forked from phoenix-oss/llama-stack-mirror
fix: agents with non-llama model (#1550)
# Summary: Includes fixes to get test_agents working with openAI model, e.g. tool parsing and message conversion # Test Plan: ``` LLAMA_STACK_CONFIG=dev pytest -s -v tests/integration/agents/test_agents.py --safety-shield meta-llama/Llama-Guard-3-8B --text-model openai/gpt-4o-mini ``` --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/meta-llama/llama-stack/pull/1550). * #1556 * __->__ #1550
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ehhuang
GitHub
parent
0bdfc71f8d
commit
c23a7af5d6
4 changed files with 161 additions and 125 deletions
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@ -527,26 +527,30 @@ async def convert_message_to_openai_dict_new(
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async def _convert_message_content(
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content: InterleavedContent,
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) -> Union[str, Iterable[OpenAIChatCompletionContentPartParam]]:
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async def impl():
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async def impl(
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content_: InterleavedContent,
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) -> Union[str, OpenAIChatCompletionContentPartParam, List[OpenAIChatCompletionContentPartParam]]:
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# Llama Stack and OpenAI spec match for str and text input
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if isinstance(content, str):
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return content
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elif isinstance(content, TextContentItem):
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if isinstance(content_, str):
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return content_
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elif isinstance(content_, TextContentItem):
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return OpenAIChatCompletionContentPartTextParam(
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type="text",
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text=content.text,
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text=content_.text,
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)
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elif isinstance(content, ImageContentItem):
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elif isinstance(content_, ImageContentItem):
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return OpenAIChatCompletionContentPartImageParam(
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type="image_url",
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image_url=OpenAIImageURL(url=await convert_image_content_to_url(content)),
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image_url=OpenAIImageURL(url=await convert_image_content_to_url(content_)),
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)
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elif isinstance(content, list):
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return [await _convert_message_content(item) for item in content]
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elif isinstance(content_, list):
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return [await impl(item) for item in content_]
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else:
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raise ValueError(f"Unsupported content type: {type(content)}")
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raise ValueError(f"Unsupported content type: {type(content_)}")
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ret = await impl()
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ret = await impl(content)
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# OpenAI*Message expects a str or list
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if isinstance(ret, str) or isinstance(ret, list):
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return ret
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else:
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@ -566,13 +570,14 @@ async def convert_message_to_openai_dict_new(
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OpenAIChatCompletionMessageToolCall(
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id=tool.call_id,
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function=OpenAIFunction(
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name=tool.tool_name,
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name=tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value,
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arguments=json.dumps(tool.arguments),
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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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],
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]
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or None,
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)
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elif isinstance(message, ToolResponseMessage):
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out = OpenAIChatCompletionToolMessage(
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@ -858,7 +863,8 @@ async def convert_openai_chat_completion_stream(
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event_type = ChatCompletionResponseEventType.progress
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stop_reason = None
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toolcall_buffer = {}
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tool_call_idx_to_buffer = {}
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async for chunk in stream:
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choice = chunk.choices[0] # assuming only one choice per chunk
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@ -868,7 +874,6 @@ async def convert_openai_chat_completion_stream(
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# if there's a tool call, emit an event for each tool in the list
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# if tool call and content, emit both separately
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if choice.delta.tool_calls:
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# the call may have content and a tool call. ChatCompletionResponseEvent
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# does not support both, so we emit the content first
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@ -889,44 +894,53 @@ async def convert_openai_chat_completion_stream(
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)
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if not enable_incremental_tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=next(event_type),
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delta=ToolCallDelta(
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tool_call=_convert_openai_tool_calls(choice.delta.tool_calls)[0],
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parse_status=ToolCallParseStatus.succeeded,
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),
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logprobs=_convert_openai_logprobs(logprobs),
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for tool_call in choice.delta.tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=event_type,
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delta=ToolCallDelta(
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tool_call=_convert_openai_tool_calls([tool_call])[0],
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parse_status=ToolCallParseStatus.succeeded,
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),
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logprobs=_convert_openai_logprobs(logprobs),
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)
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)
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)
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else:
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tool_call = choice.delta.tool_calls[0]
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if "name" not in toolcall_buffer:
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toolcall_buffer["call_id"] = tool_call.id
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toolcall_buffer["name"] = None
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toolcall_buffer["content"] = ""
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if "arguments" not in toolcall_buffer:
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toolcall_buffer["arguments"] = ""
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for tool_call in choice.delta.tool_calls:
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idx = tool_call.index if hasattr(tool_call, "index") else 0
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if tool_call.function.name:
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toolcall_buffer["name"] = tool_call.function.name
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delta = f"{toolcall_buffer['name']}("
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if tool_call.function.arguments:
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toolcall_buffer["arguments"] += tool_call.function.arguments
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delta = toolcall_buffer["arguments"]
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if idx not in tool_call_idx_to_buffer:
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tool_call_idx_to_buffer[idx] = {
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"call_id": tool_call.id,
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"name": None,
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"arguments": "",
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"content": "",
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}
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toolcall_buffer["content"] += delta
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=event_type,
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delta=ToolCallDelta(
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tool_call=delta,
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parse_status=ToolCallParseStatus.in_progress,
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),
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logprobs=_convert_openai_logprobs(logprobs),
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)
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)
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else:
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buffer = tool_call_idx_to_buffer[idx]
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if tool_call.function:
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if tool_call.function.name:
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buffer["name"] = tool_call.function.name
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delta = f"{buffer['name']}("
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buffer["content"] += delta
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if tool_call.function.arguments:
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delta = tool_call.function.arguments
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buffer["arguments"] += delta
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buffer["content"] += delta
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=event_type,
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delta=ToolCallDelta(
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tool_call=delta,
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parse_status=ToolCallParseStatus.in_progress,
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),
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logprobs=_convert_openai_logprobs(logprobs),
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)
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)
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elif choice.delta.content:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=event_type,
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@ -935,47 +949,51 @@ async def convert_openai_chat_completion_stream(
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)
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)
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if toolcall_buffer:
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delta = ")"
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toolcall_buffer["content"] += delta
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=event_type,
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delta=ToolCallDelta(
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tool_call=delta,
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parse_status=ToolCallParseStatus.in_progress,
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),
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logprobs=_convert_openai_logprobs(logprobs),
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)
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)
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try:
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arguments = json.loads(toolcall_buffer["arguments"])
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tool_call = ToolCall(
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call_id=toolcall_buffer["call_id"],
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tool_name=toolcall_buffer["name"],
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arguments=arguments,
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)
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for idx, buffer in tool_call_idx_to_buffer.items():
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logger.debug(f"toolcall_buffer[{idx}]: {buffer}")
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if buffer["name"]:
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delta = ")"
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buffer["content"] += delta
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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event_type=event_type,
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delta=ToolCallDelta(
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tool_call=tool_call,
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parse_status=ToolCallParseStatus.succeeded,
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tool_call=delta,
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parse_status=ToolCallParseStatus.in_progress,
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),
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stop_reason=stop_reason,
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logprobs=None,
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)
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)
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except json.JSONDecodeError:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.complete,
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delta=ToolCallDelta(
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tool_call=toolcall_buffer["content"],
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parse_status=ToolCallParseStatus.failed,
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),
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stop_reason=stop_reason,
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try:
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arguments = json.loads(buffer["arguments"])
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tool_call = ToolCall(
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call_id=buffer["call_id"],
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tool_name=buffer["name"],
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arguments=arguments,
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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tool_call=tool_call,
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parse_status=ToolCallParseStatus.succeeded,
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),
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stop_reason=stop_reason,
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)
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)
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except json.JSONDecodeError as e:
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print(f"Failed to parse arguments: {e}")
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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tool_call=buffer["content"],
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parse_status=ToolCallParseStatus.failed,
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),
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stop_reason=stop_reason,
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)
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)
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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