zhaoxi
209ba45477
Removes the deprecated `workflow_template` concept entirely across both backend API routers, internal logic handling within the `supervisory_node` and `consciousness_node`, and front-end components. Enables `consciousness_node` to work autonomously. Also refactors core package structure to enforce the "one python package, one Ray Actor" architectural rule. `GlobalWorkflowManager`, `WorkflowRunningEngine`, `PostgresDatabase`, and `WorkerCluster` have been moved to their own top-level decoupled package directories with properly exported `__init__.py` modules. Test suites have been relocated and import paths updated across the system. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>
133 lines
5.3 KiB
Python
133 lines
5.3 KiB
Python
# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pretor.worker_individual.base_individual import (
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BaseIndividual,
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WorkerIndividualDeps,
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)
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from pretor.utils.logger import get_logger
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import os
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import json
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from pydantic_ai import Tool
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import importlib.util
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logger = get_logger("skill_individual")
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class SkillIndividual(BaseIndividual):
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"""
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专家子个体：拥有专业 skill 的 agent。
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"""
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def __init__(self, agent_config: dict):
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super().__init__(agent_config)
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async def _load_skill_tools(self):
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"""动态加载已绑定的 skill 工具。"""
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tools = []
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bound_skill = self.agent_config.get("bound_skill", "")
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# bound_skill can be string or dict {"skill_name": ["file1", "file2"]}
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skill_mapper = {}
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if isinstance(bound_skill, str) and bound_skill:
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try:
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skill_mapper = json.loads(bound_skill)
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except json.JSONDecodeError:
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pass
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elif isinstance(bound_skill, dict):
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skill_mapper = bound_skill
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skill_base_dir = os.path.abspath(
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os.path.join(os.path.dirname(__file__), "..", "plugin", "skill")
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)
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for skill_name, _ in skill_mapper.items():
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skill_path = os.path.join(skill_base_dir, skill_name)
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metadata_path = os.path.join(skill_path, "metadata.json")
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if not os.path.exists(metadata_path):
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continue
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try:
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with open(metadata_path, "r", encoding="utf-8") as f:
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metadata = json.load(f)
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except Exception as e:
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logger.error(f"Failed to load metadata for skill {skill_name}: {e}")
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continue
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if "functions" in metadata:
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for func_info in metadata["functions"]:
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# Ensure path is absolute
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script_path = func_info.get("file_path", "")
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if not os.path.isabs(script_path):
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script_path = os.path.join(skill_path, script_path)
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if not os.path.exists(script_path):
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logger.warning(f"Skill script not found: {script_path}")
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continue
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func_name = func_info.get("name")
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try:
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# Dynamically load the python module
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spec = importlib.util.spec_from_file_location(
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func_name, script_path
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)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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func = getattr(module, func_name)
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if callable(func):
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# Convert to PydanticAI Tool
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tool = Tool(
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func,
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name=func_name,
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description=func_info.get("docstring", ""),
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)
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tools.append(tool)
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logger.info(
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f"Loaded skill tool: {func_name} from {skill_name}"
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)
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except Exception as e:
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logger.error(
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f"Failed to load function {func_name} from {script_path}: {e}"
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)
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return tools
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async def run(self, task_event: dict) -> dict:
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"""执行与 run 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: task_event (dict): 由事件总线或工作流引擎分发过来的事件载荷，封装了触发此次调用的上下文快照与任务目标指令。
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Returns: (dict): 高度聚合的字典结构数据，将多维度的属性特征或统计指标组合后一并返回。"""
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if self.agent is None:
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system_prompt = self.agent_config.get(
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"prompt",
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"你是一个拥有专业技能的专家级AI助手，请利用你的专业知识完成给定的任务。",
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)
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await self._init_agent("skill_individual", system_prompt)
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deps = WorkerIndividualDeps(task_event=task_event)
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self.agent.retries = 3
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tools = await self._load_skill_tools()
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try:
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result = await self.agent.run(
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f"请执行以下任务：\n{task_event}",
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deps=deps,
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tools=tools if tools else None,
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)
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return {"output": result.data.output}
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except Exception as e:
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logger.exception(f"SkillIndividual {self.agent_id} 执行失败: {e}")
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raise
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