mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-03 11:11:58 +00:00
Alex
574f96341e
The backend import package is now docsgpt, the name it will carry on PyPI; application was far too generic to install into anyone's site-packages. git mv plus a mechanical rewrite of every import, dotted string and path reference: 734 Python files, the compose files, Dockerfile, workflows, docs, setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage config, .gitignore. Behaviour is unchanged. Kept for one release: - A top-level application package whose meta-path finder resolves application.x.y to the already-imported docsgpt.x.y object, so old imports and entry points (celery -A application.app.celery, uvicorn application.asgi:asgi_app) keep working with a FutureWarning. - Celery registers every application.* task name as an alias of its docsgpt.* task on start-up, so messages queued by the previous release still run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries the previous release wrote are left unread instead of firing twice. The backend image builds from the repository root (docker build -f docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore allow-lists docsgpt/ and application/ and keeps caches, local data, .env files, the sample index files and the Dockerfile out. Compose and the image workflows point at the new context.
82 lines
2.4 KiB
Python
82 lines
2.4 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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**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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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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