llama_stack.apis.common.content_types import ( - URL, + _URLOrData, ImageContentItem, InterleavedContent, TextContentItem, TextDelta, ToolCallDelta, ToolCallParseStatus, - _URLOrData, + URL, ) from llama_stack.apis.inference import ( ChatCompletionRequest, @@ -99,6 +50,7 @@ from llama_stack.apis.inference import ( JsonSchemaResponseFormat, Message, OpenAIChatCompletion, + OpenAIChoice as OpenAIChatCompletionChoice, OpenAIEmbeddingData, OpenAIMessageParam, OpenAIResponseFormatParam, @@ -123,6 +75,30 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( convert_image_content_to_url, decode_assistant_message, ) +from openai.types.chat import ( + ChatCompletionMessageParam as OpenAIChatCompletionMessage, + ChatCompletionMessageToolCall, + ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage, + ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage, + ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage, +) +from openai.types.chat.chat_completion import ( + Choice as OpenAIChoice, + ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs +) +from openai.types.chat.chat_completion_chunk import ( + Choice as OpenAIChatCompletionChunkChoice, + ChoiceDelta as OpenAIChoiceDelta, + ChoiceDeltaToolCall as OpenAIChoiceDeltaToolCall, + ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction, +) +from openai.types.chat.chat_completion_content_part_image_param import ( + ImageURL as OpenAIImageURL, +) +from openai.types.chat.chat_completion_message_tool_call import ( + Function as OpenAIFunction, +) +from pydantic import BaseModel logger = get_logger(name=__name__, category="providers::utils") @@ -221,12 +197,16 @@ def convert_openai_completion_logprobs( if logprobs.tokens and logprobs.token_logprobs: return [ TokenLogProbs(logprobs_by_token={token: token_lp}) - for token, token_lp in zip(logprobs.tokens, logprobs.token_logprobs, strict=False) + for token, token_lp in zip( + logprobs.tokens, logprobs.token_logprobs, strict=False + ) ] return None -def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenAICompatLogprobs | None): +def convert_openai_completion_logprobs_stream( + text: str, logprobs: float | OpenAICompatLogprobs | None +): if logprobs is None: return None if isinstance(logprobs, float): @@ -237,31 +217,6 @@ def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenA return None -# def process_completion_response( -# response: OpenAICompatCompletionResponse, -# ) -> CompletionResponse: -# choice = response.choices[0] -# # drop suffix if present and return stop reason as end of turn -# if choice.text.endswith("<|eot_id|>"): -# return CompletionResponse( -# stop_reason=StopReason.end_of_turn, -# content=choice.text[: -len("<|eot_id|>")], -# logprobs=convert_openai_completion_logprobs(choice.logprobs), -# ) -# # drop suffix if present and return stop reason as end of message -# if choice.text.endswith("<|eom_id|>"): -# return CompletionResponse( -# stop_reason=StopReason.end_of_message, -# content=choice.text[: -len("<|eom_id|>")], -# logprobs=convert_openai_completion_logprobs(choice.logprobs), -# ) -# return CompletionResponse( -# stop_reason=get_stop_reason(choice.finish_reason), -# content=choice.text, -# logprobs=convert_openai_completion_logprobs(choice.logprobs), -# ) - - def process_chat_completion_response( response: OpenAICompatCompletionResponse, request: ChatCompletionRequest, @@ -271,7 +226,9 @@ def process_chat_completion_response( if not choice.message or not choice.message.tool_calls: 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] + tool_calls = [ + convert_tool_call(tool_call) for tool_call in choice.message.tool_calls + ] 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( @@ -295,7 +252,9 @@ def process_chat_completion_response( # TODO: This does not work well with tool calls for vLLM remote provider # Ref: https://github.com/meta-llama/llama-stack/issues/1058 - raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason)) + raw_message = decode_assistant_message( + text_from_choice(choice), get_stop_reason(choice.finish_reason) + ) # NOTE: If we do not set tools in chat-completion request, we should not # expect the ToolCall in the response. Instead, we should return the raw @@ -496,13 +455,17 @@ async def process_chat_completion_stream_response( ) -async def convert_message_to_openai_dict(message: Message, download: bool = False) -> dict: +async def convert_message_to_openai_dict( + message: Message, download: bool = False +) -> dict: async def _convert_content(content) -> dict: if isinstance(content, ImageContentItem): return { "type": "image_url", "image_url": { - "url": await convert_image_content_to_url(content, download=download), + "url": await convert_image_content_to_url( + content, download=download + ), }, } else: @@ -587,7 +550,11 @@ async def convert_message_to_openai_dict_new( ) -> str | Iterable[OpenAIChatCompletionContentPartParam]: async def impl( content_: InterleavedContent, - ) -> str | OpenAIChatCompletionContentPartParam | list[OpenAIChatCompletionContentPartParam]: + ) -> ( + str + | OpenAIChatCompletionContentPartParam + | list[OpenAIChatCompletionContentPartParam] + ): # Llama Stack and OpenAI spec match for str and text input if isinstance(content_, str): return content_ @@ -600,7 +567,9 @@ async def convert_message_to_openai_dict_new( return OpenAIChatCompletionContentPartImageParam( type="image_url", image_url=OpenAIImageURL( - url=await convert_image_content_to_url(content_, download=download_images) + url=await convert_image_content_to_url( + content_, download=download_images + ) ), ) elif isinstance(content_, list): @@ -627,7 +596,11 @@ async def convert_message_to_openai_dict_new( OpenAIChatCompletionMessageFunctionToolCall( id=tool.call_id, function=OpenAIFunction( - name=(tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value), + name=( + tool.tool_name + if not isinstance(tool.tool_name, BuiltinTool) + else tool.tool_name.value + ), arguments=tool.arguments, # Already a JSON string, don't double-encode ), type="function", @@ -807,7 +780,9 @@ def _convert_openai_finish_reason(finish_reason: str) -> StopReason: }.get(finish_reason, StopReason.end_of_turn) -def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None = None) -> ToolConfig: +def _convert_openai_request_tool_config( + tool_choice: str | dict[str, Any] | None = None +) -> ToolConfig: tool_config = ToolConfig() if tool_choice: try: @@ -818,7 +793,9 @@ def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None return tool_config -def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]: +def _convert_openai_request_tools( + tools: list[dict[str, Any]] | None = None +) -> list[ToolDefinition]: lls_tools = [] if not tools: return lls_tools @@ -917,7 +894,11 @@ def _convert_openai_logprobs( return None return [ - TokenLogProbs(logprobs_by_token={logprobs.token: logprobs.logprob for logprobs in content.top_logprobs}) + TokenLogProbs( + logprobs_by_token={ + logprobs.token: logprobs.logprob for logprobs in content.top_logprobs + } + ) for content in logprobs.content ] @@ -956,9 +937,13 @@ def openai_messages_to_messages( converted_messages = [] for message in messages: if message.role == "system": - converted_message = SystemMessage(content=openai_content_to_content(message.content)) + converted_message = SystemMessage( + content=openai_content_to_content(message.content) + ) elif message.role == "user": - converted_message = UserMessage(content=openai_content_to_content(message.content)) + converted_message = UserMessage( + content=openai_content_to_content(message.content) + ) elif message.role == "assistant": converted_message = CompletionMessage( content=openai_content_to_content(message.content), @@ -990,7 +975,9 @@ def openai_content_to_content( if content.type == "text": return TextContentItem(type="text", text=content.text) 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)) + ) else: raise ValueError(f"Unknown content type: {content.type}") else: @@ -1030,14 +1017,17 @@ def convert_openai_chat_completion_choice( end_of_message = "end_of_message" out_of_tokens = "out_of_tokens" """ - assert hasattr(choice, "message") and choice.message, "error in server response: message not found" - assert hasattr(choice, "finish_reason") and choice.finish_reason, ( - "error in server response: finish_reason not found" - ) + assert ( + hasattr(choice, "message") and choice.message + ), "error in server response: message not found" + assert ( + hasattr(choice, "finish_reason") and choice.finish_reason + ), "error in server response: finish_reason not found" return ChatCompletionResponse( completion_message=CompletionMessage( - content=choice.message.content or "", # CompletionMessage content is not optional + 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), ), @@ -1277,7 +1267,9 @@ 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 + ) return await OpenAIChatCompletionToLlamaStackMixin._process_non_stream_response( self, model, outstanding_responses @@ -1286,21 +1278,29 @@ class OpenAIChatCompletionToLlamaStackMixin: async def _process_stream_response( self, model: str, - outstanding_responses: list[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]], + outstanding_responses: list[ + Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]] + ], ): id = f"chatcmpl-{uuid.uuid4()}" for i, outstanding_response in enumerate(outstanding_responses): 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 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 + ) + ], created=int(time.time()), model=model, object="chat.completion.chunk", @@ -1322,7 +1322,9 @@ 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 + ) ], created=int(time.time()), model=model, @@ -1339,7 +1341,9 @@ 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 + ) ], created=int(time.time()), model=model, @@ -1354,7 +1358,9 @@ class OpenAIChatCompletionToLlamaStackMixin: response = await outstanding_response completion_message = response.completion_message message = await convert_message_to_openai_dict_new(completion_message) - finish_reason = _convert_stop_reason_to_openai_finish_reason(completion_message.stop_reason) + finish_reason = _convert_stop_reason_to_openai_finish_reason( + completion_message.stop_reason + ) choice = OpenAIChatCompletionChoice( index=len(choices),