df4fab1e6e
- 扩展 test_agent_execution（--homework、UTF-8 控制台） - 后端：uploads 预览、file_read、工作流与对话落盘等 - 前端：AgentChatPreview 与设计器相关调整 - 忽略 redis二进制、agent_workspaces、uploads、tessdata 等本机产物 Made-with: Cursor
303 lines
9.3 KiB
Python
303 lines
9.3 KiB
Python
"""
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每个智能体在工作区根目录下对应一个 Markdown 对话文件（自动追加）。
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与 builtin_tools._local_file_workspace_root 使用同一根目录，避免越权路径。
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"""
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from __future__ import annotations
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import json
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import logging
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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from sqlalchemy.orm import Session
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from app.core.config import settings
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from app.models.execution import Execution
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from app.models.agent import Agent
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from app.services.builtin_tools import _local_file_workspace_root
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logger = logging.getLogger(__name__)
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_MAX_BODY_CHARS = 100_000
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def _workspace_subdir() -> str:
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s = (getattr(settings, "AGENT_WORKSPACE_CHAT_SUBDIR", None) or "agent_workspaces").strip()
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return s.strip("/\\") or "agent_workspaces"
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def _extract_user_text(input_data: Optional[Dict[str, Any]]) -> str:
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if not input_data:
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return ""
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keys = ("query", "USER_INPUT", "message", "content", "text", "prompt", "user_message")
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for k in keys:
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v = input_data.get(k)
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if v is None:
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continue
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if isinstance(v, str) and v.strip():
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return v.strip()
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if v is not None and not isinstance(v, (dict, list)):
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s = str(v).strip()
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if s:
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return s
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filtered = {k: v for k, v in input_data.items() if not str(k).startswith("__")}
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try:
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raw = json.dumps(filtered, ensure_ascii=False, default=str)
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except Exception:
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raw = str(filtered)
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if len(raw) > _MAX_BODY_CHARS:
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raw = raw[:_MAX_BODY_CHARS] + "\n\n…（已截断）"
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return raw or ""
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def _extract_assistant_text(result: Optional[Dict[str, Any]]) -> str:
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if not result:
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return ""
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r = result.get("result")
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if r is None:
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try:
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raw = json.dumps(result, ensure_ascii=False, default=str)
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except Exception:
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raw = str(result)
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elif isinstance(r, str):
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return r if len(r) <= _MAX_BODY_CHARS else r[:_MAX_BODY_CHARS] + "\n\n…（已截断）"
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else:
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try:
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raw = json.dumps(r, ensure_ascii=False, default=str)
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except Exception:
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raw = str(r)
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if len(raw) > _MAX_BODY_CHARS:
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raw = raw[:_MAX_BODY_CHARS] + "\n\n…（已截断）"
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return raw
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def _file_already_has_execution_id(md_path: Path, execution_id: str) -> bool:
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"""同一执行只记一次，避免 Celery 与 API 补救重复写入。"""
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if not md_path.exists():
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return False
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try:
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text = md_path.read_text(encoding="utf-8", errors="replace")
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except OSError:
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return False
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needle = f"**执行 ID**：`{execution_id}`"
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return needle in text
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def _user_id_from_input(input_data: Optional[Dict[str, Any]]) -> Optional[str]:
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if not input_data:
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return None
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for k in ("user_id", "USER_ID", "userId"):
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v = input_data.get(k)
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if v is None:
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continue
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s = str(v).strip()
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if s:
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return s
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return None
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def fetch_agent_preview_chat_turns(
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db: Session,
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agent_id: str,
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*,
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preview_user_id: Optional[str] = None,
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limit: int = 50,
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max_scan: int = 500,
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) -> List[Dict[str, Any]]:
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"""
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设计器预览侧恢复对话：从已完成执行解析用户/助手文本。
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preview_user_id 与 input_data.user_id 一致时只返回该浏览器预览会话的记录。
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"""
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from app.models.execution import Execution
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limit = max(1, min(int(limit), 200))
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max_scan = max(limit, min(int(max_scan), 1000))
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rows = (
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db.query(Execution)
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.filter(
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Execution.agent_id == agent_id,
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Execution.status == "completed",
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)
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.order_by(Execution.created_at.desc())
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.limit(max_scan)
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.all()
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)
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picked: List[Execution] = []
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for ex in rows:
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if preview_user_id:
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inp = ex.input_data if isinstance(ex.input_data, dict) else None
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uid = _user_id_from_input(inp)
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if uid != preview_user_id:
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continue
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picked.append(ex)
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if len(picked) >= limit:
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break
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picked.reverse()
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out: List[Dict[str, Any]] = []
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for ex in picked:
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inp = ex.input_data if isinstance(ex.input_data, dict) else None
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od = ex.output_data
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wf: Dict[str, Any] = od if isinstance(od, dict) else {"result": od}
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user_text = (_extract_user_text(inp) or "").strip()
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agent_text = (_extract_assistant_text(wf) or "").strip()
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if not user_text and not agent_text:
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continue
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out.append(
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{
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"execution_id": str(ex.id),
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"created_at": ex.created_at,
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"user_text": user_text or "（无文本）",
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"agent_text": agent_text or "（无输出）",
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}
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)
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return out
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def append_agent_dialogue_md(
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*,
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db: Session,
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execution: Execution,
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input_data: Optional[Dict[str, Any]],
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workflow_result: Dict[str, Any],
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) -> Optional[Path]:
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"""
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在 agent_workspaces/<agent_id>/dialogue.md 末尾追加一轮对话。
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仅当 execution.agent_id 存在且开关开启时写入。
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"""
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if not getattr(settings, "AGENT_WORKSPACE_CHAT_LOG_ENABLED", True):
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return None
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if not execution or not getattr(execution, "agent_id", None):
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return None
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agent_id = str(execution.agent_id)
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ag = db.query(Agent).filter(Agent.id == execution.agent_id).first()
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agent_name = (ag.name if ag and ag.name else agent_id).strip() or agent_id
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root = _local_file_workspace_root()
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sub = _workspace_subdir()
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agent_dir = root / sub / agent_id
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try:
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agent_dir.mkdir(parents=True, exist_ok=True)
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except OSError as e:
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logger.warning("创建智能体对话目录失败: %s", e)
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return None
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md_path = agent_dir / "dialogue.md"
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eid = str(execution.id)
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if _file_already_has_execution_id(md_path, eid):
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logger.debug("对话 MD 已包含执行 %s，跳过追加", eid)
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return md_path
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user_text = _extract_user_text(input_data)
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assistant_text = _extract_assistant_text(workflow_result)
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uid = _user_id_from_input(input_data)
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ts = datetime.now(timezone.utc).astimezone().strftime("%Y-%m-%d %H:%M:%S %z")
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rel_note = f"{sub}/{agent_id}/dialogue.md"
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block_lines = [
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f"### {ts}",
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"",
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f"- **执行 ID**：`{eid}`",
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]
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if uid:
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block_lines.append(f"- **用户 ID**：`{uid}`")
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block_lines.extend(
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[
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"",
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"**用户**",
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"",
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user_text or "（无文本输入，详见 input_data）",
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"",
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"**助手**",
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"",
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assistant_text or "（无输出）",
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"",
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"---",
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"",
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]
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)
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block = "\n".join(block_lines)
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try:
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if not md_path.exists():
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header = "\n".join(
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[
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f"# 对话记录 · {agent_name}",
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"",
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f"- **Agent ID**：`{agent_id}`",
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f"- **文件路径**（相对工作区根）：`{rel_note}`",
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"- **说明**：每次关联本智能体的执行成功完成后自动追加。",
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"",
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"---",
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"",
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]
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)
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md_path.write_text(header + block, encoding="utf-8")
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else:
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with md_path.open("a", encoding="utf-8", newline="\n") as f:
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f.write(block)
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except OSError as e:
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logger.warning("写入智能体对话 MD 失败: %s", e)
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return None
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logger.info("智能体对话已写入: %s", md_path)
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return md_path
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def ensure_agent_dialogue_logged_from_db_execution(
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db: Session, execution: Execution
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) -> Optional[Path]:
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"""
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根据已落库的执行记录补写对话 MD（供 GET execution 等路径调用）。
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仅 completed + 有 agent_id + 有 output_data 时写入；与 Celery 路径幂等。
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"""
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if not getattr(settings, "AGENT_WORKSPACE_CHAT_LOG_ENABLED", True):
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return None
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if not execution or execution.status != "completed":
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return None
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if not getattr(execution, "agent_id", None):
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return None
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od = execution.output_data
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if od is None:
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return None
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wf: Dict[str, Any]
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if isinstance(od, dict):
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wf = od
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else:
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wf = {"result": od}
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try:
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return append_agent_dialogue_md(
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db=db,
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execution=execution,
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input_data=execution.input_data if isinstance(execution.input_data, dict) else None,
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workflow_result=wf,
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)
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except Exception as e:
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logger.warning("从执行记录补写对话 MD 失败: %s", e)
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return None
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def try_append_agent_dialogue_after_success(
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db: Session,
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execution: Optional[Execution],
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input_data: Optional[Dict[str, Any]],
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workflow_result: Optional[Dict[str, Any]],
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execution_logger: Any = None,
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) -> None:
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if not workflow_result:
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return
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try:
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append_agent_dialogue_md(
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db=db,
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execution=execution,
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input_data=input_data,
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workflow_result=workflow_result,
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)
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except Exception as e:
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msg = f"写入智能体对话 MD 异常: {e}"
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logger.warning(msg)
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if execution_logger:
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execution_logger.warn(msg)
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