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feat: add sequence output variable.

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Novice
2025-12-15 14:59:06 +08:00
parent 9941d1f160
commit ad18d084f3
4 changed files with 75 additions and 12 deletions
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1
api/core/workflow/nodes/llm/entities.py
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@ -52,6 +52,7 @@ class LLMGenerationData(BaseModel):
text: str = Field(..., description="Accumulated text content from all turns")
reasoning_contents: list[str] = Field(default_factory=list, description="Reasoning content per turn")
tool_calls: list[dict[str, Any]] = Field(default_factory=list, description="Tool calls with results")
sequence: list[dict[str, Any]] = Field(default_factory=list, description="Ordered segments for rendering")
usage: LLMUsage = Field(..., description="LLM usage statistics")
finish_reason: str | None = Field(None, description="Finish reason from LLM")
files: list[File] = Field(default_factory=list, description="Generated files")

46
api/core/workflow/nodes/llm/node.py
View File

@ -392,6 +392,7 @@ class LLMNode(Node[LLMNodeData]):
"content": generation_data.text,
"reasoning_content": generation_data.reasoning_contents, # [thought1, thought2, ...]
"tool_calls": generation_data.tool_calls,
"sequence": generation_data.sequence,
}
files_to_output = generation_data.files
else:
@ -400,6 +401,7 @@ class LLMNode(Node[LLMNodeData]):
"content": clean_text,
"reasoning_content": [reasoning_content] if reasoning_content else [],
"tool_calls": [],
"sequence": [],
}
files_to_output = self._file_outputs
@ -428,22 +430,24 @@ class LLMNode(Node[LLMNodeData]):
is_final=True,
)
metadata: dict[WorkflowNodeExecutionMetadataKey, Any] = {
WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: usage.total_tokens,
WorkflowNodeExecutionMetadataKey.TOTAL_PRICE: usage.total_price,
WorkflowNodeExecutionMetadataKey.CURRENCY: usage.currency,
}
if generation_data and generation_data.trace:
metadata[WorkflowNodeExecutionMetadataKey.LLM_TRACE] = [
segment.model_dump() for segment in generation_data.trace
]
yield StreamCompletedEvent(
node_run_result=NodeRunResult(
status=WorkflowNodeExecutionStatus.SUCCEEDED,
inputs=node_inputs,
process_data=process_data,
outputs=outputs,
metadata={
WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: usage.total_tokens,
WorkflowNodeExecutionMetadataKey.TOTAL_PRICE: usage.total_price,
WorkflowNodeExecutionMetadataKey.CURRENCY: usage.currency,
WorkflowNodeExecutionMetadataKey.LLM_TRACE: [
segment.model_dump() for segment in generation_data.trace
]
if generation_data
else [],
},
metadata=metadata,
llm_usage=usage,
)
)
@ -1783,6 +1787,27 @@ class LLMNode(Node[LLMNodeData]):
_flush_thought()
_flush_content()
# Build sequence from trace_segments for rendering
sequence: list[dict[str, Any]] = []
reasoning_index = 0
content_position = 0
tool_call_seen_index: dict[str, int] = {}
for segment in trace_segments:
if segment.type == "thought":
sequence.append({"type": "reasoning", "index": reasoning_index})
reasoning_index += 1
elif segment.type == "content":
segment_text = segment.text or ""
start = content_position
end = start + len(segment_text)
sequence.append({"type": "content", "start": start, "end": end})
content_position = end
elif segment.type == "tool_call":
tool_id = segment.tool_call_id or ""
if tool_id not in tool_call_seen_index:
tool_call_seen_index[tool_id] = len(tool_call_seen_index)
sequence.append({"type": "tool_call", "index": tool_call_seen_index[tool_id]})
# Send final events for all streams
yield StreamChunkEvent(
selector=[self._node_id, "text"],
@ -1850,6 +1875,7 @@ class LLMNode(Node[LLMNodeData]):
text=text,
reasoning_contents=reasoning_per_turn,
tool_calls=tool_calls_for_generation,
sequence=sequence,
usage=usage,
finish_reason=finish_reason,
files=files,