203 lines
8.3 KiB
Python
203 lines
8.3 KiB
Python
import logging
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import os
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from typing import Any, Dict, List
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from fastapi import HTTPException
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from sqlmodel import Session
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from core.docker_instance import docker_manager
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from core.utils import _is_video_attachment_path, _is_visual_attachment_path
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from models.bot import BotInstance
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from services.bot_service import read_bot_runtime_snapshot
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from services.platform_activity_service import record_activity_event
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from services.platform_usage_service import create_usage_request, fail_latest_usage
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from services.runtime_service import broadcast_runtime_packet, persist_runtime_packet
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from services.workspace_service import resolve_workspace_path
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logger = logging.getLogger("dashboard.backend")
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def _normalize_message_media_item(value: Any) -> str:
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return str(value or "").strip().replace("\\", "/").lstrip("/")
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def _normalize_message_media_list(raw: Any) -> List[str]:
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if not isinstance(raw, list):
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return []
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rows: List[str] = []
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for value in raw:
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normalized = _normalize_message_media_item(value)
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if normalized:
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rows.append(normalized)
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return rows
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def _build_delivery_command(command: str, checked_attachments: List[str]) -> str:
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if not checked_attachments:
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return command
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attachment_block = "\n".join(f"- {path}" for path in checked_attachments)
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if all(_is_visual_attachment_path(path) for path in checked_attachments):
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has_video = any(_is_video_attachment_path(path) for path in checked_attachments)
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media_label = "图片/视频" if has_video else "图片"
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capability_hint = (
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"1) 附件已随请求附带；图片在可用时可直接作为多模态输入理解，视频请按附件路径处理。\n"
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if has_video
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else "1) 附件中的图片已作为多模态输入提供，优先直接理解并回答。\n"
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)
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if command:
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return (
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f"{command}\n\n"
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"[Attached files]\n"
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f"{attachment_block}\n\n"
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"【附件处理要求】\n"
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f"{capability_hint}"
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"2) 若当前模型或接口不支持直接理解该附件，请明确说明后再调用工具解析。\n"
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"3) 除非用户明确要求，不要先调用工具读取附件文件。\n"
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"4) 回复语言必须遵循 USER.md；若未指定，则与用户当前输入语言保持一致。\n"
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"5) 仅基于可见内容回答；看不清或无法确认的部分请明确说明，不要猜测。"
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)
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return (
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"请先处理已附带的附件列表：\n"
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f"{attachment_block}\n\n"
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f"请直接分析已附带的{media_label}并总结关键信息。\n"
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f"{'图片在可用时可直接作为多模态输入理解，视频请按附件路径处理。' if has_video else ''}\n"
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"若当前模型或接口不支持直接理解该附件，请明确说明后再调用工具解析。\n"
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"回复语言必须遵循 USER.md；若未指定，则与用户当前输入语言保持一致。\n"
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"仅基于可见内容回答；看不清或无法确认的部分请明确说明，不要猜测。"
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)
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command_has_paths = all(path in command for path in checked_attachments) if command else False
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if command and not command_has_paths:
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return (
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f"{command}\n\n"
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"[Attached files]\n"
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f"{attachment_block}\n\n"
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"Please process the attached file(s) listed above when answering this request.\n"
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"Reply language must follow USER.md. If not specified, use the same language as the user input."
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)
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if not command:
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return (
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"Please process the uploaded file(s) listed below:\n"
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f"{attachment_block}\n\n"
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"Reply language must follow USER.md. If not specified, use the same language as the user input."
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)
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return command
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def send_bot_command(session: Session, bot_id: str, command: str, attachments: Any) -> Dict[str, Any]:
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request_id = ""
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try:
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bot = session.get(BotInstance, bot_id)
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if not bot:
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raise HTTPException(status_code=404, detail="Bot not found")
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runtime_snapshot = read_bot_runtime_snapshot(bot)
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normalized_attachments = _normalize_message_media_list(attachments)
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text_command = str(command or "").strip()
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if not text_command and not normalized_attachments:
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raise HTTPException(status_code=400, detail="Command or attachments is required")
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checked_attachments: List[str] = []
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for rel_path in normalized_attachments:
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_, target = resolve_workspace_path(bot_id, rel_path)
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if not os.path.isfile(target):
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raise HTTPException(status_code=400, detail=f"attachment not found: {rel_path}")
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checked_attachments.append(rel_path)
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delivery_media = [f"/root/.nanobot/workspace/{path.lstrip('/')}" for path in checked_attachments]
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display_command = text_command if text_command else "[attachment message]"
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delivery_command = _build_delivery_command(text_command, checked_attachments)
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request_id = create_usage_request(
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session,
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bot_id,
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display_command,
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attachments=checked_attachments,
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channel="dashboard",
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metadata={"attachment_count": len(checked_attachments)},
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provider=str(runtime_snapshot.get("llm_provider") or "").strip() or None,
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model=str(runtime_snapshot.get("llm_model") or "").strip() or None,
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)
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record_activity_event(
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session,
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bot_id,
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"command_submitted",
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request_id=request_id,
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channel="dashboard",
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detail="command submitted",
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metadata={"attachment_count": len(checked_attachments), "has_text": bool(text_command)},
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)
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session.commit()
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outbound_user_packet: Dict[str, Any] | None = None
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if display_command or checked_attachments:
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outbound_user_packet = {
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"type": "USER_COMMAND",
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"channel": "dashboard",
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"text": display_command,
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"media": checked_attachments,
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"request_id": request_id,
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}
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persist_runtime_packet(bot_id, outbound_user_packet)
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if outbound_user_packet:
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broadcast_runtime_packet(bot_id, outbound_user_packet)
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success = docker_manager.send_command(bot_id, delivery_command, media=delivery_media)
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if success:
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return {"success": True}
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detail = docker_manager.get_last_delivery_error(bot_id)
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fail_latest_usage(session, bot_id, detail or "command delivery failed")
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record_activity_event(
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session,
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bot_id,
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"command_failed",
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request_id=request_id,
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channel="dashboard",
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detail=(detail or "command delivery failed")[:400],
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)
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session.commit()
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broadcast_runtime_packet(
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bot_id,
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{
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"type": "AGENT_STATE",
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"channel": "dashboard",
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"payload": {
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"state": "ERROR",
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"action_msg": detail or "command delivery failed",
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},
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},
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)
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raise HTTPException(
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status_code=502,
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detail=f"Failed to deliver command to bot dashboard channel{': ' + detail if detail else ''}",
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)
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except HTTPException:
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raise
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except Exception as exc:
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logger.exception("send_bot_command failed for bot_id=%s", bot_id)
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try:
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session.rollback()
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except Exception:
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pass
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if request_id:
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try:
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fail_latest_usage(session, bot_id, str(exc))
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record_activity_event(
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session,
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bot_id,
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"command_failed",
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request_id=request_id,
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channel="dashboard",
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detail=str(exc)[:400],
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)
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session.commit()
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except Exception:
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try:
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session.rollback()
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except Exception:
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pass
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raise HTTPException(status_code=500, detail=f"Failed to process bot command: {exc}") from exc
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