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226 lines
7.7 KiB
226 lines
7.7 KiB
from agentscope.agent import AgentBase
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from agentscope.agent._react_agent import ReActAgent
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from agentscope.model import OpenAIChatModel
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from agentscope.formatter import OpenAIChatFormatter
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from agentscope.tool import Toolkit
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from agentscope.message import Msg
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from config import settings
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from .memory.user_memory import UserIsolatedMemory
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from .hooks.rbac_hook import register_rbac_hooks_for_user
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class AgentFactory:
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_model: OpenAIChatModel | None = None
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_formatter: OpenAIChatFormatter | None = None
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_agent_cache: dict[str, AgentBase] = {}
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_MAX_CACHE_SIZE = 50
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@classmethod
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def _get_model(cls) -> OpenAIChatModel:
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if cls._model is None:
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cls._model = OpenAIChatModel(
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config_name="enterprise_model",
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model_name=settings.LLM_MODEL,
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api_key=settings.LLM_API_KEY,
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api_base=settings.LLM_API_BASE,
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)
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return cls._model
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@classmethod
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def _get_formatter(cls) -> OpenAIChatFormatter:
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if cls._formatter is None:
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cls._formatter = OpenAIChatFormatter()
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return cls._formatter
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@classmethod
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async def create_agent(
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cls,
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agent_type: str,
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user_id: str,
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user_name: str,
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department_id: str | None = None,
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) -> AgentBase:
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cache_key = f"{agent_type}_{user_id}"
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if cache_key in cls._agent_cache:
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return cls._agent_cache[cache_key]
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model = cls._get_model()
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formatter = cls._get_formatter()
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if agent_type == "employee":
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agent = await cls._create_employee_agent(user_id, user_name, department_id, model, formatter)
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elif agent_type == "manager":
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agent = await cls._create_manager_agent(user_id, user_name, model, formatter)
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elif agent_type == "task":
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agent = await cls._create_task_agent(user_id, user_name, model, formatter)
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elif agent_type == "document":
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agent = await cls._create_document_agent(user_id, user_name, model, formatter)
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else:
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agent = await cls._create_employee_agent(user_id, user_name, department_id, model, formatter)
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if len(cls._agent_cache) >= cls._MAX_CACHE_SIZE:
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oldest_key = next(iter(cls._agent_cache))
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del cls._agent_cache[oldest_key]
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cls._agent_cache[cache_key] = agent
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return agent
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@classmethod
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async def _create_employee_agent(cls, user_id, user_name, department_id, model, formatter):
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from .tools.wecom_tools import send_notification
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from .tools.document_tools import parse_document, format_correction
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toolkit = Toolkit()
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toolkit.register_tool_function(send_notification)
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toolkit.register_tool_function(parse_document)
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toolkit.register_tool_function(format_correction)
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knowledge = None
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try:
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from modules.rag.knowledge import get_knowledge_base
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knowledge = get_knowledge_base()
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except Exception:
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pass
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agent = ReActAgent(
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name=f"EmployeeAI_{user_name}",
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sys_prompt=f"""你是 {user_name} 的专属AI工作助手。
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你可以:
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1. 回答工作中的问题，提供专业建议
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2. 帮助处理文档，修正格式
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3. 查询知识库获取信息
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4. 发送通知给相关人员
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重要约束:
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- 只能访问该员工权限范围内的数据和工具
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- 涉及敏感操作需要二次确认
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- 始终保持专业和友好的态度""",
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model=model,
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formatter=formatter,
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toolkit=toolkit,
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knowledge=knowledge,
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memory=UserIsolatedMemory(user_id=user_id),
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max_iters=8,
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)
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register_rbac_hooks_for_user(agent, {
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"user_id": user_id,
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"user_name": user_name,
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"role": "employee",
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"department_id": department_id or "",
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"data_scope": "self_only",
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})
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return agent
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@classmethod
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async def _create_manager_agent(cls, user_id, user_name, model, formatter):
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from .tools.manager_tools import list_subordinates, get_employee_dashboard, generate_efficiency_report, get_task_statistics
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from .tools.wecom_tools import send_notification
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toolkit = Toolkit()
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toolkit.register_tool_function(list_subordinates)
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toolkit.register_tool_function(get_employee_dashboard)
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toolkit.register_tool_function(generate_efficiency_report)
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toolkit.register_tool_function(get_task_statistics)
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toolkit.register_tool_function(send_notification)
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agent = ReActAgent(
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name=f"ManagerAI_{user_name}",
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sys_prompt=f"""你是 {user_name} 的管理分析助手。
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你可以:
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1. 查看下属员工列表和工作数据 (list_subordinates, get_employee_dashboard)
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2. 生成团队效率报告 (generate_efficiency_report)
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3. 统计分析任务完成情况 (get_task_statistics)
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4. 向下属发送企业微信通知提醒 (send_notification)
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重要约束:
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- 只能查看你的直接和间接下属的数据
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- 不能查看非下属或跨部门员工的数据
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- 生成报告时注意数据隐私""",
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model=model,
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formatter=formatter,
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toolkit=toolkit,
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memory=UserIsolatedMemory(user_id=user_id),
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max_iters=8,
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)
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register_rbac_hooks_for_user(agent, {
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"user_id": user_id,
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"user_name": user_name,
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"role": "dept_manager",
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"data_scope": "subordinate_only",
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})
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return agent
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@classmethod
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async def _create_task_agent(cls, user_id, user_name, model, formatter):
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from .tools.task_tools import list_tasks, create_task, get_task, update_task
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from .tools.wecom_tools import send_notification
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toolkit = Toolkit()
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toolkit.register_tool_function(list_tasks)
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toolkit.register_tool_function(create_task)
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toolkit.register_tool_function(get_task)
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toolkit.register_tool_function(update_task)
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toolkit.register_tool_function(send_notification)
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agent = ReActAgent(
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name=f"TaskAI_{user_name}",
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sys_prompt=f"""你是任务管理助手。帮助用户创建、跟踪和管理工作任务。
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你可以:
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1. 创建新任务并分配给指定人员 (create_task)
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2. 查询任务状态和进度 (list_tasks, get_task)
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3. 更新任务信息 (update_task)
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4. 推送任务通知到企业微信 (send_notification)
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重要约束:
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- 创建任务前确保标题和负责人信息完整
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- 修改任务状态前告知用户变更
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- 优先级: low/medium/high/urgent""",
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model=model,
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formatter=formatter,
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toolkit=toolkit,
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memory=UserIsolatedMemory(user_id=user_id),
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max_iters=8,
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)
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return agent
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@classmethod
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async def _create_document_agent(cls, user_id, user_name, model, formatter):
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from .tools.document_tools import parse_document, format_correction
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toolkit = Toolkit()
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toolkit.register_tool_function(parse_document)
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toolkit.register_tool_function(format_correction)
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knowledge = None
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try:
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from modules.rag.knowledge import get_knowledge_base
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knowledge = get_knowledge_base()
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except Exception:
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pass
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agent = ReActAgent(
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name=f"DocAI_{user_name}",
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sys_prompt=f"""你是文档处理专家。帮助用户处理各类文档。
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你可以:
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1. 解析PDF/Word/Excel/PPT等格式
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2. 修正文档格式
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3. 提取文档关键信息
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4. 从知识库中检索文档内容
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5. 格式转换""",
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model=model,
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formatter=formatter,
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toolkit=toolkit,
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knowledge=knowledge,
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memory=UserIsolatedMemory(user_id=user_id),
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max_iters=8,
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)
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return agent
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