254 lines
9.3 KiB
Python
254 lines
9.3 KiB
Python
"""
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模板渲染节点
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"""
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import logging
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import time
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from typing import Any, Dict
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from sqlalchemy import select
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from ..base import BaseNode, NodeContext, NodeResult
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from ..registry import NodeRegistry
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logger = logging.getLogger(__name__)
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@NodeRegistry.register
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class TemplateNode(BaseNode):
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"""
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模板渲染节点
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使用变量渲染模板字符串
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"""
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node_type = 'template'
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node_name = '模板'
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node_category = 'data'
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node_icon = 'file-text'
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node_description = '使用变量渲染模板字符串'
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inputs = [
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{
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'name': 'template',
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'type': 'string',
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'description': '模板字符串',
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},
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]
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outputs = [
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{
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'name': 'result',
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'type': 'string',
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'description': '渲染结果',
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},
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]
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def execute(self, context: NodeContext) -> NodeResult:
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"""执行模板渲染"""
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try:
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template = self.config.get('template', '')
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output_variable = self.config.get('output_variable', 'template_result')
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# 渲染模板
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result = context.resolve_template(template)
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return NodeResult(
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success=True,
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output=result,
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output_variables={output_variable: result},
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)
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except Exception as e:
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logger.exception(f'模板节点执行失败: {e}')
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return NodeResult(
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success=False,
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error=str(e),
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)
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async def execute_async(self, context: NodeContext) -> NodeResult:
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"""配置 agent_code 时，以智能体身份执行当前模板任务。"""
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agent_code = (self.config.get('agent_code') or '').strip()
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if not agent_code:
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return self.execute(context)
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start_time = time.time()
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try:
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if not context.db_session:
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return NodeResult(success=False, error='智能体模板节点需要数据库会话')
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from ai_platform.models import Agent
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from ai_platform.services.llm_service import LLMService
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result = await context.db_session.execute(
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select(Agent).where(
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Agent.code == agent_code,
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Agent.is_deleted == False,
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Agent.status == 'published',
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)
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)
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agent = result.scalar_one_or_none()
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if not agent:
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return NodeResult(success=False, error=f'智能体不存在或未发布: {agent_code}')
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model_id = self.config.get('model_id') or agent.model_id
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if not model_id:
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model_id = await self._resolve_default_chat_model_id(context)
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if not model_id:
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return NodeResult(success=False, error=f'智能体未配置模型: {agent.name}({agent.code})')
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template = self.config.get('template', '')
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rendered_prompt = context.resolve_template(template)
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if not rendered_prompt.strip():
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return NodeResult(success=False, error='智能体模板节点缺少任务内容')
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messages = [
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{'role': 'system', 'content': self._build_agent_system_prompt(agent)},
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{'role': 'user', 'content': self._build_agent_user_prompt(context, rendered_prompt)},
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]
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response = await LLMService(context.db_session).chat_async(
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model_id=str(model_id),
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messages=messages,
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temperature=self.config.get(
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'temperature',
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agent.temperature if agent.temperature is not None else 0.7,
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),
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max_tokens=self.config.get('max_tokens', agent.max_tokens or 2048),
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)
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elapsed_time = int((time.time() - start_time) * 1000)
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output_variable = self.config.get('output_variable') or f'{agent.code}_result'
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output_variables = {
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output_variable: response.content,
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f'{output_variable}_agent_code': agent.code,
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f'{output_variable}_agent_name': agent.name,
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f'{output_variable}_tokens': response.total_tokens,
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}
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return NodeResult(
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success=True,
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output=response.content,
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output_variables=output_variables,
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tokens_used=response.total_tokens,
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elapsed_time=elapsed_time,
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metadata={
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'agent_code': agent.code,
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'agent_name': agent.name,
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'model_id': str(model_id),
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'model': response.model,
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'prompt_tokens': response.prompt_tokens,
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'completion_tokens': response.completion_tokens,
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'output_variable': output_variable,
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'frontend_output_variables': output_variables,
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},
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)
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except Exception as e:
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logger.exception(f'智能体模板节点执行失败: {e}')
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return NodeResult(
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success=False,
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error=str(e),
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elapsed_time=int((time.time() - start_time) * 1000),
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)
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@staticmethod
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async def _resolve_default_chat_model_id(context: NodeContext) -> str:
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from ai_platform.models import LLMModel
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result = await context.db_session.execute(
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select(LLMModel)
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.where(
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LLMModel.is_deleted == False,
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LLMModel.is_active == True,
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LLMModel.model_type == 'chat',
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)
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.order_by(LLMModel.sort.desc(), LLMModel.sys_create_datetime.desc())
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)
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model = result.scalars().first()
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return str(model.id) if model else ''
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@staticmethod
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def _build_agent_system_prompt(agent: Any) -> str:
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base_prompt = agent.system_prompt or ''
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persona = agent.persona or {}
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if not base_prompt and isinstance(persona, dict):
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parts = []
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if persona.get('role'):
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parts.append(persona['role'])
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if persona.get('skills'):
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parts.append('你擅长：' + '、'.join(str(item) for item in persona['skills']) + '。')
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if persona.get('constraints'):
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parts.append('注意事项：\n' + '\n'.join(f'- {item}' for item in persona['constraints']))
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if persona.get('background'):
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parts.append(str(persona['background']))
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base_prompt = '\n\n'.join(parts)
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if not base_prompt:
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base_prompt = '你是一个智能体，请用中文完成当前任务。'
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return (
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f'{base_prompt}\n\n'
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'你正在作为流程编排中的协作智能体执行当前步骤。'
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'请只输出本步骤的结论、交付证据、风险和下一步，避免空泛说明。'
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)
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@staticmethod
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def _format_context_value(value: Any, max_length: int = 1200) -> str:
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if isinstance(value, str):
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text = value
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else:
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import json
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try:
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text = json.dumps(value, ensure_ascii=False, indent=2)
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except TypeError:
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text = str(value)
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return text if len(text) <= max_length else f'{text[:max_length]}...'
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def _build_agent_user_prompt(self, context: NodeContext, rendered_prompt: str) -> str:
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context_lines = []
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for key, value in context.variables.items():
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if key.startswith('_') or key.endswith('_tokens') or key.endswith('_agent_code') or key.endswith('_agent_name'):
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continue
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context_lines.append(f'- {key}: {self._format_context_value(value)}')
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if len('\n'.join(context_lines)) > 6000:
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context_lines.append('- 其余上下文因长度限制已省略')
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break
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sections = [f'当前步骤任务：\n{rendered_prompt}']
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if context.previous_output:
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sections.append(f'上一节点输出：\n{self._format_context_value(context.previous_output, 2000)}')
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if context_lines:
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sections.append('可用工作流上下文：\n' + '\n'.join(context_lines))
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return '\n\n'.join(sections)
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@classmethod
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def get_config_schema(cls) -> Dict[str, Any]:
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"""获取配置 Schema"""
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return {
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'type': 'object',
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'properties': {
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'template': {
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'type': 'string',
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'title': '模板',
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'description': '支持变量引用，如 {{variable_name}}',
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'format': 'textarea',
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},
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'output_variable': {
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'type': 'string',
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'title': '输出变量名',
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'default': 'template_result',
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},
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'agent_code': {
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'type': 'string',
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'title': '智能体编码',
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'description': '配置后会以该智能体身份调用模型执行模板任务',
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},
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'model_id': {
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'type': 'string',
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'title': '覆盖模型 ID',
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'description': '为空时使用智能体默认模型',
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},
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},
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'required': ['template'],
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}
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