Build lightweight AI agent admin
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"""
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节点基类
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"""
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import logging
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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logger = logging.getLogger(__name__)
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@dataclass
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class NodeContext:
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"""
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节点执行上下文
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包含节点执行所需的所有信息
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"""
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# 工作流运行实例
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workflow_run_id: str = ''
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# 变量存储（所有节点共享）
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variables: Dict[str, Any] = field(default_factory=dict)
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# 用户输入
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user_input: str = ''
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# 当前用户
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user_id: str = ''
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# 对话历史（用于 LLM 节点）
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conversation_history: List[Dict[str, str]] = field(default_factory=list)
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# 节点配置
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node_config: Dict[str, Any] = field(default_factory=dict)
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# 上一个节点的输出
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previous_output: Any = None
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# 元数据
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metadata: Dict[str, Any] = field(default_factory=dict)
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# 数据库会话（用于需要数据库访问的节点）
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db_session: Any = None
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def get_variable(self, name: str, default: Any = None) -> Any:
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"""获取变量"""
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return self.variables.get(name, default)
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def set_variable(self, name: str, value: Any) -> None:
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"""设置变量"""
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self.variables[name] = value
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def resolve_template(self, template: str) -> str:
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"""
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解析模板中的变量引用
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支持格式：
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- {{variable_name}} - 直接变量引用
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- {{variable_name[0]}} - 数组索引访问
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- {{variable_name[-1]}} - 负数索引（最后一个）
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- {{NodeID.key}} - 节点输出引用（如果存在）
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- {{NodeID.key}}.property - 访问解析结果的嵌套属性
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- {{NodeID.key[0].property}} - 数组索引 + 属性访问
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"""
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import re
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import json
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result = template
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# 匹配 {{...}} 格式的变量引用，以及可选的后续属性访问 .property1.property2...
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pattern = r'\{\{([^}]+)\}\}((?:\.[a-zA-Z_][a-zA-Z0-9_]*)*)'
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def get_nested_value(obj, path: str):
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"""从对象中获取嵌套属性值，支持数组索引"""
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if not path:
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return obj
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# 移除开头的点
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if path.startswith('.'):
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path = path[1:]
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current = obj
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# 使用正则分割路径，支持 .property 和 [index] 格式
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# 例如: "items[0].name" -> ["items", "[0]", "name"]
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parts = re.split(r'(?=\[)|\.', path)
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for part in parts:
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if not part:
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continue
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# 如果是字符串，尝试解析为 JSON
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if isinstance(current, str):
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try:
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current = json.loads(current)
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except (json.JSONDecodeError, TypeError):
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return None
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# 检查是否是数组索引 [n]
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index_match = re.match(r'\[(-?\d+)\]', part)
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if index_match:
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index = int(index_match.group(1))
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if isinstance(current, (list, tuple)):
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try:
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current = current[index]
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except IndexError:
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return None
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else:
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return None
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# 从字典中获取属性
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elif isinstance(current, dict):
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if part in current:
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current = current[part]
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else:
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return None
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else:
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return None
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return current
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def replace_var(match):
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var_ref = match.group(1).strip()
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extra_path = match.group(2) or '' # 额外的属性路径，如 .customer_name
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value = None
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index_path = '' # 变量名后的索引/属性路径
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# 检查是否有数组索引 [n]，如果有则分割
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# 例如: "loop_results[0]" -> var_name="loop_results", index_path="[0]"
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# 例如: "llm-123.llm_response" -> var_name="llm-123.llm_response", index_path=""
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bracket_pos = var_ref.find('[')
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if bracket_pos > 0:
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var_name = var_ref[:bracket_pos]
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index_path = var_ref[bracket_pos:]
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else:
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var_name = var_ref
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# 尝试解析 NodeID.key.subkey... 格式（支持多层属性访问）
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if '.' in var_name:
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parts = var_name.split('.')
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node_id = parts[0]
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remaining_path = '.'.join(parts[1:]) # 剩余路径，如 "item.module_name"
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# 先尝试从节点输出命名空间获取
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node_outputs = self.variables.get(f'_node_{node_id}')
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if isinstance(node_outputs, dict):
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# 尝试获取第一层 key
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first_key = parts[1] if len(parts) > 1 else None
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if first_key and first_key in node_outputs:
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value = node_outputs[first_key]
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# 如果还有更多层级，继续递归获取
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if len(parts) > 2:
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nested_path = '.'.join(parts[2:])
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nested_value = get_nested_value(value, nested_path)
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if nested_value is not None:
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value = nested_value
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# 回退：尝试直接从变量获取（node_id 作为变量名）
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if value is None and node_id in self.variables:
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node_data = self.variables[node_id]
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if isinstance(node_data, dict):
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first_key = parts[1] if len(parts) > 1 else None
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if first_key and first_key in node_data:
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value = node_data[first_key]
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if len(parts) > 2:
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nested_path = '.'.join(parts[2:])
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nested_value = get_nested_value(value, nested_path)
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if nested_value is not None:
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value = nested_value
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# 再回退：直接从顶层变量获取完整路径
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if value is None and remaining_path in self.variables:
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value = self.variables[remaining_path]
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else:
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# 直接变量引用
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if var_name in self.variables:
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value = self.variables[var_name]
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# 如果找到了值，处理索引路径和额外的属性路径
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if value is not None:
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# 合并索引路径和额外路径
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full_path = index_path + extra_path
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if full_path:
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nested_value = get_nested_value(value, full_path)
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if nested_value is not None:
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return str(nested_value)
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# 嵌套属性未找到，返回原始值
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return str(value)
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return str(value)
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# 未找到变量，保持原样
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return match.group(0)
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result = re.sub(pattern, replace_var, result)
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return result
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@dataclass
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class NodeResult:
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"""
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节点执行结果
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"""
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# 是否成功
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success: bool = True
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# 输出数据
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output: Any = None
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# 输出变量（会合并到上下文的 variables 中）
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output_variables: Dict[str, Any] = field(default_factory=dict)
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# 错误信息
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error: str = ''
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# 下一个节点 ID（用于条件分支）
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next_node_id: str = ''
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# Token 使用（LLM 节点）
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tokens_used: int = 0
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# 耗时（毫秒）
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elapsed_time: int = 0
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# 元数据
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metadata: Dict[str, Any] = field(default_factory=dict)
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# ========== 对话流相关 ==========
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# 是否等待用户输入（对话流模式）
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waiting_for_input: bool = False
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# 等待配置（描述需要什么类型的输入）
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waiting_config: Dict[str, Any] = field(default_factory=dict)
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# 事件列表（如发送消息）
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events: List[Dict[str, Any]] = field(default_factory=list)
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# ========== 设计预览相关 ==========
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# 设计预览数据（用于工作流中显示设计结果并允许编辑）
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preview: Optional[Dict[str, Any]] = None
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class BaseNode(ABC):
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"""
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节点基类
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所有节点必须继承此类并实现 execute 方法
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"""
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# 节点类型标识（必须唯一）
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node_type: str = ''
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# 节点显示名称
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node_name: str = ''
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# 节点分类
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node_category: str = 'basic' # basic, llm, logic, data, tool, knowledge
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# 节点图标
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node_icon: str = ''
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# 节点描述
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node_description: str = ''
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# 输入参数定义
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inputs: List[Dict[str, Any]] = []
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# 输出参数定义
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outputs: List[Dict[str, Any]] = []
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# 是否支持多个输出分支
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supports_branches: bool = False
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def __init__(self, config: Dict[str, Any] = None):
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"""
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初始化节点
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Args:
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config: 节点配置
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"""
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self.config = config or {}
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@abstractmethod
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def execute(self, context: NodeContext) -> NodeResult:
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"""
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执行节点
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Args:
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context: 执行上下文
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Returns:
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NodeResult
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"""
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pass
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async def execute_async(self, context: NodeContext) -> NodeResult:
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"""
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异步执行节点（默认调用同步方法）
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Args:
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context: 执行上下文
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Returns:
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NodeResult
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"""
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return self.execute(context)
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def validate_config(self) -> tuple:
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"""
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验证节点配置
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Returns:
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(is_valid, error_message)
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"""
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return True, ''
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@classmethod
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def get_schema(cls) -> Dict[str, Any]:
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"""
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获取节点 Schema（供前端渲染）
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Returns:
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节点 Schema
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"""
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return {
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'type': cls.node_type,
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'name': cls.node_name,
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'category': cls.node_category,
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'icon': cls.node_icon,
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'description': cls.node_description,
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'inputs': cls.inputs,
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'outputs': cls.outputs,
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'supports_branches': cls.supports_branches,
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}
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@classmethod
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def get_config_schema(cls) -> Dict[str, Any]:
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"""
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获取节点配置 Schema（供前端表单渲染）
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Returns:
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配置 Schema
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"""
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return {}
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def resolve_require_confirmation(self, context: NodeContext, default: bool = True) -> bool:
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"""
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解析 require_confirmation 配置
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支持三种模式：
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1. 布尔值：直接使用 True/False
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2. 字符串 'always'/'never'：始终确认/从不确认
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3. 变量引用：{{variable_name}} 格式，解析变量值作为布尔值
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Args:
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context: 节点执行上下文
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default: 默认值
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Returns:
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是否需要确认
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"""
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value = self.config.get('require_confirmation', default)
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# 布尔值直接返回
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if isinstance(value, bool):
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return value
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# 字符串处理
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if isinstance(value, str):
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value_lower = value.lower().strip()
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# 固定模式
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if value_lower in ('always', 'true', '1', 'yes'):
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return True
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if value_lower in ('never', 'false', '0', 'no'):
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return False
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# 变量引用模式：{{variable_name}}
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if '{{' in value and '}}' in value:
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resolved = context.resolve_template(value)
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# 解析后的值转换为布尔值
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if isinstance(resolved, bool):
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return resolved
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if isinstance(resolved, str):
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resolved_lower = resolved.lower().strip()
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if resolved_lower in ('true', '1', 'yes'):
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return True
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if resolved_lower in ('false', '0', 'no'):
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return False
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# 非空字符串视为 True
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return bool(resolved and resolved != value)
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# 其他情况返回默认值
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return default
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