youngkingdom/dify
1
0
Fork 0
mirror of https://github.com/langgenius/dify.git synced 2026-04-30 23:48:04 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'feat/agent-node-v2' into deploy/dev

Browse Source
This commit is contained in:
Novice
2026-01-04 11:10:38 +08:00
parent 7c78d42627 dc8a618b6a
commit 8fb50d52dc
16 changed files with 198 additions and 45 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
api/core/workflow/entities/tool_entities.py
View File

@ -22,6 +22,7 @@ class ToolResult(BaseModel):
output: str | None = Field(default=None, description="Tool output text, error or success message")
files: list[str] = Field(default_factory=list, description="File produced by tool")
status: ToolResultStatus | None = Field(default=ToolResultStatus.SUCCESS, description="Tool execution status")
elapsed_time: float | None = Field(default=None, description="Elapsed seconds spent executing the tool")
class ToolCallResult(BaseModel):
@ -31,3 +32,4 @@ class ToolCallResult(BaseModel):
output: str | None = Field(default=None, description="Tool output text, error or success message")
files: list[File] = Field(default_factory=list, description="File produced by tool")
status: ToolResultStatus = Field(default=ToolResultStatus.SUCCESS, description="Tool execution status")
elapsed_time: float | None = Field(default=None, description="Elapsed seconds spent executing the tool")

2
api/core/workflow/graph_events/node.py
View File

@ -29,6 +29,8 @@ class ChunkType(StrEnum):
TOOL_CALL = "tool_call" # Tool call arguments streaming
TOOL_RESULT = "tool_result" # Tool execution result
THOUGHT = "thought" # Agent thinking process (ReAct)
THOUGHT_START = "thought_start" # Agent thought start
THOUGHT_END = "thought_end" # Agent thought end
class NodeRunStreamChunkEvent(GraphNodeEventBase):

14
api/core/workflow/node_events/node.py
View File

@ -41,6 +41,8 @@ class ChunkType(StrEnum):
TOOL_CALL = "tool_call" # Tool call arguments streaming
TOOL_RESULT = "tool_result" # Tool execution result
THOUGHT = "thought" # Agent thinking process (ReAct)
THOUGHT_START = "thought_start" # Agent thought start
THOUGHT_END = "thought_end" # Agent thought end
class StreamChunkEvent(NodeEventBase):
@ -70,6 +72,18 @@ class ToolResultChunkEvent(StreamChunkEvent):
tool_result: ToolResult | None = Field(default=None, description="structured tool result payload")
class ThoughtStartChunkEvent(StreamChunkEvent):
"""Agent thought start streaming event - Agent thinking process (ReAct)."""
chunk_type: ChunkType = Field(default=ChunkType.THOUGHT_START, frozen=True)
class ThoughtEndChunkEvent(StreamChunkEvent):
"""Agent thought end streaming event - Agent thinking process (ReAct)."""
chunk_type: ChunkType = Field(default=ChunkType.THOUGHT_END, frozen=True)
class ThoughtChunkEvent(StreamChunkEvent):
"""Agent thought streaming event - Agent thinking process (ReAct)."""

15
api/core/workflow/nodes/base/node.py
View File

@ -580,9 +580,10 @@ class Node(Generic[NodeDataT]):
from core.workflow.entities import ToolResult, ToolResultStatus
from core.workflow.graph_events import ChunkType
tool_result = event.tool_result
status: ToolResultStatus = (
tool_result.status if tool_result and tool_result.status is not None else ToolResultStatus.SUCCESS
tool_result = event.tool_result or ToolResult()
status: ToolResultStatus = tool_result.status or ToolResultStatus.SUCCESS
tool_result = tool_result.model_copy(
update={"status": status, "files": tool_result.files or []},
)
return NodeRunStreamChunkEvent(
@ -593,13 +594,7 @@ class Node(Generic[NodeDataT]):
chunk=event.chunk,
is_final=event.is_final,
chunk_type=ChunkType.TOOL_RESULT,
tool_result=ToolResult(
id=tool_result.id if tool_result else None,
name=tool_result.name if tool_result else None,
output=tool_result.output if tool_result else None,
files=tool_result.files if tool_result else [],
status=status,
),
tool_result=tool_result,
)
@_dispatch.register

15
api/core/workflow/nodes/llm/entities.py
View File

@ -163,6 +163,7 @@ class ThinkTagStreamParser:
thought_text = self._buffer[: end_match.start()]
if thought_text:
parts.append(("thought", thought_text))
parts.append(("thought_end", ""))
self._buffer = self._buffer[end_match.end() :]
self._in_think = False
continue
@ -180,6 +181,7 @@ class ThinkTagStreamParser:
if prefix:
parts.append(("text", prefix))
self._buffer = self._buffer[start_match.end() :]
parts.append(("thought_start", ""))
self._in_think = True
continue
@ -195,7 +197,7 @@ class ThinkTagStreamParser:
# Extra safeguard: strip any stray tags that slipped through.
content = self._START_PATTERN.sub("", content)
content = self._END_PATTERN.sub("", content)
if content:
if content or kind in {"thought_start", "thought_end"}:
cleaned_parts.append((kind, content))
return cleaned_parts
@ -210,12 +212,19 @@ class ThinkTagStreamParser:
if content.lower().startswith(self._START_PREFIX) or content.lower().startswith(self._END_PREFIX):
content = ""
self._buffer = ""
if not content:
if not content and not self._in_think:
return []
# Strip any complete tags that might still be present.
content = self._START_PATTERN.sub("", content)
content = self._END_PATTERN.sub("", content)
return [(kind, content)] if content else []
result: list[tuple[str, str]] = []
if content:
result.append((kind, content))
if self._in_think:
result.append(("thought_end", ""))
self._in_think = False
return result
class StreamBuffers(BaseModel):

76
api/core/workflow/nodes/llm/node.py
View File

@ -80,6 +80,7 @@ from core.workflow.node_events import (
ToolCallChunkEvent,
ToolResultChunkEvent,
)
from core.workflow.node_events.node import ThoughtEndChunkEvent, ThoughtStartChunkEvent
from core.workflow.nodes.base.entities import VariableSelector
from core.workflow.nodes.base.node import Node
from core.workflow.nodes.base.variable_template_parser import VariableTemplateParser
@ -565,15 +566,28 @@ class LLMNode(Node[LLMNodeData]):
# Generation output: split out thoughts, forward only non-thought content chunks
for kind, segment in think_parser.process(text_part):
if not segment:
continue
if kind not in {"thought_start", "thought_end"}:
continue
if kind == "thought":
if kind == "thought_start":
yield ThoughtStartChunkEvent(
selector=[node_id, "generation", "thought"],
chunk="",
is_final=False,
)
elif kind == "thought":
reasoning_chunks.append(segment)
yield ThoughtChunkEvent(
selector=[node_id, "generation", "thought"],
chunk=segment,
is_final=False,
)
elif kind == "thought_end":
yield ThoughtEndChunkEvent(
selector=[node_id, "generation", "thought"],
chunk="",
is_final=False,
)
else:
yield StreamChunkEvent(
selector=[node_id, "generation", "content"],
@ -596,15 +610,27 @@ class LLMNode(Node[LLMNodeData]):
raise LLMNodeError(f"Failed to parse structured output: {e}")
for kind, segment in think_parser.flush():
if not segment:
if not segment and kind not in {"thought_start", "thought_end"}:
continue
if kind == "thought":
if kind == "thought_start":
yield ThoughtStartChunkEvent(
selector=[node_id, "generation", "thought"],
chunk="",
is_final=False,
)
elif kind == "thought":
reasoning_chunks.append(segment)
yield ThoughtChunkEvent(
selector=[node_id, "generation", "thought"],
chunk=segment,
is_final=False,
)
elif kind == "thought_end":
yield ThoughtEndChunkEvent(
selector=[node_id, "generation", "thought"],
chunk="",
is_final=False,
)
else:
yield StreamChunkEvent(
selector=[node_id, "generation", "content"],
@ -1649,6 +1675,7 @@ class LLMNode(Node[LLMNodeData]):
"output": tool_call.output,
"files": files,
"status": tool_call.status.value if hasattr(tool_call.status, "value") else tool_call.status,
"elapsed_time": tool_call.elapsed_time,
}
def _flush_thought_segment(self, buffers: StreamBuffers, trace_state: TraceState) -> None:
@ -1707,6 +1734,7 @@ class LLMNode(Node[LLMNodeData]):
id=tool_call_id,
name=tool_name,
arguments=tool_arguments,
elapsed_time=output.metadata.get(AgentLog.LogMetadata.ELAPSED_TIME) if output.metadata else None,
),
)
trace_state.trace_segments.append(tool_call_segment)
@ -1755,6 +1783,7 @@ class LLMNode(Node[LLMNodeData]):
id=tool_call_id,
name=tool_name,
arguments=None,
elapsed_time=output.metadata.get(AgentLog.LogMetadata.ELAPSED_TIME) if output.metadata else None,
),
)
if existing_tool_segment is None:
@ -1767,6 +1796,7 @@ class LLMNode(Node[LLMNodeData]):
id=tool_call_id,
name=tool_name,
arguments=None,
elapsed_time=output.metadata.get(AgentLog.LogMetadata.ELAPSED_TIME) if output.metadata else None,
)
tool_call_segment.tool_call.output = (
str(tool_output) if tool_output is not None else str(tool_error) if tool_error is not None else None
@ -1785,6 +1815,7 @@ class LLMNode(Node[LLMNodeData]):
output=result_output,
files=tool_files,
status=ToolResultStatus.ERROR if tool_error else ToolResultStatus.SUCCESS,
elapsed_time=output.metadata.get(AgentLog.LogMetadata.ELAPSED_TIME) if output.metadata else None,
),
is_final=False,
)
@ -1806,10 +1837,17 @@ class LLMNode(Node[LLMNodeData]):
chunk_text = str(chunk_text)
for kind, segment in buffers.think_parser.process(chunk_text):
if not segment:
if not segment and kind not in {"thought_start", "thought_end"}:
continue
if kind == "thought":
if kind == "thought_start":
self._flush_content_segment(buffers, trace_state)
yield ThoughtStartChunkEvent(
selector=[self._node_id, "generation", "thought"],
chunk="",
is_final=False,
)
elif kind == "thought":
self._flush_content_segment(buffers, trace_state)
buffers.current_turn_reasoning.append(segment)
buffers.pending_thought.append(segment)
@ -1818,6 +1856,13 @@ class LLMNode(Node[LLMNodeData]):
chunk=segment,
is_final=False,
)
elif kind == "thought_end":
self._flush_thought_segment(buffers, trace_state)
yield ThoughtEndChunkEvent(
selector=[self._node_id, "generation", "thought"],
chunk="",
is_final=False,
)
else:
self._flush_thought_segment(buffers, trace_state)
aggregate.text += segment
@ -1843,9 +1888,16 @@ class LLMNode(Node[LLMNodeData]):
self, buffers: StreamBuffers, trace_state: TraceState, aggregate: AggregatedResult
) -> Generator[NodeEventBase, None, None]:
for kind, segment in buffers.think_parser.flush():
if not segment:
if not segment and kind not in {"thought_start", "thought_end"}:
continue
if kind == "thought":
if kind == "thought_start":
self._flush_content_segment(buffers, trace_state)
yield ThoughtStartChunkEvent(
selector=[self._node_id, "generation", "thought"],
chunk="",
is_final=False,
)
elif kind == "thought":
self._flush_content_segment(buffers, trace_state)
buffers.current_turn_reasoning.append(segment)
buffers.pending_thought.append(segment)
@ -1854,6 +1906,13 @@ class LLMNode(Node[LLMNodeData]):
chunk=segment,
is_final=False,
)
elif kind == "thought_end":
self._flush_thought_segment(buffers, trace_state)
yield ThoughtEndChunkEvent(
selector=[self._node_id, "generation", "thought"],
chunk="",
is_final=False,
)
else:
self._flush_thought_segment(buffers, trace_state)
aggregate.text += segment
@ -1960,6 +2019,7 @@ class LLMNode(Node[LLMNodeData]):
arguments=json.dumps(tool_args) if tool_args else "",
output=result_text,
status=status,
elapsed_time=log.metadata.get(AgentLog.LogMetadata.ELAPSED_TIME) if log.metadata else None,
)
)