mirror of
https://github.com/meta-llama/llama-stack.git
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Merge branch 'main' into milvus/search-modes
This commit is contained in:
Francisco Arceo
GitHub
commit
2d0d13b826
7 changed files with 621 additions and 56 deletions
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@ -623,6 +623,62 @@ class OpenAIResponseObjectStreamResponseMcpCallCompleted(BaseModel):
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type: Literal["response.mcp_call.completed"] = "response.mcp_call.completed"
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@json_schema_type
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class OpenAIResponseContentPartOutputText(BaseModel):
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type: Literal["output_text"] = "output_text"
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text: str
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# TODO: add annotations, logprobs, etc.
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@json_schema_type
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class OpenAIResponseContentPartRefusal(BaseModel):
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type: Literal["refusal"] = "refusal"
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refusal: str
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OpenAIResponseContentPart = Annotated[
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OpenAIResponseContentPartOutputText | OpenAIResponseContentPartRefusal,
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Field(discriminator="type"),
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]
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register_schema(OpenAIResponseContentPart, name="OpenAIResponseContentPart")
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@json_schema_type
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class OpenAIResponseObjectStreamResponseContentPartAdded(BaseModel):
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"""Streaming event for when a new content part is added to a response item.
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:param response_id: Unique identifier of the response containing this content
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:param item_id: Unique identifier of the output item containing this content part
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:param part: The content part that was added
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:param sequence_number: Sequential number for ordering streaming events
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:param type: Event type identifier, always "response.content_part.added"
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"""
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response_id: str
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item_id: str
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part: OpenAIResponseContentPart
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sequence_number: int
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type: Literal["response.content_part.added"] = "response.content_part.added"
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@json_schema_type
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class OpenAIResponseObjectStreamResponseContentPartDone(BaseModel):
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"""Streaming event for when a content part is completed.
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:param response_id: Unique identifier of the response containing this content
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:param item_id: Unique identifier of the output item containing this content part
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:param part: The completed content part
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:param sequence_number: Sequential number for ordering streaming events
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:param type: Event type identifier, always "response.content_part.done"
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"""
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response_id: str
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item_id: str
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part: OpenAIResponseContentPart
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sequence_number: int
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type: Literal["response.content_part.done"] = "response.content_part.done"
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OpenAIResponseObjectStream = Annotated[
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OpenAIResponseObjectStreamResponseCreated
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| OpenAIResponseObjectStreamResponseOutputItemAdded
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@ -642,6 +698,8 @@ OpenAIResponseObjectStream = Annotated[
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| OpenAIResponseObjectStreamResponseMcpCallInProgress
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| OpenAIResponseObjectStreamResponseMcpCallFailed
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| OpenAIResponseObjectStreamResponseMcpCallCompleted
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| OpenAIResponseObjectStreamResponseContentPartAdded
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| OpenAIResponseObjectStreamResponseContentPartDone
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| OpenAIResponseObjectStreamResponseCompleted,
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Field(discriminator="type"),
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]
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@ -20,6 +20,7 @@ from llama_stack.apis.agents.openai_responses import (
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ListOpenAIResponseInputItem,
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ListOpenAIResponseObject,
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OpenAIDeleteResponseObject,
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OpenAIResponseContentPartOutputText,
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OpenAIResponseInput,
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OpenAIResponseInputFunctionToolCallOutput,
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OpenAIResponseInputMessageContent,
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@ -32,12 +33,22 @@ from llama_stack.apis.agents.openai_responses import (
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OpenAIResponseObject,
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OpenAIResponseObjectStream,
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OpenAIResponseObjectStreamResponseCompleted,
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OpenAIResponseObjectStreamResponseContentPartAdded,
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OpenAIResponseObjectStreamResponseContentPartDone,
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OpenAIResponseObjectStreamResponseCreated,
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OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta,
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OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone,
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OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta,
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OpenAIResponseObjectStreamResponseMcpCallArgumentsDone,
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OpenAIResponseObjectStreamResponseMcpCallCompleted,
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OpenAIResponseObjectStreamResponseMcpCallFailed,
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OpenAIResponseObjectStreamResponseMcpCallInProgress,
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OpenAIResponseObjectStreamResponseOutputItemAdded,
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OpenAIResponseObjectStreamResponseOutputItemDone,
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OpenAIResponseObjectStreamResponseOutputTextDelta,
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OpenAIResponseObjectStreamResponseWebSearchCallCompleted,
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OpenAIResponseObjectStreamResponseWebSearchCallInProgress,
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OpenAIResponseObjectStreamResponseWebSearchCallSearching,
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OpenAIResponseOutput,
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OpenAIResponseOutputMessageContent,
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OpenAIResponseOutputMessageContentOutputText,
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@ -87,6 +98,15 @@ logger = get_logger(name=__name__, category="openai_responses")
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OPENAI_RESPONSES_PREFIX = "openai_responses:"
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class ToolExecutionResult(BaseModel):
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"""Result of streaming tool execution."""
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stream_event: OpenAIResponseObjectStream | None = None
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sequence_number: int
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final_output_message: OpenAIResponseOutput | None = None
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final_input_message: OpenAIMessageParam | None = None
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async def _convert_response_content_to_chat_content(
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content: (str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]),
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) -> str | list[OpenAIChatCompletionContentPartParam]:
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@ -460,6 +480,8 @@ class OpenAIResponsesImpl:
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message_item_id = f"msg_{uuid.uuid4()}"
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# Track tool call items for streaming events
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tool_call_item_ids: dict[int, str] = {}
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# Track content parts for streaming events
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content_part_emitted = False
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async for chunk in completion_result:
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chat_response_id = chunk.id
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@ -468,6 +490,18 @@ class OpenAIResponsesImpl:
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for chunk_choice in chunk.choices:
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# Emit incremental text content as delta events
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if chunk_choice.delta.content:
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# Emit content_part.added event for first text chunk
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if not content_part_emitted:
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content_part_emitted = True
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sequence_number += 1
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yield OpenAIResponseObjectStreamResponseContentPartAdded(
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response_id=response_id,
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item_id=message_item_id,
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part=OpenAIResponseContentPartOutputText(
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text="", # Will be filled incrementally via text deltas
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),
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sequence_number=sequence_number,
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)
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sequence_number += 1
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yield OpenAIResponseObjectStreamResponseOutputTextDelta(
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content_index=0,
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@ -514,16 +548,33 @@ class OpenAIResponsesImpl:
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sequence_number=sequence_number,
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)
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# Stream function call arguments as they arrive
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# Stream tool call arguments as they arrive (differentiate between MCP and function calls)
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if tool_call.function and tool_call.function.arguments:
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tool_call_item_id = tool_call_item_ids[tool_call.index]
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sequence_number += 1
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yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta(
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delta=tool_call.function.arguments,
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item_id=tool_call_item_id,
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output_index=len(output_messages),
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sequence_number=sequence_number,
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# Check if this is an MCP tool call
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is_mcp_tool = (
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ctx.mcp_tool_to_server
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and tool_call.function.name
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and tool_call.function.name in ctx.mcp_tool_to_server
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)
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if is_mcp_tool:
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# Emit MCP-specific argument delta event
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yield OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta(
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delta=tool_call.function.arguments,
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item_id=tool_call_item_id,
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output_index=len(output_messages),
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sequence_number=sequence_number,
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)
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else:
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# Emit function call argument delta event
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yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta(
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delta=tool_call.function.arguments,
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item_id=tool_call_item_id,
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output_index=len(output_messages),
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sequence_number=sequence_number,
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)
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# Accumulate arguments for final response (only for subsequent chunks)
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if not is_new_tool_call:
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@ -531,27 +582,55 @@ class OpenAIResponsesImpl:
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response_tool_call.function.arguments or ""
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) + tool_call.function.arguments
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# Emit function_call_arguments.done events for completed tool calls
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# Emit arguments.done events for completed tool calls (differentiate between MCP and function calls)
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for tool_call_index in sorted(chat_response_tool_calls.keys()):
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tool_call_item_id = tool_call_item_ids[tool_call_index]
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final_arguments = chat_response_tool_calls[tool_call_index].function.arguments or ""
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tool_call_name = chat_response_tool_calls[tool_call_index].function.name
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# Check if this is an MCP tool call
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is_mcp_tool = ctx.mcp_tool_to_server and tool_call_name and tool_call_name in ctx.mcp_tool_to_server
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sequence_number += 1
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yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone(
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arguments=final_arguments,
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item_id=tool_call_item_id,
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output_index=len(output_messages),
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sequence_number=sequence_number,
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)
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if is_mcp_tool:
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# Emit MCP-specific argument done event
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yield OpenAIResponseObjectStreamResponseMcpCallArgumentsDone(
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arguments=final_arguments,
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item_id=tool_call_item_id,
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output_index=len(output_messages),
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sequence_number=sequence_number,
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)
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else:
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# Emit function call argument done event
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yield OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone(
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arguments=final_arguments,
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item_id=tool_call_item_id,
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output_index=len(output_messages),
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sequence_number=sequence_number,
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)
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# Convert collected chunks to complete response
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if chat_response_tool_calls:
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tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())]
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# when there are tool calls, we need to clear the content
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chat_response_content = []
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else:
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tool_calls = None
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# Emit content_part.done event if text content was streamed (before content gets cleared)
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if content_part_emitted:
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final_text = "".join(chat_response_content)
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sequence_number += 1
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yield OpenAIResponseObjectStreamResponseContentPartDone(
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response_id=response_id,
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item_id=message_item_id,
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part=OpenAIResponseContentPartOutputText(
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text=final_text,
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),
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sequence_number=sequence_number,
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)
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# Clear content when there are tool calls (OpenAI spec behavior)
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if chat_response_tool_calls:
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chat_response_content = []
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assistant_message = OpenAIAssistantMessageParam(
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content="".join(chat_response_content),
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tool_calls=tool_calls,
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@ -587,19 +666,38 @@ class OpenAIResponsesImpl:
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# execute non-function tool calls
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for tool_call in non_function_tool_calls:
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tool_call_log, tool_response_message = await self._execute_tool_call(tool_call, ctx)
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# Find the item_id for this tool call
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matching_item_id = None
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for index, item_id in tool_call_item_ids.items():
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response_tool_call = chat_response_tool_calls.get(index)
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if response_tool_call and response_tool_call.id == tool_call.id:
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matching_item_id = item_id
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break
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# Use a fallback item_id if not found
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if not matching_item_id:
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matching_item_id = f"tc_{uuid.uuid4()}"
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# Execute tool call with streaming
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tool_call_log = None
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tool_response_message = None
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async for result in self._execute_tool_call(
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tool_call, ctx, sequence_number, response_id, len(output_messages), matching_item_id
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):
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if result.stream_event:
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# Forward streaming events
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sequence_number = result.sequence_number
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yield result.stream_event
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if result.final_output_message is not None:
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tool_call_log = result.final_output_message
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tool_response_message = result.final_input_message
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sequence_number = result.sequence_number
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if tool_call_log:
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output_messages.append(tool_call_log)
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# Emit output_item.done event for completed non-function tool call
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# Find the item_id for this tool call
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matching_item_id = None
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for index, item_id in tool_call_item_ids.items():
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response_tool_call = chat_response_tool_calls.get(index)
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if response_tool_call and response_tool_call.id == tool_call.id:
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matching_item_id = item_id
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break
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if matching_item_id:
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sequence_number += 1
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yield OpenAIResponseObjectStreamResponseOutputItemDone(
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@ -848,7 +946,11 @@ class OpenAIResponsesImpl:
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self,
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tool_call: OpenAIChatCompletionToolCall,
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ctx: ChatCompletionContext,
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) -> tuple[OpenAIResponseOutput | None, OpenAIMessageParam | None]:
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sequence_number: int,
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response_id: str,
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output_index: int,
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item_id: str,
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) -> AsyncIterator[ToolExecutionResult]:
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from llama_stack.providers.utils.inference.prompt_adapter import (
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interleaved_content_as_str,
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)
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@ -858,8 +960,41 @@ class OpenAIResponsesImpl:
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tool_kwargs = json.loads(function.arguments) if function.arguments else {}
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if not function or not tool_call_id or not function.name:
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return None, None
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yield ToolExecutionResult(sequence_number=sequence_number)
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return
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# Emit in_progress event based on tool type (only for tools with specific streaming events)
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progress_event = None
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if ctx.mcp_tool_to_server and function.name in ctx.mcp_tool_to_server:
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sequence_number += 1
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progress_event = OpenAIResponseObjectStreamResponseMcpCallInProgress(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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elif function.name == "web_search":
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sequence_number += 1
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progress_event = OpenAIResponseObjectStreamResponseWebSearchCallInProgress(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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# Note: knowledge_search and other custom tools don't have specific streaming events in OpenAI spec
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if progress_event:
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yield ToolExecutionResult(stream_event=progress_event, sequence_number=sequence_number)
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# For web search, emit searching event
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if function.name == "web_search":
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sequence_number += 1
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searching_event = OpenAIResponseObjectStreamResponseWebSearchCallSearching(
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item_id=item_id,
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output_index=output_index,
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sequence_number=sequence_number,
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)
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yield ToolExecutionResult(stream_event=searching_event, sequence_number=sequence_number)
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# Execute the actual tool call
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error_exc = None
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result = None
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try:
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@ -894,6 +1029,33 @@ class OpenAIResponsesImpl:
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except Exception as e:
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error_exc = e
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# Emit completion or failure event based on result (only for tools with specific streaming events)
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has_error = error_exc or (result and ((result.error_code and result.error_code > 0) or result.error_message))
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completion_event = None
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if ctx.mcp_tool_to_server and function.name in ctx.mcp_tool_to_server:
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sequence_number += 1
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if has_error:
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completion_event = OpenAIResponseObjectStreamResponseMcpCallFailed(
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sequence_number=sequence_number,
|
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)
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else:
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completion_event = OpenAIResponseObjectStreamResponseMcpCallCompleted(
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sequence_number=sequence_number,
|
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)
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elif function.name == "web_search":
|
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sequence_number += 1
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completion_event = OpenAIResponseObjectStreamResponseWebSearchCallCompleted(
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item_id=item_id,
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output_index=output_index,
|
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sequence_number=sequence_number,
|
||||
)
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# Note: knowledge_search and other custom tools don't have specific completion events in OpenAI spec
|
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|
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if completion_event:
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yield ToolExecutionResult(stream_event=completion_event, sequence_number=sequence_number)
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|
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# Build the result message and input message
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if function.name in ctx.mcp_tool_to_server:
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from llama_stack.apis.agents.openai_responses import (
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OpenAIResponseOutputMessageMCPCall,
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|
|
@ -907,9 +1069,9 @@ class OpenAIResponsesImpl:
|
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)
|
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if error_exc:
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message.error = str(error_exc)
|
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elif (result.error_code and result.error_code > 0) or result.error_message:
|
||||
elif (result and result.error_code and result.error_code > 0) or (result and result.error_message):
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message.error = f"Error (code {result.error_code}): {result.error_message}"
|
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elif result.content:
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elif result and result.content:
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message.output = interleaved_content_as_str(result.content)
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else:
|
||||
if function.name == "web_search":
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|
|
@ -917,7 +1079,7 @@ class OpenAIResponsesImpl:
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id=tool_call_id,
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status="completed",
|
||||
)
|
||||
if error_exc or (result.error_code and result.error_code > 0) or result.error_message:
|
||||
if has_error:
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message.status = "failed"
|
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elif function.name == "knowledge_search":
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message = OpenAIResponseOutputMessageFileSearchToolCall(
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|
|
@ -925,7 +1087,7 @@ class OpenAIResponsesImpl:
|
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queries=[tool_kwargs.get("query", "")],
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status="completed",
|
||||
)
|
||||
if "document_ids" in result.metadata:
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if result and "document_ids" in result.metadata:
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||||
message.results = []
|
||||
for i, doc_id in enumerate(result.metadata["document_ids"]):
|
||||
text = result.metadata["chunks"][i] if "chunks" in result.metadata else None
|
||||
|
|
@ -939,7 +1101,7 @@ class OpenAIResponsesImpl:
|
|||
attributes={},
|
||||
)
|
||||
)
|
||||
if error_exc or (result.error_code and result.error_code > 0) or result.error_message:
|
||||
if has_error:
|
||||
message.status = "failed"
|
||||
else:
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||||
raise ValueError(f"Unknown tool {function.name} called")
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||||
|
|
@ -971,10 +1133,13 @@ class OpenAIResponsesImpl:
|
|||
raise ValueError(f"Unknown result content type: {type(result.content)}")
|
||||
input_message = OpenAIToolMessageParam(content=content, tool_call_id=tool_call_id)
|
||||
else:
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||||
text = str(error_exc)
|
||||
text = str(error_exc) if error_exc else "Tool execution failed"
|
||||
input_message = OpenAIToolMessageParam(content=text, tool_call_id=tool_call_id)
|
||||
|
||||
return message, input_message
|
||||
# Yield the final result
|
||||
yield ToolExecutionResult(
|
||||
sequence_number=sequence_number, final_output_message=message, final_input_message=input_message
|
||||
)
|
||||
|
||||
|
||||
def _is_function_tool_call(
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue