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A node's tools resolved as the person running the workflow, so a teammate or public-link user lost every owner tool they could not use themselves. They now resolve as the workflow owner, then as the editor who attached them, like an agent's own tools. The runner stays the invoker, so a member-mode connection still uses their own account.
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
"""Workflow Node Agents - defines specialized agents for workflow nodes."""
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from typing import Dict, List, Optional, Type
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from docsgpt.agents.agentic_agent import AgenticAgent
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from docsgpt.agents.base import BaseAgent
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from docsgpt.agents.classic_agent import ClassicAgent
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from docsgpt.agents.research_agent import ResearchAgent
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from docsgpt.agents.workflows.schemas import AgentType
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class _WorkflowNodeMixin:
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"""Common __init__ for all workflow node agents."""
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def __init__(
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self,
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endpoint: str,
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llm_name: str,
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model_id: str,
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api_key: str,
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tool_ids: Optional[List[str]] = None,
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tool_principals: Optional[Dict[str, str]] = None,
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tool_owner: Optional[str] = None,
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**kwargs,
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):
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super().__init__(
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endpoint=endpoint,
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llm_name=llm_name,
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model_id=model_id,
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api_key=api_key,
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**kwargs,
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)
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# Scope the executor to exactly the node's configured tools. Agents
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# fetch their toolset via ``tool_executor.get_tools()``, so the scope
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# must live on the executor — it resolves builtin synthetic ids
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# (Artifact / Code Executor / Read Document) and ``user_tools`` rows
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# alike, and an empty list means the node's LLM gets no tools.
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self.tool_executor.allowed_tool_ids = [str(t) for t in (tool_ids or [])]
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# The node's tools are the workflow owner's whoever runs it; tools the
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# owner can't use resolve as the editor who attached them.
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self.tool_executor.tool_owner = tool_owner
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self.tool_executor.tool_principals = dict(tool_principals or {})
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class WorkflowNodeClassicAgent(_WorkflowNodeMixin, ClassicAgent):
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pass
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class WorkflowNodeAgenticAgent(_WorkflowNodeMixin, AgenticAgent):
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pass
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class WorkflowNodeResearchAgent(_WorkflowNodeMixin, ResearchAgent):
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pass
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class WorkflowNodeAgentFactory:
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_agents: Dict[AgentType, Type[BaseAgent]] = {
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AgentType.CLASSIC: WorkflowNodeClassicAgent,
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AgentType.REACT: WorkflowNodeClassicAgent, # backwards compat
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AgentType.AGENTIC: WorkflowNodeAgenticAgent,
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AgentType.RESEARCH: WorkflowNodeResearchAgent,
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}
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@classmethod
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def create(
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cls,
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agent_type: AgentType,
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endpoint: str,
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llm_name: str,
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model_id: str,
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api_key: str,
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tool_ids: Optional[List[str]] = None,
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**kwargs,
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) -> BaseAgent:
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agent_class = cls._agents.get(agent_type)
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if not agent_class:
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raise ValueError(f"Unsupported agent type: {agent_type}")
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return agent_class(
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endpoint=endpoint,
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llm_name=llm_name,
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model_id=model_id,
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api_key=api_key,
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tool_ids=tool_ids,
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**kwargs,
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)
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