style(workflow): remove unnecessary indentation
This commit is contained in:
mengyonghao
@@ -33,7 +33,7 @@ class EndNode(BaseNode):
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# 获取配置的输出模板
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output_template = self.config.get("output")
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# 如果配置了输出模板，使用模板渲染；否则使用默认输出
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if output_template:
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output = self._render_template(output_template, state)
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@@ -45,17 +45,17 @@ class EndNode(BaseNode):
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total_nodes = len(node_outputs)
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logger.info(f"节点 {self.node_id} (End) 执行完成，共执行 {total_nodes} 个节点")
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return output
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def _extract_referenced_nodes(self, template: str) -> list[str]:
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"""从模板中提取引用的节点 ID
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例如：'结果：{{llm_qa.output}}' -> ['llm_qa']
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Args:
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template: 模板字符串
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Returns:
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引用的节点 ID 列表
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"""
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@@ -63,44 +63,44 @@ class EndNode(BaseNode):
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pattern = r'\{\{([a-zA-Z0-9_]+)\.[a-zA-Z0-9_]+\}\}'
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matches = re.findall(pattern, template)
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return list(set(matches)) # 去重
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def _parse_template_parts(self, template: str, state: WorkflowState) -> list[dict]:
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"""解析模板，分离静态文本和动态引用
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例如：'你好 {{llm.output}}, 这是后缀'
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返回：[
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{"type": "static", "content": "你好 "},
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{"type": "dynamic", "node_id": "llm", "field": "output"},
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{"type": "static", "content": ", 这是后缀"}
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]
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Args:
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template: 模板字符串
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state: 工作流状态
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Returns:
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模板部分列表
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"""
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import re
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parts = []
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last_end = 0
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# 匹配 {{xxx}} 或 {{ xxx }} 格式（支持空格）
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pattern = r'\{\{\s*([^}]+?)\s*\}\}'
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for match in re.finditer(pattern, template):
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start, end = match.span()
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# 添加前面的静态文本
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if start > last_end:
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static_text = template[last_end:start]
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if static_text:
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parts.append({"type": "static", "content": static_text})
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# 解析动态引用
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ref = match.group(1).strip()
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# 检查是否是节点引用（如 llm.output 或 llm_qa.output）
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if '.' in ref:
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node_id, field = ref.split('.', 1)
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@@ -115,62 +115,62 @@ class EndNode(BaseNode):
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# 直接渲染这部分
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rendered = self._render_template(f"{{{{{ref}}}}}", state)
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parts.append({"type": "static", "content": rendered})
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last_end = end
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# 添加最后的静态文本
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if last_end < len(template):
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static_text = template[last_end:]
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if static_text:
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parts.append({"type": "static", "content": static_text})
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return parts
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async def execute_stream(self, state: WorkflowState):
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"""流式执行 end 节点业务逻辑
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智能输出策略：
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1. 检测模板中是否引用了直接上游节点
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2. 如果引用了，只输出该引用**之后**的部分（后缀）
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3. 前缀和引用内容已经在上游节点流式输出时发送了
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示例：'{{start.test}}hahaha {{ llm_qa.output }} lalalalala a'
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- 直接上游节点是 llm_qa
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- 前缀 '{{start.test}}hahaha ' 已在 LLM 节点流式输出前发送
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- LLM 内容在 LLM 节点流式输出
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- End 节点只输出 ' lalalalala a'（后缀，一次性输出）
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Args:
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state: 工作流状态
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Yields:
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完成标记
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"""
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logger.info(f"节点 {self.node_id} (End) 开始执行（流式）")
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# 获取配置的输出模板
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output_template = self.config.get("output")
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if not output_template:
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output = "工作流已完成"
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yield {"__final__": True, "result": output}
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return
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# 找到直接上游节点
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direct_upstream_nodes = []
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for edge in self.workflow_config.get("edges", []):
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if edge.get("target") == self.node_id:
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source_node_id = edge.get("source")
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direct_upstream_nodes.append(source_node_id)
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logger.info(f"节点 {self.node_id} 的直接上游节点: {direct_upstream_nodes}")
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# 解析模板部分
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parts = self._parse_template_parts(output_template, state)
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logger.info(f"节点 {self.node_id} 解析模板，共 {len(parts)} 个部分")
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for i, part in enumerate(parts):
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logger.info(f"[模板解析] part[{i}]: {part}")
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# 找到第一个引用直接上游节点的动态引用
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upstream_ref_index = None
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for i, part in enumerate(parts):
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@@ -178,12 +178,12 @@ class EndNode(BaseNode):
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upstream_ref_index = i
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logger.info(f"节点 {self.node_id} 找到直接上游节点 {part['node_id']} 的引用，索引: {i}")
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break
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if upstream_ref_index is None:
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# 没有引用直接上游节点，输出完整模板内容
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output = self._render_template(output_template, state)
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logger.info(f"节点 {self.node_id} 没有引用直接上游节点，输出完整内容: '{output[:50]}...'")
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# 通过 writer 发送完整内容（作为一个 message chunk）
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from langgraph.config import get_stream_writer
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writer = get_stream_writer()
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@@ -196,14 +196,14 @@ class EndNode(BaseNode):
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"is_suffix": False
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})
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logger.info(f"节点 {self.node_id} 已通过 writer 发送完整内容")
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# yield 完成标记
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yield {"__final__": True, "result": output}
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return
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# 有引用直接上游节点，只输出该引用之后的部分（后缀）
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logger.info(f"节点 {self.node_id} 检测到直接上游节点引用，只输出后缀部分（从索引 {upstream_ref_index + 1} 开始）")
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# 收集后缀部分
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suffix_parts = []
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logger.info(f"[后缀调试] 开始收集后缀，从索引 {upstream_ref_index + 1} 到 {len(parts) - 1}")
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@@ -214,7 +214,7 @@ class EndNode(BaseNode):
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# 静态文本
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logger.info(f"[后缀调试] 添加静态文本: '{part['content']}'")
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suffix_parts.append(part["content"])
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elif part["type"] == "dynamic":
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# Other dynamic references (if there are multiple references)
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node_id = part["node_id"]
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@@ -229,21 +229,21 @@ class EndNode(BaseNode):
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except Exception as e:
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logger.warning(f"[后缀调试] 获取变量 {node_id}.{field} 失败: {e}")
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content = ""
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# Convert to string if not None
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suffix_parts.append(str(content) if content is not None else "")
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# 拼接后缀
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suffix = "".join(suffix_parts)
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# 构建完整输出（用于返回，包含前缀 + 动态内容 + 后缀）
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full_output = self._render_template(output_template, state)
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logger.info(f"[后缀调试] 节点 {self.node_id} 后缀部分数量: {len(suffix_parts)}")
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logger.info(f"[后缀调试] 后缀内容: '{suffix}'")
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logger.info(f"[后缀调试] 后缀长度: {len(suffix)}")
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logger.info(f"[后缀调试] 后缀是否为空: {not suffix}")
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if suffix:
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logger.info(f"节点 {self.node_id} 输出后缀: '{suffix}...' (长度: {len(suffix)})")
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# 一次性输出后缀（作为单个 chunk）
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@@ -266,8 +266,8 @@ class EndNode(BaseNode):
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# 统计信息
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node_outputs = state.get("node_outputs", {})
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total_nodes = len(node_outputs)
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logger.info(f"节点 {self.node_id} (End) 执行完成（流式），共执行了 {total_nodes} 个节点")
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# yield 完成标记（包含完整输出）
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yield {"__final__": True, "result": full_output}
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@@ -11,6 +11,7 @@ from langchain_core.messages import AIMessage, SystemMessage, HumanMessage
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from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
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from app.core.models import RedBearLLM, RedBearModelConfig
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from app.db import get_db_context
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from app.models import ModelType
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from app.services.model_service import ModelConfigService
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from app.core.exceptions import BusinessException
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@@ -136,7 +137,7 @@ class LLMNode(BaseNode):
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base_url=api_base,
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extra_params=extra_params
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),
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type=model_type
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type=ModelType(model_type)
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)
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logger.debug(f"创建 LLM 实例: provider={provider}, model={model_name}, streaming={stream}")
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@@ -7,6 +7,7 @@
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import logging
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from typing import Any, Union
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from app.core.workflow.nodes.knowledge import KnowledgeRetrievalNode
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from app.core.workflow.nodes.agent import AgentNode
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from app.core.workflow.nodes.base_node import BaseNode
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from app.core.workflow.nodes.end import EndNode
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@@ -15,6 +16,7 @@ from app.core.workflow.nodes.if_else import IfElseNode
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from app.core.workflow.nodes.llm import LLMNode
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from app.core.workflow.nodes.start import StartNode
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from app.core.workflow.nodes.transform import TransformNode
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from app.core.workflow.nodes.assigner import AssignerNode
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logger = logging.getLogger(__name__)
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@@ -26,6 +28,8 @@ WorkflowNode = Union[
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IfElseNode,
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AgentNode,
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TransformNode,
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AssignerNode,
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KnowledgeRetrievalNode,
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]
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@@ -42,7 +46,9 @@ class NodeFactory:
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NodeType.LLM: LLMNode,
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NodeType.AGENT: AgentNode,
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NodeType.TRANSFORM: TransformNode,
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NodeType.IF_ELSE: IfElseNode
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NodeType.IF_ELSE: IfElseNode,
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NodeType.KNOWLEDGE_RETRIEVAL: KnowledgeRetrievalNode,
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NodeType.ASSIGNER: AssignerNode,
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}
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@classmethod
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@@ -82,10 +88,6 @@ class NodeFactory:
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"""
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node_type = node_config.get("type")
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# 跳过条件节点（由 LangGraph 处理）
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if node_type == "condition":
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return None
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# 获取节点类
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node_class = cls._node_types.get(node_type)
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if not node_class:
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@@ -10,7 +10,10 @@
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"""
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import logging
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from typing import Any
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from typing import Any, TYPE_CHECKING
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if TYPE_CHECKING:
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from app.core.workflow.nodes import WorkflowState
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logger = logging.getLogger(__name__)
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@@ -82,7 +85,7 @@ class VariablePool:
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>>> pool.set(["conv", "user_name"], "张三")
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"""
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def __init__(self, state: dict[str, Any]):
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def __init__(self, state: "WorkflowState"):
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"""初始化变量池
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Args:
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@@ -15,25 +15,6 @@ class ModelType(StrEnum):
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EMBEDDING = "embedding"
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RERANK = "rerank"
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@classmethod
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def from_str(cls, value: str) -> "ModelType":
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"""
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Get a ModelType enum instance from a string value.
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Args:
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value (str): The string representation of the model type.
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Returns:
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ModelType: The corresponding ModelType enum object.
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Raises:
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ValueError: If the given value does not match any ModelType.
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"""
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try:
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return cls(value)
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except ValueError:
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raise ValueError(f"Invalid ModelType: {value}")
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class ModelProvider(StrEnum):
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"""模型提供商枚举"""
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@@ -169,7 +169,7 @@ class PromptOptimizerService:
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provider=api_config.provider,
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api_key=api_config.api_key,
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base_url=api_config.api_base
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), type=ModelType.from_str(model_config.type))
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), type=ModelType(model_config.type))
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# build message
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messages = [
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