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Merge pull request #1049 from SuanmoSuanyangTechnology/feat/wxy-dev

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feat(LLM node): integrate exception handling and enable branch routing
This commit is contained in:
山程漫悟
2026-05-07 17:34:31 +08:00
committed by GitHub
parent 2edfaa3863 cef33fce0d
commit 8967b00303
5 changed files with 182 additions and 76 deletions
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11
api/app/core/workflow/engine/graph_builder.py
View File

@@ -2,6 +2,7 @@
# Author: Eternity
# @Email: 1533512157@qq.com
# @Time : 2026/2/10 13:33
import json
import logging
import re
import uuid
@@ -141,6 +142,7 @@ class GraphBuilder:
for node_info in source_nodes:
if self.get_node_type(node_info["id"]) in BRANCH_NODES:
if node_info.get("branch") is not None:
branch_nodes.append(
(node_info["id"], node_info["branch"])
)
@@ -314,9 +316,12 @@ class GraphBuilder:
for idx in range(len(related_edge)):
# Generate a condition expression for each edge
# Used later to determine which branch to take based on the node's output
# Assumes node output `node.<node_id>.output` matches the edge's label
# For example, if node.123.output == 'CASE1', take the branch labeled 'CASE1'
related_edge[idx]['condition'] = f"node['{node_id}']['output'] == '{related_edge[idx]['label']}'"
# For LLM nodes, use branch_signal field for routing (output is dynamic text)
# For other branch nodes (e.g. HTTP), use output field
route_field = "branch_signal" if node_type == NodeType.LLM else "output"
related_edge[idx]['condition'] = (
f"node[{json.dumps(node_id)}][{json.dumps(route_field)}] == {json.dumps(related_edge[idx]['label'])}"
)
if node_instance:
# Wrap node's run method to avoid closure issues

10
api/app/core/workflow/nodes/assigner/node.py
View File

@@ -18,10 +18,17 @@ class AssignerNode(BaseNode):
super().__init__(node_config, workflow_config, down_stream_nodes)
self.variable_updater = True
self.typed_config: AssignerNodeConfig | None = None
self._input_data: dict[str, Any] | None = None
def _output_types(self) -> dict[str, VariableType]:
return {}
def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
"""提取节点输入，如果有缓存的执行前数据则使用缓存"""
if self._input_data is not None:
return self._input_data
return {"config": self._resolve_config(self.config, variable_pool)}
async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> Any:
"""
Execute the assignment operation defined by this node.
@@ -34,6 +41,9 @@ class AssignerNode(BaseNode):
Returns:
None or the result of the assignment operation.
"""
# 在执行前提取并缓存输入数据（捕获执行前的变量值）
self._input_data = {"config": self._resolve_config(self.config, variable_pool)}
# Initialize a variable pool for accessing conversation, node, and system variables
self.typed_config = AssignerNodeConfig(**self.config)
logger.info(f"节点 {self.node_id} 开始执行")

2
api/app/core/workflow/nodes/enums.py
View File

@@ -31,7 +31,7 @@ class NodeType(StrEnum):
NOTES = "notes"
BRANCH_NODES = frozenset({NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER})
BRANCH_NODES = frozenset({NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER, NodeType.LLM})
class ComparisonOperator(StrEnum):

20
api/app/core/workflow/nodes/llm/config.py
View File

@@ -6,6 +6,7 @@ import uuid
from pydantic import BaseModel, Field, field_validator
from app.core.workflow.nodes.base_config import BaseNodeConfig, VariableDefinition
from app.core.workflow.nodes.enums import HttpErrorHandle
from app.core.workflow.variable.base_variable import VariableType
@@ -49,6 +50,20 @@ class MemoryWindowSetting(BaseModel):
)
class LLMErrorHandleConfig(BaseModel):
"""LLM 异常处理配置"""
method: HttpErrorHandle = Field(
default=HttpErrorHandle.NONE,
description="异常处理策略：'none' 抛出异常, 'default' 返回默认值, 'branch' 走异常分支",
)
output: str = Field(
default="",
description="LLM 异常时返回的默认输出文本（method=default 时生效）",
)
class LLMNodeConfig(BaseNodeConfig):
"""LLM 节点配置
@@ -152,6 +167,11 @@ class LLMNodeConfig(BaseNodeConfig):
description="输出变量定义（自动生成，通常不需要修改）"
)
error_handle: LLMErrorHandleConfig = Field(
default_factory=LLMErrorHandleConfig,
description="LLM 异常处理配置",
)
@field_validator("messages", "prompt")
@classmethod
def validate_input_mode(cls, v):

99
api/app/core/workflow/nodes/llm/node.py
View File

@@ -15,6 +15,7 @@ from app.core.models import RedBearLLM, RedBearModelConfig
from app.core.workflow.engine.state_manager import WorkflowState
from app.core.workflow.engine.variable_pool import VariablePool
from app.core.workflow.nodes.base_node import BaseNode
from app.core.workflow.nodes.enums import HttpErrorHandle
from app.core.workflow.nodes.llm.config import LLMNodeConfig
from app.core.workflow.variable.base_variable import VariableType
from app.db import get_db_context
@@ -76,7 +77,7 @@ class LLMNode(BaseNode):
self.messages = []
def _output_types(self) -> dict[str, VariableType]:
return {"output": VariableType.STRING}
return {"output": VariableType.STRING, "branch_signal": VariableType.STRING}
def _render_context(self, message: str, variable_pool: VariablePool):
context = f"<context>{self._render_template(self.typed_config.context, variable_pool)}</context>"
@@ -239,7 +240,7 @@ class LLMNode(BaseNode):
return llm
async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> AIMessage:
async def execute(self, state: WorkflowState, variable_pool: VariablePool):
"""非流式执行 LLM 调用
Args:
@@ -247,8 +248,10 @@ class LLMNode(BaseNode):
variable_pool: 变量池
Returns:
LLM 响应消息
dict: {"llm_result": AIMessage, "branch_signal": "SUCCESS"} on success,
{"llm_result": None, "branch_signal": "ERROR"} on branch error
"""
try:
# self.typed_config = LLMNodeConfig(**self.config)
llm = await self._prepare_llm(state, variable_pool, False)
@@ -265,10 +268,16 @@ class LLMNode(BaseNode):
logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(content)}")
# 返回 AIMessage（包含响应元数据）
return AIMessage(content=content, response_metadata={
return {
"llm_result": AIMessage(content=content, response_metadata={
**response.response_metadata,
"token_usage": getattr(response, 'usage_metadata', None) or response.response_metadata.get('token_usage')
})
}),
"branch_signal": "SUCCESS",
}
except Exception as e:
logger.error(f"节点 {self.node_id} LLM 调用失败: {e}")
return self._handle_llm_error(e)
def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
"""提取输入数据（用于记录）"""
@@ -286,16 +295,36 @@ class LLMNode(BaseNode):
}
}
def _extract_output(self, business_result: Any) -> str:
"""从 AIMessage 中提取文本内容"""
def _extract_output(self, business_result: Any) -> dict:
"""从业务结果中提取输出变量
支持新旧两种格式：
- 新格式：{"llm_result": AIMessage, "branch_signal": "SUCCESS"}
- 旧格式：AIMessage（向后兼容）
"""
if isinstance(business_result, dict) and "branch_signal" in business_result:
llm_result = business_result.get("llm_result")
if isinstance(llm_result, AIMessage):
return {
"output": llm_result.content,
"branch_signal": business_result["branch_signal"],
}
return {
"output": str(llm_result) if llm_result else "",
"branch_signal": business_result["branch_signal"],
}
# 旧格式向后兼容
if isinstance(business_result, AIMessage):
return business_result.content
return str(business_result)
return {"output": business_result.content, "branch_signal": "SUCCESS"}
return {"output": str(business_result), "branch_signal": "SUCCESS"}
def _extract_token_usage(self, business_result: Any) -> dict[str, int] | None:
"""从 AIMessage 中提取 token 使用情况"""
if isinstance(business_result, AIMessage) and hasattr(business_result, 'response_metadata'):
usage = business_result.response_metadata.get('token_usage')
"""从业务结果中提取 token 使用情况"""
llm_result = business_result
if isinstance(business_result, dict):
llm_result = business_result.get("llm_result", business_result)
if isinstance(llm_result, AIMessage) and hasattr(llm_result, 'response_metadata'):
usage = llm_result.response_metadata.get('token_usage')
if usage:
return {
"prompt_tokens": usage.get('input_tokens', 0),
@@ -304,6 +333,44 @@ class LLMNode(BaseNode):
}
return None
def _handle_llm_error(self, error: Exception) -> dict:
"""处理 LLM 调用异常，根据 error_handle 配置决定行为
Args:
error: LLM 调用中捕获的异常
Returns:
dict: {"llm_result": None, "branch_signal": "ERROR"} for branch mode,
or default output for default mode
Raises:
原异常（当 error_handle.method 为 NONE 时）
"""
if self.typed_config is None:
raise error
match self.typed_config.error_handle.method:
case HttpErrorHandle.NONE:
raise error
case HttpErrorHandle.DEFAULT:
logger.warning(
f"节点 {self.node_id}: LLM 调用失败，返回默认输出"
)
default_output = self.typed_config.error_handle.output or ""
return {
"llm_result": AIMessage(content=default_output, response_metadata={}),
"branch_signal": "SUCCESS",
}
case HttpErrorHandle.BRANCH:
logger.warning(
f"节点 {self.node_id}: LLM 调用失败，切换到异常处理分支"
)
return {
"llm_result": None,
"branch_signal": "ERROR",
}
raise error
async def execute_stream(self, state: WorkflowState, variable_pool: VariablePool):
"""流式执行 LLM 调用
@@ -316,10 +383,10 @@ class LLMNode(BaseNode):
"""
self.typed_config = LLMNodeConfig(**self.config)
try:
llm = await self._prepare_llm(state, variable_pool, True)
logger.info(f"节点 {self.node_id} 开始执行 LLM 调用（流式）")
# logger.debug(f"LLM 配置: streaming={getattr(llm._model, 'streaming', 'unknown')}")
# 累积完整响应
full_response = ""
@@ -366,4 +433,8 @@ class LLMNode(BaseNode):
)
# yield 完成标记
yield {"__final__": True, "result": final_message}
yield {"__final__": True, "result": {"llm_result": final_message, "branch_signal": "SUCCESS"}}
except Exception as e:
logger.error(f"节点 {self.node_id} LLM 流式调用失败: {e}")
error_result = self._handle_llm_error(e)
yield {"__final__": True, "result": error_result}