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feat: add sequence output variable.

This commit is contained in:
Novice 2025-12-15 14:59:06 +08:00
parent 9941d1f160
commit ad18d084f3
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4 changed files with 75 additions and 12 deletions
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1
api/core/workflow/nodes/llm/entities.py
View File

@ -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,

1
api/fields/workflow_run_fields.py
View File

@ -81,6 +81,7 @@ workflow_run_detail_fields = {
"inputs": fields.Raw(attribute="inputs_dict"),
"status": fields.String,
"outputs": fields.Raw(attribute="outputs_dict"),
"outputs_as_generation": fields.Boolean,
"error": fields.String,
"elapsed_time": fields.Float,
"total_tokens": fields.Integer,

39
api/services/workflow_run_service.py
View File

@ -1,5 +1,6 @@
import threading
from collections.abc import Sequence
from collections.abc import Mapping, Sequence
from typing import Any
from sqlalchemy import Engine
from sqlalchemy.orm import sessionmaker
@ -102,12 +103,17 @@ class WorkflowRunService:
:param app_model: app model
:param run_id: workflow run id
"""
return self._workflow_run_repo.get_workflow_run_by_id(
workflow_run = self._workflow_run_repo.get_workflow_run_by_id(
tenant_id=app_model.tenant_id,
app_id=app_model.id,
run_id=run_id,
)
if workflow_run:
workflow_run.outputs_as_generation = self._are_all_generation_outputs(workflow_run.outputs_dict)
return workflow_run
def get_workflow_runs_count(
self,
app_model: App,
@ -159,3 +165,32 @@ class WorkflowRunService:
app_id=app_model.id,
workflow_run_id=run_id,
)
@staticmethod
def _are_all_generation_outputs(outputs: Mapping[str, Any]) -> bool:
if not outputs:
return False
allowed_sequence_types = {"reasoning", "content", "tool_call"}
for value in outputs.values():
if not isinstance(value, Mapping):
return False
content = value.get("content")
reasoning_content = value.get("reasoning_content")
tool_calls = value.get("tool_calls")
sequence = value.get("sequence")
if not isinstance(content, str):
return False
if not isinstance(reasoning_content, list) or any(not isinstance(item, str) for item in reasoning_content):
return False
if not isinstance(tool_calls, list) or any(not isinstance(item, Mapping) for item in tool_calls):
return False
if not isinstance(sequence, list) or any(
not isinstance(item, Mapping) or item.get("type") not in allowed_sequence_types for item in sequence
):
return False
return True