qixinbo
150 lines
5.2 KiB
Python
150 lines
5.2 KiB
Python
from typing import Any, Optional
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import json
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from nanobot.agent.tools.base import Tool
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from app.database import SessionLocal
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from app.models.subagent import Subagent
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from app.core.nanobot import nanobot_service
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from app.services.llm_cache import get_llm_configs, get_active_llm_config
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class ListSubagentsTool(Tool):
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"""
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Tool to list available subagents for the current project.
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"""
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def __init__(self, project_id: Optional[int] = None):
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super().__init__()
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self.project_id = project_id
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@property
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def name(self) -> str:
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return "list_subagents"
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@property
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def description(self) -> str:
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return "List all available subagents in the current project, including their names and descriptions."
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@property
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def parameters(self) -> dict[str, Any]:
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return {
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"type": "object",
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"properties": {},
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"required": [],
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}
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async def execute(self, **kwargs: Any) -> str:
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if not self.project_id:
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return "Error: No project context available to list subagents."
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with SessionLocal() as db:
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subagents = db.query(Subagent).filter(Subagent.project_id == self.project_id).all()
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if not subagents:
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return "No subagents found in the current project."
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result = []
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for sa in subagents:
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result.append({
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"id": sa.id,
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"name": sa.name,
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"description": sa.description,
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})
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return json.dumps(result, ensure_ascii=False, indent=2)
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class InvokeSubagentTool(Tool):
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"""
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Tool to invoke a specific subagent to perform a task.
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"""
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def __init__(self, project_id: Optional[int] = None):
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super().__init__()
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self.project_id = project_id
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@property
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def name(self) -> str:
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return "invoke_subagent"
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@property
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def description(self) -> str:
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return (
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"Invoke a subagent by name to perform a specific task. "
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"You should first use list_subagents to find the correct subagent name."
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)
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@property
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def parameters(self) -> dict[str, Any]:
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return {
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"type": "object",
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"properties": {
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"subagent_name": {
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"type": "string",
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"description": "The name of the subagent to invoke.",
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},
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"task": {
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"type": "string",
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"description": "The specific task or query to send to the subagent.",
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}
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},
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"required": ["subagent_name", "task"],
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}
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async def execute(self, **kwargs: Any) -> str:
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subagent_name = kwargs.get("subagent_name")
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task = kwargs.get("task")
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if not self.project_id:
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return "Error: No project context available to invoke subagent."
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if not subagent_name or not task:
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return "Error: subagent_name and task are required."
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with SessionLocal() as db:
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subagent = db.query(Subagent).filter(
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Subagent.project_id == self.project_id,
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Subagent.name == subagent_name
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).first()
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if not subagent:
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return f"Error: Subagent '{subagent_name}' not found."
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# Construct the message with subagent instructions
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instructions = subagent.instructions or "You are a helpful assistant."
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message = f"[System: You are acting as subagent '{subagent.name}'. Instructions: {instructions}]\n\nTask: {task}"
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resolved_model_id = None
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llm_configs = get_llm_configs()
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target = None
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raw_model = (getattr(subagent, "model", None) or "").strip()
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if raw_model:
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target = next((item for item in llm_configs if item.get("id") == raw_model), None)
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if target is None:
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normalized = raw_model.lower()
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target = next(
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(
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item for item in llm_configs
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if (
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str(item.get("model") or "").strip().lower() == normalized
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or str(item.get("name") or "").strip().lower() == normalized
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)
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),
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None,
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)
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if target is None:
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target = get_active_llm_config()
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if target and target.get("id"):
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resolved_model_id = target.get("id")
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try:
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from app.context import current_session_id
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parent_session_id = current_session_id.get() or "default"
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subagent_session_id = f"{parent_session_id}:subagent:{subagent.id}"
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response = await nanobot_service.process_message(
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message=message,
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session_id=subagent_session_id,
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project_id=self.project_id,
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model_id=resolved_model_id,
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)
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return f"Subagent '{subagent.name}' completed the task.\nResult:\n{response}"
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except Exception as e:
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return f"Error invoking subagent '{subagent.name}': {str(e)}"
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